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Sample records for ensemble downscaling mred

  1. The ENSEMBLES Statistical Downscaling Portal

    NASA Astrophysics Data System (ADS)

    Cofino, Antonio S.; San-Martín, Daniel; Gutiérrez, Jose M.

    2010-05-01

    The demand for high-resolution seasonal and ACC predictions is continuously increasing due to the multiple end-user applications in a variety of sectors (hydrology, agronomy, energy, etc.) which require regional meteorological inputs. To fill the gap between the coarse-resolution grids used by global weather models and the regional needs of applications, a number of statistical downscaling techniques have been proposed. Statistical downscaling is a complex multi-disciplinary problem which requires a cascade of different scientific tools to access and process different sources of data, from GCM outputs to local observations and to run complex statistical algorithms. Thus, an end-to-end approach is needed in order to link the outputs of the ensemble prediction systems to a range of impact applications. To accomplish this task in an interactive and user-friendly form, a Web portal has been developed within the European ENSEMBLES project, integrating the necessary tools and providing the appropriate technology for distributed data access and computing. In this form, users can obtain their downscaled data testing and validating different statistical methods (from the categories weather typing, regression or weather generators) in a transparent form, not worrying about the details of the downscaling techniques and the data formats and access.

  2. Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

    NASA Astrophysics Data System (ADS)

    Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

    2013-04-01

    Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the

  3. Dynamically downscaled multi-model ensemble seasonal forecasts over Ethiopia

    NASA Astrophysics Data System (ADS)

    Asharaf, Shakeel; Fröhlich, Kristina; Fernandez, Jesus; Cardoso, Rita; Nikulin, Grigory; Früh, Barbara

    2016-04-01

    Truthful and reliable seasonal rainfall predictions have an important social and economic value for the east African countries as their economy is highly dependent on rain-fed agriculture and pastoral systems. Only June to September (JJAS) seasonal rainfall accounts to more than 80% crop production in Ethiopia. Hence, seasonal foresting is a crucial concern for the region. The European Provision of Regional Impact Assessment on a seasonal to decadal timescale (EUPORIAS) project offers a common framework to understand hindcast uncertainties through the use of multi-model and multi-member simulations over east Africa. Under this program, the participating regional climate models (RCMs) were driven by the atmospheric-only version of the ECEARTH global climate model, which provides hindcasts of a five-months period (May to September) from 1991-2012. In this study the RCMs downscaled rainfall is evaluated with respect to the observed JJAS rainfall over Ethiopia. Both deterministic and probabilistic based forecast skills are assessed. Our preliminary results show the potential usefulness of multi-model ensemble simulations in forecasting the seasonal rainfall over the region.

  4. Downscaling medium-range ensemble forecasts using a neural network approach

    NASA Astrophysics Data System (ADS)

    Ohba, M.; Kadokura, S.; Yoshida, Y.; Nohara, D.; Toyoda, Y.

    2015-06-01

    In this study, we present an application of self-organizing maps (SOMs) to downscaling weekly ensemble forecasts for probabilistic prediction of local precipitation in Japan. SOM is simultaneously employed on four elemental variables derived from the JRA55 reanalysis over area of study (Southwestern Japan), whereby a two-dimensional lattice of weather patterns (WPs) dominated during the 1958-2008 period is obtained. Downscaling weekly ensemble forecasts to local precipitation are conducted by using the obtained SOM lattice based on the WPs of the global model ensemble forecast. A probabilistic local precipitation is easily and quickly obtained from the ensemble forecast. The predictability skill of the ensemble forecasts for the precipitation is significantly improved under the downscaling technique.

  5. A comparison study of three statistical downscaling methods and their model-averaging ensemble for precipitation downscaling in China

    NASA Astrophysics Data System (ADS)

    Duan, Kai; Mei, Yadong

    2014-05-01

    This study evaluated the performance of three frequently applied statistical downscaling tools including SDSM, SVM, and LARS-WG, and their model-averaging ensembles under diverse moisture conditions with respect to the capability of reproducing the extremes as well as mean behaviors of precipitation. Daily observed precipitation and NCEP reanalysis data of 30 stations across China were collected for the period 1961-2000, and model parameters were calibrated for each season at individual site with 1961-1990 as the calibration period and 1991-2000 as the validation period. A flexible framework of multi-criteria model averaging was established in which model weights were optimized by the shuffled complex evolution algorithm. Model performance was compared for the optimal objective and nine more specific metrics. Results indicate that different downscaling methods can gain diverse usefulness and weakness in simulating various precipitation characteristics under different circumstances. SDSM showed more adaptability by acquiring better overall performance at a majority of the stations while LARS-WG revealed better accuracy in modeling most of the single metrics, especially extreme indices. SVM provided more usefulness under drier conditions, but it had less skill in capturing temporal patterns. Optimized model averaging, aiming at certain objective functions, can achieve a promising ensemble with increasing model complexity and computational cost. However, the variation of different methods' performances highlighted the tradeoff among different criteria, which compromised the ensemble forecast in terms of single metrics. As the superiority over single models cannot be guaranteed, model averaging technique should be used cautiously in precipitation downscaling.

  6. Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting

    PubMed Central

    Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

    2014-01-01

    Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind “noise,” which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical “downscaling” of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Key Points Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations PMID:26213518

  7. Downscaling a perturbed physics ensemble over the CORDEX Africa domain

    NASA Astrophysics Data System (ADS)

    Buontempo, Carlo; Williams, Karina; McSweeney, Carol; Jones, Richard; Mathison, Camilla; Wang, Chang

    2014-05-01

    We present here the methodology and the results of 5-member ensemble simulation of the climate of Africa for the period 1950-2100 using climate modelling system PRECIS over the CORDEX Africa domain. The boundary conditions for the regional model simulations were selected from a 17-member perturbed physics ensemble based on the HadCM3 global climate model (Murphy et al. 2007) following the methodology described in McSweeney et al 2012. Such an approach was selected in order to provide a good representation of the overall ensemble spread over a number of sub regions while at the same time avoiding members which have demonstrate particularly unrealistic characteristics in their baseline climate. In the simulations a special attention was given to the representation of some inland water bodies, such as lake Victoria, whose impact on the regional climate was believed to be significant thus allowing for the representation of some regional processes (e.g. land-lake breezes) that were not represented in the global models. In particular the SSTs of the lakes were corrected to better represent the local climatological values. The results suggest that RCM simulations improve the fit to observations of precipitation and temperature in most of the African sub-regions (e.g. North Africa, West Sahel). Also, the range of RCM projections is often different to those from the GCMs in these regions. We discuss the reasons for and links between these results and their implications for use in informing adaptation policy at regional level.

  8. A robust framework for probabilistic precipitations downscaling from an ensemble of climate predictions applied to Switzerland

    NASA Astrophysics Data System (ADS)

    Beuchat, X.; Schaefli, B.; Soutter, M.; Mermoud, A.

    2012-02-01

    Rainfall is poorly modeled by general circulation models (GCMs) and requires appropriate downscaling for local-scale hydrological impact studies. Such downscaling methods should be robust and accurate (to handle, e.g., extreme events and uncertainties), but the noncontinuous and highly nonlinear nature of rainfall makes this task particularly challenging. This paper brings together and extends state-of-the-art methods into an integrated and robust probabilistic methodology to downscale local daily rainfall series from an ensemble of climate simulations. The downscaling is based on generalized linear models (GLMs) that relate monthly GCM-scale atmospheric variables to local-scale daily rainfall series. A cross-validation step ensures that the fitted models are correctly conditioned by the climate variables, and a statistical procedure is proposed to test whether the statistical relationships identified for the reference period also hold in a future perturbed climate (i.e., to test the stationarity assumption). Additionally, we propose a strategy to downweigh poorly performing GCM-GLM couples. The methodology is assessed at 27 locations covering Switzerland and is shown to perform well in reproducing historical rainfall statistics including extremes and interannual variability. Furthermore, the projections are consistent with the simulations of physically based dynamical models. Using an original visualization method based on heat maps, we show that although the downscaling models were fitted at each of the 27 sites independently, their projections follow a spatially coherent pattern and that regions exhibiting different climate change impacts can be identified.

  9. The regional MiKlip decadal forecast ensemble for Europe: the added value of downscaling

    NASA Astrophysics Data System (ADS)

    Mieruch, S.; Feldmann, H.; Schädler, G.; Lenz, C.-J.; Kothe, S.; Kottmeier, C.

    2014-12-01

    The prediction of climate on time scales of years to decades is attracting the interest of both climate researchers and stakeholders. The German Ministry for Education and Research (BMBF) has launched a major research programme on decadal climate prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal Climate Prediction) in order to investigate the prediction potential of global and regional climate models (RCMs). In this paper we describe a regional predictive hindcast ensemble, its validation, and the added value of regional downscaling. Global predictions are obtained from an ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs), which were downscaled for Europe using the COSMO-CLM regional model. Decadal hindcasts were produced for the 5 decades starting in 1961 until 2001. Observations were taken from the E-OBS data set. To identify decadal variability and predictability, we removed the long-term mean, as well as the long-term linear trend from the data. We split the resulting anomaly time series into two parts, the first including lead times of 1-5 years, reflecting the skill which originates mainly from the initialisation, and the second including lead times from 6-10 years, which are more related to the representation of low frequency climate variability and the effects of external forcing. We investigated temperature averages and precipitation sums for the summer and winter half-year. Skill assessment was based on correlation coefficient and reliability. We found that regional downscaling preserves, but mostly does not improve the skill and the reliability of the global predictions for summer half-year temperature anomalies. In contrast, regionalisation improves global decadal predictions of half-year precipitation sums in most parts of Europe. The added value results from an increased predictive skill on grid-point basis together with an improvement of the ensemble spread, i.e. the reliability.

  10. An evaluation of the seasonal added value of downscaling over the United States using new verification measures

    NASA Astrophysics Data System (ADS)

    De Haan, Laurel L.; Kanamitsu, Masao; De Sales, Fernando; Sun, Liqiang

    2015-10-01

    Two separate dynamically downscaled ensembles are used to assess the added value of downscaling over the continental United States on a seasonal timescale. One data set is from a 55-year continuous run forced with observed sea surface temperatures. The other data set has downscaling results from seven regional models for 21 winters forced from a single coupled global model. The second data set, known as the Multi-RCM Ensemble Downscaling (MRED) project was used as a collection of individual models as well as a multi-model ensemble. The data was first tested for the potential loss of small-scale details due to averaging, and it was found that the number of small-scale details is not reduced when averaging over several models or several years. The added value of the downscaling was then calculated by standard measures, including climatology and correlation, as well as two newer measures: the added value index (AVI) and fraction skill score (FSS). The additional verification measures provided more information about the added value than was found with the standard measures. In general, more added value was found with the multi-model ensemble than with individual models. While it was clear that the added value was dependent on the forcing model, regional model, season, variable, and region, there were some areas where the downscale consistently added value, particularly near the coast and in topographically interesting areas.

  11. Early flood warnings from empirical (expanded) downscaling of the full ECMWF Ensemble Prediction System

    NASA Astrophysics Data System (ADS)

    Bürger, Gerd; Reusser, Dominik; Kneis, David

    2009-10-01

    A prototype early warning system for floods is introduced. For a small headwater catchment, probabilistic streamflow predictions in 24-hourly steps are obtained from downscaling all members of the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System and feeding the resulting precipitation and temperature series into a hydrologic model. We apply "expanded downscaling," a scheme that was previously used for climate scenarios and that is particularly suited to extreme events and the simulation of flood-triggering heavy rainfall. The entire model chain is thoroughly verified, using daily precipitation and streamflow observations and forecasts from the decade 1997-2006. It turns out that strong meteorologic (precipitation) events are skillfully predicted for at least 5 days lead time by the downscaling. That skill, however, is partly lost by deficiencies in the hydrological modeling as revealed in this study. We discuss ways to overcome these difficulties, along with the prospect of employing the whole system operationally, for example, for reservoir regulations. We close with an outlook for early flash flood warnings.

  12. Generation of Daily Rainfall Scenario Based on Nonstationary Spatial-Temporal Downscaling Techniques with Multimodel Ensemble of Different GCMs

    NASA Astrophysics Data System (ADS)

    Kim, T. J.; Kwon, H. H.

    2014-12-01

    Recently, extreme weather occurrences associated with climate change are gradually increasing in frequency, causing unprecedented major weather-related disasters. General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the discrepancy between the spatio-temporal scale at which the models deliver output and the scales that are generally required for applied studies has led to the development of various downscaling methods. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a multimodel ensemble of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. In particular, this study uses MMEs from the APEC Climate Center (APCC) as a predictor. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management. Acknowledgement: This research was supported by a grant (13SCIPA01) from Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and the Korea Agency for Infrastructure Technology Advancement (KAIA). Keywords: Climate Change, GCM, Hidden Markov Chain Model, Multi-Model Ensemble

  13. Local seasonal forecasts over France: what can we expect from statistical downscaling ? Results with the DEMETER and ENSEMBLES systems

    NASA Astrophysics Data System (ADS)

    Qu, Z.; Dubus, L.; Gutiérrez, J. M.

    2009-04-01

    The management of the power generation system at the scale of a country is a very complex problem which involves in particular climatic variables at different spatial and time scales. Air temperature and precipitation are among the most important ones, as they explain respectively an important part of the demand variability and the hydro power production capacity. Direct GCMs forecasts of local variables are not very skilful, especially over mid-latitudes. Downscaling of large scale fields at upper levels to station points might be an efficient way to improve seasonal forecasts for application models. In this study, we evaluated the 2m temperature and precipitations hindcasts of the DEMETER and ENSEMBLES systems on a number of stations in France. We used the University of Cantabria's web portal for statistical downscaling to downscale the most predictable large scale fields, and compared direct raw hindcasts with indirect downscaled hindcasts. The portal also allowed to test different large scale predictors and different downscaling methods, in order to optimize the process.

  14. Six month-lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble

    NASA Astrophysics Data System (ADS)

    Sohn, Soo-Jin; Ahn, Joong-Bae; Tam, Chi-Yung

    2013-02-01

    Abstract The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center one-tier multimodel <span class="hlt">ensemble</span> (MME) was <span class="hlt">downscaled</span> for predicting the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both precipitation and temperature, and can incorporate the effect of global warming on the balance between precipitation and evapotranspiration. It was found that the one-tier MME has difficulty in capturing the local temperature and rainfall variations over extratropical land areas, and has no skill in predicting SPEI during boreal winter and spring. On the other hand, temperature and precipitation predictions were substantially improved in the <span class="hlt">downscaled</span> MME. In conjunction with variance inflation, <span class="hlt">downscaled</span> MME can give reasonably skillful 6 month-lead forecasts of SPEI for the winter to spring period. Our results could lead to more reliable hydrological extreme predictions for policymakers and stakeholders in the water management sector, and for better mitigation and climate adaptations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C41A0318M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C41A0318M"><span id="translatedtitle"><span class="hlt">Ensemble</span> Predictions of Future Snowfall Scenarios in the Karakorum and Hindu-Kush Mountains Using <span class="hlt">Downscaled</span> GCM Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mosier, T. M.; Hill, D. F.; Sharp, K. V.</p> <p>2014-12-01</p> <p>Climate change is affecting the seasonality and mass of snow, and impacting the water resources of hundreds of millions of people who depend on streamflow originating in High Asia. Global climate model (GCM) outputs are the primary forcing data used to investigate future projections of changes in snow and glacier processes; however, these processes occur at a much finer spatial scale than the resolution of current GCMs. To facilitate studying the cryosphere in High Asia, we developed a software package to <span class="hlt">downscale</span> monthly GCM data to 30-arcseconds for any global land area. Using this <span class="hlt">downscaling</span> package, we produce an <span class="hlt">ensemble</span> of <span class="hlt">downscaled</span> GCM data from 2020-2100, corresponding to representative concentration pathways (RCPs) 4.5 and 8.5. We then use these data to model changes to snowfall in the Karakorum and Hindu Kush (KHK) region, which is located in High Asia. The <span class="hlt">ensemble</span> mean of these data predict that total annual snowfall in 2095 will decrease by 22% under RCP 4.5 and 46% under RCP 8.5, relative to 1950-2000 climatological values. For both scenarios, the changes in snowfall are dependent on elevation, with the maximum decreases in snowfall occurring at approximately 2,300 m. While total snowfall decreases, an interesting feature of snowfall change for the RCP 8.5 scenario is that the <span class="hlt">ensemble</span> mean projection shows an increase in snowfall for elevations between 3,000- 5,000 m relative to historic values. These fine-scale spatial, temporal, and elevation-dependent patterns of changes in projected snowfall significantly affect the energy balance of the snowpack, in turn affecting timing of melt and discharge. Therefore, our work can be coupled with a glacio-hydrological model to assess effects of these snowfall patterns on other processes or compared to existing model results to assess treatment of snow processes in the existing model. Our method is designed to <span class="hlt">downscale</span> climate data for any global land area, allowing for the production of these fine</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A21E0185D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A21E0185D"><span id="translatedtitle">Probabilistic Predictions and <span class="hlt">Downscaling</span> with an Analog <span class="hlt">Ensemble</span> for Weather, Renewable Energy, Air Quality, and Hurricane Intensity</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Delle Monache, L.</p> <p>2015-12-01</p> <p>The analog of a forecast for a given location and time is defined as the observation that corresponds to a past prediction matching selected features of the current forecast. The best analogs form the analog <span class="hlt">ensemble</span> (AnEn). First AnEn skill is analyzed for predictions of 10-m wind speed and 2-m temperature. We show that AnEn produces accurate predictions and a reliable quantification of their uncertainty with similar or superior skill compared to cutting-edge methods, while requiring considerably less computational resources. A preliminary example of an application of AnEn in 3D will also be shown. Second, results for wind power predictions are presented, which confirm AnEn performance obtained for meteorological variables. Further improvements can be obtained by implementing analog-predictor weighting strategies, as will be shown. Third, AnEn is implemented for <span class="hlt">downscaling</span> the wind speed and precipitation fields from a reanalysis data set. AnEn significantly reduces the systematic and random errors in the <span class="hlt">downscaled</span> estimates, and simultaneously improves correlation between the <span class="hlt">downscaled</span> time series and the measurements, over what is provided by a reanalysis field alone. The AnEn also provides a reliable quantification of uncertainties in the estimate, thereby permitting decision makers to objectively define confidence intervals to the estimated long-term energy yield. We inckude also a discussion of the implementation of AnEn in data-sparse regions, where in that case it can be used as a technique to drastically reduce the computational cost of NWP-based dynamical <span class="hlt">downscaling</span>. We conclude we the latest novel inplementations of AnEn for air quality and hurricane intensity predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.6770S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.6770S"><span id="translatedtitle">Six-month lead <span class="hlt">downscaling</span> prediction of winter-spring drought in South Korea based on multi-model <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sohn, Soo-Jin; Ahn, Joong-Bae; Tam, Chi-Yung</p> <p>2013-04-01</p> <p>Given the changing climate, advance information on hydrological extremes such as droughts will help in planning for disaster mitigation and facilitate better decision making for water availability management. A deficit of precipitation for long-term time scales beyond 6 months has impacts on the hydrological sectors such as ground water, streamflow, and reservoir storage. The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center (APCC) 1-Tier multi-model <span class="hlt">ensemble</span> (MME) was <span class="hlt">downscaled</span> for predicting the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both of precipitation and temperature, and can incorporate the impact of global warming on the balance between precipitation and evapotranspiration. It was found that 1-Tier MME has difficulties in capturing the local temperature and rainfall variations over extratropical land areas, and has no skill in predicting SPEI during boreal winter and spring. On the other hand, temperature and precipitation predictions were substantially improved in the <span class="hlt">downscaled</span> MME (DMME). In conjunction with variance inflation, DMME can give reasonably skillful six-month-lead forecasts of SPEI for the winter-to-spring period. The results could potentially improve hydrological extreme predictions using meteorological forecasts for policymaker and stakeholders in water management sector for better climate adaption.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016AtmRe.178..138S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016AtmRe.178..138S&link_type=ABSTRACT"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of CMIP5 multi-model <span class="hlt">ensemble</span> for projected changes of climate in the Indus River Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Su, Buda; Huang, Jinlong; Gemmer, Marco; Jian, Dongnan; Tao, Hui; Jiang, Tong; Zhao, Chengyi</p> <p>2016-09-01</p> <p>The simulation results of CMIP5 (Coupled Model Inter-comparison Project phase 5) multi-model <span class="hlt">ensemble</span> in the Indus River Basin (IRB) are compared with the CRU (Climatic Research Unit) and APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation) datasets. The systematic bias between simulations and observations is corrected by applying the equidistant Cumulative Distribution Functions matching method (EDCDFm) and high-resolution simulations are statistically <span class="hlt">downscaled</span>. Then precipitation and temperature are projected for the IRB for the mid-21st century (2046-2065) and late 21st century (2081-2100). The results show that the CMIP5 <span class="hlt">ensemble</span> captures the dominant features of annual and monthly mean temperature and precipitation in the IRB. Based on the <span class="hlt">downscaling</span> results, it is projected that the annual mean temperature will increase over the entire basin, relative to the 1986-2005 reference period, with greatest changes in the Upper Indus Basin (UIB). Heat waves are more likely to occur. An increase in summer temperature is projected, particularly for regions of higher altitudes in the UIB. The persistent increase of summer temperature might accelerate the melting of glaciers, and has negative impact on the local freshwater availability. Projections under all RCP scenarios show an increase in monsoon precipitation, which will increase the possibility of flood disaster. A decreasing trend in winter and spring precipitation in the IRB is projected except for the RCP2.6 scenario which will cause a lower contribution of winter and spring precipitation to water resources in the mid and high altitude areas of the IRB.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ThApC.tmp..153P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ThApC.tmp..153P&link_type=ABSTRACT"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of regional climate over eastern China using RSM with multiple physics scheme <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peishu, Zong; Jianping, Tang; Shuyu, Wang; Lingyun, Xie; Jianwei, Yu; Yunqian, Zhu; Xiaorui, Niu; Chao, Li</p> <p>2016-06-01</p> <p>The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to conduct detailed analyses on the effect of physical parameterization schemes on regional climate simulation, to provide more reliable regional climate change information. In this paper, we evaluate the 25-year (1983-2007) summer monsoon climate characteristics of precipitation and surface air temperature by using the regional spectral model (RSM) with different physical schemes. The <span class="hlt">ensemble</span> results using the reliability <span class="hlt">ensemble</span> averaging (REA) method are also assessed. The result shows that the RSM model has the capacity to reproduce the spatial patterns, the variations, and the temporal tendency of surface air temperature and precipitation over eastern China. And it tends to predict better climatology characteristics over the Yangtze River basin and the South China. The impact of different physical schemes on RSM simulations is also investigated. Generally, the CLD3 cloud water prediction scheme tends to produce larger precipitation because of its overestimation of the low-level moisture. The systematic biases derived from the KF2 cumulus scheme are larger than those from the RAS scheme. The scale-selective bias correction (SSBC) method improves the simulation of the temporal and spatial characteristics of surface air temperature and precipitation and advances the circulation simulation capacity. The REA <span class="hlt">ensemble</span> results show significant improvement in simulating temperature and precipitation distribution, which have much higher correlation coefficient and lower root mean square error. The REA result of selected experiments is better than that of nonselected experiments, indicating the necessity of choosing better <span class="hlt">ensemble</span> samples for <span class="hlt">ensemble</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005TellA..57..488M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005TellA..57..488M"><span id="translatedtitle">Evaluation of <span class="hlt">downscaled</span> DEMETER multi-model <span class="hlt">ensemble</span> seasonal hindcasts in a northern Italy location by means of a model of wheat growth and soil water balance</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Marletto, V.; Zinoni, F.; Criscuolo, L.; Fontana, G.; Marchesi, S.; Morgillo, A.; van Soetendael, M.; Ceotto, E.; Andersen, U.</p> <p>2005-05-01</p> <p>In this paper we explore the new possibilities for early crop yield assessment at the local scale arising from the availability of dynamic crop growth models and of <span class="hlt">downscaled</span> multi-model <span class="hlt">ensemble</span> seasonal forecasts. We compare the use of the latter with other methods, based on crop growth models driven by observed climatic data only. The soil water balance model developed and used at ARPA Emilia-Romagna (CRITERIA) was integrated with crop growth routines from the model WOFOST 7.1. Some validation runs were first carried out and we verified with independent field data that the new integrated model satisfactorily simulated above-ground biomass and leaf area index. The model was then used to test the feasibility of using <span class="hlt">downscaled</span> multi-model <span class="hlt">ensemble</span> seasonal hindcasts, coming from the DEMETER European research project, in order to obtain early (i.e. 90, 60 and 30 d before harvest) yield assessments for winter wheat in northern Italy. For comparison, similar runs with climatology instead of hindcasts were also carried out. For the same purpose, we also produced six simple linear regression models of final crop yields on within season (end of March, April and May) storage organs and above-ground biomass values. Median yields obtained using <span class="hlt">downscaled</span> DEMETER hindcasts always outperformed the simple regression models and were substantially equivalent to the climatology runs, with the exception of the June experiment, where the <span class="hlt">downscaled</span> seasonal hindcasts were clearly better than all other methods in reproducing the winter wheat yields simulated with observed weather data. The crop growth model output dispersion was almost always significantly lower than the dispersion of the <span class="hlt">downscaled</span> <span class="hlt">ensemble</span> seasonal hindcast used as input for crop simulations.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li class="active"><span>1</span></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_1 --> <div id="page_2" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="21"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC23B0622F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC23B0622F"><span id="translatedtitle">Simulating expected elevation dependent warming (EDW) mechanisms in a dynamically-<span class="hlt">downscaled</span> perturbed physics climate model <span class="hlt">ensemble</span> over the Himalayan region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Forsythe, N. D.; Blenkinsop, S.; Fowler, H. J.; Betts, R.; Janes, T.</p> <p>2014-12-01</p> <p>Current theoretical climatology suggests three key climate processes - snow cover contribution to surface albedo, cloud cover prevalence and near surface water vapour - influencing the surface energy balance are expected to exhibit elevation-gradients in global warming-driven changes. These gradients are in turn expected to act as mechanisms contributing to EDW. This study examines the simulation of these mechanisms and their influence on projections of EDW in a dynamically <span class="hlt">downscaled</span> transient perturbed physics <span class="hlt">ensemble</span> (PPE). The <span class="hlt">downscaling</span> experiment in question is the Hadley Centre Regional Model version 3 PRECIS configuration (HadRM3P) 25km simulation over the South Asian domain driven by the MetOffice 17-member QUMP (Quantifying Uncertainty in Model Projections) <span class="hlt">ensemble</span> of the Hadley Centre Climate Model version 3 (HadCM3). Use of the multi-member PPE enables quantification of uncertainty in projected changes in climate variables - albedo, cloud cover, water vapour and near surface temperature - while the spatial resolution of a RCM improves insight into the role of elevation in projected rates of change. This work specifically addresses the Regional Climate Model (RCM) representation of expected EDW mechanisms by calculating vertical profiles (relative to modelled surface elevation of <span class="hlt">downscaled</span> grid cells) for changes in: [1] albedo, i.e. the ratio of future to control period albedo where albedo is calculated as one minus the ratio of absorbed surface solar radiation to incoming surface solar radiation; [2] shortwave cloud radiative effect (CRE), i.e. the ratio of future to present CRE where CRE is calculated as incoming "top of atmosphere" shortwave radiation minus incoming surface shortwave radiation; [3] near surface water vapour -- in terms of specific humidity (Qair) - and related down-welling longwave radiation, but because previous EDW research has shown non-linearity in Qair radiative influence, changes in Qair is evaluated in both delta (additive</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080040695','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080040695"><span id="translatedtitle">Simulation of SEU Cross-sections using <span class="hlt">MRED</span> under Conditions of Limited Device Information</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lauenstein, J. M.; Reed, R. A.; Weller, R. A.; Mendenhall, M. H.; Warren, K. M.; Pellish, J. A.; Schrimpf, R. D.; Sierawski, B. D.; Massengill, L. W.; Dodd, P. E.; Shaneyfelt, M. R.; Felix, J. A.; Schwank, J. R.</p> <p>2007-01-01</p> <p>This viewgraph presentation reviews the simulation of Single Event Upset (SEU) cross sections using the membrane electrode assembly (MEA) resistance and electrode diffusion (<span class="hlt">MRED</span>) tool using "Best guess" assumptions about the process and geometry, and direct ionization, low-energy beam test results. This work will also simulate SEU cross-sections including angular and high energy responses and compare the simulated results with beam test data for the validation of the model. Using <span class="hlt">MRED</span>, we produced a reasonably accurate upset response model of a low-critical charge SRAM without detailed information about the circuit, device geometry, or fabrication process</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H43H1635L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H43H1635L"><span id="translatedtitle"><span class="hlt">Downscaling</span> Satellite Data for Predicting Catchment-scale Root Zone Soil Moisture with Ground-based Sensors and an <span class="hlt">Ensemble</span> Kalman Filter</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, H.; Baldwin, D. C.; Smithwick, E. A. H.</p> <p>2015-12-01</p> <p>Predicting root zone (0-100 cm) soil moisture (RZSM) content at a catchment-scale is essential for drought and flood predictions, irrigation planning, weather forecasting, and many other applications. Satellites, such as the NASA Soil Moisture Active Passive (SMAP), can estimate near-surface (0-5 cm) soil moisture content globally at coarse spatial resolutions. We develop a hierarchical <span class="hlt">Ensemble</span> Kalman Filter (EnKF) data assimilation modeling system to <span class="hlt">downscale</span> satellite-based near-surface soil moisture and to estimate RZSM content across the Shale Hills Critical Zone Observatory at a 1-m resolution in combination with ground-based soil moisture sensor data. In this example, a simple infiltration model within the EnKF-model has been parameterized for 6 soil-terrain units to forecast daily RZSM content in the catchment from 2009 - 2012 based on AMSRE. LiDAR-derived terrain variables define intra-unit RZSM variability using a novel covariance localization technique. This method also allows the mapping of uncertainty with our RZSM estimates for each time-step. A catchment-wide satellite-to-surface <span class="hlt">downscaling</span> parameter, which nudges the satellite measurement closer to in situ near-surface data, is also calculated for each time-step. We find significant differences in predicted root zone moisture storage for different terrain units across the experimental time-period. Root mean square error from a cross-validation analysis of RZSM predictions using an independent dataset of catchment-wide in situ Time-Domain Reflectometry (TDR) measurements ranges from 0.060-0.096 cm3 cm-3, and the RZSM predictions are significantly (p < 0.05) correlated with TDR measurements [r = 0.47-0.68]. The predictive skill of this data assimilation system is similar to the Penn State Integrated Hydrologic Modeling (PIHM) system. Uncertainty estimates are significantly (p < 0.05) correlated to cross validation error during wet and dry conditions, but more so in dry summer seasons. Developing an</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4607420','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4607420"><span id="translatedtitle">MreC and <span class="hlt">MreD</span> Proteins Are Not Required for Growth of Staphylococcus aureus</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tavares, Andreia C.; Fernandes, Pedro B.; Carballido-López, Rut; Pinho, Mariana G.</p> <p>2015-01-01</p> <p>The transmembrane proteins MreC and <span class="hlt">MreD</span> are present in a wide variety of bacteria and are thought to be involved in cell shape determination. Together with the actin homologue MreB and other morphological elements, they play an essential role in the synthesis of the lateral cell wall in rod-shaped bacteria. In ovococcus, which lack MreB homologues, mreCD are also essential and have been implicated in peripheral cell wall synthesis. In this work we addressed the possible roles of MreC and <span class="hlt">MreD</span> in the spherical pathogen Staphylococcus aureus. We show that MreC and <span class="hlt">MreD</span> are not essential for cell viability and do not seem to affect cell morphology, cell volume or cell cycle control. MreC and <span class="hlt">MreD</span> localize preferentially to the division septa, but do not appear to influence peptidoglycan composition, nor the susceptibility to different antibiotics and to oxidative and osmotic stress agents. Our results suggest that the function of MreCD in S. aureus is not critical for cell division and cell shape determination. PMID:26470021</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016IJBm...60..307S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016IJBm...60..307S&link_type=ABSTRACT"><span id="translatedtitle">Future projections of labor hours based on WBGT for Tokyo and Osaka, Japan, using multi-period <span class="hlt">ensemble</span> dynamical <span class="hlt">downscale</span> simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Suzuki-Parker, Asuka; Kusaka, Hiroyuki</p> <p>2016-02-01</p> <p>Following the heatstroke prevention guideline by the Ministry of Health, Labor, and Welfare of Japan, "safe hours" for heavy and light labor are estimated based on hourly wet-bulb globe temperature (WBGT) obtained from the three-member <span class="hlt">ensemble</span> multi-period (the 2000s, 2030s, 2050s, 2070s, and 2090s) climate projections using dynamical <span class="hlt">downscaling</span> approach. Our target cities are Tokyo and Osaka, Japan. The results show that most of the current climate daytime hours are "light labor safe,", but these hours are projected to decrease by 30-40 % by the end of the twenty-first century. A 60-80 % reduction is projected for heavy labor hours, resulting in less than 2 hours available for safe performance of heavy labor. The number of "heavy labor restricted days" (days with minimum daytime WBGT exceeding the safe level threshold for heavy labor) is projected to increase from ~5 days in the 2000s to nearly two-thirds of the days in August in the 2090s.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25935576','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25935576"><span id="translatedtitle">Future projections of labor hours based on WBGT for Tokyo and Osaka, Japan, using multi-period <span class="hlt">ensemble</span> dynamical <span class="hlt">downscale</span> simulations.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Suzuki-Parker, Asuka; Kusaka, Hiroyuki</p> <p>2016-02-01</p> <p>Following the heatstroke prevention guideline by the Ministry of Health, Labor, and Welfare of Japan, "safe hours" for heavy and light labor are estimated based on hourly wet-bulb globe temperature (WBGT) obtained from the three-member <span class="hlt">ensemble</span> multi-period (the 2000s, 2030s, 2050s, 2070s, and 2090s) climate projections using dynamical <span class="hlt">downscaling</span> approach. Our target cities are Tokyo and Osaka, Japan. The results show that most of the current climate daytime hours are "light labor safe,", but these hours are projected to decrease by 30-40% by the end of the twenty-first century. A 60-80% reduction is projected for heavy labor hours, resulting in less than 2 hours available for safe performance of heavy labor. The number of "heavy labor restricted days" (days with minimum daytime WBGT exceeding the safe level threshold for heavy labor) is projected to increase from ~5 days in the 2000s to nearly two-thirds of the days in August in the 2090s. PMID:25935576</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1813579G&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1813579G&link_type=ABSTRACT"><span id="translatedtitle">An intercomparison of a large <span class="hlt">ensemble</span> of statistical <span class="hlt">downscaling</span> methods for Europe: Overall results from the VALUE perfect predictor cross-validation experiment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutiérrez, Jose Manuel; Maraun, Douglas; Widmann, Martin; Huth, Radan; Hertig, Elke; Benestad, Rasmus; Roessler, Ole; Wibig, Joanna; Wilcke, Renate; Kotlarski, Sven</p> <p>2016-04-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical <span class="hlt">downscaling</span> methods. This framework is based on a user-focused validation tree, guiding the selection of relevant validation indices and performance measures for different aspects of the validation (marginal, temporal, spatial, multi-variable). Moreover, several experiments have been designed to isolate specific points in the <span class="hlt">downscaling</span> procedure where problems may occur (assessment of intrinsic performance, effect of errors inherited from the global models, effect of non-stationarity, etc.). The list of <span class="hlt">downscaling</span> experiments includes 1) cross-validation with perfect predictors, 2) GCM predictors -aligned with EURO-CORDEX experiment- and 3) pseudo reality predictors (see Maraun et al. 2015, Earth's Future, 3, doi:10.1002/2014EF000259, for more details). The results of these experiments are gathered, validated and publicly distributed through the VALUE validation portal, allowing for a comprehensive community-open <span class="hlt">downscaling</span> intercomparison study. In this contribution we describe the overall results from Experiment 1), consisting of a European wide 5-fold cross-validation (with consecutive 6-year periods from 1979 to 2008) using predictors from ERA-Interim to <span class="hlt">downscale</span> precipitation and temperatures (minimum and maximum) over a set of 86 ECA&D stations representative of the main geographical and climatic regions in Europe. As a result of the open call for contribution to this experiment (closed in Dec. 2015), over 40 methods representative of the main approaches (MOS and Perfect Prognosis, PP) and techniques (linear scaling, quantile mapping, analogs, weather typing, linear and generalized regression, weather generators, etc.) were submitted, including information both data</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005WRR....41.2024G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005WRR....41.2024G"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> using K-nearest neighbors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gangopadhyay, Subhrendu; Clark, Martyn; Rajagopalan, Balaji</p> <p>2005-02-01</p> <p>Statistical <span class="hlt">downscaling</span> provides a technique for deriving local-scale information of precipitation and temperature from numerical weather prediction model output. The K-nearest neighbor (K-nn) is a new analog-type approach that is used in this paper to <span class="hlt">downscale</span> the National Centers for Environmental Prediction 1998 medium-range forecast model output. The K-nn algorithm queries days similar to a given feature vector in this archive and using empirical orthogonal function analysis identifies a subset of days (K) similar to the feature day. These K days are then weighted using a bisquare weight function and randomly sampled to generate <span class="hlt">ensembles</span>. A set of 15 medium-range forecast runs was used, and seven <span class="hlt">ensemble</span> members were generated from each run. The <span class="hlt">ensemble</span> of 105 members was then used to select the local-scale precipitation and temperature values in four diverse basins across the contiguous United States. These <span class="hlt">downscaled</span> precipitation and temperature estimates were subsequently analyzed to test the performance of this <span class="hlt">downscaling</span> approach. The <span class="hlt">downscaled</span> <span class="hlt">ensembles</span> were evaluated in terms of bias, the ranked probability skill score as a measure of forecast skill, spatial covariability between stations, temporal persistence, consistency between variables, and conditional bias and to develop spread-skill relationships. Though this approach does not explicitly model the space-time variability of the weather fields at each individual station, the above statistics were extremely well captured. The K-nn method was also compared with a multiple-linear-regression-based <span class="hlt">downscaling</span> model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H53A1633A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H53A1633A"><span id="translatedtitle">Utilizing Multi-<span class="hlt">Ensemble</span> of <span class="hlt">Downscaled</span> CMIP5 GCMs to Investigate Trends and Spatial and Temporal Extent of Drought in Willamette Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ahmadalipour, A.; Beal, B.; Moradkhani, H.</p> <p>2015-12-01</p> <p>Changing climate and potential future increases in global temperature are likely to have impacts on drought characteristics and hydrologic cylce. In this study, we analyze changes in temporal and spatial extent of meteorological and hydrological droughts in future, and their trends. Three statistically <span class="hlt">downscaled</span> datasets from NASA Earth Exchange Global Daily <span class="hlt">Downscaled</span> Projections (NEX-GDDP), Multivariate Adaptive Constructed Analogs (MACA), and Bias Correction and Spatial Disagregation (BCSD-PSU) each consisting of 10 CMIP5 Global Climate Models (GCM) are utilized for RCP4.5 and RCP8.5 scenarios. Further, Precipitation Runoff Modeling System (PRMS) hydrologic model is used to simulate streamflow from GCM inputs and assess the hydrological drought characteristics. Standard Precipitation Index (SPI) and Streamflow Drought Index (SDI) are the two indexes used to investigate meteorological and hydrological drought, respectively. Study is done for Willamette Basin with a drainage area of 29,700 km2 accommodating more than 3 million inhabitants and 25 dams. We analyze our study for annual time scale as well as three future periods of near future (2010-2039), intermediate future (2040-2069), and far future (2070-2099). Large uncertainty is found from GCM predictions. Results reveal that meteorological drought events are expected to increase in near future. Severe to extreme drought with large areal coverage and several years of occurance is predicted around year 2030 with the likelihood of exceptional drought for both drought types. SPI is usually showing positive trends, while SDI indicates negative trends in most cases.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.A24A..05D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A24A..05D"><span id="translatedtitle">Statistical Properties of <span class="hlt">Downscaled</span> CMIP3 Global Climate Model Simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Duffy, P.; Tyan, S.; Thrasher, B.; Maurer, E. P.; Tebaldi, C.</p> <p>2009-12-01</p> <p>Spatial <span class="hlt">downscaling</span> of global climate model projections adds physically meaningful spatial detail, and brings the results down to a scale that is more relevant to human and ecological systems. Statistical/empirical <span class="hlt">downscaling</span> methods are computationally inexpensive, and thus can be applied to large <span class="hlt">ensembles</span> of global climate model projections. Here we examine some of the statistical properties of a large <span class="hlt">ensemble</span> of empirically <span class="hlt">downscale</span> global climate projections. The projections are the CMIP3 global climate model projections that were performed by modeling groups around the world and archived by the Program for Climate Model Diagnosis and Intercomparison at Lawrence Livermore National Laboratory. <span class="hlt">Downscaled</span> versions of 112 of these simulations were created on 2007 and are archived at http://gdo-dcp.ucllnl.org/<span class="hlt">downscaled</span>_cmip3_projections/dcpInterface.html. The <span class="hlt">downscaling</span> methodology employed, “Bias Correction/Spatial Downscaling” (BCSD), includes a correction of GCM biases relative to observations during a historical reference period, as well as empirical <span class="hlt">downscaling</span> to grid scale of ~12 km. We analyzed these <span class="hlt">downscaled</span> projections and some of the original global model results to assess effects of the bias correction and <span class="hlt">downscaling</span> on the statistical properties of the <span class="hlt">ensemble</span>. We also assessed uncertainty in the climate response to increased greenhouse gases from initial conditions relative to the uncertainty introduced by choice of global climate model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC31D..02F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC31D..02F"><span id="translatedtitle">Regional <span class="hlt">downscaling</span> of decadal predictions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Feldmann, H.</p> <p>2014-12-01</p> <p>During the last years the research field of decadal predictions gained increased attention. Its intention is to exploit the predictability derived from slowly varying components of the climate system on inter-annual to decadal time-scales. Such predictions are mostly performed using <span class="hlt">ensembles</span> of global earth system models. The prediction systems are able to achieve a relatively high predictive skill over some oceanic regions, like the North Atlantic sector. But potential users of decadal predictions are often interested in forecasts over land areas and require a higher resolution, too. Therefore, the German research program MiKlip develops a decadal <span class="hlt">ensemble</span> predictions system with regional <span class="hlt">downscaling</span> as an additional option. Dynamical <span class="hlt">downscaling</span> and a statistical-dynamical <span class="hlt">downscaling</span> approach are applied within the MiKlip regionalization module. The global prediction system consists of the MPI-ESM model. Different RCMs are used for the <span class="hlt">downscaling</span>, e.g. CCLM and REMO. The focus regions are Europe and Western Africa. Hindcast experiments for the period 1960 - 2013 were performed to assess the general skill of the prediction system. Of special interest is the value added by the regional <span class="hlt">downscaling</span>. For mean quantities, like annual mean temperature and precipitation, the predictive skill is comparable between the global and the <span class="hlt">downscaled</span> systems. For extremes on the other hand there seems to be an improvement by the RCM <span class="hlt">ensemble</span>. The skill strongly varies on sub-continental regions and with the season. The lead time up to which a positive predictive skill can be achieved depends on the parameter and season, too. A further goal is to assess the potential for valuable information, which can be derived from predicting long-term variations of the European climate. The leading mode of decadal variability in the European/Atlantic sector is the Atlantic Multidecadal Variation (AMV). The potential predictability from AMV teleconnections especially for extreme value</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1812217O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812217O"><span id="translatedtitle">Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model <span class="hlt">ensembles</span> <span class="hlt">downscaled</span> by analog <span class="hlt">ensemble</span> using self-organizing maps</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji</p> <p>2016-04-01</p> <p>Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the <span class="hlt">downscaling</span> technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1069M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1069M"><span id="translatedtitle">New statistical <span class="hlt">downscaling</span> for Canada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Murdock, T. Q.; Cannon, A. J.; Sobie, S.</p> <p>2013-12-01</p> <p>This poster will document the production of a set of statistically <span class="hlt">downscaled</span> future climate projections for Canada based on the latest available RCM and GCM simulations - the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2007) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). The main stages of the project included (1) <span class="hlt">downscaling</span> method evaluation, (2) scenarios selection, (3) production of statistically <span class="hlt">downscaled</span> results, and (4) applications of results. We build upon a previous <span class="hlt">downscaling</span> evaluation project (Bürger et al. 2012, Bürger et al. 2013) in which a quantile-based method (Bias Correction/Spatial Disaggregation - BCSD; Werner 2011) provided high skill compared with four other methods representing the majority of types of <span class="hlt">downscaling</span> used in Canada. Additional quantile-based methods (Bias-Correction/Constructed Analogues; Maurer et al. 2010 and Bias-Correction/Climate Imprint ; Hunter and Meentemeyer 2005) were evaluated. A subset of 12 CMIP5 simulations was chosen based on an objective set of selection criteria. This included hemispheric skill assessment based on the CLIMDEX indices (Sillmann et al. 2013), historical criteria used previously at the Pacific Climate Impacts Consortium (Werner 2011), and refinement based on a modified clustering algorithm (Houle et al. 2012; Katsavounidis et al. 1994). Statistical <span class="hlt">downscaling</span> was carried out on the NARCCAP <span class="hlt">ensemble</span> and a subset of the CMIP5 <span class="hlt">ensemble</span>. We produced <span class="hlt">downscaled</span> scenarios over Canada at a daily time resolution and 300 arc second (~10 km) spatial resolution from historical runs for 1951-2005 and from RCP 2.6, 4.5, and 8.5 projections for 2006-2100. The ANUSPLIN gridded daily dataset (McKenney et al. 2011) was used as a target. It has national coverage, spans the historical period of interest 1951-2005, and has daily time resolution. It uses interpolation of station data based on thin-plate splines. This type of method has been shown to have</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1064W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1064W"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span>: Lessons Learned</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Walton, D.; Hall, A. D.; Sun, F.</p> <p>2013-12-01</p> <p>In this study, we examine ways to improve statistical <span class="hlt">downscaling</span> of general circulation model (GCM) output. Why do we <span class="hlt">downscale</span> GCM output? GCMs have low resolution, so they cannot represent local dynamics and topographic effects that cause spatial heterogeneity in the regional climate change signal. Statistical <span class="hlt">downscaling</span> recovers fine-scale information by utilizing relationships between the large-scale and fine-scale signals to bridge this gap. In theory, the <span class="hlt">downscaled</span> climate change signal is more credible and accurate than its GCM counterpart, but in practice, there may be little improvement. Here, we tackle the practical problems that arise in statistical <span class="hlt">downscaling</span>, using temperature change over the Los Angeles region as a test case. This region is an ideal place to apply <span class="hlt">downscaling</span> since its complex topography and shoreline are poorly simulated by GCMs. By comparing two popular statistical <span class="hlt">downscaling</span> methods and one dynamical <span class="hlt">downscaling</span> method, we identify issues with statistically <span class="hlt">downscaled</span> climate change signals and develop ways to fix them. We focus on scale mismatch, domain of influence, and other problems - many of which users may be unaware of - and discuss practical solutions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009HESSD...6.6535H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009HESSD...6.6535H"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of precipitation: state-of-the-art and application of bayesian multi-model approach for uncertainty assessment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hashmi, M. Z.; Shamseldin, A. Y.; Melville, B. W.</p> <p>2009-10-01</p> <p>Global Circulation Models (GCMs) are a major tool used for future projections of climate change using different emission scenarios. However, for assessing the hydrological impacts of climate change at the watershed and the regional scale, the GCM outputs cannot be used directly due to the mismatch in the spatial resolution between the GCMs and hydrological models. In order to use the output of a GCM for conducting hydrological impact studies, <span class="hlt">downscaling</span> is used. However, the <span class="hlt">downscaling</span> results may contain considerable uncertainty which needs to be quantified before making the results available. Among the variables usually <span class="hlt">downscaled</span>, precipitation <span class="hlt">downscaling</span> is quite challenging and is more prone to uncertainty issues than other climatological variables. This paper addresses the uncertainty analysis associated with statistical <span class="hlt">downscaling</span> of a watershed precipitation (Clutha River above Balclutha, New Zealand) using results from three well reputed <span class="hlt">downscaling</span> methods and Bayesian weighted multi-model <span class="hlt">ensemble</span> approach. The <span class="hlt">downscaling</span> methods used for this study belong to the following <span class="hlt">downscaling</span> categories; (1) Multiple linear regression; (2) Multiple non-linear regression; and (3) Stochastic weather generator. The results obtained in this study have shown that this <span class="hlt">ensemble</span> strategy is very efficient in combining the results from multiple <span class="hlt">downscaling</span> methods on the basis of their performance and quantifying the uncertainty contained in this <span class="hlt">ensemble</span> output. This will encourage any future attempts on quantifying <span class="hlt">downscaling</span> uncertainties using the multi-model <span class="hlt">ensemble</span> framework.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC51E1033C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC51E1033C"><span id="translatedtitle">An extreme comparison of two <span class="hlt">downscaling</span> approaches using Bayes factors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chun, K.; Wheater, H. S.; Onof, C. J.</p> <p>2011-12-01</p> <p>Extreme rainfall events are the long-standing hydrological interest of flood defence and water resources management. Although traditional extreme value theory allows stationary extreme assessment, recent development of rainfall <span class="hlt">downscaling</span> approaches driven by projections of Global Climate models (GCMs) facilitates non-stationary extreme assessments. Additionally, using stochastic <span class="hlt">downscaling</span>, the <span class="hlt">downscaled</span> rainfall series can be probabilistic so that the inherent uncertainty of the used approaches can be explicitly presented. However, there is little work on performance benchmarking of extremes simulated by alternative <span class="hlt">downscaling</span> approaches. In the United Kingdom (UK), two independently developed <span class="hlt">downscaling</span> methodologies are (1) the UK climate projections (UKCP09) weather generators and (2) the Generalised linear model (GLM) approach. Both <span class="hlt">downscaling</span> approaches can provide daily rainfall series at catchment scale. As a quantitative benchmark, Bayes factors are proposed as a tool for comparing <span class="hlt">ensemble</span> extremes generated from the two UK models. Using Monte Carlo Integration and Laplace's approximation, Bayes factors for the 30th largest annual event within a 30 year period of the two methods are approximated for six catchments in the UK. Despite their similar average monthly statistics (i.e. mean, variance, autocorrelation and skewness), results show that the preferred approach for extreme results is catchment specific. The implications and possible interpretations of diverse extreme results from different <span class="hlt">downscaling</span> approaches are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PhDT.......150W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhDT.......150W"><span id="translatedtitle">Development and Evaluation of a Hybrid Dynamical-Statistical <span class="hlt">Downscaling</span> Method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Walton, Daniel Burton</p> <p></p> <p>Regional climate change studies usually rely on <span class="hlt">downscaling</span> of global climate model (GCM) output in order to resolve important fine-scale features and processes that govern local climate. Previous efforts have used one of two techniques: (1) dynamical <span class="hlt">downscaling</span>, in which a regional climate model is forced at the boundaries by GCM output, or (2) statistical <span class="hlt">downscaling</span>, which employs historical empirical relationships to go from coarse to fine resolution. Studies using these methods have been criticized because they either dynamical <span class="hlt">downscaled</span> only a few GCMs, or used statistical <span class="hlt">downscaling</span> on an <span class="hlt">ensemble</span> of GCMs, but missed important dynamical effects in the climate change signal. This study describes the development and evaluation of a hybrid dynamical-statstical <span class="hlt">downscaling</span> method that utilizes aspects of both dynamical and statistical <span class="hlt">downscaling</span> to address these concerns. The first step of the hybrid method is to use dynamical <span class="hlt">downscaling</span> to understand the most important physical processes that contribute to the climate change signal in the region of interest. Then a statistical model is built based on the patterns and relationships identified from dynamical <span class="hlt">downscaling</span>. This statistical model can be used to <span class="hlt">downscale</span> an entire <span class="hlt">ensemble</span> of GCMs quickly and efficiently. The hybrid method is first applied to a domain covering Los Angeles Region to generate projections of temperature change between the 2041-2060 and 1981-2000 periods for 32 CMIP5 GCMs. The hybrid method is also applied to a larger region covering all of California and the adjacent ocean. The hybrid method works well in both areas, primarily because a single feature, the land-sea contrast in the warming, controls the overwhelming majority of the spatial detail. Finally, the dynamically <span class="hlt">downscaled</span> temperature change patterns are compared to those produced by two commonly-used statistical methods, BCSD and BCCA. Results show that dynamical <span class="hlt">downscaling</span> recovers important spatial features that the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC51I0837F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC51I0837F"><span id="translatedtitle">Accounting for <span class="hlt">downscaling</span> and model uncertainties in examining the impacts of climate change on hydrological systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Franklin, M.; Yan, E.; Demissie, Y.</p> <p>2010-12-01</p> <p>Statistical <span class="hlt">downscaling</span> is a widely used method of transforming global climate model output to a regional or local scale for impact assessment studies. Uncertainties, both in the predictions generated through statistical <span class="hlt">downscaling</span> and in the climate model simulations themselves, are rarely accounted for in the resultant <span class="hlt">downscaled</span> climate parameters. Using observational meteorological data from 130 weather stations located in the upper midwest region of the U.S. and the 30-member <span class="hlt">ensemble</span> of Community Climate System Model forecasts under the A1B SRES scenario, probability distribution functions (PDF) accounting for the aforementioned <span class="hlt">downscaling</span> and model uncertainties were generated for daily precipitation, maximum and minimum temperature. Two-stage <span class="hlt">downscaling</span> was performed for each model <span class="hlt">ensemble</span> member resulting in 30 daily estimates of temperature and precipitation for each weather station. As temperature is a much smoother spatial and temporal process than precipitation, separate <span class="hlt">downscaling</span> methods were developed for these two parameters. The standard errors from the <span class="hlt">downscaling</span> stages were retained to quantify uncertainty in the estimates. Combined with the 30 realizations for each day, PDFs were generated that characterize both sources of uncertainty. Repeated samples drawn from the resultant PDFs served as inputs to the Soil and Water Assessment Tool (SWAT) hydrological model. The impact of climate change, accounting for uncertainty in <span class="hlt">downscaling</span> and the climate model, on the hydrological cycle of the upper Mississippi river basin was assessed. Sensitivity in the SWAT model to uncertainty in the input parameters was also examined.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1611668A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1611668A"><span id="translatedtitle"><span class="hlt">Downscaling</span> of inundation extents</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Aires, Filipe; Prigent, Catherine; Papa, Fabrice</p> <p>2014-05-01</p> <p>The Global Inundation Extent from Multi-Satellite (GIEMS) provides multi-year monthly variations of the global surface water extent at about 25 kmx25 km resolution, from 1993 to 2007. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. This paper present three approaches to do <span class="hlt">downscale</span> GIEMS: The first one is based on a image-processing technique using neighborhood constraints. The third approach uses a PCA representation to perform an algebraic inversion. The PCA-representation is also very convenient to perform temporal and spatial interpolation of complexe inundation fields. The third <span class="hlt">downscaling</span> method uses topography information from Hydroshed Digital Elevation Model (DEM). Information such as the elevation, distance to river and flow accumulation are used to define a ``flood ability index'' that is used by the <span class="hlt">downscaling</span>. Three basins will be considered for illustrative purposes: Amazon, Niger and Mekong. Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and <span class="hlt">downscaling</span> of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeorology, in press, 2014. Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, <span class="hlt">downscaled</span> from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..525..286D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..525..286D"><span id="translatedtitle">Dynamic coupling of support vector machine and K-nearest neighbour for <span class="hlt">downscaling</span> daily rainfall</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Devak, Manjula; Dhanya, C. T.; Gosain, A. K.</p> <p>2015-06-01</p> <p>Climate change impact assessment studies in water resources section demand the simulations of climatic variables at coarser scales from dynamic General Circulation Models (GCMs) to be mapped to even finer scales. Related studies in this area have mostly been relying on statistical techniques for <span class="hlt">downscaling</span> variables to finer resolution. This demands a careful selection of a suitable <span class="hlt">downscaling</span> model, to alleviate the <span class="hlt">downscaling</span> uncertainty. In this study, it is proposed to develop a dynamic framework for <span class="hlt">downscaling</span> purpose by integrating the frequently used techniques, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). In order to give flexibility in future predictors-predictand relationships and to account the sensitivity in model parameters, it is also proposed to generate an <span class="hlt">ensemble</span> of outputs by identifying various plausible model parameter combinations. The performance of this framework for <span class="hlt">downscaling</span> daily precipitation values at different locations is compared with simple KNN and SVM models. The proposed hybrid model is found to be better in capturing various characteristics of daily precipitation than individual models, especially in simulating the extremes, both in magnitude and duration. The mean <span class="hlt">ensemble</span> is found to be efficient than single best simulation with optimum parameter combinations. The efficacy of hybrid SVM-KNN <span class="hlt">ensemble</span> <span class="hlt">downscaling</span> model is established through detailed investigations. The future <span class="hlt">downscaled</span> projection for mid-century and late century employing this hybrid model indicates an increased variability in future precipitation, though the intensity varies for various locations. The developed methodology hence ensures lesser <span class="hlt">downscaling</span> uncertainty and also eliminates the inherent assumption of relationship stationarity considered in many <span class="hlt">downscaling</span> models.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_2 --> <div id="page_3" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="41"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/22086963','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/22086963"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2012.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Flicek, Paul; Amode, M Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; Gordon, Leo; Hendrix, Maurice; Hourlier, Thibaut; Johnson, Nathan; Kähäri, Andreas K; Keefe, Damian; Keenan, Stephen; Kinsella, Rhoda; Komorowska, Monika; Koscielny, Gautier; Kulesha, Eugene; Larsson, Pontus; Longden, Ian; McLaren, William; Muffato, Matthieu; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Riat, Harpreet Singh; Ritchie, Graham R S; Ruffier, Magali; Schuster, Michael; Sobral, Daniel; Tang, Y Amy; Taylor, Kieron; Trevanion, Stephen; Vandrovcova, Jana; White, Simon; Wilson, Mark; Wilder, Steven P; Aken, Bronwen L; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Durbin, Richard; Fernández-Suarez, Xosé M; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J P; Parker, Anne; Proctor, Glenn; Spudich, Giulietta; Vogel, Jan; Yates, Andy; Zadissa, Amonida; Searle, Stephen M J</p> <p>2012-01-01</p> <p>The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) provides genome resources for chordate genomes with a particular focus on human genome data as well as data for key model organisms such as mouse, rat and zebrafish. Five additional species were added in the last year including gibbon (Nomascus leucogenys) and Tasmanian devil (Sarcophilus harrisii) bringing the total number of supported species to 61 as of <span class="hlt">Ensembl</span> release 64 (September 2011). Of these, 55 species appear on the main <span class="hlt">Ensembl</span> website and six species are provided on the <span class="hlt">Ensembl</span> preview site (Pre!<span class="hlt">Ensembl</span>; http://pre.<span class="hlt">ensembl</span>.org) with preliminary support. The past year has also seen improvements across the project. PMID:22086963</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESSD..11.6167S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESSD..11.6167S"><span id="translatedtitle">Inter-comparison of statistical <span class="hlt">downscaling</span> methods for projection of extreme precipitation in Europe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sunyer, M. A.; Hundecha, Y.; Lawrence, D.; Madsen, H.; Willems, P.; Martinkova, M.; Vormoor, K.; Bürger, G.; Hanel, M.; Kriaučiūnienė, J.; Loukas, A.; Osuch, M.; Yücel, I.</p> <p>2014-06-01</p> <p>Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical <span class="hlt">downscaling</span> is necessary to address climate change impacts at the catchment scale. This study compares eight statistical <span class="hlt">downscaling</span> methods often used in climate change impact studies. Four methods are based on change factors, three are bias correction methods, and one is a perfect prognosis method. The eight methods are used to <span class="hlt">downscale</span> precipitation output from fifteen regional climate models (RCMs) from the <span class="hlt">ENSEMBLES</span> project for eleven catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the <span class="hlt">downscaled</span> time series tend to agree on the direction of the change but differ in the magnitude. Differences between the statistical <span class="hlt">downscaling</span> methods vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between change factor and bias correction methods. The performance of the bias correction methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the <span class="hlt">ensemble</span> of RCMs and statistical <span class="hlt">downscaling</span> methods indicates that up to half of the total variance is derived from the statistical <span class="hlt">downscaling</span> methods. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need of considering an <span class="hlt">ensemble</span> of both statistical <span class="hlt">downscaling</span> methods and climate models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFMPA13A2184T&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFMPA13A2184T&link_type=ABSTRACT"><span id="translatedtitle">Quantifying the Value of <span class="hlt">Downscaled</span> Climate Model Information for Adaptation Decisions: When is <span class="hlt">Downscaling</span> a Smart Decision?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Terando, A. J.; Wootten, A.; Eaton, M. J.; Runge, M. C.; Littell, J. S.; Bryan, A. M.; Carter, S. L.</p> <p>2015-12-01</p> <p>Two types of decisions face society with respect to anthropogenic climate change: (1) whether to enact a global greenhouse gas abatement policy, and (2) how to adapt to the local consequences of current and future climatic changes. The practice of <span class="hlt">downscaling</span> global climate models (GCMs) is often used to address (2) because GCMs do not resolve key features that will mediate global climate change at the local scale. In response, the development of <span class="hlt">downscaling</span> techniques and models has accelerated to aid decision makers seeking adaptation guidance. However, quantifiable estimates of the value of information are difficult to obtain, particularly in decision contexts characterized by deep uncertainty and low system-controllability. Here we demonstrate a method to quantify the additional value that decision makers could expect if research investments are directed towards developing new <span class="hlt">downscaled</span> climate projections. As a proof of concept we focus on a real-world management problem: whether to undertake assisted migration for an endangered tropical avian species. We also take advantage of recently published multivariate methods that account for three vexing issues in climate impacts modeling: maximizing climate model quality information, accounting for model dependence in <span class="hlt">ensembles</span> of opportunity, and deriving probabilistic projections. We expand on these global methods by including regional (Caribbean Basin) and local (Puerto Rico) domains. In the local domain, we test whether a high resolution (2km) dynamically <span class="hlt">downscaled</span> GCM reduces the multivariate error estimate compared to the original coarse-scale GCM. Initial tests show little difference between the <span class="hlt">downscaled</span> and original GCM multivariate error. When propagated through to a species population model, the Value of Information analysis indicates that the expected utility that would accrue to the manager (and species) if this <span class="hlt">downscaling</span> were completed may not justify the cost compared to alternative actions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1999JGR...10419705V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1999JGR...10419705V"><span id="translatedtitle">A space-time <span class="hlt">downscaling</span> model for rainfall</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Venugopal, V.; Foufoula-Georgiou, Efi; Sapozhnikov, Victor</p> <p>1999-08-01</p> <p>Interpretation of the impact of climate change or climate variability on water resources management requires information at scales much smaller than the current resolution of regional climate models. Subgrid-scale variability of precipitation is typically resolved by running nested or variable resolution models or by statistical <span class="hlt">downscaling</span>, the latter being especially attractive in <span class="hlt">ensemble</span> predictions due to its computational efficiency. Most existing precipitation <span class="hlt">downscaling</span> schemes are based on spatial disaggregation of rainfall patterns, independently at different times, and do not properly account for the temporal persistence of rainfall at the subgrid spatial scales. Such a temporal persistence in rainfall directly relates to the spatial variability of accumulated local soil moisture and might be important if the <span class="hlt">downscaled</span> values were to be used in a coupled atmospheric-hydrologic model. In this paper we propose a rainfall <span class="hlt">downscaling</span> model which utilizes the presence of dynamic scaling in rainfall [Venugopal et al., 1999] and which in conjunction with a spatial disaggregation scheme preserves both the temporal and spatial correlation structure of rainfall at the subgrid scales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFM.A41I0098T&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFM.A41I0098T&link_type=ABSTRACT"><span id="translatedtitle">Inter-comparison of precipitable water among reanalyses and its effect on <span class="hlt">downscaling</span> in the tropics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Takahashi, H. G.; Fujita, M.; Hara, M.</p> <p>2012-12-01</p> <p>This paper compared precipitable water (PW) among four major reanalyses. In addition, we also investigated the effect of the boundary conditions on <span class="hlt">downscaling</span> in the tropics, using a regional climate model. The spatial pattern of PW in the reanalyses agreed closely with observations. However, the absolute amounts of PW in some reanalyses were very small compared to observations. The discrepancies of the 12-year mean PW in July over the Southeast Asian monsoon region exceeded the inter-annual standard deviation of the PW. There was also a discrepancy in tropical PWs throughout the year, an indication that the problem is not regional, but global. The <span class="hlt">downscaling</span> experiments were conducted, which were forced by the different four reanalyses. The atmospheric circulation, including monsoon westerlies and various disturbances, was very small among the reanalyses. However, simulated precipitation was only 60 % of observed precipitation, although the dry bias in the boundary conditions was only 6 %. This result indicates that dry bias has large effects on precipitation in <span class="hlt">downscaling</span> over the tropics. This suggests that a simulated regional climate <span class="hlt">downscaled</span> from <span class="hlt">ensemble</span>-mean boundary conditions is quite different from an <span class="hlt">ensemble</span>-mean regional climate averaged over the several regional ones <span class="hlt">downscaled</span> from boundary conditions of the <span class="hlt">ensemble</span> members in the tropics. <span class="hlt">Downscaled</span> models can provide realistic simulations of regional tropical climates only if the boundary conditions include realistic absolute amounts of PW. Use of boundary conditions that include realistic absolute amounts of PW in <span class="hlt">downscaling</span> in the tropics is imperative at the present time. This work was partly supported by the Global Environment Research Fund (RFa-1101) of the Ministry of the Environment, Japan.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25833698','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25833698"><span id="translatedtitle"><span class="hlt">Downscaled</span> projections of Caribbean coral bleaching that can inform conservation planning.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>van Hooidonk, Ruben; Maynard, Jeffrey Allen; Liu, Yanyun; Lee, Sang-Ki</p> <p>2015-09-01</p> <p>Projections of climate change impacts on coral reefs produced at the coarse resolution (~1°) of Global Climate Models (GCMs) have informed debate but have not helped target local management actions. Here, projections of the onset of annual coral bleaching conditions in the Caribbean under Representative Concentration Pathway (RCP) 8.5 are produced using an <span class="hlt">ensemble</span> of 33 Coupled Model Intercomparison Project phase-5 models and via dynamical and statistical <span class="hlt">downscaling</span>. A high-resolution (~11 km) regional ocean model (MOM4.1) is used for the dynamical <span class="hlt">downscaling</span>. For statistical <span class="hlt">downscaling</span>, sea surface temperature (SST) means and annual cycles in all the GCMs are replaced with observed data from the ~4-km NOAA Pathfinder SST dataset. Spatial patterns in all three projections are broadly similar; the average year for the onset of annual severe bleaching is 2040-2043 for all projections. However, <span class="hlt">downscaled</span> projections show many locations where the onset of annual severe bleaching (ASB) varies 10 or more years within a single GCM grid cell. Managers in locations where this applies (e.g., Florida, Turks and Caicos, Puerto Rico, and the Dominican Republic, among others) can identify locations that represent relative albeit temporary refugia. Both <span class="hlt">downscaled</span> projections are different for the Bahamas compared to the GCM projections. The dynamically <span class="hlt">downscaled</span> projections suggest an earlier onset of ASB linked to projected changes in regional currents, a feature not resolved in GCMs. This result demonstrates the value of dynamical <span class="hlt">downscaling</span> for this application and means statistically <span class="hlt">downscaled</span> projections have to be interpreted with caution. However, aside from west of Andros Island, the projections for the two types of <span class="hlt">downscaling</span> are mostly aligned; projected onset of ASB is within ±10 years for 72% of the reef locations. PMID:25833698</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010ems..confE.188T&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010ems..confE.188T&link_type=ABSTRACT"><span id="translatedtitle">Hydrological <span class="hlt">Ensemble</span> Prediction System (HEPS)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thielen-Del Pozo, J.; Schaake, J.; Martin, E.; Pailleux, J.; Pappenberger, F.</p> <p>2010-09-01</p> <p>Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Following on the success of the use of <span class="hlt">ensembles</span> for weather forecasting, the hydrological community now moves increasingly towards Hydrological <span class="hlt">Ensemble</span> Prediction Systems (HEPS) for improved flood forecasting using operationally available NWP products as inputs. However, these products are often generated on relatively coarse scales compared to hydrologically relevant basin units and suffer systematic biases that may have considerable impact when passed through the non-linear hydrological filters. Therefore, a better understanding on how best to produce, communicate and use hydrologic <span class="hlt">ensemble</span> forecasts in hydrological short-, medium- und long term prediction of hydrological processes is necessary. The "Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment" (HEPEX), is an international initiative consisting of hydrologists, meteorologist and end-users to advance probabilistic hydrologic forecast techniques for flood, drought and water management applications. Different aspects of the hydrological <span class="hlt">ensemble</span> processor are being addressed including • Production of useful meteorological products relevant for hydrological applications, ranging from nowcasting products to seasonal forecasts. The importance of hindcasts that are consistent with the operational weather forecasts will be discussed to support bias correction and <span class="hlt">downscaling</span>, statistically meaningful verification of HEPS, and the development and testing of operating rules; • Need for <span class="hlt">downscaling</span> and post-processing of weather <span class="hlt">ensembles</span> to reduce bias before entering hydrological applications; • Hydrological model and parameter uncertainty and how to correct and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.5859D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.5859D"><span id="translatedtitle"><span class="hlt">Downscaling</span> precipitation extremes in a complex Alpine catchment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dobler, C.</p> <p>2012-04-01</p> <p>Climate change is expected to have significant effects on the frequency and intensity of heavy precipitation events. Assessing the impacts of climate change on precipitation extremes is a challenging task. On the one hand, the output of Regional Climate Models (RCMs) is subjected to systematic biases in the case of precipitation, especially in a complex mountain topography, and on the other hand, yet only a few statistical <span class="hlt">downscaling</span> techniques are known to <span class="hlt">downscale</span> precipitation extremes reliably. In this investigation two statistical <span class="hlt">downscaling</span> approaches were applied to simulate precipitation extremes in the Alpine part of the Lech catchment. The first one, Expanded <span class="hlt">Downscaling</span> (EDS), is a perfect prognosis approach that is based on regression. EDS has been calibrated and validated using large-scale predictor variables derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset and local station data. The EDS model was then applied to <span class="hlt">downscale</span> the output of two GCMs (ECHAM5, HadGEM2) for current (1971-2000) and future (2071-2100) time horizons, forced with the SRES A1B emission scenario. The second approach is the Long Ashton Research Station Weather Generator (LARS-WG) which can be characterized as a change factor conditioned weather generator. LARS-WG was calibrated on local station data only and then applied to <span class="hlt">downscale</span> the output of five different GCM-RCM combinations to meteorological stations. The RCMs have a horizontal resolution of ~25 km and were obtained from the <span class="hlt">ENSEMBLES</span> project of the European Union. In order to assess precipitation extremes with higher return values, a generalized extreme value distribution was applied to the data. Confidence intervals were calculated by using the non-parametric bootstrapping technique. The results show that both <span class="hlt">downscaling</span> approaches reproduce observed precipitation extremes fairly well. Even for very extreme precipitation events such as the 20-year event a good agreement</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3531136','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3531136"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2013</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Flicek, Paul; Ahmed, Ikhlak; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; García-Girón, Carlos; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah; Juettemann, Thomas; Kähäri, Andreas K.; Keenan, Stephen; Komorowska, Monika; Kulesha, Eugene; Longden, Ian; Maurel, Thomas; McLaren, William M.; Muffato, Matthieu; Nag, Rishi; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Pritchard, Emily; Riat, Harpreet Singh; Ritchie, Graham R. S.; Ruffier, Magali; Schuster, Michael; Sheppard, Daniel; Sobral, Daniel; Taylor, Kieron; Thormann, Anja; Trevanion, Stephen; White, Simon; Wilder, Steven P.; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J. P.; Johnson, Nathan; Kinsella, Rhoda; Parker, Anne; Spudich, Giulietta; Yates, Andy; Zadissa, Amonida; Searle, Stephen M. J.</p> <p>2013-01-01</p> <p>The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) provides genome information for sequenced chordate genomes with a particular focus on human, mouse, zebrafish and rat. Our resources include evidenced-based gene sets for all supported species; large-scale whole genome multiple species alignments across vertebrates and clade-specific alignments for eutherian mammals, primates, birds and fish; variation data resources for 17 species and regulation annotations based on ENCODE and other data sets. <span class="hlt">Ensembl</span> data are accessible through the genome browser at http://www.<span class="hlt">ensembl</span>.org and through other tools and programmatic interfaces. PMID:23203987</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013EGUGA..15.3380Z&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013EGUGA..15.3380Z&link_type=ABSTRACT"><span id="translatedtitle">Atmospheric <span class="hlt">Downscaling</span> using Genetic Programming</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zerenner, Tanja; Venema, Victor; Simmer, Clemens</p> <p>2013-04-01</p> <p>Coupling models for the different components of the Soil-Vegetation-Atmosphere-System requires up-and <span class="hlt">downscaling</span> procedures. Subject of our work is the <span class="hlt">downscaling</span> scheme used to derive high resolution forcing data for land-surface and subsurface models from coarser atmospheric model output. The current <span class="hlt">downscaling</span> scheme [Schomburg et. al. 2010, 2012] combines a bi-quadratic spline interpolation, deterministic rules and autoregressive noise. For the development of the scheme, training and validation data sets have been created by carrying out high-resolution runs of the atmospheric model. The deterministic rules in this scheme are partly based on known physical relations and partly determined by an automated search for linear relationships between the high resolution fields of the atmospheric model output and high resolution data on surface characteristics. Up to now deterministic rules are available for <span class="hlt">downscaling</span> surface pressure and partially, depending on the prevailing weather conditions, for near surface temperature and radiation. Aim of our work is to improve those rules and to find deterministic rules for the remaining variables, which require <span class="hlt">downscaling</span>, e.g. precipitation or near surface specifc humidity. To accomplish that, we broaden the search by allowing for interdependencies between different atmospheric parameters, non-linear relations, non-local and time-lagged relations. To cope with the vast number of possible solutions, we use genetic programming, a method from machine learning, which is based on the principles of natural evolution. We are currently working with GPLAB, a Genetic Programming toolbox for Matlab. At first we have tested the GP system to retrieve the known physical rule for <span class="hlt">downscaling</span> surface pressure, i.e. the hydrostatic equation, from our training data. We have found this to be a simple task to the GP system. Furthermore we have improved accuracy and efficiency of the GP solution by implementing constant variation and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012amld.book..563R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012amld.book..563R"><span id="translatedtitle"><span class="hlt">Ensemble</span> Methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Re, Matteo; Valentini, Giorgio</p> <p>2012-03-01</p> <p><span class="hlt">Ensemble</span> methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. <span class="hlt">Ensembles</span> are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote <span class="hlt">ensemble</span>, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall <span class="hlt">ensemble</span> [158]. In the literature, a plethora of terms other than <span class="hlt">ensembles</span> has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term <span class="hlt">ensemble</span> in its widest meaning, in order to include the whole range of combination methods. Nowadays, <span class="hlt">ensemble</span> methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on <span class="hlt">ensemble</span> methods is witnessed by conferences and workshops specifically devoted to <span class="hlt">ensembles</span>, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012amld.book..563R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012amld.book..563R&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Ensemble</span> Methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Re, Matteo; Valentini, Giorgio</p> <p>2012-03-01</p> <p><span class="hlt">Ensemble</span> methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. <span class="hlt">Ensembles</span> are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote <span class="hlt">ensemble</span>, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall <span class="hlt">ensemble</span> [158]. In the literature, a plethora of terms other than <span class="hlt">ensembles</span> has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term <span class="hlt">ensemble</span> in its widest meaning, in order to include the whole range of combination methods. Nowadays, <span class="hlt">ensemble</span> methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on <span class="hlt">ensemble</span> methods is witnessed by conferences and workshops specifically devoted to <span class="hlt">ensembles</span>, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=235875&keyword=Kalman&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=77967723&CFTOKEN=81361224','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=235875&keyword=Kalman&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=77967723&CFTOKEN=81361224"><span id="translatedtitle"><span class="hlt">Ensemble</span> Models</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p><span class="hlt">Ensemble</span> forecasting has been used for operational numerical weather prediction in the United States and Europe since the early 1990s. An <span class="hlt">ensemble</span> of weather or climate forecasts is used to characterize the two main sources of uncertainty in computer models of physical systems: ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=540092','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=540092"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2005</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Hubbard, T.; Andrews, D.; Caccamo, M.; Cameron, G.; Chen, Y.; Clamp, M.; Clarke, L.; Coates, G.; Cox, T.; Cunningham, F.; Curwen, V.; Cutts, T.; Down, T.; Durbin, R.; Fernandez-Suarez, X. M.; Gilbert, J.; Hammond, M.; Herrero, J.; Hotz, H.; Howe, K.; Iyer, V.; Jekosch, K.; Kahari, A.; Kasprzyk, A.; Keefe, D.; Keenan, S.; Kokocinsci, F.; London, D.; Longden, I.; McVicker, G.; Melsopp, C.; Meidl, P.; Potter, S.; Proctor, G.; Rae, M.; Rios, D.; Schuster, M.; Searle, S.; Severin, J.; Slater, G.; Smedley, D.; Smith, J.; Spooner, W.; Stabenau, A.; Stalker, J.; Storey, R.; Trevanion, S.; Ureta-Vidal, A.; Vogel, J.; White, S.; Woodwark, C.; Birney, E.</p> <p>2005-01-01</p> <p>The <span class="hlt">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org/) project provides a comprehensive and integrated source of annotation of large genome sequences. Over the last year the number of genomes available from the <span class="hlt">Ensembl</span> site has increased by 7 to 16, with the addition of the six vertebrate genomes of chimpanzee, dog, cow, chicken, tetraodon and frog and the insect genome of honeybee. The majority have been annotated automatically using the <span class="hlt">Ensembl</span> gene build system, showing its flexibility to reliably annotate a wide variety of genomes. With the increased number of vertebrate genomes, the comparative analysis provided to users has been greatly improved, with new website interfaces allowing annotation of different genomes to be directly compared. The <span class="hlt">Ensembl</span> software system is being increasingly widely reused in different projects showing the benefits of a completely open approach to software development and distribution. PMID:15608235</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1616101E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1616101E"><span id="translatedtitle"><span class="hlt">Downscaling</span> GCM-simulated precipitation for the last millennium</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Eden, Jonathan; Widmann, Martin; Smith, Richard</p> <p>2014-05-01</p> <p> precipitation from a simulation of ECHAM5 (nudged to ERA-40) and gridded observations. Both methods are then applied to simulated precipitation for the last millennium. Our findings show that, under cross-validation for the period 1958-2001, <span class="hlt">downscaling</span> with MOS from the T31 resolution to a 0.5° x 0.5° target grid produces precipitation estimates that generally match the temporal variability of the observed record in large parts of Europe. MOS also shows good skill in estimating monthly precipitation amounts at small scales that are more realistic than raw model output. In comparison with a multi-proxy gridded reconstruction (Pauling et al., 2006) it is shown that reconstructed precipitation falls within the range of the <span class="hlt">downscaled</span> <span class="hlt">ensemble</span> spread in some parts of Europe. However, in many areas MOS fails to produce <span class="hlt">downscaled</span> estimates that are in agreement with either the temporal evolution or magnitude indicated by the proxy record. Ultimately, this inconsistency limits the potential for such a comparison to be used as a validation tool except in individual cases.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006513','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006513"><span id="translatedtitle">Evaluating <span class="hlt">Downscaling</span> Methods for Seasonal Climate Forecasts over East Africa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris</p> <p>2013-01-01</p> <p>The U.S. National Multi-Model <span class="hlt">Ensemble</span> seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of <span class="hlt">downscaling</span> methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially <span class="hlt">downscaled</span> and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available <span class="hlt">downscaling</span> methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006440','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006440"><span id="translatedtitle">Evaluating <span class="hlt">Downscaling</span> Methods for Seasonal Climate Forecasts over East Africa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Robertson, Franklin R.; Roberts, J. Brent; Bosilovich, Michael; Lyon, Bradfield</p> <p>2013-01-01</p> <p>The U.S. National Multi-Model <span class="hlt">Ensemble</span> seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of <span class="hlt">downscaling</span> methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially <span class="hlt">downscaled</span> and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available <span class="hlt">downscaling</span> methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4383879','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4383879"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2015</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Cunningham, Fiona; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Billis, Konstantinos; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.; Janacek, Sophie H.; Johnson, Nathan; Juettemann, Thomas; Kähäri, Andreas K.; Keenan, Stephen; Martin, Fergal J.; Maurel, Thomas; McLaren, William; Murphy, Daniel N.; Nag, Rishi; Overduin, Bert; Parker, Anne; Patricio, Mateus; Perry, Emily; Pignatelli, Miguel; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P.; Zadissa, Amonida; Aken, Bronwen L.; Birney, Ewan; Harrow, Jennifer; Kinsella, Rhoda; Muffato, Matthieu; Ruffier, Magali; Searle, Stephen M.J.; Spudich, Giulietta; Trevanion, Stephen J.; Yates, Andy; Zerbino, Daniel R.; Flicek, Paul</p> <p>2015-01-01</p> <p><span class="hlt">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org) is a genomic interpretation system providing the most up-to-date annotations, querying tools and access methods for chordates and key model organisms. This year we released updated annotation (gene models, comparative genomics, regulatory regions and variation) on the new human assembly, GRCh38, although we continue to support researchers using the GRCh37.p13 assembly through a dedicated site (http://grch37.<span class="hlt">ensembl</span>.org). Our Regulatory Build has been revamped to identify regulatory regions of interest and to efficiently highlight their activity across disparate epigenetic data sets. A number of new interfaces allow users to perform large-scale comparisons of their data against our annotations. The REST server (http://rest.<span class="hlt">ensembl</span>.org), which allows programs written in any language to query our databases, has moved to a full service alongside our upgraded website tools. Our online Variant Effect Predictor tool has been updated to process more variants and calculate summary statistics. Lastly, the WiggleTools package enables users to summarize large collections of data sets and view them as single tracks in <span class="hlt">Ensembl</span>. The <span class="hlt">Ensembl</span> code base itself is more accessible: it is now hosted on our GitHub organization page (https://github.com/<span class="hlt">Ensembl</span>) under an Apache 2.0 open source license. PMID:25352552</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012GeoRL..3923804H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012GeoRL..3923804H"><span id="translatedtitle">A combined statistical and dynamical approach for <span class="hlt">downscaling</span> large-scale footprints of European windstorms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Haas, R.; Pinto, J. G.</p> <p>2012-12-01</p> <p>The occurrence of mid-latitude windstorms is related to strong socio-economic effects. For detailed and reliable regional impact studies, large datasets of high-resolution wind fields are required. In this study, a statistical <span class="hlt">downscaling</span> approach in combination with dynamical <span class="hlt">downscaling</span> is introduced to derive storm related gust speeds on a high-resolution grid over Europe. Multiple linear regression models are trained using reanalysis data and wind gusts from regional climate model simulations for a sample of 100 top ranking windstorm events. The method is computationally inexpensive and reproduces individual windstorm footprints adequately. Compared to observations, the results for Germany are at least as good as pure dynamical <span class="hlt">downscaling</span>. This new tool can be easily applied to large <span class="hlt">ensembles</span> of general circulation model simulations and thus contribute to a better understanding of the regional impact of windstorms based on decadal and climate change projections.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..529.1407N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..529.1407N"><span id="translatedtitle">Transient stochastic <span class="hlt">downscaling</span> of quantitative precipitation estimates for hydrological applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nogueira, M.; Barros, A. P.</p> <p>2015-10-01</p> <p>Rainfall fields are heavily thresholded and highly intermittent resulting in large areas of zero values. This deforms their stochastic spatial scale-invariant behavior, introducing scaling breaks and curvature in the spatial scale spectrum. To address this problem, spatial scaling analysis was performed inside continuous rainfall features (CRFs) delineated via cluster analysis. The results show that CRFs from single realizations of hourly rainfall display ubiquitous multifractal behavior that holds over a wide range of scales (from ≈1 km up to 100's km). The results further show that the aggregate scaling behavior of rainfall fields is intrinsically transient with the scaling parameters explicitly dependent on the atmospheric environment. These findings provide a framework for robust stochastic <span class="hlt">downscaling</span>, bridging the gap between spatial scales of observed and simulated rainfall fields and the high-resolution requirements of hydrometeorological and hydrological studies. Here, a fractal <span class="hlt">downscaling</span> algorithm adapted to CRFs is presented and applied to generate stochastically <span class="hlt">downscaled</span> hourly rainfall products from radar derived Stage IV (∼4 km grid resolution) quantitative precipitation estimates (QPE) over the Integrated Precipitation and Hydrology Experiment (IPHEx) domain in the southeast USA. The methodology can produce large <span class="hlt">ensembles</span> of statistically robust high-resolution fields without additional data or any calibration requirements, conserving the coarse resolution information and generating coherent small-scale variability and field statistics, hence adding value to the original fields. Moreover, it is computationally inexpensive enabling fast production of high-resolution rainfall realizations with latency adequate for forecasting applications. When the transient nature of the scaling behavior is considered, the results show a better ability to reproduce the statistical structure of observed rainfall compared to using fixed scaling parameters</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.553D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.553D"><span id="translatedtitle"><span class="hlt">Downscaling</span> of seasonal forecasts and possible application to hydro-power production forecasts in France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubus, L.; Berthelot, M.; Qu, Z.; Gailhard, J.</p> <p>2009-09-01</p> <p>Managing the power generation system at the scale of a country is a very complex problem which involves in particular climatic variables at different space and time scales. Air temperature and precipitation are among the most important ones, as they explain respectively an important part of the demand variability and the hydro power production capacity. If direct GCMs forecasts of local variables are not very skilful, specially over mid-latitudes, large scale fields such as geopotential height or mean sea level pressure show some positive skill over the North Atlantic / european region, that can be used to make local predictions of surface variables, using <span class="hlt">downscaling</span> technics. In this study, we evaluated the 2m temperature and precipitation hindcasts of the DEMETER and <span class="hlt">ENSEMBLES</span> systems on a number of hydrological basins in France. We used the University of Cantabria web portal for statistical <span class="hlt">downscaling</span>, developed in the <span class="hlt">ENSEMBLES</span> project, to <span class="hlt">downscale</span> the most predictable large scale fields, and compared direct raw hindcasts with indirect <span class="hlt">downscaled</span> hindcasts. Both direct and indirect hindcasts are then used in an hydrolocial model to evaluate their respective interest for hydro-power production forecasts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4742469','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4742469"><span id="translatedtitle"><span class="hlt">Ensemble</span> Tractography</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Wandell, Brian A.</p> <p>2016-01-01</p> <p>Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using <span class="hlt">ensemble</span> methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an <span class="hlt">ensemble</span> of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The <span class="hlt">ensemble</span> approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the <span class="hlt">ensemble</span> approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic <span class="hlt">ensemble</span> tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles. PMID:26845558</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006JHyd..319..357K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006JHyd..319..357K"><span id="translatedtitle">Uncertainty analysis of statistical <span class="hlt">downscaling</span> methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khan, Mohammad Sajjad; Coulibaly, Paulin; Dibike, Yonas</p> <p>2006-03-01</p> <p>Three <span class="hlt">downscaling</span> models namely Statistical <span class="hlt">Down-Scaling</span> Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG) model and Artificial Neural Network (ANN) model have been compared in terms various uncertainty assessments exhibited in their <span class="hlt">downscaled</span> results of daily precipitation, daily maximum and minimum temperatures. In case of daily maximum and minimum temperature, uncertainty is assessed by comparing monthly mean and variance of <span class="hlt">downscaled</span> and observed daily maximum and minimum temperature at each month of the year at 95% confidence level. In addition, uncertainties of the monthly means and variances of <span class="hlt">downscaled</span> daily temperature have been calculated using 95% confidence intervals, which are compared with the observed uncertainties of means and variances. In daily precipitation <span class="hlt">downscaling</span>, in addition to comparing means and variances, uncertainties have been assessed by comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions (cdfs) of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and <span class="hlt">downscaled</span> daily precipitation. The study has been carried out using 40 years of observed and <span class="hlt">downscaled</span> daily precipitation, daily maximum and minimum temperature data using NCEP (National Center for Environmental Prediction) reanalysis predictors starting from 1961 to 2000. The uncertainty assessment results indicate that the SDSM is the most capable of reproducing various statistical characteristics of observed data in its <span class="hlt">downscaled</span> results with 95% confidence level, the ANN is the least capable in this respect, and the LARS-WG is in between SDSM and ANN.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/17148474','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/17148474"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2007.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hubbard, T J P; Aken, B L; Beal, K; Ballester, B; Caccamo, M; Chen, Y; Clarke, L; Coates, G; Cunningham, F; Cutts, T; Down, T; Dyer, S C; Fitzgerald, S; Fernandez-Banet, J; Graf, S; Haider, S; Hammond, M; Herrero, J; Holland, R; Howe, K; Howe, K; Johnson, N; Kahari, A; Keefe, D; Kokocinski, F; Kulesha, E; Lawson, D; Longden, I; Melsopp, C; Megy, K; Meidl, P; Ouverdin, B; Parker, A; Prlic, A; Rice, S; Rios, D; Schuster, M; Sealy, I; Severin, J; Slater, G; Smedley, D; Spudich, G; Trevanion, S; Vilella, A; Vogel, J; White, S; Wood, M; Cox, T; Curwen, V; Durbin, R; Fernandez-Suarez, X M; Flicek, P; Kasprzyk, A; Proctor, G; Searle, S; Smith, J; Ureta-Vidal, A; Birney, E</p> <p>2007-01-01</p> <p>The <span class="hlt">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org/) project provides a comprehensive and integrated source of annotation of chordate genome sequences. Over the past year the number of genomes available from <span class="hlt">Ensembl</span> has increased from 15 to 33, with the addition of sites for the mammalian genomes of elephant, rabbit, armadillo, tenrec, platypus, pig, cat, bush baby, common shrew, microbat and european hedgehog; the fish genomes of stickleback and medaka and the second example of the genomes of the sea squirt (Ciona savignyi) and the mosquito (Aedes aegypti). Some of the major features added during the year include the first complete gene sets for genomes with low-sequence coverage, the introduction of new strain variation data and the introduction of new orthology/paralog annotations based on gene trees. PMID:17148474</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.7619V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.7619V"><span id="translatedtitle">A Combined Bias Correction and Stochastic <span class="hlt">Downscaling</span> Method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Volosciuk, Claudia; Maraun, Douglas; Vrac, Mathieu</p> <p>2015-04-01</p> <p>Precipitation is highly variable in space and time, especially its extremes. Much of our knowledge about future changes in precipitation relies on global (GCM) and/or regional climate models (RCM) that have resolutions which are much coarser than typical spatial scales of extreme precipitation. The major problems with these projections are both GCM/RCM-biases in simulated precipitation and the scale gap between grid box and point scale. In particular, traditional bias correction methods (e.g., quantile mapping) cannot bridge this scale gap, and empirical statistical <span class="hlt">downscaling</span> methods have a very limited ability to correct biases. Wong et al. presented a first attempt to jointly bias correct and <span class="hlt">downscale</span> precipitation at daily scales. However, this approach relies on spectrally nudged RCM simulations, which are rarely available. Here we present a combined statistical bias correction and stochastic <span class="hlt">downscaling</span> method, with the aim of combining their respective advantages, that in principle also works for free running RCMs, such as those available from <span class="hlt">ENSEMBLES</span> or CORDEX. Thereby, we separate bias correction from <span class="hlt">downscaling</span>. In a first step, we bias correct the RCMs (EURO-CORDEX) against gridded observational datasets (e.g., E-OBS) at the same scale using a quantile mapping approach that relies on distribution transformation. To correct the whole precipitation distribution including extreme tails we apply a mixture distribution of a gamma distribution for the precipitation mass and a generalized Pareto distribution for the extreme tail. In a second step, we bridge the scale gap: we add small scale variability to the bias corrected precipitation time series using a vector generalized linear gamma model (VGLM gamma). To calibrate the VGLM gamma model we determine the statistical relationship between precipitation observations on different scales, i.e. between gridded (e.g., E-OBS) and station (ECA&D) observations. We apply this combined bias correction and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3147673','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3147673"><span id="translatedtitle">The Requirement for Pneumococcal MreC and <span class="hlt">MreD</span> Is Relieved by Inactivation of the Gene Encoding PBP1a ▿†</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Land, Adrian D.; Winkler, Malcolm E.</p> <p>2011-01-01</p> <p>MreC and <span class="hlt">MreD</span>, along with the actin homologue MreB, are required to maintain the shape of rod-shaped bacteria. The depletion of MreCD in rod-shaped bacteria leads to the formation of spherical cells and the accumulation of suppressor mutations. Ovococcus bacteria, such as Streptococcus pneumoniae, lack MreB homologues, and the functions of the S. pneumoniae MreCD (MreCDSpn) proteins are unknown. mreCD are located upstream from the pcsB cell division gene in most Streptococcus species, but we found that mreCD and pcsB are transcribed independently. Similarly to rod-shaped bacteria, we show that mreCD are essential in the virulent serotype 2 D39 strain of S. pneumoniae, and the depletion of MreCD results in cell rounding and lysis. In contrast, laboratory strain R6 contains suppressors that allow the growth of ΔmreCD mutants, and bypass suppressors accumulate in D39 ΔmreCD mutants. One class of suppressors eliminates the function of class A penicillin binding protein 1a (PBP1a). Unencapsulated Δpbp1a D39 mutants have smaller diameters than their pbp1a+ parent or Δpbp2a and Δpbp1b mutants, which lack other class A PBPs and do not show the suppression of ΔmreCD mutations. Suppressed ΔmreCD Δpbp1a double mutants form aberrantly shaped cells, some with misplaced peptidoglycan (PG) biosynthesis compared to that of single Δpbp1a mutants. Quantitative Western blotting showed that MreCSpn is abundant (≈8,500 dimers per cell), and immunofluorescent microscopy (IFM) located MreCDSpn to the equators and septa of dividing cells, similarly to the PBPs and PG pentapeptides indicative of PG synthesis. These combined results are consistent with a model in which MreCDSpn direct peripheral PG synthesis and control PBP1a localization or activity. PMID:21685290</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702834','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702834"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2016</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yates, Andrew; Akanni, Wasiu; Amode, M. Ridwan; Barrell, Daniel; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.; Janacek, Sophie H.; Johnson, Nathan; Juettemann, Thomas; Keenan, Stephen; Lavidas, Ilias; Martin, Fergal J.; Maurel, Thomas; McLaren, William; Murphy, Daniel N.; Nag, Rishi; Nuhn, Michael; Parker, Anne; Patricio, Mateus; Pignatelli, Miguel; Rahtz, Matthew; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P.; Zadissa, Amonida; Birney, Ewan; Harrow, Jennifer; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Spudich, Giulietta; Trevanion, Stephen J.; Cunningham, Fiona; Aken, Bronwen L.; Zerbino, Daniel R.; Flicek, Paul</p> <p>2016-01-01</p> <p>The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) is a system for genome annotation, analysis, storage and dissemination designed to facilitate the access of genomic annotation from chordates and key model organisms. It provides access to data from 87 species across our main and early access Pre! websites. This year we introduced three newly annotated species and released numerous updates across our supported species with a concentration on data for the latest genome assemblies of human, mouse, zebrafish and rat. We also provided two data updates for the previous human assembly, GRCh37, through a dedicated website (http://grch37.<span class="hlt">ensembl</span>.org). Our tools, in particular the VEP, have been improved significantly through integration of additional third party data. REST is now capable of larger-scale analysis and our regulatory data BioMart can deliver faster results. The website is now capable of displaying long-range interactions such as those found in cis-regulated datasets. Finally we have launched a website optimized for mobile devices providing views of genes, variants and phenotypes. Our data is made available without restriction and all code is available from our GitHub organization site (http://github.com/<span class="hlt">Ensembl</span>) under an Apache 2.0 license. PMID:26687719</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26687719','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26687719"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2016.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yates, Andrew; Akanni, Wasiu; Amode, M Ridwan; Barrell, Daniel; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E; Janacek, Sophie H; Johnson, Nathan; Juettemann, Thomas; Keenan, Stephen; Lavidas, Ilias; Martin, Fergal J; Maurel, Thomas; McLaren, William; Murphy, Daniel N; Nag, Rishi; Nuhn, Michael; Parker, Anne; Patricio, Mateus; Pignatelli, Miguel; Rahtz, Matthew; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P; Zadissa, Amonida; Birney, Ewan; Harrow, Jennifer; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Spudich, Giulietta; Trevanion, Stephen J; Cunningham, Fiona; Aken, Bronwen L; Zerbino, Daniel R; Flicek, Paul</p> <p>2016-01-01</p> <p>The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) is a system for genome annotation, analysis, storage and dissemination designed to facilitate the access of genomic annotation from chordates and key model organisms. It provides access to data from 87 species across our main and early access Pre! websites. This year we introduced three newly annotated species and released numerous updates across our supported species with a concentration on data for the latest genome assemblies of human, mouse, zebrafish and rat. We also provided two data updates for the previous human assembly, GRCh37, through a dedicated website (http://grch37.<span class="hlt">ensembl</span>.org). Our tools, in particular the VEP, have been improved significantly through integration of additional third party data. REST is now capable of larger-scale analysis and our regulatory data BioMart can deliver faster results. The website is now capable of displaying long-range interactions such as those found in cis-regulated datasets. Finally we have launched a website optimized for mobile devices providing views of genes, variants and phenotypes. Our data is made available without restriction and all code is available from our GitHub organization site (http://github.com/<span class="hlt">Ensembl</span>) under an Apache 2.0 license. PMID:26687719</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5667B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5667B"><span id="translatedtitle">Using satellite products to evaluate statistical <span class="hlt">downscaling</span> with generalised linear models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bergin, Emma; Buytaert, Wouter; Kwok-Pan, Chun; Turner, Andrew; Chawla, Ila; Mujumdar, Pradeep</p> <p>2015-04-01</p> <p>Generalised linear models (GLMs) have been around for some time and are routinely used for statistical <span class="hlt">downscaling</span> of rainfall data. However, in many regions it is difficult to evaluate them because of a lack of in situ data. <span class="hlt">Downscaling</span> models are frequently fitted using data from rain gauges. Therefore the validation of models using the same data can result in over-confidence of the model. One such region is northern India owing to the complexity of the monsoon system and relative lack of availability of raw raingauge data. Here we present a method to evaluate GLM-based <span class="hlt">downscaling</span> using satellite products. We fit a multi-site <span class="hlt">downscaling</span> model using generalised linear models for a case study region in the Upper Ganges, using data from 32 daily rain gauges from the Indian Meteorological Department for our study. The Asian monsoon is one of the largest manifestations of the annual cycle in the Earth System And given its importance for water resources in northern India, the analysis and projection of rainfall series in the Upper Ganges basin is of great significance for the region. We use correlations analyses to select physically meaningful predictors for the monsoon season for JJAS. Our GLM is fitted using rain gauge data for the period 1951-1999 using separate regressions for rainfall occurrence and amount. For the amounts model, we use sea surface temperature predictors from the Niño-3 region, moisture flux across the zonal plane at 850hPa over the Arabian Sea, specific humidity at 850hPa and air temperature at 2m over the Ganges basin. For the occurrence model we use air temperature at 2m over the Ganges basin. Additional predictors were trialled but were not significant. Our model is validated using a split-sample test for 1999-2005 using rain gauge data and independent satellite and reanalysis rainfall products. We use the TRMM 3B42 v7a and APHRODITE satellite rainfall products and the Princeton <span class="hlt">downscaled</span> NCEP reanalysis rainfall to form an <span class="hlt">ensemble</span> of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241249&keyword=point+AND+Fusion&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=77027838&CFTOKEN=64626316','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241249&keyword=point+AND+Fusion&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=77027838&CFTOKEN=64626316"><span id="translatedtitle">User's Manual for <span class="hlt">Downscaler</span> Fusion Software</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Recently, a series of 3 papers has been published in the statistical literature that details the use of <span class="hlt">downscaling</span> to obtain more accurate and precise predictions of air pollution across the conterminous U.S. This <span class="hlt">downscaling</span> approach combines CMAQ gridded numerical model output...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3964975','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3964975"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2014</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Flicek, Paul; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Billis, Konstantinos; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah; Johnson, Nathan; Juettemann, Thomas; Kähäri, Andreas K.; Keenan, Stephen; Kulesha, Eugene; Martin, Fergal J.; Maurel, Thomas; McLaren, William M.; Murphy, Daniel N.; Nag, Rishi; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Pritchard, Emily; Riat, Harpreet S.; Ruffier, Magali; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Trevanion, Stephen J.; Vullo, Alessandro; Wilder, Steven P.; Wilson, Mark; Zadissa, Amonida; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J.P.; Kinsella, Rhoda; Muffato, Matthieu; Parker, Anne; Spudich, Giulietta; Yates, Andy; Zerbino, Daniel R.; Searle, Stephen M.J.</p> <p>2014-01-01</p> <p><span class="hlt">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org) creates tools and data resources to facilitate genomic analysis in chordate species with an emphasis on human, major vertebrate model organisms and farm animals. Over the past year we have increased the number of species that we support to 77 and expanded our genome browser with a new scrollable overview and improved variation and phenotype views. We also report updates to our core datasets and improvements to our gene homology relationships from the addition of new species. Our REST service has been extended with additional support for comparative genomics and ontology information. Finally, we provide updated information about our methods for data access and resources for user training. PMID:24316576</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESS...19.1827S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESS...19.1827S"><span id="translatedtitle">Inter-comparison of statistical <span class="hlt">downscaling</span> methods for projection of extreme precipitation in Europe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sunyer, M. A.; Hundecha, Y.; Lawrence, D.; Madsen, H.; Willems, P.; Martinkova, M.; Vormoor, K.; Bürger, G.; Hanel, M.; Kriaučiūnienė, J.; Loukas, A.; Osuch, M.; Yücel, I.</p> <p>2015-04-01</p> <p>Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical <span class="hlt">downscaling</span> is necessary to address climate change impacts at the catchment scale. This study compares eight statistical <span class="hlt">downscaling</span> methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to <span class="hlt">downscale</span> precipitation output from 15 regional climate models (RCMs) from the <span class="hlt">ENSEMBLES</span> project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the <span class="hlt">downscaled</span> time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the <span class="hlt">ensemble</span> of RCMs and SDMs indicates that at least 30% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an <span class="hlt">ensemble</span> of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H21A1005Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H21A1005Z"><span id="translatedtitle">Atmospheric <span class="hlt">Downscaling</span> using Genetic Programming</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zerenner, T.; Venema, V.; Simmer, C.</p> <p>2013-12-01</p> <p>The coupling of models for the different components of the soil-vegetation-atmosphere system is required to understand component interactions and feedback processes. The Transregional Collaborative Research Center 32 (TR 32) has developed a coupled modeling platform, TerrSysMP, consisting of the atmospheric model COSMO, the land-surface model CLM, and the hydrological model ParFlow. These component models are usually operated at different resolutions in space and time owing to the dominant processes. These different scales should also be considered in the coupling mode, because it is for instance unfeasible to run the computationally quite expensive atmospheric models at the usually much higher spatial resolution required by hydrological models. Thus up- and <span class="hlt">downscaling</span> procedures are required at the interface between atmospheric model and land-surface/subsurface models. Here we present an advanced atmospheric <span class="hlt">downscaling</span> scheme, that creates realistic fine-scale fields (e.g. 400 m resolution) of the atmospheric state variables from the coarse atmospheric model output (e.g. 2.8 km resolution). The mixed physical/statistical scheme is developed from a training data set of high-resolution atmospheric model runs covering a range different weather conditions using Genetic Programming (GP). GP originates from machine learning: From a set of functions (arithmetic expressions, IF-statements, etc.) and terminals (constants or variables) GP generates potential solutions to a given problem while minimizing a fitness or cost function. We use a multi-objective approach that aims at fitting spatial structures, spatially distributed variance and spatio-temporal correlation of the fields. We account for the spatio-temporal nature of the data in two ways. On the one hand we offer GP potential predictors, which are based on our physical understanding of the atmospheric processes involved (spatial and temporal gradients, etc.). On the other hand we include functions operating on</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESS...18.5077S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESS...18.5077S"><span id="translatedtitle">Satellite-driven <span class="hlt">downscaling</span> of global reanalysis precipitation products for hydrological applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Seyyedi, H.; Anagnostou, E. N.; Beighley, E.; McCollum, J.</p> <p>2014-12-01</p> <p>Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be <span class="hlt">downscaled</span> to a spatial and temporal resolution applicable for flood modeling. This study evaluates such <span class="hlt">downscaling</span> and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation data set and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002-2011) in the Susquehanna River basin located in the northeastern United States. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity, and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall data set, while runoff simulations forced with the stage IV precipitation data set were considered as the runoff reference. Results show that the generated rainfall <span class="hlt">ensembles</span> from the <span class="hlt">downscaled</span> reanalysis product encapsulate the reference rainfall. The statistical analysis consists of frequency and quantile plots plus mean relative error and root-mean-square error statistics. The results demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven <span class="hlt">downscaled</span> reanalysis data set compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the <span class="hlt">downscaled</span> product has 3 times lower mean relative error than the original product; this ratio increases to 4 times for the simulated runoff values. The proposed <span class="hlt">downscaling</span> scheme is modular in design and can be applied on any gridded satellite and reanalysis data set.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014HESSD..11.9067S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014HESSD..11.9067S"><span id="translatedtitle">Satellite-driven <span class="hlt">downscaling</span> of global reanalysis precipitation products for hydrological applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Seyyedi, H.; Anagnostou, E. N.; Beighley, E.; McCollum, J.</p> <p>2014-07-01</p> <p>Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be <span class="hlt">downscaled</span> to a spatial and temporal resolution applicable for flood modeling. This study evaluates such <span class="hlt">downscaling</span> and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation dataset and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002-2011) in the Susquehanna River basin located in the northeast US. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall dataset, while runoff simulations forced with the stage IV precipitation dataset were considered as the runoff reference. Results show that the generated rainfall <span class="hlt">ensembles</span> from the <span class="hlt">downscaled</span> reanalysis products encapsulate the reference rainfall. The statistical analysis, including frequency and quantile plots plus mean relative error and root mean square error statistics, demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven <span class="hlt">downscaled</span> reanalysis dataset compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the <span class="hlt">downscaled</span> product has three times lower mean relative error than the original product; this ratio increases to four times for the simulated runoff values. The proposed <span class="hlt">downscaling</span> scheme is modular in design and can be applied on gridded satellite and reanalysis dataset.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H52F..02A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H52F..02A"><span id="translatedtitle">Improving Flood Modeling Applications of Global Reanalysis Precipitation Products through Satellite-driven <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Anagnostou, E. N.; Seyyedi, H.; Beighley, E., II; McCollum, J.</p> <p>2014-12-01</p> <p>Deriving flood vulnerability maps at basin scale typically requires simulating a long record of annual maximum discharges. To improve this approach, long precipitation records from global reanalysis systems must be <span class="hlt">downscaled</span> to a spatio-temporal resolution applicable for flood modeling. This study evaluates a combined spatial <span class="hlt">downscaling</span> and error correction technique based on high-resolution satellite precipitation products applied on NASA's Global Land Data Assimilation System (GLDAS) reanalysis precipitation dataset. The TRMM 3B42 25-km and 3-hourly blended satellite precipitation product is used for driving the GLDAS reanalysis <span class="hlt">downscaling</span>. The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002-2011) in the Susquehanna River basin located in the northeast United States. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall dataset, while runoff simulations forced with the stage IV precipitation dataset were considered as the runoff reference. Results show that the generated rainfall <span class="hlt">ensembles</span> from the <span class="hlt">downscaled</span> reanalysis products encapsulate the reference rainfall. Frequency analysis of rainfall and runoff and mean relative error and root mean square error statistics exhibited improvements in the precipitation and runoff simulation error statistics of the 3B42-driven <span class="hlt">downscaled</span> GLDAS reanalysis dataset compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. The proposed <span class="hlt">downscaling</span> scheme is modular in design and can be applied on different satellite and reanalysis dataset over different regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A11L..02D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A11L..02D"><span id="translatedtitle">Future hub-height wind speed distributions from statistically <span class="hlt">downscaled</span> CMIP5 simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Devis, A.; Demuzere, M.; van Lipzig, N.</p> <p>2013-12-01</p> <p>In order to realistically estimate wind-power yields, we need to know the hub-height wind speed under future climate conditions. Climate conditions of the upper atmosphere are commonly simulated using general circulation models (GCMs). However their typical resolutions are too coarse to assess the climate at the height of a wind turbine. This study simulates the hub-height wind speed probability distributions (PDFs) over Europe under future climate conditions. The analysis is based on the simulations of the CMIP5 earth system models, which are the latest development of GCMs. They include more components and feedbacks and their runs are performed at higher resolutions. In a first step, the <span class="hlt">ensemble</span> of GCMs is evaluated on their representation of the wind speed PDFs in the lower atmosphere using ERA-Interim data. The evaluation indicates that GCMs are skillful down to their lowest model levels apart for the south of Europe, which is affected by a large scale winter bias and for certain coastal and orographical regions. Secondly, a statistical approach is developed which <span class="hlt">downscales</span> the GCM output to the wind speed PDF at the height of the wind turbine hub. Since the evaluation analysis shows that GCMs are also skillful at the lower model levels, the statistical <span class="hlt">downscaling</span> uses GCM variables describing the lower atmosphere, instead of the commonly used large scale circulation variables of the upper atmosphere. By doing so less uncertainty will be added trough the <span class="hlt">downscaling</span> implementation. The <span class="hlt">downscaling</span> methodology is developed for an observational site in the Netherlands, using hub-height wind speed observations and ERA-Interim data for the period 1989-2009. The statistical approach is based on a regression analysis of the parameters of the PDFs. Results indicate that the predictor selection is very much defined by the stability conditions of the atmospheric boundary layer. During convective summer-day conditions, the observed hub-height wind speed can skillfully</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC33C0518D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC33C0518D"><span id="translatedtitle">On Reliability of Regional Decadal <span class="hlt">Ensemble</span> Prediction for Europe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Davary Adalatpanah, F.; Frueh, B.; Lenz, C. J.</p> <p>2014-12-01</p> <p>Within the MiKlip project the coupled model MPI-ESM is used to perform global decadal hindcast experiments. These experiments, baseline0, are performed in a low-resolution configuration (MPI-ESM-LR) with the latest version of the ocean model MPIOM and the atmospheric component ECHAM6, in a resolution GR15L40 and T063L47 respectively. The MPI-ESM-LR hindcasts are <span class="hlt">downscaled</span> to the CORDEX-Europe domain with a horizontal grid resolution of 0.22° using the mesoscale non-hydrostatic regional climate model COSMO-CLM (CCLM) (Rockel et al. 2008) with the version of COSMO4.8-clm17 for the time period 1961-2010 realizing hindcasts from 1961 to 2001 each 10 years for one decade. The evaluation run (ERA40 extended by ERA-Interim and <span class="hlt">downscaled</span> by CCLM) are used to initialize temperature and humidity in/at the soil/surface in the hindcasts. By using driving data with 1-day-lagged initialization, the "initial conditions" perturbation strategy is implemented. The gridded observational E-OBS data in version 8.0 (Haylock et al., 2008) and the CCLM evaluation run are used for evaluation. The focus of this study is on the 2-m temperature over Europe. To filter out the systematic error, anomalies are calculated by considering the time period 1981-2010 as reference period. Before the evaluation of reliability, the forecast quality is assessed by the anomaly correlation (Fig. 1) and the root mean square error (Fig. 2) (Wilks, 2006). The low-pass filtered 2-m temperature anomaly averaged over Europe from reference datasets and the <span class="hlt">ensemble</span> mean reveals that the CCLM captures the climate change signal. An <span class="hlt">ensemble</span> prediction system is perfectly reliable when the mean <span class="hlt">ensemble</span> spread equals the mean RMSE of the <span class="hlt">ensemble</span>-mean forecast (Palmer, 2006 and Doblas-Reyes, 2013). Therefore the ratio of the <span class="hlt">ensemble</span> spread to <span class="hlt">ensemble</span> error defined as <span class="hlt">ensemble</span> spread score (ESS) (Keller, 2011), is assessed for reliability. The evaluation shows that there is added value for reliability in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC54A..01H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC54A..01H"><span id="translatedtitle">Recent Developments in Statistical <span class="hlt">Downscaling</span> of Extremes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hertig, E.</p> <p>2014-12-01</p> <p>Based on the output of general circulation models (GCMs) regionalization techniques are usually applied to obtain fine-scale climate change information. Different types of regionalization techniques have been developed which comprise regional climate models and statistical <span class="hlt">downscaling</span> approaches such as conditional weather generators, artificial neural networks, synoptic studies, and transfer functions. In the scope of climate variability and climate change the variations and changes of extremes are of special importance. Extreme events are not only of scientific interest but also have a profound impact on society. For the statistical <span class="hlt">downscaling</span> of extremes, promising approaches have been introduced and/or developed further in the last few years. Aspects of recent developments in the scope of statistical <span class="hlt">downscaling</span> of extremes will be presented. In this context, various approaches to <span class="hlt">downscale</span> extremes, particularly those associated with extreme precipitation events, will be discussed. Key problems related to statistical <span class="hlt">downscaling</span> of extremes will be addressed. Furthermore, information on Working Group 4 "Extremes" of the EU COST action VALUE (www.value-cost.eu) will be provided. VALUE systematically validates and develops <span class="hlt">downscaling</span> methods for climate change research in order to improve regional climate change scenarios for use in climate impact studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AdAtS..20..951H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AdAtS..20..951H"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> based on dynamically <span class="hlt">downscaled</span> predictors: Application to monthly precipitation in Sweden</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hellström, Cecilia; Chen, Deliang</p> <p>2003-11-01</p> <p>A prerequisite of a successful statistical <span class="hlt">downscaling</span> is that large-scale predictors simulated by the General Circulation Model (GCM) must be realistic. It is assumed here that features smaller than the GCM resolution are important in determining the realism of the large-scale predictors. It is tested whether a three-step method can improve conventional one-step statistical <span class="hlt">downscaling</span>. The method uses predictors that are upscaled from a dynamical <span class="hlt">downscaling</span> instead of predictors taken directly from a GCM simulation. The method is applied to <span class="hlt">downscaling</span> of monthly precipitation in Sweden. The statistical model used is a multiple regression model that uses indices of large-scale atmospheric circulation and 850-hPa specific humidity as predictors. Data from two GCMs (HadCM2 and ECHAM4) and two RCM experiments of the Rossby Centre model (RCA1) driven by the GCMs are used. It is found that upscaled RCA1 predictors capture the seasonal cycle better than those from the GCMs, and hence increase the reliability of the <span class="hlt">downscaled</span> precipitation. However, there are only slight improvements in the simulation of the seasonal cycle of <span class="hlt">downscaled</span> precipitation. Due to the cost of the method and the limited improvements in the <span class="hlt">downscaling</span> results, the three-step method is not justified to replace the one-step method for <span class="hlt">downscaling</span> of Swedish precipitation.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.tmp..234G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.tmp..234G"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of meteorological time series and climatic projections in a watershed in Turkey</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Göncü, S.; Albek, E.</p> <p>2015-07-01</p> <p>In this study, meteorological time series from five meteorological stations in and around a watershed in Turkey were used in the statistical <span class="hlt">downscaling</span> of global climate model results to be used for future projections. Two general circulation models (GCMs), Canadian Climate Center (CGCM3.1(T63)) and Met Office Hadley Centre (2012) (HadCM3) models, were used with three Special Report Emission Scenarios, A1B, A2, and B2. The statistical <span class="hlt">downscaling</span> model SDSM was used for the <span class="hlt">downscaling</span>. The <span class="hlt">downscaled</span> <span class="hlt">ensembles</span> were put to validation with GCM predictors against observations using nonparametric statistical tests. The two most important meteorological variables, temperature and precipitation, passed validation statistics, and partial validation was achieved with other time series relevant in hydrological studies, namely, cloudiness, relative humidity, and wind velocity. Heat waves, number of dry days, length of dry and wet spells, and maximum precipitation were derived from the primary time series as annual series. The change in monthly predictor sets used in constructing the multiple regression equations for <span class="hlt">downscaling</span> was examined over the watershed and over the months in a year. Projections between 1962 and 2100 showed that temperatures and dryness indicators show increasing trends while precipitation, relative humidity, and cloudiness tend to decrease. The spatial changes over the watershed and monthly temporal changes revealed that the western parts of the watershed where water is produced for subsequent downstream use will get drier than the rest and the precipitation distribution over the year will shift. Temperatures showed increasing trends over the whole watershed unparalleled with another period in history. The results emphasize the necessity of mitigation efforts to combat climate change on local and global scales and the introduction of adaptation strategies for the region under study which was shown to be vulnerable to climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010EGUGA..1213747B&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010EGUGA..1213747B&link_type=ABSTRACT"><span id="translatedtitle">Methodology for Air Quality Forecast <span class="hlt">Downscaling</span> from Regional- to Street-Scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob</p> <p>2010-05-01</p> <p>The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how <span class="hlt">downscaling</span> from the European MACC <span class="hlt">ensemble</span> to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of <span class="hlt">downscaling</span> from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of <span class="hlt">downscaling</span> according to the proposed methodology are presented. The potential for <span class="hlt">downscaling</span> of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" <span class="hlt">downscaling</span> of European air-quality forecasts to the city and street levels with different approaches will be formulated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1812353V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812353V"><span id="translatedtitle">Evaluating a Combined Bias Correction and Stochastic <span class="hlt">Downscaling</span> Method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Volosciuk, Claudia; Maraun, Douglas; Vrac, Mathieu; Widmann, Martin</p> <p>2016-04-01</p> <p>Much of our knowledge about future changes in precipitation relies on global (GCM) and/or regional climate models (RCM) that have resolutions which are much coarser than typical spatial scales of extreme precipitation. The major problems with these projections are both climate model biases and the scale gap between grid box and point scale. Wong et al. presented a first attempt to jointly bias correct and <span class="hlt">downscale</span> precipitation at daily scales. This approach however relied on spectrally nudged RCM simulations and was not able to post-process GCM biases. Previously, we have presented an extension of this approach that separates the <span class="hlt">downscaling</span> from the bias correction and in principle is applicable to free running RCMs, such as those available from <span class="hlt">ENSEMBLES</span> or CORDEX. In a first step, we bias correct the RCMs (EURO-CORDEX) against gridded observational datasets (e.g., E-OBS) at the same scale using a quantile mapping approach that relies on distribution transformation. To correct the whole precipitation distribution including extreme tails we apply a mixture distribution of a gamma distribution for the precipitation mass and a generalized Pareto distribution for the extreme tail. In a second step, we bridge the scale gap: we add small scale variability to the bias corrected precipitation time series using a vector generalized linear gamma model (VGLM gamma). To calibrate the VGLM gamma model we determine the statistical relationship between precipitation observations on different scales, i.e. between gridded (e.g., E-OBS) and station (ECA&D) observations. Here we present a comprehensive evaluation of this approach against 86 weather stations in Europe based on the VALUE perfect predictor experiment, including a comparison with standard bias correction techniques.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012EGUGA..1412266Y&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012EGUGA..1412266Y&link_type=ABSTRACT"><span id="translatedtitle">A hybrid <span class="hlt">downscaling</span> procedure for estimating the vertical distribution of ambient temperature in local scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yiannikopoulou, I.; Philippopoulos, K.; Deligiorgi, D.</p> <p>2012-04-01</p> <p>The vertical thermal structure of the atmosphere is defined by a combination of dynamic and radiation transfer processes and plays an important role in describing the meteorological conditions at local scales. The scope of this work is to develop and quantify the predictive ability of a hybrid dynamic-statistical <span class="hlt">downscaling</span> procedure to estimate the vertical profile of ambient temperature at finer spatial scales. The study focuses on the warm period of the year (June - August) and the method is applied to an urban coastal site (Hellinikon), located in eastern Mediterranean. The two-step methodology initially involves the dynamic <span class="hlt">downscaling</span> of coarse resolution climate data via the RegCM4.0 regional climate model and subsequently the statistical <span class="hlt">downscaling</span> of the modeled outputs by developing and training site-specific artificial neural networks (ANN). The 2.5ox2.5o gridded NCEP-DOE Reanalysis 2 dataset is used as initial and boundary conditions for the dynamic <span class="hlt">downscaling</span> element of the methodology, which enhances the regional representivity of the dataset to 20km and provides modeled fields in 18 vertical levels. The regional climate modeling results are compared versus the upper-air Hellinikon radiosonde observations and the mean absolute error (MAE) is calculated between the four grid point values nearest to the station and the ambient temperature at the standard and significant pressure levels. The statistical <span class="hlt">downscaling</span> element of the methodology consists of an <span class="hlt">ensemble</span> of ANN models, one for each pressure level, which are trained separately and employ the regional scale RegCM4.0 output. The ANN models are theoretically capable of estimating any measurable input-output function to any desired degree of accuracy. In this study they are used as non-linear function approximators for identifying the relationship between a number of predictor variables and the ambient temperature at the various vertical levels. An insight of the statistically derived input</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1611854A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1611854A"><span id="translatedtitle">Improving GEFS Weather Forecasts for Indian Monsoon with Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal</p> <p>2014-05-01</p> <p>Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global <span class="hlt">Ensemble</span> Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical <span class="hlt">downscaling</span> technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5462V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5462V"><span id="translatedtitle">Selecting <span class="hlt">downscaled</span> climate projections for water resource impacts and adaptation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vidal, Jean-Philippe; Hingray, Benoît</p> <p>2015-04-01</p> <p>Increasingly large <span class="hlt">ensembles</span> of global and regional climate projections are being produced and delivered to the climate impact community. However, such an enormous amount of information can hardly been dealt with by some impact models due to computational constraints. Strategies for transparently selecting climate projections are therefore urgently needed for informing small-scale impact and adaptation studies and preventing potential pitfalls in interpreting <span class="hlt">ensemble</span> results from impact models. This work proposes results from a selection approach implemented for an integrated water resource impact and adaptation study in the Durance river basin (Southern French Alps). A large <span class="hlt">ensemble</span> of 3000 daily transient gridded climate projections was made available for this study. It was built from different runs of 4 <span class="hlt">ENSEMBLES</span> Stream2 GCMs, statistically <span class="hlt">downscaled</span> by 3 probabilistic methods based on the K-nearest neighbours resampling approach (Lafaysse et al., 2014). The selection approach considered here exemplifies one of the multiple possible approaches described in a framework for identifying tailored subsets of climate projections for impact and adaptation studies proposed by Vidal & Hingray (2014). It was chosen based on the specificities of both the study objectives and the characteristics of the projection dataset. This selection approach aims at propagating as far as possible the relative contributions of the four different sources of uncertainties considered, namely GCM structure, large-scale natural variability, structure of the <span class="hlt">downscaling</span> method, and catchment-scale natural variability. Moreover, it took the form of a hierarchical structure to deal with the specific constraints of several types of impact models (hydrological models, irrigation demand models and reservoir management models). The implemented 3-layer selection approach is therefore mainly based on conditioned Latin Hypercube sampling (Christierson et al., 2012). The choice of conditioning</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMIN23A1414R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMIN23A1414R"><span id="translatedtitle"><span class="hlt">Downscaling</span> Climate Data from Distributed Archives</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Radhakrishnan, A.; Guentchev, G.; Cinquini, L.; Schweitzer, R.; Nikonov, S.; Balaji, V.</p> <p>2013-12-01</p> <p>Model refinement -- numerical estimates of climate change at higher resolution than climate models are currently capable of producing -- is an essential weapon in the arsenal of decision makers and researchers in climate change. We describe here steps toward a general-purpose system for model refinement. We envision a system wherein multiple climate models, alone or in combination, can be used as predictors; multiple refinement methods, alone or in combination, can be deployed and trained, including evaluation within a perfect-model framework, described below; time periods and locations of training can be chosen at will; and providing all of these options as standard web services within the Earth System Grid Federation (ESGF) global data infrastructure for the distribution of climate model output. The perfect-model framework for systematic testing of model refinement using empirical-statistical <span class="hlt">downscaling</span> (ESD) schemes is being developed at NOAA/GFDL under the National Climate Predictions and Projections Platform (NCPP) project. It uses the approach that Laprise and collaborators call the "big-brother" framework for evaluating dynamical <span class="hlt">downscaling</span>. High-resolution model output is used as a "nature run" and used in place of observations to train the ESD scheme under testing. The data is interpolated to a coarse grid (the "little brother") and the ESD scheme attempts to <span class="hlt">downscale</span> and bias-correct the "future", i.e beyond the period of training. The output of ESD can then be rigorously compared to the original nature run on a chosen list of metrics. Initial work was performed in collaboration with Texas Tech University: the high-resolution time-slice models that GFDL submitted to CMIP5 are used as training sets for the <span class="hlt">downscaling</span> methods developed by Katharine Hayhoe and collaborators. The approach is being extended to using other <span class="hlt">downscaling</span> schemes, such as BCSD, Delta, quantile mapping, constructed analogs, and machine learning algorithms; and in future to using</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.H33E1667M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.H33E1667M&link_type=ABSTRACT"><span id="translatedtitle">Probabilistic <span class="hlt">Downscaling</span> Methods for Developing Categorical Streamflow Forecasts using Climate Forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mazrooei, A. H.</p> <p>2015-12-01</p> <p>Statistical information from climate forecast <span class="hlt">ensembles</span> can be utilized in developing probabilistic streamflow forecasts for providing the uncertainty in streamflow forecast potential. This study examines the use of Multinomial Logistic Regression (MLR) in <span class="hlt">downscaling</span> the probabilistic information from the large-scale climate forecast <span class="hlt">ensembles</span> into a point-scale categorical streamflow forecasts. Performance of MLR in developing one-month lead categorical forecasts is evaluated for various river basins over the US Sunbelt. Comparison of MLR with the estimated categorical forecasts from Principle Component Regression (PCR) method under both cross-validation and split-sampling validation reveals that in general the forecasts from MLR has better performance and lower Rank Probability Score (RPS) compared to the PCR forecasts. In addition, MLR performs better than PCR method particularly in arid basins that exhibit strong skewness in seasonal flows with records of distinct dry years. A theoretical underpinning for this improved performance of MLR is also provided.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GeoRL..4210847W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GeoRL..4210847W"><span id="translatedtitle">Incremental dynamical <span class="hlt">downscaling</span> for probabilistic analysis based on multiple GCM projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wakazuki, Y.; Rasmussen, R.</p> <p>2015-12-01</p> <p>A dynamical <span class="hlt">downscaling</span> method for probabilistic regional-scale climate change projections was developed to cover the inherent uncertainty associated with multiple general circulation model (GCM) climate simulations. The climatological increments estimated by GCM results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the <span class="hlt">ensemble</span> mean with the magnitudes of their standard deviations were extracted and added to the <span class="hlt">ensemble</span> mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding them to reanalysis data. The incremental handling of GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. For the probabilistic analysis, three values of a climatological variable simulated by RCMs for a mode were analyzed under an assumption of linear response to the multiple modal perturbations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26293893','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26293893"><span id="translatedtitle">Climate change effects on extreme flows of water supply area in Istanbul: utility of regional climate models and <span class="hlt">downscaling</span> method.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kara, Fatih; Yucel, Ismail</p> <p>2015-09-01</p> <p>This study investigates the climate change impact on the changes of mean and extreme flows under current and future climate conditions in the Omerli Basin of Istanbul, Turkey. The 15 regional climate model output from the EU-<span class="hlt">ENSEMBLES</span> project and a <span class="hlt">downscaling</span> method based on local implications from geophysical variables were used for the comparative analyses. Automated calibration algorithm is used to optimize the parameters of Hydrologiska Byråns Vattenbalansavdel-ning (HBV) model for the study catchment using observed daily temperature and precipitation. The calibrated HBV model was implemented to simulate daily flows using precipitation and temperature data from climate models with and without <span class="hlt">downscaling</span> method for reference (1960-1990) and scenario (2071-2100) periods. Flood indices were derived from daily flows, and their changes throughout the four seasons and year were evaluated by comparing their values derived from simulations corresponding to the current and future climate. All climate models strongly underestimate precipitation while <span class="hlt">downscaling</span> improves their underestimation feature particularly for extreme events. Depending on precipitation input from climate models with and without <span class="hlt">downscaling</span> the HBV also significantly underestimates daily mean and extreme flows through all seasons. However, this underestimation feature is importantly improved for all seasons especially for spring and winter through the use of <span class="hlt">downscaled</span> inputs. Changes in extreme flows from reference to future increased for the winter and spring and decreased for the fall and summer seasons. These changes were more significant with <span class="hlt">downscaling</span> inputs. With respect to current time, higher flow magnitudes for given return periods will be experienced in the future and hence, in the planning of the Omerli reservoir, the effective storage and water use should be sustained. PMID:26293893</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/974391','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/974391"><span id="translatedtitle">Accounting for Global Climate Model Projection Uncertainty in Modern Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Johannesson, G</p> <p>2010-03-17</p> <p>Future climate change has emerged as a national and a global security threat. To carry out the needed adaptation and mitigation steps, a quantification of the expected level of climate change is needed, both at the global and the regional scale; in the end, the impact of climate change is felt at the local/regional level. An important part of such climate change assessment is uncertainty quantification. Decision and policy makers are not only interested in 'best guesses' of expected climate change, but rather probabilistic quantification (e.g., Rougier, 2007). For example, consider the following question: What is the probability that the average summer temperature will increase by at least 4 C in region R if global CO{sub 2} emission increases by P% from current levels by time T? It is a simple question, but one that remains very difficult to answer. It is answering these kind of questions that is the focus of this effort. The uncertainty associated with future climate change can be attributed to three major factors: (1) Uncertainty about future emission of green house gasses (GHG). (2) Given a future GHG emission scenario, what is its impact on the global climate? (3) Given a particular evolution of the global climate, what does it mean for a particular location/region? In what follows, we assume a particular GHG emission scenario has been selected. Given the GHG emission scenario, the current batch of the state-of-the-art global climate models (GCMs) is used to simulate future climate under this scenario, yielding an <span class="hlt">ensemble</span> of future climate projections (which reflect, to some degree our uncertainty of being able to simulate future climate give a particular GHG scenario). Due to the coarse-resolution nature of the GCM projections, they need to be spatially <span class="hlt">downscaled</span> for regional impact assessments. To <span class="hlt">downscale</span> a given GCM projection, two methods have emerged: dynamical <span class="hlt">downscaling</span> and statistical (empirical) <span class="hlt">downscaling</span> (SDS). Dynamic <span class="hlt">downscaling</span> involves</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1812384C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1812384C&link_type=ABSTRACT"><span id="translatedtitle">FORWINE - Statistical <span class="hlt">Downscaling</span> of Seasonal forecasts for wine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.</p> <p>2016-04-01</p> <p>The most renowned viticulture regions in the Iberian Peninsula have a long standing tradition in winemaking and are considered world-class grapevine (Vitis Vinifera L.) producing regions. Portugal is the 11th wine producer in the world, with internationally acclaimed wines, such as Port wine, and vineyards across the whole territory. Climate is widely acknowledged of one of the most important factors for grapevine development and growth (Fraga et al. 2014a and b; Jackson et al. 1993; Keller 2010). During the growing season (April-October in the Northern Hemisphere) of this perennial and deciduous crop, the climatic conditions are responsible for numerous morphologically and physiological changes. Anomalously low February-March mean temperature, anomalously high May mean temperature and anomalously high March precipitation tend to be favourable to wine production in the Douro Valley. Seasonal forecast of precipitation and temperature tailored to fit critical thresholds, for crucial seasons, can be used to inform management practices (viz. phytosanitary measures, land operations, marketing campaigns) and develop a wine production forecast. Statistical <span class="hlt">downscaling</span> of precipitation, maximum, minimum temperatures is used to model wine production following Santos et al. (2013) and to calculate bioclimatic indices. The skill of the <span class="hlt">ensemble</span> forecast is evaluated through anomaly correlation, ROC area, spread-error ratio and CRPS</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A41D0096W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A41D0096W"><span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> of Climate Change over the Hawaiian Islands</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Y.; Zhang, C.; Hamilton, K. P.; Lauer, A.</p> <p>2015-12-01</p> <p>The pseudo-global-warming (PGW) method was applied to the Hawaii Regional Climate Model (HRCM) to dynamically <span class="hlt">downscale</span> the projected climate in the late 21st century over the Hawaiian Islands. The initial and boundary conditions were adopted from MERRA reanalysis and NOAA SST data for the present-day simulations. The global warming increments constructed from the CMIP3 multi-model <span class="hlt">ensemble</span> mean were added to the reanalysis and SST data to perform the future climate simulations. We found that the Hawaiian Islands are vulnerable to global warming effects and the changes are diverse due to the varied topography. The windward side will have more clouds and receive more rainfall. The increase of the moisture in the boundary layer makes the major contribution. On the contrary, the leeward side will have less clouds and rainfall. The clouds and rain can slightly slow down the warming trend over the windward side. The temperature increases almost linearly with the terrain height. Cloud base and top heights will slightly decline in response to the slightly lower trade wind inversion base height, while the trade wind occurrence frequency will increase by about 8% in the future. More extreme rainfall events will occur in the warming climate over the Hawaiian Islands. And the snow cover on the top of Mauna Kea and Mauna Loa will nearly disappear in the future winter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3278305','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3278305"><span id="translatedtitle">Exploring <span class="hlt">Ensemble</span> Visualization</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Phadke, Madhura N.; Pinto, Lifford; Alabi, Femi; Harter, Jonathan; Taylor, Russell M.; Wu, Xunlei; Petersen, Hannah; Bass, Steffen A.; Healey, Christopher G.</p> <p>2012-01-01</p> <p>An <span class="hlt">ensemble</span> is a collection of related datasets. Each dataset, or member, of an <span class="hlt">ensemble</span> is normally large, multidimensional, and spatio-temporal. <span class="hlt">Ensembles</span> are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an <span class="hlt">ensemble</span> to see how parameter choices affect the simulation. To draw inferences from an <span class="hlt">ensemble</span>, scientists need to compare data both within and between <span class="hlt">ensemble</span> members. We propose two techniques to support <span class="hlt">ensemble</span> exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of <span class="hlt">ensemble</span> data. PMID:22347540</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26026419','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26026419"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rashid, Md Mamunur; Beecham, Simon; Chowdhury, Rezaul K</p> <p>2015-10-15</p> <p>A generalized linear model was fitted to stochastically <span class="hlt">downscaled</span> multi-site daily rainfall projections from CMIP5 General Circulation Models (GCMs) for the Onkaparinga catchment in South Australia to assess future changes to hydrologically relevant metrics. For this purpose three GCMs, two multi-model <span class="hlt">ensembles</span> (one by averaging the predictors of GCMs and the other by regressing the predictors of GCMs against reanalysis datasets) and two scenarios (RCP4.5 and RCP8.5) were considered. The <span class="hlt">downscaling</span> model was able to reasonably reproduce the observed historical rainfall statistics when the model was driven by NCEP reanalysis datasets. Significant bias was observed in the rainfall when <span class="hlt">downscaled</span> from historical outputs of GCMs. Bias was corrected using the Frequency Adapted Quantile Mapping technique. Future changes in rainfall were computed from the bias corrected <span class="hlt">downscaled</span> rainfall forced by GCM outputs for the period 2041-2060 and these were then compared to the base period 1961-2000. The results show that annual and seasonal rainfalls are likely to significantly decrease for all models and scenarios in the future. The number of dry days and maximum consecutive dry days will increase whereas the number of wet days and maximum consecutive wet days will decrease. Future changes of daily rainfall occurrence sequences combined with a reduction in rainfall amounts will lead to a drier catchment, thereby reducing the runoff potential. Because this is a catchment that is a significant source of Adelaide's water supply, irrigation water and water for maintaining environmental flows, an effective climate change adaptation strategy is needed in order to face future potential water shortages. PMID:26026419</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140009212','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140009212"><span id="translatedtitle"><span class="hlt">Downscaling</span> Reanalysis over Continental Africa with a Regional Model: NCEP Versus ERA Interim Forcing</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Druyan, Leonard M.; Fulakeza, Matthew B.</p> <p>2013-01-01</p> <p>Five annual climate cycles (1998-2002) are simulated for continental Africa and adjacent oceans by a regional atmospheric model (RM3). RM3 horizontal grid spacing is 0.44deg at 28 vertical levels. Each of 2 simulation <span class="hlt">ensembles</span> is driven by lateral boundary conditions from each of 2 alternative reanalysis data sets. One simulation downs cales National Center for Environmental Prediction reanalysis 2 (NCPR2) and the other the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I). NCPR2 data are archived at 2.5deg grid spacing, while a recent version of ERA-I provides data at 0.75deg spacing. ERA-I-forced simulations are recomrp. ended by the Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX). Comparisons of the 2 sets of simulations with each other and with observational evidence assess the relative performance of each <span class="hlt">downscaling</span> system. A third simulation also uses ERA-I forcing, but degraded to the same horizontal resolution as NCPR2. RM3-simulated pentad and monthly mean precipitation data are compared to Tropical Rainfall Measuring Mission (TRMM) data, gridded at 0.5deg, and RM3-simulated circulation is compared to both reanalyses. Results suggest that each <span class="hlt">downscaling</span> system provides advantages and disadvantages relative to the other. The RM3/NCPR2 achieves a more realistic northward advance of summer monsoon rains over West Africa, but RM3/ERA-I creates the more realistic monsoon circulation. Both systems recreate some features of JulySeptember 1999 minus 2002 precipitation differences. Degrading the resolution of ERA-I driving data unrealistically slows the monsoon circulation and considerably diminishes summer rainfall rates over West Africa. The high resolution of ERA-I data, therefore, contributes to the quality of the <span class="hlt">downscaling</span>, but NCPR2laterai boundary conditions nevertheless produce better simulations of some features.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=philippines&pg=5&id=EJ969636','ERIC'); return false;" href="http://eric.ed.gov/?q=philippines&pg=5&id=EJ969636"><span id="translatedtitle">World Music <span class="hlt">Ensemble</span>: Kulintang</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Beegle, Amy C.</p> <p>2012-01-01</p> <p>As instrumental world music <span class="hlt">ensembles</span> such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music <span class="hlt">ensembles</span> just starting to gain popularity in particular parts of the United States. The kulintang <span class="hlt">ensemble</span>, a drum and gong ensemble…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.4328V&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.4328V&link_type=ABSTRACT"><span id="translatedtitle">The role of <span class="hlt">ensemble</span> post-processing for modeling the <span class="hlt">ensemble</span> tail</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Van De Vyver, Hans; Van Schaeybroeck, Bert; Vannitsem, Stéphane</p> <p>2016-04-01</p> <p>. Soc. 134: 2051-2066.Buizza and Leutbecher, 2015: The forecast skill horizon, Q. J. R. Meteorol. Soc. 141: 3366-3382.Ferro, 2007: A probability model for verifying deterministic forecasts of extreme events. Weather and Forecasting 22 (5), 1089-1100.Friederichs, 2010: Statistical <span class="hlt">downscaling</span> of extreme precipitation events using extreme value theory. Extremes 13, 109-132.Van Schaeybroeck and Vannitsem, 2015: <span class="hlt">Ensemble</span> post-processing using member-by-member approaches: theoretical aspects. Q.J.R. Meteorol. Soc., 141: 807-818.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A51H3129C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A51H3129C"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> for the Northern Great Plains</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Coburn, J.</p> <p>2014-12-01</p> <p>The need for detailed, local scale information about the warming climate has led to the use of ever more complex and geographically realistic computer models as well as the use of regional models capable of capturing much finer details. Another class of methods for ascertaining localized data is known as statistical <span class="hlt">downscaling</span>, which offers some advantages over regional models, especially in the realm of computational efficiency. Statistical <span class="hlt">downscaling</span> can be described as the process of linking coarse resolution climate model output to that of fine resolution or even station-level data via statistical relationships with the purpose of correcting model biases at the local scale. The development and application of <span class="hlt">downscaling</span> has given rise to a plethora of techniques which have been applied to many spatial scales and multiple climate variables. In this study two <span class="hlt">downscaling</span> processes, bias-corrected statistical <span class="hlt">downscaling</span> (BCSD) and canonical correlation analysis (CCA), are applied to minimum and maximum temperatures and precipitation for the Northern Great Plains (NGP, 40 - 53°N and 95 - 120°W) region at both daily and monthly time steps. The abilities of the methods were tested by assessing their ability to recreate local variations in a set of both spatial and temporal climate metrics obtained through the analysis of 1/16 degree station data for the period 1950 to 2000. Model data for temperature, precipitation and a set of predictor variables were obtained from CMIP5 for 15 models. BCSD was applied using direct comparison and correction of the variable distributions via quadrant mapping. CCA was calibrated on the data for the period 1950 to 1980 using a series of model-based predictor variables screened for increasing skill, with the derived model being applied to the period 1980 to 2000 so as to verify that it could recreate the overall climate patterns and trends. As in previous studies done on other regions, it was found that the CCA method recreated</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015PEPS....2...42S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015PEPS....2...42S&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Ensemble</span> experiments using a nested LETKF system to reproduce intense vortices associated with tornadoes of 6 May 2012 in Japan</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Seko, Hiromu; Kunii, Masaru; Yokota, Sho; Tsuyuki, Tadashi; Miyoshi, Takemasa</p> <p>2015-12-01</p> <p>Experiments simulating intense vortices associated with tornadoes that occurred on 6 May 2012 on the Kanto Plain, Japan, were performed with a nested local <span class="hlt">ensemble</span> transform Kalman filter (LETKF) system. Intense vortices were reproduced by <span class="hlt">downscale</span> experiments with a 12-member <span class="hlt">ensemble</span> in which the initial conditions were obtained from the nested LETKF system analyses. The <span class="hlt">downscale</span> experiments successfully generated intense vortices in three regions similar to the observed vortices, whereas only one tornado was reproduced by a deterministic forecast. The intense vorticity of the strongest tornado, which was observed in the southernmost region, was successfully reproduced by 10 of the 12 <span class="hlt">ensemble</span> members. An examination of the results of the <span class="hlt">ensemble</span> <span class="hlt">downscale</span> experiments showed that the duration of intense vorticities tended to be longer when the vertical shear of the horizontal wind was larger and the lower airflow was more humid. Overall, the study results show that <span class="hlt">ensemble</span> forecasts have the following merits: (1) probabilistic forecasts of the outbreak of intense vortices associated with tornadoes are possible; (2) the miss rate of outbreaks should decrease; and (3) environmental factors favoring outbreaks can be obtained by comparing the multiple possible scenarios of the <span class="hlt">ensemble</span> forecasts.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFMOS51A0962C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014AGUFMOS51A0962C&link_type=ABSTRACT"><span id="translatedtitle">Comparison of Statistical <span class="hlt">Downscaling</span> Methods for Seasonal Precipitation Prediction: An Application Toward a Fire and Haze Early Warning System for Southeast Asia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cho, J.; Lee, H.; Lee, E.; Field, R. D.; Hameed, S. N.; Foo, K. K.; Albar, I.; Sopaheluwakan, A.</p> <p>2014-12-01</p> <p>Smoke haze from forest fires is among Southeast Asia's most serious environmental problems and there is a clear need for a long-lead fire and haze early warning system (EWS) for the regions. The seasonal forecast supplied by the APEC Climate Center (APCC) is one of available information can be used to predict drought conditions triggering forest fires in the region. The objective of this study is to assess the skill of the current and <span class="hlt">downscaled</span> products of APCC's seasonal forecast of 6-month lead-time for predicting ASO precipitation over the fire-prone regions. First, seasonal forecast skill by six individual models (MSC_CANCM3, MSC_CANCM4, NASA, NCEP, PNU, POAMA) and simple composite model (SCM) <span class="hlt">ensemble</span> was assessed by considering available each <span class="hlt">ensemble</span> members. Second, three different statistical <span class="hlt">downscaling</span> methods including simple bias-correction (SBC), moving window regression (MWReg), and climate index regression (CIReg) were applied and the forecast sill were compared. Both current and <span class="hlt">downscaled</span> seasonal forecast showed higher predictability over Sumatra regions compared to the Kalimantan regions. Statistical <span class="hlt">downscaling</span> of forecasts showed the skill improvement over the Kalimantan region where current APCC's forecast shows low predictability. Study also shows that temporal correlation coefficient (TCC) between observed and forecasted ASO precipitation increases as lead-time decrease.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC21B0879Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC21B0879Z"><span id="translatedtitle">Joint Variable Spatial <span class="hlt">Downscaling</span> (JVSD): A New <span class="hlt">Downscaling</span> Method with Application to the Southeast US</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, F.; Georgakakos, A. P.</p> <p>2011-12-01</p> <p>Joint Variable Spatial <span class="hlt">Downscaling</span> (JVSD) is a new <span class="hlt">downscaling</span> method developed to produce high resolution gridded hydrological datasets suitable for regional watershed modeling and assessments. JVSD differs from other statistical <span class="hlt">downscaling</span> methods in that multiple climatic variables are <span class="hlt">downscaled</span> simultaneously to produce realistic and consistent climate fields. JVSD includes two major steps: bias correction and spatial <span class="hlt">downscaling</span>. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being <span class="hlt">downscaled</span>. Bias correction is then based on quantile-to-quantile mapping of these stationary frequency distributions probability space. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons with 20th Century Climate in Coupled Models (20C3M) data show that JVSD reproduces the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with the North American regional climate change assessment program (NARCCAP) and other statistical <span class="hlt">downscaling</span> methods over the southeastern US. The results show that JVSD performs favorably. JVSD is applied for all A1B and A2 CMIP3 GCM scenarios in the Apalachicola-Chattahoochee-Flint River Basin (southeast US) with the following general findings: (i) Mean monthly temperature exhibits increasing trends over the ACF basin for all seasons and all A1B and A2 scenarios; Most significant are the A2 temperature increases in the 2050 - 2099 time periods; (ii</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4785R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4785R"><span id="translatedtitle">Statistical-dynamical <span class="hlt">downscaling</span> for wind energy potentials: Evaluation and applications to decadal hindcasts and climate change projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reyers, Mark; Pinto, Joaquim G.; Moemken, Julia</p> <p>2015-04-01</p> <p>A statistical-dynamical <span class="hlt">downscaling</span> (SDD) approach for the regionalisation of wind energy output (Eout) over Europe with special focus on Germany is proposed. SDD uses an extended circulation weather type (CWT) analysis on global daily MSLP fields with the central point being located over Germany. 77 weather classes based on the associated circulation weather type and the intensity of the geostrophic flow are identified. Representatives of these classes are dynamical <span class="hlt">downscaled</span> with the regional climate model COSMO-CLM. By using weather class frequencies of different datasets the simulated representatives are recombined to probability density functions (PDFs) of near-surface wind speed and finally to Eout of a sample wind turbine for present and future climate. This is performed for reanalysis, decadal hindcasts and long-term future projections. For evaluation purposes results of SDD are compared to wind observations and to simulated Eout of purely dynamical <span class="hlt">downscaling</span> (DD) methods. For the present climate SDD is able to simulate realistic PDFs of 10m-wind speed for most stations in Germany. The resulting spatial Eout patterns are similar to DD simulated Eout. In terms of decadal hindcasts results of SDD are similar to DD simulated Eout over Germany, Poland, Czech Republic, and Benelux, for which high correlations between annual Eout timeseries of SDD and DD are detected for selected hindcasts. Lower correlation is found for other European countries. It is demonstrated that SDD can be used to <span class="hlt">downscale</span> the full <span class="hlt">ensemble</span> of the MPI-ESM decadal prediction system. Long-term climate change projections in SRES scenarios of ECHAM5/MPI-OM as obtained by SDD agree well to results of other studies using DD methods, with increasing Eout over Northern Europe and a negative trend over Southern Europe. Despite some biases it is concluded that SDD is an adequate tool to assess regional wind energy changes in large model <span class="hlt">ensembles</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70022063','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70022063"><span id="translatedtitle">Hydrological responses to dynamically and statistically <span class="hlt">downscaled</span> climate model output</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Wilby, R.L.; Hay, L.E.; Gutowski, W.J., Jr.; Arritt, R.W.; Takle, E.S.; Pan, Z.; Leavesley, G.H.; Clark, M.P.</p> <p>2000-01-01</p> <p>Daily rainfall and surface temperature series were simulated for the Animas River basin, Colorado using dynamically and statistically <span class="hlt">downscaled</span> output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis. A distributed hydrological model was then applied to the <span class="hlt">downscaled</span> data. Relative to raw NCEP output, <span class="hlt">downscaled</span> climate variables provided more realistic stimulations of basin scale hydrology. However, the results highlight the sensitivity of modeled processes to the choice of <span class="hlt">downscaling</span> technique, and point to the need for caution when interpreting future hydrological scenarios.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H33M..07W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H33M..07W"><span id="translatedtitle">Assessing short to medium range <span class="hlt">ensemble</span> streamflow forecast approaches in small to medium scale watersheds across CONUS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, A. W.; Newman, A. J.; Brekke, L. D.; Arnold, J. R.; Clark, M. P.</p> <p>2014-12-01</p> <p>As part of the Hydrologic <span class="hlt">Ensemble</span> Forecast Service, the US National Weather Service River Forecasting Centers have implemented short to medium range <span class="hlt">ensemble</span> streamflow forecasts. Hydrologic models are forced with meteorological forecast <span class="hlt">ensembles</span> derived using a <span class="hlt">downscaling</span> and calibration technique, MEFP, that leverages correlations at multiple temporal scales between large scale GEFS forecast <span class="hlt">ensemble</span> mean and local scale observed precipitation and temperature. Strengths of MEFP include its use of multi-decade hindcast for calibration of local scale forecasts and production of verification information, but possible weaknesses include the use of precipitation and temperature <span class="hlt">ensemble</span> mean information only, which requires the statistical synthesis of <span class="hlt">ensemble</span> members. We explore whether using a larger set of atmospheric predictors and full <span class="hlt">ensemble</span> members from the GEFS can lead to greater meteorological and hydrological predictability. Using 30+ year streamflow hindcasts, we evaluate 1-15 day streamflow predictions using the Snow-17/Sacramento hydrologic modeling approach in small to medium-sized watersheds across CONUS. We compare the MEFP approach and performance with regressive and analog-based statistical <span class="hlt">downscaling</span> and calibration methods that rely on a range of atmospheric predictors to produce watershed-scale <span class="hlt">ensemble</span> forecasts. This presentation describes the strengths and weaknesses of the two approaches.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRD..121.2110T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRD..121.2110T"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> and dynamical <span class="hlt">downscaling</span> of regional climate in China: Present climate evaluations and future climate projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tang, Jianping; Niu, Xiaorui; Wang, Shuyu; Gao, Hongxia; Wang, Xueyuan; Wu, Jian</p> <p>2016-03-01</p> <p>Statistical <span class="hlt">downscaling</span> and dynamical <span class="hlt">downscaling</span> are two approaches to generate high-resolution regional climate models based on the large-scale information from either reanalysis data or global climate models. In this study, these two <span class="hlt">downscaling</span> methods are used to simulate the surface climate of China and compared. The Statistical <span class="hlt">Downscaling</span> Model (SDSM) is cross validated and used to <span class="hlt">downscale</span> the regional climate of China. Then, the <span class="hlt">downscaled</span> historical climate of 1981-2000 and future climate of 2041-2060 are compared with that from the Weather Research and Forecasting (WRF) model driven by the European Center-Hamburg atmosphere model and the Max Planck Institute Ocean Model (ECHAM5/MPI-OM) and the L'Institut Pierre-Simon Laplace Coupled Model, version 5, coupled with the Nucleus for European Modelling of the ocean, low resolution (IPSL-CM5A-LR). The SDSM can reproduce the surface temperature characteristics of the present climate in China, whereas the WRF tends to underestimate the surface temperature over most of China. Both the SDSM and WRF require further work to improve their ability to <span class="hlt">downscale</span> precipitation. Both statistical and dynamical <span class="hlt">downscaling</span> methods produce future surface temperatures for 2041-2060 that are markedly different from the historical climatology. However, the changes in projected precipitation differ between the two <span class="hlt">downscaling</span> methods. Indeed, large uncertainties remain in terms of the direction and magnitude of future precipitation changes over China.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110013410','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110013410"><span id="translatedtitle">The <span class="hlt">Ensemble</span> Canon</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>MIittman, David S</p> <p>2011-01-01</p> <p><span class="hlt">Ensemble</span> is an open architecture for the development, integration, and deployment of mission operations software. Fundamentally, it is an adaptation of the Eclipse Rich Client Platform (RCP), a widespread, stable, and supported framework for component-based application development. By capitalizing on the maturity and availability of the Eclipse RCP, <span class="hlt">Ensemble</span> offers a low-risk, politically neutral path towards a tighter integration of operations tools. The <span class="hlt">Ensemble</span> project is a highly successful, ongoing collaboration among NASA Centers. Since 2004, the <span class="hlt">Ensemble</span> project has supported the development of mission operations software for NASA's Exploration Systems, Science, and Space Operations Directorates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280783&keyword=weather+AND+modification&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=64175374&CFTOKEN=97310681','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280783&keyword=weather+AND+modification&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=64175374&CFTOKEN=97310681"><span id="translatedtitle">"Going the Extra Mile in <span class="hlt">Downscaling</span>: Why <span class="hlt">Downscaling</span> is not jut "Plug-and-Play"</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of <span class="hlt">downscaling</span> the Comm...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.6789R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.6789R&link_type=ABSTRACT"><span id="translatedtitle">Stochastic <span class="hlt">Downscaling</span> of Digital Elevation Models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rasera, Luiz Gustavo; Mariethoz, Gregoire; Lane, Stuart N.</p> <p>2016-04-01</p> <p>High-resolution digital elevation models (HR-DEMs) are extremely important for the understanding of small-scale geomorphic processes in Alpine environments. In the last decade, remote sensing techniques have experienced a major technological evolution, enabling fast and precise acquisition of HR-DEMs. However, sensors designed to measure elevation data still feature different spatial resolution and coverage capabilities. Terrestrial altimetry allows the acquisition of HR-DEMs with centimeter to millimeter-level precision, but only within small spatial extents and often with dead ground problems. Conversely, satellite radiometric sensors are able to gather elevation measurements over large areas but with limited spatial resolution. In the present study, we propose an algorithm to <span class="hlt">downscale</span> low-resolution satellite-based DEMs using topographic patterns extracted from HR-DEMs derived for example from ground-based and airborne altimetry. The method consists of a multiple-point geostatistical simulation technique able to generate high-resolution elevation data from low-resolution digital elevation models (LR-DEMs). Initially, two collocated DEMs with different spatial resolutions serve as an input to construct a database of topographic patterns, which is also used to infer the statistical relationships between the two scales. High-resolution elevation patterns are then retrieved from the database to <span class="hlt">downscale</span> a LR-DEM through a stochastic simulation process. The output of the simulations are multiple equally probable DEMs with higher spatial resolution that also depict the large-scale geomorphic structures present in the original LR-DEM. As these multiple models reflect the uncertainty related to the <span class="hlt">downscaling</span>, they can be employed to quantify the uncertainty of phenomena that are dependent on fine topography, such as catchment hydrological processes. The proposed methodology is illustrated for a case study in the Swiss Alps. A swissALTI3D HR-DEM (with 5 m resolution</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950024819','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950024819"><span id="translatedtitle">The Personal Software Process: <span class="hlt">Downscaling</span> the factory</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roy, Daniel M.</p> <p>1994-01-01</p> <p>It is argued that the next wave of software process improvement (SPI) activities will be based on a people-centered paradigm. The most promising such paradigm, Watts Humphrey's personal software process (PSP), is summarized and its advantages are listed. The concepts of the PSP are shown also to fit a <span class="hlt">down-scaled</span> version of Basili's experience factory. The author's data and lessons learned while practicing the PSP are presented along with personal experience, observations, and advice from the perspective of a consultant and teacher for the personal software process.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.2060T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.2060T"><span id="translatedtitle">Sampling <span class="hlt">downscaling</span> in summertime precipitation over Hokkaido</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tamaki, Yuta; Inatsu, Masaru; Kuno, Ryusuke; Nakano, Naoto</p> <p>2016-04-01</p> <p>1. Introduction Recently, the mixture method of dynamical and statistical <span class="hlt">downscaling</span> have been developed (cf. Kuno and Inatsu 2014, Pinto et al. 2014). Kuno and Inatsu (2014) developed the sampling <span class="hlt">downscaling</span> (SmDS) method in which a regional atmospheric model is integrated for sampled years. However, in order to know how these mixture methods are able to effectively reduce the computational costs for dynamical <span class="hlt">downscaling</span>, we need to apply them to other cases. The purpose of this study is to apply SmDS to summertime precipitation over Hokkaido as another case study. 2. Method Singular value decomposition (SVD) analysis is performed from 1981 to 2010 in June-July-August (JJA) months using the moisture flux convergence (JRA25/JCDAS) around Japan and precipitation (APHRO_JP/V1207) over Hokkaido. Next, we selected the top and bottom two years of the moisture flux convergence of the general circulation model projection onto the first SVD mode. This study conducts the dynamical <span class="hlt">downscaling</span> for 30 years (full DDS) under the current climate experiment in advance to investigate the reproducibility of SmDS. 3. Result The spatial correlation coefficient between SmDS and full DDS shows 0.96 in daily-mean precipitation and 0.85 in 99 percentile value of daily precipitation. This indicates that SmDS can be applied to the place where the synoptic field strongly controls the local precipitation. In addition, we also statistically considered the error in SmDS and it turned out that the mean in SmDS depended on the correlation coefficient between local and synoptic variables, the number of samples, and the standard deviation of seasonal mean precipitation. It was also demonstrated the SmDS selected the group of years where extreme events likely occurred and another group where they rarely occurred. References Kuno, R., and M. Inatsu, 2014, Clim. Dyn., 43, 375-387. Pinto, J. O., A. J. Monaghan, L. D. Monache, E. Vanvyve, and D. L. Rife, 2014, J. Climate, 27, 1524-1538.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006IJCli..26.1315H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006IJCli..26.1315H"><span id="translatedtitle">Consensus between GCM climate change projections with empirical <span class="hlt">downscaling</span>: precipitation <span class="hlt">downscaling</span> over South Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hewitson, B. C.; Crane, R. G.</p> <p>2006-08-01</p> <p>This paper discusses issues that surround the development of empirical <span class="hlt">downscaling</span> techniques as context for presenting a new approach based on self-organizing maps (SOMs). The technique is applied to the <span class="hlt">downscaling</span> of daily precipitation over South Africa. SOMs are used to characterize the state of the atmosphere on a localized domain surrounding each target location on the basis of NCEP 6-hourly reanalysis data from 1979 to 2002, and using surface and 700-hPa u and v wind vectors, specific and relative humidities, and surface temperature. Each unique atmospheric state is associated with an observed precipitation probability density function (PDF). Future climate states are derived from three global climate models (GCMs): HadAM3, ECHAM4.5, CSIRO Mk2. In each case, the GCM data are mapped to the NCEP SOMs for each target location and a precipitation value is drawn at random from the associated precipitation PDF. The <span class="hlt">downscaling</span> approach combines the advantages of a direct transfer function and a stochastic weather generator, and provides an indication of the strength of the regional versus stochastic forcing, as well as a measure of stationarity in the atmosphere-precipitation relationship.The methodology is applied to South Africa. The <span class="hlt">downscaling</span> reveals a similarity in the projected climate change between the models. Each GCM projects similar changes in atmospheric state and they converge on a <span class="hlt">downscaled</span> solution that points to increased summer rainfall in the interior and the eastern part of the country, and a decrease in winter rainfall in the Western Cape. The actual GCM precipitation projections from the three models show large areas of intermodel disagreement, suggesting that the model differences may be due to their precipitation parameterization schemes, rather than to basic disagreements in their projections of the changing atmospheric state over South Africa.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016BGeo...13.4271F&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016BGeo...13.4271F&link_type=ABSTRACT"><span id="translatedtitle">Technical note: 3-hourly temporal <span class="hlt">downscaling</span> of monthly global terrestrial biosphere model net ecosystem exchange</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fisher, Joshua B.; Sikka, Munish; Huntzinger, Deborah N.; Schwalm, Christopher; Liu, Junjie</p> <p>2016-07-01</p> <p>The land surface provides a boundary condition to atmospheric forward and flux inversion models. These models require prior estimates of CO2 fluxes at relatively high temporal resolutions (e.g., 3-hourly) because of the high frequency of atmospheric mixing and wind heterogeneity. However, land surface model CO2 fluxes are often provided at monthly time steps, typically because the land surface modeling community focuses more on time steps associated with plant phenology (e.g., seasonal) than on sub-daily phenomena. Here, we describe a new dataset created from 15 global land surface models and 4 <span class="hlt">ensemble</span> products in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), temporally <span class="hlt">downscaled</span> from monthly to 3-hourly output. We provide 3-hourly output for each individual model over 7 years (2004-2010), as well as an <span class="hlt">ensemble</span> mean, a weighted <span class="hlt">ensemble</span> mean, and the multi-model standard deviation. Output is provided in three different spatial resolutions for user preferences: 0.5° × 0.5°, 2.0° × 2.5°, and 4.0° × 5.0° (latitude × longitude). These data are publicly available from <a href="http://dx.doi.org/10.3334/ORNLDAAC/1315" target="_blank">doi:10.3334/ORNLDAAC/1315</a>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26888907','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26888907"><span id="translatedtitle"><span class="hlt">Ensembl</span> regulation resources.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zerbino, Daniel R; Johnson, Nathan; Juetteman, Thomas; Sheppard, Dan; Wilder, Steven P; Lavidas, Ilias; Nuhn, Michael; Perry, Emily; Raffaillac-Desfosses, Quentin; Sobral, Daniel; Keefe, Damian; Gräf, Stefan; Ahmed, Ikhlak; Kinsella, Rhoda; Pritchard, Bethan; Brent, Simon; Amode, Ridwan; Parker, Anne; Trevanion, Steven; Birney, Ewan; Dunham, Ian; Flicek, Paul</p> <p>2016-01-01</p> <p>New experimental techniques in epigenomics allow researchers to assay a diversity of highly dynamic features such as histone marks, DNA modifications or chromatin structure. The study of their fluctuations should provide insights into gene expression regulation, cell differentiation and disease. The <span class="hlt">Ensembl</span> project collects and maintains the <span class="hlt">Ensembl</span> regulation data resources on epigenetic marks, transcription factor binding and DNA methylation for human and mouse, as well as microarray probe mappings and annotations for a variety of chordate genomes. From this data, we produce a functional annotation of the regulatory elements along the human and mouse genomes with plans to expand to other species as data becomes available. Starting from well-studied cell lines, we will progressively expand our library of measurements to a greater variety of samples. <span class="hlt">Ensembl</span>'s regulation resources provide a central and easy-to-query repository for reference epigenomes. As with all <span class="hlt">Ensembl</span> data, it is freely available at http://www.<span class="hlt">ensembl</span>.org, from the Perl and REST APIs and from the public <span class="hlt">Ensembl</span> MySQL database server at ensembldb.<span class="hlt">ensembl</span>.org. Database URL: http://www.<span class="hlt">ensembl</span>.org. PMID:26888907</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JHyd..376..463R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JHyd..376..463R"><span id="translatedtitle">Verification of <span class="hlt">ensemble</span> flow forecasts for the River Rhine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Renner, M.; Werner, M. G. F.; Rademacher, S.; Sprokkereef, E.</p> <p>2009-10-01</p> <p>Summary<span class="hlt">Ensemble</span> stream flow predictions obtained by forcing rainfall-runoff models with probabilistic weather forecasting products are becoming more commonly used in operational flood forecasting applications. In this paper the performance of <span class="hlt">ensemble</span> flow forecasts at various stations in the Rhine basin are studied by the means of probabilistic verification statistics. When compared to climatology positive skill scores are found at all river gauges for lead times of up to 9 days, thus proving the medium-range flow forecasts to be useful. A preliminary comparison between the low resolution ECMWF-EPS forecast and the high-resolution COSMO-LEPS forecast products shows that <span class="hlt">downscaling</span> of global meteorological forecast products is recommended before use in forcing rainfall-runoff models in flow forecasting.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUSM.H31B..02T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUSM.H31B..02T"><span id="translatedtitle">Bayesian Processor of <span class="hlt">Ensemble</span> for Precipitation Forecasting: A Development Plan</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Toth, Z.; Krzysztofowicz, R.</p> <p>2006-05-01</p> <p>The Bayesian Processor of <span class="hlt">Ensemble</span> (BPE) is a new, theoretically-based technique for probabilistic forecasting of weather variates. It is a generalization of the Bayesian Processor of Output (BPO) developed by Krzysztofowicz and Maranzano for processing single values of multiple predictors into a posterior distribution function of a predictand. The BPE processes an <span class="hlt">ensemble</span> of a predictand generated by multiple integrations of a numerical weather prediction (NWP) model, and optimally fuses the <span class="hlt">ensemble</span> with climatic data in order to quantify uncertainty about the predictand. As is well known, Bayes theorem provides the optimal theoretical framework for fusing information from different sources and for obtaining the posterior distribution function of a predictand. Using a family of such distribution functions, a given raw <span class="hlt">ensemble</span> can be mapped into a posterior <span class="hlt">ensemble</span>, which is well calibrated, has maximum informativeness, and preserves the spatio-temporal and cross-variate dependence structure of the NWP output fields. The challenge is to develop and test the BPE suitable for operational forecasting. This talk will present the basic design components of the BPE, along with a discussion of the climatic and training data to be used in its potential application at the National Centers for Environmental Prediction (NCEP). The technique will be tested first on quasi-normally distributed variates and next on precipitation variates. For reasons of economy, the BPE will be applied on the relatively coarse resolution grid corresponding to the <span class="hlt">ensemble</span> output, and then the posterior <span class="hlt">ensemble</span> will be <span class="hlt">downscaled</span> to finer grids such as that of the National Digital Forecast Database (NDFD).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5019R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5019R"><span id="translatedtitle">Defining predictand areas with homogeneous predictors for spatially coherent precipitation <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Radanovics, Sabine; Vidal, Jean-Philippe; Sauquet, Eric; Ben Daoud, Aurélien; Bontron, Guillaume</p> <p>2013-04-01</p> <p> the 5 ones that lead to the highest CRPSS for the zone in question are retained. The 5 retained domains were found to be equally skillfull with a maximum difference of around 1% of CRPSS on average, and are thus all candidates for clustering predictand zones. An objective procedure has then been implemented for clustering zones together, based on their sharing a common predictor domain inside their 5 near-optimal domain <span class="hlt">ensemble</span>. For zones sharing several near-optimal predictor domains, the aim was to minimise the number of disjoint predictand areas. Furthermore solutions that lead to more similar sized areas were preferred. This procedure defines areas with natural spatial coherence and reduces the number of different predictor domains using a procedure based on objective rules, unlike most of studies where this is done either subjectively or arbitrarily. It allowed to reduce significantly the number of independent zones and to identify large homogeneous areas encompassing relatively large river basins. Further developments will address the issue of spatial coherent <span class="hlt">downscaling</span> for predictand areas that do not share any near-optimal predictor domains. Ben Daoud, A., Sauquet, E., Lang, M., Bontron, G., and Obled, C. (2011). Precipitation forecasting through an analog sorting technique: a comparative study. Advances in Geosciences, 29:103-107. doi: 10.5194/adgeo-29-103-2011 Timbal, B., Dufour, A., and McAvaney, B. (2003). An estimate of future climate change for western France using a statistical <span class="hlt">downscaling</span> technique. Climate Dynamics, 20(7-8):807-823. doi: 10.1007/s00382-002-0298-9 Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M. (2010) A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology, 30:1627-1644. doi: 10.1002/joc.2003</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4756621','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4756621"><span id="translatedtitle"><span class="hlt">Ensembl</span> regulation resources</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zerbino, Daniel R.; Johnson, Nathan; Juetteman, Thomas; Sheppard, Dan; Wilder, Steven P.; Lavidas, Ilias; Nuhn, Michael; Perry, Emily; Raffaillac-Desfosses, Quentin; Sobral, Daniel; Keefe, Damian; Gräf, Stefan; Ahmed, Ikhlak; Kinsella, Rhoda; Pritchard, Bethan; Brent, Simon; Amode, Ridwan; Parker, Anne; Trevanion, Steven; Birney, Ewan; Dunham, Ian; Flicek, Paul</p> <p>2016-01-01</p> <p>New experimental techniques in epigenomics allow researchers to assay a diversity of highly dynamic features such as histone marks, DNA modifications or chromatin structure. The study of their fluctuations should provide insights into gene expression regulation, cell differentiation and disease. The <span class="hlt">Ensembl</span> project collects and maintains the <span class="hlt">Ensembl</span> regulation data resources on epigenetic marks, transcription factor binding and DNA methylation for human and mouse, as well as microarray probe mappings and annotations for a variety of chordate genomes. From this data, we produce a functional annotation of the regulatory elements along the human and mouse genomes with plans to expand to other species as data becomes available. Starting from well-studied cell lines, we will progressively expand our library of measurements to a greater variety of samples. Ensembl’s regulation resources provide a central and easy-to-query repository for reference epigenomes. As with all <span class="hlt">Ensembl</span> data, it is freely available at http://www.<span class="hlt">ensembl</span>.org, from the Perl and REST APIs and from the public <span class="hlt">Ensembl</span> MySQL database server at ensembldb.<span class="hlt">ensembl</span>.org. Database URL: http://www.<span class="hlt">ensembl</span>.org PMID:26888907</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25887522','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25887522"><span id="translatedtitle">The <span class="hlt">ensembl</span> regulatory build.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zerbino, Daniel R; Wilder, Steven P; Johnson, Nathan; Juettemann, Thomas; Flicek, Paul R</p> <p>2015-01-01</p> <p>Most genomic variants associated with phenotypic traits or disease do not fall within gene coding regions, but in regulatory regions, rendering their interpretation difficult. We collected public data on epigenetic marks and transcription factor binding in human cell types and used it to construct an intuitive summary of regulatory regions in the human genome. We verified it against independent assays for sensitivity. The <span class="hlt">Ensembl</span> Regulatory Build will be progressively enriched when more data is made available. It is freely available on the <span class="hlt">Ensembl</span> browser, from the <span class="hlt">Ensembl</span> Regulation MySQL database server and in a dedicated track hub. PMID:25887522</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.4151V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.4151V"><span id="translatedtitle">Comparing climate change impacts on crops in Belgium based on CMIP3 and EU-<span class="hlt">ENSEMBLES</span> multi-model <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vanuytrecht, E.; Raes, D.; Willems, P.; Semenov, M.</p> <p>2012-04-01</p> <p>Global Circulation Models (GCMs) are sophisticated tools to study the future evolution of the climate. Yet, the coarse scale of GCMs of hundreds of kilometers raises questions about the suitability for agricultural impact assessments. These assessments are often made at field level and require consideration of interactions at sub-GCM grid scale (e.g., elevation-dependent climatic changes). Regional climate models (RCMs) were developed to provide climate projections at a spatial scale of 25-50 km for limited regions, e.g. Europe (Giorgi and Mearns, 1991). Climate projections from GCMs or RCMs are available as multi-model <span class="hlt">ensembles</span>. These <span class="hlt">ensembles</span> are based on large data sets of simulations produced by modelling groups worldwide, who performed a set of coordinated climate experiments in which climate models were run for a common set of experiments and various emissions scenarios (Knutti et al., 2010). The use of multi-model <span class="hlt">ensembles</span> in climate change studies is an important step in quantifying uncertainty in impact predictions, which will underpin more informed decisions for adaptation and mitigation to changing climate (Semenov and Stratonovitch, 2010). The objective of our study was to evaluate the effect of the spatial scale of climate projections on climate change impacts for cereals in Belgium. Climate scenarios were based on two multi-model <span class="hlt">ensembles</span>, one comprising 15 GCMs of the Coupled Model Intercomparison Project phase 3 (CMIP3; Meehl et al., 2007) with spatial resolution of 200-300 km, the other comprising 9 RCMs of the EU-<span class="hlt">ENSEMBLES</span> project (van der Linden and Mitchell, 2009) with spatial resolution of 25 km. To be useful for agricultural impact assessments, the projections of GCMs and RCMs were <span class="hlt">downscaled</span> to the field level. Long series (240 cropping seasons) of local-scale climate scenarios were generated by the LARS-WG weather generator (Semenov et al., 2010) via statistical inference. Crop growth and development were simulated with the Aqua</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009EGUGA..1112640C&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009EGUGA..1112640C&link_type=ABSTRACT"><span id="translatedtitle">High resolution probabilistic precipitation forecast over Spain combining the statistical <span class="hlt">downscaling</span> tool PROMETEO and the AEMET short range EPS system (AEMET/SREPS)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cofino, A. S.; Santos, C.; Garcia-Moya, J. A.; Gutierrez, J. M.; Orfila, B.</p> <p>2009-04-01</p> <p>The Short-Range <span class="hlt">Ensemble</span> Prediction System (SREPS) is a multi-LAM (UM, HIRLAM, MM5, LM and HRM) multi analysis/boundary conditions (ECMWF, UKMetOffice, DWD and GFS) run twice a day by AEMET (72 hours lead time) over a European domain, with a total of 5 (LAMs) x 4 (GCMs) = 20 members. One of the main goals of this project is analyzing the impact of models and boundary conditions in the short-range high-resolution forecasted precipitation. A previous validation of this method has been done considering a set of climate networks in Spain, France and Germany, by interpolating the prediction to the gauge locations (SREPS, 2008). In this work we compare these results with those obtained by using a statistical <span class="hlt">downscaling</span> method to post-process the global predictions, obtaining an "advanced interpolation" for the local precipitation using climate network precipitation observations. In particular, we apply the PROMETEO <span class="hlt">downscaling</span> system based on analogs and compare the SREPS <span class="hlt">ensemble</span> of 20 members with the PROMETEO statistical <span class="hlt">ensemble</span> of 5 (analog <span class="hlt">ensemble</span>) x 4 (GCMs) = 20 members. Moreover, we will also compare the performance of a combined approach post-processing the SREPS outputs using the PROMETEO system. References: SREPS 2008. 2008 EWGLAM-SRNWP Meeting (http://www.aemet.es/documentos/va/divulgacion/conferencias/prediccion/Ewglam/PRED_CSantos.pdf)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20060015642','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20060015642"><span id="translatedtitle"><span class="hlt">Ensemble</span> Data Mining Methods</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oza, Nikunj C.</p> <p>2004-01-01</p> <p><span class="hlt">Ensemble</span> Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an <span class="hlt">ensemble</span> is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in <span class="hlt">ensemble</span> methods has largely revolved around designing <span class="hlt">ensembles</span> consisting of competent yet complementary models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/881929','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/881929"><span id="translatedtitle">Physically Based Global <span class="hlt">Downscaling</span>: Regional Evaluation</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Ghan, Steven J.; Shippert, Timothy R.; Fox, Jared</p> <p>2006-02-01</p> <p>The climate simulated by a global atmosphere/land model with a physically-based subgrid orography scheme is evaluated in ten selected regions. Climate variables simulated for each of multiple elevation classes within each grid cell are mapped according the high-resolution distribution of surface elevation in each region. Comparison of the simulated annual mean climate with gridded observations leads to the following conclusions. At low to moderate elevations the <span class="hlt">downscaling</span> scheme correctly simulates increasing precipitation, decreasing temperature, and increasing snow with increasing elevation within regions smaller than 100 km. At high elevations the <span class="hlt">downscaling</span> scheme correctly simulates a decrease in precipitation with increasing elevation. Too little precipitation is simulated on the windward side of mountain ranges and too much precipitation is simulated on the lee side. The simulated sensitivity of surface air temperature to surface elevation is too strong, particularly in valleys influenced by drainage circulations. Observations show little evidence of a “snow shadow”, so the neglect of the subgrid rainshadow does not produce an unrealistic simulation of the snow distribution. Summertime snow area, which is a proxy for land ice, is much larger than observed. Summertime snow water equivalent is far less than the observed thickness of glaciers because a 1 m upper bound on snow water is applied to the simulations and because snow transport by slides is neglected. The 1 m upper bound on snow water equivalent also causes an underestimate of seasonal snow water during late winter, compared with gridded station measurements. Potential solutions to these problems are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5500I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5500I"><span id="translatedtitle">Enhancing Local Climate Projections of Precipitation: Assets and Limitations of Quantile Mapping Techniques for Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ivanov, Martin; Kotlarski, Sven; Schär, Christoph</p> <p>2015-04-01</p> <p>The Swiss CH2011 scenarios provide a portfolio of climate change scenarios for the region of Switzerland, specifically tailored for use in climate impact research. Although widely applied by a variety of end-users, these scenarios are subject to several limitations related to the underlying delta change methodology. Examples are difficulties to appropriately account for changes in the spatio-temporal variability of meteorological fields and for changes in extreme events. The recently launched ELAPSE project (Enhancing local and regional climate change projections for Switzerland) is connected to the EU COST Action VALUE (www.value-cost.eu) and aims at complementing CH2011 by further scenario products, including a bias-corrected version of daily scenarios at the site scale. For this purpose the well-established empirical quantile mapping (QM) methodology is employed. Here, daily temperature and precipitation output of 15 GCM-RCM model chains of the <span class="hlt">ENSEMBLES</span> project is <span class="hlt">downscaled</span> and bias-corrected to match observations at weather stations in Switzerland. We consider established QM techniques based on all empirical quantiles or linear interpolation between the empirical percentiles. In an attempt to improve the <span class="hlt">downscaling</span> of extreme precipitation events, we also apply a parametric approximation of the daily precipitation distribution by a dynamically weighted mixture of a Gamma distribution for the bulk and a Pareto distribution for the right tail for the first time in the context of QM. All techniques are evaluated and intercompared in a cross-validation framework. The statistical <span class="hlt">downscaling</span> substantially improves virtually all considered distributional and temporal characteristics as well as their spatial distribution. The empirical methods have in general very similar performances. The parametric method does not show an improvement over the empirical ones. Critical sites and seasons are highlighted and discussed. Special emphasis is placed on investigating the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016CliPa..12..635C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016CliPa..12..635C"><span id="translatedtitle">Probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin</p> <p>2016-03-01</p> <p>This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871-2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue <span class="hlt">Downscaling</span> method for HYdrology) statistical <span class="hlt">downscaling</span> method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily <span class="hlt">ensemble</span> of 125 analogue dates over the 1871-2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature (T). Comparisons to the Safran reanalysis over 1959-2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/pages/biblio/1268294-probabilistic-precipitation-temperature-downscaling-twentieth-century-reanalysis-over-france','SCIGOV-DOEP'); return false;" href="http://www.osti.gov/pages/biblio/1268294-probabilistic-precipitation-temperature-downscaling-twentieth-century-reanalysis-over-france"><span id="translatedtitle">Probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGESBeta</a></p> <p>Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric; Graff, Benjamin</p> <p>2016-03-16</p> <p>In this study, this work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue <span class="hlt">Downscaling</span> method for HYdrology) statistical <span class="hlt">downscaling</span> method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safranmore » high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily <span class="hlt">ensemble</span> of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature (T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.3876K&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.3876K&link_type=ABSTRACT"><span id="translatedtitle">Testing a Weather Generator for <span class="hlt">Downscaling</span> Climate Change Projections over Switzerland</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Keller, Denise E.; Fischer, Andreas M.; Liniger, Mark A.; Appenzeller, Christof; Knutti, Reto</p> <p>2016-04-01</p> <p>Climate information provided by global or regional climate models (RCMs) are often too coarse and prone to substantial biases, making it impossible to directly use daily time-series of the RCMs for local assessments and in climate impact models. Hence, statistical <span class="hlt">downscaling</span> becomes necessary. For the Swiss National Climate Change Initiative (CH2011), a delta-change approach was used to provide daily climate projections at the local scale. This data have the main limitations that changes in variability, extremes and in the temporal structure, such as changes in the wet day frequency, are not reproduced. The latter is a considerable downside of the delta-change approach for many impact applications. In this regard, stochastic weather generators (WGs) are an appealing technique that allow the simulation of multiple realizations of synthetic weather sequences consistent with the locally observed weather statistics and its future changes. Here, we analyse a Richardson-type weather generator (WG) as an alternative method to <span class="hlt">downscale</span> daily precipitation, minimum and maximum temperature. The WG is calibrated for 26 Swiss stations and the reference period 1980-2009. It is perturbed with change factors derived from 12 RCMs (<span class="hlt">ENSEMBLES</span>) to represent the climate of 2070-2099 assuming the SRES A1B emission scenario. The WG can be run in multi-site mode, making it especially attractive for impact-modelers that rely on a realistic spatial structure in <span class="hlt">downscaled</span> time-series. The results from the WG are benchmarked against the original delta-change approach that applies mean additive or multiplicative adjustments to the observations. According to both <span class="hlt">downscaling</span> methods, the results reveal area-wide mean temperature increases and a precipitation decrease in summer, consistent with earlier studies. For the summer drying, the WG indicates primarily a decrease in wet-day frequency and correspondingly an increase in mean dry spell length by around 18% - 40% at low</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015CliPD..11.4425C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015CliPD..11.4425C"><span id="translatedtitle">Probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caillouet, L.; Vidal, J.-P.; Sauquet, E.; Graff, B.</p> <p>2015-09-01</p> <p>This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871-2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late 19th century onwards. The SANDHY (Stepwise ANalogue <span class="hlt">Downscaling</span> method for HYdrology) statistical <span class="hlt">downscaling</span> method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the SAFRAN high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily <span class="hlt">ensemble</span> of 125 analogues dates over the 1871-2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping unchanged the structure of the SANDHY method while reducing those seasonal biases. The calendar selection keeps the closest analogue dates in the year for each target date. The stepwise selection applies two new analogy steps based on similarity of the Sea Surface Temperature (SST) and the large-scale Two-metre Temperature (T2m). Comparisons to the SAFRAN reanalysis over 1959-2007 and to homogenized series over the whole 20th century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8627L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8627L"><span id="translatedtitle">Improved large-scale hydrological modelling through the assimilation of streamflow and <span class="hlt">downscaled</span> satellite soil moisture observations.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>López López, Patricia; Wanders, Niko; Sutanudjaja, Edwin; Renzullo, Luigi; Sterk, Geert; Schellekens, Jaap; Bierkens, Marc</p> <p>2015-04-01</p> <p>The coarse spatial resolution of global hydrological models (typically > 0.25o) often limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tunes river models. A possible solution to the problem may be to drive the coarse resolution models with high-resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the modelling resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigated the impact that assimilating streamflow and satellite soil moisture observations have on global hydrological model estimation, driven by coarse- and high-resolution meteorological observations, for the Murrumbidgee river basin in Australia. The PCR-GLOBWB global hydrological model is forced with <span class="hlt">downscaled</span> global climatological data (from 0.5o <span class="hlt">downscaled</span> to 0.1o resolution) obtained from the WATCH Forcing Data (WFDEI) and local high resolution gauging station based gridded datasets (0.05o), sourced from the Australian Bureau of Meteorology. <span class="hlt">Downscaled</span> satellite derived soil moisture (from 0.5o <span class="hlt">downscaled</span> to 0.1o resolution) from AMSR-E and streamflow observations collected from 25 gauging stations are assimilated using an <span class="hlt">ensemble</span> Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global climatological data. Results show that the assimilation of streamflow observations result in the largest improvement of the model estimates. The joint assimilation of both streamflow and <span class="hlt">downscaled</span> soil moisture observations leads to further improved in streamflow simulations (10% reduction in RMSE), mainly in the headwater catchments (up to 10,000 km2). Results also show that the added contribution of data assimilation, for both soil</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016HESS...20.3059L&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016HESS...20.3059L&link_type=ABSTRACT"><span id="translatedtitle">Improved large-scale hydrological modelling through the assimilation of streamflow and <span class="hlt">downscaled</span> satellite soil moisture observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>López López, Patricia; Wanders, Niko; Schellekens, Jaap; Renzullo, Luigi J.; Sutanudjaja, Edwin H.; Bierkens, Marc F. P.</p> <p>2016-07-01</p> <p>The coarse spatial resolution of global hydrological models (typically >  0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with <span class="hlt">downscaled</span> global meteorological data (<span class="hlt">downscaled</span> from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). <span class="hlt">Downscaled</span> satellite-derived soil moisture (<span class="hlt">downscaled</span> from ˜  0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an <span class="hlt">ensemble</span> Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and <span class="hlt">downscaled</span> soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..1210559L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..1210559L"><span id="translatedtitle">Improved large-scale hydrological modelling through the assimilation of streamflow and <span class="hlt">downscaled</span> satellite soil moisture observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lopez Lopez, P.; Wanders, N.; Schellekens, J.; Renzullo, L. J.; Sutanudjaja, E. H.; Bierkens, M. F. P.</p> <p>2015-10-01</p> <p>The coarse spatial resolution of global hydrological models (typically > 0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tuned river models. A possible solution to the problem may be to drive the coarse resolution models with locally available high spatial resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee river basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with <span class="hlt">downscaled</span> global meteorological data (from 0.5° <span class="hlt">downscaled</span> to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high resolution gauging station based gridded dataset (0.05°). <span class="hlt">Downscaled</span> satellite derived soil moisture (from approx. 0.5° <span class="hlt">downscaled</span> to 0.08° resolution) from AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an <span class="hlt">ensemble</span> Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and <span class="hlt">downscaled</span> soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H43G1551C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H43G1551C"><span id="translatedtitle">Assimilation of <span class="hlt">Downscaled</span> SMOS Soil Moisture for Quantifying Drought Impacts on Crop Yield in Agricultural Regions in Brazil</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chakrabarti, S.; Bongiovanni, T. E.; Judge, J.; Principe, J. C.; Fraisse, C.</p> <p>2013-12-01</p> <p>Reliable soil moisture (SM) information in the root zone (RZSM) is critical for quantification of agricultural drought impacts on crop yields and for recommending management and adaptation strategies for crop management, commodity trading and food security.The recently launched European Space Agency-Soil Moisture and Ocean Salinity (ESA-SMOS) and the near-future National Aeronautics and Space Administration-Soil Moisture Active Passive (NASA-SMAP) missions provide SM at unprecedented spatial resolutions of 10-25 km, but these resolutions are still too coarse for agricultural applications in heterogeneous landscapes, making <span class="hlt">downscaling</span> a necessity. This <span class="hlt">downscaled</span> near-surface SM can be merged with crop growth models in a data assimilation framework to provide optimal estimates of RZSM and crop yield. The objectives of the study include: 1) to implement a novel downscalingalgorithm based on the Information theoretical learning principlesto <span class="hlt">downscale</span> SMOS soil moisture at 25 km to 1km in the Brazilian La Plata Basin region and2) to assimilate the 1km-soil moisture in the crop model for a normal and a drought year to understand the impact on crop yield. In this study, a novel <span class="hlt">downscaling</span> algorithm based on the Principle of Relevant Information (PRI) was applied to in-situ and remotely sensed precipitation, SM, land surface temperature and leaf area index in the Brazilian Lower La Plata region in South America. An <span class="hlt">Ensemble</span> Kalman Filter (EnKF) based assimilation algorithm was used to assimilate the <span class="hlt">downscaled</span> soil moisture to update both states and parameters. The <span class="hlt">downscaled</span> soil moisture for two growing seasons in2010-2011 and 2011-2012 was assimilated into the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model over 161 km2 rain-fed region in the Brazilian LPB regionto improve the estimates of soybean yield. The first season experienced normal precipitation, while the second season was impacted by drought. Assimilation improved yield</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ClDy..tmp..157T&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ClDy..tmp..157T&link_type=ABSTRACT"><span id="translatedtitle">A framework for investigating large-scale patterns as an alternative to precipitation for <span class="hlt">downscaling</span> to local drought</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Towler, Erin; PaiMazumder, Debasish; Holland, Greg</p> <p>2016-04-01</p> <p>Global Climate Model (GCM) projections suggest that drought will increase across large areas of the globe, but lack skill at simulating climate variations at local-scales where adaptation decisions are made. As such, GCMs are often <span class="hlt">downscaled</span> using statistical methods. This study develops a 3-step framework to assess the use of large-scale environmental patterns to assess local precipitation in statistically <span class="hlt">downscaling</span> to local drought. In Step 1, two statistical <span class="hlt">downscaling</span> models are developed: one based on temperature and precipitation and another based on temperature and a large-scale predictor that serves as a proxy for precipitation. A key component is identifying the large-scale predictor, which is customized for the location of interest. In Step 2, the statistical models are evaluated using NCEP/NCAR Reanalysis data. In Step 3, we apply a large <span class="hlt">ensemble</span> of future GCM projections to the statistical models. The technique is demonstrated for predicting drought, as measured by the Palmer Drought Severity Index, in South-central Oklahoma, but the framework is general and applicable to other locations. Case study results using the Reanalysis show that the large-scale predictor explains slightly more variance than precipitation when predicting local drought. Applying future GCM projections to both statistical models indicates similar drying trends, but demonstrates notable internal variability. The case study demonstrates: (1) where a large-scale predictor performs comparably (or better) than precipitation directly, then it is an appealing predictor choice to use with future projections, (2) when statistically <span class="hlt">downscaling</span> to local scales, it is critical to consider internal variability, as it may be more important than predictor selection.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016WRR....52..471L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016WRR....52..471L"><span id="translatedtitle">Assessing the relative effectiveness of statistical <span class="hlt">downscaling</span> and distribution mapping in reproducing rainfall statistics based on climate model results</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Langousis, Andreas; Mamalakis, Antonios; Deidda, Roberto; Marrocu, Marino</p> <p>2016-01-01</p> <p>To improve the level skill of climate models (CMs) in reproducing the statistics of daily rainfall at a basin level, two types of statistical approaches have been suggested. One is statistical correction of CM rainfall outputs based on historical series of precipitation. The other, usually referred to as statistical rainfall <span class="hlt">downscaling</span>, is the use of stochastic models to conditionally simulate rainfall series, based on large-scale atmospheric forcing from CMs. While promising, the latter approach attracted reduced attention in recent years, since the developed <span class="hlt">downscaling</span> schemes involved complex weather identification procedures, while demonstrating limited success in reproducing several statistical features of rainfall. In a recent effort, Langousis and Kaleris (<link href="#wrcr21852-bib-0084"/>) developed a statistical framework for simulation of daily rainfall intensities conditional on upper-air variables, which is simpler to implement and more accurately reproduces several statistical properties of actual rainfall records. Here we study the relative performance of: (a) direct statistical correction of CM rainfall outputs using nonparametric distribution mapping, and (b) the statistical <span class="hlt">downscaling</span> scheme of Langousis and Kaleris (<link href="#wrcr21852-bib-0084"/>), in reproducing the historical rainfall statistics, including rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e., the Flumendosa catchment, using rainfall and atmospheric data from four CMs of the <span class="hlt">ENSEMBLES</span> project. The obtained results are promising, since the proposed <span class="hlt">downscaling</span> scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the CM used and the characteristics of the calibration period. This is particularly the case for yearly rainfall maxima.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4761110','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4761110"><span id="translatedtitle"><span class="hlt">Ensembl</span> comparative genomics resources</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Muffato, Matthieu; Beal, Kathryn; Fitzgerald, Stephen; Gordon, Leo; Pignatelli, Miguel; Vilella, Albert J.; Searle, Stephen M. J.; Amode, Ridwan; Brent, Simon; Spooner, William; Kulesha, Eugene; Yates, Andrew; Flicek, Paul</p> <p>2016-01-01</p> <p>Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The <span class="hlt">Ensembl</span> comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. <span class="hlt">Ensembl</span> computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define <span class="hlt">Ensembl</span> Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The <span class="hlt">Ensembl</span> comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including <span class="hlt">Ensembl</span> Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes <span class="hlt">Ensembl</span> an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available. Database URL: http://www.<span class="hlt">ensembl</span>.org. PMID:26896847</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26896847','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26896847"><span id="translatedtitle"><span class="hlt">Ensembl</span> comparative genomics resources.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Herrero, Javier; Muffato, Matthieu; Beal, Kathryn; Fitzgerald, Stephen; Gordon, Leo; Pignatelli, Miguel; Vilella, Albert J; Searle, Stephen M J; Amode, Ridwan; Brent, Simon; Spooner, William; Kulesha, Eugene; Yates, Andrew; Flicek, Paul</p> <p>2016-01-01</p> <p>Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The <span class="hlt">Ensembl</span> comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. <span class="hlt">Ensembl</span> computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define <span class="hlt">Ensembl</span> Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The <span class="hlt">Ensembl</span> comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including <span class="hlt">Ensembl</span> Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes <span class="hlt">Ensembl</span> an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available. Database URL: http://www.<span class="hlt">ensembl</span>.org. PMID:26896847</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC33G..08M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC33G..08M"><span id="translatedtitle">Effect of <span class="hlt">downscaling</span> methodology on decision-making</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McCrary, R. R.; Mearns, L. O.; McGinnis, S. A.; McDaniel, L. R.</p> <p>2015-12-01</p> <p>There is increasing demand from decision makers for fine scale climate information that is relevant and useful for regional and local adaptation planning. While global climate models (GCMs) are vital for understanding large-scale changes in global circulation patterns, the horizontal resolution of a typical GCM is too coarse for use in local impact studies. A number of methods have been implemented to translate coarse GCM climate projections down to the regional and local scale. These range from the simplest delta approach to complex dynamical <span class="hlt">downscaling</span> models. With so many diverse methods of <span class="hlt">downscaling</span> now available, there is a need to perform robust comparisons and evaluations of the different techniques. In this study we explore how the choice of <span class="hlt">downscaling</span> method may influence the climate change response of important impacts related variables. Our goal is to identify the uncertainty in future climate change associated with different <span class="hlt">downscaling</span> methods. We then examine how the uncertainty associated with <span class="hlt">downscaling</span> can affect vulnerability assessments and adaptation planning. We focus on the impact of climate change to extremes in three sectors: forest fire risk management, heat stress and human health, and energy consumption by buildings. For each sector, an impacts relevant index is used to assess current and future risk. The Keetch-Byram Drought Index (KBDI) is used for fire, the Wet Bulb Globe Temperature (WBGT) is used for heat stress, and heating and cooling degree-days are used for energy consumption. Local climate changes have been calculated for each sector using four <span class="hlt">downscaling</span> techniques: the delta method, a bias correction method (KDDM), the statistical <span class="hlt">downscaling</span> model (SDSM), and dynamical <span class="hlt">downscaling</span> with NARCCAP. Climate response surfaces (e.g. response of KBDI to changes in temp. and precip.) are generated at four locations in the United States. Response surfaces are a useful tool to help decision makers estimate the vulnerability to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.S51A2313Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.S51A2313Y"><span id="translatedtitle"><span class="hlt">Downscaling</span> of slip distribution for strong earthquakes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yoshida, T.; Oya, S.; Kuzuha, Y.</p> <p>2013-12-01</p> <p>We intend to develop a <span class="hlt">downscaling</span> model to enhance the earthquake slip distribution resolution. Slip distributions have been obtained by other researchers using various inversion methods. As a <span class="hlt">downscaling</span> model, we are discussing fractal models that include mono-fractal models (fractional Brownian motion, fBm; fractional Lévy motion, fLm) and multi-fractal models as candidates. Log - log-linearity of k (wave number) versus E (k) (power spectrum) is the necessary condition for fractality: the slip distribution is expected to satisfy log - log-linearity described above if we can apply fractal model to a slip distribution as a <span class="hlt">downscaling</span> model. Therefore, we conducted spectrum analyses using slip distributions of 11 earthquakes as explained below. 1) Spectrum analyses using one-dimensional slip distributions (strike direction) were conducted. 2) Averaging of some results of power spectrum (dip direction) was conducted. Results show that, from the viewpoint of log - log-linearity, applying a fractal model to slip distributions can be inferred as valid. We adopt the filtering method after Lavallée (2008) to generate fBm/ fLm. In that method, generated white noises (random numbers) are filtered using a power law type filter (log - log-linearity of the spectrum). Lavallée (2008) described that Lévy white noise that generates fLm is more appropriate than the Gaussian white noise which generates fBm. In addition, if the 'alpha' parameter of the Lévy law, which governs the degree of attenuation of tails of the probability distribution, is 2.0, then the Lévy distribution is equivalent to the Gauss distribution. We analyzed slip distributions of 11 earthquakes: the Tohoku earthquake (Wei et al., 2011), Haiti earthquake (Sladen, 2010), Simeulue earthquake (Sladen, 2008), eastern Sichuan earthquake (Sladen, 2008), Peru earthquake (Konca, 2007), Tocopilla earthquake (Sladen, 2007), Kuril earthquake (Sladen, 2007), Benkulu earthquake (Konca, 2007), and southern Java</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H42D..04N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H42D..04N"><span id="translatedtitle">Satellite-Enhanced Dynamical <span class="hlt">Downscaling</span> of Extreme Events</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nunes, A.</p> <p>2015-12-01</p> <p>Severe weather events can be the triggers of environmental disasters in regions particularly susceptible to changes in hydrometeorological conditions. In that regard, the reconstruction of past extreme weather events can help in the assessment of vulnerability and risk mitigation actions. Using novel modeling approaches, dynamical <span class="hlt">downscaling</span> of long-term integrations from global circulation models can be useful for risk analysis, providing more accurate climate information at regional scales. Originally developed at the National Centers for Environmental Prediction (NCEP), the Regional Spectral Model (RSM) is being used in the dynamical <span class="hlt">downscaling</span> of global reanalysis, within the South American Hydroclimate Reconstruction Project. Here, RSM combines scale-selective bias correction with assimilation of satellite-based precipitation estimates to <span class="hlt">downscale</span> extreme weather occurrences. Scale-selective bias correction is a method employed in the <span class="hlt">downscaling</span>, similar to the spectral nudging technique, in which the <span class="hlt">downscaled</span> solution develops in agreement with its coarse boundaries. Precipitation assimilation acts on modeled deep-convection, drives the land-surface variables, and therefore the hydrological cycle. During the <span class="hlt">downscaling</span> of extreme events that took place in Brazil in recent years, RSM continuously assimilated NCEP Climate Prediction Center morphing technique precipitation rates. As a result, RSM performed better than its global (reanalysis) forcing, showing more consistent hydrometeorological fields compared with more sophisticated global reanalyses. Ultimately, RSM analyses might provide better-quality initial conditions for high-resolution numerical predictions in metropolitan areas, leading to more reliable short-term forecasting of severe local storms.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020052415','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020052415"><span id="translatedtitle">Input Decimated <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)</p> <p>2001-01-01</p> <p>Using an <span class="hlt">ensemble</span> of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated <span class="hlt">ensembles</span> (IDEs) outperform <span class="hlt">ensembles</span> whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1231194','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1231194"><span id="translatedtitle">Matlab Cluster <span class="hlt">Ensemble</span> Toolbox</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Sapio, Vincent De; Kegelmeyer, Philip</p> <p>2009-04-27</p> <p>This is a Matlab toolbox for investigating the application of cluster <span class="hlt">ensembles</span> to data classification, with the objective of improving the accuracy and/or speed of clustering. The toolbox divides the cluster <span class="hlt">ensemble</span> problem into four areas, providing functionality for each. These include, (1) synthetic data generation, (2) clustering to generate individual data partitions and similarity matrices, (3) consensus function generation and final clustering to generate <span class="hlt">ensemble</span> data partitioning, and (4) implementation of accuracy metrics. With regard to data generation, Gaussian data of arbitrary dimension can be generated. The kcenters algorithm can then be used to generate individual data partitions by either, (a) subsampling the data and clustering each subsample, or by (b) randomly initializing the algorithm and generating a clustering for each initialization. In either case an overall similarity matrix can be computed using a consensus function operating on the individual similarity matrices. A final clustering can be performed and performance metrics are provided for evaluation purposes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1160288','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1160288"><span id="translatedtitle">The ultimate <span class="hlt">downscaling</span> limit of FETs.</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Mamaluy, Denis; Gao, Xujiao; Tierney, Brian David</p> <p>2014-10-01</p> <p>We created a highly efficient, universal 3D quant um transport simulator. We demonstrated that the simulator scales linearly - both with the problem size (N) and number of CPUs, which presents an important break-through in the field of computational nanoelectronics. It allowed us, for the first time, to accurately simulate and optim ize a large number of realistic nanodevices in a much shorter time, when compared to other methods/codes such as RGF[%7EN 2.333 ]/KNIT, KWANT, and QTBM[%7EN 3 ]/NEMO5. In order to determine the best-in-class for different beyond-CMOS paradigms, we performed rigorous device optimization for high-performance logic devices at 6-, 5- and 4-nm gate lengths. We have discovered that there exists a fundamental <span class="hlt">down-scaling</span> limit for CMOS technology and other Field-Effect Transistors (FETs). We have found that, at room temperatures, all FETs, irre spective of their channel material, will start experiencing unacceptable level of thermally induced errors around 5-nm gate lengths.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27516599','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27516599"><span id="translatedtitle">Imprinting and recalling cortical <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S; Yuste, Rafael</p> <p>2016-08-12</p> <p>Neuronal <span class="hlt">ensembles</span> are coactive groups of neurons that may represent building blocks of cortical circuits. These <span class="hlt">ensembles</span> could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from <span class="hlt">ensembles</span> in the visual cortex of awake mice builds neuronal <span class="hlt">ensembles</span> that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted <span class="hlt">ensembles</span> can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal <span class="hlt">ensembles</span> that can perform pattern completion. PMID:27516599</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17..986C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17..986C"><span id="translatedtitle">Representative meteorological <span class="hlt">ensembles</span> of change climate change in the Araucanía Region, Chile.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cepeda, Javier; Vargas, Ximena</p> <p>2015-04-01</p> <p>One of the main uncertainties in hydrologic modeling is attributed to meteorological inputs. When climate change impact analysis is performed, uncertainty increases due to that meteorological time series are obtained through Global Circulation Models (GCM) for a specific climate change scenario. The Intergovernmental Panel on Climate Change (IPCC) in their last report (AR5, 2013 ) recommend the Representative Concentration Pathway. RCP scenarios, developed under the Coupled Model Intercomparison Project Phase 5 (CMIP5). Pathways for stabilization of radiative forcing by 2100 characterize these scenarios being a radiative forcing of 8.5 w/m2, the highest future condition considered. In order to reduce the meteorological uncertainties, we study the behavior of the daily precipitation series I three meteorological stations in the valley of the Araucanía region, in southern Chile, using ten <span class="hlt">ensembles</span> from CGM MK-3.6 model for RCP 8.5. The main hypothesis is that good transformer functions between the observations and data obtained from the model is essential to have suitable future projections. To obtain these functions, statistical <span class="hlt">downscaling</span> is performed; first spatial <span class="hlt">downscaling</span> is carried out, and then a temporal <span class="hlt">downscaling</span> of the daily precipitation data for each month is made. <span class="hlt">Ensembles</span> whit transfer functions without discontinuities or those with the least were preferred. From this analysis we selected four <span class="hlt">ensembles</span>. For the three gage stations we apply the transfer's functions during the observed period and compared the average seasonal variation curve, the duration curve of daily, monthly and annually precipitation and average number of rainy days. Finally, based on qualitative analysis and quantitative criteria we suggest which <span class="hlt">ensemble</span> are the most representative historical conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5800S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5800S"><span id="translatedtitle">Regional climate change projections over South America based on the CLARIS-LPB RCM <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Samuelsson, Patrick; Solman, Silvina; Sanchez, Enrique; Rocha, Rosmeri; Li, Laurent; Marengo, José; Remedio, Armelle; Berbery, Hugo</p> <p>2013-04-01</p> <p>CLARIS-LPB was an EU FP7 financed Europe-South America Network for Climate Change Assessment and Impact Studies in La Plata Basin. CLARIS-LPB has created the first <span class="hlt">ensemble</span> ever of RCM <span class="hlt">downscalings</span> over South America. Here we present the climate change scenarios for a near future period (2011-2040) and for a far future period (2071-2100). The <span class="hlt">ensemble</span> is based on seven RCMs driven by three CMIP3 GCMs for emission scenario SRES A1B. The RCM model domains cover all of South America, with a horizontal resolution of approximately 50 km, but project focus has been on results over the La Plata Basin. The <span class="hlt">ensemble</span> mean for temperature change shows more warming over tropical South America than over the southern part of the continent. During summer (DJF) the Low-Parana and Uruguay regions show less warming than the surrounding regions. For the <span class="hlt">ensemble</span> mean of precipitation changes the patterns are almost the same for near and far future but with larger values for far future. Thus overall trends do not change with time. The near future shows in general small changes over large areas (less than ±10%). For JJA a dry tendency is seen over eastern Brazil that becomes stronger and extends geographically with time. In near future most models show a drying trend over this area. In far future almost all models agree on the drying. For DJF a wet tendency is seen over the La Plata basin area which becomes stronger with time. In near future almost all <span class="hlt">downscalings</span> agree on this wet tendency and in far future all <span class="hlt">downscalings</span> agree on the sign. The RCM <span class="hlt">ensemble</span> is unbalanced with respect to forcing GCMs. 6 out of 11(10) simulations use ECHAM5 for the near(far) future period while 4(3) use HadCM3 and only one IPSL. Thus, all <span class="hlt">ensemble</span> mean values will be tilted towards ECHAM5. It is of course possible to compensate for this imbalance among GCMs by some weighting but no such weighting has been applied for the current analysis. The north-south gradient in warming is in general stronger in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009WRR....4511411M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009WRR....4511411M"><span id="translatedtitle">Using probabilistic climate change information from a multimodel <span class="hlt">ensemble</span> for water resources assessment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Manning, L. J.; Hall, J. W.; Fowler, H. J.; Kilsby, C. G.; Tebaldi, C.</p> <p>2009-11-01</p> <p>Increasing availability of <span class="hlt">ensemble</span> outputs from general circulation models (GCMs) and regional climate models (RCMs) permits fuller examination of the implications of climate uncertainties in hydrological systems. A Bayesian statistical framework is used to combine projections by weighting and to generate probability distributions of local climate change from an <span class="hlt">ensemble</span> of RCM outputs. A stochastic weather generator produces corresponding daily series of rainfall and potential evapotranspiration, which are input into a catchment rainfall-runoff model to estimate future water abstraction availability. The method is applied to the Thames catchment in the United Kingdom, where comparison with previous studies shows that different <span class="hlt">downscaling</span> methods produce significantly different flow predictions and that this is partly attributable to potential evapotranspiration predictions. An extended sensitivity test exploring the effect of the weights and assumptions associated with combining climate model projections illustrates that under all plausible assumptions the <span class="hlt">ensemble</span> implies a significant reduction in catchment water resource availability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=music+AND+management&pg=5&id=EJ663675','ERIC'); return false;" href="http://eric.ed.gov/?q=music+AND+management&pg=5&id=EJ663675"><span id="translatedtitle">Classroom Management for <span class="hlt">Ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Bauer, William I.</p> <p>2001-01-01</p> <p>Discusses topics essential to good classroom management for <span class="hlt">ensemble</span> music teachers. Explores the importance of planning and preparation, good teaching practice within the classroom, and using an effective discipline plan to deal with any behavior problems in the classroom. Includes a bibliography of further resources. (CMK)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19810000222&hterms=Garment&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DGarment','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19810000222&hterms=Garment&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DGarment"><span id="translatedtitle">Protective Garment <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wakefield, M. E.</p> <p>1982-01-01</p> <p>Protective garment <span class="hlt">ensemble</span> with internally-mounted environmental- control unit contains its own air supply. Alternatively, a remote-environmental control unit or an air line is attached at the umbilical quick disconnect. Unit uses liquid air that is vaporized to provide both breathing air and cooling. Totally enclosed garment protects against toxic substances.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..12.6179W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..12.6179W"><span id="translatedtitle">Hydrologic extremes - an intercomparison of multiple gridded statistical <span class="hlt">downscaling</span> methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Werner, A. T.; Cannon, A. J.</p> <p>2015-06-01</p> <p>Gridded statistical <span class="hlt">downscaling</span> methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate <span class="hlt">downscaling</span> methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, <span class="hlt">downscaling</span> comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded <span class="hlt">downscaling</span> models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven <span class="hlt">downscaling</span> methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as <span class="hlt">downscaling</span> target data. The skill of the <span class="hlt">downscaling</span> methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1512041M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1512041M"><span id="translatedtitle">VALUE - Validating and Integrating <span class="hlt">Downscaling</span> Methods for Climate Change Research</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maraun, Douglas; Widmann, Martin; Benestad, Rasmus; Kotlarski, Sven; Huth, Radan; Hertig, Elke; Wibig, Joanna; Gutierrez, Jose</p> <p>2013-04-01</p> <p>Our understanding of global climate change is mainly based on General Circulation Models (GCMs) with a relatively coarse resolution. Since climate change impacts are mainly experienced on regional scales, high-resolution climate change scenarios need to be derived from GCM simulations by <span class="hlt">downscaling</span>. Several projects have been carried out over the last years to validate the performance of statistical and dynamical <span class="hlt">downscaling</span>, yet several aspects have not been systematically addressed: variability on sub-daily, decadal and longer time-scales, extreme events, spatial variability and inter-variable relationships. Different <span class="hlt">downscaling</span> approaches such as dynamical <span class="hlt">downscaling</span>, statistical <span class="hlt">downscaling</span> and bias correction approaches have not been systematically compared. Furthermore, collaboration between different communities, in particular regional climate modellers, statistical <span class="hlt">downscalers</span> and statisticians has been limited. To address these gaps, the EU Cooperation in Science and Technology (COST) action VALUE (www.value-cost.eu) has been brought into life. VALUE is a research network with participants from currently 23 European countries running from 2012 to 2015. Its main aim is to systematically validate and develop <span class="hlt">downscaling</span> methods for climate change research in order to improve regional climate change scenarios for use in climate impact studies. Inspired by the co-design idea of the international research initiative "future earth", stakeholders of climate change information have been involved in the definition of research questions to be addressed and are actively participating in the network. The key idea of VALUE is to identify the relevant weather and climate characteristics required as input for a wide range of impact models and to define an open framework to systematically validate these characteristics. Based on a range of benchmark data sets, in principle every <span class="hlt">downscaling</span> method can be validated and compared with competing methods. The results of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016HESS...20.1483W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016HESS...20.1483W"><span id="translatedtitle">Hydrologic extremes - an intercomparison of multiple gridded statistical <span class="hlt">downscaling</span> methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Werner, Arelia T.; Cannon, Alex J.</p> <p>2016-04-01</p> <p>Gridded statistical <span class="hlt">downscaling</span> methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate <span class="hlt">downscaling</span> methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, <span class="hlt">downscaling</span> comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded <span class="hlt">downscaling</span> models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven <span class="hlt">downscaling</span> methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as <span class="hlt">downscaling</span> target data. The skill of the <span class="hlt">downscaling</span> methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24701932','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24701932"><span id="translatedtitle"><span class="hlt">Downscaling</span> the chemical oxygen demand test.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Carbajal-Palacios, Patricia; Balderas-Hernandez, Patricia; Ibanez, Jorge G; Roa-Morales, Gabriela</p> <p>2014-01-01</p> <p>The usefulness of the standard chemical oxygen demand (COD) test for water characterization is offset to some extent by its requirement for highly toxic or expensive Cr, Ag, and Hg species. In addition, oxidation of the target samples by chromate requires a 2-3 h heating step. We have <span class="hlt">downscaled</span> this method to obtain a reduction of up to ca. 80% in the use and generation of toxic residues and a time reduction of up to ca. 67%. This also translates into considerable energy savings by reducing the time required for heating as well as costly labour time. Such reductions can be especially important for analytical laboratories with heavy loads of COD analyses. Numerical results obtained with the standard COD method for laboratory KHP samples (potassium hydrogen phthalate) show an average relative error of 1.41% vs. an average of 2.14% obtained with the downsized or small-scale version. The average % standard deviation when using the former is 2.16% vs. 3.24% obtained with the latter. When analysing municipal wastewater samples, the relative error is smaller for the proposed small-scale method than for the standard method (0.05 vs. 0.58, respectively), and the % std. dev. is 1.25% vs. 1.06%. The results obtained with various industrial wastewaters show good agreement with those obtained using the standard method. Chloride ions do not interfere at concentrations below 2000 mg Nacl/L. This highly encouraging proof-of-concept offers a potentially alternative greener approach to COD analysis. PMID:24701932</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy...46.1991S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...46.1991S"><span id="translatedtitle">Credibility of statistical <span class="hlt">downscaling</span> under nonstationary climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salvi, Kaustubh; Ghosh, Subimal; Ganguly, Auroop R.</p> <p>2016-03-01</p> <p>Statistical <span class="hlt">downscaling</span> (SD) establishes empirical relationships between coarse-resolution climate model simulations with higher-resolution climate variables of interest to stakeholders. These statistical relations are estimated based on historical observations at the finer resolutions and used for future projections. The implicit assumption is that the SD relations, extracted from data are stationary or remain unaltered, despite non-stationary change in climate. The validity of this assumption relates directly to the credibility of SD. Falsifiability of climate projections is a challenging proposition. Calibration and verification, while necessary for SD, are unlikely to be able to reproduce the full range of behavior that could manifest at decadal to century scale lead times. We propose a design-of-experiments (DOE) strategy to assess SD performance under nonstationary climate and evaluate the strategy via a transfer-function based SD approach. The strategy relies on selection of calibration and validation periods such that they represent contrasting climatic conditions like hot-versus-cold and ENSO-versus-non-ENSO years. The underlying assumption is that conditions such as warming or predominance of El Niño may be more prevalent under climate change. In addition, two different historical time periods are identified, which resemble pre-industrial and the most severe future emissions scenarios. The ability of the empirical relations to generalize under these proxy conditions is considered an indicator of their performance under future nonstationarity. Case studies over two climatologically disjoint study regions, specifically India and Northeast United States, reveal robustness of DOE in identifying the locations where nonstationarity prevails as well as the role of effective predictor selection under nonstationarity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.H33A1483K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.H33A1483K"><span id="translatedtitle">Spatiostatistical <span class="hlt">downscaling</span> of soil moisture in an assimilation framework</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kaheil, Y. H.; Gill, M.; McKee, M.; Bastidas, L.</p> <p>2006-12-01</p> <p>The scale reconciliation issue has gained in extra attention with remote sensing data coming in and the shift towards the distributed approach for hydrologic modeling. The purpose of the current research is to develop a method to disaggregate coarse resolution remote sensing data to fine resolutions more appropriate in hydrologic studies. Disaggregation is done with the help of point measurements on the ground. The <span class="hlt">downscaling</span> of remote sensing data is achieved by three main steps namely: initialization, spatial pattern mimicking, and assimilation. The assimilation step also excerpts the information coming from the point measurements. These three steps provide means of capturing both spatial trend and physics of the process at multiple resolution levels while <span class="hlt">downscaling</span>. The approach has been applied and validated by <span class="hlt">downscaling</span> images for two cases. In the first case a synthetically generated random field based on the statistical properties of point measurements is reproduced at fine scale and coarse resolutions. The algorithm was able to account for spatial and vertical properties for this synthetic case. In the second case a soil moisture field from SGP 97 experiments is <span class="hlt">downscaled</span> from a resolution of 800m x 800m to a resolution of 50m x 50m. It is also shown that how the assimilation step helped to improve the approximation of the <span class="hlt">downscaled</span> fields.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014OcMod..84...35L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014OcMod..84...35L"><span id="translatedtitle">Wave climate projections along the French coastline: Dynamical versus statistical <span class="hlt">downscaling</span> methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Méndez, Fernando</p> <p>2014-12-01</p> <p>The estimation of possible impacts related to climate change on the wave climate is subject to several levels of uncertainty. In this work, we focus on the uncertainties inherent in the method applied to project the wave climate using atmospheric simulations. Two approaches are commonly used to obtain the regional wave climate: dynamical and statistical <span class="hlt">downscaling</span> from atmospheric data. We apply both approaches based on the outputs of a global climate model (GCM), ARPEGE-CLIMAT, under three possible future scenarios (B1, A1B and A2) of the Fourth Assessment Report, AR4 (IPCC, 2007), along the French coast and evaluate their results for the wave climate with a high level of precision. The performance of the dynamical and the statistical methods is determined through a comparative analysis of the estimated means, standard deviations and monthly quantile distributions of significant wave heights, the joint probability distributions of wave parameters and seasonal and interannual variability. Analysis of the results shows that the statistical projections are able to reproduce the wave climatology as well as the dynamical projections, with some deficiencies being observed in the summer and for the upper tail of the significant wave height. In addition, with its low computational time requirements, the statistical <span class="hlt">downscaling</span> method allows an <span class="hlt">ensemble</span> of simulations to be calculated faster than the dynamical method. It then becomes possible to quantify the uncertainties associated with the choice of the GCM or the socio-economic scenarios, which will improve estimates of the impact of wave climate change along the French coast.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..519.2978G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..519.2978G"><span id="translatedtitle">Evaluation of real-time hydrometeorological <span class="hlt">ensemble</span> prediction on hydrologic scales in Northern California</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Georgakakos, Konstantine P.; Graham, Nicholas E.; Modrick, Theresa M.; Murphy, Michael J.; Shamir, Eylon; Spencer, Cristopher R.; Sperfslage, Jason A.</p> <p>2014-11-01</p> <p>The paper presents an evaluation of real time <span class="hlt">ensemble</span> forecasts produced during 2010-2012 by the demonstration project INFORM (Integrated Forecast and Reservoir Management) in Northern California. In addition, the innovative elements of the forecast component of the INFORM project are highlighted. The forecast component is designed to dynamically <span class="hlt">downscale</span> operational multi-lead <span class="hlt">ensemble</span> forecasts from the Global <span class="hlt">Ensemble</span> Forecast System (GEFS) and the Climate Forecast system (CFS) of the National Centers of Environmental Prediction (NCEP), and to use adaptations of the operational hydrologic models of the US National Weather Service California Nevada River Forecast Center to provide <span class="hlt">ensemble</span> reservoir inflow forecasts in real time. A full-physics 10-km resolution (10 km on the side) mesoscale model was implemented for the <span class="hlt">ensemble</span> prediction of surface precipitation and temperature over the domain of Northern California with lead times out to 16 days with 6-hourly temporal resolution. An intermediate complexity regional model with a 10 km resolution was implemented to <span class="hlt">downscale</span> the NCEP CFS <span class="hlt">ensemble</span> forecasts for lead times out to 41.5 days. Methodologies for precipitation and temperature model forecast adjustment to comply with the corresponding observations were formulated and tested as regards their effectiveness for improving the <span class="hlt">ensemble</span> predictions of these two variables and also for improving reservoir inflow forecasts. The evaluation is done using the real time databases of INFORM and concerns the snow accumulation and melt seasons. Performance is measured by metrics that range from those that use forecast means to those that use the entire forecast <span class="hlt">ensemble</span>. The results show very good skill in forecasting precipitation and temperature over the subcatchments of the INFORM domain out to a week in advance for all basins, models and seasons. For temperature, in some cases, non-negligible skill has been obtained out to four weeks for the melt season</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1814006M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1814006M&link_type=ABSTRACT"><span id="translatedtitle">On the combination of Stochastic Pertubation of Physical Tendencies and parameter perturbation for convection-permitting <span class="hlt">ensemble</span> forecast</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Marsigli, Chiara; Montani, Andrea; Paccagnella, Tiziana; Torrisi, Lucio; Marcucci, Francesca</p> <p>2016-04-01</p> <p>A convection-permitting <span class="hlt">ensemble</span> based on the COSMO model (COSMO-IT-EPS) has been developed for Italy. The <span class="hlt">ensemble</span> is run at 2.8 km of horizontal resolution, with 10 members, and receive initial and boundary conditions from a coarser resolution <span class="hlt">ensemble</span> covering the entire Mediterranean area. A deficiency in the spread/skill relation of the <span class="hlt">ensemble</span> in terms of near-surface weather parameter had been found in a previous study, in agreement with results reported in literature for similar limited-area <span class="hlt">ensemble</span> systems. In order to address this issue, the physics perturbation methodology applied to the <span class="hlt">ensemble</span> is here studied, with the aim of combining different sources of model uncertainties. Three configurations of the <span class="hlt">ensemble</span> have been run for one month period in Autumn 2015: i) a control configuration, which is a pure <span class="hlt">downscaling</span> <span class="hlt">ensemble</span>, ii) a configuration where the model physics is perturbed by making use of the Stochastic Pertubation of Physical Tendencies (SPPT) scheme implemented in the COSMO model and iii) a configuration where the SPPT scheme is combined with perturbed physics parameters. The aim is to assess the relative impact of SPPT and parameter perturbation and to study their complementarity, both in a statistical way and on selected events. Objective evaluation of the forecast quality is performed for 2-meter temperature and humidity, against data from the SYNOP network, as well as for precipitation, using high density raingauge data to allow the application of spatial verification methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009GeoRL..3611708M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009GeoRL..3611708M"><span id="translatedtitle">Probabilistic <span class="hlt">downscaling</span> approaches: Application to wind cumulative distribution functions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Michelangeli, P.-A.; Vrac, M.; Loukos, H.</p> <p>2009-06-01</p> <p>A statistical method is developed to generate local cumulative distribution functions (CDFs) of surface climate variables from large-scale fields. Contrary to most <span class="hlt">downscaling</span> methods producing continuous time series, our “probabilistic <span class="hlt">downscaling</span> methods” (PDMs), named “CDF-transform”, is designed to deal with and provide local-scale CDFs through a transformation applied to large-scale CDFs. First, our PDM is compared to a reference method (Quantile-matching), and validated on a historical time period by <span class="hlt">downscaling</span> CDFs of wind intensity anomalies over France, for reanalyses and simulations from a general circulation model (GCM). Then, CDF-transform is applied to GCM output fields to project changes in wind intensity anomalies for the 21st century under A2 scenario. Results show a decrease in wind anomalies for most weather stations, ranging from less than 1% (in the South) to nearly 9% (in the North), with a maximum in the Brittany region.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3673440','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3673440"><span id="translatedtitle"><span class="hlt">Downscaling</span> Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Liu, Desheng; Pu, Ruiliang</p> <p>2008-01-01</p> <p>Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for <span class="hlt">downscaling</span> coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to <span class="hlt">downscale</span> simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both <span class="hlt">downscaling</span> methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26387108','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26387108"><span id="translatedtitle">The Protein <span class="hlt">Ensemble</span> Database.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Varadi, Mihaly; Tompa, Peter</p> <p>2015-01-01</p> <p>The scientific community's major conceptual notion of structural biology has recently shifted in emphasis from the classical structure-function paradigm due to the emergence of intrinsically disordered proteins (IDPs). As opposed to their folded cousins, these proteins are defined by the lack of a stable 3D fold and a high degree of inherent structural heterogeneity that is closely tied to their function. Due to their flexible nature, solution techniques such as small-angle X-ray scattering (SAXS), nuclear magnetic resonance (NMR) spectroscopy and fluorescence resonance energy transfer (FRET) are particularly well-suited for characterizing their biophysical properties. Computationally derived structural <span class="hlt">ensembles</span> based on such experimental measurements provide models of the conformational sampling displayed by these proteins, and they may offer valuable insights into the functional consequences of inherent flexibility. The Protein <span class="hlt">Ensemble</span> Database (http://pedb.vib.be) is the first openly accessible, manually curated online resource storing the <span class="hlt">ensemble</span> models, protocols used during the calculation procedure, and underlying primary experimental data derived from SAXS and/or NMR measurements. By making this previously inaccessible data freely available to researchers, this novel resource is expected to promote the development of more advanced modelling methodologies, facilitate the design of standardized calculation protocols, and consequently lead to a better understanding of how function arises from the disordered state. PMID:26387108</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1231194-matlab-cluster-ensemble-toolbox','SCIGOV-ESTSC'); return false;" href="http://www.osti.gov/scitech/biblio/1231194-matlab-cluster-ensemble-toolbox"><span id="translatedtitle">Matlab Cluster <span class="hlt">Ensemble</span> Toolbox</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech/">Energy Science and Technology Software Center (ESTSC)</a></p> <p></p> <p>2009-04-27</p> <p>This is a Matlab toolbox for investigating the application of cluster <span class="hlt">ensembles</span> to data classification, with the objective of improving the accuracy and/or speed of clustering. The toolbox divides the cluster <span class="hlt">ensemble</span> problem into four areas, providing functionality for each. These include, (1) synthetic data generation, (2) clustering to generate individual data partitions and similarity matrices, (3) consensus function generation and final clustering to generate <span class="hlt">ensemble</span> data partitioning, and (4) implementation of accuracy metrics. Withmore » regard to data generation, Gaussian data of arbitrary dimension can be generated. The kcenters algorithm can then be used to generate individual data partitions by either, (a) subsampling the data and clustering each subsample, or by (b) randomly initializing the algorithm and generating a clustering for each initialization. In either case an overall similarity matrix can be computed using a consensus function operating on the individual similarity matrices. A final clustering can be performed and performance metrics are provided for evaluation purposes.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26529728','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26529728"><span id="translatedtitle">Effective Visualization of Temporal <span class="hlt">Ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hao, Lihua; Healey, Christopher G; Bass, Steffen A</p> <p>2016-01-01</p> <p>An <span class="hlt">ensemble</span> is a collection of related datasets, called members, built from a series of runs of a simulation or an experiment. <span class="hlt">Ensembles</span> are large, temporal, multidimensional, and multivariate, making them difficult to analyze. Another important challenge is visualizing <span class="hlt">ensembles</span> that vary both in space and time. Initial visualization techniques displayed <span class="hlt">ensembles</span> with a small number of members, or presented an overview of an entire <span class="hlt">ensemble</span>, but without potentially important details. Recently, researchers have suggested combining these two directions, allowing users to choose subsets of members to visualization. This manual selection process places the burden on the user to identify which members to explore. We first introduce a static <span class="hlt">ensemble</span> visualization system that automatically helps users locate interesting subsets of members to visualize. We next extend the system to support analysis and visualization of temporal <span class="hlt">ensembles</span>. We employ 3D shape comparison, cluster tree visualization, and glyph based visualization to represent different levels of detail within an <span class="hlt">ensemble</span>. This strategy is used to provide two approaches for temporal <span class="hlt">ensemble</span> analysis: (1) segment based <span class="hlt">ensemble</span> analysis, to capture important shape transition time-steps, clusters groups of similar members, and identify common shape changes over time across multiple members; and (2) time-step based <span class="hlt">ensemble</span> analysis, which assumes <span class="hlt">ensemble</span> members are aligned in time by combining similar shapes at common time-steps. Both approaches enable users to interactively visualize and analyze a temporal <span class="hlt">ensemble</span> from different perspectives at different levels of detail. We demonstrate our techniques on an <span class="hlt">ensemble</span> studying matter transition from hadronic gas to quark-gluon plasma during gold-on-gold particle collisions. PMID:26529728</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H32F..01B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H32F..01B"><span id="translatedtitle">Revealing Risks in Adaptation Planning: expanding Uncertainty Treatment and dealing with Large Projection <span class="hlt">Ensembles</span> during Planning Scenario development</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brekke, L. D.; Clark, M. P.; Gutmann, E. D.; Wood, A.; Mizukami, N.; Mendoza, P. A.; Rasmussen, R.; Ikeda, K.; Pruitt, T.; Arnold, J. R.; Rajagopalan, B.</p> <p>2015-12-01</p> <p>Adaptation planning assessments often rely on single methods for climate projection <span class="hlt">downscaling</span> and hydrologic analysis, do not reveal uncertainties from associated method choices, and thus likely produce overly confident decision-support information. Recent work by the authors has highlighted this issue by identifying strengths and weaknesses of widely applied methods for <span class="hlt">downscaling</span> climate projections and assessing hydrologic impacts. This work has shown that many of the methodological choices made can alter the magnitude, and even the sign of the climate change signal. Such results motivate consideration of both sources of method uncertainty within an impacts assessment. Consequently, the authors have pursued development of improved <span class="hlt">downscaling</span> techniques spanning a range of method classes (quasi-dynamical and circulation-based statistical methods) and developed approaches to better account for hydrologic analysis uncertainty (multi-model; regional parameter estimation under forcing uncertainty). This presentation summarizes progress in the development of these methods, as well as implications of pursuing these developments. First, having access to these methods creates an opportunity to better reveal impacts uncertainty through multi-method <span class="hlt">ensembles</span>, expanding on present-practice <span class="hlt">ensembles</span> which are often based only on emissions scenarios and GCM choices. Second, such expansion of uncertainty treatment combined with an ever-expanding wealth of global climate projection information creates a challenge of how to use such a large <span class="hlt">ensemble</span> for local adaptation planning. To address this challenge, the authors are evaluating methods for <span class="hlt">ensemble</span> selection (considering the principles of fidelity, diversity and sensitivity) that is compatible with present-practice approaches for abstracting change scenarios from any "<span class="hlt">ensemble</span> of opportunity". Early examples from this development will also be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JESS..123.1603A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JESS..123.1603A"><span id="translatedtitle">Assessment of climate change impacts on rainfall using large scale climate variables and <span class="hlt">downscaling</span> models - A case study</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ahmadi, Azadeh; Moridi, Ali; Lafdani, Elham Kakaei; Kianpisheh, Ghasem</p> <p>2014-10-01</p> <p>Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and <span class="hlt">downscaling</span> numerical weather <span class="hlt">ensemble</span> forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash-Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical <span class="hlt">downscaling</span> model (SDSM) as a popular <span class="hlt">downscaling</span> tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.7513L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.7513L"><span id="translatedtitle">Rainfall <span class="hlt">Downscaling</span> Conditional on Upper-air Atmospheric Predictors: Improved Assessment of Rainfall Statistics in a Changing Climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Langousis, Andreas; Mamalakis, Antonis; Deidda, Roberto; Marrocu, Marino</p> <p>2015-04-01</p> <p> regional level. This is done for an intermediate-sized catchment in Italy, i.e. the Flumendosa catchment, using climate model rainfall and atmospheric data from the <span class="hlt">ENSEMBLES</span> project (http://ensembleseu.metoffice.com). In doing so, we split the historical rainfall record of mean areal precipitation (MAP) in 15-year calibration and 45-year validation periods, and compare the historical rainfall statistics to those obtained from: a) Q-Q corrected climate model rainfall products, and b) synthetic rainfall series generated by the suggested <span class="hlt">downscaling</span> scheme. To our knowledge, this is the first time that climate model rainfall and statistically <span class="hlt">downscaled</span> precipitation are compared to catchment-averaged MAP at a daily resolution. The obtained results are promising, since the proposed <span class="hlt">downscaling</span> scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the climate model used and the length of the calibration period. This is particularly the case for the yearly rainfall maxima, where direct statistical correction of climate model rainfall outputs shows increased sensitivity to the length of the calibration period and the climate model used. The robustness of the suggested <span class="hlt">downscaling</span> scheme in modeling rainfall extremes at a daily resolution, is a notable feature that can effectively be used to assess hydrologic risk at a regional level under changing climatic conditions. Acknowledgments The research project is implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State. CRS4 highly acknowledges the contribution of the Sardinian regional authorities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.6616N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.6616N"><span id="translatedtitle">Predicting future wind power generation and power demand in France using statistical <span class="hlt">downscaling</span> methods developed for hydropower applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Najac, Julien</p> <p>2014-05-01</p> <p>For many applications in the energy sector, it is crucial to dispose of <span class="hlt">downscaling</span> methods that enable to conserve space-time dependences at very fine spatial and temporal scales between variables affecting electricity production and consumption. For climate change impact studies, this is an extremely difficult task, particularly as reliable climate information is usually found at regional and monthly scales at best, although many industry oriented applications need further refined information (hydropower production model, wind energy production model, power demand model, power balance model…). Here we thus propose to investigate the question of how to predict and quantify the influence of climate change on climate-related energies and the energy demand. To do so, statistical <span class="hlt">downscaling</span> methods originally developed for studying climate change impacts on hydrological cycles in France (and which have been used to compute hydropower production in France), have been applied for predicting wind power generation in France and an air temperature indicator commonly used for predicting power demand in France. We show that those methods provide satisfactory results over the recent past and apply this methodology to several climate model runs from the <span class="hlt">ENSEMBLES</span> project.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003PhDT........88W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003PhDT........88W"><span id="translatedtitle">Using climate model <span class="hlt">ensemble</span> forecasts for seasonal hydrologic prediction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, Andrew Whitaker</p> <p></p> <p>Seasonal hydrologic forecasting has long played an invaluable role in the development and use of water resources. Despite notable advances in the science and practice of climate prediction, current approaches of hydrologists and water managers largely fail to incorporate seasonal climate forecast information that has become operationally available during the last decade. This study is motivated by the view that a combination of hydrologic and climate prediction methods affords a new opportunity to improve hydrologic forecast skill. A relatively direct statistical approach for achieving this combination (i.e., <span class="hlt">downscaling</span>) was formulated that used <span class="hlt">ensemble</span> climate model forecasts with a six month lead time produced by the NCEP/CPC Global Spectral Model (GSM) as input to the macroscale Variable Infiltration Capacity hydrologic model to produce <span class="hlt">ensemble</span> runoff and streamflow forecasts. The approach involved the bias correction of climate model precipitation and temperature fields, and spatial and temporal disaggregation from monthly climate model scale (about 2 degrees latitude by longitude) fields to daily hydrology model scale (1/8 degrees) inputs. A qualitative evaluation of the approach in the eastern U.S. suggested that it was successful in translating climate forecast signals to local hydrologic variables and streamflow, but that the dominant influence on forecast results tended to be persistence in initial hydrologic conditions. The suitability of the statistical <span class="hlt">downscaling</span> approach for supporting hydrologic simulation was then assessed (using a continuous retrospective 20-year climate simulation from the DOE Parallel Climate Model) relative to dynamical <span class="hlt">downscaling</span> via a regional, meso-scale climate model. The statistical approach generally outperformed the dynamical approach, in that the dynamical approach alone required additional bias-correction to reproduce the retrospective hydrology as well as the statistical approach. Finally, using 21 years of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC41E..05G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41E..05G"><span id="translatedtitle">Evaluating the utility of dynamical <span class="hlt">downscaling</span> in agricultural impacts projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Glotter, M.; Elliott, J. W.; McInerney, D. J.; Moyer, E. J.</p> <p>2013-12-01</p> <p>The need to understand the future impacts of climate change has driven the increasing use of dynamical <span class="hlt">downscaling</span> to produce fine-spatial-scale climate projections for impacts models. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield. Our results suggest that it does not. We simulate U.S. maize yields under current and future CO2 concentrations with the widely-used DSSAT crop model, driven by a variety of climate inputs including two general circulation models (GCMs), each in turn <span class="hlt">downscaled</span> by two regional climate models (RCMs). We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven yields are essentially indistinguishable in all scenarios (<10% discrepancy in national yield, equivalent to error from observations). While RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kms) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the added value of dynamically <span class="hlt">downscaling</span> raw GCM output for impacts assessments may not justify its computational demands, and that some rethinking of <span class="hlt">downscaling</span> methods is warranted.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1616115S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1616115S"><span id="translatedtitle">Evaluation of a vector autoregressive approach for <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salonen, Sebastian; Sauter, Tobias</p> <p>2014-05-01</p> <p>Statisical <span class="hlt">downscaling</span> has become a well-established tool in regional and local impact assessments over the last few years. Robust and universal <span class="hlt">downscaling</span> methods are required to reliably correct the spatial and temporal structures from coarse models. In this study we set up and evaluate the application of VAR-models for automated temperature and precipitation <span class="hlt">downscaling</span>. VAR-models belong to the vectorial regression-techniques, that include autoregressive effects of the considered time series. They might be seen as an extension of univariate time-series analysis to multivariate perspective. Including autoregressive effects is one of the great advantages of this method, but also includes some pitfalls. Before the model can be applied the structure of the data must be carfully examined and require appropriate data preprocessing. We study in detail different preprocessing techniques and the possibility of the automatization. The proposed method has been applied and evaluated to temperature and precipitation data in the Rhineland region (Germany) and Svalbard. The large-scale atmospheric data are derived from ERA-40 as NCEP/NCAR reanalysis. These datasets offer the possibility to determine the applicability of VAR-models in a <span class="hlt">downscaling</span> approach, their need for data-preparation techniques and the possibility of an automatization of an approach based on these models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007WRR....43.7402V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007WRR....43.7402V"><span id="translatedtitle">Stochastic <span class="hlt">downscaling</span> of precipitation: From dry events to heavy rainfalls</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vrac, M.; Naveau, P.</p> <p>2007-07-01</p> <p><span class="hlt">Downscaling</span> precipitation is a difficult challenge for the climate community. We propose and study a new stochastic weather typing approach to perform such a task. In addition to providing accurate small and medium precipitation, our procedure possesses built-in features that allow us to model adequately extreme precipitation distributions. First, we propose a new distribution for local precipitation via a probability mixture model of Gamma and Generalized Pareto (GP) distributions. The latter one stems from Extreme Value Theory (EVT). The performance of this mixture is tested on real and simulated data, and also compared to classical rainfall densities. Then our <span class="hlt">downscaling</span> method, extending the recently developed nonhomogeneous stochastic weather typing approach, is presented. It can be summarized as a three-step program. First, regional weather precipitation patterns are constructed through a hierarchical ascending clustering method. Second, daily transitions among our precipitation patterns are represented by a nonhomogeneous Markov model influenced by large-scale atmospheric variables like NCEP reanalyses. Third, conditionally on these regional patterns, precipitation occurrence and intensity distributions are modeled as statistical mixtures. Precipitation amplitudes are assumed to follow our mixture of Gamma and GP densities. The proposed <span class="hlt">downscaling</span> approach is applied to 37 weather stations in Illinois and compared to various possible parameterizations and to a direct modeling. Model selection procedures show that choosing one GP distribution shape parameter per pattern for all stations provides the best rainfall representation amongst all tested models. This work highlights the importance of EVT distributions to improve the modeling and <span class="hlt">downscaling</span> of local extreme precipitations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=282623&keyword=LAKE+AND+ICE&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65321814&CFTOKEN=12462783','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=282623&keyword=LAKE+AND+ICE&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65321814&CFTOKEN=12462783"><span id="translatedtitle">Using a Coupled Lake Model with WRF for Dynamical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The Weather Research and Forecasting (WRF) model is used to <span class="hlt">downscale</span> a coarse reanalysis (National Centers for Environmental Prediction–Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=331601','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=331601"><span id="translatedtitle">Evaluation of <span class="hlt">downscaled</span> daily precipitation for FIELD scale hydrologic applications</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Hydrologic and agronomic applications often require a reliable representation of precipitation sequence as well as physical consistency of precipitation series for climate change impact assessment. Herein, we evaluate the daily sequence of the state –of –art <span class="hlt">downscaled</span> Bias Corrected Constructed Ana...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.7437R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.7437R"><span id="translatedtitle">Stepwise analogue <span class="hlt">downscaling</span> for hydrology (SANDHY): validation experiments over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Radanovics, Sabine; Vidal, Jean-Philippe; Sauquet, Eric; Ben Daoud, Aurélien; Bontron, Guillaume</p> <p>2014-05-01</p> <p>Statistical <span class="hlt">downscaling</span> aims at finding relationships between local precipitation (predictand) and large-scale predictor fields, in various contexts, from medium-term forecasting to climate change impact studies. One of the challenges of statistical <span class="hlt">downscaling</span> in a climate change context is that the predictor-predictand relationship should still be valid under climate change conditions. A minimum requirement is therefore to test the performance of the <span class="hlt">downscaling</span> method on independent data under current climate conditions. The <span class="hlt">downscaling</span> method considered is the Stepwise ANalog <span class="hlt">Downscaling</span> method for HYdrology (SANDHY). ERA-40 reanalysis data are used as large scale predictors and daily precipitation from the French near surface reanalysis (Safran) as predictand. Two 20-year periods have been selected from the common archive period of the two data sources: 1958-1978 ('early') and 1982-2002 ('late'). SANDHY has been optimised over the late period in terms of geopotential predictor domains individually for 608 target zones covering France. The validation setup consists of 4 experiments, that all use the parameters as optimised for the late period and that are compared in terms of continous ranked probability skill score (CRPSS) with climatology as reference: Reference simulation. A simulation of the late period is performed using the late period as an archive for searching the analogue dates, thus representing the best possible case. The CRPSS shows a spatial distribution similar to the one of the mean precipitation. Out-of-sample validation. The early period is simulated using the late period as an archive for searching the analogue dates. The idea is to simulate a period whose local data is not 'known' by the model as it would be the case in any application. The average skill loss compared to the reference simulation is reasonable with some more skill loss in the northern part of the country and no loss in the southeastern part. Alternative archive. The late</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009pcms.confE.194B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009pcms.confE.194B"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of daily precipitation over Llobregat river basin in Catalonia (Spain) using three <span class="hlt">downscaling</span> methods.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.</p> <p>2009-09-01</p> <p> environmental impact studies. <span class="hlt">Downscaling</span> methods to assess the effect of large-scale circulations on local parameters have. Statistical <span class="hlt">downscaling</span> methods are based on the view that regional climate can be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or "predictors" for which GCMs are trustable to regional or local surface "predictands" for which models are less skilful. The main advantage of these methods is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. Three statistical <span class="hlt">downscaling</span> methods are applied: Analogue method, Delta Change and Direct Forcing. These methods have been used to determine daily precipitation projections at rain gauge location to study the intensity, frequency and variability of storms in a context of climate change in the Llobregat River Basin in Catalonia, Spain. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change adaptation. Two stakeholders involved in the project provided the historical time series: Catalan Water Agency (ACA) and the State Meteorological Agency (AEMET).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/pages/biblio/1302921-multilevel-ensemble-kalman-filtering','SCIGOV-DOEP'); return false;" href="http://www.osti.gov/pages/biblio/1302921-multilevel-ensemble-kalman-filtering"><span id="translatedtitle">Multilevel <span class="hlt">ensemble</span> Kalman filtering</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGESBeta</a></p> <p>Hoel, Hakon; Law, Kody J. H.; Tempone, Raul</p> <p>2016-06-14</p> <p>This study embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the <span class="hlt">ensemble</span> Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. Finally, the resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRD..120.4534B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..120.4534B"><span id="translatedtitle">A spatial hybrid approach for <span class="hlt">downscaling</span> of extreme precipitation fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bechler, Aurélien; Vrac, Mathieu; Bel, Liliane</p> <p>2015-05-01</p> <p>For a few decades, climate models are used to provide future scenarios of precipitation with increasingly higher spatial resolution. However, this resolution is not yet sufficient to describe efficiently what happens at local scale. Dynamical and statistical methods of <span class="hlt">downscaling</span> have been developed and allow us to make the link between two levels of resolution and enable us to get values at a local scale based on large-scale information from global or regional climate models. Nevertheless, both the extreme behavior and the spatial structures are not well described by these <span class="hlt">downscaling</span> methods. We propose a two-step methodology, called spatial hybrid <span class="hlt">downscaling</span> (SHD), to solve this problem. The first step consists in applying a univariate (i.e., one-dimensional) statistical <span class="hlt">downscaling</span> to link the high- and low-resolution variables at some given locations. Once this 1d-link is performed, a conditional simulation algorithm of max-stable processes adapted to the extremal t process enables us to get conditional distributions of extreme precipitation at any point of the region. An application is performed on precipitation data in the south of France where extreme (Cevenol) events have major impacts (e.g., floods). Different versions of the SHD approach are tested. Most of them show particularly good results regarding univariate and multivariate criteria and overcome classical <span class="hlt">downscaling</span> techniques tested in comparison. Furthermore, these conclusions are robust to the choice of the 1d-link functions tested and to the choice of the conditioning points to drive the conditional local-scale simulations performed by the SHD approach.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC41E..02M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41E..02M"><span id="translatedtitle"><span class="hlt">Downscaling</span> climate model output for water resources impacts assessment (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maurer, E. P.; Pierce, D. W.; Cayan, D. R.</p> <p>2013-12-01</p> <p>Water agencies in the U.S. and around the globe are beginning to wrap climate change projections into their planning procedures, recognizing that ongoing human-induced changes to hydrology can affect water management in significant ways. Future hydrology changes are derived using global climate model (GCM) projections, though their output is at a spatial scale that is too coarse to meet the needs of those concerned with local and regional impacts. Those investigating local impacts have employed a range of techniques for <span class="hlt">downscaling</span>, the process of translating GCM output to a more locally-relevant spatial scale. Recent projects have produced libraries of publicly-available <span class="hlt">downscaled</span> climate projections, enabling managers, researchers and others to focus on impacts studies, drawing from a shared pool of fine-scale climate data. Besides the obvious advantage to data users, who no longer need to develop expertise in <span class="hlt">downscaling</span> prior to examining impacts, the use of the <span class="hlt">downscaled</span> data by hundreds of people has allowed a crowdsourcing approach to examining the data. The wide variety of applications employed by different users has revealed characteristics not discovered during the initial data set production. This has led to a deeper look at the <span class="hlt">downscaling</span> methods, including the assumptions and effect of bias correction of GCM output. Here new findings are presented related to the assumption of stationarity in the relationships between large- and fine-scale climate, as well as the impact of quantile mapping bias correction on precipitation trends. The validity of these assumptions can influence the interpretations of impacts studies using data derived using these standard statistical methods and help point the way to improved methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015PIAHS.369..147H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015PIAHS.369..147H&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Downscaling</span> approach to develop future sub-daily IDF relations for Canberra Airport Region, Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Herath, H. M. S. M.; Sarukkalige, P. R.; Nguyen, V. T. V.</p> <p>2015-06-01</p> <p><span class="hlt">Downscaling</span> of climate projections is the most adopted method to assess the impacts of climate change at regional and local scale. In the last decade, <span class="hlt">downscaling</span> techniques which provide reasonable improvement to resolution of General Circulation Models' (GCMs) output are developed in notable manner. Most of these techniques are limited to spatial <span class="hlt">downscaling</span> of GCMs' output and still there is a high demand to develop temporal <span class="hlt">downscaling</span> approaches. As the main objective of this study, combined approach of spatial and temporal <span class="hlt">downscaling</span> is developed to improve the resolution of rainfall predicted by GCMs. Canberra airport region is subjected to this study and the applicability of proposed <span class="hlt">downscaling</span> approach is evaluated for Sydney, Melbourne, Brisbane, Adelaide, Perth and Darwin regions. Statistical <span class="hlt">Downscaling</span> Model (SDSM) is used to spatial <span class="hlt">downscaling</span> and numerical model based on scaling invariant concept is used to temporal <span class="hlt">downscaling</span> of rainfalls. National Centre of Environmental Prediction (NCEP) data is used in SDSM model calibration and validation. Regression based bias correction function is used to improve the accuracy of <span class="hlt">downscaled</span> annual maximum rainfalls using HadCM3-A2. By analysing the non-central moments of observed rainfalls, single time regime (from 30 min to 24 h) is identified which exist scaling behaviour and it is used to estimate the sub daily extreme rainfall depths from daily <span class="hlt">downscaled</span> rainfalls. Finally, as the major output of this study, Intensity Duration Frequency (IDF) relations are developed for the future periods of 2020s, 2050s and 2080s in the context of climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.122..159S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.122..159S"><span id="translatedtitle">Potential improvements to statistical <span class="hlt">downscaling</span> of general circulation model outputs to catchment streamflows with <span class="hlt">downscaled</span> precipitation and evaporation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sachindra, D. A.; Huang, F.; Barton, A.; Perera, B. J. C.</p> <p>2015-10-01</p> <p>An existing streamflow <span class="hlt">downscaling</span> model (SDM(original)), was modified with the outputs of a precipitation <span class="hlt">downscaling</span> model (PDM) and an evaporation <span class="hlt">downscaling</span> model (EDM) as additional inputs, for improving streamflow projections. For this purpose, lag 0, lag 1 and lag 2 outputs of PDM were individually introduced to SDM(original) as additional inputs, and then it was calibrated and validated. Performances of the resulting modified models were assessed using Nash-Sutcliffe efficiency (NSE) during calibration and validation. It was found that the use of lag 0 precipitation as an additional input to SDM(original) improves NSE in calibration and validation. This modified streamflow <span class="hlt">downscaling</span> model is called SDM(lag0_preci). Then lag 0, lag 1 and lag 2 evaporation of EDM were individually introduced to SDM(lag0_preci) as additional inputs and it was calibrated and validated. The resulting models showed signs of over-fitting in calibration and under-fitting in validation. Hence, SDM(lag0_preci) was selected as the best model. When SDM(lag0_preci) was run with observed lag 0 precipitation, a large improvement in NSE was seen. This proved that if precipitation produced by the PDM can accurately reproduce the observations, improved precipitation predictions will produce better streamflow predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/23611203','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/23611203"><span id="translatedtitle">Multinomial logistic regression <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lee, Kyewon; Ahn, Hongshik; Moon, Hojin; Kodell, Ralph L; Chen, James J</p> <p>2013-05-01</p> <p>This article proposes a method for multiclass classification problems using <span class="hlt">ensembles</span> of multinomial logistic regression models. A multinomial logit model is used as a base classifier in <span class="hlt">ensembles</span> from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the proposed method can handle a huge database without a constraint needed for analyzing high-dimensional data, and the random partition can improve the prediction accuracy by reducing the correlation among base classifiers. The proposed method is implemented using R, and the performance including overall prediction accuracy, sensitivity, and specificity for each category is evaluated on two real data sets and simulation data sets. To investigate the quality of prediction in terms of sensitivity and specificity, the area under the receiver operating characteristic (ROC) curve (AUC) is also examined. The performance of the proposed model is compared to a single multinomial logit model and it shows a substantial improvement in overall prediction accuracy. The proposed method is also compared with other classification methods such as the random forest, support vector machines, and random multinomial logit model. PMID:23611203</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.3465R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.3465R"><span id="translatedtitle">Sensitivity and dependence of mesoscale <span class="hlt">downscaled</span> prediction results on different parameterizations of convection and cloud microphysics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Remesan, R.; Bellerby, T.</p> <p>2012-04-01</p> <p>These days as operational real-time flood forecasting and warning systems rely more on high resolution mesoscale models employed with coupling system of hydrological models. So it is inevitable to assess prediction sensitivity or disparity in collection with selection of different cumulus and microphysical parameterization schemes, to assess the possible uncertainties associated with mesoscale <span class="hlt">downscaling</span>. This study investigates the role of physical parameterization in mesoscale model simulations on simulation of unprecedented heavy rainfall over Yorkshire-Humberside in United Kingdom during 1-14th March, 1999. The study has used a popular mesoscale numerical weather prediction model named Advanced Research Weather Research Forecast model (version 3.3) which was developed at the National Center for Atmospheric Research (NCAR) in the USA. This study has performed a comprehensive evaluation of four cumulus parameterization schemes (CPSs) [Kian-Fritsch (KF), Betts-Miller-Janjic (BMJ) and Grell-Devenyi <span class="hlt">ensemble</span> (GD)] and five microphysical schemes Lin et al scheme, older Thompson scheme, new Thompson scheme, WRF Single Moment - 6 class scheme, and WRF Single Moment - 5 class scheme] to identify how their inclusion influences the mesoscale model's meteorological parameter estimation capabilities and related uncertainties in prediction. The case study was carried out at the Upper River Derwent catchment in Northern Yorkshire, England using both the ERA-40 reanalysis data and the land based observation data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.A33A0226D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A33A0226D"><span id="translatedtitle">Development of dynamical <span class="hlt">downscaling</span> for regional climate modeling and decision aid applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Darmenova, K.; Higgins, G.; Alliss, R.; Kiley, H.; Apling, D.</p> <p>2009-12-01</p> <p>Current General Circulation Models (GCMs) provide a valuable estimate of both natural and anthropogenic climate changes and variability on global scales. At the same time, future climate projections calculated with GCMs are not of sufficient spatial resolution to address regional needs. There is a growing interest from various industry sectors such as health, energy, agriculture, transportation and water planning in incorporating climate change into their strategic and development plans. To address current deficiencies in local planning and decision making with respect to regional climate change, our research is focused on developing a dynamical <span class="hlt">downscaling</span> capability with the Weather Research and Forecasting (WRF) model and developing decision aids that translate the regional climate data into actionable information for users. Our methodology involves detailed analysis of <span class="hlt">ensemble</span> runs performed with the WRF model initialized with the NCEP-NCAR reanalysis data and the ECHAM5 GCM. The WRF model is also run with different physical schemes and spatial resolutions, and compared with ground-based observations to delineate the uncertainties associated with the use of different initial conditions, grid sizes and physical parameterizations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22251869','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22251869"><span id="translatedtitle">Density of states for Gaussian unitary <span class="hlt">ensemble</span>, Gaussian orthogonal <span class="hlt">ensemble</span>, and interpolating <span class="hlt">ensembles</span> through supersymmetric approach</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Shamis, Mira</p> <p>2013-11-15</p> <p>We use the supersymmetric formalism to derive an integral formula for the density of states of the Gaussian Orthogonal <span class="hlt">Ensemble</span>, and then apply saddle-point analysis to give a new derivation of the 1/N-correction to Wigner's law. This extends the work of Disertori on the Gaussian Unitary <span class="hlt">Ensemble</span>. We also apply our method to the interpolating <span class="hlt">ensembles</span> of Mehta–Pandey.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H43I1585P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H43I1585P"><span id="translatedtitle">Stochastic Cascade Dynamical <span class="hlt">Downscaling</span> of Precipitation over Complex Terrain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Posadas, A.; Duffaut, L. E.; Jones, C.; Carvalho, L. V.; Carbajal, M.; Heidinger, H.; Quiroz, R.</p> <p>2013-12-01</p> <p>Global Climate Models (GCMs) suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called <span class="hlt">downscaling</span> techniques are used to bridge the spatial and temporal resolution gaps between what climate modelers are currently able to provide and what decision-makers require. Among the most important impacts of regional-scale prediction of climate change is to assess how food production and security will be affected. Regional scale precipitation and temperature simulations are crucial to understand how global warming will affect fresh water storage and the ability to grow agricultural crops. Precipitation and temperature <span class="hlt">downscaling</span> improve the coarse resolution and poor local representation of global climate models and help decision-makers to assess the likely hydrological impacts of climate change, and it would also help crop modelers to generate more realistic climatic-change scenarios. Thus, a spatial <span class="hlt">downscaling</span> method was developed based on the multiplicative random cascade disaggregation theory, considering a β-lognormal model describing the rainfall precipitation distribution and using the Mandelbrot-Kahane-Peyriere (MKP) function. In this paper, gridded 15 km resolution rainfall data over a 220 x 220 km section of the Andean Plateau and surroundings, generated by the Weather Research and Forecasting model (WRF), were <span class="hlt">downscaled</span> to gridded 1 km layers with the Multifractal <span class="hlt">downscaling</span> technique, complemented by a local heterogeneity filter. The process was tested for daily data over a period of five years (01/01/2001 - 12/31/2005). Specifically, The model parameters were estimated from 5 years of observed daily rainfall data from 18 rain gauges located in the region. A detailed testing of the model was undertaken on the basis of a comparison of the statistical characteristics of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007IJMPB..21...69Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007IJMPB..21...69Y"><span id="translatedtitle">Representative <span class="hlt">Ensembles</span> in Statistical Mechanics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yukalov, V. I.</p> <p></p> <p>The notion of representative statistical <span class="hlt">ensembles</span>, correctly representing statistical systems, is strictly formulated. This notion allows for a proper description of statistical systems, avoiding inconsistencies in theory. As an illustration, a Bose-condensed system is considered. It is shown that a self-consistent treatment of the latter, using a representative <span class="hlt">ensemble</span>, always yields a conserving and gapless theory.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Chamber+AND+Music&pg=2&id=EJ594156','ERIC'); return false;" href="http://eric.ed.gov/?q=Chamber+AND+Music&pg=2&id=EJ594156"><span id="translatedtitle">The Importance of Bass <span class="hlt">Ensemble</span>.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Bitz, Michael</p> <p>1997-01-01</p> <p>States that bass players should be allowed to play chamber music because it is an essential component to all string students' musical development. Expounds that bassists can successfully enjoy chamber music through participation in a bass <span class="hlt">ensemble</span>. Gives suggestions on how to form a bass <span class="hlt">ensemble</span> and on the repertoire of music. (CMK)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AcPPB..34.4699B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AcPPB..34.4699B"><span id="translatedtitle"><span class="hlt">Ensemble</span> of Causal Trees</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bialas, Piotr</p> <p>2003-10-01</p> <p>We discuss the geometry of trees endowed with a causal structure using the conventional framework of equilibrium statistical mechanics. We show how this <span class="hlt">ensemble</span> is related to popular growing network models. In particular we demonstrate that on a class of afine attachment kernels the two models are identical but they can differ substantially for other choice of weights. We show that causal trees exhibit condensation even for asymptotically linear kernels. We derive general formulae describing the degree distribution, the ancestor--descendant correlation and the probability that a randomly chosen node lives at a given geodesic distance from the root. It is shown that the Hausdorff dimension dH of the causal networks is generically infinite.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010JHyd..381...18B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010JHyd..381...18B"><span id="translatedtitle"><span class="hlt">Downscaling</span> transient climate change using a Neyman-Scott Rectangular Pulses stochastic rainfall model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Burton, A.; Fowler, H. J.; Blenkinsop, S.; Kilsby, C. G.</p> <p>2010-02-01</p> <p>SummaryThe future management of hydrological systems must be informed by climate change projections at relevant time horizons and at appropriate spatial scales. Furthermore, the robustness of such management decisions is dependent on both the uncertainty inherent in future climate change scenarios and the natural climate system. Addressing these needs, we present a new transient rainfall simulation methodology which combines dynamical and statistical <span class="hlt">downscaling</span> techniques to produce transient (i.e. temporally non-stationary) climate change scenarios. This is used to generate a transient multi-model <span class="hlt">ensemble</span> of simulated point-scale rainfall time series for 1997-2085 for the polluted Brévilles spring in Northern France. The recovery of this previously potable source may be affected by climatic changes and variability over the next few decades. The provision of locally-relevant transient climate change scenarios for use as input to hydrological models of both water quality and quantity will ultimately provide a valuable resource for planning and decision making. Observed rainfall from 1988-2006 was characterised in terms of a set of statistics for each calendar month: the daily mean, variance, probability dry, lag-1 autocorrelation and skew, and the monthly variance. The Neyman-Scott Rectangular Pulses (NSRP) stochastic rainfall model was fitted to these observed statistics and correctly simulated both monthly statistics and extreme rainfall properties. Multiplicative change factors which quantify the change in each statistic between the periods 1961-1990 and 2071-2100 were estimated for each month and for each of 13 Regional Climate Models (RCMs) from the PRUDENCE <span class="hlt">ensemble</span>. To produce transient climate change scenarios, pattern scaling factors were estimated and interpolated from four time-slice integrations of two General Circulation Models which condition the RCMs, ECHAM4/OPYC and HadCM3. Applying both factors to the observed statistics provided projected</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014GMD.....7..621E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GMD.....7..621E"><span id="translatedtitle">Design of a regional climate modelling projection <span class="hlt">ensemble</span> experiment - NARCliM</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Evans, J. P.; Ji, F.; Lee, C.; Smith, P.; Argüeso, D.; Fita, L.</p> <p>2014-04-01</p> <p>Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of <span class="hlt">ensemble</span> members that can be simulated such that choices must be made concerning which global climate models (GCMs) to <span class="hlt">downscale</span> from, and which regional climate models (RCMs) to <span class="hlt">downscale</span> with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM <span class="hlt">ensembles</span>, as well as spanning the range of future climate projections present in the GCM <span class="hlt">ensemble</span>. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created <span class="hlt">ensemble</span> provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22399060','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22399060"><span id="translatedtitle">The fundamental <span class="hlt">downscaling</span> limit of field effect transistors</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Mamaluy, Denis Gao, Xujiao</p> <p>2015-05-11</p> <p>We predict that within next 15 years a fundamental <span class="hlt">down-scaling</span> limit for CMOS technology and other Field-Effect Transistors (FETs) will be reached. Specifically, we show that at room temperatures all FETs, irrespective of their channel material, will start experiencing unacceptable level of thermally induced errors around 5-nm gate lengths. These findings were confirmed by performing quantum mechanical transport simulations for a variety of 6-, 5-, and 4-nm gate length Si devices, optimized to satisfy high-performance logic specifications by ITRS. Different channel materials and wafer/channel orientations have also been studied; it is found that altering channel-source-drain materials achieves only insignificant increase in switching energy, which overall cannot sufficiently delay the approaching <span class="hlt">downscaling</span> limit. Alternative possibilities are discussed to continue the increase of logic element densities for room temperature operation below the said limit.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC41D0594M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC41D0594M"><span id="translatedtitle">Developing Climate-Informed <span class="hlt">Ensemble</span> Streamflow Forecasts over the Colorado River Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miller, W. P.; Lhotak, J.; Werner, K.; Stokes, M.</p> <p>2014-12-01</p> <p>As climate change is realized, the assumption of hydrometeorologic stationarity embedded within many hydrologic models is no longer valid over the Colorado River Basin. As such, resource managers have begun to request more information to support decisions, specifically with regards to the incorporation of climate change information and operational risk. To this end, <span class="hlt">ensemble</span> methodologies have become increasingly popular among the scientific and forecasting communities, and resource managers have begun to incorporate this information into decision support tools and operational models. Over the Colorado River Basin, reservoir operations are determined, in large part, by forecasts issued by the Colorado Basin River Forecast Center (CBRFC). The CBRFC produces both single value and <span class="hlt">ensemble</span> forecasts for use by resource managers in their operational decision-making process. These <span class="hlt">ensemble</span> forecasts are currently driven by a combination of daily updating model states used as initial conditions and weather forecasts plus historical meteorological information used to generate forecasts with the assumption that past hydroclimatological conditions are representative of future hydroclimatology. Recent efforts have produced updated bias-corrected and spatially <span class="hlt">downscaled</span> projections of future climate over the Colorado River Basin. In this study, the historical climatology used as input to the CBRFC forecast model is adjusted to represent future projections of climate based on data developed by the updated projections of future climate data. <span class="hlt">Ensemble</span> streamflow forecasts reflecting the impacts of climate change are then developed. These forecasts are subsequently compared to non-informed <span class="hlt">ensemble</span> streamflow forecasts to evaluate the changing range of streamflow forecasts and risk over the Colorado River Basin. <span class="hlt">Ensemble</span> forecasts may be compared through the use of a reservoir operations planning model, providing resource managers with <span class="hlt">ensemble</span> information regarding changing</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011WRR....4710502C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011WRR....4710502C"><span id="translatedtitle">Stochastic <span class="hlt">downscaling</span> of precipitation with neural network conditional mixture models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Carreau, Julie; Vrac, Mathieu</p> <p>2011-10-01</p> <p>We present a new class of stochastic <span class="hlt">downscaling</span> models, the conditional mixture models (CMMs), which builds on neural network models. CMMs are mixture models whose parameters are functions of predictor variables. These functions are implemented with a one-layer feed-forward neural network. By combining the approximation capabilities of mixtures and neural networks, CMMs can, in principle, represent arbitrary conditional distributions. We evaluate the CMMs at <span class="hlt">downscaling</span> precipitation data at three stations in the French Mediterranean region. A discrete (Dirac) component is included in the mixture to handle the "no-rain" events. Positive rainfall is modeled with a mixture of continuous densities, which can be either Gaussian, log-normal, or hybrid Pareto (an extension of the generalized Pareto). CMMs are stochastic weather generators in the sense that they provide a model for the conditional density of local variables given large-scale information. In this study, we did not look for the most appropriate set of predictors, and we settled for a decent set as the basis to compare the <span class="hlt">downscaling</span> models. The set of predictors includes the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalyses sea level pressure fields on a 6 × 6 grid cell region surrounding the stations plus three date variables. We compare the three distribution families of CMMs with a simpler benchmark model, which is more common in the <span class="hlt">downscaling</span> community. The difference between the benchmark model and CMMs is that positive rainfall is modeled with a single Gamma distribution. The results show that CMM with hybrid Pareto components outperforms both the CMM with Gaussian components and the benchmark model in terms of log-likelihood. However, there is no significant difference with the log-normal CMM. In general, the additional flexibility of mixture models, as opposed to using a single distribution, allows us to better represent the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DClimate','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DClimate"><span id="translatedtitle"><span class="hlt">Downscaling</span> GISS ModelE Boreal Summer Climate over Africa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Druyan, Leonard M.; Fulakeza, Matthew</p> <p>2015-01-01</p> <p>The study examines the perceived added value of <span class="hlt">downscaling</span> atmosphere-ocean global climate model simulations over Africa and adjacent oceans by a nested regional climate model. NASA/Goddard Institute for Space Studies (GISS) coupled ModelE simulations for June- September 1998-2002 are used to form lateral boundary conditions for synchronous simulations by the GISS RM3 regional climate model. The ModelE computational grid spacing is 2deg latitude by 2.5deg longitude and the RM3 grid spacing is 0.44deg. ModelE precipitation climatology for June-September 1998-2002 is shown to be a good proxy for 30-year means so results based on the 5-year sample are presumed to be generally representative. Comparison with observational evidence shows several discrepancies in ModelE configuration of the boreal summer inter-tropical convergence zone (ITCZ). One glaring shortcoming is that ModelE simulations do not advance the West African rain band northward during the summer to represent monsoon precipitation onset over the Sahel. Results for 1998-2002 show that onset simulation is an important added value produced by <span class="hlt">downscaling</span> with RM3. ModelE Eastern South Atlantic Ocean computed sea-surface temperatures (SST) are some 4 K warmer than reanalysis, contributing to large positive biases in overlying surface air temperatures (Tsfc). ModelE Tsfc are also too warm over most of Africa. RM3 <span class="hlt">downscaling</span> somewhat mitigates the magnitude of Tsfc biases over the African continent, it eliminates the ModelE double ITCZ over the Atlantic and it produces more realistic orographic precipitation maxima. Parallel ModelE and RM3 simulations with observed SST forcing (in place of the predicted ocean) lower Tsfc errors but have mixed impacts on circulation and precipitation biases. <span class="hlt">Downscaling</span> improvements of the meridional movement of the rain band over West Africa and the configuration of orographic precipitation maxima are realized irrespective of the SST biases.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC51J..03I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC51J..03I"><span id="translatedtitle">Information content of <span class="hlt">downscaled</span> GCM precipitation variables for crop simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ines, A. V. M.; Mishra, A. K.</p> <p>2015-12-01</p> <p>A simple statistical <span class="hlt">downscaling</span> procedure for transforming daily global climate model (GCM) rainfall was applied at the local scale in Katumani, Kenya. We corrected the rainfall frequency bias of the GCM by truncating its daily rainfall cumulative distribution into the station's distribution using a wet-day threshold. Then, we corrected the GCM's rainfall intensity bias by mapping its truncated rainfall distribution into the station's truncated distribution. Additional tailoring was made to the bias corrected GCM rainfall by linking it with a stochastic disaggregation scheme based on a conditional stochastic weather generator to correct the temporal structure inherent with daily GCM rainfall. Results of the simple and hybridized GCM <span class="hlt">downscaled</span> precipitation variables (total, probability of occurrence, intensity and dry spell length) were linked with a crop model. An objective evaluation of the tailored GCM data was done using entropy. This study is useful for the identification of the most suitable <span class="hlt">downscaling</span> technique, as well as the most effective precipitation variables for forecasting crop yields.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.tmp..247V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.tmp..247V"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> rainfall using artificial neural network: significantly wetter Bangkok?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vu, Minh Tue; Aribarg, Thannob; Supratid, Siriporn; Raghavan, Srivatsan V.; Liong, Shie-Yui</p> <p>2015-08-01</p> <p>Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically <span class="hlt">downscaling</span> global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses <span class="hlt">downscaled</span> results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final <span class="hlt">downscaled</span> results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JSP...153...10H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JSP...153...10H"><span id="translatedtitle">The Polyanalytic Ginibre <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Haimi, Antti; Hedenmalm, Haakan</p> <p>2013-10-01</p> <p>For integers n, q=1,2,3,… , let Pol n, q denote the -linear space of polynomials in z and , of degree ≤ n-1 in z and of degree ≤ q-1 in . We supply Pol n, q with the inner product structure of the resulting Hilbert space is denoted by Pol m, n, q . Here, it is assumed that m is a positive real. We let K m, n, q denote the reproducing kernel of Pol m, n, q , and study the associated determinantal process, in the limit as m, n→+∞ while n= m+O(1); the number q, the degree of polyanalyticity, is kept fixed. We call these processes polyanalytic Ginibre <span class="hlt">ensembles</span>, because they generalize the Ginibre ensemble—the eigenvalue process of random (normal) matrices with Gaussian weight. There is a physical interpretation in terms of a system of free fermions in a uniform magnetic field so that a fixed number of the first Landau levels have been filled. We consider local blow-ups of the polyanalytic Ginibre <span class="hlt">ensembles</span> around points in the spectral droplet, which is here the closed unit disk . We obtain asymptotics for the blow-up process, using a blow-up to characteristic distance m -1/2; the typical distance is the same both for interior and for boundary points of . This amounts to obtaining the asymptotical behavior of the generating kernel K m, n, q . Following (Ameur et al. in Commun. Pure Appl. Math. 63(12):1533-1584, 2010), the asymptotics of the K m, n, q are rather conveniently expressed in terms of the Berezin measure (and density) [Equation not available: see fulltext.] For interior points | z|<1, we obtain that in the weak-star sense, where δ z denotes the unit point mass at z. Moreover, if we blow up to the scale of m -1/2 around z, we get convergence to a measure which is Gaussian for q=1, but exhibits more complicated Fresnel zone behavior for q>1. In contrast, for exterior points | z|>1, we have instead that , where is the harmonic measure at z with respect to the exterior disk . For boundary points, | z|=1, the Berezin measure converges to the unit</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1816302S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1816302S&link_type=ABSTRACT"><span id="translatedtitle">Statistical dynamical <span class="hlt">downscaling</span> of present day and future precipitation regimes in southern Vietnam</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schubert, David; Reyers, Mark; Pinto, Joaquim; Fink, Andreas; Massmeyer, Klaus</p> <p>2016-04-01</p> <p>Southeast Asia has been identified as one of the hot-spots of climate change. While the projected changes in annual precipitation are comparatively small, there is a clear tendency towards more rainfall in the dry season and an increase in extreme precipitation events. In this study, a statistical dynamical <span class="hlt">downscaling</span> (SDD) approach is applied to obtain higher resolution and more robust regional climate change projections for tropical Southeast Asia with focus on Vietnam. First, a recent climate (RC) simulation with the regional climate model COSMO-CLM with a spatial resolution of ~50 km driven by ERA-Interim (1979-2008) is performed for the tropical region of Southeast Asia. For the SDD, six weather types (WTs) are selected for Vietnam during the wet season (April - October) using a k-means cluster analysis of daily zonal wind component in 850 hPa and 200 hPa from the RC run. For each calculated weather type, simulated representatives are selected from the RC run and are then further dynamically <span class="hlt">downscaled</span> to a resolution of 0.0625° (7 km). By using historical WT frequencies, the simulated representatives are recombined to a high resolution rainfall climatology for the recent climate. It is shown that the SDD is generally able to capture the present day climatology and that the employment of the higher resolved simulated representatives enhances the performance of the SDD. However, an overestimation of rainfall at higher altitudes is found. To obtain future climate projections, an <span class="hlt">ensemble</span> of eight CMIP5 model members are selected to study precipitation changes. For these projections, WT frequencies of future scenarios under two representative Concentration Pathways (RCP4.5 and RCP8.5) are taken into account for the mid-term scenario (2046-2065) and the long-term scenario (2081-2100). The strongest precipitation changes are found for the RCP8.5 scenario. Most of the models indicate a generally increase in precipitation amount in the wet period over Southeast</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H33F0879F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H33F0879F"><span id="translatedtitle">Developing High-Resolution Inundation Estimates through a <span class="hlt">Downscaling</span> of Brightness Temperature Measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fisher, C. K.; Wood, E. F.</p> <p>2014-12-01</p> <p>There is currently a large demand for high-resolution estimates of inundation extent and flooding for applications in water management, risk assessment and hydrologic modeling. In many regions of the world it is possible to examine the extent of past inundation from visible and infrared imagery provided by sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS); however, this is not possible in regions that are densely vegetated or are under persistent cloud cover. As a result of this, there is a need for alternative methodologies that make use of other remotely sensed data sources to inform high-resolution estimates of inundation. One such data source is the AMSR-E/Aqua 37 GHz vertically and horizontally polarized brightness temperature measurements, which have been used in previous studies to estimate the extent of inundated areas and which can make observations in vegetated or cloudy regions. The objective of this work was to develop a decision tree classifier based <span class="hlt">downscaling</span> methodology by which inundation extent can be obtained at a high resolution, based on microwave brightness temperature measurements and high resolution topographic information. Using a random forest classifier that combined the AMSR-E 37GHz brightness temperatures (~12km mean spatial resolution) and a number of high-resolution topographic indices derived from the National Elevation Dataset for the United States (30m spatial resolution), a high-resolution estimate of inundation was created. A case study of this work is presented for the severe flooding that occurred in Iowa during the summer of 2008. Training and validation data for the random forest classifier were derived from an <span class="hlt">ensemble</span> of previously existing estimates of inundation from sources such as MODIS imagery, as well as simulated inundation extents generated from a hydrologic routing model. Results of this work suggest that the decision tree based <span class="hlt">downscaling</span> has skill in producing high-resolution estimates</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009pcms.confE..18F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009pcms.confE..18F"><span id="translatedtitle">Evaluation of the mean and extreme precipitation regimes of the <span class="hlt">ENSEMBLES</span> RCM multi-model over Spain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fita, L.; Herrera, S.; Fernández, J.; Gutiérrez, J. M.</p> <p>2009-09-01</p> <p><span class="hlt">ENSEMBLES</span> is an European project devoted (in Working Package RT3) to the use of Regional Climate Models (RCMs) to produce dynamical <span class="hlt">downscaling</span> of climate change global models over Europe. A large variety of models have been used allowing the generation of an <span class="hlt">ensemble</span> of high resolution projections of the future scenarios. In this study, the results of 9 RCMs driven by ERA40 ECMWF re-analyses during the control period of the <span class="hlt">ensemble</span> (1961 to 2000) are depicted. Validation of the climatology retrieved from simulations is done by comparison to a 0.2 degree horizontal resolution precipitation climatology (Spain02). Spain02 grid has been produced in the Universidad de Cantabria throughout a kriging over 2772 surface observations from the secondary AEMET network for the whole the period. Simulated and observed climatologies are compared in terrms of the average and extreme precipitation regimes. The average regime comparison is carried out by <span class="hlt">ensemble</span> mean results and seasonality is studied by temporal evolution of the monthly anual cycle over 11 Spanish river basins. The extreme precipitation regime is analysed by the use of different extreme precipitation indicators. Results show a large variety of concordance of the members of the <span class="hlt">ensemble</span> to the observed climatology. Climatologic patterns over Spain are significantly well represented by RCMs when the 5 best correlated members of the <span class="hlt">ensemble</span> are considered.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.2322G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.2322G"><span id="translatedtitle">Multilevel <span class="hlt">Ensemble</span> Transform Particle Filtering</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gregory, Alastair; Cotter, Colin; Reich, Sebastian</p> <p>2016-04-01</p> <p>This presentation extends the Multilevel Monte Carlo variance reduction technique to nonlinear filtering. In particular, Multilevel Monte Carlo is applied to a certain variant of the particle filter, the <span class="hlt">Ensemble</span> Transform Particle Filter (ETPF). A key aspect is the use of optimal transport methods to re-establish correlation between coarse and fine <span class="hlt">ensembles</span> after resampling; this controls the variance of the estimator. Numerical examples present a proof of concept of the effectiveness of the proposed method, demonstrating significant computational cost reductions (relative to the single-level ETPF counterpart) in the propagation of <span class="hlt">ensembles</span>.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27337980','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27337980"><span id="translatedtitle">The <span class="hlt">Ensembl</span> gene annotation system.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Aken, Bronwen L; Ayling, Sarah; Barrell, Daniel; Clarke, Laura; Curwen, Valery; Fairley, Susan; Fernandez Banet, Julio; Billis, Konstantinos; García Girón, Carlos; Hourlier, Thibaut; Howe, Kevin; Kähäri, Andreas; Kokocinski, Felix; Martin, Fergal J; Murphy, Daniel N; Nag, Rishi; Ruffier, Magali; Schuster, Michael; Tang, Y Amy; Vogel, Jan-Hinnerk; White, Simon; Zadissa, Amonida; Flicek, Paul; Searle, Stephen M J</p> <p>2016-01-01</p> <p>The <span class="hlt">Ensembl</span> gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the <span class="hlt">Ensembl</span> website. Here, we describe the annotation process in detail.Database URL: http://www.<span class="hlt">ensembl</span>.org/index.html. PMID:27337980</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4919035','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4919035"><span id="translatedtitle">The <span class="hlt">Ensembl</span> gene annotation system</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Aken, Bronwen L.; Ayling, Sarah; Barrell, Daniel; Clarke, Laura; Curwen, Valery; Fairley, Susan; Fernandez Banet, Julio; Billis, Konstantinos; García Girón, Carlos; Hourlier, Thibaut; Howe, Kevin; Kähäri, Andreas; Kokocinski, Felix; Martin, Fergal J.; Murphy, Daniel N.; Nag, Rishi; Ruffier, Magali; Schuster, Michael; Tang, Y. Amy; Vogel, Jan-Hinnerk; White, Simon; Zadissa, Amonida; Flicek, Paul</p> <p>2016-01-01</p> <p>The <span class="hlt">Ensembl</span> gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the <span class="hlt">Ensembl</span> website. Here, we describe the annotation process in detail. Database URL: http://www.<span class="hlt">ensembl</span>.org/index.html PMID:27337980</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.2405M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.2405M"><span id="translatedtitle"><span class="hlt">Downscaling</span> site rainfall from daily to 11.25-minute resolution: event, diurnal, seasonal and decadal controls on <span class="hlt">downscaling</span> parameters</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McIntyre, Neil; Shi, Shirley; Onof, Christian</p> <p>2016-04-01</p> <p><span class="hlt">Downscaling</span> site rainfall from daily to sub-daily resolution is often approached using the multiplicative discrete random cascade (MDRC) class of models, with mixed success. Questions in any application - for MDRCs or indeed other classes of <span class="hlt">downscaling</span> model - is to what extent and in what way are model parameters functions of rainfall event type and/or large scale climate controls for example those linked to the El Nino Southern Oscillation (ENSO). These questions underlie the applicability of <span class="hlt">downscaling</span> models for analysing rainfall and hydrological extremes, in particular for synthesising long-term historical or future sub-daily extremes conditional on historic or projected daily data. Coastal Queensland, Australia, is subject to combinations of multiple weather systems, including tropical cyclones, blocking systems, convective storms, frontal systems and ENSO influences. Using 100 years of fine resolution data from two gauges in central Brisbane, microcanonical MDRC models are fitted to data from 1 day to 11.25 minutes in seven cascade levels, each level dividing the time interval and its rainfall volume into two sub-intervals. Each cascade level involves estimating: the probabilities that all the rainfall observed in a time interval is concentrated in only the first of the two sub-intervals and that all the rainfall observed in a time interval is concentrated in only the second of the two sub-intervals; and also two beta distribution parameters that define the probability of a given division of the rainfall into both sub-intervals. These parameters are found to vary systematically with time of day, rainfall volume, event temporal structure, month of year, and ENSO anomaly. Reasonable <span class="hlt">downscaling</span> performance is achieved (in terms of replicating extreme values of 11.25 minute rainfall given the observed daily data) by including the parameter dependence on the rainfall volume and event structure, although particular applications may justify development of more</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC23B0922M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC23B0922M"><span id="translatedtitle">Precipitation Prediction in North Africa Based on Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Molina, J. M.; Zaitchik, B.</p> <p>2013-12-01</p> <p>Although Global Climate Models (GCM) outputs should not be used directly to predict precipitation variability and change at the local scale, GCM projections of large-scale features in ocean and atmosphere can be applied to infer future statistical properties of climate at finer resolutions through empirical statistical <span class="hlt">downscaling</span> techniques. A number of such <span class="hlt">downscaling</span> methods have been proposed in the literature, and although all of them have advantages and limitations depending on the specific <span class="hlt">downscaling</span> problem, most of them have been developed and tested in developed countries. In this research, we explore the use of statistical <span class="hlt">downscaling</span> to generate future local precipitation scenarios in different locations in Northern Africa, where available data is sparse and missing values are frequently observed in the historical records. The presence of arid and semiarid regions in North African countries and the persistence of long periods with no rain pose challenges to the <span class="hlt">downscaling</span> exercise since normality assumptions may be a serious limitation in the application of traditional linear regression methods. In our work, the development of monthly statistical relationships between the local precipitation and the large-scale predictors considers common Empirical Orthogonal Functions (EOFs) from different NCAR/Reanalysis climate fields (e.g., Sea Level Pressure (SLP) and Global Precipitation). GCM/CMIP5 data is considered in the predictor data set to analyze the future local precipitation. Both parametric (e.g., Generalized Linear Models (GLM)) and nonparametric (e,g,, Bootstrapping) approaches are considered in the regression analysis, and different spatial windows in the predictor fields are tested in the prediction experiments. In the latter, seasonal spatial cross-covariance between predictant and predictors is estimated by means of a teleconnections algorithm which was implemented to define the regions in the predictor domain that better captures the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.6035T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.6035T"><span id="translatedtitle">Large-Scale Weather Generator for <span class="hlt">Downscaling</span> Precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thober, Stephan; Samaniego, Luis; Bardossy, Andras</p> <p>2013-04-01</p> <p>Well parametrized distributed precipitation-runoff models are able to correctly quantify hydrological state variables (e.g. streamflow, soil moisture, among others) for the past decades. In order to estimate future risks associated with hydrometeorological extremes, it is necessary to incorporate information about the future weather and climate. A common approach is to <span class="hlt">downscale</span> Regional Climate Model (RCM) projections. Therefore, various statistical <span class="hlt">downscaling</span> schemes, utilizing diverse mathematical methods, have been developed. One kind of statistical <span class="hlt">downscaling</span> technique is the so called Weather Generator (WG). These algorithms provide meteorological time series as the realization of a stochastic process. First, single- and multi-site models were developed. Recently, however WG at sub-daily scales and on gridded spatial resolution have captured the interest because of the new development in distributed hydrological modelling. A standard approach for a multi-site WG is to sample a multivariate normal process for all locations. Doing so, it is necessary to calculate the Cholesky factor of the cross-covariance matrix to guarantee a spatially consistent sampling. In general, gridded WGs are an extension of multi-site WGs to larger domains (i.e. >10000 grid cells). On these large grids, it is not possible to accurately determine the Cholesky factor and further enhancements are required. In this work, a framework for a WG is proposed, which provides meteorological time-series on a large scale grid, e.g. 4 km grid of Germany. It employs a sequential Gaussian simulation method, conditioning the value of a grid cell only on a neighborhood, not on the whole field. This methodology is incorporated into a multi-scale <span class="hlt">downscaling</span> scheme, which is able to provide precipitation data sets at different spatial and temporal resolutions, ranging from 4 km to 32 km, and from days to months, respectively. This framework uses a copula approach for spatial <span class="hlt">downscaling</span>, exploiting</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy..tmp..394P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy..tmp..394P"><span id="translatedtitle"><span class="hlt">Downscaling</span> humidity with Localized Constructed Analogs (LOCA) over the conterminous United States</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pierce, D. W.; Cayan, D. R.</p> <p>2015-09-01</p> <p>Humidity is important to climate impacts in hydrology, agriculture, ecology, energy demand, and human health and comfort. Nonetheless humidity is not available in some widely-used archives of statistically <span class="hlt">downscaled</span> climate projections for the western U.S. In this work the Localized Constructed Analogs (LOCA) statistical <span class="hlt">downscaling</span> method is used to <span class="hlt">downscale</span> specific humidity to a 1°/16° grid over the conterminous U.S. and the results compared to observations. LOCA reproduces observed monthly climatological values with a mean error of ~0.5 % and RMS error of ~2 %. Extreme (1-day in 1- and 20-years) maximum values (relevant to human health and energy demand) are within ~5 % of observed, while extreme minimum values (relevant to agriculture and wildfire) are within ~15 %. The asymmetry between extreme maximum and minimum errors is largely due to residual errors in the bias correction of extreme minimum values. The temporal standard deviations of <span class="hlt">downscaled</span> daily specific humidity values have a mean error of ~1 % and RMS error of ~3 %. LOCA increases spatial coherence in the final <span class="hlt">downscaled</span> field by ~13 %, but the <span class="hlt">downscaled</span> coherence depends on the spatial coherence in the data being <span class="hlt">downscaled</span>, which is not addressed by bias correction. Temporal correlations between daily, monthly, and annual time series of the original and <span class="hlt">downscaled</span> data typically yield values >0.98. LOCA captures the observed correlations between temperature and specific humidity even when the two are <span class="hlt">downscaled</span> independently.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ClDy...47..411P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ClDy...47..411P&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Downscaling</span> humidity with Localized Constructed Analogs (LOCA) over the conterminous United States</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pierce, D. W.; Cayan, D. R.</p> <p>2016-07-01</p> <p>Humidity is important to climate impacts in hydrology, agriculture, ecology, energy demand, and human health and comfort. Nonetheless humidity is not available in some widely-used archives of statistically <span class="hlt">downscaled</span> climate projections for the western U.S. In this work the Localized Constructed Analogs (LOCA) statistical <span class="hlt">downscaling</span> method is used to <span class="hlt">downscale</span> specific humidity to a 1°/16° grid over the conterminous U.S. and the results compared to observations. LOCA reproduces observed monthly climatological values with a mean error of ~0.5 % and RMS error of ~2 %. Extreme (1-day in 1- and 20-years) maximum values (relevant to human health and energy demand) are within ~5 % of observed, while extreme minimum values (relevant to agriculture and wildfire) are within ~15 %. The asymmetry between extreme maximum and minimum errors is largely due to residual errors in the bias correction of extreme minimum values. The temporal standard deviations of <span class="hlt">downscaled</span> daily specific humidity values have a mean error of ~1 % and RMS error of ~3 %. LOCA increases spatial coherence in the final <span class="hlt">downscaled</span> field by ~13 %, but the <span class="hlt">downscaled</span> coherence depends on the spatial coherence in the data being <span class="hlt">downscaled</span>, which is not addressed by bias correction. Temporal correlations between daily, monthly, and annual time series of the original and <span class="hlt">downscaled</span> data typically yield values >0.98. LOCA captures the observed correlations between temperature and specific humidity even when the two are <span class="hlt">downscaled</span> independently.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27063736','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27063736"><span id="translatedtitle">Projecting malaria hazard from climate change in eastern Africa using large <span class="hlt">ensembles</span> to estimate uncertainty.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Leedale, Joseph; Tompkins, Adrian M; Caminade, Cyril; Jones, Anne E; Nikulin, Grigory; Morse, Andrew P</p> <p>2016-01-01</p> <p>The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented <span class="hlt">ensemble</span> of climate projections, employing three diverse bias correction and <span class="hlt">downscaling</span> techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate <span class="hlt">ensembles</span> drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model <span class="hlt">ensemble</span> generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model <span class="hlt">ensemble</span>. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach. PMID:27063736</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.5721H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.5721H&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Downscaling</span> 20th century flooding events in complex terrain (Switzerland) using the WRF regional climate model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heikkilä, Ulla; Gómez Navarro, Juan Jose; Franke, Jörg; Brönnimann, Stefan; Cattin, Réne</p> <p>2016-04-01</p> <p>Switzerland has experienced a number of severe precipitation events during the last few decades, such as during the 14-16 November of 2002 or during the 21-22 August of 2005. Both events, and subsequent extreme floods, caused fatalities and severe financial losses, and have been well studied both in terms of atmospheric conditions leading to extreme precipitation, and their consequences [e.g. Hohenegger et al., 2008, Stucki et al., 2012]. These examples highlight the need to better characterise the frequency and severity of flooding in the Alpine area. In a larger framework we will ultimately produce a high-resolution data set covering the entire 20th century to be used for detailed hydrological studies including all atmospheric parameters relevant for flooding events. In a first step, we <span class="hlt">downscale</span> the aforementioned two events of 2002 and 2005 to assess the model performance regarding precipitation extremes. The complexity of the topography in the Alpine area demands high resolution datasets. To achieve a sufficient detail in resolution we employ the Weather Research and Forecasting regional climate model (WRF). A set of 4 nested domains is used with a 2-km resolution horizontal resolution over Switzerland. The NCAR 20th century reanalysis (20CR) with a horizontal resolution of 2.5° serves as boundary condition [Compo et al., 2011]. First results of the <span class="hlt">downscaling</span> the 2002 and 2005 extreme precipitation events show that, compared to station observations provided by the Swiss Meteorological Office MeteoSwiss, the model strongly underestimates the strength of these events. This is mainly due to the coarse resolution of the 20CR data, which underestimates the moisture fluxes during these events. We tested driving WRF with the higher-resolved NCEP reanalysis and found a significant improvement in the amount of precipitation of the 2005 event. In a next step we will <span class="hlt">downscale</span> the precipitation and wind fields during a 6-year period 2002-2007 to investigate and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H33E0922B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H33E0922B"><span id="translatedtitle">Expansion of the On-line Archive "Statistically <span class="hlt">Downscaled</span> WCRP CMIP3 Climate Projections"</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brekke, L. D.; Pruitt, T.; Maurer, E. P.; Das, T.; Duffy, P.; White, K.</p> <p>2009-12-01</p> <p> response, archive developers are adding content in 2010, teaming with Scripps Institution of Oceanography (through their NOAA-RISA California-Nevada Applications Program and the California Climate Change Center) to apply a new daily <span class="hlt">downscaling</span> technique to a sub-<span class="hlt">ensemble</span> of the archive’s CMIP3 projections. The new technique, Bias-Corrected Constructed Analogs, combines the BC part of BCSD with a recently developed technique that preserves the daily sequencing structure of CMIP3 projections (Constructed Analogs, or CA). Such data will more easily serve hydrologic and ecological impacts assessments, and offer an opportunity to evaluate projection uncertainty associated with <span class="hlt">downscaling</span> technique. Looking ahead to the arrival CMIP5 projections, archive collaborators have plans apply both BCSD and BCCA over the contiguous U.S. consistent with CMIP3 applications above, and also apply BCSD globally at a 0.5 degree spatial resolution. The latter effort involves collaboration with U.S. Army Corps of Engineers (USACE) and Climate Central.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5044C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5044C"><span id="translatedtitle">Probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin</p> <p>2015-04-01</p> <p>This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the last century built on the NOAA 20th century global extended atmospheric reanalysis (20CR, Compo et al., 2011). It aims at delivering appropriate meteorological forcings for continuous distributed hydrological modelling over the last 140 years. The longer term objective is to improve our knowledge of major historical hydrometeorological events having occurred outside of the last 50-year period, over which comprehensive reconstructions and observations are available. It would constitute a perfect framework for assessing the recent observed events but also future events projected by climate change impact studies. The Sandhy (Stepwise ANalogue <span class="hlt">Downscaling</span> method for Hydrology) statistical <span class="hlt">downscaling</span> method (Radanovics et al., 2013), initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between 20CR predictors - temperature, geopotential shape, vertical velocity and relative humidity - and local predictands - precipitation and temperature - relevant for catchment-scale hydrology. Multiple predictor domains for geopotential shape are retained from a local optimisation over France using the Safran near-surface reanalysis (Vidal et al., 2010). Sandhy gives an <span class="hlt">ensemble</span> of 125 equally plausible gridded precipitation and temperature time series over the whole 1871-2012 period. Previous studies showed that Sandhy precipitation outputs are very slightly biased at the annual time scale. Nevertheless, the seasonal precipitation signal for areas with a high interannual variability is not well simulated. Moreover, winter and summer temperatures are respectively over- and underestimated. Reliable seasonal precipitation and temperature signals are however necessary for hydrological modelling, especially for evapotranspiration and snow accumulation/snowmelt processes. Two different post-processing methods are</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1813320M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1813320M&link_type=ABSTRACT"><span id="translatedtitle">Future changes of wind energy potentials over Europe in a large CMIP5 multi-model <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moemken, Julia; Reyers, Mark; Pinto, Joaquim G.</p> <p>2016-04-01</p> <p>A statistical-dynamical <span class="hlt">downscaling</span> method is used to estimate future changes of wind energy output (Eout) of a benchmark wind turbine across Europe at the regional scale. With this aim, 22 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) <span class="hlt">ensemble</span> are considered. The <span class="hlt">downscaling</span> method uses circulation weather types and regional climate modelling with the COSMO-CLM model. Future projections are computed for two time periods (2021-2060 and 2061-2100) following two scenarios (RCP4.5 and RCP8.5). The CMIP5 <span class="hlt">ensemble</span> mean response reveals a more likely than not increase of mean annual Eout over Northern and Central Europe and a likely decrease over Southern Europe. There is some uncertainty with respect to the magnitude and the sign of the changes. Higher robustness in future changes is observed for specific seasons. Except from the Mediterranean area, an <span class="hlt">ensemble</span> mean increase of Eout is simulated for winter and a decreasing for the summer season, resulting in a strong increase of the intra-annual variability for most of Europe. The latter is, in particular, probable during the second half of the 21st century under the RCP8.5 scenario. In general, signals are stronger for 2061-2100 compared to 2021-2060 and for RCP8.5 compared to RCP4.5. Regarding changes of the inter-annual variability of Eout for Central Europe, the future projections strongly vary between individual models and also between future periods and scenarios within single models. This study showed for an <span class="hlt">ensemble</span> of 22 CMIP5 models that changes in the wind energy potentials over Europe may take place in future decades. However, due to the uncertainties detected in this research, further investigations with multi-model <span class="hlt">ensembles</span> are needed to provide a better quantification and understanding of the future changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4857R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4857R"><span id="translatedtitle">Future changes of wind energy potentials over Europe in a large CMIP5 multi-model <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reyers, Mark; Moemken, Julia; Pinto, Joaquim G.</p> <p>2015-04-01</p> <p>A statistical-dynamical <span class="hlt">downscaling</span> method is used to estimate future changes of wind energy output (Eout) of an idealized wind turbine across Europe at the regional scale. With this aim, 22 GCMs of the CMIP5 <span class="hlt">ensemble</span> are considered. The <span class="hlt">downscaling</span> method uses circulation weather types and regional climate modelling with the COSMO-CLM model. Future projections are computed for two time periods (2021-2060 and 2061-2100) following two scenarios (RCP4.5 and RCP8.5). The CMIP5 <span class="hlt">ensemble</span> mean response reveal a more likely than not increase of mean annual Eout over Northern and Central Europe and a likely decrease over Southern Europe. There is some uncertainty with respect to the magnitude and the sign of the changes. Higher robustness in future changes is observed for specific seasons. Except from the Mediterranean area, an <span class="hlt">ensemble</span> mean increase of Eout is simulated for winter and a decreasing for the summer season, resulting in a strong increase of the intra-annual variability for most of Europe. The latter is in particular likely during the 2nd half of the 21st century under the RCP8.5 scenario. In general, signals are stronger for 2061-2100 compared to 2021-2060 and for RCP8.5 compared to RCP4.5. Regarding changes of the inter-annual variability of Eout for Central Europe, the future projections strongly vary between individual models and also between future periods and scenarios within single models. This study showed for an <span class="hlt">ensemble</span> of 22 CMIP5 models that changes in the wind energy potentials over Europe may take place in future decades. However, due to the uncertainties detected in this research, further investigations with multi-model <span class="hlt">ensembles</span> are needed to provide a better quantification and understanding of the future changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1062O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1062O"><span id="translatedtitle">"Uncertainty in <span class="hlt">downscaling</span> using high-resolution observational datasets"</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Oswald, E.; Rood, R. B.</p> <p>2013-12-01</p> <p>In order to bridge the gap between the resolution of global climate modeling efforts and the scale that decision-makers work at statistical <span class="hlt">downscaling</span> is often employed. The performance of any statistical <span class="hlt">downscaling</span> is dependant on the quality of the observational data at the location(s) of <span class="hlt">downscaling</span> (whether gridded or point-source). However, discussions of the assumptions made during statistical <span class="hlt">downscaling</span>, such as the stationariness of the relationships between predictor(s) and predictand, normally do not acknowledge the uncertainty introduced by the observational dataset. Many observational datasets do not have the erroneous temporal discontinuities caused by non-climatic biases, such as instrument changes or station relocations, diminished by a homogenization process. Moreover stations included within the underlying networks of high-resolution gridded datasets are typically not required to meet high standards of quality. Hence we evaluated three popular observational climate datasets, of the high-resolution gridded type, for their depiction of temperature values over the span of the datasets and the continental U.S. This was done using the homogenized United States Historical Climatology Network (USHCN) dataset version 2.0. The summer average temperatures at selected stations within the USHCN were compared to those created by interpolating gridpoints to the locations of those stations. The relationships these datasets have with more traditional climate datasets (e.g. the GISS, CRU, USHCN) have not formally been evaluated. The comparisons were not to judge which dataset was closest aligned with the USHCN dataset, but rather to discuss the common features (across datasets) of the residuals (i.e. differences with the USHCN dataset). We found that the lack of homogenization was a primary cause of the residuals, but that proxies for the non-climatic biases were not as well related to the residuals as expected. This was due in part to the gridding process that</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.4754R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.4754R"><span id="translatedtitle">Optimising predictor domains for spatially coherent precipitation <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Radanovics, S.; Vidal, J.-P.; Sauquet, E.; Ben Daoud, A.; Bontron, G.</p> <p>2012-04-01</p> <p>Relationships between local precipitation (predictands) and large-scale circulation (predictors) are used for statistical <span class="hlt">downscaling</span> purposes in various contexts, from medium-term forecasting to climate change impact studies. For hydrological purposes like flood forecasting, the <span class="hlt">downscaled</span> precipitation spatial fields have furthermore to be coherent over possibly large basins. This thus first requires to know what predictor domain can be associated to the precipitation over each part of the studied basin. This study addresses this issue by identifying the optimum predictor domains over the whole of France, for a specific <span class="hlt">downscaling</span> method based on a analogue approach and developed by Ben Daoud et al. (2011). The <span class="hlt">downscaling</span> method used here is based on analogies on different variables: temperature, relative humidity, vertical velocity and geopotentials. The optimum predictor domain has been found to consist of the nearest grid cell for all variables except geopotentials (Ben Daoud et al., 2011). Moreover, geopotential domains have been found to be sensitive to the target location by Obled et al. (2002), and the present study thus focuses on optimizing the domains of this specific predictor over France. The predictor domains for geopotential at 500 hPa and 1000 hPa are optimised for 608 climatologically homogeneous zones in France using the ERA-40 reanalysis data for the large-scale predictors and local precipitation from the Safran near-surface atmospheric reanalysis (Vidal et al., 2010). The similarity of geopotential fields is measured by the Teweles and Wobus shape criterion. The predictive skill of different predictor domains for the different regions is tested with the Continuous Ranked Probability Score (CRPS) for the 25 best analogue days found with the statistical <span class="hlt">downscaling</span> method. Rectangular predictor domains of different sizes, shapes and locations are tested, and the one that leads to the smallest CRPS for the zone in question is retained. The</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1817062Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817062Y"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of CMIP5 outputs for projecting future maximum and minimum temperature over the Haihe River Bain, China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yan, Tiezhu; Shen, Zhenyao; Heng, Lee; Dercon, Gerd</p> <p>2016-04-01</p> <p>Future climate change information is important to formulate adaptation and mitigation strategies for climate change. In this study, a statistical <span class="hlt">downscaling</span> model (SDSM) was established using both NCEP reanalysis data and ground observations (daily maximum and minimum temperature) during the period 1971-2010, and then calibrated model was applied to generate the future maximum and minimum temperature projections using predictors from the two CMIP5 models (MPI-ESM-LR and CNRM-CM5) under two Representative Concentration Pathway (RCP2.6 and RCP8.5) during the period 2011-2100 for the Haihe River Basin, China. Compared to the baseline period, future change in annual and seasonal maximum and minimum temperature was computed after bias correction. The spatial distribution and trend change of annual maximum and minimum temperature were also analyzed using <span class="hlt">ensemble</span> projections. The results shows that: (1)The <span class="hlt">downscaling</span> model had a good applicability on reproducing daily and monthly mean maximum and minimum temperature over the whole basin. (2) Bias was observed when using historical predictors from CMIP5 models and the performance of CNRM-CM5 was a little worse than that of MPI-ESM-LR. (3) The change in annual mean maximum and minimum temperature under the two scenarios in 2020s, 2050s and 2070s will increase and magnitude of maximum temperature will be higher than minimum temperature. (4) The increase in temperature in the mountains and along the coastline is remarkably high than the other parts of the studies basin. (5) For annual maximum and minimum temperature, the significant upward trend will be obtained under RCP 8.5 scenario and the magnitude will be 0.37 and 0.39 ℃ per decade, respectively; the increase in magnitude under RCP 2.6 scenario will be upward in 2020s and then decrease in 2050s and 2070s, and the magnitude will be 0.01 and 0.01℃ per decade, respectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009AGUFM.U13B0061H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009AGUFM.U13B0061H&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Downscaling</span> of Minimum Surface Temperature in the Semi-arid Great Basin Region and Implications for Bio-geophysical Processes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hatchett, B. J.; Vellore, R.; Koracin, D.</p> <p>2009-12-01</p> <p>This study addresses <span class="hlt">downscaling</span> methodology for monthly surface air temperature from global climate model (GCM) horizontal grid resolutions (> 100 km) to regional scales (< 10 km) appropriate for climate impact studies. Preliminary hindcast analysis for the period 1950-2008 indicated that the minimum temperatures extracted from the GCMs at 46 individual stations in Nevada show correct seasonal trends, but the monthly mean minima are significantly underestimated compared to three observational networks (Western Regional Climate Center (WRCC), DRI), National Climate Data Center (NCDC), and Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate data sets. The daily mean surface air temperature, from the three GCMs (NCAR-CCSM3, ECHAM5, and CSIRO-Mk3.5) and a regional climate model (RCM) using the Weather Research and Forecasting (WRF) model forced by the CCSM3 outputs, is generally under-predicted with root-mean-square errors as large as 6 K on an annual scale. The underlying premise of this study is that changes in minimum temperature are manifested on the landscape via changes in hydrological parameters viz., runoff timing and evapotranspiration rates, ecological parameters viz., rates of invasion of exotic species and fire hazards, and socio-economic parameters viz., urban energy use. The systematic error or bias in surface minimum temperature simulated by the GCMs and their <span class="hlt">ensembles</span> under designated Intergovernmental Panel on Climate Change (IPCC) climate change scenarios (A1B, A2, and B1) is investigated to assess and substantiate this argument. The present study employs the <span class="hlt">downscaling</span> technique of bias correction and spatial disaggregation (BCSD) to improve GCM representation of monthly minimum temperature characteristics at local and regional scales which are critical to properly quantify for ecologic, hydrologic, and socio-economic forecasting under future climate change scenarios.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1818263L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1818263L"><span id="translatedtitle">A copula-based <span class="hlt">downscaling</span> methodology of RCM precipitation fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lorenz, Manuel</p> <p>2016-04-01</p> <p>Many hydrological studies require long term precipitation time series at a fine spatial resolution. While regional climate models are nowadays capable of simulating reasonable high-resolution precipitation fields, the long computing time makes the generation of such long term time series often infeasible for practical purposes. We introduce a comparatively fast stochastic approach to simulate precipitation fields which resemble the spatial dependencies and density distributions of the dynamic model. Nested RCM simulations at two different spatial resolutions serve as a training set to derive the statistics which will then be used in a random path simulation where fine scale precipitation values are simulated from a multivariate Gaussian Copula. The chosen RCM is the Weather Research and Forecasting Model (WRF). Simulated daily precipitation fields of the RCM are based on ERA-Interim reanalysis data from 1971 to 2000 and are available at a spatial resolution of 42 km (Europe) and 7 km (Germany). In order to evaluate the method, the stochastic algorithm is applied to the nested German domain and the resulting spatial dependencies and density distributions are compared to the original 30 years long 7 km WRF simulations. Preliminary evaluations based on QQ-plots for one year indicate that the distributions of the <span class="hlt">downscaled</span> values are very similar to the original values for most cells. In this presentation, a detailed overview of the stochastic <span class="hlt">downscaling</span> algorithm and the evaluation of the long term simulations are given. Additionally, an outlook for a 5 km and 1 km <span class="hlt">downscaling</span> experiment for urban hydrology studies is presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006WRR....4211423W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006WRR....4211423W"><span id="translatedtitle">Daily precipitation-<span class="hlt">downscaling</span> techniques in three Chinese regions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wetterhall, Fredrik; BáRdossy, AndráS.; Chen, Deliang; Halldin, Sven; Xu, Chong-Yu</p> <p>2006-11-01</p> <p>Four methods of statistical <span class="hlt">downscaling</span> of daily precipitation were evaluated on three catchments located in southern, eastern, and central China. The evaluation focused on seasonal variation of statistical properties of precipitation and indices describing the precipitation regime, e.g., maximum length of dry spell and maximum 5-day precipitation, as well as interannual and intra-annual variations of precipitation. The predictors used in this study were mean sea level pressure, geopotential heights at 1000, 850, 700, and 500 hPa, and specific humidity as well as horizontal winds at 850, 700, and 500 hPa levels from the NCEP/NCAR reanalysis with 2.5° × 2.5° resolution for 1961-2000. The predictand was daily precipitation from 13 stations. Two analogue methods, one using principal components analysis (PCA) and the other Teweles-Wobus scores (TWS), a multiregression technique with a weather generator producing precipitation (SDSM) and a fuzzy-rule-based weather-pattern-classification method (MOFRBC), were used. Temporal and spatial properties of the predictors were carefully evaluated to derive the optimum setting for each method, and MOFRBC and SDSM were implemented in two modes, with and without humidity as predictor. The results showed that (1) precipitation was most successfully <span class="hlt">downscaled</span> in the southern and eastern catchments located close to the coast, (2) winter properties were generally better <span class="hlt">downscaled</span>, (3) MOFRBC and SDSM performed overall better than the analogue methods, (4) the modeled interannual variation in precipitation was improved when humidity was added to the predictor set, and (5), the annual precipitation cycle was well captured with all methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.7362B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.7362B"><span id="translatedtitle"><span class="hlt">Downscaling</span> of rainfall in Peru using Generalised Linear Models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.</p> <p>2012-04-01</p> <p>The assessment of water resources in the Peruvian Andes is particularly important because the Peruvian economy relies heavily on agriculture. Much of the agricultural land is situated near to the coast and relies on large quantities of water for irrigation. The simulation of synthetic rainfall series is thus important to evaluate the reliability of water supplies for current and future scenarios of climate change. In addition to water resources concerns, there is also a need to understand extreme heavy rainfall events, as there was significant flooding in Machu Picchu in 2010. The region exhibits a reduction of rainfall in 1983, associated with El Nino Southern Oscillation (SOI). NCEP Reanalysis 1 data was used to provide weather variable data. Correlations were calculated for several weather variables using raingauge data in the Andes. These were used to evaluate teleconnections and provide suggested covariates for the <span class="hlt">downscaling</span> model. External covariates used in the model include sea level pressure and sea surface temperature over the region of the Humboldt Current. Relative humidity and temperature data over the region are also included. The SOI teleconnection is also used. Covariates are standardised using observations for 1960-1990. The GlimClim <span class="hlt">downscaling</span> model was used to fit a stochastic daily rainfall model to 13 sites in the Peruvian Andes. Results indicate that the model is able to reproduce rainfall statistics well, despite the large area used. Although the correlation between individual rain gauges is generally quite low, all sites are affected by similar weather patterns. This is an assumption of the GlimClim <span class="hlt">downscaling</span> model. Climate change scenarios are considered using several GCM outputs for the A1B scenario. GCM data was corrected for bias using 1960-1990 outputs from the 20C3M scenario. Rainfall statistics for current and future scenarios are compared. The region shows an overall decrease in mean rainfall but with an increase in variance.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A11F0122D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A11F0122D"><span id="translatedtitle">Comparing the skill of precipitation forecasts from high resolution simulations and statistically <span class="hlt">downscaled</span> products in the Australian Snowy Mountains</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dai, J.; Chubb, T.; Manton, M.; Siems, S. T.</p> <p>2013-12-01</p> <p>Statistically significant improvements to a 'Poor Man's <span class="hlt">Ensemble</span>' (PME) of coarse-resolution numeral precipitation forecast for the Australian Snowy Mountains can be achieved using a clustering algorithm. Daily upwind soundings are classified according to one of four clusters, which are employed to adjust the precipitation forecasts using a linear regression. This approach is a type of 'statistical <span class="hlt">downscaling</span>', in that it relies on a historical relationship between observed and forecast precipitation amounts, and is a computationally cheap and fast way to improve forecast skill. For the 'wettest' class, the root-mean-square error for the one-day forecast was reduced from 26.98 to 17.08 mm, and for the second 'wet' class the improvement was from 8.43 to 5.57 mm. Regressions performed for the two 'dry' classes were not shown to significantly improve the forecast. Statistical measures of the probability of precipitation and the quantitative precipitation forecast were evaluated for the whole of the 2011 winter (May-September). With a 'hit rate' (fraction of correctly-forecast rain days) of 0.9, and a 'false alarm rate' (fraction of forecast rain days which did not occur) of 0.16 the PME forecast performs well in identifying rain days. The precipitation amount is, however systematically under-predicted, with a mean bias of -5.76 mm and RMSE of 12.86 mm for rain days during the 2011 winter. To compare the statistically <span class="hlt">downscaled</span> results with the capabilities of a state of the art numerical prediction system, the WRF model was run at 4 km resolution over the Australian Alpine region for the same period, and precipitation forecasts analysed in a similar manner. It had a hit rate of 0.955 and RMSE of 5.16 mm for rain days. The main reason for the improved performance relative to the PME is that the high resolution of the simulations better captures the orographic forcing due to the terrain, and consequently resolves the precipitation processes more realistically, but</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.387D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.387D"><span id="translatedtitle">Moroccan precipitation in a regional climate change simulation, evaluating a statistical <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Driouech, F.; Déqué, M.; Sánchez-Gómez, E.</p> <p>2009-09-01</p> <p> range covered by these RCMs for all the climate indices considered. In order to validate, in the case of Moroccan winter precipitation, a statistical <span class="hlt">downscaling</span> approach that uses large scale fields to construct local scenarios of future climate change, the link between north Atlantic weather regimes and Moroccan local precipitation has been investigated, in terms of precipitation average, and the frequencies of occurrence of wet and intense precipitation days. The robustness of the statistical approach considered is evaluated using the outputs of ARPEGE-Climate and also those of the 10 <span class="hlt">ENSEMBLES</span>-RCMs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy...45.2541Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy...45.2541Z"><span id="translatedtitle">A new statistical precipitation <span class="hlt">downscaling</span> method with Bayesian model averaging: a case study in China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Xianliang; Yan, Xiaodong</p> <p>2015-11-01</p> <p>A new statistical <span class="hlt">downscaling</span> method was developed and applied to <span class="hlt">downscale</span> monthly total precipitation from 583 stations in China. Generally, there are two steps involved in statistical <span class="hlt">downscaling</span>: first, the predictors are selected (large-scale variables) and transformed; and second, a model between the predictors and the predictand (in this case, precipitation) is established. In the first step, a selection process of the predictor domain, called the optimum correlation method (OCM), was developed to transform the predictors. The transformed series obtained by the OCM showed much better correlation with the predictand than those obtained by the traditional transform method for the same predictor. Moreover, the method combining OCM and linear regression obtained better <span class="hlt">downscaling</span> results than the traditional linear regression method, suggesting that the OCM could be used to improve the results of statistical <span class="hlt">downscaling</span>. In the second step, Bayesian model averaging (BMA) was adopted as an alternative to linear regression. The method combining the OCM and BMA showed much better performance than the method combining the OCM and linear regression. Thus, BMA could be used as an alternative to linear regression in the second step of statistical <span class="hlt">downscaling</span>. In conclusion, the <span class="hlt">downscaling</span> method combining OCM and BMA produces more accurate results than the multiple linear regression method when used to statistically <span class="hlt">downscale</span> large-scale variables.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=311127&keyword=tropical+AND+agriculture&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=77941987&CFTOKEN=29713851','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=311127&keyword=tropical+AND+agriculture&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=77941987&CFTOKEN=29713851"><span id="translatedtitle">Assessing the Added Value of Dynamical <span class="hlt">Downscaling</span> Using the Standardized Precipitation Index</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>In this study, the Standardized Precipitation Index (SPI) is used to ascertain the added value of dynamical <span class="hlt">downscaling</span> over the contiguous United States. WRF is used as a regional climate model (RCM) to dynamically <span class="hlt">downscale</span> reanalysis fields to compare values of SPI over drough...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8005G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8005G"><span id="translatedtitle">Looking for added value in Australian <span class="hlt">downscaling</span> for climate change studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grose, Michael</p> <p>2015-04-01</p> <p><span class="hlt">Downscaling</span> gives the prospect of added value in the regional pattern and temporal nature of rainfall change with a warmer climate. However, such value is not guaranteed and the use of <span class="hlt">downscaling</span> can raise rather than diminish uncertainties. Validation of <span class="hlt">downscaling</span> methods tends to focus on the ability to simulate current climate statistics, rather than the robustness of simulated future climate change. Here we compare the future climate change signal in average rainfall from various dynamical and statistical <span class="hlt">downscaling</span> outputs used for all of Australia and in regional climate change studies over smaller domains. We show that <span class="hlt">downscaling</span> can generate different regional patterns of projected change compared to the global climate models used as input, indicating the potential for added value in projections. These differences often make physical sense in regions of complex topography such as in southeast Australia, the eastern seaboard and Tasmania. However, results from different methods are not always consistent. In addition, <span class="hlt">downscaling</span> can produce projected changes that are not clearly related to finer resolution and are difficult to interpret. In some cases, each <span class="hlt">downscaling</span> method gives a different range of results and different messages about projected rainfall change for a region. This shows that <span class="hlt">downscaling</span> has the potential to add value to projections, but also brings the potential for uncertain or contradictory messages. We conclude that each method has strengths and weaknesses, and these should be clearly communicated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/991999','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/991999"><span id="translatedtitle"><span class="hlt">Downscaling</span> socioeconomic and emissions scenarios for global environmental change research:a review</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Van Vuuren, Detlet; Smith, Steven J.; Riahi, Keywan</p> <p>2010-05-01</p> <p>Abstract: Global change research encompasses global to local scale analysis. Impacts analysis in particular often requires spatial <span class="hlt">downscaling</span>, whereby socio-economic and emissions variables specified at relatively large spatial scales are translated to values at a country or grid level. The methods used for spatial <span class="hlt">downscaling</span> are reviewed, classified, and current applications discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMSH53A4205P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMSH53A4205P"><span id="translatedtitle">CME <span class="hlt">Ensemble</span> Forecasting - A Primer</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pizzo, V. J.; de Koning, C. A.; Cash, M. D.; Millward, G. H.; Biesecker, D. A.; Codrescu, M.; Puga, L.; Odstrcil, D.</p> <p>2014-12-01</p> <p>SWPC has been evaluating various approaches for <span class="hlt">ensemble</span> forecasting of Earth-directed CMEs. We have developed the software infrastructure needed to support broad-ranging CME <span class="hlt">ensemble</span> modeling, including composing, interpreting, and making intelligent use of <span class="hlt">ensemble</span> simulations. The first step is to determine whether the physics of the interplanetary propagation of CMEs is better described as chaotic (like terrestrial weather) or deterministic (as in tsunami propagation). This is important, since different <span class="hlt">ensemble</span> strategies are to be pursued under the two scenarios. We present the findings of a comprehensive study of CME <span class="hlt">ensembles</span> in uniform and structured backgrounds that reveals systematic relationships between input cone parameters and ambient flow states and resulting transit times and velocity/density amplitudes at Earth. These results clearly indicate that the propagation of single CMEs to 1 AU is a deterministic process. Thus, the accuracy with which one can forecast the gross properties (such as arrival time) of CMEs at 1 AU is determined primarily by the accuracy of the inputs. This is no tautology - it means specifically that efforts to improve forecast accuracy should focus upon obtaining better inputs, as opposed to developing better propagation models. In a companion paper (deKoning et al., this conference), we compare in situ solar wind data with forecast events in the SWPC operational archive to show how the qualitative and quantitative findings presented here are entirely consistent with the observations and may lead to improved forecasts of arrival time at Earth.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/18632380','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/18632380"><span id="translatedtitle"><span class="hlt">Ensemble</span> algorithms in reinforcement learning.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wiering, Marco A; van Hasselt, Hado</p> <p>2008-08-01</p> <p>This paper describes several <span class="hlt">ensemble</span> methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different <span class="hlt">ensemble</span> methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed <span class="hlt">ensemble</span> methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where <span class="hlt">ensemble</span> methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity; the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV <span class="hlt">ensembles</span> significantly outperform the single RL algorithms. PMID:18632380</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC51A0729P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC51A0729P"><span id="translatedtitle">New Daily <span class="hlt">Downscaled</span> Information at the "Bias-Corrected <span class="hlt">Downscaled</span> WCRP CMIP3 Climate Projections" online archive</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pruitt, T.; Thrasher, B.; Das, T.; Maurer, E. P.; Duffy, P.; Long, J.; Brekke, L. D.</p> <p>2010-12-01</p> <p>Recent efforts have generated a new empirical <span class="hlt">downscaling</span> technique that is well-positioned to inform climate change vulnerability assessments for ecosystems as well as studies on future storm and flood frequency. The technique combines bias-correction (BC) of general circulation model (GCM) outputs with a constructed analogs approach (CA) for spatially <span class="hlt">downscale</span> the daily solutions from GCM simulations. These combined steps are referred to as BCCA. A recent methods intercomparison (Maurer et al. 2010, HESS, 14:1125-1139) shows that BCCA outperforms CA and the archive's current underlying methodology (BCSD, Wood et al. 2002) when applied to NCEP/NCAR Reanalysis. Given how BCCA is designed to translate daily sequences from GCM simulations, it offers the opportunity to provide <span class="hlt">downscaled</span> projection information on diurnal temperature range (relevant to ecohydrological investigations) and interarrival frequencies of daily to multi-day precipitation events. The information on diurnal temperature range also has significance to watershed hydrologic studies in arid to semi-arid regions, where evapotranspiration (ET) is the dominant fate of precipitation and simulation of ET processes is sensitive to diurnal temperature range. Recognizing these benefits, archive collaborators initiated an effort to develop a daily BCCA CMIP3 data archive that complements the archive's existing monthly BCSD CMIP3 dataset. The two datasets' have the following attributes: -- Space: BCSD coverage = NLDAS domain), resolution = 1/8°; BCCA has same attributes -- Time: BCSD period = GCM-simulated 1950-2099, BCCA has three nested periods based on common availability of daily GCM outputs at PCMDI (1961-2000, 2045-2064, and 2080-2099) -- Variables: BCSD has been performed for monthly mean temperature and precipitation; BCCA has been performed for daily minimum and maximum temperature and precipitation. Presentation highlights BCCA implementation for archive expansions, illustrates key differences in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012EGUGA..14.2551I&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012EGUGA..14.2551I&link_type=ABSTRACT"><span id="translatedtitle">A new project on development and application of comprehensive <span class="hlt">downscaling</span> methods over Hokkaido.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Inatsu, M.; Yamada, T. J.; Sato, T.; Nakamura, K.; Matsuoka, N.; Komatsu, A.; Pokhrel, Y. N.; Sugimoto, S.; Miyazaki, S.</p> <p>2012-04-01</p> <p>A new project on development and application of comprehensive <span class="hlt">downscaling</span> methods over Hokkaido started as one of the branches of "Research Program on climate change adaptation" funded by Ministry of Education, Sports, Culture, Science, and Technology of Japan in 2010. Our group will develop two new <span class="hlt">downscaling</span> algorithms in order to get more information on the uncertainty of high/low temperatures or heavy rainfall. Both of the algorithms called "sampling <span class="hlt">downscaling</span>" and "hybrid <span class="hlt">downscaling</span>" are based upon the mixed use of statistical and dynamical <span class="hlt">downscaling</span> ideas. Another point of the project is to evaluate the effect of land-use changes in Hokkaido, where the major pioneering began only about a century ago. Scientific outcomes on climate changes in Hokkaido from the project will be provided to not only public sectors in Hokkaido but also people who live in Hokkaido through a graphical-user-interface system just like a weather forecast system in a forecast-center's webpage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.5258A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.5258A"><span id="translatedtitle">Future changes in the West African Monsoon: A COSMO-CLM and RCA4 multimodel <span class="hlt">ensemble</span> study</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Anders, Ivonne; Gbobaniyi, Emiola</p> <p>2014-05-01</p> <p>In this multi-model multi-<span class="hlt">ensemble</span> study, we intercompare results from two regional climate simulation <span class="hlt">ensembles</span> to see how well they reproduce the known main features of the West African Monsoon (WAM). Each <span class="hlt">ensemble</span> was created under the ongoing CORDEX-Africa activities by using the regional climate models (RCA4 and COSMO-CLM) to <span class="hlt">downscale</span> four coupled atmosphere ocean general circulation models (AOGCMs), namely, CNRM-CM5, HadGEM2-ES, EC-EARTH, and MPI-ESM-LR. Spatial resolution of the driving AOGCMs varies from about 1° to 3° while all regional simulations are at the same 0.44° resolution. Future climate projections from the RCP8.5 scenario are analyzed and inter-compared for both <span class="hlt">ensembles</span> in order to assess deviations and uncertainties. The main focus in our analysis is on the projected WAM rainy season statistics. We look at projected changes in onset and cessation, total precipitation and temperature toward the end of the century (2071-2100) for different time scales spanning seasonal climatologies, annual cycles and interannual variability, and a number of spatial scales covering the Sahel, the Gulf of Guinea and the entire West Africa. Differences in the <span class="hlt">ensemble</span> projections are linked to the parameterizations employed in both regional models and the influence of this is discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4066535','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4066535"><span id="translatedtitle">Evaluating the utility of dynamical <span class="hlt">downscaling</span> in agricultural impacts projections</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J.</p> <p>2014-01-01</p> <p>Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn <span class="hlt">downscaled</span> by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically <span class="hlt">downscaling</span> raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections. PMID:24872455</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC41D0853V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC41D0853V"><span id="translatedtitle">Toward Robust and Efficient Climate <span class="hlt">Downscaling</span> for Wind Energy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vanvyve, E.; Rife, D.; Pinto, J. O.; Monaghan, A. J.; Davis, C. A.</p> <p>2011-12-01</p> <p>This presentation describes a more accurate and economical (less time, money and effort) wind resource assessment technique for the renewable energy industry, that incorporates innovative statistical techniques and new global mesoscale reanalyzes. The technique judiciously selects a collection of "case days" that accurately represent the full range of wind conditions observed at a given site over a 10-year period, in order to estimate the long-term energy yield. We will demonstrate that this new technique provides a very accurate and statistically reliable estimate of the 10-year record of the wind resource by intelligently choosing a sample of ±120 case days. This means that the expense of <span class="hlt">downscaling</span> to quantify the wind resource at a prospective wind farm can be cut by two thirds from the current industry practice of <span class="hlt">downscaling</span> a randomly chosen 365-day sample to represent winds over a "typical" year. This new estimate of the long-term energy yield at a prospective wind farm also has far less statistical uncertainty than the current industry standard approach. This key finding has the potential to reduce significantly market barriers to both onshore and offshore wind farm development, since insurers and financiers charge prohibitive premiums on investments that are deemed to be high risk. Lower uncertainty directly translates to lower perceived risk, and therefore far more attractive financing terms could be offered to wind farm developers who employ this new technique.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy...44.2637D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy...44.2637D"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of CMIP5 global circulation models over CORDEX-Africa with COSMO-CLM: evaluation over the present climate and analysis of the added value</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dosio, Alessandro; Panitz, Hans-Jürgen; Schubert-Frisius, Martina; Lüthi, Daniel</p> <p>2015-05-01</p> <p>In this work we present the results of the application of the consortium for small-scale modeling (COSMO) regional climate model (COSMO-CLM, hereafter, CCLM) over Africa in the context of the coordinated regional climate <span class="hlt">downscaling</span> experiment. An <span class="hlt">ensemble</span> of climate change projections has been created by <span class="hlt">downscaling</span> the simulations of four global climate models (GCM), namely: MPI-ESM-LR, HadGEM2-ES, CNRM-CM5, and EC-Earth. Here we compare the results of CCLM to those of the driving GCMs over the present climate, in order to investigate whether RCMs are effectively able to add value, at regional scale, to the performances of GCMs. It is found that, in general, the geographical distribution of mean sea level pressure, surface temperature and seasonal precipitation is strongly affected by the boundary conditions (i.e. driving GCMs), and seasonal statistics are not always improved by the <span class="hlt">downscaling</span>. However, CCLM is generally able to better represent the annual cycle of precipitation, in particular over Southern Africa and the West Africa monsoon (WAM) area. By performing a singular spectrum analysis it is found that CCLM is able to reproduce satisfactorily the annual and sub-annual principal components of the precipitation time series over the Guinea Gulf, whereas the GCMs are in general not able to simulate the bimodal distribution due to the passage of the WAM and show a unimodal precipitation annual cycle. Furthermore, it is shown that CCLM is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet and dry days, and the frequency of heavy rain events.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..522..645K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..522..645K"><span id="translatedtitle">A holistic, multi-scale dynamic <span class="hlt">downscaling</span> framework for climate impact assessments and challenges of addressing finer-scale watershed dynamics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, Jongho; Ivanov, Valeriy Y.</p> <p>2015-03-01</p> <p>We present a state-of-the-art holistic, multi-scale dynamic <span class="hlt">downscaling</span> approach suited to address climate change impacts on hydrologic metrics and hydraulic regime of surface flow at the "scale of human decisions" in ungauged basins. The framework rests on stochastic and physical <span class="hlt">downscaling</span> techniques that permit one-way crossing 106-100 m scales, with a specific emphasis on 'nesting' hydraulic assessments within a coarser-scale hydrologic model. Future climate projections for the location of Manchester watershed (MI) are obtained from an <span class="hlt">ensemble</span> of General Circulation Models of the 3rd phase of the Coupled Model Intercomparison Project database and <span class="hlt">downscaled</span> to a "point" scale using a weather generator. To represent the natural variability of historic and future climates, we generated continuous time series of 300 years for the locations of 3 meteorological stations located in the vicinity of the ungauged basin. To make such a multi-scale approach computationally feasible, we identified the months of May and August as the periods of specific interest based on ecohydrologic considerations. Analyses of historic and future simulation results for the identified periods show that the same median rainfall obtained by accounting for climate natural variability triggers hydrologically-mediated non-uniqueness in flow variables resolved at the hydraulic scale. An emerging challenge is that uncertainty initiated at the hydrologic scale is not necessarily preserved at smaller-scale flow variables, because of non-linearity of underlying physical processes, which ultimately can mask climate uncertainty. We stress the necessity of augmenting climate-level uncertainties of emission scenario, multi-model, and natural variability with uncertainties arising due to non-linearities in smaller-scale processes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=200778','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=200778"><span id="translatedtitle">USING <span class="hlt">ENSEMBLE</span> PREDICTIONS TO SIMULATE FIELD-SCALE SOIL WATER TIME SERIES WITH UPSCALED AND <span class="hlt">DOWNSCALED</span> SOIL HYDRAULIC PROPERTIES</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Simulations of soil water flow require measurements of soil hydraulic properties which are particularly difficult at field scale. Laboratory measurements provide hydraulic properties at scales finer than the field scale, whereas pedotransfer functions (PTFs) integrate information on hydraulic prope...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20020020435&hterms=Franco&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAuthor-Name%26N%3D0%26No%3D50%26Ntt%3DFranco','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20020020435&hterms=Franco&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAuthor-Name%26N%3D0%26No%3D50%26Ntt%3DFranco"><span id="translatedtitle"><span class="hlt">Ensemble</span> Cannonical Correlation Prediction of Seasonal Precipitation Over the US</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lau, William K. M.; Kim, Kyu-Myong; Shen, Samuel; Einaudi, Franco (Technical Monitor)</p> <p>2001-01-01</p> <p>This paper presents preliminary results of an <span class="hlt">ensemble</span> cannonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate <span class="hlt">downscaling</span> studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into nonoverlapping sectors. The cannonical SST modes for each sector are used as the predictor for the <span class="hlt">ensemble</span> hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all regions of the US and for all seasonal compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible for enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduced the spring predictability barrier over all regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and regional regional data. Moreover, the ECC forecasts can be applied to other climate subsystems and, in conjunction with further diagnostic or model studies will enables a better understanding of the dynamic links between climate variations and precipitation, not only for the US, but also for other regions of the world.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4224315','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4224315"><span id="translatedtitle">The <span class="hlt">ensemble</span> nature of allostery</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Motlagh, Hesam N.; Wrabl, James O.; Li, Jing; Hilser, Vincent J.</p> <p>2014-01-01</p> <p>Allostery is the process by which biological macromolecules (mostly proteins) transmit the effect of binding at one site to another, often distal, functional site, allowing for regulation of activity. Recent experimental observations demonstrating that allostery can be facilitated by dynamic and intrinsically disordered proteins have resulted in a new paradigm for understanding allosteric mechanisms, which focuses on the conformational <span class="hlt">ensemble</span> and the statistical nature of the interactions responsible for the transmission of information. Analysis of allosteric <span class="hlt">ensembles</span> reveals a rich spectrum of regulatory strategies, as well as a framework to unify the description of allosteric mechanisms from different systems. PMID:24740064</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27268795','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27268795"><span id="translatedtitle">The <span class="hlt">Ensembl</span> Variant Effect Predictor.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>McLaren, William; Gil, Laurent; Hunt, Sarah E; Riat, Harpreet Singh; Ritchie, Graham R S; Thormann, Anja; Flicek, Paul; Cunningham, Fiona</p> <p>2016-01-01</p> <p>The <span class="hlt">Ensembl</span> Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The <span class="hlt">Ensembl</span> Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs. PMID:27268795</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006HyPr...20.3085K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006HyPr...20.3085K"><span id="translatedtitle">Uncertainty analysis of statistical <span class="hlt">downscaling</span> methods using Canadian Global Climate Model predictors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khan, Mohammad Sajjad; Coulibaly, Paulin; Dibike, Yonas</p> <p>2006-09-01</p> <p>Three <span class="hlt">downscaling</span> models, namely the Statistical <span class="hlt">Down-Scaling</span> Model (SDSM), the Long Ashton Research Station Weather Generator (LARS-WG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their <span class="hlt">downscaled</span> results of daily precipitation, daily maximum and minimum temperature. The uncertainty attributes are described by the model errors and the 95% confidence intervals in the estimates of means and variances of <span class="hlt">downscaled</span> data. The significance of those errors has been examined by suitable statistical tests at the 95% confidence level. The 95% confidence intervals in the estimates of means and variances of <span class="hlt">downscaled</span> data have been estimated using the bootstrapping method and compared with the observed data. The study has been carried out using 40 years of observed and <span class="hlt">downscaled</span> daily precipitation data and daily maximum and minimum temperature data, starting from 1961 to 2000. In all the <span class="hlt">downscaling</span> experiments, the simulated predictors of the Canadian Global Climate Model (CGCM1) have been used. The uncertainty assessment results indicate that, in daily precipitation <span class="hlt">downscaling</span>, the LARS-WG model errors are significant at the 95% confidence level only in a very few months, the SDSM errors are significant in some months, and the ANN model errors are significant in almost all months of the year. In <span class="hlt">downscaling</span> daily maximum and minimum temperature, the performance of all three models is similar in terms of model errors evaluation at the 95% confidence level. But, according to the evaluation of variability and uncertainty in the estimates of means and variances of <span class="hlt">downscaled</span> precipitation and temperature, the performances of the LARS-WG model and the SDSM are almost similar, whereas the ANN model performance is found to be poor in that consideration. Further assessment of those models, in terms of skewness and average dry-spell length comparison between observed and <span class="hlt">downscaled</span> daily</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2000JGR...10529523D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2000JGR...10529523D"><span id="translatedtitle">Intra-annual and interannual <span class="hlt">ensemble</span> forcing of a regional climate model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dutton, Jan F.; Barron, Eric J.</p> <p>2000-12-01</p> <p>The use of <span class="hlt">ensemble</span> modeling within the framework of dynamical <span class="hlt">downscaling</span> of climate change scenarios derived from global climate model scenarios has not been fully explored. This study uses a six member <span class="hlt">ensemble</span> of RegCM2 regional climate model simulations forced by the CCM3 global climate model to explore the one-way boundary forcing of regional interannual variability of 500 mbar heights, precipitation, and surface temperature. Anomaly pattern correlations (APCs) between the CCM3 and the RegCM2 500 mbar heights, precipitation, and surface temperature show distinct annual cycles. The January <span class="hlt">ensemble</span>-averaged APCs for 500 mbar heights, precipitation, and surface temperature are 0.95, 0.65, and 0.90, respectively. The July correlations for the same variables are 0.63, 0.14, and 0.52, respectively. This indicates that the RegCM2 winter interannual variability is strongly dependent on the GCM interannual variability. The summer interannual variability of precipitation is found to contain little GCM-supplied signal. The <span class="hlt">ensemble</span> run variance of the CCM3 and RegCM2 is also explored. The ratio of RegCM2 to CCM3 500 mbar height normalized <span class="hlt">ensemble</span> run variance (NERV), a measure of climate reproducibility, is near 1.0 for various regions in the simulated domain. The RegCM2 precipitation NERV is greater than CCM3 NERV, suggesting less reproducibility and therefore less predictability. Certain regions show statistically significant reduced RegCM2 surface temperature NERV, suggesting that greater reproducibility may exist in these regions. The effect of increased topographic resolution in the RegCM2 domain was not found to significantly enhance reproducibility.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1712462P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1712462P"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of summer precipitation over northwestern South America</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Palomino Lemus, Reiner; Córdoba Machado, Samir; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda; Jesús Esteban Parra, María</p> <p>2015-04-01</p> <p>In this study a statistical <span class="hlt">downscaling</span> (SD) model using Principal Component Regression (PCR) for simulating summer precipitation in Colombia during the period 1950-2005, has been developed, and climate projections during the 2071-2100 period by applying the obtained SD model have been obtained. For these ends the Principal Components (PCs) of the SLP reanalysis data from NCEP were used as predictor variables, while the observed gridded summer precipitation was the predictand variable. Period 1950-1993 was utilized for calibration and 1994-2010 for validation. The Bootstrap with replacement was applied to provide estimations of the statistical errors. All models perform reasonably well at regional scales, and the spatial distribution of the correlation coefficients between predicted and observed gridded precipitation values show high values (between 0.5 and 0.93) along Andes range, north and north Pacific of Colombia. Additionally, the ability of the MIROC5 GCM to simulate the summer precipitation in Colombia, for present climate (1971-2005), has been analyzed by calculating the differences between the simulated and observed precipitation values. The simulation obtained by this GCM strongly overestimates the precipitation along a horizontal sector through the center of Colombia, especially important at the east and west of this country. However, the SD model applied to the SLP of the GCM shows its ability to faithfully reproduce the rainfall field. Finally, in order to get summer precipitation projections in Colombia for the period 1971-2100, the <span class="hlt">downscaled</span> model, recalibrated for the total period 1950-2010, has been applied to the SLP output from MIROC5 model under the RCP2.6, RCP4.5 and RCP8.5 scenarios. The changes estimated by the SD models are not significant under the RCP2.6 scenario, while for the RCP4.5 and RCP8.5 scenarios a significant increase of precipitation appears regard to the present values in all the regions, reaching around the 27% in northern</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1412222S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1412222S"><span id="translatedtitle">Stochastic <span class="hlt">Downscaling</span> for Hydrodynamic and Ecological Modeling of Lakes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schlabing, D.; Eder, M.; Frassl, M.; Rinke, K.; Bárdossy, A.</p> <p>2012-04-01</p> <p>Weather generators are of interest in climate impact studies, because they allow different modi operandi: (1) More realizations of the past, (2) possible futures as defined by the modeler and (3) possible futures according to the combination of greenhouse gas emission scenarios and their Global Circulation Model (GCM) consequences. Climate modeling has huge inherently unquantifiable uncertainties, yet the results present themselves as single point values without any measure of uncertainty. Given this reduction of risk-relevant information, stochastic <span class="hlt">downscaling</span> offers itself as a tool to recover the variability present in local measurements. One should bear in mind that the lake models that are fed with <span class="hlt">downscaling</span> results are themselves deterministic and single runs may prove to be misleading. Especially population dynamics simulated by ecological models are sensitive to very particular events in the input data. A way to handle this sensitivity is to perform Monte Carlo studies with varying meteorological driving forces using a weather generator. For these studies, the Vector-Autoregressive Weather generator (VG), which was first presented at the EGU 2011, was developed further. VG generates daily air temperature, humidity, long- and shortwave radiance and wind. Wind and shortwave radiation is subsequently disaggregated to hourly values, because their short term variability has proven important for the application. Changes relative to the long-term values are modeled as disturbances that act during the autoregressive generation of the synthetic time series. The method preserves the dependence structure between the variables, as changes in the disturbed variable, say temperature, are propagated to the other variables. The approach is flexible because the disturbances can be chosen freely. Changes in mean can be represented as constant disturbance, changes in variability as episodes of certain length and amplitude. The disturbances can also be extracted from GCMs</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.1634B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.1634B"><span id="translatedtitle">The application of a multimodel <span class="hlt">ensemble</span> to quantify uncertainty and produce weighted probabilistic projections of hydrological change.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Broderick, Ciaran; Fealy, Rowan; Murphy, Conor</p> <p>2013-04-01</p> <p>Multimodel experiments have provided the data necessary for undertaking probabilistic assessments of the likely impacts which projected climate change may have on hydrological systems. The availability of <span class="hlt">ensemble</span> data has also facilitated a more comprehensive exploration of uncertainty and a greater understanding of the implications it has for future resource management. In this study a probabilistic framework is used to examine changes in the flow regime of the Burrishoole catchment - characterised as a responsive peatland system typical of many upland catchments found along Ireland's Atlantic seaboard. For the study a sampling procedure is used to generate probability distributions which quantify the range of uncertainty in the projected hydrological response. The sampling scheme combines model projections by weighting; to this end a likelihood value is attached to each member of a multimodel <span class="hlt">ensemble</span>. Model reliability is quantified based on performance at capturing different aspects of the observed system behaviour. The dynamically <span class="hlt">downscaled</span> climate data used is obtained from the EU-FP6 <span class="hlt">ENSEMBLES</span> project; to overcome some of the limitations associated with this dataset it is used alongside statistically <span class="hlt">downscaled</span> climate scenarios. To address uncertainty in the hydrological simulations multiple realizations of the catchment system - obtained by altering both the model structure and parameter values in search of behavioural solutions - are employed. The overriding aim of the paper is to examine how <span class="hlt">ensemble</span> data can be most effectively exploited when conducting impact assessments. The probabilistic framework outlined is used to explore whether the application of a weighting scheme produces a different outcome than if uniform probabilities are applied; also examined is whether the weighting enables the uncertainty space to be constrained in a methodologically rigorous way. In order to understand how we can more effectively manage uncertainty the study</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/ofr20141190','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/ofr20141190"><span id="translatedtitle"><span class="hlt">Downscaled</span> climate projections for the Southeast United States: evaluation and use for ecological applications</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Wootten, Adrienne; Smith, Kara; Boyles, Ryan; Terando, Adam; Stefanova, Lydia; Misra, Vasru; Smith, Tom; Blodgett, David L.; Semazzi, Fredrick</p> <p>2014-01-01</p> <p>Climate change is likely to have many effects on natural ecosystems in the Southeast U.S. The National Climate Assessment Southeast Technical Report (SETR) indicates that natural ecosystems in the Southeast are likely to be affected by warming temperatures, ocean acidification, sea-level rise, and changes in rainfall and evapotranspiration. To better assess these how climate changes could affect multiple sectors, including ecosystems, climatologists have created several <span class="hlt">downscaled</span> climate projections (or <span class="hlt">downscaled</span> datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these <span class="hlt">downscaled</span> datasets, known as <span class="hlt">downscaling</span>, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for <span class="hlt">downscaling</span> and the number of <span class="hlt">downscaled</span> datasets produced in recent years present many challenges for scientists and decisionmakers in assessing the impact or vulnerability of a given species or ecosystem to climate change. Given the number of available <span class="hlt">downscaled</span> datasets, how do these model outputs compare to each other? Which variables are available, and are certain <span class="hlt">downscaled</span> datasets more appropriate for assessing vulnerability of a particular species? Given the desire to use these datasets for impact and vulnerability assessments and the lack of comparison between these datasets, the goal of this report is to synthesize the information available in these <span class="hlt">downscaled</span> datasets and provide guidance to scientists and natural resource managers with specific interests in ecological modeling and conservation planning related to climate change in the Southeast U.S. This report enables the Southeast Climate Science Center (SECSC) to address an important strategic goal of providing scientific information and guidance that will enable resource managers and other participants in Landscape Conservation Cooperatives to make science</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1411047F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1411047F"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> inter-comparison for high resolution climate reconstruction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ferreira, J.; Rocha, A.; Castanheira, J. M.; Carvalho, A. C.</p> <p>2012-04-01</p> <p>In the scope of the project: "High-resolution Rainfall EroSivity analysis and fORecasTing - RESORT", an evaluation of various methods of dynamic <span class="hlt">downscaling</span> is presented. The methods evaluated range from the classic method of nesting a regional model results in a global model, in this case the ECMWF reanalysis, to more recently proposed methods, which consist in using Newtonian relaxation methods in order to nudge the results of the regional model to the reanalysis. The method with better results involves using a system of variational data assimilation to incorporate observational data with results from the regional model. The climatology of a simulation of 5 years using this method is tested against observations on mainland Portugal and the ocean in the area of the Portuguese Continental Shelf, which shows that the method developed is suitable for the reconstruction of high resolution climate over continental Portugal.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=steel&pg=7&id=EJ631682','ERIC'); return false;" href="http://eric.ed.gov/?q=steel&pg=7&id=EJ631682"><span id="translatedtitle">African Drum and Steel Pan <span class="hlt">Ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Sunkett, Mark E.</p> <p>2000-01-01</p> <p>Discusses how to develop both African drum and steel pan <span class="hlt">ensembles</span> providing information on teacher preparation, instrument choice, beginning the <span class="hlt">ensemble</span>, and lesson planning. Includes additional information for the drum <span class="hlt">ensembles</span>. Lists references and instructional materials, sources of drums and pans, and common note layout/range for steel pan…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=326566','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=326566"><span id="translatedtitle"><span class="hlt">Ensembl</span> genomes 2016: more genomes, more complexity</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p><span class="hlt">Ensembl</span> Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org). Together, the two resources provide a consistent...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.7926K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.7926K"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> Of Local Climate In The Alpine Region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kaspar, Severin; Philipp, Andreas; Jacobeit, Jucundus</p> <p>2016-04-01</p> <p>The impact of climate change on the alpine region was disproportional strong in the past decades compared to the surrounding areas, which becomes manifest in a higher increase in surface air temperature. Beside the thermal changes also implications for the hydrological cycle may be expected, acting as a very important factor not only for the ecosystem but also for mankind, in the form of water security or considering economical aspects like winter tourism etc. Therefore, in climate impact studies, it is necessary to focus on variables with high influence on the hydrological cycle, for example temperature, precipitation, wind, humidity and radiation. The aim of this study is to build statistical <span class="hlt">downscaling</span> models which are able to reproduce temperature and precipitation at the mountainous alpine weather stations Zugspitze and Sonnblick and to further project these models into the future to identify possible changes in the behavior of these climate variables and with that in the hydrological cycle. Beside facing a in general very complex terrain in this high elevated regions, we have the advantage of a more direct atmospheric influence on the meteorology of the exposed weather stations from the large scale circulation. Two nonlinear statistical methods are developed to model the station-data series on a daily basis: On the one hand a conditional classification approach was used and on the other hand a model based on artificial neural networks (ANNs) was built. The latter is in focus of this presentation. One of the important steps of developing a new model approach is to find a reliable predictor setup with e.g. informative predictor variables or adequate location and size of the spatial domain. The question is: Can we include synoptic background knowledge to identify an optimal domain for an ANN approach? The yet developed ANN setups and configurations show promising results in <span class="hlt">downscaling</span> both, temperature (up to 80 % of explained variance) and precipitation (up</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1710540J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1710540J"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> for winter streamflow in Douro River</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jesús Esteban Parra, María; Hidalgo Muñoz, José Manuel; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda</p> <p>2015-04-01</p> <p>In this paper we have obtained climate change projections for winter flow of the Douro River in the period 2071-2100 by applying the technique of Partial Regression and various General Circulation Models of CMIP5. The streamflow data base used has been provided by the Center for Studies and Experimentation of Public Works, CEDEX. Series from gauing stations and reservoirs with less than 10% of missing data (filled by regression with well correlated neighboring stations) have been considered. The homogeneity of these series has been evaluated through the Pettit test and degree of human alteration by the Common Area Index. The application of these criteria led to the selection of 42 streamflow time series homogeneously distributed over the basin, covering the period 1951-2011. For these streamflow data, winter seasonal values were obtained by averaging the monthly values from January to March. Statistical <span class="hlt">downscaling</span> models for the streamflow have been fitted using as predictors the main atmospheric modes of variability over the North Atlantic region. These modes have been obtained using winter sea level pressure data of the NCEP reanalysis, averaged for the months from December to February. Period 1951-1995 was used for calibration, while 1996-2011 period was used in validating the adjusted models. In general, these models are able to reproduce about 70% of the variability of the winter streamflow of the Douro River. Finally, the obtained statistical models have been applied to obtain projections for 2071-2100 period, using outputs from different CMIP5 models under the RPC8.5 scenario. The results for the end of the century show modest declines of winter streamflow in this river for most of the models. Keywords: Statistical <span class="hlt">downscaling</span>, streamflow, Douro River, climate change. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24051840','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24051840"><span id="translatedtitle">Coupled <span class="hlt">ensemble</span> flow line advection and analysis.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Guo, Hanqi; Yuan, Xiaoru; Huang, Jian; Zhu, Xiaomin</p> <p>2013-12-01</p> <p><span class="hlt">Ensemble</span> run simulations are becoming increasingly widespread. In this work, we couple particle advection with pathline analysis to visualize and reveal the differences among the flow fields of <span class="hlt">ensemble</span> runs. Our method first constructs a variation field using a Lagrangian-based distance metric. The variation field characterizes the variation between vector fields of the <span class="hlt">ensemble</span> runs, by extracting and visualizing the variation of pathlines within <span class="hlt">ensemble</span>. Parallelism in a MapReduce style is leveraged to handle data processing and computing at scale. Using our prototype system, we demonstrate how scientists can effectively explore and investigate differences within <span class="hlt">ensemble</span> simulations. PMID:24051840</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4301745','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4301745"><span id="translatedtitle">Triticeae Resources in <span class="hlt">Ensembl</span> Plants</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Bolser, Dan M.; Kerhornou, Arnaud; Walts, Brandon; Kersey, Paul</p> <p>2015-01-01</p> <p>Recent developments in DNA sequencing have enabled the large and complex genomes of many crop species to be determined for the first time, even those previously intractable due to their polyploid nature. Indeed, over the course of the last 2 years, the genome sequences of several commercially important cereals, notably barley and bread wheat, have become available, as well as those of related wild species. While still incomplete, comparison with other, more completely assembled species suggests that coverage of genic regions is likely to be high. <span class="hlt">Ensembl</span> Plants (http://plants.<span class="hlt">ensembl</span>.org) is an integrative resource organizing, analyzing and visualizing genome-scale information for important crop and model plants. Available data include reference genome sequence, variant loci, gene models and functional annotation. For variant loci, individual and population genotypes, linkage information and, where available, phenotypic information are shown. Comparative analyses are performed on DNA and protein sequence alignments. The resulting genome alignments and gene trees, representing the implied evolutionary history of the gene family, are made available for visualization and analysis. Driven by the case of bread wheat, specific extensions to the analysis pipelines and web interface have recently been developed to support polyploid genomes. Data in <span class="hlt">Ensembl</span> Plants is accessible through a genome browser incorporating various specialist interfaces for different data types, and through a variety of additional methods for programmatic access and data mining. These interfaces are consistent with those offered through the <span class="hlt">Ensembl</span> interface for the genomes of non-plant species, including those of plant pathogens, pests and pollinators, facilitating the study of the plant in its environment. PMID:25432969</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25432969','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25432969"><span id="translatedtitle">Triticeae resources in <span class="hlt">Ensembl</span> Plants.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bolser, Dan M; Kerhornou, Arnaud; Walts, Brandon; Kersey, Paul</p> <p>2015-01-01</p> <p>Recent developments in DNA sequencing have enabled the large and complex genomes of many crop species to be determined for the first time, even those previously intractable due to their polyploid nature. Indeed, over the course of the last 2 years, the genome sequences of several commercially important cereals, notably barley and bread wheat, have become available, as well as those of related wild species. While still incomplete, comparison with other, more completely assembled species suggests that coverage of genic regions is likely to be high. <span class="hlt">Ensembl</span> Plants (http://plants.<span class="hlt">ensembl</span>.org) is an integrative resource organizing, analyzing and visualizing genome-scale information for important crop and model plants. Available data include reference genome sequence, variant loci, gene models and functional annotation. For variant loci, individual and population genotypes, linkage information and, where available, phenotypic information are shown. Comparative analyses are performed on DNA and protein sequence alignments. The resulting genome alignments and gene trees, representing the implied evolutionary history of the gene family, are made available for visualization and analysis. Driven by the case of bread wheat, specific extensions to the analysis pipelines and web interface have recently been developed to support polyploid genomes. Data in <span class="hlt">Ensembl</span> Plants is accessible through a genome browser incorporating various specialist interfaces for different data types, and through a variety of additional methods for programmatic access and data mining. These interfaces are consistent with those offered through the <span class="hlt">Ensembl</span> interface for the genomes of non-plant species, including those of plant pathogens, pests and pollinators, facilitating the study of the plant in its environment. PMID:25432969</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016IJTP...55.3017K&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016IJTP...55.3017K&link_type=ABSTRACT"><span id="translatedtitle">State <span class="hlt">Ensembles</span> and Quantum Entropy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kak, Subhash</p> <p>2016-06-01</p> <p>This paper considers quantum communication involving an <span class="hlt">ensemble</span> of states. Apart from the von Neumann entropy, it considers other measures one of which may be useful in obtaining information about an unknown pure state and another that may be useful in quantum games. It is shown that under certain conditions in a two-party quantum game, the receiver of the states can increase the entropy by adding another pure state.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020008664','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020008664"><span id="translatedtitle">Statistical <span class="hlt">Ensemble</span> of Large Eddy Simulations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carati, Daniele; Rogers, Michael M.; Wray, Alan A.; Mansour, Nagi N. (Technical Monitor)</p> <p>2001-01-01</p> <p>A statistical <span class="hlt">ensemble</span> of large eddy simulations (LES) is run simultaneously for the same flow. The information provided by the different large scale velocity fields is used to propose an <span class="hlt">ensemble</span> averaged version of the dynamic model. This produces local model parameters that only depend on the statistical properties of the flow. An important property of the <span class="hlt">ensemble</span> averaged dynamic procedure is that it does not require any spatial averaging and can thus be used in fully inhomogeneous flows. Also, the <span class="hlt">ensemble</span> of LES's provides statistics of the large scale velocity that can be used for building new models for the subgrid-scale stress tensor. The <span class="hlt">ensemble</span> averaged dynamic procedure has been implemented with various models for three flows: decaying isotropic turbulence, forced isotropic turbulence, and the time developing plane wake. It is found that the results are almost independent of the number of LES's in the statistical <span class="hlt">ensemble</span> provided that the <span class="hlt">ensemble</span> contains at least 16 realizations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25314405','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25314405"><span id="translatedtitle">Heat fluctuations and initial <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kim, Kwangmoo; Kwon, Chulan; Park, Hyunggyu</p> <p>2014-09-01</p> <p>Time-integrated quantities such as work and heat increase incessantly in time during nonequilibrium processes near steady states. In the long-time limit, the average values of work and heat become asymptotically equivalent to each other, since they only differ by a finite energy change in average. However, the fluctuation theorem (FT) for the heat is found not to hold with the equilibrium initial <span class="hlt">ensemble</span>, while the FT for the work holds. This reveals an intriguing effect of everlasting initial memory stored in rare events. We revisit the problem of a Brownian particle in a harmonic potential dragged with a constant velocity, which is in contact with a thermal reservoir. The heat and work fluctuations are investigated with initial Boltzmann <span class="hlt">ensembles</span> at temperatures generally different from the reservoir temperature. We find that, in the infinite-time limit, the FT for the work is fully recovered for arbitrary initial temperatures, while the heat fluctuations significantly deviate from the FT characteristics except for the infinite initial-temperature limit (a uniform initial <span class="hlt">ensemble</span>). Furthermore, we succeed in calculating finite-time corrections to the heat and work distributions analytically, using the modified saddle point integral method recently developed by us. Interestingly, we find noncommutativity between the infinite-time limit and the infinite-initial-temperature limit for the probability distribution function (PDF) of the heat. PMID:25314405</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/23288332','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/23288332"><span id="translatedtitle"><span class="hlt">Ensemble</span> learning incorporating uncertain registration.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Simpson, Ivor J A; Woolrich, Mark W; Andersson, Jesper L R; Groves, Adrian R; Schnabel, Julia A</p> <p>2013-04-01</p> <p>This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalized analysis. This is achieved by incorporating registration uncertainty into an <span class="hlt">ensemble</span> learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalized feature data, e.g., grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an <span class="hlt">ensemble</span> learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel-based morphometry and deformation tensor-based morphometry over bootstrap aggregating, a common <span class="hlt">ensemble</span> learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important. PMID:23288332</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000102382','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000102382"><span id="translatedtitle">Dimensionality Reduction Through Classifier <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)</p> <p>1999-01-01</p> <p>In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in <span class="hlt">ensembles</span> of classifiers, yielding results superior to single classifiers, <span class="hlt">ensembles</span> that use the full set of features, and <span class="hlt">ensembles</span> based on principal component analysis on both real and synthetic datasets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308919&keyword=nasa&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65355685&CFTOKEN=44239461','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308919&keyword=nasa&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65355685&CFTOKEN=44239461"><span id="translatedtitle">Examining Projected Changes in Weather & Air Quality Extremes Between 2000 & 2030 using Dynamical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Climate change may alter regional weather extremes resulting in a range of environmental impacts including changes in air quality, water quality and availability, energy demands, agriculture, and ecology. Dynamical <span class="hlt">downscaling</span> simulations were conducted with the Weather Research...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..1210067G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..1210067G"><span id="translatedtitle">Sensitivity analysis of runoff modeling to statistical <span class="hlt">downscaling</span> models in the western Mediterranean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grouillet, B.; Ruelland, D.; Ayar, P. V.; Vrac, M.</p> <p>2015-10-01</p> <p>This paper analyzes the sensitivity of a hydrological model to different methods to statistically <span class="hlt">downscale</span> climate precipitation and temperature over four western Mediterranean basins illustrative of different hydro-meteorological situations. The comparison was conducted over a common 20 year period (1986-2005) to capture different climatic conditions in the basins. Streamflow was simulated using the GR4j conceptual model. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically <span class="hlt">downscaled</span> datasets to reproduce various aspects of the hydrograph. Three different statistical <span class="hlt">downscaling</span> models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the "cumulative distribution function - transform" approach (CDFt). We used the models to <span class="hlt">downscale</span> precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two GCMs (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various <span class="hlt">downscaled</span> data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution <span class="hlt">downscaled</span> climate values leads to a major improvement of runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical <span class="hlt">downscaling</span> models to be used in climate change impact studies to minimize the range of uncertainty associated with such <span class="hlt">downscaling</span> methods.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016HESS...20.1031G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016HESS...20.1031G"><span id="translatedtitle">Sensitivity analysis of runoff modeling to statistical <span class="hlt">downscaling</span> models in the western Mediterranean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grouillet, Benjamin; Ruelland, Denis; Vaittinada Ayar, Pradeebane; Vrac, Mathieu</p> <p>2016-03-01</p> <p>This paper analyzes the sensitivity of a hydrological model to different methods to statistically <span class="hlt">downscale</span> climate precipitation and temperature over four western Mediterranean basins illustrative of different hydro-meteorological situations. The comparison was conducted over a common 20-year period (1986-2005) to capture different climatic conditions in the basins. The daily GR4j conceptual model was used to simulate streamflow that was eventually evaluated at a 10-day time step. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically <span class="hlt">downscaled</span> data sets to reproduce various aspects of the hydrograph. Three different statistical <span class="hlt">downscaling</span> models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the cumulative distribution function-transform approach (CDFt). We used the models to <span class="hlt">downscale</span> precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two general circulation models (GCMs) (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various <span class="hlt">downscaled</span> data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution <span class="hlt">downscaled</span> climate values leads to a major improvement in runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical <span class="hlt">downscaling</span> models to be used in climate change impact studies to minimize the range</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JHyd..375..578G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JHyd..375..578G"><span id="translatedtitle">A cluster-optimizing regression-based approach for precipitation spatial <span class="hlt">downscaling</span> in mountainous terrain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guan, Huade; Wilson, John L.; Xie, Hongjie</p> <p>2009-09-01</p> <p>SummaryPrecipitation temporal and spatial variability often controls terrestrial hydrological processes and states. Common remote-sensing and modeling precipitation products have a spatial resolution that is often too coarse to reveal hydrologically important spatial variability. A statistical algorithm was developed for <span class="hlt">downscaling</span> low-resolution spatial precipitation fields. This algorithm auto-searches precipitation spatial structures (rain-pixel clusters), and orographic effects on precipitation distribution without prior knowledge of atmospheric setting. It is composed of three components: rain-pixel clustering, multivariate regression, and random cascade. The only required input data for the <span class="hlt">downscaling</span> algorithm are coarse-pixel precipitation map and a topographic map. The algorithm was demonstrated with 4 km × 4 km Next Generation Radar (NEXRAD) precipitation fields, and tested by <span class="hlt">downscaling</span> NEXRAD-aggregated 16 km × 16 km precipitation fields to 4 km × 4 km pixel precipitation, which was then compared to the original NEXRAD data. The demonstration and testing were performed at both daily and hourly temporal resolutions for the northern New Mexico mountainous terrain and the central Texas Hill Country. The algorithm <span class="hlt">downscaled</span> daily precipitation fields are in good agreement with the original 4 km × 4 km NEXRAD precipitation, as measured by precipitation spatial structures and the statistics between the <span class="hlt">downscaling</span> and the original NEXRAD precipitation maps. For three daily precipitation events, <span class="hlt">downscaled</span> precipitation map reproduces precipitation variance of the disaggregation field, and with Pearson correlation coefficients between the <span class="hlt">downscaled</span> map and the NEXRAD map of 0.65, 0.71, and 0.80. The algorithm does not perform as well on <span class="hlt">downscaling</span> hourly precipitation fields at the examined scale range (from 16 km to 4 km), which underestimates precipitation variance of the disaggregation field. For a scale range from 4 km to 1 km, the algorithm has</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.4655C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4655C"><span id="translatedtitle"><span class="hlt">Ensemble</span> reconstruction of severe low flow events in France since 1871</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Devers, Alexandre; Graff, Benjamin</p> <p>2016-04-01</p> <p>This work presents a study of severe low flow events that occurred from 1871 onwards for a large number of near-natural catchments in France. It aims at assessing and comparing their characteristics to improve our knowledge on historical events and to provide a selection of benchmark events for climate change adaptation purposes. The historical depth of streamflow observations is generally limited to the last 50 years and therefore offers too small a sample of severe low flow events to properly explore the long-term evolution of their characteristics and associated impacts. In order to overcome this limit, this work takes advantage of a 140-year <span class="hlt">ensemble</span> hydrometeorological dataset over France based on: (1) a probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France (Caillouet et al., 2015), and (2) a continuous hydrological modelling that uses the high-resolution meteorological reconstructions as forcings over the whole period. This dataset provides an <span class="hlt">ensemble</span> of 25 equally plausible daily streamflow time series for a reference network of stations in France over the whole 1871-2012 period. Severe low flow events are identified based on a combination of a fixed threshold and a daily variable threshold. Each event is characterized by its deficit, duration and timing by applying the Sequent Peak Algorithm. The procedure is applied to the 25 simulated time series as well as to the observed time series in order to compare observed and simulated events over the recent period, and to characterize in a probabilistic way unrecorded historical events. The <span class="hlt">ensemble</span> aspect of the reconstruction leads to address specific issues, for properly defining events across <span class="hlt">ensemble</span> simulations, as well as for adequately comparing the simulated characteristics to the observed ones. This study brings forward the outstanding 1921 and 1940s events but also older and less known ones that occurred during the last decade of the 19th century. For</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.U13B0059T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.U13B0059T"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of CMIP5 Global Climate Model Simulations for Use in Regional Impact Studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thrasher, B.; Duffy, P.; Maurer, E. P.; White, K.; Das, T.; Brekke, L. D.; Girvetz, E. H.</p> <p>2009-12-01</p> <p>Global climate models encapsulate our best understanding of the physics of the climate system, but at a spatial scale that is too coarse to meet the needs of societal impacts researchers and decision makers. To meet these needs, we are undertaking systematic spatial <span class="hlt">downscaling</span> of CMIP5 GCM simulations now being performed by modeling groups around the world and archived by the Program for Climate Model Diagnosis and Intercomparison at Lawrence Livermore National Laboratory. A user group of particular interest is researchers contributing to IPCC Working Group II AR5. We are using two empirical <span class="hlt">downscaling</span> methods, which both add spatial detail based upon fine-scale gridded observations of historical climate: the “Bias-Corrected Constructed Analogs” method and the “Bias-Corrected Spatial <span class="hlt">Downscaling</span> method.” We will <span class="hlt">downscale</span> several hundred simulations from all participating models, and from several Representative Concentration Pathways. This effort complements ongoing, related work that produced <span class="hlt">downscaled</span> versions of CMIP3 global climate model results (http://gdo-dcp.ucllnl.org/<span class="hlt">downscaled</span>_cmip3_projections/dcpInterface.html). A Google Maps-based user interface will allow at-archive data exploration and visualization, and selection and downloading of desired data subsets. High-resolution monthly results focusing on temperature and precipitation projections for the western United States will be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1070A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1070A"><span id="translatedtitle">Applying <span class="hlt">downscaled</span> climate data to wildlife areas in Washington State, USA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Allan, A.; Shafer, S. L.; Bartlein, P. J.; Helbrecht, L.; Pelltier, R.; Thompson, B.</p> <p>2013-12-01</p> <p>Conservation and natural resource managers require information about potential climate change effects for the species and ecosystems they manage. We evaluated potential future climate and bioclimate changes for wildlife areas in Washington State (USA) using five climate simulations for the 21st century from the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset run under the A2 greenhouse gases emissions scenario. These data were <span class="hlt">downscaled</span> to a 30-arc-second (~1-km) grid encompassing the state of Washington by calculating and interpolating future climate anomalies, and then applying the interpolated data to observed historical climate data. This climate data <span class="hlt">downscaling</span> technique (also referred to as the 'delta' method) is relatively simple and makes a number of assumptions that affect how the <span class="hlt">downscaled</span> data can be used and interpreted. We used the <span class="hlt">downscaled</span> climate data to calculate bioclimatic variables (e.g., growing degree days) that represent important physiological and environmental limits for Washington species and habitats of management concern. Multivariate descriptive plots and maps were used to evaluate the direction, magnitude, and spatial patterns of projected future climate and bioclimatic changes. The results indicate which managed areas experience the largest climate and bioclimatic changes under each of the potential future climate simulations. We discuss these changes while accounting for some of the limitations of our <span class="hlt">downscaling</span> technique and the uncertainties associated with using these <span class="hlt">downscaled</span> data for conservation and natural resource management applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..1712011M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..1712011M&link_type=ABSTRACT"><span id="translatedtitle">VALUE - A Framework to Validate <span class="hlt">Downscaling</span> Approaches for Climate Change Studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilke, Renate A. I.</p> <p>2015-04-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary <span class="hlt">downscaling</span> community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical <span class="hlt">downscaling</span> methods. Here, we present the key ingredients of this framework. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the <span class="hlt">downscaling</span> procedure where problems may occur: what is the isolated <span class="hlt">downscaling</span> skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open <span class="hlt">downscaling</span> intercomparison study, but is intended also to provide general guidance for other validation studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EaFut...3....1M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EaFut...3....1M&link_type=ABSTRACT"><span id="translatedtitle">VALUE: A framework to validate <span class="hlt">downscaling</span> approaches for climate change studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilcke, Renate A. I.</p> <p>2015-01-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary <span class="hlt">downscaling</span> community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical <span class="hlt">downscaling</span> methods. In this paper, we present the key ingredients of this framework. VALUE's main approach to validation is user- focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the <span class="hlt">downscaling</span> procedure where problems may occur: what is the isolated <span class="hlt">downscaling</span> skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open <span class="hlt">downscaling</span> intercomparison study, but is intended also to provide general guidance for other validation studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70168924','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70168924"><span id="translatedtitle">Evaluation of <span class="hlt">downscaled</span>, gridded climate data for the conterminous United States</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Robert J. Behnke; Stephen J. Vavrus; Andrew Allstadt; Thomas P. Albright; Thogmartin, Wayne E.; Volker C. Radeloff</p> <p>2016-01-01</p> <p>Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous <span class="hlt">downscaled</span> weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are <span class="hlt">downscaled</span>, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used <span class="hlt">downscaled</span> data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between <span class="hlt">downscaled</span> and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect <span class="hlt">downscaled</span> weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a <span class="hlt">downscaled</span> weather data set for a given ecological application.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1043326','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/1043326"><span id="translatedtitle">Sub-daily Statistical <span class="hlt">Downscaling</span> of Meteorological Variables Using Neural Networks</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kumar, Jitendra; Brooks, Bjørn-Gustaf J.; Thornton, Peter E; Dietze, Michael</p> <p>2012-01-01</p> <p>A new open source neural network temporal <span class="hlt">downscaling</span> model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We <span class="hlt">downscaled</span> multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between <span class="hlt">downscaled</span> output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates that this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by <span class="hlt">downscaling</span> multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the <span class="hlt">downscaled</span> data as in the training data with probabilities that differed by no more than 6%. Our <span class="hlt">downscaling</span> approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016OcMod.100...20V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcMod.100...20V"><span id="translatedtitle"><span class="hlt">Downscaling</span> and extrapolating dynamic seasonal marine forecasts for coastal ocean users</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vanhatalo, Jarno; Hobday, Alistair J.; Little, L. Richard; Spillman, Claire M.</p> <p>2016-04-01</p> <p>Marine weather and climate forecasts are essential in planning strategies and activities on a range of temporal and spatial scales. However, seasonal dynamical forecast models, that provide forecasts in monthly scale, often have low offshore resolution and limited information for inshore coastal areas. Hence, there is increasing demand for methods capable of fine scale seasonal forecasts covering coastal waters. Here, we have developed a method to combine observational data with dynamical forecasts from POAMA (Predictive Ocean Atmosphere Model for Australia; Australian Bureau of Meteorology) in order to produce seasonal <span class="hlt">downscaled</span>, corrected forecasts, extrapolated to include inshore regions that POAMA does not cover. We demonstrate the method in forecasting the monthly sea surface temperature anomalies in the Great Australian Bight (GAB) region. The resolution of POAMA in the GAB is approximately 2° × 1° (lon. × lat.) and the resolution of our <span class="hlt">downscaled</span> forecast is approximately 1° × 0.25°. We use data and model hindcasts for the period 1994-2010 for forecast validation. The predictive performance of our statistical <span class="hlt">downscaling</span> model improves on the original POAMA forecast. Additionally, this statistical <span class="hlt">downscaling</span> model extrapolates forecasts to coastal regions not covered by POAMA and its forecasts are probabilistic which allows straightforward assessment of uncertainty in <span class="hlt">downscaling</span> and prediction. A range of marine users will benefit from access to <span class="hlt">downscaled</span> and nearshore forecasts at seasonal timescales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1816538A&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1816538A&link_type=ABSTRACT"><span id="translatedtitle">Islands Climatology at Local Scale. <span class="hlt">Downscaling</span> with CIELO model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Azevedo, Eduardo; Reis, Francisco; Tomé, Ricardo; Rodrigues, Conceição</p> <p>2016-04-01</p> <p>Islands with horizontal scales of the order of tens of km, as is the case of the Atlantic Islands of Macaronesia, are subscale orographic features for Global Climate Models (GCMs) since the horizontal scales of these models are too coarse to give a detailed representation of the islands' topography. Even the Regional Climate Models (RCMs) reveals limitations when they are forced to reproduce the climate of small islands mainly by the way they flat and lowers the elevation of the islands, reducing the capacity of the model to reproduce important local mechanisms that lead to a very deep local climate differentiation. Important local thermodynamics mechanisms like Foehn effect, or the influence of topography on radiation balance, have a prominent role in the climatic spatial differentiation. Advective transport of air - and the consequent induced adiabatic cooling due to orography - lead to transformations of the state parameters of the air that leads to the spatial configuration of the fields of pressure, temperature and humidity. The same mechanism is in the origin of the orographic clouds cover that, besides the direct role as water source by the reinforcement of precipitation, act like a filter to direct solar radiation and as a source of long-wave radiation that affect the local balance of energy. Also, the saturation (or near saturation) conditions that they provide constitute a barrier to water vapour diffusion in the mechanisms of evapotranspiration. Topographic factors like slope, aspect and orographic mask have also significant importance in the local energy balance. Therefore, the simulation of the local scale climate (past, present and future) in these archipelagos requires the use of <span class="hlt">downscaling</span> techniques to adjust locally outputs obtained at upper scales. This presentation will discuss and analyse the evolution of the CIELO model (acronym for Clima Insular à Escala LOcal) a statistical/dynamical technique developed at the University of the Azores</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.8417C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.8417C"><span id="translatedtitle">Dynamically <span class="hlt">Downscaling</span> Precipitation from Extra-Tropical Cyclones</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Champion, A.; Hodges, K.; Bengtsson, L.</p> <p>2012-04-01</p> <p> suitability of the LAM for <span class="hlt">downscaling</span> was evaluated by running the LAM for the events of June and July 2007 (UK floods) and comparing the output to observations. The results from this comparison provide confidence that the model is able of reproducing realistic intensities for extreme precipitation events. Whilst this method does not allow for a robust comparison between the climates it does for allow for an analysis of the method, and whether dynamically <span class="hlt">downscaling</span> individual events is suitable. It was found that by nesting the LAM within the GCM, large increases in the precipitation intensities were seen, as well as gaining a greater temporal resolution. Analysis of more events will allow a more robust comparison between climates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.A13D0243S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A13D0243S"><span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> of Tropical Storm Ivan in the Southern Appalachians</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sun, X.; Barros, A. P.</p> <p>2010-12-01</p> <p>To understand the mechanisms associated with the spatial and temporal rainfall distribution over the Southern Appalachians during tropical storms, two-way nested high resolution dynamical <span class="hlt">downscaling</span> of Hurricane Ivan, 2004 was conducted using WRF3.1 with the outer domain covering most of the southeast US at 3km resolution and the inner domain focusing on the trail of the Southern Appalachians at 1km grid increment. Model forcing was extracted from the North American Regional Reanalysis (NARR) and NCEP Final Operational Global Analysis (NCEP-FNL) data sets. Compared with different observations [satellite based, station measurement, combined products and the best track data from National Hurricane Center (NHC)], it is found both NARR and FNL reproduce the precipitation patterns reasonably well, but NARR is generally better than the FNL over the eastern slopes where orographic effects dominate. Timing errors are more significant in the NARR DDS because NARR underestimated the intensity of Hurricane Ivan. Rainfall intensity errors that result from underestimating localized heavy rainfall in the FNL forced experiment were attributed to the poorly resolved vertical wind shear in the FNL reanalysis. Independently of forcing, both dynamical <span class="hlt">downscaling</span> simulations (DDS) overestimate rainfall at low elevations, and all around better performance of the DDS files vis-à-vis the original forcing fields is generally found at high elevations. Although the rainfall distribution is still dominated by the large scale forcing, how well the topography is resolved is of significance on simulating localized extreme rainfall. The early arrival of rainfall in the vicinity of NCDC raingauges is due to excessive horizontal wind speed, potentially resulting from the parameterization of land surface roughness in the model, which may not be appropriate for high wind regimes. Sensitivity experiments on boundary layer dynamics showed that by transporting moisture away from the surface faster</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMIN33A3761A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMIN33A3761A"><span id="translatedtitle">A Modified <span class="hlt">Ensemble</span> Framework for Drought Estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alobaidi, M. H.; Marpu, P. R.; Ouarda, T.</p> <p>2014-12-01</p> <p>Drought estimation at ungauged sites is a difficult task due to various challenges such as scale and limited availability and information about hydrologic neighborhoods. <span class="hlt">Ensemble</span> regression has been recently utilized in modeling various hydrologic systems and showed advantage over classical regression approaches to such studies. A challenging task in <span class="hlt">ensemble</span> modeling is the proper training of the <span class="hlt">ensemble</span>'s individual learners and the <span class="hlt">ensemble</span> combiners. In this work, an <span class="hlt">ensemble</span> framework is proposed to enhance the generalization ability of the sub-<span class="hlt">ensemble</span> models and its combiner. Information mixtures between the subsamples are introduced. Such measure is dedicated to the <span class="hlt">ensemble</span> members and <span class="hlt">ensemble</span> combiners. Controlled homogeneity magnitudes are then stimulated and induced in the proposed model via a two-stage resampling algorithm. Artificial neural networks (ANNs) were used as <span class="hlt">ensemble</span> members in addition to different <span class="hlt">ensemble</span> integration plans. The model provided superior results when compared to previous models applied to the case study in this work. The root mean squared error (RMSE) in the testing phase for the drought quantiles improved by 67% - 76%. The bias error (BIAS) also showed 61% - 95% improvement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010PhDT.......137H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010PhDT.......137H&link_type=ABSTRACT"><span id="translatedtitle">A standardized framework for evaluating the skill of regional climate <span class="hlt">downscaling</span> techniques</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hayhoe, Katharine Anne</p> <p></p> <p>Regional climate impact assessments require high-resolution projections to resolve local factors that modify the impact of global-scale forcing. To generate these projections, global climate model simulations are commonly <span class="hlt">downscaled</span> using a variety of statistical and dynamical techniques. Despite the essential role of <span class="hlt">downscaling</span> in regional assessments, there is no standard approach to evaluating various <span class="hlt">downscaling</span> methods. Hence, impact communities often have little awareness of limitations and uncertainties associated with <span class="hlt">downscaled</span> projections. To develop a standardized framework for evaluating and comparing <span class="hlt">downscaling</span> approaches, I first identify three primary characteristics of a distribution directly relevant to impact analyses that can be used to evaluate a simulated variable such as temperature or precipitation at a given location: (1) annual, seasonal, and monthly mean values; (2) thresholds, extreme values, and accumulated quantities such as 24h precipitation or degree-days; and (3) persistence, reflecting multi-day events such as heat waves, cold spells, and wet periods. Based on a survey of the literature and solicitation of expert opinion, I select a set of ten statistical tests to evaluate these characteristics, including measures of error, skill, and correlation. I apply this framework to evaluate the skill of four <span class="hlt">downscaling</span> methods, from a simple delta approach to a complex asynchronous quantile regression, in simulating daily temperature at twenty stations across North America. Identical global model fields force each <span class="hlt">downscaling</span> method, and the historical observational record at each location is randomly divided by year into two equal parts, such that each statistical method is trained on one set of historical observations, and evaluated on an entirely independent set of observations. Biases relative to observations are calculated for the historical evaluation period, and differences between projections for the future. Application of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/15020771','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/15020771"><span id="translatedtitle">Changes in Seasonal and Extreme Hydrologic Conditions of the Georgia Basin/Puget Sound in an <span class="hlt">Ensemble</span> Regional Climate Simulation for the Mid-Century</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Leung, Lai R.; Qian, Yun</p> <p>2003-12-15</p> <p>This study examines an <span class="hlt">ensemble</span> of climate change projections simulated by a global climate model (GCM) and <span class="hlt">downscaled</span> with a region climate model (RCM) to 40 km spatial resolution for the western North America. One control and three <span class="hlt">ensemble</span> future climate simulations were produced by the GCM following a business as usual scenario for greenhouse gases and aerosols emissions from 1995 to 2100. The RCM was used to <span class="hlt">downscale</span> the GCM control simulation (1995-2015) and each <span class="hlt">ensemble</span> future GCM climate (2040-2060) simulation. Analyses of the regional climate simulations for the Georgia Basin/Puget Sound showed a warming of 1.5-2oC and statistically insignificant changes in precipitation by the mid-century. Climate change has large impacts on snowpack (about 50% reduction) but relatively smaller impacts on the total runoff for the basin as a whole. However, climate change can strongly affect small watersheds such as those located in the transient snow zone, causing a higher likelihood of winter flooding as a higher percentage of precipitation falls in the form of rain rather than snow, and reduced streamflow in early summer. In addition, there are large changes in the monthly total runoff above the upper 1% threshold (or flood volume) from October through May, and the December flood volume of the future climate is 60% above the maximum monthly flood volume of the control climate. Uncertainty of the climate change projections, as characterized by the spread among the <span class="hlt">ensemble</span> future climate simulations, is relatively small for the basin mean snowpack and runoff, but increases in smaller watersheds, especially in the transient snow zone, and associated with extreme events. This emphasizes the importance of characterizing uncertainty through <span class="hlt">ensemble</span> simulations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ClDy...46.3305S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ClDy...46.3305S&link_type=ABSTRACT"><span id="translatedtitle">Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sohn, Soo-Jin; Tam, Chi-Yung</p> <p>2016-05-01</p> <p>Capturing climatic variations in boreal winter to spring (December-May) is essential for properly predicting droughts in South Korea. This study investigates the variability and predictability of the South Korean climate during this extended season, based on observations from 60 station locations and multi-model <span class="hlt">ensemble</span> (MME) hindcast experiments (1983/1984-2005/2006) archived at the APEC Climate Center (APCC). Multivariate empirical orthogonal function (EOF) analysis results based on observations show that the first two leading modes of winter-to-spring precipitation and temperature variability, which together account for ~80 % of the total variance, are characterized by regional-scale anomalies covering the whole South Korean territory. These modes were also closely related to some of the recurrent large-scale circulation changes in the northern hemisphere during the same season. Consistent with the above, examination of the standardized precipitation evapotranspiration index (SPEI) indicates that drought conditions in South Korea tend to be accompanied by regional-to-continental-scale circulation anomalies over East Asia to the western north Pacific. Motivated by the aforementioned findings on the spatial-temporal coherence among station-scale precipitation and temperature anomalies, a new bivariate and pattern-based <span class="hlt">downscaling</span> method was developed. The novelty of this method is that precipitation and temperature data were first filtered using multivariate EOFs to enhance their spatial-temporal coherence, before being linked to large-scale circulation variables using canonical correlation analysis (CCA). To test its applicability and to investigate its related potential predictability, a perfect empirical model was first constructed with observed datasets as predictors. Next, a model output statistics (MOS)-type hybrid dynamical-statistical model was developed, using products from nine one-tier climate models as inputs. It was found that, with model sea</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy..tmp..302S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy..tmp..302S"><span id="translatedtitle">Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sohn, Soo-Jin; Tam, Chi-Yung</p> <p>2015-07-01</p> <p>Capturing climatic variations in boreal winter to spring (December-May) is essential for properly predicting droughts in South Korea. This study investigates the variability and predictability of the South Korean climate during this extended season, based on observations from 60 station locations and multi-model <span class="hlt">ensemble</span> (MME) hindcast experiments (1983/1984-2005/2006) archived at the APEC Climate Center (APCC). Multivariate empirical orthogonal function (EOF) analysis results based on observations show that the first two leading modes of winter-to-spring precipitation and temperature variability, which together account for ~80 % of the total variance, are characterized by regional-scale anomalies covering the whole South Korean territory. These modes were also closely related to some of the recurrent large-scale circulation changes in the northern hemisphere during the same season. Consistent with the above, examination of the standardized precipitation evapotranspiration index (SPEI) indicates that drought conditions in South Korea tend to be accompanied by regional-to-continental-scale circulation anomalies over East Asia to the western north Pacific. Motivated by the aforementioned findings on the spatial-temporal coherence among station-scale precipitation and temperature anomalies, a new bivariate and pattern-based <span class="hlt">downscaling</span> method was developed. The novelty of this method is that precipitation and temperature data were first filtered using multivariate EOFs to enhance their spatial-temporal coherence, before being linked to large-scale circulation variables using canonical correlation analysis (CCA). To test its applicability and to investigate its related potential predictability, a perfect empirical model was first constructed with observed datasets as predictors. Next, a model output statistics (MOS)-type hybrid dynamical-statistical model was developed, using products from nine one-tier climate models as inputs. It was found that, with model sea</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A34E..03S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A34E..03S"><span id="translatedtitle">Mid-Century Warming in the Los Angeles Region and its Uncertainty using Dynamical and Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sun, F.; Hall, A. D.; Walton, D.; Capps, S. B.; Qu, X.; Huang, H. J.; Berg, N.; Jousse, A.; Schwartz, M.; Nakamura, M.; Cerezo-Mota, R.</p> <p>2012-12-01</p> <p>Using a combination of dynamical and statistical <span class="hlt">downscaling</span> techniques, we projected mid-21st century warming in the Los Angeles region at 2-km resolution. To account for uncertainty associated with the trajectory of future greenhouse gas emissions, we examined projections for both "business-as-usual" (RCP8.5) and "mitigation" (RCP2.6) emissions scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5). To account for the considerable uncertainty associated with choice of global climate model, we <span class="hlt">downscaled</span> results for all available global climate models in CMIP5. For the business-as-usual scenario, we find that by the mid-21st century, the most likely warming is roughly 2.6°C averaged over the region's land areas, with a 95% confidence that the warming lies between 0.9 and 4.2°C. The high resolution of the projections reveals a pronounced spatial pattern in the warming: High elevations and inland areas separated from the coast by at least one mountain complex warm 20 to 50% more than the areas near the coast or within the Los Angeles basin. This warming pattern is especially apparent in summertime. The summertime warming contrast between the inland and coastal zones has a large effect on the most likely expected number of extremely hot days per year. Coastal locations and areas within the Los Angeles basin see roughly two to three times the number of extremely hot days, while high elevations and inland areas typically experience approximately three to five times the number of extremely hot days. Under the mitigation emissions scenario, the most likely warming and increase in heat extremes are somewhat smaller. However, the majority of the warming seen in the business-as-usual scenario still occurs at all locations in the most likely case under the mitigation scenario, and heat extremes still increase significantly. This warming study is the first part of a series studies of our project. More climate change impacts on the Santa Ana wind, rainfall</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.B53E0725Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.B53E0725Y"><span id="translatedtitle">Intercomparison of <span class="hlt">Downscaling</span> Methods on Hydrological Impact for Earth System Model of NE United States</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.</p> <p>2012-12-01</p> <p>Atmospheric dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be <span class="hlt">downscaled</span> from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this <span class="hlt">downscaling</span> procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several <span class="hlt">downscaling</span> techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four <span class="hlt">downscaling</span> approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial <span class="hlt">downscaling</span> (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic <span class="hlt">Downscaling</span> based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different <span class="hlt">downscaling</span> methods against observational datasets was carried out for assessment of the <span class="hlt">downscaled</span> climate model outputs. The effects of using the</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMSM24A..03L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMSM24A..03L"><span id="translatedtitle"><span class="hlt">Ensemble</span> modeling of CME propagation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, C. O.; Arge, C. N.; Henney, C. J.; Odstrcil, D.; Millward, G. H.; Pizzo, V. J.</p> <p>2014-12-01</p> <p>The Wang-Sheeley-Arge(WSA)-Enlil-cone modeling system is used for making routine arrival time forecasts of the Earth-directed "halo" coronal mass ejections (CMEs), since they typically produce the most geoeffective events. A major objective of this work is to better understand the sensitivity of the WSA-Enlil modeling results to input model parameters and how these parameters contribute to the overall model uncertainty and performance. We present <span class="hlt">ensemble</span> modeling results for a simple halo CME event that occurred on 15 February 2011 and a succession of three halo CME events that occurred on 2-4 August 2011. During this period the Solar TErrestrial RElations Observatory (STEREO) A and B spacecraft viewed the CMEs over the solar limb, thereby providing more reliable constraints on the initial CME geometries during the manual cone fitting process. To investigate the sensitivity of the modeled CME arrival times to small variations in the input cone properties, for each CME event we create an <span class="hlt">ensemble</span> of numerical simulations based on multiple sets of cone parameters. We find that the accuracy of the modeled arrival times not only depends on the initial input CME geometry, but also on the reliable specification of the background solar wind, which is driven by the input maps of the photospheric magnetic field. As part of the modeling <span class="hlt">ensemble</span>, we simulate the CME events using the traditional daily updated maps as well as those that are produced by the Air Force data Assimilative Photospheric flux Transport (ADAPT) model, which provide a more instantaneous snapshot of the photospheric field distribution. For the August 2011 events, in particular, we find that the accuracy in the arrival time predictions also depends on whether the cone parameters for all three CMEs are specified in a single WSA-Enlil simulation. The inclusion/exclusion of one or two of the preceding CMEs affects the solar wind conditions through which the succeeding CME propagates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006432','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006432"><span id="translatedtitle"><span class="hlt">Downscaling</span> MODIS Land Surface Temperature for Urban Public Health Applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel</p> <p>2013-01-01</p> <p>This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for <span class="hlt">downscaling</span> the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC53A1041A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC53A1041A"><span id="translatedtitle"><span class="hlt">Downscaling</span> MODIS Land Surface Temperature for Urban Public Health Applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G., Jr.; Estes, S. M.; Quattrochi, D. A.; Johnson, D.</p> <p>2013-12-01</p> <p>This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heat-related mortality data. The current HWWS do not take into account intra-urban spatial variations in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with land surface temperature (LST) estimates derived from thermal remote sensing data. In order to further improve the assessment of intra-urban variations in risk from extreme heat, we developed and evaluated a number of spatial statistical techniques for <span class="hlt">downscaling</span> the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. We will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011613','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011613"><span id="translatedtitle"><span class="hlt">Downscaling</span> NASA Climatological Data to Produce Detailed Climate Zone Maps</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chandler, William S.; Hoell, James M.; Westberg, David J.; Whitlock, Charles H.; Zhang, Taiping; Stackhouse, P. W.</p> <p>2011-01-01</p> <p>The design of energy efficient sustainable buildings is heavily dependent on accurate long-term and near real-time local weather data. To varying degrees the current meteorological networks over the globe have been used to provide these data albeit often from sites far removed from the desired location. The national need is for access to weather and solar resource data accurate enough to use to develop preliminary building designs within a short proposal time limit, usually within 60 days. The NASA Prediction Of Worldwide Energy Resource (POWER) project was established by NASA to provide industry friendly access to globally distributed solar and meteorological data. As a result, the POWER web site (power.larc.nasa.gov) now provides global information on many renewable energy parameters and several buildings-related items but at a relatively coarse resolution. This paper describes a method of <span class="hlt">downscaling</span> NASA atmospheric assimilation model results to higher resolution and maps those parameters to produce building climate zone maps using estimates of temperature and precipitation. The distribution of climate zones for North America with an emphasis on the Pacific Northwest for just one year shows very good correspondence to the currently defined distribution. The method has the potential to provide a consistent procedure for deriving climate zone information on a global basis that can be assessed for variability and updated more regularly.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27069054','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27069054"><span id="translatedtitle">Measuring social interaction in music <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Volpe, Gualtiero; D'Ausilio, Alessandro; Badino, Leonardo; Camurri, Antonio; Fadiga, Luciano</p> <p>2016-05-01</p> <p>Music <span class="hlt">ensembles</span> are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music <span class="hlt">ensembles</span> offer a broad variety of scenarios which are particularly suitable for investigation. Small <span class="hlt">ensembles</span>, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger <span class="hlt">ensembles</span>, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music <span class="hlt">ensembles</span> with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of <span class="hlt">ensemble</span> music performances. PMID:27069054</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20130001779&hterms=Kalman&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DKalman','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20130001779&hterms=Kalman&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DKalman"><span id="translatedtitle">A Localized <span class="hlt">Ensemble</span> Kalman Smoother</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Butala, Mark D.</p> <p>2012-01-01</p> <p>Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized <span class="hlt">ensemble</span> Kalman smoother.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820026226','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820026226"><span id="translatedtitle"><span class="hlt">Ensemble</span> averaging of acoustic data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stefanski, P. K.</p> <p>1982-01-01</p> <p>A computer program called <span class="hlt">Ensemble</span> Averaging of Acoustic Data is documented. The program samples analog data, analyzes the data, and displays them in the time and frequency domains. Hard copies of the displays are the program's output. The documentation includes a description of the program and detailed user instructions for the program. This software was developed for use on the Ames 40- by 80-Foot Wind Tunnel's Dynamic Analysis System consisting of a PDP-11/45 computer, two RK05 disk drives, a tektronix 611 keyboard/display terminal, and FPE-4 Fourier Processing Element, and an analog-to-digital converter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/22482774','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/22482774"><span id="translatedtitle">Potential and limitations of <span class="hlt">ensemble</span> docking.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Korb, Oliver; Olsson, Tjelvar S G; Bowden, Simon J; Hall, Richard J; Verdonk, Marcel L; Liebeschuetz, John W; Cole, Jason C</p> <p>2012-05-25</p> <p>A major problem in structure-based virtual screening applications is the appropriate selection of a single or even multiple protein structures to be used in the virtual screening process. A priori it is unknown which protein structure(s) will perform best in a virtual screening experiment. We investigated the performance of <span class="hlt">ensemble</span> docking, as a function of <span class="hlt">ensemble</span> size, for eight targets of pharmaceutical interest. Starting from single protein structure docking results, for each <span class="hlt">ensemble</span> size up to 500,000 combinations of protein structures were generated, and, for each <span class="hlt">ensemble</span>, pose prediction and virtual screening results were derived. Comparison of single to multiple protein structure results suggests improvements when looking at the performance of the worst and the average over all single protein structures to the performance of the worst and average over all protein <span class="hlt">ensembles</span> of size two or greater, respectively. We identified several key factors affecting <span class="hlt">ensemble</span> docking performance, including the sampling accuracy of the docking algorithm, the choice of the scoring function, and the similarity of database ligands to the cocrystallized ligands of ligand-bound protein structures in an <span class="hlt">ensemble</span>. Due to these factors, the prospective selection of optimum <span class="hlt">ensembles</span> is a challenging task, shown by a reassessment of published <span class="hlt">ensemble</span> selection protocols. PMID:22482774</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160007028','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160007028"><span id="translatedtitle">Multi-Model <span class="hlt">Ensemble</span> Wake Vortex Prediction</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.</p> <p>2015-01-01</p> <p>Several multi-model <span class="hlt">ensemble</span> methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model <span class="hlt">ensemble</span> capability using their wake models. An overview of different multi-model <span class="hlt">ensemble</span> methods and their feasibility for wake applications is presented. The methods include Reliability <span class="hlt">Ensemble</span> Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1614315G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1614315G"><span id="translatedtitle">Comparison among different <span class="hlt">downscaling</span> approaches in building water scarcity scenarios in an Alpine basin.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guyennon, Nicolas; Romano, Emanuele; Mariani, Davide; Bruna Petrangeli, Anna; Portoghese, Ivan</p> <p>2014-05-01</p> <p>Various <span class="hlt">downscaling</span> techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Although statistical <span class="hlt">downscaling</span> (SD) has been traditionally seen as an alternative to dynamical <span class="hlt">downscaling</span> (DD), recent works on statistical <span class="hlt">downscaling</span> have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD is able to provide more reliable climate forcing for crop water demand models. The case study presented here focuses on the Maggiore Lake (Alpine region), with a watershed of approximately 4750 km2 and whose waters are mainly used for irrigation purposes in the Lombardia and Piemonte regions. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction of the precipitation data collected in the period 1950-2012 by the 19 rainfall gauges located in the watershed area (some of them operating not continuously during the study period). The relationship between the precipitation regime and the inflow to the reservoir is obtained through a simple multilinear regression model, validated using both precipitation data and inflow measurements to the lake in the period 1996-2012 then, the same relation has been applied to the control (20c) and scenario (a1b) simulations <span class="hlt">downscaled</span> by means of the different <span class="hlt">downscaling</span> approaches (DD, SD and combined DD-SD). The resulting forcing has been used as input to a daily water balance model taking into account the inflow to the lake, the demand for irrigation and the reservoir management policies. The impact of the different <span class="hlt">downscaling</span> approaches on the water budget scenarios has been evaluated in terms of occurrence, duration and intensity of water scarcity periods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..12.8505P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..12.8505P"><span id="translatedtitle">Evaluation of soil moisture <span class="hlt">downscaling</span> using a simple thermal based proxy - the REMEDHUS network (Spain) example</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, J.; Niesel, J.; Loew, A.</p> <p>2015-08-01</p> <p>Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution in the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought predication. Therefore, various <span class="hlt">downscaling</span> methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple Vegetation Temperature Condition Index (VTCI) <span class="hlt">downscaling</span> scheme over a dense soil moisture observational network (REMEDHUS) in Spain. Firstly, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography and land cover heterogeneity, using data from MODIS and MSG SEVIRI. Then the <span class="hlt">downscaling</span> scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the <span class="hlt">downscaling</span> method can significantly improve the spatial details of CCI soil moisture while maintain the accuracy of CCI soil moisture. The accuracy level is comparable to other <span class="hlt">downscaling</span> methods that were also validated against REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying geostationary satellite for <span class="hlt">downscaling</span> soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI <span class="hlt">downscaling</span> method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1812161R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1812161R&link_type=ABSTRACT"><span id="translatedtitle">Actor groups, related needs, and challenges at the climate <span class="hlt">downscaling</span> interface</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rössler, Ole; Benestad, Rasmus; Diamando, Vlachogannis; Heike, Hübener; Kanamaru, Hideki; Pagé, Christian; Margarida Cardoso, Rita; Soares, Pedro; Maraun, Douglas; Kreienkamp, Frank; Christodoulides, Paul; Fischer, Andreas; Szabo, Peter</p> <p>2016-04-01</p> <p>At the climate <span class="hlt">downscaling</span> interface, numerous <span class="hlt">downscaling</span> techniques and different philosophies compete on being the best method in their specific terms. Thereby, it remains unclear to what extent and for which purpose these <span class="hlt">downscaling</span> techniques are valid or even the most appropriate choice. A common validation framework that compares all the different available methods was missing so far. The initiative VALUE closes this gap with such a common validation framework. An essential part of a validation framework for <span class="hlt">downscaling</span> techniques is the definition of appropriate validation measures. The selection of validation measures should consider the needs of the stakeholder: some might need a temporal or spatial average of a certain variable, others might need temporal or spatial distributions of some variables, still others might need extremes for the variables of interest or even inter-variable dependencies. Hence, a close interaction of climate data providers and climate data users is necessary. Thus, the challenge in formulating a common validation framework mirrors also the challenges between the climate data providers and the impact assessment community. This poster elaborates the issues and challenges at the <span class="hlt">downscaling</span> interface as it is seen within the VALUE community. It suggests three different actor groups: one group consisting of the climate data providers, the other two groups being climate data users (impact modellers and societal users). Hence, the <span class="hlt">downscaling</span> interface faces classical transdisciplinary challenges. We depict a graphical illustration of actors involved and their interactions. In addition, we identified four different types of issues that need to be considered: i.e. data based, knowledge based, communication based, and structural issues. They all may, individually or jointly, hinder an optimal exchange of data and information between the actor groups at the <span class="hlt">downscaling</span> interface. Finally, some possible ways to tackle these issues are</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012adm..book...89C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012adm..book...89C"><span id="translatedtitle">Probabilistic Description of Stellar <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cerviño, Miguel</p> <p></p> <p>I describe the modeling of stellar <span class="hlt">ensembles</span> in terms of probability distributions. This modeling is primary characterized by the number of stars included in the considered resolution element, whatever its physical (stellar cluster) or artificial (pixel/IFU) nature. It provides a solution of the direct problem of characterizing probabilistically the observables of stellar <span class="hlt">ensembles</span> as a function of their physical properties. In addition, this characterization implies that intensive properties (like color indices) are intrinsically biased observables, although the bias decreases when the number of stars in the resolution element increases. In the case of a low number of stars in the resolution element (N<105), the distributions of intensive and extensive observables follow nontrivial probability distributions. Such a situation ​​​ can be computed by means of Monte Carlo simulations where data mining techniques would be applied. Regarding the inverse problem of obtaining physical parameters from observational data, I show how some of the scatter in the data provides valuable physical information since it is related to the system size (and the number of stars in the resolution element). However, making use of such ​​​ information requires following iterative procedures in the data analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27179343','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27179343"><span id="translatedtitle">Visualizing <span class="hlt">ensembles</span> in structural biology.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Melvin, Ryan L; Salsbury, Freddie R</p> <p>2016-06-01</p> <p>Displaying a single representative conformation of a biopolymer rather than an <span class="hlt">ensemble</span> of states mistakenly conveys a static nature rather than the actual dynamic personality of biopolymers. However, there are few apparent options due to the fixed nature of print media. Here we suggest a standardized methodology for visually indicating the distribution width, standard deviation and uncertainty of <span class="hlt">ensembles</span> of states with little loss of the visual simplicity of displaying a single representative conformation. Of particular note is that the visualization method employed clearly distinguishes between isotropic and anisotropic motion of polymer subunits. We also apply this method to ligand binding, suggesting a way to indicate the expected error in many high throughput docking programs when visualizing the structural spread of the output. We provide several examples in the context of nucleic acids and proteins with particular insights gained via this method. Such examples include investigating a therapeutic polymer of FdUMP (5-fluoro-2-deoxyuridine-5-O-monophosphate) - a topoisomerase-1 (Top1), apoptosis-inducing poison - and nucleotide-binding proteins responsible for ATP hydrolysis from Bacillus subtilis. We also discuss how these methods can be extended to any macromolecular data set with an underlying distribution, including experimental data such as NMR structures. PMID:27179343</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/5206885','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/5206885"><span id="translatedtitle">Forecast of iceberg <span class="hlt">ensemble</span> drift</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>El-Tahan, M.S.; El-Tahan, H.W.; Venkatesh, S.</p> <p>1983-05-01</p> <p>The objectives of the study are to gain a better understanding of the characteristics of iceberg motion and the factors controlling iceberg drift, and to develop an iceberg <span class="hlt">ensemble</span> drift forecast system to be operated by the Canadian Atmospheric Environment Service. An extensive review of field and theoretical studies on iceberg behaviour, and the factors controlling iceberg motion has been carried out. Long term and short term behaviour of icebergs are critically examined. A quantitative assessment of the effects of the factors controlling iceberg motion is presented. The study indicated that wind and currents are the primary driving forces. Coriolis Force and ocean surface slope also have significant effects. As for waves, only the higher waves have a significant effect. Iceberg drift is also affected by iceberg size characteristics. Based on the findings of the study a comprehensive computerized forecast system to predict the drift of iceberg <span class="hlt">ensembles</span> off Canada's east coast has been designed. The expected accuracy of the forecast system is discussed and recommendations are made for future improvements to the system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26032515','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26032515"><span id="translatedtitle">Residue-level global and local <span class="hlt">ensemble-ensemble</span> comparisons of protein domains.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Clark, Sarah A; Tronrud, Dale E; Karplus, P Andrew</p> <p>2015-09-01</p> <p>Many methods of protein structure generation such as NMR-based solution structure determination and template-based modeling do not produce a single model, but an <span class="hlt">ensemble</span> of models consistent with the available information. Current strategies for comparing <span class="hlt">ensembles</span> lose information because they use only a single representative structure. Here, we describe the <span class="hlt">ENSEMBLATOR</span> and its novel strategy to directly compare two <span class="hlt">ensembles</span> containing the same atoms to identify significant global and local backbone differences between them on per-atom and per-residue levels, respectively. The <span class="hlt">ENSEMBLATOR</span> has four components: eePREP (ee for <span class="hlt">ensemble-ensemble</span>), which selects atoms common to all models; eeCORE, which identifies atoms belonging to a cutoff-distance dependent common core; eeGLOBAL, which globally superimposes all models using the defined core atoms and calculates for each atom the two intraensemble variations, the interensemble variation, and the closest approach of members of the two <span class="hlt">ensembles</span>; and eeLOCAL, which performs a local overlay of each dipeptide and, using a novel measure of local backbone similarity, reports the same four variations as eeGLOBAL. The combination of eeGLOBAL and eeLOCAL analyses identifies the most significant differences between <span class="hlt">ensembles</span>. We illustrate the <span class="hlt">ENSEMBLATOR</span>'s capabilities by showing how using it to analyze NMR <span class="hlt">ensembles</span> and to compare NMR <span class="hlt">ensembles</span> with crystal structures provides novel insights compared to published studies. One of these studies leads us to suggest that a "consistency check" of NMR-derived <span class="hlt">ensembles</span> may be a useful analysis step for NMR-based structure determinations in general. The <span class="hlt">ENSEMBLATOR</span> 1.0 is available as a first generation tool to carry out <span class="hlt">ensemble-ensemble</span> comparisons. PMID:26032515</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=music+AND+development+AND+social&pg=4&id=EJ971454','ERIC'); return false;" href="http://eric.ed.gov/?q=music+AND+development+AND+social&pg=4&id=EJ971454"><span id="translatedtitle">Joys of Community <span class="hlt">Ensemble</span> Playing: The Case of the Happy Roll Elastic <span class="hlt">Ensemble</span> in Taiwan</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Hsieh, Yuan-Mei; Kao, Kai-Chi</p> <p>2012-01-01</p> <p>The Happy Roll Elastic <span class="hlt">Ensemble</span> (HREE) is a community music <span class="hlt">ensemble</span> supported by Tainan Culture Centre in Taiwan. With enjoyment and friendship as its primary goals, it aims to facilitate the joys of <span class="hlt">ensemble</span> playing and the spirit of social networking. This article highlights the key aspects of HREE's development in its first two years…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMGC11B1004B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMGC11B1004B"><span id="translatedtitle">A Comprehensive Framework for Quantitative Evaluation of <span class="hlt">Downscaled</span> Climate Predictions and Projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barsugli, J. J.; Guentchev, G.</p> <p>2012-12-01</p> <p>The variety of methods used for <span class="hlt">downscaling</span> climate predictions and projections is large and growing larger. Comparative studies of <span class="hlt">downscaling</span> techniques to date are often initiated in relation to specific projects, are focused on limited sets of <span class="hlt">downscaling</span> techniques, and hence do not allow for easy comparison of outcomes. In addition, existing information about the quality of <span class="hlt">downscaled</span> datasets is not available in digital form. There is a strong need for systematic evaluation of <span class="hlt">downscaling</span> methods using standard protocols which will allow for a fair comparison of their advantages and disadvantages with respect to specific user needs. The National Climate Predictions and Projections platform, with the contributions of NCPP's Climate Science Advisory Team, is developing community-based standards and a prototype framework for the quantitative evaluation of <span class="hlt">downscaling</span> techniques and datasets. Certain principles guide the development of this framework. We want the evaluation procedures to be reproducible and transparent, simple to understand, and straightforward to implement. To this end we propose a set of open standards that will include the use of specific data sets, time periods of analysis, evaluation protocols, evaluation tests and metrics. Secondly, we want the framework to be flexible and extensible to <span class="hlt">downscaling</span> techniques which may be developed in the future, to high-resolution global models, and to evaluations that are meaningful for additional applications and sectors. Collaboration among practitioners who will be using the <span class="hlt">downscaled</span> data and climate scientists who develop <span class="hlt">downscaling</span> methods will therefore be essential to the development of this framework. The proposed framework consists of three analysis protocols, along with two tiers of specific metrics and indices that are to be calculated. The protocols describe the following types of evaluation that can be performed: 1) comparison to observations, 2) comparison to a "perfect model" simulation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1511726K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1511726K"><span id="translatedtitle">Assessing Fire Weather Index using statistical <span class="hlt">downscaling</span> and spatial interpolation techniques in Greece</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Karali, Anna; Giannakopoulos, Christos; Frias, Maria Dolores; Hatzaki, Maria; Roussos, Anargyros; Casanueva, Ana</p> <p>2013-04-01</p> <p>Forest fires have always been present in the Mediterranean ecosystems, thus they constitute a major ecological and socio-economic issue. The last few decades though, the number of forest fires has significantly increased, as well as their severity and impact on the environment. Local fire danger projections are often required when dealing with wild fire research. In the present study the application of statistical <span class="hlt">downscaling</span> and spatial interpolation methods was performed to the Canadian Fire Weather Index (FWI), in order to assess forest fire risk in Greece. The FWI is used worldwide (including the Mediterranean basin) to estimate the fire danger in a generalized fuel type, based solely on weather observations. The meteorological inputs to the FWI System are noon values of dry-bulb temperature, air relative humidity, 10m wind speed and precipitation during the previous 24 hours. The statistical <span class="hlt">downscaling</span> methods are based on a statistical model that takes into account empirical relationships between large scale variables (used as predictors) and local scale variables. In the framework of the current study the statistical <span class="hlt">downscaling</span> portal developed by the Santander Meteorology Group (https://www.meteo.unican.es/<span class="hlt">downscaling</span>) in the framework of the EU project CLIMRUN (www.climrun.eu) was used to <span class="hlt">downscale</span> non standard parameters related to forest fire risk. In this study, two different approaches were adopted. Firstly, the analogue <span class="hlt">downscaling</span> technique was directly performed to the FWI index values and secondly the same <span class="hlt">downscaling</span> technique was performed indirectly through the meteorological inputs of the index. In both cases, the statistical <span class="hlt">downscaling</span> portal was used considering the ERA-Interim reanalysis as predictands due to the lack of observations at noon. Additionally, a three-dimensional (3D) interpolation method of position and elevation, based on Thin Plate Splines (TPS) was used, to interpolate the ERA-Interim data used to calculate the index</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013OcMod..72..231K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013OcMod..72..231K"><span id="translatedtitle"><span class="hlt">Downscaling</span> ocean conditions: Experiments with a quasi-geostrophic model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Katavouta, A.; Thompson, K. R.</p> <p>2013-12-01</p> <p>The predictability of small-scale ocean variability, given the time history of the associated large-scales, is investigated using a quasi-geostrophic model of two wind-driven gyres separated by an unstable, mid-ocean jet. Motivated by the recent theoretical study of Henshaw et al. (2003), we propose a straightforward method for assimilating information on the large-scale in order to recover the small-scale details of the quasi-geostrophic circulation. The similarity of this method to the spectral nudging of limited area atmospheric models is discussed. Results from the spectral nudging of the quasi-geostrophic model, and an independent multivariate regression-based approach, show that important features of the ocean circulation, including the position of the meandering mid-ocean jet and the associated pinch-off eddies, can be recovered from the time history of a small number of large-scale modes. We next propose a hybrid approach for assimilating both the large-scales and additional observed time series from a limited number of locations that alone are too sparse to recover the small scales using traditional assimilation techniques. The hybrid approach improved significantly the recovery of the small-scales. The results highlight the importance of the coupling between length scales in <span class="hlt">downscaling</span> applications, and the value of assimilating limited point observations after the large-scales have been set correctly. The application of the hybrid and spectral nudging to practical ocean forecasting, and projecting changes in ocean conditions on climate time scales, is discussed briefly.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1110243C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1110243C"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> in Multi-dimensional Wave Climate Forecast</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Camus, P.; Méndez, F. J.; Medina, R.; Losada, I. J.; Cofiño, A. S.; Gutiérrez, J. M.</p> <p>2009-04-01</p> <p>Wave climate at a particular site is defined by the statistical distribution of sea state parameters, such as significant wave height, mean wave period, mean wave direction, wind velocity, wind direction and storm surge. Nowadays, long-term time series of these parameters are available from reanalysis databases obtained by numerical models. The Self-Organizing Map (SOM) technique is applied to characterize multi-dimensional wave climate, obtaining the relevant "wave types" spanning the historical variability. This technique summarizes multi-dimension of wave climate in terms of a set of clusters projected in low-dimensional lattice with a spatial organization, providing Probability Density Functions (PDFs) on the lattice. On the other hand, wind and storm surge depend on instantaneous local large-scale sea level pressure (SLP) fields while waves depend on the recent history of these fields (say, 1 to 5 days). Thus, these variables are associated with large-scale atmospheric circulation patterns. In this work, a nearest-neighbors analog method is used to predict monthly multi-dimensional wave climate. This method establishes relationships between the large-scale atmospheric circulation patterns from numerical models (SLP fields as predictors) with local wave databases of observations (monthly wave climate SOM PDFs as predictand) to set up statistical models. A wave reanalysis database, developed by Puertos del Estado (Ministerio de Fomento), is considered as historical time series of local variables. The simultaneous SLP fields calculated by NCEP atmospheric reanalysis are used as predictors. Several applications with different size of sea level pressure grid and with different temporal domain resolution are compared to obtain the optimal statistical model that better represents the monthly wave climate at a particular site. In this work we examine the potential skill of this <span class="hlt">downscaling</span> approach considering perfect-model conditions, but we will also analyze the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1812252T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812252T"><span id="translatedtitle">CORDEX.be: COmbining Regional climate <span class="hlt">Downscaling</span> EXpertise in Belgium</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Termonia, Piet; Van Schaeybroeck, Bert; De Ridder, Koen; Fettweis, Xavier; Gobin, Anne; Luyten, Patrick; Marbaix, Philippe; Pottiaux, Eric; Stavrakou, Trissevgeni; Van Lipzig, Nicole; van Ypersele, Jean-Pascal; Willems, Patrick</p> <p>2016-04-01</p> <p>The main objective of the ongoing project CORDEX.be, "COmbining Regional <span class="hlt">Downscaling</span> EXpertise in Belgium: CORDEX and Beyond" is to gather existing and ongoing Belgian research activities in the domain of climate modelling to create a coherent scientific basis for future climate services in Belgium. The project regroups eight Belgian Institutes under a single research program of the Belgian Science Policy (BELSPO). The project involves three regional climate models: the ALARO model, the COSMO-CLM model and the MAR model running according to the guidelines of the CORDEX project and at convection permitting resolution on small domains over Belgium. The project creates a framework to address four objectives/challenges. First, this projects aims to contribute to the EURO-CORDEX project. Secondly, RCP simulations are executed at convection-permitting resolutions (3 to 5 km) on small domains. Thirdly, the output of the atmospheric models is used to drive land surface models (the SURFEX model and the Urbclim model) with urban modules, a crop model (REGCROP), a tides and storm model (COHERENS) and the MEGAN-MOHYCAN model that simulates the fluxes emitted by vegetation. Finally, one work package will translate the uncertainty present in the CORDEX database to the high-resolution output of the CORDEX.be project. The organization of the project will be presented and first results will be shown, demonstrating that convection-permitting models can add extra skill to the mesoscale version of the regional climate models, in particular regarding the extreme value statistics and the diurnal cycle.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H52E..04T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H52E..04T"><span id="translatedtitle"><span class="hlt">Downscaling</span> Alkaline Phosphatase Activity in a Subtropical Reservoir</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tseng, Y.</p> <p>2011-12-01</p> <p>This research was conducted by <span class="hlt">downscaling</span> study to understand phosphorus (P)-deficient status of different plankton and the role of alkaline phosphatase activity (APA) in subtropical Feitsui Reservoir. Results from field survey showed that bulk APA (1.6~95.2 nM h-1) was widely observed in the epilimnion (0~20 m) with an apparent seasonal variations, suggesting that plankton in the system were subjected to P-deficient seasonally. Mixed layer depth (an index of phosphate availability) is the major factor influencing the variation of bulk APA and specific APA (124~1,253 nmol mg C-1 h-1), based on multiple linear regression analysis. Size-fractionated APA assays showed that picoplankton (size 0.2~3 um) contributed most of the bulk APA in the system. In addition, single-cell APA detected by enzyme-labeled fluorescence (ELF) assay indicated that heterotrophic bacteria are the major contributors of APA. Thus, we can infer that bacteria play an important role in accelerating P-cycle within P-deficient systems. Light/nutrient manipulation bioassays showed that bacterial growth was directly controlled by phosphate, while picocyanobacterial growth is controlled by light and can out-compete bacteria under P-limited condition with the aid of light. Further analysis revealed that the strength of summer typhoon is a factor responsible for the inter-annual variability of bulk and specific APA. APA study demonstrated the episodic events (e.g. strong typhoon and extreme precipitation) had significant influence on APA variability in sub-tropical to tropical aquatic ecosystems. Hence, the results herein will allow future studies on monitoring typhoon disturbance (intensity and frequency) as well as the APA of plankton during summer-to-autumn in subtropical systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JHyd..398...65C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JHyd..398...65C"><span id="translatedtitle"><span class="hlt">Downscaling</span> climate variability associated with quasi-periodic climate signals: A new statistical approach using MSSA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cañón, Julio; Domínguez, Francina; Valdés, Juan B.</p> <p>2011-02-01</p> <p>SummaryA statistical method is introduced to <span class="hlt">downscale</span> hydroclimatic variables while incorporating the variability associated with quasi-periodic global climate signals. The method extracts statistical information of distributed variables from historic time series available at high resolution and uses Multichannel Singular Spectrum Analysis (MSSA) to reconstruct, on a cell-by-cell basis, specific frequency signatures associated with both the variable at a coarse scale and the global climate signals. Historical information is divided in two sets: a reconstruction set to identify the dominant modes of variability of the series for each cell and a validation set to compare the <span class="hlt">downscaling</span> relative to the observed patterns. After validation, the coarse projections from Global Climate Models (GCMs) are disaggregated to higher spatial resolutions by using an iterative gap-filling MSSA algorithm to <span class="hlt">downscale</span> the projected values of the variable, using the distributed series statistics and the MSSA analysis. The method is data adaptive and useful for <span class="hlt">downscaling</span> short-term forecasts as well as long-term climate projections. The method is applied to the <span class="hlt">downscaling</span> of temperature and precipitation from observed records and GCM projections over a region located in the US Southwest, taking into account the seasonal variability associated with ENSO.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.6336K&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.6336K&link_type=ABSTRACT"><span id="translatedtitle">The role of observational reference data for climate <span class="hlt">downscaling</span>: Insights from the VALUE COST Action</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kotlarski, Sven; Gutiérrez, José M.; Boberg, Fredrik; Bosshard, Thomas; Cardoso, Rita M.; Herrera, Sixto; Maraun, Douglas; Mezghani, Abdelkader; Pagé, Christian; Räty, Olle; Stepanek, Petr; Soares, Pedro M. M.; Szabo, Peter</p> <p>2016-04-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of <span class="hlt">downscaling</span> methods. Such assessments can be expected to crucially depend on the existence of accurate and reliable observational reference data. In dynamical <span class="hlt">downscaling</span>, observational data can influence model development itself and, later on, model evaluation, parameter calibration and added value assessment. In empirical-statistical <span class="hlt">downscaling</span>, observations serve as predictand data and directly influence model calibration with corresponding effects on <span class="hlt">downscaled</span> climate change projections. We here present a comprehensive assessment of the influence of uncertainties in observational reference data and of scale-related issues on several of the above-mentioned aspects. First, temperature and precipitation characteristics as simulated by a set of reanalysis-driven EURO-CORDEX RCM experiments are validated against three different gridded reference data products, namely (1) the EOBS dataset (2) the recently developed EURO4M-MESAN regional re-analysis, and (3) several national high-resolution and quality-controlled gridded datasets that recently became available. The analysis reveals a considerable influence of the choice of the reference data on the evaluation results, especially for precipitation. It is also illustrated how differences between the reference data sets influence the ranking of RCMs according to a comprehensive set of performance measures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26938544','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26938544"><span id="translatedtitle">Evaluating the Appropriateness of <span class="hlt">Downscaled</span> Climate Information for Projecting Risks of Salmonella.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Guentchev, Galina S; Rood, Richard B; Ammann, Caspar M; Barsugli, Joseph J; Ebi, Kristie; Berrocal, Veronica; O'Neill, Marie S; Gronlund, Carina J; Vigh, Jonathan L; Koziol, Ben; Cinquini, Luca</p> <p>2016-03-01</p> <p>Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be <span class="hlt">downscaled</span> to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically <span class="hlt">downscaled</span> GCM simulations for 1971-2000--a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) <span class="hlt">downscaling</span> methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically <span class="hlt">downscaled</span> data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of <span class="hlt">downscaled</span> climate data and the potential for misinterpretation of future estimates of Salmonella infections. PMID:26938544</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFMNH51C1906N&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFMNH51C1906N&link_type=ABSTRACT"><span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> of Typhoon Vera (1959) and related Storm Surge based on JRA-55 Reanalysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ninomiya, J.; Takemi, T.; Mori, N.; Shibutani, Y.; Kim, S.</p> <p>2015-12-01</p> <p>Typhoon Vera in 1959 is historical extreme typhoon that caused severest typhoon damage mainly due to the storm surge up to 389 cm in Japan. Vera developed 895 hPa on offshore and landed with 929.2 hPa. There are many studies of the dynamical <span class="hlt">downscaling</span> of Vera but it is difficult to simulate accurately because of the lack of the accuracy of global reanalysis data. This study carried out dynamical <span class="hlt">downscaling</span> experiment of Vera using WRF <span class="hlt">downscaling</span> forced by JRA-55 that are latest atmospheric model and reanalysis data. In this study, the reproducibility of five global reanalysis data for Typhoon Vera were compered. Comparison shows that reanalysis data doesn't have strong typhoon information except for JRA-55, so that <span class="hlt">downscaling</span> with conventional reanalysis data goes wrong. The dynamical <span class="hlt">downscaling</span> method for storm surge is studied very much (e.g. choice of physical model, nudging, 4D-VAR, bogus and so on). In this study, domain size and resolution of the coarse domain were considered. The coarse domain size influences the typhoon route and central pressure, and larger domain restrains the typhoon strength. The results of simulations with different domain size show that the threshold of developing restrain is whether the coarse domain fully includes the area of wind speed more than 15 m/s around the typhoon. The results of simulations with different resolution show that the resolution doesn't affect the typhoon route, and higher resolution gives stronger typhoon simulation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JESS..tmp...90A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JESS..tmp...90A"><span id="translatedtitle">Multilayer perceptron neural network for <span class="hlt">downscaling</span> rainfall in arid region: A case study of Baluchistan, Pakistan</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ahmed, Kamal; Shahid, Shamsuddin; Haroon, Sobri Bin; Xiao-jun, Wang</p> <p>2015-08-01</p> <p><span class="hlt">Downscaling</span> rainfall in an arid region is much challenging compared to wet region due to erratic and infrequent behaviour of rainfall in the arid region. The complexity is further aggregated due to scarcity of data in such regions. A multilayer perceptron (MLP) neural network has been proposed in the present study for the <span class="hlt">downscaling</span> of rainfall in the data scarce arid region of Baluchistan province of Pakistan, which is considered as one of the most vulnerable areas of Pakistan to climate change. The National Center for Environmental Prediction (NCEP) reanalysis datasets from 20 grid points surrounding the study area were used to select the predictors using principal component analysis. Monthly rainfall data for the time periods 1961-1990 and 1991-2001 were used for the calibration and validation of the MLP model, respectively. The performance of the model was assessed using various statistics including mean, variance, quartiles, root mean square error (RMSE), mean bias error (MBE), coefficient of determination (R 2) and Nash-Sutcliffe efficiency (NSE). Comparisons of mean monthly time series of observed and <span class="hlt">downscaled</span> rainfall showed good agreement during both calibration and validation periods, while the <span class="hlt">downscaling</span> model was found to underpredict rainfall variance in both periods. Other statistical parameters also revealed good agreement between observed and <span class="hlt">downscaled</span> rainfall during both calibration and validation periods in most of the stations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4808930','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4808930"><span id="translatedtitle">Evaluating the Appropriateness of <span class="hlt">Downscaled</span> Climate Information for Projecting Risks of Salmonella</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Guentchev, Galina S.; Rood, Richard B.; Ammann, Caspar M.; Barsugli, Joseph J.; Ebi, Kristie; Berrocal, Veronica; O’Neill, Marie S.; Gronlund, Carina J.; Vigh, Jonathan L.; Koziol, Ben; Cinquini, Luca</p> <p>2016-01-01</p> <p>Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be <span class="hlt">downscaled</span> to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically <span class="hlt">downscaled</span> GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) <span class="hlt">downscaling</span> methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically <span class="hlt">downscaled</span> data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of <span class="hlt">downscaled</span> climate data and the potential for misinterpretation of future estimates of Salmonella infections. PMID:26938544</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25751882','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25751882"><span id="translatedtitle">Layered <span class="hlt">Ensemble</span> Architecture for Time Series Forecasting.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin</p> <p>2016-01-01</p> <p>Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered <span class="hlt">ensemble</span> architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an <span class="hlt">ensemble</span> of multilayer perceptron (MLP) networks. While the first <span class="hlt">ensemble</span> layer tries to find an appropriate lag, the second <span class="hlt">ensemble</span> layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an <span class="hlt">ensemble</span>. LEA trains different networks in the <span class="hlt">ensemble</span> by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the <span class="hlt">ensemble</span>. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other <span class="hlt">ensemble</span> and nonensemble methods. PMID:25751882</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=guitar&pg=7&id=EJ430552','ERIC'); return false;" href="http://eric.ed.gov/?q=guitar&pg=7&id=EJ430552"><span id="translatedtitle">Fine-Tuning Your <span class="hlt">Ensemble</span>'s Jazz Style.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Garcia, Antonio J.</p> <p>1991-01-01</p> <p>Proposes instructional strategies for directors of jazz groups, including guidelines for developing of skills necessary for good performance. Includes effective methods for positive changes in <span class="hlt">ensemble</span> style. Addresses jazz group problems such as beat, tempo, staying in tune, wind power, and solo/<span class="hlt">ensemble</span> lines. Discusses percussionists, bassists,…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1093136','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/1093136"><span id="translatedtitle">Image Change Detection via <span class="hlt">Ensemble</span> Learning</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Martin, Benjamin W; Vatsavai, Raju</p> <p>2013-01-01</p> <p>The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work, we explore the use of <span class="hlt">ensemble</span> learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. <span class="hlt">Ensemble</span> learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the <span class="hlt">ensemble</span>. The strength of the <span class="hlt">ensemble</span> lies in the fact that the individual classifiers in the <span class="hlt">ensemble</span> create a mixture of experts in which the final classification made by the <span class="hlt">ensemble</span> classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of <span class="hlt">ensemble</span> learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the <span class="hlt">ensemble</span> method has approximately 11.5% higher change detection accuracy than an individual classifier.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4183303','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4183303"><span id="translatedtitle">Visual stimuli recruit intrinsically generated cortical <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Miller, Jae-eun Kang; Ayzenshtat, Inbal; Carrillo-Reid, Luis; Yuste, Rafael</p> <p>2014-01-01</p> <p>The cortical microcircuit is built with recurrent excitatory connections, and it has long been suggested that the purpose of this design is to enable intrinsically driven reverberating activity. To understand the dynamics of neocortical intrinsic activity better, we performed two-photon calcium imaging of populations of neurons from the primary visual cortex of awake mice during visual stimulation and spontaneous activity. In both conditions, cortical activity is dominated by coactive groups of neurons, forming <span class="hlt">ensembles</span> whose activation cannot be explained by the independent firing properties of their contributing neurons, considered in isolation. Moreover, individual neurons flexibly join multiple <span class="hlt">ensembles</span>, vastly expanding the encoding potential of the circuit. Intriguingly, the same coactive <span class="hlt">ensembles</span> can repeat spontaneously and in response to visual stimuli, indicating that stimulus-evoked responses arise from activating these intrinsic building blocks. Although the spatial properties of stimulus-driven and spontaneous <span class="hlt">ensembles</span> are similar, spontaneous <span class="hlt">ensembles</span> are active at random intervals, whereas visually evoked <span class="hlt">ensembles</span> are time-locked to stimuli. We conclude that neuronal <span class="hlt">ensembles</span>, built by the coactivation of flexible groups of neurons, are emergent functional units of cortical activity and propose that visual stimuli recruit intrinsically generated <span class="hlt">ensembles</span> to represent visual attributes. PMID:25201983</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016APS..MARL48015S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016APS..MARL48015S"><span id="translatedtitle">Characterizing <span class="hlt">Ensembles</span> of Superconducting Qubits</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sears, Adam; Birenbaum, Jeff; Hover, David; Rosenberg, Danna; Weber, Steven; Yoder, Jonilyn L.; Kerman, Jamie; Gustavsson, Simon; Kamal, Archana; Yan, Fei; Oliver, William</p> <p></p> <p>We investigate <span class="hlt">ensembles</span> of up to 48 superconducting qubits embedded within a superconducting cavity. Such arrays of qubits have been proposed for the experimental study of Ising Hamiltonians, and efficient methods to characterize and calibrate these types of systems are still under development. Here we leverage high qubit coherence (> 70 μs) to characterize individual devices as well as qubit-qubit interactions, utilizing the common resonator mode for a joint readout. This research was funded by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) under Air Force Contract No. FA8721-05-C-0002. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the US Government.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016APS..DMP.K1077G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016APS..DMP.K1077G"><span id="translatedtitle">Rydberg <span class="hlt">ensemble</span> based CNOTN gates using STIRAP</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gujarati, Tanvi; Duan, Luming</p> <p>2016-05-01</p> <p>Schemes for implementation of CNOT gates in atomic <span class="hlt">ensembles</span> are important for realization of quantum computing. We present here a theoretical scheme of a CNOTN gate with an <span class="hlt">ensemble</span> of three-level atoms in the lambda configuration and a single two-level control atom. We work in the regime of Rydberg blockade for the <span class="hlt">ensemble</span> atoms due to excitation of the Rydberg control atom. It is shown that using STIRAP, atoms from one ground state of the <span class="hlt">ensemble</span> can be adiabatically transferred to the other ground state, depending on the state of the control atom. A thorough analysis of adiabatic conditions for this scheme and the influence of the radiative decay is provided. We show that the CNOTN process is immune to the decay rate of the excited level in <span class="hlt">ensemble</span> atoms. This work is supported by the ARL, the IARPA LogiQ program, and the AFOSR MURI program.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4624683','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4624683"><span id="translatedtitle">ENCORE: Software for Quantitative <span class="hlt">Ensemble</span> Comparison</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tiberti, Matteo; Papaleo, Elena; Bengtsen, Tone; Boomsma, Wouter; Lindorff-Larsen, Kresten</p> <p>2015-01-01</p> <p>There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or <span class="hlt">ensemble</span> refinement approaches have provided conformational <span class="hlt">ensembles</span> that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural <span class="hlt">ensembles</span> in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational <span class="hlt">ensembles</span> generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for <span class="hlt">ensemble</span> comparison, that each can be used to quantify the similarity between conformational <span class="hlt">ensembles</span> by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing <span class="hlt">ensembles</span> generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural <span class="hlt">ensembles</span> refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the <span class="hlt">ensembles</span>. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads <span class="hlt">ensemble</span> data in many common formats, and can work with large</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26505632','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26505632"><span id="translatedtitle">ENCORE: Software for Quantitative <span class="hlt">Ensemble</span> Comparison.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tiberti, Matteo; Papaleo, Elena; Bengtsen, Tone; Boomsma, Wouter; Lindorff-Larsen, Kresten</p> <p>2015-10-01</p> <p>There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or <span class="hlt">ensemble</span> refinement approaches have provided conformational <span class="hlt">ensembles</span> that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural <span class="hlt">ensembles</span> in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational <span class="hlt">ensembles</span> generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for <span class="hlt">ensemble</span> comparison, that each can be used to quantify the similarity between conformational <span class="hlt">ensembles</span> by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing <span class="hlt">ensembles</span> generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural <span class="hlt">ensembles</span> refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the <span class="hlt">ensembles</span>. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads <span class="hlt">ensemble</span> data in many common formats, and can work with large</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1817117S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1817117S&link_type=ABSTRACT"><span id="translatedtitle">ECOMS-UDG. A User-friendly Data access Gateway to seasonal forecast datasets allowing R-based remote data access, visualization-validation, bias correction and <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Santiago Cofiño, Antonio; Gutiérrez, José Manuel; Fernández, Jesús; Bedia, Joaquín; Vega, Manuel; Herrera, Sixto; Frías, María Dolores; Iturbide, Maialen; Magariño, Maria Eugenia; Manzanas, Rodrigo</p> <p>2016-04-01</p> <p>Seasonal forecasting data from state-or-the-art forecasting systems (e.g. NCEP/CFSv2 or ECMWF/System4) can be obtained directly from the data providers, but the resulting formats, aggregations and vocabularies may not be homogeneous across datasets, requiring some post processing. Moreover, different data policies hold for the various datasets - which are freely available only in some cases - and therefore data access may not be straightforward. Thus, obtaining seasonal climate forecast data is typically a time consuming task. The ECOMS-UDG (User Data Gateway for the ECOMS initiative) has been developed building in the ​User Data Gateway (UDG, http://meteo.unican.es/udg-wiki) in order to facilitate seasonal (re)forecast data access to end users. The required variables have been downloaded from data providers and stored locally in a THREDDS data server implementing fine-grained user authorization. Thus, users can efficiently retrieve the subsets that best suits their particular research aims (typically surface variables for certain regions, periods and/or <span class="hlt">ensemble</span> members) from a large volume of information. Moreover, an interface layer developed in R allows remote data exploration, access (including homogenization, collocation and sub-setting) and the integration of ECOMS-UDG with a number of R packages developed in the framework of ECOMS for forecast visualization, validation, bias correction and <span class="hlt">downscaling</span>. This unique framework oriented to climate services allows users from different sectors to easily access seasonal forecasting data (typically surface variables), calibrating and/or <span class="hlt">downscaling</span> (using upper air information from large scale predictors) this data at local level and validating the different results (using observations). The documentation delivered with the packages includes worked examples showing that the whole visualization, bias correction and/or <span class="hlt">downscaling</span> tasks requires only a few lines of code and are fully reproducible and adaptable to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRD..120.7316S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..120.7316S"><span id="translatedtitle">Toward a seasonal precipitation prediction system for West Africa: Performance of CFSv2 and high-resolution dynamical <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Siegmund, Jonatan; Bliefernicht, Jan; Laux, Patrick; Kunstmann, Harald</p> <p>2015-08-01</p> <p>Seasonal precipitation forecasts are important sources of information for early drought and famine warnings in West Africa. This study presents an assessment of the monthly precipitation forecast of the Climate Forecast System version 2 (CFSv2) for three agroecological zones (Sudan-Sahel, Sudan, and Guinean zone) of the Volta Basin. The CFSv2 performance is evaluated for the Sahel drought 1983 and for all August months of the reforecast period (1982-2009) with lead times up to 8 months using a quantile-quantile transformation for bias correction. In addition, an operational experiment is performed for the rainy season 2013 to analyze the performance of a dynamical <span class="hlt">downscaling</span> approach for this region. Twenty-two CFSv2 <span class="hlt">ensemble</span> members initialized in February 2013 are transferred to a resolution of 10 km × 10 km using the Weather and Research Forecasting (WRF) model. Since the uncorrected CFSv2 precipitation forecasts are characterized by a high uncertainty (up to 175% of the observed variability), the quantile-quantile transformation can clearly reduce this overestimation with the potential to provide skillful and valuable early warnings of precipitation deficits and excess up to 6 months in ahead, particularly for the Sudan-Sahel zone. The operational experiment illustrates that CFSv2-WRF can reduce the CFSv2 uncertainty (up to 69%) for monthly precipitation and the onset of the rainy season but has still strong deficits regarding the northward progression of the rain belt. Further studies are necessary for a more robust assessment of the techniques applied in this study to confirm these promising outcomes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ClDy...40.1141B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ClDy...40.1141B"><span id="translatedtitle"><span class="hlt">Downscaling</span> large-scale climate variability using a regional climate model: the case of ENSO over Southern Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Boulard, Damien; Pohl, Benjamin; Crétat, Julien; Vigaud, Nicolas; Pham-Xuan, Thanh</p> <p>2013-03-01</p> <p>This study documents methodological issues arising when <span class="hlt">downscaling</span> modes of large-scale atmospheric variability with a regional climate model, over a remote region that is yet under their influence. The retained case study is El Niño Southern Oscillation and its impacts on Southern Africa and the South West Indian Ocean. Regional simulations are performed with WRF model, driven laterally by ERA40 reanalyses over the 1971-1998 period. We document the sensitivity of simulated climate variability to the model physics, the constraint of relaxing the model solutions towards reanalyses, the size of the relaxation buffer zone towards the lateral forcings and the forcing fields through ERA-Interim driven simulations. The model's internal variability is quantified using 15-member <span class="hlt">ensemble</span> simulations for seasons of interest, single 30-year integrations appearing as inappropriate to investigate the simulated interannual variability properly. The incidence of SST prescription is also assessed through additional integrations using a simple ocean mixed-layer model. Results show a limited skill of the model to reproduce the seasonal droughts associated with El Niño conditions. The model deficiencies are found to result from biased atmospheric forcings and/or biased response to these forcings, whatever the physical package retained. In contrast, regional SST forcing over adjacent oceans favor realistic rainfall anomalies over the continent, although their amplitude remains too weak. These results confirm the significant contribution of nearby ocean SST to the regional effects of ENSO, but also illustrate that regionalizing large-scale climate variability can be a demanding exercise.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002EGSGA..27.1715S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002EGSGA..27.1715S&link_type=ABSTRACT"><span id="translatedtitle">On The Choce of The Temporal Aggregation Level For Statistical <span class="hlt">Downscaling</span> For Precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shabalova, M. V.; Buishand, T. A.</p> <p></p> <p>Statistical <span class="hlt">downscaling</span> methods are used in hydrological applications to translate cli- mate change scenarios generated by GCMs to the finer spatial grids. In these meth- ods the present-day statistical relationships between the local precipitation and atmo- spheric predictor variables are used to predict the change in precipitation given the changes in predictors. The predictors thus should be realistically modelled by GCMs and should fully represent the climate change signal. The proper choice of the tem- poral aggregation level of the data in statistical relationships is crucial. An inadequate choice may exclude vital for climate change predictors as statistically not significant. The chosen aggregation level also affects the sensitivity of the <span class="hlt">downscaling</span> model to changes in predictors, as well as the model's efficiency. The results from various <span class="hlt">downscaling</span> models for daily and monthly rainfall occurrence and amount developed in the class of generalised linear models for a number of stations in the River Rhine basin are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GMD.....8.1085M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GMD.....8.1085M"><span id="translatedtitle">Technical challenges and solutions in representing lakes when using WRF in <span class="hlt">downscaling</span> applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mallard, M. S.; Nolte, C. G.; Spero, T. L.; Bullock, O. R.; Alapaty, K.; Herwehe, J. A.; Gula, J.; Bowden, J. H.</p> <p>2015-04-01</p> <p>The Weather Research and Forecasting (WRF) model is commonly used to make high-resolution future projections of regional climate by <span class="hlt">downscaling</span> global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional <span class="hlt">downscaled</span> fields, lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for <span class="hlt">downscaling</span> simulations are presented and contrasted.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.4703K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.4703K"><span id="translatedtitle">Future changes in African temperature and precipitation in an <span class="hlt">ensemble</span> of Africa-CORDEX regional climate model simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kjellström, Erik; Nikulin, Grigory; Gbobaniyi, Emiola; Jones, Colin</p> <p>2013-04-01</p> <p>In this study we investigate possible changes in temperature and precipitation on a regional scale over Africa from 1961 to 2100. We use data from two <span class="hlt">ensembles</span> of climate simulations, one global and one regional, over the Africa-CORDEX domain. The global <span class="hlt">ensemble</span> includes eight coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CNRM-CM5, HadGEM2-ES, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. In the regional <span class="hlt">ensemble</span> all 8 AOGCMs are <span class="hlt">downscaled</span> at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Two forcing scenarios are considered, RCP 4.5 and 8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, we investigate the benefit of the higher horizontal resolution in RCA4 by comparing the results to the coarser driving AOGCM data. The significance of the results is investigated by comparing to i) the model simulated natural variability, and, ii) the biases in the control period. Results dealing with changes in the seasonal cycle of temperature and precipitation are presented. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=277355','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=277355"><span id="translatedtitle">Assessment of the scale effect on statistical <span class="hlt">downscaling</span> quality at a station scale using a weather generator-based model</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>The resolution of General Circulation Models (GCMs) is too coarse to assess the fine scale or site-specific impacts of climate change. <span class="hlt">Downscaling</span> approaches including dynamical and statistical <span class="hlt">downscaling</span> have been developed to meet this requirement. As the resolution of climate model increases, it...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.A31D0934K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.A31D0934K"><span id="translatedtitle">California Reanalysis <span class="hlt">Downscaling</span> at 10km: Implication to regional reanalysis over south Asia.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kanamaru, H.; Kanamitsu, M.</p> <p>2006-12-01</p> <p>We have completed 57 year dynamical <span class="hlt">downscaling</span> of NCEP/NCAR Reanalysis over California at 10km resolution (CaRD10) for the purpose of regional climate research and application. The unique feature of the <span class="hlt">downscaling</span> method by the Regional Spectral Model is the use of Scale Selective Bias Correction (Kanamaru and Kanamitsu 2006a) which preserves the reanalysis of the scale greater than 1000km within the regional domain. The detailed validation of the analysis with station observation and comparison with the North American Regional Reanalysis (NARR) have been performed and submitted for publication (Kanamitsu and Kanamaru, 2006; Kanamaru and Kanamitsu, 2006b). The study indicated that the CaRD10 generally produce analysis better fit with observation than NARR over land, due to higher spatial resolution. Precipitation suffers from wet bias, but their temporal variation agrees well with observation on time scales ranging from hourly to decadal. Comparison of moisture budget with NARR indicated that large budget residual exists for both analyses, which makes it difficult to use them in some hydrological studies. High resolution <span class="hlt">downscaling</span> well simulates large-scale forced meso-scale features, such as Catalina eddies and Santa Ana events. The quality of the simulations strongly depends on the model resolution. These results suggest that 1) the spatial resolution as high as 10km is desirable particularly for hydrological application, 2) our <span class="hlt">downscaling</span> technique is an economical alternative to full regional data assimilation. When combined with assimilation of observed precipitation, it has a potential of producing analysis as good and as useful as regional data assimilation product and 3) regional data assimilation technique is not mature enough to fully utilize surface observation where spatial inhomogeneity dominates. Thus, regional data assimilation near the surface tends to give higher weight to model forecast guess and the resulting analysis becomes very similar to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AtmRe.164...27M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AtmRe.164...27M"><span id="translatedtitle">Bayesian Inference aided analog <span class="hlt">downscaling</span> for near-surface winds in complex terrain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Manor, Alon; Berkovic, Sigalit</p> <p>2015-10-01</p> <p>Assessing atmospheric boundary layer flows in complex terrain for short-range real-time applications demands fast and reliable <span class="hlt">downscaling</span> from coarser-resolution meteorological data to the relevant scale. An ideal statistical <span class="hlt">downscaling</span> numerical experiment was performed for surface winds above complex terrain in Israel's northern Negev desert region. Dynamical <span class="hlt">downscaling</span> have been performed by the WRF model to create a historical database by the following two sets. The first set used 5 nested domains from 40.5 km to 0.5 km. The second set used 3 nested domains ranging from 40.5 km to 4.5 km. The 4.5 km data (stage 2) was defined as predictors while data on 0.5 km (stage 1) served as predictands for statistical <span class="hlt">downscaling</span>. Two statistical <span class="hlt">downscaling</span> algorithms: minimal distance analog and a Bayesian inference aided analog (hereafter Bayesian algorithm) were tested by the above data. Unlike most analog algorithms, the Bayesian algorithm refers to the probability to get the best analog instead of the minimal differences between predictands. The comparison of the two algorithms shows that the Bayesian approach yields improved results. The Bayesian algorithm reproduces the 0.5 km resolution dynamically <span class="hlt">downscaled</span> surface winds with an average absolute direction difference of 43 and 20 for calm winds and moderate/strong winds respectively. Its average wind speed error is ~ 1.1 ms- 1. ~ 40 days are sufficient to create a representative database. Given the database, the procedure is extremely fast (a few seconds) and easy to operate, which makes it suitable for real-time non-expert fast-response applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC51A0740P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC51A0740P"><span id="translatedtitle">Weather Typing Statistical <span class="hlt">downscaling</span> with dsclim: diagnostics, and uncertainties in data provision for the impact community</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Page, C.; Sanchez, E.; Terray, L.</p> <p>2010-12-01</p> <p>Recently, an innovative statistical methodology has been developed to <span class="hlt">downscale</span> climate simulations in France using a weather-typing approach (Boé et al., 2006), and further developed (Pagé et al., 2009). It has been used to <span class="hlt">downscale</span> 15 CMIP3 models as well as 7 Météo-France ARPEGE climate numerical model (Salas et al., 2005) simulations. In the framework of the ANR-SCAMPEI project, dsclim has been carefully configured to be able to <span class="hlt">downscale</span> climate simulations over France mountainous areas. Some new diagnostics have been developed to analyze the performance of the methodology and its configuration. In parallel, several projects to make these <span class="hlt">downscaled</span> climate scenarios available to the impact community are going on, notably GICC-DRIAS and EU-IS-ENES. In the context of IS-ENES, several national Use Cases have been developed to formalize the steps needed to provide climate scenarios suitable for the impact community starting from the global climate scenarios data, and also taking into account the uncertainties. References Pagé, C., L. Terray et J. Boé, 2009: dsclim: A software package to <span class="hlt">downscale</span> climate scenarios at regional scale using a weather-typing based statistical methodology. Technical Report TR/CMGC/09/21, CERFACS, Toulouse, France. Boé, J., L. Terray, F. Habets, et E. Martin, 2006: A simple statistical-dynamical <span class="hlt">downscaling</span> scheme based on weather types and conditional resampling. J. Geophys. Res., 111, D21106. Salas y Mélia, D., F. Chauvin, M. Déqué, H. Douville, J.-F. Guérémy, P. Marquet, S. Planton, J.-F. Royer, and S. Tyteca, 2005: Description and validation of CNRM-CM3 global coupled climate model. Technical report, Centre national de recherches météorologiques, Groupe de Météorologie de Grande Echelle et Climat, Météo-France.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1814396W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1814396W&link_type=ABSTRACT"><span id="translatedtitle">Validation of spatial variability in <span class="hlt">downscaling</span> results from the VALUE perfect predictor experiment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Widmann, Martin; Bedia, Joaquin; Gutiérrez, Jose Manuel; Maraun, Douglas; Huth, Radan; Fischer, Andreas; Keller, Denise; Hertig, Elke; Vrac, Mathieu; Wibig, Joanna; Pagé, Christian; Cardoso, Rita M.; Soares, Pedro MM; Bosshard, Thomas; Casado, Maria Jesus; Ramos, Petra</p> <p>2016-04-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research. Within VALUE a systematic validation framework to enable the assessment and comparison of both dynamical and statistical <span class="hlt">downscaling</span> methods has been developed. In the first validation experiment the <span class="hlt">downscaling</span> methods are validated in a setup with perfect predictors taken from the ERA-interim reanalysis for the period 1997 - 2008. This allows to investigate the isolated skill of <span class="hlt">downscaling</span> methods without further error contributions from the large-scale predictors. One aspect of the validation is the representation of spatial variability. As part of the VALUE validation we have compared various properties of the spatial variability of <span class="hlt">downscaled</span> daily temperature and precipitation with the corresponding properties in observations. We have used two test validation datasets, one European-wide set of 86 stations, and one higher-density network of 50 stations in Germany. Here we present results based on three approaches, namely the analysis of i.) correlation matrices, ii.) pairwise joint threshold exceedances, and iii.) regions of similar variability. We summarise the information contained in correlation matrices by calculating the dependence of the correlations on distance and deriving decorrelation lengths, as well as by determining the independent degrees of freedom. Probabilities for joint threshold exceedances and (where appropriate) non-exceedances are calculated for various user-relevant thresholds related for instance to extreme precipitation or frost and heat days. The dependence of these probabilities on distance is again characterised by calculating typical length scales that separate dependent from independent exceedances. Regionalisation is based on rotated Principal Component Analysis. The results indicate which <span class="hlt">downscaling</span> methods are preferable if the dependency of variability at different locations is relevant for the user.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150010221','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150010221"><span id="translatedtitle"><span class="hlt">Downscaling</span> Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.</p> <p>2013-01-01</p> <p>The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for <span class="hlt">downscaling</span> and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for <span class="hlt">downscaling</span> satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the <span class="hlt">downscaling</span> problem as a discrete inverse problem and solve it via a regularized variational approach (variational <span class="hlt">downscaling</span>) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse <span class="hlt">downscaling</span> methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.A21G0182F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A21G0182F"><span id="translatedtitle">Comparison of Grid Nudging and Spectral Nudging Techniques for Dynamical Climate <span class="hlt">Downscaling</span> within the WRF Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fan, X.; Chen, L.; Ma, Z.</p> <p>2010-12-01</p> <p>Climate <span class="hlt">downscaling</span> has been an active research and application area in the past several decades focusing on regional climate studies. Dynamical <span class="hlt">downscaling</span>, in addition to statistical methods, has been widely used in <span class="hlt">downscaling</span> as the advanced modern numerical weather and regional climate models emerge. The utilization of numerical models enables that a full set of climate variables are generated in the process of <span class="hlt">downscaling</span>, which are dynamically consistent due to the constraints of physical laws. While we are generating high resolution regional climate, the large scale climate patterns should be retained. To serve this purpose, nudging techniques, including grid analysis nudging and spectral nudging, have been used in different models. There are studies demonstrating the benefit and advantages of each nudging technique; however, the results are sensitive to many factors such as nudging coefficients and the amount of information to nudge to, and thus the conclusions are controversy. While in a companion work of developing approaches for quantitative assessment of the <span class="hlt">downscaled</span> climate, in this study, the two nudging techniques are under extensive experiments in the Weather Research and Forecasting (WRF) model. Using the same model provides fair comparability. Applying the quantitative assessments provides objectiveness of comparison. Three types of <span class="hlt">downscaling</span> experiments were performed for one month of choice. The first type is serving as a base whereas the large scale information is communicated through lateral boundary conditions only; the second is using the grid analysis nudging; and the third is using spectral nudging. Emphases are given to the experiments of different nudging coefficients and nudging to different variables in the grid analysis nudging; while in spectral nudging, we focus on testing the nudging coefficients, different wave numbers on different model levels to nudge.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..510..182L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..510..182L"><span id="translatedtitle">Nonparametric statistical temporal <span class="hlt">downscaling</span> of daily precipitation to hourly precipitation and implications for climate change scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, Taesam; Jeong, Changsam</p> <p>2014-03-01</p> <p>Hydro-meteorological time series on finer temporal scales, such as hourly, are essential for assessing the hydrological effects of land use or climate change on medium and small watersheds. However, these time series are, in general, available at no finer than daily time intervals. An alternative method of obtaining finer time series is temporal <span class="hlt">downscaling</span> of daily time series to hourly time series. In the current study, a temporal <span class="hlt">downscaling</span> model that combines a nonparametric stochastic simulation approach with a genetic algorithm is proposed. The proposed model was applied to Jinju station in South Korea for a historical time period to validate the model performance. The results revealed that the proposed model preserves the key statistics (i.e., the mean, standard deviation, skewness, lag-1 correlation, and maximum) of the historical hourly precipitation data. In addition, the occurrence and transition probabilities are well preserved in the <span class="hlt">downscaled</span> hourly precipitation data. Furthermore, the RCP 4.5 and RCP 8.5 climate scenarios for the Jinju station were also analyzed, revealing that the mean and the wet-hour probability (P1) significantly increased and the standard deviation and maximum slightly increased in these scenarios. The magnitude of the increase was greater in RCP 8.5 than RCP 4.5. Extreme events of different durations were also tested. The <span class="hlt">downscaled</span> hourly precipitation adequately reproduced the statistical behavior of the extremes of the historical hourly precipitation data for all durations considered. However, the inter-daily relation between the 1st hour of the present day and the last hour of the previous day was not preserved. Overall, the results demonstrated that the proposed temporal <span class="hlt">downscaling</span> model is a good alternative method for <span class="hlt">downscaling</span> simulated daily precipitation data from weather generators or RCM outputs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20150019486&hterms=wildlife+conservation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dwildlife%2Bconservation','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20150019486&hterms=wildlife+conservation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dwildlife%2Bconservation"><span id="translatedtitle">The Practitioner's Dilemma: How to Assess the Credibility of <span class="hlt">Downscaled</span> Climate Projections</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barsugli, Joseph J.; Guentchev, Galina; Horton, Radley M.; Wood, Andrew; Mearns, Lindo O.; Liang, Xin-Zhong; Winkler, Julia A.; Dixon, Keith; Hayhoe, Katharine; Rood, Richard B.; Goddard, Lisa; Ray, Andrea; Buja, Lawrence; Ammann, Caspar</p> <p>2013-01-01</p> <p>Suppose you are a city planner, regional water manager, or wildlife conservation specialist who is asked to include the potential impacts of climate variability and change in your risk management and planning efforts. What climate information would you use? The choice is often regional or local climate projections <span class="hlt">downscaled</span> from global climate models (GCMs; also known as general circulation models) to include detail at spatial and temporal scales that align with those of the decision problem. A few years ago this information was hard to come by. Now there is Web-based access to a proliferation of high-resolution climate projections derived with differing <span class="hlt">downscaling</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016GPC...144..129K&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016GPC...144..129K&link_type=ABSTRACT"><span id="translatedtitle">An evaluation of how <span class="hlt">downscaled</span> climate data represents historical precipitation characteristics beyond the means and variances</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kusangaya, Samuel; Toucher, Michele L. Warburton; van Garderen, Emma Archer; Jewitt, Graham P. W.</p> <p>2016-09-01</p> <p>Precipitation is the main driver of the hydrological cycle. For climate change impact analysis, use of <span class="hlt">downscaled</span> precipitation, amongst other factors, determines accuracy of modelled runoff. Precipitation is, however, considerably more difficult to model than temperature, largely due to its high spatial and temporal variability and its nonlinear nature. Due to such qualities of precipitation, a key challenge for water resources management is thus how to incorporate potentially significant but highly uncertain precipitation characteristics when modelling potential changes in climate for water resources management in order to support local management decisions. Research undertaken here was aimed at evaluating how <span class="hlt">downscaled</span> climate data represented the underlying historical precipitation characteristics beyond the means and variances. Using the uMngeni Catchment in KwaZulu-Natal, South Africa as a case study, the occurrence of rainfall, rainfall threshold events and wet dry sequence was analysed for current climate (1961-1999). The number of rain days with daily rainfall > 1 mm, > 5 mm, > 10 mm, > 20 mm and > 40 mm for each of the 10 selected climate models was, compared to the number of rain days at 15 rain stations. Results from graphical and statistical analysis indicated that on a monthly basis rain days are over estimated for all climate models. Seasonally, the number of rain days were overestimated in autumn and winter and underestimated in summer and spring. The overall conclusion was that despite the advancement in <span class="hlt">downscaling</span> and the improved spatial scale for a better representation of the climate variables, such as rainfall for use in hydrological impact studies, <span class="hlt">downscaled</span> rainfall data still does not simulate well some important rainfall characteristics, such as number of rain days and wet-dry sequences. This is particularly critical, since, whilst for climatologists, means and variances might be simulated well in <span class="hlt">downscaled</span> GCMs, for hydrologists</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EOSTr..94..424B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EOSTr..94..424B"><span id="translatedtitle">The Practitioner's Dilemma: How to Assess the Credibility of <span class="hlt">Downscaled</span> Climate Projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barsugli, Joseph J.; Guentchev, Galina; Horton, Radley M.; Wood, Andrew; Mearns, Linda O.; Liang, Xin-Zhong; Winkler, Julie A.; Dixon, Keith; Hayhoe, Katharine; Rood, Richard B.; Goddard, Lisa; Ray, Andrea; Buja, Lawrence; Ammann, Caspar</p> <p>2013-11-01</p> <p>Suppose you are a city planner, regional water manager, or wildlife conservation specialist who is asked to include the potential impacts of climate variability and change in your risk management and planning efforts. What climate information would you use? The choice is often regional or local climate projections <span class="hlt">downscaled</span> from global climate models (GCMs; also known as general circulation models) to include detail at spatial and temporal scales that align with those of the decision problem. A few years ago this information was hard to come by. Now there is Web-based access to a proliferation of high-resolution climate projections derived with differing <span class="hlt">downscaling</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H43A1168F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H43A1168F"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of Gridded Rainfall and Their Impacts on Hydrological Response Analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fu, G.; Charles, S. P.; Chiew, F. H.; Teng, J.; Frost, A. J.</p> <p>2011-12-01</p> <p>Water resource management and planning increasingly need to incorporate the effects of global climate change on regional climate variability in order to accurately assess future water supplies. Therefore future climate projections, particularly of rainfall, are of utmost interest to water resource management and water-users. General circulation models (GCMs) are the primary tool used to simulate present climate and project future climate. The outputs of GCMs are useful in understanding how future global climate responds to prescribed greenhouse gases emission scenarios. However GCMs do not provide realistic daily rainfall at scales below about 200 km, at which hydrological processes are typically assessed. Statistical <span class="hlt">downscaling</span> techniques have been developed to resolve the scale discrepancy between GCM climate change scenarios and the resolution required for hydrological impact assessment, based on the assumption that large-scale atmospheric conditions have a strong influence on local-scale weather. Gridded rainfall is important for a variety of scientific and engineering applications, including climate change detection, the evaluation of climate models, the parameterization of stochastic weather generators, as well as assessment of climate change impacts on regional hydrological regimes and water availability, whereas statistical <span class="hlt">downscaling</span> has predominantly provided daily rainfall series at the site (point) scale. The first part of the study explores the application of statistical <span class="hlt">downscaling</span> to gridded rainfall datasets using three methods: 1) statistically <span class="hlt">downscaling</span> to sites and then post-processing to interpolate to gridded rainfall; 2) treating each grid cell as an "observed" site for statistical <span class="hlt">downscaling</span> directly; and 3) treating each sub-catchment as an "observed" site and statistically <span class="hlt">downscaling</span> to sub-catchment averaged rainfall. The statistical <span class="hlt">downscaling</span> Nonhomogeneous Hidden Markov Model (NHMM), which models multi-site patterns of daily</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://dx.doi.org/10.1186/2192-1709-1-2','USGSPUBS'); return false;" href="http://dx.doi.org/10.1186/2192-1709-1-2"><span id="translatedtitle"><span class="hlt">Downscaling</span> future climate scenarios to fine scales for hydrologic and ecological modeling and analysis</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Flint, Lorraine E.; Flint, Alan L.</p> <p>2012-01-01</p> <p>The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the <span class="hlt">downscaling</span> while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale <span class="hlt">downscaling</span> to analyses of ecological processes influenced by topographic complexity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.A33A0210G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A33A0210G"><span id="translatedtitle">Performance of dynamical <span class="hlt">downscaling</span> for Colorado River basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gao, Y.; Zhu, C.; Lettenmaier, D. P.</p> <p>2009-12-01</p> <p> RCM results for current and future climate with inferred changes taken directly from the GCM via statistical <span class="hlt">downscaling</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.213M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.213M"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of wind fields for wind power applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mengelkamp, H.-T.; Huneke, S.; Geyer, J.</p> <p>2010-09-01</p> <p>Dynamical <span class="hlt">downscaling</span> of wind fields for wind power applications H.-T. Mengelkamp*,**, S. Huneke**, J, Geyer** *GKSS Research Center Geesthacht GmbH **anemos Gesellschaft für Umweltmeteorologie mbH Investments in wind power require information on the long-term mean wind potential and its temporal variations on daily to annual and decadal time scales. This information is rarely available at specific wind farm sites. Short-term on-site measurements usually are only performed over a 12 months period. These data have to be set into the long-term perspective through correlation to long-term consistent wind data sets. Preliminary wind information is often asked for to select favourable wind sites over regional and country wide scales. Lack of high-quality wind measurements at weather stations was the motivation to start high resolution wind field simulations The simulations are basically a refinement of global scale reanalysis data by means of high resolution simulations with an atmospheric mesoscale model using high-resolution terrain and land-use data. The 3-dimensional representation of the atmospheric state available every six hours at 2.5 degree resolution over the globe, known as NCAR/NCEP reanalysis data, forms the boundary conditions for continuous simulations with the non-hydrostatic atmospheric mesoscale model MM5. MM5 is nested in itself down to a horizontal resolution of 5 x 5 km². The simulation is performed for different European countries and covers the period 2000 to present and is continuously updated. Model variables are stored every 10 minutes for various heights. We have analysed the wind field primarily. The wind data set is consistent in space and time and provides information on the regional distribution of the long-term mean wind potential, the temporal variability of the wind potential, the vertical variation of the wind potential, and the temperature, and pressure distribution (air density). In the context of wind power these data are used </p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2990198','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2990198"><span id="translatedtitle">A Spatio-Temporal <span class="hlt">Downscaler</span> for Output From Numerical Models</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Berrocal, Veronica J.; Gelfand, Alan E.; Holland, David M.</p> <p>2010-01-01</p> <p>Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this article we propose a simple, fully model-based strategy to <span class="hlt">downscale</span> the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially-varying coefficients which are specified through a correlated spatial Gaussian process. As an example, we apply our method to ozone concentration data for the eastern U.S. and compare it to Bayesian melding (Fuentes and Raftery 2005) and ordinary kriging (Cressie 1993; Chilès and Delfiner 1999). Our results show that our method outperforms Bayesian melding in terms of computing speed and it is superior to both Bayesian melding and ordinary kriging in terms of predictive performance; predictions obtained with our method are better calibrated and predictive intervals have empirical coverage closer to the nominal values. Moreover, our model can be easily extended to accommodate for the temporal dimension. In this regard, we consider several spatio-temporal versions of the static model. We compare them using out-of-sample predictions of ozone concentration for the eastern U.S. for the period May 1–October 15, 2001. For the best choice, we present a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A41H0062U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A41H0062U"><span id="translatedtitle">Diamond-NICAM-SPRINTARS: <span class="hlt">downscaling</span> and simulation results</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Uchida, J.</p> <p>2012-12-01</p> <p>As a part of initiative "Research Program on Climate Change Adaptation" (RECCA) which investigates how predicted large-scale climate change may affect a local weather, and further examines possible atmospheric hazards that cities may encounter due to such a climate change, thus to guide policy makers on implementing new environmental measures, a "Development of Seamless Chemical AssimiLation System and its Application for Atmospheric Environmental Materials" (SALSA) project is funded by the Japanese Ministry of Education, Culture, Sports, Science and Technology and is focused on creating a regional (local) scale assimilation system that can accurately recreate and predict a transport of carbon dioxide and other air pollutants. In this study, a regional model of the next generation global cloud-resolving model NICAM (Non-hydrostatic ICosahedral Atmospheric Model) (Tomita and Satoh, 2004) is used and ran together with a transport model SPRINTARS (Spectral Radiation Transport Model for Aerosol Species) (Takemura et al, 2000) and a chemical transport model CHASER (Sudo et al, 2002) to simulate aerosols across urban cities (over a Kanto region including metropolitan Tokyo). The presentation will mainly be on a "Diamond-NICAM" (Figure 1), a regional climate model version of the global climate model NICAM, and its dynamical <span class="hlt">downscaling</span> methodologies. Originally, a global NICAM can be described as twenty identical equilateral triangular-shaped panels covering the entire globe where grid points are at the corners of those panels, and to increase a resolution (called a "global-level" in NICAM), additional points are added at the middle of existing two adjacent points, so a number of panels increases by fourfold with an increment of one global-level. On the other hand, a Diamond-NICAM only uses two of those initial triangular-shaped panels, thus only covers part of the globe. In addition, NICAM uses an adaptive mesh scheme and its grid size can gradually decrease, as the grid</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011180','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011180"><span id="translatedtitle">Hybrid Data Assimilation without <span class="hlt">Ensemble</span> Filtering</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Todling, Ricardo; Akkraoui, Amal El</p> <p>2014-01-01</p> <p>The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the <span class="hlt">ensemble</span> is generated using a square-root <span class="hlt">ensemble</span> Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member <span class="hlt">ensemble</span> solution close to the variational solution; we also found it necessary to re-center the members of the <span class="hlt">ensemble</span> about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the <span class="hlt">ensemble</span>. This led us to consider a hybrid strategy in which the members of the <span class="hlt">ensemble</span> are generated by simply converting the variational analysis to the resolution of the <span class="hlt">ensemble</span> and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1213118B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1213118B"><span id="translatedtitle">Long-range Prediction of climatic Change in the Eastern Seaboard of Thailand over the 21st Century using various <span class="hlt">Downscaling</span> Approaches</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bejranonda, Werapol; Koch, Manfred; Koontanakulvong, Sucharit</p> <p>2010-05-01</p> <p> the different scales of the hydrological (local to regional) and of the GCM (global), one is faced with the problem of '<span class="hlt">downscaling</span>' the coarse grid resolution output of the GCM to the fine grid of the hydrological model. Although there have been numerous <span class="hlt">downscaling</span> approaches proposed to that regard over the last decade, the jury is still out about the best method to use in a particular application. The focus here is on the <span class="hlt">downscaling</span> part of the investigation, i.e. the proper preparation of the GCM's output to serve as input, i.e. the driving force, to the hydrological model (which is not further discussed here). Daily <span class="hlt">ensembles</span> of climate variables computed by means of the CGCM3 model of the Canadian Climate Center which has a horizontal grid resolution of approximately the size of the whole study basin are used here, indicating clearly the need for <span class="hlt">downscaling</span>. Daily observations of local climate variables available since 1971 are used as additional input to the various <span class="hlt">downscaling</span> tools proposed which are, namely, the stochastic weather generator (LARS-WG), the statistical <span class="hlt">downscaling</span> model (SDSM), and a multiple linear regression model between the observed variables and the CGCM3 predictors. Both the 2D and the 3D versions of the CGCM3 model are employed to predict, 100 years ahead up to year 2100, the monthly rainfall and temperatures, based on the past calibration period (training period) 1971-2000. To investigate the prediction performance, multiple linear regression, autoregressive (AR) and autoregressive integrated moving average (ARIMA) models are applied to the time series of the observation data which are aggregated into monthly time steps to be able compare them with the <span class="hlt">downscaling</span> results above. Likewise, multiple linear regression and ARIMA models also executed on the CGCM3 predictors and the Pacific / Indian oceans indices as external regressors to predict short-term local climate variations. The results of the various <span class="hlt">downscaling</span> method are</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.401T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.401T"><span id="translatedtitle">Climate change scenarios of temperature and precipitation over five Italian regions for the period 2021-2050 obtained by statistical <span class="hlt">downscaling</span> models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tomozeiu, R.; Tomei, F.; Villani, G.; Pasqui, M.</p> <p>2010-09-01</p> <p>Climate change scenarios of seasonal maximum, minimum temperature and precipitation in five Italian regions, over the period 2021-2050 against 1961-1990 are assessed. The regions selected by the AGROSCENARI project are important from the local agricultural practises and are situated as follows: in the Northern Italy - Po valley and hilly area of Faenza; in Central part of Italy- Marche, Beneventano and Destra Sele, and in Sardinia Island - Oristano. A statistical <span class="hlt">downscaling</span> technique applied to the <span class="hlt">ENSEMBLES</span> global climate simulations, A1B scenario, is used to reach this objective. The method consists of a multivariate regression, based on Canonical Correlation Analysis, using as possible predictors mean sea level pressure, geopotential height at 500hPa and temperature at 850 hPa. The observational data set (predictands) for the selected regions is composed by a reconstruction of minimum, maximum temperature and precipitation daily data on a regular grid with a spatial resolution of 35 km, for 1951-2009 period (managed by the Meteorological and Climatological research unit for agriculture - Agricultural Research Council, CRA - CMA). First, a set-up of statistical model has been made using predictors from ERA40 reanalysis and the seasonal indices of temperature and precipitation from local scale, 1958-2002 period. Then, the statistical <span class="hlt">downscaling</span> model has been applied to the predictors derived from the <span class="hlt">ENSEMBLES</span> global climate models, A1B scenario, in order to obtain climate change scenario of temperature and precipitation at local scale, 2021-2050 period. The projections show that increases could be expected to occur under scenario conditions in all seasons, in both minimum and maximum temperature. The magnitude of changes is more intense during summer when the changes could reach values around 2°C for minimum and maximum temperature. In the case of precipitation, the pattern of changes is more complex, different from season to season and over the regions, a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJSyS..47..406C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJSyS..47..406C"><span id="translatedtitle">MSEBAG: a dynamic classifier <span class="hlt">ensemble</span> generation based on `minimum-sufficient <span class="hlt">ensemble</span>' and bagging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Lei; Kamel, Mohamed S.</p> <p>2016-01-01</p> <p>In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient <span class="hlt">ensemble</span>' and bagging at the <span class="hlt">ensemble</span> level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient <span class="hlt">ensemble</span>', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient <span class="hlt">ensemble</span>', a backward stepwise algorithm is employed to generate a collection of <span class="hlt">ensembles</span>. The objective is to create a collection of <span class="hlt">ensembles</span> with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the <span class="hlt">ensembles</span> from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the <span class="hlt">ensemble</span> level, is employed for this task. EAA searches for the competent <span class="hlt">ensembles</span> using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected <span class="hlt">ensembles</span> to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4910T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4910T"><span id="translatedtitle">Forecasting European Droughts using the North American Multi-Model <span class="hlt">Ensemble</span> (NMME)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thober, Stephan; Kumar, Rohini; Samaniego, Luis; Sheffield, Justin; Schäfer, David; Mai, Juliane</p> <p>2015-04-01</p> <p>Soil moisture droughts have the potential to diminish crop yields causing economic damage or even threatening the livelihood of societies. State-of-the-art drought forecasting systems incorporate seasonal meteorological forecasts to estimate future drought conditions. Meteorological forecasting skill (in particular that of precipitation), however, is limited to a few weeks because of the chaotic behaviour of the atmosphere. One of the most important challenges in drought forecasting is to understand how the uncertainty in the atmospheric forcings (e.g., precipitation and temperature) is further propagated into hydrologic variables such as soil moisture. The North American Multi-Model <span class="hlt">Ensemble</span> (NMME) provides the latest collection of a multi-institutional seasonal forecasting <span class="hlt">ensemble</span> for precipitation and temperature. In this study, we analyse the skill of NMME forecasts for predicting European drought events. The monthly NMME forecasts are <span class="hlt">downscaled</span> to daily values to force the mesoscale hydrological model (mHM). The mHM soil moisture forecasts obtained with the forcings of the dynamical models are then compared against those obtained with the <span class="hlt">Ensemble</span> Streamflow Prediction (ESP) approach. ESP recombines historical meteorological forcings to create a new <span class="hlt">ensemble</span> forecast. Both forecasts are compared against reference soil moisture conditions obtained using observation based meteorological forcings. The study is conducted for the period from 1982 to 2009 and covers a large part of the Pan-European domain (10°W to 40°E and 35°N to 55°N). Results indicate that NMME forecasts are better at predicting the reference soil moisture variability as compared to ESP. For example, NMME explains 50% of the variability in contrast to only 31% by ESP at a six-month lead time. The Equitable Threat Skill Score (ETS), which combines the hit and false alarm rates, is analysed for drought events using a 0.2 threshold of a soil moisture percentile index. On average, the NMME</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.U21A0001H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.U21A0001H"><span id="translatedtitle"><span class="hlt">Ensemble</span> Exigent Forecasting of Critical Weather Events</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hoffman, R. N.; Gombos, D.</p> <p>2011-12-01</p> <p>To improve the forecasting of and society's preparedness for "worst-case" weather damage scenarios, we have developed <span class="hlt">ensemble</span> exigent analysis. Exigent analysis determines worst cast scenarios and associated probability quantiles from the joint spatial properties of multivariate damaging weather events. Using the <span class="hlt">ensemble</span>-estimated forecast covariance, we (1) identify the forecast exigent analysis perturbation (ExAP) and (2) find the contemporaneous and antecedent meteorological conditions that are most likely to coexist with or to evolve into the ExAP at the forecast time. Here we focus on the first objective, the ExAP identification problem. The ExAP is the perturbation wrt to the <span class="hlt">ensemble</span> mean at the forecast time that maximizes the damage in the subspace of the <span class="hlt">ensemble</span> with respect to a user-defined damage metric (i.e. maximizes the sum of the damage perturbation over the domain of interest) and to a user-specified <span class="hlt">ensemble</span> probability quantile (EPQ) defined in terms of the Mahalanobis distance of the perturbation to the <span class="hlt">ensemble</span> mean. Making use of a universal relationship (for Gaussian <span class="hlt">ensembles</span>) between the quantile of the damage functional and the EPQ, we explain the ExAP using topological arguments. Then, we formally define the ExAP by making use of the <span class="hlt">ensemble</span>-estimated covariance of the damage <span class="hlt">ensemble</span> in a Lagrangian minimization technique according to an exigent analysis theorem. Two case studies with varying complexities and expected accuracies are used to illustrate <span class="hlt">ensemble</span> exigent analysis. The first case study employs the gridded forecast number of heating degree days (HDD) to analyze forecast heating demand over a large portion of the United Sates for a cold event on 9 January 2010. The second case uses <span class="hlt">ensemble</span> forecasts of 2-meter temperature and estimates of the spatial distribution of citrus trees to define the damage functional as the percentage of Florida citrus trees damaged by the 11 January 2010 Florida freeze event. The ExAP of this</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1175481','DOE-PATENT-XML'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1175481"><span id="translatedtitle">Creating <span class="hlt">ensembles</span> of decision trees through sampling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Kamath, Chandrika; Cantu-Paz, Erick</p> <p>2005-08-30</p> <p>A system for decision tree <span class="hlt">ensembles</span> that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in <span class="hlt">ensembles</span>. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in <span class="hlt">ensembles</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/8542966','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/8542966"><span id="translatedtitle">Cooperative effects of neuronal <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rose, G; Siebler, M</p> <p>1995-01-01</p> <p>Electrophysiological properties of neurons as the basic cellular elements of the central nervous system and their synaptic connections are well characterized down to a molecular level. However, the behavior of complex noisy networks formed by these constituents usually cannot simply be derived from the knowledge of its microscopic parameters. As a consequence, cooperative phenomena based on the interaction of neurons were postulated. This is a report on a study of global network spike activity as a function of synaptic interaction. We performed experiments in dissociated cultured hippocampal neurons and, for comparison, simulations of a mathematical model closely related to electrophysiology. Numeric analyses revealed that at a critical level of synaptic connectivity the firing behavior undergoes a phase transition. This cooperative effect depends crucially on the interaction of numerous cells and cannot be attributed to the spike threshold of individual neurons. In the experiment a drastic increase in the firing level was observed upon increase of synaptic efficacy by lowering of the extracellular magnesium concentration, which is compatible with our theoretical predictions. This "on-off" phenomenon demonstrates that even in small neuronal <span class="hlt">ensembles</span> collective behavior can emerge which is not explained by the characteristics of single neurons. PMID:8542966</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=wind+AND+industry&pg=2&id=EJ176993','ERIC'); return false;" href="http://eric.ed.gov/?q=wind+AND+industry&pg=2&id=EJ176993"><span id="translatedtitle">Music Literature for Band and Wind <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>De Young, Derald</p> <p>1977-01-01</p> <p>Provides some music literature sources for band and wind <span class="hlt">ensembles</span>. Since music literature is crucial to both musical groups and the music curriculum evolves from music, these references are most important. (RK)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25859041','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25859041"><span id="translatedtitle">Experimental observation of a generalized Gibbs <span class="hlt">ensemble</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Langen, Tim; Erne, Sebastian; Geiger, Remi; Rauer, Bernhard; Schweigler, Thomas; Kuhnert, Maximilian; Rohringer, Wolfgang; Mazets, Igor E; Gasenzer, Thomas; Schmiedmayer, Jörg</p> <p>2015-04-10</p> <p>The description of the non-equilibrium dynamics of isolated quantum many-body systems within the framework of statistical mechanics is a fundamental open question. Conventional thermodynamical <span class="hlt">ensembles</span> fail to describe the large class of systems that exhibit nontrivial conserved quantities, and generalized <span class="hlt">ensembles</span> have been predicted to maximize entropy in these systems. We show experimentally that a degenerate one-dimensional Bose gas relaxes to a state that can be described by such a generalized <span class="hlt">ensemble</span>. This is verified through a detailed study of correlation functions up to 10th order. The applicability of the generalized <span class="hlt">ensemble</span> description for isolated quantum many-body systems points to a natural emergence of classical statistical properties from the microscopic unitary quantum evolution. PMID:25859041</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=berliner&pg=3&id=EJ092927','ERIC'); return false;" href="http://eric.ed.gov/?q=berliner&pg=3&id=EJ092927"><span id="translatedtitle">"Verfremdung" in Action at the Berliner <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Brown, Thomas K.</p> <p>1973-01-01</p> <p>Discussion of Brecht's aesthetic principles, particularly "Verfremdung" (the device of renewal and estrangement), including the opinions of the Berliner <span class="hlt">Ensemble</span> concerning to what degree they have retained Brecht's principles in productions of his plays. (DD)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Chamber+AND+Music&pg=2&id=EJ075665','ERIC'); return false;" href="http://eric.ed.gov/?q=Chamber+AND+Music&pg=2&id=EJ075665"><span id="translatedtitle">Effectiveness of Chamber Music <span class="hlt">Ensemble</span> Experience</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Zorn, Jay D.</p> <p>1973-01-01</p> <p>This investigation was concerned with the effectiveness of chamber music <span class="hlt">ensemble</span> experience for certain members of a ninth grade band and the evaluation of the effectiveness in terms of performing abilities, cognitive learnings, and attitude changes. (Author)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/21962010','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/21962010"><span id="translatedtitle">The <span class="hlt">ensemble</span> performance index: an improved measure for assessing <span class="hlt">ensemble</span> pose prediction performance.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Korb, Oliver; McCabe, Patrick; Cole, Jason</p> <p>2011-11-28</p> <p>We present a theoretical study on the performance of <span class="hlt">ensemble</span> docking methodologies considering multiple protein structures. We perform a theoretical analysis of pose prediction experiments which is completely unbiased, as we make no assumptions about specific scoring functions, search paradigms, protein structures, or ligand data sets. We introduce a novel interpretable measure, the <span class="hlt">ensemble</span> performance index (EPI), for the assessment of scoring performance in <span class="hlt">ensemble</span> docking, which will be applied to simulated and real data sets. PMID:21962010</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010JGRD..11513106S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010JGRD..11513106S"><span id="translatedtitle">Development of daily precipitation projections for the United States based on probabilistic <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schoof, J. T.; Pryor, S. C.; Surprenant, J.</p> <p>2010-07-01</p> <p>Projections of mid and late 21st century precipitation for 963 stations across the contiguous United States are derived using probabilistic <span class="hlt">downscaling</span> of 10 coupled atmosphere-ocean general circulation models (AOGCMs). The projections are constructed by <span class="hlt">downscaling</span> the statistical parameters describing precipitation occurrence and intensity, using a first-order Markov chain and two-parameter gamma distribution, respectively. Future <span class="hlt">downscaled</span> values of the parameters are used to derive projections of wet day probability, wet day precipitation intensity and its distribution, and total seasonal precipitation for the cold season (November through March) and the warm season (May through September). <span class="hlt">Downscaled</span> results for the 10 AOGCMs indicate several robust features of possible changes in the U.S. regional precipitation climatology. Cold season projections are characterized by increases in precipitation in the northwest and northeast regions, decreases in precipitation in the southwest region, and smaller or inconsistent changes in other regions. With the exception of the northeast region, warm season projections reflect drier conditions overall resulting primarily from fewer wet days. In both the cold and warm seasons, changes in both the occurrence and intensity processes contribute to changes in total precipitation. Changes in total precipitation, and the relative roles of the occurrence and intensity processes, are found to be sensitive to the change in the distribution of wet day precipitation intensities. Regions with increasing seasonal precipitation totals are characterized by disproportionate increases in large precipitation events, while those with decreasing seasonal precipitation totals are characterized by the largest fractional decreases in small precipitation events.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=308425','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=308425"><span id="translatedtitle">A method to <span class="hlt">downscale</span> soil moisture to fine-resolutions using topographic, vegetation, and soil data</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Soil moisture can be estimated over large regions with spatial resolutions greater than 500 m, but many applications require finer resolutions (10 – 100 m grid cells). Several methods use topographic data to <span class="hlt">downscale</span>, but vegetation and soil patterns can also be important. In this paper, a downsc...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H13F1188G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H13F1188G"><span id="translatedtitle">Optimal Selection of Predictor Variables in Statistical <span class="hlt">Downscaling</span> Models of Precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Goly, A.; Teegavarapu, R. S. V.</p> <p>2014-12-01</p> <p>Statistical <span class="hlt">downscaling</span> models developed for precipitation rely heavily on predictors chosen and on accurate relationships between regional scale predictand and GCM-scale predictor for providing future precipitation projections at different spatial and temporal scales. This study provides two new screening methods for selection of predictor variables for use in <span class="hlt">downscaling</span> methods based on predictand-predictors relationships. Methods to characterize predictand-predictors relationships via rigid and flexible functional relationships using mixed integer nonlinear programming (MINLP) model with binary variables and artificial neural network (ANN) models respectively are developed and evaluated in this study. In addition to these two methods, a stepwise regression (SWR) and two models that do not use any pre-screening of variables are also evaluated. A two-step process is used to <span class="hlt">downscale</span> precipitation data with optimal selection of predictors and using them in a statistical <span class="hlt">downscaling</span> model based on support vector machine (SVM) approach. Experiments with the proposed two new methods and three additional methods based on correlation between predictors and predictand and the other based on principal component analysis are evaluated in this study. Results suggest that optimal selection of variables using MINLP albeit with linear relationship and ANN method provided improved performance and error measures compared to two other models that did not use these methods for screening the variables. Of all the three screening methods tested in this study, SWR method selected the least number of variables and also ranked lowest based on several performance measures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ASCMO...2...39B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ASCMO...2...39B"><span id="translatedtitle">Calibrating regionally <span class="hlt">downscaled</span> precipitation over Norway through quantile-based approaches</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bolin, David; Frigessi, Arnoldo; Guttorp, Peter; Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Wallin, Jonas</p> <p>2016-06-01</p> <p>Dynamical <span class="hlt">downscaling</span> of earth system models is intended to produce high-resolution climate information at regional to local scales. Current models, while adequate for describing temperature distributions at relatively small scales, struggle when it comes to describing precipitation distributions. In order to better match the distribution of observed precipitation over Norway, we consider approaches to statistical adjustment of the output from a regional climate model when forced with ERA-40 reanalysis boundary conditions. As a second step, we try to correct <span class="hlt">downscalings</span> of historical climate model runs using these transformations built from <span class="hlt">downscaled</span> ERA-40 data. Unless such calibrations are successful, it is difficult to argue that scenario-based <span class="hlt">downscaled</span> climate projections are realistic and useful for decision makers. We study both full quantile calibrations and several different methods that correct individual quantiles separately using random field models. Results based on cross-validation show that while a full quantile calibration is not very effective in this case, one can correct individual quantiles satisfactorily if the spatial structure in the data are accounted for. Interestingly, different methods are favoured depending on whether ERA-40 data or historical climate model runs are adjusted.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H43G1537C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H43G1537C"><span id="translatedtitle">Spatial <span class="hlt">Downscaling</span> of TRMM Precipitation using MODIS product in the Korean Peninsula</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cho, H.; Choi, M.</p> <p>2013-12-01</p> <p>Precipitation is a major driving force in the water cycle. But, it is difficult to provide spatially distributed precipitation data from isolated individual in situ. The Tropical Rainfall Monitoring Mission (TRMM) satellite can provide precipitation data with relatively coarse spatial resolution (0.25° scale) at daily basis. In order to overcome the coarse spatial resolution of TRMM precipitation products, we conducted a <span class="hlt">downscaling</span> technique using a scaling parameter from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor. In this study, statistical relations between precipitation estimates derived from the TRMM satellite and the normalized difference vegetation index (NDVI) which is obtained from the MODIS sensor in TERRA satellite are found for different spatial scales on the Korean peninsula in northeast Asia. We obtain the <span class="hlt">downscaled</span> precipitation mapping by regression equation between yearly TRMM precipitations values and annual average NDVI aggregating 1km to 25 degree. The <span class="hlt">downscaled</span> precipitation is validated using time series of the ground measurements precipitation dataset provided by Korea Meteorological Organization (KMO) from 2002 to 2005. To improve the spatial <span class="hlt">downscaling</span> of precipitation, we will conduct a study about correlation between precipitation and land surface temperature, perceptible water and other hydrological parameters.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1812447R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1812447R&link_type=ABSTRACT"><span id="translatedtitle">Multi objective climate change impact assessment using multi <span class="hlt">downscaled</span> climate scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rana, Arun; Moradkhani, Hamid</p> <p>2016-04-01</p> <p>Global Climate Models (GCMs) are often used to <span class="hlt">downscale</span> the climatic parameters on a regional and global scale. In the present study, we have analyzed the changes in precipitation and temperature for future scenario period of 2070-2099 with respect to historical period of 1970-2000 from a set of statistically <span class="hlt">downscaled</span> GCM projections for Columbia River Basin (CRB). Analysis is performed using 2 different statistically <span class="hlt">downscaled</span> climate projections namely the Bias Correction and Spatial <span class="hlt">Downscaling</span> (BCSD) technique generated at Portland State University and the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. Analysis is performed on spatial, temporal and frequency based parameters in the future period at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice-versa for temperature. Frequency analysis provided insights into possible explanation to changes in precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015WRR....51.6244J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015WRR....51.6244J"><span id="translatedtitle">A space and time scale-dependent nonlinear geostatistical approach for <span class="hlt">downscaling</span> daily precipitation and temperature</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason; McCabe, Matthew F.; Sharma, Ashish</p> <p>2015-08-01</p> <p>A geostatistical framework is proposed to <span class="hlt">downscale</span> daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 and 10 km resolution for a 20 year period ranging from 1985 to 2004. The data are used to predict <span class="hlt">downscaled</span> climate variables for the year 2005. The result, for each <span class="hlt">downscaled</span> pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference data set indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple-point geostatistics approach is an efficient tool to be used for statistical <span class="hlt">downscaling</span> to obtain local-scale estimates of precipitation and temperature from General Circulation Models.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=296239','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=296239"><span id="translatedtitle">Passive microwave soil moisture <span class="hlt">downscaling</span> using vegetation index and skin surface temperature</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture <span class="hlt">downscaling</span> algorithm based on a regression relationship bet...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1510642B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1510642B"><span id="translatedtitle">A comparison of two classification based approaches for <span class="hlt">downscaling</span> of monthly PM10 concentrations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Beck, Christoph; Weitnauer, Claudia; Jacobeit, Jucundus</p> <p>2013-04-01</p> <p>Circulation type classifications may be utilised for the <span class="hlt">downscaling</span> of local climatic and environmental target variables in different methodological settings. In this contribution we apply and compare two different classification based approaches for <span class="hlt">downscaling</span> of monthly indices of PM10 concentrations (monthly mean and number of days exceeding a certain threshold) at different stations in Bavaria (Germany) during the period 1979 to 2010. The first approach uses monthly frequencies of circulation types as predictors in multiple linear regression models (stepwise regression) to estimate monthly predictand values (monthly PM10 indices). The second approach utilizes type specific mean values of the target variable - determined for a calibration period - to estimate predictand values in the validation period. Both approaches are run using varying circulation classifications. This comprises different methodological concepts for circulation classification (e.g. threshold based methods, leader algorithms, cluster analysis) and as well different temporal (1-day or multiple day sequences) and spatial domains (synoptic to continental scale). All models are applied to multiple calibration and validation samples and different skill scores (e.g. reduction of variance, Pearson R) are estimated for each of the validation samples in order to quantify model performance. As main preliminary findings we may state that: - the regression based <span class="hlt">downscaling</span> approach in most cases clearly outperforms the approach that uses type specific mean values (reference forecasting), - best skill is reached in winter (DJF) and spring (MAM), - comparable model skill is reached for the <span class="hlt">downscaling</span> of monthly means and extremes indicators (number of days exceeding a certain threshold).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=312466','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=312466"><span id="translatedtitle"><span class="hlt">Downscaling</span> Landsat 7 canopy reflectance employing a multi soil sensor platform</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Crop growth and yield can be efficiently monitored using canopy reflectance. The spatial resolution of freely available remote sensing data is however too coarse to fully understand spatial dynamics of crop status. In this manuscript Landsat 7 (L7) reflectance is <span class="hlt">downscaled</span> from the native resolutio...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=213342','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=213342"><span id="translatedtitle">Reductions in seasonal climate forecast dependability as a result of <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>This research determines whether NOAA/CPC seasonal climate forecasts are skillful enough to retain utility after they have been <span class="hlt">downscaled</span> for use in crop models. Utility is assessed using net dependability, the product of the large-scale 3-month forecast dependability and a factor accounting for l...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ACP....16.5229W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ACP....16.5229W&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Downscaling</span> surface wind predictions from numerical weather prediction models in complex terrain with WindNinja</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wagenbrenner, Natalie S.; Forthofer, Jason M.; Lamb, Brian K.; Shannon, Kyle S.; Butler, Bret W.</p> <p>2016-04-01</p> <p>Wind predictions in complex terrain are important for a number of applications. Dynamic <span class="hlt">downscaling</span> of numerical weather prediction (NWP) model winds with a high-resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for <span class="hlt">downscaling</span> near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. <span class="hlt">Downscaling</span> improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with <span class="hlt">downscaling</span>. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26489417','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26489417"><span id="translatedtitle">Dynamically <span class="hlt">downscaling</span> predictions for deciduous tree leaf emergence in California under current and future climate.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Medvigy, David; Kim, Seung Hee; Kim, Jinwon; Kafatos, Menas C</p> <p>2016-07-01</p> <p>Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical <span class="hlt">downscaling</span> of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically <span class="hlt">downscaled</span> to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of <span class="hlt">downscaling</span> from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of <span class="hlt">downscaling</span> are unlikely to be stationary. PMID:26489417</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1713722K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713722K"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of WRF-Chem Model: An Air Quality Analysis over Bogota, Colombia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kumar, Anikender; Rojas, Nestor</p> <p>2015-04-01</p> <p>Statistical <span class="hlt">downscaling</span> is a technique that is used to extract high-resolution information from regional scale variables produced by coarse resolution models such as Chemical Transport Models (CTMs). The fully coupled WRF-Chem (Weather Research and Forecasting with Chemistry) model is used to simulate air quality over Bogota. Bogota is a tropical Andean megacity located over a high-altitude plateau in the middle of very complex terrain. The WRF-Chem model was adopted for simulating the hourly ozone concentrations. The computational domains were chosen of 120x120x32, 121x121x32 and 121x121x32 grid points with horizontal resolutions of 27, 9 and 3 km respectively. The model was initialized with real boundary conditions using NCAR-NCEP's Final Analysis (FNL) and a 1ox1o (~111 km x 111 km) resolution. Boundary conditions were updated every 6 hours using reanalysis data. The emission rates were obtained from global inventories, namely the REanalysis of the TROpospheric (RETRO) chemical composition and the Emission Database for Global Atmospheric Research (EDGAR). Multiple linear regression and artificial neural network techniques are used to <span class="hlt">downscale</span> the model output at each monitoring stations. The results confirm that the statistically <span class="hlt">downscaled</span> outputs reduce simulated errors by up to 25%. This study provides a general overview of statistical <span class="hlt">downscaling</span> of chemical transport models and can constitute a reference for future air quality modeling exercises over Bogota and other Colombian cities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJBm...60..935M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJBm...60..935M"><span id="translatedtitle">Dynamically <span class="hlt">downscaling</span> predictions for deciduous tree leaf emergence in California under current and future climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medvigy, David; Kim, Seung Hee; Kim, Jinwon; Kafatos, Menas C.</p> <p>2016-07-01</p> <p>Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical <span class="hlt">downscaling</span> of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically <span class="hlt">downscaled</span> to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of <span class="hlt">downscaling</span> from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of <span class="hlt">downscaling</span> are unlikely to be stationary.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=307740&keyword=LAKE+AND+ICE&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65321814&CFTOKEN=12462783','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=307740&keyword=LAKE+AND+ICE&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65321814&CFTOKEN=12462783"><span id="translatedtitle">Technical Challenges and Solutions in Representing Lakes when using WRF in <span class="hlt">Downscaling</span> Applications</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by <span class="hlt">downscaling</span> global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUSM.H23C..29G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUSM.H23C..29G"><span id="translatedtitle">A Physically-Based Multivariate-Regression Approach for <span class="hlt">Downscaling</span> NEXRAD Precipitation in Mountainous Terrain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guan, H.; Xie, H.; Wilson, J. L.</p> <p>2006-05-01</p> <p>Precipitation temporal and spatial variability often controls terrestrial hydrologic processes and states. Common remotely-sensed precipitation products have a spatial resolution that is often too coarse to reveal hydrologically important spatial variability. A parsimonious physically-based multivariate-regression algorithm, referred to as multi-level cluster-optimizing ASOADeK regression, is developed for <span class="hlt">downscaling</span> low-resolution spatial precipitation fields. This algorithm auto-searches precipitation spatial structures (e.g., rain cells), and atmospheric and orographic effects, to estimate precipitation distribution without prior knowledge of the atmospheric setting. The only required input data for the <span class="hlt">downscaling</span> algorithm are a large-pixel precipitation map and the DEM map of the area of interest. We tested the algorithm on NEXRAD precipitation fields with 4km x 4km large pixels. The algorithm generated 1km x 1km <span class="hlt">downscaled</span> daily precipitation maps, which we judge successful for the mountainous terrain in terms of precipitation spatial statistics and pair comparisons of pixel values and rain gauges. It produced acceptable <span class="hlt">downscaled</span> hourly precipitation maps in terms of precipitation spatial statistics, but not in regard of pixel-gauge comparison. The algorithm also successfully retrieves the overall moisture flux direction for the precipitation field. These promising results suggest that the algorithm is worthy of further exploration and development.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1056M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1056M"><span id="translatedtitle">Addressing impacts of different statistical <span class="hlt">downscaling</span> methods on large scale hydrologic simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mizukami, N.; Clark, M. P.; Gutmann, E. D.; Mendoza, P. A.; Brekke, L. D.; Arnold, J.; Raff, D. A.</p> <p>2013-12-01</p> <p>Many hydrologic assessments, such as evaluations of climate change impacts on water resources, require <span class="hlt">downscaled</span> climate model outputs to force hydrologic simulations at a spatial resolution finer than the climate models' native scale. Statistical <span class="hlt">downscaling</span> is an attractive alternative to dynamical <span class="hlt">downscaling</span> methods for continental scale hydrologic applications because of its lower computational cost. The goal of this study is to illustrate and compare how the errors in precipitation and temperature produced by different statistical <span class="hlt">downscaling</span> methods propagate into hydrologic simulations. Multi-decadal hydrologic simulations were performed with three process-based hydrologic models (CLM, VIC, and PRMS) forced by multiple climate datasets over the contiguous United States. The forcing datasets include climate data derived from gauge observations (M02) as well as climate data <span class="hlt">downscaled</span> from the NCEP-NCAR reanalysis using 4 statistical <span class="hlt">downscaling</span> methods for a domain with 12-km grid spacing: two forms of Bias Corrected Spatially Disaggregated methods (BCSD-monthly and BCSD-daily), Bias Corrected Constructed Analogue (BCCA), and Asynchronous Regression (AR). Our results show that both BCCA and BCSD-daily underestimate extreme precipitation events while AR produces these correctly at the scale at which the simulations were run but does not scale them up appropriately to larger basin scales like HUC-4 and HUC-2. These artifacts lead to a poor representation of flooding events when hydrologic models are forced by these methods over a range of spatial scales. We also illustrate that errors in precipitation depths dominate impacts on runoff depth estimations, and that errors in wet day frequency have a larger effect on shortwave radiation estimations than do the errors in temperatures; this error subsequently affects the partitioning of precipitation into evaporation and runoff as we show over mountainous areas of the upper Colorado River. Finally we show the inter</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.H32A..03P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.H32A..03P&link_type=ABSTRACT"><span id="translatedtitle">Drought monitoring using <span class="hlt">downscaled</span> soil moisture through machine learning approaches over North and South Korea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Park, S.; Im, J.; Rhee, J.; Park, S.</p> <p>2015-12-01</p> <p>Soil moisture is one of the most important key variables for drought monitoring. It reflects hydrological and agricultural processes because soil moisture is a function of precipitation and energy flux and crop yield is highly related to soil moisture. Many satellites including Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E), Soil Moisture and Ocean Salinity sensor (SMOS), and Soil Moisture Active Passive (SMAP) provide global scale soil moisture products through microwave sensors. However, as the spatial resolution of soil moisture products is typically tens of kilometers, it is difficult to monitor drought using soil moisture at local or regional scale. In this study, AMSR-E and AMSR2 soil moisture were <span class="hlt">downscaled</span> up to 1 km spatial resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) data—Evapotranspiration, Land Surface Temperature, Leaf Area Index, Normalized Difference Vegetation Index, Enhanced Vegetation Index and Albedo—through machine learning approaches over Korean peninsula. To monitor drought from 2003 to 2014, each pixel of the <span class="hlt">downscaled</span> soil moisture was scaled from 0 to 1 (1 is the wettest and 0 is the driest). The soil moisture based drought maps were validated using Standardized Precipitation Index (SPI) and crop yield data. Spatial distribution of drought status was also compared with other drought indices such as Scaled Drought Condition Index (SDCI). Machine learning approaches were performed well (R=0.905) for <span class="hlt">downscaling</span>. <span class="hlt">Downscaled</span> soil moisture was validated using in situ Asia flux data. The Root Mean Square Errors (RMSE) improved from 0.172 (25 km AMSR2) to 0.065 (<span class="hlt">downscaled</span> soil moisture). The correlation coefficients improved from 0.201 (25 km AMSR2) to 0.341 (<span class="hlt">downscaled</span> soil moisture). The soil moisture based drought maps and SDCI showed similar spatial distribution that caught both extreme drought and no drought. Since the proposed drought monitoring approach based on the <span class="hlt">downscaled</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H11N..07N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H11N..07N"><span id="translatedtitle"><span class="hlt">Downscaling</span> Soil Moisture Product from SMOS for Monitoring Agricultural Droughts in South America</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nagarajan, K.; Fu, C.; Judge, J.; Fraisse, C.</p> <p>2012-12-01</p> <p>Availability of reliable near-surface soil moisture (SM) estimates at fine spatial resolutions of 1 km and at temporal resolutions of a few days is critical for accurate quantification of drought impacts on crop yields and recommending meaningful management and adaptation strategies. The recently launched European Space Agency-Soil Moisture and Ocean Salinity (ESA-SMOS) and the near-future NASA-Soil Moisture Active Passive (SMAP) missions provide unprecedented, global SM product every 2-3 days at spatial resolutions of ~50 km. In addition, the SMAP will provide a SM product at 10 km . <span class="hlt">Downscaling</span> the above SM products to 1km is essential for any meaningful drought-related application in agricultural terrains. Optimal <span class="hlt">downscaling</span> should retain information from higher-order moments and leverage information from auxiliary remote sensing products that are available at fine resolutions. In this study, a novel <span class="hlt">downscaling</span> methodology based upon information theory was implemented to obtain distributed SM at 1 km every 3 days, using the SM product from SMOS. Observations of land surface temperature (LST), leaf area index (LAI) and land cover (LC) at 1 km from MODIS, and precipitation at 25 km from TRMM, were used as auxiliary information to facilitate the <span class="hlt">downscaling</span> process. The use of information-theory in <span class="hlt">downscaling</span> provides a hierarchical decomposition of image data that is optimal in terms of the transfer of information across scales and is therefore a better alternative to methods that use second-order statistics only. The <span class="hlt">downscaling</span> methodology was implemented over the agricultural regions in the lower La Plata Basin (L-LPB) in South America. The L-LPB region is of great economic value in South America, where agricultural cover makes up about 25% of the continent's land area and is vulnerable to high losses in crop yields due to agricultural drought . Both remote sensing and in situ observations (precipitation, temperature, and soil moisture) obtained during the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26903095','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26903095"><span id="translatedtitle">Meaning of temperature in different thermostatistical <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hänggi, Peter; Hilbert, Stefan; Dunkel, Jörn</p> <p>2016-03-28</p> <p>Depending on the exact experimental conditions, the thermodynamic properties of physical systems can be related to one or more thermostatistical <span class="hlt">ensembles</span>. Here, we survey the notion of thermodynamic temperature in different statistical <span class="hlt">ensembles</span>, focusing in particular on subtleties that arise when <span class="hlt">ensembles</span> become non-equivalent. The 'mother' of all <span class="hlt">ensembles</span>, the microcanonical <span class="hlt">ensemble</span>, uses entropy and internal energy (the most fundamental, dynamically conserved quantity) to derive temperature as a secondary thermodynamic variable. Over the past century, some confusion has been caused by the fact that several competing microcanonical entropy definitions are used in the literature, most commonly the volume and surface entropies introduced by Gibbs. It can be proved, however, that only the volume entropy satisfies exactly the traditional form of the laws of thermodynamics for a broad class of physical systems, including all standard classical Hamiltonian systems, regardless of their size. This mathematically rigorous fact implies that negative 'absolute' temperatures and Carnot efficiencies more than 1 are not achievable within a standard thermodynamical framework. As an important offspring of microcanonical thermostatistics, we shall briefly consider the canonical <span class="hlt">ensemble</span> and comment on the validity of the Boltzmann weight factor. We conclude by addressing open mathematical problems that arise for systems with discrete energy spectra. PMID:26903095</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25104944','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25104944"><span id="translatedtitle">Conductor gestures influence evaluations of <span class="hlt">ensemble</span> performance.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Morrison, Steven J; Price, Harry E; Smedley, Eric M; Meals, Cory D</p> <p>2014-01-01</p> <p>Previous research has found that listener evaluations of <span class="hlt">ensemble</span> performances vary depending on the expressivity of the conductor's gestures, even when performances are otherwise identical. It was the purpose of the present study to test whether this effect of visual information was evident in the evaluation of specific aspects of <span class="hlt">ensemble</span> performance: articulation and dynamics. We constructed a set of 32 music performances that combined auditory and visual information and were designed to feature a high degree of contrast along one of two target characteristics: articulation and dynamics. We paired each of four music excerpts recorded by a chamber <span class="hlt">ensemble</span> in both a high- and low-contrast condition with video of four conductors demonstrating high- and low-contrast gesture specifically appropriate to either articulation or dynamics. Using one of two equivalent test forms, college music majors and non-majors (N = 285) viewed sixteen 30 s performances and evaluated the quality of the <span class="hlt">ensemble</span>'s articulation, dynamics, technique, and tempo along with overall expressivity. Results showed significantly higher evaluations for performances featuring high rather than low conducting expressivity regardless of the <span class="hlt">ensemble</span>'s performance quality. Evaluations for both articulation and dynamics were strongly and positively correlated with evaluations of overall <span class="hlt">ensemble</span> expressivity. PMID:25104944</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70048367','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70048367"><span id="translatedtitle">Climate <span class="hlt">downscaling</span> effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.</p> <p>2013-01-01</p> <p>High-resolution (<span class="hlt">downscaled</span>) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different <span class="hlt">downscaling</span> approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between <span class="hlt">downscaling</span> approaches and that the variation attributable to <span class="hlt">downscaling</span> technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between <span class="hlt">downscaling</span> techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of <span class="hlt">downscaling</span> applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative <span class="hlt">downscaling</span> methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016JHyd..538...49M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016JHyd..538...49M&link_type=ABSTRACT"><span id="translatedtitle">Use of beta regression for statistical <span class="hlt">downscaling</span> of precipitation in the Campbell River basin, British Columbia, Canada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mandal, Sohom; Srivastav, Roshan K.; Simonovic, Slobodan P.</p> <p>2016-07-01</p> <p>Impacts of global climate change on water resources systems are assessed by <span class="hlt">downscaling</span> coarse scale climate variables into regional scale hydro-climate variables. In this study, a new multisite statistical <span class="hlt">downscaling</span> method based on beta regression (BR) is developed for generating synthetic precipitation series, which can preserve temporal and spatial dependence along with other historical statistics. The beta regression based <span class="hlt">downscaling</span> method includes two main steps: (1) prediction of precipitation states for the study area using classification and regression trees, and (2) generation of precipitation at different stations in the study area conditioned on the precipitation states. Daily precipitation data for 53 years from the ANUSPLIN data set is used to predict precipitation states of the study area where predictor variables are extracted from the NCEP/NCAR reanalysis data set for the same interval. The proposed model is applied to <span class="hlt">downscaling</span> daily precipitation at ten different stations in the Campbell River basin, British Columbia, Canada. Results show that the proposed <span class="hlt">downscaling</span> model can capture spatial and temporal variability of local precipitation very well at various locations. The performance of the model is compared with a recently developed non-parametric kernel regression based <span class="hlt">downscaling</span> model. The BR model performs better regarding extrapolation compared to the non-parametric kernel regression model. Future precipitation changes under different GHG (greenhouse gas) emission scenarios also projected with the developed <span class="hlt">downscaling</span> model that reveals a significant amount of changes in future seasonal precipitation and number of wet days in the river basin.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140010385','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140010385"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard</p> <p>2013-01-01</p> <p>Statistical <span class="hlt">downscaling</span> can be used to efficiently <span class="hlt">downscale</span> a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically <span class="hlt">downscales</span> (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical <span class="hlt">Downscaling</span> and Bias Correction (SDBC) approach. Based on these <span class="hlt">downscaled</span> data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical <span class="hlt">downscaling</span> as an intermediate step does not lead to considerable differences in the results of statistical <span class="hlt">downscaling</span> for the study domain.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014GMD.....7..387F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GMD.....7..387F"><span id="translatedtitle">TopoSCALE v.1.0: <span class="hlt">downscaling</span> gridded climate data in complex terrain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fiddes, J.; Gruber, S.</p> <p>2014-02-01</p> <p>Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by, for example, topography, and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to <span class="hlt">downscale</span> coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM). The method makes use of an interpolation of pressure-level data according to topographic height of the subgrid. An elevation and topography correction is used to <span class="hlt">downscale</span> short-wave radiation. Long-wave radiation is <span class="hlt">downscaled</span> by deriving a cloud-component of all-sky emissivity at grid level and using <span class="hlt">downscaled</span> temperature and relative humidity fields to describe variability with elevation. Precipitation is <span class="hlt">downscaled</span> with a simple non-linear lapse and optionally disaggregated using a climatology approach. We test the method in comparison with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure levels: air temperature, relative humidity, wind speed and incoming long-wave radiation. This method may be of use in improving inputs to numerical simulations in heterogeneous and/or remote terrain, especially when statistical methods are not possible, due to lack of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001JCli...14.4047H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001JCli...14.4047H"><span id="translatedtitle">Time Structure of Observed, GCM-Simulated, <span class="hlt">Downscaled</span>, and Stochastically Generated Daily Temperature Series.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huth, Radan; Kyselý, Jan; Dubrovský, Martin</p> <p>2001-10-01</p> <p>The time structure of simulated daily maximum and minimum temperature series, produced by several different methods, is compared with observations at six stations in central Europe. The methods are statistical <span class="hlt">downscaling</span>, stochastic weather generator, and general circulation models (GCMs). Outputs from control runs of two GCMs are examined: ECHAM3 and CCCM2. Four time series are constructed by statistical <span class="hlt">downscaling</span> using multiple linear regression of 500-hPa heights and 1000-/500-hPa thickness: (i) from observations with variance reproduced by the inflation technique, (ii) from observations with variance reproduced by adding a white noise process, and (iii) from the two GCMs. Two runs of the weather generator were performed, one considering and one neglecting the annual cycle of lag-0 and lag-1 correlations among daily weather characteristics. Standard deviation and skewness of day-to-day temperature changes and lag-1 autocorrelations are examined. For heat and cold waves, the occurrence frequency, mean duration, peak temperature, and mean position within the year are studied.Possible causes of discrepancies between the simulated and observed time series are discussed and identified. They are shown to stem, among others, from (i) the absence of physics in <span class="hlt">downscaled</span> and stochastically generated series, (ii) inadequacies of treatment of physical processes in GCMs, (iii) assumptions of linearity in <span class="hlt">downscaling</span> equations, and (iv) properties of the underlying statistical model of the weather generator. In <span class="hlt">downscaling</span>, variance inflation is preferable to the white noise addition in most aspects as the latter results in highly overestimated day-to-day variability. The inclusion of the annual cycle of correlations into the weather generator does not lead to an overall improvement of the temperature series produced. None of the methods appears to be able to reproduce all the characteristics of time structure correctly.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005TellA..57..409D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005TellA..57..409D"><span id="translatedtitle">Statistical and dynamical <span class="hlt">downscaling</span> of precipitation over Spain from DEMETER seasonal forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Díez, E.; Primo, C.; García-Moya, J. A.; Gutiérrez, J. M.; Orfila, B.</p> <p>2005-05-01</p> <p>Statistical and dynamical <span class="hlt">downscaling</span> methods are tested and compared for <span class="hlt">downscaling</span> seasonal precipitation forecasts over Spain from two DEMETER models: the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Meteorological Office (UKMO). The statistical method considered is a particular implementation of the standard analogue technique, based on close neighbours of the predicted atmospheric geopotential and humidity fields. Dynamical <span class="hlt">downscaling</span> is performed using the Rossby Centre Climate Atmospheric model, which has been nested to the ECMWF model output, and run in climate mode for six months. We first check the performance of the direct output models in the period 1986 1997 and compare it with the results obtained applying the analogue method. We have found that the direct outputs underestimate the precipitation amount and that the statistical <span class="hlt">downscaling</span> method improves the results as the skill of the direct forecast increases. The highest skills relative operating characteristic skill areas (RSAs) above 0.6 are associated with early and late spring, summer and autumn seasons at zero- and one-month lead times. On the other hand, models have poor skill during winter with the exception of the El Niño period (1986 1988), especially in the south of Spain. In this case, high RSAs and economic values have been found. We also compare statistical and dynamical <span class="hlt">downscaling</span> during four seasons, obtaining no concluding result. Both methods outperform direct output from DEMETER models, but depending on the season and on the region of Spain one method is better than the other. Moreover, we have seen that dynamical and statistical methods can be used in combination, yielding the best skill scores in some cases of the study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70131483','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70131483"><span id="translatedtitle">On the <span class="hlt">downscaling</span> of actual evapotranspiration maps based on combination of MODIS and landsat-based actual evapotranspiration estimates</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Singh, Ramesh K.; Senay, Gabriel B.; Velpuri, Naga Manohar; Bohms, Stefanie; Verdin, James P.</p> <p>2014-01-01</p> <p> <span class="hlt">Downscaling</span> is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for <span class="hlt">downscaling</span> MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and <span class="hlt">downscaling</span>. The regression with intercept method does have limitations in <span class="hlt">downscaling</span> if the slope and intercept are computed over a large area. A good agreement was obtained between <span class="hlt">downscaled</span> monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from −16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and <span class="hlt">downscaled</span> monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between <span class="hlt">downscaled</span> monthly AET using LinZI method and the gridded FLUXNET dataset. The <span class="hlt">downscaled</span> monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed <span class="hlt">downscaling</span> method will be very useful in advancing the application of remotely sensed images in water resources planning and management.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013HESSD..10.7857T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013HESSD..10.7857T"><span id="translatedtitle">Influence of <span class="hlt">downscaling</span> methods in projecting climate change impact on hydrological extremes of upper Blue Nile basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Taye, M. T.; Willems, P.</p> <p>2013-06-01</p> <p>Methods from two statistical <span class="hlt">downscaling</span> categories were used to investigate the impact of climate change on high rainfall and flow extremes of the upper Blue Nile basin. The main <span class="hlt">downscaling</span> differences considered were on the rainfall variable while a generally similar method was applied for temperature. The applied <span class="hlt">downscaling</span> methods are a stochastic weather generator, LARS-WG, and an advanced change factor method, the Quantile Perturbation Method (QPM). These were applied on 10 GCM runs and two emission scenarios (A1B and B1). The <span class="hlt">downscaled</span> rainfall and evapotranspiration were input into a calibrated and validated lumped conceptual model. The future simulations were conducted for 2050s and 2090s horizon and were compared with 1980-2000 control period. From the results all <span class="hlt">downscaling</span> methods agree in projecting increase in temperature for both periods. Nevertheless, the change signal on the rainfall was dependent on the climate model and the <span class="hlt">downscaling</span> method applied. LARS weather generator was good for monthly statistics although caution has to be taken when it is applied for impact analysis dealing with extremes, as it showed a deviation from the extreme value distribution's tail shape. Contrary, the QPM method was good for extreme cases but only for good quality daily climate model data. The study showed the choice of <span class="hlt">downscaling</span> method is an important factor to be considered and results based on one <span class="hlt">downscaling</span> method may not give the full picture. Regardless, the projections on the extreme high flows and the mean main rainy season flow mostly showed a decreasing change signal for both periods. This is either by decreasing rainfall or increasing evapotranspiration depending on the <span class="hlt">downscaling</span> method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC33G..07E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC33G..07E"><span id="translatedtitle">Identification of robust statistical <span class="hlt">downscaling</span> methods based on a comprehensive suite of performance metrics for South Korea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Eum, H. I.; Cannon, A. J.</p> <p>2015-12-01</p> <p>Climate models are a key provider to investigate impacts of projected future climate conditions on regional hydrologic systems. However, there is a considerable mismatch of spatial resolution between GCMs and regional applications, in particular a region characterized by complex terrain such as Korean peninsula. Therefore, a <span class="hlt">downscaling</span> procedure is an essential to assess regional impacts of climate change. Numerous statistical <span class="hlt">downscaling</span> methods have been used mainly due to the computational efficiency and simplicity. In this study, four statistical <span class="hlt">downscaling</span> methods [Bias-Correction/Spatial Disaggregation (BCSD), Bias-Correction/Constructed Analogue (BCCA), Multivariate Adaptive Constructed Analogs (MACA), and Bias-Correction/Climate Imprint (BCCI)] are applied to <span class="hlt">downscale</span> the latest Climate Forecast System Reanalysis data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. By split sampling scheme, all methods are calibrated with observational station data for 19 years from 1973 to 1991 are and tested for the recent 19 years from 1992 to 2010. To assess skill of the <span class="hlt">downscaling</span> methods, we construct a comprehensive suite of performance metrics that measure an ability of reproducing temporal correlation, distribution, spatial correlation, and extreme events. In addition, we employ Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to identify robust statistical <span class="hlt">downscaling</span> methods based on the performance metrics for each season. The results show that <span class="hlt">downscaling</span> skill is considerably affected by the skill of CFSR and all methods lead to large improvements in representing all performance metrics. According to seasonal performance metrics evaluated, when TOPSIS is applied, MACA is identified as the most reliable and robust method for all variables and seasons. Note that such result is derived from CFSR output which is recognized as near perfect climate data in climate studies. Therefore, the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFM.H41J..03B&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012AGUFM.H41J..03B&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Ensemble</span> postprocessing for probabilistic quantitative precipitation forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bentzien, S.; Friederichs, P.</p> <p>2012-12-01</p> <p>Precipitation is one of the most difficult weather variables to predict in hydrometeorological applications. In order to assess the uncertainty inherent in deterministic numerical weather prediction (NWP), meteorological services around the globe develop <span class="hlt">ensemble</span> prediction systems (EPS) based on high-resolution NWP systems. With non-hydrostatic model dynamics and without parameterization of deep moist convection, high-resolution NWP models are able to describe convective processes in more detail and provide more realistic mesoscale structures. However, precipitation forecasts are still affected by displacement errors, systematic biases and fast error growth on small scales. Probabilistic guidance can be achieved from an <span class="hlt">ensemble</span> setup which accounts for model error and uncertainty of initial and boundary conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) provides such an <span class="hlt">ensemble</span> system based on the German-focused limited-area model COSMO-DE. With a horizontal grid-spacing of 2.8 km, COSMO-DE is the convection-permitting high-resolution part of the operational model chain at DWD. The COSMO-DE-EPS consists of 20 realizations of COSMO-DE, driven by initial and boundary conditions derived from 4 global models and 5 perturbations of model physics. <span class="hlt">Ensemble</span> systems like COSMO-DE-EPS are often limited with respect to <span class="hlt">ensemble</span> size due to the immense computational costs. As a consequence, they can be biased and exhibit insufficient <span class="hlt">ensemble</span> spread, and probabilistic forecasts may be not well calibrated. In this study, probabilistic quantitative precipitation forecasts are derived from COSMO-DE-EPS and evaluated at more than 1000 rain gauges located all over Germany. COSMO-DE-EPS is a frequently updated <span class="hlt">ensemble</span> system, initialized 8 times a day. We use the time-lagged approach to inexpensively increase <span class="hlt">ensemble</span> spread, which results in more reliable forecasts especially for extreme precipitation events. Moreover, we will show that statistical</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010WRR....46.3532B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010WRR....46.3532B"><span id="translatedtitle">A multisite seasonal <span class="hlt">ensemble</span> streamflow forecasting technique</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bracken, Cameron; Rajagopalan, Balaji; Prairie, James</p> <p>2010-03-01</p> <p>We present a technique for providing seasonal <span class="hlt">ensemble</span> streamflow forecasts at several locations simultaneously on a river network. The framework is an integration of two recent approaches: the nonparametric multimodel <span class="hlt">ensemble</span> forecast technique and the nonparametric space-time disaggregation technique. The four main components of the proposed framework are as follows: (1) an index gauge streamflow is constructed as the sum of flows at all the desired spatial locations; (2) potential predictors of the spring season (April-July) streamflow at this index gauge are identified from the large-scale ocean-atmosphere-land system, including snow water equivalent; (3) the multimodel <span class="hlt">ensemble</span> forecast approach is used to generate the <span class="hlt">ensemble</span> flow forecast at the index gauge; and (4) the <span class="hlt">ensembles</span> are disaggregated using a nonparametric space-time disaggregation technique resulting in forecast <span class="hlt">ensembles</span> at the desired locations and for all the months within the season. We demonstrate the utility of this technique in skillful forecast of spring seasonal streamflows at four locations in the Upper Colorado River Basin at different lead times. Where applicable, we compare the forecasts to the Colorado Basin River Forecast Center's <span class="hlt">Ensemble</span> Streamflow Prediction (ESP) and the National Resource Conservation Service "coordinated" forecast, which is a combination of the ESP, Statistical Water Supply, a principal component regression technique, and modeler knowledge. We find that overall, the proposed method is equally skillful to existing operational models while tending to better predict wet years. The forecasts from this approach can be a valuable input for efficient planning and management of water resources in the basin.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25844624','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25844624"><span id="translatedtitle">Individual differences in <span class="hlt">ensemble</span> perception reveal multiple, independent levels of <span class="hlt">ensemble</span> representation.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Haberman, Jason; Brady, Timothy F; Alvarez, George A</p> <p>2015-04-01</p> <p><span class="hlt">Ensemble</span> perception, including the ability to "see the average" from a group of items, operates in numerous feature domains (size, orientation, speed, facial expression, etc.). Although the ubiquity of <span class="hlt">ensemble</span> representations is well established, the large-scale cognitive architecture of this process remains poorly defined. We address this using an individual differences approach. In a series of experiments, observers saw groups of objects and reported either a single item from the group or the average of the entire group. High-level <span class="hlt">ensemble</span> representations (e.g., average facial expression) showed complete independence from low-level <span class="hlt">ensemble</span> representations (e.g., average orientation). In contrast, low-level <span class="hlt">ensemble</span> representations (e.g., orientation and color) were correlated with each other, but not with high-level <span class="hlt">ensemble</span> representations (e.g., facial expression and person identity). These results suggest that there is not a single domain-general <span class="hlt">ensemble</span> mechanism, and that the relationship among various <span class="hlt">ensemble</span> representations depends on how proximal they are in representational space. PMID:25844624</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.5582B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.5582B"><span id="translatedtitle"><span class="hlt">Downscaling</span> Smooth Tomographic Models: Separating Intrinsic and Apparent Anisotropy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bodin, Thomas; Capdeville, Yann; Romanowicz, Barbara</p> <p>2016-04-01</p> <p>In recent years, a number of tomographic models based on full waveform inversion have been published. Due to computational constraints, the fitted waveforms are low pass filtered, which results in an inability to map features smaller than half the shortest wavelength. However, these tomographic images are not a simple spatial average of the true model, but rather an effective, apparent, or equivalent model that provides a similar 'long-wave' data fit. For example, it can be shown that a series of horizontal isotropic layers will be seen by a 'long wave' as a smooth anisotropic medium. In this way, the observed anisotropy in tomographic models is a combination of intrinsic anisotropy produced by lattice-preferred orientation (LPO) of minerals, and apparent anisotropy resulting from the incapacity of mapping discontinuities. Interpretations of observed anisotropy (e.g. in terms of mantle flow) requires therefore the separation of its intrinsic and apparent components. The "up-scaling" relations that link elastic properties of a rapidly varying medium to elastic properties of the effective medium as seen by long waves are strongly non-linear and their inverse highly non-unique. That is, a smooth homogenized effective model is equivalent to a large number of models with discontinuities. In the 1D case, Capdeville et al (GJI, 2013) recently showed that a tomographic model which results from the inversion of low pass filtered waveforms is an homogenized model, i.e. the same as the model computed by upscaling the true model. Here we propose a stochastic method to sample the <span class="hlt">ensemble</span> of layered models equivalent to a given tomographic profile. We use a transdimensional formulation where the number of layers is variable. Furthermore, each layer may be either isotropic (1 parameter) or intrinsically anisotropic (2 parameters). The parsimonious character of the Bayesian inversion gives preference to models with the least number of parameters (i.e. least number of layers, and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26565367','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26565367"><span id="translatedtitle">Simulations in generalized <span class="hlt">ensembles</span> through noninstantaneous switches.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Giovannelli, Edoardo; Cardini, Gianni; Chelli, Riccardo</p> <p>2015-10-01</p> <p>Generalized-<span class="hlt">ensemble</span> simulations, such as replica exchange and serial generalized-<span class="hlt">ensemble</span> methods, are powerful simulation tools to enhance sampling of free energy landscapes in systems with high energy barriers. In these methods, sampling is enhanced through instantaneous transitions of replicas, i.e., copies of the system, between different <span class="hlt">ensembles</span> characterized by some control parameter associated with thermodynamical variables (e.g., temperature or pressure) or collective mechanical variables (e.g., interatomic distances or torsional angles). An interesting evolution of these methodologies has been proposed by replacing the conventional instantaneous (trial) switches of replicas with noninstantaneous switches, realized by varying the control parameter in a finite time and accepting the final replica configuration with a Metropolis-like criterion based on the Crooks nonequilibrium work (CNW) theorem. Here we revise these techniques focusing on their correlation with the CNW theorem in the framework of Markovian processes. An outcome of this report is the derivation of the acceptance probability for noninstantaneous switches in serial generalized-<span class="hlt">ensemble</span> simulations, where we show that explicit knowledge of the time dependence of the weight factors entering such simulations is not necessary. A generalized relationship of the CNW theorem is also provided in terms of the underlying equilibrium probability distribution at a fixed control parameter. Illustrative calculations on a toy model are performed with serial generalized-<span class="hlt">ensemble</span> simulations, especially focusing on the different behavior of instantaneous and noninstantaneous replica transition schemes. PMID:26565367</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3721968','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3721968"><span id="translatedtitle">Multiscale Macromolecular Simulation: Role of Evolving <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Singharoy, A.; Joshi, H.; Ortoleva, P.J.</p> <p>2013-01-01</p> <p>Multiscale analysis provides an algorithm for the efficient simulation of macromolecular assemblies. This algorithm involves the coevolution of a quasiequilibrium probability density of atomic configurations and the Langevin dynamics of spatial coarse-grained variables denoted order parameters (OPs) characterizing nanoscale system features. In practice, implementation of the probability density involves the generation of constant OP <span class="hlt">ensembles</span> of atomic configurations. Such <span class="hlt">ensembles</span> are used to construct thermal forces and diffusion factors that mediate the stochastic OP dynamics. Generation of all-atom <span class="hlt">ensembles</span> at every Langevin timestep is computationally expensive. Here, multiscale computation for macromolecular systems is made more efficient by a method that self-consistently folds in <span class="hlt">ensembles</span> of all-atom configurations constructed in an earlier step, history, of the Langevin evolution. This procedure accounts for the temporal evolution of these <span class="hlt">ensembles</span>, accurately providing thermal forces and diffusions. It is shown that efficiency and accuracy of the OP-based simulations is increased via the integration of this historical information. Accuracy improves with the square root of the number of historical timesteps included in the calculation. As a result, CPU usage can be decreased by a factor of 3-8 without loss of accuracy. The algorithm is implemented into our existing force-field based multiscale simulation platform and demonstrated via the structural dynamics of viral capsomers. PMID:22978601</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26736910','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26736910"><span id="translatedtitle"><span class="hlt">Ensembling</span> brain regions for brain decoding.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Alkan, Sarper; Yarman-Vural, Fatos T</p> <p>2015-08-01</p> <p>In this study, we propose a new method which <span class="hlt">ensembles</span> the brain regions for brain decoding. The <span class="hlt">ensemble</span> is generated by clustering the fMRI images recorded during an experimental set-up which measures the cognitive states associated to semantic categories. Initially, voxel clusters are formed by using hierarchical agglomerative clustering with correlation as the similarity metric. Then, for each voxel cluster, a support vector machine (SVM) classifier is trained to estimate the class-posteriori probabilities. Lastly, the class-posteriori probabilities are <span class="hlt">ensembled</span> by concatenating them under the same feature space, which are then used to train a meta-layer SVM for the final classification of the cognitive states. By using the voxel clusters, we aim to utilize the distributed, but complementing nature of the semantic representations in the brain and improve the classification accuracy. Thus, we make an existential claim that the brain regions provide a natural basis for <span class="hlt">ensemble</span> learning which should be superior to the random clusters formed over a selected set of voxels. Our approach yields to better classification accuracies in Mitchell dataset on most of the subjects, when compared to state-of-the-art which emphasizes voxel selection and <span class="hlt">ensemble</span> learning with random subspaces. PMID:26736910</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015PhRvE..92d3310G&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015PhRvE..92d3310G&link_type=ABSTRACT"><span id="translatedtitle">Simulations in generalized <span class="hlt">ensembles</span> through noninstantaneous switches</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Giovannelli, Edoardo; Cardini, Gianni; Chelli, Riccardo</p> <p>2015-10-01</p> <p>Generalized-<span class="hlt">ensemble</span> simulations, such as replica exchange and serial generalized-<span class="hlt">ensemble</span> methods, are powerful simulation tools to enhance sampling of free energy landscapes in systems with high energy barriers. In these methods, sampling is enhanced through instantaneous transitions of replicas, i.e., copies of the system, between different <span class="hlt">ensembles</span> characterized by some control parameter associated with thermodynamical variables (e.g., temperature or pressure) or collective mechanical variables (e.g., interatomic distances or torsional angles). An interesting evolution of these methodologies has been proposed by replacing the conventional instantaneous (trial) switches of replicas with noninstantaneous switches, realized by varying the control parameter in a finite time and accepting the final replica configuration with a Metropolis-like criterion based on the Crooks nonequilibrium work (CNW) theorem. Here we revise these techniques focusing on their correlation with the CNW theorem in the framework of Markovian processes. An outcome of this report is the derivation of the acceptance probability for noninstantaneous switches in serial generalized-<span class="hlt">ensemble</span> simulations, where we show that explicit knowledge of the time dependence of the weight factors entering such simulations is not necessary. A generalized relationship of the CNW theorem is also provided in terms of the underlying equilibrium probability distribution at a fixed control parameter. Illustrative calculations on a toy model are performed with serial generalized-<span class="hlt">ensemble</span> simulations, especially focusing on the different behavior of instantaneous and noninstantaneous replica transition schemes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/22978601','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/22978601"><span id="translatedtitle">Multiscale macromolecular simulation: role of evolving <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Singharoy, A; Joshi, H; Ortoleva, P J</p> <p>2012-10-22</p> <p>Multiscale analysis provides an algorithm for the efficient simulation of macromolecular assemblies. This algorithm involves the coevolution of a quasiequilibrium probability density of atomic configurations and the Langevin dynamics of spatial coarse-grained variables denoted order parameters (OPs) characterizing nanoscale system features. In practice, implementation of the probability density involves the generation of constant OP <span class="hlt">ensembles</span> of atomic configurations. Such <span class="hlt">ensembles</span> are used to construct thermal forces and diffusion factors that mediate the stochastic OP dynamics. Generation of all-atom <span class="hlt">ensembles</span> at every Langevin time step is computationally expensive. Here, multiscale computation for macromolecular systems is made more efficient by a method that self-consistently folds in <span class="hlt">ensembles</span> of all-atom configurations constructed in an earlier step, history, of the Langevin evolution. This procedure accounts for the temporal evolution of these <span class="hlt">ensembles</span>, accurately providing thermal forces and diffusions. It is shown that efficiency and accuracy of the OP-based simulations is increased via the integration of this historical information. Accuracy improves with the square root of the number of historical timesteps included in the calculation. As a result, CPU usage can be decreased by a factor of 3-8 without loss of accuracy. The algorithm is implemented into our existing force-field based multiscale simulation platform and demonstrated via the structural dynamics of viral capsomers. PMID:22978601</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70035550','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70035550"><span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.</p> <p>2010-01-01</p> <p><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002AGUFM.H12G..03W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002AGUFM.H12G..03W&link_type=ABSTRACT"><span id="translatedtitle">Streamflow <span class="hlt">Ensemble</span> Generation using Climate Forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Watkins, D. W.; O'Connell, S.; Wei, W.; Nykanen, D.; Mahmoud, M.</p> <p>2002-12-01</p> <p>Although significant progress has been made in understanding the correlation between large-scale atmospheric circulation patterns and regional streamflow anomalies, there is a general perception that seasonal climate forecasts are not being used to the fullest extent possible for optimal water resources management. Possible contributing factors are limited knowledge and understanding of climate processes and prediction capabilities, noise in climate signals and inaccuracies in forecasts, and hesitancy on the part of water managers to apply new information or methods that could expose them to greater liability. This work involves a decision support model based on streamflow <span class="hlt">ensembles</span> developed for the Lower Colorado River Authority in Central Texas. Predicative skill is added to <span class="hlt">ensemble</span> forecasts that are based on climatology by conditioning the <span class="hlt">ensembles</span> on observable climate indicators, including streamflow (persistence), soil moisture, land surface temperatures, and large-scale recurrent patterns such as the El Ni¤o-Southern Oscillation, Pacific Decadal Oscillation, and the North Atlantic Oscillation. A Bayesian procedure for updating <span class="hlt">ensemble</span> probabilities is outlined, and various skill scores are reviewed for evaluating forecast performance. Verification of the <span class="hlt">ensemble</span> forecasts using a resampling procedure indicates a small but potentially significant improvement in forecast skill that could be exploited in seasonal water management decisions. The ultimate goal of this work will be explicit incorporation of climate forecasts in reservoir operating rules and estimation of the value of the forecasts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4415763','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4415763"><span id="translatedtitle">A Bayesian <span class="hlt">Ensemble</span> Approach for Epidemiological Projections</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lindström, Tom; Tildesley, Michael; Webb, Colleen</p> <p>2015-01-01</p> <p>Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, <span class="hlt">ensemble</span> modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model <span class="hlt">ensembles</span> based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the <span class="hlt">ensemble</span> prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for <span class="hlt">ensembles</span> with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for <span class="hlt">ensemble</span> modeling of disease outbreaks. PMID:25927892</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H43A1313W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H43A1313W"><span id="translatedtitle">The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment (HEPEX)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, A. W.; Thielen, J.; Pappenberger, F.; Schaake, J. C.; Hartman, R. K.</p> <p>2012-12-01</p> <p>The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF). With support from the US National Weather Service (NWS) and the European Commission (EC), the HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological <span class="hlt">ensemble</span> forecasts for decision support in emergency management and water resources sectors. The strategy to meet this goal includes meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. HEPEX has organized about a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Today, the HEPEX mission is to demonstrate the added value of hydrological <span class="hlt">ensemble</span> prediction systems (HEPS) for emergency management and water resources sectors to make decisions that have important consequences for economy, public health, safety, and the environment. HEPEX is now organised around six major themes that represent core elements of a hydrologic <span class="hlt">ensemble</span> prediction enterprise: input and pre-processing, <span class="hlt">ensemble</span> techniques, data assimilation, post-processing, verification, and communication and use in decision making. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/20136746','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/20136746"><span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Stohlgren, Thomas J; Ma, Peter; Kumar, Sunil; Rocca, Monique; Morisette, Jeffrey T; Jarnevich, Catherine S; Benson, Nate</p> <p>2010-02-01</p> <p><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis. PMID:20136746</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.A33A0222M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A33A0222M"><span id="translatedtitle">High-resolution climate simulations for Central Europe: An assessment of dynamical and statistical <span class="hlt">downscaling</span> techniques</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miksovsky, J.; Huth, R.; Halenka, T.; Belda, M.; Farda, A.; Skalak, P.; Stepanek, P.</p> <p>2009-12-01</p> <p>To bridge the resolution gap between the outputs of global climate models (GCMs) and finer-scale data needed for studies of the climate change impacts, two approaches are widely used: dynamical <span class="hlt">downscaling</span>, based on application of regional climate models (RCMs) embedded into the domain of the GCM simulation, and statistical <span class="hlt">downscaling</span> (SDS), using empirical transfer functions between the large-scale data generated by the GCM and local measurements. In our contribution, we compare the performance of different variants of both techniques for the region of Central Europe. The dynamical <span class="hlt">downscaling</span> is represented by the outputs of two regional models run in the 10 km horizontal grid, ALADIN-CLIMATE/CZ (co-developed by the Czech Hydrometeorological Institute and Meteo-France) and RegCM3 (developed by the Abdus Salam Centre for Theoretical Physics). The applied statistical methods were based on multiple linear regression, as well as on several of its nonlinear alternatives, including techniques employing artificial neural networks. Validation of the <span class="hlt">downscaling</span> outputs was carried out using measured data, gathered from weather stations in the Czech Republic, Slovakia, Austria and Hungary for the end of the 20th century; series of daily values of maximum and minimum temperature, precipitation and relative humidity were analyzed. None of the regional models or statistical <span class="hlt">downscaling</span> techniques could be identified as the universally best one. For instance, while most statistical methods misrepresented the shape of the statistical distribution of the target variables (especially in the more challenging cases such as estimation of daily precipitation), RCM-generated data often suffered from severe biases. It is also shown that further enhancement of the simulated fields of climate variables can be achieved through a combination of dynamical <span class="hlt">downscaling</span> and statistical postprocessing. This can not only be used to reduce biases and other systematic flaws in the generated time</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27516861','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27516861"><span id="translatedtitle"><span class="hlt">Downscaling</span> patterns of complementarity to a finer resolution and its implications for conservation prioritization.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>de Albuquerque, Fábio Suzart; Beier, Paul</p> <p>2016-06-01</p> <p>Given species inventories of all sites in a planning area, integer programming or heuristic algorithms can prioritize sites in terms of the site's complementary value, that is, the ability of the site to complement (add unrepresented species to) other sites prioritized for conservation. The utility of these procedures is limited because distributions of species are typically available only as coarse atlases or range maps, whereas conservation planners need to prioritize relatively small sites. If such coarse-resolution information can be used to identify small sites that efficiently represent species (i.e., <span class="hlt">downscaled</span>), then such data can be useful for conservation planning. We develop and test a new type of surrogate for biodiversity, which we call <span class="hlt">downscaled</span> complementarity. In this approach, complementarity values from large cells are <span class="hlt">downscaled</span> to small cells, using statistical methods or simple map overlays. We illustrate our approach for birds in Spain by building models at coarse scale (50 × 50 km atlas of European birds, and global range maps of birds interpreted at the same 50 × 50 km grid size), using this model to predict complementary value for 10 × 10 km cells in Spain, and testing how well-prioritized cells represented bird distributions in an independent bird atlas of those 10 × 10 km cells. <span class="hlt">Downscaled</span> complementarity was about 63-77% as effective as having full knowledge of the 10-km atlas data in its ability to improve on random selection of sites. <span class="hlt">Downscaled</span> complementarity has relatively low data acquisition cost and meets representation goals well compared with other surrogates currently in use. Our study justifies additional tests to determine whether <span class="hlt">downscaled</span> complementarity is an effective surrogate for other regions and taxa, and at spatial resolution finer than 10 × 10 km cells. Until such tests have been completed, we caution against assuming that any surrogate can reliably prioritize sites for species representation</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..1710850Z&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..1710850Z&link_type=ABSTRACT"><span id="translatedtitle">Employing multi-objective Genetic Programming to the <span class="hlt">downscaling</span> of near-surface atmospheric fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zerenner, Tanja; Venema, Victor; Friederichs, Petra; Simmer, Clemens</p> <p>2015-04-01</p> <p>The coupling of models for the different components of the Soil-Vegetation-Atmosphere-System is required to investigate component interactions and feedback processes. However, the component models for atmosphere, land-surface and subsurface are usually operated at different resolutions in space and time owing to the dominant processes. The computationally expensive atmospheric models are typically employed at a coarser resolution than land-surface and subsurface models. Thus up- and <span class="hlt">downscaling</span> procedures are required at the interface between the atmospheric model and the land-surface/subsurface models. We apply multi-objective Genetic Programming (GP) to a training data set of high-resolution atmospheric model runs to learn <span class="hlt">downscaling</span> rules, i. e., equations or short programs that reconstruct the fine-scale fields of the near-surface atmospheric state variables from the coarse atmospheric model output. Like artificial neural networks, GP can flexibly incorporate multivariate and nonlinear relations, but offers the advantage that the solutions are human readable and thus can be checked for physical consistency. Further, the Strength Pareto Approach for multi-objective fitness assignment allows to consider multiple characteristics of the fine-scale fields during the learning procedure. We have applied the described machine learning methodology to a training data set of 400 m resolution COSMO model runs to learn <span class="hlt">downscaling</span> rules which recover realistic fine-scale structures from the coarsened fields at 2.8 km resolution. Hence we are currently <span class="hlt">downscaling</span> by a factor of 7. The COSMO model is the weather forecast model developed and maintained by the German Weather Service and is contained in the Terrestrial Systems Modeling Platform (TerrSysMP), which couples the atmospheric COSMO model to land-surface model CLM and subsurface hydrological model ParFlow. Finally we aim at implementing the learned <span class="hlt">downscaling</span> rules in the TerrSysMP to achieve scale</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=315550&keyword=Atmospheric+AND+pressure&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65309539&CFTOKEN=27543274','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=315550&keyword=Atmospheric+AND+pressure&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65309539&CFTOKEN=27543274"><span id="translatedtitle">The Impact of Incongruous Lake Temperatures on Regional Climate Extremes <span class="hlt">Downscaled</span> from the CMIP5 Archive Using the WRF Model</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The impact of incongruous lake temperatures is demonstrated using the Weather Research and Forecasting (WRF) Model to <span class="hlt">downscale</span> global climate fields. Unrealistic lake temperatures prescribed by the default WRF configuration cause obvious biases near the lakes and also affect pre...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&keyword=management+AND+journals&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65283372&CFTOKEN=55719609','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&keyword=management+AND+journals&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=65283372&CFTOKEN=55719609"><span id="translatedtitle">An Observation-base investigation of nudging in WRF for <span class="hlt">downscaling</span> surface climate information to 12-km Grid Spacing</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Previous research has demonstrated the ability to use the Weather Research and Forecast (WRF) model and contemporary dynamical <span class="hlt">downscaling</span> methods to refine global climate modeling results to a horizontal resolution of 36 km. Environmental managers and urban planners have expre...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005JGRD..110.4105W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005JGRD..110.4105W"><span id="translatedtitle">A retrospective assessment of National Centers for Environmental Prediction climate model-based <span class="hlt">ensemble</span> hydrologic forecasting in the western United States</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, Andrew W.; Kumar, Arun; Lettenmaier, Dennis P.</p> <p>2005-02-01</p> <p>We assess the potential forecast skill of a climate model-based approach for seasonal <span class="hlt">ensemble</span> hydrologic and streamflow forecasting for the western United States. By using climate model <span class="hlt">ensemble</span> forecasts and <span class="hlt">ensembles</span> formed via the resampling of observations, we distinguish hydrologic forecast skill resulting from the predictable evolution of initial hydrologic conditions from that derived from the climate model forecasts. Monthly climate model <span class="hlt">ensembles</span> of precipitation and temperature produced by the National Centers for Environmental prediction global spectral model (GSM) are <span class="hlt">downscaled</span> for use as forcings of the variable infiltration capacity (VIC) hydrologic model. VIC then simulates <span class="hlt">ensembles</span> of streamflow and spatially distributed hydrologic variables such as snowpack, soil moisture, and runoff. The regional averages of the <span class="hlt">ensemble</span> forcings and derived hydrologic variables were evaluated over five regions: the Pacific Northwest, California, the Great Basin, the Colorado River basin, and the upper Rio Grande River basin. The skill assessment focuses on a retrospective 21-year period (1979-1999) during which GSM retrospective forecast <span class="hlt">ensembles</span> (termed hindcasts), created using similar procedures to GSM real-time forecasts, are available. The observational verification data set for the hindcasts was a retrospective hydroclimatology at 1/8°-1/4° consisting of gridded observations of temperature and precipitation and gridded hydrologic simulation results (for hydrologic variables and streamflow) based on the observed meteorological inputs. The GSM hindcast skill was assessed relative to that of a naive <span class="hlt">ensemble</span> climatology forecast and to that of <span class="hlt">ensemble</span> streamflow prediction (ESP) hindcasts, a forecast baseline sharing the same initial condition information as the GSM-based hindcasts. We found that the unconditional (all years) GSM hindcasts for regionally averaged variables provided practically no skill improvement over the ESP hindcasts and did not</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015SPIE.9534E..15R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015SPIE.9534E..15R&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Ensemble</span> approach for differentiation of malignant melanoma</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rastgoo, Mojdeh; Morel, Olivier; Marzani, Franck; Garcia, Rafael</p> <p>2015-04-01</p> <p>Melanoma is the deadliest type of skin cancer, yet it is the most treatable kind depending on its early diagnosis. The early prognosis of melanoma is a challenging task for both clinicians and dermatologists. Due to the importance of early diagnosis and in order to assist the dermatologists, we propose an automated framework based on <span class="hlt">ensemble</span> learning methods and dermoscopy images to differentiate melanoma from dysplastic and benign lesions. The evaluation of our framework on the recent and public dermoscopy benchmark (PH2 dataset) indicates the potential of proposed method. Our evaluation, using only global features, revealed that <span class="hlt">ensembles</span> such as random forest perform better than single learner. Using random forest <span class="hlt">ensemble</span> and combination of color and texture features, our framework achieved the highest sensitivity of 94% and specificity of 92%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3680205','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3680205"><span id="translatedtitle">Optimized gold nanoshell <span class="hlt">ensembles</span> for biomedical applications</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p></p> <p>2013-01-01</p> <p>We theoretically study the properties of the optimal size distribution in the <span class="hlt">ensemble</span> of hollow gold nanoshells (HGNs) that exhibits the best performance at in vivo biomedical applications. For the first time, to the best of our knowledge, we analyze the dependence of the optimal geometric means of the nanoshells’ thicknesses and core radii on the excitation wavelength and the type of human tissue, while assuming lognormal fit to the size distribution in a real HGN <span class="hlt">ensemble</span>. Regardless of the tissue type, short-wavelength, near-infrared lasers are found to be the most effective in both absorption- and scattering-based applications. We derive approximate analytical expressions enabling one to readily estimate the parameters of optimal distribution for which an HGN <span class="hlt">ensemble</span> exhibits the maximum efficiency of absorption or scattering inside a human tissue irradiated by a near-infrared laser. PMID:23537206</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/15006172','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/15006172"><span id="translatedtitle">Creating <span class="hlt">Ensembles</span> of Decision Trees Through Sampling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kamath,C; Cantu-Paz, E</p> <p>2001-07-26</p> <p>Recent work in classification indicates that significant improvements in accuracy can be obtained by growing an <span class="hlt">ensemble</span> of classifiers and having them vote for the most popular class. This paper focuses on <span class="hlt">ensembles</span> of decision trees that are created with a randomized procedure based on sampling. Randomization can be introduced by using random samples of the training data (as in bagging or boosting) and running a conventional tree-building algorithm, or by randomizing the induction algorithm itself. The objective of this paper is to describe the first experiences with a novel randomized tree induction method that uses a sub-sample of instances at a node to determine the split. The empirical results show that <span class="hlt">ensembles</span> generated using this approach yield results that are competitive in accuracy and superior in computational cost to boosting and bagging.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/15005459','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/15005459"><span id="translatedtitle">Creating <span class="hlt">ensembles</span> of decision trees through sampling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kamath, C; Cantu-Paz, E</p> <p>2001-02-02</p> <p>Recent work in classification indicates that significant improvements in accuracy can be obtained by growing an <span class="hlt">ensemble</span> of classifiers and having them vote for the most popular class. This paper focuses on <span class="hlt">ensembles</span> of decision trees that are created with a randomized procedure based on sampling. Randomization can be introduced by using random samples of the training data (as in bagging or arcing) and running a conventional tree-building algorithm, or by randomizing the induction algorithm itself. The objective of this paper is to describe our first experiences with a novel randomized tree induction method that uses a subset of samples at a node to determine the split. Our empirical results show that <span class="hlt">ensembles</span> generated using this approach yield results that are competitive in accuracy and superior in computational cost.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFM.H14A..05S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFM.H14A..05S"><span id="translatedtitle">Hydrologic <span class="hlt">Ensemble</span> Prediction: Challenges and Opportunities</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schaake, J.; Bradley, A.</p> <p>2005-12-01</p> <p><span class="hlt">Ensemble</span> forecast techniques are beginning to be used for hydrological prediction by operational hydrological services throughout the world. These techniques are attractive because they allow effects of a wide range of sources of uncertainty on hydrological forecasts to be accounted for. Not only does <span class="hlt">ensemble</span> prediction in hydrology offer a general approach to probabilistic prediction, it offers a significant new approach to improve hydrological forecast accuracy as well. But, there are many scientific challenges that must be overcome to provide users with high quality hydrologic <span class="hlt">ensemble</span> forecasts. A new international project the Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment (HEPEX) was started last year to organize the scientific community to meet these challenges. Its main objective is to bring the international hydrological community together with the meteorological community to demonstrate how to produce reliable hydrological <span class="hlt">ensemble</span> for decisions for the benefit of public health and safety, the economy and the environment. Topics that will be addressed by the HEPEX scientific community include techniques for using weather and climate information in hydrologic prediction systems, new methods in hydrologic prediction, data assimilation issues in hydrology and hydrometeorology, verification and correction of <span class="hlt">ensemble</span> weather and hydrologic forecasts, and better quantification of uncertainty in hydrological prediction. As pathway for addressing these topics, HEPEX will set up demonstration test bed projects and compile data sets for the intercomparison of coupled systems for atmospheric and hydrologic forecasting, and their assessment for meeting end users' needs for decision-making. Test bed projects have been proposed in North and South America, Europe, and Asia, and have a focus ranging from short-range flood forecasting to seasonal predictions for water supply. For example, within the United States, ongoing activities in seasonal prediction as part of the GEWEX</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.tmp..152I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp..152I"><span id="translatedtitle">Robust intensification of hydroclimatic intensity over East Asia from multi-model <span class="hlt">ensemble</span> regional projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Im, Eun-Soon; Choi, Yeon-Woo; Ahn, Joong-Bae</p> <p>2016-06-01</p> <p>This study assesses the hydroclimatic response to global warming over East Asia from multi-model <span class="hlt">ensemble</span> regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical <span class="hlt">downscaling</span> of the Hadley Centre Global Environmental Model version 2-Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981-2100) daily precipitation. <span class="hlt">Ensemble</span> projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhRvA..93c2139G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhRvA..93c2139G"><span id="translatedtitle">Incoherent <span class="hlt">ensemble</span> dynamics in disordered systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gneiting, Clemens; Anger, Felix R.; Buchleitner, Andreas</p> <p>2016-03-01</p> <p>We derive a quantum master equation which describes the dynamics of the <span class="hlt">ensemble</span>-averaged state of homogeneous disorder models at short times, and mediates a transition from coherent superpositions into classical mixtures. While each single realization follows unitary dynamics, this decoherencelike behavior arises as a consequence of the <span class="hlt">ensemble</span> average. The master equation manifestly reflects the translational invariance of the disorder correlations and allows us to relate the disorder-induced dynamics to a collisional decoherence process, where the disorder correlations determine the spatial decay of coherences. We apply our theory to the (one-dimensional) Anderson model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JPhA...49c5101A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JPhA...49c5101A"><span id="translatedtitle">Native ultrametricity of sparse random <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Avetisov, V.; Krapivsky, P. L.; Nechaev, S.</p> <p>2016-01-01</p> <p>We investigate the eigenvalue density in <span class="hlt">ensembles</span> of large sparse Bernoulli random matrices. Analyzing in detail the spectral density of <span class="hlt">ensembles</span> of linear subgraphs, we discuss its ultrametric nature and show that near the spectrum boundary, the tails of the spectral density exhibit a Lifshitz singularity typical for Anderson localization. We pay attention to an intriguing connection of the spectral density to the Dedekind η-function. We conjecture that ultrametricity emerges in rare-event statistics and is inherit to generic complex sparse systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/20857669','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/20857669"><span id="translatedtitle">Quantum measurement of a mesoscopic spin <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Giedke, G.; Taylor, J. M.; Lukin, M. D.; D'Alessandro, D.; Imamoglu, A.</p> <p>2006-09-15</p> <p>We describe a method for precise estimation of the polarization of a mesoscopic spin <span class="hlt">ensemble</span> by using its coupling to a single two-level system. Our approach requires a minimal number of measurements on the two-level system for a given measurement precision. We consider the application of this method to the case of nuclear-spin <span class="hlt">ensemble</span> defined by a single electron-charged quantum dot: we show that decreasing the electron spin dephasing due to nuclei and increasing the fidelity of nuclear-spin-based quantum memory could be within the reach of present day experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120003771','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120003771"><span id="translatedtitle">Electrostatic Evaluation of the Propellant Handlers <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hogue, Michael D.; Calle, Carlos I.; Buhler, Charles</p> <p>2006-01-01</p> <p>The Self-Contained Atmospheric Protective <span class="hlt">Ensemble</span> (SCAPE) used in propellant handling at NASA's Kennedy Space Center (KSC) has recently completed a series of tests to determine its electrostatic properties of the coverall fabric used in the Propellant Handlers <span class="hlt">Ensemble</span> (PHE). Understanding these electrostatic properties are fundamental to ensuring safe operations when working with flammable rocket propellants such as hydrazine, methyl hydrazine, and unsymmetrical dimethyl hydrazine. These tests include surface resistivity, charge decay, triboelectric charging, and flame incendivity. In this presentation, we will discuss the results of these tests on the current PHE as well as new fabrics and materials being evaluated for the next generation of PHE.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.tmp..351G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.tmp..351G"><span id="translatedtitle">Validating the dynamic <span class="hlt">downscaling</span> ability of WRF for East Asian summer climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gao, Jiangbo; Hou, Wenjuan; Xue, Yongkang; Wu, Shaohong</p> <p>2015-12-01</p> <p>To better understand the regional climate model (RCM) performance for East Asian summer climate and the influencing factors, this study evaluated the dynamic <span class="hlt">downscaling</span> ability of the Weather Research Forecast (WRF) RCM. According to the comprehensive comparison studies on different physical processes and experimental settings, the optimal combination of WRF model setups can be obtained for East Asian precipitation and temperature simulations. Furthermore, based on the optimal combination, when compared with climate observations, WRF shows high ability to <span class="hlt">downscale</span> NCEP DOE Reanalysis-2, which provided initial and lateral boundary conditions for the WRF, especially for the precipitation simulation due to the better simulated low-level water vapor flux. However, the strengthened Western North Pacific Subtropical High (WPSH) from WRF simulation results in the positive anomaly for summer rainfall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ClDy...43.1731J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ClDy...43.1731J"><span id="translatedtitle">Rainfall anomaly prediction using statistical <span class="hlt">downscaling</span> in a multimodel superensemble over tropical South America</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Johnson, Bradford; Kumar, Vinay; Krishnamurti, T. N.</p> <p>2014-10-01</p> <p>This study addresses the predictability of rainfall variations over South America and the Amazon basin. A primary factor leading to model inaccuracy in precipitation forecasts is the coarse resolution data utilized by coupled models during the training phase. By using MERRA reanalysis and statistical <span class="hlt">downscaling</span> along with the superensemble methodology, it is possible to obtain more precise forecast of rainfall anomalies over tropical South America during austral fall. Selective inclusion (and exclusion) of member models also allows for increased accuracy of superensemble forecasts. The use of coupled atmospheric-ocean numerical models to predict the rainfall anomalies has had mixed results. Improvement in individual member models is also possible on smaller spatial scales and in regions where substantial topographical changes were not handled well under original model initial conditions. The combination of <span class="hlt">downscaling</span> and superensemble methodologies with other research methods presents the potential opportunity for increased accuracy not only in seasonal forecasts but on shorter temporal scales as well.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC12C..05G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC12C..05G"><span id="translatedtitle">Lessons learned from the National Climate Predictions and Projections (NCPP) platform Workshop on Quantitative Evaluation of <span class="hlt">Downscaling</span> 2013</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guentchev, G.</p> <p>2013-12-01</p> <p>The mission of NCPP is to accelerate the provision of climate information on regional and local scale for use in adaptation planning and decision making through collaborative participation of a community of scientists and practitioners. A major focus is the development of a capability for objective and quantitative evaluation of <span class="hlt">downscaled</span> climate information in support of applications. NCPP recognizes the importance of focusing this evaluation effort on real-world applications and the necessity to work closely with the user community to deliver usable evaluations and guidance. This summer NCPP organized our first workshop on quantitative evaluation of <span class="hlt">downscaled</span> climate datasets (http://earthsystemcog.org/projects/<span class="hlt">downscaling</span>-2013/). Workshop participants included representatives from <span class="hlt">downscaling</span> efforts, applications partners from the health, ecological, agriculture and water resources impacts communities, and people working on data infrastructure, metadata, and standards development. The workshop exemplifies NCPP's approach of collaborative and participatory problem-solving where scientists are working together with practitioners to develop applications related evaluation. The set of observed and <span class="hlt">downscaled</span> datasets included for evaluation in the workshop were assessed using a variety of metrics to elucidate the statistical characteristics of temperature and precipitation time series. In addition, the <span class="hlt">downscaled</span> datasets were evaluated in terms of their representation of indices relevant to the participating applications working groups, more specifically related to human health and ecological impacts. The presentation will focus on sharing the lessons we learned from our workshop.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130013812','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130013812"><span id="translatedtitle"><span class="hlt">Ensemble</span> Eclipse: A Process for Prefab Development Environment for the <span class="hlt">Ensemble</span> Project</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wallick, Michael N.; Mittman, David S.; Shams, Khawaja, S.; Bachmann, Andrew G.; Ludowise, Melissa</p> <p>2013-01-01</p> <p>This software simplifies the process of having to set up an Eclipse IDE programming environment for the members of the cross-NASA center project, <span class="hlt">Ensemble</span>. It achieves this by assembling all the necessary add-ons and custom tools/preferences. This software is unique in that it allows developers in the <span class="hlt">Ensemble</span> Project (approximately 20 to 40 at any time) across multiple NASA centers to set up a development environment almost instantly and work on <span class="hlt">Ensemble</span> software. The software automatically has the source code repositories and other vital information and settings included. The Eclipse IDE is an open-source development framework. The NASA (<span class="hlt">Ensemble</span>-specific) version of the software includes <span class="hlt">Ensemble</span>-specific plug-ins as well as settings for the <span class="hlt">Ensemble</span> project. This software saves developers the time and hassle of setting up a programming environment, making sure that everything is set up in the correct manner for <span class="hlt">Ensemble</span> development. Existing software (i.e., standard Eclipse) requires an intensive setup process that is both time-consuming and error prone. This software is built once by a single user and tested, allowing other developers to simply download and use the software</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20020061294&hterms=soil+study+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dsoil%2Bstudy%2Bmodel','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20020061294&hterms=soil+study+model&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dsoil%2Bstudy%2Bmodel"><span id="translatedtitle"><span class="hlt">Ensemble</span> Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.</p> <p>2001-01-01</p> <p>This paper presents preliminary results of an <span class="hlt">ensemble</span> canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate <span class="hlt">downscaling</span> studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the <span class="hlt">ensemble</span> hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26977807','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26977807"><span id="translatedtitle">Using Random Forest to Improve the <span class="hlt">Downscaling</span> of Global Livestock Census Data.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Nicolas, Gaëlle; Robinson, Timothy P; Wint, G R William; Conchedda, Giulia; Cinardi, Giuseppina; Gilbert, Marius</p> <p>2016-01-01</p> <p>Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a <span class="hlt">downscaling</span> methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and <span class="hlt">downscaling</span> at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to <span class="hlt">downscale</span> census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better <span class="hlt">downscaled</span> estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4792414','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4792414"><span id="translatedtitle">Using Random Forest to Improve the <span class="hlt">Downscaling</span> of Global Livestock Census Data</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Nicolas, Gaëlle; Robinson, Timothy P.; Wint, G. R. William; Conchedda, Giulia; Cinardi, Giuseppina; Gilbert, Marius</p> <p>2016-01-01</p> <p>Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a <span class="hlt">downscaling</span> methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and <span class="hlt">downscaling</span> at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to <span class="hlt">downscale</span> census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better <span class="hlt">downscaled</span> estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.120..341K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.120..341K"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> and future scenario generation of temperatures for Pakistan Region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kazmi, Dildar Hussain; Li, Jianping; Rasul, Ghulam; Tong, Jiang; Ali, Gohar; Cheema, Sohail Babar; Liu, Luliu; Gemmer, Marco; Fischer, Thomas</p> <p>2015-04-01</p> <p>Finer climate change information on spatial scale is required for impact studies than that presently provided by global or regional climate models. It is especially true for regions like South Asia with complex topography, coastal or island locations, and the areas of highly heterogeneous land-cover. To deal with the situation, an inexpensive method (statistical <span class="hlt">downscaling</span>) has been adopted. Statistical <span class="hlt">DownScaling</span> Model (SDSM) employed for <span class="hlt">downscaling</span> of daily minimum and maximum temperature data of 44 national stations for base time (1961-1990) and then the future scenarios generated up to 2099. Observed as well as Predictors (product of National Oceanic and Atmospheric Administration) data were calibrated and tested on individual/multiple basis through linear regression. Future scenario was generated based on HadCM3 daily data for A2 and B2 story lines. The <span class="hlt">downscaled</span> data has been tested, and it has shown a relatively strong relationship with the observed in comparison to ECHAM5 data. Generally, the southern half of the country is considered vulnerable in terms of increasing temperatures, but the results of this study projects that in future, the northern belt in particular would have a possible threat of increasing tendency in air temperature. Especially, the northern areas (hosting the third largest ice reserves after the Polar Regions), an important feeding source for Indus River, are projected to be vulnerable in terms of increasing temperatures. Consequently, not only the hydro-agricultural sector but also the environmental conditions in the area may be at risk, in future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.124..919R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.124..919R"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of rainfall: a non-stationary and multi-resolution approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rashid, Md. Mamunur; Beecham, Simon; Chowdhury, Rezaul Kabir</p> <p>2016-05-01</p> <p>A novel <span class="hlt">downscaling</span> technique is proposed in this study whereby the original rainfall and reanalysis variables are first decomposed by wavelet transforms and rainfall is modelled using the semi-parametric additive model formulation of Generalized Additive Model in Location, Scale and Shape (GAMLSS). The flexibility of the GAMLSS model makes it feasible as a framework for non-stationary modelling. Decomposition of a rainfall series into different components is useful to separate the scale-dependent properties of the rainfall as this varies both temporally and spatially. The study was conducted at the Onkaparinga river catchment in South Australia. The model was calibrated over the period 1960 to 1990 and validated over the period 1991 to 2010. The model reproduced the monthly variability and statistics of the observed rainfall well with Nash-Sutcliffe efficiency (NSE) values of 0.66 and 0.65 for the calibration and validation periods, respectively. It also reproduced well the seasonal rainfall over the calibration (NSE = 0.37) and validation (NSE = 0.69) periods for all seasons. The proposed model was better than the tradition modelling approach (application of GAMLSS to the original rainfall series without decomposition) at reproducing the time-frequency properties of the observed rainfall, and yet it still preserved the statistics produced by the traditional modelling approach. When <span class="hlt">downscaling</span> models were developed with general circulation model (GCM) historical output datasets, the proposed wavelet-based <span class="hlt">downscaling</span> model outperformed the traditional <span class="hlt">downscaling</span> model in terms of reproducing monthly rainfall for both the calibration and validation periods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011JESS..120..375A&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011JESS..120..375A&link_type=ABSTRACT"><span id="translatedtitle">Uncertainties in <span class="hlt">downscaled</span> relative humidity for a semi-arid region in India</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Anandhi, Aavudai</p> <p>2011-06-01</p> <p>Monthly scenarios of relative humidity ( R H) were obtained for the Malaprabha river basin in India using a statistical <span class="hlt">downscaling</span> technique. Large-scale atmospheric variables (air temperature and specific humidity at 925 mb, surface air temperature and latent heat flux) were chosen as predictors. The predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis dataset for the period 1978-2000, and (2) simulations of the third generation Canadian Coupled Global Climate Model for the period 1978-2100. The objective of this study was to investigate the uncertainties in regional scenarios developed for R H due to the choice of emission scenarios (A1B, A2, B1 and COMMIT) and the predictors selected. Multi-linear regression with stepwise screening is the <span class="hlt">downscaling</span> technique used in this study. To study the uncertainty in the regional scenarios of R H, due to the selected predictors, eight sets of predictors were chosen and a <span class="hlt">downscaling</span> model was developed for each set. Performance of the <span class="hlt">downscaling</span> models in the baseline period (1978-2000) was studied using three measures (1) Nash-Sutcliffe error estimate ( E f), (2) mean absolute error (MAE), and (3) product moment correlation ( P). Results show that the performances vary between 0.59 and 0.68, 0.42 and 0.50 and 0.77 and 0.82 for E f, MAE and P. Cumulative distribution functions were prepared from the regional scenarios of R H developed for combinations of predictors and emission scenarios. Results show a variation of 1 to 6% R H in the scenarios developed for combination of predictor sets for baseline period. For a future period (2001-2100), a variation of 6 to 15% R H was observed for the combination of emission scenarios and predictors. The variation was highest for A2 scenario and least for COMMIT and B1 scenario.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC53G1293H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC53G1293H"><span id="translatedtitle">Some Advances in <span class="hlt">Downscaling</span> Probabilistic Climate Forecasts for Agricultural Decision Support</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Han, E.; Ines, A.</p> <p>2015-12-01</p> <p>Seasonal climate forecasts, commonly provided in tercile-probabilities format (below-, near- and above-normal), need to be translated into more meaningful information for decision support of practitioners in agriculture. In this paper, we will present two new novel approaches to temporally <span class="hlt">downscale</span> probabilistic seasonal climate forecasts: one non-parametric and another parametric method. First, the non-parametric <span class="hlt">downscaling</span> approach called FResampler1 uses the concept of 'conditional block sampling' of weather data to create daily weather realizations of a tercile-based seasonal climate forecasts. FResampler1 randomly draws time series of daily weather parameters (e.g., rainfall, maximum and minimum temperature and solar radiation) from historical records, for the season of interest from years that belong to a certain rainfall tercile category (e.g., being below-, near- and above-normal). In this way, FResampler1 preserves the covariance between rainfall and other weather parameters as if conditionally sampling maximum and minimum temperature and solar radiation if that day is wet or dry. The second approach called predictWTD is a parametric method based on a conditional stochastic weather generator. The tercile-based seasonal climate forecast is converted into a theoretical forecast cumulative probability curve. Then the deviates for each percentile is converted into rainfall amount or frequency or intensity to <span class="hlt">downscale</span> the 'full' distribution of probabilistic seasonal climate forecasts. Those seasonal deviates are then disaggregated on a monthly basis and used to constrain the <span class="hlt">downscaling</span> of forecast realizations at different percentile values of the theoretical forecast curve. As well as the theoretical basis of the approaches we will discuss sensitivity analysis (length of data and size of samples) of them. In addition their potential applications for managing climate-related risks in agriculture will be shown through a couple of case studies based on</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC41E..06G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41E..06G"><span id="translatedtitle">Combining Global Climate Model Outputs and Insights from <span class="hlt">Downscaling</span> for Australian Climate Projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grose, M. R.; Timbal, B.; Katzfey, J. J.; Moise, A. F.; Eksrtrom, M.; Whetton, P.</p> <p>2013-12-01</p> <p>Dynamical and statistical <span class="hlt">downscaling</span> of global climate model (GCM) outputs has the potential to provide valuable insights when making regional climate projections. It may reveal regional detail in the projected climate change signal through higher resolution and accounting for local influences such as topography and coastlines. However, climate change adaptation research and planning desires a coherent view of possible future climate that accounts for the various sources of uncertainty and at a relevant spatial scale. This means there is value in combining the most useful insights from all available <span class="hlt">downscaling</span> with a more comprehensive set of designed global climate model (GCM) projections (e.g. the CMIP5 archive), and this is done for the next set of national climate projections products in Australia. There are several practical considerations in this process that affect the process, primarily because <span class="hlt">downscaling</span> is done using various disparate methods for a limited set of models and scenarios. There is no objective framework to combine different sets of ad hoc <span class="hlt">downscaling</span> simulations with a set of GCMs, so some degree of expert judgment is used. We emphasize cases where there is the most apparent ';added value' and report these insights in complement, and in some cases in preference to, GCM projections. Confidence in such insights first requires understanding of what input data is used from the host model, what biases are reduced and what new biases are potentially introduced. We then seek an understanding of how the climate change signal differs from that of the host model, and an attribution of the cause of this difference. Several case studies within Australia are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A43F3330H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A43F3330H"><span id="translatedtitle">Extended-Range High-Resolution Dynamical <span class="hlt">Downscaling</span> over a Continental-Scale Domain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Husain, S. Z.; Separovic, L.; Yu, W.; Fernig, D.</p> <p>2014-12-01</p> <p>High-resolution mesoscale simulations, when applied for <span class="hlt">downscaling</span> meteorological fields over large spatial domains and for extended time periods, can provide valuable information for many practical application scenarios including the weather-dependent renewable energy industry. In the present study, a strategy has been proposed to dynamically <span class="hlt">downscale</span> coarse-resolution meteorological fields from Environment Canada's regional analyses for a period of multiple years over the entire Canadian territory. The study demonstrates that a continuous mesoscale simulation over the entire domain is the most suitable approach in this regard. Large-scale deviations in the different meteorological fields pose the biggest challenge for extended-range simulations over continental scale domains, and the enforcement of the lateral boundary conditions is not sufficient to restrict such deviations. A scheme has therefore been developed to spectrally nudge the simulated high-resolution meteorological fields at the different model vertical levels towards those embedded in the coarse-resolution driving fields derived from the regional analyses. A series of experiments were carried out to determine the optimal nudging strategy including the appropriate nudging length scales, nudging vertical profile and temporal relaxation. A forcing strategy based on grid nudging of the different surface fields, including surface temperature, soil-moisture, and snow conditions, towards their expected values obtained from a high-resolution offline surface scheme was also devised to limit any considerable deviation in the evolving surface fields due to extended-range temporal integrations. The study shows that ensuring large-scale atmospheric similarities helps to deliver near-surface statistical scores for temperature, dew point temperature and horizontal wind speed that are better or comparable to the operational regional forecasts issued by Environment Canada. Furthermore, the meteorological fields</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015NHESD...3.3077M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015NHESD...3.3077M"><span id="translatedtitle">Runup parameterization and beach vulnerability assessment on a barrier island: a <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medellín, G.; Brinkkemper, J. A.; Torres-Freyermuth, A.; Appendini, C. M.; Mendoza, E. T.; Salles, P.</p> <p>2015-05-01</p> <p>We present a <span class="hlt">downscaling</span> approach for the study of wave-induced extreme water levels at a location on a barrier island in Yucatan (Mexico). Wave information from a 30 year wave hindcast is validated with in situ measurements at 8 m water depth. The Maximum Dissimilarity Algorithm is employed for the selection of 600 representative cases, encompassing different wave characteristics and tidal level combinations. The selected cases are propagated from 8 m water depth till the shore using the coupling of a third-generation wave model and a phase-resolving non-hydrostatic Nonlinear Shallow Water Equations model. Extreme wave runup, R2%, is estimated for the simulated cases and can be further employed to reconstruct the 30 year period using an interpolation algorithm. <span class="hlt">Downscaling</span> results show runup saturation during more energetic wave conditions and modulation owing to tides. The latter suggests that the R2% can be parameterized using a hyperbolic-like formulation with dependency on both wave height and tidal level. The new parametric formulation is in agreement with the <span class="hlt">downscaling</span> results (r2 = 0.78), allowing a fast calculation of wave-induced extreme water levels at this location. Finally, an assessment of beach vulnerability to wave-induced extreme water level is conducted at the study area by employing the two approaches (reconstruction/parametrization) and a storm impact scale. The 30 year extreme water level hindcast allows the calculation of beach vulnerability as a function of return periods. It is shown that the <span class="hlt">downscaling</span>-derived parameterization provides reasonable results as compared with the numerical approach. This methodology can be extended to other locations and can be further improved by incorporating the storm surge contributions to the extreme water level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016NHESS..16..167M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016NHESS..16..167M&link_type=ABSTRACT"><span id="translatedtitle">Run-up parameterization and beach vulnerability assessment on a barrier island: a <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medellín, G.; Brinkkemper, J. A.; Torres-Freyermuth, A.; Appendini, C. M.; Mendoza, E. T.; Salles, P.</p> <p>2016-01-01</p> <p>We present a <span class="hlt">downscaling</span> approach for the study of wave-induced extreme water levels at a location on a barrier island in Yucatán (Mexico). Wave information from a 30-year wave hindcast is validated with in situ measurements at 8 m water depth. The maximum dissimilarity algorithm is employed for the selection of 600 representative cases, encompassing different combinations of wave characteristics and tidal level. The selected cases are propagated from 8 m water depth to the shore using the coupling of a third-generation wave model and a phase-resolving non-hydrostatic nonlinear shallow-water equation model. Extreme wave run-up, R2%, is estimated for the simulated cases and can be further employed to reconstruct the 30-year time series using an interpolation algorithm. <span class="hlt">Downscaling</span> results show run-up saturation during more energetic wave conditions and modulation owing to tides. The latter suggests that the R2% can be parameterized using a hyperbolic-like formulation with dependency on both wave height and tidal level. The new parametric formulation is in agreement with the <span class="hlt">downscaling</span> results (r2 = 0.78), allowing a fast calculation of wave-induced extreme water levels at this location. Finally, an assessment of beach vulnerability to wave-induced extreme water levels is conducted at the study area by employing the two approaches (reconstruction/parameterization) and a storm impact scale. The 30-year extreme water level hindcast allows the calculation of beach vulnerability as a function of return periods. It is shown that the <span class="hlt">downscaling</span>-derived parameterization provides reasonable results as compared with the numerical approach. This methodology can be extended to other locations and can be further improved by incorporating the storm surge contributions to the extreme water level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H33E0921N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H33E0921N"><span id="translatedtitle">A Procedure for Statistical <span class="hlt">Downscaling</span> of Precipitation with an Objective Method for Predictor Selection</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Najafi, M.; Moradkhani, H.; Wherry, S.</p> <p>2009-12-01</p> <p><span class="hlt">Downscaling</span> General Circulation Models’ (GCM) outputs to a finer grid cell size is an important step in climate change impact and adaptation studies in particular for hydrologic applications. Many investigations have been focused on presenting techniques to <span class="hlt">downscale</span> GCM data utilizing statistical approaches. Nevertheless there is currently the need to present techniques on predictor selection and also to compare different <span class="hlt">downscaling</span> models’ capabilities. Hence in this study an algorithm has been developed to select GCM predictors in a subseasonal to seasonal time scale. Independent component analysis was used to find the statistically independent signals of CGCM3 variables in the 4*7 grid cells covering the Willamette river basin in Oregon, USA. Using the multi-linear regression cross validation (MLR-CV) the GCM predictors were selected for each period. The selected predictors were then applied to train the ANFIS (Adaptive Network-based Fuzzy Inference System) and the SVM (Support Vector Machine) models, and their performances were assessed on the test data. To design more robust networks that are less dependent on training data set, the cross validation was performed. . Predictors with the best performance for each season in the test set (using both ANFIS and SVM models) were selected for that specific season. The comparison of ANFIS and SVM models using statistical measures showed that ANFIS presents better results suitable for climate impact studies. Also application of ICA allowed reducing the size of many dependent GCM variables in 28 grid cells considerably resulting in higher accuracy in <span class="hlt">downscaling</span> and more effectiveness in the procedure.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H31B0994H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H31B0994H"><span id="translatedtitle"><span class="hlt">Downscaling</span> of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.</p> <p>2010-12-01</p> <p>High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image <span class="hlt">downscaling</span> has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform <span class="hlt">downscaling</span> of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for <span class="hlt">downscaling</span> problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the <span class="hlt">downscaled</span> 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JGRD..11412108N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JGRD..11412108N"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of short-term climate fluctuations: On the benefits of precipitation assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nunes, Ana M. B.; Roads, John O.</p> <p>2009-06-01</p> <p>Regional <span class="hlt">downscaling</span> has proven useful in adding details to the global solution. However, the parameterized physical processes can systematically deviate the large-scale features in the regional solution. To demonstrate the precipitation assimilation beneficial impact on the dynamical <span class="hlt">downscaling</span>, a regional spectral model driven by the National Centers for Environmental Prediction/Department of Energy Atmospheric Model Intercomparison Project II (NCEP/DOE AMIP-II) Reanalysis was used to <span class="hlt">downscale</span> the large-scale features over most of North America. The North American Regional Reanalysis provided the 3-hourly precipitation rates that the regional model employed to simulate two opposite extreme climate events: the upper Mississippi River Basin 1988 drought and 1993 floods. In addition to these two cases, the 1990 summer anomalous precipitation over the same area was also investigated. Precipitation assimilation positively influences the dynamical <span class="hlt">downscaling</span> of these extreme climate events. The regional model when assimilating precipitation was particularly successful in reproducing the observed precipitation patterns over the central United States, where the large-scale circulation affects the precipitation variability. Particularly for the flood year, the intensity and location of the subtropical upper-level westerly jet and its associated transverse circulations were noticeably improved in the regional simulations, where the heavy precipitation core was found. This also suggests that the cumulus convection scheme, in this case the Relaxed Arakawa-Schubert parameterization scheme, can cause the large-scale features to drift during the regional simulation, and precipitation assimilation reduces this departure from the global solution. These changes in the upper-level winds were also followed by better characterization of the drought of 1988 as well as the 1990 summer heavy precipitation simulation, in comparison to regional control simulations, where precipitation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1813618O&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1813618O&link_type=ABSTRACT"><span id="translatedtitle">Total probabilities of <span class="hlt">ensemble</span> runoff forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian</p> <p>2016-04-01</p> <p><span class="hlt">Ensemble</span> forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the <span class="hlt">ensembles</span> often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and <span class="hlt">Ensemble</span> Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of <span class="hlt">ensemble</span> forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff <span class="hlt">ensembles</span> for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast <span class="hlt">ensemble</span> individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all <span class="hlt">ensembles</span>. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFMGC23F1191A&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFMGC23F1191A&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Downscaling</span> SSPs in Bangladesh - Integrating Science, Modelling and Stakeholders Through Qualitative and Quantitative Scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Allan, A.; Barbour, E.; Salehin, M.; Hutton, C.; Lázár, A. N.; Nicholls, R. J.; Rahman, M. M.</p> <p>2015-12-01</p> <p>A <span class="hlt">downscaled</span> scenario development process was adopted in the context of a project seeking to understand relationships between ecosystem services and human well-being in the Ganges-Brahmaputra delta. The aim was to link the concerns and priorities of relevant stakeholders with the integrated biophysical and poverty models used in the project. A 2-stage process was used to facilitate the connection between stakeholders concerns and available modelling capacity: the first to qualitatively describe what the future might look like in 2050; the second to translate these qualitative descriptions into the quantitative form required by the numerical models. An extended, modified SSP approach was adopted, with stakeholders <span class="hlt">downscaling</span> issues identified through interviews as being priorities for the southwest of Bangladesh. Detailed qualitative futures were produced, before modellable elements were quantified in conjunction with an expert stakeholder cadre. Stakeholder input, using the methods adopted here, allows the top-down focus of the RCPs to be aligned with the bottom-up approach needed to make the SSPs appropriate at the more local scale, and also facilitates the translation of qualitative narrative scenarios into a quantitative form that lends itself to incorporation of biophysical and socio-economic indicators. The presentation will describe the <span class="hlt">downscaling</span> process in detail, and conclude with findings regarding the importance of stakeholder involvement (and logistical considerations), balancing model capacity with expectations and recommendations on SSP refinement at local levels.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1814589A&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1814589A&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Downscaling</span> SSPs in the GBM Delta - Integrating Science, Modelling and Stakeholders Through Qualitative and Quantitative Scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Allan, Andrew; Barbour, Emily; Salehin, Mashfiqus; Munsur Rahman, Md.; Hutton, Craig; Lazar, Attila</p> <p>2016-04-01</p> <p>A <span class="hlt">downscaled</span> scenario development process was adopted in the context of a project seeking to understand relationships between ecosystem services and human well-being in the Ganges-Brahmaputra delta. The aim was to link the concerns and priorities of relevant stakeholders with the integrated biophysical and poverty models used in the project. A 2-stage process was used to facilitate the connection between stakeholders concerns and available modelling capacity: the first to qualitatively describe what the future might look like in 2050; the second to translate these qualitative descriptions into the quantitative form required by the numerical models. An extended, modified SSP approach was adopted, with stakeholders <span class="hlt">downscaling</span> issues identified through interviews as being priorities for the southwest of Bangladesh. Detailed qualitative futures were produced, before modellable elements were quantified in conjunction with an expert stakeholder cadre. Stakeholder input, using the methods adopted here, allows the top-down focus of the RCPs to be aligned with the bottom-up approach needed to make the SSPs appropriate at the more local scale, and also facilitates the translation of qualitative narrative scenarios into a quantitative form that lends itself to incorporation of biophysical and socio-economic indicators. The presentation will describe the <span class="hlt">downscaling</span> process in detail, and conclude with findings regarding the importance of stakeholder involvement (and logistical considerations), balancing model capacity with expectations and recommendations on SSP refinement at local levels.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC43C0728S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC43C0728S"><span id="translatedtitle">Evaluation of Future Precipitation Scenario Using Statistical <span class="hlt">Downscaling</span> MODEL over Three Climatic Region of Nepal Himalaya</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sigdel, M.</p> <p>2014-12-01</p> <p>Statistical <span class="hlt">downscaling</span> model (SDSM) was applied in <span class="hlt">downscaling</span> precipitation in the three climatic regions such as humid, sub-humid and arid region of Nepal Himalaya. The study includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP reanalysis data, the validation of the model and the outputs of <span class="hlt">downscaled</span> scenarios A2 (high green house gases emission) and B2 (low green house gases emission) of the HadCM3 model for the future. Under both scenarios H3A2 and H3B2, during the prediction period of 2010-2099, the change of annual mean precipitation in the three climatic regions would present a tendency of surplus of precipitation as compared to the mean values of the base period. On the average for all three climatic regions of Nepal the annual mean precipitation would increase by about 13.75% under scenario H3A2 and increase near about 11.68% under scenario H3B2 in the 2050s. For the 2080s there would be increase of 8.28% and 13.30% under H3A2 and H3B2 respectively compared to the base period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010TellB..62..242S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010TellB..62..242S"><span id="translatedtitle">A <span class="hlt">downscaling</span> scheme for atmospheric variables to drive soil-vegetation-atmosphere transfer models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schomburg, A.; Venema, V.; Lindau, R.; Ament, F.; Simmer, C.</p> <p>2010-09-01</p> <p>For driving soil-vegetation-transfer models or hydrological models, high-resolution atmospheric forcing data is needed. For most applications the resolution of atmospheric model output is too coarse. To avoid biases due to the non-linear processes, a <span class="hlt">downscaling</span> system should predict the unresolved variability of the atmospheric forcing. For this purpose we derived a disaggregation system consisting of three steps: (1) a bi-quadratic spline-interpolation of the low-resolution data, (2) a so-called `deterministic' part, based on statistical rules between high-resolution surface variables and the desired atmospheric near-surface variables and (3) an autoregressive noise-generation step. The disaggregation system has been developed and tested based on high-resolution model output (400m horizontal grid spacing). A novel automatic search-algorithm has been developed for deriving the deterministic <span class="hlt">downscaling</span> rules of step 2. When applied to the atmospheric variables of the lowest layer of the atmospheric COSMO-model, the disaggregation is able to adequately reconstruct the reference fields. Applying <span class="hlt">downscaling</span> step 1 and 2, root mean square errors are decreased. Step 3 finally leads to a close match of the subgrid variability and temporal autocorrelation with the reference fields. The scheme can be applied to the output of atmospheric models, both for stand-alone offline simulations, and a fully coupled model system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ClDy...42.2899E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ClDy...42.2899E"><span id="translatedtitle">Uncertainty analysis of statistical <span class="hlt">downscaling</span> models using general circulation model over an international wetland</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Etemadi, H.; Samadi, S.; Sharifikia, M.</p> <p>2014-06-01</p> <p>Regression-based statistical <span class="hlt">downscaling</span> model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely "Shadegan" in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of <span class="hlt">downscaled</span> and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation <span class="hlt">downscaling</span> using 1987-2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012JSemi..33g5008L&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012JSemi..33g5008L&link_type=ABSTRACT"><span id="translatedtitle">A high-speed mixed-signal <span class="hlt">down-scaling</span> circuit for DAB tuners</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, Tang; Zhigong, Wang; Jiahui, Xuan; Yang, Yang; Jian, Xu; Yong, Xu</p> <p>2012-07-01</p> <p>A high-speed mixed-signal <span class="hlt">down-scaling</span> circuit with low power consumption and low phase noise for use in digital audio broadcasting tuners has been realized and characterized. Some new circuit techniques are adopted to improve its performance. A dual-modulus prescaler (DMP) with low phase noise is realized with a kind of improved source-coupled logic (SCL) D-flip-flop (DFF) in the synchronous divider and a kind of improved complementary metal oxide semiconductor master-slave (CMOS MS)-DFF in the asynchronous divider. A new more accurate wire-load model is used to realize the pulse-swallow counter (PS counter). Fabricated in a 0.18-μm CMOS process, the total chip size is 0.6 × 0.2 mm2. The DMP in the proposed <span class="hlt">down-scaling</span> circuit exhibits a low phase noise of -118.2 dBc/Hz at 10 kHz off the carrier frequency. At a supply voltage of 1.8 V, the power consumption of the <span class="hlt">down-scaling</span> circuit's core part is only 2.7 mW.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016AdAtS..33.1071S&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016AdAtS..33.1071S&link_type=ABSTRACT"><span id="translatedtitle">A timescale decomposed threshold regression <span class="hlt">downscaling</span> approach to forecasting South China early summer rainfall</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Song, Linye; Duan, Wansuo; Li, Yun; Mao, Jiangyu</p> <p>2016-09-01</p> <p>A timescale decomposed threshold regression (TSDTR) <span class="hlt">downscaling</span> approach to forecasting South China early summer rainfall (SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression <span class="hlt">downscaling</span> models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares (PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation (PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR <span class="hlt">downscaling</span> approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24824947','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24824947"><span id="translatedtitle">Design of a <span class="hlt">downscaling</span> method to estimate continuous data from discrete pollen monitoring in Tunisia.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Orlandi, Fabio; Oteros, Jose; Aguilera, Fátima; Ben Dhiab, Ali; Msallem, Monji; Fornaciari, Marco</p> <p>2014-07-01</p> <p>The study of microorganisms and biological particulate matter that transport passively through air is very important for an understanding of the real quality of air. Such monitoring is essential in several specific areas, such as public health, allergy studies, agronomy, indoor and outdoor conservation, and climate-change impact studies. Choosing the suitable monitoring method is an important step in aerobiological studies, so as to obtain reliable airborne data. In this study, we compare olive pollen data from two of the main air traps used in aerobiology, the Hirst and Cour air samplers, at three Tunisian sampling points, for 2009 to 2011. Moreover, a <span class="hlt">downscaling</span> method to perform daily Cour air sampler data estimates is designed. While Hirst air samplers can offer daily, and even bi-hourly data, Cour air samplers provide data for longer discrete sampling periods, which limits their usefulness for daily monitoring. Higher quantities of olive pollen capture were generally detected for the Hirst air sampler, and a <span class="hlt">downscaling</span> method that is developed in this study is used to model these differences. The effectiveness of this <span class="hlt">downscaling</span> method is demonstrated, which allows the potential use of Cour air sampler data series. These results improve the information that new Cour data and, importantly, historical Cour databases can provide for the understanding of phenological dates, airborne pollination curves, and allergenicity levels of air. PMID:24824947</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016OcSci..12...39G&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016OcSci..12...39G&link_type=ABSTRACT"><span id="translatedtitle">On the feasibility of the use of wind SAR to <span class="hlt">downscale</span> waves on shallow water</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutiérrez, O. Q.; Filipponi, F.; Taramelli, A.; Valentini, E.; Camus, P.; Méndez, F. J.</p> <p>2016-01-01</p> <p>In recent years, wave reanalyses have become popular as a powerful source of information for wave climate research and engineering applications. These wave reanalyses provide continuous time series of offshore wave parameters; nevertheless, in coastal areas or shallow water, waves are poorly described because spatial resolution is not detailed. By means of wave <span class="hlt">downscaling</span>, it is possible to increase spatial resolution in high temporal coverage simulations, using forcing from wind and offshore wave databases. Meanwhile, the reanalysis wave databases are enough to describe the wave climate at the limit of simulations; wind reanalyses at an adequate spatial resolution to describe the wind structure near the coast are not frequently available. Remote sensing synthetic aperture radar (SAR) has the ability to detect sea surface signatures and estimate wind fields at high resolution (up to 300 m) and high frequency. In this work a wave <span class="hlt">downscaling</span> is done on the northern Adriatic Sea, using a hybrid methodology and global wave and wind reanalysis as forcing. The wave fields produced were compared to wave fields produced with SAR winds that represent the two dominant wind regimes in the area: the bora (ENE direction) and sirocco (SE direction). Results show a good correlation between the waves forced with reanalysis wind and SAR wind. In addition, a validation of reanalysis is shown. This research demonstrates how Earth observation products, such as SAR wind fields, can be successfully up-taken into oceanographic modeling, producing similar <span class="hlt">downscaled</span> wave fields when compared to waves forced with reanalysis wind.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1052P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1052P"><span id="translatedtitle">Weather Typing Statistical <span class="hlt">Downscaling</span> with dsclim: Diagnostic methodology and configuration sensitivity</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Page, C.; Albertus, G.</p> <p>2013-12-01</p> <p>The 8-km output of the statistical <span class="hlt">downscaling</span> methodology dsclim has been used since a few years to perform impacts and adaptation studies in France. The dsclim method is resampling the Météo-France SAFRAN observation mesoscale analysis. Since then, the SAFRAN observation period has been extended from 1981-2005 to 1958-2012. At the same time, there are strong needs of cross-national impact studies, hence the required use of an European observation dataset in the methodology. In this context, a diagnostic package has been developed to properly evaluate the <span class="hlt">downscaling</span> methodology and its performance: it enables to evaluate the sensitivity and the impacts of the changes in its configuration, taking also properly into account stochastic aspects. In this study we evaluated the impacts on the results with respect to the extension of the learning period from 1981-2005 to 1958-2012, as well as the comparison on the use of the EOBS dataset instead of SAFRAN, having the objective of running dsclim over a larger region within the EU FP7 SPECS project and the EU COST Action VALUE <span class="hlt">downscaling</span> methods intercomparison. This study was funded by the EU project SPECS funded by the European Commission's Seventh Framework Research Programme under the grant agreement 243964.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70095788','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70095788"><span id="translatedtitle">Applying <span class="hlt">downscaled</span> global climate model data to a hydrodynamic surface-water and groundwater model</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Swain, Eric; Stefanova, Lydia; Smith, Thomas</p> <p>2014-01-01</p> <p>Precipitation data from Global Climate Models have been <span class="hlt">downscaled</span> to smaller regions. Adapting this <span class="hlt">downscaled</span> precipitation data to a coupled hydrodynamic surface-water/groundwater model of southern Florida allows an examination of future conditions and their effect on groundwater levels, inundation patterns, surface-water stage and flows, and salinity. The <span class="hlt">downscaled</span> rainfall data include the 1996-2001 time series from the European Center for Medium-Range Weather Forecasting ERA-40 simulation and both the 1996-1999 and 2038-2057 time series from two global climate models: the Community Climate System Model (CCSM) and the Geophysical Fluid Dynamic Laboratory (GFDL). Synthesized surface-water inflow datasets were developed for the 2038-2057 simulations. The resulting hydrologic simulations, with and without a 30-cm sea-level rise, were compared with each other and field data to analyze a range of projected conditions. Simulations predicted generally higher future stage and groundwater levels and surface-water flows, with sea-level rise inducing higher coastal salinities. A coincident rise in sea level, precipitation and surface-water flows resulted in a narrower inland saline/fresh transition zone. The inland areas were affected more by the rainfall difference than the sea-level rise, and the rainfall differences make little difference in coastal inundation, but a larger difference in coastal salinities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AtmRe.167..156K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AtmRe.167..156K"><span id="translatedtitle">High resolution WRF <span class="hlt">ensemble</span> forecasting for irrigation: Multi-variable evaluation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kioutsioukis, Ioannis; de Meij, Alexander; Jakobs, Hermann; Katragkou, Eleni; Vinuesa, Jean-Francois; Kazantzidis, Andreas</p> <p>2016-01-01</p> <p>An <span class="hlt">ensemble</span> of meteorological simulations with the WRF model at convection-allowing resolution (2 km) is analysed in a multi-variable evaluation framework over Europe. Besides temperature and precipitation, utilized variables are relative humidity, boundary layer height, shortwave radiation, wind speed, convective and large-scale precipitation in view of explaining some of the biases. Furthermore, the forecast skill of evapotranspiration and irrigation water need is ultimately assessed. It is found that the modelled temperature exhibits a small but significant negative bias during the cold period in the snow-covered northeast regions. Total precipitation exhibits positive bias during all seasons but autumn, peaking in the spring months. The varying physics configurations resulted in significant differences for the simulated minimum temperature, summer rainfall, relative humidity, solar radiation and planetary boundary layer height. The interaction of the temperature and moisture profiles with the different microphysics schemes, results in excess convective precipitation using MYJ/WSM6 compared to YSU/Thompson. With respect to evapotranspiration and irrigation need, the errors using the MYJ configuration were in opposite directions and eventually cancel out, producing overall smaller biases. WRF was able to dynamically <span class="hlt">downscale</span> global forecast data into finer resolutions in space and time for hydro-meteorological applications such as the irrigation management. Its skill was sensitive to the geographical location and physical configuration, driven by the variable relative importance of evapotranspiration and rainfall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005QJRMS.131..965W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005QJRMS.131..965W"><span id="translatedtitle">Improvement of <span class="hlt">ensemble</span> reliability with a new dressing kernel</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Xuguang; Bishop, Craig H.</p> <p>2005-04-01</p> <p>A new method of combining dynamical and statistical <span class="hlt">ensembles</span> for the purpose of improving <span class="hlt">ensemble</span> reliability for underdispersive <span class="hlt">ensembles</span> is introduced. The method involves adding independent sets of N random four-dimensional 'dressing' perturbations to each of the K members of a dynamical <span class="hlt">ensemble</span> forecast to obtain an N × K dressed <span class="hlt">ensemble</span>. The new method mathematically constrains the stochastic process used to generate the statistical dressing perturbations so that it removes seasonally averaged errors in the second moment measures for originally underdispersive <span class="hlt">ensembles</span>. A random-number generator experiment and an experiment with the <span class="hlt">ensemble</span> transform Kalman filter (ETKF) <span class="hlt">ensemble</span> generation scheme show that the previously proposed 'bestmember' dressing method fails to reliably predict the second moment of the distribution of forecast errors, whereas the new dressing method reliably predicts this second moment. After being dressed with the second moment constraint method, the ETKF <span class="hlt">ensemble</span> is more skilful than the undressed <span class="hlt">ensemble</span>. The ETKF <span class="hlt">ensemble</span> postprocessed with the new dressing method is applied for probabilistic forecasts of cooling degree-days (CDD) for Boston. It is shown that the new kernel's ability to account for temporally correlated forecast errors results in <span class="hlt">ensemble</span> fore