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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. Use of multi-model ensembles for regional climate downscaling

    NASA Astrophysics Data System (ADS)

    Reichler, Thomas; Andrade, Marcos; Ohara, Noriaki

    2014-05-01

    Dynamic regional downscaling requires use of a regional model driven at its boundaries by the output from coarse-scale global climate models. But individual members from global multi-model ensembles often lead to contradicting answers, and the important question arises of which of the many global models to select for the downscaling work. The perhaps most obvious solution to downscale various models is usually too expensive. Numerous studies have shown that the performance of the multi-model mean of an ensemble is usually superior to that of any individual model. However, it is unclear how to employ the multi-model mean framework for regional downscaling. We propose a simple method that allows use of a multi-model mean for downscaling work. We demonstrate the performance of our method using the WRF regional model system coupled to CMIP5 output. The system is used to perform high-resolution climate change simulations over our prototypical study region of tropical South America. We use objective criteria to select three CMIP5 models that perform best in terms of simulating present day climate. The outcomes from using these three individual global models are contrasted against that from using the CMIP5 multi-model mean. We discuss the advantages and limitations of the new method, and conclude that it represents a promising and computationally inexpensive alternative to the traditional downscaling of individual models.

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

    2012-04-01

    Rainfall is poorly modeled by general circulation models (GCMs) and has to be downscaled to drive local hydrological impact studies. Such downscaling methods should be robust and accurate (to handle e.g. extreme events), but the non-continuous and highly non-linear nature of rainfall makes this task particularly challenging. Building upon state-of-the-art methods, we propose a robust probabilistic framework 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 daily rainfall series. The other key elements of the framework are i) a cross-validation step to ensure that the fitted models are correctly conditioned by the climate variables, and ii) a statistical procedure to test the stationarity assumption that the statistical relationships identified for the reference period also hold in a future perturbed climate. Additionally, we propose a strategy to downweight poor-performing GCMs-GLMs couples. The methodology is assessed at 27 locations covering Switzerland and is shown to perform well in reproducing historical rainfall statistics, including extremes and inter-annual variability, and their projections are consistent with the simulations of physically-based dynamical models. Although the downscaling models were fitted for each of the 27 sites independently, their projections follow a spatially coherent pattern, exhibiting regions with different climate change impacts, which we identified using an original visualization method based on heatmaps.

  10. Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

    NASA Astrophysics Data System (ADS)

    Fatichi, S.; Ivanov, V. Y.; Caporali, E.

    2013-04-01

    This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000-2009, 2046-2065 and 2081-2100, using the period of 1962-1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000-2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.

  11. 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.

  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. Evaluations of three high-resolution dynamical downscaled ensembles over North America

    NASA Astrophysics Data System (ADS)

    Zobel, Z.

    2015-12-01

    Six 12 kilometer resolution WRF simulations are performed driven by three GCM outputs --- CCSM4, GFDL, and HadGEM --- in 10-yr or 15-yr historical period. The model fidelity is measured in terms of correlations, RMSEs, and probability distribution functions. The model evaluations are conducted over 20 subregions for North America. The 3D and surface variables considered in this study are precipitation, 2m-temperature, tmax/tmin, relative humidity, geopotential height, u-wind/v-wind, and sea-level pressure. We aim to find which downscaled GCM run showed the best skill over each subregion for all the variables. Depending on the variable and type of metric being examined, the output from each historical run will be primarily compared to North American Regional Reanalysis (NARR) and Variable Infiltration Capacity (VIC) data for the statistical analysis. It is important to both evaluate which high resolution ensemble member captures the climatological mean of the variables as well as to determine which run does the best at observing the extremes values, such as maximum/minimum temperature and large precipitation events. High RMSE values frequently occur because of errors in either the seasonal mean, which could indicate a specific bias in the model, or a failure to capture the tails of the PDF correctly, which indicates the model had trouble capturing the seasonal variability. This will give us valuable information at which GCM is the most effective at simulating historical periods using dynamic downscaling over specific regions of the United States and give us added confidence of their ability to forecast future time periods based on model performance.

  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/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/abs/2016AtmRe.178..138S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AtmRe.178..138S"><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/abs/2016ThApC.tmp..153P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp..153P"><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/2012EGUGA..14.2843C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.2843C"><span id="translatedtitle">Regional Climate Models <span class="hlt">Downscaling</span> in the Alpine Area with Multimodel Super<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>Cane, D.; Barbarino, S.; Renier, L.; Ronchi, C.</p> <p>2012-04-01</p> <p>The climatic scenarios show a strong signal of warming in the Alpine area already for the mid XXI century. The climate simulation, however, even when obtained with Regional Climate Models (RCMs), are affected by strong errors where compared with observations in the control period, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization. In this work we use a selection of RCMs runs from the <span class="hlt">ENSEMBLES</span> project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project <span class="hlt">ENSEMBLES</span> with an available resolution of 25 km. For the study area of Piemonte daily temperature and precipitation observations (1957-present) were carefully gridded on a 14-km grid over Piemonte Region with an Optimal Interpolation technique. We applied the Multimodel Super<span class="hlt">Ensemble</span> technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We propose also the first application to RCMs of a brand new probabilistic Multimodel Super<span class="hlt">Ensemble</span> Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces the monthly behaviour of observed precipitation in the control period far better than the direct model outputs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012HESSD...9.9425C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012HESSD...9.9425C"><span id="translatedtitle">Regional climate models <span class="hlt">downscaling</span> in the Alpine area with Multimodel Super<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>Cane, D.; Barbarino, S.; Renier, L. A.; Ronchi, C.</p> <p>2012-08-01</p> <p>The climatic scenarios show a strong signal of warming in the Alpine area already for the mid XXI century. The climate simulations, however, even when obtained with Regional Climate Models (RCMs), are affected by strong errors where compared with observations, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization. Therefore the aim of this work is reducing these model biases using a specific post processing statistic technique to obtain a more suitable projection of climate change scenarios in the Alpine area. For our purposes we use a selection of RCMs runs from the <span class="hlt">ENSEMBLES</span> project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project <span class="hlt">ENSEMBLES</span> with an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (1957-present) were carefully gridded on a 14-km grid over Piedmont Region with an Optimal Interpolation technique. Hence, we applied the Multimodel Super<span class="hlt">Ensemble</span> technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We propose also the first application to RCMS of a brand new probabilistic Multimodel Super<span class="hlt">Ensemble</span> Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces well the monthly behaviour of precipitation in the control period.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li class="active"><span>1</span></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_1 --> <div id="page_2" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="21"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009AGUFM.A33A0217L&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009AGUFM.A33A0217L&link_type=ABSTRACT"><span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> NCEP Global Climate Forecast System (CFS) Seasonal Predictions Using Regional Atmospheric Modeling System (RAMS)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, L.; Zheng, Y.; Pielke, R. A.</p> <p>2009-12-01</p> <p>As part of the NOAA CPPA-sponsored <span class="hlt">MRED</span> project, the state-of-the-art Regional Atmospheric Modeling System (RAMS) version 6.0 is used to dynamically and progressively <span class="hlt">downscale</span> NCEP global Climate Forecast System (CFS, at 100s-km grid increment) seasonal predictions to a regional domain that covers the conterminous United States at 30-km grid increment. The first set of RCM prediction experiment focuses on the winter seasons, during which the precipitation is largely dependent on synoptic-scale mid-latitude storms and orographic dominant mesoscale processes. Our first suite of numerical experiment includes one <span class="hlt">ensemble</span> member for each year from 1982 through 2008, with all the simulations starting on December 1 and ending on April 30. Driven by the same atmospheric and SST forcings, RAMS will be compared with other RCMs, and evaluated against observations and reanalysis (NARR) to see if the simulations capture the climatology and interannual variability of temperature and precipitation distributions. The overall strengths and weaknesses of the modeling systems will be identified, as well as the consistent model biases. In addition, we will analyze the changes in kinetic energy spectra before and after the spectral nudging algorithm is implemented. The results show that with the spectral nudging scheme, RAMS can better preserve large-scale kinetic energy than standard boundary forcing method, and allow more large-scale energy to cascade to smaller scales.</p> </li> <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://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('https://www.ncbi.nlm.nih.gov/pubmed/25935576','PUBMED'); return false;" href="https://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="https://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.</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="https://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/abs/2016IJBm...60..307S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJBm...60..307S"><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/abs/2014AGUFM.H41E0866E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H41E0866E"><span id="translatedtitle">A Novel approach for monitoring cyanobacterial blooms using an <span class="hlt">ensemble</span> based system from MODIS imagery <span class="hlt">downscaled</span> to 250 metres spatial resolution</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>El Alem, A.; Chokmani, K.; Laurion, I.; El-Adlouni, S. E.</p> <p>2014-12-01</p> <p>In reason of inland freshwaters sensitivity to Harmful algae blooms (HAB) development and the limits coverage of standards monitoring programs, remote sensing data have become increasingly used for monitoring HAB extension. Usually, HAB monitoring using remote sensing data is based on empirical and semi-empirical models. Development of such models requires a great number of continuous in situ measurements to reach an acceptable accuracy. However, Ministries and water management organizations often use two thresholds, established by the World Health Organization, to determine water quality. Consequently, the available data are ordinal «semi-qualitative» and they are mostly unexploited. Use of such databases with remote sensing data and statistical classification algorithms can produce hazard management maps linked to the presence of cyanobacteria. Unlike standard classification algorithms, which are generally unstable, classifiers based on <span class="hlt">ensemble</span> systems are more general and stable. In the present study, an <span class="hlt">ensemble</span> based classifier was developed and compared to a standard classification method called CART (Classification and Regression Tree) in a context of HAB monitoring in freshwaters using MODIS images <span class="hlt">downscaled</span> to 250 spatial resolution and ordinal in situ data. Calibration and validation data on cyanobacteria densities were collected by the Ministère du Développement durable, de l'Environnement et de la Lutte contre les changements climatiques on 22 waters bodies between 2000 and 2010. These data comprise three density classes: waters poorly (< 20,000 cells mL-1), moderately (20,000 - 100,000 cells mL-1), and highly (> 100,000 cells mL-1) loaded in cyanobacteria. Results were very interesting and highlighted that inland waters exhibit different spectral response allowing them to be classified into the three above classes for water quality monitoring. On the other, even if the accuracy (Kappa-index = 0.86) of the proposed approach is relatively lower</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813579G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813579G"><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/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/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/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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3245178','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3245178"><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=pmc">PubMed Central</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/2015AGUFMPA13A2184T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMPA13A2184T"><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/2012AGUFM.A41I0098T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A41I0098T"><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="https://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('https://www.ncbi.nlm.nih.gov/pubmed/25833698','PUBMED'); return false;" href="https://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="https://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.</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/2002EGSGA..27.6136B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.6136B"><span id="translatedtitle">Dam Management and Multifractal <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>Biou, A.; Hubert, P.; Schertzer, D.</p> <p></p> <p>In order to get a more efficient production management of reservoirs, it would be helpful to apply long-term meteorological forecasts to hydrological models. Unfortu- nately, the explicit scales of present meteorological models are quite larger than those of hydrological models. Therefore it is indispensable to proceed to a <span class="hlt">downscaling</span> of the output of the former in order to obtain an input for the latter. In this paper, we discuss a multifractal <span class="hlt">downscaling</span> procedure. This type of procedure was motivated because it deals with scaling variability of the fields. We first present the results of a detailed multifractal analysis of various data bases. Concerning the development of our <span class="hlt">downscaling</span> model, we show how to develop a scaling space-time cascade, which takes into account the distinct space and time scaling. We will present it first in the framework of the pedagogical β-model and a- model, then in the framework of universal multifractal models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.3380Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.3380Z"><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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_2 --> <div id="page_3" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="41"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3531136','PMC'); return false;" href="https://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=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://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://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=79415872&CFTOKEN=69517922','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=79415872&CFTOKEN=69517922"><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://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.ncbi.nlm.nih.gov/pubmed/25352552','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25352552"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2015.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4383879','PMC'); return false;" href="https://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/2003EAEJA......467B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003EAEJA......467B"><span id="translatedtitle">Dam management and multifractal <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>Biaou, A.; Hubert, P.; Schertzer, D.; Hendrickx, F.; Tchiguirinskaia, I.</p> <p>2003-04-01</p> <p>In order to get a more efficient production management of reservoirs, it would be helpful to apply long-term meteorological forecasts to hydrological models. Unfortunately, the explicit scales of present meteorological models are quite larger than those of hydrological models. Therefore it is indispensable to proceed to a <span class="hlt">downscaling</span> of the output of the former in order to obtain an input for the latter. In this paper, we discuss a multifractal <span class="hlt">downscaling</span> procedure. This type of procedure was motivated because it deals with scaling variability of the fields. The site of the study is the region of the Doubs, but we make an extension on the whole France for the multifractale analysis to take into account well the spatial variabilities. We first present the results of a detailed multifractal analysis of various data bases. Concerning the development of our <span class="hlt">downscaling</span> model, we show how to develop a scaling space-time cascade, which takes into account the distinct space and time scaling. We will present it first in the framework of the pedagogical b-model and a-model, then in the framework of universal multifractal models. The obtained results can be the object of an relief and microclimate conditioning before being compared with the real values.</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> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4742469','PMC'); return false;" href="https://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/2014AGUFMGC44B..06P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC44B..06P"><span id="translatedtitle">The Influence of <span class="hlt">Downscaling</span> Models and Observations on Future Hydrochemistry Reponses of Forest Watersheds</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pourmokhtarian, A.; Driscoll, C. T.; Campbell, J. L.; Hayhoe, K.; Stoner, A. M. K.</p> <p>2014-12-01</p> <p>Most projections of climate change impacts on ecosystems rely on multiple climate model projections, but utilize only one <span class="hlt">downscaling</span> approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate <span class="hlt">downscaling</span> method and training observations used in the complex mountainous terrain of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three different <span class="hlt">downscaling</span> methods: the monthly delta method (or the "change factor method"); monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD); and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs, from four AOGCMs (CCSM3, HadCM3, PCM, and GFDL-CM2) driven by higher (A1fi) and lower (B1) future emission scenarios, on two sets of observations (1/8th degree resolution grid vs. individual weather station) to generate the high-resolution climate input for the hydrochemical model PnET-BGC (<span class="hlt">ensemble</span> of 48 runs). The choice of <span class="hlt">downscaling</span> approach and spatial resolution of the observations used to train the <span class="hlt">downscaling</span> model both had a major impact on modeled soil moisture and streamflow which in turn affected forest growth, net nitrification and stream chemistry. Specifically, the delta method, the simplest <span class="hlt">downscaling</span> approach evaluated, was highly sensitive to the observations used, resulting in projections that were significantly different than those produced with the BCSD and ARRM methods. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce poor results in model applications run at higher temporal and/or spatial resolutions. These results underscore the importance of carefully considering the observations and <span class="hlt">downscaling</span> method used to generate climate change projections for smaller scale modeling</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17148474','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17148474"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2007.</span></a></p> <p><a target="_blank" href="https://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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26687719','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26687719"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2016.</span></a></p> <p><a target="_blank" href="https://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.</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="https://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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702834','PMC'); return false;" href="https://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://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241249&keyword=matlab&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=79362706&CFTOKEN=25662470','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241249&keyword=matlab&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=79362706&CFTOKEN=25662470"><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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3964975','PMC'); return false;" href="https://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/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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.126..191G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.126..191G"><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>2016-10-01</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/abs/2010EGUGA..1213747B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1213747B"><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/2009AGUSMIN23A..04Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUSMIN23A..04Z"><span id="translatedtitle">Research and operational applications in multi-center <span class="hlt">ensemble</span> forecasting</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhu, Y.; Toth, Z.</p> <p>2009-05-01</p> <p>The North American <span class="hlt">Ensemble</span> Forecast System (NAEFS) was built up in 2004 by the Meteorological Service of Canada (MSC), the National Meteorological Service of Mexico (NMSM), and the US National Weather Service (NWS) as an operational multi-center <span class="hlt">ensemble</span> forecast system. Currently it combines the 20-member MSC and NWS <span class="hlt">ensembles</span> to form a joint <span class="hlt">ensemble</span> of 40 members twice a day. The joint <span class="hlt">ensemble</span> forecast, after bias correction and statistical <span class="hlt">downscaling</span>, is used to generate a suite of products for CONUS, North America and for other regions of the globe. The THORPEX Interactive Grand Global <span class="hlt">Ensemble</span> (TIGGE) project has been established a few years ago to collect operational global <span class="hlt">ensemble</span> forecasts from world centers, and distribute to the scientific community, to encourage research leading to the acceleration of improvements in the skill and utility of high impact weather forecasts. TIGGE research is expected to advise the development of the operational NAEFS system and eventually the two projects are expected to converge into a single operational system, the Global Interactive Forecast System (GIFS). This presentation will review recent developments, the current status, and plans related to the TIGGE research and NAEFS operational multi-center <span class="hlt">ensemble</span> projects.</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/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/2012EGUGA..1412266Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1412266Y"><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/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/abs/2010EGUGA..1213219B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1213219B"><span id="translatedtitle">Transient climate rainfall <span class="hlt">downscaling</span> using a combined dynamic-stochastic methodology</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Burton, Aidan; Blenkinsop, Stephen; Fowler, Hayley J.; Kilsby, Chris G.</p> <p>2010-05-01</p> <p>Managers of water resource systems need <span class="hlt">downscaled</span> climate change projections that are relevant at the catchment scale and at a range of future time horizons. However, the uncertainty in future climate projections and the natural variability of the climate system affect the robustness of their decisions. Dynamic <span class="hlt">downscaling</span> of discrete future time-slices also limits the analysis of the temporal development of climate change impacts, as only steady state scenarios are widely available. Addressing these issues a new transient (i.e. temporally non-stationary) rainfall simulation methodology has been developed which combines dynamical and statistical <span class="hlt">downscaling</span> to generate a multi-model <span class="hlt">ensemble</span> of transient daily point-scale rainfall timeseries. Each timeseries is sampled from a continuous stochastic simulation of the control-future time period and exhibits climatic non-stationarity in accordance with GCM/RCM projections. The <span class="hlt">ensemble</span> as a whole represents aspects of both climate model uncertainty and natural variability and provides a basis for probabilistic time-horizon analyses such as when a particular impact will occur or when a particular threshold will be reached. The methodology is demonstrated for a case study raingauge located near the Brévilles spring in Northern France. Thirteen RCM projections from the PRUDENCE project for both control (1961-1990) and future (2071-2100) time-slices were obtained to form the basis of a multi-model representation of climate change. Each dynamically <span class="hlt">downscales</span> the climate from either the ECHAM4/OPYC or the HadCM3 GCM. Multiplicative ‘change factors' were evaluated for a set of statistics of daily rainfall for each RCM. These quantify the future value of each statistic as a multiple of the control value for each calendar month in turn. Multiplying the case study raingauge statistics by the change factors provides future projections with an implicit correction for biases in the RCM control runs and a representation of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFMGC21A0136Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFMGC21A0136Y"><span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> Technique for Global 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>Yoshimura, K.; Kanamitsu, M.</p> <p>2007-12-01</p> <p>Aiming at producing higher resolution global reanalysis datasets from coarse 200 km resolution reanalysis, a global version of the dynamical <span class="hlt">downscaling</span> using a global spectral model (GSM) is developed. A variant of spectral nudging, the scale-selective bias correction (SSBC) developed for regional models is modified in the following manner to adapt it to the global domain; 1) temperature is nudged in addition to the zonal and meridional components of winds, and 2) humidity is excluded from any nudging or correction. The <span class="hlt">downscaling</span> was performed using T248L28 (about 50 km resolution) global model for 2001, driven by NCEP/NCAR Reanalysis 2 (T62L28 resolution). Evaluation with high-resolution observations showed that the monthly averaged surface temperature and daily variation of precipitation become better than the Reanalysis over the globe. It was found that humidity plays a significant role for a significant positive bias of global precipitation in the <span class="hlt">downscaled</span> simulation. Over North America, surface wind speed and temperature become better, and over Japan, the diurnal pattern of surface temperature is much improved, as are wind speed and precipitation, but not humidity. This study suggests that the global <span class="hlt">downscaling</span> is a viable and economical method to obtain high- resolution reanalysis without re-running a very expensive high-resolution full data assimilation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1110820M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1110820M"><span id="translatedtitle">Improving <span class="hlt">downscaling</span> in South America Sector</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mendes, D.; Marengo, J.</p> <p>2009-04-01</p> <p>The mathematical models used to simulate the present climate and project future climate with forcing by greenhouse gases and aerosols are generally referred to as General Circulation Models or Global Climate Models (GCMs). However, the spatial resolution of GCMs remains quite coarse, in the order of 300 x 300 km, and at scale, the regional and local details of the climate which are influenced by spatial heterogeneities in the regional physiography are lost. Therefore, there is the need to convert the GCM outputs into a reliable data set with higher spatial resolution, with daily rainfall and temperature time series at the scale of the watershed or a region to which the climate impact is going to be investigated. The methods used to convert GCM outputs into local meteorological variables required for reliable climate modeling are usually referred to as <span class="hlt">downscaling</span> techniques. There are a variety of <span class="hlt">downscaling</span> techniques in the literature, but two major approaches can be identified at the moment, namely, dynamic <span class="hlt">downscaling</span> and empirical (statistical) <span class="hlt">downscaling</span>. The most widely used empirical <span class="hlt">downscaling</span> methods are the multiple linear regression and stochastic weather generation. However, the interest in nonlinear regression methods, namely, artificial neural network (ANN), is nowadays increasing because of their high potential for complex, nonlinear and time-varying input-output mapping. The main aim of this work is to develop and test a novel type of statistical <span class="hlt">downscaling</span> technique based on the Artificial Neural Network (ANN), applied of the climate change. This work analyses the performance of the IPCC models (CGCM3.1, CSIRO-MK3.5, ECHAM5-MPI, GFDL-CM2.1, and MIROC3.2-MEDRES ) in simulate the present and future climate using ANN. The ANN used here are based on a feed forward configuration of the multilayer perception that has been used by a growing number of authors. To carry out statistical <span class="hlt">downscaling</span> for each meteorological date (grid point), the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUSM.H53A..03S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUSM.H53A..03S"><span id="translatedtitle"><span class="hlt">Downscaling</span> GCM Output with Genetic Programming Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shi, X.; Dibike, Y. B.; Coulibaly, P.</p> <p>2004-05-01</p> <p>Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called '<span class="hlt">downscaling</span> techniques'. This study applies Genetic Programming (GP) based technique to <span class="hlt">downscale</span> daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP <span class="hlt">downscaling</span> technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation <span class="hlt">downscaling</span>, in addition to the mean daily</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H33E1667M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H33E1667M"><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('https://www.ncbi.nlm.nih.gov/pubmed/26293893','PUBMED'); return false;" href="https://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="https://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.</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/abs/2016EGUGA..1812384C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812384C"><span id="translatedtitle">FORWINE - Statistical <span class="hlt">Downscaling</span> of Seasonal forecasts for wine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.</p> <p>2016-04-01</p> <p>The most renowned viticulture regions in the Iberian Peninsula have a long standing tradition in winemaking and are considered world-class grapevine (Vitis Vinifera L.) producing regions. Portugal is the 11th wine producer in the world, with internationally acclaimed wines, such as Port wine, and vineyards across the whole territory. Climate is widely acknowledged of one of the most important factors for grapevine development and growth (Fraga et al. 2014a and b; Jackson et al. 1993; Keller 2010). During the growing season (April-October in the Northern Hemisphere) of this perennial and deciduous crop, the climatic conditions are responsible for numerous morphologically and physiological changes. Anomalously low February-March mean temperature, anomalously high May mean temperature and anomalously high March precipitation tend to be favourable to wine production in the Douro Valley. Seasonal forecast of precipitation and temperature tailored to fit critical thresholds, for crucial seasons, can be used to inform management practices (viz. phytosanitary measures, land operations, marketing campaigns) and develop a wine production forecast. Statistical <span class="hlt">downscaling</span> of precipitation, maximum, minimum temperatures is used to model wine production following Santos et al. (2013) and to calculate bioclimatic indices. The skill of the <span class="hlt">ensemble</span> forecast is evaluated through anomaly correlation, ROC area, spread-error ratio and CRPS</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005TellA..57..435G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005TellA..57..435G"><span id="translatedtitle">Analysis and <span class="hlt">downscaling</span> multi-model seasonal forecasts in Peru 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>Gutiérrez, J. M.; Cano, R.; Cofiño, A. S.; Sordo, C.</p> <p>2005-05-01</p> <p>We present an application of self-organizing maps (SOMs) for analysing multi-model <span class="hlt">ensemble</span> seasonal forecasts from the DEMETER project in the tropical area of Northern Peru. 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 (cluster centroids). Moreover, it has outstanding analysis and visualization properties, because the reference vectors can be projected into a two-dimensional lattice, preserving their high-dimensional topology.In the first part of the paper, the SOM is applied to analyse both atmospheric patterns over Peru and local precipitation observations at two nearby stations. First, the method is applied to cluster the ERA40 reanalysis patterns on the area of study (Northern Peru), obtaining a two-dimensional lattice which represents the climatology. Then, each particular phenomenon or event (e.g. El Niño or La Niña) is shown to define a probability density function (PDF) on the lattice, which represents its characteristic 'location' within the climatology. On the other hand, the climatological lattice is also used to represent the local precipitation regime associated with a given station. For instance, we show that the precipitation regime is strongly associated with El Niño events for one station, whereas it is more uniform for the other.The second part of the paper is devoted to <span class="hlt">downscaling</span> seasonal <span class="hlt">ensemble</span> forecasts from the multi-model DEMETER <span class="hlt">ensemble</span> to local stations. To this aim, the PDF generated on the lattice by the patterns predicted for a particular season is combined with the local precipitation lattice for a given station. Thus, a probabilistic or numeric local forecast is easily obtained from the resulting PDF. Moreover, a measure of predictability for the <span class="hlt">downscaled</span> forecast can be computed in terms of the entropy of the <span class="hlt">ensemble</span> PDF. We present some evidence that accurate local predictions for accumulated seasonal</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://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="https://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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3278305','PMC'); return false;" href="https://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://adsabs.harvard.edu/abs/2012AGUFM.H14C..05P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H14C..05P"><span id="translatedtitle">Data Assimilation Methods for Hydrologic <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>Pan, M.; Wood, E. F.; Luo, L.</p> <p>2012-12-01</p> <p>Data assimilation techniques have been among the most useful tools in Earth sciences. As for their applications in hydrology, significant efforts have been devoted to improving the predictions of dynamic models, e.g., catchment hydrologic models, land surface models (LSM), and ultimately general circulation models (GCM), using various types of observational data, e.g. remotely sensed surface parameters. Here we focus on the applications to a fundamentally important but less explored category of problems - estimating hydrologic quantities of interest across different spatial and temporal scales, and the primarily problem is <span class="hlt">downscaling</span> in space and time (since upscaling is in most cases trivial). <span class="hlt">Downscaling</span> plays a vital role in bridging the scale gaps between various types of modeling and observation systems, for example, from the relatively coarse GCM to LSM, and to catchment scale models, and from coarse resolution remote sensors (long wavelength or gravitational) to fine resolution sensors (visible/infrared). Through <span class="hlt">downscaling</span>, fine scale applications (e.g. catchment hydrologic models, local geo-chemical and geo-biological models) can make use of predictions from coarse scale models (e.g. weather/climate models) or coarse resolution remote sensing measurements. Our <span class="hlt">downscaling</span> approach will rely on both (a) the physical models to parameterize the related cross-scale physical processes and to link hydrologic variables defined at one scale to another, and (b) the mathematical tools to properly handle the uncertainties during the estimation and as well as to help quantify those cross-scale relationships too difficult for the physical models. We showcase the <span class="hlt">downscaling</span> of two hydrologic variables: (1) deriving spatial fields of land surface runoff from river streamflow measurements and (2) creating fine resolution soil moisture data from coarse resolution remote sensing retrievals or dynamic models. In the runoff case, all the measurements are collected in the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=steel&pg=2&id=EJ969636','ERIC'); return false;" href="http://eric.ed.gov/?q=steel&pg=2&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/2010AGUFMGC51A0736C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC51A0736C"><span id="translatedtitle">Simulation of an <span class="hlt">ensemble</span> of future climate time series with an hourly weather generator</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.</p> <p>2010-12-01</p> <p>There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous <span class="hlt">downscaling</span> techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic <span class="hlt">downscaling</span> technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the <span class="hlt">downscaling</span> procedure derives distributions of factors of change for several climate statistics from a multi-model <span class="hlt">ensemble</span> of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model <span class="hlt">ensemble</span>. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an <span class="hlt">ensemble</span> of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated <span class="hlt">ensemble</span> of scenarios also accounts for the uncertainty derived from multiple GCMs used in <span class="hlt">downscaling</span>. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.4328V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4328V"><span id="translatedtitle">The role of <span class="hlt">ensemble</span> post-processing for modeling the <span class="hlt">ensemble</span> tail</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Van De Vyver, Hans; Van Schaeybroeck, Bert; Vannitsem, Stéphane</p> <p>2016-04-01</p> <p>. Soc. 134: 2051-2066.Buizza and Leutbecher, 2015: The forecast skill horizon, Q. J. R. Meteorol. Soc. 141: 3366-3382.Ferro, 2007: A probability model for verifying deterministic forecasts of extreme events. Weather and Forecasting 22 (5), 1089-1100.Friederichs, 2010: Statistical <span class="hlt">downscaling</span> of extreme precipitation events using extreme value theory. Extremes 13, 109-132.Van Schaeybroeck and Vannitsem, 2015: <span class="hlt">Ensemble</span> post-processing using member-by-member approaches: theoretical aspects. Q.J.R. Meteorol. Soc., 141: 807-818.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PEPS....2...42S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PEPS....2...42S"><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> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1051D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1051D"><span id="translatedtitle">Evaluation of the applicability in the future climate of a statistical <span class="hlt">downscaling</span> method 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>Dayon, G.; Boé, J.; Martin, E.</p> <p>2013-12-01</p> <p>The uncertainties in climate projections during the next decades generally remain large, with an important contribution of internal climate variability. To quantify and capture the impact of those uncertainties in impact projections, multi-model and multi-member approaches are essential. Statistical <span class="hlt">downscaling</span> (SD) methods are computationally inexpensive allowing for large <span class="hlt">ensemble</span> approaches. The main weakness of SD is that it relies on a stationarity hypothesis, namely that the statistical relation established in the present climate remains valid in the climate change context. In this study, the evaluation of SD methods developed for a future study of hydrological changes during the next decades over France is presented, focusing on precipitation. The SD methods are all based on the analogs method which is quite simple to set up and permits to easily test different combinations of predictors, the only changing parameter in the methods discussed in this presentation. The basic idea of the analogs method is that for a same large scale climatic state, the state of local variables will be identical. In a climate change context, the statistical relation established on past climate is assumed to remain valid in the future climate. In practice, this stationarity assumption is impossible to verify until the future climate is effectively observed. It is possible to evaluate the ability of SD methods to reproduce the interannual variability in the present climate, but this approach does not guarantee their validity in the future climate as the mechanisms that play in the interannual and climate change contexts may not be identical. Another common approach is to test whether a SD method is able to reproduce observed, as they may be partly caused by climate changes. The observed trends in precipitation are compared to those obtained by <span class="hlt">downscaling</span> 4 different atmospheric reanalyses with analogs methods. The uncertainties in <span class="hlt">downscaled</span> trends due to renalyses are very large</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/2014AGUFMOS51A0962C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMOS51A0962C"><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/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://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://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280783&keyword=Preprocessing+AND+Data&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=79432653&CFTOKEN=62989258','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280783&keyword=Preprocessing+AND+Data&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=79432653&CFTOKEN=62989258"><span id="translatedtitle">"Going the Extra Mile in <span class="hlt">Downscaling</span>: Why <span class="hlt">Downscaling</span> is not jut "Plug-and-Play"</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of <span class="hlt">downscaling</span> the Comm...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.6789R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..18.6789R&link_type=ABSTRACT"><span id="translatedtitle">Stochastic <span class="hlt">Downscaling</span> of Digital Elevation Models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rasera, Luiz Gustavo; Mariethoz, Gregoire; Lane, Stuart N.</p> <p>2016-04-01</p> <p>High-resolution digital elevation models (HR-DEMs) are extremely important for the understanding of small-scale geomorphic processes in Alpine environments. In the last decade, remote sensing techniques have experienced a major technological evolution, enabling fast and precise acquisition of HR-DEMs. However, sensors designed to measure elevation data still feature different spatial resolution and coverage capabilities. Terrestrial altimetry allows the acquisition of HR-DEMs with centimeter to millimeter-level precision, but only within small spatial extents and often with dead ground problems. Conversely, satellite radiometric sensors are able to gather elevation measurements over large areas but with limited spatial resolution. In the present study, we propose an algorithm to <span class="hlt">downscale</span> low-resolution satellite-based DEMs using topographic patterns extracted from HR-DEMs derived for example from ground-based and airborne altimetry. The method consists of a multiple-point geostatistical simulation technique able to generate high-resolution elevation data from low-resolution digital elevation models (LR-DEMs). Initially, two collocated DEMs with different spatial resolutions serve as an input to construct a database of topographic patterns, which is also used to infer the statistical relationships between the two scales. High-resolution elevation patterns are then retrieved from the database to <span class="hlt">downscale</span> a LR-DEM through a stochastic simulation process. The output of the simulations are multiple equally probable DEMs with higher spatial resolution that also depict the large-scale geomorphic structures present in the original LR-DEM. As these multiple models reflect the uncertainty related to the <span class="hlt">downscaling</span>, they can be employed to quantify the uncertainty of phenomena that are dependent on fine topography, such as catchment hydrological processes. The proposed methodology is illustrated for a case study in the Swiss Alps. A swissALTI3D HR-DEM (with 5 m resolution</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950024819','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950024819"><span id="translatedtitle">The Personal Software Process: <span class="hlt">Downscaling</span> the factory</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roy, Daniel M.</p> <p>1994-01-01</p> <p>It is argued that the next wave of software process improvement (SPI) activities will be based on a people-centered paradigm. The most promising such paradigm, Watts Humphrey's personal software process (PSP), is summarized and its advantages are listed. The concepts of the PSP are shown also to fit a <span class="hlt">down-scaled</span> version of Basili's experience factory. The author's data and lessons learned while practicing the PSP are presented along with personal experience, observations, and advice from the perspective of a consultant and teacher for the personal software process.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1214308V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1214308V"><span id="translatedtitle">Wave model <span class="hlt">downscaling</span> for coastal applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Valchev, Nikolay; Davidan, Georgi; Trifonova, Ekaterina; Andreeva, Nataliya</p> <p>2010-05-01</p> <p><span class="hlt">Downscaling</span> is a suitable technique for obtaining high-resolution estimates from relatively coarse-resolution global models. Dynamical and statistical <span class="hlt">downscaling</span> has been applied to the multidecadal simulations of ocean waves. Even as large-scale variability might be plausibly estimated from these simulations, their value for the small scale applications such as design of coastal protection structures and coastal risk assessment is limited due to their relatively coarse spatial and temporal resolutions. Another advantage of the high resolution wave modeling is that it accounts for shallow water effects. Therefore, it can be used for both wave forecasting at specific coastal locations and engineering applications that require knowledge about extreme wave statistics at or near the coastal facilities. In the present study <span class="hlt">downscaling</span> is applied to both ECMWF and NCEP/NCAR global reanalysis of atmospheric pressure over the Black Sea with 2.5 degrees spatial resolution. A simplified regional atmospheric model is employed for calculation of the surface wind field at 0.5 degrees resolution that serves as forcing for the wave models. Further, a high-resolution nested WAM/SWAN wave model suite of nested wave models is applied for spatial <span class="hlt">downscaling</span>. It aims at resolving the wave conditions in a limited area at the close proximity to the shore. The pilot site is located in the northern part the Bulgarian Black Sea shore. The system involves the WAM wave model adapted for basin scale simulation at 0.5 degrees spatial resolution. The WAM output for significant wave height, mean wave period and mean angle of wave approach is used in terms of external boundary conditions for the SWAN wave model, which is set up for the western Black Sea shelf at 4km resolution. The same model set up on about 400m resolution is nested to the first SWAN run. In this case the SWAN 2D spectral output provides boundary conditions for the high-resolution model run. The models are implemented for a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016BGeo...13.4271F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016BGeo...13.4271F"><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="https://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/2013NHESS..13.2089C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013NHESS..13.2089C"><span id="translatedtitle">Evaluation and projection of daily temperature percentiles from statistical and dynamical <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>Casanueva, A.; Herrera, S.; Fernández, J.; Frías, M. D.; Gutiérrez, J. M.</p> <p>2013-08-01</p> <p>The study of extreme events has become of great interest in recent years due to their direct impact on society. Extremes are usually evaluated by using extreme indicators, based on order statistics on the tail of the probability distribution function (typically percentiles). In this study, we focus on the tail of the distribution of daily maximum and minimum temperatures. For this purpose, we analyse high (95th) and low (5th) percentiles in daily maximum and minimum temperatures on the Iberian Peninsula, respectively, derived from different <span class="hlt">downscaling</span> methods (statistical and dynamical). First, we analyse the performance of reanalysis-driven <span class="hlt">downscaling</span> methods in present climate conditions. The comparison among the different methods is performed in terms of the bias of seasonal percentiles, considering as observations the public gridded data sets E-OBS and Spain02, and obtaining an estimation of both the mean and spatial percentile errors. Secondly, we analyse the increments of future percentile projections under the SRES A1B scenario and compare them with those corresponding to the mean temperature, showing that their relative importance depends on the method, and stressing the need to consider an <span class="hlt">ensemble</span> of methodologies.</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/2016EGUGA..18.2207P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.2207P"><span id="translatedtitle">Future changes in Australian midlatitude cyclones using a regional climate 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>Pepler, Acacia; Di Luca, Alejandro; Ji, Fei; Alexander, Lisa; Evans, Jason; Sherwood, Steven</p> <p>2016-04-01</p> <p>Midlatitude cyclones cause the majority of strong winds, high seas and coastal flooding along the east coast of Australia, and are also an important contributor to annual rainfall variability and water security. For this reason, there is substantial interest in how the frequency or behaviour of these cyclones may change during the 21st century. A recent regional <span class="hlt">downscaling</span> project in southeastern Australia (NARCliM) provides an <span class="hlt">ensemble</span> of climate model projections at both 50km and 10km resolutions for the 20-year periods 1990-2009 and 2060-2079. This allows us to analyse and assess the projections of midlatitude cyclones in significantly more detail than previous studies. NARCliM is an <span class="hlt">ensemble</span> of 4 CMIP3 Global Climate Models (GCMs) which have been <span class="hlt">downscaled</span> using three different configurations of the Weather Research and Forecasting model, with both GCMs and regional <span class="hlt">downscaling</span> parameters chosen to optimise both model skill and independence of errors during the current climate. In addition to the <span class="hlt">ensemble</span> of regional climate projections, we also employ three different cyclone identification and tracking methods which have been recently evaluated in the study region. This provides the most robust assessment to date of future changes in cyclone activity in this region, drawing attention to both areas of consistency and seasons and locations of high inter-model or inter-method uncertainty. The high resolution regional models also allow the first assessment of future changes in the frequency of heavy rainfall and strong winds associated with these systems.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/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> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/16881400','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/16881400"><span id="translatedtitle"><span class="hlt">Downscaling</span> climate information for local disease mapping.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bernardi, M; Gommes, R; Grieser, J</p> <p>2006-06-01</p> <p>The study of the impacts of climate on human health requires the interdisciplinary efforts of health professionals, climatologists, biologists, and social scientists to analyze the relationships among physical, biological, ecological, and social systems. As the disease dynamics respond to variations in regional and local climate, climate variability affects every region of the world and the diseases are not necessarily limited to specific regions, so that vectors may become endemic in other regions. Climate data at local level are thus essential to evaluate the dynamics of vector-borne disease through health-climate models and most of the times the climatological databases are not adequate. Climate data at high spatial resolution can be derived by statistical <span class="hlt">downscaling</span> using historical observations but the method is limited by the availability of historical data at local level. Since the 90s', the statistical interpolation of climate data has been an important priority of the Agrometeorology Group of the Food and Agriculture Organization of the United Nations (FAO), as they are required for agricultural planning and operational activities at the local level. Since 1995, date of the first FAO spatial interpolation software for climate data, more advanced applications have been developed such as SEDI (Satellite Enhanced Data Interpolation) for the <span class="hlt">downscaling</span> of climate data, LOCCLIM (Local Climate Estimator) and the NEW_LOCCLIM in collaboration with the Deutscher Wetterdienst (German Weather Service) to estimate climatic conditions at locations for which no observations are available. In parallel, an important effort has been made to improve the FAO climate database including at present more than 30,000 stations worldwide and expanding the database from developing countries coverage to global coverage.</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/2009EGUGA..11.9937B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.9937B"><span id="translatedtitle"><span class="hlt">Downscaling</span> transient climate change scenarios for water resource management</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Blenkinsop, S.; Burton, A.; Fowler, H. J.; Harpham, C.; Goderniaux, P.</p> <p>2009-04-01</p> <p>The management of hydrological systems in response to climate change requires reliable projections at relevant time horizons and at appropriate spatial scales. Furthermore the robustness of decisions is dependent on both the uncertainty of future climate scenarios and climatic variability. The current generation of climate models do not adequately meet these requirements for hydrological impacts assessments and so new techniques are required to meet the needs of hydrologists and water resource managers. Here, a new methodology is described and implemented which addresses these issues by adopting a hybrid dynamical and stochastic <span class="hlt">downscaling</span> approach to produce a multi-model <span class="hlt">ensemble</span> of transient scenarios of daily weather variables. These scenarios will be used to drive hydrological simulations for two groundwater systems in north-west Europe, the Brévilles and the Geer, studied as part of the EU FP6 AQUATERRA project. In so doing, the impact of climate change on the challenges facing these aquifers can be assessed on relevant timescales and provide the means to answer wide-ranging questions relating to water quality and flow. The framework described here integrates two components which use projections of future change derived from regional climate models (RCMs) to generate stochastic climate series. Firstly, a new, transient version of the Neyman Scott Rectangular Pulses (NSRP) stochastic rainfall model is implemented to produce transient rainfall scenarios for the 21st century. Secondly, a novel, transient implementation of the Climatic Research Unit (CRU) daily weather generator is adopted, conditioned with daily rainfall series simulated by the NSRP model. This two-stage process is thus able to produce consistent transient series of rainfall, temperature and other variables. Both of these stages apply monthly change factors (CFs) derived from 13 RCM experiments from the PRUDENCE <span class="hlt">ensemble</span> to current rainfall and temperature statistics respectively to project</p> </li> <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://adsabs.harvard.edu/abs/2016EGUGA..18.3876K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.3876K"><span id="translatedtitle">Testing a Weather Generator for <span class="hlt">Downscaling</span> Climate Change Projections over Switzerland</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Keller, Denise E.; Fischer, Andreas M.; Liniger, Mark A.; Appenzeller, Christof; Knutti, Reto</p> <p>2016-04-01</p> <p>Climate information provided by global or regional climate models (RCMs) are often too coarse and prone to substantial biases, making it impossible to directly use daily time-series of the RCMs for local assessments and in climate impact models. Hence, statistical <span class="hlt">downscaling</span> becomes necessary. For the Swiss National Climate Change Initiative (CH2011), a delta-change approach was used to provide daily climate projections at the local scale. This data have the main limitations that changes in variability, extremes and in the temporal structure, such as changes in the wet day frequency, are not reproduced. The latter is a considerable downside of the delta-change approach for many impact applications. In this regard, stochastic weather generators (WGs) are an appealing technique that allow the simulation of multiple realizations of synthetic weather sequences consistent with the locally observed weather statistics and its future changes. Here, we analyse a Richardson-type weather generator (WG) as an alternative method to <span class="hlt">downscale</span> daily precipitation, minimum and maximum temperature. The WG is calibrated for 26 Swiss stations and the reference period 1980-2009. It is perturbed with change factors derived from 12 RCMs (<span class="hlt">ENSEMBLES</span>) to represent the climate of 2070-2099 assuming the SRES A1B emission scenario. The WG can be run in multi-site mode, making it especially attractive for impact-modelers that rely on a realistic spatial structure in <span class="hlt">downscaled</span> time-series. The results from the WG are benchmarked against the original delta-change approach that applies mean additive or multiplicative adjustments to the observations. According to both <span class="hlt">downscaling</span> methods, the results reveal area-wide mean temperature increases and a precipitation decrease in summer, consistent with earlier studies. For the summer drying, the WG indicates primarily a decrease in wet-day frequency and correspondingly an increase in mean dry spell length by around 18% - 40% at low</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5500I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5500I"><span id="translatedtitle">Enhancing Local Climate Projections of Precipitation: Assets and Limitations of Quantile Mapping Techniques for Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ivanov, Martin; Kotlarski, Sven; Schär, Christoph</p> <p>2015-04-01</p> <p>The Swiss CH2011 scenarios provide a portfolio of climate change scenarios for the region of Switzerland, specifically tailored for use in climate impact research. Although widely applied by a variety of end-users, these scenarios are subject to several limitations related to the underlying delta change methodology. Examples are difficulties to appropriately account for changes in the spatio-temporal variability of meteorological fields and for changes in extreme events. The recently launched ELAPSE project (Enhancing local and regional climate change projections for Switzerland) is connected to the EU COST Action VALUE (www.value-cost.eu) and aims at complementing CH2011 by further scenario products, including a bias-corrected version of daily scenarios at the site scale. For this purpose the well-established empirical quantile mapping (QM) methodology is employed. Here, daily temperature and precipitation output of 15 GCM-RCM model chains of the <span class="hlt">ENSEMBLES</span> project is <span class="hlt">downscaled</span> and bias-corrected to match observations at weather stations in Switzerland. We consider established QM techniques based on all empirical quantiles or linear interpolation between the empirical percentiles. In an attempt to improve the <span class="hlt">downscaling</span> of extreme precipitation events, we also apply a parametric approximation of the daily precipitation distribution by a dynamically weighted mixture of a Gamma distribution for the bulk and a Pareto distribution for the right tail for the first time in the context of QM. All techniques are evaluated and intercompared in a cross-validation framework. The statistical <span class="hlt">downscaling</span> substantially improves virtually all considered distributional and temporal characteristics as well as their spatial distribution. The empirical methods have in general very similar performances. The parametric method does not show an improvement over the empirical ones. Critical sites and seasons are highlighted and discussed. Special emphasis is placed on investigating the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016CliPa..12..635C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016CliPa..12..635C"><span id="translatedtitle">Probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin</p> <p>2016-03-01</p> <p>This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871-2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue <span class="hlt">Downscaling</span> method for HYdrology) statistical <span class="hlt">downscaling</span> method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily <span class="hlt">ensemble</span> of 125 analogue dates over the 1871-2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature (T). Comparisons to the Safran reanalysis over 1959-2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://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/2016HESS...20.3059L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016HESS...20.3059L"><span id="translatedtitle">Improved large-scale hydrological modelling through the assimilation of streamflow and <span class="hlt">downscaled</span> satellite soil moisture observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>López López, Patricia; Wanders, Niko; Schellekens, Jaap; Renzullo, Luigi J.; Sutanudjaja, Edwin H.; Bierkens, Marc F. P.</p> <p>2016-07-01</p> <p>The coarse spatial resolution of global hydrological models (typically >  0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with <span class="hlt">downscaled</span> global meteorological data (<span class="hlt">downscaled</span> from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). <span class="hlt">Downscaled</span> satellite-derived soil moisture (<span class="hlt">downscaled</span> from ˜  0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an <span class="hlt">ensemble</span> Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and <span class="hlt">downscaled</span> soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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://adsabs.harvard.edu/abs/2016ClDy..tmp..157T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy..tmp..157T"><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/2013EGUGA..1511304T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1511304T"><span id="translatedtitle">AMIC Project: Comparison of WRF High Resolution Dynamical <span class="hlt">Downscaling</span> of ERA-Interim and EC-Earth for Azores Islands</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tomé, Ricardo; Miranda, Pedro; Azevedo, Eduardo; Santo, Fátima</p> <p>2013-04-01</p> <p>Project AMIC integrates the Portuguese members of the new EC-Earth climate modeling consortium. The aim is to contribute to the IPCC fifth report with a significant set of simulations with a state of the art model, while giving the group timely access to the complete <span class="hlt">ensemble</span> of simulations for diagnostic studies, and regional <span class="hlt">downscaling</span>. Additionally, Project AMIC will produce a new set of high resolution simulations of the Portuguese islands climate, using a state of the art model (WRF) at 6km horizontal resolution, with boundary conditions from the new ERA-Interim reanalysis (1989-2009) and from the EC-Earth decadal (20 year) runs. These simulations will allow for validation of the <span class="hlt">downscaling</span> methodology, and will characterize both the current and near future climate. This study aims to compare two present day climate high resolution dynamical <span class="hlt">downscaling</span> WRF simulations for the Portuguese islands of Azores using the ECMWF ERA-Interim reanalysis and the EC-Earth v2.3 boundary conditions for the period 1989-2010. In small volcanic islands the local scale climate is influenced by the regional scale climate and by the orography and orientation of air masses over the islands. In these environments the climatological conditions are a vital importance for the local agriculture and water management. With this study we aim to see how well the dynamical <span class="hlt">downscaling</span> using EC-Earth v2.3 behaves when put against to the ERA-Interim reanalysis. To achieve this goal results from both simulations are compared against with the available observation network in both islands. This study results will show us what kind of deviations we can expect for the future scenarios runs using EC-Earth boundaries currently being made in IDL.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC44A..08C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC44A..08C"><span id="translatedtitle">Trend of climate extremes in North America: A comparison between dynamically <span class="hlt">downscaled</span> CMIP3 and 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>Castro, C. L.; Chang, H. I.; Mearns, L. O.; Bukovsky, M. S.</p> <p>2015-12-01</p> <p>Ascertaining the impact of anthropogenically-influenced climate change on climate extremes is of high priority for civil infrastructure and water resource planning. The current future projections based on IPCC models, for example as documented in the recent Climate Change Assessment for the Southwest, indicate a declining trend in precipitation with a warming climate, with associated dramatic reductions in streamflow in the Colorado River basin. However, inconsistent precipitation trends are projected by individual IPCC global climate models (i.e. Sheffield et al. 2013, Bukovsky et al., 2013). The North American Monsoon interannual variability is partly controlled by warm season atmospheric teleconnections emanating from the western tropical Pacific, related to the El Niño Southern Oscillation (ENSO) and Pacific Decadal Variability (PDV). Departure from the <span class="hlt">ensemble</span> mean approach for long-term climate projection analysis, a physics-based methodology is designed to analyze the relationship between climate extremes and the large scale forcing (Chang et al. 2015). Analysis from the observational record and <span class="hlt">downscaled</span> CMIP3 regional climate runs has shown intensifying warm season precipitation and temperature extremes following the natural variability of large scale forcing. We will utilize the ongoing community effort in dynamically <span class="hlt">downscaling</span> the CMIP5 climate projection datasets, part of the North American Coordinated Regional Climate <span class="hlt">Downscaling</span> Experiment (NA-CORDEX), and compare with the previous generation of CMIP3 <span class="hlt">downscaled</span> products for future climate assessment. We aim to examine the difference in large scale forcing from different generations of the CMIP models, and the related impact on regional scale climate extreme characteristics.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015NPGeo..22..383P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015NPGeo..22..383P"><span id="translatedtitle">Spatial random <span class="hlt">downscaling</span> of rainfall signals in Andean heterogeneous 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 Espinosa, L. A.; Yarlequé, C.; Carbajal, M.; Heidinger, H.; Carvalho, L.; Jones, C.; Quiroz, R.</p> <p>2015-07-01</p> <p>Remotely sensed data are often used as proxies for indirect precipitation measures over data-scarce and complex-terrain areas such as the Peruvian Andes. Although this information might be appropriate for some research requirements, the extent at which local sites could be related to such information is very limited because of the resolution of the available satellite data. <span class="hlt">Downscaling</span> techniques are used to bridge the gap between what climate modelers (global and regional) are able to provide and what decision-makers require (local). Precipitation <span class="hlt">downscaling</span> improves the poor local representation of satellite data and helps end-users acquire more accurate estimates of water availability. Thus, a multifractal <span class="hlt">downscaling</span> technique complemented by a heterogeneity filter was applied to TRMM (Tropical Rainfall Measuring Mission) 3B42 gridded data (spatial resolution ~ 28 km) from the Peruvian Andean high plateau or Altiplano to generate <span class="hlt">downscaled</span> rainfall fields that are relevant at an agricultural scale (spatial resolution ~ 1 km).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003JGRD..108.8863P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003JGRD..108.8863P"><span id="translatedtitle">A <span class="hlt">downscaling</span> framework for L band radiobrightness temperature imagery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Parada, Laura M.; Liang, Xu</p> <p>2003-11-01</p> <p>In this paper we introduce a general <span class="hlt">downscaling</span> framework and apply it to L band microwave radiobrightness temperature fields retrieved from electronically scanned thinned array radiometer (ESTAR). The gist of the <span class="hlt">downscaling</span> scheme presented in this paper is the statistical characterization of scale-invariant properties of the wavelet coefficients or fluctuations from long memory 1/f processes. We test the proposed <span class="hlt">downscaling</span> framework with the radiobrightness temperature images collected during the Southern Great Plains hydrology experiment of 1997. We produce realizations of radiobrightness temperature at 800-m resolution given a mean-area value at approximately 30-km resolution (the near-future expected operational scale). The results obtained evince that the proposed <span class="hlt">downscaling</span> methodology is capable of accurately preserving the variability and overall structure of spatial dependence of the observed radiobrightness temperature fields.</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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4761110','PMC'); return false;" href="https://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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26896847','PUBMED'); return false;" href="https://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="https://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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AtmRe..94..448F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AtmRe..94..448F"><span id="translatedtitle">Evaluation of statistical <span class="hlt">downscaling</span> in short range precipitation forecasting</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fernández-Ferrero, A.; Sáenz, J.; Ibarra-Berastegi, G.; Fernández, J.</p> <p>2009-11-01</p> <p>The objective of this study is to compare several statistical <span class="hlt">downscaling</span> methods for the development of an operational short-term forecast of precipitation in the area of Bilbao (Spain). The ability of statistical <span class="hlt">downscaling</span> methods nested inside numerical simulations run by both coarse and regional model simulations is tested with several selections of predictors and domain sizes. The selection of predictors is performed both in terms of sound physical mechanisms and also by means of "blind" criteria, such as "give the statistical <span class="hlt">downscaling</span> methods all the information they can process". Results show that the use of statistical <span class="hlt">downscaling</span> methods improves the ability of the mesoscale and coarse resolution models to provide quantitative precipitation forecasts. The selection of predictors in terms of sound physical principles does not necessarily improve the ability of the statistical <span class="hlt">downscaling</span> method to select the most relevant inputs to feed the precipitation forecasting model, due to the fact that the numerical models do not always fulfil conservation laws or because precipitation events do not reflect simple phenomenological laws. Coarse resolution models are able to provide information usable in combination with a statistical <span class="hlt">downscaling</span> method to achieve a quantitative precipitation forecast skill comparable to that obtained by other systems currently in use.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ClDy...38..635C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ClDy...38..635C"><span id="translatedtitle"><span class="hlt">Downscaling</span> of South America present climate driven by 4-member HadCM3 runs</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chou, Sin Chan; Marengo, José A.; Lyra, André A.; Sueiro, Gustavo; Pesquero, José F.; Alves, Lincoln M.; Kay, Gillian; Betts, Richard; Chagas, Diego J.; Gomes, Jorge L.; Bustamante, Josiane F.; Tavares, Priscila</p> <p>2012-02-01</p> <p>The objective of this work is to evaluate climate simulations over South America using the regional Eta Model driven by four members of an <span class="hlt">ensemble</span> of the UK Met Office Hadley Centre HadCM3 global model. The Eta Model has been modified with the purpose of performing long-term decadal integrations and has shown to reproduce "present climate"—the period 1961-1990—reasonably well when forced by HadCM3. The global model lateral conditions with a resolution of 2.5° latitude × 3.75° longitude were provided at a frequency of 6 h. Each member of the global model <span class="hlt">ensemble</span> has a different climate sensitivity, and the four members were selected to span the range of uncertainty encompassed by the <span class="hlt">ensemble</span>. The Eta Model nested in the HadCM3 global model was configured with 40-km horizontal resolution and 38 layers in the vertical. No large-scale internal nudging was applied. Results are shown for austral summer and winter at present climate defined as 1961-90. The upper and low-level circulation patterns produced by the Eta-CPTEC/HadCM3 experiment set-up show good agreement with reanalysis data and the mean precipitation and temperature with CRU observation data. The spread in the <span class="hlt">downscaled</span> mean precipitation and temperature is small when compared against model errors. On the other hand, the benefits in using an <span class="hlt">ensemble</span> is clear in the improved representation of the seasonal cycle by the <span class="hlt">ensemble</span> mean over any one realization. El Niño and La Niña years were identified in the HadCM3 member runs based on the NOAA Climate Prediction Center criterion of sea surface temperature anomalies in the Niño 3.4 area. The frequency of the El Niño and La Niña events in the studied period is underestimated by HadCM3. The precipitation and temperature anomalies typical of these events are reproduced by most of the Eta-CPTEC/HadCM3 <span class="hlt">ensemble</span>, although small displacements of the positions of the anomalies occur. This experiment configuration is the first step on the implementation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011PhDT.......163P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011PhDT.......163P"><span id="translatedtitle">Complex System <span class="hlt">Ensemble</span> Analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pearson, Carl A.</p> <p></p> <p>A new measure for interaction network <span class="hlt">ensembles</span> and their dynamics is presented: the <span class="hlt">ensemble</span> transition matrix, T, the proportions of networks in an <span class="hlt">ensemble</span> that support particular transitions. That presentation begins with generation of the <span class="hlt">ensemble</span> and application of constraint perturbations to compute T, including estimating alternatives to accommodate cases where the problem size becomes computationally intractable. Then, T is used to predict <span class="hlt">ensemble</span> dynamics properties in expected-value like calculations. Finally, analyses from the two complementary assumptions about T - that it represents uncertainty about a unique system or that it represents stochasticity around a unique constraint - are presented: entropy-based experiment selection and generalized potentials/heat dissipation of the system, respectively. Extension of these techniques to more general graph models is described, but not demonstrated. Future directions for research using T are proposed in the summary chapter. Throughout this work, the presentation of various calculations involving T are motivated by the Budding Yeast Cell Cycle example, with argument for the generality of the approaches presented by the results of their application to a database of pseudo-randomly generated dynamic constraints.</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://adsabs.harvard.edu/abs/2008AGUFM.A13A0217S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFM.A13A0217S"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of Era40 in Norway</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sorteberg, A.; Barstad, I.; Flatoy, F.; Deque, M.</p> <p>2008-12-01</p> <p>A novel approach for <span class="hlt">downscaling</span> of the Era40 data set has been taken and results from comparison with observations in Norway will be presented. The approach make use of a nudging technique in a stretched global model with the grid focus at (67N, 5W). The effective resolution is three times the one of the Era40, equivalent to about 30km grid spacing in the area of focus. Longer waves are nudged towards Era40 while the short waves are set free to evolve. The comparison to observations incorporate numerous station data points of i) precipitation (#357), ii) temperature (#98) and iii) wind (#10), and the new data set shows large improvements over Era40. The results from daily precipitation show considerably reduction in bias (from 50% to 11%), and a two-fold reduction (-59% to 29%) at 99.9%tile level. The daily temperature bias was reduced by about a degree in most areas, and the RMSE was reduced significantly (from 7.5 to 5.0 except winter). The wind comparison indicated a slight improvement in bias, and significant improvement in RMSE.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1710270C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1710270C"><span id="translatedtitle">Comparison between dynamical and stochastic <span class="hlt">downscaling</span> methods in central Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Camici, Stefania; Palazzi, Elisa; Pieri, Alexandre; Brocca, Luca; Moramarco, Tommaso; Provenzale, Antonello</p> <p>2015-04-01</p> <p>Global climate models (GCMs) are the primary tool to assess future climate change. However, most GCMs currently do not provide reliable information on scales below about 100 km and, hence, cannot be used as a direct input of hydrological models for climate change impact assessments. Therefore, a wide range of statistical and dynamical <span class="hlt">downscaling</span> methods have been developed to overcome the scale discrepancy between the GCM climatic scenarios and the resolution required for hydrological applications and impact studies. In this context, the selection of a suitable <span class="hlt">downscaling</span> method is an important issue. The use of different spatial domains, predictor variables, predictands and assessment criteria makes the relative performance of different methods difficult to achieve and general rules to select a priori the best <span class="hlt">downscaling</span> method do not exist. Additionally, many studies have shown that, depending on the hydrological variable, each <span class="hlt">downscaling</span> method significantly contributes to the overall uncertainty of the final hydrological response. Therefore, it is strongly recommended to test/evaluate different <span class="hlt">downscaling</span> methods by using ground-based data before applying them to climate model data. In this study, the daily rainfall data from the ERA-Interim re-analysis database (provided by the European Centre for Medium-Range Weather Forecasts, ECMWF) for the period 1979-2008 and with a resolution of about 80 km, are <span class="hlt">downscaled</span> using both dynamical and statistical methods. In the first case, the Weather Research and Forecasting (WRF) model was nested into the ERA-Interim re-analysis system to achieve a spatial resolution of about 4 km; in the second one, the stochastic rainfall <span class="hlt">downscaling</span> method called RainFARM was applied to the ERA-Interim data to obtain one stochastic realization of the rainfall field with a resolution of ~1 km. The <span class="hlt">downscaled</span> rainfall data obtained with the two methods are then used to force a continuous rainfall-runoff model in order to obtain a</p> </li> <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://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> <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="https://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/2015OcMod..90...57M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015OcMod..90...57M"><span id="translatedtitle"><span class="hlt">Downscaling</span> biogeochemistry in the Benguela eastern boundary current</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Machu, E.; Goubanova, K.; Le Vu, B.; Gutknecht, E.; Garçon, V.</p> <p>2015-06-01</p> <p>Dynamical <span class="hlt">downscaling</span> is developed to better predict the regional impact of global changes in the framework of scenarios. As an intermediary step towards this objective we used the Regional Ocean Modeling System (ROMS) to <span class="hlt">downscale</span> a low resolution coupled atmosphere-ocean global circulation model (AOGCM; IPSL-CM4) for simulating the recent-past dynamics and biogeochemistry of the Benguela eastern boundary current. Both physical and biogeochemical improvements are discussed over the present climate scenario (1980-1999) under the light of <span class="hlt">downscaling</span>. Despite biases introduced through boundary conditions (atmospheric and oceanic), the physical and biogeochemical processes in the Benguela Upwelling System (BUS) have been improved by the ROMS model, relative to the IPSL-CM4 simulation. Nevertheless, using coarse-resolution AOGCM daily atmospheric forcing interpolated on ROMS grids resulted in a shifted SST seasonality in the southern BUS, a deterioration of the northern Benguela region and a very shallow mixed layer depth over the whole regional domain. We then investigated the effect of wind <span class="hlt">downscaling</span> on ROMS solution. Together with a finer resolution of dynamical processes and of bathymetric features (continental shelf and Walvis Ridge), wind <span class="hlt">downscaling</span> allowed correction of the seasonality, the mixed layer depth, and provided a better circulation over the domain and substantial modifications of subsurface biogeochemical properties. It has also changed the structure of the lower trophic levels by shifting large offshore areas from autotrophic to heterotrophic regimes with potential important consequences on ecosystem functioning. The regional <span class="hlt">downscaling</span> also improved the phytoplankton distribution and the southward extension of low oxygen waters in the Northern Benguela. It allowed simulating low oxygen events in the northern BUS and highlighted a potential upscaling effect related to the nitrogen irrigation from the productive BUS towards the tropical</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010EGUGA..12.9475M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010EGUGA..12.9475M&link_type=ABSTRACT"><span id="translatedtitle">South America <span class="hlt">downscaling</span>: using spatial artificial neural network</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mendes, David; Marengo, José</p> <p>2010-05-01</p> <p>The mathematical models used to simulate the present climate and project future climate with forcing by greenhouse gases and aerosols are generally referred to as General Circulation Models or Global Climate Models (GCMs). However, the spatial resolution of GCMs remains quite coarse, in the order of 300 x 300 km, and at scale, the regional and local details of the climate which are influenced by spatial heterogeneities in the regional physiography are lost. Therefore, there is the need to convert the GCM outputs into a reliable data set with higher spatial resolution, with daily rainfall and temperature time series at the scale of the watershed or a region to which the climate impact is going to be investigated. The methods used to convert GCM outputs into local meteorological variables required for reliable climate modeling are usually referred to as <span class="hlt">downscaling</span> techniques. There are a variety of <span class="hlt">downscaling</span> techniques in the literature, but two major approaches can be identified at the moment, namely, dynamic <span class="hlt">downscaling</span> and empirical (statistical) <span class="hlt">downscaling</span>. The most widely used empirical <span class="hlt">downscaling</span> methods are the multiple linear regression and stochastic weather generation. However, the interest in nonlinear regression methods, namely, artificial neural network (ANN), is nowadays increasing because of their high potential for complex, nonlinear and time-varying input-output mapping. The main aim of this work is to develop and test a novel type of statistical <span class="hlt">downscaling</span> technique based on the Artificial Neural Network (ANN), applied of the climate change. This work analyses the performance of the IPCC models in simulate the present and future climate using ANN. The ANN used here are based on a feed forward configuration of the multilayer perception that has been used by a growing number of authors. To carry out statistical <span class="hlt">downscaling</span> for each meteorological date (grid point), the predictors and predictands were supplied to the models (ANN) and spatial</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4271150','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4271150"><span id="translatedtitle">The <span class="hlt">Ensembl</span> REST API: <span class="hlt">Ensembl</span> Data for Any Language</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; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R. S.; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul</p> <p>2015-01-01</p> <p>Motivation: We present a Web service to access <span class="hlt">Ensembl</span> data using Representational State Transfer (REST). The <span class="hlt">Ensembl</span> REST server enables the easy retrieval of a wide range of <span class="hlt">Ensembl</span> data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular <span class="hlt">Ensembl</span> Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. Availability and implementation: The <span class="hlt">Ensembl</span> REST API can be accessed at http://rest.<span class="hlt">ensembl</span>.org and source code is freely available under an Apache 2.0 license from http://github.com/<span class="hlt">Ensembl/ensembl</span>-rest. Contact: ayates@ebi.ac.uk or flicek@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25236461</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=crescendo&id=ED254263','ERIC'); return false;" href="http://eric.ed.gov/?q=crescendo&id=ED254263"><span id="translatedtitle">Music <span class="hlt">Ensemble</span>: Course Proposal.</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>Kovach, Brian</p> <p></p> <p>A proposal is presented for a Music <span class="hlt">Ensemble</span> course to be offered at the Community College of Philadelphia for music students who have had previous vocal or instrumental training. A standardized course proposal cover form is followed by a statement of purpose for the course, a list of major course goals, a course outline, and a bibliography. Next,…</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://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="https://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/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://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/2012EGUGA..1411238R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1411238R"><span id="translatedtitle"><span class="hlt">Ensemble</span> simulation and uncertainty estimation - Mountain soil moisture variability under climate change</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rössler, O.; Diekkrüger, B.; Löffler, J.</p> <p>2012-04-01</p> <p> areas below 2000 m a.s.l. to be affected at most by climate change in 2070-2100 (-10 vol-%). Thereby, the variability of the results from the six <span class="hlt">ensembles</span> were remarkably high, offering a bandwidth of possibilities from nearly unchanged soil moisture conditions to strong expansion of drought stress in the future. In addition we found uncertainties from the applied hydrological model and <span class="hlt">downscaling</span> approaches in the magnitude of the predicted changes (+/- 10 vol-%).</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AdG....23...65Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AdG....23...65Y"><span id="translatedtitle"><span class="hlt">Downscaling</span>, parameterization, decomposition, compression: a perspective from the multiresolution analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yano, J.-I.</p> <p>2010-06-01</p> <p>Geophysical models in general, and atmospheric models more specifically, are always limited in spatial resolutions. Due to this limitation, we face with two different needs. The first is a need for knowing (or "<span class="hlt">downscaling</span>") more spatial details (e.g., precipitation distribution) than having model simulations for practical applications, such as hydrological modelling. The second is a need for "parameterizing" the subgrid-scale physical processes in order to represent the feedbacks of these processes on to the resolved scales (e.g., the convective heating rate). The present article begins by remarking that it is essential to consider the <span class="hlt">downscaling</span> and parametrization as an "inverse" of each other: <span class="hlt">downscaling</span> seeks a detail of the subgrid-scale processes, then the parameterization seeks an integrated effect of the former into the resolved scales. A consideration on why those two closely-related operations are traditionally treated separately, gives insights of the fundamental limitations of the current <span class="hlt">downscalings</span> and parameterizations. The multiresolution analysis (such as those based on wavelet) provides an important conceptual framework for developing a unified formulation for the <span class="hlt">downscaling</span> and parameterization. In the vocabulary of multiresolution analysis, these two operations may be considered as types of decompression and compression. A new type of a subgrid-scale representation scheme, NAM-SCA (nonhydrostatic anelastic model with segmentally-constant approximation), is introduced under this framework.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JGRD..11717116H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JGRD..11717116H"><span id="translatedtitle">Predictor selection for <span class="hlt">downscaling</span> GCM data with LASSO</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hammami, Dorra; Lee, Tae Sam; Ouarda, Taha B. M. J.; Lee, Jonghyun</p> <p>2012-09-01</p> <p>Over the last 10 years, <span class="hlt">downscaling</span> techniques, including both dynamical (i.e., the regional climate model) and statistical methods, have been widely developed to provide climate change information at a finer resolution than that provided by global climate models (GCMs). Because one of the major aims of <span class="hlt">downscaling</span> techniques is to provide the most accurate information possible, data analysts have tried a number of approaches to improve predictor selection, which is one of the most important steps in <span class="hlt">downscaling</span> techniques. Classical methods such as regression techniques, particularly stepwise regression (SWR), have been employed for <span class="hlt">downscaling</span>. However, SWR presents some limits, such as deficiencies in dealing with collinearity problems, while also providing overly complex models. Thus, the least absolute shrinkage and selection operator (LASSO) technique, which is a penalized regression method, is presented as another alternative for predictor selection in <span class="hlt">downscaling</span> GCM data. It may allow for more accurate and clear models that can properly deal with collinearity problems. Therefore, the objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Québec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters for comparison.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.H13E1618L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H13E1618L"><span id="translatedtitle">Seasonal River Flow Forecasting Using Multi-model <span class="hlt">Ensemble</span> Climate Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lavers, D.; Prudhomme, C.; Hannah, D.; Troccoli, A.</p> <p>2007-12-01</p> <p>Developing skilful seasonal forecasting of river flows is important for many societal applications. Long-lead forecasts have potential to aid water management decision making and preparation for human response to hydrological extremes. The seasonal prediction of river flows has been a topic of increasing interest due to the recent 2004-06 drought and 2007 floods experienced in the UK. We compare the relative skill of predictions of river flow using: (1) a multi-Global Climate Model (GCM) <span class="hlt">ensemble</span> data set and (2) <span class="hlt">downscaled</span> multi-GCM data as input to a hydrological model. The period considered is 1980- 2001. The River Dyfi basin in West Wales, UK is the focus of this research. This basin is near natural, hence the climate-flow signal should be clearer. The DEMETER project is the source of the multi-model climate data, and this consists of 7 GCMs each with 9 <span class="hlt">ensemble</span> members. Hindcasts with lead times up to 6 months are available from 1st February, 1st May, 1st August and 1st November initial conditions. Each hindcast was split into the first 3 and last 3 months, and the subsequent concatenation of the split hindcasts produced 2 time series (total of 7×9×2 <span class="hlt">ensemble</span> series), which were run through the Probability Distributed Model (PDM). PDM is a lumped rainfall-runoff model that transforms rainfall and potential evaporation data to river flow at the basin outlet. PDM was calibrated with observations from 1980-1990, and then validated from 1991-2001. The coarse resolution of the DEMETER data (standardised to 2.5° × 2.5° resolution) means that the atmospheric motions at sub-grid scales are not captured by the models. The large spatial disparity between the GCM grids and the scale of the study (471.3 km2) lead to underestimation of precipitation by DEMETER models. This difference is addressed through the use of a statistical <span class="hlt">downscaling</span> tool, the Statistical <span class="hlt">Downscaling</span> Model (SDSM). The SDSM was calibrated on the ERA-40 re-analysis data set (from the ECMWF), as</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H24F..08H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H24F..08H"><span id="translatedtitle">Development of Spatiotemporal Bias-Correction Techniques for <span class="hlt">Downscaling</span> GCM Predictions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.</p> <p>2010-12-01</p> <p>Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM <span class="hlt">downscaling</span> techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. <span class="hlt">Downscaled</span> GCM results (e.g., CMIP3 <span class="hlt">ensembles</span>) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and <span class="hlt">downscaled</span> CMIP3 data for the</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/abs/2015AGUFMGC33G..06M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC33G..06M"><span id="translatedtitle">Evaluation of Statistical <span class="hlt">Downscaling</span> Skill at Reproducing 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>McGinnis, S. A.; Tye, M. R.; Nychka, D. W.; Mearns, L. O.</p> <p>2015-12-01</p> <p>Climate model outputs usually have much coarser spatial resolution than is needed by impacts models. Although higher resolution can be achieved using regional climate models for dynamical <span class="hlt">downscaling</span>, further <span class="hlt">downscaling</span> is often required. The final resolution gap is often closed with a combination of spatial interpolation and bias correction, which constitutes a form of statistical <span class="hlt">downscaling</span>. We use this technique to <span class="hlt">downscale</span> regional climate model data and evaluate its skill in reproducing extreme events. We <span class="hlt">downscale</span> output from the North American Regional Climate Change Assessment Program (NARCCAP) dataset from its native 50-km spatial resolution to the 4-km resolution of University of Idaho's METDATA gridded surface meterological dataset, which derives from the PRISM and NLDAS-2 observational datasets. We operate on the major variables used in impacts analysis at a daily timescale: daily minimum and maximum temperature, precipitation, humidity, pressure, solar radiation, and winds. To interpolate the data, we use the patch recovery method from the Earth System Modeling Framework (ESMF) regridding package. We then bias correct the data using Kernel Density Distribution Mapping (KDDM), which has been shown to exhibit superior overall performance across multiple metrics. Finally, we evaluate the skill of this technique in reproducing extreme events by comparing raw and <span class="hlt">downscaled</span> output with meterological station data in different bioclimatic regions according to the the skill scores defined by Perkins et al in 2013 for evaluation of AR4 climate models. We also investigate techniques for improving bias correction of values in the tails of the distributions. These techniques include binned kernel density estimation, logspline kernel density estimation, and transfer functions constructed by fitting the tails with a generalized pareto distribution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1814006M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1814006M"><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/2016AcASn..57..326Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AcASn..57..326Y"><span id="translatedtitle"><span class="hlt">Ensemble</span> Pulsar Time Scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yin, D. S.; Gao, Y. P.; Zhao, S. H.</p> <p>2016-05-01</p> <p>Millisecond pulsars can generate another type of time scale that is totally independent of the atomic time scale, because the physical mechanisms of the pulsar time scale and the atomic time scale are quite different from each other. Usually the pulsar timing observational data are not evenly sampled, and the internals between data points range from several hours to more than half a month. What's more, these data sets are sparse. And all these make it difficult to generate an <span class="hlt">ensemble</span> pulsar time scale. Hence, a new algorithm to calculate the <span class="hlt">ensemble</span> pulsar time scale is proposed. Firstly, we use cubic spline interpolation to densify the data set, and make the intervals between data points even. Then, we employ the Vondrak filter to smooth the data set, and get rid of high-frequency noise, finally adopt the weighted average method to generate the <span class="hlt">ensemble</span> pulsar time scale. The pulsar timing residuals represent clock difference between the pulsar time and atomic time, and the high precision pulsar timing data mean the clock difference measurement between the pulsar time and atomic time with a high signal to noise ratio, which is fundamental to generate pulsar time. We use the latest released NANOGRAV (North American Nanohertz Observatory for Gravitational Waves) 9-year data set to generate the <span class="hlt">ensemble</span> pulsar time scale. This data set is from the newest NANOGRAV data release, which includes 9-year observational data of 37 millisecond pulsars using the 100-meter Green Bank telescope and 305-meter Arecibo telescope. We find that the algorithm used in this paper can lower the influence caused by noises in timing residuals, and improve long-term stability of pulsar time. Results show that the long-term (> 1 yr) frequency stability of the pulsar time is better than 3.4×10-15.</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=""></a></p> <p></p> <p>2009-04-27</p> <p>This is a Matlab toolbox for investigating the application of cluster <span class="hlt">ensembles</span> to data classification, with the objective of improving the accuracy and/or speed of clustering. The toolbox divides the cluster <span class="hlt">ensemble</span> problem into four areas, providing functionality for each. These include, (1) synthetic data generation, (2) clustering to generate individual data partitions and similarity matrices, (3) consensus function generation and final clustering to generate <span class="hlt">ensemble</span> data partitioning, and (4) implementation of accuracy metrics. Withmore » regard to data generation, Gaussian data of arbitrary dimension can be generated. The kcenters algorithm can then be used to generate individual data partitions by either, (a) subsampling the data and clustering each subsample, or by (b) randomly initializing the algorithm and generating a clustering for each initialization. In either case an overall similarity matrix can be computed using a consensus function operating on the individual similarity matrices. A final clustering can be performed and performance metrics are provided for evaluation purposes.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26387108','PUBMED'); return false;" href="https://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="https://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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26529728','PUBMED'); return false;" href="https://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="https://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.</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="https://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.H53A1653L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H53A1653L"><span id="translatedtitle">Seasonal Hydrometeorological <span class="hlt">Ensemble</span> Prediction System; Forecast of Irrigation Potentials in Denmark</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lucatero, D.; Jensen, K. H.; Madsen, H.; Refsgaard, J. C.; Kidmose, J.</p> <p>2015-12-01</p> <p>The European Center for Medium Weather Forecast seasonal <span class="hlt">ensemble</span> prediction system (ECMWF-SEPS) of weather variables such as precipitation, temperature and evapotranspiration is used as input to an integrated surface-subsurface hydrological model based on the MIKE SHE system to generate probabilistic forecasts of the irrigation requirements in the Skjern river catchment in Denmark. We demonstrate the usability of the ECMWF-SEPS and discuss the challenges and areas of opportunities when issuing forecasts generated with this methodology. A simple bias-correction and <span class="hlt">downscaling</span> technique, namely linear scaling, is applied to the raw inputs to remove the bias intrinsic in <span class="hlt">ensemble</span> prediction systems and to <span class="hlt">downscale</span> the data to a scale appropriate for hydrological modelling. The forecasts of the meteorological variables are analysed for accuracy and reliability by comparing them to meteorological observations. Additionally, weather <span class="hlt">ensembles</span> will be generated using the nearest-neighbour resampling technique with the purpose of exploring additional possibilities of hydrometeorological system input for complementing situations where the SEPS is lacking skill.</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/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/2015JESS..124..843S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JESS..124..843S"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> and projection of future temperature and precipitation change in middle catchment of Sutlej River Basin, India</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Singh, Dharmaveer; Jain, Sanjay K.; Gupta, R. D.</p> <p>2015-06-01</p> <p><span class="hlt">Ensembles</span> of two Global Climate Models (GCMs), CGCM3 and HadCM3, are used to project future maximum temperature ( T Max), minimum temperature ( T Min) and precipitation in a part of Sutlej River Basin, northwestern Himalayan region, India. Large scale atmospheric variables of CGCM3 and HadCM3 under different emission scenarios and the National Centre for Environmental Prediction/National Centre for Atmospheric Research reanalysis datasets are <span class="hlt">downscaled</span> using Statistical <span class="hlt">Downscaling</span> Model (SDSM). Variability and changes in T Max, T Min and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model are presented for future periods: 2020s, 2050s and 2080s. The study reveals rise in annual average T Max, T Min and precipitation under scenarios A1B and A2 for CGCM3 model as well as under A2 and B2 scenarios for HadCM3 model in 2020s, 2050s and 2080s. Increase in mean monthly T Min is also observed for all months of the year under all scenarios of both the models. This is followed by decrease in T Max during June, July August and September. However, the model projects rise in precipitation in months of July, August and September under A1B and A2 scenarios of CGCM3 model and A2 and B2 of HadCM3 model for future periods.</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=80636758&CFTOKEN=42828612','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=80636758&CFTOKEN=42828612"><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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.4656P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4656P"><span id="translatedtitle">Soil moisture <span class="hlt">downscaling</span> using a simple thermal based proxy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, Jian; Loew, Alexander; Niesel, Jonathan</p> <p>2016-04-01</p> <p>Microwave remote sensing has been largely applied to retrieve soil moisture (SM) from active and passive sensors. The obvious advantage of microwave sensor is that SM can be obtained regardless of atmospheric conditions. However, existing global SM products only provide observations at coarse spatial resolutions, which often hamper their applications in regional hydrological studies. Therefore, various <span class="hlt">downscaling</span> methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of a simple Vegetation Temperature Condition Index (VTCI) <span class="hlt">downscaling</span> scheme over different climates and regions. Both polar orbiting (MODIS) and geostationary (MSG SEVIRI) satellite data are used to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture in-situ measurements, spatial pattern comparison, as well as seasonal and land use analyses show that the <span class="hlt">downscaling</span> method can significantly improve the spatial details of CCI soil moisture while maintain the accuracy of CCI soil moisture. The application of the scheme with different satellite platforms and over different regions further demonstrate the robustness and effectiveness of the proposed method. Therefore, the VTCI <span class="hlt">downscaling</span> method has the potential to facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/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/2013AGUFMGC41E..05G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41E..05G"><span id="translatedtitle">Evaluating the utility of dynamical <span class="hlt">downscaling</span> in agricultural impacts projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Glotter, M.; Elliott, J. W.; McInerney, D. J.; Moyer, E. J.</p> <p>2013-12-01</p> <p>The need to understand the future impacts of climate change has driven the increasing use of dynamical <span class="hlt">downscaling</span> to produce fine-spatial-scale climate projections for impacts models. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield. Our results suggest that it does not. We simulate U.S. maize yields under current and future CO2 concentrations with the widely-used DSSAT crop model, driven by a variety of climate inputs including two general circulation models (GCMs), each in turn <span class="hlt">downscaled</span> by two regional climate models (RCMs). We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven yields are essentially indistinguishable in all scenarios (<10% discrepancy in national yield, equivalent to error from observations). While RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kms) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the added value of dynamically <span class="hlt">downscaling</span> raw GCM output for impacts assessments may not justify its computational demands, and that some rethinking of <span class="hlt">downscaling</span> methods is warranted.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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/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://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://www.osti.gov/scitech/servlets/purl/799409','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/799409"><span id="translatedtitle"><span class="hlt">Ensemble</span> Atmospheric Dispersion Modeling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Addis, R.P.</p> <p>2002-06-24</p> <p>Prognostic atmospheric dispersion models are used to generate consequence assessments, which assist decision-makers in the event of a release from a nuclear facility. Differences in the forecast wind fields generated by various meteorological agencies, differences in the transport and diffusion models, as well as differences in the way these models treat the release source term, result in differences in the resulting plumes. Even dispersion models using the same wind fields may produce substantially different plumes. This talk will address how <span class="hlt">ensemble</span> techniques may be used to enable atmospheric modelers to provide decision-makers with a more realistic understanding of how both the atmosphere and the models behave.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFMGC53A0687D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMGC53A0687D"><span id="translatedtitle">Assessment of dynamical <span class="hlt">downscaling</span> in Japan using an atmosphere-biosphere-river coupling 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>Dairaku, K.; Iizuka, S.; Sasaki, W.; Beltran, A.; Pielke, R. A.</p> <p>2008-12-01</p> <p>The responses of the climate system to increases in carbon dioxide concentrations and to changes in land use/land cover and the subsequent impacts of climatic variability on humans and natural ecosystems are of fundamental concern. Because regional responses of surface hydrological and biogeochemical changes are particularly complex, it is necessary to add spatial resolution to accurately assess critical interactions within the regional climate system for climate change impacts assessments. We investigated the reproducibility of present climate using two regional climate models with 20km horizontal grid spacing, the atmosphere- biosphere-river coupling regional climate model(GEMRAMS) and the Meteorological Research Institute Nonhydrostatic Model(MRI-NHM), both of which used Japanese 25-year ReAnalysis (JRA-25) as lateral boundary conditions. Two key variables for impact studies, surface air temperature and precipitation, were compared with the Japanese high-resolution surface observation, Automated Meteorological Data Acquisition System (AMeDAS) on 78 river basins. Results simulated by the two models were relatively in good agreement with the observation on the basin scale. The differences of surface air temperature between the models and the observation were less than 2K and the ratio of precipitation of the models to the observation was within 0.5-2 on seasonal averages. By adding other two regional climate models, a multi-model <span class="hlt">ensemble</span> will be applied in climate change impact studies in combination with additional statistical <span class="hlt">downscaling</span> approaches.</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/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://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://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://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%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%3D50%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/2015ClDy..tmp..429D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy..tmp..429D"><span id="translatedtitle"><span class="hlt">Downscaling</span> GISS ModelE boreal summer climate over Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Druyan, Leonard M.; Fulakeza, Matthew</p> <p>2015-11-01</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 2° latitude by 2.5° longitude and the RM3 grid spacing is 0.44°. 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/2012GMD.....5.1177C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012GMD.....5.1177C"><span id="translatedtitle"><span class="hlt">Downscaling</span> the climate change for oceans around Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chamberlain, M. A.; Sun, C.; Matear, R. J.; Feng, M.; Phipps, S. J.</p> <p>2012-09-01</p> <p>At present, global climate models used to project changes in climate poorly resolve mesoscale ocean features such as boundary currents and eddies. These missing features may be important to realistically project the marine impacts of climate change. Here we present a framework for dynamically <span class="hlt">downscaling</span> coarse climate change projections utilising a near-global ocean model that resolves these features in the Australasian region, with coarser resolution elsewhere. A time-slice projection for a 2060s ocean was obtained by adding climate change anomalies to initial conditions and surface fluxes of a near-global eddy-resolving ocean model. Climate change anomalies are derived from the differences between present and projected climates from a coarse global climate model. These anomalies are added to observed fields, thereby reducing the effect of model bias from the climate model. The <span class="hlt">downscaling</span> model used here is ocean-only and does not include the effects that changes in the ocean state will have on the atmosphere and air-sea fluxes. We use restoring of the sea surface temperature and salinity to approximate real-ocean feedback on heat flux and to keep the salinity stable. Extra experiments with different feedback parameterisations are run to test the sensitivity of the projection. Consistent spatial differences emerge in sea surface temperature, salinity, stratification and transport between the <span class="hlt">downscaled</span> projections and those of the climate model. Also, the spatial differences become established rapidly (< 3 yr), indicating the importance of mesoscale resolution. However, the differences in the magnitude of the difference between experiments show that feedback of the ocean onto the air-sea fluxes is still important in determining the state of the ocean in these projections. Until such a time when it is feasible to regularly run a global climate model with eddy resolution, our framework for ocean climate change <span class="hlt">downscaling</span> provides an attractive way to explore the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24872455','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24872455"><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="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J</p> <p>2014-06-17</p> <p>Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical <span class="hlt">downscaling</span>--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.</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/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/2016EGUGA..1816302S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1816302S"><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/2016ThApC.tmp..257Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp..257Y"><span id="translatedtitle">Performance comparison of three predictor selection methods for statistical <span class="hlt">downscaling</span> of daily precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, Chunli; Wang, Ninglian; Wang, Shijin; Zhou, Liang</p> <p>2016-10-01</p> <p>Predictor selection is a critical factor affecting the statistical <span class="hlt">downscaling</span> of daily precipitation. This study provides a general comparison between uncertainties in <span class="hlt">downscaled</span> results from three commonly used predictor selection methods (correlation analysis, partial correlation analysis, and stepwise regression analysis). Uncertainty is analyzed by comparing statistical indices, including the mean, variance, and the distribution of monthly mean daily precipitation, wet spell length, and the number of wet days. The <span class="hlt">downscaled</span> results are produced by the artificial neural network (ANN) statistical <span class="hlt">downscaling</span> model and 50 years (1961-2010) of observed daily precipitation together with reanalysis predictors. Although results show little difference between <span class="hlt">downscaling</span> methods, stepwise regression analysis is generally the best method for selecting predictors for the ANN statistical <span class="hlt">downscaling</span> model of daily precipitation, followed by partial correlation analysis and then correlation analysis.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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://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="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Aken, Bronwen L; Ayling, Sarah; Barrell, Daniel; Clarke, Laura; Curwen, Valery; Fairley, Susan; Fernandez Banet, Julio; Billis, Konstantinos; García Girón, Carlos; Hourlier, Thibaut; Howe, Kevin; Kähäri, Andreas; Kokocinski, Felix; Martin, Fergal J; Murphy, Daniel N; Nag, Rishi; Ruffier, Magali; Schuster, Michael; Tang, Y Amy; Vogel, Jan-Hinnerk; White, Simon; Zadissa, Amonida; Flicek, Paul; Searle, Stephen M J</p> <p>2016-01-01</p> <p>The <span class="hlt">Ensembl</span> gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the <span class="hlt">Ensembl</span> website. Here, we describe the annotation process in detail.Database URL: http://www.<span class="hlt">ensembl</span>.org/index.html. PMID:27337980</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4919035','PMC'); return false;" href="https://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/2009pcms.confE..18F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009pcms.confE..18F"><span id="translatedtitle">Evaluation of the mean and extreme precipitation regimes of the <span class="hlt">ENSEMBLES</span> RCM multi-model over Spain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fita, L.; Herrera, S.; Fernández, J.; Gutiérrez, J. M.</p> <p>2009-09-01</p> <p><span class="hlt">ENSEMBLES</span> is an European project devoted (in Working Package RT3) to the use of Regional Climate Models (RCMs) to produce dynamical <span class="hlt">downscaling</span> of climate change global models over Europe. A large variety of models have been used allowing the generation of an <span class="hlt">ensemble</span> of high resolution projections of the future scenarios. In this study, the results of 9 RCMs driven by ERA40 ECMWF re-analyses during the control period of the <span class="hlt">ensemble</span> (1961 to 2000) are depicted. Validation of the climatology retrieved from simulations is done by comparison to a 0.2 degree horizontal resolution precipitation climatology (Spain02). Spain02 grid has been produced in the Universidad de Cantabria throughout a kriging over 2772 surface observations from the secondary AEMET network for the whole the period. Simulated and observed climatologies are compared in terrms of the average and extreme precipitation regimes. The average regime comparison is carried out by <span class="hlt">ensemble</span> mean results and seasonality is studied by temporal evolution of the monthly anual cycle over 11 Spanish river basins. The extreme precipitation regime is analysed by the use of different extreme precipitation indicators. Results show a large variety of concordance of the members of the <span class="hlt">ensemble</span> to the observed climatology. Climatologic patterns over Spain are significantly well represented by RCMs when the 5 best correlated members of the <span class="hlt">ensemble</span> are considered.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy...47..411P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...47..411P"><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://adsabs.harvard.edu/abs/2016EGUGA..1812689S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812689S"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of Climate Data for the River Severn Basin: A Comparative Study</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sharifi, Soroosh; Drapier, Christopher</p> <p>2016-04-01</p> <p>Global Climate Models (GCMs) are the main tools for the assessment of climate change impacts at a global level. However, they have a poor level of resolution, and to assess the impacts of climate change at a local-scale, their outputs need to be <span class="hlt">downscaled</span>. In this paper, the ability of three well known statistical <span class="hlt">downscaling</span> methods, namely, K-nn, SDSM and LARS-WG are compared in performing statistical <span class="hlt">downscaling</span> over the future period 2020 to 2039 within the River Severn Basin in the UK. The GCM outputs were obtained from the Hadley Centre's HadCM3 coupled model. To assess each method's skill at <span class="hlt">downscaling</span>, observed station data within the River Severn Basin was calibrated and verified with historic GCM data over the period 1960 to 1999 drawn from the 20C3M experiment. In general, <span class="hlt">downscaling</span> captured the seasonal trend for minimum and maximum temperature within the River Severn Basin. However all methods underestimated the observed weather information by up to 1.5 oC. LARS-WG showed the lowest annual and seasonal variation for temperature <span class="hlt">downscaling</span>. Provided a sufficiently long period of historic data was available for calibration, this method captured the climate characteristics most effectively. <span class="hlt">Downscaling</span> for precipitation was poor across all methods, but SDSM and LARS-WG were considered better than K-nn in their skill at <span class="hlt">downscaling</span>. However, <span class="hlt">downscaling</span> under SDSM showed a marginally closer match to the observed data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.5772N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.5772N"><span id="translatedtitle">Stochastic <span class="hlt">downscaling</span> of numerically simulated spatial rain and cloud fields using a transient multifractal approach</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.; Miranda, P. M.</p> <p>2012-04-01</p> <p>Atmospheric fields can be extremely variable over wide ranges of spatial scales, with a scale ratio of 109-1010 between largest (planetary) and smallest (viscous dissipation) scale. Furthermore atmospheric fields with strong variability over wide ranges in scale most likely should not be artificially split apart into large and small scales, as in reality there is no scale separation between resolved and unresolved motions. Usually the effects of the unresolved scales are modeled by a deterministic bulk formula representing an <span class="hlt">ensemble</span> of incoherent subgrid processes on the resolved flow. This is a pragmatic approach to the problem and not the complete solution to it. These models are expected to underrepresent the small-scale spatial variability of both dynamical and scalar fields due to implicit and explicit numerical diffusion as well as physically based subgrid scale turbulent mixing, resulting in smoother and less intermittent fields as compared to observations. Thus, a fundamental change in the way we formulate our models is required. Stochastic approaches equipped with a possible realization of subgrid processes and potentially coupled to the resolved scales over the range of significant scale interactions range provide one alternative to address the problem. Stochastic multifractal models based on the cascade phenomenology of the atmosphere and its governing equations in particular are the focus of this research. Previous results have shown that rain and cloud fields resulting from both idealized and realistic numerical simulations display multifractal behavior in the resolved scales. This result is observed even in the absence of scaling in the initial conditions or terrain forcing, suggesting that multiscaling is a general property of the nonlinear solutions of the Navier-Stokes equations governing atmospheric dynamics. Our results also show that the corresponding multiscaling parameters for rain and cloud fields exhibit complex nonlinear behavior</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H33E0922B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H33E0922B"><span id="translatedtitle">Expansion of the On-line Archive "Statistically <span class="hlt">Downscaled</span> WCRP CMIP3 Climate Projections"</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brekke, L. D.; Pruitt, T.; Maurer, E. P.; Das, T.; Duffy, P.; White, K.</p> <p>2009-12-01</p> <p> response, archive developers are adding content in 2010, teaming with Scripps Institution of Oceanography (through their NOAA-RISA California-Nevada Applications Program and the California Climate Change Center) to apply a new daily <span class="hlt">downscaling</span> technique to a sub-<span class="hlt">ensemble</span> of the archive’s CMIP3 projections. The new technique, Bias-Corrected Constructed Analogs, combines the BC part of BCSD with a recently developed technique that preserves the daily sequencing structure of CMIP3 projections (Constructed Analogs, or CA). Such data will more easily serve hydrologic and ecological impacts assessments, and offer an opportunity to evaluate projection uncertainty associated with <span class="hlt">downscaling</span> technique. Looking ahead to the arrival CMIP5 projections, archive collaborators have plans apply both BCSD and BCCA over the contiguous U.S. consistent with CMIP3 applications above, and also apply BCSD globally at a 0.5 degree spatial resolution. The latter effort involves collaboration with U.S. Army Corps of Engineers (USACE) and Climate Central.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.5721H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.5721H"><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://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="https://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/2012AGUFMGC43B1029L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMGC43B1029L"><span id="translatedtitle">Decadal Trends in <span class="hlt">Ensemble</span> Projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liess, S.; Snyder, P. K.; Kumar, A.; Kumar, V.</p> <p>2012-12-01</p> <p>A simple method to rank multi-model <span class="hlt">ensemble</span> members of CMIP3 simulations by their representation of phases of decadal oscillations is introduced. A period of 22 years (1979-2000) from the 20th century simulations is used to generate <span class="hlt">ensemble</span> projections of trends for an 11-year (2001-2011) lead time for the SRES A1B scenario. Although greenhouse-gas forcing is identical for all 20th century simulations, the phases of decadal oscillations are quite different. Thus, the suggested minimum requirements for a simple selection criterion for adequate <span class="hlt">ensemble</span> members are that (a) trends in high-, mid-, and low-latitude zones need to be treated separately and (b) information about the state of teleconnections between the zones needs to be included, when projecting decadal variability and trends in climate. The new method indicates that half (19 out of 38) <span class="hlt">ensemble</span> members retain their rank when each GCM is treated separately without any assumptions of which model might be superior. Thus, the overall <span class="hlt">ensemble</span> size can be reduced without a large loss of information but with a greatly reduced range of uncertainty, when only the least well performing <span class="hlt">ensemble</span> members of each GCM are omitted for an 11-year projection.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/15783619','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15783619"><span id="translatedtitle">Teleportation of an atomic <span class="hlt">ensemble</span> quantum state.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Dantan, A; Treps, N; Bramati, A; Pinard, M</p> <p>2005-02-11</p> <p>We propose a protocol to achieve high fidelity quantum state teleportation of a macroscopic atomic <span class="hlt">ensemble</span> using a pair of quantum-correlated atomic <span class="hlt">ensembles</span>. We show how to prepare this pair of <span class="hlt">ensembles</span> using quasiperfect quantum state transfer processes between light and atoms. Our protocol relies on optical joint measurements of the atomic <span class="hlt">ensemble</span> states and magnetic feedback reconstruction.</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/2010AGUFM.H23F1275P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H23F1275P"><span id="translatedtitle">A strategy for <span class="hlt">downscaling</span> SMOS-based soil moisture</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pan, M.; Sahoo, A. K.; Wood, E. F.</p> <p>2010-12-01</p> <p>The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission was launched in November 2009, and has been providing 1.4GHz (L-band) observations. A number of ongoing SMOS-related research efforts have been focusing on retrieving top surface soil moisture from the measurements and validation of such measurements and retrievals. For soil moisture detection, the SMOS sensor can only achieve a relatively low spatial resolution of about 50km. But the variability of soil moisture field is still quite high below 50km scale due to land surface heterogeneities like elevation, vegetation cover, soil texture, etc. For this reason, a lot of hydrologic applications, for example, regional land surface modeling and data assimilation studies, are performed at an increasingly finer resolution (down to 1km) and they would expect finer soil moisture fields. So in the long run, the relatively coarse soil moisture retrievals will limit their value in many applications, and spatially <span class="hlt">downscaled</span> products are very much needed. We propose and test a strategy to <span class="hlt">downscale</span> the SMOS-based soil moisture products to ~1km or finer. The basic idea is to relate soil moisture to other physical parameters available at higher resolution, for example, elevation, topography, vegetation cover, soil texture, land surface temperature and so on. At places with strong topography, the fine scale soil moisture is primarily controlled by gravity-driven horizontal movement of surface water. In such areas, we can relate soil moisture to topographic features through catchment hydrologic models like the TOPMODEL. In flat areas, soil texture and vegetation properties may pose a greater impact than topography. In this case, we will explore the use of high resolution vegetation information or land surface temperature for <span class="hlt">downscaling</span>.</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..1813320M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813320M"><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/2012WRR....48.4521R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012WRR....48.4521R"><span id="translatedtitle">Potential drought stress in a Swiss mountain catchment—<span class="hlt">Ensemble</span> forecasting of high mountain soil moisture reveals a drastic decrease, despite major uncertainties</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>RöSsler, Ole; Diekkrüger, Bernd; LöFfler, JöRg</p> <p>2012-04-01</p> <p>Climate change is expected to profoundly influence the hydrosphere of mountain ecosystems. The focus of current process-based research is centered on the reaction of glaciers and runoff to climate change; spatially explicit impacts on soil moisture remain widely neglected. We spatio-temporally analyzed the impact of the climate on soil moisture in a mesoscale high mountain catchment to facilitate the development of mitigation and adaptation strategies at the level of vegetation patterns. Two regional climate models were <span class="hlt">downscaled</span> using three different approaches (statistical <span class="hlt">downscaling</span>, delta change, and direct use) to drive a hydrological model (WaSiM-ETH) for reference and scenario period (1960-1990 and 2070-2100), resulting in an <span class="hlt">ensemble</span> forecast of six members. For all <span class="hlt">ensembles</span> members we found large changes in temperature, resulting in decreasing snow and ice storage and earlier runoff, but only small changes in evapotranspiration. The occurrence of <span class="hlt">downscaled</span> dry spells was found to fluctuate greatly, causing soil moisture depletion and drought stress potential to show high variability in both space and time. In general, the choice of the <span class="hlt">downscaling</span> approach had a stronger influence on the results than the applied regional climate model. All of the results indicate that summer soil moisture decreases, which leads to more frequent declines below a critical soil moisture level and an advanced evapotranspiration deficit. Forests up to an elevation of 1800 m a.s.l. are likely to be threatened the most, while alpine areas and most pastures remain nearly unaffected. Nevertheless, the <span class="hlt">ensemble</span> variability was found to be extremely high and should be interpreted as a bandwidth of possible future drought stress situations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=311127&keyword=meteorology&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=68467179&CFTOKEN=89165054','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=311127&keyword=meteorology&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=68467179&CFTOKEN=89165054"><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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013WRR....49.1360K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013WRR....49.1360K"><span id="translatedtitle">A nonparametric kernel regression model for <span class="hlt">downscaling</span> multisite daily precipitation in the Mahanadi basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kannan, S.; Ghosh, Subimal</p> <p>2013-03-01</p> <p>Hydrologic impacts of global climate change are usually assessed by <span class="hlt">downscaling</span> large-scale climate variables, simulated by general circulation models (GCMs), to local-scale hydrometeorological variables. Conventional multisite statistical <span class="hlt">downscaling</span> techniques often fail to capture spatial dependence of rainfall amounts as well as hydrometeorological extremes. To overcome these limitations, a <span class="hlt">downscaling</span> algorithm is proposed, which first simulates the rainfall state of an entire study area/river basin, from large-scale climate variables, with classification and regression trees, and then projects multisite rainfall amounts using a nonparametric kernel regression estimator, conditioned on the estimated rainfall state. The concept of a common rainfall state for the entire study area, using it as an input for projections of rainfall amount, is found to be advantageous in capturing the cross correlation between rainfalls at different <span class="hlt">downscaling</span> locations. Temporal variability and extremities of rainfall are captured in <span class="hlt">downscaling</span> with multivariate kernel regression. The proposed model is applied for <span class="hlt">downscaling</span> daily monsoon precipitation at eight locations in the Mahanadi River basin of eastern India. The model performance is compared, with a recently developed conditional random field based as well as with established multisite <span class="hlt">downscaling</span> models, and is found to be superior. Analysis of future rainfall scenarios, projected with the developed <span class="hlt">downscaling</span> model, reveals considerable changes in rainfall intensity and dry and wet spell lengths, among other things, at different locations. An increasing trend of rainfall is projected for the lower (southern) Mahanadi River basin, and a decreasing trend is observed in the upper (northern) Mahanadi River basin.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3614370','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3614370"><span id="translatedtitle">Comparative Visualization of <span class="hlt">Ensembles</span> Using <span class="hlt">Ensemble</span> Surface Slicing</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Alabi, Oluwafemi S.; Wu, Xunlei; Harter, Jonathan M.; Phadke, Madhura; Pinto, Lifford; Petersen, Hannah; Bass, Steffen; Keifer, Michael; Zhong, Sharon; Healey, Chris; Taylor, Russell M.</p> <p>2012-01-01</p> <p>By definition, an <span class="hlt">ensemble</span> is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an <span class="hlt">ensemble</span> is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call <span class="hlt">Ensemble</span> Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators. PMID:23560167</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('https://www.ncbi.nlm.nih.gov/pubmed/18632380','PUBMED'); return false;" href="https://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="https://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.</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="https://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('https://www.ncbi.nlm.nih.gov/pubmed/22302520','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22302520"><span id="translatedtitle">Identifying representative trees from <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Banerjee, Mousumi; Ding, Ying; Noone, Anne-Michelle</p> <p>2012-07-10</p> <p>Tree-based methods have become popular for analyzing complex data structures where the primary goal is risk stratification of patients. <span class="hlt">Ensemble</span> techniques improve the accuracy in prediction and address the instability in a single tree by growing an <span class="hlt">ensemble</span> of trees and aggregating. However, in the process, individual trees get lost. In this paper, we propose a methodology for identifying the most representative trees in an <span class="hlt">ensemble</span> on the basis of several tree distance metrics. Although our focus is on binary outcomes, the methods are applicable to censored data as well. For any two trees, the distance metrics are chosen to (1) measure similarity of the covariates used to split the trees; (2) reflect similar clustering of patients in the terminal nodes of the trees; and (3) measure similarity in predictions from the two trees. Whereas the latter focuses on prediction, the first two metrics focus on the architectural similarity between two trees. The most representative trees in the <span class="hlt">ensemble</span> are chosen on the basis of the average distance between a tree and all other trees in the <span class="hlt">ensemble</span>. Out-of-bag estimate of error rate is obtained using neighborhoods of representative trees. Simulations and data examples show gains in predictive accuracy when averaging over such neighborhoods. We illustrate our methods using a dataset of kidney cancer treatment receipt (binary outcome) and a second dataset of breast cancer survival (censored outcome).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22093453','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22093453"><span id="translatedtitle">Estimating preselected and postselected <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Massar, Serge; Popescu, Sandu</p> <p>2011-11-15</p> <p>In analogy with the usual quantum state-estimation problem, we introduce the problem of state estimation for a pre- and postselected <span class="hlt">ensemble</span>. The problem has fundamental physical significance since, as argued by Y. Aharonov and collaborators, pre- and postselected <span class="hlt">ensembles</span> are the most basic quantum <span class="hlt">ensembles</span>. Two new features are shown to appear: (1) information is flowing to the measuring device both from the past and from the future; (2) because of the postselection, certain measurement outcomes can be forced never to occur. Due to these features, state estimation in such <span class="hlt">ensembles</span> is dramatically different from the case of ordinary, preselected-only <span class="hlt">ensembles</span>. We develop a general theoretical framework for studying this problem and illustrate it through several examples. We also prove general theorems establishing that information flowing from the future is closely related to, and in some cases equivalent to, the complex conjugate information flowing from the past. Finally, we illustrate our approach on examples involving covariant measurements on spin-1/2 particles. We emphasize that all state-estimation problems can be extended to the pre- and postselected situation. The present work thus lays the foundations of a much more general theory of quantum state estimation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4066535','PMC'); return false;" href="https://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/2015AIPC.1702i0049B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AIPC.1702i0049B"><span id="translatedtitle">Excitation energies from <span class="hlt">ensemble</span> DFT</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Borgoo, Alex; Teale, Andy M.; Helgaker, Trygve</p> <p>2015-12-01</p> <p>We study the evaluation of the Gross-Oliveira-Kohn expression for excitation energies E1-E0=ɛ1-ɛ0+∂E/xc,w[ρ] ∂w | ρ =ρ0. This expression gives the difference between an excitation energy E1 - E0 and the corresponding Kohn-Sham orbital energy difference ɛ1 - ɛ0 as a partial derivative of the exchange-correlation energy of an <span class="hlt">ensemble</span> of states Exc,w[ρ]. Through Lieb maximisation, on input full-CI density functions, the exchange-correlation energy is evaluated accurately and the partial derivative is evaluated numerically using finite difference. The equality is studied numerically for different geometries of the H2 molecule and different <span class="hlt">ensemble</span> weights. We explore the adiabatic connection for the <span class="hlt">ensemble</span> exchange-correlation energy. The latter may prove useful when modelling the unknown weight dependence of the exchange-correlation energy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20020020435&hterms=CCA&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DCCA','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20020020435&hterms=CCA&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DCCA"><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://adsabs.harvard.edu/abs/2015EGUGA..1712144H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1712144H"><span id="translatedtitle">EuroCORDEX <span class="hlt">ensemble</span> analysis and comparison to <span class="hlt">ENSEMBLES</span> <span class="hlt">ensemble</span> of regional simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Halenka, Tomas; Klukova, Zuzana; Belda, Michal</p> <p>2015-04-01</p> <p>Basic assessment of the <span class="hlt">ensemble</span> of available EuroCORDEX simulations is provided in terms of monthly mean analysis of surface temperature and precipitation monthly amount. Both ERA-Interim perfect boundary conditions simulations and historical runs driven by different GCMs from CMIP5 are validated against E.OBS data and compared for both available resolutions (0.11 and 0.44 deg.). The results are presented using the maps of model biases as well as in terms of the areal statistics for PRUDENCE regions, where former <span class="hlt">ENSEMBLES</span> <span class="hlt">ensemble</span> of regional simulations is used for comparison. No significantly better results can be seen when comparing the results of 0.11 deg. resolution with respect to the 0.44 deg. Moreover, while both <span class="hlt">ensembles</span> (basically all the members) are in very good agreement in annual cycle for temperature and very close to the reality, for precipitation quite significant disagreements appear for many of the simulations over some regions, in both <span class="hlt">ensembles</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/21450714','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/21450714"><span id="translatedtitle">Quantum metrology with molecular <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Schaffry, Marcus; Gauger, Erik M.; Morton, John J. L.; Fitzsimons, Joseph; Benjamin, Simon C.; Lovett, Brendon W.</p> <p>2010-10-15</p> <p>The field of quantum metrology promises measurement devices that are fundamentally superior to conventional technologies. Specifically, when quantum entanglement is harnessed, the precision achieved is supposed to scale more favorably with the resources employed, such as system size and time required. Here, we consider measurement of magnetic-field strength using an <span class="hlt">ensemble</span> of spin-active molecules. We identify a third essential resource: the change in <span class="hlt">ensemble</span> polarization (entropy increase) during the metrology experiment. We find that performance depends crucially on the form of decoherence present; for a plausible dephasing model, we describe a quantum strategy, which can indeed beat the standard strategy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22308400','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22308400"><span id="translatedtitle">Quantum Gibbs <span class="hlt">ensemble</span> Monte Carlo</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Fantoni, Riccardo; Moroni, Saverio</p> <p>2014-09-21</p> <p>We present a path integral Monte Carlo method which is the full quantum analogue of the Gibbs <span class="hlt">ensemble</span> Monte Carlo method of Panagiotopoulos to study the gas-liquid coexistence line of a classical fluid. Unlike previous extensions of Gibbs <span class="hlt">ensemble</span> Monte Carlo to include quantum effects, our scheme is viable even for systems with strong quantum delocalization in the degenerate regime of temperature. This is demonstrated by an illustrative application to the gas-superfluid transition of {sup 4}He in two dimensions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19884133','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19884133"><span id="translatedtitle"><span class="hlt">Ensembl</span> Genomes: extending <span class="hlt">Ensembl</span> across the taxonomic space.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kersey, P J; Lawson, D; Birney, E; Derwent, P S; Haimel, M; Herrero, J; Keenan, S; Kerhornou, A; Koscielny, G; Kähäri, A; Kinsella, R J; Kulesha, E; Maheswari, U; Megy, K; Nuhn, M; Proctor, G; Staines, D; Valentin, F; Vilella, A J; Yates, A</p> <p>2010-01-01</p> <p><span class="hlt">Ensembl</span> Genomes (http://www.ensemblgenomes.org) is a new portal offering integrated access to genome-scale data from non-vertebrate species of scientific interest, developed using the <span class="hlt">Ensembl</span> genome annotation and visualisation platform. <span class="hlt">Ensembl</span> Genomes consists of five sub-portals (for bacteria, protists, fungi, plants and invertebrate metazoa) designed to complement the availability of vertebrate genomes in <span class="hlt">Ensembl</span>. Many of the databases supporting the portal have been built in close collaboration with the scientific community, which we consider as essential for maintaining the accuracy and usefulness of the resource. A common set of user interfaces (which include a graphical genome browser, FTP, BLAST search, a query optimised data warehouse, programmatic access, and a Perl API) is provided for all domains. Data types incorporated include annotation of (protein and non-protein coding) genes, cross references to external resources, and high throughput experimental data (e.g. data from large scale studies of gene expression and polymorphism visualised in their genomic context). Additionally, extensive comparative analysis has been performed, both within defined clades and across the wider taxonomy, and sequence alignments and gene trees resulting from this can be accessed through the site.</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/2014GeoRL..41.4013K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GeoRL..41.4013K"><span id="translatedtitle">Uncertainty resulting from multiple data usage in 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>Kannan, S.; Ghosh, Subimal; Mishra, Vimal; Salvi, Kaustubh</p> <p>2014-06-01</p> <p>Statistical <span class="hlt">downscaling</span> (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalysis data products in model calibration. In the training of SD, for Indian Summer Monsoon Rainfall (ISMR), we use two reanalysis data as predictors and three gridded data products for ISMR from different sources. We observe that the uncertainty resulting from six possible training options is comparable to that resulting from multiple GCMs. Though the original GCM simulations project spatially uniform increasing change of ISMR, at the end of 21st century, the same is not obtained with SD, which projects spatially heterogeneous and mixed changes of ISMR. This is due to the differences in statistical relationship between rainfall and predictors in GCM simulations and observed/reanalysis data, and SD considers the latter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://pubs.usgs.gov/of/2014/1190/pdf/ofr2014-1190.pdf','USGSPUBS'); return false;" href="https://pubs.usgs.gov/of/2014/1190/pdf/ofr2014-1190.pdf"><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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GPC...146...30Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GPC...146...30Y"><span id="translatedtitle">CMIP5 <span class="hlt">downscaling</span> and its uncertainty 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>Yue, TianXiang; Zhao, Na; Fan, ZeMeng; Li, Jing; Chen, ChuanFa; Lu, YiMin; Wang, ChenLiang; Xu, Bing; Wilson, John</p> <p>2016-11-01</p> <p>A comparison between the Coupled Model Intercomparison Project Phase 5 (CMIP5) data and observations at 735 meteorological stations indicated that mean annual temperature (MAT) was underestimated about 1.8 °C while mean annual precipitation (MAP) was overestimated about 263 mm in general across the whole of China. A statistical analysis of China-CMIP5 data demonstrated that MAT exhibits spatial stationarity, while MAP exhibits spatial non-stationarity. MAT and MAP data from the China-CMIP5 dataset were <span class="hlt">downscaled</span> by combining statistical approaches with a method for high accuracy surface modeling (HASM). A statistical transfer function (STF) of MAT was formulated using minimized residuals output by HASM with an ordinary least squares (OLS) linear equation that used latitude and elevation as independent variables, abbreviated as HASM-OLS. The STF of MAP under a BOX-COX transformation was derived as a combination of minimized residuals output by HASM with a geographically weight regression (GWR) using latitude, longitude, elevation and impact coefficient of aspect as independent variables, abbreviated as HASM-GB. Cross validation, using observational data from the 735 meteorological stations across China for the period 1976 to 2005, indicates that the largest uncertainty occurred on the Tibet plateau with mean absolute errors (MAEs) of MAT and MAP as high as 4.64 °C and 770.51 mm, respectively. The <span class="hlt">downscaling</span> processes of HASM-OLS and HASM-GB generated MAEs of MAT and MAP that were 67.16% and 77.43% lower, respectively across the whole of China on average, and 88.48% and 97.09% lower for the Tibet plateau.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015JGRD..120.8227B&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015JGRD..120.8227B&link_type=ABSTRACT"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> simulation and future projection of precipitation over China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bao, Jiawei; Feng, Jinming; Wang, Yongli</p> <p>2015-08-01</p> <p>This study assesses present-day and future precipitation changes over China by using the Weather Research and Forecasting (WRF) model version 3.5.1. The WRF model was driven by the Geophysical Fluid Dynamics Laboratory Earth System Model with the Generalized Ocean Layer Dynamics component (GFDL-ESM2G) output over China at the resolution of 30 km for the present day (1976-2005) and near future (2031-2050) under the Representative Concentration Pathways 4.5 (RCP4.5) scenario. The results demonstrate that with improved resolution and better representation of finer-scale physical process, dynamical <span class="hlt">downscaling</span> adds value to the regional precipitation simulation. WRF <span class="hlt">downscaling</span> generally simulates more reliable spatial distributions of total precipitation and extreme precipitation in China with higher spatial pattern correlations and closer magnitude. It is able to successfully eliminate the artificial precipitation maximum area simulated by GFDL-ESM2G over the west of the Sichuan Basin, along the eastern edge of the Tibetan Plateau in both summer and winter. Besides, the regional annual cycle and frequencies of precipitation intensity are also well depicted by WRF. In the future projections, under the RCP4.5 scenario, both models project that summer precipitation over most parts of China will increase, especially in western and northern China, and that precipitation over some southern regions is projected to decrease. The projected increase of future extreme precipitation makes great contributions to the total precipitation increase. In southern regions, the projected larger extreme precipitation amounts accompanied with fewer extreme precipitation frequencies suggest that future daily extreme precipitation intensity is likely to increase in these regions.</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://pubs.er.usgs.gov/publication/70124278','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70124278"><span id="translatedtitle">Projections of the Ganges-Brahmaputra precipitation: <span class="hlt">downscaled</span> from GCM predictors</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Pervez, Md Shahriar; Henebry, Geoffrey M.</p> <p>2014-01-01</p> <p><span class="hlt">Downscaling</span> Global Climate Model (GCM) projections of future climate is critical for impact studies. <span class="hlt">Downscaling</span> enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical <span class="hlt">Downscaling</span> Model (SDSM) to <span class="hlt">downscale</span> 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia—the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in <span class="hlt">downscaling</span> the precipitation. <span class="hlt">Downscaling</span> was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the <span class="hlt">downscaled</span> precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 <span class="hlt">downscaled</span> precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change</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/2010ems..confE.373F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.373F"><span id="translatedtitle">Evolution of the Canadian regional <span class="hlt">ensemble</span> prediction system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frenette, R.; Charron, M.; Li, X.; Gagnon, N.; Lavaysse, C.; Belair, S.; Carrera, M.; Yau, P.; Candille, G.</p> <p>2010-09-01</p> <p>A regional <span class="hlt">ensemble</span> prediction system (REPS) over North America is expected to become operational at the Canadian Meteorological Centre (CMC) in late 2010 or early 2011. Different configurations of the REPS have already been tested and verified at different locations and time periods. The system was used during the Beijing 2008 summer Olympics and for the North American domain with a focus over southern British Columbia, Canada, during the 2010 Vancouver Olympics. It will also provide forecasts for tropical storms and hurricanes for the Haïti area during the summer and autumn of 2010. The Canadian Global Environmental Multiscale (GEM) model has been designed with the possibility to be run as a limited area model (GEM-LAM). The Canadian REPS is composed of 20 members running the GEM-LAM at a near 33 km grid spacing and with the same physical parameterizations as those present in the operational global deterministic prediction system at CMC. Two initial perturbation strategies (moist targeted singular vectors [SV] and the <span class="hlt">ensemble</span> Kalman filter [EnKF]), as well as two stochastic methods for perturbations of parameterizations were verified against surface and upper air (rawinsondes) observations during summer and winter periods to determine which system has the best forecast abilities. For the SV-based REPS, 20 initial conditions (IC) are generated using a targeted SV perturbation method. These ICs are then used to run 20 global GEMs that will provide the lateral boundary conditions (LBCs) for each GEM-LAM. For the EnKF-based REPS, the 20 LBCs are built by <span class="hlt">downscaling</span> the 20 members of the Canadian global <span class="hlt">ensemble</span> prediction system (GEPS) to the resolution of the REPS. Verifications indicate that the EnKF approach gives better skill for summer and winter periods. The skill difference between the two systems comes mainly from the reliability attribute (smaller bias and reduced under-dispersion). Stochastic perturbations on model physical tendencies and on physical</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AdWR...76...81R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AdWR...76...81R"><span id="translatedtitle">A method to <span class="hlt">downscale</span> soil moisture to fine resolutions using topographic, vegetation, and soil data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ranney, Kayla J.; Niemann, Jeffrey D.; Lehman, Brandon M.; Green, Timothy R.; Jones, Andrew S.</p> <p>2015-02-01</p> <p>Soil moisture can be estimated over large regions with spatial resolutions greater than 500 m, but many applications require finer resolutions (10-100 m). Several methods use topographic data to <span class="hlt">downscale</span>, but vegetation and soil patterns can also be important. In this paper, a <span class="hlt">downscaling</span> model that uses fine-resolution topographic, vegetation, and soil data is presented. The method is tested at the Cache la Poudre catchment where detailed vegetation and soil data were collected. Additional testing is performed at the Tarrawarra and Nerrigundah catchments where limited soil data are available. <span class="hlt">Downscaled</span> soil moisture patterns at Cache la Poudre improve when vegetation and soil data are used, and model performance is similar to an EOF method. Using interpolated soil data at Tarrawarra and Nerrigundah decreases model performance and results in worse performance than an EOF method, suggesting that soil data needs greater spatial detail and accuracy to be useful for <span class="hlt">downscaling</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308919&keyword=agriculture+AND+environment&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=68445180&CFTOKEN=66695991','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308919&keyword=agriculture+AND+environment&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=68445180&CFTOKEN=66695991"><span id="translatedtitle">Examining Projected Changes in Weather & Air Quality Extremes Between 2000 & 2030 using Dynamical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Climate change may alter regional weather extremes resulting in a range of environmental impacts including changes in air quality, water quality and availability, energy demands, agriculture, and ecology. Dynamical <span class="hlt">downscaling</span> simulations were conducted with the Weather Research...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25432969','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25432969"><span id="translatedtitle">Triticeae resources in <span class="hlt">Ensembl</span> Plants.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bolser, Dan M; Kerhornou, Arnaud; Walts, Brandon; Kersey, Paul</p> <p>2015-01-01</p> <p>Recent developments in DNA sequencing have enabled the large and complex genomes of many crop species to be determined for the first time, even those previously intractable due to their polyploid nature. Indeed, over the course of the last 2 years, the genome sequences of several commercially important cereals, notably barley and bread wheat, have become available, as well as those of related wild species. While still incomplete, comparison with other, more completely assembled species suggests that coverage of genic regions is likely to be high. <span class="hlt">Ensembl</span> Plants (http://plants.<span class="hlt">ensembl</span>.org) is an integrative resource organizing, analyzing and visualizing genome-scale information for important crop and model plants. Available data include reference genome sequence, variant loci, gene models and functional annotation. For variant loci, individual and population genotypes, linkage information and, where available, phenotypic information are shown. Comparative analyses are performed on DNA and protein sequence alignments. The resulting genome alignments and gene trees, representing the implied evolutionary history of the gene family, are made available for visualization and analysis. Driven by the case of bread wheat, specific extensions to the analysis pipelines and web interface have recently been developed to support polyploid genomes. Data in <span class="hlt">Ensembl</span> Plants is accessible through a genome browser incorporating various specialist interfaces for different data types, and through a variety of additional methods for programmatic access and data mining. These interfaces are consistent with those offered through the <span class="hlt">Ensembl</span> interface for the genomes of non-plant species, including those of plant pathogens, pests and pollinators, facilitating the study of the plant in its environment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016IJTP...55.3017K&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016IJTP...55.3017K&link_type=ABSTRACT"><span id="translatedtitle">State <span class="hlt">Ensembles</span> and Quantum Entropy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kak, Subhash</p> <p>2016-06-01</p> <p>This paper considers quantum communication involving an <span class="hlt">ensemble</span> of states. Apart from the von Neumann entropy, it considers other measures one of which may be useful in obtaining information about an unknown pure state and another that may be useful in quantum games. It is shown that under certain conditions in a two-party quantum game, the receiver of the states can increase the entropy by adding another pure state.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020008664','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020008664"><span id="translatedtitle">Statistical <span class="hlt">Ensemble</span> of Large Eddy Simulations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carati, Daniele; Rogers, Michael M.; Wray, Alan A.; Mansour, Nagi N. (Technical Monitor)</p> <p>2001-01-01</p> <p>A statistical <span class="hlt">ensemble</span> of large eddy simulations (LES) is run simultaneously for the same flow. The information provided by the different large scale velocity fields is used to propose an <span class="hlt">ensemble</span> averaged version of the dynamic model. This produces local model parameters that only depend on the statistical properties of the flow. An important property of the <span class="hlt">ensemble</span> averaged dynamic procedure is that it does not require any spatial averaging and can thus be used in fully inhomogeneous flows. Also, the <span class="hlt">ensemble</span> of LES's provides statistics of the large scale velocity that can be used for building new models for the subgrid-scale stress tensor. The <span class="hlt">ensemble</span> averaged dynamic procedure has been implemented with various models for three flows: decaying isotropic turbulence, forced isotropic turbulence, and the time developing plane wake. It is found that the results are almost independent of the number of LES's in the statistical <span class="hlt">ensemble</span> provided that the <span class="hlt">ensemble</span> contains at least 16 realizations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000102382','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000102382"><span id="translatedtitle">Dimensionality Reduction Through Classifier <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)</p> <p>1999-01-01</p> <p>In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in <span class="hlt">ensembles</span> of classifiers, yielding results superior to single classifiers, <span class="hlt">ensembles</span> that use the full set of features, and <span class="hlt">ensembles</span> based on principal component analysis on both real and synthetic datasets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3353412','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3353412"><span id="translatedtitle"><span class="hlt">Ensemble</span> Modeling of Cancer Metabolism</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Khazaei, Tahmineh; McGuigan, Alison; Mahadevan, Radhakrishnan</p> <p>2012-01-01</p> <p>The metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the <span class="hlt">Ensemble</span> Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire <span class="hlt">ensemble</span> of models to obtain a sub-set, which is increasingly predictive. EM allows for incorporation of regulatory information and captures the behavior of enzymatic reactions at the molecular level by representing reactions in the elementary reaction form. In this study, a metabolic network consisting of 58 reactions is considered and accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation of key enzymes. Experimentally measured intracellular and extracellular metabolite concentrations are used for developing the <span class="hlt">ensemble</span> of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinyl-CoA ligase (SUCOAS1m) to cause a significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets. PMID:22623918</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PhRvD..90d4064B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhRvD..90d4064B"><span id="translatedtitle">Thermodynamic curvature and <span class="hlt">ensemble</span> nonequivalence</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bravetti, Alessandro; Nettel, Francisco</p> <p>2014-08-01</p> <p>In this work we consider thermodynamic geometries defined as Hessians of different potentials and derive some useful formulas that show their complementary role in the description of thermodynamic systems with 2 degrees of freedom that show <span class="hlt">ensemble</span> nonequivalence. From the expressions derived for the metrics, we can obtain the curvature scalars in a very simple and compact form. We explain here the reason why each curvature scalar diverges over the line of divergence of one of the specific heats. This application is of special interest in the study of changes of stability in black holes as defined by Davies. From these results we are able to prove on a general footing a conjecture first formulated by Liu, Lü, Luo, and Shao stating that different Hessian metrics can correspond to different behaviors in the various <span class="hlt">ensembles</span>. We study the case of two thermodynamic dimensions. Moreover, comparing our result with the more standard turning point method developed by Poincaré, we obtain that the divergence of the scalar curvature of the Hessian metric of one potential exactly matches the change of stability in the corresponding <span class="hlt">ensemble</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC41E..07W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41E..07W"><span id="translatedtitle">SDSM-DC: A smarter approach to <span class="hlt">downscaling</span> for decision-making? (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wilby, R. L.; Dawson, C. W.</p> <p>2013-12-01</p> <p>General Circulation Model (GCM) output has been used for <span class="hlt">downscaling</span> and impact assessments for at least 25 years. <span class="hlt">Downscaling</span> methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' <span class="hlt">downscaling</span> typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use <span class="hlt">downscaling</span> tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical <span class="hlt">DownScaling</span> Model (SDSM) over the last decade. This sample offers insights to <span class="hlt">downscaling</span> practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new <span class="hlt">downscaling</span> tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by <span class="hlt">downscaling</span> multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..1210067G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..1210067G"><span id="translatedtitle">Sensitivity analysis of runoff modeling to statistical <span class="hlt">downscaling</span> models in the western Mediterranean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grouillet, B.; Ruelland, D.; Ayar, P. V.; Vrac, M.</p> <p>2015-10-01</p> <p>This paper analyzes the sensitivity of a hydrological model to different methods to statistically <span class="hlt">downscale</span> climate precipitation and temperature over four western Mediterranean basins illustrative of different hydro-meteorological situations. The comparison was conducted over a common 20 year period (1986-2005) to capture different climatic conditions in the basins. Streamflow was simulated using the GR4j conceptual model. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically <span class="hlt">downscaled</span> datasets to reproduce various aspects of the hydrograph. Three different statistical <span class="hlt">downscaling</span> models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the "cumulative distribution function - transform" approach (CDFt). We used the models to <span class="hlt">downscale</span> precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two GCMs (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various <span class="hlt">downscaled</span> data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution <span class="hlt">downscaled</span> climate values leads to a major improvement of runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical <span class="hlt">downscaling</span> models to be used in climate change impact studies to minimize the range of uncertainty associated with such <span class="hlt">downscaling</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016HESS...20.1031G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016HESS...20.1031G"><span id="translatedtitle">Sensitivity analysis of runoff modeling to statistical <span class="hlt">downscaling</span> models in the western Mediterranean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grouillet, Benjamin; Ruelland, Denis; Vaittinada Ayar, Pradeebane; Vrac, Mathieu</p> <p>2016-03-01</p> <p>This paper analyzes the sensitivity of a hydrological model to different methods to statistically <span class="hlt">downscale</span> climate precipitation and temperature over four western Mediterranean basins illustrative of different hydro-meteorological situations. The comparison was conducted over a common 20-year period (1986-2005) to capture different climatic conditions in the basins. The daily GR4j conceptual model was used to simulate streamflow that was eventually evaluated at a 10-day time step. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically <span class="hlt">downscaled</span> data sets to reproduce various aspects of the hydrograph. Three different statistical <span class="hlt">downscaling</span> models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the cumulative distribution function-transform approach (CDFt). We used the models to <span class="hlt">downscale</span> precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two general circulation models (GCMs) (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various <span class="hlt">downscaled</span> data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution <span class="hlt">downscaled</span> climate values leads to a major improvement in runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical <span class="hlt">downscaling</span> models to be used in climate change impact studies to minimize the range</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..1712011M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..1712011M&link_type=ABSTRACT"><span id="translatedtitle">VALUE - A Framework to Validate <span class="hlt">Downscaling</span> Approaches for Climate Change Studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilke, Renate A. I.</p> <p>2015-04-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary <span class="hlt">downscaling</span> community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical <span class="hlt">downscaling</span> methods. Here, we present the key ingredients of this framework. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the <span class="hlt">downscaling</span> procedure where problems may occur: what is the isolated <span class="hlt">downscaling</span> skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open <span class="hlt">downscaling</span> intercomparison study, but is intended also to provide general guidance for other validation studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EaFut...3....1M&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EaFut...3....1M&link_type=ABSTRACT"><span id="translatedtitle">VALUE: A framework to validate <span class="hlt">downscaling</span> approaches for climate change studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilcke, Renate A. I.</p> <p>2015-01-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary <span class="hlt">downscaling</span> community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical <span class="hlt">downscaling</span> methods. In this paper, we present the key ingredients of this framework. VALUE's main approach to validation is user- focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the <span class="hlt">downscaling</span> procedure where problems may occur: what is the isolated <span class="hlt">downscaling</span> skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open <span class="hlt">downscaling</span> intercomparison study, but is intended also to provide general guidance for other validation studies.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70168924','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70168924"><span id="translatedtitle">Evaluation of <span class="hlt">downscaled</span>, gridded climate data for the conterminous United States</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Robert J. Behnke,; Stephen J. Vavrus,; Andrew Allstadt,; Thomas P. Albright,; Thogmartin, Wayne E.; Volker C. Radeloff,</p> <p>2016-01-01</p> <p>Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous <span class="hlt">downscaled</span> weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are <span class="hlt">downscaled</span>, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used <span class="hlt">downscaled</span> data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between <span class="hlt">downscaled</span> and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect <span class="hlt">downscaled</span> weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a <span class="hlt">downscaled</span> weather data set for a given ecological application.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016OcMod.100...20V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcMod.100...20V"><span id="translatedtitle"><span class="hlt">Downscaling</span> and extrapolating dynamic seasonal marine forecasts for coastal ocean users</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vanhatalo, Jarno; Hobday, Alistair J.; Little, L. Richard; Spillman, Claire M.</p> <p>2016-04-01</p> <p>Marine weather and climate forecasts are essential in planning strategies and activities on a range of temporal and spatial scales. However, seasonal dynamical forecast models, that provide forecasts in monthly scale, often have low offshore resolution and limited information for inshore coastal areas. Hence, there is increasing demand for methods capable of fine scale seasonal forecasts covering coastal waters. Here, we have developed a method to combine observational data with dynamical forecasts from POAMA (Predictive Ocean Atmosphere Model for Australia; Australian Bureau of Meteorology) in order to produce seasonal <span class="hlt">downscaled</span>, corrected forecasts, extrapolated to include inshore regions that POAMA does not cover. We demonstrate the method in forecasting the monthly sea surface temperature anomalies in the Great Australian Bight (GAB) region. The resolution of POAMA in the GAB is approximately 2° × 1° (lon. × lat.) and the resolution of our <span class="hlt">downscaled</span> forecast is approximately 1° × 0.25°. We use data and model hindcasts for the period 1994-2010 for forecast validation. The predictive performance of our statistical <span class="hlt">downscaling</span> model improves on the original POAMA forecast. Additionally, this statistical <span class="hlt">downscaling</span> model extrapolates forecasts to coastal regions not covered by POAMA and its forecasts are probabilistic which allows straightforward assessment of uncertainty in <span class="hlt">downscaling</span> and prediction. A range of marine users will benefit from access to <span class="hlt">downscaled</span> and nearshore forecasts at seasonal timescales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014ThApC.117..343O&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014ThApC.117..343O&link_type=ABSTRACT"><span id="translatedtitle">Evaluating climate change effects on runoff by statistical <span class="hlt">downscaling</span> and hydrological model GR2M</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Okkan, Umut; Fistikoglu, Okan</p> <p>2014-07-01</p> <p>The main purpose of this study is to evaluate the impacts of climate change on Izmir-Tahtali freshwater basin, which is located in the Aegean Region of Turkey. For this purpose, a developed strategy involving statistical <span class="hlt">downscaling</span> and hydrological modeling is illustrated through its application to the basin. Prior to statistical <span class="hlt">downscaling</span> of precipitation and temperature, the explanatory variables are obtained from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data set. All possible regression approach is used to establish the most parsimonious relationship between precipitation, temperature, and climatic variables. Selected predictors have been used in training of artificial neural networks-based <span class="hlt">downscaling</span> models and the trained models with the obtained relationships have been operated to produce scenario precipitation and temperature from the simulations of third Generation Coupled Climate Model. Biases from <span class="hlt">downscaled</span> outputs have been reduced after <span class="hlt">downscaling</span> process. Finally, the corrected <span class="hlt">downscaled</span> outputs have been transformed to runoff by means of a monthly parametric hydrological model GR2M to assess the probable impacts of temperature and precipitation changes on runoff. According to the A1B climate scenario results, statistically significant trends are foreseen for precipitation, temperature, and runoff in the study basin.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1043326','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/1043326"><span id="translatedtitle">Sub-daily Statistical <span class="hlt">Downscaling</span> of Meteorological Variables Using Neural Networks</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kumar, Jitendra; Brooks, Bjørn-Gustaf J.; Thornton, Peter E; Dietze, Michael</p> <p>2012-01-01</p> <p>A new open source neural network temporal <span class="hlt">downscaling</span> model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We <span class="hlt">downscaled</span> multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between <span class="hlt">downscaled</span> output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates that this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by <span class="hlt">downscaling</span> multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the <span class="hlt">downscaled</span> data as in the training data with probabilities that differed by no more than 6%. Our <span class="hlt">downscaling</span> approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1070A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1070A"><span id="translatedtitle">Applying <span class="hlt">downscaled</span> climate data to wildlife areas in Washington State, USA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Allan, A.; Shafer, S. L.; Bartlein, P. J.; Helbrecht, L.; Pelltier, R.; Thompson, B.</p> <p>2013-12-01</p> <p>Conservation and natural resource managers require information about potential climate change effects for the species and ecosystems they manage. We evaluated potential future climate and bioclimate changes for wildlife areas in Washington State (USA) using five climate simulations for the 21st century from the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset run under the A2 greenhouse gases emissions scenario. These data were <span class="hlt">downscaled</span> to a 30-arc-second (~1-km) grid encompassing the state of Washington by calculating and interpolating future climate anomalies, and then applying the interpolated data to observed historical climate data. This climate data <span class="hlt">downscaling</span> technique (also referred to as the 'delta' method) is relatively simple and makes a number of assumptions that affect how the <span class="hlt">downscaled</span> data can be used and interpreted. We used the <span class="hlt">downscaled</span> climate data to calculate bioclimatic variables (e.g., growing degree days) that represent important physiological and environmental limits for Washington species and habitats of management concern. Multivariate descriptive plots and maps were used to evaluate the direction, magnitude, and spatial patterns of projected future climate and bioclimatic changes. The results indicate which managed areas experience the largest climate and bioclimatic changes under each of the potential future climate simulations. We discuss these changes while accounting for some of the limitations of our <span class="hlt">downscaling</span> technique and the uncertainties associated with using these <span class="hlt">downscaled</span> data for conservation and natural resource management applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.H23F1682V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H23F1682V"><span id="translatedtitle">Verification of a <span class="hlt">Downscaling</span> Sequence Applied to Medium Range Meteorological Predictions for Global Flood Prediction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Voisin, N.; Wood, A. W.; Lettenmaier, D. P.</p> <p>2007-12-01</p> <p>We describe a prototype system for medium range (up to two week lead) flood prediction in large rivers, which is intended for global implementation - particularly in river basins having limited in situ meteorological observations. The procedure draws from the experimental North American Land Data Assimilation System (NLDAS) and the University of Washington West-wide Seasonal Hydrologic Forecast System for streamflow prediction. Meteorological forecasts based on a numerical weather prediction model serve both as the forcing for hydrologic model initialization and forecasts for lead times up to fifteen days. The hydrologic component of the system is the Variable Infiltration Capacity (VIC) macroscale hydrology model. In the prototype, VIC is spun up for forecast initialization using daily ERA-40 precipitation, wind, and surface air temperature. In hindcast mode, VIC is driven by global NCEP <span class="hlt">ensemble</span> 15-day re-forecasts (NOAA/ESRL) that are bias corrected with respect to ERA- 40 and spatially disaggregated using two higher spatial resolution satellite products: Global Precipitation Climatology Project (GPCP) 1DD daily precipitation and Tropical Rainfall Measuring System (TRMM) 3B42 precipitation are used to spatially disaggregate NCEP re-forecasts precipitation during the 15-day forecast period. The use of forecast models and satellite remote sensing data in this procedure reduces the need for in situ precipitation and other observations in parts of the world where surface networks are critically deficient, but where a global hydrologic forecast capability arguably would have the greatest value. The prototype system was implemented at one-half degree spatial resolution and tested during the 1979-August 2002 period. For the Mississippi R. Basin (where ample data for model evaluation exist) we evaluate the spatial disaggregation step in which observed precipitation products (NARR) are first aggregated to a coarser resolution (for the sole purpose of the evaluation) and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.4655C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4655C"><span id="translatedtitle"><span class="hlt">Ensemble</span> reconstruction of severe low flow events in France since 1871</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Devers, Alexandre; Graff, Benjamin</p> <p>2016-04-01</p> <p>This work presents a study of severe low flow events that occurred from 1871 onwards for a large number of near-natural catchments in France. It aims at assessing and comparing their characteristics to improve our knowledge on historical events and to provide a selection of benchmark events for climate change adaptation purposes. The historical depth of streamflow observations is generally limited to the last 50 years and therefore offers too small a sample of severe low flow events to properly explore the long-term evolution of their characteristics and associated impacts. In order to overcome this limit, this work takes advantage of a 140-year <span class="hlt">ensemble</span> hydrometeorological dataset over France based on: (1) a probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France (Caillouet et al., 2015), and (2) a continuous hydrological modelling that uses the high-resolution meteorological reconstructions as forcings over the whole period. This dataset provides an <span class="hlt">ensemble</span> of 25 equally plausible daily streamflow time series for a reference network of stations in France over the whole 1871-2012 period. Severe low flow events are identified based on a combination of a fixed threshold and a daily variable threshold. Each event is characterized by its deficit, duration and timing by applying the Sequent Peak Algorithm. The procedure is applied to the 25 simulated time series as well as to the observed time series in order to compare observed and simulated events over the recent period, and to characterize in a probabilistic way unrecorded historical events. The <span class="hlt">ensemble</span> aspect of the reconstruction leads to address specific issues, for properly defining events across <span class="hlt">ensemble</span> simulations, as well as for adequately comparing the simulated characteristics to the observed ones. This study brings forward the outstanding 1921 and 1940s events but also older and less known ones that occurred during the last decade of the 19th century. For</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.8417C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.8417C"><span id="translatedtitle">Dynamically <span class="hlt">Downscaling</span> Precipitation from Extra-Tropical Cyclones</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Champion, A.; Hodges, K.; Bengtsson, L.</p> <p>2012-04-01</p> <p> suitability of the LAM for <span class="hlt">downscaling</span> was evaluated by running the LAM for the events of June and July 2007 (UK floods) and comparing the output to observations. The results from this comparison provide confidence that the model is able of reproducing realistic intensities for extreme precipitation events. Whilst this method does not allow for a robust comparison between the climates it does for allow for an analysis of the method, and whether dynamically <span class="hlt">downscaling</span> individual events is suitable. It was found that by nesting the LAM within the GCM, large increases in the precipitation intensities were seen, as well as gaining a greater temporal resolution. Analysis of more events will allow a more robust comparison between climates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1816538A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1816538A"><span id="translatedtitle">Islands Climatology at Local Scale. <span class="hlt">Downscaling</span> with CIELO model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Azevedo, Eduardo; Reis, Francisco; Tomé, Ricardo; Rodrigues, Conceição</p> <p>2016-04-01</p> <p>Islands with horizontal scales of the order of tens of km, as is the case of the Atlantic Islands of Macaronesia, are subscale orographic features for Global Climate Models (GCMs) since the horizontal scales of these models are too coarse to give a detailed representation of the islands' topography. Even the Regional Climate Models (RCMs) reveals limitations when they are forced to reproduce the climate of small islands mainly by the way they flat and lowers the elevation of the islands, reducing the capacity of the model to reproduce important local mechanisms that lead to a very deep local climate differentiation. Important local thermodynamics mechanisms like Foehn effect, or the influence of topography on radiation balance, have a prominent role in the climatic spatial differentiation. Advective transport of air - and the consequent induced adiabatic cooling due to orography - lead to transformations of the state parameters of the air that leads to the spatial configuration of the fields of pressure, temperature and humidity. The same mechanism is in the origin of the orographic clouds cover that, besides the direct role as water source by the reinforcement of precipitation, act like a filter to direct solar radiation and as a source of long-wave radiation that affect the local balance of energy. Also, the saturation (or near saturation) conditions that they provide constitute a barrier to water vapour diffusion in the mechanisms of evapotranspiration. Topographic factors like slope, aspect and orographic mask have also significant importance in the local energy balance. Therefore, the simulation of the local scale climate (past, present and future) in these archipelagos requires the use of <span class="hlt">downscaling</span> techniques to adjust locally outputs obtained at upper scales. This presentation will discuss and analyse the evolution of the CIELO model (acronym for Clima Insular à Escala LOcal) a statistical/dynamical technique developed at the University of the Azores</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.4176H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.4176H"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> Based on Spartan Spatial Random Fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hristopulos, Dionissios</p> <p>2010-05-01</p> <p>Stochastic methods of space-time interpolation and conditional simulation have been used in statistical <span class="hlt">downscaling</span> approaches to increase the resolution of measured fields. One of the popular interpolation methods in geostatistics is kriging, also known as optimal interpolation in data assimilation. Kriging is a stochastic, linear interpolator which incorporates time/space variability by means of the variogram function. However, estimation of the variogram from data involves various assumptions and simplifications. At the same time, the high numerical complexity of kriging makes it difficult to use for very large data sets. We present a different approach based on the so-called Spartan Spatial Random Fields (SSRFs). SSRFs were motivated from classical field theories of statistical physics [1]. The SSRFs provide a different approach of parametrizing spatial dependence based on 'effective interactions,' which can be formulated based on general statistical principles or even incorporate physical constraints. This framework leads to a broad family of covariance functions [2], and it provides new perspectives in covariance parameter estimation and interpolation [3]. A significant advantage offered by SSRFs is reduced numerical complexity, which can lead to much faster codes for spatial interpolation and conditional simulation. In addition, on grids composed of rectangular cells, the SSRF representation leads to an explicit expression for the precision matrix (the inverse covariance). Therefore SSRFs could provide useful models of error covariance for data assimilation methods. We use simulated and real data to demonstrate SSRF properties and <span class="hlt">downscaled</span> fields. keywords: interpolation, conditional simulation, precision matrix References [1] Hristopulos, D.T., 2003. Spartan Gibbs random field models for geostatistical applications, SIAM Journal in Scientific Computation, 24, 2125-2162. [2] Hristopulos, D.T., Elogne, S. N. 2007. Analytic properties and covariance</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A23F0373F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A23F0373F"><span id="translatedtitle">Improving dynamical <span class="hlt">downscaling</span> of thunderstorms in New England</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frediani, M. E.; Anagnostou, E. N.; Hopson, T. M.; Hacker, J.</p> <p>2013-12-01</p> <p>This study aims to quantify the variability of wind speed and precipitation during summer storms events in New England by using standard verification metrics along with the Method For Object-Based Diagnostic Evaluation technique (MODE). Using WRF-ARW to dynamically <span class="hlt">downscale</span> a set of storm events, the first approach investigates potential errors propagated from global analysis products used as initial and boundary conditions. The second approach evaluates the significance of applying a topographic wind parametrization scheme in order to obtain more realistic wind speeds. This fundamental study is born out of the necessity of developing a model for power outage prediction caused by severe storms. In New England, a densely forested region of the US, severe winds and precipitation are key weather factors that cause vulnerability in the power grid infrastructure. During storms, trees are uprooted and branches break, resulting in significant interruptions to electricity distribution. The power outage prediction framework utilizes simulated values of meteorological parameters from storms that have caused outages in the past; and the geographic coordinates of the trouble spots recorded by local utilities during these storms. These two components are used as input for a generalized multi-linear regression that estimate the coefficients for these meteorological parameters, which are then applied to weather forecasts of potential hazardous events, providing an estimate of the number and spatial distribution of power outages over the region for the approaching weather system. Given that the count and location of the predicted outages rely on the weather description of past events, the accuracy of spatial patterns and intensity of meteorological fields are crucial to developing an unbiased database for the regression. With that in mind, it is important to quantify the influence that a particular global analysis product can impose to the dynamical <span class="hlt">downscaling</span> of precipitation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010PhDT.......137H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010PhDT.......137H&link_type=ABSTRACT"><span id="translatedtitle">A standardized framework for evaluating the skill of regional climate <span class="hlt">downscaling</span> techniques</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hayhoe, Katharine Anne</p> <p></p> <p>Regional climate impact assessments require high-resolution projections to resolve local factors that modify the impact of global-scale forcing. To generate these projections, global climate model simulations are commonly <span class="hlt">downscaled</span> using a variety of statistical and dynamical techniques. Despite the essential role of <span class="hlt">downscaling</span> in regional assessments, there is no standard approach to evaluating various <span class="hlt">downscaling</span> methods. Hence, impact communities often have little awareness of limitations and uncertainties associated with <span class="hlt">downscaled</span> projections. To develop a standardized framework for evaluating and comparing <span class="hlt">downscaling</span> approaches, I first identify three primary characteristics of a distribution directly relevant to impact analyses that can be used to evaluate a simulated variable such as temperature or precipitation at a given location: (1) annual, seasonal, and monthly mean values; (2) thresholds, extreme values, and accumulated quantities such as 24h precipitation or degree-days; and (3) persistence, reflecting multi-day events such as heat waves, cold spells, and wet periods. Based on a survey of the literature and solicitation of expert opinion, I select a set of ten statistical tests to evaluate these characteristics, including measures of error, skill, and correlation. I apply this framework to evaluate the skill of four <span class="hlt">downscaling</span> methods, from a simple delta approach to a complex asynchronous quantile regression, in simulating daily temperature at twenty stations across North America. Identical global model fields force each <span class="hlt">downscaling</span> method, and the historical observational record at each location is randomly divided by year into two equal parts, such that each statistical method is trained on one set of historical observations, and evaluated on an entirely independent set of observations. Biases relative to observations are calculated for the historical evaluation period, and differences between projections for the future. Application of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1213118B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1213118B"><span id="translatedtitle">Long-range Prediction of climatic Change in the Eastern Seaboard of Thailand over the 21st Century using various <span class="hlt">Downscaling</span> Approaches</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bejranonda, Werapol; Koch, Manfred; Koontanakulvong, Sucharit</p> <p>2010-05-01</p> <p> the different scales of the hydrological (local to regional) and of the GCM (global), one is faced with the problem of '<span class="hlt">downscaling</span>' the coarse grid resolution output of the GCM to the fine grid of the hydrological model. Although there have been numerous <span class="hlt">downscaling</span> approaches proposed to that regard over the last decade, the jury is still out about the best method to use in a particular application. The focus here is on the <span class="hlt">downscaling</span> part of the investigation, i.e. the proper preparation of the GCM's output to serve as input, i.e. the driving force, to the hydrological model (which is not further discussed here). Daily <span class="hlt">ensembles</span> of climate variables computed by means of the CGCM3 model of the Canadian Climate Center which has a horizontal grid resolution of approximately the size of the whole study basin are used here, indicating clearly the need for <span class="hlt">downscaling</span>. Daily observations of local climate variables available since 1971 are used as additional input to the various <span class="hlt">downscaling</span> tools proposed which are, namely, the stochastic weather generator (LARS-WG), the statistical <span class="hlt">downscaling</span> model (SDSM), and a multiple linear regression model between the observed variables and the CGCM3 predictors. Both the 2D and the 3D versions of the CGCM3 model are employed to predict, 100 years ahead up to year 2100, the monthly rainfall and temperatures, based on the past calibration period (training period) 1971-2000. To investigate the prediction performance, multiple linear regression, autoregressive (AR) and autoregressive integrated moving average (ARIMA) models are applied to the time series of the observation data which are aggregated into monthly time steps to be able compare them with the <span class="hlt">downscaling</span> results above. Likewise, multiple linear regression and ARIMA models also executed on the CGCM3 predictors and the Pacific / Indian oceans indices as external regressors to predict short-term local climate variations. The results of the various <span class="hlt">downscaling</span> method are</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMIN33A3761A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMIN33A3761A"><span id="translatedtitle">A Modified <span class="hlt">Ensemble</span> Framework for Drought Estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alobaidi, M. H.; Marpu, P. R.; Ouarda, T.</p> <p>2014-12-01</p> <p>Drought estimation at ungauged sites is a difficult task due to various challenges such as scale and limited availability and information about hydrologic neighborhoods. <span class="hlt">Ensemble</span> regression has been recently utilized in modeling various hydrologic systems and showed advantage over classical regression approaches to such studies. A challenging task in <span class="hlt">ensemble</span> modeling is the proper training of the <span class="hlt">ensemble</span>'s individual learners and the <span class="hlt">ensemble</span> combiners. In this work, an <span class="hlt">ensemble</span> framework is proposed to enhance the generalization ability of the sub-<span class="hlt">ensemble</span> models and its combiner. Information mixtures between the subsamples are introduced. Such measure is dedicated to the <span class="hlt">ensemble</span> members and <span class="hlt">ensemble</span> combiners. Controlled homogeneity magnitudes are then stimulated and induced in the proposed model via a two-stage resampling algorithm. Artificial neural networks (ANNs) were used as <span class="hlt">ensemble</span> members in addition to different <span class="hlt">ensemble</span> integration plans. The model provided superior results when compared to previous models applied to the case study in this work. The root mean squared error (RMSE) in the testing phase for the drought quantiles improved by 67% - 76%. The bias error (BIAS) also showed 61% - 95% improvement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/15020771','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/15020771"><span id="translatedtitle">Changes in Seasonal and Extreme Hydrologic Conditions of the Georgia Basin/Puget Sound in an <span class="hlt">Ensemble</span> Regional Climate Simulation for the Mid-Century</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Leung, Lai R.; Qian, Yun</p> <p>2003-12-15</p> <p>This study examines an <span class="hlt">ensemble</span> of climate change projections simulated by a global climate model (GCM) and <span class="hlt">downscaled</span> with a region climate model (RCM) to 40 km spatial resolution for the western North America. One control and three <span class="hlt">ensemble</span> future climate simulations were produced by the GCM following a business as usual scenario for greenhouse gases and aerosols emissions from 1995 to 2100. The RCM was used to <span class="hlt">downscale</span> the GCM control simulation (1995-2015) and each <span class="hlt">ensemble</span> future GCM climate (2040-2060) simulation. Analyses of the regional climate simulations for the Georgia Basin/Puget Sound showed a warming of 1.5-2oC and statistically insignificant changes in precipitation by the mid-century. Climate change has large impacts on snowpack (about 50% reduction) but relatively smaller impacts on the total runoff for the basin as a whole. However, climate change can strongly affect small watersheds such as those located in the transient snow zone, causing a higher likelihood of winter flooding as a higher percentage of precipitation falls in the form of rain rather than snow, and reduced streamflow in early summer. In addition, there are large changes in the monthly total runoff above the upper 1% threshold (or flood volume) from October through May, and the December flood volume of the future climate is 60% above the maximum monthly flood volume of the control climate. Uncertainty of the climate change projections, as characterized by the spread among the <span class="hlt">ensemble</span> future climate simulations, is relatively small for the basin mean snowpack and runoff, but increases in smaller watersheds, especially in the transient snow zone, and associated with extreme events. This emphasizes the importance of characterizing uncertainty through <span class="hlt">ensemble</span> simulations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006517','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006517"><span id="translatedtitle"><span class="hlt">Downscaling</span> MODIS Land Surface Temperature for Urban Public Health Applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice, Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel</p> <p>2013-01-01</p> <p>This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for <span class="hlt">downscaling</span> the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19148764','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19148764"><span id="translatedtitle"><span class="hlt">Downscaling</span> drug nanosuspension production: processing aspects and physicochemical characterization.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Van Eerdenbrugh, Bernard; Stuyven, Bernard; Froyen, Ludo; Van Humbeeck, Jan; Martens, Johan A; Augustijns, Patrick; Van den Mooter, Guy</p> <p>2009-01-01</p> <p>In this study, scaling down nanosuspension production to 10 mg of drug compound and evaluation of the nanosuspensions to 1 mg of drug compound per test were investigated. Media milling of seven model drug compounds (cinnarizine-indomethacin-itraconazole-loviride-mebendazole-naproxen-phenytoin) was evaluated in a 96-well plate setup (10, 20, and 30 mg) and a glass-vial-based system in a planetary mill (10, 100, and 1,000 mg). Physicochemical properties evaluated on 1 mg of drug compound were drug content (high-performance liquid chromatography), size [dynamic light scattering (DLS)], morphology (scanning electron microscopy), thermal characteristics (differential scanning calorimetry), and X-ray powder diffraction (XRPD). Scaling down nanosuspension production to 10 mg of drug compound was feasible for the seven model compounds using both designs, the planetary mill design being more robust. Similar results were obtained for both designs upon milling 10 mg of drug compound. Drug content determination was precise and accurate. DLS was the method of choice for size measurements. Morphology evaluation and thermal analysis were feasible, although sample preparation had a big influence on the results. XRPD in capillary mode was successfully performed, both in the suspended state and after freeze-drying in the capillary. Results obtained for the latter were superior. Both the production and the physicochemical evaluation of nanosuspensions can be successfully <span class="hlt">downscaled</span>, enabling nanosuspension screening applications in preclinical development settings.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24988779','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24988779"><span id="translatedtitle"><span class="hlt">Downscaling</span> the environmental associations and spatial patterns of species richness.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Keil, Petr; Jetz, Walter</p> <p>2014-06-01</p> <p>We introduce a method that enables the estimation of species richness environment association and prediction of geographic patterns of species richness at grains finer than the original grain of observation. The method is based on a hierarchical model that uses coarse-grain values of species richness and fine-grain environmental data as input. In the model, the (unobserved) fine-grain species richness is linked to the observed fine-grain environment and upscaled using a simple species-area relationship (SAR). The upscaled values are then stochastically linked to the observed coarse-grain species richness. We tested the method on Southern African Bird Atlas data by <span class="hlt">downscaling</span> richness from 2 degrees to 0.25 degrees (-250 km to -30 km) resolution. When prior knowledge of the SAR slope (average species turnover within coarse-grain cells) was available, the method predicted the fine-grain relationship between richness and environment and provided fine-grain predictions of richness that closely resembled results from native fine-grain models. Without the SAR knowledge the method still accurately quantified the richness-environment relationship, but accurately predicted only relative (rank) values of richness. The approach can be easily extended and it is a powerful path for cross-scale statistical modeling of richness-environment relationships, and for the provision of high-resolution maps for basic science and conservation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011613','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011613"><span id="translatedtitle"><span class="hlt">Downscaling</span> NASA Climatological Data to Produce Detailed Climate Zone Maps</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chandler, William S.; Hoell, James M.; Westberg, David J.; Whitlock, Charles H.; Zhang, Taiping; Stackhouse, P. W.</p> <p>2011-01-01</p> <p>The design of energy efficient sustainable buildings is heavily dependent on accurate long-term and near real-time local weather data. To varying degrees the current meteorological networks over the globe have been used to provide these data albeit often from sites far removed from the desired location. The national need is for access to weather and solar resource data accurate enough to use to develop preliminary building designs within a short proposal time limit, usually within 60 days. The NASA Prediction Of Worldwide Energy Resource (POWER) project was established by NASA to provide industry friendly access to globally distributed solar and meteorological data. As a result, the POWER web site (power.larc.nasa.gov) now provides global information on many renewable energy parameters and several buildings-related items but at a relatively coarse resolution. This paper describes a method of <span class="hlt">downscaling</span> NASA atmospheric assimilation model results to higher resolution and maps those parameters to produce building climate zone maps using estimates of temperature and precipitation. The distribution of climate zones for North America with an emphasis on the Pacific Northwest for just one year shows very good correspondence to the currently defined distribution. The method has the potential to provide a consistent procedure for deriving climate zone information on a global basis that can be assessed for variability and updated more regularly.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy...46.3305S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...46.3305S"><span id="translatedtitle">Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sohn, Soo-Jin; Tam, Chi-Yung</p> <p>2016-05-01</p> <p>Capturing climatic variations in boreal winter to spring (December-May) is essential for properly predicting droughts in South Korea. This study investigates the variability and predictability of the South Korean climate during this extended season, based on observations from 60 station locations and multi-model <span class="hlt">ensemble</span> (MME) hindcast experiments (1983/1984-2005/2006) archived at the APEC Climate Center (APCC). Multivariate empirical orthogonal function (EOF) analysis results based on observations show that the first two leading modes of winter-to-spring precipitation and temperature variability, which together account for ~80 % of the total variance, are characterized by regional-scale anomalies covering the whole South Korean territory. These modes were also closely related to some of the recurrent large-scale circulation changes in the northern hemisphere during the same season. Consistent with the above, examination of the standardized precipitation evapotranspiration index (SPEI) indicates that drought conditions in South Korea tend to be accompanied by regional-to-continental-scale circulation anomalies over East Asia to the western north Pacific. Motivated by the aforementioned findings on the spatial-temporal coherence among station-scale precipitation and temperature anomalies, a new bivariate and pattern-based <span class="hlt">downscaling</span> method was developed. The novelty of this method is that precipitation and temperature data were first filtered using multivariate EOFs to enhance their spatial-temporal coherence, before being linked to large-scale circulation variables using canonical correlation analysis (CCA). To test its applicability and to investigate its related potential predictability, a perfect empirical model was first constructed with observed datasets as predictors. Next, a model output statistics (MOS)-type hybrid dynamical-statistical model was developed, using products from nine one-tier climate models as inputs. It was found that, with model sea</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A34E..03S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A34E..03S"><span id="translatedtitle">Mid-Century Warming in the Los Angeles Region and its Uncertainty using Dynamical and Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sun, F.; Hall, A. D.; Walton, D.; Capps, S. B.; Qu, X.; Huang, H. J.; Berg, N.; Jousse, A.; Schwartz, M.; Nakamura, M.; Cerezo-Mota, R.</p> <p>2012-12-01</p> <p>Using a combination of dynamical and statistical <span class="hlt">downscaling</span> techniques, we projected mid-21st century warming in the Los Angeles region at 2-km resolution. To account for uncertainty associated with the trajectory of future greenhouse gas emissions, we examined projections for both "business-as-usual" (RCP8.5) and "mitigation" (RCP2.6) emissions scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5). To account for the considerable uncertainty associated with choice of global climate model, we <span class="hlt">downscaled</span> results for all available global climate models in CMIP5. For the business-as-usual scenario, we find that by the mid-21st century, the most likely warming is roughly 2.6°C averaged over the region's land areas, with a 95% confidence that the warming lies between 0.9 and 4.2°C. The high resolution of the projections reveals a pronounced spatial pattern in the warming: High elevations and inland areas separated from the coast by at least one mountain complex warm 20 to 50% more than the areas near the coast or within the Los Angeles basin. This warming pattern is especially apparent in summertime. The summertime warming contrast between the inland and coastal zones has a large effect on the most likely expected number of extremely hot days per year. Coastal locations and areas within the Los Angeles basin see roughly two to three times the number of extremely hot days, while high elevations and inland areas typically experience approximately three to five times the number of extremely hot days. Under the mitigation emissions scenario, the most likely warming and increase in heat extremes are somewhat smaller. However, the majority of the warming seen in the business-as-usual scenario still occurs at all locations in the most likely case under the mitigation scenario, and heat extremes still increase significantly. This warming study is the first part of a series studies of our project. More climate change impacts on the Santa Ana wind, rainfall</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.B53E0725Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.B53E0725Y"><span id="translatedtitle">Intercomparison of <span class="hlt">Downscaling</span> Methods on Hydrological Impact for Earth System Model of NE United States</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.</p> <p>2012-12-01</p> <p>Atmospheric dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be <span class="hlt">downscaled</span> from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this <span class="hlt">downscaling</span> procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several <span class="hlt">downscaling</span> techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four <span class="hlt">downscaling</span> approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial <span class="hlt">downscaling</span> (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic <span class="hlt">Downscaling</span> based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different <span class="hlt">downscaling</span> methods against observational datasets was carried out for assessment of the <span class="hlt">downscaled</span> climate model outputs. The effects of using the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20130001779&hterms=Kalman&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DKalman','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20130001779&hterms=Kalman&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DKalman"><span id="translatedtitle">A Localized <span class="hlt">Ensemble</span> Kalman Smoother</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Butala, Mark D.</p> <p>2012-01-01</p> <p>Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized <span class="hlt">ensemble</span> Kalman smoother.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010CMaPh.293..145B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010CMaPh.293..145B"><span id="translatedtitle">Gibbs <span class="hlt">Ensembles</span> of Nonintersecting Paths</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Borodin, Alexei; Shlosman, Senya</p> <p>2010-01-01</p> <p>We consider a family of determinantal random point processes on the two-dimensional lattice and prove that members of our family can be interpreted as a kind of Gibbs <span class="hlt">ensembles</span> of nonintersecting paths. Examples include probability measures on lozenge and domino tilings of the plane, some of which are non-translation-invariant. The correlation kernels of our processes can be viewed as extensions of the discrete sine kernel, and we show that the Gibbs property is a consequence of simple linear relations satisfied by these kernels. The processes depend on infinitely many parameters, which are closely related to parametrization of totally positive Toeplitz matrices.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820026226','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820026226"><span id="translatedtitle"><span class="hlt">Ensemble</span> averaging of acoustic data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stefanski, P. K.</p> <p>1982-01-01</p> <p>A computer program called <span class="hlt">Ensemble</span> Averaging of Acoustic Data is documented. The program samples analog data, analyzes the data, and displays them in the time and frequency domains. Hard copies of the displays are the program's output. The documentation includes a description of the program and detailed user instructions for the program. This software was developed for use on the Ames 40- by 80-Foot Wind Tunnel's Dynamic Analysis System consisting of a PDP-11/45 computer, two RK05 disk drives, a tektronix 611 keyboard/display terminal, and FPE-4 Fourier Processing Element, and an analog-to-digital converter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AcMeS..26...52D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AcMeS..26...52D"><span id="translatedtitle">A comparison of breeding and <span class="hlt">ensemble</span> transform vectors for global <span class="hlt">ensemble</span> generation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Deng, Guo; Tian, Hua; Li, Xiaoli; Chen, Jing; Gong, Jiandong; Jiao, Meiyan</p> <p>2012-02-01</p> <p>To compare the initial perturbation techniques using breeding vectors and <span class="hlt">ensemble</span> transform vectors, three <span class="hlt">ensemble</span> prediction systems using both initial perturbation methods but with different <span class="hlt">ensemble</span> member sizes based on the spectral model T213/L31 are constructed at the National Meteorological Center, China Meteorological Administration (NMC/CMA). A series of <span class="hlt">ensemble</span> verification scores such as forecast skill of the <span class="hlt">ensemble</span> mean, <span class="hlt">ensemble</span> resolution, and <span class="hlt">ensemble</span> reliability are introduced to identify the most important attributes of <span class="hlt">ensemble</span> forecast systems. The results indicate that the <span class="hlt">ensemble</span> transform technique is superior to the breeding vector method in light of the evaluation of anomaly correlation coefficient (ACC), which is a deterministic character of the <span class="hlt">ensemble</span> mean, the root-mean-square error (RMSE) and spread, which are of probabilistic attributes, and the continuous ranked probability score (CRPS) and its decomposition. The advantage of the <span class="hlt">ensemble</span> transform approach is attributed to its orthogonality among <span class="hlt">ensemble</span> perturbations as well as its consistence with the data assimilation system. Therefore, this study may serve as a reference for configuration of the best <span class="hlt">ensemble</span> prediction system to be used in operation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27069054','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27069054"><span id="translatedtitle">Measuring social interaction in music <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Volpe, Gualtiero; D'Ausilio, Alessandro; Badino, Leonardo; Camurri, Antonio; Fadiga, Luciano</p> <p>2016-05-01</p> <p>Music <span class="hlt">ensembles</span> are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music <span class="hlt">ensembles</span> offer a broad variety of scenarios which are particularly suitable for investigation. Small <span class="hlt">ensembles</span>, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger <span class="hlt">ensembles</span>, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music <span class="hlt">ensembles</span> with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of <span class="hlt">ensemble</span> music performances.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1812161R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812161R"><span id="translatedtitle">Actor groups, related needs, and challenges at the climate <span class="hlt">downscaling</span> interface</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rössler, Ole; Benestad, Rasmus; Diamando, Vlachogannis; Heike, Hübener; Kanamaru, Hideki; Pagé, Christian; Margarida Cardoso, Rita; Soares, Pedro; Maraun, Douglas; Kreienkamp, Frank; Christodoulides, Paul; Fischer, Andreas; Szabo, Peter</p> <p>2016-04-01</p> <p>At the climate <span class="hlt">downscaling</span> interface, numerous <span class="hlt">downscaling</span> techniques and different philosophies compete on being the best method in their specific terms. Thereby, it remains unclear to what extent and for which purpose these <span class="hlt">downscaling</span> techniques are valid or even the most appropriate choice. A common validation framework that compares all the different available methods was missing so far. The initiative VALUE closes this gap with such a common validation framework. An essential part of a validation framework for <span class="hlt">downscaling</span> techniques is the definition of appropriate validation measures. The selection of validation measures should consider the needs of the stakeholder: some might need a temporal or spatial average of a certain variable, others might need temporal or spatial distributions of some variables, still others might need extremes for the variables of interest or even inter-variable dependencies. Hence, a close interaction of climate data providers and climate data users is necessary. Thus, the challenge in formulating a common validation framework mirrors also the challenges between the climate data providers and the impact assessment community. This poster elaborates the issues and challenges at the <span class="hlt">downscaling</span> interface as it is seen within the VALUE community. It suggests three different actor groups: one group consisting of the climate data providers, the other two groups being climate data users (impact modellers and societal users). Hence, the <span class="hlt">downscaling</span> interface faces classical transdisciplinary challenges. We depict a graphical illustration of actors involved and their interactions. In addition, we identified four different types of issues that need to be considered: i.e. data based, knowledge based, communication based, and structural issues. They all may, individually or jointly, hinder an optimal exchange of data and information between the actor groups at the <span class="hlt">downscaling</span> interface. Finally, some possible ways to tackle these issues are</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/20695534','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/20695534"><span id="translatedtitle">Particle number fluctuations in a canonical <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Begun, V.V.; Gazdzicki, M.; Gorenstein, M.I.; Zozulya, O.S.</p> <p>2004-09-01</p> <p>Fluctuations of charged particle number are studied in the canonical <span class="hlt">ensemble</span>. In the infinite volume limit the fluctuations in the canonical <span class="hlt">ensemble</span> are different from the fluctuations in the grand canonical one. Thus, the well-known equivalence of both <span class="hlt">ensembles</span> for the average quantities does not extend for the fluctuations. In view of the possible relevance of the results for the analysis of fluctuations in nuclear collisions at high energies, a role of the limited kinematical acceptance is studied.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/22482774','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/22482774"><span id="translatedtitle">Potential and limitations of <span class="hlt">ensemble</span> docking.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Korb, Oliver; Olsson, Tjelvar S G; Bowden, Simon J; Hall, Richard J; Verdonk, Marcel L; Liebeschuetz, John W; Cole, Jason C</p> <p>2012-05-25</p> <p>A major problem in structure-based virtual screening applications is the appropriate selection of a single or even multiple protein structures to be used in the virtual screening process. A priori it is unknown which protein structure(s) will perform best in a virtual screening experiment. We investigated the performance of <span class="hlt">ensemble</span> docking, as a function of <span class="hlt">ensemble</span> size, for eight targets of pharmaceutical interest. Starting from single protein structure docking results, for each <span class="hlt">ensemble</span> size up to 500,000 combinations of protein structures were generated, and, for each <span class="hlt">ensemble</span>, pose prediction and virtual screening results were derived. Comparison of single to multiple protein structure results suggests improvements when looking at the performance of the worst and the average over all single protein structures to the performance of the worst and average over all protein <span class="hlt">ensembles</span> of size two or greater, respectively. We identified several key factors affecting <span class="hlt">ensemble</span> docking performance, including the sampling accuracy of the docking algorithm, the choice of the scoring function, and the similarity of database ligands to the cocrystallized ligands of ligand-bound protein structures in an <span class="hlt">ensemble</span>. Due to these factors, the prospective selection of optimum <span class="hlt">ensembles</span> is a challenging task, shown by a reassessment of published <span class="hlt">ensemble</span> selection protocols. PMID:22482774</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160006063','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160006063"><span id="translatedtitle">Multi-Model <span class="hlt">Ensemble</span> Wake Vortex Prediction</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koerner, Stephan; Ahmad, Nash'at N.; Holzaepfel, Frank; VanValkenburg, Randal L.</p> <p>2015-01-01</p> <p>Several multi-model <span class="hlt">ensemble</span> methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model <span class="hlt">ensemble</span> capability using their wake models. An overview of different multi-model <span class="hlt">ensemble</span> methods and their feasibility for wake applications is presented. The methods include Reliability <span class="hlt">Ensemble</span> Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/5206885','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/5206885"><span id="translatedtitle">Forecast of iceberg <span class="hlt">ensemble</span> drift</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>El-Tahan, M.S.; El-Tahan, H.W.; Venkatesh, S.</p> <p>1983-05-01</p> <p>The objectives of the study are to gain a better understanding of the characteristics of iceberg motion and the factors controlling iceberg drift, and to develop an iceberg <span class="hlt">ensemble</span> drift forecast system to be operated by the Canadian Atmospheric Environment Service. An extensive review of field and theoretical studies on iceberg behaviour, and the factors controlling iceberg motion has been carried out. Long term and short term behaviour of icebergs are critically examined. A quantitative assessment of the effects of the factors controlling iceberg motion is presented. The study indicated that wind and currents are the primary driving forces. Coriolis Force and ocean surface slope also have significant effects. As for waves, only the higher waves have a significant effect. Iceberg drift is also affected by iceberg size characteristics. Based on the findings of the study a comprehensive computerized forecast system to predict the drift of iceberg <span class="hlt">ensembles</span> off Canada's east coast has been designed. The expected accuracy of the forecast system is discussed and recommendations are made for future improvements to the system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27179343','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27179343"><span id="translatedtitle">Visualizing <span class="hlt">ensembles</span> in structural biology.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Melvin, Ryan L; Salsbury, Freddie R</p> <p>2016-06-01</p> <p>Displaying a single representative conformation of a biopolymer rather than an <span class="hlt">ensemble</span> of states mistakenly conveys a static nature rather than the actual dynamic personality of biopolymers. However, there are few apparent options due to the fixed nature of print media. Here we suggest a standardized methodology for visually indicating the distribution width, standard deviation and uncertainty of <span class="hlt">ensembles</span> of states with little loss of the visual simplicity of displaying a single representative conformation. Of particular note is that the visualization method employed clearly distinguishes between isotropic and anisotropic motion of polymer subunits. We also apply this method to ligand binding, suggesting a way to indicate the expected error in many high throughput docking programs when visualizing the structural spread of the output. We provide several examples in the context of nucleic acids and proteins with particular insights gained via this method. Such examples include investigating a therapeutic polymer of FdUMP (5-fluoro-2-deoxyuridine-5-O-monophosphate) - a topoisomerase-1 (Top1), apoptosis-inducing poison - and nucleotide-binding proteins responsible for ATP hydrolysis from Bacillus subtilis. We also discuss how these methods can be extended to any macromolecular data set with an underlying distribution, including experimental data such as NMR structures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1812252T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812252T"><span id="translatedtitle">CORDEX.be: COmbining Regional climate <span class="hlt">Downscaling</span> EXpertise in Belgium</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Termonia, Piet; Van Schaeybroeck, Bert; De Ridder, Koen; Fettweis, Xavier; Gobin, Anne; Luyten, Patrick; Marbaix, Philippe; Pottiaux, Eric; Stavrakou, Trissevgeni; Van Lipzig, Nicole; van Ypersele, Jean-Pascal; Willems, Patrick</p> <p>2016-04-01</p> <p>The main objective of the ongoing project CORDEX.be, "COmbining Regional <span class="hlt">Downscaling</span> EXpertise in Belgium: CORDEX and Beyond" is to gather existing and ongoing Belgian research activities in the domain of climate modelling to create a coherent scientific basis for future climate services in Belgium. The project regroups eight Belgian Institutes under a single research program of the Belgian Science Policy (BELSPO). The project involves three regional climate models: the ALARO model, the COSMO-CLM model and the MAR model running according to the guidelines of the CORDEX project and at convection permitting resolution on small domains over Belgium. The project creates a framework to address four objectives/challenges. First, this projects aims to contribute to the EURO-CORDEX project. Secondly, RCP simulations are executed at convection-permitting resolutions (3 to 5 km) on small domains. Thirdly, the output of the atmospheric models is used to drive land surface models (the SURFEX model and the Urbclim model) with urban modules, a crop model (REGCROP), a tides and storm model (COHERENS) and the MEGAN-MOHYCAN model that simulates the fluxes emitted by vegetation. Finally, one work package will translate the uncertainty present in the CORDEX database to the high-resolution output of the CORDEX.be project. The organization of the project will be presented and first results will be shown, demonstrating that convection-permitting models can add extra skill to the mesoscale version of the regional climate models, in particular regarding the extreme value statistics and the diurnal cycle.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H52E..04T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H52E..04T"><span id="translatedtitle"><span class="hlt">Downscaling</span> Alkaline Phosphatase Activity in a Subtropical Reservoir</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tseng, Y.</p> <p>2011-12-01</p> <p>This research was conducted by <span class="hlt">downscaling</span> study to understand phosphorus (P)-deficient status of different plankton and the role of alkaline phosphatase activity (APA) in subtropical Feitsui Reservoir. Results from field survey showed that bulk APA (1.6~95.2 nM h-1) was widely observed in the epilimnion (0~20 m) with an apparent seasonal variations, suggesting that plankton in the system were subjected to P-deficient seasonally. Mixed layer depth (an index of phosphate availability) is the major factor influencing the variation of bulk APA and specific APA (124~1,253 nmol mg C-1 h-1), based on multiple linear regression analysis. Size-fractionated APA assays showed that picoplankton (size 0.2~3 um) contributed most of the bulk APA in the system. In addition, single-cell APA detected by enzyme-labeled fluorescence (ELF) assay indicated that heterotrophic bacteria are the major contributors of APA. Thus, we can infer that bacteria play an important role in accelerating P-cycle within P-deficient systems. Light/nutrient manipulation bioassays showed that bacterial growth was directly controlled by phosphate, while picocyanobacterial growth is controlled by light and can out-compete bacteria under P-limited condition with the aid of light. Further analysis revealed that the strength of summer typhoon is a factor responsible for the inter-annual variability of bulk and specific APA. APA study demonstrated the episodic events (e.g. strong typhoon and extreme precipitation) had significant influence on APA variability in sub-tropical to tropical aquatic ecosystems. Hence, the results herein will allow future studies on monitoring typhoon disturbance (intensity and frequency) as well as the APA of plankton during summer-to-autumn in subtropical systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1110243C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1110243C"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> in Multi-dimensional Wave Climate Forecast</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Camus, P.; Méndez, F. J.; Medina, R.; Losada, I. J.; Cofiño, A. S.; Gutiérrez, J. M.</p> <p>2009-04-01</p> <p>Wave climate at a particular site is defined by the statistical distribution of sea state parameters, such as significant wave height, mean wave period, mean wave direction, wind velocity, wind direction and storm surge. Nowadays, long-term time series of these parameters are available from reanalysis databases obtained by numerical models. The Self-Organizing Map (SOM) technique is applied to characterize multi-dimensional wave climate, obtaining the relevant "wave types" spanning the historical variability. This technique summarizes multi-dimension of wave climate in terms of a set of clusters projected in low-dimensional lattice with a spatial organization, providing Probability Density Functions (PDFs) on the lattice. On the other hand, wind and storm surge depend on instantaneous local large-scale sea level pressure (SLP) fields while waves depend on the recent history of these fields (say, 1 to 5 days). Thus, these variables are associated with large-scale atmospheric circulation patterns. In this work, a nearest-neighbors analog method is used to predict monthly multi-dimensional wave climate. This method establishes relationships between the large-scale atmospheric circulation patterns from numerical models (SLP fields as predictors) with local wave databases of observations (monthly wave climate SOM PDFs as predictand) to set up statistical models. A wave reanalysis database, developed by Puertos del Estado (Ministerio de Fomento), is considered as historical time series of local variables. The simultaneous SLP fields calculated by NCEP atmospheric reanalysis are used as predictors. Several applications with different size of sea level pressure grid and with different temporal domain resolution are compared to obtain the optimal statistical model that better represents the monthly wave climate at a particular site. In this work we examine the potential skill of this <span class="hlt">downscaling</span> approach considering perfect-model conditions, but we will also analyze the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=joy&pg=7&id=EJ971454','ERIC'); return false;" href="http://eric.ed.gov/?q=joy&pg=7&id=EJ971454"><span id="translatedtitle">Joys of Community <span class="hlt">Ensemble</span> Playing: The Case of the Happy Roll Elastic <span class="hlt">Ensemble</span> in Taiwan</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Hsieh, Yuan-Mei; Kao, Kai-Chi</p> <p>2012-01-01</p> <p>The Happy Roll Elastic <span class="hlt">Ensemble</span> (HREE) is a community music <span class="hlt">ensemble</span> supported by Tainan Culture Centre in Taiwan. With enjoyment and friendship as its primary goals, it aims to facilitate the joys of <span class="hlt">ensemble</span> playing and the spirit of social networking. This article highlights the key aspects of HREE's development in its first two years…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26938544','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26938544"><span id="translatedtitle">Evaluating the Appropriateness of <span class="hlt">Downscaled</span> Climate Information for Projecting Risks of Salmonella.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Guentchev, Galina S; Rood, Richard B; Ammann, Caspar M; Barsugli, Joseph J; Ebi, Kristie; Berrocal, Veronica; O'Neill, Marie S; Gronlund, Carina J; Vigh, Jonathan L; Koziol, Ben; Cinquini, Luca</p> <p>2016-02-29</p> <p>Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be <span class="hlt">downscaled</span> to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically <span class="hlt">downscaled</span> GCM simulations for 1971-2000--a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) <span class="hlt">downscaling</span> methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically <span class="hlt">downscaled</span> data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of <span class="hlt">downscaled</span> climate data and the potential for misinterpretation of future estimates of Salmonella infections.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMNH51C1906N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMNH51C1906N"><span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> of Typhoon Vera (1959) and related Storm Surge based on JRA-55 Reanalysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ninomiya, J.; Takemi, T.; Mori, N.; Shibutani, Y.; Kim, S.</p> <p>2015-12-01</p> <p>Typhoon Vera in 1959 is historical extreme typhoon that caused severest typhoon damage mainly due to the storm surge up to 389 cm in Japan. Vera developed 895 hPa on offshore and landed with 929.2 hPa. There are many studies of the dynamical <span class="hlt">downscaling</span> of Vera but it is difficult to simulate accurately because of the lack of the accuracy of global reanalysis data. This study carried out dynamical <span class="hlt">downscaling</span> experiment of Vera using WRF <span class="hlt">downscaling</span> forced by JRA-55 that are latest atmospheric model and reanalysis data. In this study, the reproducibility of five global reanalysis data for Typhoon Vera were compered. Comparison shows that reanalysis data doesn't have strong typhoon information except for JRA-55, so that <span class="hlt">downscaling</span> with conventional reanalysis data goes wrong. The dynamical <span class="hlt">downscaling</span> method for storm surge is studied very much (e.g. choice of physical model, nudging, 4D-VAR, bogus and so on). In this study, domain size and resolution of the coarse domain were considered. The coarse domain size influences the typhoon route and central pressure, and larger domain restrains the typhoon strength. The results of simulations with different domain size show that the threshold of developing restrain is whether the coarse domain fully includes the area of wind speed more than 15 m/s around the typhoon. The results of simulations with different resolution show that the resolution doesn't affect the typhoon route, and higher resolution gives stronger typhoon simulation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.6336K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.6336K"><span id="translatedtitle">The role of observational reference data for climate <span class="hlt">downscaling</span>: Insights from the VALUE COST Action</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kotlarski, Sven; Gutiérrez, José M.; Boberg, Fredrik; Bosshard, Thomas; Cardoso, Rita M.; Herrera, Sixto; Maraun, Douglas; Mezghani, Abdelkader; Pagé, Christian; Räty, Olle; Stepanek, Petr; Soares, Pedro M. M.; Szabo, Peter</p> <p>2016-04-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of <span class="hlt">downscaling</span> methods. Such assessments can be expected to crucially depend on the existence of accurate and reliable observational reference data. In dynamical <span class="hlt">downscaling</span>, observational data can influence model development itself and, later on, model evaluation, parameter calibration and added value assessment. In empirical-statistical <span class="hlt">downscaling</span>, observations serve as predictand data and directly influence model calibration with corresponding effects on <span class="hlt">downscaled</span> climate change projections. We here present a comprehensive assessment of the influence of uncertainties in observational reference data and of scale-related issues on several of the above-mentioned aspects. First, temperature and precipitation characteristics as simulated by a set of reanalysis-driven EURO-CORDEX RCM experiments are validated against three different gridded reference data products, namely (1) the EOBS dataset (2) the recently developed EURO4M-MESAN regional re-analysis, and (3) several national high-resolution and quality-controlled gridded datasets that recently became available. The analysis reveals a considerable influence of the choice of the reference data on the evaluation results, especially for precipitation. It is also illustrated how differences between the reference data sets influence the ranking of RCMs according to a comprehensive set of performance measures.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC23B1138B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC23B1138B"><span id="translatedtitle">Diagnosing the drivers of rain on snow events in Alaska using dynamical <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bieniek, P.; Bhatt, U. S.; Lader, R.; Walsh, J. E.; Rupp, S. T.</p> <p>2015-12-01</p> <p>Rain on snow (ROS) events are fairly rare in Alaska but have broad impacts ranging from economic losses to hazardous driving conditions to difficult caribou foraging due to ice formation on the snow. While rare, these events have recently increased in frequency in Alaska and may continue to increase under the projected warming climate. Dynamically <span class="hlt">downscaled</span> data are now available for Alaska based on historical reanalysis for 1979-2013, while CMIP5 historical and future scenario <span class="hlt">downscaling</span> are in progress. These new data offer a detailed, gridded product of rain and snowfall not previously possible in the spatially and temporally coarser reanalysis and GCM output currently available. Preliminary analysis shows that the dynamical <span class="hlt">downscaled</span> data can identify extreme ROS events in Interior Alaska. The ROS events in the dynamically <span class="hlt">downscaled</span> data are analyzed against observations and the ERA-Interim reanalysis data used to force the historical <span class="hlt">downscaling</span> simulations. Additionally, the synoptic atmospheric circulations conditions that correspond to major ROS events in various regions of Alaska are identified with Self-Organizing Map (SOM) analysis. Such analysis is beneficial for operational forecasters with the National Weather Service and for diagnosing the mechanisms of change in future climate projections.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4808930','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4808930"><span id="translatedtitle">Evaluating the Appropriateness of <span class="hlt">Downscaled</span> Climate Information for Projecting Risks of Salmonella</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Guentchev, Galina S.; Rood, Richard B.; Ammann, Caspar M.; Barsugli, Joseph J.; Ebi, Kristie; Berrocal, Veronica; O’Neill, Marie S.; Gronlund, Carina J.; Vigh, Jonathan L.; Koziol, Ben; Cinquini, Luca</p> <p>2016-01-01</p> <p>Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be <span class="hlt">downscaled</span> to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically <span class="hlt">downscaled</span> GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) <span class="hlt">downscaling</span> methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically <span class="hlt">downscaled</span> data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of <span class="hlt">downscaled</span> climate data and the potential for misinterpretation of future estimates of Salmonella infections. PMID:26938544</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H41E0878C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H41E0878C"><span id="translatedtitle">Spatial <span class="hlt">Downscaling</span> of Remotely Sensed Soil Moisture Using Support Vector Machine in Northeast Asia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Choi, M.; Moon, H.; Kim, D.</p> <p>2014-12-01</p> <p>Recent advances in remote sensing of soil moisture have broadened the understanding of spatiotemporal behavior of soil moisture and contributed to major improvements in the associated research fields. However, large spatial coverage and short timescale notwithstanding, low spatial resolution of passive microwave soil moisture data has been frequently treated as major research problem in many studies, which suggested statistical or deterministic <span class="hlt">downscaling</span> method as a solution to obtain targeted spatial resolutions. This study suggests a methodology to <span class="hlt">downscale</span> 10 km and 25 km daily L3 volumetric soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) in 2013 in Northeast Asia using Support Vector Machine (SVM). In the presented methodology, hydrometeorological variables observed from satellite remote sensing which have physically significant relationship with soil moisture are chosen as predictor variables to estimate soil moisture in finer resolution. Separate <span class="hlt">downscaling</span> algorithms optimized for seasonal conditions are applied to achieve more accurate results of <span class="hlt">downscaled</span> soil moisture. A comparative analysis between in-situ and <span class="hlt">downscaled</span> soil moisture is also conducted for quantitatively assessing its accuracy. Further application can be carried out in hydrological modeling or prediction of extreme weather phenomena in fine spatial resolution based on the results of this study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26938544','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26938544"><span id="translatedtitle">Evaluating the Appropriateness of <span class="hlt">Downscaled</span> Climate Information for Projecting Risks of Salmonella.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Guentchev, Galina S; Rood, Richard B; Ammann, Caspar M; Barsugli, Joseph J; Ebi, Kristie; Berrocal, Veronica; O'Neill, Marie S; Gronlund, Carina J; Vigh, Jonathan L; Koziol, Ben; Cinquini, Luca</p> <p>2016-03-01</p> <p>Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be <span class="hlt">downscaled</span> to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically <span class="hlt">downscaled</span> GCM simulations for 1971-2000--a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) <span class="hlt">downscaling</span> methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically <span class="hlt">downscaled</span> data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of <span class="hlt">downscaled</span> climate data and the potential for misinterpretation of future estimates of Salmonella infections. PMID:26938544</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1093136','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/1093136"><span id="translatedtitle">Image Change Detection via <span class="hlt">Ensemble</span> Learning</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Martin, Benjamin W; Vatsavai, Raju</p> <p>2013-01-01</p> <p>The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work, we explore the use of <span class="hlt">ensemble</span> learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. <span class="hlt">Ensemble</span> learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the <span class="hlt">ensemble</span>. The strength of the <span class="hlt">ensemble</span> lies in the fact that the individual classifiers in the <span class="hlt">ensemble</span> create a mixture of experts in which the final classification made by the <span class="hlt">ensemble</span> classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of <span class="hlt">ensemble</span> learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the <span class="hlt">ensemble</span> method has approximately 11.5% higher change detection accuracy than an individual classifier.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=guitar+AND+tuning&id=EJ430552','ERIC'); return false;" href="http://eric.ed.gov/?q=guitar+AND+tuning&id=EJ430552"><span id="translatedtitle">Fine-Tuning Your <span class="hlt">Ensemble</span>'s Jazz Style.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Garcia, Antonio J.</p> <p>1991-01-01</p> <p>Proposes instructional strategies for directors of jazz groups, including guidelines for developing of skills necessary for good performance. Includes effective methods for positive changes in <span class="hlt">ensemble</span> style. Addresses jazz group problems such as beat, tempo, staying in tune, wind power, and solo/<span class="hlt">ensemble</span> lines. Discusses percussionists, bassists,…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25201983','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25201983"><span id="translatedtitle">Visual stimuli recruit intrinsically generated cortical <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Miller, Jae-eun Kang; Ayzenshtat, Inbal; Carrillo-Reid, Luis; Yuste, Rafael</p> <p>2014-09-23</p> <p>The cortical microcircuit is built with recurrent excitatory connections, and it has long been suggested that the purpose of this design is to enable intrinsically driven reverberating activity. To understand the dynamics of neocortical intrinsic activity better, we performed two-photon calcium imaging of populations of neurons from the primary visual cortex of awake mice during visual stimulation and spontaneous activity. In both conditions, cortical activity is dominated by coactive groups of neurons, forming <span class="hlt">ensembles</span> whose activation cannot be explained by the independent firing properties of their contributing neurons, considered in isolation. Moreover, individual neurons flexibly join multiple <span class="hlt">ensembles</span>, vastly expanding the encoding potential of the circuit. Intriguingly, the same coactive <span class="hlt">ensembles</span> can repeat spontaneously and in response to visual stimuli, indicating that stimulus-evoked responses arise from activating these intrinsic building blocks. Although the spatial properties of stimulus-driven and spontaneous <span class="hlt">ensembles</span> are similar, spontaneous <span class="hlt">ensembles</span> are active at random intervals, whereas visually evoked <span class="hlt">ensembles</span> are time-locked to stimuli. We conclude that neuronal <span class="hlt">ensembles</span>, built by the coactivation of flexible groups of neurons, are emergent functional units of cortical activity and propose that visual stimuli recruit intrinsically generated <span class="hlt">ensembles</span> to represent visual attributes. PMID:25201983</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016APS..MARL48015S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016APS..MARL48015S"><span id="translatedtitle">Characterizing <span class="hlt">Ensembles</span> of Superconducting Qubits</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sears, Adam; Birenbaum, Jeff; Hover, David; Rosenberg, Danna; Weber, Steven; Yoder, Jonilyn L.; Kerman, Jamie; Gustavsson, Simon; Kamal, Archana; Yan, Fei; Oliver, William</p> <p></p> <p>We investigate <span class="hlt">ensembles</span> of up to 48 superconducting qubits embedded within a superconducting cavity. Such arrays of qubits have been proposed for the experimental study of Ising Hamiltonians, and efficient methods to characterize and calibrate these types of systems are still under development. Here we leverage high qubit coherence (> 70 μs) to characterize individual devices as well as qubit-qubit interactions, utilizing the common resonator mode for a joint readout. This research was funded by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) under Air Force Contract No. FA8721-05-C-0002. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the US Government.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002EGSGA..27.1715S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002EGSGA..27.1715S"><span id="translatedtitle">On The Choce of The Temporal Aggregation Level For Statistical <span class="hlt">Downscaling</span> For Precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shabalova, M. V.; Buishand, T. A.</p> <p></p> <p>Statistical <span class="hlt">downscaling</span> methods are used in hydrological applications to translate cli- mate change scenarios generated by GCMs to the finer spatial grids. In these meth- ods the present-day statistical relationships between the local precipitation and atmo- spheric predictor variables are used to predict the change in precipitation given the changes in predictors. The predictors thus should be realistically modelled by GCMs and should fully represent the climate change signal. The proper choice of the tem- poral aggregation level of the data in statistical relationships is crucial. An inadequate choice may exclude vital for climate change predictors as statistically not significant. The chosen aggregation level also affects the sensitivity of the <span class="hlt">downscaling</span> model to changes in predictors, as well as the model's efficiency. The results from various <span class="hlt">downscaling</span> models for daily and monthly rainfall occurrence and amount developed in the class of generalised linear models for a number of stations in the River Rhine basin are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1817117S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817117S"><span id="translatedtitle">ECOMS-UDG. A User-friendly Data access Gateway to seasonal forecast datasets allowing R-based remote data access, visualization-validation, bias correction and <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Santiago Cofiño, Antonio; Gutiérrez, José Manuel; Fernández, Jesús; Bedia, Joaquín; Vega, Manuel; Herrera, Sixto; Frías, María Dolores; Iturbide, Maialen; Magariño, Maria Eugenia; Manzanas, Rodrigo</p> <p>2016-04-01</p> <p>Seasonal forecasting data from state-or-the-art forecasting systems (e.g. NCEP/CFSv2 or ECMWF/System4) can be obtained directly from the data providers, but the resulting formats, aggregations and vocabularies may not be homogeneous across datasets, requiring some post processing. Moreover, different data policies hold for the various datasets - which are freely available only in some cases - and therefore data access may not be straightforward. Thus, obtaining seasonal climate forecast data is typically a time consuming task. The ECOMS-UDG (User Data Gateway for the ECOMS initiative) has been developed building in the ​User Data Gateway (UDG, http://meteo.unican.es/udg-wiki) in order to facilitate seasonal (re)forecast data access to end users. The required variables have been downloaded from data providers and stored locally in a THREDDS data server implementing fine-grained user authorization. Thus, users can efficiently retrieve the subsets that best suits their particular research aims (typically surface variables for certain regions, periods and/or <span class="hlt">ensemble</span> members) from a large volume of information. Moreover, an interface layer developed in R allows remote data exploration, access (including homogenization, collocation and sub-setting) and the integration of ECOMS-UDG with a number of R packages developed in the framework of ECOMS for forecast visualization, validation, bias correction and <span class="hlt">downscaling</span>. This unique framework oriented to climate services allows users from different sectors to easily access seasonal forecasting data (typically surface variables), calibrating and/or <span class="hlt">downscaling</span> (using upper air information from large scale predictors) this data at local level and validating the different results (using observations). The documentation delivered with the packages includes worked examples showing that the whole visualization, bias correction and/or <span class="hlt">downscaling</span> tasks requires only a few lines of code and are fully reproducible and adaptable to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRD..120.7316S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..120.7316S"><span id="translatedtitle">Toward a seasonal precipitation prediction system for West Africa: Performance of CFSv2 and high-resolution dynamical <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Siegmund, Jonatan; Bliefernicht, Jan; Laux, Patrick; Kunstmann, Harald</p> <p>2015-08-01</p> <p>Seasonal precipitation forecasts are important sources of information for early drought and famine warnings in West Africa. This study presents an assessment of the monthly precipitation forecast of the Climate Forecast System version 2 (CFSv2) for three agroecological zones (Sudan-Sahel, Sudan, and Guinean zone) of the Volta Basin. The CFSv2 performance is evaluated for the Sahel drought 1983 and for all August months of the reforecast period (1982-2009) with lead times up to 8 months using a quantile-quantile transformation for bias correction. In addition, an operational experiment is performed for the rainy season 2013 to analyze the performance of a dynamical <span class="hlt">downscaling</span> approach for this region. Twenty-two CFSv2 <span class="hlt">ensemble</span> members initialized in February 2013 are transferred to a resolution of 10 km × 10 km using the Weather and Research Forecasting (WRF) model. Since the uncorrected CFSv2 precipitation forecasts are characterized by a high uncertainty (up to 175% of the observed variability), the quantile-quantile transformation can clearly reduce this overestimation with the potential to provide skillful and valuable early warnings of precipitation deficits and excess up to 6 months in ahead, particularly for the Sudan-Sahel zone. The operational experiment illustrates that CFSv2-WRF can reduce the CFSv2 uncertainty (up to 69%) for monthly precipitation and the onset of the rainy season but has still strong deficits regarding the northward progression of the rain belt. Further studies are necessary for a more robust assessment of the techniques applied in this study to confirm these promising outcomes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1814396W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1814396W"><span id="translatedtitle">Validation of spatial variability in <span class="hlt">downscaling</span> results from the VALUE perfect predictor experiment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Widmann, Martin; Bedia, Joaquin; Gutiérrez, Jose Manuel; Maraun, Douglas; Huth, Radan; Fischer, Andreas; Keller, Denise; Hertig, Elke; Vrac, Mathieu; Wibig, Joanna; Pagé, Christian; Cardoso, Rita M.; Soares, Pedro MM; Bosshard, Thomas; Casado, Maria Jesus; Ramos, Petra</p> <p>2016-04-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research. Within VALUE a systematic validation framework to enable the assessment and comparison of both dynamical and statistical <span class="hlt">downscaling</span> methods has been developed. In the first validation experiment the <span class="hlt">downscaling</span> methods are validated in a setup with perfect predictors taken from the ERA-interim reanalysis for the period 1997 - 2008. This allows to investigate the isolated skill of <span class="hlt">downscaling</span> methods without further error contributions from the large-scale predictors. One aspect of the validation is the representation of spatial variability. As part of the VALUE validation we have compared various properties of the spatial variability of <span class="hlt">downscaled</span> daily temperature and precipitation with the corresponding properties in observations. We have used two test validation datasets, one European-wide set of 86 stations, and one higher-density network of 50 stations in Germany. Here we present results based on three approaches, namely the analysis of i.) correlation matrices, ii.) pairwise joint threshold exceedances, and iii.) regions of similar variability. We summarise the information contained in correlation matrices by calculating the dependence of the correlations on distance and deriving decorrelation lengths, as well as by determining the independent degrees of freedom. Probabilities for joint threshold exceedances and (where appropriate) non-exceedances are calculated for various user-relevant thresholds related for instance to extreme precipitation or frost and heat days. The dependence of these probabilities on distance is again characterised by calculating typical length scales that separate dependent from independent exceedances. Regionalisation is based on rotated Principal Component Analysis. The results indicate which <span class="hlt">downscaling</span> methods are preferable if the dependency of variability at different locations is relevant for the user.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150010221','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150010221"><span id="translatedtitle"><span class="hlt">Downscaling</span> Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.</p> <p>2013-01-01</p> <p>The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for <span class="hlt">downscaling</span> and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for <span class="hlt">downscaling</span> satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the <span class="hlt">downscaling</span> problem as a discrete inverse problem and solve it via a regularized variational approach (variational <span class="hlt">downscaling</span>) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse <span class="hlt">downscaling</span> methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26505632','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26505632"><span id="translatedtitle">ENCORE: Software for Quantitative <span class="hlt">Ensemble</span> Comparison.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tiberti, Matteo; Papaleo, Elena; Bengtsen, Tone; Boomsma, Wouter; Lindorff-Larsen, Kresten</p> <p>2015-10-01</p> <p>There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or <span class="hlt">ensemble</span> refinement approaches have provided conformational <span class="hlt">ensembles</span> that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural <span class="hlt">ensembles</span> in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational <span class="hlt">ensembles</span> generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for <span class="hlt">ensemble</span> comparison, that each can be used to quantify the similarity between conformational <span class="hlt">ensembles</span> by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing <span class="hlt">ensembles</span> generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural <span class="hlt">ensembles</span> refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the <span class="hlt">ensembles</span>. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads <span class="hlt">ensemble</span> data in many common formats, and can work with large</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4624683','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4624683"><span id="translatedtitle">ENCORE: Software for Quantitative <span class="hlt">Ensemble</span> Comparison</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tiberti, Matteo; Papaleo, Elena; Bengtsen, Tone; Boomsma, Wouter; Lindorff-Larsen, Kresten</p> <p>2015-01-01</p> <p>There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or <span class="hlt">ensemble</span> refinement approaches have provided conformational <span class="hlt">ensembles</span> that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural <span class="hlt">ensembles</span> in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational <span class="hlt">ensembles</span> generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for <span class="hlt">ensemble</span> comparison, that each can be used to quantify the similarity between conformational <span class="hlt">ensembles</span> by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing <span class="hlt">ensembles</span> generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural <span class="hlt">ensembles</span> refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the <span class="hlt">ensembles</span>. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads <span class="hlt">ensemble</span> data in many common formats, and can work with large</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26505632','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26505632"><span id="translatedtitle">ENCORE: Software for Quantitative <span class="hlt">Ensemble</span> Comparison.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tiberti, Matteo; Papaleo, Elena; Bengtsen, Tone; Boomsma, Wouter; Lindorff-Larsen, Kresten</p> <p>2015-10-01</p> <p>There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or <span class="hlt">ensemble</span> refinement approaches have provided conformational <span class="hlt">ensembles</span> that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural <span class="hlt">ensembles</span> in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational <span class="hlt">ensembles</span> generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for <span class="hlt">ensemble</span> comparison, that each can be used to quantify the similarity between conformational <span class="hlt">ensembles</span> by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing <span class="hlt">ensembles</span> generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural <span class="hlt">ensembles</span> refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the <span class="hlt">ensembles</span>. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads <span class="hlt">ensemble</span> data in many common formats, and can work with large</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=277355','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=277355"><span id="translatedtitle">Assessment of the scale effect on statistical <span class="hlt">downscaling</span> quality at a station scale using a weather generator-based model</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>The resolution of General Circulation Models (GCMs) is too coarse to assess the fine scale or site-specific impacts of climate change. <span class="hlt">Downscaling</span> approaches including dynamical and statistical <span class="hlt">downscaling</span> have been developed to meet this requirement. As the resolution of climate model increases, it...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A41H0062U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A41H0062U"><span id="translatedtitle">Diamond-NICAM-SPRINTARS: <span class="hlt">downscaling</span> and simulation results</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Uchida, J.</p> <p>2012-12-01</p> <p>As a part of initiative "Research Program on Climate Change Adaptation" (RECCA) which investigates how predicted large-scale climate change may affect a local weather, and further examines possible atmospheric hazards that cities may encounter due to such a climate change, thus to guide policy makers on implementing new environmental measures, a "Development of Seamless Chemical AssimiLation System and its Application for Atmospheric Environmental Materials" (SALSA) project is funded by the Japanese Ministry of Education, Culture, Sports, Science and Technology and is focused on creating a regional (local) scale assimilation system that can accurately recreate and predict a transport of carbon dioxide and other air pollutants. In this study, a regional model of the next generation global cloud-resolving model NICAM (Non-hydrostatic ICosahedral Atmospheric Model) (Tomita and Satoh, 2004) is used and ran together with a transport model SPRINTARS (Spectral Radiation Transport Model for Aerosol Species) (Takemura et al, 2000) and a chemical transport model CHASER (Sudo et al, 2002) to simulate aerosols across urban cities (over a Kanto region including metropolitan Tokyo). The presentation will mainly be on a "Diamond-NICAM" (Figure 1), a regional climate model version of the global climate model NICAM, and its dynamical <span class="hlt">downscaling</span> methodologies. Originally, a global NICAM can be described as twenty identical equilateral triangular-shaped panels covering the entire globe where grid points are at the corners of those panels, and to increase a resolution (called a "global-level" in NICAM), additional points are added at the middle of existing two adjacent points, so a number of panels increases by fourfold with an increment of one global-level. On the other hand, a Diamond-NICAM only uses two of those initial triangular-shaped panels, thus only covers part of the globe. In addition, NICAM uses an adaptive mesh scheme and its grid size can gradually decrease, as the grid</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GPC...144..129K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GPC...144..129K"><span id="translatedtitle">An evaluation of how <span class="hlt">downscaled</span> climate data represents historical precipitation characteristics beyond the means and variances</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kusangaya, Samuel; Toucher, Michele L. Warburton; van Garderen, Emma Archer; Jewitt, Graham P. W.</p> <p>2016-09-01</p> <p>Precipitation is the main driver of the hydrological cycle. For climate change impact analysis, use of <span class="hlt">downscaled</span> precipitation, amongst other factors, determines accuracy of modelled runoff. Precipitation is, however, considerably more difficult to model than temperature, largely due to its high spatial and temporal variability and its nonlinear nature. Due to such qualities of precipitation, a key challenge for water resources management is thus how to incorporate potentially significant but highly uncertain precipitation characteristics when modelling potential changes in climate for water resources management in order to support local management decisions. Research undertaken here was aimed at evaluating how <span class="hlt">downscaled</span> climate data represented the underlying historical precipitation characteristics beyond the means and variances. Using the uMngeni Catchment in KwaZulu-Natal, South Africa as a case study, the occurrence of rainfall, rainfall threshold events and wet dry sequence was analysed for current climate (1961-1999). The number of rain days with daily rainfall > 1 mm, > 5 mm, > 10 mm, > 20 mm and > 40 mm for each of the 10 selected climate models was, compared to the number of rain days at 15 rain stations. Results from graphical and statistical analysis indicated that on a monthly basis rain days are over estimated for all climate models. Seasonally, the number of rain days were overestimated in autumn and winter and underestimated in summer and spring. The overall conclusion was that despite the advancement in <span class="hlt">downscaling</span> and the improved spatial scale for a better representation of the climate variables, such as rainfall for use in hydrological impact studies, <span class="hlt">downscaled</span> rainfall data still does not simulate well some important rainfall characteristics, such as number of rain days and wet-dry sequences. This is particularly critical, since, whilst for climatologists, means and variances might be simulated well in <span class="hlt">downscaled</span> GCMs, for hydrologists</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://dx.doi.org/10.1186/2192-1709-1-2','USGSPUBS'); return false;" href="http://dx.doi.org/10.1186/2192-1709-1-2"><span id="translatedtitle"><span class="hlt">Downscaling</span> future climate scenarios to fine scales for hydrologic and ecological modeling and analysis</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Flint, Lorraine E.; Flint, Alan L.</p> <p>2012-01-01</p> <p>The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the <span class="hlt">downscaling</span> while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale <span class="hlt">downscaling</span> to analyses of ecological processes influenced by topographic complexity.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20150019486&hterms=wildlife&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dwildlife','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20150019486&hterms=wildlife&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dwildlife"><span id="translatedtitle">The Practitioner's Dilemma: How to Assess the Credibility of <span class="hlt">Downscaled</span> Climate Projections</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barsugli, Joseph J.; Guentchev, Galina; Horton, Radley M.; Wood, Andrew; Mearns, Lindo O.; Liang, Xin-Zhong; Winkler, Julia A.; Dixon, Keith; Hayhoe, Katharine; Rood, Richard B.; Goddard, Lisa; Ray, Andrea; Buja, Lawrence; Ammann, Caspar</p> <p>2013-01-01</p> <p>Suppose you are a city planner, regional water manager, or wildlife conservation specialist who is asked to include the potential impacts of climate variability and change in your risk management and planning efforts. What climate information would you use? The choice is often regional or local climate projections <span class="hlt">downscaled</span> from global climate models (GCMs; also known as general circulation models) to include detail at spatial and temporal scales that align with those of the decision problem. A few years ago this information was hard to come by. Now there is Web-based access to a proliferation of high-resolution climate projections derived with differing <span class="hlt">downscaling</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JHyd..401..190C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JHyd..401..190C"><span id="translatedtitle">Uncertainty of <span class="hlt">downscaling</span> method in quantifying the impact of climate change on hydrology</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Jie; Brissette, François P.; Leconte, Robert</p> <p>2011-05-01</p> <p>SummaryUncertainty estimation of climate change impacts has been given a lot of attention in the recent literature. It is generally assumed that the major sources of uncertainty are linked to General Circulation Models (GCMs) and Greenhouse Gases Emissions Scenarios (GGES). However, other sources of uncertainty such as the choice of a <span class="hlt">downscaling</span> method have been given less attention. This paper focuses on this issue by comparing six <span class="hlt">downscaling</span> methods to investigate the uncertainties in quantifying the impacts of climate change on the hydrology of a Canadian (Quebec province) river basin. The <span class="hlt">downscaling</span> methods regroup dynamical and statistical approaches, including the change factor method and a weather generator-based approach. Future (2070-2099, 2085 horizon) hydrological regimes simulated with a hydrological model are compared to the reference period (1970-1999) using the average hydrograph, annual mean discharge, peak discharge and time to peak discharge as criteria. The results show that all <span class="hlt">downscaling</span> methods suggest temperature increases over the basin for the 2085 horizon. The regression-based statistical methods predict a larger increase in autumn and winter temperatures. Predicted changes in precipitation are not as unequivocal as those of temperatures, they vary depending on the <span class="hlt">downscaling</span> methods and seasons. There is a general increase in winter discharge (November-April) while decreases in summer discharge are predicted by most methods. Consistently with the large predicted increases in autumn and winter temperature, regression-based statistical methods show severe increases in winter flows and considerable reductions in peak discharge. Across all variables, a large uncertainty envelope was found to be associated with the choice of a <span class="hlt">downscaling</span> method. This envelope was compared to the envelope originating from the choice of 28 climate change projections from a combination of seven GCMs and three GGES. Both uncertainty envelopes were similar</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.4703K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.4703K"><span id="translatedtitle">Future changes in African temperature and precipitation in an <span class="hlt">ensemble</span> of Africa-CORDEX regional climate model simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kjellström, Erik; Nikulin, Grigory; Gbobaniyi, Emiola; Jones, Colin</p> <p>2013-04-01</p> <p>In this study we investigate possible changes in temperature and precipitation on a regional scale over Africa from 1961 to 2100. We use data from two <span class="hlt">ensembles</span> of climate simulations, one global and one regional, over the Africa-CORDEX domain. The global <span class="hlt">ensemble</span> includes eight coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CNRM-CM5, HadGEM2-ES, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. In the regional <span class="hlt">ensemble</span> all 8 AOGCMs are <span class="hlt">downscaled</span> at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Two forcing scenarios are considered, RCP 4.5 and 8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, we investigate the benefit of the higher horizontal resolution in RCA4 by comparing the results to the coarser driving AOGCM data. The significance of the results is investigated by comparing to i) the model simulated natural variability, and, ii) the biases in the control period. Results dealing with changes in the seasonal cycle of temperature and precipitation are presented. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC24A..02P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC24A..02P"><span id="translatedtitle">Evaluation of a Technique for <span class="hlt">Downscaling</span> Climate-Model Output in Mountainous Terrain Using Local Topographic Lapse Rates</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Praskievicz, S. J.</p> <p>2015-12-01</p> <p>One of the challenges in using general circulation model (GCM) output is the need to <span class="hlt">downscale</span> beyond the model's coarse spatial grid in order to infer climate at any particular location. Traditionally, <span class="hlt">downscaling</span> has been achieved either dynamically, through regional climate models (RCMs), or statistically, through empirical relationships between predictor variables in the GCM and observed variables. In mountainous terrain, elevation is one of the primary controls on temperature and precipitation at the local scale, which provides the potential for topographic variables to be used to adjust climate-model output. Here, local topographic lapse rates (LTLR) were estimated from gridded climate data for the Pacific Northwest, and those lapse rates were used to <span class="hlt">downscale</span> RCM output. Skill scores were calculated for the LTLR-<span class="hlt">downscaled</span> climate-model output relative to an existing set of model output <span class="hlt">downscaled</span> using the well-established statistical <span class="hlt">downscaling</span> technique of bias-corrected constructed analogs (BCCA). Spatial and temporal patterns in forecast skill and in bias of the LTLR <span class="hlt">downscaling</span> method were also examined. The results indicate that the LTLR method performs well in the mountainous study region relative to the BCCA method. There is variability in the forecast skill, however, most notably the LTLR <span class="hlt">downscaling</span> technique's better performance in the eastern part of the study region for temperature and in the western part of the study region for precipitation. LTLR <span class="hlt">downscaling</span> offers a promising method for <span class="hlt">downscaling</span> climate-model output in regions in which elevation is a strong control on climate, particularly for studying impacts of past or future climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.401T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.401T"><span id="translatedtitle">Climate change scenarios of temperature and precipitation over five Italian regions for the period 2021-2050 obtained by statistical <span class="hlt">downscaling</span> models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tomozeiu, R.; Tomei, F.; Villani, G.; Pasqui, M.</p> <p>2010-09-01</p> <p>Climate change scenarios of seasonal maximum, minimum temperature and precipitation in five Italian regions, over the period 2021-2050 against 1961-1990 are assessed. The regions selected by the AGROSCENARI project are important from the local agricultural practises and are situated as follows: in the Northern Italy - Po valley and hilly area of Faenza; in Central part of Italy- Marche, Beneventano and Destra Sele, and in Sardinia Island - Oristano. A statistical <span class="hlt">downscaling</span> technique applied to the <span class="hlt">ENSEMBLES</span> global climate simulations, A1B scenario, is used to reach this objective. The method consists of a multivariate regression, based on Canonical Correlation Analysis, using as possible predictors mean sea level pressure, geopotential height at 500hPa and temperature at 850 hPa. The observational data set (predictands) for the selected regions is composed by a reconstruction of minimum, maximum temperature and precipitation daily data on a regular grid with a spatial resolution of 35 km, for 1951-2009 period (managed by the Meteorological and Climatological research unit for agriculture - Agricultural Research Council, CRA - CMA). First, a set-up of statistical model has been made using predictors from ERA40 reanalysis and the seasonal indices of temperature and precipitation from local scale, 1958-2002 period. Then, the statistical <span class="hlt">downscaling</span> model has been applied to the predictors derived from the <span class="hlt">ENSEMBLES</span> global climate models, A1B scenario, in order to obtain climate change scenario of temperature and precipitation at local scale, 2021-2050 period. The projections show that increases could be expected to occur under scenario conditions in all seasons, in both minimum and maximum temperature. The magnitude of changes is more intense during summer when the changes could reach values around 2°C for minimum and maximum temperature. In the case of precipitation, the pattern of changes is more complex, different from season to season and over the regions, a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011180','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011180"><span id="translatedtitle">Hybrid Data Assimilation without <span class="hlt">Ensemble</span> Filtering</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Todling, Ricardo; Akkraoui, Amal El</p> <p>2014-01-01</p> <p>The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the <span class="hlt">ensemble</span> is generated using a square-root <span class="hlt">ensemble</span> Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member <span class="hlt">ensemble</span> solution close to the variational solution; we also found it necessary to re-center the members of the <span class="hlt">ensemble</span> about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the <span class="hlt">ensemble</span>. This led us to consider a hybrid strategy in which the members of the <span class="hlt">ensemble</span> are generated by simply converting the variational analysis to the resolution of the <span class="hlt">ensemble</span> and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJSyS..47..406C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJSyS..47..406C"><span id="translatedtitle">MSEBAG: a dynamic classifier <span class="hlt">ensemble</span> generation based on `minimum-sufficient <span class="hlt">ensemble</span>' and bagging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Lei; Kamel, Mohamed S.</p> <p>2016-01-01</p> <p>In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient <span class="hlt">ensemble</span>' and bagging at the <span class="hlt">ensemble</span> level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient <span class="hlt">ensemble</span>', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient <span class="hlt">ensemble</span>', a backward stepwise algorithm is employed to generate a collection of <span class="hlt">ensembles</span>. The objective is to create a collection of <span class="hlt">ensembles</span> with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the <span class="hlt">ensembles</span> from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the <span class="hlt">ensemble</span> level, is employed for this task. EAA searches for the competent <span class="hlt">ensembles</span> using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected <span class="hlt">ensembles</span> to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.8109K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.8109K"><span id="translatedtitle">The effect of member configuration of <span class="hlt">Ensemble</span> Transform Kalman Filter on the performance of <span class="hlt">ensemble</span> prediction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kay, Jun Kyung; Kim, Hyun Mee; Park, Young-Youn; Son, Joohyung; Moon, Seonok</p> <p>2010-05-01</p> <p>Using Met Office Global and Regional <span class="hlt">Ensemble</span> Prediction System (MOGREPS), the effect of member configuration of <span class="hlt">Ensemble</span> Transform Kalman Filter (ETKF) on the performance of <span class="hlt">ensemble</span> prediction is evaluated. Because Korea Meteorological Administration (KMA) is implementing Unified model (UM) and related pre/post processing imported from United Kingdom Meteorological Office (UKMO) operationally from year 2010, it is necessary to investigate the effect of <span class="hlt">ensemble</span> size on the prediction capability of MOGREPS before operating the UM in KMA. Currently the <span class="hlt">ensemble</span> size of MOGREPS is 24, 1-control and 23-perturbation members. The finite <span class="hlt">ensemble</span> size causes the sampling error of the full forecast probability distribution function (PDF), so the <span class="hlt">ensemble</span> size is closely related to the efficiency of EPS. The prediction capability depending on the <span class="hlt">ensemble</span> size has been evaluated by increasing the number of <span class="hlt">ensemble</span> from 24 to 48. In addition to that, a new method of selecting 24 <span class="hlt">ensemble</span> members that best approximate the forecast PDF from 48 <span class="hlt">ensemble</span> members of ETKF (Reduced <span class="hlt">Ensemble</span> Transform Kalman Filter; RETKF) is proposed to decrease computing cost by increasing <span class="hlt">ensemble</span> size from 24 to 48, and the result of RETKF is compared with that of ETKF in terms of the statistical reliability and resolution. In the northern hemisphere, the best performance was obtained by the 48 ETKF members, followed by the 24 ETKF members. The performance of 24 RETKF members was the worst. However, in the southern hemisphere, the 24 RETKF members showed the best performance. 48 and 24 ETKF members had a little difference, but the performance of 48 members is slightly better than 24 members.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4910T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4910T"><span id="translatedtitle">Forecasting European Droughts using the North American Multi-Model <span class="hlt">Ensemble</span> (NMME)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thober, Stephan; Kumar, Rohini; Samaniego, Luis; Sheffield, Justin; Schäfer, David; Mai, Juliane</p> <p>2015-04-01</p> <p>Soil moisture droughts have the potential to diminish crop yields causing economic damage or even threatening the livelihood of societies. State-of-the-art drought forecasting systems incorporate seasonal meteorological forecasts to estimate future drought conditions. Meteorological forecasting skill (in particular that of precipitation), however, is limited to a few weeks because of the chaotic behaviour of the atmosphere. One of the most important challenges in drought forecasting is to understand how the uncertainty in the atmospheric forcings (e.g., precipitation and temperature) is further propagated into hydrologic variables such as soil moisture. The North American Multi-Model <span class="hlt">Ensemble</span> (NMME) provides the latest collection of a multi-institutional seasonal forecasting <span class="hlt">ensemble</span> for precipitation and temperature. In this study, we analyse the skill of NMME forecasts for predicting European drought events. The monthly NMME forecasts are <span class="hlt">downscaled</span> to daily values to force the mesoscale hydrological model (mHM). The mHM soil moisture forecasts obtained with the forcings of the dynamical models are then compared against those obtained with the <span class="hlt">Ensemble</span> Streamflow Prediction (ESP) approach. ESP recombines historical meteorological forcings to create a new <span class="hlt">ensemble</span> forecast. Both forecasts are compared against reference soil moisture conditions obtained using observation based meteorological forcings. The study is conducted for the period from 1982 to 2009 and covers a large part of the Pan-European domain (10°W to 40°E and 35°N to 55°N). Results indicate that NMME forecasts are better at predicting the reference soil moisture variability as compared to ESP. For example, NMME explains 50% of the variability in contrast to only 31% by ESP at a six-month lead time. The Equitable Threat Skill Score (ETS), which combines the hit and false alarm rates, is analysed for drought events using a 0.2 threshold of a soil moisture percentile index. On average, the NMME</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1175481','DOE-PATENT-XML'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1175481"><span id="translatedtitle">Creating <span class="hlt">ensembles</span> of decision trees through sampling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Kamath, Chandrika; Cantu-Paz, Erick</p> <p>2005-08-30</p> <p>A system for decision tree <span class="hlt">ensembles</span> that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in <span class="hlt">ensembles</span>. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in <span class="hlt">ensembles</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016CoTPh..65..185W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016CoTPh..65..185W"><span id="translatedtitle">Derivation of Mayer Series from Canonical <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>Wang, Xian-Zhi</p> <p>2016-02-01</p> <p>Mayer derived the Mayer series from both the canonical <span class="hlt">ensemble</span> and the grand canonical <span class="hlt">ensemble</span> by use of the cluster expansion method. In 2002, we conjectured a recursion formula of the canonical partition function of a fluid (X.Z. Wang, Phys. Rev. E 66 (2002) 056102). In this paper we give a proof for this formula by developing an appropriate expansion of the integrand of the canonical partition function. We further derive the Mayer series solely from the canonical <span class="hlt">ensemble</span> by use of this recursion formula.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009SPIE.7347E..03C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009SPIE.7347E..03C"><span id="translatedtitle">Multiobjective information theoretic <span class="hlt">ensemble</span> selection</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Card, Stuart W.; Mohan, Chilukuri K.</p> <p>2009-05-01</p> <p>In evolutionary learning, the sine qua non is evolvability, which requires heritability of fitness and a balance between exploitation and exploration. Unfortunately, commonly used fitness measures, such as root mean squared error (RMSE), often fail to reward individuals whose presence in the population is needed to explain important data variance; and indicators of diversity generally are not only incommensurate with those of fitness but also essentially arbitrary. Thus, due to poor scaling, deception, etc., apparently relatively high fitness individuals in early generations may not contain the building blocks needed to evolve optimal solutions in later generations. To reward individuals for their potential incremental contributions to the solution of the overall problem, heritable information theoretic functionals are developed that incorporate diversity considerations into fitness, explicitly identifying building blocks suitable for recombination (e.g. for non-random mating). Algorithms for estimating these functionals from either discrete or continuous data are illustrated by application to input selection in a high dimensional industrial process control data set. Multiobjective information theoretic <span class="hlt">ensemble</span> selection is shown to avoid some known feature selection pitfalls.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=307740&keyword=LAKE+AND+ICE&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=80636758&CFTOKEN=42828612','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=307740&keyword=LAKE+AND+ICE&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=80636758&CFTOKEN=42828612"><span id="translatedtitle">Technical Challenges and Solutions in Representing Lakes when using WRF in <span class="hlt">Downscaling</span> Applications</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by <span class="hlt">downscaling</span> global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JGRD..11617110N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JGRD..11617110N"><span id="translatedtitle">Projecting changes in future heavy rainfall events for Oahu, Hawaii: 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>Norton, Chase W.; Chu, Pao-Shin; Schroeder, Thomas A.</p> <p>2011-09-01</p> <p>A statistical model based on nonlinear artificial neural networks is used to <span class="hlt">downscale</span> daily extreme precipitation events in Oahu, Hawaii, from general circulation model (GCM) outputs and projected into the future. From a suite of GCMs and their emission scenarios, two tests recommended by the International Panel on Climate Change are conducted and the ECHAM5 A2 is selected as the most appropriate one for <span class="hlt">downscaling</span> precipitation extremes for Oahu. The skill of the neural network model is highest in drier, leeward regions where orographic uplifting has less influence on daily extreme precipitation. The trained model is used with the ECHAM5 forced by emissions from the A2 scenario to simulate future daily precipitation on Oahu. A BCa bootstrap resampling method is used to provide 95% confidence intervals of the storm frequency and intensity for all three data sets (actual observations, <span class="hlt">downscaled</span> GCM output from the present-day climate, and <span class="hlt">downscaled</span> GCM output for future climate). Results suggest a tendency for increased frequency of heavy rainfall events but a decrease in rainfall intensity during the next 30 years (2011-2040) for the southern shoreline of Oahu.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=296239','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=296239"><span id="translatedtitle">Passive microwave soil moisture <span class="hlt">downscaling</span> using vegetation index and skin surface temperature</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture <span class="hlt">downscaling</span> algorithm based on a regression relationship bet...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=213342','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=213342"><span id="translatedtitle">Reductions in seasonal climate forecast dependability as a result of <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>This research determines whether NOAA/CPC seasonal climate forecasts are skillful enough to retain utility after they have been <span class="hlt">downscaled</span> for use in crop models. Utility is assessed using net dependability, the product of the large-scale 3-month forecast dependability and a factor accounting for l...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015WRR....51.6244J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015WRR....51.6244J"><span id="translatedtitle">A space and time scale-dependent nonlinear geostatistical approach for <span class="hlt">downscaling</span> daily precipitation and temperature</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason; McCabe, Matthew F.; Sharma, Ashish</p> <p>2015-08-01</p> <p>A geostatistical framework is proposed to <span class="hlt">downscale</span> daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 and 10 km resolution for a 20 year period ranging from 1985 to 2004. The data are used to predict <span class="hlt">downscaled</span> climate variables for the year 2005. The result, for each <span class="hlt">downscaled</span> pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference data set indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple-point geostatistics approach is an efficient tool to be used for statistical <span class="hlt">downscaling</span> to obtain local-scale estimates of precipitation and temperature from General Circulation Models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1812447R&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016EGUGA..1812447R&link_type=ABSTRACT"><span id="translatedtitle">Multi objective climate change impact assessment using multi <span class="hlt">downscaled</span> climate scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rana, Arun; Moradkhani, Hamid</p> <p>2016-04-01</p> <p>Global Climate Models (GCMs) are often used to <span class="hlt">downscale</span> the climatic parameters on a regional and global scale. In the present study, we have analyzed the changes in precipitation and temperature for future scenario period of 2070-2099 with respect to historical period of 1970-2000 from a set of statistically <span class="hlt">downscaled</span> GCM projections for Columbia River Basin (CRB). Analysis is performed using 2 different statistically <span class="hlt">downscaled</span> climate projections namely the Bias Correction and Spatial <span class="hlt">Downscaling</span> (BCSD) technique generated at Portland State University and the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. Analysis is performed on spatial, temporal and frequency based parameters in the future period at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice-versa for temperature. Frequency analysis provided insights into possible explanation to changes in precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1713722K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713722K"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of WRF-Chem Model: An Air Quality Analysis over Bogota, Colombia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kumar, Anikender; Rojas, Nestor</p> <p>2015-04-01</p> <p>Statistical <span class="hlt">downscaling</span> is a technique that is used to extract high-resolution information from regional scale variables produced by coarse resolution models such as Chemical Transport Models (CTMs). The fully coupled WRF-Chem (Weather Research and Forecasting with Chemistry) model is used to simulate air quality over Bogota. Bogota is a tropical Andean megacity located over a high-altitude plateau in the middle of very complex terrain. The WRF-Chem model was adopted for simulating the hourly ozone concentrations. The computational domains were chosen of 120x120x32, 121x121x32 and 121x121x32 grid points with horizontal resolutions of 27, 9 and 3 km respectively. The model was initialized with real boundary conditions using NCAR-NCEP's Final Analysis (FNL) and a 1ox1o (~111 km x 111 km) resolution. Boundary conditions were updated every 6 hours using reanalysis data. The emission rates were obtained from global inventories, namely the REanalysis of the TROpospheric (RETRO) chemical composition and the Emission Database for Global Atmospheric Research (EDGAR). Multiple linear regression and artificial neural network techniques are used to <span class="hlt">downscale</span> the model output at each monitoring stations. The results confirm that the statistically <span class="hlt">downscaled</span> outputs reduce simulated errors by up to 25%. This study provides a general overview of statistical <span class="hlt">downscaling</span> of chemical transport models and can constitute a reference for future air quality modeling exercises over Bogota and other Colombian cities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..516..304W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..516..304W"><span id="translatedtitle">Evaluation of sampling techniques to characterize topographically-dependent variability for soil moisture <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>Werbylo, Kevin L.; Niemann, Jeffrey D.</p> <p>2014-08-01</p> <p><span class="hlt">Downscaling</span> methods have been proposed to estimate catchment-scale soil moisture patterns from coarser resolution patterns. These methods usually infer the fine-scale variability in soil moisture using variations in ancillary variables like topographic attributes that have relationships to soil moisture. Previously, such relationships have been observed in catchments using soil moisture observations taken on uniform grids at hundreds of locations on multiple dates, but collecting data in this manner limits the applicability of this approach. The objective of this paper is to evaluate the effectiveness of two strategic sampling techniques for characterizing the relationships between topographic attributes and soil moisture for the purpose of constraining <span class="hlt">downscaling</span> methods. The strategic sampling methods are conditioned Latin hypercube sampling (cLHS) and stratified random sampling (SRS). Each sampling method is used to select a limited number of locations or dates for soil moisture monitoring at three catchments with detailed soil moisture datasets. These samples are then used to calibrate two available <span class="hlt">downscaling</span> methods, and the effectiveness of the sampling methods is evaluated by the ability of the <span class="hlt">downscaling</span> methods to reproduce the known soil moisture patterns. cLHS outperforms random sampling in almost every case considered. SRS usually performs better than cLHS when very few locations are sampled, but it can perform worse than random sampling for intermediate and large numbers of locations.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ACP....16.5229W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2016ACP....16.5229W&link_type=ABSTRACT"><span id="translatedtitle"><span class="hlt">Downscaling</span> surface wind predictions from numerical weather prediction models in complex terrain with WindNinja</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wagenbrenner, Natalie S.; Forthofer, Jason M.; Lamb, Brian K.; Shannon, Kyle S.; Butler, Bret W.</p> <p>2016-04-01</p> <p>Wind predictions in complex terrain are important for a number of applications. Dynamic <span class="hlt">downscaling</span> of numerical weather prediction (NWP) model winds with a high-resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for <span class="hlt">downscaling</span> near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. <span class="hlt">Downscaling</span> improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with <span class="hlt">downscaling</span>. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=312466','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=312466"><span id="translatedtitle"><span class="hlt">Downscaling</span> Landsat 7 canopy reflectance employing a multi soil sensor platform</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Crop growth and yield can be efficiently monitored using canopy reflectance. The spatial resolution of freely available remote sensing data is however too coarse to fully understand spatial dynamics of crop status. In this manuscript Landsat 7 (L7) reflectance is <span class="hlt">downscaled</span> from the native resolutio...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014APJAS..50...83H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014APJAS..50...83H"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span>: Fundamental issues from an NWP point of view and recommendations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hong, Song-You; Kanamitsu, Masao</p> <p>2014-01-01</p> <p>Dynamical <span class="hlt">downscaling</span> has been recognized as a useful tool not only for the climate community, but also for associated application communities such as the environmental and hydrological societies. Although climate projection data are available in lower-resolution general circulation models (GCMs), higher-resolution climate projections using regional climate models (RCMs) have been obtained over various regions of the globe. Various model outputs from RCMs with a high resolution of even as high as a few km have become available with heavy weight on applications. However, from a scientific point of view in numerical atmospheric modeling, it is not clear how to objectively judge the degree of added value in the RCM output against the corresponding GCM results. A key factor responsible for skepticism is based on the fundamental limitations in the nesting approach between GCMs and RCMs. In this article, we review the current status of the dynamical <span class="hlt">downscaling</span> for climate prediction, focusing on basic assumptions that are scrutinized from a numerical weather prediction (NWP) point of view. Uncertainties in <span class="hlt">downscaling</span> due to the inconsistencies in the physics packages between GCMs and RCMs were revealed. Recommendations on how to tackle the ultimate goal of dynamical <span class="hlt">downscaling</span> were also described.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJBm...60..935M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJBm...60..935M"><span id="translatedtitle">Dynamically <span class="hlt">downscaling</span> predictions for deciduous tree leaf emergence in California under current and future climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medvigy, David; Kim, Seung Hee; Kim, Jinwon; Kafatos, Menas C.</p> <p>2016-07-01</p> <p>Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical <span class="hlt">downscaling</span> of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically <span class="hlt">downscaled</span> to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of <span class="hlt">downscaling</span> from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of <span class="hlt">downscaling</span> are unlikely to be stationary.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26489417','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26489417"><span id="translatedtitle">Dynamically <span class="hlt">downscaling</span> predictions for deciduous tree leaf emergence in California under current and future climate.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Medvigy, David; Kim, Seung Hee; Kim, Jinwon; Kafatos, Menas C</p> <p>2016-07-01</p> <p>Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical <span class="hlt">downscaling</span> of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically <span class="hlt">downscaled</span> to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of <span class="hlt">downscaling</span> from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of <span class="hlt">downscaling</span> are unlikely to be stationary. PMID:26489417</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1817140B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817140B"><span id="translatedtitle">A 4D-<span class="hlt">Ensemble</span>-Variational System for Data Assimilation and <span class="hlt">Ensemble</span> Initialization</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bowler, Neill; Clayton, Adam; Jardak, Mohamed; Lee, Eunjoo; Jermey, Peter; Lorenc, Andrew; Piccolo, Chiara; Pring, Stephen; Wlasak, Marek; Barker, Dale; Inverarity, Gordon; Swinbank, Richard</p> <p>2016-04-01</p> <p>The Met Office has been developing a four-dimensional <span class="hlt">ensemble</span> variational (4DEnVar) data assimilation system over the past four years. The 4DEnVar system is intended both as data assimilation system in its own right and also an improved means of initializing the Met Office Global and Regional <span class="hlt">Ensemble</span> Prediction System (MOGREPS). The global MOGREPS <span class="hlt">ensemble</span> has been initialized by running an <span class="hlt">ensemble</span> of 4DEnVars (En-4DEnVar). The scalability and maintainability of <span class="hlt">ensemble</span> data assimilation methods make them increasingly attractive, and 4DEnVar may be adopted in the context of the Met Office's LFRic project to redevelop the technical infrastructure to enable its Unified Model (MetUM) to be run efficiently on massively parallel supercomputers. This presentation will report on the results of the 4DEnVar development project, including experiments that have been run using <span class="hlt">ensemble</span> sizes of up to 200 members.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.H32A..03P&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015AGUFM.H32A..03P&link_type=ABSTRACT"><span id="translatedtitle">Drought monitoring using <span class="hlt">downscaled</span> soil moisture through machine learning approaches over North and South Korea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Park, S.; Im, J.; Rhee, J.; Park, S.</p> <p>2015-12-01</p> <p>Soil moisture is one of the most important key variables for drought monitoring. It reflects hydrological and agricultural processes because soil moisture is a function of precipitation and energy flux and crop yield is highly related to soil moisture. Many satellites including Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E), Soil Moisture and Ocean Salinity sensor (SMOS), and Soil Moisture Active Passive (SMAP) provide global scale soil moisture products through microwave sensors. However, as the spatial resolution of soil moisture products is typically tens of kilometers, it is difficult to monitor drought using soil moisture at local or regional scale. In this study, AMSR-E and AMSR2 soil moisture were <span class="hlt">downscaled</span> up to 1 km spatial resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) data—Evapotranspiration, Land Surface Temperature, Leaf Area Index, Normalized Difference Vegetation Index, Enhanced Vegetation Index and Albedo—through machine learning approaches over Korean peninsula. To monitor drought from 2003 to 2014, each pixel of the <span class="hlt">downscaled</span> soil moisture was scaled from 0 to 1 (1 is the wettest and 0 is the driest). The soil moisture based drought maps were validated using Standardized Precipitation Index (SPI) and crop yield data. Spatial distribution of drought status was also compared with other drought indices such as Scaled Drought Condition Index (SDCI). Machine learning approaches were performed well (R=0.905) for <span class="hlt">downscaling</span>. <span class="hlt">Downscaled</span> soil moisture was validated using in situ Asia flux data. The Root Mean Square Errors (RMSE) improved from 0.172 (25 km AMSR2) to 0.065 (<span class="hlt">downscaled</span> soil moisture). The correlation coefficients improved from 0.201 (25 km AMSR2) to 0.341 (<span class="hlt">downscaled</span> soil moisture). The soil moisture based drought maps and SDCI showed similar spatial distribution that caught both extreme drought and no drought. Since the proposed drought monitoring approach based on the <span class="hlt">downscaled</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014OcDyn..64..927S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014OcDyn..64..927S"><span id="translatedtitle"><span class="hlt">Downscaling</span> IPCC control run and future scenario with focus on the Barents Sea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sandø, Anne Britt; Melsom, Arne; Budgell, William Paul</p> <p>2014-07-01</p> <p>Global atmosphere-ocean general circulation models are the tool by which projections for climate changes due to radiative forcing scenarios have been produced. Further, regional atmospheric <span class="hlt">downscaling</span> of the global models may be applied in order to evaluate the details in, e.g., temperature and precipitation patterns. Similarly, detailed regional information is needed in order to assess the implications of future climate change for the marine ecosystems. However, regional results for climate change in the ocean are sparse. We present the results for the circulation and hydrography of the Barents Sea from the ocean component of two global models and from a corresponding pair of regional model configurations. The global models used are the GISS AOM and the NCAR CCSM3. The ROMS ocean model is used for the regional <span class="hlt">downscaling</span> of these results (ROMS-G and ROMS-N configurations, respectively). This investigation was undertaken in order to shed light on two questions that are essential in the context of regional ocean projections: (1) How should a regional model be set up in order to take advantage of the results from global projections; (2) What limits to quality in the results of regional models are imposed by the quality of global models? We approached the first question by initializing the ocean model in the control simulation by a realistic ocean analysis and specifying air-sea fluxes according to the results from the global models. For the projection simulation, the global models' oceanic anomalies from their control simulation results were added upon initialization. Regarding the second question, the present set of simulations includes regional <span class="hlt">downscalings</span> of the present-day climate as well as projected climate change. Thus, we study separately how <span class="hlt">downscaling</span> changes the results in the control climate case, and how scenario results are changed. For the present-day climate, we find that <span class="hlt">downscaling</span> reduces the differences in the Barents Sea between the original</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JHyd..408....1F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JHyd..408....1F"><span id="translatedtitle">A comparison of multi-site daily rainfall <span class="hlt">downscaling</span> techniques under Australian conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frost, Andrew J.; Charles, Stephen P.; Timbal, Bertrand; Chiew, Francis H. S.; Mehrotra, R.; Nguyen, Kim C.; Chandler, Richard E.; McGregor, John L.; Fu, Guobin; Kirono, Dewi G. C.; Fernandez, Elodie; Kent, David M.</p> <p>2011-09-01</p> <p>SummarySix methods of <span class="hlt">downscaling</span> GCM simulations to multi-site daily precipitation were applied to a set of 30 rain gauges located within South-Eastern Australia. The methods were tested at reproducing a range of statistics important within hydrological studies including inter-annual variability and spatial coherency using both NCEP/NCAR reanalysis and GCM predictors, thus testing the validity of GCM <span class="hlt">downscaled</span> predictions. The methods evaluated, all having found application in Australia previously, are: (1) the dynamical <span class="hlt">downscaling</span> Conformal-Cubic Atmospheric Model (CCAM) of McGregor (2005); the historical data based (2) Scaling method applied by Chiew et al. (2009) and (3) Analogue method of Timbal (2004); and three stochastic methods, (4) the GLIMCLIM (Generalised Linear Model for daily Climate time series) software package ( Chandler, 2002), (5) the Non-homogeneous Hidden Markov Model (NHMM) of Charles et al. (1999), and (6) the modified Markov model-kernel probability density estimation (MMM-KDE) <span class="hlt">downscaling</span> technique of Mehrotra and Sharma (2007). The results showed that the simple Scaling approach provided relatively robust results for a range of statistics when GCM forcing data was used, and was therefore recommended for regional water availability and planning studies (subject to certain limitations as mentioned in conclusion section). The stochastic methods better capture changes to a fuller range of rainfall statistics and are recommended for detailed catchment modelling studies. In particular, the stochastic methods show better results for daily extreme rainfall (e.g. flooding/low flow) as the simulations are not based purely on temporal/spatial rainfall patterns observed in the past, and a hybrid GLIMCLIM occurrence-KDE amounts model is recommended based on performance for individual statistics. For GCM <span class="hlt">downscaled</span> simulations, biases in annual mean and standard deviation of ±5% and -30% were observed typically, and no single model performed well</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Chamber+AND+Music&pg=2&id=EJ075665','ERIC'); return false;" href="http://eric.ed.gov/?q=Chamber+AND+Music&pg=2&id=EJ075665"><span id="translatedtitle">Effectiveness of Chamber Music <span class="hlt">Ensemble</span> Experience</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Zorn, Jay D.</p> <p>1973-01-01</p> <p>This investigation was concerned with the effectiveness of chamber music <span class="hlt">ensemble</span> experience for certain members of a ninth grade band and the evaluation of the effectiveness in terms of performing abilities, cognitive learnings, and attitude changes. (Author)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/21962010','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/21962010"><span id="translatedtitle">The <span class="hlt">ensemble</span> performance index: an improved measure for assessing <span class="hlt">ensemble</span> pose prediction performance.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Korb, Oliver; McCabe, Patrick; Cole, Jason</p> <p>2011-11-28</p> <p>We present a theoretical study on the performance of <span class="hlt">ensemble</span> docking methodologies considering multiple protein structures. We perform a theoretical analysis of pose prediction experiments which is completely unbiased, as we make no assumptions about specific scoring functions, search paradigms, protein structures, or ligand data sets. We introduce a novel interpretable measure, the <span class="hlt">ensemble</span> performance index (EPI), for the assessment of scoring performance in <span class="hlt">ensemble</span> docking, which will be applied to simulated and real data sets. PMID:21962010</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70048367','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70048367"><span id="translatedtitle">Climate <span class="hlt">downscaling</span> effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.</p> <p>2013-01-01</p> <p>High-resolution (<span class="hlt">downscaled</span>) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different <span class="hlt">downscaling</span> approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between <span class="hlt">downscaling</span> approaches and that the variation attributable to <span class="hlt">downscaling</span> technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between <span class="hlt">downscaling</span> techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of <span class="hlt">downscaling</span> applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative <span class="hlt">downscaling</span> methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JHyd..538...49M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JHyd..538...49M"><span id="translatedtitle">Use of beta regression for statistical <span class="hlt">downscaling</span> of precipitation in the Campbell River basin, British Columbia, Canada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mandal, Sohom; Srivastav, Roshan K.; Simonovic, Slobodan P.</p> <p>2016-07-01</p> <p>Impacts of global climate change on water resources systems are assessed by <span class="hlt">downscaling</span> coarse scale climate variables into regional scale hydro-climate variables. In this study, a new multisite statistical <span class="hlt">downscaling</span> method based on beta regression (BR) is developed for generating synthetic precipitation series, which can preserve temporal and spatial dependence along with other historical statistics. The beta regression based <span class="hlt">downscaling</span> method includes two main steps: (1) prediction of precipitation states for the study area using classification and regression trees, and (2) generation of precipitation at different stations in the study area conditioned on the precipitation states. Daily precipitation data for 53 years from the ANUSPLIN data set is used to predict precipitation states of the study area where predictor variables are extracted from the NCEP/NCAR reanalysis data set for the same interval. The proposed model is applied to <span class="hlt">downscaling</span> daily precipitation at ten different stations in the Campbell River basin, British Columbia, Canada. Results show that the proposed <span class="hlt">downscaling</span> model can capture spatial and temporal variability of local precipitation very well at various locations. The performance of the model is compared with a recently developed non-parametric kernel regression based <span class="hlt">downscaling</span> model. The BR model performs better regarding extrapolation compared to the non-parametric kernel regression model. Future precipitation changes under different GHG (greenhouse gas) emission scenarios also projected with the developed <span class="hlt">downscaling</span> model that reveals a significant amount of changes in future seasonal precipitation and number of wet days in the river basin.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140010385','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140010385"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard</p> <p>2013-01-01</p> <p>Statistical <span class="hlt">downscaling</span> can be used to efficiently <span class="hlt">downscale</span> a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically <span class="hlt">downscales</span> (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical <span class="hlt">Downscaling</span> and Bias Correction (SDBC) approach. Based on these <span class="hlt">downscaled</span> data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical <span class="hlt">downscaling</span> as an intermediate step does not lead to considerable differences in the results of statistical <span class="hlt">downscaling</span> for the study domain.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC54A..02M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC54A..02M"><span id="translatedtitle">Sensitivity of Statistical <span class="hlt">Downscaling</span> Techniques to Reanalysis Choice and Implications for Regional Climate Change Scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Manzanas, R., Sr.; Brands, S.; San Martin, D., Sr.; Gutiérrez, J. M., Sr.</p> <p>2014-12-01</p> <p>This work shows that local-scale climate projections obtained by means of statistical <span class="hlt">downscaling</span> are sensitive to the choice of reanalysis used for calibration. To this aim, a Generalized Linear Model (GLM) approach is applied to <span class="hlt">downscale</span> daily precipitation in the Philippines. First, the GLMs are trained and tested -under a cross-validation scheme- separately for two distinct reanalyses (ERA-Interim and JRA-25) for the period 1981-2000. When the observed and <span class="hlt">downscaled</span> time-series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a Global Climate Model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to "delta-change" estimates differing by up to a 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is shown to importantly contribute to the uncertainty of local-scale climate change projections, and, consequently, should be treated with equal care as other, well-known, sources of uncertainty -e.g., the choice of the GCM and/or <span class="hlt">downscaling</span> method.- Implications of the results for the entire tropics, as well as for the Model Output Statistics <span class="hlt">downscaling</span> approach are also briefly discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006JHyd..330..621T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006JHyd..330..621T"><span id="translatedtitle"><span class="hlt">Downscaling</span> of precipitation for climate change scenarios: A support vector machine approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tripathi, Shivam; Srinivas, V. V.; Nanjundiah, Ravi S.</p> <p>2006-11-01</p> <p>SummaryThe Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be <span class="hlt">downscaled</span> to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical <span class="hlt">downscaling</span> of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based <span class="hlt">downscaling</span> model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional <span class="hlt">downscaling</span> using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical <span class="hlt">downscaling</span>, and are suitable for conducting climate impact studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007ThApC..90...65K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007ThApC..90...65K"><span id="translatedtitle">Simulating maximum and minimum temperature over Greece: a comparison of three <span class="hlt">downscaling</span> techniques</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kostopoulou, E.; Giannakopoulos, C.; Anagnostopoulou, C.; Tolika, K.; Maheras, P.; Vafiadis, M.; Founda, D.</p> <p>2007-09-01</p> <p>Statistical <span class="hlt">downscaling</span> techniques have been developed for the generation of maximum and minimum temperatures in Greece. This research focuses on the four conventional seasons, and three <span class="hlt">downscaling</span> approaches, Multiple Linear Regression using a circulation type approach (MLRct), Canonical Correlation Analysis (CCA) and Artificial Neural Networks (ANNs), are employed and compared to assess their performance skills. Models were developed individually for each variable (Tmax, Tmin), station and season. The accuracy of <span class="hlt">downscaled</span> values has been quantified in terms of a number of performance criteria, such as differences of the mean and standard deviation ratios between observed and modelled data, the correlation coefficients of the two sets, and also the RMSEs of the <span class="hlt">downscaled</span> values relative to the observed. All methods revealed that during the cool season Tmax tends to be better reproduced, whereas Tmin is overestimated, particularly over western Greece, which is characterised by higher orography. With respect to the warm season, the simulation of Tmax reveals greater divergence, whereas Tmin is better generated. The distinction between the three techniques is somewhat blurred. None of the methods were found to be superior to the others and each has been shown to be a good estimator in some cases. This study concludes that all proposed methods comprise useful tools for simulating daily temperatures, as the high correlation coefficients, between observed and <span class="hlt">downscaled</span> values, have demonstrated. However, the importance of local factors, which affect the natural variability of temperature, has been emphasised indicating that the geography of a region constitutes an important and rather complex factor, which should be included in models to improve their performance.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26903095','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26903095"><span id="translatedtitle">Meaning of temperature in different thermostatistical <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hänggi, Peter; Hilbert, Stefan; Dunkel, Jörn</p> <p>2016-03-28</p> <p>Depending on the exact experimental conditions, the thermodynamic properties of physical systems can be related to one or more thermostatistical <span class="hlt">ensembles</span>. Here, we survey the notion of thermodynamic temperature in different statistical <span class="hlt">ensembles</span>, focusing in particular on subtleties that arise when <span class="hlt">ensembles</span> become non-equivalent. The 'mother' of all <span class="hlt">ensembles</span>, the microcanonical <span class="hlt">ensemble</span>, uses entropy and internal energy (the most fundamental, dynamically conserved quantity) to derive temperature as a secondary thermodynamic variable. Over the past century, some confusion has been caused by the fact that several competing microcanonical entropy definitions are used in the literature, most commonly the volume and surface entropies introduced by Gibbs. It can be proved, however, that only the volume entropy satisfies exactly the traditional form of the laws of thermodynamics for a broad class of physical systems, including all standard classical Hamiltonian systems, regardless of their size. This mathematically rigorous fact implies that negative 'absolute' temperatures and Carnot efficiencies more than 1 are not achievable within a standard thermodynamical framework. As an important offspring of microcanonical thermostatistics, we shall briefly consider the canonical <span class="hlt">ensemble</span> and comment on the validity of the Boltzmann weight factor. We conclude by addressing open mathematical problems that arise for systems with discrete energy spectra.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26903095','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26903095"><span id="translatedtitle">Meaning of temperature in different thermostatistical <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hänggi, Peter; Hilbert, Stefan; Dunkel, Jörn</p> <p>2016-03-28</p> <p>Depending on the exact experimental conditions, the thermodynamic properties of physical systems can be related to one or more thermostatistical <span class="hlt">ensembles</span>. Here, we survey the notion of thermodynamic temperature in different statistical <span class="hlt">ensembles</span>, focusing in particular on subtleties that arise when <span class="hlt">ensembles</span> become non-equivalent. The 'mother' of all <span class="hlt">ensembles</span>, the microcanonical <span class="hlt">ensemble</span>, uses entropy and internal energy (the most fundamental, dynamically conserved quantity) to derive temperature as a secondary thermodynamic variable. Over the past century, some confusion has been caused by the fact that several competing microcanonical entropy definitions are used in the literature, most commonly the volume and surface entropies introduced by Gibbs. It can be proved, however, that only the volume entropy satisfies exactly the traditional form of the laws of thermodynamics for a broad class of physical systems, including all standard classical Hamiltonian systems, regardless of their size. This mathematically rigorous fact implies that negative 'absolute' temperatures and Carnot efficiencies more than 1 are not achievable within a standard thermodynamical framework. As an important offspring of microcanonical thermostatistics, we shall briefly consider the canonical <span class="hlt">ensemble</span> and comment on the validity of the Boltzmann weight factor. We conclude by addressing open mathematical problems that arise for systems with discrete energy spectra. PMID:26903095</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25104944','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25104944"><span id="translatedtitle">Conductor gestures influence evaluations of <span class="hlt">ensemble</span> performance.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Morrison, Steven J; Price, Harry E; Smedley, Eric M; Meals, Cory D</p> <p>2014-01-01</p> <p>Previous research has found that listener evaluations of <span class="hlt">ensemble</span> performances vary depending on the expressivity of the conductor's gestures, even when performances are otherwise identical. It was the purpose of the present study to test whether this effect of visual information was evident in the evaluation of specific aspects of <span class="hlt">ensemble</span> performance: articulation and dynamics. We constructed a set of 32 music performances that combined auditory and visual information and were designed to feature a high degree of contrast along one of two target characteristics: articulation and dynamics. We paired each of four music excerpts recorded by a chamber <span class="hlt">ensemble</span> in both a high- and low-contrast condition with video of four conductors demonstrating high- and low-contrast gesture specifically appropriate to either articulation or dynamics. Using one of two equivalent test forms, college music majors and non-majors (N = 285) viewed sixteen 30 s performances and evaluated the quality of the <span class="hlt">ensemble</span>'s articulation, dynamics, technique, and tempo along with overall expressivity. Results showed significantly higher evaluations for performances featuring high rather than low conducting expressivity regardless of the <span class="hlt">ensemble</span>'s performance quality. Evaluations for both articulation and dynamics were strongly and positively correlated with evaluations of overall <span class="hlt">ensemble</span> expressivity. PMID:25104944</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC33G..07E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC33G..07E"><span id="translatedtitle">Identification of robust statistical <span class="hlt">downscaling</span> methods based on a comprehensive suite of performance metrics for South Korea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Eum, H. I.; Cannon, A. J.</p> <p>2015-12-01</p> <p>Climate models are a key provider to investigate impacts of projected future climate conditions on regional hydrologic systems. However, there is a considerable mismatch of spatial resolution between GCMs and regional applications, in particular a region characterized by complex terrain such as Korean peninsula. Therefore, a <span class="hlt">downscaling</span> procedure is an essential to assess regional impacts of climate change. Numerous statistical <span class="hlt">downscaling</span> methods have been used mainly due to the computational efficiency and simplicity. In this study, four statistical <span class="hlt">downscaling</span> methods [Bias-Correction/Spatial Disaggregation (BCSD), Bias-Correction/Constructed Analogue (BCCA), Multivariate Adaptive Constructed Analogs (MACA), and Bias-Correction/Climate Imprint (BCCI)] are applied to <span class="hlt">downscale</span> the latest Climate Forecast System Reanalysis data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. By split sampling scheme, all methods are calibrated with observational station data for 19 years from 1973 to 1991 are and tested for the recent 19 years from 1992 to 2010. To assess skill of the <span class="hlt">downscaling</span> methods, we construct a comprehensive suite of performance metrics that measure an ability of reproducing temporal correlation, distribution, spatial correlation, and extreme events. In addition, we employ Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to identify robust statistical <span class="hlt">downscaling</span> methods based on the performance metrics for each season. The results show that <span class="hlt">downscaling</span> skill is considerably affected by the skill of CFSR and all methods lead to large improvements in representing all performance metrics. According to seasonal performance metrics evaluated, when TOPSIS is applied, MACA is identified as the most reliable and robust method for all variables and seasons. Note that such result is derived from CFSR output which is recognized as near perfect climate data in climate studies. Therefore, the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.mdpi.com/2072-4292/6/11/10483','USGSPUBS'); return false;" href="http://www.mdpi.com/2072-4292/6/11/10483"><span id="translatedtitle">On the <span class="hlt">downscaling</span> of actual evapotranspiration maps based on combination of MODIS and landsat-based actual evapotranspiration estimates</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Singh, Ramesh K.; Senay, Gabriel B.; Velpuri, Naga Manohar; Bohms, Stefanie; Verdin, James P.</p> <p>2014-01-01</p> <p> <span class="hlt">Downscaling</span> is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for <span class="hlt">downscaling</span> MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and <span class="hlt">downscaling</span>. The regression with intercept method does have limitations in <span class="hlt">downscaling</span> if the slope and intercept are computed over a large area. A good agreement was obtained between <span class="hlt">downscaled</span> monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from −16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and <span class="hlt">downscaled</span> monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between <span class="hlt">downscaled</span> monthly AET using LinZI method and the gridded FLUXNET dataset. The <span class="hlt">downscaled</span> monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed <span class="hlt">downscaling</span> method will be very useful in advancing the application of remotely sensed images in water resources planning and management.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008ThApC..91..129C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008ThApC..91..129C"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of hourly and daily climate scenarios for various meteorological variables in South-central Canada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cheng, C. S.; Li, G.; Li, Q.; Auld, H.</p> <p>2008-02-01</p> <p>A regression-based methodology was used to <span class="hlt">downscale</span> hourly and daily station-scale meteorological variables from outputs of large-scale general circulation models (GCMs). Meteorological variables include air temperature, dew point, and west east and south north wind velocities at the surface and three upper atmospheric levels (925, 850, and 500 hPa), as well as mean sea-level air pressure and total cloud cover. Different regression methods were used to construct <span class="hlt">downscaling</span> transfer functions for different weather variables. Multiple stepwise regression analysis was used for all weather variables, except total cloud cover. Cumulative logit regression was employed for analysis of cloud cover, since cloud cover is an ordered categorical data format. For both regression procedures, to avoid multicollinearity between explanatory variables, principal components analysis was used to convert inter-correlated weather variables into uncorrelated principal components that were used as predictors. The results demonstrated that the <span class="hlt">downscaling</span> method was able to capture the relationship between the premises and the response; for example, most hourly <span class="hlt">downscaling</span> transfer functions could explain over 95% of the total variance for several variables (e.g. surface air temperature, dew point, and air pressure). <span class="hlt">Downscaling</span> transfer functions were validated using a cross-validation scheme, and it was concluded that the functions for all weather variables used in the study are reliable. Performance of the <span class="hlt">downscaling</span> method was also evaluated by comparing data distributions and extreme weather characteristics of <span class="hlt">downscaled</span> GCM historical runs and observations during the period 1961 2000. The results showed that data distributions of <span class="hlt">downscaled</span> GCM historical runs for all weather variables are significantly similar to those of observations. In addition, extreme characteristics of the <span class="hlt">downscaled</span> meteorological variables (e.g. temperature, dew point, air pressure, and total cloud cover</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H41J..03B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H41J..03B"><span id="translatedtitle"><span class="hlt">Ensemble</span> postprocessing for probabilistic quantitative precipitation forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bentzien, S.; Friederichs, P.</p> <p>2012-12-01</p> <p>Precipitation is one of the most difficult weather variables to predict in hydrometeorological applications. In order to assess the uncertainty inherent in deterministic numerical weather prediction (NWP), meteorological services around the globe develop <span class="hlt">ensemble</span> prediction systems (EPS) based on high-resolution NWP systems. With non-hydrostatic model dynamics and without parameterization of deep moist convection, high-resolution NWP models are able to describe convective processes in more detail and provide more realistic mesoscale structures. However, precipitation forecasts are still affected by displacement errors, systematic biases and fast error growth on small scales. Probabilistic guidance can be achieved from an <span class="hlt">ensemble</span> setup which accounts for model error and uncertainty of initial and boundary conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) provides such an <span class="hlt">ensemble</span> system based on the German-focused limited-area model COSMO-DE. With a horizontal grid-spacing of 2.8 km, COSMO-DE is the convection-permitting high-resolution part of the operational model chain at DWD. The COSMO-DE-EPS consists of 20 realizations of COSMO-DE, driven by initial and boundary conditions derived from 4 global models and 5 perturbations of model physics. <span class="hlt">Ensemble</span> systems like COSMO-DE-EPS are often limited with respect to <span class="hlt">ensemble</span> size due to the immense computational costs. As a consequence, they can be biased and exhibit insufficient <span class="hlt">ensemble</span> spread, and probabilistic forecasts may be not well calibrated. In this study, probabilistic quantitative precipitation forecasts are derived from COSMO-DE-EPS and evaluated at more than 1000 rain gauges located all over Germany. COSMO-DE-EPS is a frequently updated <span class="hlt">ensemble</span> system, initialized 8 times a day. We use the time-lagged approach to inexpensively increase <span class="hlt">ensemble</span> spread, which results in more reliable forecasts especially for extreme precipitation events. Moreover, we will show that statistical</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.5582B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.5582B"><span id="translatedtitle"><span class="hlt">Downscaling</span> Smooth Tomographic Models: Separating Intrinsic and Apparent Anisotropy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bodin, Thomas; Capdeville, Yann; Romanowicz, Barbara</p> <p>2016-04-01</p> <p>In recent years, a number of tomographic models based on full waveform inversion have been published. Due to computational constraints, the fitted waveforms are low pass filtered, which results in an inability to map features smaller than half the shortest wavelength. However, these tomographic images are not a simple spatial average of the true model, but rather an effective, apparent, or equivalent model that provides a similar 'long-wave' data fit. For example, it can be shown that a series of horizontal isotropic layers will be seen by a 'long wave' as a smooth anisotropic medium. In this way, the observed anisotropy in tomographic models is a combination of intrinsic anisotropy produced by lattice-preferred orientation (LPO) of minerals, and apparent anisotropy resulting from the incapacity of mapping discontinuities. Interpretations of observed anisotropy (e.g. in terms of mantle flow) requires therefore the separation of its intrinsic and apparent components. The "up-scaling" relations that link elastic properties of a rapidly varying medium to elastic properties of the effective medium as seen by long waves are strongly non-linear and their inverse highly non-unique. That is, a smooth homogenized effective model is equivalent to a large number of models with discontinuities. In the 1D case, Capdeville et al (GJI, 2013) recently showed that a tomographic model which results from the inversion of low pass filtered waveforms is an homogenized model, i.e. the same as the model computed by upscaling the true model. Here we propose a stochastic method to sample the <span class="hlt">ensemble</span> of layered models equivalent to a given tomographic profile. We use a transdimensional formulation where the number of layers is variable. Furthermore, each layer may be either isotropic (1 parameter) or intrinsically anisotropic (2 parameters). The parsimonious character of the Bayesian inversion gives preference to models with the least number of parameters (i.e. least number of layers, and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25844624','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25844624"><span id="translatedtitle">Individual differences in <span class="hlt">ensemble</span> perception reveal multiple, independent levels of <span class="hlt">ensemble</span> representation.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Haberman, Jason; Brady, Timothy F; Alvarez, George A</p> <p>2015-04-01</p> <p><span class="hlt">Ensemble</span> perception, including the ability to "see the average" from a group of items, operates in numerous feature domains (size, orientation, speed, facial expression, etc.). Although the ubiquity of <span class="hlt">ensemble</span> representations is well established, the large-scale cognitive architecture of this process remains poorly defined. We address this using an individual differences approach. In a series of experiments, observers saw groups of objects and reported either a single item from the group or the average of the entire group. High-level <span class="hlt">ensemble</span> representations (e.g., average facial expression) showed complete independence from low-level <span class="hlt">ensemble</span> representations (e.g., average orientation). In contrast, low-level <span class="hlt">ensemble</span> representations (e.g., orientation and color) were correlated with each other, but not with high-level <span class="hlt">ensemble</span> representations (e.g., facial expression and person identity). These results suggest that there is not a single domain-general <span class="hlt">ensemble</span> mechanism, and that the relationship among various <span class="hlt">ensemble</span> representations depends on how proximal they are in representational space. PMID:25844624</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27516861','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27516861"><span id="translatedtitle"><span class="hlt">Downscaling</span> patterns of complementarity to a finer resolution and its implications for conservation prioritization.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>de Albuquerque, Fábio Suzart; Beier, Paul</p> <p>2016-06-01</p> <p>Given species inventories of all sites in a planning area, integer programming or heuristic algorithms can prioritize sites in terms of the site's complementary value, that is, the ability of the site to complement (add unrepresented species to) other sites prioritized for conservation. The utility of these procedures is limited because distributions of species are typically available only as coarse atlases or range maps, whereas conservation planners need to prioritize relatively small sites. If such coarse-resolution information can be used to identify small sites that efficiently represent species (i.e., <span class="hlt">downscaled</span>), then such data can be useful for conservation planning. We develop and test a new type of surrogate for biodiversity, which we call <span class="hlt">downscaled</span> complementarity. In this approach, complementarity values from large cells are <span class="hlt">downscaled</span> to small cells, using statistical methods or simple map overlays. We illustrate our approach for birds in Spain by building models at coarse scale (50 × 50 km atlas of European birds, and global range maps of birds interpreted at the same 50 × 50 km grid size), using this model to predict complementary value for 10 × 10 km cells in Spain, and testing how well-prioritized cells represented bird distributions in an independent bird atlas of those 10 × 10 km cells. <span class="hlt">Downscaled</span> complementarity was about 63-77% as effective as having full knowledge of the 10-km atlas data in its ability to improve on random selection of sites. <span class="hlt">Downscaled</span> complementarity has relatively low data acquisition cost and meets representation goals well compared with other surrogates currently in use. Our study justifies additional tests to determine whether <span class="hlt">downscaled</span> complementarity is an effective surrogate for other regions and taxa, and at spatial resolution finer than 10 × 10 km cells. Until such tests have been completed, we caution against assuming that any surrogate can reliably prioritize sites for species representation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.A33A0222M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A33A0222M"><span id="translatedtitle">High-resolution climate simulations for Central Europe: An assessment of dynamical and statistical <span class="hlt">downscaling</span> techniques</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miksovsky, J.; Huth, R.; Halenka, T.; Belda, M.; Farda, A.; Skalak, P.; Stepanek, P.</p> <p>2009-12-01</p> <p>To bridge the resolution gap between the outputs of global climate models (GCMs) and finer-scale data needed for studies of the climate change impacts, two approaches are widely used: dynamical <span class="hlt">downscaling</span>, based on application of regional climate models (RCMs) embedded into the domain of the GCM simulation, and statistical <span class="hlt">downscaling</span> (SDS), using empirical transfer functions between the large-scale data generated by the GCM and local measurements. In our contribution, we compare the performance of different variants of both techniques for the region of Central Europe. The dynamical <span class="hlt">downscaling</span> is represented by the outputs of two regional models run in the 10 km horizontal grid, ALADIN-CLIMATE/CZ (co-developed by the Czech Hydrometeorological Institute and Meteo-France) and RegCM3 (developed by the Abdus Salam Centre for Theoretical Physics). The applied statistical methods were based on multiple linear regression, as well as on several of its nonlinear alternatives, including techniques employing artificial neural networks. Validation of the <span class="hlt">downscaling</span> outputs was carried out using measured data, gathered from weather stations in the Czech Republic, Slovakia, Austria and Hungary for the end of the 20th century; series of daily values of maximum and minimum temperature, precipitation and relative humidity were analyzed. None of the regional models or statistical <span class="hlt">downscaling</span> techniques could be identified as the universally best one. For instance, while most statistical methods misrepresented the shape of the statistical distribution of the target variables (especially in the more challenging cases such as estimation of daily precipitation), RCM-generated data often suffered from severe biases. It is also shown that further enhancement of the simulated fields of climate variables can be achieved through a combination of dynamical <span class="hlt">downscaling</span> and statistical postprocessing. This can not only be used to reduce biases and other systematic flaws in the generated time</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1710850Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1710850Z"><span id="translatedtitle">Employing multi-objective Genetic Programming to the <span class="hlt">downscaling</span> of near-surface atmospheric fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zerenner, Tanja; Venema, Victor; Friederichs, Petra; Simmer, Clemens</p> <p>2015-04-01</p> <p>The coupling of models for the different components of the Soil-Vegetation-Atmosphere-System is required to investigate component interactions and feedback processes. However, the component models for atmosphere, land-surface and subsurface are usually operated at different resolutions in space and time owing to the dominant processes. The computationally expensive atmospheric models are typically employed at a coarser resolution than land-surface and subsurface models. Thus up- and <span class="hlt">downscaling</span> procedures are required at the interface between the atmospheric model and the land-surface/subsurface models. We apply multi-objective Genetic Programming (GP) to a training data set of high-resolution atmospheric model runs to learn <span class="hlt">downscaling</span> rules, i. e., equations or short programs that reconstruct the fine-scale fields of the near-surface atmospheric state variables from the coarse atmospheric model output. Like artificial neural networks, GP can flexibly incorporate multivariate and nonlinear relations, but offers the advantage that the solutions are human readable and thus can be checked for physical consistency. Further, the Strength Pareto Approach for multi-objective fitness assignment allows to consider multiple characteristics of the fine-scale fields during the learning procedure. We have applied the described machine learning methodology to a training data set of 400 m resolution COSMO model runs to learn <span class="hlt">downscaling</span> rules which recover realistic fine-scale structures from the coarsened fields at 2.8 km resolution. Hence we are currently <span class="hlt">downscaling</span> by a factor of 7. The COSMO model is the weather forecast model developed and maintained by the German Weather Service and is contained in the Terrestrial Systems Modeling Platform (TerrSysMP), which couples the atmospheric COSMO model to land-surface model CLM and subsurface hydrological model ParFlow. Finally we aim at implementing the learned <span class="hlt">downscaling</span> rules in the TerrSysMP to achieve scale</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&keyword=meteorology+AND+weather&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=80557227&CFTOKEN=36190490','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&keyword=meteorology+AND+weather&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=80557227&CFTOKEN=36190490"><span id="translatedtitle">An Observation-base investigation of nudging in WRF for <span class="hlt">downscaling</span> surface climate information to 12-km Grid Spacing</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Previous research has demonstrated the ability to use the Weather Research and Forecast (WRF) model and contemporary dynamical <span class="hlt">downscaling</span> methods to refine global climate modeling results to a horizontal resolution of 36 km. Environmental managers and urban planners have expre...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=315550&keyword=Weather&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=68460992&CFTOKEN=73316500','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=315550&keyword=Weather&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=68460992&CFTOKEN=73316500"><span id="translatedtitle">The Impact of Incongruous Lake Temperatures on Regional Climate Extremes <span class="hlt">Downscaled</span> from the CMIP5 Archive Using the WRF Model</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The impact of incongruous lake temperatures is demonstrated using the Weather Research and Forecasting (WRF) Model to <span class="hlt">downscale</span> global climate fields. Unrealistic lake temperatures prescribed by the default WRF configuration cause obvious biases near the lakes and also affect pre...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20136746','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20136746"><span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Stohlgren, Thomas J; Ma, Peter; Kumar, Sunil; Rocca, Monique; Morisette, Jeffrey T; Jarnevich, Catherine S; Benson, Nate</p> <p>2010-02-01</p> <p><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25927892','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25927892"><span id="translatedtitle">A Bayesian <span class="hlt">ensemble</span> approach for epidemiological projections.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lindström, Tom; Tildesley, Michael; Webb, Colleen</p> <p>2015-04-01</p> <p>Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, <span class="hlt">ensemble</span> modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model <span class="hlt">ensembles</span> based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the <span class="hlt">ensemble</span> prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for <span class="hlt">ensembles</span> with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for <span class="hlt">ensemble</span> modeling of disease outbreaks.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70035550','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70035550"><span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.</p> <p>2010-01-01</p> <p><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015PhRvE..92d3310G&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015PhRvE..92d3310G&link_type=ABSTRACT"><span id="translatedtitle">Simulations in generalized <span class="hlt">ensembles</span> through noninstantaneous switches</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Giovannelli, Edoardo; Cardini, Gianni; Chelli, Riccardo</p> <p>2015-10-01</p> <p>Generalized-<span class="hlt">ensemble</span> simulations, such as replica exchange and serial generalized-<span class="hlt">ensemble</span> methods, are powerful simulation tools to enhance sampling of free energy landscapes in systems with high energy barriers. In these methods, sampling is enhanced through instantaneous transitions of replicas, i.e., copies of the system, between different <span class="hlt">ensembles</span> characterized by some control parameter associated with thermodynamical variables (e.g., temperature or pressure) or collective mechanical variables (e.g., interatomic distances or torsional angles). An interesting evolution of these methodologies has been proposed by replacing the conventional instantaneous (trial) switches of replicas with noninstantaneous switches, realized by varying the control parameter in a finite time and accepting the final replica configuration with a Metropolis-like criterion based on the Crooks nonequilibrium work (CNW) theorem. Here we revise these techniques focusing on their correlation with the CNW theorem in the framework of Markovian processes. An outcome of this report is the derivation of the acceptance probability for noninstantaneous switches in serial generalized-<span class="hlt">ensemble</span> simulations, where we show that explicit knowledge of the time dependence of the weight factors entering such simulations is not necessary. A generalized relationship of the CNW theorem is also provided in terms of the underlying equilibrium probability distribution at a fixed control parameter. Illustrative calculations on a toy model are performed with serial generalized-<span class="hlt">ensemble</span> simulations, especially focusing on the different behavior of instantaneous and noninstantaneous replica transition schemes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002AGUFM.H12G..03W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002AGUFM.H12G..03W&link_type=ABSTRACT"><span id="translatedtitle">Streamflow <span class="hlt">Ensemble</span> Generation using Climate Forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Watkins, D. W.; O'Connell, S.; Wei, W.; Nykanen, D.; Mahmoud, M.</p> <p>2002-12-01</p> <p>Although significant progress has been made in understanding the correlation between large-scale atmospheric circulation patterns and regional streamflow anomalies, there is a general perception that seasonal climate forecasts are not being used to the fullest extent possible for optimal water resources management. Possible contributing factors are limited knowledge and understanding of climate processes and prediction capabilities, noise in climate signals and inaccuracies in forecasts, and hesitancy on the part of water managers to apply new information or methods that could expose them to greater liability. This work involves a decision support model based on streamflow <span class="hlt">ensembles</span> developed for the Lower Colorado River Authority in Central Texas. Predicative skill is added to <span class="hlt">ensemble</span> forecasts that are based on climatology by conditioning the <span class="hlt">ensembles</span> on observable climate indicators, including streamflow (persistence), soil moisture, land surface temperatures, and large-scale recurrent patterns such as the El Ni¤o-Southern Oscillation, Pacific Decadal Oscillation, and the North Atlantic Oscillation. A Bayesian procedure for updating <span class="hlt">ensemble</span> probabilities is outlined, and various skill scores are reviewed for evaluating forecast performance. Verification of the <span class="hlt">ensemble</span> forecasts using a resampling procedure indicates a small but potentially significant improvement in forecast skill that could be exploited in seasonal water management decisions. The ultimate goal of this work will be explicit incorporation of climate forecasts in reservoir operating rules and estimation of the value of the forecasts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22978601','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22978601"><span id="translatedtitle">Multiscale macromolecular simulation: role of evolving <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Singharoy, A; Joshi, H; Ortoleva, P J</p> <p>2012-10-22</p> <p>Multiscale analysis provides an algorithm for the efficient simulation of macromolecular assemblies. This algorithm involves the coevolution of a quasiequilibrium probability density of atomic configurations and the Langevin dynamics of spatial coarse-grained variables denoted order parameters (OPs) characterizing nanoscale system features. In practice, implementation of the probability density involves the generation of constant OP <span class="hlt">ensembles</span> of atomic configurations. Such <span class="hlt">ensembles</span> are used to construct thermal forces and diffusion factors that mediate the stochastic OP dynamics. Generation of all-atom <span class="hlt">ensembles</span> at every Langevin time step is computationally expensive. Here, multiscale computation for macromolecular systems is made more efficient by a method that self-consistently folds in <span class="hlt">ensembles</span> of all-atom configurations constructed in an earlier step, history, of the Langevin evolution. This procedure accounts for the temporal evolution of these <span class="hlt">ensembles</span>, accurately providing thermal forces and diffusions. It is shown that efficiency and accuracy of the OP-based simulations is increased via the integration of this historical information. Accuracy improves with the square root of the number of historical timesteps included in the calculation. As a result, CPU usage can be decreased by a factor of 3-8 without loss of accuracy. The algorithm is implemented into our existing force-field based multiscale simulation platform and demonstrated via the structural dynamics of viral capsomers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/20136746','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/20136746"><span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Stohlgren, Thomas J; Ma, Peter; Kumar, Sunil; Rocca, Monique; Morisette, Jeffrey T; Jarnevich, Catherine S; Benson, Nate</p> <p>2010-02-01</p> <p><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis. PMID:20136746</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.tmp..351G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.tmp..351G"><span id="translatedtitle">Validating the dynamic <span class="hlt">downscaling</span> ability of WRF for East Asian summer climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gao, Jiangbo; Hou, Wenjuan; Xue, Yongkang; Wu, Shaohong</p> <p>2015-12-01</p> <p>To better understand the regional climate model (RCM) performance for East Asian summer climate and the influencing factors, this study evaluated the dynamic <span class="hlt">downscaling</span> ability of the Weather Research Forecast (WRF) RCM. According to the comprehensive comparison studies on different physical processes and experimental settings, the optimal combination of WRF model setups can be obtained for East Asian precipitation and temperature simulations. Furthermore, based on the optimal combination, when compared with climate observations, WRF shows high ability to <span class="hlt">downscale</span> NCEP DOE Reanalysis-2, which provided initial and lateral boundary conditions for the WRF, especially for the precipitation simulation due to the better simulated low-level water vapor flux. However, the strengthened Western North Pacific Subtropical High (WPSH) from WRF simulation results in the positive anomaly for summer rainfall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC51E1057S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC51E1057S"><span id="translatedtitle">Dynamically <span class="hlt">downscaled</span> simulations of the north Georgia flood of 2009 under different land-use scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shem, W.; Preston, B. L.; Parish, E. S.</p> <p>2011-12-01</p> <p>The Weather Forecasting and Research (WRF) model was used to simulate a week-long heavy rainfall event which occurred in north Georgia from September 15-23, 2009. Metropolitan area of Atlanta and the surrounding areas in northern Georgia experienced severe flooding. The study investigated whether the National Center for Environmental Prediction's (NCEP)-North American Regional Reanalysis (NARR) driven WRF dynamic <span class="hlt">downscaling</span> simulates this extreme event in size and duration comparable to and consistent with the observational data. The study also explored the possibility that land-use change, particularly urbanization, might have facilitated boundary interactions leading to enhancement of precipitation in some localized, specific regions as suggested in some previous studies. The results indicate that the <span class="hlt">downscaling</span> exercise, under certain land-use scenarios, does a better job than the NARR in reproducing the higher values of the accumulated rainfall totals from this event</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3099348','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3099348"><span id="translatedtitle">Disease and Phenotype Data at <span class="hlt">Ensembl</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Spudich, Giulietta M.; Fernández-Suárez, Xosè M.</p> <p>2011-01-01</p> <p>Biological databases are an important resource for the life sciences community. Accessing the hundreds of databases supporting molecular biology and related fields is a daunting and time-consuming task. Integrating this information into one access point is a necessity for the life sciences community, which includes researchers focusing on human disease. Here we discuss the <span class="hlt">Ensembl</span> genome browser, which acts as a single entry point with Graphical User Interface to data from multiple projects, including OMIM, dbSNP, and the NHGRI GWAS catalog. <span class="hlt">Ensembl</span> provides a comprehensive source of annotation for the human genome, along with other species of biomedical interest. In this unit, we explore how to use the <span class="hlt">Ensembl</span> genome browser in example queries related to human genetic diseases. Support protocols demonstrate quick sequence export using the BioMart tool. PMID:21400687</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/15006172','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/15006172"><span id="translatedtitle">Creating <span class="hlt">Ensembles</span> of Decision Trees Through Sampling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kamath,C; Cantu-Paz, E</p> <p>2001-07-26</p> <p>Recent work in classification indicates that significant improvements in accuracy can be obtained by growing an <span class="hlt">ensemble</span> of classifiers and having them vote for the most popular class. This paper focuses on <span class="hlt">ensembles</span> of decision trees that are created with a randomized procedure based on sampling. Randomization can be introduced by using random samples of the training data (as in bagging or boosting) and running a conventional tree-building algorithm, or by randomizing the induction algorithm itself. The objective of this paper is to describe the first experiences with a novel randomized tree induction method that uses a sub-sample of instances at a node to determine the split. The empirical results show that <span class="hlt">ensembles</span> generated using this approach yield results that are competitive in accuracy and superior in computational cost to boosting and bagging.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015SPIE.9534E..15R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015SPIE.9534E..15R"><span id="translatedtitle"><span class="hlt">Ensemble</span> approach for differentiation of malignant melanoma</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rastgoo, Mojdeh; Morel, Olivier; Marzani, Franck; Garcia, Rafael</p> <p>2015-04-01</p> <p>Melanoma is the deadliest type of skin cancer, yet it is the most treatable kind depending on its early diagnosis. The early prognosis of melanoma is a challenging task for both clinicians and dermatologists. Due to the importance of early diagnosis and in order to assist the dermatologists, we propose an automated framework based on <span class="hlt">ensemble</span> learning methods and dermoscopy images to differentiate melanoma from dysplastic and benign lesions. The evaluation of our framework on the recent and public dermoscopy benchmark (PH2 dataset) indicates the potential of proposed method. Our evaluation, using only global features, revealed that <span class="hlt">ensembles</span> such as random forest perform better than single learner. Using random forest <span class="hlt">ensemble</span> and combination of color and texture features, our framework achieved the highest sensitivity of 94% and specificity of 92%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC12C..05G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC12C..05G"><span id="translatedtitle">Lessons learned from the National Climate Predictions and Projections (NCPP) platform Workshop on Quantitative Evaluation of <span class="hlt">Downscaling</span> 2013</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guentchev, G.</p> <p>2013-12-01</p> <p>The mission of NCPP is to accelerate the provision of climate information on regional and local scale for use in adaptation planning and decision making through collaborative participation of a community of scientists and practitioners. A major focus is the development of a capability for objective and quantitative evaluation of <span class="hlt">downscaled</span> climate information in support of applications. NCPP recognizes the importance of focusing this evaluation effort on real-world applications and the necessity to work closely with the user community to deliver usable evaluations and guidance. This summer NCPP organized our first workshop on quantitative evaluation of <span class="hlt">downscaled</span> climate datasets (http://earthsystemcog.org/projects/<span class="hlt">downscaling</span>-2013/). Workshop participants included representatives from <span class="hlt">downscaling</span> efforts, applications partners from the health, ecological, agriculture and water resources impacts communities, and people working on data infrastructure, metadata, and standards development. The workshop exemplifies NCPP's approach of collaborative and participatory problem-solving where scientists are working together with practitioners to develop applications related evaluation. The set of observed and <span class="hlt">downscaled</span> datasets included for evaluation in the workshop were assessed using a variety of metrics to elucidate the statistical characteristics of temperature and precipitation time series. In addition, the <span class="hlt">downscaled</span> datasets were evaluated in terms of their representation of indices relevant to the participating applications working groups, more specifically related to human health and ecological impacts. The presentation will focus on sharing the lessons we learned from our workshop.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013IJAEO..23...95E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013IJAEO..23...95E"><span id="translatedtitle"><span class="hlt">Downscaling</span> of thermal images over urban areas using the land surface temperature-impervious percentage relationship</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Essa, W.; van der Kwast, J.; Verbeiren, B.; Batelaan, O.</p> <p>2013-08-01</p> <p>Intensive expansion and densification of urban areas decreases environmental quality and quality of urban life as exemplified by the urban heat island effect. For this reason, thermal information is becoming an increasingly important data source for integration in urban studies. It is expected that future spaceborne thermal sensors will provide data at appropriate spatial and temporal resolutions for urban studies. Until they become operational, research has to rely on <span class="hlt">downscaling</span> algorithms increasing the spatial resolution of relatively coarse resolution thermal images albeit having a high temporal resolution. Existing <span class="hlt">downscaling</span> algorithms, however, have been developed for sharpening images over rural and natural areas, resulting in large errors when applied to urban areas. The objective of this study is to adapt the DisTrad method for <span class="hlt">downscaling</span> land surface temperature (LST) over urban areas using the relationship between LST and impervious percentage. The proposed approach is evaluated by sharpening aggregated LST derived from Landsat 7 ETM+ imagery collected over the city of Dublin on May 24th 2001. The new approach shows improved <span class="hlt">downscaling</span> results over urban areas for all evaluated resolutions, especially in an environment with mixed land cover. The adapted DisTrad approach was most successful at a resolution of 480 m, resulting in a correlation of R2 = 0.84 with an observed image at the same resolution. Furthermore, sharpening using the adapted DisTrad approach was able to preserve the spatial autocorrelation present in urban environments. The unmixing performance of the adapted DisTrad approach improves with decreasing resolution due to the fact that the functional relationship between LST and impervious percentage was defined at coarse resolutions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A43F3330H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A43F3330H"><span id="translatedtitle">Extended-Range High-Resolution Dynamical <span class="hlt">Downscaling</span> over a Continental-Scale Domain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Husain, S. Z.; Separovic, L.; Yu, W.; Fernig, D.</p> <p>2014-12-01</p> <p>High-resolution mesoscale simulations, when applied for <span class="hlt">downscaling</span> meteorological fields over large spatial domains and for extended time periods, can provide valuable information for many practical application scenarios including the weather-dependent renewable energy industry. In the present study, a strategy has been proposed to dynamically <span class="hlt">downscale</span> coarse-resolution meteorological fields from Environment Canada's regional analyses for a period of multiple years over the entire Canadian territory. The study demonstrates that a continuous mesoscale simulation over the entire domain is the most suitable approach in this regard. Large-scale deviations in the different meteorological fields pose the biggest challenge for extended-range simulations over continental scale domains, and the enforcement of the lateral boundary conditions is not sufficient to restrict such deviations. A scheme has therefore been developed to spectrally nudge the simulated high-resolution meteorological fields at the different model vertical levels towards those embedded in the coarse-resolution driving fields derived from the regional analyses. A series of experiments were carried out to determine the optimal nudging strategy including the appropriate nudging length scales, nudging vertical profile and temporal relaxation. A forcing strategy based on grid nudging of the different surface fields, including surface temperature, soil-moisture, and snow conditions, towards their expected values obtained from a high-resolution offline surface scheme was also devised to limit any considerable deviation in the evolving surface fields due to extended-range temporal integrations. The study shows that ensuring large-scale atmospheric similarities helps to deliver near-surface statistical scores for temperature, dew point temperature and horizontal wind speed that are better or comparable to the operational regional forecasts issued by Environment Canada. Furthermore, the meteorological fields</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NHESS..16..167M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NHESS..16..167M"><span id="translatedtitle">Run-up parameterization and beach vulnerability assessment on a barrier island: a <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medellín, G.; Brinkkemper, J. A.; Torres-Freyermuth, A.; Appendini, C. M.; Mendoza, E. T.; Salles, P.</p> <p>2016-01-01</p> <p>We present a <span class="hlt">downscaling</span> approach for the study of wave-induced extreme water levels at a location on a barrier island in Yucatán (Mexico). Wave information from a 30-year wave hindcast is validated with in situ measurements at 8 m water depth. The maximum dissimilarity algorithm is employed for the selection of 600 representative cases, encompassing different combinations of wave characteristics and tidal level. The selected cases are propagated from 8 m water depth to the shore using the coupling of a third-generation wave model and a phase-resolving non-hydrostatic nonlinear shallow-water equation model. Extreme wave run-up, R2%, is estimated for the simulated cases and can be further employed to reconstruct the 30-year time series using an interpolation algorithm. <span class="hlt">Downscaling</span> results show run-up saturation during more energetic wave conditions and modulation owing to tides. The latter suggests that the R2% can be parameterized using a hyperbolic-like formulation with dependency on both wave height and tidal level. The new parametric formulation is in agreement with the <span class="hlt">downscaling</span> results (r2 = 0.78), allowing a fast calculation of wave-induced extreme water levels at this location. Finally, an assessment of beach vulnerability to wave-induced extreme water levels is conducted at the study area by employing the two approaches (reconstruction/parameterization) and a storm impact scale. The 30-year extreme water level hindcast allows the calculation of beach vulnerability as a function of return periods. It is shown that the <span class="hlt">downscaling</span>-derived parameterization provides reasonable results as compared with the numerical approach. This methodology can be extended to other locations and can be further improved by incorporating the storm surge contributions to the extreme water level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/15010453','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/15010453"><span id="translatedtitle">Hydrologic Implications of Dynamical and Statistical Approaches to <span class="hlt">Downscaling</span> Climate Model Outputs</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Wood, Andrew W; Leung, Lai R; Sridhar, V; Lettenmaier, D P</p> <p>2004-01-01</p> <p>Six approaches for <span class="hlt">downscaling</span> climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical <span class="hlt">downscaling</span> methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical <span class="hlt">downscaling</span> via a Regional Climate Model (RCM – at ½-degree spatial resolution), for <span class="hlt">downscaling</span> the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011JESS..120..375A&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011JESS..120..375A&link_type=ABSTRACT"><span id="translatedtitle">Uncertainties in <span class="hlt">downscaled</span> relative humidity for a semi-arid region in India</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Anandhi, Aavudai</p> <p>2011-06-01</p> <p>Monthly scenarios of relative humidity ( R H) were obtained for the Malaprabha river basin in India using a statistical <span class="hlt">downscaling</span> technique. Large-scale atmospheric variables (air temperature and specific humidity at 925 mb, surface air temperature and latent heat flux) were chosen as predictors. The predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis dataset for the period 1978-2000, and (2) simulations of the third generation Canadian Coupled Global Climate Model for the period 1978-2100. The objective of this study was to investigate the uncertainties in regional scenarios developed for R H due to the choice of emission scenarios (A1B, A2, B1 and COMMIT) and the predictors selected. Multi-linear regression with stepwise screening is the <span class="hlt">downscaling</span> technique used in this study. To study the uncertainty in the regional scenarios of R H, due to the selected predictors, eight sets of predictors were chosen and a <span class="hlt">downscaling</span> model was developed for each set. Performance of the <span class="hlt">downscaling</span> models in the baseline period (1978-2000) was studied using three measures (1) Nash-Sutcliffe error estimate ( E f), (2) mean absolute error (MAE), and (3) product moment correlation ( P). Results show that the performances vary between 0.59 and 0.68, 0.42 and 0.50 and 0.77 and 0.82 for E f, MAE and P. Cumulative distribution functions were prepared from the regional scenarios of R H developed for combinations of predictors and emission scenarios. Results show a variation of 1 to 6% R H in the scenarios developed for combination of predictor sets for baseline period. For a future period (2001-2100), a variation of 6 to 15% R H was observed for the combination of emission scenarios and predictors. The variation was highest for A2 scenario and least for COMMIT and B1 scenario.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4792414','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4792414"><span id="translatedtitle">Using Random Forest to Improve the <span class="hlt">Downscaling</span> of Global Livestock Census Data</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Nicolas, Gaëlle; Robinson, Timothy P.; Wint, G. R. William; Conchedda, Giulia; Cinardi, Giuseppina; Gilbert, Marius</p> <p>2016-01-01</p> <p>Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a <span class="hlt">downscaling</span> methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and <span class="hlt">downscaling</span> at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to <span class="hlt">downscale</span> census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better <span class="hlt">downscaled</span> estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26977807','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26977807"><span id="translatedtitle">Using Random Forest to Improve the <span class="hlt">Downscaling</span> of Global Livestock Census Data.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Nicolas, Gaëlle; Robinson, Timothy P; Wint, G R William; Conchedda, Giulia; Cinardi, Giuseppina; Gilbert, Marius</p> <p>2016-01-01</p> <p>Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a <span class="hlt">downscaling</span> methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and <span class="hlt">downscaling</span> at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to <span class="hlt">downscale</span> census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better <span class="hlt">downscaled</span> estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.120..341K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.120..341K"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> and future scenario generation of temperatures for Pakistan Region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kazmi, Dildar Hussain; Li, Jianping; Rasul, Ghulam; Tong, Jiang; Ali, Gohar; Cheema, Sohail Babar; Liu, Luliu; Gemmer, Marco; Fischer, Thomas</p> <p>2015-04-01</p> <p>Finer climate change information on spatial scale is required for impact studies than that presently provided by global or regional climate models. It is especially true for regions like South Asia with complex topography, coastal or island locations, and the areas of highly heterogeneous land-cover. To deal with the situation, an inexpensive method (statistical <span class="hlt">downscaling</span>) has been adopted. Statistical <span class="hlt">DownScaling</span> Model (SDSM) employed for <span class="hlt">downscaling</span> of daily minimum and maximum temperature data of 44 national stations for base time (1961-1990) and then the future scenarios generated up to 2099. Observed as well as Predictors (product of National Oceanic and Atmospheric Administration) data were calibrated and tested on individual/multiple basis through linear regression. Future scenario was generated based on HadCM3 daily data for A2 and B2 story lines. The <span class="hlt">downscaled</span> data has been tested, and it has shown a relatively strong relationship with the observed in comparison to ECHAM5 data. Generally, the southern half of the country is considered vulnerable in terms of increasing temperatures, but the results of this study projects that in future, the northern belt in particular would have a possible threat of increasing tendency in air temperature. Especially, the northern areas (hosting the third largest ice reserves after the Polar Regions), an important feeding source for Indus River, are projected to be vulnerable in terms of increasing temperatures. Consequently, not only the hydro-agricultural sector but also the environmental conditions in the area may be at risk, in future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC53G1293H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC53G1293H"><span id="translatedtitle">Some Advances in <span class="hlt">Downscaling</span> Probabilistic Climate Forecasts for Agricultural Decision Support</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Han, E.; Ines, A.</p> <p>2015-12-01</p> <p>Seasonal climate forecasts, commonly provided in tercile-probabilities format (below-, near- and above-normal), need to be translated into more meaningful information for decision support of practitioners in agriculture. In this paper, we will present two new novel approaches to temporally <span class="hlt">downscale</span> probabilistic seasonal climate forecasts: one non-parametric and another parametric method. First, the non-parametric <span class="hlt">downscaling</span> approach called FResampler1 uses the concept of 'conditional block sampling' of weather data to create daily weather realizations of a tercile-based seasonal climate forecasts. FResampler1 randomly draws time series of daily weather parameters (e.g., rainfall, maximum and minimum temperature and solar radiation) from historical records, for the season of interest from years that belong to a certain rainfall tercile category (e.g., being below-, near- and above-normal). In this way, FResampler1 preserves the covariance between rainfall and other weather parameters as if conditionally sampling maximum and minimum temperature and solar radiation if that day is wet or dry. The second approach called predictWTD is a parametric method based on a conditional stochastic weather generator. The tercile-based seasonal climate forecast is converted into a theoretical forecast cumulative probability curve. Then the deviates for each percentile is converted into rainfall amount or frequency or intensity to <span class="hlt">downscale</span> the 'full' distribution of probabilistic seasonal climate forecasts. Those seasonal deviates are then disaggregated on a monthly basis and used to constrain the <span class="hlt">downscaling</span> of forecast realizations at different percentile values of the theoretical forecast curve. As well as the theoretical basis of the approaches we will discuss sensitivity analysis (length of data and size of samples) of them. In addition their potential applications for managing climate-related risks in agriculture will be shown through a couple of case studies based on</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H31B0994H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H31B0994H"><span id="translatedtitle"><span class="hlt">Downscaling</span> of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.</p> <p>2010-12-01</p> <p>High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image <span class="hlt">downscaling</span> has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform <span class="hlt">downscaling</span> of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for <span class="hlt">downscaling</span> problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the <span class="hlt">downscaled</span> 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.124..919R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.124..919R"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of rainfall: a non-stationary and multi-resolution approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rashid, Md. Mamunur; Beecham, Simon; Chowdhury, Rezaul Kabir</p> <p>2016-05-01</p> <p>A novel <span class="hlt">downscaling</span> technique is proposed in this study whereby the original rainfall and reanalysis variables are first decomposed by wavelet transforms and rainfall is modelled using the semi-parametric additive model formulation of Generalized Additive Model in Location, Scale and Shape (GAMLSS). The flexibility of the GAMLSS model makes it feasible as a framework for non-stationary modelling. Decomposition of a rainfall series into different components is useful to separate the scale-dependent properties of the rainfall as this varies both temporally and spatially. The study was conducted at the Onkaparinga river catchment in South Australia. The model was calibrated over the period 1960 to 1990 and validated over the period 1991 to 2010. The model reproduced the monthly variability and statistics of the observed rainfall well with Nash-Sutcliffe efficiency (NSE) values of 0.66 and 0.65 for the calibration and validation periods, respectively. It also reproduced well the seasonal rainfall over the calibration (NSE = 0.37) and validation (NSE = 0.69) periods for all seasons. The proposed model was better than the tradition modelling approach (application of GAMLSS to the original rainfall series without decomposition) at reproducing the time-frequency properties of the observed rainfall, and yet it still preserved the statistics produced by the traditional modelling approach. When <span class="hlt">downscaling</span> models were developed with general circulation model (GCM) historical output datasets, the proposed wavelet-based <span class="hlt">downscaling</span> model outperformed the traditional <span class="hlt">downscaling</span> model in terms of reproducing monthly rainfall for both the calibration and validation periods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMNG22A..06H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMNG22A..06H"><span id="translatedtitle">Accounting for Skewness in <span class="hlt">Ensemble</span> Data Assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hodyss, D.</p> <p>2011-12-01</p> <p>I will discuss a new framework for understanding how a non-normal probability density function (pdf) may affect a state estimate and how one might usefully exploit the non-normal properties of the pdf when constructing a state estimate. A Bayesian framework is constructed that leads naturally to an expansion of the expected forecast error in a polynomial series consisting of powers of the innovation vector. This polynomial expansion in the innovation reveals a new view of the geometric nature of the state estimation problem. Among other things a direct relationship is shown between the degree to which the state estimate varies with the innovation and the moments of the distribution. A practical data assimilation algorithm will also be presented that explicitly accounts for skewness in the prior distribution. The algorithm operates as a global-solve using a conjugate-gradient technique and Schur/Hadamard (element-wise) localization, and as a general rule is only a factor of four more expensive than the traditional <span class="hlt">ensemble</span> Kalman filter. The central feature of this technique is the squaring of the innovation and the <span class="hlt">ensemble</span> perturbations so as to create an extended state-space that accounts for the second, third and fourth moments of the prior distribution. This new technique is illustrated in a simple scalar system as well as in a Boussinesq model of O(10000) variables configured to simulate nonlinearly evolving Kelvin-Helmholtz waves in shear flow. It is shown that <span class="hlt">ensemble</span> sizes of at least 100 members is needed to adequately resolve the third and fourth moments required for the algorithm. For <span class="hlt">ensembles</span> of this size it is shown that this new technique is superior to a state-of-the-art <span class="hlt">Ensemble</span> Kalman Filter in situations with significant skewness, otherwise the new algorithm reduces to the performance of the <span class="hlt">Ensemble</span> Kalman Filter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A33E0270C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A33E0270C"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of General Circulation Model Output for the Northern Great Plains: A Comparative Analysis of <span class="hlt">Downscaling</span> Methods for Temperature and Precipitation</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>2013-12-01</p> <p>General Circulation Models have come to be the foremost dynamical tools to understanding and predicting the complex changes associated with climate change, yet the grid spacing of these models are far too course for use in local and regional impact studies. Recent research has highlighted the possibility to <span class="hlt">downscale</span> these course resolution data through regional climate models (RCMs) or through statistical means. Given the current changes in climate due to natural and human made forcings and the importance of the Northern Great Plains (NGP) to the global agricultural food supply, it is important to gain insight into the small scale effects these changes will have on this important region. Here is presented an analysis of three statistical <span class="hlt">downscaling</span> methods for translating the course scale information to a more usable scale planners and the public can use for more effective decision making. Regression and weather typing methods are applied to ten GCM outputs and compared for their effectiveness and ability to accurately generate fine scale daily and monthly temperature and precipitation data for the NGP. The implications are explored by utilizing the most robust method to extrapolate the outcomes of the 2.6, 4.5 and 8.5 RCP experiments for future climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.tmp..152I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp..152I"><span id="translatedtitle">Robust intensification of hydroclimatic intensity over East Asia from multi-model <span class="hlt">ensemble</span> regional projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Im, Eun-Soon; Choi, Yeon-Woo; Ahn, Joong-Bae</p> <p>2016-06-01</p> <p>This study assesses the hydroclimatic response to global warming over East Asia from multi-model <span class="hlt">ensemble</span> regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical <span class="hlt">downscaling</span> of the Hadley Centre Global Environmental Model version 2-Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981-2100) daily precipitation. <span class="hlt">Ensemble</span> projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/20857669','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/20857669"><span id="translatedtitle">Quantum measurement of a mesoscopic spin <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Giedke, G.; Taylor, J. M.; Lukin, M. D.; D'Alessandro, D.; Imamoglu, A.</p> <p>2006-09-15</p> <p>We describe a method for precise estimation of the polarization of a mesoscopic spin <span class="hlt">ensemble</span> by using its coupling to a single two-level system. Our approach requires a minimal number of measurements on the two-level system for a given measurement precision. We consider the application of this method to the case of nuclear-spin <span class="hlt">ensemble</span> defined by a single electron-charged quantum dot: we show that decreasing the electron spin dephasing due to nuclei and increasing the fidelity of nuclear-spin-based quantum memory could be within the reach of present day experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/39927','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/39927"><span id="translatedtitle"><span class="hlt">Ensemble</span> computing for the petroleum industry</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Annaratone, M.; Dossa, D.</p> <p>1995-02-01</p> <p>Computer downsizing is one of the most often used buzzwords in today`s competitive business, and the petroleum industry is at the forefront of this revolution. <span class="hlt">Ensemble</span> computing provides the key for computer downsizing with its first incarnation, i.e., workstation farms. This paper concerns the importance of increasing the productivity cycle and not just the execution time of a job. The authors introduce the concept of <span class="hlt">ensemble</span> computing and workstation farms. The they discuss how different computing paradigms can be addressed by workstation farms.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1410829G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1410829G"><span id="translatedtitle">Application of statistical <span class="hlt">downscaling</span> technique for the production of wine grapes (Vitis vinifera L.) in Spain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gaitán Fernández, E.; García Moreno, R.; Pino Otín, M. R.; Ribalaygua Batalla, J.</p> <p>2012-04-01</p> <p>Climate and soil are two of the most important limiting factors for agricultural production. Nowadays climate change has been documented in many geographical locations affecting different cropping systems. The General Circulation Models (GCM) has become important tools to simulate the more relevant aspects of the climate expected for the XXI century in the frame of climatic change. These models are able to reproduce the general features of the atmospheric dynamic but their low resolution (about 200 Km) avoids a proper simulation of lower scale meteorological effects. <span class="hlt">Downscaling</span> techniques allow overcoming this problem by adapting the model outcomes to local scale. In this context, FIC (Fundación para la Investigación del Clima) has developed a statistical <span class="hlt">downscaling</span> technique based on a two step analogue methods. This methodology has been broadly tested on national and international environments leading to excellent results on future climate models. In a collaboration project, this statistical <span class="hlt">downscaling</span> technique was applied to predict future scenarios for the grape growing systems in Spain. The application of such model is very important to predict expected climate for the different growing crops, mainly for grape, where the success of different varieties are highly related to climate and soil. The model allowed the implementation of agricultural conservation practices in the crop production, detecting highly sensible areas to negative impacts produced by any modification of climate in the different regions, mainly those protected with protected designation of origin, and the definition of new production areas with optimal edaphoclimatic conditions for the different varieties.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AdAtS..33.1071S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AdAtS..33.1071S"><span id="translatedtitle">A timescale decomposed threshold regression <span class="hlt">downscaling</span> approach to forecasting South China early summer rainfall</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Song, Linye; Duan, Wansuo; Li, Yun; Mao, Jiangyu</p> <p>2016-09-01</p> <p>A timescale decomposed threshold regression (TSDTR) <span class="hlt">downscaling</span> approach to forecasting South China early summer rainfall (SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression <span class="hlt">downscaling</span> models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares (PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation (PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR <span class="hlt">downscaling</span> approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC23F1191A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC23F1191A"><span id="translatedtitle"><span class="hlt">Downscaling</span> SSPs in Bangladesh - Integrating Science, Modelling and Stakeholders Through Qualitative and Quantitative Scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Allan, A.; Barbour, E.; Salehin, M.; Hutton, C.; Lázár, A. N.; Nicholls, R. J.; Rahman, M. M.</p> <p>2015-12-01</p> <p>A <span class="hlt">downscaled</span> scenario development process was adopted in the context of a project seeking to understand relationships between ecosystem services and human well-being in the Ganges-Brahmaputra delta. The aim was to link the concerns and priorities of relevant stakeholders with the integrated biophysical and poverty models used in the project. A 2-stage process was used to facilitate the connection between stakeholders concerns and available modelling capacity: the first to qualitatively describe what the future might look like in 2050; the second to translate these qualitative descriptions into the quantitative form required by the numerical models. An extended, modified SSP approach was adopted, with stakeholders <span class="hlt">downscaling</span> issues identified through interviews as being priorities for the southwest of Bangladesh. Detailed qualitative futures were produced, before modellable elements were quantified in conjunction with an expert stakeholder cadre. Stakeholder input, using the methods adopted here, allows the top-down focus of the RCPs to be aligned with the bottom-up approach needed to make the SSPs appropriate at the more local scale, and also facilitates the translation of qualitative narrative scenarios into a quantitative form that lends itself to incorporation of biophysical and socio-economic indicators. The presentation will describe the <span class="hlt">downscaling</span> process in detail, and conclude with findings regarding the importance of stakeholder involvement (and logistical considerations), balancing model capacity with expectations and recommendations on SSP refinement at local levels.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy...47.1383B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...47.1383B"><span id="translatedtitle">Multisite and multivariable statistical <span class="hlt">downscaling</span> using a Gaussian copula quantile regression model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ben Alaya, M. A.; Chebana, F.; Ouarda, T. B. M. J.</p> <p>2016-09-01</p> <p>Statistical <span class="hlt">downscaling</span> techniques are required to refine atmosphere-ocean global climate data and provide reliable meteorological information such as a realistic temporal variability and relationships between sites and variables in a changing climate. To this end, the present paper introduces a modular structure combining two statistical tools of increasing interest during the last years: (1) Gaussian copula and (2) quantile regression. The quantile regression tool is employed to specify the entire conditional distribution of <span class="hlt">downscaled</span> variables and to address the limitations of traditional regression-based approaches whereas the Gaussian copula is performed to describe and preserve the dependence between both variables and sites. A case study based on precipitation and maximum and minimum temperatures from the province of Quebec, Canada, is used to evaluate the performance of the proposed model. Obtained results suggest that this approach is capable of generating series with realistic correlation structures and temporal variability. Furthermore, the proposed model performed better than a classical multisite multivariate statistical <span class="hlt">downscaling</span> model for most evaluation criteria.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016OcSci..12...39G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcSci..12...39G"><span id="translatedtitle">On the feasibility of the use of wind SAR to <span class="hlt">downscale</span> waves on shallow water</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutiérrez, O. Q.; Filipponi, F.; Taramelli, A.; Valentini, E.; Camus, P.; Méndez, F. J.</p> <p>2016-01-01</p> <p>In recent years, wave reanalyses have become popular as a powerful source of information for wave climate research and engineering applications. These wave reanalyses provide continuous time series of offshore wave parameters; nevertheless, in coastal areas or shallow water, waves are poorly described because spatial resolution is not detailed. By means of wave <span class="hlt">downscaling</span>, it is possible to increase spatial resolution in high temporal coverage simulations, using forcing from wind and offshore wave databases. Meanwhile, the reanalysis wave databases are enough to describe the wave climate at the limit of simulations; wind reanalyses at an adequate spatial resolution to describe the wind structure near the coast are not frequently available. Remote sensing synthetic aperture radar (SAR) has the ability to detect sea surface signatures and estimate wind fields at high resolution (up to 300 m) and high frequency. In this work a wave <span class="hlt">downscaling</span> is done on the northern Adriatic Sea, using a hybrid methodology and global wave and wind reanalysis as forcing. The wave fields produced were compared to wave fields produced with SAR winds that represent the two dominant wind regimes in the area: the bora (ENE direction) and sirocco (SE direction). Results show a good correlation between the waves forced with reanalysis wind and SAR wind. In addition, a validation of reanalysis is shown. This research demonstrates how Earth observation products, such as SAR wind fields, can be successfully up-taken into oceanographic modeling, producing similar <span class="hlt">downscaled</span> wave fields when compared to waves forced with reanalysis wind.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JSemi..33g5008L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JSemi..33g5008L"><span id="translatedtitle">A high-speed mixed-signal <span class="hlt">down-scaling</span> circuit for DAB tuners</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, Tang; Zhigong, Wang; Jiahui, Xuan; Yang, Yang; Jian, Xu; Yong, Xu</p> <p>2012-07-01</p> <p>A high-speed mixed-signal <span class="hlt">down-scaling</span> circuit with low power consumption and low phase noise for use in digital audio broadcasting tuners has been realized and characterized. Some new circuit techniques are adopted to improve its performance. A dual-modulus prescaler (DMP) with low phase noise is realized with a kind of improved source-coupled logic (SCL) D-flip-flop (DFF) in the synchronous divider and a kind of improved complementary metal oxide semiconductor master-slave (CMOS MS)-DFF in the asynchronous divider. A new more accurate wire-load model is used to realize the pulse-swallow counter (PS counter). Fabricated in a 0.18-μm CMOS process, the total chip size is 0.6 × 0.2 mm2. The DMP in the proposed <span class="hlt">down-scaling</span> circuit exhibits a low phase noise of -118.2 dBc/Hz at 10 kHz off the carrier frequency. At a supply voltage of 1.8 V, the power consumption of the <span class="hlt">down-scaling</span> circuit's core part is only 2.7 mW.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC43C0728S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC43C0728S"><span id="translatedtitle">Evaluation of Future Precipitation Scenario Using Statistical <span class="hlt">Downscaling</span> MODEL over Three Climatic Region of Nepal Himalaya</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sigdel, M.</p> <p>2014-12-01</p> <p>Statistical <span class="hlt">downscaling</span> model (SDSM) was applied in <span class="hlt">downscaling</span> precipitation in the three climatic regions such as humid, sub-humid and arid region of Nepal Himalaya. The study includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP reanalysis data, the validation of the model and the outputs of <span class="hlt">downscaled</span> scenarios A2 (high green house gases emission) and B2 (low green house gases emission) of the HadCM3 model for the future. Under both scenarios H3A2 and H3B2, during the prediction period of 2010-2099, the change of annual mean precipitation in the three climatic regions would present a tendency of surplus of precipitation as compared to the mean values of the base period. On the average for all three climatic regions of Nepal the annual mean precipitation would increase by about 13.75% under scenario H3A2 and increase near about 11.68% under scenario H3B2 in the 2050s. For the 2080s there would be increase of 8.28% and 13.30% under H3A2 and H3B2 respectively compared to the base period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013SPIE.8795E..08S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013SPIE.8795E..08S"><span id="translatedtitle">A spatial <span class="hlt">downscaling</span> procedure of MODIS derived actual evapotranspiration using Landsat images at central Greece</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spiliotopoulos, M.; Adaktylou, N.; Loukas, A.; Michalopoulou, H.; Mylopoulos, N.; Toulios, L.</p> <p>2013-08-01</p> <p>In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to derive daily actual evapotranspiration (ETa) distributions from Landsat and MODIS images separately. The study area is the Lake Karla basin in Thessaly, Central Greece. Meteorological data from the archive of Center for Research and Technology, Thessaly (CERETETH) have also been used. The methodology was developed using satellite and ground data for the period of summer 2007. Landsat and MODIS imagery were combined in order to have data with high temporal and spatial resolution (<span class="hlt">downscaling</span>). The <span class="hlt">downscaling</span> technique applied is the output <span class="hlt">downscaling</span> with regression between images. This technique disaggregates imagery by applying linear regression between two MODIS products to the previous or subsequent Landsat product. After the calculation of a first order linear regression between two MODIS-derived ETa maps the next step is the regression to the ETa map derived from the prior Landsat image to predict the disaggregated subsequent Landsat ETa map. The results are satisfactory, giving the general trend of ETa derived from the original SEBAL procedure.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1814589A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1814589A"><span id="translatedtitle"><span class="hlt">Downscaling</span> SSPs in the GBM Delta - Integrating Science, Modelling and Stakeholders Through Qualitative and Quantitative Scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Allan, Andrew; Barbour, Emily; Salehin, Mashfiqus; Munsur Rahman, Md.; Hutton, Craig; Lazar, Attila</p> <p>2016-04-01</p> <p>A <span class="hlt">downscaled</span> scenario development process was adopted in the context of a project seeking to understand relationships between ecosystem services and human well-being in the Ganges-Brahmaputra delta. The aim was to link the concerns and priorities of relevant stakeholders with the integrated biophysical and poverty models used in the project. A 2-stage process was used to facilitate the connection between stakeholders concerns and available modelling capacity: the first to qualitatively describe what the future might look like in 2050; the second to translate these qualitative descriptions into the quantitative form required by the numerical models. An extended, modified SSP approach was adopted, with stakeholders <span class="hlt">downscaling</span> issues identified through interviews as being priorities for the southwest of Bangladesh. Detailed qualitative futures were produced, before modellable elements were quantified in conjunction with an expert stakeholder cadre. Stakeholder input, using the methods adopted here, allows the top-down focus of the RCPs to be aligned with the bottom-up approach needed to make the SSPs appropriate at the more local scale, and also facilitates the translation of qualitative narrative scenarios into a quantitative form that lends itself to incorporation of biophysical and socio-economic indicators. The presentation will describe the <span class="hlt">downscaling</span> process in detail, and conclude with findings regarding the importance of stakeholder involvement (and logistical considerations), balancing model capacity with expectations and recommendations on SSP refinement at local levels.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.scirp.org/journal/PaperInformation.aspx?PaperID=43632#.Ux9OwPRDuVM','USGSPUBS'); return false;" href="http://www.scirp.org/journal/PaperInformation.aspx?PaperID=43632#.Ux9OwPRDuVM"><span id="translatedtitle">Applying <span class="hlt">downscaled</span> global climate model data to a hydrodynamic surface-water and groundwater model</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Swain, Eric; Stefanova, Lydia; Smith, Thomas</p> <p>2014-01-01</p> <p>Precipitation data from Global Climate Models have been <span class="hlt">downscaled</span> to smaller regions. Adapting this <span class="hlt">downscaled</span> precipitation data to a coupled hydrodynamic surface-water/groundwater model of southern Florida allows an examination of future conditions and their effect on groundwater levels, inundation patterns, surface-water stage and flows, and salinity. The <span class="hlt">downscaled</span> rainfall data include the 1996-2001 time series from the European Center for Medium-Range Weather Forecasting ERA-40 simulation and both the 1996-1999 and 2038-2057 time series from two global climate models: the Community Climate System Model (CCSM) and the Geophysical Fluid Dynamic Laboratory (GFDL). Synthesized surface-water inflow datasets were developed for the 2038-2057 simulations. The resulting hydrologic simulations, with and without a 30-cm sea-level rise, were compared with each other and field data to analyze a range of projected conditions. Simulations predicted generally higher future stage and groundwater levels and surface-water flows, with sea-level rise inducing higher coastal salinities. A coincident rise in sea level, precipitation and surface-water flows resulted in a narrower inland saline/fresh transition zone. The inland areas were affected more by the rainfall difference than the sea-level rise, and the rainfall differences make little difference in coastal inundation, but a larger difference in coastal salinities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24824947','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24824947"><span id="translatedtitle">Design of a <span class="hlt">downscaling</span> method to estimate continuous data from discrete pollen monitoring in Tunisia.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Orlandi, Fabio; Oteros, Jose; Aguilera, Fátima; Ben Dhiab, Ali; Msallem, Monji; Fornaciari, Marco</p> <p>2014-07-01</p> <p>The study of microorganisms and biological particulate matter that transport passively through air is very important for an understanding of the real quality of air. Such monitoring is essential in several specific areas, such as public health, allergy studies, agronomy, indoor and outdoor conservation, and climate-change impact studies. Choosing the suitable monitoring method is an important step in aerobiological studies, so as to obtain reliable airborne data. In this study, we compare olive pollen data from two of the main air traps used in aerobiology, the Hirst and Cour air samplers, at three Tunisian sampling points, for 2009 to 2011. Moreover, a <span class="hlt">downscaling</span> method to perform daily Cour air sampler data estimates is designed. While Hirst air samplers can offer daily, and even bi-hourly data, Cour air samplers provide data for longer discrete sampling periods, which limits their usefulness for daily monitoring. Higher quantities of olive pollen capture were generally detected for the Hirst air sampler, and a <span class="hlt">downscaling</span> method that is developed in this study is used to model these differences. The effectiveness of this <span class="hlt">downscaling</span> method is demonstrated, which allows the potential use of Cour air sampler data series. These results improve the information that new Cour data and, importantly, historical Cour databases can provide for the understanding of phenological dates, airborne pollination curves, and allergenicity levels of air.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130013812','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130013812"><span id="translatedtitle"><span class="hlt">Ensemble</span> Eclipse: A Process for Prefab Development Environment for the <span class="hlt">Ensemble</span> Project</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wallick, Michael N.; Mittman, David S.; Shams, Khawaja, S.; Bachmann, Andrew G.; Ludowise, Melissa</p> <p>2013-01-01</p> <p>This software simplifies the process of having to set up an Eclipse IDE programming environment for the members of the cross-NASA center project, <span class="hlt">Ensemble</span>. It achieves this by assembling all the necessary add-ons and custom tools/preferences. This software is unique in that it allows developers in the <span class="hlt">Ensemble</span> Project (approximately 20 to 40 at any time) across multiple NASA centers to set up a development environment almost instantly and work on <span class="hlt">Ensemble</span> software. The software automatically has the source code repositories and other vital information and settings included. The Eclipse IDE is an open-source development framework. The NASA (<span class="hlt">Ensemble</span>-specific) version of the software includes <span class="hlt">Ensemble</span>-specific plug-ins as well as settings for the <span class="hlt">Ensemble</span> project. This software saves developers the time and hassle of setting up a programming environment, making sure that everything is set up in the correct manner for <span class="hlt">Ensemble</span> development. Existing software (i.e., standard Eclipse) requires an intensive setup process that is both time-consuming and error prone. This software is built once by a single user and tested, allowing other developers to simply download and use the software</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20020061294&hterms=CCA&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DCCA','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20020061294&hterms=CCA&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DCCA"><span id="translatedtitle"><span class="hlt">Ensemble</span> Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.</p> <p>2001-01-01</p> <p>This paper presents preliminary results of an <span class="hlt">ensemble</span> canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate <span class="hlt">downscaling</span> studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the <span class="hlt">ensemble</span> hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813618O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813618O"><span id="translatedtitle">Total probabilities of <span class="hlt">ensemble</span> runoff forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian</p> <p>2016-04-01</p> <p><span class="hlt">Ensemble</span> forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the <span class="hlt">ensembles</span> often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and <span class="hlt">Ensemble</span> Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of <span class="hlt">ensemble</span> forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff <span class="hlt">ensembles</span> for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast <span class="hlt">ensemble</span> individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all <span class="hlt">ensembles</span>. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..522..110S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..522..110S"><span id="translatedtitle">Hydrological modelling using <span class="hlt">ensemble</span> satellite rainfall estimates in a sparsely gauged river basin: The need for whole-<span class="hlt">ensemble</span> calibration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Skinner, Christopher J.; Bellerby, Timothy J.; Greatrex, Helen; Grimes, David I. F.</p> <p>2015-03-01</p> <p>The potential for satellite rainfall estimates to drive hydrological models has been long understood, but at the high spatial and temporal resolutions often required by these models the uncertainties in satellite rainfall inputs are both significant in magnitude and spatiotemporally autocorrelated. Conditional stochastic modelling of <span class="hlt">ensemble</span> observed fields provides one possible approach to representing this uncertainty in a form suitable for hydrological modelling. Previous studies have concentrated on the uncertainty within the satellite rainfall estimates themselves, sometimes applying <span class="hlt">ensemble</span> inputs to a pre-calibrated hydrological model. This approach does not account for the interaction between input uncertainty and model uncertainty and in particular the impact of input uncertainty on model calibration. Moreover, it may not be appropriate to use deterministic inputs to calibrate a model that is intended to be driven by using an <span class="hlt">ensemble</span>. A novel whole-<span class="hlt">ensemble</span> calibration approach has been developed to overcome some of these issues. This study used <span class="hlt">ensemble</span> rainfall inputs produced by a conditional satellite-driven stochastic rainfall generator (TAMSIM) to drive a version of the Pitman rainfall-runoff model, calibrated using the whole-<span class="hlt">ensemble</span> approach. Simulated <span class="hlt">ensemble</span> discharge outputs were assessed using metrics adapted from <span class="hlt">ensemble</span> forecast verification, showing that the <span class="hlt">ensemble</span> outputs produced using the whole-<span class="hlt">ensemble</span> calibrated Pitman model outperformed equivalent <span class="hlt">ensemble</span> outputs created using a Pitman model calibrated against either the <span class="hlt">ensemble</span> mean or a theoretical infinite-<span class="hlt">ensemble</span> expected value. Overall, for the verification period the whole-<span class="hlt">ensemble</span> calibration provided a mean RMSE of 61.7% of the mean wet season discharge, compared to 83.6% using a calibration based on the daily mean of the <span class="hlt">ensemble</span> estimates. Using a Brier's Skill Score to assess the performance of the <span class="hlt">ensemble</span> against a climatic estimate, the whole-<span class="hlt">ensemble</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AtmRe.167..156K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AtmRe.167..156K"><span id="translatedtitle">High resolution WRF <span class="hlt">ensemble</span> forecasting for irrigation: Multi-variable evaluation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kioutsioukis, Ioannis; de Meij, Alexander; Jakobs, Hermann; Katragkou, Eleni; Vinuesa, Jean-Francois; Kazantzidis, Andreas</p> <p>2016-01-01</p> <p>An <span class="hlt">ensemble</span> of meteorological simulations with the WRF model at convection-allowing resolution (2 km) is analysed in a multi-variable evaluation framework over Europe. Besides temperature and precipitation, utilized variables are relative humidity, boundary layer height, shortwave radiation, wind speed, convective and large-scale precipitation in view of explaining some of the biases. Furthermore, the forecast skill of evapotranspiration and irrigation water need is ultimately assessed. It is found that the modelled temperature exhibits a small but significant negative bias during the cold period in the snow-covered northeast regions. Total precipitation exhibits positive bias during all seasons but autumn, peaking in the spring months. The varying physics configurations resulted in significant differences for the simulated minimum temperature, summer rainfall, relative humidity, solar radiation and planetary boundary layer height. The interaction of the temperature and moisture profiles with the different microphysics schemes, results in excess convective precipitation using MYJ/WSM6 compared to YSU/Thompson. With respect to evapotranspiration and irrigation need, the errors using the MYJ configuration were in opposite directions and eventually cancel out, producing overall smaller biases. WRF was able to dynamically <span class="hlt">downscale</span> global forecast data into finer resolutions in space and time for hydro-meteorological applications such as the irrigation management. Its skill was sensitive to the geographical location and physical configuration, driven by the variable relative importance of evapotranspiration and rainfall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C41A0334F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C41A0334F"><span id="translatedtitle">Combination of remote sensing data products to derive spatial climatologies of "degree days" and <span class="hlt">downscale</span> meteorological reanalyses: application to the Upper Indus Basin</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.; Rutter, N.; Brock, B. W.; Fowler, H. J.; Blenkinsop, S.</p> <p>2014-12-01</p> <p> "degree day" product to <span class="hlt">downscale</span> an <span class="hlt">ensemble</span> of modern global meteorological reanalyses including ERA-Interim, NCEP CFSR, NASA MERRA and JRA-55 which overlap MODIS instrument record. This <span class="hlt">downscaling</span> feasibility assessment is a prerequisite to applying the method to regional climate projections.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JSMTE..05..018C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JSMTE..05..018C"><span id="translatedtitle">A class of energy-based <span class="hlt">ensembles</span> in Tsallis statistics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chandrashekar, R.; Naina Mohammed, S. S.</p> <p>2011-05-01</p> <p>A comprehensive investigation is carried out on the class of energy-based <span class="hlt">ensembles</span>. The eight <span class="hlt">ensembles</span> are divided into two main classes. In the isothermal class of <span class="hlt">ensembles</span> the individual members are at the same temperature. A unified framework is evolved to describe the four isothermal <span class="hlt">ensembles</span> using the currently accepted third constraint formalism. The isothermal-isobaric, grand canonical and generalized <span class="hlt">ensembles</span> are illustrated through a study of the classical nonrelativistic and extreme relativistic ideal gas models. An exact calculation is possible only in the case of the isothermal-isobaric <span class="hlt">ensemble</span>. The study of the ideal gas models in the grand canonical and the generalized <span class="hlt">ensembles</span> has been carried out using a perturbative procedure with the nonextensivity parameter (1 - q) as the expansion parameter. Though all the thermodynamic quantities have been computed up to a particular order in (1 - q) the procedure can be extended up to any arbitrary order in the expansion parameter. In the adiabatic class of <span class="hlt">ensembles</span> the individual members of the <span class="hlt">ensemble</span> have the same value of the heat function and a unified formulation to described all four <span class="hlt">ensembles</span> is given. The nonrelativistic and the extreme relativistic ideal gases are studied in the isoenthalpic-isobaric <span class="hlt">ensemble</span>, the adiabatic <span class="hlt">ensemble</span> with number fluctuations and the adiabatic <span class="hlt">ensemble</span> with number and particle fluctuations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC41F0662S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC41F0662S"><span id="translatedtitle">Uncertainty Analysis of <span class="hlt">Downscaled</span> CMIP5 Precipitation Data for Louisiana, USA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sumi, S. J.; Tamanna, M.; Chivoiu, B.; Habib, E. H.</p> <p>2014-12-01</p> <p>The <span class="hlt">downscaled</span> CMIP3 and CMIP5 Climate and Hydrology Projections dataset contains fine spatial resolution translations of climate projections over the contiguous United States developed using two <span class="hlt">downscaling</span> techniques (monthly Bias Correction Spatial Disaggregation (BCSD) and daily Bias Correction Constructed Analogs (BCCA)). The objective of this study is to assess the uncertainty of the CMIP5 <span class="hlt">downscaled</span> general circulation models (GCM). We performed an analysis of the daily, monthly, seasonal and annual variability of precipitation downloaded from the <span class="hlt">Downscaled</span> CMIP3 and CMIP5 Climate and Hydrology Projections website for the state of Louisiana, USA at 0.125° x 0.125° resolution. A data set of daily gridded observations of precipitation of a rectangular boundary covering Louisiana is used to assess the validity of 21 <span class="hlt">downscaled</span> GCMs for the 1950-1999 period. The following statistics are computed using the CMIP5 observed dataset with respect to the 21 models: the correlation coefficient, the bias, the normalized bias, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). A measure of variability simulated by each model is computed as the ratio of its standard deviation, in both space and time, to the corresponding standard deviation of the observation. The correlation and MAPE statistics are also computed for each of the nine climate divisions of Louisiana. Some of the patterns that we observed are: 1) Average annual precipitation rate shows similar spatial distribution for all the models within a range of 3.27 to 4.75 mm/day from Northwest to Southeast. 2) Standard deviation of summer (JJA) precipitation (mm/day) for the models maintains lower value than the observation whereas they have similar spatial patterns and range of values in winter (NDJ). 3) Correlation coefficients of annual precipitation of models against observation have a range of -0.48 to 0.36 with variable spatial distribution by model</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.tmp...65Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp...65Z"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of precipitation using local regression and high accuracy surface modeling method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhao, Na; Yue, Tianxiang; Zhou, Xun; Zhao, Mingwei; Liu, Yu; Du, Zhengping; Zhang, Lili</p> <p>2016-03-01</p> <p><span class="hlt">Downscaling</span> precipitation is required in local scale climate impact studies. In this paper, a statistical <span class="hlt">downscaling</span> scheme was presented with a combination of geographically weighted regression (GWR) model and a recently developed method, high accuracy surface modeling method (HASM). This proposed method was compared with another <span class="hlt">downscaling</span> method using the Coupled Model Intercomparison Project Phase 5 (CMIP5) database and ground-based data from 732 stations across China for the period 1976-2005. The residual which was produced by GWR was modified by comparing different interpolators including HASM, Kriging, inverse distance weighted method (IDW), and Spline. The spatial <span class="hlt">downscaling</span> from 1° to 1-km grids for period 1976-2005 and future scenarios was achieved by using the proposed <span class="hlt">downscaling</span> method. The prediction accuracy was assessed at two separate validation sites throughout China and Jiangxi Province on both annual and seasonal scales, with the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE). The results indicate that the developed model in this study outperforms the method that builds transfer function using the gauge values. There is a large improvement in the results when using a residual correction with meteorological station observations. In comparison with other three classical interpolators, HASM shows better performance in modifying the residual produced by local regression method. The success of the developed technique lies in the effective use of the datasets and the modification process of the residual by using HASM. The results from the future climate scenarios show that precipitation exhibits overall increasing trend from T1 (2011-2040) to T2 (2041-2070) and T2 to T3 (2071-2100) in RCP2.6, RCP4.5, and RCP8.5 emission scenarios. The most significant increase occurs in RCP8.5 from T2 to T3, while the lowest increase is found in RCP2.6 from T2 to T3, increased by 47.11 and 2.12 mm, respectively.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1413756M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1413756M"><span id="translatedtitle">Assessment of CMIP5 GCM daily predictor variables 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>Mpelasoka, F. S.; Charles, S.; Chiew, F. H.; Fu, G.; Beecham, S.</p> <p>2012-04-01</p> <p>Assessment of CMIP5 GCM daily predictor variables for statistical <span class="hlt">downscaling</span> To support adaptation to climate change in the water resource sector in South Australia, <span class="hlt">downscaled</span> climate projections are being constructed within the Goyder Institute for Water Research - a 5-year multi-million dollar collaborative research partnership between the Government of South Australia, CSIRO and the university sector. Statistical <span class="hlt">downscaling</span> is a robust approach providing a link between observed (re-analysis) large-scale atmospheric variables (predictors) and local or regional surface climate variables such as daily station rainfall. When applied to outputs of Global Climate Models (GCMs), the credibility of statistically <span class="hlt">downscaled</span> future projections is dependent on the ability of GCMs to reproduce the re-analysis data statistics for the current climate. The main objective of this study is thus to assess daily predictor variables simulated by phase Five of Coupled Model Inter-comparison Project (CMIP5) GCMs, while acknowledging that an optimal measure of overall GCM performance does not exist and the usefulness of any assessment approach varies with the intended application. Here we assess GCMs by comparing cumulative probability density functions of predictor variables against the re-analysis data using the Kolmogorov test metric. Historical daily data simulations from 12 GCMs (BCC-csm1, CanESM2, CSIRO-Mk3.6.0, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC4h, MIROC-ESM-CHEM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M) for the period 1961-2005 are used. The variables assessed include specific/relative humidity, winds, geopotential heights at different atmospheric levels and sea-level pressure over the Australian region (7-45oS, 100-160oE). We present a summary of results for the South Australia region quantifying the ability of these GCMs in reproducing the mean state and the relative frequency of extremes for these predictors. The complexity and challenges in GCM</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC41E..04D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41E..04D"><span id="translatedtitle">Cluster analysis of explicitly and <span class="hlt">downscaled</span> simulated North Atlantic tropical cyclone tracks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Daloz, A.; Camargo, S. J.; Kossin, J. P.; Emanuel, K.</p> <p>2013-12-01</p> <p>The response of tropical cyclone (TC) activity to climate change is a question of major interest. In order to address this crucial issue, several types of models have been developed in the past, such as Global Climate Models (GCMs). However, the horizontal resolution of those models usually leads to some difficulties in resolving the inner core of TCs and then to properly simulate TC activity. In order to avoid this problem, an alternative tool has been developed by Emanuel (2005). This <span class="hlt">downscaling</span> technique uses tracks that are initiated by randomly seeding large areas of the tropics with weak vortices. Then the survival of the tracks is based on large-scale environmental conditions produced by GCMs in our case. Here we compare the statistics of TC tracks simulated explicitly in four GCMs to the results of the <span class="hlt">downscaling</span> technique driven by the four same GCMs in the present and future climates over the North Atlantic basin. Simulated tracks are objectively separated into four groups using a cluster technique (Kossin et al. 2010). The four clusters form zonal and meridional separations of tracks as shown in Figure 1. The meridional separation largely captures the separation between hybrid or baroclinic storms (clusters 1 and 2) and deep tropical systems (clusters 3 and 4), while the zonal separation segregates Gulf of Mexico and Cape Verde storms. Except for the seasonality, the <span class="hlt">downscaled</span> simulations better capture the general characteristics of the clusters (mean duration of the tracks, intensity...) compared with the explicit simulations, which present strong biases. In the second part of this study, we use three different scenarios to examine the possible future changes of the clusters from the <span class="hlt">downscaled</span> simulations. We explored the role of a warming of the SST, an increase in carbon dioxide and a combination of both ones. The results show that the response to each scenario is highly varying depending on the simulation examined. References - Kossin, J. P., S</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED294775.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED294775.pdf"><span id="translatedtitle">The Honolulu Symphony In-School <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>Higa, Harold</p> <p></p> <p>The Honolulu (Hawaii) Symphony Orchestra's commitment to education includes young people's concerts and in-school <span class="hlt">ensembles</span>. The purpose of this booklet is to enhance the educational potential of in-school concerts through the presentation of information about the orchestra and music related concepts. Part 1 describes the orchestra's personnel,…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4912128','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4912128"><span id="translatedtitle">AUC-Maximizing <span class="hlt">Ensembles</span> through Metalearning</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>LeDell, Erin; van der Laan, Mark J.; Peterson, Maya</p> <p>2016-01-01</p> <p>Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner <span class="hlt">ensemble</span>, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the <span class="hlt">ensemble</span> fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to <span class="hlt">ensemble</span> AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner <span class="hlt">ensemble</span> outperforms the top base algorithm by a larger degree. PMID:27227721</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27227721','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27227721"><span id="translatedtitle">AUC-Maximizing <span class="hlt">Ensembles</span> through Metalearning.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>LeDell, Erin; van der Laan, Mark J; Peterson, Maya</p> <p>2016-05-01</p> <p>Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner <span class="hlt">ensemble</span>, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the <span class="hlt">ensemble</span> fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to <span class="hlt">ensemble</span> AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner <span class="hlt">ensemble</span> outperforms the top base algorithm by a larger degree. PMID:27227721</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22525639','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22525639"><span id="translatedtitle">Cosmological <span class="hlt">ensemble</span> and directional averages of observables</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Bonvin, Camille; Clarkson, Chris; Durrer, Ruth; Maartens, Roy; Umeh, Obinna E-mail: chris.clarkson@gmail.com E-mail: roy.maartens@gmail.com</p> <p>2015-07-01</p> <p>We show that at second order, <span class="hlt">ensemble</span> averages of observables and directional averages do not commute due to gravitational lensing—observing the same thing in many directions over the sky is not the same as taking an <span class="hlt">ensemble</span> average. In principle this non-commutativity is significant for a variety of quantities that we often use as observables and can lead to a bias in parameter estimation. We derive the relation between the <span class="hlt">ensemble</span> average and the directional average of an observable, at second order in perturbation theory. We discuss the relevance of these two types of averages for making predictions of cosmological observables, focusing on observables related to distances and magnitudes. In particular, we show that the <span class="hlt">ensemble</span> average of the distance in a given observed direction is increased by gravitational lensing, whereas the directional average of the distance is decreased. For a generic observable, there exists a particular function of the observable that is not affected by second-order lensing perturbations. We also show that standard areas have an advantage over standard rulers, and we discuss the subtleties involved in averaging in the case of supernova observations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JChPh.143x3131C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JChPh.143x3131C"><span id="translatedtitle">Predicting protein dynamics from structural <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>Copperman, J.; Guenza, M. G.</p> <p>2015-12-01</p> <p>The biological properties of proteins are uniquely determined by their structure and dynamics. A protein in solution populates a structural <span class="hlt">ensemble</span> of metastable configurations around the global fold. From overall rotation to local fluctuations, the dynamics of proteins can cover several orders of magnitude in time scales. We propose a simulation-free coarse-grained approach which utilizes knowledge of the important metastable folded states of the protein to predict the protein dynamics. This approach is based upon the Langevin Equation for Protein Dynamics (LE4PD), a Langevin formalism in the coordinates of the protein backbone. The linear modes of this Langevin formalism organize the fluctuations of the protein, so that more extended dynamical cooperativity relates to increasing energy barriers to mode diffusion. The accuracy of the LE4PD is verified by analyzing the predicted dynamics across a set of seven different proteins for which both relaxation data and NMR solution structures are available. Using experimental NMR conformers as the input structural <span class="hlt">ensembles</span>, LE4PD predicts quantitatively accurate results, with correlation coefficient ρ = 0.93 to NMR backbone relaxation measurements for the seven proteins. The NMR solution structure derived <span class="hlt">ensemble</span> and predicted dynamical relaxation is compared with molecular dynamics simulation-derived structural <span class="hlt">ensembles</span> and LE4PD predictions and is consistent in the time scale of the simulations. The use of the experimental NMR conformers frees the approach from computationally demanding simulations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4109431','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4109431"><span id="translatedtitle">Conductor gestures influence evaluations of <span class="hlt">ensemble</span> performance</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Morrison, Steven J.; Price, Harry E.; Smedley, Eric M.; Meals, Cory D.</p> <p>2014-01-01</p> <p>Previous research has found that listener evaluations of <span class="hlt">ensemble</span> performances vary depending on the expressivity of the conductor’s gestures, even when performances are otherwise identical. It was the purpose of the present study to test whether this effect of visual information was evident in the evaluation of specific aspects of <span class="hlt">ensemble</span> performance: articulation and dynamics. We constructed a set of 32 music performances that combined auditory and visual information and were designed to feature a high degree of contrast along one of two target characteristics: articulation and dynamics. We paired each of four music excerpts recorded by a chamber <span class="hlt">ensemble</span> in both a high- and low-contrast condition with video of four conductors demonstrating high- and low-contrast gesture specifically appropriate to either articulation or dynamics. Using one of two equivalent test forms, college music majors and non-majors (N = 285) viewed sixteen 30 s performances and evaluated the quality of the ensemble’s articulation, dynamics, technique, and tempo along with overall expressivity. Results showed significantly higher evaluations for performances featuring high rather than low conducting expressivity regardless of the ensemble’s performance quality. Evaluations for both articulation and dynamics were strongly and positively correlated with evaluations of overall <span class="hlt">ensemble</span> expressivity. PMID:25104944</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..1714639W&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2015EGUGA..1714639W&link_type=ABSTRACT"><span id="translatedtitle">The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment (HEPEX)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, Andy; Wetterhall, Fredrik; Ramos, Maria-Helena</p> <p>2015-04-01</p> <p>The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF), and co-sponsored by the US National Weather Service (NWS) and the European Commission (EC). The HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological <span class="hlt">ensemble</span> forecasts for decision support. HEPEX pursues this goal through research efforts and practical implementations involving six core elements of a hydrologic <span class="hlt">ensemble</span> prediction enterprise: input and pre-processing, <span class="hlt">ensemble</span> techniques, data assimilation, post-processing, verification, and communication and use in decision making. HEPEX has grown through meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. In the last decade, HEPEX has organized over a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Through these interactions and an active online blog (www.hepex.org), HEPEX has built a strong and active community of nearly 400 researchers & practitioners around the world. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=dot&pg=5&id=EJ934197','ERIC'); return false;" href="http://eric.ed.gov/?q=dot&pg=5&id=EJ934197"><span id="translatedtitle">Memory for Multiple Visual <span class="hlt">Ensembles</span> in Infancy</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>Zosh, Jennifer M.; Halberda, Justin; Feigenson, Lisa</p> <p>2011-01-01</p> <p>The number of individual items that can be maintained in working memory is limited. One solution to this problem is to store representations of <span class="hlt">ensembles</span> that contain summary information about large numbers of items (e.g., the approximate number or cumulative area of a group of many items). Here we explored the developmental origins of ensemble…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702859','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702859"><span id="translatedtitle"><span class="hlt">Ensembl</span> Genomes 2016: more genomes, more complexity</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kersey, Paul Julian; Allen, James E.; Armean, Irina; Boddu, Sanjay; Bolt, Bruce J.; Carvalho-Silva, Denise; Christensen, Mikkel; Davis, Paul; Falin, Lee J.; Grabmueller, Christoph; Humphrey, Jay; Kerhornou, Arnaud; Khobova, Julia; Aranganathan, Naveen K.; Langridge, Nicholas; Lowy, Ernesto; McDowall, Mark D.; Maheswari, Uma; Nuhn, Michael; Ong, Chuang Kee; Overduin, Bert; Paulini, Michael; Pedro, Helder; Perry, Emily; Spudich, Giulietta; Tapanari, Electra; Walts, Brandon; Williams, Gareth; Tello–Ruiz, Marcela; Stein, Joshua; Wei, Sharon; Ware, Doreen; Bolser, Daniel M.; Howe, Kevin L.; Kulesha, Eugene; Lawson, Daniel; Maslen, Gareth; Staines, Daniel M.</p> <p>2016-01-01</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 set of programmatic and interactive interfaces to a rich range of data including reference sequence, gene models, transcriptional data, genetic variation and comparative analysis. This paper provides an update to the previous publications about the resource, with a focus on recent developments. These include the development of new analyses and views to represent polyploid genomes (of which bread wheat is the primary exemplar); and the continued up-scaling of the resource, which now includes over 23 000 bacterial genomes, 400 fungal genomes and 100 protist genomes, in addition to 55 genomes from invertebrate metazoa and 39 genomes from plants. This dramatic increase in the number of included genomes is one part of a broader effort to automate the integration of archival data (genome sequence, but also associated RNA sequence data and variant calls) within the context of reference genomes and make it available through the <span class="hlt">Ensembl</span> user interfaces. PMID:26578574</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/919456','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/919456"><span id="translatedtitle">Marking up lattice QCD configurations and <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>P.Coddington; B.Joo; C.M.Maynard; D.Pleiter; T.Yoshie</p> <p>2007-10-01</p> <p>QCDml is an XML-based markup language designed for sharing QCD configurations and <span class="hlt">ensembles</span> world-wide via the International Lattice Data Grid (ILDG). Based on the latest release, we present key ingredients of the QCDml in order to provide some starting points for colleagues in this community to markup valuable configurations and submit them to the ILDG.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4325279','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4325279"><span id="translatedtitle">NMR Studies of Dynamic Biomolecular Conformational <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Torchia, Dennis A.</p> <p>2015-01-01</p> <p>Multidimensional heteronuclear NMR approaches can provide nearly complete sequential signal assignments of isotopically enriched biomolecules. The availability of assignments together with measurements of spin relaxation rates, residual spin interactions, J-couplings and chemical shifts provides information at atomic resolution about internal dynamics on timescales ranging from ps to ms, both in solution and in the solid state. However, due to the complexity of biomolecules, it is not possible to extract a unique atomic-resolution description of biomolecular motions even from extensive NMR data when many conformations are sampled on multiple timescales. For this reason, powerful computational approaches are increasingly applied to large NMR data sets to elucidate conformational <span class="hlt">ensembles</span> sampled by biomolecules. In the past decade, considerable attention has been directed at an important class of biomolecules that function by binding to a wide variety of target molecules. Questions of current interest are: “Does the free biomolecule sample a conformational <span class="hlt">ensemble</span> that encompasses the conformations found when it binds to various targets; and if so, on what time scale is the <span class="hlt">ensemble</span> sampled?” This article reviews recent efforts to answer these questions, with a focus on comparing <span class="hlt">ensembles</span> obtained for the same biomolecules by different investigators. A detailed comparison of results obtained is provided for three biomolecules: ubiquitin, calmodulin and the HIV-1 trans-activation response RNA. PMID:25669739</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.4952G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4952G"><span id="translatedtitle">Performance of Statistical Temporal <span class="hlt">Downscaling</span> Techniques of Wind Speed Data Over Aegean Sea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gokhan Guler, Hasan; Baykal, Cuneyt; Ozyurt, Gulizar; Kisacik, Dogan</p> <p>2016-04-01</p> <p>Wind speed data is a key input for many meteorological and engineering applications. Many institutions provide wind speed data with temporal resolutions ranging from one hour to twenty four hours. Higher temporal resolution is generally required for some applications such as reliable wave hindcasting studies. One solution to generate wind data at high sampling frequencies is to use statistical <span class="hlt">downscaling</span> techniques to interpolate values of the finer sampling intervals from the available data. In this study, the major aim is to assess temporal <span class="hlt">downscaling</span> performance of nine statistical interpolation techniques by quantifying the inherent uncertainty due to selection of different techniques. For this purpose, hourly 10-m wind speed data taken from 227 data points over Aegean Sea between 1979 and 2010 having a spatial resolution of approximately 0.3 degrees are analyzed from the National Centers for Environmental Prediction (NCEP) The Climate Forecast System Reanalysis database. Additionally, hourly 10-m wind speed data of two in-situ measurement stations between June, 2014 and June, 2015 are considered to understand effect of dataset properties on the uncertainty generated by interpolation technique. In this study, nine statistical interpolation techniques are selected as w0 (left constant) interpolation, w6 (right constant) interpolation, averaging step function interpolation, linear interpolation, 1D Fast Fourier Transform interpolation, 2nd and 3rd degree Lagrange polynomial interpolation, cubic spline interpolation, piecewise cubic Hermite interpolating polynomials. Original data is down sampled to 6 hours (i.e. wind speeds at 0th, 6th, 12th and 18th hours of each day are selected), then 6 hourly data is temporally <span class="hlt">downscaled</span> to hourly data (i.e. the wind speeds at each hour between the intervals are computed) using nine interpolation technique, and finally original data is compared with the temporally <span class="hlt">downscaled</span> data. A penalty point system based on</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.6372M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.6372M"><span id="translatedtitle">Large <span class="hlt">Ensembles</span> of Regional 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>Massey, Neil; Allen, Myles; Hall, Jim</p> <p>2016-04-01</p> <p>Projections of regional climate change have great utility for impact assessment at a local scale. The CORDEX climate projection framework presents a method of providing these regional projections by driving a regional climate model (RCM) with output from CMIP5 climate projection runs of global climate models (GCM). This produces an <span class="hlt">ensemble</span> of regional climate projections, sampling the model uncertainty, the forcing uncertainty and the uncertainty of the response of the climate system to the increase in greenhouse gas (GHG) concentrations. Using the weather@home project to compute large <span class="hlt">ensembles</span> of RCMs via volunteer distributed computing presents another method of generating projections of climate variables and also allows the sampling of the uncertainty due to internal variability. weather@home runs both a RCM and GCM on volunteer's home computers, with the free-running GCM driving the boundaries of the RCM. The GCM is an atmosphere only model and requires forcing at the lower boundary with sea-surface temperature (SST) and sea-ice concentration (SIC) data. By constructing SST and SIC projections, using projections of GHG and other atmospheric gases, and running the weather@home RCM and GCM with these forcings, large <span class="hlt">ensembles</span> of projections of climate variables at regional scales can be made. To construct the SSTs and SICs, a statistical model is built to represent the response of SST and SIC to increases in GHG concentrations in the CMIP5 <span class="hlt">ensemble</span>, for both the RCP4.5 and RCP8.5 scenarios. This statistical model uses empirical orthogonal functions (EOFs) to represent the change in the long term trend of SSTs in the CMIP5 projections. A multivariate distribution of the leading principle components (PC) is produced using a copula and sampled to produce a timeseries of PCs which are recombined with the EOFs to generate a timeseries of SSTs, with internal variability added from observations. Hence, a large <span class="hlt">ensemble</span> of SST projections is generated, with each SST</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24830256','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24830256"><span id="translatedtitle">[Evaluating the performance of the UCLA method for spatially <span class="hlt">downscaling</span> soil moisture products using three Ts/VI indices].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ling, Zi-Wei; He, Long-Bin; Zeng, Hui</p> <p>2014-02-01</p> <p>Soil moisture products derived from microwave remote sensing data are commonly used in the studies of large-scale water resources or climate change. However, the spatial resolutions of these products are usually too coarse to be used in regional- or watershed-scale studies. Therefore, it is necessary to spatially <span class="hlt">downscale</span> the coarse-resolution soil moisture products for use in regional- or watershed-scale studies. The UCLA method is one of the methods for spatially <span class="hlt">downscaling</span> soil moisture products. In this method, the spatial indices (Ts/VI indices) calculated from land surface temperature and vegetation index are used as auxiliary variables for spatial <span class="hlt">downscaling</span>. In this paper, we compared the performance of the UCLA method for spatially <span class="hlt">downscaling</span> the coarse-resolution AMSR-E soil moisture products, using three Ts/VI indices as auxiliary variables, i. e., the soil wetness index (SW), temperature vegetation dryness index (TVDI), and vegetation temperature condition index (VTCI). These auxiliary variables were calculated from the products of MODIS land surface temperature (MYD11A1) and MODIS vegetation index (MYD13A2). The <span class="hlt">downscaled</span> results using the three Ts/VI indices were all reasonable. However, the <span class="hlt">downscaled</span> results using TVDI and VTCI were better than using SW. Therefore, we concluded that TVDI and VTCI are more suitable than SW to be used as the auxiliary variable when applying the UCLA method for <span class="hlt">downscaling</span> soil moisture products. Finally, we discussed the error sources of applying the UCLA method, such as measurement errors of coarse resolution soil products, calculation errors from spatial indices, and errors from the UCLA method itself, and we also discussed the potential improvements of future research.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESS...19.4765P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESS...19.4765P"><span id="translatedtitle">Evaluation of soil moisture <span class="hlt">downscaling</span> using a simple thermal-based proxy - the REMEDHUS network (Spain) example</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, J.; Niesel, J.; Loew, A.</p> <p>2015-12-01</p> <p>Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution on the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought prediction. Therefore, various <span class="hlt">downscaling</span> methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple vegetation temperature condition index (VTCI) <span class="hlt">downscaling</span> scheme over a dense soil moisture observational network (REMEDHUS) in Spain. First, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography, and land cover heterogeneity, using data from Moderate Resolution Imaging Spectroradiometer~(MODIS) and MSG SEVIRI (METEOSAT Second Generation - Spinning Enhanced Visible and Infrared Imager). Then the <span class="hlt">downscaling</span> scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the <span class="hlt">downscaling</span> method can significantly improve the spatial details of CCI soil moisture while maintaining the accuracy of CCI soil moisture. The accuracy level is comparable to other <span class="hlt">downscaling</span> methods that were also validated against the REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying a geostationary satellite for <span class="hlt">downscaling</span> soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI <span class="hlt">downscaling</span> method can facilitate relevant</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016GMD.....9.3055L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GMD.....9.3055L"><span id="translatedtitle"><span class="hlt">Downscaling</span> land use and land cover from the Global Change Assessment Model for coupling with Earth system models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Le Page, Yannick; West, Tris O.; Link, Robert; Patel, Pralit</p> <p>2016-09-01</p> <p>The Global Change Assessment Model (GCAM) is a global integrated assessment model used to project future societal and environmental scenarios, based on economic modeling and on a detailed representation of food and energy production systems. The terrestrial module in GCAM represents agricultural activities and ecosystems dynamics at the subregional scale, and must be <span class="hlt">downscaled</span> to be used for impact assessments in gridded models (e.g., climate models). In this study, we present the <span class="hlt">downscaling</span> algorithm of the GCAM model, which generates gridded time series of global land use and land cover (LULC) from any GCAM scenario. The <span class="hlt">downscaling</span> is based on a number of user-defined rules and drivers, including transition priorities (e.g., crop expansion preferentially into grasslands rather than forests) and spatial constraints (e.g., nutrient availability). The default parameterization is evaluated using historical LULC change data, and a sensitivity experiment provides insights on the most critical parameters and how their influence changes regionally and in time. Finally, a reference scenario and a climate mitigation scenario are <span class="hlt">downscaled</span> to illustrate the gridded land use outcomes of different policies on agricultural expansion and forest management. Several features of the <span class="hlt">downscaling</span> can be modified by providing new input data or changing the parameterization, without any edits to the code. Those features include spatial resolution as well as the number and type of land classes being <span class="hlt">downscaled</span>, thereby providing flexibility to adapt GCAM LULC scenarios to the requirements of a wide range of models and applications. The <span class="hlt">downscaling</span> system is version controlled and freely available.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24830256','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24830256"><span id="translatedtitle">[Evaluating the performance of the UCLA method for spatially <span class="hlt">downscaling</span> soil moisture products using three Ts/VI indices].</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ling, Zi-Wei; He, Long-Bin; Zeng, Hui</p> <p>2014-02-01</p> <p>Soil moisture products derived from microwave remote sensing data are commonly used in the studies of large-scale water resources or climate change. However, the spatial resolutions of these products are usually too coarse to be used in regional- or watershed-scale studies. Therefore, it is necessary to spatially <span class="hlt">downscale</span> the coarse-resolution soil moisture products for use in regional- or watershed-scale studies. The UCLA method is one of the methods for spatially <span class="hlt">downscaling</span> soil moisture products. In this method, the spatial indices (Ts/VI indices) calculated from land surface temperature and vegetation index are used as auxiliary variables for spatial <span class="hlt">downscaling</span>. In this paper, we compared the performance of the UCLA method for spatially <span class="hlt">downscaling</span> the coarse-resolution AMSR-E soil moisture products, using three Ts/VI indices as auxiliary variables, i. e., the soil wetness index (SW), temperature vegetation dryness index (TVDI), and vegetation temperature condition index (VTCI). These auxiliary variables were calculated from the products of MODIS land surface temperature (MYD11A1) and MODIS vegetation index (MYD13A2). The <span class="hlt">downscaled</span> results using the three Ts/VI indices were all reasonable. However, the <span class="hlt">downscaled</span> results using TVDI and VTCI were better than using SW. Therefore, we concluded that TVDI and VTCI are more suitable than SW to be used as the auxiliary variable when applying the UCLA method for <span class="hlt">downscaling</span> soil moisture products. Finally, we discussed the error sources of applying the UCLA method, such as measurement errors of coarse resolution soil products, calculation errors from spatial indices, and errors from the UCLA method itself, and we also discussed the potential improvements of future research. PMID:24830256</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016SPIE.9917E..2CB','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016SPIE.9917E..2CB"><span id="translatedtitle">Polarizing properties of molecular <span class="hlt">ensembles</span>: new approaches to calculations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bokarev, Andrey N.; Plastun, Inna L.</p> <p>2016-04-01</p> <p>Polarizing properties of molecular <span class="hlt">ensembles</span> with different structures are investigated by numerical simulation. Carbon nanotubes with zigzag configuration and nucleobases are considered. By numerical simulation total polarizability is investigated for different structures of molecules <span class="hlt">ensembles</span>. New semi-analytical procedure for calculation of total polarizability for <span class="hlt">ensembles</span> with different configuration is offered and tested by its application to <span class="hlt">ensembles</span> which contain single-wall carbon nanotubes and nucleobases.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3021968','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3021968"><span id="translatedtitle">Identifying <span class="hlt">Ensembles</span> of Signal Transduction Models using Pareto Optimal <span class="hlt">Ensemble</span> Techniques (POETs)</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Song, Sang Ok; Chakrabarti, Anirikh; Varner, Jeffrey D.</p> <p>2010-01-01</p> <p>Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model <span class="hlt">ensembles</span> using multiobjective optimization. In this study, we used Pareto Optimal <span class="hlt">Ensemble</span> Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass action kinetics within an ordinary differential equation (ODE) framework (64-ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an <span class="hlt">ensemble</span> of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the <span class="hlt">ensemble</span> following the addition of extracellular ligand. Also, the <span class="hlt">ensemble</span> recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model <span class="hlt">ensembles</span> could capture qualitatively important network features without exact parameter information. PMID:20665647</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/20665647','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/20665647"><span id="translatedtitle"><span class="hlt">Ensembles</span> of signal transduction models using Pareto Optimal <span class="hlt">Ensemble</span> Techniques (POETs).</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Song, Sang Ok; Chakrabarti, Anirikh; Varner, Jeffrey D</p> <p>2010-07-01</p> <p>Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model <span class="hlt">ensembles</span> using multiobjective optimization. In this study, we used Pareto Optimal <span class="hlt">Ensemble</span> Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (64 ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an <span class="hlt">ensemble</span> of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the <span class="hlt">ensemble</span> following the addition of extracellular ligand. Also, the <span class="hlt">ensemble</span> recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model <span class="hlt">ensembles</span> could capture qualitatively important network features without exact parameter information.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.4714K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.4714K"><span id="translatedtitle">Future changes in European temperature and precipitation in an <span class="hlt">ensemble</span> of Europe-CORDEX regional climate model simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kjellström, Erik; Nikulin, Grigory; Jones, Colin</p> <p>2013-04-01</p> <p>In this study we investigate possible changes in temperature and precipitation on a regional scale over Europe from 1961 to 2100. We use data from two <span class="hlt">ensembles</span> of climate simulations, one global and one regional, over the Europe-CORDEX domain. The global <span class="hlt">ensemble</span> includes nine coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CNRM-CM5, HadGEM2-ES, IPSL-CM5A-MR, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. In the regional <span class="hlt">ensemble</span> all 9 AOGCMs are <span class="hlt">downscaled</span> at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Two forcing scenarios are considered, RCP 4.5 and 8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, we investigate the benefit of the higher horizontal resolution, in RCA4 by comparing the results to the coarser driving AOGCM data. The significance of the results is investigated by comparing to i) the model simulated natural variability, and, ii) the biases in the control period. Results dealing with changes in the seasonal cycle of temperature and precipitation and their relation to changes in the large-scale atmospheric circulation are presented. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Chamber+AND+Music&pg=4&id=EJ149452','ERIC'); return false;" href="http://eric.ed.gov/?q=Chamber+AND+Music&pg=4&id=EJ149452"><span id="translatedtitle">The Symphonic Wind <span class="hlt">Ensemble</span>: Seating for Sound Improvement</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>Garofalo, Robert; Whaley, Garwood</p> <p>1976-01-01</p> <p>Inherent in the basic principles of the symphonic wind <span class="hlt">ensemble</span> concept are several important concepts about seating--concepts that are intended to apply only to the seating of a full symphonic wind <span class="hlt">ensemble</span> and not to small or large chamber <span class="hlt">ensembles</span>. (Author/RK)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1715091B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1715091B"><span id="translatedtitle">HEPS4Power - Extended-range Hydrometeorological <span class="hlt">Ensemble</span> Predictions for Improved Hydropower Operations and Revenues</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bogner, Konrad; Monhart, Samuel; Liniger, Mark; Spririg, Christoph; Jordan, Fred; Zappa, Massimiliano</p> <p>2015-04-01</p> <p>In recent years large progresses have been achieved in the operational prediction of floods and hydrological drought with up to ten days lead time. Both the public and the private sectors are currently using probabilistic runoff forecast in order to monitoring water resources and take actions when critical conditions are to be expected. The use of extended-range predictions with lead times exceeding 10 days is not yet established. The hydropower sector in particular might have large benefits from using hydro meteorological forecasts for the next 15 to 60 days in order to optimize the operations and the revenues from their watersheds, dams, captions, turbines and pumps. The new Swiss Competence Centers in Energy Research (SCCER) targets at boosting research related to energy issues in Switzerland. The objective of HEPS4POWER is to demonstrate that operational extended-range hydro meteorological forecasts have the potential to become very valuable tools for fine tuning the production of energy from hydropower systems. The project team covers a specific system-oriented value chain starting from the collection and forecast of meteorological data (MeteoSwiss), leading to the operational application of state-of-the-art hydrological models (WSL) and terminating with the experience in data presentation and power production forecasts for end-users (e-dric.ch). The first task of the HEPS4POWER will be the <span class="hlt">downscaling</span> and post-processing of <span class="hlt">ensemble</span> extended-range meteorological forecasts (EPS). The goal is to provide well-tailored forecasts of probabilistic nature that should be reliable in statistical and localized at catchment or even station level. The hydrology related task will consist in feeding the post-processed meteorological forecasts into a HEPS using a multi-model approach by implementing models with different complexity. Also in the case of the hydrological <span class="hlt">ensemble</span> predictions, post-processing techniques need to be tested in order to improve the quality of the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160007438','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160007438"><span id="translatedtitle">Improving Climate Projections Using "Intelligent" <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>Baker, Noel C.; Taylor, Patrick C.</p> <p>2015-01-01</p> <p>Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model <span class="hlt">ensemble</span>, in which each model is given an equal weight. It has been shown that the <span class="hlt">ensemble</span> mean generally outperforms any single model. It is possible to use unequal weights to produce <span class="hlt">ensemble</span> means, in which models are weighted based on performance (called "intelligent" <span class="hlt">ensembles</span>). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" <span class="hlt">ensemble</span> method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" <span class="hlt">ensemble</span>-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27377537','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27377537"><span id="translatedtitle"><span class="hlt">Downscaled</span> and debiased climate simulations for North America from 21,000 years ago to 2100AD.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Lorenz, David J; Nieto-Lugilde, Diego; Blois, Jessica L; Fitzpatrick, Matthew C; Williams, John W</p> <p>2016-07-05</p> <p>Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and <span class="hlt">downscaled</span> before they can be used by ecological models. <span class="hlt">Downscaling</span> methods and observational baselines vary among researchers, which produces confounding biases among <span class="hlt">downscaled</span> climate simulations. We present unified datasets of debiased and <span class="hlt">downscaled</span> climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950-2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This <span class="hlt">downscaling</span> includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850-2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4932881','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4932881"><span id="translatedtitle"><span class="hlt">Downscaled</span> and debiased climate simulations for North America from 21,000 years ago to 2100AD</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lorenz, David J.; Nieto-Lugilde, Diego; Blois, Jessica L.; Fitzpatrick, Matthew C.; Williams, John W.</p> <p>2016-01-01</p> <p>Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and <span class="hlt">downscaled</span> before they can be used by ecological models. <span class="hlt">Downscaling</span> methods and observational baselines vary among researchers, which produces confounding biases among <span class="hlt">downscaled</span> climate simulations. We present unified datasets of debiased and <span class="hlt">downscaled</span> climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950–2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This <span class="hlt">downscaling</span> includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850–2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity. PMID:27377537</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1211444H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1211444H"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of the ERA-40 reanalysis in complex terrain in Norway</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; Kvamstø, Nils Gunnar; Sandvik, Anne</p> <p>2010-05-01</p> <p>The increase in resolution of numerical mesoscale models in the last few years has enhanced the level of detail of atmospheric parameters, such as precipitation. It is however not straightforward to evaluate if the higher resolution actually improves the representaton of these parameters. This is an especially interesting issue in complex terrain, such as the west coast of Norway where there is a strong orographic enhancement of the precipitation. A high-resolution model allowing for a more accurate representation of the orography can be expected to improve the modelled precipitation in comparison with GCM or reanalysis data. In this work dynamical <span class="hlt">downscaling</span> of the ERA-40 reanalysis data down to 10 km resolution over Norway was performed. We used the WRF regional climate model (www.wrf-model.org). Results from a 30-year period ranging from 1961 to 1990 are presented and evaluated against daily mean observations of precipitation, 2-meter temperature and 10-meter wind speed from a number of surface stations. The WRF model is reproducing the probability density functions of the modelled and observed daily mean parameters reasonably well. We also investigate the frequency of wet days as well as the occurrence of extreme events which is of high importance for future climate studies. The <span class="hlt">downscaled</span> WRF results show clear improvement from the ERA-40 reanalysis in precipitation. Especially the number and intensity of high precipitation events is much improved due to the higher model resolution and therefore a better representation of the mountains on the Norwegian west coast. On the other hand, temperature and wind are reasonably well represented in the ERA-40 reanalysis and not significant improvement was found in the <span class="hlt">downscaled</span> data set. We will also present a model intercomparison of these parameters with some of the models used in the PRUDENCE project.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1411806V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1411806V"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.</p> <p>2012-04-01</p> <p>Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical <span class="hlt">downscaling</span> methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical <span class="hlt">downscaling</span>, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were <span class="hlt">downscaled</span> dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014ThApC.116..243H&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2014ThApC.116..243H&link_type=ABSTRACT"><span id="translatedtitle">Application of SDSM and LARS-WG for simulating and <span class="hlt">downscaling</span> of rainfall and temperature</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hassan, Zulkarnain; Shamsudin, Supiah; Harun, Sobri</p> <p>2014-04-01</p> <p>Climate change is believed to have significant impacts on the water basin and region, such as in a runoff and hydrological system. However, impact studies on the water basin and region are difficult, since general circulation models (GCMs), which are widely used to simulate future climate scenarios, do not provide reliable hours of daily series rainfall and temperature for hydrological modeling. There is a technique named as "<span class="hlt">downscaling</span> techniques", which can derive reliable hour of daily series rainfall and temperature due to climate scenarios from the GCMs output. In this study, statistical <span class="hlt">downscaling</span> models are used to generate the possible future values of local meteorological variables such as rainfall and temperature in the selected stations in Peninsular of Malaysia. The models are: (1) statistical <span class="hlt">downscaling</span> model (SDSM) that utilized the regression models and stochastic weather generators and (2) Long Ashton research station weather generator (LARS-WG) that only utilized the stochastic weather generators. The LARS-WG and SDSM models obviously are feasible methods to be used as tools in quantifying effects of climate change condition in a local scale. SDSM yields a better performance compared to LARS-WG, except SDSM is slightly underestimated for the wet and dry spell lengths. Although both models do not provide identical results, the time series generated by both methods indicate a general increasing trend in the mean daily temperature values. Meanwhile, the trend of the daily rainfall is not similar to each other, with SDSM giving a relatively higher change of annual rainfall compared to LARS-WG.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1998IJCli..18.1051G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1998IJCli..18.1051G"><span id="translatedtitle">Development of daily rainfall scenarios for southeast Spain using a circulation-type approach to <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>Goodess, Clare M.; Palutikof, Jean P.</p> <p>1998-08-01</p> <p>A method for <span class="hlt">downscaling</span> from the relatively coarse General Circulation Model (GCM) spatial scale to the finer spatial scale required for impact assessment has been developed and tested in the Guadalentin Basin, southeast Spain. The method uses a circulation-type approach and relates large-scale patterns of a predictor variable, gridded sea level pressure, to local values of a surface climate variable (daily rainfall at six stations). The large-scale patterns are defined using an automated version of the Lamb Weather Type classification scheme, originally developed for the British Isles. It is demonstrated that this scheme can be successfully transferred to another region, southeast Spain. The 14 basic circulation types are combined into eight groups. These provide a legitimate basis for <span class="hlt">downscaling</span> because each has a characteristic pressure pattern which produces the expected type and direction of flow over the study region. Furthermore, a set of consistent and distinct relationships is identified between these circulation types and daily rainfall in the Guadalentin Basin. The ability of the GCM to reproduce the observed circulation types is assessed before applying these relationships to control and perturbed-run GCM output using a statistical weather generator. The effects of the GCM's failure to reproduce the observed frequency of the circulation types are detectable in the weather generator output. The GCM changes in SLP and circulation-type frequency between the control and perturbed-runs are generally small. Nonetheless the weather generator results indicate significant changes in the number of rain days in spring and summer. These scenarios are presented as illustrative results rather than as reliable predictions. It is concluded that the circulation-type based approach to <span class="hlt">downscaling</span> offers great potential.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JPRS...97...78W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JPRS...97...78W"><span id="translatedtitle">Modeling diurnal land temperature cycles over Los Angeles using <span class="hlt">downscaled</span> GOES imagery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weng, Qihao; Fu, Peng</p> <p>2014-11-01</p> <p>Land surface temperature is a key parameter for monitoring urban heat islands, assessing heat related risks, and estimating building energy consumption. These environmental issues are characterized by high temporal variability. A possible solution from the remote sensing perspective is to utilize geostationary satellites images, for instance, images from Geostationary Operational Environmental System (GOES) and Meteosat Second Generation (MSG). These satellite systems, however, with coarse spatial but high temporal resolution (sub-hourly imagery at 3-10 km resolution), often limit their usage to meteorological forecasting and global climate modeling. Therefore, how to develop efficient and effective methods to disaggregate these coarse resolution images to a proper scale suitable for regional and local studies need be explored. In this study, we propose a least square support vector machine (LSSVM) method to achieve the goal of <span class="hlt">downscaling</span> of GOES image data to half-hourly 1-km LSTs by fusing it with MODIS data products and Shuttle Radar Topography Mission (SRTM) digital elevation data. The result of <span class="hlt">downscaling</span> suggests that the proposed method successfully disaggregated GOES images to half-hourly 1-km LSTs with accuracy of approximately 2.5 K when validated against with MODIS LSTs at the same over-passing time. The synthetic LST datasets were further explored for monitoring of surface urban heat island (UHI) in the Los Angeles region by extracting key diurnal temperature cycle (DTC) parameters. It is found that the datasets and DTC derived parameters were more suitable for monitoring of daytime- other than nighttime-UHI. With the <span class="hlt">downscaled</span> GOES 1-km LSTs, the diurnal temperature variations can well be characterized. An accuracy of about 2.5 K was achieved in terms of the fitted results at both 1 km and 5 km resolutions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5228S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5228S"><span id="translatedtitle">A Time-scale Decomposed Threshold Regression <span class="hlt">Downscaling</span> Approach to Forecasting South China Early Summer Rainfall</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Song, Linye; Duan, Wansuo; Li, Yun; Mao, Jiangyu</p> <p>2015-04-01</p> <p>A time-scale decomposed threshold regression (TSDTR) <span class="hlt">downscaling</span> approach to forecasting South China early summer rainfall (SCESR) is described by using long-term observed station rainfall data and the National Oceanic and Atmospheric Administration Extended Reconstructed sea surface temperature (SST) data. It makes use of two distinct regression <span class="hlt">downscaling</span> models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models were developed based on the partial least square (PLS) regression technique linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specially, using the datasets in the calibration period 1915-1984, the variability of SCESR and SST were decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model was fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation (PDO). On the interdecadal timescale, a standard PLS regression model was fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction was obtained by the sum of the outputs from both interannual and interdecadal models. Results show that the TSDTR <span class="hlt">downscaling</span> approach achieved a reasonable skill to predict the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns may provide a new perspective to improve the climate predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhRvE..93e2114Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhRvE..93e2114Y"><span id="translatedtitle">Rotationally invariant <span class="hlt">ensembles</span> of integrable matrices</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yuzbashyan, Emil A.; Shastry, B. Sriram; Scaramazza, Jasen A.</p> <p>2016-05-01</p> <p>We construct <span class="hlt">ensembles</span> of random integrable matrices with any prescribed number of nontrivial integrals and formulate integrable matrix theory (IMT)—a counterpart of random matrix theory (RMT) for quantum integrable models. A type-M family of integrable matrices consists of exactly N -M independent commuting N ×N matrices linear in a real parameter. We first develop a rotationally invariant parametrization of such matrices, previously only constructed in a preferred basis. For example, an arbitrary choice of a vector and two commuting Hermitian matrices defines a type-1 family and vice versa. Higher types similarly involve a random vector and two matrices. The basis-independent formulation allows us to derive the joint probability density for integrable matrices, similar to the construction of Gaussian <span class="hlt">ensembles</span> in the RMT.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012PhyA..391.4839V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012PhyA..391.4839V"><span id="translatedtitle">Statistical <span class="hlt">ensembles</span> for money and debt</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Viaggiu, Stefano; Lionetto, Andrea; Bargigli, Leonardo; Longo, Michele</p> <p>2012-10-01</p> <p>We build a statistical <span class="hlt">ensemble</span> representation of two economic models describing respectively, in simplified terms, a payment system and a credit market. To this purpose we adopt the Boltzmann-Gibbs distribution where the role of the Hamiltonian is taken by the total money supply (i.e. including money created from debt) of a set of interacting economic agents. As a result, we can read the main thermodynamic quantities in terms of monetary ones. In particular, we define for the credit market model a work term which is related to the impact of monetary policy on credit creation. Furthermore, with our formalism we recover and extend some results concerning the temperature of an economic system, previously presented in the literature by considering only the monetary base as a conserved quantity. Finally, we study the statistical <span class="hlt">ensemble</span> for the Pareto distribution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003EAEJA.....2355R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003EAEJA.....2355R"><span id="translatedtitle"><span class="hlt">Ensemble</span>-based multi-scale assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ravela, S.; Hansen, J.; Hill, C.; Hill, H.; Marshall, J.</p> <p>2003-04-01</p> <p>We develop <span class="hlt">ensemble</span> methods for constraining numerical models due to errors induced both by uncertain initial states and model structure. In the present paper, circulation phenomena are physically simulated in a laboratory and sensors are used to extract observations (velocity, temperature, etc.). <span class="hlt">Ensembles</span> of the MITGCM constructed across variations in state and model-parameterizations are assimilated with observations over sliding multi-scale assimilation windows to regulate the trajectory of the model attractors vis a vis the system attractor. The novel contribution of this work is in bringing together the use of multi-scale assimilations, physical processes of moderate complexity, techniques for extracting flow and providing physically meaningful ways to alter analyses for minimizing model/data misfit.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22472437','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22472437"><span id="translatedtitle">Quark <span class="hlt">ensembles</span> with the infinite correlation length</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Zinov’ev, G. M.; Molodtsov, S. V.</p> <p>2015-01-15</p> <p>A number of exactly integrable (quark) models of quantum field theory with the infinite correlation length have been considered. It has been shown that the standard vacuum quark ensemble—Dirac sea (in the case of the space-time dimension higher than three)—is unstable because of the strong degeneracy of a state, which is due to the character of the energy distribution. When the momentum cutoff parameter tends to infinity, the distribution becomes infinitely narrow, leading to large (unlimited) fluctuations. Various vacuum ensembles—Dirac sea, neutral <span class="hlt">ensemble</span>, color superconductor, and BCS state—have been compared. In the case of the color interaction between quarks, the BCS state has been certainly chosen as the ground state of the quark <span class="hlt">ensemble</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhRvL.115r8701F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvL.115r8701F"><span id="translatedtitle">Sampling Motif-Constrained <span class="hlt">Ensembles</span> of Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fischer, Rico; Leitão, Jorge C.; Peixoto, Tiago P.; Altmann, Eduardo G.</p> <p>2015-10-01</p> <p>The statistical significance of network properties is conditioned on null models which satisfy specified properties but that are otherwise random. Exponential random graph models are a principled theoretical framework to generate such constrained <span class="hlt">ensembles</span>, but which often fail in practice, either due to model inconsistency or due to the impossibility to sample networks from them. These problems affect the important case of networks with prescribed clustering coefficient or number of small connected subgraphs (motifs). In this Letter we use the Wang-Landau method to obtain a multicanonical sampling that overcomes both these problems. We sample, in polynomial time, networks with arbitrary degree sequences from <span class="hlt">ensembles</span> with imposed motifs counts. Applying this method to social networks, we investigate the relation between transitivity and homophily, and we quantify the correlation between different types of motifs, finding that single motifs can explain up to 60% of the variation of motif profiles.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19880000017&hterms=jesus&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Djesus','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19880000017&hterms=jesus&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Djesus"><span id="translatedtitle">The Mark III Hypercube-<span class="hlt">Ensemble</span> Computers</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Peterson, John C.; Tuazon, Jesus O.; Lieberman, Don; Pniel, Moshe</p> <p>1988-01-01</p> <p>Mark III Hypercube concept applied in development of series of increasingly powerful computers. Processor of each node of Mark III Hypercube <span class="hlt">ensemble</span> is specialized computer containing three subprocessors and shared main memory. Solves problem quickly by simultaneously processing part of problem at each such node and passing combined results to host computer. Disciplines benefitting from speed and memory capacity include astrophysics, geophysics, chemistry, weather, high-energy physics, applied mechanics, image processing, oil exploration, aircraft design, and microcircuit design.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016PhRvL.117f4101R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhRvL.117f4101R"><span id="translatedtitle">Microwave Realization of the Gaussian Symplectic <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>Rehemanjiang, A.; Allgaier, M.; Joyner, C. H.; Müller, S.; Sieber, M.; Kuhl, U.; Stöckmann, H.-J.</p> <p>2016-08-01</p> <p>Following an idea by Joyner et al. [Europhys. Lett. 107, 50004 (2014)], a microwave graph with an antiunitary symmetry T obeying T2=-1 is realized. The Kramers doublets expected for such systems are clearly identified and can be lifted by a perturbation which breaks the antiunitary symmetry. The observed spectral level spacings distribution of the Kramers doublets is in agreement with the predictions from the Gaussian symplectic <span class="hlt">ensemble</span> expected for chaotic systems with such a symmetry.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhRvA..92b2314V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvA..92b2314V"><span id="translatedtitle">Dynamical rephasing of <span class="hlt">ensembles</span> of qudits</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vitanov, Nikolay V.</p> <p>2015-08-01</p> <p>Dynamical decoupling is an established tool for protecting the quantum state of a qubit from decoherence. This paper extends the simplest dynamical decoupling technique for qubits—the Carr-Purcell-Meiboom-Gill's two-pulse rephasing sequence—to qudits with an arbitrary number of states. The pulse sequences introduced here are particularly well suited for dynamical rephasing of the collective coherence of inhomogeneously broadened <span class="hlt">ensembles</span> of atomic qudits.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/27541466','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/27541466"><span id="translatedtitle">Microwave Realization of the Gaussian Symplectic <span class="hlt">Ensemble</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rehemanjiang, A; Allgaier, M; Joyner, C H; Müller, S; Sieber, M; Kuhl, U; Stöckmann, H-J</p> <p>2016-08-01</p> <p>Following an idea by Joyner et al. [Europhys. Lett. 107, 50004 (2014)], a microwave graph with an antiunitary symmetry T obeying T^{2}=-1 is realized. The Kramers doublets expected for such systems are clearly identified and can be lifted by a perturbation which breaks the antiunitary symmetry. The observed spectral level spacings distribution of the Kramers doublets is in agreement with the predictions from the Gaussian symplectic <span class="hlt">ensemble</span> expected for chaotic systems with such a symmetry. PMID:27541466</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AGUFM.A31E0140A&link_type=ABSTRACT','NASAADS'); return false;" href="http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2013AGUFM.A31E0140A&link_type=ABSTRACT"><span id="translatedtitle">Reproduction of surface air temperature over South Korea using dynamical <span class="hlt">downscaling</span> and statistical correction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ahn, J.; Lee, J.; Shim, K.; Kim, Y.</p> <p>2013-12-01</p> <p>In spite of dense meteorological observation conducting over South Korea (The average distance between stations: ~ 12.7km), the detailed topographical effect is not reflected properly due to its mountainous terrains and observation sites mostly situated on low altitudes. A model represents such a topographical effect well, but due to systematic biases in the model, the general temperature distribution is sometimes far different from actual observation. This study attempts to produce a detailed mean temperature distribution for South Korea through a method combining dynamical <span class="hlt">downscaling</span> and statistical correction. For the dynamical <span class="hlt">downscaling</span>, a multi-nesting technique is applied to obtain 3-km resolution data with a focus on the domain for the period of 10 years (1999-2008). For the correction of systematic biases, a perturbation method divided into the mean and the perturbation part was used with a different correction method being applied to each part. The mean was corrected by a weighting function while the perturbation was corrected by the self-organizing maps method. The results with correction agree well with the observed pattern compared to those without correction, improving the spatial and temporal correlations as well as the RMSE. In addition, they represented detailed spatial features of temperature including topographic signals, which cannot be expressed properly by gridded observation. Through comparison with in-situ observation with gridded values after objective analysis, it was found that the detailed structure correctly reflected topographically diverse signals that could not be derived from limited observation data. We expect that the correction method developed in this study can be effectively used for the analyses and projections of climate <span class="hlt">downscaled</span> by using region climate models. Acknowledgements This work was carried out with the support of Korea Meteorological Administration Research and Development Program under Grant CATER 2012-3083 and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC53E1249M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC53E1249M"><span id="translatedtitle">Trends in 100m Wind Speed using Global High-Resolution <span class="hlt">Downscaled</span> Analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McGraw, Z.</p> <p>2015-12-01</p> <p>The strength and variability of the wind energy resource are expected to be susceptible to the complex changes undergoing Earth's climate system. A variety of physical mechanisms for long-term wind speed changes has been proposed, including modified temperature gradients, shifting storm tracks and altered land use. This study is an analysis of multi-decadal wind speed trends within a high-resolution <span class="hlt">downscaled</span> global analysis provided by our collaborators at Vestas Wind Systems A/S. We have sought to identify the regions and landscape types that most exhibit long-term changes to wind speed and identify the mechanisms responsible.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/945745','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/945745"><span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> of GCM Simulations: Toward the Improvement of Forecast Bias over California</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Chin, H S</p> <p>2008-09-24</p> <p>The effects of climate change will mostly be felt on local to regional scales. However, global climate models (GCMs) are unable to produce reliable climate information on the scale needed to assess regional climate-change impacts and variability as a result of coarse grid resolution and inadequate model physics though their capability is improving. Therefore, dynamical and statistical <span class="hlt">downscaling</span> (SD) methods have become popular methods for filling the gap between global and local-to-regional climate applications. Recent inter-comparison studies of these <span class="hlt">downscaling</span> techniques show that both <span class="hlt">downscaling</span> methods have similar skill in simulating the mean and variability of present climate conditions while they show significant differences for future climate conditions (Leung et al., 2003). One difficulty with the SD method is that it relies on predictor-predict and relationships, which may not hold in future climate conditions. In addition, it is now commonly accepted that the dynamical <span class="hlt">downscaling</span> with the regional climate model (RCM) is more skillful at the resolving orographic climate effect than the driving coarser-grid GCM simulations. To assess the possible societal impacts of climate changes, many RCMs have been developed and used to provide a better projection of future regional-scale climates for guiding policies in economy, ecosystem, water supply, agriculture, human health, and air quality (Giorgi et al., 1994; Leung and Ghan, 1999; Leung et al., 2003; Liang et al., 2004; Kim, 2004; Duffy et al., 2006). Although many regional climate features, such as seasonal mean and extreme precipitation have been successfully captured in these RCMs, obvious biases of simulated precipitation remain, particularly the winter wet bias commonly seen in mountain regions of the Western United States. The importance of regional climate research over California is not only because California has the largest population in the nation, but California has one of the most</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H41C1049F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H41C1049F"><span id="translatedtitle">Developing a regional retrospective <span class="hlt">ensemble</span> precipitation dataset for watershed hydrology modeling, Idaho, USA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Flores, A. N.; Smith, K.; LaPorte, P.</p> <p>2011-12-01</p> <p>Applications like flood forecasting, military trafficability assessment, and slope stability analysis necessitate the use of models capable of resolving hydrologic states and fluxes at spatial scales of hillslopes (e.g., 10s to 100s m). These models typically require precipitation forcings at spatial scales of kilometers or better and time intervals of hours. Yet in especially rugged terrain that typifies much of the Western US and throughout much of the developing world, precipitation data at these spatiotemporal resolutions is difficult to come by. Ground-based weather radars have significant problems in high-relief settings and are sparsely located, leaving significant gaps in coverage and high uncertainties. Precipitation gages provide accurate data at points but are very sparsely located and their placement is often not representative, yielding significant coverage gaps in a spatial and physiographic sense. Numerical weather prediction efforts have made precipitation data, including critically important information on precipitation phase, available globally and in near real-time. However, these datasets present watershed modelers with two problems: (1) spatial scales of many of these datasets are tens of kilometers or coarser, (2) numerical weather models used to generate these datasets include a land surface parameterization that in some circumstances can significantly affect precipitation predictions. We report on the development of a regional precipitation dataset for Idaho that leverages: (1) a dataset derived from a numerical weather prediction model, (2) gages within Idaho that report hourly precipitation data, and (3) a long-term precipitation climatology dataset. Hourly precipitation estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA) are stochastically <span class="hlt">downscaled</span> using a hybrid orographic and statistical model from their native resolution (1/2 x 2/3 degrees) to a resolution of approximately 1 km. <span class="hlt">Downscaled</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23454721','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23454721"><span id="translatedtitle">Complementary <span class="hlt">ensemble</span> clustering of biomedical data.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fodeh, Samah Jamal; Brandt, Cynthia; Luong, Thai Binh; Haddad, Ali; Schultz, Martin; Murphy, Terrence; Krauthammer, Michael</p> <p>2013-06-01</p> <p>The rapidly growing availability of electronic biomedical data has increased the need for innovative data mining methods. Clustering in particular has been an active area of research in many different application areas, with existing clustering algorithms mostly focusing on one modality or representation of the data. Complementary <span class="hlt">ensemble</span> clustering (CEC) is a recently introduced framework in which Kmeans is applied to a weighted, linear combination of the coassociation matrices obtained from separate <span class="hlt">ensemble</span> clustering of different data modalities. The strength of CEC is its extraction of information from multiple aspects of the data when forming the final clusters. This study assesses the utility of CEC in biomedical data, which often have multiple data modalities, e.g., text and images, by applying CEC to two distinct biomedical datasets (PubMed images and radiology reports) that each have two modalities. Referent to five different clustering approaches based on the Kmeans algorithm, CEC exhibited equal or better performance in the metrics of micro-averaged precision and Normalized Mutual Information across both datasets. The reference methods included clustering of single modalities as well as <span class="hlt">ensemble</span> clustering of separate and merged data modalities. Our experimental results suggest that CEC is equivalent or more efficient than comparable Kmeans based clustering methods using either single or merged data modalities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3540257','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3540257"><span id="translatedtitle">Optimal Superpositioning of Flexible Molecule <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Gapsys, Vytautas; de Groot, Bert L.</p> <p>2013-01-01</p> <p>Analysis of the internal dynamics of a biological molecule requires the successful removal of overall translation and rotation. Particularly for flexible or intrinsically disordered peptides, this is a challenging task due to the absence of a well-defined reference structure that could be used for superpositioning. In this work, we started the analysis with a widely known formulation of an objective for the problem of superimposing a set of multiple molecules as variance minimization over an <span class="hlt">ensemble</span>. A negative effect of this superpositioning method is the introduction of ambiguous rotations, where different rotation matrices may be applied to structurally similar molecules. We developed two algorithms to resolve the suboptimal rotations. The first approach minimizes the variance together with the distance of a structure to a preceding molecule in the <span class="hlt">ensemble</span>. The second algorithm seeks for minimal variance together with the distance to the nearest neighbors of each structure. The newly developed methods were applied to molecular-dynamics trajectories and normal-mode <span class="hlt">ensembles</span> of the Aβ peptide, RS peptide, and lysozyme. These new (to our knowledge) superpositioning methods combine the benefits of variance and distance between nearest-neighbor(s) minimization, providing a solution for the analysis of intrinsic motions of flexible molecules and resolving ambiguous rotations. PMID:23332072</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22365877','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22365877"><span id="translatedtitle">On large deviations for <span class="hlt">ensembles</span> of distributions</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Khrychev, D A</p> <p>2013-11-30</p> <p>The paper is concerned with the large deviations problem in the Freidlin-Wentzell formulation without the assumption of the uniqueness of the solution to the equation involving white noise. In other words, it is assumed that for each ε>0 the nonempty set P{sub ε} of weak solutions is not necessarily a singleton. Analogues of a number of concepts in the theory of large deviations are introduced for the set (P{sub ε}, ε>0), hereafter referred to as an <span class="hlt">ensemble</span> of distributions. The <span class="hlt">ensembles</span> of weak solutions of an n-dimensional stochastic Navier-Stokes system and stochastic wave equation with power-law nonlinearity are shown to be uniformly exponentially tight. An idempotent Wiener process in a Hilbert space and idempotent partial differential equations are defined. The accumulation points in the sense of large deviations of the <span class="hlt">ensembles</span> in question are shown to be weak solutions of the corresponding idempotent equations. Bibliography: 14 titles.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1329227','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1329227"><span id="translatedtitle">Gradient Flow Analysis on MILC HISQ <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Brown, Nathan; Bazavov, Alexei; Bernard, Claude; DeTar, Carleton; Foley, Justin; Gottlieb, Steven; Heller, Urs M.; Hetrick, J. E.; Komijani, Javad; Laiho, Jack; Levkova, Ludmila; Oktay, M. B.; Sugar, Robert; Toussaint, Doug; Van de Water, Ruth S.; Zhou, Ran</p> <p>2014-11-14</p> <p>We report on a preliminary scale determination with gradient-flow techniques on the $N_f = 2 + 1 + 1$ HISQ <span class="hlt">ensembles</span> generated by the MILC collaboration. The <span class="hlt">ensembles</span> include four lattice spacings, ranging from 0.15 to 0.06 fm, and both physical and unphysical values of the quark masses. The scales $\\sqrt{t_0}/a$ and $w_0/a$ are computed using Symanzik flow and the cloverleaf definition of $\\langle E \\rangle$ on each <span class="hlt">ensemble</span>. Then both scales and the meson masses $aM_\\pi$ and $aM_K$ are adjusted for mistunings in the charm mass. Using a combination of continuum chiral perturbation theory and a Taylor series ansatz in the lattice spacing, the results are simultaneously extrapolated to the continuum and interpolated to physical quark masses. Our preliminary results are $\\sqrt{t_0} = 0.1422(7)$fm and $w_0 = 0.1732(10)$fm. We also find the continuum mass-dependence of $w_0$.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2143983','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2143983"><span id="translatedtitle">Flexible ligand docking using conformational <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lorber, D. M.; Shoichet, B. K.</p> <p>1998-01-01</p> <p>Molecular docking algorithms suggest possible structures for molecular complexes. They are used to model biological function and to discover potential ligands. A present challenge for docking algorithms is the treatment of molecular flexibility. Here, the rigid body program, DOCK, is modified to allow it to rapidly fit multiple conformations of ligands. Conformations of a given molecule are pre-calculated in the same frame of reference, so that each conformer shares a common rigid fragment with all other conformations. The ligand conformers are then docked together, as an <span class="hlt">ensemble</span>, into a receptor binding site. This takes advantage of the redundancy present in differing conformers of the same molecule. The algorithm was tested using three organic ligand protein systems and two protein-protein systems. Both the bound and unbound conformations of the receptors were used. The ligand <span class="hlt">ensemble</span> method found conformations that resembled those determined in X-ray crystal structures (RMS values typically less than 1.5 A). To test the method's usefulness for inhibitor discovery, multi-compound and multi-conformer databases were screened for compounds known to bind to dihydrofolate reductase and compounds known to bind to thymidylate synthase. In both cases, known inhibitors and substrates were identified in conformations resembling those observed experimentally. The ligand <span class="hlt">ensemble</span> method was 100-fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 molecules in one to four CPU days on a workstation. PMID:9568900</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26244742','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26244742"><span id="translatedtitle">Retinal Conformation Changes Rhodopsin's Dynamic <span class="hlt">Ensemble</span>.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Leioatts, Nicholas; Romo, Tod D; Danial, Shairy Azmy; Grossfield, Alan</p> <p>2015-08-01</p> <p>G protein-coupled receptors are vital membrane proteins that allosterically transduce biomolecular signals across the cell membrane. However, the process by which ligand binding induces protein conformation changes is not well understood biophysically. Rhodopsin, the mammalian dim-light receptor, is a unique test case for understanding these processes because of its switch-like activity; the ligand, retinal, is bound throughout the activation cycle, switching from inverse agonist to agonist after absorbing a photon. By contrast, the ligand-free opsin is outside the activation cycle and may behave differently. We find that retinal influences rhodopsin dynamics using an <span class="hlt">ensemble</span> of all-atom molecular dynamics simulations that in aggregate contain 100 μs of sampling. Active retinal destabilizes the inactive state of the receptor, whereas the active <span class="hlt">ensemble</span> was more structurally homogenous. By contrast, simulations of an active-like receptor without retinal present were much more heterogeneous than those containing retinal. These results suggest allosteric processes are more complicated than a ligand inducing protein conformational changes or simply capturing a shifted <span class="hlt">ensemble</span> as outlined in classic models of allostery.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JGRD..114.5307M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JGRD..114.5307M"><span id="translatedtitle">Ozone <span class="hlt">ensemble</span> forecast with machine learning algorithms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mallet, Vivien; Stoltz, Gilles; Mauricette, Boris</p> <p>2