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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_13");'>»</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_13");'>»</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://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=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://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_13");'>»</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_13");'>»</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_13");'>»</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_13");'>»</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_13");'>»</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_13");'>»</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/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://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://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://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://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/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_13");'>»</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_13");'>»</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/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/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/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_13");'>»</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_13");'>»</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/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://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/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/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://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/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/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/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.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://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://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_13");'>»</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_13");'>»</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/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://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://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.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> </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_13");'>»</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_13");'>»</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.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> <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://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/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://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/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_13");'>»</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_13");'>»</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/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/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/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://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> </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_13");'>»</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_13");'>»</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.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..18.2322G','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> <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/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/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/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/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..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/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_13");'>»</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_9");'>9</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_13");'>»</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://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://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://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://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://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/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://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://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_9");'>9</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_13");'>»</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_9");'>9</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><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_13");'>»</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://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://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://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> </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><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_13");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <center> <div class="footer-extlink text-muted"><small>Some links on this page may take you to non-federal websites. 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