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Sample records for ensemble downscaling mred

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. Investigating Statistical Downscaling Methods and Applications for the NCEP/GEFS Ensemble Precipitation Forecasts

    NASA Astrophysics Data System (ADS)

    Luo, Y.; Zhu, Y.; Hou, D.

    2015-12-01

    Significant discrepancies exist when coarse resolution model precipitation forecast products on standard output grids are verified against high-resolution analyses, remaining a challenge for NWP model guidance products. To enhance the usefulness of the model products, tremendous efforts with various statistical post-processing techniques are being made to reduce those discrepancies and recover small scale features using observations and a long-term reforecast climatology as the baseline. Among them, downscaling ensemble using forecast analogs (Hamill et al., 2006) and multiplicative downscaling using Parameter-elevation Regressions on Independent Slopes Model (PRISM) Mountain Mapper by WPC show promising improvement in skill of forecasts. This work concentrates on these two commonly used statistical downscaling approaches along with the Frequency Matching Method (FMM, Zhu and Luo, 2015) developed at NCEP/EMC. In this work, these three approaches will be investigated when applied to the standard one degree NCEP Global Ensemble Forecast System (GEFS) ensemble precipitation forecasts based on the 5-km high resolution NCEP Climatology-Calibrated Precipitation Analysis (CCPA) and 18 years ensemble control only reforecast data from the latest version of GEFS (GEFS v11.0). We will explore the effectiveness and feasibility of these approaches and to discover their strengths and weaknesses, with a focus mainly on generation of much higher 5km NDGD grid GEFS ensemble precipitation forecasts over the CONUS. This work is also expected to identify factors that influence the performance for each approach, such as the choice of case matching methods, the sample size, weighting function, regime definition, etc. A summary of the investigations and outcomes will be shown. Suggestions for some possible directions to produce such a high resolution ensemble precipitation forecast products in the future will be provided.

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

    PubMed

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

    2014-06-01

    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.

  7. A WRF-based ensemble data assimilation system for dynamic downscaling of satellite precipitation information (Invited)

    NASA Astrophysics Data System (ADS)

    Zhang, S. Q.; Hou, A. Y.; Zupanski, M.; Cheung, S.

    2010-12-01

    For many hydrological applications, dynamic downscaling from global analyses has been used to provide local scale information on spatial and temporal distribution of precipitation and other associated environmental parameters. In the near future the NASA Global Precipitation Measurement (GPM) Mission will provide new sources of precipitation observations with unprecedented spatial and temporal coverage for better understanding and prediction of climate, weather and hydro-meteorological processes. However, in terms of using precipitation observations in global analyses and forecasts, the capability of current operational systems is generally limited by the global model resolution, the requirement of linearization of parameterized cloud physics, and the static forecast error statistics often with no distinction for clear sky or storm. In order to maximize the utilization of satellite precipitation observations in dynamic downscaling for hydrological applications, an ensemble data assimilation system (Goddard-WRF-EDAS) has been developed jointly by NASA Goddard and Colorado State University (CSU). The system takes advantages of the cloud-resolving high-resolution of the Weather Research and Forecasting (WRF) model with NASA Goddard microphysics and the flow-dependent estimation of forecast error covariance from the Maximum Likelihood Ensemble Filter (MLEF). Satellite observed radiances in precipitation regions are assimilated using Goddard Satellite Data Simulator Unit (SDSU) as the observation operator. Experimental results using current available satellite precipitation data (AMSR-E and TRMM-TMI) are presented to investigate the ability of the assimilation system in ingesting information from in-situ and satellite observations to produce dynamically downscaled precipitation. The results from the assimilation of precipitation-affected microwave radiances in a storm case and in a heavy rainfall event demonstrate the data impact to down-scaled precipitation and

  8. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-02-01

    Abstract The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center one-tier multimodel <span class="hlt">ensemble</span> (MME) was <span class="hlt">downscaled</span> for predicting the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both precipitation and temperature, and can incorporate the effect of global warming on the balance between precipitation and evapotranspiration. It was found that the one-tier MME has difficulty in capturing the local temperature and rainfall variations over extratropical land areas, and has no skill in predicting SPEI during boreal winter and spring. On the other hand, temperature and precipitation predictions were substantially improved in the <span class="hlt">downscaled</span> MME. In conjunction with variance inflation, <span class="hlt">downscaled</span> MME can give reasonably skillful 6 month-lead forecasts of SPEI for the winter to spring period. Our results could lead to more reliable hydrological extreme predictions for policymakers and stakeholders in the water management sector, and for better mitigation and climate adaptations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C41A0318M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C41A0318M"><span><span class="hlt">Ensemble</span> Predictions of Future Snowfall Scenarios in the Karakorum and Hindu-Kush Mountains Using <span class="hlt">Downscaled</span> GCM Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mosier, T. M.; Hill, D. F.; Sharp, K. V.</p> <p>2014-12-01</p> <p>Climate change is affecting the seasonality and mass of snow, and impacting the water resources of hundreds of millions of people who depend on streamflow originating in High Asia. Global climate model (GCM) outputs are the primary forcing data used to investigate future projections of changes in snow and glacier processes; however, these processes occur at a much finer spatial scale than the resolution of current GCMs. To facilitate studying the cryosphere in High Asia, we developed a software package to <span class="hlt">downscale</span> monthly GCM data to 30-arcseconds for any global land area. Using this <span class="hlt">downscaling</span> package, we produce an <span class="hlt">ensemble</span> of <span class="hlt">downscaled</span> GCM data from 2020-2100, corresponding to representative concentration pathways (RCPs) 4.5 and 8.5. We then use these data to model changes to snowfall in the Karakorum and Hindu Kush (KHK) region, which is located in High Asia. The <span class="hlt">ensemble</span> mean of these data predict that total annual snowfall in 2095 will decrease by 22% under RCP 4.5 and 46% under RCP 8.5, relative to 1950-2000 climatological values. For both scenarios, the changes in snowfall are dependent on elevation, with the maximum decreases in snowfall occurring at approximately 2,300 m. While total snowfall decreases, an interesting feature of snowfall change for the RCP 8.5 scenario is that the <span class="hlt">ensemble</span> mean projection shows an increase in snowfall for elevations between 3,000- 5,000 m relative to historic values. These fine-scale spatial, temporal, and elevation-dependent patterns of changes in projected snowfall significantly affect the energy balance of the snowpack, in turn affecting timing of melt and discharge. Therefore, our work can be coupled with a glacio-hydrological model to assess effects of these snowfall patterns on other processes or compared to existing model results to assess treatment of snow processes in the existing model. Our method is designed to <span class="hlt">downscale</span> climate data for any global land area, allowing for the production of these fine</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A21E0185D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A21E0185D"><span>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/2016AtmRe.178..138S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AtmRe.178..138S"><span>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>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>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>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> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A51P0335S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A51P0335S"><span>An Evaluation of the Parallel <span class="hlt">Ensemble</span> Empirical Mode Decomposition Method in Revealing the Role of <span class="hlt">Downscaling</span> Processes Associated with African Easterly Waves in Tropical Cyclone Genesis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shen, B. W.; Wu, Y.</p> <p>2015-12-01</p> <p>In this study, we applied the parallel version of the <span class="hlt">Ensemble</span> Empirical Mode Decomposition (PEEMD) for an analysis of 10-year (2004-2013) ERA-Interim global reanalysis data in order to explore multiscale interaction of tropical cyclone genesis associated with African Easterly Waves (AEWs) in sheared flows. Our focus was aimed at understanding the <span class="hlt">downscaling</span> process in multiscale flows during storm intensification. To represent the various length scales of atmospheric systems, we extracted Intrinsic Function Modes (IMFs) from raw data using the PEEMD and found that the non-oscillatory trend mode can be used to represent large scale environmental flow and the third oscillatory mode (IMF3) is to represent AEW/TC scale systems. Our results: 1) identified 42 developing cases from 272 AEWs, with 25 eventually developing into hurricanes; 2) indicated that maximum shear largely occurs over the ocean for the IMF3 mode and over land near the coast for the trend mode for developing cases, suggesting shear transfer between the trend mode and the IMF3; 3) displayed opposite wind shear tendencies for the trend mode and the IMF3 during storm intensification, signifying the <span class="hlt">downscaling</span> process in 13 hurricane cases along their tracks; 4) showed that among the 42 developing cases, only 13 of the 25 hurricanes were found with significant <span class="hlt">downscaling</span> transfer features, so other processes such as upscaling processes may play an important role in the other developing cases, especially the remaining 12 hurricane cases. Investigating the upscaling process between the convection scale and the AEW/TC requires data from the finer grid resolution and will be the subject of a future study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080040695','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080040695"><span>Simulation of SEU Cross-sections using <span class="hlt">MRED</span> under Conditions of Limited Device Information</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lauenstein, J. M.; Reed, R. A.; Weller, R. A.; Mendenhall, M. H.; Warren, K. M.; Pellish, J. A.; Schrimpf, R. D.; Sierawski, B. D.; Massengill, L. W.; Dodd, P. E.; Shaneyfelt, M. R.; Felix, J. A.; Schwank, J. R.</p> <p>2007-01-01</p> <p>This viewgraph presentation reviews the simulation of Single Event Upset (SEU) cross sections using the membrane electrode assembly (MEA) resistance and electrode diffusion (<span class="hlt">MRED</span>) tool using "Best guess" assumptions about the process and geometry, and direct ionization, low-energy beam test results. This work will also simulate SEU cross-sections including angular and high energy responses and compare the simulated results with beam test data for the validation of the model. Using <span class="hlt">MRED</span>, we produced a reasonably accurate upset response model of a low-critical charge SRAM without detailed information about the circuit, device geometry, or fabrication process</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26470021','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26470021"><span>MreC and <span class="hlt">MreD</span> Proteins Are Not Required for Growth of Staphylococcus aureus.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tavares, Andreia C; Fernandes, Pedro B; Carballido-López, Rut; Pinho, Mariana G</p> <p>2015-01-01</p> <p>The transmembrane proteins MreC and <span class="hlt">MreD</span> are present in a wide variety of bacteria and are thought to be involved in cell shape determination. Together with the actin homologue MreB and other morphological elements, they play an essential role in the synthesis of the lateral cell wall in rod-shaped bacteria. In ovococcus, which lack MreB homologues, mreCD are also essential and have been implicated in peripheral cell wall synthesis. In this work we addressed the possible roles of MreC and <span class="hlt">MreD</span> in the spherical pathogen Staphylococcus aureus. We show that MreC and <span class="hlt">MreD</span> are not essential for cell viability and do not seem to affect cell morphology, cell volume or cell cycle control. MreC and <span class="hlt">MreD</span> localize preferentially to the division septa, but do not appear to influence peptidoglycan composition, nor the susceptibility to different antibiotics and to oxidative and osmotic stress agents. Our results suggest that the function of MreCD in S. aureus is not critical for cell division and cell shape determination.</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><span class="hlt">Downscaling</span> Satellite Data for Predicting Catchment-scale Root Zone Soil Moisture with Ground-based Sensors and an <span class="hlt">Ensemble</span> Kalman Filter</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, H.; Baldwin, D. C.; Smithwick, E. A. H.</p> <p>2015-12-01</p> <p>Predicting root zone (0-100 cm) soil moisture (RZSM) content at a catchment-scale is essential for drought and flood predictions, irrigation planning, weather forecasting, and many other applications. Satellites, such as the NASA Soil Moisture Active Passive (SMAP), can estimate near-surface (0-5 cm) soil moisture content globally at coarse spatial resolutions. We develop a hierarchical <span class="hlt">Ensemble</span> Kalman Filter (EnKF) data assimilation modeling system to <span class="hlt">downscale</span> satellite-based near-surface soil moisture and to estimate RZSM content across the Shale Hills Critical Zone Observatory at a 1-m resolution in combination with ground-based soil moisture sensor data. In this example, a simple infiltration model within the EnKF-model has been parameterized for 6 soil-terrain units to forecast daily RZSM content in the catchment from 2009 - 2012 based on AMSRE. LiDAR-derived terrain variables define intra-unit RZSM variability using a novel covariance localization technique. This method also allows the mapping of uncertainty with our RZSM estimates for each time-step. A catchment-wide satellite-to-surface <span class="hlt">downscaling</span> parameter, which nudges the satellite measurement closer to in situ near-surface data, is also calculated for each time-step. We find significant differences in predicted root zone moisture storage for different terrain units across the experimental time-period. Root mean square error from a cross-validation analysis of RZSM predictions using an independent dataset of catchment-wide in situ Time-Domain Reflectometry (TDR) measurements ranges from 0.060-0.096 cm3 cm-3, and the RZSM predictions are significantly (p < 0.05) correlated with TDR measurements [r = 0.47-0.68]. The predictive skill of this data assimilation system is similar to the Penn State Integrated Hydrologic Modeling (PIHM) system. Uncertainty estimates are significantly (p < 0.05) correlated to cross validation error during wet and dry conditions, but more so in dry summer seasons. Developing an</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJBm...60..307S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJBm...60..307S"><span>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> </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/2016ThApC.tmp..281K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp..281K"><span>Climate change effects on wildland fire risk in the Northeastern and Great Lakes states predicted by a <span class="hlt">downscaled</span> 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>Kerr, Gaige Hunter; DeGaetano, Arthur T.; Stoof, Cathelijne R.; Ward, Daniel</p> <p>2016-11-01</p> <p>This study is among the first to investigate wildland fire risk in the Northeastern and the Great Lakes states under a changing climate. We use a multi-model <span class="hlt">ensemble</span> (MME) of regional climate models from the Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX) together with the Canadian Forest Fire Weather Index System (CFFWIS) to understand changes in wildland fire risk through differences between historical simulations and future projections. Our results are relatively homogeneous across the focus region and indicate modest increases in the magnitude of fire weather indices (FWIs) during northern hemisphere summer. The most pronounced changes occur in the date of the initialization of CFFWIS and peak of the wildland fire season, which in the future are trending earlier in the year, and in the significant increases in the length of high-risk episodes, defined by the number of consecutive days with FWIs above the current 95th percentile. Further analyses show that these changes are most closely linked to expected changes in the focus region's temperature and precipitation. These findings relate to the current understanding of particulate matter vis-à-vis wildfires and have implications for human health and local and regional changes in radiative forcings. When considering current fire management strategies which could be challenged by increasing wildland fire risk, fire management agencies could adapt new strategies to improve awareness, prevention, and resilience to mitigate potential impacts to critical infrastructure and population.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.A21G0175D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A21G0175D"><span>Dynamic <span class="hlt">downscaling</span> of CFS winter seasonal simulations over the United States using the ETA/SSIB-3 model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>de Sales, F.; Xue, Y.</p> <p>2010-12-01</p> <p>The NCEP ETA/SSiB-3 regional circulation model (RCM) was 1-way nested in the NCEP Climate Forecast System (CFS) for a series of 22-year <span class="hlt">downscaling</span> simulations of the winter season (December through April) over North America from 1982 through 2003. Each year’ simulation has 5 <span class="hlt">ensemble</span> members producing a total of 110 winter hindcasts. These simulations are part of the Multi-RCM <span class="hlt">Ensemble</span> <span class="hlt">Downscaling</span> (<span class="hlt">MRED</span>), which aims to explore the utility and value of RCMs in operational seasonal to interannual climate prediction. The Eta/SSiB-3 shows good <span class="hlt">downscaling</span> ability for seasonal mean precipitation. The model reproduced well the winter precipitation pattern over the country, especially the high precipitation regions; over the East, the Northwest, and central California, with a large drier region in between. The RCM, however, did not simulate the precipitation maximum over the Southeast, which was well captured by the global model. The December-January-February-March-April (DJFMA) of 22-year mean bias averaged over the whole country for the CFS is 1.52 mm day-1, while for the ETA-SSIB-3 model it is -0.1 mm day-1. Similar comparison yielded a 66.7% reduction in RMSE of precipitation with <span class="hlt">downscaling</span>. The analysis of average precipitation time series indicates that overall the RCM improved the simulation by reducing excessive rainfall produced by the GCM, especially over the western states. RCM reduced the countrywide CFS’ RMSE of time series from 1.60 to 0.33 mm day-1. The error reduction was larger over the western states (nearly 83%) than over the eastern states (approximately 67%). However, the temporal correlation with observation shows little difference between GCM and RCM, indicating the dominant role of lateral boundary forcing from CFS in producing the temporal variability. The simulation of seasonal snow water equivalent was also improved by the regional model. Comparison between models simulations and the Rutgers University observational data shows that the</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>An intercomparison of a large <span class="hlt">ensemble</span> of statistical <span class="hlt">downscaling</span> methods for Europe: Overall results from the VALUE perfect predictor cross-validation experiment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutiérrez, Jose Manuel; Maraun, Douglas; Widmann, Martin; Huth, Radan; Hertig, Elke; Benestad, Rasmus; Roessler, Ole; Wibig, Joanna; Wilcke, Renate; Kotlarski, Sven</p> <p>2016-04-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical <span class="hlt">downscaling</span> methods. This framework is based on a user-focused validation tree, guiding the selection of relevant validation indices and performance measures for different aspects of the validation (marginal, temporal, spatial, multi-variable). Moreover, several experiments have been designed to isolate specific points in the <span class="hlt">downscaling</span> procedure where problems may occur (assessment of intrinsic performance, effect of errors inherited from the global models, effect of non-stationarity, etc.). The list of <span class="hlt">downscaling</span> experiments includes 1) cross-validation with perfect predictors, 2) GCM predictors -aligned with EURO-CORDEX experiment- and 3) pseudo reality predictors (see Maraun et al. 2015, Earth's Future, 3, doi:10.1002/2014EF000259, for more details). The results of these experiments are gathered, validated and publicly distributed through the VALUE validation portal, allowing for a comprehensive community-open <span class="hlt">downscaling</span> intercomparison study. In this contribution we describe the overall results from Experiment 1), consisting of a European wide 5-fold cross-validation (with consecutive 6-year periods from 1979 to 2008) using predictors from ERA-Interim to <span class="hlt">downscale</span> precipitation and temperatures (minimum and maximum) over a set of 86 ECA&D stations representative of the main geographical and climatic regions in Europe. As a result of the open call for contribution to this experiment (closed in Dec. 2015), over 40 methods representative of the main approaches (MOS and Perfect Prognosis, PP) and techniques (linear scaling, quantile mapping, analogs, weather typing, linear and generalized regression, weather generators, etc.) were submitted, including information both data</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005WRR....4102024G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005WRR....4102024G"><span>Statistical <span class="hlt">downscaling</span> using K-nearest neighbors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gangopadhyay, Subhrendu; Clark, Martyn; Rajagopalan, Balaji</p> <p>2005-02-01</p> <p>Statistical <span class="hlt">downscaling</span> provides a technique for deriving local-scale information of precipitation and temperature from numerical weather prediction model output. The K-nearest neighbor (K-nn) is a new analog-type approach that is used in this paper to <span class="hlt">downscale</span> the National Centers for Environmental Prediction 1998 medium-range forecast model output. The K-nn algorithm queries days similar to a given feature vector in this archive and using empirical orthogonal function analysis identifies a subset of days (K) similar to the feature day. These K days are then weighted using a bisquare weight function and randomly sampled to generate <span class="hlt">ensembles</span>. A set of 15 medium-range forecast runs was used, and seven <span class="hlt">ensemble</span> members were generated from each run. The <span class="hlt">ensemble</span> of 105 members was then used to select the local-scale precipitation and temperature values in four diverse basins across the contiguous United States. These <span class="hlt">downscaled</span> precipitation and temperature estimates were subsequently analyzed to test the performance of this <span class="hlt">downscaling</span> approach. The <span class="hlt">downscaled</span> <span class="hlt">ensembles</span> were evaluated in terms of bias, the ranked probability skill score as a measure of forecast skill, spatial covariability between stations, temporal persistence, consistency between variables, and conditional bias and to develop spread-skill relationships. Though this approach does not explicitly model the space-time variability of the weather fields at each individual station, the above statistics were extremely well captured. The K-nn method was also compared with a multiple-linear-regression-based <span class="hlt">downscaling</span> model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H53A1633A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H53A1633A"><span>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>Climatological attribution of wind power ramp events in East Japan and their probabilistic forecast based on multi-model <span class="hlt">ensembles</span> <span class="hlt">downscaled</span> by analog <span class="hlt">ensemble</span> using self-organizing maps</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ohba, Masamichi; Nohara, Daisuke; Kadokura, Shinji</p> <p>2016-04-01</p> <p>Severe storms or other extreme weather events can interrupt the spin of wind turbines in large scale that cause unexpected "wind ramp events". In this study, we present an application of self-organizing maps (SOMs) for climatological attribution of the wind ramp events and their probabilistic prediction. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors. The SOM is applied to analyze and connect the relationship between atmospheric patterns over Japan and wind power generation. SOM is employed on sea level pressure derived from the JRA55 reanalysis over the target area (Tohoku region in Japan), whereby a two-dimensional lattice of weather patterns (WPs) classified during the 1977-2013 period is obtained. To compare with the atmospheric data, the long-term wind power generation is reconstructed by using a high-resolution surface observation network AMeDAS (Automated Meteorological Data Acquisition System) in Japan. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of wind ramp events. Probabilistic forecasts to wind power generation and ramps are conducted by using the obtained SOM. The probability are derived from the multiple SOM lattices based on the matching of output from TIGGE multi-model global forecast to the WPs on the lattices. Since this method effectively takes care of the empirical uncertainties from the historical data, wind power generation and ramp is probabilistically forecasted from the forecasts of global models. The predictability skill of the forecasts for the wind power generation and ramp events show the relatively good skill score under the <span class="hlt">downscaling</span> technique. It is expected that the results of this study provides better guidance to the user community and contribute to future development of system operation model for the transmission grid operator.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1069M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1069M"><span>New statistical <span class="hlt">downscaling</span> for Canada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Murdock, T. Q.; Cannon, A. J.; Sobie, S.</p> <p>2013-12-01</p> <p>This poster will document the production of a set of statistically <span class="hlt">downscaled</span> future climate projections for Canada based on the latest available RCM and GCM simulations - the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2007) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). The main stages of the project included (1) <span class="hlt">downscaling</span> method evaluation, (2) scenarios selection, (3) production of statistically <span class="hlt">downscaled</span> results, and (4) applications of results. We build upon a previous <span class="hlt">downscaling</span> evaluation project (Bürger et al. 2012, Bürger et al. 2013) in which a quantile-based method (Bias Correction/Spatial Disaggregation - BCSD; Werner 2011) provided high skill compared with four other methods representing the majority of types of <span class="hlt">downscaling</span> used in Canada. Additional quantile-based methods (Bias-Correction/Constructed Analogues; Maurer et al. 2010 and Bias-Correction/Climate Imprint ; Hunter and Meentemeyer 2005) were evaluated. A subset of 12 CMIP5 simulations was chosen based on an objective set of selection criteria. This included hemispheric skill assessment based on the CLIMDEX indices (Sillmann et al. 2013), historical criteria used previously at the Pacific Climate Impacts Consortium (Werner 2011), and refinement based on a modified clustering algorithm (Houle et al. 2012; Katsavounidis et al. 1994). Statistical <span class="hlt">downscaling</span> was carried out on the NARCCAP <span class="hlt">ensemble</span> and a subset of the CMIP5 <span class="hlt">ensemble</span>. We produced <span class="hlt">downscaled</span> scenarios over Canada at a daily time resolution and 300 arc second (~10 km) spatial resolution from historical runs for 1951-2005 and from RCP 2.6, 4.5, and 8.5 projections for 2006-2100. The ANUSPLIN gridded daily dataset (McKenney et al. 2011) was used as a target. It has national coverage, spans the historical period of interest 1951-2005, and has daily time resolution. It uses interpolation of station data based on thin-plate splines. This type of method has been shown to have</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AtmRe.147...68X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AtmRe.147...68X"><span>A review on regional dynamical <span class="hlt">downscaling</span> in intraseasonal to seasonal simulation/prediction and major factors that affect <span class="hlt">downscaling</span> ability</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xue, Yongkang; Janjic, Zavisa; Dudhia, Jimy; Vasic, Ratko; De Sales, Fernando</p> <p>2014-10-01</p> <p>Regional climate models (RCMs) have been developed and extensively applied for dynamically <span class="hlt">downscaling</span> coarse resolution information from different sources, such as general circulation models (GCMs) and reanalyses, for different purposes including past climate simulations and future climate projection. Thus far, the nature, the methods, and a number of crucial issues concerning the use of dynamic <span class="hlt">downscaling</span> are still not well understood. The most important issue is whether, and if so, under what conditions dynamic <span class="hlt">downscaling</span> is really capable of adding more information at different scales compared to the GCM or reanalysis that imposes lateral boundary conditions (LBCs) to the RCMs. There are controversies regarding the <span class="hlt">downscaling</span> ability. In this review paper we present several factors that have consistently demonstrated strong impact on dynamic <span class="hlt">downscaling</span> ability in intraseasonal and seasonal simulations/predictions and future projection. Those factors include setting of the RCM experiment (e.g. imposed LBC quality, domain size and position, LBC coupling, and horizontal resolution); as well as physical processes, mainly convective schemes and vegetation and soil processes that include initializations, vegetation specifications, and planetary boundary layer and surface coupling. These studies indicate that RCMs have <span class="hlt">downscaling</span> ability in some aspects but only under certain conditions. Any significant weaknesses in one of these aspects would cause an RCM to lose its dynamic <span class="hlt">downscaling</span> ability. This paper also briefly presents challenges faced in current RCM dynamic <span class="hlt">downscaling</span> and future prospective, which cover the application of coupled ocean-atmosphere RCMs, <span class="hlt">ensemble</span> applications, and future projections.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PhDT.......150W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhDT.......150W"><span>Development and Evaluation of a Hybrid Dynamical-Statistical <span class="hlt">Downscaling</span> Method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Walton, Daniel Burton</p> <p></p> <p>Regional climate change studies usually rely on <span class="hlt">downscaling</span> of global climate model (GCM) output in order to resolve important fine-scale features and processes that govern local climate. Previous efforts have used one of two techniques: (1) dynamical <span class="hlt">downscaling</span>, in which a regional climate model is forced at the boundaries by GCM output, or (2) statistical <span class="hlt">downscaling</span>, which employs historical empirical relationships to go from coarse to fine resolution. Studies using these methods have been criticized because they either dynamical <span class="hlt">downscaled</span> only a few GCMs, or used statistical <span class="hlt">downscaling</span> on an <span class="hlt">ensemble</span> of GCMs, but missed important dynamical effects in the climate change signal. This study describes the development and evaluation of a hybrid dynamical-statstical <span class="hlt">downscaling</span> method that utilizes aspects of both dynamical and statistical <span class="hlt">downscaling</span> to address these concerns. The first step of the hybrid method is to use dynamical <span class="hlt">downscaling</span> to understand the most important physical processes that contribute to the climate change signal in the region of interest. Then a statistical model is built based on the patterns and relationships identified from dynamical <span class="hlt">downscaling</span>. This statistical model can be used to <span class="hlt">downscale</span> an entire <span class="hlt">ensemble</span> of GCMs quickly and efficiently. The hybrid method is first applied to a domain covering Los Angeles Region to generate projections of temperature change between the 2041-2060 and 1981-2000 periods for 32 CMIP5 GCMs. The hybrid method is also applied to a larger region covering all of California and the adjacent ocean. The hybrid method works well in both areas, primarily because a single feature, the land-sea contrast in the warming, controls the overwhelming majority of the spatial detail. Finally, the dynamically <span class="hlt">downscaled</span> temperature change patterns are compared to those produced by two commonly-used statistical methods, BCSD and BCCA. Results show that dynamical <span class="hlt">downscaling</span> recovers important spatial features that the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1611668A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1611668A"><span><span class="hlt">Downscaling</span> of inundation extents</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Aires, Filipe; Prigent, Catherine; Papa, Fabrice</p> <p>2014-05-01</p> <p>The Global Inundation Extent from Multi-Satellite (GIEMS) provides multi-year monthly variations of the global surface water extent at about 25 kmx25 km resolution, from 1993 to 2007. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. This paper present three approaches to do <span class="hlt">downscale</span> GIEMS: The first one is based on a image-processing technique using neighborhood constraints. The third approach uses a PCA representation to perform an algebraic inversion. The PCA-representation is also very convenient to perform temporal and spatial interpolation of complexe inundation fields. The third <span class="hlt">downscaling</span> method uses topography information from Hydroshed Digital Elevation Model (DEM). Information such as the elevation, distance to river and flow accumulation are used to define a ``flood ability index'' that is used by the <span class="hlt">downscaling</span>. Three basins will be considered for illustrative purposes: Amazon, Niger and Mekong. Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and <span class="hlt">downscaling</span> of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeorology, in press, 2014. Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, <span class="hlt">downscaled</span> from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMPA13A2184T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMPA13A2184T"><span>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>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('https://www.ncbi.nlm.nih.gov/pubmed/25833698','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25833698"><span><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/abs/2013EGUGA..15.3380Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.3380Z"><span>Atmospheric <span class="hlt">Downscaling</span> using Genetic Programming</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zerenner, Tanja; Venema, Victor; Simmer, Clemens</p> <p>2013-04-01</p> <p>Coupling models for the different components of the Soil-Vegetation-Atmosphere-System requires up-and <span class="hlt">downscaling</span> procedures. Subject of our work is the <span class="hlt">downscaling</span> scheme used to derive high resolution forcing data for land-surface and subsurface models from coarser atmospheric model output. The current <span class="hlt">downscaling</span> scheme [Schomburg et. al. 2010, 2012] combines a bi-quadratic spline interpolation, deterministic rules and autoregressive noise. For the development of the scheme, training and validation data sets have been created by carrying out high-resolution runs of the atmospheric model. The deterministic rules in this scheme are partly based on known physical relations and partly determined by an automated search for linear relationships between the high resolution fields of the atmospheric model output and high resolution data on surface characteristics. Up to now deterministic rules are available for <span class="hlt">downscaling</span> surface pressure and partially, depending on the prevailing weather conditions, for near surface temperature and radiation. Aim of our work is to improve those rules and to find deterministic rules for the remaining variables, which require <span class="hlt">downscaling</span>, e.g. precipitation or near surface specifc humidity. To accomplish that, we broaden the search by allowing for interdependencies between different atmospheric parameters, non-linear relations, non-local and time-lagged relations. To cope with the vast number of possible solutions, we use genetic programming, a method from machine learning, which is based on the principles of natural evolution. We are currently working with GPLAB, a Genetic Programming toolbox for Matlab. At first we have tested the GP system to retrieve the known physical rule for <span class="hlt">downscaling</span> surface pressure, i.e. the hydrostatic equation, from our training data. We have found this to be a simple task to the GP system. Furthermore we have improved accuracy and efficiency of the GP solution by implementing constant variation and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.188T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.188T"><span>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/2016JHyd..541.1273H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JHyd..541.1273H"><span>Inter-comparison of statistical <span class="hlt">downscaling</span> methods for projection of extreme flow indices across Europe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hundecha, Yeshewatesfa; Sunyer, Maria A.; Lawrence, Deborah; Madsen, Henrik; Willems, Patrick; Bürger, Gerd; Kriaučiūnienė, Jurate; Loukas, Athanasios; Martinkova, Marta; Osuch, Marzena; Vasiliades, Lampros; von Christierson, Birgitte; Vormoor, Klaus; Yücel, Ismail</p> <p>2016-10-01</p> <p>The effect of methods of statistical <span class="hlt">downscaling</span> of daily precipitation on changes in extreme flow indices under a plausible future climate change scenario was investigated in 11 catchments selected from 9 countries in different parts of Europe. The catchments vary from 67 to 6171 km2 in size and cover different climate zones. 15 regional climate model outputs and 8 different statistical <span class="hlt">downscaling</span> methods, which are broadly categorized as change factor and bias correction based methods, were used for the comparative analyses. Different hydrological models were implemented in different catchments to simulate daily runoff. A set of flood indices were derived from daily flows and their changes have been evaluated by comparing their values derived from simulations corresponding to the current and future climate. Most of the implemented <span class="hlt">downscaling</span> methods project an increase in the extreme flow indices in most of the catchments. The catchments where the extremes are expected to increase have a rainfall-dominated flood regime. In these catchments, the <span class="hlt">downscaling</span> methods also project an increase in the extreme precipitation in the seasons when the extreme flows occur. In catchments where the flooding is mainly caused by spring/summer snowmelt, the <span class="hlt">downscaling</span> methods project a decrease in the extreme flows in three of the four catchments considered. A major portion of the variability in the projected changes in the extreme flow indices is attributable to the variability of the climate model <span class="hlt">ensemble</span>, although the statistical <span class="hlt">downscaling</span> methods contribute 35-60% of the total variance.</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>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>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://adsabs.harvard.edu/abs/2012amld.book..563R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012amld.book..563R"><span><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=air+AND+pollution+AND+europe&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=90781921&CFTOKEN=18571020','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=235875&keyword=air+AND+pollution+AND+europe&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=90781921&CFTOKEN=18571020"><span><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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_2 --> <div id="page_3" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="41"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy...44.2097B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy...44.2097B"><span>An <span class="hlt">ensemble</span> climate projection for Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Buontempo, Carlo; Mathison, Camilla; Jones, Richard; Williams, Karina; Wang, Changgui; McSweeney, Carol</p> <p>2015-04-01</p> <p>The Met Office Hadley Centre's PRECIS regional climate modelling system has been used to generate a five member <span class="hlt">ensemble</span> of climate projections for Africa over the 50 km resolution Coordinated Regional climate <span class="hlt">Downscaling</span> Experiment-Africa domain. The <span class="hlt">ensemble</span> comprises the <span class="hlt">downscaling</span> of a subset of the Hadley Centre's perturbed physics global climate model (GCM) <span class="hlt">ensemble</span> chosen to exclude <span class="hlt">ensemble</span> members unable to represent the African climate realistically and then to capture the spread in outcomes from the projections of the remaining models. The PRECIS simulations were run from December 1949 to December 2100. The regional climate model (RCM) <span class="hlt">ensemble</span> captures the annual cycle of temperatures well both for Africa as a whole and the sub-regions. It slightly overestimates precipitation over Africa as a whole and captures the annual cycle of rainfall for most of the African regions. The RCM <span class="hlt">ensemble</span> substantially improve the patterns and magnitude of precipitation simulation compared to their driving GCM which is particularly noticeable in the Sahel for both the magnitude and timing of the wet season. Present-day simulations of the RCM <span class="hlt">ensemble</span> are more similar to each other than those of the driving GCM <span class="hlt">ensemble</span> which indicates that their climatologies are influenced significantly by the RCM formulation and less so by their driving GCMs. Consistent with this, the spread and magnitudes of the large-scale responses of the RCMs are often different than the driving GCMs and arguably more credible given the improved performance of the RCM. This also suggests that local climate forcing will be a significant driver of the regional response to climate change over Africa.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ClDy..tmp..275B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ClDy..tmp..275B"><span>An <span class="hlt">ensemble</span> climate projection for Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Buontempo, Carlo; Mathison, Camilla; Jones, Richard; Williams, Karina; Wang, Changgui; McSweeney, Carol</p> <p>2014-09-01</p> <p>The Met Office Hadley Centre's PRECIS regional climate modelling system has been used to generate a five member <span class="hlt">ensemble</span> of climate projections for Africa over the 50 km resolution Coordinated Regional climate <span class="hlt">Downscaling</span> Experiment-Africa domain. The <span class="hlt">ensemble</span> comprises the <span class="hlt">downscaling</span> of a subset of the Hadley Centre's perturbed physics global climate model (GCM) <span class="hlt">ensemble</span> chosen to exclude <span class="hlt">ensemble</span> members unable to represent the African climate realistically and then to capture the spread in outcomes from the projections of the remaining models. The PRECIS simulations were run from December 1949 to December 2100. The regional climate model (RCM) <span class="hlt">ensemble</span> captures the annual cycle of temperatures well both for Africa as a whole and the sub-regions. It slightly overestimates precipitation over Africa as a whole and captures the annual cycle of rainfall for most of the African regions. The RCM <span class="hlt">ensemble</span> substantially improve the patterns and magnitude of precipitation simulation compared to their driving GCM which is particularly noticeable in the Sahel for both the magnitude and timing of the wet season. Present-day simulations of the RCM <span class="hlt">ensemble</span> are more similar to each other than those of the driving GCM <span class="hlt">ensemble</span> which indicates that their climatologies are influenced significantly by the RCM formulation and less so by their driving GCMs. Consistent with this, the spread and magnitudes of the large-scale responses of the RCMs are often different than the driving GCMs and arguably more credible given the improved performance of the RCM. This also suggests that local climate forcing will be a significant driver of the regional response to climate change over Africa.</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>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('http://adsabs.harvard.edu/abs/2010AGUFM.A21G0199C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A21G0199C"><span>Analogue <span class="hlt">Downscaling</span> of Seasonal Rainfall Forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Charles, A. N.; Timbal, B.; Hendon, H.</p> <p>2010-12-01</p> <p>We have taken an existing statistical <span class="hlt">downscaling</span> model (SDM), based on meteorological analogues that was developed for <span class="hlt">downscaling</span> climate change projections (Timbal et al 2009), and applied it in the seasonal forecasting context to produce <span class="hlt">downscaled</span> rainfall hindcasts from a coupled model seasonal forecast system (POAMA). <span class="hlt">Downscaling</span> of POAMA forecasts is required to provide seasonal climate information at local scales of interest. Analogue <span class="hlt">downscaling</span> is a simple technique to generate rainfall forecasts appropriate to the local scale by conditioning on the large scale predicted GCM circulation and the local topography and climate. Analogue methods are flexible and have been shown to produce good results when <span class="hlt">downscaling</span> 20th century South Eastern Australian rainfall output from climate models. A set of re-forecasts for three month rainfall at 170 observing stations in the South Murray Darling region of Australia were generated using predictors from the POAMA re-forecasts as input for the analogue SDM. The predictors were optimised over a number of different GCMS in previous climate change <span class="hlt">downscaling</span> studies. <span class="hlt">Downscaling</span> with the analogue SDM results in predicted rainfall with realistic variance while maintaining the modest predictive skill of the dynamical model. Evaluation of the consistency between the large scale mean of <span class="hlt">downscaled</span> and direct GCM output precipitation is encouraging.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27755746','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27755746"><span>The effects of climate <span class="hlt">downscaling</span> technique and observational data set on modeled ecological responses.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Pourmokhtarian, Afshin; Driscoll, Charles T; Campbell, John L; Hayhoe, Katharine; Stoner, Anne M K</p> <p>2016-07-01</p> <p>Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often 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 at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three <span class="hlt">downscaling</span> methods: the 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 atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (eight <span class="hlt">ensembles</span> of six 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 impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three <span class="hlt">downscaling</span> methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of <span class="hlt">downscaling</span> method also slightly affected the results, however not as much as the choice of observations. 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 biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241249&keyword=biometrics&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=78676012&CFTOKEN=86770661','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241249&keyword=biometrics&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=78676012&CFTOKEN=86770661"><span>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://ntrs.nasa.gov/search.jsp?R=20170002266&hterms=nasa&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dnasa','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20170002266&hterms=nasa&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dnasa"><span>NASA <span class="hlt">Downscaling</span> Project: Final Report</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa</p> <p>2017-01-01</p> <p>A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether <span class="hlt">downscaling</span> coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for <span class="hlt">downscaling</span> is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA - 2 reanalyses were used to drive the NU - WRF regional climate model and a GEOS - 5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000 - 2010. The results of these experiments were compared to observational datasets to evaluate the output.</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><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.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><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://adsabs.harvard.edu/abs/2013AGUFM.A11L..02D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A11L..02D"><span>Future hub-height wind speed distributions from statistically <span class="hlt">downscaled</span> CMIP5 simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Devis, A.; Demuzere, M.; van Lipzig, N.</p> <p>2013-12-01</p> <p>In order to realistically estimate wind-power yields, we need to know the hub-height wind speed under future climate conditions. Climate conditions of the upper atmosphere are commonly simulated using general circulation models (GCMs). However their typical resolutions are too coarse to assess the climate at the height of a wind turbine. This study simulates the hub-height wind speed probability distributions (PDFs) over Europe under future climate conditions. The analysis is based on the simulations of the CMIP5 earth system models, which are the latest development of GCMs. They include more components and feedbacks and their runs are performed at higher resolutions. In a first step, the <span class="hlt">ensemble</span> of GCMs is evaluated on their representation of the wind speed PDFs in the lower atmosphere using ERA-Interim data. The evaluation indicates that GCMs are skillful down to their lowest model levels apart for the south of Europe, which is affected by a large scale winter bias and for certain coastal and orographical regions. Secondly, a statistical approach is developed which <span class="hlt">downscales</span> the GCM output to the wind speed PDF at the height of the wind turbine hub. Since the evaluation analysis shows that GCMs are also skillful at the lower model levels, the statistical <span class="hlt">downscaling</span> uses GCM variables describing the lower atmosphere, instead of the commonly used large scale circulation variables of the upper atmosphere. By doing so less uncertainty will be added trough the <span class="hlt">downscaling</span> implementation. The <span class="hlt">downscaling</span> methodology is developed for an observational site in the Netherlands, using hub-height wind speed observations and ERA-Interim data for the period 1989-2009. The statistical approach is based on a regression analysis of the parameters of the PDFs. Results indicate that the predictor selection is very much defined by the stability conditions of the atmospheric boundary layer. During convective summer-day conditions, the observed hub-height wind speed can skillfully</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5210575','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5210575"><span><span class="hlt">Ensembl</span> 2017</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.; Achuthan, Premanand; Akanni, Wasiu; Amode, M. Ridwan; Bernsdorff, Friederike; Bhai, Jyothish; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.; Janacek, Sophie H.; Juettemann, Thomas; Keenan, Stephen; Laird, Matthew R.; Lavidas, Ilias; Maurel, Thomas; McLaren, William; Moore, Benjamin; Murphy, Daniel N.; Nag, Rishi; Newman, Victoria; Nuhn, Michael; Ong, Chuang Kee; Parker, Anne; Patricio, Mateus; Riat, Harpreet Singh; Sheppard, Daniel; Sparrow, Helen; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Walts, Brandon; Wilder, Steven P.; Zadissa, Amonida; Kostadima, Myrto; Martin, Fergal J.; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Staines, Daniel M.; Trevanion, Stephen J.; Cunningham, Fiona; Yates, Andrew; Zerbino, Daniel R.; Flicek, Paul</p> <p>2017-01-01</p> <p><span class="hlt">Ensembl</span> (www.<span class="hlt">ensembl</span>.org) is a database and genome browser for enabling research on vertebrate genomes. We import, analyse, curate and integrate a diverse collection of large-scale reference data to create a more comprehensive view of genome biology than would be possible from any individual dataset. Our extensive data resources include evidence-based gene and regulatory region annotation, genome variation and gene trees. An accompanying suite of tools, infrastructure and programmatic access methods ensure uniform data analysis and distribution for all supported species. Together, these provide a comprehensive solution for large-scale and targeted genomics applications alike. Among many other developments over the past year, we have improved our resources for gene regulation and comparative genomics, and added CRISPR/Cas9 target sites. We released new browser functionality and tools, including improved filtering and prioritization of genome variation, Manhattan plot visualization for linkage disequilibrium and eQTL data, and an ontology search for phenotypes, traits and disease. We have also enhanced data discovery and access with a track hub registry and a selection of new REST end points. All <span class="hlt">Ensembl</span> data are freely released to the scientific community and our source code is available via the open source Apache 2.0 license. PMID:27899575</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EOSTr..93...52P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EOSTr..93...52P"><span>Regional climate <span class="hlt">downscaling</span>: What's the point?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pielke, Roger A., Sr.; Wilby, Robert L.</p> <p>2012-01-01</p> <p>Dynamical and statistical <span class="hlt">downscaling</span> of multidecadal global climate models provides finer spatial resolution information for climate impact assessments [Wilby and Fowler, 2010]. Increasingly, some scientists are using the language of "prediction" with respect to future regional climate change and impacts [e.g., Hurrell et al., 2009; Shapiro et al., 2010], yet others note serious reservations about the capability of <span class="hlt">downscaling</span> to provide detailed, accurate predictions [see Kerr, 2011]. Dynamic <span class="hlt">downscaling</span> is based on regional climate models (usually just the atmospheric part) that have finer horizontal grid resolution of surface features such as terrain [Castro et al., 2005]. Statistical <span class="hlt">downscaling</span> uses transfer functions (e.g., regression relationships) representing observed relationships between larger-scale atmospheric variables and local quantities such as daily precipitation and/or temperature [Wilby and Fowler, 2010]. These approaches have been successful in improving the skill of numerical weather prediction. Statistical <span class="hlt">downscaling</span> can also be used as the benchmark (the control) against which dynamic <span class="hlt">downscaling</span> skill is judged [Landsea and Knaff, 2000</p> </li> <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>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>Methodology for Air Quality Forecast <span class="hlt">Downscaling</span> from Regional- to Street-Scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob</p> <p>2010-05-01</p> <p>The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how <span class="hlt">downscaling</span> from the European MACC <span class="hlt">ensemble</span> to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of <span class="hlt">downscaling</span> from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of <span class="hlt">downscaling</span> according to the proposed methodology are presented. The potential for <span class="hlt">downscaling</span> of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" <span class="hlt">downscaling</span> of European air-quality forecasts to the city and street levels with different approaches will be formulated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1812353V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812353V"><span>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/2013AGUFMGC11F..06G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC11F..06G"><span>Precipitation <span class="hlt">Downscaling</span> Products for Hydrologic Applications (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutmann, E. D.; Pruitt, T.; Liu, C.; Clark, M. P.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Rasmussen, R.</p> <p>2013-12-01</p> <p>Hydrologists and engineers require climate data on high-resolution grids (4-12km) for many water resources applications. To get such data from climate models, users have traditionally relied on statistical <span class="hlt">downscaling</span> techniques, with only limited use of dynamic <span class="hlt">downscaling</span> techniques. Statistical techniques utilize a variety of assumptions, data, and methodologies that result in statistical artifacts that may impact hydroclimate representations. These impacts are often pronounced when <span class="hlt">downscaling</span> precipitation. We will discuss four major statistical <span class="hlt">downscaling</span> techniques: Bias Corrected Constructed Analogue (BCCA), Asynchronous Regression (AR), and two forms of Bias Corrected Spatial Disaggregation (BCSD.) The hydroclimate representations within many statistical methods often have too much drizzle, too small extreme events, and an improper representation of spatial scaling characteristics. These scaling problems lead some statistical methods substantially over estimate extreme events at hydrologically important scales (e.g., basin totals.) This can lead to large errors in future hydrologic predictions. In contrast, high-resolution dynamic <span class="hlt">downscaling</span> using the Weather Research and Forecasting model (WRF) provides a better representation of precipitation in many respects, but at a much higher computational cost. This computational constraint prevents the use of high-resolution WRF simulations when examining the range of possible future scenarios generated as part of the Coupled Model Intercomparison Project (CMIP.) Finally, we will present a next generation psuedo-dynamical model that provides dynamic <span class="hlt">downscaling</span> information for a fraction of the computational requirements. This simple weather model uses large scale circulation patterns from a GCM, for example wind, temperature and humidity, but performs advection and microphysical calculations on a high-resolution grid, thus permitting topography to be adequately represented. This model is capable of generating</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>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/2013AGUFMIN23A1414R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMIN23A1414R"><span><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/2015EGUGA..17.5462V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5462V"><span>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/2012EGUGA..1412266Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1412266Y"><span>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> </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/2009AGUSMIN23A..04Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUSMIN23A..04Z"><span>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/2010EGUGA..1213219B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1213219B"><span>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/2010AGUFMGC23C0926S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC23C0926S"><span>Assessing the performance of dynamical and statistical <span class="hlt">downscaling</span> techniques to simulate crop yield in West Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sultan, B.; Oettli, P.; Vrac, M.; Baron, C.</p> <p>2010-12-01</p> <p>Global circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. It therefore requires the use of <span class="hlt">downscaling</span> methods. This study analyzes the performance of both dynamical and statistical <span class="hlt">downscaling</span> techniques in simulating crop yield at local scale. A detailed case study is conducted using historical weather data for Senegal, applied to the crop model SARRAH for simulating several tropical cereals (sorghum, millet, maize) at local scale. This control simulation is used as a benchmark to evaluate a set of Regional Climate Models (RCM) simulations, forced by ERA-Interim, from the <span class="hlt">ENSEMBLES</span> project and a statistical <span class="hlt">downscaling</span> method, the CDF-Transform, used to correct biases in RCM outputs. We first evaluate each climate variable that drives the simulated yield in the control simulation (radiation, rainfall, temperatures). We then simulate crop yields with RCM outputs (with or without applying the CDG-Transform) and evaluate the performance of each RCM in regards to crop yield simulations.</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>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://adsabs.harvard.edu/abs/2016EGUGA..1812384C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812384C"><span>FORWINE - Statistical <span class="hlt">Downscaling</span> of Seasonal forecasts for wine</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.</p> <p>2016-04-01</p> <p>The most renowned viticulture regions in the Iberian Peninsula have a long standing tradition in winemaking and are considered world-class grapevine (Vitis Vinifera L.) producing regions. Portugal is the 11th wine producer in the world, with internationally acclaimed wines, such as Port wine, and vineyards across the whole territory. Climate is widely acknowledged of one of the most important factors for grapevine development and growth (Fraga et al. 2014a and b; Jackson et al. 1993; Keller 2010). During the growing season (April-October in the Northern Hemisphere) of this perennial and deciduous crop, the climatic conditions are responsible for numerous morphologically and physiological changes. Anomalously low February-March mean temperature, anomalously high May mean temperature and anomalously high March precipitation tend to be favourable to wine production in the Douro Valley. Seasonal forecast of precipitation and temperature tailored to fit critical thresholds, for crucial seasons, can be used to inform management practices (viz. phytosanitary measures, land operations, marketing campaigns) and develop a wine production forecast. Statistical <span class="hlt">downscaling</span> of precipitation, maximum, minimum temperatures is used to model wine production following Santos et al. (2013) and to calculate bioclimatic indices. The skill of the <span class="hlt">ensemble</span> forecast is evaluated through anomaly correlation, ROC area, spread-error ratio and CRPS</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A41D0096W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A41D0096W"><span>Dynamical <span class="hlt">Downscaling</span> of Climate Change over the Hawaiian Islands</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Y.; Zhang, C.; Hamilton, K. P.; Lauer, A.</p> <p>2015-12-01</p> <p>The pseudo-global-warming (PGW) method was applied to the Hawaii Regional Climate Model (HRCM) to dynamically <span class="hlt">downscale</span> the projected climate in the late 21st century over the Hawaiian Islands. The initial and boundary conditions were adopted from MERRA reanalysis and NOAA SST data for the present-day simulations. The global warming increments constructed from the CMIP3 multi-model <span class="hlt">ensemble</span> mean were added to the reanalysis and SST data to perform the future climate simulations. We found that the Hawaiian Islands are vulnerable to global warming effects and the changes are diverse due to the varied topography. The windward side will have more clouds and receive more rainfall. The increase of the moisture in the boundary layer makes the major contribution. On the contrary, the leeward side will have less clouds and rainfall. The clouds and rain can slightly slow down the warming trend over the windward side. The temperature increases almost linearly with the terrain height. Cloud base and top heights will slightly decline in response to the slightly lower trade wind inversion base height, while the trade wind occurrence frequency will increase by about 8% in the future. More extreme rainfall events will occur in the warming climate over the Hawaiian Islands. And the snow cover on the top of Mauna Kea and Mauna Loa will nearly disappear in the future winter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/974391','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/974391"><span>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/2012AGUFM.H14C..05P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H14C..05P"><span>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://adsabs.harvard.edu/abs/2014AGUFM.A51H3129C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A51H3129C"><span>Statistical <span class="hlt">Downscaling</span> for the Northern Great Plains</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Coburn, J.</p> <p>2014-12-01</p> <p>The need for detailed, local scale information about the warming climate has led to the use of ever more complex and geographically realistic computer models as well as the use of regional models capable of capturing much finer details. Another class of methods for ascertaining localized data is known as statistical <span class="hlt">downscaling</span>, which offers some advantages over regional models, especially in the realm of computational efficiency. Statistical <span class="hlt">downscaling</span> can be described as the process of linking coarse resolution climate model output to that of fine resolution or even station-level data via statistical relationships with the purpose of correcting model biases at the local scale. The development and application of <span class="hlt">downscaling</span> has given rise to a plethora of techniques which have been applied to many spatial scales and multiple climate variables. In this study two <span class="hlt">downscaling</span> processes, bias-corrected statistical <span class="hlt">downscaling</span> (BCSD) and canonical correlation analysis (CCA), are applied to minimum and maximum temperatures and precipitation for the Northern Great Plains (NGP, 40 - 53°N and 95 - 120°W) region at both daily and monthly time steps. The abilities of the methods were tested by assessing their ability to recreate local variations in a set of both spatial and temporal climate metrics obtained through the analysis of 1/16 degree station data for the period 1950 to 2000. Model data for temperature, precipitation and a set of predictor variables were obtained from CMIP5 for 15 models. BCSD was applied using direct comparison and correction of the variable distributions via quadrant mapping. CCA was calibrated on the data for the period 1950 to 1980 using a series of model-based predictor variables screened for increasing skill, with the derived model being applied to the period 1980 to 2000 so as to verify that it could recreate the overall climate patterns and trends. As in previous studies done on other regions, it was found that the CCA method recreated</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005TellA..57..435G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005TellA..57..435G"><span>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://adsabs.harvard.edu/abs/2012JHyd..475..350T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JHyd..475..350T"><span>Comparison of two analog-based <span class="hlt">downscaling</span> methods for regional reference evapotranspiration forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tian, Di; Martinez, Christopher J.</p> <p>2012-12-01</p> <p>SummaryThe objective of this study was to compare the performance of natural analog (NA) and constructed analog (CA) methods to produce both probabilistic and deterministic <span class="hlt">downscaled</span> daily reference evapotranspiration (ETo) forecasts in the southeastern United States. The 1-15 day, 15-member ETo forecasts were produced from 1979 to 2009 using the Penman-Monteith equation and a forecast analog approach with a combination of the Global Forecast System (GFS) reforecasts and NCEP-DOE Reanalysis 2 climatology, and were <span class="hlt">downscaled</span> using the North American Regional Reanalysis (NARR). The Pearson correlation coefficient (R), mean squared error skill score (MSESS), and Bias were used to evaluate the skill of <span class="hlt">downscaled</span> deterministic forecasts. The Linear Error in Probability Space (LEPS) skill score, Brier Skill Score (BSS), relative operating characteristic, and reliability diagrams were used to evaluate the skill of <span class="hlt">downscaled</span> probabilistic forecasts. Overall, CA showed slightly higher skill than NA in terms of the metrics for deterministic forecasts, while for probabilistic forecasts NA showed higher skill than CA regarding the BSS in five categories (terciles, and 10th and 90th percentiles) and lower skill than CA regarding the LEPS skill score. Both CA and NA produced skillful deterministic results in the first 3 lead days, while the skill was higher for CA than for NA. Probabilistic NA forecasts exhibited higher resolution and reliability than CA, likely due to a larger <span class="hlt">ensemble</span> size. Forecasts by both methods showed the lowest skill in the Florida peninsula and in mountainous areas, likely due to the fact that these features were not well-resolved in the model forecast.</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><span class="hlt">Downscaling</span> Reanalysis over Continental Africa with a Regional Model: NCEP Versus ERA Interim Forcing</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Druyan, Leonard M.; Fulakeza, Matthew B.</p> <p>2013-01-01</p> <p>Five annual climate cycles (1998-2002) are simulated for continental Africa and adjacent oceans by a regional atmospheric model (RM3). RM3 horizontal grid spacing is 0.44deg at 28 vertical levels. Each of 2 simulation <span class="hlt">ensembles</span> is driven by lateral boundary conditions from each of 2 alternative reanalysis data sets. One simulation downs cales National Center for Environmental Prediction reanalysis 2 (NCPR2) and the other the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I). NCPR2 data are archived at 2.5deg grid spacing, while a recent version of ERA-I provides data at 0.75deg spacing. ERA-I-forced simulations are recomrp. ended by the Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX). Comparisons of the 2 sets of simulations with each other and with observational evidence assess the relative performance of each <span class="hlt">downscaling</span> system. A third simulation also uses ERA-I forcing, but degraded to the same horizontal resolution as NCPR2. RM3-simulated pentad and monthly mean precipitation data are compared to Tropical Rainfall Measuring Mission (TRMM) data, gridded at 0.5deg, and RM3-simulated circulation is compared to both reanalyses. Results suggest that each <span class="hlt">downscaling</span> system provides advantages and disadvantages relative to the other. The RM3/NCPR2 achieves a more realistic northward advance of summer monsoon rains over West Africa, but RM3/ERA-I creates the more realistic monsoon circulation. Both systems recreate some features of JulySeptember 1999 minus 2002 precipitation differences. Degrading the resolution of ERA-I driving data unrealistically slows the monsoon circulation and considerably diminishes summer rainfall rates over West Africa. The high resolution of ERA-I data, therefore, contributes to the quality of the <span class="hlt">downscaling</span>, but NCPR2laterai boundary conditions nevertheless produce better simulations of some features.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012SPIE.8294E..0BP','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012SPIE.8294E..0BP"><span>Exploring <span class="hlt">ensemble</span> visualization</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Phadke, Madhura N.; Pinto, Lifford; Alabi, Oluwafemi; Harter, Jonathan; Taylor, Russell M., II; 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.</p> </li> <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>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/2011AGUFMGC21B0879Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC21B0879Z"><span>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/2016EGUGA..18.4328V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4328V"><span>The role of <span class="hlt">ensemble</span> post-processing for modeling the <span class="hlt">ensemble</span> tail</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Van De Vyver, Hans; Van Schaeybroeck, Bert; Vannitsem, Stéphane</p> <p>2016-04-01</p> <p>. Soc. 134: 2051-2066.Buizza and Leutbecher, 2015: The forecast skill horizon, Q. J. R. Meteorol. Soc. 141: 3366-3382.Ferro, 2007: A probability model for verifying deterministic forecasts of extreme events. Weather and Forecasting 22 (5), 1089-1100.Friederichs, 2010: Statistical <span class="hlt">downscaling</span> of extreme precipitation events using extreme value theory. Extremes 13, 109-132.Van Schaeybroeck and Vannitsem, 2015: <span class="hlt">Ensemble</span> post-processing using member-by-member approaches: theoretical aspects. Q.J.R. Meteorol. Soc., 141: 807-818.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=steel&pg=2&id=EJ969636','ERIC'); return false;" href="http://eric.ed.gov/?q=steel&pg=2&id=EJ969636"><span>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://pubs.er.usgs.gov/publication/70022063','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70022063"><span>Hydrological responses to dynamically and statistically <span class="hlt">downscaled</span> climate model output</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Wilby, R.L.; Hay, L.E.; Gutowski, W.J.; Arritt, R.W.; Takle, E.S.; Pan, Z.; Leavesley, G.H.; Clark, M.P.</p> <p>2000-01-01</p> <p>Daily rainfall and surface temperature series were simulated for the Animas River basin, Colorado using dynamically and statistically <span class="hlt">downscaled</span> output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis. A distributed hydrological model was then applied to the <span class="hlt">downscaled</span> data. Relative to raw NCEP output, <span class="hlt">downscaled</span> climate variables provided more realistic stimulations of basin scale hydrology. However, the results highlight the sensitivity of modeled processes to the choice of <span class="hlt">downscaling</span> technique, and point to the need for caution when interpreting future hydrological scenarios.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ThApC.tmp...64G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ThApC.tmp...64G"><span>Application of physical scaling towards <span class="hlt">downscaling</span> climate model precipitation data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gaur, Abhishek; Simonovic, Slobodan P.</p> <p>2017-03-01</p> <p>Physical scaling (SP) method <span class="hlt">downscales</span> climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards <span class="hlt">downscaling</span> North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art <span class="hlt">downscaling</span> methods: statistical <span class="hlt">downscaling</span> model (SDSM) and generalized linear modeling (GLM). The <span class="hlt">downscaled</span> precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the <span class="hlt">downscaling</span> methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in <span class="hlt">downscaling</span> precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to <span class="hlt">downscale</span> future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The <span class="hlt">downscaled</span> future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2-4 day), and long (more than 5-day) precipitation events is projected.</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>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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PEPS....2...42S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PEPS....2...42S"><span><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/2014ClDy...43.3201G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ClDy...43.3201G"><span>Comparison of statistically <span class="hlt">downscaled</span> precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gaitan, Carlos F.; Hsieh, William W.; Cannon, Alex J.</p> <p>2014-12-01</p> <p>Given the coarse resolution of global climate models, <span class="hlt">downscaling</span> techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical <span class="hlt">downscaling</span> experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically <span class="hlt">downscaled</span> daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and <span class="hlt">ensembles</span>, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971-2000) and A2 (2041-2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree <span class="hlt">ensembles</span> outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of <span class="hlt">downscaling</span> models deteriorated in future climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JMetR..30..867W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JMetR..30..867W"><span>Projection of summer precipitation over the Yangtze-Huaihe River basin using multimodel statistical <span class="hlt">downscaling</span> based on canonical correlation analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Dan; Jiang, Zhihong; Ma, Tingting</p> <p>2016-12-01</p> <p>By using observational daily precipitation data over the Yangtze-Huaihe River basin, ERA-40 data, and the data from eight CMIP5 climate models, statistical <span class="hlt">downscaling</span> models are constructed based on BP-CCA (combination of empirical orthogonal function and canonical correlation analysis) to project future changes of precipitation. The results show that the absolute values of domain-averaged precipitation relative errors of most models are reduced from 8%-46% to 1%-7% after statistical <span class="hlt">downscaling</span>. The spatial correlations are all improved from less than 0.40 to more than 0.60. As a result of the statistical <span class="hlt">downscaling</span> multimodel <span class="hlt">ensemble</span> (SDMME), the relative error is improved from-15.8% to-1.3%, and the spatial correlation increases significantly from 0.46 to 0.88. These results demonstrate that the simulation skill of SDMME is relatively better than that of the multimodel <span class="hlt">ensemble</span> (MME) and the <span class="hlt">downscaling</span> of most individual models. The projections of SDMME reveal that under the RCP (Representative Concentration Pathway) 4.5 scenario, the projected domain-averaged precipitation changes for the early (2016-2035), middle (2046-2065), and late (2081-2100) 21st century are-1.8%, 6.1%, and 9.9%, respectively. For the early period, the increasing trends of precipitation in the western region are relatively weak, while the precipitation in the east shows a decreasing trend. Furthermore, the reliability of the projected changes over the area east of 115 ◦ E is higher than that in the west. The stations with significant increasing trends are primarily located over the western region in both the middle and late periods, with larger magnitude for the latter. Stations with high reliability mainly appear in the region north of 28.5 ◦ N for both periods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280783&keyword=Weather+AND+forecasting&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=90684389&CFTOKEN=76431295','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280783&keyword=Weather+AND+forecasting&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=90684389&CFTOKEN=76431295"><span>"Going the Extra Mile in <span class="hlt">Downscaling</span>: Why <span class="hlt">Downscaling</span> is not jut "Plug-and-Play"</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of <span class="hlt">downscaling</span> the Comm...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.8856G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.8856G"><span>Validation of a Universal Multifractal <span class="hlt">downscaling</span> process with the help of dense networks of disdrometers</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Berne, Alexis; Lovejoy, Shaun</p> <p>2013-04-01</p> <p>The resolution of the rainfall data usually provided by operational C-band radar networks of Western European meteorological services is 1 km in space and 5 min in time. It has been shown that higher resolutions are needed for various applications, notably in the field of urban hydrology. A way of dealing with this unmeasured small scale rainfall variability is to input stochastically <span class="hlt">downscaled</span> rainfall fields to urban hydrological models and simulate not a single response for the studied catchment but an <span class="hlt">ensemble</span>. In this paper we suggest to discuss a <span class="hlt">downscaling</span> procedure for the rainfall field. It relies on the Universal Multifractals which have been extensively used to model and simulate geophysical fields extremely variable over a wide range of spatio-temporal scales such as rainfall. Here this standard framework of multiplicative cascades has been modified in a discrete case to better take into account the numerous zeros of the rainfall field (i.e. a pixel with no rainfall recorded). More precisely the zeros are introduced at each scale within the cascade process in a probabilistic scale invariant way. The <span class="hlt">downscaling</span> suggested here consists in retrieving the scaling properties of the rainfall field on the available range of scales and stochastically continuing the underlying process below the scale of observation. Rainfall data coming from a dense network of 16 optical disdrometers (Particle Size and Velocity, PARSIVEL, 1st generation) that was deployed for 16 month over an area of approximately 1 km2 in the campus of Ecole Polytechnique Federale de Lausanne (Switzerland) will be used to validate this <span class="hlt">downscaling</span> procedure. Preliminary results with a network of second generation PARSIVEL currently under construction in Ecole des Ponts ParisTech (France) will also be shown. The methodology implemented consists in <span class="hlt">downscaling</span> a rainfall field with a resolution of 1 km and 5 min to a resolution comparable with the disdrometers' one (few tens of cm and 1 min</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC21E..08B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC21E..08B"><span>Multisite stochastic <span class="hlt">downscaling</span> of climate model precipitation outputs to high resolution scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bordoy, R.; Burlando, P.</p> <p>2011-12-01</p> <p> show that, between certain scaling regimes, it is possible to use observed precipitation scaling properties to extrapolate RCMs statistics at resolutions lower than those conventionally provided by RCMs. It is shown that the explored <span class="hlt">downscaling</span> technique has several advantages over other existing techniques. Among others it allows the investigation of changes of the internal properties of precipitation, thereby including variability and extremes, and the generation of an <span class="hlt">ensemble</span> of stochastic realizations through a Monte Carlo approach. These features are an advantage over other <span class="hlt">downscaling</span> procedures since they allow the explicit investigation of uncertainty due to the internal variability of the precipitation process. The results of this novel methodology are presented with regard to future scenarios generation in an alpine catchment in Switzerland using the RCM RegCM3 and the emission scenario A1B for the period 2011-2050.</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>The Personal Software Process: <span class="hlt">Downscaling</span> the factory</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roy, Daniel M.</p> <p>1994-01-01</p> <p>It is argued that the next wave of software process improvement (SPI) activities will be based on a people-centered paradigm. The most promising such paradigm, Watts Humphrey's personal software process (PSP), is summarized and its advantages are listed. The concepts of the PSP are shown also to fit a <span class="hlt">down-scaled</span> version of Basili's experience factory. The author's data and lessons learned while practicing the PSP are presented along with personal experience, observations, and advice from the perspective of a consultant and teacher for the personal software process.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.6789R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.6789R"><span>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://adsabs.harvard.edu/abs/2016BGeo...13.4271F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016BGeo...13.4271F"><span>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://adsabs.harvard.edu/abs/2007PhyA..385..199S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007PhyA..385..199S"><span>Subspace <span class="hlt">ensembles</span> for classification</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sun, Shiliang; Zhang, Changshui</p> <p>2007-11-01</p> <p><span class="hlt">Ensemble</span> learning constitutes one of the principal current directions in machine learning and data mining. In this paper, we explore subspace <span class="hlt">ensembles</span> for classification by manipulating different feature subspaces. Commencing with the nature of <span class="hlt">ensemble</span> efficacy, we probe into the microcosmic meaning of <span class="hlt">ensemble</span> diversity, and propose to use region partitioning and region weighting to implement effective subspace <span class="hlt">ensembles</span>. Individual classifiers possessing eminent performance on a partitioned region reflected by high neighborhood accuracies are deemed to contribute largely to this region, and are assigned large weights in determining the labels of instances in this area. A robust algorithm “Sena” that incarnates the mechanism is presented, which is insensitive to the number of nearest neighbors chosen to calculate neighborhood accuracies. The algorithm exhibits improved performance over the well-known <span class="hlt">ensembles</span> of bagging, AdaBoost and random subspace. The difference of its effectivity with varying base classifiers is also investigated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8278P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8278P"><span>Statistical Testing of Dynamically <span class="hlt">Downscaled</span> Rainfall Data for the East Coast of Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Parana Manage, Nadeeka; Lockart, Natalie; Willgoose, Garry; Kuczera, George</p> <p>2015-04-01</p> <p>This study performs a validation of statistical properties of <span class="hlt">downscaled</span> climate data, concentrating on the rainfall which is required for hydrology predictions used in reservoir simulations. The data sets used in this study have been produced by the NARCliM (NSW/ACT Regional Climate Modelling) project which provides a dynamically <span class="hlt">downscaled</span> climate dataset for South-East Australia at 10km resolution. NARCliM has used three configurations of the Weather Research Forecasting Regional Climate Model and four different GCMs (MIROC-medres 3.2, ECHAM5, CCCMA 3.1 and CSIRO mk3.0) from CMIP3 to perform twelve <span class="hlt">ensembles</span> of simulations for current and future climates. Additionally to the GCM-driven simulations, three control run simulations driven by the NCEP/NCAR reanalysis for the entire period of 1950-2009 has also been performed by the project. The validation has been performed in the Upper Hunter region of Australia which is a semi-arid to arid region 200 kilometres North-West of Sydney. The analysis used the time series of <span class="hlt">downscaled</span> rainfall data and ground based measurements for selected Bureau of Meteorology rainfall stations within the study area. The initial testing of the gridded rainfall was focused on the autoregressive characteristics of time series because the reservoir performance depends on long-term average runoffs. A correlation analysis was performed for fortnightly, monthly and annual averaged time resolutions showing a good statistical match between reanalysis and ground truth. The spatial variation of the statistics of gridded rainfall series were calculated and plotted at the catchment scale. The spatial correlation analysis shows a poor agreement between NARCliM data and ground truth at each time resolution. However, the spatial variability plots show a strong link between the statistics and orography at the catchment scale.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110013410','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110013410"><span>The <span class="hlt">Ensemble</span> Canon</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>MIittman, David S</p> <p>2011-01-01</p> <p><span class="hlt">Ensemble</span> is an open architecture for the development, integration, and deployment of mission operations software. Fundamentally, it is an adaptation of the Eclipse Rich Client Platform (RCP), a widespread, stable, and supported framework for component-based application development. By capitalizing on the maturity and availability of the Eclipse RCP, <span class="hlt">Ensemble</span> offers a low-risk, politically neutral path towards a tighter integration of operations tools. The <span class="hlt">Ensemble</span> project is a highly successful, ongoing collaboration among NASA Centers. Since 2004, the <span class="hlt">Ensemble</span> project has supported the development of mission operations software for NASA's Exploration Systems, Science, and Space Operations Directorates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5019R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5019R"><span>Defining predictand areas with homogeneous predictors for spatially coherent precipitation <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Radanovics, Sabine; Vidal, Jean-Philippe; Sauquet, Eric; Ben Daoud, Aurélien; Bontron, Guillaume</p> <p>2013-04-01</p> <p> the 5 ones that lead to the highest CRPSS for the zone in question are retained. The 5 retained domains were found to be equally skillfull with a maximum difference of around 1% of CRPSS on average, and are thus all candidates for clustering predictand zones. An objective procedure has then been implemented for clustering zones together, based on their sharing a common predictor domain inside their 5 near-optimal domain <span class="hlt">ensemble</span>. For zones sharing several near-optimal predictor domains, the aim was to minimise the number of disjoint predictand areas. Furthermore solutions that lead to more similar sized areas were preferred. This procedure defines areas with natural spatial coherence and reduces the number of different predictor domains using a procedure based on objective rules, unlike most of studies where this is done either subjectively or arbitrarily. It allowed to reduce significantly the number of independent zones and to identify large homogeneous areas encompassing relatively large river basins. Further developments will address the issue of spatial coherent <span class="hlt">downscaling</span> for predictand areas that do not share any near-optimal predictor domains. Ben Daoud, A., Sauquet, E., Lang, M., Bontron, G., and Obled, C. (2011). Precipitation forecasting through an analog sorting technique: a comparative study. Advances in Geosciences, 29:103-107. doi: 10.5194/adgeo-29-103-2011 Timbal, B., Dufour, A., and McAvaney, B. (2003). An estimate of future climate change for western France using a statistical <span class="hlt">downscaling</span> technique. Climate Dynamics, 20(7-8):807-823. doi: 10.1007/s00382-002-0298-9 Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M. (2010) A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology, 30:1627-1644. doi: 10.1002/joc.2003</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014WRR....50..562B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014WRR....50..562B"><span>Stochastic <span class="hlt">downscaling</span> of climate model precipitation outputs in orographically complex regions: 2. <span class="hlt">Downscaling</span> methodology</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bordoy, R.; Burlando, P.</p> <p>2014-01-01</p> <p>A new methodology of stochastic <span class="hlt">downscaling</span> of climate model precipitation outputs to subdaily temporal resolution and in a multisite framework is presented. The methodology is based on the reparameterization for future climate of the Spatiotemporal Neyman-Scott Rectangular Pulses model. The reparameterization is carried out by estimating the model parameters as done for the calibration of the model for the historical climate and using future statistics that are obtained: (i) applying to the daily historical statistics a factor of change computed from the control and future climate model outputs and (ii) by rescaling the altered daily statistics according to the scaling properties exhibited by the historical raw moments, in order to generate the future statistics at the temporal resolutions required by the reparameterization procedure. The <span class="hlt">downscaled</span> scenarios are obtained in a multisite framework accounting for cross correlations among the stations. The methodology represents a robust, efficient, and unique approach to generate multiple series of spatially distributed subdaily precipitation scenarios by Monte Carlo simulation. It presents thus a unique alternative for addressing the internal variability of the precipitation process at high temporal and spatial resolution, as compared to other <span class="hlt">downscaling</span> techniques, which are affected by both computational and resolution problems. The application of the presented approach is demonstrated for a region of complex orography where the model has proved to provide good results, in order to analyze potential changes in such vulnerable areas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/881929','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/881929"><span>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/2016ThApC.tmp..256S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp..256S"><span>Annual statistical <span class="hlt">downscaling</span> of precipitation and evaporation and monthly disaggregation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sachindra, D. A.; Perera, B. J. C.</p> <p>2016-10-01</p> <p>Development of <span class="hlt">downscaling</span> models for each calendar month using the data of predictors specifically selected for each calendar month may assists in better capturing the time-varying nature of the predictor-predictand relationships. Such approach will not allow the explicit modelling of the persistence of the predictand (e.g. lag-1 correlation). However, <span class="hlt">downscaling</span> at an annual time step and subsequent disaggregation to monthly values can explicitly consider the modelling of the persistence of the predictand. This study investigated the potential of annual <span class="hlt">downscaling</span> of a predictand and subsequent disaggregation of annual values to monthly values, in comparison to the potential of <span class="hlt">downscaling</span> models separately developed for each calendar month. In the case study, annual and monthly <span class="hlt">downscaling</span> models were developed for precipitation and evaporation at two stations located in Victoria, Australia. The output of the annual <span class="hlt">downscaling</span> models was then disaggregated into monthly values using four different methods based on the method of fragments. It was found that the annual to monthly disaggregation methods and monthly <span class="hlt">downscaling</span> models are able to reproduce the average of monthly observations with relatively higher accuracy in comparison to their ability in reproducing standard deviation, skewness and lag-1 serial correlation. <span class="hlt">Downscaling</span> models separately developed for each calendar month were able to show relatively smaller root mean square errors for their time series indicating better overall agreement with observations in comparison to their counterpart annual to monthly disaggregation methods. Furthermore, it was found that not only the bias in the output of an annual <span class="hlt">downscaling</span> model but also the presence of annual totals in the records of observations of a predictand that are very similar in magnitude, but having significantly different sets of fragments, can largely contribute to the poor performance of an annual to monthly disaggregation method.</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><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/abs/2015EGUGA..17.5500I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5500I"><span>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>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/2015CliPD..11.4425C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015CliPD..11.4425C"><span>Probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caillouet, L.; Vidal, J.-P.; Sauquet, E.; Graff, B.</p> <p>2015-09-01</p> <p>This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871-2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late 19th century onwards. The SANDHY (Stepwise ANalogue <span class="hlt">Downscaling</span> method for HYdrology) statistical <span class="hlt">downscaling</span> method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the SAFRAN high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily <span class="hlt">ensemble</span> of 125 analogues dates over the 1871-2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping unchanged the structure of the SANDHY method while reducing those seasonal biases. The calendar selection keeps the closest analogue dates in the year for each target date. The stepwise selection applies two new analogy steps based on similarity of the Sea Surface Temperature (SST) and the large-scale Two-metre Temperature (T2m). Comparisons to the SAFRAN reanalysis over 1959-2007 and to homogenized series over the whole 20th century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method</p> </li> </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/2016EGUGA..18.3876K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.3876K"><span>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/2016HESS...20.3059L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016HESS...20.3059L"><span>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/2016ClDy..tmp..157T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy..tmp..157T"><span>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/2015AGUFMGC44A..08C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC44A..08C"><span>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/2017ClDy...48..881T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy...48..881T"><span>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>2017-02-01</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/2003JGRD..108.8863P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003JGRD..108.8863P"><span>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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4756621','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4756621"><span><span class="hlt">Ensembl</span> regulation resources</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zerbino, Daniel R.; Johnson, Nathan; Juetteman, Thomas; Sheppard, Dan; Wilder, Steven P.; Lavidas, Ilias; Nuhn, Michael; Perry, Emily; Raffaillac-Desfosses, Quentin; Sobral, Daniel; Keefe, Damian; Gräf, Stefan; Ahmed, Ikhlak; Kinsella, Rhoda; Pritchard, Bethan; Brent, Simon; Amode, Ridwan; Parker, Anne; Trevanion, Steven; Birney, Ewan; Dunham, Ian; Flicek, Paul</p> <p>2016-01-01</p> <p>New experimental techniques in epigenomics allow researchers to assay a diversity of highly dynamic features such as histone marks, DNA modifications or chromatin structure. The study of their fluctuations should provide insights into gene expression regulation, cell differentiation and disease. The <span class="hlt">Ensembl</span> project collects and maintains the <span class="hlt">Ensembl</span> regulation data resources on epigenetic marks, transcription factor binding and DNA methylation for human and mouse, as well as microarray probe mappings and annotations for a variety of chordate genomes. From this data, we produce a functional annotation of the regulatory elements along the human and mouse genomes with plans to expand to other species as data becomes available. Starting from well-studied cell lines, we will progressively expand our library of measurements to a greater variety of samples. Ensembl’s regulation resources provide a central and easy-to-query repository for reference epigenomes. As with all <span class="hlt">Ensembl</span> data, it is freely available at http://www.<span class="hlt">ensembl</span>.org, from the Perl and REST APIs and from the public <span class="hlt">Ensembl</span> MySQL database server at ensembldb.<span class="hlt">ensembl</span>.org. Database URL: http://www.<span class="hlt">ensembl</span>.org PMID:26888907</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC33G..08M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC33G..08M"><span>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><span class="hlt">Downscaling</span> of slip distribution for strong earthquakes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yoshida, T.; Oya, S.; Kuzuha, Y.</p> <p>2013-12-01</p> <p>We intend to develop a <span class="hlt">downscaling</span> model to enhance the earthquake slip distribution resolution. Slip distributions have been obtained by other researchers using various inversion methods. As a <span class="hlt">downscaling</span> model, we are discussing fractal models that include mono-fractal models (fractional Brownian motion, fBm; fractional Lévy motion, fLm) and multi-fractal models as candidates. Log - log-linearity of k (wave number) versus E (k) (power spectrum) is the necessary condition for fractality: the slip distribution is expected to satisfy log - log-linearity described above if we can apply fractal model to a slip distribution as a <span class="hlt">downscaling</span> model. Therefore, we conducted spectrum analyses using slip distributions of 11 earthquakes as explained below. 1) Spectrum analyses using one-dimensional slip distributions (strike direction) were conducted. 2) Averaging of some results of power spectrum (dip direction) was conducted. Results show that, from the viewpoint of log - log-linearity, applying a fractal model to slip distributions can be inferred as valid. We adopt the filtering method after Lavallée (2008) to generate fBm/ fLm. In that method, generated white noises (random numbers) are filtered using a power law type filter (log - log-linearity of the spectrum). Lavallée (2008) described that Lévy white noise that generates fLm is more appropriate than the Gaussian white noise which generates fBm. In addition, if the 'alpha' parameter of the Lévy law, which governs the degree of attenuation of tails of the probability distribution, is 2.0, then the Lévy distribution is equivalent to the Gauss distribution. We analyzed slip distributions of 11 earthquakes: the Tohoku earthquake (Wei et al., 2011), Haiti earthquake (Sladen, 2010), Simeulue earthquake (Sladen, 2008), eastern Sichuan earthquake (Sladen, 2008), Peru earthquake (Konca, 2007), Tocopilla earthquake (Sladen, 2007), Kuril earthquake (Sladen, 2007), Benkulu earthquake (Konca, 2007), and southern Java</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H42D..04N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H42D..04N"><span>Satellite-Enhanced Dynamical <span class="hlt">Downscaling</span> of Extreme Events</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nunes, A.</p> <p>2015-12-01</p> <p>Severe weather events can be the triggers of environmental disasters in regions particularly susceptible to changes in hydrometeorological conditions. In that regard, the reconstruction of past extreme weather events can help in the assessment of vulnerability and risk mitigation actions. Using novel modeling approaches, dynamical <span class="hlt">downscaling</span> of long-term integrations from global circulation models can be useful for risk analysis, providing more accurate climate information at regional scales. Originally developed at the National Centers for Environmental Prediction (NCEP), the Regional Spectral Model (RSM) is being used in the dynamical <span class="hlt">downscaling</span> of global reanalysis, within the South American Hydroclimate Reconstruction Project. Here, RSM combines scale-selective bias correction with assimilation of satellite-based precipitation estimates to <span class="hlt">downscale</span> extreme weather occurrences. Scale-selective bias correction is a method employed in the <span class="hlt">downscaling</span>, similar to the spectral nudging technique, in which the <span class="hlt">downscaled</span> solution develops in agreement with its coarse boundaries. Precipitation assimilation acts on modeled deep-convection, drives the land-surface variables, and therefore the hydrological cycle. During the <span class="hlt">downscaling</span> of extreme events that took place in Brazil in recent years, RSM continuously assimilated NCEP Climate Prediction Center morphing technique precipitation rates. As a result, RSM performed better than its global (reanalysis) forcing, showing more consistent hydrometeorological fields compared with more sophisticated global reanalyses. Ultimately, RSM analyses might provide better-quality initial conditions for high-resolution numerical predictions in metropolitan areas, leading to more reliable short-term forecasting of severe local storms.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20060015642','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20060015642"><span><span class="hlt">Ensemble</span> Data Mining Methods</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oza, Nikunj C.</p> <p>2004-01-01</p> <p><span class="hlt">Ensemble</span> Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an <span class="hlt">ensemble</span> is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in <span class="hlt">ensemble</span> methods has largely revolved around designing <span class="hlt">ensembles</span> consisting of competent yet complementary models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1160288','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1160288"><span>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/2015OcMod..90...57M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015OcMod..90...57M"><span><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('https://www.ncbi.nlm.nih.gov/pubmed/26896847','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26896847"><span><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('https://www.ncbi.nlm.nih.gov/pubmed/24701932','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24701932"><span><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.</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>Credibility of statistical <span class="hlt">downscaling</span> under nonstationary climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salvi, Kaustubh; Ghosh, Subimal; Ganguly, Auroop R.</p> <p>2016-03-01</p> <p>Statistical <span class="hlt">downscaling</span> (SD) establishes empirical relationships between coarse-resolution climate model simulations with higher-resolution climate variables of interest to stakeholders. These statistical relations are estimated based on historical observations at the finer resolutions and used for future projections. The implicit assumption is that the SD relations, extracted from data are stationary or remain unaltered, despite non-stationary change in climate. The validity of this assumption relates directly to the credibility of SD. Falsifiability of climate projections is a challenging proposition. Calibration and verification, while necessary for SD, are unlikely to be able to reproduce the full range of behavior that could manifest at decadal to century scale lead times. We propose a design-of-experiments (DOE) strategy to assess SD performance under nonstationary climate and evaluate the strategy via a transfer-function based SD approach. The strategy relies on selection of calibration and validation periods such that they represent contrasting climatic conditions like hot-versus-cold and ENSO-versus-non-ENSO years. The underlying assumption is that conditions such as warming or predominance of El Niño may be more prevalent under climate change. In addition, two different historical time periods are identified, which resemble pre-industrial and the most severe future emissions scenarios. The ability of the empirical relations to generalize under these proxy conditions is considered an indicator of their performance under future nonstationarity. Case studies over two climatologically disjoint study regions, specifically India and Northeast United States, reveal robustness of DOE in identifying the locations where nonstationarity prevails as well as the role of effective predictor selection under nonstationarity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016HESS...20.1483W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016HESS...20.1483W"><span>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>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/2013ClDy...41..255D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ClDy...41..255D"><span>Dynamic <span class="hlt">downscaling</span> of 22-year CFS winter seasonal hindcasts with the UCLA-ETA regional climate model over the 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>De Sales, Fernando; Xue, Yongkang</p> <p>2013-07-01</p> <p>This study evaluates the UCLA-ETA regional model's dynamic <span class="hlt">downscaling</span> ability to improve the National Center for Environmental Prediction Climate Forecast System (NCEP CFS), winter season predictions over the contiguous United States (US). Spatial distributions and temporal variations of seasonal and monthly precipitation are the main focus. A multi-member <span class="hlt">ensemble</span> means of 22 winters from 1982 through 2004 are included in the study. CFS over-predicts the precipitation in eastern and western US by as much as 45 and 90 % on average compared to observations, respectively. Dynamic <span class="hlt">downscaling</span> improves the precipitation hindcasts across the domain, except in the southern States, by substantially reducing the excessive precipitation produced by the CFS. Average precipitation root-mean-square error for CFS and UCLA-ETA are 1.5 and 0.9 mm day-1, respectively. In addition, <span class="hlt">downscaling</span> improves the simulation of spatial distribution of snow water equivalent and land surface heat fluxes. Despite these large improvements, the UCLA-ETA's ability to improve the inter-annual and intra-seasonal precipitation variability is not clear, probably because of the imposed CFS' lateral boundary conditions. Preliminary analysis of the cause for the large precipitation differences between the models reveals that the CFS appears to underestimate the moisture flux convergence despite producing excessive precipitation amounts. Additionally, the comparison of modeled monthly surface sensible and latent heat fluxes with Global Land Data Assimilation System land data set shows that the CFS incorrectly partitioned most of surface energy into evaporation, unlike the UCLA-ETA. These findings suggest that the <span class="hlt">downscaling</span> improvements are mostly due to a better representation of land-surface processes by the UCLA-ETA. Sensitivity tests also reveal that higher-resolution topography only played a secondary role in the dynamic <span class="hlt">downscaling</span> improvement.</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>Input Decimated <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)</p> <p>2001-01-01</p> <p>Using an <span class="hlt">ensemble</span> of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated <span class="hlt">ensembles</span> (IDEs) outperform <span class="hlt">ensembles</span> whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1231194','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1231194"><span>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://adsabs.harvard.edu/abs/2015EGUGA..17..986C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17..986C"><span>Representative meteorological <span class="hlt">ensembles</span> of change climate change in the Araucanía Region, Chile.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cepeda, Javier; Vargas, Ximena</p> <p>2015-04-01</p> <p>One of the main uncertainties in hydrologic modeling is attributed to meteorological inputs. When climate change impact analysis is performed, uncertainty increases due to that meteorological time series are obtained through Global Circulation Models (GCM) for a specific climate change scenario. The Intergovernmental Panel on Climate Change (IPCC) in their last report (AR5, 2013 ) recommend the Representative Concentration Pathway. RCP scenarios, developed under the Coupled Model Intercomparison Project Phase 5 (CMIP5). Pathways for stabilization of radiative forcing by 2100 characterize these scenarios being a radiative forcing of 8.5 w/m2, the highest future condition considered. In order to reduce the meteorological uncertainties, we study the behavior of the daily precipitation series I three meteorological stations in the valley of the Araucanía region, in southern Chile, using ten <span class="hlt">ensembles</span> from CGM MK-3.6 model for RCP 8.5. The main hypothesis is that good transformer functions between the observations and data obtained from the model is essential to have suitable future projections. To obtain these functions, statistical <span class="hlt">downscaling</span> is performed; first spatial <span class="hlt">downscaling</span> is carried out, and then a temporal <span class="hlt">downscaling</span> of the daily precipitation data for each month is made. <span class="hlt">Ensembles</span> whit transfer functions without discontinuities or those with the least were preferred. From this analysis we selected four <span class="hlt">ensembles</span>. For the three gage stations we apply the transfer's functions during the observed period and compared the average seasonal variation curve, the duration curve of daily, monthly and annually precipitation and average number of rainy days. Finally, based on qualitative analysis and quantitative criteria we suggest which <span class="hlt">ensemble</span> are the most representative historical conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC33G..06M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC33G..06M"><span>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/2016SPIE10004E..1ZA','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016SPIE10004E..1ZA"><span><span class="hlt">Downscaling</span> soil moisture using multisource data 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>An, Ru; Wang, Hui-Lin; You, Jia-jun; Wang, Ying; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballardd, Jonathan Arthur; Chen, Yuehong</p> <p>2016-10-01</p> <p>Soil moisture plays an important role in the water cycle within the surface ecosystem and it is the basic condition for the growth and development of plants. Currently, the spatial resolution of most soil moisture data from remote sensing ranges from ten to several tens of kilometres whilst those observed in situ and simulated for watershed hydrology, ecology, agriculture, weather and drought research are generally less than 1 kilometre. Therefore, the existing coarse resolution remotely sensed soil moisture data needs to be <span class="hlt">down-scaled</span>. In this paper, a universal soil moisture <span class="hlt">downscaling</span> model through stepwise regression with moving window suitable for large areas and multi temporal has been established. Datasets comprise land surface, brightness temperature, precipitation, soil and topographic parameters from high resolution data, and active/passive microwave remotely sensed soil moisture data from Essential Climate Variables (ECV_SM) with 25 km spatial resolution were used. With this model, a total of 288 soil moisture maps of 1 km resolution from the first ten-day of January 2003 to the last tenth-day of December 2010 were derived. The in situ observations were used to validate the <span class="hlt">down-scaled</span> ECV_SM for different land cover and land use types and seasons. In addition, various errors comparative analysis was also carried out for the <span class="hlt">down-scaled</span> ECV_SM and original one. In general, the <span class="hlt">down-scaled</span> soil moisture for different land cover and land use types is consistent with the in situ observations. The accuracy is relatively high in autumn and winter. The validation results show that <span class="hlt">downscaled</span> soil moisture can be improved not only on spatial resolution, but also on estimation accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Forte&pg=2&id=ED254263','ERIC'); return false;" href="http://eric.ed.gov/?q=Forte&pg=2&id=ED254263"><span>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('https://ntrs.nasa.gov/search.jsp?R=19810000222&hterms=wakefield&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dwakefield','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19810000222&hterms=wakefield&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dwakefield"><span>Protective Garment <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wakefield, M. E.</p> <p>1982-01-01</p> <p>Protective garment <span class="hlt">ensemble</span> with internally-mounted environmental- control unit contains its own air supply. Alternatively, a remote-environmental control unit or an air line is attached at the umbilical quick disconnect. Unit uses liquid air that is vaporized to provide both breathing air and cooling. Totally enclosed garment protects against toxic substances.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..519.2978G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..519.2978G"><span>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://www.ars.usda.gov/research/publications/publication/?seqNo115=331905','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=331905"><span><span class="hlt">Downscaled</span> soil moisture from SMAP evaluated using high density observations</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>Recently, a soil moisture <span class="hlt">downscaling</span> algorithm based on a regression relationship between daily temperature changes and daily average soil moisture was developed to produce an enhanced spatial resolution on soil moisture product for the Advanced Microwave Scanning Radiometer–EOS (AMSR-E) satellite ...</p> </li> <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>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://www.ars.usda.gov/research/publications/publication/?seqNo115=331601','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=331601"><span>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://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=282623&keyword=sound+AND+temperature&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=78778811&CFTOKEN=10033070','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=282623&keyword=sound+AND+temperature&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=78778811&CFTOKEN=10033070"><span>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://adsabs.harvard.edu/abs/2009pcms.confE.194B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009pcms.confE.194B"><span>Statistical <span class="hlt">downscaling</span> of daily precipitation over Llobregat river basin in Catalonia (Spain) using three <span class="hlt">downscaling</span> methods.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.</p> <p>2009-09-01</p> <p> environmental impact studies. <span class="hlt">Downscaling</span> methods to assess the effect of large-scale circulations on local parameters have. Statistical <span class="hlt">downscaling</span> methods are based on the view that regional climate can be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or "predictors" for which GCMs are trustable to regional or local surface "predictands" for which models are less skilful. The main advantage of these methods is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. Three statistical <span class="hlt">downscaling</span> methods are applied: Analogue method, Delta Change and Direct Forcing. These methods have been used to determine daily precipitation projections at rain gauge location to study the intensity, frequency and variability of storms in a context of climate change in the Llobregat River Basin in Catalonia, Spain. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change adaptation. Two stakeholders involved in the project provided the historical time series: Catalan Water Agency (ACA) and the State Meteorological Agency (AEMET).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.6616N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.6616N"><span>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>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://adsabs.harvard.edu/abs/2014EGUGA..16.7437R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.7437R"><span>Stepwise analogue <span class="hlt">downscaling</span> for hydrology (SANDHY): validation experiments over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Radanovics, Sabine; Vidal, Jean-Philippe; Sauquet, Eric; Ben Daoud, Aurélien; Bontron, Guillaume</p> <p>2014-05-01</p> <p>Statistical <span class="hlt">downscaling</span> aims at finding relationships between local precipitation (predictand) and large-scale predictor fields, in various contexts, from medium-term forecasting to climate change impact studies. One of the challenges of statistical <span class="hlt">downscaling</span> in a climate change context is that the predictor-predictand relationship should still be valid under climate change conditions. A minimum requirement is therefore to test the performance of the <span class="hlt">downscaling</span> method on independent data under current climate conditions. The <span class="hlt">downscaling</span> method considered is the Stepwise ANalog <span class="hlt">Downscaling</span> method for HYdrology (SANDHY). ERA-40 reanalysis data are used as large scale predictors and daily precipitation from the French near surface reanalysis (Safran) as predictand. Two 20-year periods have been selected from the common archive period of the two data sources: 1958-1978 ('early') and 1982-2002 ('late'). SANDHY has been optimised over the late period in terms of geopotential predictor domains individually for 608 target zones covering France. The validation setup consists of 4 experiments, that all use the parameters as optimised for the late period and that are compared in terms of continous ranked probability skill score (CRPSS) with climatology as reference: Reference simulation. A simulation of the late period is performed using the late period as an archive for searching the analogue dates, thus representing the best possible case. The CRPSS shows a spatial distribution similar to the one of the mean precipitation. Out-of-sample validation. The early period is simulated using the late period as an archive for searching the analogue dates. The idea is to simulate a period whose local data is not 'known' by the model as it would be the case in any application. The average skill loss compared to the reference simulation is reasonable with some more skill loss in the northern part of the country and no loss in the southeastern part. Alternative archive. The late</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.191R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.191R"><span>Climate change at local level : let's look around <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>Ravenel, H.; Jan, J.; Moisselin, J. M.; Pagé, C.</p> <p>2009-09-01</p> <p>Weather services and climatologists in research centre are overwhelmed by requests from local authorities about climate change in their regions. Most of the times local authorities want initially a level of precision in terms of time and space scale which far beyond the scientific knowledge we have for the time being. The communication will build upon several experiences of such requests and show the importance of building common language and confidence between the different actors that are to be involved in <span class="hlt">downscaling</span> exercise. The goal is to bridge the gap between initial requests by decision makers and existing scientific knowledge. UNDP (United Nations Development Program) set up recently a unit called ClimSAT to help regions (sub national authorities) to establish mitigation and adaptation action plans. ClimSAT already initiated such plans in Uruguay, Albania, Uganda, Senegal, Morocco, … Météo-France takes part to ClimSAT for instance by explaining the importance of data rescue, providing with latest information about climate change impacts and stressing the interests to involve national weather services in regional climate change action plans, … In Basse Normandie, Bretagne and Pays de Loire, Météo-France has been involved in several processes aiming ultimately at building local climate change action plans. For the time being, no real dynamical or statistical <span class="hlt">downscaling</span> exercise have been launched : For impacts on precipitation pattern, IPCC models do not really agree on this zone, so <span class="hlt">downscaling</span> is not really pertinent. For temperature, the climate change signal is clearer, but <span class="hlt">downscaling</span> won't give much more information. Of course on other meteorogical parameters or on other variable that are linked to meteorological parameters, <span class="hlt">downscaling</span> could be of interest and will probably be necessary. With or without <span class="hlt">downscaling</span>, the stake is to build, at a local level, mechanisms which are similar to IPCC and UNFCCC. In that context, <span class="hlt">downscaling</span> could</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016WRR....52.8217H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016WRR....52.8217H"><span>Spatial <span class="hlt">downscaling</span> of precipitation using adaptable random forests</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>He, Xiaogang; Chaney, Nathaniel W.; Schleiss, Marc; Sheffield, Justin</p> <p>2016-10-01</p> <p>This paper introduces Prec-DWARF (Precipitation <span class="hlt">Downscaling</span> With Adaptable Random Forests), a novel machine-learning based method for statistical <span class="hlt">downscaling</span> of precipitation. Prec-DWARF sets up a nonlinear relationship between precipitation at fine resolution and covariates at coarse/fine resolution, based on the advanced binary tree method known as Random Forests (RF). In addition to a single RF, we also consider a more advanced implementation based on two independent RFs which yield better results for extreme precipitation. Hourly gauge-radar precipitation data at 0.125° from NLDAS-2 are used to conduct synthetic experiments with different spatial resolutions (0.25°, 0.5°, and 1°). Quantitative evaluation of these experiments demonstrates that Prec-DWARF consistently outperforms the baseline (i.e., bilinear interpolation in this case) and can reasonably reproduce the spatial and temporal patterns, occurrence and distribution of observed precipitation fields. However, Prec-DWARF with a single RF significantly underestimates precipitation extremes and often cannot correctly recover the fine-scale spatial structure, especially for the 1° experiments. Prec-DWARF with a double RF exhibits improvement in the simulation of extreme precipitation as well as its spatial and temporal structures, but variogram analyses show that the spatial and temporal variability of the <span class="hlt">downscaled</span> fields are still strongly underestimated. Covariate importance analysis shows that the most important predictors for the <span class="hlt">downscaling</span> are the coarse-scale precipitation values over adjacent grid cells as well as the distance to the closest dry grid cell (i.e., the dry drift). The encouraging results demonstrate the potential of Prec-DWARF and machine-learning based techniques in general for the statistical <span class="hlt">downscaling</span> of precipitation.</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><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://adsabs.harvard.edu/abs/2015AGUFM.H53A1653L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H53A1653L"><span>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/2015PIAHS.369..147H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PIAHS.369..147H"><span><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> </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('https://www.ncbi.nlm.nih.gov/pubmed/26529728','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26529728"><span>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://adsabs.harvard.edu/abs/2008AGUFMOS52B..02R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMOS52B..02R"><span><span class="hlt">Downscaling</span> an Eddy-Resolving Global Model for the Continental Shelf off South Eastern Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roughan, M.; Baird, M.; MacDonald, H.; Oke, P.</p> <p>2008-12-01</p> <p>The Australian Bluelink collaboration between CSIRO, the Bureau of Meteorology and the Royal Australian Navy has made available to the research community the output of BODAS (Bluelink ocean data assimilation system), an <span class="hlt">ensemble</span> optimal interpolation reanalysis system with ~10 km resolution around Australia. Within the Bluelink project, BODAS fields are assimilated into a dynamic ocean model of the same resolution to produce BRAN (BlueLink ReANalysis, a hindcast of water properties around Australia from 1992 to 2004). In this study, BODAS hydrographic fields are assimilated into a ~ 3 km resolution Princeton Ocean Model (POM) configuration of the coastal ocean off SE Australia. Experiments were undertaken to establish the optimal strength and duration of the assimilation of BODAS fields into the 3 km resolution POM configuration for the purpose of producing hindcasts of ocean state. It is shown that the resultant <span class="hlt">downscaling</span> of Bluelink products is better able to reproduce coastal features, particularly velocities and hydrography over the continental shelf off south eastern Australia. The BODAS-POM modelling system is used to provide a high-resolution simulation of the East Australian Current over the period 1992 to 2004. One of the applications that we will present is an investigation of the seasonal and inter-annual variability in the dispersion of passive particles in the East Australian Current. The practical outcome is an estimate of the connectivity of estuaries along the coast of southeast Australia, which is relevant for the dispersion of marine pests.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.3465R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.3465R"><span>Sensitivity and dependence of mesoscale <span class="hlt">downscaled</span> prediction results on different parameterizations of convection and cloud microphysics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Remesan, R.; Bellerby, T.</p> <p>2012-04-01</p> <p>These days as operational real-time flood forecasting and warning systems rely more on high resolution mesoscale models employed with coupling system of hydrological models. So it is inevitable to assess prediction sensitivity or disparity in collection with selection of different cumulus and microphysical parameterization schemes, to assess the possible uncertainties associated with mesoscale <span class="hlt">downscaling</span>. This study investigates the role of physical parameterization in mesoscale model simulations on simulation of unprecedented heavy rainfall over Yorkshire-Humberside in United Kingdom during 1-14th March, 1999. The study has used a popular mesoscale numerical weather prediction model named Advanced Research Weather Research Forecast model (version 3.3) which was developed at the National Center for Atmospheric Research (NCAR) in the USA. This study has performed a comprehensive evaluation of four cumulus parameterization schemes (CPSs) [Kian-Fritsch (KF), Betts-Miller-Janjic (BMJ) and Grell-Devenyi <span class="hlt">ensemble</span> (GD)] and five microphysical schemes Lin et al scheme, older Thompson scheme, new Thompson scheme, WRF Single Moment - 6 class scheme, and WRF Single Moment - 5 class scheme] to identify how their inclusion influences the mesoscale model's meteorological parameter estimation capabilities and related uncertainties in prediction. The case study was carried out at the Upper River Derwent catchment in Northern Yorkshire, England using both the ERA-40 reanalysis data and the land based observation data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1302921-multilevel-ensemble-kalman-filtering','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1302921-multilevel-ensemble-kalman-filtering"><span>Multilevel <span class="hlt">ensemble</span> Kalman filtering</span></a></p> <p><a target="_blank" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Hoel, Hakon; Law, Kody J. H.; Tempone, Raul</p> <p>2016-06-14</p> <p>This study embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the <span class="hlt">ensemble</span> Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. Finally, the resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010JHyd..381...18B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010JHyd..381...18B"><span><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('https://www.osti.gov/scitech/biblio/22399060','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/22399060"><span>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://www.dtic.mil/docs/citations/ADA616716','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA616716"><span>ESPC Coupled Global <span class="hlt">Ensemble</span> Design</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2014-09-30</p> <p>1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. ESPC Coupled Global <span class="hlt">Ensemble</span> Design Justin McLay...range global atmospheric <span class="hlt">ensemble</span> forecasting system using the Navy Global Environmental Model (NAVGEM). Couple NAVGEM to a simple SST model that...SEP 2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE ESPC Coupled Global <span class="hlt">Ensemble</span> Design 5a. CONTRACT NUMBER</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC42A..06G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC42A..06G"><span>The Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX): A Framework for Mitigation and Adaptation Information (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutowski, W. J.; Wcrp Task Force On Regional Climate Downscaling</p> <p>2010-12-01</p> <p>The Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX) is a program developed by the Task Force on Regional Climate <span class="hlt">Downscaling</span> of World Climate Research Programme (WCRP). The Task Force’s mandate is to develop a framework to evaluate regional climate <span class="hlt">downscaling</span> techniques; foster an international coordinated effort to develop improved <span class="hlt">downscaling</span> techniques and to provide feedback to the global modeling community; and promote greater interactions between global climate modelers, <span class="hlt">downscalers</span> and end-users. Within this mandate, the primary goal of CORDEX is to extend to a global framework the lessons learned from regional climate <span class="hlt">downscaling</span> programs focused on one continent. The framework includes statistical <span class="hlt">downscaling</span> as well as regional climate models (RCMs), with an aim of evaluating the strengths and weaknesses of <span class="hlt">downscaled</span> climate information. CORDEX also provides coordination among existing and emerging <span class="hlt">downscaling</span> programs around the world. This talk will emphasize the statistical <span class="hlt">downscaling</span> component of CORDEX. CORDEX has defined a set of target regions covering most land areas of the planet. A primary region of emphasis is Africa, which has received less attention than most other continents in regional climate-change and climate-impacts research. Baseline <span class="hlt">downscaling</span> efforts by statistical <span class="hlt">downscaling</span> and RCMs have started, focusing on the period covered by the ERA-Interim Reanalysis: 1987-2007. Future work will include <span class="hlt">downscaling</span> GCM output for extended periods in the twentieth and twenty-first centuries, where future projections will be based on Representative Concentration Pathway (RCP) greenhouse gas and aerosol scenarios, specifically RCP 4.5 and RCP 8.5. CORDEX has established a preliminary set of archival protocols and targeted variables for output that will be stored in a central, openly accessible repository. Although CORDEX intends to produce simulations and analyses for the IPCC Fifth Assessment Report, the WCRP Task Force views CORDEX</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy...47.3499D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...47.3499D"><span><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>2016-12-01</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('https://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Africa&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DAfrica','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Africa&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DAfrica"><span><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/2013AGUFMGC43C1071M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1071M"><span>Characterizing Uncertainties in Hydrologic Extremes: Statistical vs. 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>Mauger, G. S.; Salathe, E. P., Jr.</p> <p>2013-12-01</p> <p>Numerous agencies are now charged with considering the impacts of climate change in management decisions, both from the standpoint of adapting to changing conditions and minimizing emissions of greenhouse gases. These decisions require robust projections of change and defensible estimates of their uncertainty. We present work that is specifically focused on characterizing the uncertainty in projections of hydrologic extremes. Much recent work has been devoted to characterizing the uncertainty in hydrologic projections due to differences in <span class="hlt">downscaling</span> methodology (e.g., Abatzoglou and Brown, 2012; Bürger et al., 2012; Rasmussen et al., 2011; Wetterhall et al., 2012) and among hydrologic models (e.g., Bennett et al., 2012; Clark et al., 2008; Fenicia et al., 2008; Smith and Marshall, 2010; Vano et al., 2012). These have established a basis for such analyses, but have generally focused on the implications for monthly and annual flows rather than flow extremes. In addition, few among these have been focused within the Pacific Northwest. In this work we assess the uncertainty in projected changes to hydrologic extremes associated with dynamical vs. statistical <span class="hlt">downscaling</span>. The analysis is focused on 3 distinct watersheds within the Pacific Northwest - the Skagit, Green, and Willamette river basins. Results highlight the sensitivity of flood projections to <span class="hlt">downscaling</span> approach and hydrologic model assumptions. Sensitivities are characterized as a function of geographic location, hydrologic regime, and climate - identifying circumstances under which projections are reliable and others in which answers differ markedly based on methodology. For example, one notable result is that dynamically <span class="hlt">downscaled</span> projections appear to refute the assumed relationship between watershed type (snow-dominant vs. rain-dominant) and projected changes to flood risk - currently considered a key indicator of future flood risk. Results presented here provide key information for decision</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>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/2016ThApC.126..453V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.126..453V"><span>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>2016-11-01</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('https://www.osti.gov/scitech/biblio/22251869','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/22251869"><span>Density of states for Gaussian unitary <span class="hlt">ensemble</span>, Gaussian orthogonal <span class="hlt">ensemble</span>, and interpolating <span class="hlt">ensembles</span> through supersymmetric approach</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Shamis, Mira</p> <p>2013-11-15</p> <p>We use the supersymmetric formalism to derive an integral formula for the density of states of the Gaussian Orthogonal <span class="hlt">Ensemble</span>, and then apply saddle-point analysis to give a new derivation of the 1/N-correction to Wigner's law. This extends the work of Disertori on the Gaussian Unitary <span class="hlt">Ensemble</span>. We also apply our method to the interpolating <span class="hlt">ensembles</span> of Mehta–Pandey.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.2405M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.2405M"><span><span class="hlt">Downscaling</span> site rainfall from daily to 11.25-minute resolution: event, diurnal, seasonal and decadal controls on <span class="hlt">downscaling</span> parameters</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>McIntyre, Neil; Shi, Shirley; Onof, Christian</p> <p>2016-04-01</p> <p><span class="hlt">Downscaling</span> site rainfall from daily to sub-daily resolution is often approached using the multiplicative discrete random cascade (MDRC) class of models, with mixed success. Questions in any application - for MDRCs or indeed other classes of <span class="hlt">downscaling</span> model - is to what extent and in what way are model parameters functions of rainfall event type and/or large scale climate controls for example those linked to the El Nino Southern Oscillation (ENSO). These questions underlie the applicability of <span class="hlt">downscaling</span> models for analysing rainfall and hydrological extremes, in particular for synthesising long-term historical or future sub-daily extremes conditional on historic or projected daily data. Coastal Queensland, Australia, is subject to combinations of multiple weather systems, including tropical cyclones, blocking systems, convective storms, frontal systems and ENSO influences. Using 100 years of fine resolution data from two gauges in central Brisbane, microcanonical MDRC models are fitted to data from 1 day to 11.25 minutes in seven cascade levels, each level dividing the time interval and its rainfall volume into two sub-intervals. Each cascade level involves estimating: the probabilities that all the rainfall observed in a time interval is concentrated in only the first of the two sub-intervals and that all the rainfall observed in a time interval is concentrated in only the second of the two sub-intervals; and also two beta distribution parameters that define the probability of a given division of the rainfall into both sub-intervals. These parameters are found to vary systematically with time of day, rainfall volume, event temporal structure, month of year, and ENSO anomaly. Reasonable <span class="hlt">downscaling</span> performance is achieved (in terms of replicating extreme values of 11.25 minute rainfall given the observed daily data) by including the parameter dependence on the rainfall volume and event structure, although particular applications may justify development of more</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.tmp..257Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp..257Y"><span>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> <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>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/2014ClDy...42.2931R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ClDy...42.2931R"><span>Performance assessment of three convective parameterization schemes in WRF for <span class="hlt">downscaling</span> summer rainfall over South Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ratna, Satyaban B.; Ratnam, J. V.; Behera, S. K.; Rautenbach, C. J. deW.; Ndarana, T.; Takahashi, K.; Yamagata, T.</p> <p>2014-06-01</p> <p>Austral summer rainfall over the period 1991/1992 to 2010/2011 was dynamically <span class="hlt">downscaled</span> by the weather research and forecasting (WRF) model at 9 km resolution for South Africa. Lateral boundary conditions for WRF were provided from the European Centre for medium-range weather (ECMWF) reanalysis (ERA) interim data. The model biases for the rainfall were evaluated over the South Africa as a whole and its nine provinces separately by employing three different convective parameterization schemes, namely the (1) Kain-Fritsch (KF), (2) Betts-Miller-Janjic (BMJ) and (3) Grell-Devenyi <span class="hlt">ensemble</span> (GDE) schemes. All three schemes have generated positive rainfall biases over South Africa, with the KF scheme producing the largest biases and mean absolute errors. Only the BMJ scheme could reproduce the intensity of rainfall anomalies, and also exhibited the highest correlation with observed interannual summer rainfall variability. In the KF scheme, a significantly high amount of moisture was transported from the tropics into South Africa. The vertical thermodynamic profiles show that the KF scheme has caused low level moisture convergence, due to the highly unstable atmosphere, and hence contributed to the widespread positive biases of rainfall. The negative bias in moisture, along with a stable atmosphere and negative biases of vertical velocity simulated by the GDE scheme resulted in negative rainfall biases, especially over the Limpopo Province. In terms of rain rate, the KF scheme generated the lowest number of low rain rates and the maximum number of moderate to high rain rates associated with more convective unstable environment. KF and GDE schemes overestimated the convective rain and underestimated the stratiform rain. However, the simulated convective and stratiform rain with BMJ scheme is in more agreement with the observations. This study also documents the performance of regional model in <span class="hlt">downscaling</span> the large scale climate mode such as El Niño Southern Oscillation</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><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/2014AGUFMGC41D0594M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC41D0594M"><span>Developing Climate-Informed <span class="hlt">Ensemble</span> Streamflow Forecasts over the Colorado River Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miller, W. P.; Lhotak, J.; Werner, K.; Stokes, M.</p> <p>2014-12-01</p> <p>As climate change is realized, the assumption of hydrometeorologic stationarity embedded within many hydrologic models is no longer valid over the Colorado River Basin. As such, resource managers have begun to request more information to support decisions, specifically with regards to the incorporation of climate change information and operational risk. To this end, <span class="hlt">ensemble</span> methodologies have become increasingly popular among the scientific and forecasting communities, and resource managers have begun to incorporate this information into decision support tools and operational models. Over the Colorado River Basin, reservoir operations are determined, in large part, by forecasts issued by the Colorado Basin River Forecast Center (CBRFC). The CBRFC produces both single value and <span class="hlt">ensemble</span> forecasts for use by resource managers in their operational decision-making process. These <span class="hlt">ensemble</span> forecasts are currently driven by a combination of daily updating model states used as initial conditions and weather forecasts plus historical meteorological information used to generate forecasts with the assumption that past hydroclimatological conditions are representative of future hydroclimatology. Recent efforts have produced updated bias-corrected and spatially <span class="hlt">downscaled</span> projections of future climate over the Colorado River Basin. In this study, the historical climatology used as input to the CBRFC forecast model is adjusted to represent future projections of climate based on data developed by the updated projections of future climate data. <span class="hlt">Ensemble</span> streamflow forecasts reflecting the impacts of climate change are then developed. These forecasts are subsequently compared to non-informed <span class="hlt">ensemble</span> streamflow forecasts to evaluate the changing range of streamflow forecasts and risk over the Colorado River Basin. <span class="hlt">Ensemble</span> forecasts may be compared through the use of a reservoir operations planning model, providing resource managers with <span class="hlt">ensemble</span> information regarding changing</p> </li> </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/2006AGUFM.H41A0372L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.H41A0372L"><span>A seasonal hydrologic <span class="hlt">ensemble</span> prediction system 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>Luo, L.; Wood, E. F.</p> <p>2006-12-01</p> <p>A seasonal hydrologic <span class="hlt">ensemble</span> prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing <span class="hlt">ensemble</span> prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically <span class="hlt">downscaled</span>, seasonal forecast from dynamic climate models. The seasonal hydrologic <span class="hlt">ensemble</span> prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.</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>Probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin</p> <p>2015-04-01</p> <p>This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the last century built on the NOAA 20th century global extended atmospheric reanalysis (20CR, Compo et al., 2011). It aims at delivering appropriate meteorological forcings for continuous distributed hydrological modelling over the last 140 years. The longer term objective is to improve our knowledge of major historical hydrometeorological events having occurred outside of the last 50-year period, over which comprehensive reconstructions and observations are available. It would constitute a perfect framework for assessing the recent observed events but also future events projected by climate change impact studies. The Sandhy (Stepwise ANalogue <span class="hlt">Downscaling</span> method for Hydrology) statistical <span class="hlt">downscaling</span> method (Radanovics et al., 2013), initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between 20CR predictors - temperature, geopotential shape, vertical velocity and relative humidity - and local predictands - precipitation and temperature - relevant for catchment-scale hydrology. Multiple predictor domains for geopotential shape are retained from a local optimisation over France using the Safran near-surface reanalysis (Vidal et al., 2010). Sandhy gives an <span class="hlt">ensemble</span> of 125 equally plausible gridded precipitation and temperature time series over the whole 1871-2012 period. Previous studies showed that Sandhy precipitation outputs are very slightly biased at the annual time scale. Nevertheless, the seasonal precipitation signal for areas with a high interannual variability is not well simulated. Moreover, winter and summer temperatures are respectively over- and underestimated. Reliable seasonal precipitation and temperature signals are however necessary for hydrological modelling, especially for evapotranspiration and snow accumulation/snowmelt processes. Two different post-processing methods are</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H33E0922B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H33E0922B"><span>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><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/2012EGUGA..14.4754R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.4754R"><span>Optimising predictor domains for spatially coherent precipitation <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Radanovics, S.; Vidal, J.-P.; Sauquet, E.; Ben Daoud, A.; Bontron, G.</p> <p>2012-04-01</p> <p>Relationships between local precipitation (predictands) and large-scale circulation (predictors) are used for statistical <span class="hlt">downscaling</span> purposes in various contexts, from medium-term forecasting to climate change impact studies. For hydrological purposes like flood forecasting, the <span class="hlt">downscaled</span> precipitation spatial fields have furthermore to be coherent over possibly large basins. This thus first requires to know what predictor domain can be associated to the precipitation over each part of the studied basin. This study addresses this issue by identifying the optimum predictor domains over the whole of France, for a specific <span class="hlt">downscaling</span> method based on a analogue approach and developed by Ben Daoud et al. (2011). The <span class="hlt">downscaling</span> method used here is based on analogies on different variables: temperature, relative humidity, vertical velocity and geopotentials. The optimum predictor domain has been found to consist of the nearest grid cell for all variables except geopotentials (Ben Daoud et al., 2011). Moreover, geopotential domains have been found to be sensitive to the target location by Obled et al. (2002), and the present study thus focuses on optimizing the domains of this specific predictor over France. The predictor domains for geopotential at 500 hPa and 1000 hPa are optimised for 608 climatologically homogeneous zones in France using the ERA-40 reanalysis data for the large-scale predictors and local precipitation from the Safran near-surface atmospheric reanalysis (Vidal et al., 2010). The similarity of geopotential fields is measured by the Teweles and Wobus shape criterion. The predictive skill of different predictor domains for the different regions is tested with the Continuous Ranked Probability Score (CRPS) for the 25 best analogue days found with the statistical <span class="hlt">downscaling</span> method. Rectangular predictor domains of different sizes, shapes and locations are tested, and the one that leads to the smallest CRPS for the zone in question is retained. The</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.7362B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.7362B"><span><span class="hlt">Downscaling</span> of rainfall in Peru using Generalised Linear Models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.</p> <p>2012-04-01</p> <p>The assessment of water resources in the Peruvian Andes is particularly important because the Peruvian economy relies heavily on agriculture. Much of the agricultural land is situated near to the coast and relies on large quantities of water for irrigation. The simulation of synthetic rainfall series is thus important to evaluate the reliability of water supplies for current and future scenarios of climate change. In addition to water resources concerns, there is also a need to understand extreme heavy rainfall events, as there was significant flooding in Machu Picchu in 2010. The region exhibits a reduction of rainfall in 1983, associated with El Nino Southern Oscillation (SOI). NCEP Reanalysis 1 data was used to provide weather variable data. Correlations were calculated for several weather variables using raingauge data in the Andes. These were used to evaluate teleconnections and provide suggested covariates for the <span class="hlt">downscaling</span> model. External covariates used in the model include sea level pressure and sea surface temperature over the region of the Humboldt Current. Relative humidity and temperature data over the region are also included. The SOI teleconnection is also used. Covariates are standardised using observations for 1960-1990. The GlimClim <span class="hlt">downscaling</span> model was used to fit a stochastic daily rainfall model to 13 sites in the Peruvian Andes. Results indicate that the model is able to reproduce rainfall statistics well, despite the large area used. Although the correlation between individual rain gauges is generally quite low, all sites are affected by similar weather patterns. This is an assumption of the GlimClim <span class="hlt">downscaling</span> model. Climate change scenarios are considered using several GCM outputs for the A1B scenario. GCM data was corrected for bias using 1960-1990 outputs from the 20C3M scenario. Rainfall statistics for current and future scenarios are compared. The region shows an overall decrease in mean rainfall but with an increase in variance.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JGRD..119.2131M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JGRD..119.2131M"><span>Genetic particle filter application to land surface temperature <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>Mechri, Rihab; Ottlé, Catherine; Pannekoucke, Olivier; Kallel, Abdelaziz</p> <p>2014-03-01</p> <p>Thermal infrared data are widely used for surface flux estimation giving the possibility to assess water and energy budgets through land surface temperature (LST). Many applications require both high spatial resolution (HSR) and high temporal resolution (HTR), which are not presently available from space. It is therefore necessary to develop methodologies to use the coarse spatial/high temporal resolutions LST remote-sensing products for a better monitoring of fluxes at appropriate scales. For that purpose, a data assimilation method was developed to <span class="hlt">downscale</span> LST based on particle filtering. The basic tenet of our approach is to constrain LST dynamics simulated at both HSR and HTR, through the optimization of aggregated temperatures at the coarse observation scale. Thus, a genetic particle filter (GPF) data assimilation scheme was implemented and applied to a land surface model which simulates prior subpixel temperatures. First, the GPF <span class="hlt">downscaling</span> scheme was tested on pseudoobservations generated in the framework of the study area landscape (Crau-Camargue, France) and climate for the year 2006. The GPF performances were evaluated against observation errors and temporal sampling. Results show that GPF outperforms prior model estimations. Finally, the GPF method was applied on Spinning Enhanced Visible and InfraRed Imager time series and evaluated against HSR data provided by an Advanced Spaceborne Thermal Emission and Reflection Radiometer image acquired on 26 July 2006. The temperatures of seven land cover classes present in the study area were estimated with root-mean-square errors less than 2.4 K which is a very promising result for <span class="hlt">downscaling</span> LST satellite products.</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>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('https://www.ncbi.nlm.nih.gov/pubmed/22371429','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22371429"><span><span class="hlt">Ensemble</span> manifold regularization.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Geng, Bo; Tao, Dacheng; Xu, Chao; Yang, Linjun; Hua, Xian-Sheng</p> <p>2012-06-01</p> <p>We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, cross validation is applied, but it does not necessarily scale up. Other problems derive from the suboptimality incurred by discrete grid search and the overfitting. Therefore, we develop an <span class="hlt">ensemble</span> manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR carefully so it 1) learns both the composite manifold and the semi-supervised learner jointly, 2) is fully automatic for learning the intrinsic manifold hyperparameters implicitly, 3) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption, and 4) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Furthermore, we prove the convergence property of EMR to the deterministic matrix at rate root-n. Extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A11F0122D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A11F0122D"><span>Comparing the skill of precipitation forecasts from high resolution simulations and statistically <span class="hlt">downscaled</span> products in the Australian Snowy Mountains</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dai, J.; Chubb, T.; Manton, M.; Siems, S. T.</p> <p>2013-12-01</p> <p>Statistically significant improvements to a 'Poor Man's <span class="hlt">Ensemble</span>' (PME) of coarse-resolution numeral precipitation forecast for the Australian Snowy Mountains can be achieved using a clustering algorithm. Daily upwind soundings are classified according to one of four clusters, which are employed to adjust the precipitation forecasts using a linear regression. This approach is a type of 'statistical <span class="hlt">downscaling</span>', in that it relies on a historical relationship between observed and forecast precipitation amounts, and is a computationally cheap and fast way to improve forecast skill. For the 'wettest' class, the root-mean-square error for the one-day forecast was reduced from 26.98 to 17.08 mm, and for the second 'wet' class the improvement was from 8.43 to 5.57 mm. Regressions performed for the two 'dry' classes were not shown to significantly improve the forecast. Statistical measures of the probability of precipitation and the quantitative precipitation forecast were evaluated for the whole of the 2011 winter (May-September). With a 'hit rate' (fraction of correctly-forecast rain days) of 0.9, and a 'false alarm rate' (fraction of forecast rain days which did not occur) of 0.16 the PME forecast performs well in identifying rain days. The precipitation amount is, however systematically under-predicted, with a mean bias of -5.76 mm and RMSE of 12.86 mm for rain days during the 2011 winter. To compare the statistically <span class="hlt">downscaled</span> results with the capabilities of a state of the art numerical prediction system, the WRF model was run at 4 km resolution over the Australian Alpine region for the same period, and precipitation forecasts analysed in a similar manner. It had a hit rate of 0.955 and RMSE of 5.16 mm for rain days. The main reason for the improved performance relative to the PME is that the high resolution of the simulations better captures the orographic forcing due to the terrain, and consequently resolves the precipitation processes more realistically, but</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27063736','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27063736"><span>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>2016-03-31</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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8005G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8005G"><span>Looking for added value in Australian <span class="hlt">downscaling</span> for climate change studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grose, Michael</p> <p>2015-04-01</p> <p><span class="hlt">Downscaling</span> gives the prospect of added value in the regional pattern and temporal nature of rainfall change with a warmer climate. However, such value is not guaranteed and the use of <span class="hlt">downscaling</span> can raise rather than diminish uncertainties. Validation of <span class="hlt">downscaling</span> methods tends to focus on the ability to simulate current climate statistics, rather than the robustness of simulated future climate change. Here we compare the future climate change signal in average rainfall from various dynamical and statistical <span class="hlt">downscaling</span> outputs used for all of Australia and in regional climate change studies over smaller domains. We show that <span class="hlt">downscaling</span> can generate different regional patterns of projected change compared to the global climate models used as input, indicating the potential for added value in projections. These differences often make physical sense in regions of complex topography such as in southeast Australia, the eastern seaboard and Tasmania. However, results from different methods are not always consistent. In addition, <span class="hlt">downscaling</span> can produce projected changes that are not clearly related to finer resolution and are difficult to interpret. In some cases, each <span class="hlt">downscaling</span> method gives a different range of results and different messages about projected rainfall change for a region. This shows that <span class="hlt">downscaling</span> has the potential to add value to projections, but also brings the potential for uncertain or contradictory messages. We conclude that each method has strengths and weaknesses, and these should be clearly communicated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=311127&keyword=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=79986002&CFTOKEN=46085959','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=79986002&CFTOKEN=46085959"><span>Assessing the Added Value of Dynamical <span class="hlt">Downscaling</span> Using the Standardized Precipitation Index</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>In this study, the Standardized Precipitation Index (SPI) is used to ascertain the added value of dynamical <span class="hlt">downscaling</span> over the contiguous United States. WRF is used as a regional climate model (RCM) to dynamically <span class="hlt">downscale</span> reanalysis fields to compare values of SPI over drough...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.387D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.387D"><span>Moroccan precipitation in a regional climate change simulation, evaluating a statistical <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Driouech, F.; Déqué, M.; Sánchez-Gómez, E.</p> <p>2009-09-01</p> <p> range covered by these RCMs for all the climate indices considered. In order to validate, in the case of Moroccan winter precipitation, a statistical <span class="hlt">downscaling</span> approach that uses large scale fields to construct local scenarios of future climate change, the link between north Atlantic weather regimes and Moroccan local precipitation has been investigated, in terms of precipitation average, and the frequencies of occurrence of wet and intense precipitation days. The robustness of the statistical approach considered is evaluated using the outputs of ARPEGE-Climate and also those of the 10 <span class="hlt">ENSEMBLES</span>-RCMs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.2551I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.2551I"><span>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/15783619','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/15783619"><span>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://eric.ed.gov/?q=music+AND+funding&pg=4&id=EJ640050','ERIC'); return false;" href="http://eric.ed.gov/?q=music+AND+funding&pg=4&id=EJ640050"><span>Is It Curtains for Traditional <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>Van Zandt, Kathryn</p> <p>2001-01-01</p> <p>Focuses on traditional music <span class="hlt">ensembles</span> (orchestra, bands, and choir) discussing such issues as the affects of block scheduling and how to deal with scheduling issues, the effects of funding on large <span class="hlt">ensemble</span> programs, nontraditional <span class="hlt">ensembles</span> in music programs, and trying to teach the National Standards for Music Education within a large <span class="hlt">ensemble</span>.…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.297F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.297F"><span>Definition of <span class="hlt">Ensemble</span> Error Statistics for Optimal <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>Frehlich, R.</p> <p>2009-09-01</p> <p>Next generation data assimilation methods must include the state dependent observation errors, i.e., the spatial and temporal variations produced by the atmospheric turbulent field. A rigorous analysis of optimal data assimilation algorithms and <span class="hlt">ensemble</span> forecast systems requires a definition of model "truth" or perfect measurement which then defines the total observation error and forecast error. Truth is defined as the spatial average of the continuous atmospheric state variables centered on the model grid locations. To be consistent with the climatology of turbulence, the spatial average is chosen as the effective spatial filter of the numerical model. The observation errors then consist of two independent components: an instrument error and an observation sampling error which describes the mismatch of the spatial average of the observation and the spatial average of the perfect measurement or "truth". The observation sampling error is related to the "error of representativeness" but is defined only in terms of the local statistics of the atmosphere and the sampling pattern of the observation. Optimal data assimilation requires an estimate of the local background error correlation as well as the local observation error correlation. Both of these local correlations can be estimated from <span class="hlt">ensemble</span> assimilation techniques where each member of the <span class="hlt">ensemble</span> are produced by generating and assimilating random observations consistent with the estimates of the local sampling errors based on estimates of the local turbulent statistics. A rigorous evaluation of these optimal <span class="hlt">ensemble</span> data assimilation techniques requires a definition of the <span class="hlt">ensemble</span> members and the <span class="hlt">ensemble</span> average that describes the error correlations. A new formulation is presented that is consistent with the climatology of atmospheric turbulence and the implications of this formulation for <span class="hlt">ensemble</span> forecast systems is discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JPRS..126...56M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPRS..126...56M"><span>A comparison of two <span class="hlt">downscaling</span> procedures to increase the spatial resolution of mapping actual evapotranspiration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mahour, Milad; Tolpekin, Valentyn; Stein, Alfred; Sharifi, Ali</p> <p>2017-04-01</p> <p>This research addressed the effects of <span class="hlt">downscaling</span> cokriging Land Surface Temperature (LST) on estimation of Actual Evapotranspiration (AET) from remote sensing images. Two procedures were followed. We first applied <span class="hlt">downscaling</span> cokriging to a coarse resolution LST product of MODIS at 1000 m. With its outcome, daily AET of a medium spatial resolution (250 m) was obtained using the Surface Energy Balance System (SEBS). Second, we <span class="hlt">downscaled</span> a coarse AET map to medium spatial resolution (250 m). For both procedures, the 250 m resolution MODIS NDVI product was used as a co-variable. Validation was carried out using Landsat 8 images, from which LST was derived from the thermal bands. The two procedures were applied to an agricultural area with a traditional irrigation network in Iran. We obtained an average LST value of 305.8 K as compared to a <span class="hlt">downscaled</span> LST value of 307.0 K. Reference AET estimated with SEBS using Landsat 8 data was equal to 5.756 mm day-1, as compared with a <span class="hlt">downscaled</span> AET value of 5.571 mm day-1. The RMSE between reference AET and <span class="hlt">downscaled</span> AET was equal to 1.26 mm day-1 (r = 0.49) and between reference and <span class="hlt">downscaled</span> LST to 3.67 K (r = 0.48). The study showed that AET values obtained with the two <span class="hlt">downscaling</span> procedures were similar to each other, but that AET showed a higher spatial variability if obtained with <span class="hlt">downscaled</span> LST. We concluded that LST had a large effect on producing AET maps from Remote Sensing (RS) images, and that <span class="hlt">downscaling</span> cokriging was helpful to provide daily AET maps at medium spatial resolution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A11F0104S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A11F0104S"><span>Impact of Dynamical <span class="hlt">Downscaling</span> on Model Representation of Land-Atmosphere Coupling Strength</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Santanello, J. A., Jr.; Roundy, J. K.; Ferguson, C. R.</p> <p>2015-12-01</p> <p>Extremes in the water cycle, such as drought and flood, threaten the sustainability of water resources and cause significant impacts on society that will likely increase due to growing populations and a changing climate. Reducing the impact of extreme events requires preparations enabled by reliable and relevant predictions of the climate. Climate predictions are made using Global Climate Models (GCMs) that typically have spatial resolutions that are too course for application at the local level where the prediction is needed to ensure a society resilient to extremes. Therefore, a common practice is to dynamically <span class="hlt">downscale</span> results from GCM's using a regional model that can provide predictions at scales consistent with the application. <span class="hlt">Downscaled</span> predictions are dependent on model physics and model setup (e.g. boundary conditions, nudging) and as a result the overall validity of dynamically <span class="hlt">downscaling</span> has not been fully demonstrated to date. NASA has recently sponsored an intra-agency <span class="hlt">downscaling</span> project to better understand the validity of dynamical <span class="hlt">downscaling</span>. As part of this project, several 10-year simulations of the NASA Unified Weather Research and Forecast (NU-WRF) model that vary in resolution and large scale nudging were used to <span class="hlt">downscale</span> MERRA-2 reanalyses over the continental U.S. This work leverages these model runs in order to understand the impact of model resolution and nudging on the representation of land-atmosphere coupling strength and its impact on <span class="hlt">downscaled</span> predictions of the water cycle. The representation of land-atmosphere coupling strength is analyzed through a suite of local land-atmosphere coupling (LoCo) metrics that are compared across <span class="hlt">downscaling</span> runs as well as coarse scale predictions from GEOS-5 and MERRA-2. The impact of <span class="hlt">downscaling</span> approaches and resolution on the representation of land-atmosphere coupling is presented and the implications for future <span class="hlt">downscaling</span> applications are discussed.</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/2009AGUFM.A33A0225S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A33A0225S"><span><span class="hlt">Downscaling</span> of climate parameters using Active Learning Method (ALM)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sodoudi, S.; Reimer, E.</p> <p>2009-12-01</p> <p>This study is a part of main program RIMAX "risk management of extreme flood events“, which concerns itself of extremes floodwater and damage potential in the Bode river basin in Germany with the variable occurrence of flood events in this area for the past 1000 years. The objective of the project is to produce the local climate time series (climate <span class="hlt">downscaling</span>) as the input for a runoff model in the Bode basin for the last 1000 years on a grid of 5x5 km as well as the estimation of the spatial distributions and temporal variability of the precipitation, the amount of precipitation and further meteorological parameter (temperature, radiation and relative humidity) for this area. A nonlinear <span class="hlt">downscaling</span> based on Fuzzy rules has been used to produce 1000 year climate time series. The global model ECHO from Max Planck institute for Meteorology (MPI) with T30 resolution and 1000 years data has been used as the global model (GCM). The regional model REMO, with 10 km resolution and 20 years data has been used as the regional input. The observations, which include 30 years precipitation, radiation, temperature, wind and relative humidity, have been used as output (predictand). In this study, two set fuzzy rules have been trained to describe the relationship between ECHO/REMO and REMO/Observation. The Fuzzy method used in this work is Active Learning Method (ALM). The heart of calculation of ALM is a fuzzy interpolation and curve fitting which is entitled Ink Drop Spread (IDS). The IDS searches fuzzily for continuous possible paths of interpolated data points on data planes. The ability of ALM to simulate the high values as well as the fluctuation of time series is much better than Takagi-Sugeno models, which have been used for <span class="hlt">downscaling</span> in the last decade. In the next steps, considering predictors from the ECHO time series As well as the predictands from the REMO grid points, some ALM models are developed, which describe the fuzzy rules and the relationship between</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>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('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>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/abs/2013AGUFM.H44C..05C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H44C..05C"><span>Meteorological Drought Prediction Using a Multi-Model <span class="hlt">Ensemble</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>Chen, L.; Mo, K. C.; Zhang, Q.; Huang, J.</p> <p>2013-12-01</p> <p>In the United States, drought is among the costliest natural hazards, with an annual average of 6 billion dollars in damage. Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Started in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the National Multi-Model <span class="hlt">Ensemble</span> (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the meteorological drought predictability using the retrospective NMME forecasts for the period from 1982 to 2010. Before predicting SPI, monthly-mean precipitation (P) forecasts from each model were bias corrected and spatially <span class="hlt">downscaled</span> (BCSD) to regional grids of 0.5-degree resolution over the contiguous United States based on the probability distribution functions derived from the hindcasts. The corrected P forecasts were then appended to the CPC Unified Precipitation Analysis to form a P time series for computing 3-month and 6-month SPIs. The <span class="hlt">ensemble</span> SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation and root-mean-square errors against the observations, are used to evaluate forecast skill. For P forecasts, errors vary among models and skill generally is low after the second month. All model P forecasts have higher skill in winter and lower skill in summer. In wintertime, BCSD improves both P and SPI forecast skill. Most improvements are over the western mountainous regions and along the Great Lake. Overall, SPI predictive skill is regionally and seasonally dependent. The six-month SPI forecasts are skillful out to four months. For</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21475662','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21475662"><span>Algorithms on <span class="hlt">ensemble</span> quantum computers.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Boykin, P Oscar; Mor, Tal; Roychowdhury, Vwani; Vatan, Farrokh</p> <p>2010-06-01</p> <p>In <span class="hlt">ensemble</span> (or bulk) quantum computation, all computations are performed on an <span class="hlt">ensemble</span> of computers rather than on a single computer. Measurements of qubits in an individual computer cannot be performed; instead, only expectation values (over the complete <span class="hlt">ensemble</span> of computers) can be measured. As a result of this limitation on the model of computation, many algorithms cannot be processed directly on such computers, and must be modified, as the common strategy of delaying the measurements usually does not resolve this <span class="hlt">ensemble</span>-measurement problem. Here we present several new strategies for resolving this problem. Based on these strategies we provide new versions of some of the most important quantum algorithms, versions that are suitable for implementing on <span class="hlt">ensemble</span> quantum computers, e.g., on liquid NMR quantum computers. These algorithms are Shor's factorization algorithm, Grover's search algorithm (with several marked items), and an algorithm for quantum fault-tolerant computation. The first two algorithms are simply modified using a randomizing and a sorting strategies. For the last algorithm, we develop a classical-quantum hybrid strategy for removing measurements. We use it to present a novel quantum fault-tolerant scheme. More explicitly, we present schemes for fault-tolerant measurement-free implementation of Toffoli and σ(z)(¼) as these operations cannot be implemented "bitwise", and their standard fault-tolerant implementations require measurement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/22093453','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/22093453"><span>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('http://adsabs.harvard.edu/abs/2016GMD.....9.4087G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GMD.....9.4087G"><span>WCRP COordinated Regional <span class="hlt">Downscaling</span> EXperiment (CORDEX): a diagnostic MIP for CMIP6</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutowski, William J., Jr.; Giorgi, Filippo; Timbal, Bertrand; Frigon, Anne; Jacob, Daniela; Kang, Hyun-Suk; Raghavan, Krishnan; Lee, Boram; Lennard, Christopher; Nikulin, Grigory; O'Rourke, Eleanor; Rixen, Michel; Solman, Silvina; Stephenson, Tannecia; Tangang, Fredolin</p> <p>2016-11-01</p> <p>The COordinated Regional <span class="hlt">Downscaling</span> EXperiment (CORDEX) is a diagnostic model intercomparison project (MIP) in CMIP6. CORDEX builds on a foundation of previous <span class="hlt">downscaling</span> intercomparison projects to provide a common framework for <span class="hlt">downscaling</span> activities around the world. The CORDEX Regional Challenges provide a focus for <span class="hlt">downscaling</span> research and a basis for making use of CMIP6 global climate model (GCM) output to produce <span class="hlt">downscaled</span> projected changes in regional climates and assess sources of uncertainties in the projections, all of which can potentially be distilled into climate change information for vulnerability, impacts and adaptation studies. CORDEX Flagship Pilot Studies advance regional <span class="hlt">downscaling</span> by targeting one or more of the CORDEX Regional Challenges. A CORDEX-CORE framework is planned that will produce a baseline set of homogeneous high-resolution, <span class="hlt">downscaled</span> projections for regions worldwide. In CMIP6, CORDEX coordinates with ScenarioMIP and is structured to allow cross comparisons with HighResMIP and interaction with the CMIP6 VIACS Advisory Board.</p> </li> <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>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/abs/2016EGUGA..18.7926K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.7926K"><span>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('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><span class="hlt">Downscaled</span> climate projections for the Southeast United States: evaluation and use for ecological applications</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Wootten, Adrienne; Smith, Kara; Boyles, Ryan; Terando, Adam; Stefanova, Lydia; Misra, Vasru; Smith, Tom; Blodgett, David L.; Semazzi, Fredrick</p> <p>2014-01-01</p> <p>Climate change is likely to have many effects on natural ecosystems in the Southeast U.S. The National Climate Assessment Southeast Technical Report (SETR) indicates that natural ecosystems in the Southeast are likely to be affected by warming temperatures, ocean acidification, sea-level rise, and changes in rainfall and evapotranspiration. To better assess these how climate changes could affect multiple sectors, including ecosystems, climatologists have created several <span class="hlt">downscaled</span> climate projections (or <span class="hlt">downscaled</span> datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these <span class="hlt">downscaled</span> datasets, known as <span class="hlt">downscaling</span>, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for <span class="hlt">downscaling</span> and the number of <span class="hlt">downscaled</span> datasets produced in recent years present many challenges for scientists and decisionmakers in assessing the impact or vulnerability of a given species or ecosystem to climate change. Given the number of available <span class="hlt">downscaled</span> datasets, how do these model outputs compare to each other? Which variables are available, and are certain <span class="hlt">downscaled</span> datasets more appropriate for assessing vulnerability of a particular species? Given the desire to use these datasets for impact and vulnerability assessments and the lack of comparison between these datasets, the goal of this report is to synthesize the information available in these <span class="hlt">downscaled</span> datasets and provide guidance to scientists and natural resource managers with specific interests in ecological modeling and conservation planning related to climate change in the Southeast U.S. This report enables the Southeast Climate Science Center (SECSC) to address an important strategic goal of providing scientific information and guidance that will enable resource managers and other participants in Landscape Conservation Cooperatives to make science</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://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="https://ntrs.nasa.gov/search.jsp?R=20020020435&hterms=cca&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dcca"><span><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://pubs.er.usgs.gov/publication/70124278','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70124278"><span>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('https://www.ncbi.nlm.nih.gov/pubmed/28125607','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28125607"><span>Multifractal <span class="hlt">Downscaling</span> of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Duffaut Espinosa, L A; Posadas, A N; Carbajal, M; Quiroz, R</p> <p>2017-01-01</p> <p>In this paper, a multifractal <span class="hlt">downscaling</span> technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This <span class="hlt">downscaling</span> procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the <span class="hlt">downscaling</span> process. The results were validated against on-site measurements of meteorological stations distributed in the area under study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5268785','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5268785"><span>Multifractal <span class="hlt">Downscaling</span> of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Posadas, A. N.; Carbajal, M.; Quiroz, R.</p> <p>2017-01-01</p> <p>In this paper, a multifractal <span class="hlt">downscaling</span> technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This <span class="hlt">downscaling</span> procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the <span class="hlt">downscaling</span> process. The results were validated against on-site measurements of meteorological stations distributed in the area under study. PMID:28125607</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/22308400','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/22308400"><span>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.osti.gov/scitech/biblio/21450714','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/21450714"><span>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://adsabs.harvard.edu/abs/2015AdWR...76...81R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AdWR...76...81R"><span>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=sound+AND+temperature&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=78778811&CFTOKEN=10033070','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308919&keyword=sound+AND+temperature&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=78778811&CFTOKEN=10033070"><span>Examining Projected Changes in Weather & Air Quality Extremes Between 2000 & 2030 using Dynamical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Climate change may alter regional weather extremes resulting in a range of environmental impacts including changes in air quality, water quality and availability, energy demands, agriculture, and ecology. Dynamical <span class="hlt">downscaling</span> simulations were conducted with the Weather Research...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A23E0369K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A23E0369K"><span>Future changes in daily snowfall intensity projected by large <span class="hlt">ensemble</span> regional climate experiments</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kawase, H.</p> <p>2015-12-01</p> <p>We investigate the future changes in daily snowfall intensity in Japan analyzing the large <span class="hlt">ensemble</span> regional climate experiments. Dynamical <span class="hlt">downscalings</span> are conducted by Non-Hydrostatic Regional Climate Model (NHRCM) with 20 km from the global climate projections using Meteorological Research Institute-Atmospheric General Circulation Model (MRI-AGCM). Fifty <span class="hlt">ensemble</span> experiments are performed in the present climate. For the future climate projections, 90 <span class="hlt">ensemble</span> experiments are performed based on the six patterns of SST changes in the periods when 4 K rise in global-mean surface air temperature is projected. The accumulated snowfall in winter decreases in Japan except for the northern parts of Japan. Especially, the inland areas in the Sea of Japan side, which is famous for the heaviest snowfall region in the world, shows the remarkable decrease in snowfall in the future climate. The experiments also show increased number of days without snowfall and decreased number of days with weak snowfall due to significant warming in the most parts of Japan. On the other hand, the extreme daily snowfall, which occurs once ten years, would increase at higher elevations in the Sea of Japan side. This means that extreme daily snowfall in the present climate would occur more frequently in the future climate. The warmer atmosphere can contain more water vapor and warmer ocean can supply more water vapor to the low atmosphere. The surface air temperature at higher elevations is still lower than 0 degree Celsius, which could result in the increased extreme daily snowfall.</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>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> </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/2015HESSD..1210067G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..1210067G"><span>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/2016EGUGA..1816538A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1816538A"><span>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/2013AGUFM.A23F0373F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A23F0373F"><span>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/abs/2013AGUFMGC43C1070A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1070A"><span>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/2016OcMod.100...20V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcMod.100...20V"><span><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://pubs.er.usgs.gov/publication/70168924','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70168924"><span>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('https://www.osti.gov/scitech/biblio/1043326','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1043326"><span>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://www.ars.usda.gov/research/publications/publication/?seqNo115=326566','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=326566"><span><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>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://hdl.handle.net/2060/20110011613','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011613"><span><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/2013AGUFMGC53A1041A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC53A1041A"><span><span class="hlt">Downscaling</span> MODIS Land Surface Temperature for Urban Public Health Applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G., Jr.; Estes, S. M.; Quattrochi, D. A.; Johnson, D.</p> <p>2013-12-01</p> <p>This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heat-related mortality data. The current HWWS do not take into account intra-urban spatial variations in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with land surface temperature (LST) estimates derived from thermal remote sensing data. In order to further improve the assessment of intra-urban variations in risk from extreme heat, we developed and evaluated a number of spatial statistical techniques for <span class="hlt">downscaling</span> the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. We will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24988779','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24988779"><span><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/2060/20140006517','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006517"><span><span class="hlt">Downscaling</span> MODIS Land Surface Temperature for Urban Public Health Applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice, Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel</p> <p>2013-01-01</p> <p>This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for <span class="hlt">downscaling</span> the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1213118B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1213118B"><span>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/2013AGUFM.H41M..08Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H41M..08Z"><span><span class="hlt">Ensemble</span> data assimilation using passive and active microwave observations of precipitation in mountainous regions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>zhang, S. Q.; Lin, X.; Hou, A. Y.; Barros, A. P.</p> <p>2013-12-01</p> <p>The Goddard WRF <span class="hlt">ensemble</span> data assimilation system has been developed to assimilate precipitation information into WRF model to improve QPF and QPE at high resolution. The flow-dependent forecast error covariance estimated in the assimilation procedure aims to capture the large temporal and spatial variability of precipitation and clouds. The microphysics at cloud-resolving scales and all-sky radiative transfer simulator serve as non-linear observation operators to link observables with model states. We present results of assimilating precipitation-affected microwave radiance and precipitation radar reflectivity from a pre-GPM constellation overland in the southeast US region. Observational bias correction for all-sky radiance is developed based on innovation statistics and a situation-dependent bias estimation model. The data impact is assessed with independent ground-based precipitation observations and evaluated in applications to dynamical <span class="hlt">downscaling</span> and hydrological prediction.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014FrP.....2...20M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014FrP.....2...20M"><span>Statistical Analysis of Protein <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>Máté, Gabriell; Heermann, Dieter</p> <p>2014-04-01</p> <p>As 3D protein-configuration data is piling up, there is an ever-increasing need for well-defined, mathematically rigorous analysis approaches, especially that the vast majority of the currently available methods rely heavily on heuristics. We propose an analysis framework which stems from topology, the field of mathematics which studies properties preserved under continuous deformations. First, we calculate a barcode representation of the molecules employing computational topology algorithms. Bars in this barcode represent different topological features. Molecules are compared through their barcodes by statistically determining the difference in the set of their topological features. As a proof-of-principle application, we analyze a dataset compiled of <span class="hlt">ensembles</span> of different proteins, obtained from the <span class="hlt">Ensemble</span> Protein Database. We demonstrate that our approach correctly detects the different protein groupings.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A34E..03S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A34E..03S"><span>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/2016ClDy...46.3305S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...46.3305S"><span>Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sohn, Soo-Jin; Tam, Chi-Yung</p> <p>2016-05-01</p> <p>Capturing climatic variations in boreal winter to spring (December-May) is essential for properly predicting droughts in South Korea. This study investigates the variability and predictability of the South Korean climate during this extended season, based on observations from 60 station locations and multi-model <span class="hlt">ensemble</span> (MME) hindcast experiments (1983/1984-2005/2006) archived at the APEC Climate Center (APCC). Multivariate empirical orthogonal function (EOF) analysis results based on observations show that the first two leading modes of winter-to-spring precipitation and temperature variability, which together account for ~80 % of the total variance, are characterized by regional-scale anomalies covering the whole South Korean territory. These modes were also closely related to some of the recurrent large-scale circulation changes in the northern hemisphere during the same season. Consistent with the above, examination of the standardized precipitation evapotranspiration index (SPEI) indicates that drought conditions in South Korea tend to be accompanied by regional-to-continental-scale circulation anomalies over East Asia to the western north Pacific. Motivated by the aforementioned findings on the spatial-temporal coherence among station-scale precipitation and temperature anomalies, a new bivariate and pattern-based <span class="hlt">downscaling</span> method was developed. The novelty of this method is that precipitation and temperature data were first filtered using multivariate EOFs to enhance their spatial-temporal coherence, before being linked to large-scale circulation variables using canonical correlation analysis (CCA). To test its applicability and to investigate its related potential predictability, a perfect empirical model was first constructed with observed datasets as predictors. Next, a model output statistics (MOS)-type hybrid dynamical-statistical model was developed, using products from nine one-tier climate models as inputs. It was found that, with model sea</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020008664','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020008664"><span>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>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> </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/2016EGUGA..18.4655C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.4655C"><span><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/2015HESSD..12.8505P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..12.8505P"><span>Evaluation of soil moisture <span class="hlt">downscaling</span> using a simple thermal based proxy - the REMEDHUS network (Spain) example</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, J.; Niesel, J.; Loew, A.</p> <p>2015-08-01</p> <p>Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution in the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought predication. Therefore, various <span class="hlt">downscaling</span> methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple Vegetation Temperature Condition Index (VTCI) <span class="hlt">downscaling</span> scheme over a dense soil moisture observational network (REMEDHUS) in Spain. Firstly, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography and land cover heterogeneity, using data from MODIS and MSG SEVIRI. Then the <span class="hlt">downscaling</span> scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the <span class="hlt">downscaling</span> method can significantly improve the spatial details of CCI soil moisture while maintain the accuracy of CCI soil moisture. The accuracy level is comparable to other <span class="hlt">downscaling</span> methods that were also validated against REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying geostationary satellite for <span class="hlt">downscaling</span> soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI <span class="hlt">downscaling</span> method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20170000928','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20170000928"><span>Uncertainty Assessment of the NASA Earth Exchange Global Daily <span class="hlt">Downscaled</span> Climate Projections (NEX-GDDP) Dataset</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wang, Weile; Nemani, Ramakrishna R.; Michaelis, Andrew; Hashimoto, Hirofumi; Dungan, Jennifer L.; Thrasher, Bridget L.; Dixon, Keith W.</p> <p>2016-01-01</p> <p>The NASA Earth Exchange Global Daily <span class="hlt">Downscaled</span> Projections (NEX-GDDP) dataset is comprised of <span class="hlt">downscaled</span> climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km x 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the <span class="hlt">downscaling</span> algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical <span class="hlt">downscaling</span> techniques. We developed a set of metrics to evaluate <span class="hlt">downscaling</span> errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the <span class="hlt">downscaled</span> daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical <span class="hlt">downscaling</span> algorithms and products in the future.</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>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('https://www.osti.gov/scitech/biblio/15020771','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/15020771"><span>Changes in Seasonal and Extreme Hydrologic Conditions of the Georgia Basin/Puget Sound in an <span class="hlt">Ensemble</span> Regional Climate Simulation for the Mid-Century</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Leung, Lai R.; Qian, Yun</p> <p>2003-12-15</p> <p>This study examines an <span class="hlt">ensemble</span> of climate change projections simulated by a global climate model (GCM) and <span class="hlt">downscaled</span> with a region climate model (RCM) to 40 km spatial resolution for the western North America. One control and three <span class="hlt">ensemble</span> future climate simulations were produced by the GCM following a business as usual scenario for greenhouse gases and aerosols emissions from 1995 to 2100. The RCM was used to <span class="hlt">downscale</span> the GCM control simulation (1995-2015) and each <span class="hlt">ensemble</span> future GCM climate (2040-2060) simulation. Analyses of the regional climate simulations for the Georgia Basin/Puget Sound showed a warming of 1.5-2oC and statistically insignificant changes in precipitation by the mid-century. Climate change has large impacts on snowpack (about 50% reduction) but relatively smaller impacts on the total runoff for the basin as a whole. However, climate change can strongly affect small watersheds such as those located in the transient snow zone, causing a higher likelihood of winter flooding as a higher percentage of precipitation falls in the form of rain rather than snow, and reduced streamflow in early summer. In addition, there are large changes in the monthly total runoff above the upper 1% threshold (or flood volume) from October through May, and the December flood volume of the future climate is 60% above the maximum monthly flood volume of the control climate. Uncertainty of the climate change projections, as characterized by the spread among the <span class="hlt">ensemble</span> future climate simulations, is relatively small for the basin mean snowpack and runoff, but increases in smaller watersheds, especially in the transient snow zone, and associated with extreme events. This emphasizes the importance of characterizing uncertainty through <span class="hlt">ensemble</span> simulations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H52E..04T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H52E..04T"><span><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/2016EGUGA..1812252T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812252T"><span>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/2009EGUGA..1110243C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1110243C"><span>Statistical <span class="hlt">Downscaling</span> in Multi-dimensional Wave Climate Forecast</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Camus, P.; Méndez, F. J.; Medina, R.; Losada, I. J.; Cofiño, A. S.; Gutiérrez, J. M.</p> <p>2009-04-01</p> <p>Wave climate at a particular site is defined by the statistical distribution of sea state parameters, such as significant wave height, mean wave period, mean wave direction, wind velocity, wind direction and storm surge. Nowadays, long-term time series of these parameters are available from reanalysis databases obtained by numerical models. The Self-Organizing Map (SOM) technique is applied to characterize multi-dimensional wave climate, obtaining the relevant "wave types" spanning the historical variability. This technique summarizes multi-dimension of wave climate in terms of a set of clusters projected in low-dimensional lattice with a spatial organization, providing Probability Density Functions (PDFs) on the lattice. On the other hand, wind and storm surge depend on instantaneous local large-scale sea level pressure (SLP) fields while waves depend on the recent history of these fields (say, 1 to 5 days). Thus, these variables are associated with large-scale atmospheric circulation patterns. In this work, a nearest-neighbors analog method is used to predict monthly multi-dimensional wave climate. This method establishes relationships between the large-scale atmospheric circulation patterns from numerical models (SLP fields as predictors) with local wave databases of observations (monthly wave climate SOM PDFs as predictand) to set up statistical models. A wave reanalysis database, developed by Puertos del Estado (Ministerio de Fomento), is considered as historical time series of local variables. The simultaneous SLP fields calculated by NCEP atmospheric reanalysis are used as predictors. Several applications with different size of sea level pressure grid and with different temporal domain resolution are compared to obtain the optimal statistical model that better represents the monthly wave climate at a particular site. In this work we examine the potential skill of this <span class="hlt">downscaling</span> approach considering perfect-model conditions, but we will also analyze the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.A23E0374T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.A23E0374T"><span>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, P.</p> <p>2015-12-01</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 8 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/2013EGUGA..1511726K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1511726K"><span>Assessing Fire Weather Index using statistical <span class="hlt">downscaling</span> and spatial interpolation techniques in Greece</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Karali, Anna; Giannakopoulos, Christos; Frias, Maria Dolores; Hatzaki, Maria; Roussos, Anargyros; Casanueva, Ana</p> <p>2013-04-01</p> <p>Forest fires have always been present in the Mediterranean ecosystems, thus they constitute a major ecological and socio-economic issue. The last few decades though, the number of forest fires has significantly increased, as well as their severity and impact on the environment. Local fire danger projections are often required when dealing with wild fire research. In the present study the application of statistical <span class="hlt">downscaling</span> and spatial interpolation methods was performed to the Canadian Fire Weather Index (FWI), in order to assess forest fire risk in Greece. The FWI is used worldwide (including the Mediterranean basin) to estimate the fire danger in a generalized fuel type, based solely on weather observations. The meteorological inputs to the FWI System are noon values of dry-bulb temperature, air relative humidity, 10m wind speed and precipitation during the previous 24 hours. The statistical <span class="hlt">downscaling</span> methods are based on a statistical model that takes into account empirical relationships between large scale variables (used as predictors) and local scale variables. In the framework of the current study the statistical <span class="hlt">downscaling</span> portal developed by the Santander Meteorology Group (https://www.meteo.unican.es/<span class="hlt">downscaling</span>) in the framework of the EU project CLIMRUN (www.climrun.eu) was used to <span class="hlt">downscale</span> non standard parameters related to forest fire risk. In this study, two different approaches were adopted. Firstly, the analogue <span class="hlt">downscaling</span> technique was directly performed to the FWI index values and secondly the same <span class="hlt">downscaling</span> technique was performed indirectly through the meteorological inputs of the index. In both cases, the statistical <span class="hlt">downscaling</span> portal was used considering the ERA-Interim reanalysis as predictands due to the lack of observations at noon. Additionally, a three-dimensional (3D) interpolation method of position and elevation, based on Thin Plate Splines (TPS) was used, to interpolate the ERA-Interim data used to calculate the index</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ClDy...40..839P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ClDy...40..839P"><span>Probabilistic estimates of future changes in California temperature and precipitation using statistical and 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>Pierce, David W.; Das, Tapash; Cayan, Daniel R.; Maurer, Edwin P.; Miller, Norman L.; Bao, Yan; Kanamitsu, M.; Yoshimura, Kei; Snyder, Mark A.; Sloan, Lisa C.; Franco, Guido; Tyree, Mary</p> <p>2013-02-01</p> <p>Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were <span class="hlt">downscaled</span> with two statistical techniques and three nested dynamical regional climate models, although not all global models were <span class="hlt">downscaled</span> with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across <span class="hlt">downscaling</span> techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical <span class="hlt">downscaling</span> techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical <span class="hlt">downscaling</span>.</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>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/2015AGUFMGC23B1138B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC23B1138B"><span>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.ncbi.nlm.nih.gov/pubmed/26938544','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26938544"><span>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>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>The role of observational reference data for climate <span class="hlt">downscaling</span>: Insights from the VALUE COST Action</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kotlarski, Sven; Gutiérrez, José M.; Boberg, Fredrik; Bosshard, Thomas; Cardoso, Rita M.; Herrera, Sixto; Maraun, Douglas; Mezghani, Abdelkader; Pagé, Christian; Räty, Olle; Stepanek, Petr; Soares, Pedro M. M.; Szabo, Peter</p> <p>2016-04-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of <span class="hlt">downscaling</span> methods. Such assessments can be expected to crucially depend on the existence of accurate and reliable observational reference data. In dynamical <span class="hlt">downscaling</span>, observational data can influence model development itself and, later on, model evaluation, parameter calibration and added value assessment. In empirical-statistical <span class="hlt">downscaling</span>, observations serve as predictand data and directly influence model calibration with corresponding effects on <span class="hlt">downscaled</span> climate change projections. We here present a comprehensive assessment of the influence of uncertainties in observational reference data and of scale-related issues on several of the above-mentioned aspects. First, temperature and precipitation characteristics as simulated by a set of reanalysis-driven EURO-CORDEX RCM experiments are validated against three different gridded reference data products, namely (1) the EOBS dataset (2) the recently developed EURO4M-MESAN regional re-analysis, and (3) several national high-resolution and quality-controlled gridded datasets that recently became available. The analysis reveals a considerable influence of the choice of the reference data on the evaluation results, especially for precipitation. It is also illustrated how differences between the reference data sets influence the ranking of RCMs according to a comprehensive set of performance measures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JGRD..12110617N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JGRD..12110617N"><span>Satellite-enhanced dynamical <span class="hlt">downscaling</span> for the analysis of extreme events</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nunes, Ana M. B.</p> <p>2016-09-01</p> <p>The use of regional models in the <span class="hlt">downscaling</span> of general circulation models provides a strategy to generate more detailed climate information. In that case, boundary-forcing techniques can be useful to maintain the large-scale features from the coarse-resolution global models in agreement with the inner modes of the higher-resolution regional models. Although those procedures might improve dynamics, <span class="hlt">downscaling</span> via regional modeling still aims for better representation of physical processes. With the purpose of improving dynamics and physical processes in regional <span class="hlt">downscaling</span> of global reanalysis, the Regional Spectral Model—originally developed at the National Centers for Environmental Prediction—employs a newly reformulated scale-selective bias correction, together with the 3-hourly assimilation of the satellite-based precipitation estimates constructed from the Climate Prediction Center morphing technique. The two-scheme technique for the dynamical <span class="hlt">downscaling</span> of global reanalysis can be applied in analyses of environmental disasters and risk assessment, with hourly outputs, and resolution of about 25 km. Here the satellite-enhanced dynamical <span class="hlt">downscaling</span> added value is demonstrated in simulations of the first reported hurricane in the western South Atlantic Ocean basin through comparisons with global reanalyses and satellite products available in ocean areas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/21076261','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/21076261"><span>Extended Gibbs <span class="hlt">ensembles</span> with flow</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Ison, M. J.</p> <p>2007-11-15</p> <p>A recently proposed [Ph. Chomaz, F. Gulminelli, and O. Juillet, Ann. Phys. (Paris) 320, 135 (2005)] statistical treatment of finite unbound systems in the presence of collective motions is applied to a classical Lennard-Jones system, numerically simulated through molecular dynamics. In the ideal gas limit, the flow dynamics can be exactly recast into effective time-dependent Lagrange parameters acting on a standard Gibbs <span class="hlt">ensemble</span> with an extra total energy conservation constraint. Using this same ansatz for the low-density freeze-out configurations of an interacting expanding system, we show that the presence of flow can have a sizable effect on the microstate distribution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23496593','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23496593"><span>Heteroclinic contours in oscillatory <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>Komarov, M A; Osipov, G V; Zhou, C S</p> <p>2013-02-01</p> <p>In this work, we study the onset of sequential activity in <span class="hlt">ensembles</span> of neuronlike oscillators with inhibitorylike coupling between them. The winnerless competition (WLC) principle is a dynamical concept underlying sequential activity generation. According to the WLC principle, stable heteroclinic sequences in the phase space of a network model represent sequential metastable dynamics. We show that stable heteroclinic sequences and stable heteroclinic channels, connecting saddle limit cycles, can appear in oscillatory models of neural activity. We find the key bifurcations which lead to the occurrence of sequential activity as well as heteroclinic sequences and channels.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20130001779&hterms=localized+state+ensemble&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dlocalized%2Bstate%2Bensemble','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20130001779&hterms=localized+state+ensemble&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dlocalized%2Bstate%2Bensemble"><span>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> </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('https://www.ncbi.nlm.nih.gov/pubmed/27069054','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27069054"><span>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>2016-05-05</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/2016OcDyn..66.1589C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcDyn..66.1589C"><span>An atmospheric-to-marine synoptic classification for statistical <span class="hlt">downscaling</span> marine climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Camus, Paula; Rueda, Ana; Méndez, Fernando J.; Losada, Iñigo J.</p> <p>2016-12-01</p> <p>A regression-guided classification is implemented in statistical <span class="hlt">downscaling</span> models based on weather types for <span class="hlt">downscaling</span> multivariate wave climate and modelling extreme events. The semi-supervised method classifies the atmospheric circulation conditions (predictor) and the estimations from a regression model linking the circulation with local marine climate (filtered predictand). A weighted factor controls the influence of the predictor and predictand in the weather patterns to improve the performance of the classification to reflect local marine climate characteristics. In addition to the analysis of the variance explained by the predictor and the predictand, the selection of the optimal value of the weighted factor is based on the predictand response in order to avoid subjectivity in the solution. The statistical models using the guided classification are applied in the North Atlantic. The new technique reduces the dispersion of the multivariate predictand within weather types and improves the model skill to <span class="hlt">downscale</span> waves and to reproduce extremes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GMD.....8.1085M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GMD.....8.1085M"><span>Technical challenges and solutions in representing lakes when using WRF in <span class="hlt">downscaling</span> applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mallard, M. S.; Nolte, C. G.; Spero, T. L.; Bullock, O. R.; Alapaty, K.; Herwehe, J. A.; Gula, J.; Bowden, J. H.</p> <p>2015-04-01</p> <p>The Weather Research and Forecasting (WRF) model is commonly used to make high-resolution future projections of regional climate by <span class="hlt">downscaling</span> global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional <span class="hlt">downscaled</span> fields, lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for <span class="hlt">downscaling</span> simulations are presented and contrasted.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC31C1190P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC31C1190P"><span>Analysis for Spatiotemporal Characteristics of <span class="hlt">Downscaled</span> Hourly Precipitation for Climate Scenarios and Hydrological Responses</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>PARK, T.; Lee, T. S.; Lee, H.; Kim, J.</p> <p>2015-12-01</p> <p>lobal Climate Models (GCMs) have been widely used for adapting and mitigating water-related disasters affected by climate change. However, GCM outputs are too coarse to apply at a small basin scale. GCM outputs provide only daily precipitation data that are inadequate to analyze a small or medium basin because only few or several hours are used to determine the peak flows after it rains. Therefore, in the current study, we <span class="hlt">downscale</span> the outputs to hourly time scale over South Korea for climate change scenarios (RCP 4.5 and RCP 8.5) and illustrate the spatiotemporal distribution of <span class="hlt">downscaled</span> hourly precipitation. Furthermore, the hydrological application of <span class="hlt">downscaled</span> scenarios is conducted for its hydrological responses employing a distributed rainfall-runoff model, Vflo.AcknowledgementsThis work was supported by the National Research Foundation of Korea (NRF) grant that was funded by the Korean Government (MEST) (2015R1A1A1A05001007).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AdWR..100..139H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AdWR..100..139H"><span>Groundwater flow parameter estimation using refinement and coarsening indicators for adaptive <span class="hlt">downscaling</span> parameterization</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hassane, Mamadou Maina F. Z.; Ackerer, P.</p> <p>2017-02-01</p> <p>In the context of parameter identification by inverse methods, an optimized adaptive <span class="hlt">downscaling</span> parameterization is described in this work. The adaptive <span class="hlt">downscaling</span> parameterization consists of (i) defining a parameter mesh for each parameter, independent of the flow model mesh, (ii) optimizing the parameters set related to the parameter mesh, and (iii) if the match between observed and computed heads is not accurate enough, creating a new parameter mesh via refinement (<span class="hlt">downscaling</span>) and performing a new optimization of the parameters. Refinement and coarsening indicators are defined to optimize the parameter mesh refinement. The robustness of the refinement and coarsening indicators was tested by comparing the results of inversions using refinement without indicators, refinement with only refinement indicators and refinement with coarsening and refinement indicators. These examples showed that the indicators significantly reduce the number of degrees of freedom necessary to solve the inverse problem without a loss of accuracy. They, therefore, limit over-parameterization.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160007028','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160007028"><span>Multi-Model <span class="hlt">Ensemble</span> Wake Vortex Prediction</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.</p> <p>2015-01-01</p> <p>Several multi-model <span class="hlt">ensemble</span> methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model <span class="hlt">ensemble</span> capability using their wake models. An overview of different multi-model <span class="hlt">ensemble</span> methods and their feasibility for wake applications is presented. The methods include Reliability <span class="hlt">Ensemble</span> Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRD..120.7316S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..120.7316S"><span>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/2012AGUFM.A41H0062U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A41H0062U"><span>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('https://www.osti.gov/scitech/biblio/5206885','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/5206885"><span>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/28076446','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28076446"><span><span class="hlt">Downscaling</span> Global Emissions and Its Implications Derived from Climate Model Experiments.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fujimori, Shinichiro; Abe, Manabu; Kinoshita, Tsuguki; Hasegawa, Tomoko; Kawase, Hiroaki; Kushida, Kazuhide; Masui, Toshihiko; Oka, Kazutaka; Shiogama, Hideo; Takahashi, Kiyoshi; Tatebe, Hiroaki; Yoshikawa, Minoru</p> <p>2017-01-01</p> <p>In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10-30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, <span class="hlt">downscaling</span> emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in <span class="hlt">downscaling</span> methods significantly affect climate variables such as temperature and precipitation. We tested two <span class="hlt">downscaling</span> methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The <span class="hlt">downscaled</span> emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the <span class="hlt">downscaled</span> spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two <span class="hlt">downscaling</span> methods.</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>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://adsabs.harvard.edu/abs/2015AGUFM.C43F..07W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.C43F..07W"><span><span class="hlt">Downscaling</span> Regional Wind Forecasts for Use in High Resolution, Operational Snow Models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Winstral, A. H.; Jonas, T.; Helbig, N.</p> <p>2015-12-01</p> <p>High resolution model forcings are required to adequately simulate snow accumulation, melt, and streamflow in mountain environments. Wind, especially the high winds that induce snow redistribution and drive turbulent heat fluxes during rain-on-snow events, have been shown to play a vital role in these processes. Yet wind observations are sparse and rarely capture the large variability present in alpine regions. High resolution (1-10km) climate data is becoming more readily available but even these data are too coarse to properly represent alpine snow processes. Much attention has been focused on <span class="hlt">downscaling</span> precipitation and air temperature for fine resolution modeling. However there is very little in the literature that has addressed techniques for deterministically <span class="hlt">downscaling</span> wind speeds. This work addresses means of <span class="hlt">downscaling</span> large-scale wind products for high-resolution operational modeling purposes. Though both dynamical and statistical means are available for <span class="hlt">downscaling</span> purposes, the time constraints imposed by operational modeling restricts this work to the latter. The statistical <span class="hlt">downscaling</span> is done by means of terrain parameters that determine topographic position related to wind exposure and shelter. First, raw hourly wind data from ~2km and ~7km resolution weather forecasts were compared to observations at well over 100 sites located throughout the Swiss Alps. As might be expected, there was a large range of scatter between model-predicted and observed winds, and predictions at high wind sites were biased low. Terrain parameters derived from a 25m resolution DEM aptly identified high and low wind speed sites and climate model biases related to the higher resolution terrain structure. The statistical <span class="hlt">downscaling</span> differentiated windward and leeward slopes not resolved in the climate models, reduced modeling errors, and substantially reduced biases at the all-important high wind sites.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5226776','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5226776"><span><span class="hlt">Downscaling</span> Global Emissions and Its Implications Derived from Climate Model Experiments</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Abe, Manabu; Kinoshita, Tsuguki; Hasegawa, Tomoko; Kawase, Hiroaki; Kushida, Kazuhide; Masui, Toshihiko; Oka, Kazutaka; Shiogama, Hideo; Takahashi, Kiyoshi; Tatebe, Hiroaki; Yoshikawa, Minoru</p> <p>2017-01-01</p> <p>In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10–30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, <span class="hlt">downscaling</span> emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in <span class="hlt">downscaling</span> methods significantly affect climate variables such as temperature and precipitation. We tested two <span class="hlt">downscaling</span> methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The <span class="hlt">downscaled</span> emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the <span class="hlt">downscaled</span> spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two <span class="hlt">downscaling</span> methods. PMID:28076446</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><span class="hlt">Downscaling</span> Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.</p> <p>2013-01-01</p> <p>The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for <span class="hlt">downscaling</span> and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for <span class="hlt">downscaling</span> satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the <span class="hlt">downscaling</span> problem as a discrete inverse problem and solve it via a regularized variational approach (variational <span class="hlt">downscaling</span>) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse <span class="hlt">downscaling</span> methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1817117S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817117S"><span>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://www.ars.usda.gov/research/publications/publication/?seqNo115=277355','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=277355"><span>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/2016GPC...144..129K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016GPC...144..129K"><span>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><span class="hlt">Downscaling</span> future climate scenarios to fine scales for hydrologic and ecological modeling and analysis</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Flint, Lorraine E.; Flint, Alan L.</p> <p>2012-01-01</p> <p>The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the <span class="hlt">downscaling</span> while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale <span class="hlt">downscaling</span> to analyses of ecological processes influenced by topographic complexity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20150019486&hterms=Wildlife+Conservation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DWildlife%2BConservation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150019486&hterms=Wildlife+Conservation&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DWildlife%2BConservation"><span>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/2010ems..confE.401T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.401T"><span>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> </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('https://www.ncbi.nlm.nih.gov/pubmed/25751882','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25751882"><span>Layered <span class="hlt">Ensemble</span> Architecture for Time Series Forecasting.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin</p> <p>2016-01-01</p> <p>Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered <span class="hlt">ensemble</span> architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an <span class="hlt">ensemble</span> of multilayer perceptron (MLP) networks. While the first <span class="hlt">ensemble</span> layer tries to find an appropriate lag, the second <span class="hlt">ensemble</span> layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an <span class="hlt">ensemble</span>. LEA trains different networks in the <span class="hlt">ensemble</span> by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the <span class="hlt">ensemble</span>. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other <span class="hlt">ensemble</span> and nonensemble methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25201983','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25201983"><span>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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Wind+AND+power&pg=7&id=EJ430552','ERIC'); return false;" href="http://eric.ed.gov/?q=Wind+AND+power&pg=7&id=EJ430552"><span>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('https://www.osti.gov/scitech/biblio/1093136','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/1093136"><span>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('https://www.ncbi.nlm.nih.gov/pubmed/24507778','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24507778"><span>Predicting the predictive power of IDP <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>Tompa, Peter; Varadi, Mihaly</p> <p>2014-02-04</p> <p>The function of intrinsically disordered proteins may be interpreted in terms of their structural <span class="hlt">ensembles</span>. The article by Schwalbe and colleagues in this issue of Structure combines NMR and SAXS constraints to generate structural <span class="hlt">ensembles</span> that unveil important functional and pathological features.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27918894','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27918894"><span>Perception of <span class="hlt">ensemble</span> statistics requires attention.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jackson-Nielsen, Molly; Cohen, Michael A; Pitts, Michael A</p> <p>2017-02-01</p> <p>To overcome inherent limitations in perceptual bandwidth, many aspects of the visual world are represented as summary statistics (e.g., average size, orientation, or density of objects). Here, we investigated the relationship between summary (<span class="hlt">ensemble</span>) statistics and visual attention. Recently, it was claimed that one <span class="hlt">ensemble</span> statistic in particular, color diversity, can be perceived without focal attention. However, a broader debate exists over the attentional requirements of conscious perception, and it is possible that some form of attention is necessary for <span class="hlt">ensemble</span> perception. To test this idea, we employed a modified inattentional blindness paradigm and found that multiple types of summary statistics (color and size) often go unnoticed without attention. In addition, we found attentional costs in dual-task situations, further implicating a role for attention in statistical perception. Overall, we conclude that while visual <span class="hlt">ensembles</span> may be processed efficiently, some amount of attention is necessary for conscious perception of <span class="hlt">ensemble</span> statistics.</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=90758774&CFTOKEN=92528598','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=90758774&CFTOKEN=92528598"><span>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/2015EGUGA..1713722K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713722K"><span>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/2014APJAS..50...83H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014APJAS..50...83H"><span>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('https://www.ncbi.nlm.nih.gov/pubmed/26489417','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26489417"><span>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.</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>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://adsabs.harvard.edu/abs/2016EGUGA..1812447R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812447R"><span>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://www.ars.usda.gov/research/publications/publication/?seqNo115=308425','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=308425"><span>A method to <span class="hlt">downscale</span> soil moisture to fine-resolutions using topographic, vegetation, and soil data</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Soil moisture can be estimated over large regions with spatial resolutions greater than 500 m, but many applications require finer resolutions (10 – 100 m grid cells). Several methods use topographic data to <span class="hlt">downscale</span>, but vegetation and soil patterns can also be important. In this paper, a downsc...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ACP....16.5229W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ACP....16.5229W"><span><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><span class="hlt">Downscaling</span> Landsat 7 canopy reflectance employing a multi soil sensor platform</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Crop growth and yield can be efficiently monitored using canopy reflectance. The spatial resolution of freely available remote sensing data is however too coarse to fully understand spatial dynamics of crop status. In this manuscript Landsat 7 (L7) reflectance is <span class="hlt">downscaled</span> from the native resolutio...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=296239','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=296239"><span>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>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/2016OcMod.106..104P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcMod.106..104P"><span>Optimizing dynamic <span class="hlt">downscaling</span> in one-way nesting using a regional ocean model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pham, Van Sy; Hwang, Jin Hwan; Ku, Hyeyun</p> <p>2016-10-01</p> <p>Dynamical <span class="hlt">downscaling</span> with nested regional oceanographic models has been demonstrated to be an effective approach for both operationally forecasted sea weather on regional scales and projections of future climate change and its impact on the ocean. However, when nesting procedures are carried out in dynamic <span class="hlt">downscaling</span> from a larger-scale model or set of observations to a smaller scale, errors are unavoidable due to the differences in grid sizes and updating intervals. The present work assesses the impact of errors produced by nesting procedures on the <span class="hlt">downscaled</span> results from Ocean Regional Circulation Models (ORCMs). Errors are identified and evaluated based on their sources and characteristics by employing the Big-Brother Experiment (BBE). The BBE uses the same model to produce both nesting and nested simulations; so it addresses those error sources separately (i.e., without combining the contributions of errors from different sources). Here, we focus on discussing errors resulting from the spatial grids' differences, the updating times and the domain sizes. After the BBE was separately run for diverse cases, a Taylor diagram was used to analyze the results and recommend an optimal combination of grid size, updating period and domain sizes. Finally, suggested setups for the <span class="hlt">downscaling</span> were evaluated by examining the spatial correlations of variables and the relative magnitudes of variances between the nested model and the original data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.9432H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.9432H"><span>Statistical <span class="hlt">Downscaling</span> of Large-Scale Wind Signatures Using a Two-Step Approach</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.; Born, K.; Georgiadis, A.; Karremann, M. K.; Pinto, J. G.</p> <p>2012-04-01</p> <p><span class="hlt">Downscaling</span> global scale climate data is an important issue in order to obtain high-resolution data desired for most applications in meteorology and hydrology and to gain a better understanding of local climate variability. Statistical <span class="hlt">downscaling</span> transforms data from large to local scale by relating punctual climate observations, climate model outputs and high-resolution surface data. In this study, a statistical <span class="hlt">downscaling</span> approach is used in combination with dynamical <span class="hlt">downscaling</span> in order to produce gust characteristics of wind storms on a small-scale grid over Europe. The idea is to relate large-scale data, regional climate model (RCM) data and observations by transfer functions, which are calibrated using physically consistent features of the RCM model simulations. In comparison to purely dynamical <span class="hlt">downscaling</span> by a regional model, such a statistical <span class="hlt">downscaling</span> approach has several advantages. The computing time is much shorter and, therefore, such an approach can be easily applied on very large numbers of windstorm cases provided e.g. by long-term GCM model simulations, like millennium runs. The first step of the approach constructs a relation between observations and COSMO-CLM signatures with the aim of calibrating the modelled signatures to the observations in terms of model output statistics. For this purpose, parameters of the theoretical Weibull distribution, estimated from the observations at each test site, are interpolated to a 7km RCM grid with Gaussian weights and are compared to Weibull parameters from the COSMO-CLM modelled gust distributions. This allows for an evaluation and correction of gust signatures by quantile mapping. The second step links the RCM wind signatures and large-scale data by a multiple linear regression (MLR) model. One model per grid point is trained using the COSMO-CLM simulated and MOS-corrected gusts for selected wind storm events as predictands, and the according NCEP reanalysis wind speeds of the surrounding NCEP grid</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H32A..03P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H32A..03P"><span>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> </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/2016PhDT.......129R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhDT.......129R"><span><span class="hlt">Downscaling</span> modis evapotranspiration via cokriging in Wellton-Mohawk Irrigation and Drainage District, Yuma, AZ</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rodriguez Rodriguez, Jesus</p> <p></p> <p>Evapotranspiration (ET) is a key parameter for irrigation planning and management, and it is a crucial factor for water conservation practices considering the challenges associated with agricultural water availability. Field ET determination is the most accurate, but remains to be expensive and limited in scope. On the other hand, remote sensing is becoming an alternative tool for the estimation of ET. Operational ET algorithms, like the Moderate Resolution Imaging Spectroradiometer (MODIS)-based ET, are now successful at generating ET estimates globally at 1km resolution, however their intent is not management of agriculture irrigation. This research was done to develop an integrated method for <span class="hlt">downscaling</span> MODIS ET appropriate for farm-level applications using geostatistical and remote sensing techniques. The proposed methodology was applied in the Wellton-Mohawk Irrigation and Drainage District of Yuma, Arizona. In a first effort, ET data was <span class="hlt">downscaled</span> from standard 1-km-MODIS to a medium 250-m-spatial resolution via cokriging using Land Surface Temperature and Enhanced Vegetation Index as covariates. Results showed consistent <span class="hlt">downscaled</span> ET with a variance greater than the variance of the coarse scale input and nearly similar mean values. This 250m product can serve larger irrigation districts in developed countries, where plot size is fairly large and regular. However, the size and shapes of most farms in developing countries makes the 250m ET challenging. For this reason, the second part of this work was done to <span class="hlt">downscale</span> global scale 1km ET to 30m farm level application for irrigation use. This approach involved the generation of daily vegetation indices (VI) at 30m in order to support the <span class="hlt">downscaling</span> of MODIS 1km ET. Landsat and MODIS reflectances were combined with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm and the resulting VI data was used as a covariate to <span class="hlt">downscale</span> ET with the cokriging approach. The results showed</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AGUFM.H12G..02G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AGUFM.H12G..02G"><span>Statistical <span class="hlt">Downscaling</span>: A Comparison of Multiple Linear Regression and k-Nearest Neighbor Approaches</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gangopadhyay, S.; Clark, M. P.; Rajagopalan, B.</p> <p>2002-12-01</p> <p>The success of short term (days to fortnight) streamflow forecasting largely depends on the skill of surface climate (e.g., precipitation and temperature) forecasts at local scales in the individual river basins. The surface climate forecasts are used to drive the hydrologic models for streamflow forecasting. Typically, Medium Range Forecast (MRF) models provide forecasts of large scale circulation variables (e.g. pressures, wind speed, relative humidity etc.) at different levels in the atmosphere on a regular grid - which are then used to "<span class="hlt">downscale</span>" to the surface climate at locations within the model grid box. Several statistical and dynamical methods are available for <span class="hlt">downscaling</span>. This paper compares the utility of two statistical <span class="hlt">downscaling</span> methodologies: (1) multiple linear regression (MLR) and (2) a nonparametric approach based on k-nearest neighbor (k-NN) bootstrap method, in providing local-scale information of precipitation and temperature at a network of stations in the Upper Colorado River Basin. <span class="hlt">Downscaling</span> to the stations is based on output of large scale circulation variables (i.e. predictors) from the NCEP Medium Range Forecast (MRF) database. Fourteen-day six hourly forecasts are developed using these two approaches, and their forecast skill evaluated. A stepwise regression is performed at each location to select the predictors for the MLR. The k-NN bootstrap technique resamples historical data based on their "nearness" to the current pattern in the predictor space. Prior to resampling a Principal Component Analysis (PCA) is performed on the predictor set to identify a small subset of predictors. Preliminary results using the MLR technique indicate a significant value in the <span class="hlt">downscaled</span> MRF output in predicting runoff in the Upper Colorado Basin. It is expected that the k-NN approach will match the skill of the MLR approach at individual stations, and will have the added advantage of preserving the spatial co-variability between stations, capturing</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014GMD.....7..387F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GMD.....7..387F"><span>TopoSCALE v.1.0: <span class="hlt">downscaling</span> gridded climate data in complex terrain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fiddes, J.; Gruber, S.</p> <p>2014-02-01</p> <p>Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by, for example, topography, and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to <span class="hlt">downscale</span> coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM). The method makes use of an interpolation of pressure-level data according to topographic height of the subgrid. An elevation and topography correction is used to <span class="hlt">downscale</span> short-wave radiation. Long-wave radiation is <span class="hlt">downscaled</span> by deriving a cloud-component of all-sky emissivity at grid level and using <span class="hlt">downscaled</span> temperature and relative humidity fields to describe variability with elevation. Precipitation is <span class="hlt">downscaled</span> with a simple non-linear lapse and optionally disaggregated using a climatology approach. We test the method in comparison with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure levels: air temperature, relative humidity, wind speed and incoming long-wave radiation. This method may be of use in improving inputs to numerical simulations in heterogeneous and/or remote terrain, especially when statistical methods are not possible, due to lack of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JHyd..538...49M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JHyd..538...49M"><span>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>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://pubs.er.usgs.gov/publication/70048367','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70048367"><span>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://hdl.handle.net/2060/20140011180','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011180"><span>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/2015AGUFMGC33G..07E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC33G..07E"><span>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>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/2016IJSyS..47..406C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJSyS..47..406C"><span>MSEBAG: a dynamic classifier <span class="hlt">ensemble</span> generation based on `minimum-sufficient <span class="hlt">ensemble</span>' and bagging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Lei; Kamel, Mohamed S.</p> <p>2016-01-01</p> <p>In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient <span class="hlt">ensemble</span>' and bagging at the <span class="hlt">ensemble</span> level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient <span class="hlt">ensemble</span>', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient <span class="hlt">ensemble</span>', a backward stepwise algorithm is employed to generate a collection of <span class="hlt">ensembles</span>. The objective is to create a collection of <span class="hlt">ensembles</span> with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the <span class="hlt">ensembles</span> from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the <span class="hlt">ensemble</span> level, is employed for this task. EAA searches for the competent <span class="hlt">ensembles</span> using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected <span class="hlt">ensembles</span> to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4910T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4910T"><span>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>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/2014JSP...tmp...64B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JSP...tmp...64B"><span>Statistical <span class="hlt">Ensembles</span> for Economic Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bargigli, Leonardo</p> <p>2014-03-01</p> <p>Economic networks share with other social networks the fundamental property of sparsity. It is well known that the maximum entropy techniques usually employed to estimate or simulate weighted networks produce unrealistic dense topologies. At the same time, strengths should not be neglected, since they are related to core economic variables like supply and demand. To overcome this limitation, the exponential Bosonic model has been previously extended in order to obtain <span class="hlt">ensembles</span> where the average degree and strength sequences are simultaneously fixed (conditional geometric model). In this paper a new exponential model, which is the network equivalent of Boltzmann ideal systems, is introduced and then extended to the case of joint degree-strength constraints (conditional Poisson model). Finally, the fitness of these alternative models is tested against a number of networks. While the conditional geometric model generally provides a better goodness-of-fit in terms of log-likelihoods, the conditional Poisson model could nevertheless be preferred whenever it provides a higher similarity with original data. If we are interested instead only in topological properties, the simple Bernoulli model appears to be preferable to the correlated topologies of the two more complex models.</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><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://adsabs.harvard.edu/abs/2016EGUGA..1817140B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817140B"><span>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/abs/2010EGUGA..12.7376Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.7376Z"><span>Assimilation of precipitation-affected microwave radiances in a cloud-resolving WRF <span class="hlt">ensemble</span> data assimilation system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Sara; Zupanski, Dusanka; Zupanski, Milija; Hou, Arthur; Cheung, Samson</p> <p>2010-05-01</p> <p>In the last decade the progress in satellite precipitation estimation and the advance in precipitation assimilation techniques proved to have positive impact on the quality of atmospheric analyses and forecasts. Direct assimilation of rain-affected radiances presents new challenge to optimal utilization of satellite precipitation observations. Currently operational and research experiences in using precipitation observations have focused on a global model resolution with prescribed static forecast error statistics, while a high-resolution with cloud-resolving physics and flow-dependent forecast error information are needed for applications such as for <span class="hlt">downscaling</span> precipitation information from rain-affected radiances and for improving hydrological forecasts. To address some of these challenges, a WRF <span class="hlt">ensemble</span> data assimilation system (WRF-EDAS) at cloud-resolving scales has been developed jointly by NASA/GSFC and Colorado State University. The high-resolution WRF-EDAS is designed to assimilate precipitation-affected radiances in addition to the NOAA/NCEP operational data stream of in-situ data and clear-sky satellite observations. The <span class="hlt">ensemble</span> data assimilation technique opens a new pathway to provide dynamically updated background error covariance, and to utilize full nonlinear microphysics and radiative transfer model in precipitation observation operators. The high resolution of nested domain WRF model first guess allows more realistic representation of precipitation distribution in the field of view (FOV) of microwave radiance observations in low and medium range of frequencies. We present experimental results of assimilating AMSR-E microwave radiances in case studies of summer storm events over land. The assimilation of precipitation-affected radiances from multiple channels of AMSR-E has shown positive impact on the <span class="hlt">downscaled</span> precipitation analysis and short term forecast of microphysical variables. The sensitivity of precipitation analyses to the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhRvB..95k5128I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhRvB..95k5128I"><span>From interacting particles to equilibrium statistical <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>Ilievski, Enej; Quinn, Eoin; Caux, Jean-Sébastien</p> <p>2017-03-01</p> <p>We argue that a particle language provides a conceptually simple framework for the description of anomalous equilibration in isolated quantum systems. We address this paradigm in the context of integrable models, which are those where particles scatter completely elastically and are stable against decay. In particular, we demonstrate that a complete description of equilibrium <span class="hlt">ensembles</span> for interacting integrable models requires a formulation built from the mode occupation numbers of the underlying particle content, mirroring the case of noninteracting particles. This yields an intuitive physical interpretation of generalized Gibbs <span class="hlt">ensembles</span>, and reconciles them with the microcanonical <span class="hlt">ensemble</span>. We explain how previous attempts to identify an appropriate <span class="hlt">ensemble</span> overlooked an essential piece of information, and provide explicit examples in the context of quantum quenches.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009APS..HAW.BM003H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009APS..HAW.BM003H"><span><span class="hlt">Ensemble</span> treatments of thermal pairing in nuclei</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hung, Nguyen Quang; Dang, Nguyen Dinh</p> <p>2009-10-01</p> <p>A systematic comparison is conducted for pairing properties of finite systems at nonzero temperature as predicted by the exact solutions of the pairing problem embedded in three principal statistical <span class="hlt">ensembles</span>, namely the grandcanonical <span class="hlt">ensemble</span>, canonical <span class="hlt">ensemble</span> and microcanonical <span class="hlt">ensemble</span>, as well as the unprojected (FTBCS1+SCQRPA) and Lipkin-Nogami projected (FTLN1+SCQRPA) theories that include the quasiparticle number fluctuation and coupling to pair vibrations within the self-consistent quasiparticle random-phase approximation. The numerical calculations are performed for the pairing gap, total energy, heat capacity, entropy, and microcanonical temperature within the doubly-folded equidistant multilevel pairing model. The FTLN1+SCQRPA predictions are found to agree best with the exact grand-canonical results. In general, all approaches clearly show that the superfluid-normal phase transition is smoothed out in finite systems. A novel formula is suggested for extracting the empirical pairing gap in reasonable agreement with the exact canonical results.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005PhRvC..71e4904B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005PhRvC..71e4904B"><span>Particle number fluctuations in the microcanonical <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>Begun, V. V.; Gorenstein, M. I.; Kostyuk, A. P.; Zozulya, O. S.</p> <p>2005-05-01</p> <p>Particle number fluctuations are studied in the microcanonical <span class="hlt">ensemble</span>. For the Boltzmann statistics we deduce exact analytical formulas for the microcanonical partition functions in the case of noninteracting massless neutral particles and charged particles with zero net charge. The particle number fluctuations are calculated and we find that in the microcanonical <span class="hlt">ensemble</span> they are suppressed in comparison to the fluctuations in the canonical and grand canonical <span class="hlt">ensembles</span>. This remains valid in the thermodynamic limit too, so that the well-known equivalence of all statistical <span class="hlt">ensembles</span> refers to average quantities, but does not apply to fluctuations. In the thermodynamic limit we are able to calculate the particle number fluctuations in the system of massive bosons and fermions when the exact conservation laws of both the energy and charge are taken into account.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/EJ092927.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/EJ092927.pdf"><span>"Verfremdung" in Action at the Berliner <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Brown, Thomas K.</p> <p>1973-01-01</p> <p>Discussion of Brecht's aesthetic principles, particularly "Verfremdung" (the device of renewal and estrangement), including the opinions of the Berliner <span class="hlt">Ensemble</span> concerning to what degree they have retained Brecht's principles in productions of his plays. (DD)</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.A33A0222M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A33A0222M"><span>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>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('https://www.ncbi.nlm.nih.gov/pubmed/26903095','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26903095"><span>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://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&keyword=2+AND+bodies+AND+problem&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=85845982&CFTOKEN=71237265','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&keyword=2+AND+bodies+AND+problem&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=85845982&CFTOKEN=71237265"><span>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=sound+AND+temperature&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=78778811&CFTOKEN=10033070','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=315550&keyword=sound+AND+temperature&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=78778811&CFTOKEN=10033070"><span>The Impact of Incongruous Lake Temperatures on Regional Climate Extremes <span class="hlt">Downscaled</span> from the CMIP5 Archive Using the WRF Model</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The impact of incongruous lake temperatures is demonstrated using the Weather Research and Forecasting (WRF) Model to <span class="hlt">downscale</span> global climate fields. Unrealistic lake temperatures prescribed by the default WRF configuration cause obvious biases near the lakes and also affect pre...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H21E1083Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H21E1083Z"><span>Assimilation of precipitation-affected microwave radiances in a cloud-resolving WRF <span class="hlt">ensemble</span> data assimilation system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, S. Q.; Zupanski, M.; Hou, A. Y.; Lin, X.; Cheung, S.</p> <p>2010-12-01</p> <p>In the last decade the progress in satellite precipitation estimation and the advance in precipitation assimilation techniques proved to have positive impact on the quality of atmospheric analyses and forecasts. Direct assimilation of rain-affected radiances presents new challenge to optimal utilization of satellite precipitation observations in numeric weather and climate predictions. Current operational and research methodologies are generally limited to relatively coarse resolution models and prescribed static error statistics, and commonly require tangent linear model and adjoint model for the highly non-linear cloud and precipitation physics. To address some of these challenges, a WRF <span class="hlt">ensemble</span> data assimilation system (Goddard-WRF-EDAS) at cloud-resolving scales has been developed jointly by NASA/GSFC and Colorado State University (CSU). The system employs the Weather Research and Forecasting (WRF) model with NASA Goddard microphysics schemes, and the Maximum Likelihood <span class="hlt">Ensemble</span> Filter (MLEF). Precipitation affected radiances are assimilated with Goddard Satellite Data Simulator Unit (SDSU) as the observation operator. In addition to the boundary forcing constructed from operational global analysis, NCEP operational data stream is also assimilated to ensure realistic representation of dynamic circulation in the regional domains. Using the <span class="hlt">ensemble</span> assimilation approach, the forecast error-statistics is updated by <span class="hlt">ensemble</span> forecasts, and information is extracted from precipitation observations along with other types of data to produce dynamically consistent precipitation analyses and forecasts. We present experimental results of assimilating precipitation-affected microwave radiances over land in middle latitudes. The results demonstrate the data impact to the <span class="hlt">downscaled</span> precipitation short term forecasts and information propagation from precipitation data to dynamic fields. The error statistics of microphysical control variables and their relationship to the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AdSpR..54..655M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AdSpR..54..655M"><span>A comparison of different regression models for <span class="hlt">downscaling</span> Landsat and MODIS land surface temperature images over heterogeneous landscape</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mukherjee, Sandip; Joshi, P. K.; Garg, R. D.</p> <p>2014-08-01</p> <p>Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (∼1000 m) thermal images with high revisiting capability (∼1 day) are freely available. To bridge this gap, present study attempts to <span class="hlt">downscale</span> coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression <span class="hlt">downscaling</span> (LMSDS) and (v) Pace regression <span class="hlt">downscaling</span> (PRDS) are applied to <span class="hlt">downscale</span> LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and <span class="hlt">downscaled</span> to 480 m and 240 m resolution. The <span class="hlt">downscale</span> accuracy is achieved using LMSDS and PRDS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data <span class="hlt">downscaled</span> from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMSDS and PRDS models, respectively. The LMSDS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. <span class="hlt">Downscaling</span> model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMNG23B1794D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMNG23B1794D"><span>Efficient Simulation and <span class="hlt">Downscaling</span> of Large Non-Stationary Fields with Varying Local Anisotropy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dodov, B.</p> <p>2015-12-01</p> <p>Simulation of locally anisotropic, non-stationary random fields is a relatively new topic in geostatistics with applications currently restricted to the construction of an admissible covariance matrix. In this paper, we introduce an efficient algorithm for constructing large non-stationary random fields with arbitrary local covariance structure and anisotropy. At the heart of our approach is a newly developed robust directional multiresolution framework combined with a local tensor anisotropy model. The use of our algorithm is illustrated with local anisotropy analysis, simulation and <span class="hlt">downscaling</span> of complex pseudo-precipitation (PP) fields* related to tropical and extra-tropical cyclones. The efficiency of the algorithm allows obtaining realistic <span class="hlt">downscaled</span> global GCM precipitation fields down to a few kilometers resolution in seconds.* Reference: Unpublished work by Huiling Yuan and Zoltan Toth. PP fields are constructed by taking the precipitation as the positive component of the field and the water vapor saturation deficit as its negative complement.</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>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> <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><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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4024107','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4024107"><span>ON THE CONVERGENCE OF THE <span class="hlt">ENSEMBLE</span> KALMAN FILTER</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Mandel, Jan; Cobb, Loren; Beezley, Jonathan D.</p> <p>2013-01-01</p> <p>Convergence of the <span class="hlt">ensemble</span> Kalman filter in the limit for large <span class="hlt">ensembles</span> to the Kalman filter is proved. In each step of the filter, convergence of the <span class="hlt">ensemble</span> sample covariance follows from a weak law of large numbers for exchangeable random variables, the continuous mapping theorem gives convergence in probability of the <span class="hlt">ensemble</span> members, and Lp bounds on the <span class="hlt">ensemble</span> then give Lp convergence. PMID:24843228</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017ThApC.tmp...62D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ThApC.tmp...62D"><span>Precipitation projections under GCMs perspective and Turkish Water Foundation (TWF) statistical <span class="hlt">downscaling</span> model procedures</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dabanlı, İsmail; Şen, Zekai</p> <p>2017-02-01</p> <p>The statistical climate <span class="hlt">downscaling</span> model by the Turkish Water Foundation (TWF) is further developed and applied to a set of monthly precipitation records. The model is structured by two phases as spatial (regional) and temporal <span class="hlt">downscaling</span> of global circulation model (GCM) scenarios. The TWF model takes into consideration the regional dependence function (RDF) for spatial structure and Markov whitening process (MWP) for temporal characteristics of the records to set projections. The impact of climate change on monthly precipitations is studied by <span class="hlt">downscaling</span> Intergovernmental Panel on Climate Change-Special Report on Emission Scenarios (IPCC-SRES) A2 and B2 emission scenarios from Max Plank Institute (EH40PYC) and Hadley Center (HadCM3). The main purposes are to explain the TWF statistical climate <span class="hlt">downscaling</span> model procedures and to expose the validation tests, which are rewarded in same specifications as "very good" for all stations except one (Suhut) station in the Akarcay basin that is in the west central part of Turkey. Eventhough, the validation score is just a bit lower at the Suhut station, the results are "satisfactory." It is, therefore, possible to say that the TWF model has reasonably acceptable skill for highly accurate estimation regarding standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS) criteria. Based on the validated model, precipitation predictions are generated from 2011 to 2100 by using 30-year reference observation period (1981-2010). Precipitation arithmetic average and standard deviation have less than 5% error for EH40PYC and HadCM3 SRES (A2 and B2) scenarios.</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>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/2013AGUFMGC11F..05D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC11F..05D"><span>A framework for evaluating statistical <span class="hlt">downscaling</span> performance under changing climatic conditions (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dixon, K. W.; Balaji, V.; Lanzante, J.; Radhakrishnan, A.; Hayhoe, K.; Stoner, A. K.; Gaitan, C. F.</p> <p>2013-12-01</p> <p>Statistical <span class="hlt">downscaling</span> (SD) methods may be viewed as generating a value-added product - a refinement of global climate model (GCM) output designed to add finer scale detail and to address GCM shortcomings via a process that gleans information from a combination of observations and GCM-simulated climate change responses. Making use of observational data sets and GCM simulations representing the same historical period, cross-validation techniques allow one to assess how well an SD method meets this goal. However, lacking observations of future, the extent to which a particular SD method's skill might degrade when applied to future climate projections cannot be assessed in the same manner. Here we illustrate and describe extensions to a 'perfect model' experimental design that seeks to quantify aspects of SD method performance both for a historical period (1979-2008) and for late 21st century climate projections. Examples highlighting cases in which <span class="hlt">downscaling</span> performance deteriorates in future climate projections will be discussed. Also, results will be presented showing how synthetic datasets having known statistical properties may be used to further isolate factors responsible for degradations in SD method skill under changing climatic conditions. We will describe a set of input files used to conduct these analyses that are being made available to researchers who wish to utilize this experimental framework to evaluate SD methods they have developed. The gridded data sets cover a region centered on the contiguous 48 United States with a grid spacing of approximately 25km, have daily time resolution (e.g., maximum and minimum near-surface temperature and precipitation), and represent a total of 120 years of model simulations. This effort is consistent with the 2013 National Climate Predictions and Projections Platform Quantitative Evaluation of <span class="hlt">Downscaling</span> Workshop goal of supporting a community approach to promote the informed use of <span class="hlt">downscaled</span> climate projections.</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>Using Random Forest to Improve the <span class="hlt">Downscaling</span> of Global Livestock Census Data</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Nicolas, Gaëlle; Robinson, Timothy P.; Wint, G. R. William; Conchedda, Giulia; Cinardi, Giuseppina; Gilbert, Marius</p> <p>2016-01-01</p> <p>Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a <span class="hlt">downscaling</span> methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and <span class="hlt">downscaling</span> at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to <span class="hlt">downscale</span> census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better <span class="hlt">downscaled</span> estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.120..341K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.120..341K"><span>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/2009JGRD..11412108N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JGRD..11412108N"><span>Dynamical <span class="hlt">downscaling</span> of short-term climate fluctuations: On the benefits of precipitation assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nunes, Ana M. B.; Roads, John O.</p> <p>2009-06-01</p> <p>Regional <span class="hlt">downscaling</span> has proven useful in adding details to the global solution. However, the parameterized physical processes can systematically deviate the large-scale features in the regional solution. To demonstrate the precipitation assimilation beneficial impact on the dynamical <span class="hlt">downscaling</span>, a regional spectral model driven by the National Centers for Environmental Prediction/Department of Energy Atmospheric Model Intercomparison Project II (NCEP/DOE AMIP-II) Reanalysis was used to <span class="hlt">downscale</span> the large-scale features over most of North America. The North American Regional Reanalysis provided the 3-hourly precipitation rates that the regional model employed to simulate two opposite extreme climate events: the upper Mississippi River Basin 1988 drought and 1993 floods. In addition to these two cases, the 1990 summer anomalous precipitation over the same area was also investigated. Precipitation assimilation positively influences the dynamical <span class="hlt">downscaling</span> of these extreme climate events. The regional model when assimilating precipitation was particularly successful in reproducing the observed precipitation patterns over the central United States, where the large-scale circulation affects the precipitation variability. Particularly for the flood year, the intensity and location of the subtropical upper-level westerly jet and its associated transverse circulations were noticeably improved in the regional simulations, where the heavy precipitation core was found. This also suggests that the cumulus convection scheme, in this case the Relaxed Arakawa-Schubert parameterization scheme, can cause the large-scale features to drift during the regional simulation, and precipitation assimilation reduces this departure from the global solution. These changes in the upper-level winds were also followed by better characterization of the drought of 1988 as well as the 1990 summer heavy precipitation simulation, in comparison to regional control simulations, where precipitation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.B31D0049B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.B31D0049B"><span>Site Level Climate <span class="hlt">Downscaling</span> for Forecasting Water Balance Stress and Reslience of Acadian Boreal Trees</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brooks, B. G.; Serbin, S.</p> <p>2014-12-01</p> <p>A <span class="hlt">downscaling</span> framework is presented and applied to physiological and climatic data for projecting future climate resilience of one key boreal tree species, black spruce, in Cape Breton Highlands, Nova Scotia. The technique is based on a combination of probabilistic <span class="hlt">downscaling</span> methods and control system theory, which together are used to transform large-scale future climate input (air temperature, humidity) to local scale climate parameters important to plant biophysical processes (vapor pressure deficit). Large-scale forecast data from the Community Earth System Model were <span class="hlt">downscaled</span> spatially then temporally based on the cumulative distributions and sub-daily patterns from corresponding observational data at North Mountain (Cape Breton). Validation over historical decades shows that this technique provides hourly temperature and vapor pressure deficit data accurate to within 0.7%. Further we applied these environmental factors to a species specific empirical model of stomatal conductance for black spruce to compare differences in predicted water regulation response when large-scale (ESM) data are used as drivers versus localized data transformed using this new site-level <span class="hlt">downscaling</span> technique. We observe through this synthetic study that over historical to contemporary periods (1850-2006) differences between large-scale and localized forecasts of stomatal conductance were small but that future climate extremes (2006-2100) have a strong effect on derived water balance in black spruce. These results also suggest that black spruce in the Cape Breton Highlands may have biophysical responses to climate change that are not predicted by spatially coarse (1°) data, which does not include site level extremes that in this study are shown to strongly curb future growth rates in black spruce as present day climate extremes become common place.</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>Run-up parameterization and beach vulnerability assessment on a barrier island: a <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medellín, G.; Brinkkemper, J. A.; Torres-Freyermuth, A.; Appendini, C. M.; Mendoza, E. T.; Salles, P.</p> <p>2016-01-01</p> <p>We present a <span class="hlt">downscaling</span> approach for the study of wave-induced extreme water levels at a location on a barrier island in Yucatán (Mexico). Wave information from a 30-year wave hindcast is validated with in situ measurements at 8 m water depth. The maximum dissimilarity algorithm is employed for the selection of 600 representative cases, encompassing different combinations of wave characteristics and tidal level. The selected cases are propagated from 8 m water depth to the shore using the coupling of a third-generation wave model and a phase-resolving non-hydrostatic nonlinear shallow-water equation model. Extreme wave run-up, R2%, is estimated for the simulated cases and can be further employed to reconstruct the 30-year time series using an interpolation algorithm. <span class="hlt">Downscaling</span> results show run-up saturation during more energetic wave conditions and modulation owing to tides. The latter suggests that the R2% can be parameterized using a hyperbolic-like formulation with dependency on both wave height and tidal level. The new parametric formulation is in agreement with the <span class="hlt">downscaling</span> results (r2 = 0.78), allowing a fast calculation of wave-induced extreme water levels at this location. Finally, an assessment of beach vulnerability to wave-induced extreme water levels is conducted at the study area by employing the two approaches (reconstruction/parameterization) and a storm impact scale. The 30-year extreme water level hindcast allows the calculation of beach vulnerability as a function of return periods. It is shown that the <span class="hlt">downscaling</span>-derived parameterization provides reasonable results as compared with the numerical approach. This methodology can be extended to other locations and can be further improved by incorporating the storm surge contributions to the extreme water level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015NHESD...3.3077M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015NHESD...3.3077M"><span>Runup parameterization and beach vulnerability assessment on a barrier island: a <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medellín, G.; Brinkkemper, J. A.; Torres-Freyermuth, A.; Appendini, C. M.; Mendoza, E. T.; Salles, P.</p> <p>2015-05-01</p> <p>We present a <span class="hlt">downscaling</span> approach for the study of wave-induced extreme water levels at a location on a barrier island in Yucatan (Mexico). Wave information from a 30 year wave hindcast is validated with in situ measurements at 8 m water depth. The Maximum Dissimilarity Algorithm is employed for the selection of 600 representative cases, encompassing different wave characteristics and tidal level combinations. The selected cases are propagated from 8 m water depth till the shore using the coupling of a third-generation wave model and a phase-resolving non-hydrostatic Nonlinear Shallow Water Equations model. Extreme wave runup, R2%, is estimated for the simulated cases and can be further employed to reconstruct the 30 year period using an interpolation algorithm. <span class="hlt">Downscaling</span> results show runup saturation during more energetic wave conditions and modulation owing to tides. The latter suggests that the R2% can be parameterized using a hyperbolic-like formulation with dependency on both wave height and tidal level. The new parametric formulation is in agreement with the <span class="hlt">downscaling</span> results (r2 = 0.78), allowing a fast calculation of wave-induced extreme water levels at this location. Finally, an assessment of beach vulnerability to wave-induced extreme water level is conducted at the study area by employing the two approaches (reconstruction/parametrization) and a storm impact scale. The 30 year extreme water level hindcast allows the calculation of beach vulnerability as a function of return periods. It is shown that the <span class="hlt">downscaling</span>-derived parameterization provides reasonable results as compared with the numerical approach. This methodology can be extended to other locations and can be further improved by incorporating the storm surge contributions to the extreme water level.</p> </li> </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/abs/2012EGUGA..1410829G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1410829G"><span>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/2017ClDy..tmp...43E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy..tmp...43E"><span>Future intensification of hydro-meteorological extremes: <span class="hlt">downscaling</span> using the weather research and forecasting model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>El-Samra, R.; Bou-Zeid, E.; Bangalath, H. K.; Stenchikov, G.; El-Fadel, M.</p> <p>2017-02-01</p> <p>A set of ten <span class="hlt">downscaling</span> simulations at high spatial resolution (3 km horizontally) were performed using the Weather Research and Forecasting (WRF) model to generate future climate projections of annual and seasonal temperature and precipitation changes over the Eastern Mediterranean (with a focus on Lebanon). The model was driven with the High Resolution Atmospheric Model (HiRAM), running over the whole globe at a resolution of 25 km, under the conditions of two Representative Concentration Pathways (RCP) (4.5 and 8.5). Each <span class="hlt">downscaling</span> simulation spanned one year. Two past years (2003 and 2008), also forced by HiRAM without data assimilation, were simulated to evaluate the model's ability to capture the cold and wet (2003) and hot and dry (2008) extremes. The <span class="hlt">downscaled</span> data were in the range of recent observed climatic variability, and therefore corrected for the cold bias of HiRAM. Eight future years were then selected based on an anomaly score that relies on the mean annual temperature and accumulated precipitation to identify the worst year per decade from a water resources perspective. One hot and dry year per decade, from 2011 to 2050, and per scenario was simulated and compared to the historic 2008 reference. The results indicate that hot and dry future extreme years will be exacerbated and the study area might be exposed to a significant decrease in annual precipitation (rain and snow), reaching up to 30% relative to the current extreme conditions.</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>Applying <span class="hlt">downscaled</span> global climate model data to a hydrodynamic surface-water and groundwater model</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Swain, Eric; Stefanova, Lydia; Smith, Thomas</p> <p>2014-01-01</p> <p>Precipitation data from Global Climate Models have been <span class="hlt">downscaled</span> to smaller regions. Adapting this <span class="hlt">downscaled</span> precipitation data to a coupled hydrodynamic surface-water/groundwater model of southern Florida allows an examination of future conditions and their effect on groundwater levels, inundation patterns, surface-water stage and flows, and salinity. The <span class="hlt">downscaled</span> rainfall data include the 1996-2001 time series from the European Center for Medium-Range Weather Forecasting ERA-40 simulation and both the 1996-1999 and 2038-2057 time series from two global climate models: the Community Climate System Model (CCSM) and the Geophysical Fluid Dynamic Laboratory (GFDL). Synthesized surface-water inflow datasets were developed for the 2038-2057 simulations. The resulting hydrologic simulations, with and without a 30-cm sea-level rise, were compared with each other and field data to analyze a range of projected conditions. Simulations predicted generally higher future stage and groundwater levels and surface-water flows, with sea-level rise inducing higher coastal salinities. A coincident rise in sea level, precipitation and surface-water flows resulted in a narrower inland saline/fresh transition zone. The inland areas were affected more by the rainfall difference than the sea-level rise, and the rainfall differences make little difference in coastal inundation, but a larger difference in coastal salinities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1052P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1052P"><span>Weather Typing Statistical <span class="hlt">Downscaling</span> with dsclim: Diagnostic methodology and configuration sensitivity</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Page, C.; Albertus, G.</p> <p>2013-12-01</p> <p>The 8-km output of the statistical <span class="hlt">downscaling</span> methodology dsclim has been used since a few years to perform impacts and adaptation studies in France. The dsclim method is resampling the Météo-France SAFRAN observation mesoscale analysis. Since then, the SAFRAN observation period has been extended from 1981-2005 to 1958-2012. At the same time, there are strong needs of cross-national impact studies, hence the required use of an European observation dataset in the methodology. In this context, a diagnostic package has been developed to properly evaluate the <span class="hlt">downscaling</span> methodology and its performance: it enables to evaluate the sensitivity and the impacts of the changes in its configuration, taking also properly into account stochastic aspects. In this study we evaluated the impacts on the results with respect to the extension of the learning period from 1981-2005 to 1958-2012, as well as the comparison on the use of the EOBS dataset instead of SAFRAN, having the objective of running dsclim over a larger region within the EU FP7 SPECS project and the EU COST Action VALUE <span class="hlt">downscaling</span> methods intercomparison. This study was funded by the EU project SPECS funded by the European Commission's Seventh Framework Research Programme under the grant agreement 243964.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24824947','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24824947"><span>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://adsabs.harvard.edu/abs/2009EGUGA..11.7642V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.7642V"><span>Test of a dynamical <span class="hlt">downscaling</span> chain for assessing climate at regional scale.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vargiu, A.; Peneva, E.; Marrocu, M.</p> <p>2009-04-01</p> <p>During last years reanalysis datasets (ECMWF ERA40 or NCEP Reanalysis Project) have been widely used to investigate climate and detect some signals of global climate changes. Heavy limitations of those datasets are found when investigating the variables with intrinsic small coherence: precipitation, local winds, fogs, etc. Our aim was to perform a dynamical <span class="hlt">downscaling</span> of ERA40 dataset using a local model (BOLAM, developed at the ISAC-CNR, Bologna, Italy). We focused our study mainly on precipitation verification. More specifically we verified the <span class="hlt">downscaling</span> chain with CRU daily precipitation over Europe at 0.25 degrees. A test period, covering about a year, was studied adding up runs of 36 hours forecast. Some common verification indexes for precipitation, (ETS, POD, FAR, HK, etc.) were computed at different thresholds. The verification results have shown the benefits of the <span class="hlt">downscaling</span> chain particularly for events of deep convective precipitation and precipitation forced by orography. Comparison of the results obtained using the BOLAM model and a specific regional climate model (REGCM3, developed at the ICTP, Trieste, Italy) will be also discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70179088','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70179088"><span>A multimodal wave spectrum-based approach for statistical <span class="hlt">downscaling</span> of local wave climate</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Hegermiller, Christie; Antolinez, Jose A A; Rueda, Ana C; Camus, Paula; Perez, Jorge; Erikson, Li; Barnard, Patrick; Mendez, Fernando J</p> <p>2017-01-01</p> <p>Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical <span class="hlt">downscaling</span> of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g. the Pacific). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, we address these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in statistical <span class="hlt">downscaling</span> of local wave climate. The improved methodology separates the local wave spectrum into “wave families,” defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical <span class="hlt">downscaling</span> by weather types, which has recently been applied to coastal flooding and morphodynamic applications.</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><span class="hlt">Downscaling</span> SSPs in the GBM Delta - Integrating Science, Modelling and Stakeholders Through Qualitative and Quantitative Scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Allan, Andrew; Barbour, Emily; Salehin, Mashfiqus; Munsur Rahman, Md.; Hutton, Craig; Lazar, Attila</p> <p>2016-04-01</p> <p>A <span class="hlt">downscaled</span> scenario development process was adopted in the context of a project seeking to understand relationships between ecosystem services and human well-being in the Ganges-Brahmaputra delta. The aim was to link the concerns and priorities of relevant stakeholders with the integrated biophysical and poverty models used in the project. A 2-stage process was used to facilitate the connection between stakeholders concerns and available modelling capacity: the first to qualitatively describe what the future might look like in 2050; the second to translate these qualitative descriptions into the quantitative form required by the numerical models. An extended, modified SSP approach was adopted, with stakeholders <span class="hlt">downscaling</span> issues identified through interviews as being priorities for the southwest of Bangladesh. Detailed qualitative futures were produced, before modellable elements were quantified in conjunction with an expert stakeholder cadre. Stakeholder input, using the methods adopted here, allows the top-down focus of the RCPs to be aligned with the bottom-up approach needed to make the SSPs appropriate at the more local scale, and also facilitates the translation of qualitative narrative scenarios into a quantitative form that lends itself to incorporation of biophysical and socio-economic indicators. The presentation will describe the <span class="hlt">downscaling</span> process in detail, and conclude with findings regarding the importance of stakeholder involvement (and logistical considerations), balancing model capacity with expectations and recommendations on SSP refinement at local levels.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.9406B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.9406B"><span>Statistical <span class="hlt">downscaling</span> of extreme rainfall events in Romania using artificial neural networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Birsan, Marius-Victor; Busuioc, Aristita; Dumitrescu, Alexandru</p> <p>2013-04-01</p> <p>The main purpose of statistical <span class="hlt">downscaling</span> methods is to model the relationship between large-scale atmospheric circulation and climatic variables on a regional and subregional scale. <span class="hlt">Downscaling</span> is an important area of research as it bridges the gap between predictions of future circulation generated by General Circulation Models (GCMs) and the effects of climate change on smaller areas. In this study we present the first results of a statistical <span class="hlt">downscaling</span> model, using a neural network-based approach by means of multi-layer perceptron networks. As predictands, various indices associated to temperature and precipitation extremes in Romania are used over the entire country (for temperature extremes) and on selected homogenous areas (for precipitation extremes). Several large-scale predictors (sea-level pressure, temperature at 850 / 700 hPa, specific humidity at 850 / 700 hPa) are tested, in order to select the optimum statistical model for each predictand. Predictands are considered separately or in various combinations. This work has been realised within the research project "Changes in climate extremes and associated impact in hydrological events in Romania" (CLIMHYDEX), code PN II-ID-2011-2-0073, financed by the Romanian Executive Agency for Higher Education Research, Development and Innovation Funding (UEFISCDI).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013PhDT.......275M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013PhDT.......275M"><span>An Evaluation of Using Dynamical <span class="hlt">Downscaling</span> to Reconstruct Historical Atmospheric Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mann, Hilary M.</p> <p></p> <p>The purpose of this project is to determine whether climate data reconstruction by dynamical <span class="hlt">downscaling</span> of NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) historical global reanalysis atmospheric data can provide an accurate input for hydrological models to be used in watersheds with limited ground observations. To do this, historical global reanalysis atmospheric data were dynamically <span class="hlt">downscaled</span> to a 3km resolution over the Upper Feather, Yuba, and American River watersheds using the Fifth-Generation NCAR / Penn State Mesoscale Model (MM5). To validate the reconstructed precipitation, historical data from California Data Exchange Center (CDEC) stations were first used. However, the data that can be obtained from them are coarse throughout the watersheds, so historical data from PRISM (parameter-elevation regressions on independent slopes model) were also used. The observation data from CDEC stations were compared to the model-simulated (reconstructed) hourly, 72-hour, and monthly averaged precipitation data. These comparisons all had fairly accurate results, allowing the temporal accuracy of the model to be validated. PRISM observation data were used to compare the simulated monthly basin-average precipitation and its spatial distribution over each watershed. These comparisons showed very good results for timing and peak values. Consequently, it was determined that the current MM5 Mesoscale Model can produce the accurate precipitation field data by dynamical <span class="hlt">downscaling</span> for use in practice.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JHyd..547...21C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JHyd..547...21C"><span>A novel approach for statistical <span class="hlt">downscaling</span> of future precipitation over the Indo-Gangetic Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chaudhuri, Chiranjib; Srivastava, Rajesh</p> <p>2017-04-01</p> <p>We propose a novel statistical <span class="hlt">downscaling</span> method using Global Circulation Model (GCM) rainfall and satellite based precipitation estimate Tropical Rainfall Measurement Mission (TRMM; 3B43v7) to generate a high-resolution rainfall (0.25° × 0.25°) estimate over the Indo-Gangetic Basin (IGB) for 9 GCM and 4 Special Report on Emissions Scenarios (SRES) combinations. These precipitation values, along with the precipitation dataset from the APHRODITE's Water Resources project are then seasonally segregated (winter, pre-monsoon, monsoon and post-monsoon) and combined into a Bayesian framework to generate probability distribution of future precipitation change at regional scale. We considered present time as 2001-2010, and 3 non-overlapping time slices 2011-2040, 2041-2070, and 2071-2100 as future. The precipitation trends are heterogeneous in space and seasons, but there is an overall consistency in trends for different future time slices. The shapes of the final probability density functions given by the kernel density estimators show varying characteristics. Compared to traditional transfer function based statistical <span class="hlt">downscaling</span> methods our framework allows <span class="hlt">downscaling</span> to basin level gridded rainfall rather than station specific precipitation. It also allows an integrated estimate of uncertainties arising from different sources which is an essential diagnostic when datasets from various sources are considered. Furthermore, the Bayesian framework allows the analysis of means and precisions of precipitation, even when they reveal characteristics, such as multi-modality and long tails.</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>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/2016JAMES...8.1229Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JAMES...8.1229Y"><span>Dynamical <span class="hlt">downscaling</span> of tropical cyclones from CCSM4 simulations of the Last Glacial Maximum</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yoo, Jinwoong; Galewsky, Joseph; Camargo, Suzana J.; Korty, Robert; Zamora, Ryan</p> <p>2016-09-01</p> <p>Dynamical <span class="hlt">downscaling</span> of simulations of the Last Glacial Maximum (LGM) and late twentieth century (20C) were conducted using the Weather Research and Forecasting (WRF) model with the aim of (1) understanding how the <span class="hlt">downscaled</span> kinematic and thermodynamic variables influence simulated tropical cyclone (TC) activity over the western North Pacific during the LGM and the 20C periods and (2) to test the relevance of TC genesis factors for the colder LGM climate. The results show that, despite the lower temperatures during the LGM, the <span class="hlt">downscaled</span> TC climatology over the western North Pacific in the LGM simulation does not differ significantly from that in the 20C simulation. Among the TC environmental factors, the TC potential intensity, mid-tropospheric entropy deficit, and vertical wind shear during the LGM were consistent with previous analyses of TC genesis factors in LGM global climate model simulations. Changes in TC genesis density between the LGM and the 20C simulations seem to be well represented by the ventilation index, a nondimensional measure of the combined effects of vertical wind shear, and thermodynamic properties, suggesting the potential applicability of those factors for TC activity evaluation during the LGM and possibly other climates.</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>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/2015AGUFMGC23F1191A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC23F1191A"><span><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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5338072','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5338072"><span>Statistical <span class="hlt">Downscaling</span> for Rainfall Forecasts Using Modified Constructed Analog Method in Thailand</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Anuchaivong, Patchalai; Sukawat, Dusadee</p> <p>2017-01-01</p> <p>The simulations of rainfall from historical data were created in this study by using statistical <span class="hlt">downscaling</span>. Statistical <span class="hlt">downscaling</span> techniques are based on a relationship between the variables that are solved by the General Circulation Models (GCMs) and the observed predictions. The Modified Constructed Analog Method (MCAM) is a technique in <span class="hlt">downscaling</span> estimation, suitable for rainfall simulation accuracy using weather forecasting. In this research, the MCAM was used to calculate the Euclidean distance to obtain the number of analog days. Afterwards, a linear combination of 30 analog days is created with simulated rainfall data which are determined by the corresponding 5 days from the adjusted weights of the appropriate forecast day. This method is used to forecast the daily rainfall and was received from the Thai Meteorological Department (TMD) from the period during 1979 to 2010 at thirty stations. The experiment involved the use of rainfall forecast data that was combined with the historical data during the rainy season in 2010. The result showed that the MCAM gave the correlation value of 0.8 resulting in a reduced percentage error of 13.66%. The MCAM gave the value of 1094.10 mm which was the closest value to the observed precipitation of 1119.53 mm. PMID:28317010</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA586466','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA586466"><span>Inclusion of Sea-Surface Temperature Variation in the U.S. Navy <span class="hlt">Ensemble</span>-Transform Global <span class="hlt">Ensemble</span> Prediction System</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2012-10-13</p> <p>Inclusion of sea-surface temperature variation in the U.S. Navy <span class="hlt">ensemble</span>-transform global <span class="hlt">ensemble</span> prediction system J. G. McLay,1 M. K. Flatau,1 C...Operational Global Atmospheric Prediction System (NOGAPS) global spectral model to generate a medium-range forecast <span class="hlt">ensemble</span>. When compared to a control...Navy <span class="hlt">ensemble</span>-transform global <span class="hlt">ensemble</span> prediction system, J. Geophys. Res., 117, D19120, doi:10.1029/2011JD016937. 1. Introduction [2] The uppermost</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28301770','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28301770"><span>Imaging and Optically Manipulating Neuronal <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; Kang Miller, Jae-Eun; Peterka, Darcy S; Yuste, Rafael</p> <p>2017-03-15</p> <p>The neural code that relates the firing of neurons to the generation of behavior and mental states must be implemented by spatiotemporal patterns of activity across neuronal populations. These patterns engage selective groups of neurons, called neuronal <span class="hlt">ensembles</span>, which are emergent building blocks of neural circuits. We review optical and computational methods, based on two-photon calcium imaging and two-photon optogenetics, to detect, characterize, and manipulate neuronal <span class="hlt">ensembles</span> in three dimensions. We review data using these methods in the mammalian cortex that demonstrate the existence of neuronal <span class="hlt">ensembles</span> in the spontaneous and evoked cortical activity in vitro and in vivo. Moreover, two-photon optogenetics enable the possibility of artificially imprinting neuronal <span class="hlt">ensembles</span> into awake, behaving animals and of later recalling those <span class="hlt">ensembles</span> selectively by stimulating individual cells. These methods could enable deciphering the neural code and also be used to understand the pathophysiology of neurological and mental diseases and design novel therapies. Expected final online publication date for the Annual Review of Biophysics Volume 46 is May 20, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.</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><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.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4415763','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4415763"><span>A Bayesian <span class="hlt">Ensemble</span> Approach for Epidemiological Projections</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lindström, Tom; Tildesley, Michael; Webb, Colleen</p> <p>2015-01-01</p> <p>Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, <span class="hlt">ensemble</span> modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model <span class="hlt">ensembles</span> based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the <span class="hlt">ensemble</span> prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for <span class="hlt">ensembles</span> with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for <span class="hlt">ensemble</span> modeling of disease outbreaks. PMID:25927892</p> </li> </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('https://www.ncbi.nlm.nih.gov/pubmed/22978601','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22978601"><span>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('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3932860','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3932860"><span>Response times from <span class="hlt">ensembles</span> of accumulators</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zandbelt, Bram; Purcell, Braden A.; Palmeri, Thomas J.; Logan, Gordon D.; Schall, Jeffrey D.</p> <p>2014-01-01</p> <p>Decision-making is explained by psychologists through stochastic accumulator models and by neurophysiologists through the activity of neurons believed to instantiate these models. We investigated an overlooked scaling problem: How does a response time (RT) that can be explained by a single model accumulator arise from numerous, redundant accumulator neurons, each of which individually appears to explain the variability of RT? We explored this scaling problem by developing a unique <span class="hlt">ensemble</span> model of RT, called e pluribus unum, which embodies the well-known dictum “out of many, one.” We used the e pluribus unum model to analyze the RTs produced by <span class="hlt">ensembles</span> of redundant, idiosyncratic stochastic accumulators under various termination mechanisms and accumulation rate correlations in computer simulations of <span class="hlt">ensembles</span> of varying size. We found that predicted RT distributions are largely invariant to <span class="hlt">ensemble</span> size if the accumulators share at least modestly correlated accumulation rates and RT is not governed by the most extreme accumulators. Under these regimes the termination times of individual accumulators was predictive of <span class="hlt">ensemble</span> RT. We also found that the threshold measured on individual accumulators, corresponding to the firing rate of neurons measured at RT, can be invariant with RT but is equivalent to the specified model threshold only when the rate correlation is very high. PMID:24550315</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><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/abs/2012AGUFM.H43A1313W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H43A1313W"><span>The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment (HEPEX)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, A. W.; Thielen, J.; Pappenberger, F.; Schaake, J. C.; Hartman, R. K.</p> <p>2012-12-01</p> <p>The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF). With support from the US National Weather Service (NWS) and the European Commission (EC), the HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological <span class="hlt">ensemble</span> forecasts for decision support in emergency management and water resources sectors. The strategy to meet this goal includes meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. HEPEX has organized about a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Today, the HEPEX mission is to demonstrate the added value of hydrological <span class="hlt">ensemble</span> prediction systems (HEPS) for emergency management and water resources sectors to make decisions that have important consequences for economy, public health, safety, and the environment. HEPEX is now organised around six major themes that represent core elements of a hydrologic <span class="hlt">ensemble</span> prediction enterprise: input and pre-processing, <span class="hlt">ensemble</span> techniques, data assimilation, post-processing, verification, and communication and use in decision making. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.tmp...65Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp...65Z"><span>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> <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>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://www.osti.gov/scitech/servlets/purl/15006172','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/15006172"><span>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('https://www.ncbi.nlm.nih.gov/pubmed/24580576','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24580576"><span>Cavity cooling of an <span class="hlt">ensemble</span> spin system.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wood, Christopher J; Borneman, Troy W; Cory, David G</p> <p>2014-02-07</p> <p>We describe how sideband cooling techniques may be applied to large spin <span class="hlt">ensembles</span> in magnetic resonance. Using the Tavis-Cummings model in the presence of a Rabi drive, we solve a Markovian master equation describing the joint spin-cavity dynamics to derive cooling rates as a function of <span class="hlt">ensemble</span> size. Our calculations indicate that the coupled angular momentum subspaces of a spin <span class="hlt">ensemble</span> containing roughly 10(11) electron spins may be polarized in a time many orders of magnitude shorter than the typical thermal relaxation time. The described techniques should permit efficient removal of entropy for spin-based quantum information processors and fast polarization of spin samples. The proposed application of a standard technique in quantum optics to magnetic resonance also serves to reinforce the connection between the two fields, which has recently begun to be explored in further detail due to the development of hybrid designs for manufacturing noise-resilient quantum devices.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3680205','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3680205"><span>Optimized gold nanoshell <span class="hlt">ensembles</span> for biomedical applications</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p></p> <p>2013-01-01</p> <p>We theoretically study the properties of the optimal size distribution in the <span class="hlt">ensemble</span> of hollow gold nanoshells (HGNs) that exhibits the best performance at in vivo biomedical applications. For the first time, to the best of our knowledge, we analyze the dependence of the optimal geometric means of the nanoshells’ thicknesses and core radii on the excitation wavelength and the type of human tissue, while assuming lognormal fit to the size distribution in a real HGN <span class="hlt">ensemble</span>. Regardless of the tissue type, short-wavelength, near-infrared lasers are found to be the most effective in both absorption- and scattering-based applications. We derive approximate analytical expressions enabling one to readily estimate the parameters of optimal distribution for which an HGN <span class="hlt">ensemble</span> exhibits the maximum efficiency of absorption or scattering inside a human tissue irradiated by a near-infrared laser. PMID:23537206</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23537206','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23537206"><span>Optimized gold nanoshell <span class="hlt">ensembles</span> for biomedical applications.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sikdar, Debabrata; Rukhlenko, Ivan D; Cheng, Wenlong; Premaratne, Malin</p> <p>2013-03-28</p> <p>: We theoretically study the properties of the optimal size distribution in the <span class="hlt">ensemble</span> of hollow gold nanoshells (HGNs) that exhibits the best performance at in vivo biomedical applications. For the first time, to the best of our knowledge, we analyze the dependence of the optimal geometric means of the nanoshells' thicknesses and core radii on the excitation wavelength and the type of human tissue, while assuming lognormal fit to the size distribution in a real HGN <span class="hlt">ensemble</span>. Regardless of the tissue type, short-wavelength, near-infrared lasers are found to be the most effective in both absorption- and scattering-based applications. We derive approximate analytical expressions enabling one to readily estimate the parameters of optimal distribution for which an HGN <span class="hlt">ensemble</span> exhibits the maximum efficiency of absorption or scattering inside a human tissue irradiated by a near-infrared laser.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC13C..05A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC13C..05A"><span>Weather extremes in very large, high-resolution <span class="hlt">ensembles</span>: the weatherathome experiment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Allen, M. R.; Rosier, S.; Massey, N.; Rye, C.; Bowery, A.; Miller, J.; Otto, F.; Jones, R.; Wilson, S.; Mote, P.; Stone, D. A.; Yamazaki, Y. H.; Carrington, D.</p> <p>2011-12-01</p> <p>Resolution and <span class="hlt">ensemble</span> size are often seen as alternatives in climate modelling. Models with sufficient resolution to simulate many classes of extreme weather cannot normally be run often enough to assess the statistics of rare events, still less how these statistics may be changing. As a result, assessments of the impact of external forcing on regional climate extremes must be based either on statistical <span class="hlt">downscaling</span> from relatively coarse-resolution models, or statistical extrapolation from 10-year to 100-year events. Under the weatherathome experiment, part of the climateprediction.net initiative, we have compiled the Met Office Regional Climate Model HadRM3P to run on personal computer volunteered by the general public at 25 and 50km resolution, embedded within the HadAM3P global atmosphere model. With a global network of about 50,000 volunteers, this allows us to run time-slice <span class="hlt">ensembles</span> of essentially unlimited size, exploring the statistics of extreme weather under a range of scenarios for surface forcing and atmospheric composition, allowing for uncertainty in both boundary conditions and model parameters. Current experiments, developed with the support of Microsoft Research, focus on three regions, the Western USA, Europe and Southern Africa. We initially simulate the period 1959-2010 to establish which variables are realistically simulated by the model and on what scales. Our next experiments are focussing on the Event Attribution problem, exploring how the probability of various types of extreme weather would have been different over the recent past in a world unaffected by human influence, following the design of Pall et al (2011), but extended to a longer period and higher spatial resolution. We will present the first results of the unique, global, participatory experiment and discuss the implications for the attribution of recent weather events to anthropogenic influence on 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>Robust intensification of hydroclimatic intensity over East Asia from multi-model <span class="hlt">ensemble</span> regional projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Im, Eun-Soon; Choi, Yeon-Woo; Ahn, Joong-Bae</p> <p>2016-06-01</p> <p>This study assesses the hydroclimatic response to global warming over East Asia from multi-model <span class="hlt">ensemble</span> regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical <span class="hlt">downscaling</span> of the Hadley Centre Global Environmental Model version 2-Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981-2100) daily precipitation. <span class="hlt">Ensemble</span> projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESS...19.4765P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESS...19.4765P"><span>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><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://adsabs.harvard.edu/abs/2002PhRvL..88g2502V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002PhRvL..88g2502V"><span>Spectroscopy with Random and Displaced Random <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Velázquez, V.; Zuker, A. P.</p> <p>2002-02-01</p> <p>Because of the time reversal invariance of the angular momentum operator J2, the average energies and variances at fixed J for random two-body Hamiltonians exhibit odd-even- J staggering that may be especially strong for J = 0. It is shown that upon <span class="hlt">ensemble</span> averaging over random runs, this behavior is reflected in the yrast states. Displaced (attractive) random <span class="hlt">ensembles</span> lead to rotational spectra with strongly enhanced B(E2) transitions for a certain class of model spaces. It is explained how to generalize these results to other forms of collectivity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/20857669','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/20857669"><span>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('https://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="https://ntrs.nasa.gov/search.jsp?R=20020061294&hterms=cca&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dcca"><span><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://hdl.handle.net/2060/20130013812','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130013812"><span><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://adsabs.harvard.edu/abs/2016EGUGA..18.1659T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.1659T"><span>Calibrated <span class="hlt">Ensemble</span> Forecasts using Quantile Regression Forests and <span class="hlt">Ensemble</span> Model Output Statistics.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Taillardat, Maxime; Mestre, Olivier; Zamo, Michaël; Naveau, Philippe</p> <p>2016-04-01</p> <p><span class="hlt">Ensembles</span> used for probabilistic weather forecasting tend to be biased and underdispersive. This presentation proposes a statistical method for postprocessing <span class="hlt">ensembles</span> based on Quantile Regression Forests (QRF), a generalization of random forests for quantile regression. This method does not fit a parametric probability density function like in <span class="hlt">Ensemble</span> Model Output Statistics (EMOS) but provides an estimation of desired quantiles. This is a non-parametric approach which eliminates any assumption on the variable subject to calibration. This method can estimate quantiles using not only members of the <span class="hlt">ensemble</span> but any predictor available including statistics on other variables for example. The method is applied to the Météo-France 35-members <span class="hlt">ensemble</span> forecast (PEARP) for surface temperature and wind-speed for available lead times from 3 up to 54 hours and compared to EMOS. All postprocessed <span class="hlt">ensembles</span> are much better calibrated than the PEARP raw <span class="hlt">ensemble</span> and experiments on real data also show that QRF performs better than EMOS, and can bring a real gain for forecasters compared to EMOS. QRF provides sharp and reliable probabilistic forecasts. At last, classical scoring rules to verify predictive forecasts are completed by the introduction of entropy as a general measure of reliability.</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>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> </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://adsabs.harvard.edu/abs/2016AtmRe.167..156K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AtmRe.167..156K"><span>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/2017ClDy..tmp...72M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017ClDy..tmp...72M"><span>Seasonal drought <span class="hlt">ensemble</span> predictions based on multiple climate models in the upper Han River Basin, China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ma, Feng; Ye, Aizhong; Duan, Qingyun</p> <p>2017-03-01</p> <p>An experimental seasonal drought forecasting system is developed based on 29-year (1982-2010) seasonal meteorological hindcasts generated by the climate models from the North American Multi-Model <span class="hlt">Ensemble</span> (NMME) project. This system made use of a bias correction and spatial <span class="hlt">downscaling</span> method, and a distributed time-variant gain model (DTVGM) hydrologic model. DTVGM was calibrated using observed daily hydrological data and its streamflow simulations achieved Nash-Sutcliffe efficiency values of 0.727 and 0.724 during calibration (1978-1995) and validation (1996-2005) periods, respectively, at the Danjiangkou reservoir station. The experimental seasonal drought forecasting system (known as NMME-DTVGM) is used to generate seasonal drought forecasts. The forecasts were evaluated against the reference forecasts (i.e., persistence forecast and climatological forecast). The NMME-DTVGM drought forecasts have higher detectability and accuracy and lower false alarm rate than the reference forecasts at different lead times (from 1 to 4 months) during the cold-dry season. No apparent advantage is shown in drought predictions during spring and summer seasons because of a long memory of the initial conditions in spring and a lower predictive skill for precipitation in summer. Overall, the NMME-based seasonal drought forecasting system has meaningful skill in predicting drought several months in advance, which can provide critical information for drought preparedness and response planning as well as the sustainable practice of water resource conservation over the basin.</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><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('https://www.ncbi.nlm.nih.gov/pubmed/27377537','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27377537"><span><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('http://adsabs.harvard.edu/abs/2016NatSD...360048L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NatSD...360048L"><span><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://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lorenz, David J.; Nieto-Lugilde, Diego; Blois, Jessica L.; Fitzpatrick, Matthew C.; Williams, John W.</p> <p>2016-07-01</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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/7758443','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/7758443"><span>Transfer of radiative heat through clothing <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>Lotens, W A; Pieters, A M</p> <p>1995-06-01</p> <p>A mathematical model was designed to calculate the temperature and dry heat transfer in the various layers of a clothing <span class="hlt">ensemble</span>, and the total heat loss of a human who is irradiated for a certain fraction of his or her area. The clothing <span class="hlt">ensemble</span> that is irradiated by an external heat source is considered to be composed of underclothing, trapped air, and outer fabric. The model was experimentally tested with heat balance methods, using subjects, varying the activity, wind, and radiation characteristics of the outer garment of two-layer <span class="hlt">ensembles</span>. In two experiments the subjects could only give off dry heat because they were wrapped in plastic foil. The model appeared to be correct within about 1 degree C (rms error) and 10 Wm-2 (rms error). In a third experiment, sweat evaporation was also taken into account, showing that the resulting physiological heat load of 10 to 30% of the intercepted additional radiation is compensated by additional sweating. The resulting heat strain was rather mild. It is concluded that the mathematical model is a valid tool for the investigation of heat transfer through two-layer <span class="hlt">ensembles</span> in radiant environments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhyD..338....1T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhyD..338....1T"><span>A Hierarchical Bayes <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>Tsyrulnikov, Michael; Rakitko, Alexander</p> <p>2017-01-01</p> <p>A new <span class="hlt">ensemble</span> filter that allows for the uncertainty in the prior distribution is proposed and tested. The filter relies on the conditional Gaussian distribution of the state given the model-error and predictability-error covariance matrices. The latter are treated as random matrices and updated in a hierarchical Bayes scheme along with the state. The (hyper)prior distribution of the covariance matrices is assumed to be inverse Wishart. The new Hierarchical Bayes <span class="hlt">Ensemble</span> Filter (HBEF) assimilates <span class="hlt">ensemble</span> members as generalized observations and allows ordinary observations to influence the covariances. The actual probability distribution of the <span class="hlt">ensemble</span> members is allowed to be different from the true one. An approximation that leads to a practicable analysis algorithm is proposed. The new filter is studied in numerical experiments with a doubly stochastic one-variable model of "truth". The model permits the assessment of the variance of the truth and the true filtering error variance at each time instance. The HBEF is shown to outperform the EnKF and the HEnKF by Myrseth and Omre (2010) in a wide range of filtering regimes in terms of performance of its primary and secondary filters.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4325279','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4325279"><span>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('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><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('https://www.osti.gov/scitech/biblio/22525639','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/22525639"><span>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://eric.ed.gov/?q=habituation+AND+time&pg=7&id=EJ934197','ERIC'); return false;" href="http://eric.ed.gov/?q=habituation+AND+time&pg=7&id=EJ934197"><span>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('http://www.osti.gov/scitech/servlets/purl/919456','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/919456"><span>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('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>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/abs/2016PhRvB..94x5131N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016PhRvB..94x5131N"><span>Eigenstate Gibbs <span class="hlt">ensemble</span> in integrable quantum systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nandy, Sourav; Sen, Arnab; Das, Arnab; Dhar, Abhishek</p> <p>2016-12-01</p> <p>The eigenstate thermalization hypothesis conjectures that for a thermodynamically large system in one of its energy eigenstates, the reduced density matrix describing any finite subsystem is determined solely by a set of relevant conserved quantities. In a chaotic quantum system, only the energy is expected to play that role and hence eigenstates appear locally thermal. Integrable systems, on the other hand, possess an extensive number of such conserved quantities and therefore the reduced density matrix requires specification of all the corresponding parameters (generalized Gibbs <span class="hlt">ensemble</span>). However, here we show by unbiased statistical sampling of the individual eigenstates with a given finite energy density that the local description of an overwhelming majority of these states of even such an integrable system is actually Gibbs-like, i.e., requires only the energy density of the eigenstate. Rare eigenstates that cannot be represented by the Gibbs <span class="hlt">ensemble</span> can also be sampled efficiently by our method and their local properties are then shown to be described by appropriately truncated generalized Gibbs <span class="hlt">ensembles</span>. We further show that the presence of these rare eigenstates differentiates the model from the chaotic case and leads to the system being described by a generalized Gibbs <span class="hlt">ensemble</span> at long time under a unitary dynamics following a sudden quench, even when the initial state is a typical (Gibbs-like) eigenstate of the prequench Hamiltonian.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26578574','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26578574"><span><span class="hlt">Ensembl</span> Genomes 2016: more genomes, more complexity.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</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>2016-01-04</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.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1714639W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1714639W"><span>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://adsabs.harvard.edu/abs/2015EGUGA..17.1490G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.1490G"><span>ICEMAP250: Sea Ice Mapping At 250m Resolution Using <span class="hlt">Downscaled</span> Modis Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gignac, Charles; Bernier, Monique; Chokmani, Karem; Poulin, Jimmy</p> <p>2015-04-01</p> <p>IceMap250 is an automated and autonomous algorithm, focused on producing sea ice presence maps for any area covered by the MODIS Terra sensor at a 250m spatial resolution and on a daily basis. The IceMap250 algorithm, like its parent lower resolution version, uses data from reflective bands 2,4 & 6 and emissive bands 31 & 32 of the MODIS Terra sensor to build ancillary conditions dataset used to detect sea ice presence. The first condition of ice presence is the detection of snow at the surface. This is done using a threshold of >0.4 on the Normalized Difference Snow Index (NDSI). The second condition, determined empirically during the development of the original IceMap algorithm, is a reflectance greater than 11% in MODIS Terra Band 2. The final condition, based on thermal information, is to detect an ice surface temperature (IST) lower than 271.4 K, which corresponds to the freezing point of sea salt water. If these three conditions are respected, ice is detected; otherwise, water is expected to be present. To achieve a 250m spatial resolution in NDSI, Band 2 and IST, two <span class="hlt">downscaling</span> approaches were used. To <span class="hlt">downscale</span> bands 3-7 to a 250m spatial resolution, the Canadian Centre for Remote Sensing algorithm, based on focal regression, is used. An innovative method to <span class="hlt">downscale</span> the IST to 250m, uses a KNN regression between cloud masked NDSI and IST at 1KM to, after the initial CCRS <span class="hlt">downscaling</span>, injects 250m NDSI values into the KNN regression parameters therefore building a new, 250m <span class="hlt">downscaled</span> IST. Validation tests have been run on 5 days periods for each 'season' of the ice regime; the freeze-up, the stable cover and the meltdown. The first results of the IceMap250 algorithm make it clear that adaptations have to be taken to correct the diverse seasonal effects due to cloud cover and the smoothing effect caused by the regression approaches. During the freeze-up season, the dense cloud cover makes it difficult to precisely distinguish ice and water from clouds</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5307253','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5307253"><span>A Theoretical Analysis of Why Hybrid <span class="hlt">Ensembles</span> Work</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p></p> <p>2017-01-01</p> <p>Inspired by the group decision making process, <span class="hlt">ensembles</span> or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid <span class="hlt">ensemble</span>. Why does such an <span class="hlt">ensemble</span> work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of <span class="hlt">ensembles</span>, we conduct a theoretical analysis of why hybrid <span class="hlt">ensembles</span> work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid <span class="hlt">ensembles</span> of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid <span class="hlt">ensembles</span>. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid <span class="hlt">ensembles</span>. PMID:28255296</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>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/2017JPRS..126...79K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPRS..126...79K"><span>Spatiotemporal <span class="hlt">downscaling</span> approaches for monitoring 8-day 30 m actual evapotranspiration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ke, Yinghai; Im, Jungho; Park, Seonyoung; Gong, Huili</p> <p>2017-04-01</p> <p>Continuous monitoring of actual evapotranspiration (ET) is critical for water resources management at both regional and local scales. Although the MODIS ET product (MOD16A2) provides viable sources for ET monitoring at 8-day intervals, the spatial resolution (1 km) is too coarse for local scale applications. In this study, we propose a machine learning and spatial temporal fusion (STF)-integrated approach in order to generate 8-day 30 m ET based on both MOD16A2 and Landsat 8 data with three schemes. Random forest machine learning was used to <span class="hlt">downscale</span> MODIS 1 km ET to 30 m resolution based on nine Landsat-derived indicators including vegetation indices (VIs) and land surface temperature (LST). STF-based models including Spatial and Temporal Adaptive Reflectance Fusion Model and Spatio-Temporal Image Fusion Model were used to derive synthetic Landsat surface reflectance (scheme 1)/VIs (scheme 2)/ET (scheme 3) on Landsat-unavailable dates. The approach was tested over two study sites in the United States. The results showed that fusion of Landsat VIs produced the best accuracy of predicted ET (R2 = 0.52-0.97, RMSE = 0.47-3.0 mm/8 days and rRMSE = 6.4-37%). High density of cloud-clear Landsat image acquisitions and low spatial heterogeneity of Landsat VIs benefit the ET prediction. The <span class="hlt">downscaled</span> 30 m ET had good agreement with MODIS ET (RMSE = 0.42-3.4 mm/8 days, rRMSE = 3.2-26%). Comparison with the in situ ET measurements showed that the <span class="hlt">downscaled</span> ET had higher accuracy than MODIS ET.</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/2013AGUFM.A31E0140A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A31E0140A"><span>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/2016JHyd..542..896M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JHyd..542..896M"><span>Incorporating parameter dependencies into temporal <span class="hlt">downscaling</span> of extreme rainfall using a random cascade approach</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, Meng; Onof, Christian</p> <p>2016-11-01</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. 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. Using fine resolution data from two gauges in central Brisbane, Australia, covering the period 1908-2015, microcanonical MDRC models are fitted using data from 1 day to 11.25 min resolutions 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 the first and 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, month of year, decade, rainfall volume, event temporal structure and ENSO anomaly. Reasonable <span class="hlt">downscaling</span> performance is achieved in an evaluation period - in terms of replicating extreme values and autocorrelation structure of 11.25-min rainfall given the observed daily data - by including the parameter dependence on the rainfall volume and event structure, which involves 16 parameters per cascade level. Using only a volume dependence and assuming symmetrical probability distributions reduces the number of parameters to two per level with only a small loss of performance; and empirical relationships between parameter values and cascade level reduces the total number of</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUSM.H53B..03G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUSM.H53B..03G"><span>Climatological <span class="hlt">Downscaling</span> and Evaluation of AGRMET Precipitation Analyses Over the Continental U.S.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Tian, Y.; Zeng, J.</p> <p>2007-05-01</p> <p>The spatially distributed application of a land surface model (LSM) over a region of interest requires the application of similarly distributed precipitation fields that can be derived from various sources, including surface gauge networks, surface-based radar, and orbital platforms. The spatial variability of precipitation influences the spatial organization of soil temperature and moisture states and, consequently, the spatial variability of land- atmosphere fluxes. The accuracy of spatially-distributed precipitation fields can contribute significantly to the uncertainty of model-based hydrological states and fluxes at the land surface. Collaborations between the Air Force Weather Agency (AFWA), NASA, and Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and <span class="hlt">downscaling</span> of precipitation fields. Climatological information included in the LIS-based <span class="hlt">downscaling</span> procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1111255B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1111255B"><span>Statistical <span class="hlt">downscaling</span> of daily precipitation over Llobregat River Basin in Catalunya, Spain using analog method.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.</p> <p>2009-04-01</p> <p>Since anthropogenic climate change has become an important issue, the need to provide regional climate change information has increased, both for impact assessment studies and policy making. A regional climate is determined by interactions at large, regional and local scales. The general circulation models (GCMs) are run at too coarse resolution to permit accurate description of these regional and local interactions. So far, they have been unable to provide consistent estimates of climate change on a local scale. Several regionalization techniques have been developed to bridge the gap between the large-scale information provided by GCMs and fine spatial scales required for regional and environmental impact studies. Statistical <span class="hlt">downscaling</span> technique is based on the view that regional climate may be seen to be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or ‘‘predictors'' for which GCMs are trustable to regional or local surface ‘‘predictands'' for which models are less skilful. The main advantage of these techniques is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. In dynamical <span class="hlt">downscaling</span> methods, a regional climate model (RCM) uses GCM outputs as its initial and boundary conditions. A statistical <span class="hlt">downscaling</span> procedure based on an analogue technique has been used to determine projections for future climate change in the Llobregat River Basin in Catalunya, Spain. Llobregat Basin is one of the most important of Catalonia because it provides a significant amount of water for numerous cities that make up including Barcelona. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change</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>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/2010EGUGA..12.2389Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.2389Z"><span>Comparing empirical <span class="hlt">downscaling</span> methods within different kinds of terrain applied on the edge to climate impact research</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zuvela-Aloise, Maja; Matulla, Christoph; Auer, Inge; Böhm, Reinhard; Lexer, Manfred J.; Scheifinger, Helfried</p> <p>2010-05-01</p> <p>We use some statistical <span class="hlt">downscaling</span> techniques to derive local scale scenarios of future daily and monthly temperature and precipitation for the Alpine region. We utilize large scale NCEP/NCAR reanalysis data to establish empirical models and evaluate their performance against long term climate records from Austrian monitoring stations (forest sites, riverside fish population distributions, glaciers or phenological gardens across Europe etc.) for the second half of the 20th century. The performance of different <span class="hlt">downscaling</span> methods (multiple linear regression, canonical correlation analysis, the analog method) is analyzed. These methods are applied to derive transient climate change scenarios from ECHAM4/5 runs. <span class="hlt">Downscaled</span> data have been used in climate risk assessment studies to evaluate the sensitivity of the Austrian forests, fish stocks, phenological occurrence dates etc. to scenarios of climatic change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.7872E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.7872E"><span><span class="hlt">Downscaling</span> of Extreme Precipitation: Proposing a New Statistical Approach and Investigating a Taken-for-Granted Assumption</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Elshorbagy, Amin; Alam, Shahabul</p> <p>2015-04-01</p> <p>In spite of the ability of General Circulation Models (GCMs) to predict and generate atmospheric variables under pre-identified climate change scenarios, their coarse horizontal scale is an obstacle for impact studies. Therefore, <span class="hlt">downscaling</span> of variables (e.g., precipitation) from coarse spatial and temporal scales to finer ones is inevitable. <span class="hlt">Downscaling</span> methods are classified into various types ranging from applications related to short term numerical weather prediction to multidecadal global climate prediction. For engineering applications of impact assessment of climate change on infrastructure, the multidecadal global climate projection, is the most widely used type. One of the important engineering applications of climate change impact assessment is the development and reconstruction of intensity-duration-frequency (IDF) curves under possible climate change. IDF curves are widely used for design and management of urban hydrosystems. Their construction requires accurate information about intense short duration rainfall, including sub-hourly, extremes. Previous attempts were made to construct IDF curves in various places under climate change using dynamical and statistical <span class="hlt">downscaling</span>. The deficiency of GCMs, and even RCMs, in representing local surface conditions, especially extreme weather and convective precipitation in many areas, necessitates the use of statistical <span class="hlt">downscaling</span> for IDF-related applications. In statistical <span class="hlt">downscaling</span> methods, and in particular regression-based methods, the search is always for the optimum set of inputs at a coarser scale that act as predictors for the desired surface weather variable (predictand) at the local finer scale. The grid box nearest to the local site may not provide the optimum predictor-predictand relationship. In fact, even the set of predictors varies from one region to another. In this study, a novel approach using genetic programming (GP) for specific application of <span class="hlt">downscaling</span> annual maximum precipitation</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>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://adsabs.harvard.edu/abs/2016ClDy...47..579R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...47..579R"><span>Spatial, temporal and frequency based climate change assessment in Columbia River Basin using multi <span class="hlt">downscaled</span>-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>2016-07-01</p> <p>Uncertainties in climate modelling are well documented in literature. Global Climate Models (GCMs) are often used to <span class="hlt">downscale</span> the climatic parameters on a regional scale. In the present work, 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 statistically <span class="hlt">downscaled</span> GCM projections in Columbia River Basin (CRB). Analysis is performed using two different statistically <span class="hlt">downscaled</span> climate projections (with ten GCMs <span class="hlt">downscaled</span> products each, for RCP 4.5 and RCP 8.5, from CMIP5 dataset) namely, those from the Bias Correction and Spatial <span class="hlt">Downscaling</span> (BCSD) technique generated at Portland State University and from the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. The two datasets for BCSD and MACA are <span class="hlt">downscaled</span> from observed data for both scenarios projections i.e. RCP4.5 and RCP8.5. Analysis is performed using spatial change (yearly scale), temporal change (monthly scale), percentile change (seasonal scale), quantile change (yearly scale), and wavelet analysis (yearly scale) in the future period from the historical period, respectively, 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. Most of the models have indicated considerate positive change in quantiles and percentiles for both precipitation and temperature. Wavelet analysis provided insights into possible explanation to changes in precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/publication/?seqNo115=276487','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/publication/?seqNo115=276487"><span>Evaluation of a weather generator-based method for statistically <span class="hlt">downscaling</span> non-stationary climate scenarios for impact assessment at a point scale</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 non-stationarity is a major concern for statistically <span class="hlt">downscaling</span> climate change scenarios for impact assessment. This study is to evaluate whether a statistical <span class="hlt">downscaling</span> method is fully applicable to generate daily precipitation under non-stationary conditions in a wide range of climatic zo...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160007388','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160007388"><span>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('http://hdl.handle.net/2060/20120016072','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120016072"><span>Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, Randal; Walker, Greg; Mahanama, Sarith; Reichle, Rolf</p> <p>2012-01-01</p> <p>Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the <span class="hlt">downscaling</span> of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation <span class="hlt">downscaling</span> is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010pcms.confE..30F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010pcms.confE..30F"><span>Orographic Signature on Multiscale Statistics of Extreme Rainfall: Conditional <span class="hlt">downscaling</span> with emphasis on extremes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Foufoula-Georgiou, E.; Ebtehaj, M.</p> <p>2010-09-01</p> <p>Rainfall intensity and spatio-temporal patterns often show a strong dependency on the underlying terrain. The main objective of this work is to study the statistical signature imprinted by orography on the spatial structure of rainfall and its temporal evolution at multiple scales, with the aim to develop a consistent theoretical basis for conditional <span class="hlt">downscaling</span> of precipitation given the topographic information of the underlying terrain. The results of an extensive analysis of the high resolution stage II Doppler radar data of the Rapidan storm, June 1995, over the Appalachian Mountains is reported in this study. The orographic signature on the elementary statistical structure of the precipitation fields is studied via a variable-intensity thresholding scheme. This signature is further explored at multiple scales via analysis of the dependence of precipitation fields on the underlying terrain both in Fourier and Wavelet domains. The Generalized Normal distribution is found to be a suitable probability model to explain the variability of the rainfall wavelet coefficients and its dependence on the underlying elevations. These results provide a new perspective for more accurate statistical <span class="hlt">downscaling</span> of the orographic precipitation over complex terrain with emphasis on extremes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015APJAS..51...77J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015APJAS..51...77J"><span>Projected change in East Asian summer monsoon by dynamic <span class="hlt">downscaling</span>: Moisture budget analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jung, Chun-Yong; Shin, Ho-Jeong; Jang, Chan Joo; Kim, Hyung-Jin</p> <p>2015-02-01</p> <p>The summer monsoon considerably affects water resource and natural hazards including flood and drought in East Asia, one of the world's most densely populated area. In this study, we investigate future changes in summer precipitation over East Asia induced by global warming through dynamical <span class="hlt">downscaling</span> with the Weather Research and Forecast model. We have selected a global model from the Coupled Model Intercomparison Project Phase 5 based on an objective evaluation for East Asian summer monsoon and applied its climate change under Representative Concentration Pathway 4.5 scenario to a pseudo global warming method. Unlike the previous studies that focused on a qualitative description of projected precipitation changes over East Asia, this study tried to identify the physical causes of the precipitation changes by analyzing a local moisture budget. Projected changes in precipitation over the eastern foothills area of Tibetan Plateau including Sichuan Basin and Yangtze River displayed a contrasting pattern: a decrease in its northern area and an increase in its southern area. A local moisture budget analysis indicated the precipitation increase over the southern area can be mainly attributed to an increase in horizontal wind convergence and surface evaporation. On the other hand, the precipitation decrease over the northern area can be largely explained by horizontal advection of dry air from the northern continent and by divergent wind flow. Regional changes in future precipitation in East Asia are likely to be attributed to different mechanisms which can be better resolved by regional dynamical <span class="hlt">downscaling</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ThApC.tmp...89A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ThApC.tmp...89A"><span>Drought episodes over Greece as simulated by dynamical and statistical <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>Anagnostopoulou, Christina</p> <p>2016-04-01</p> <p>Drought over the Greek region is characterized by a strong seasonal cycle and large spatial variability. Dry spells longer than 10 consecutive days mainly characterize the duration and the intensity of Greek drought. Moreover, an increasing trend of the frequency of drought episodes has been observed, especially during the last 20 years of the 20th century. Moreover, the most recent regional circulation models (RCMs) present discrepancies compared to observed precipitation, while they are able to reproduce the main patterns of atmospheric circulation. In this study, both a statistical and a dynamical <span class="hlt">downscaling</span> approach are used to quantify drought episodes over Greece by simulating the Standardized Precipitation Index (SPI) for different time steps (3, 6, and 12 months). A statistical <span class="hlt">downscaling</span> technique based on artificial neural network is employed for the estimation of SPI over Greece, while this drought index is also estimated using the RCM precipitation for the time period of 1961-1990. Overall, it was found that the drought characteristics (intensity, duration, and spatial extent) were well reproduced by the regional climate models for long term drought indices (SPI12) while ANN simulations are better for the short-term drought indices (SPI3).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy...47.3235Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...47.3235Y"><span>Biases and improvements in three dynamical <span class="hlt">downscaling</span> climate simulations 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>Yang, Hao; Jiang, Zhihong; Li, Laurent</p> <p>2016-11-01</p> <p>A dynamical <span class="hlt">downscaling</span> is performed to improve the regional climate simulation in China. It consists of using a variable resolution model LMDZ4 nested into three global climate models (GCMs): BCC-csm1-1-m, FGOALS-g2 and IPSL-CM5A-MR, respectively. The regional climate from different simulations is assessed in terms of surface air temperature and rainfalls through a comparison to observations (both station data and gridded data). The comparison includes climatic trends during the last 40 years, statistical distribution of sub-regional climate, and the seasonal cycle. For surface air temperature, a significant part of the improvement provided by LMDZ4 is related to the effect of surface elevation which is more realistic in high-resolution simulations; the rest is related to changes in regional or local atmospheric general circulation. All GCMs and the <span class="hlt">downscaling</span> model LMDZ4 are, more or less, able to describe the spatial distribution of surface air temperature and precipitation in China. LMDZ4 does show its superiority, compared to GCMs, in depicting a good regional terrain including the Tibetan Plateau, the Sichuan Basin and the Qilian Mountains.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy...47.2833W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...47.2833W"><span>Evaluation of dynamically <span class="hlt">downscaled</span> extreme temperature using a spatially-aggregated generalized extreme value (GEV) model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wang, Jiali; Han, Yuefeng; Stein, Michael L.; Kotamarthi, Veerabhadra R.; Huang, Whitney K.</p> <p>2016-11-01</p> <p>The weather research and forecast (WRF) model <span class="hlt">downscaling</span> skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-<span class="hlt">downscaled</span> extreme maximum temperature through comparison with North American regional reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting a novel bootstrap procedure that makes no assumption of temporal or spatial independence within a year, which is especially important for climate data. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4913252','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4913252"><span>Nucleus <span class="hlt">downscaling</span> in mouse embryos is regulated by cooperative developmental and geometric programs</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tsichlaki, Elina; FitzHarris, Greg</p> <p>2016-01-01</p> <p>Maintaining appropriate nucleus size is important for cell health, but the mechanisms by which this is achieved are poorly understood. Controlling nucleus size is a particular challenge in early development, where the nucleus must <span class="hlt">downscale</span> in size with progressive reductive cell divisions. Here we use live and fixed imaging, micromanipulation approaches, and small molecule analyses during preimplantation mouse development to probe the mechanisms by which nucleus size is determined. We find a close correlation between cell and nuclear size at any given developmental stage, and show that experimental cytoplasmic reduction can alter nuclear size, together indicating that cell size helps dictate nuclear proportions. Additionally, however, by creating embryos with over-sized blastomeres we present evidence of a developmental program that drives nuclear <span class="hlt">downscaling</span> independently of cell size. We show that this developmental program does not correspond with nuclear import rates, but provide evidence that PKC activity may contribute to this mechanism. We propose a model in which nuclear size regulation during early development is a multi-mode process wherein nucleus size is set by cytoplasmic factors, and fine-tuned on a cell-by-cell basis according to cell size. PMID:27320842</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1813768L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1813768L"><span>Statiscal <span class="hlt">Downscaling</span> of a surface wind field over northeastern North America: annual to centennial variability.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lucio-Eceiza, Etor E.; Fidel González-Rouco, J.; Navarro, Jorge; García-Bustamante, Elena; Beltrami, Hugo</p> <p>2016-04-01</p> <p>The region of North Eastern North America is located in a privileged geographical position that allows for interesting relationships between various large-scale circulation modes and the seasonal surface wind. This is a region where the large-scale configurations foster the transit of tropical cyclonic events during the summer season and even more intense extratropical cyclones during winter. In this work we present a statistical <span class="hlt">downscaling</span> method based on Canonical Correlation Analysis (CCA) that exploits the relationships among the main modes of circulation over the North Atlantic and Pacific Sectors and the behaviour of surface wind. The statistical technique has been implemented with predictor variables (mean sea level pressure and geopotential height at different levels) provided by all the reanalysis products available to date. The regional scale data consist of a set of 526 sites distributed over North Eastern North America that span over a period of about 60 years (1953-2010). These data have been previously subjected to an exhaustive quality control process, height standardization and wind direction homogenization. Several decades of observations allow for the study of intra to multidecadal variability. Also, the sensitivity of the <span class="hlt">downscaling</span> methodology to the selection of a systematic sampling of model parameter values has been explored. The statistical relationship obtained by this method also allows for the reconstruction of the regional wind behaviour back to the mid 19th century through various 20th century reanalysis and instrumental sea level pressure datasets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1812946T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1812946T"><span>Assessment of Projected Changes in Temperature and Precipitation Climatology over the CORDEX-Region 9 via Multi-Model <span class="hlt">Ensemble</span> Mean of CMIP5 Models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Turp, M. Tufan; Ozturk, Tugba; Kurnaz, M. Levent</p> <p>2016-04-01</p> <p>In this study, we conduct a multi-model <span class="hlt">ensemble</span> mean approach in order to investigate of projected changes in fundamental climate variables (i.e. mean air temperature, minimum temperature, maximum temperature, and precipitation total) over the CORDEX-Australasia domain based on the outputs of various coupled global climate models (GCMs) participating in the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project (CMIP5). In this respect, in order to analyze projected future changes in temperature and precipitation climatology, seasonal averages, and inter-annual variability over the Australasia (known as Region 9) domain, where is one of fourteen domains of the Coordinated Regional Climate <span class="hlt">Downscaling</span> Experiment (CORDEX), we focus on historical, RCP4.5 and RCP8.5 experiments of the GCMs for reference- (1981 - 2000), near- (2016 - 2035), mid- (2046 - 2065), and long-term (2081 - 2100), respectively. This research has been supported by Boǧaziçi University Research Fund Grant Number 10421.</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('https://www.ncbi.nlm.nih.gov/pubmed/25361244','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25361244"><span>Quantum data compression of a qubit <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>Rozema, Lee A; Mahler, Dylan H; Hayat, Alex; Turner, Peter S; Steinberg, Aephraim M</p> <p>2014-10-17</p> <p>Data compression is a ubiquitous aspect of modern information technology, and the advent of quantum information raises the question of what types of compression are feasible for quantum data, where it is especially relevant given the extreme difficulty involved in creating reliable quantum memories. We present a protocol in which an <span class="hlt">ensemble</span> of quantum bits (qubits) can in principle be perfectly compressed into exponentially fewer qubits. We then experimentally implement our algorithm, compressing three photonic qubits into two. This protocol sheds light on the subtle differences between quantum and classical information. Furthermore, since data compression stores all of the available information about the quantum state in fewer physical qubits, it could allow for a vast reduction in the amount of quantum memory required to store a quantum <span class="hlt">ensemble</span>, making even today's limited quantum memories far more powerful than previously recognized.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27300837','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27300837"><span>Rotationally invariant <span class="hlt">ensembles</span> of integrable matrices.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</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/2015PhRvL.115r8701F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvL.115r8701F"><span>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://adsabs.harvard.edu/abs/2014PhRvL.113j4102B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhRvL.113j4102B"><span>Dysonian Dynamics of the Ginibre <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>Burda, Zdzislaw; Grela, Jacek; Nowak, Maciej A.; Tarnowski, Wojciech; Warchoł, Piotr</p> <p>2014-09-01</p> <p>We study the time evolution of Ginibre matrices whose elements undergo Brownian motion. The non-Hermitian character of the Ginibre <span class="hlt">ensemble</span> binds the dynamics of eigenvalues to the evolution of eigenvectors in a nontrivial way, leading to a system of coupled nonlinear equations resembling those for turbulent systems. We formulate a mathematical framework allowing simultaneous description of the flow of eigenvalues and eigenvectors, and we unravel a hidden dynamics as a function of a new complex variable, which in the standard description is treated as a regulator only. We solve the evolution equations for large matrices and demonstrate that the nonanalytic behavior of the Green's functions is associated with a shock wave stemming from a Burgers-like equation describing correlations of eigenvectors. We conjecture that the hidden dynamics that we observe for the Ginibre <span class="hlt">ensemble</span> is a general feature of non-Hermitian random matrix models and is relevant to related physical applications.</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>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('https://www.osti.gov/scitech/biblio/22472437','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/22472437"><span>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/2011AGUFM.H41C1049F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H41C1049F"><span>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('http://www.dtic.mil/docs/citations/AD1001134','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1001134"><span>Deep <span class="hlt">Ensemble</span> Learning for Monaural Speech Separation</span></a></p> <p><a target="_blank" href="https://publicaccess.dtic.mil/psm/api/service/search/search">DTIC Science & Technology</a></p> <p></p> <p>2015-02-01</p> <p><span class="hlt">Ensemble</span> Learning for Monaural Speech Separation Xiao-Lei Zhang Department of Computer Science and Engineering The Ohio State University, Columbus...State University, Columbus, OH 43210, USA dwang@cse.ohio-state.edu Abstract – Monaural speech separation is a fundamental problem in robust speech...processing. Recently, deep neural network (DNN) based speech separation methods, which predict either clean speech or an ideal time-frequency mask, have</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AdAtS..33...10Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AdAtS..33...10Z"><span><span class="hlt">Ensemble</span> transform sensitivity method for adaptive observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Yu; Xie, Yuanfu; Wang, Hongli; Chen, Dehui; Toth, Zoltan</p> <p>2016-01-01</p> <p>The <span class="hlt">Ensemble</span> Transform (ET) method has been shown to be useful in providing guidance for adaptive observation deployment. It predicts forecast error variance reduction for each possible deployment using its corresponding transformation matrix in an <span class="hlt">ensemble</span> subspace. In this paper, a new ET-based sensitivity (ETS) method, which calculates the gradient of forecast error variance reduction in terms of analysis error variance reduction, is proposed to specify regions for possible adaptive observations. ETS is a first order approximation of the ET; it requires just one calculation of a transformation matrix, increasing computational efficiency (60%-80% reduction in computational cost). An explicit mathematical formulation of the ETS gradient is derived and described. Both the ET and ETS methods are applied to the Hurricane Irene (2011) case and a heavy rainfall case for comparison. The numerical results imply that the sensitive areas estimated by the ETS and ET are similar. However, ETS is much more efficient, particularly when the resolution is higher and the number of <span class="hlt">ensemble</span> members is larger.</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>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('http://hdl.handle.net/2060/20000093260','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000093260"><span>Decimated Input <span class="hlt">Ensembles</span> for Improved Generalization</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.; Norvig, Peter (Technical Monitor)</p> <p>1999-01-01</p> <p>Recently, many researchers have demonstrated that using classifier <span class="hlt">ensembles</span> (e.g., averaging the outputs of multiple classifiers before reaching a classification decision) leads to improved performance for many difficult generalization problems. However, in many domains there are serious impediments to such "turnkey" classification accuracy improvements. Most notable among these is the deleterious effect of highly correlated classifiers on the <span class="hlt">ensemble</span> performance. One particular solution to this problem is generating "new" training sets by sampling the original one. However, with finite number of patterns, this causes a reduction in the training patterns each classifier sees, often resulting in considerably worsened generalization performance (particularly for high dimensional data domains) for each individual classifier. Generally, this drop in the accuracy of the individual classifier performance more than offsets any potential gains due to combining, unless diversity among classifiers is actively promoted. In this work, we introduce a method that: (1) reduces the correlation among the classifiers; (2) reduces the dimensionality of the data, thus lessening the impact of the 'curse of dimensionality'; and (3) improves the classification performance of the <span class="hlt">ensemble</span>.</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>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('http://adsabs.harvard.edu/abs/2015PhRvL.115z8701S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvL.115z8701S"><span>Breaking of <span class="hlt">Ensemble</span> Equivalence in Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Squartini, Tiziano; de Mol, Joey; den Hollander, Frank; Garlaschelli, Diego</p> <p>2015-12-01</p> <p>It is generally believed that, in the thermodynamic limit, the microcanonical description as a function of energy coincides with the canonical description as a function of temperature. However, various examples of systems for which the microcanonical and canonical <span class="hlt">ensembles</span> are not equivalent have been identified. A complete theory of this intriguing phenomenon is still missing. Here we show that <span class="hlt">ensemble</span> nonequivalence can manifest itself also in random graphs with topological constraints. We find that, while graphs with a given number of links are <span class="hlt">ensemble</span> equivalent, graphs with a given degree sequence are not. This result holds irrespective of whether the energy is nonadditive (as in unipartite graphs) or additive (as in bipartite graphs). In contrast with previous expectations, our results show that (1) physically, nonequivalence can be induced by an extensive number of local constraints, and not necessarily by long-range interactions or nonadditivity, (2) mathematically, nonequivalence is determined by a different large-deviation behavior of microcanonical and canonical probabilities for a single microstate, and not necessarily for almost all microstates. The latter criterion, which is entirely local, is not restricted to networks and holds in general.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016APS..DPPB10018C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016APS..DPPB10018C"><span>On the <span class="hlt">ensemble</span> averaging of PIC simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Codur, R. J. B.; Tsung, F. S.; Mori, W. B.</p> <p>2016-10-01</p> <p>Particle-in-cell simulations are used ubiquitously in plasma physics to study a variety of phenomena. They can be an efficient tool for modeling the Vlasov or Vlasov Fokker Planck equations in multi-dimensions. However, the PIC method actually models the Klimontovich equation for finite size particles. The Vlasov Fokker Planck equation can be derived as the <span class="hlt">ensemble</span> average of the Klimontovich equation. We present results of studying Landau damping and Stimulated Raman Scattering using PIC simulations where we use identical ``drivers'' but change the random number generator seeds. We show that even for cases where a plasma wave is excited below the noise in a single simulation that the plasma wave can clearly be seen and studied if an <span class="hlt">ensemble</span> average over O(10) simulations is made. Comparison between the results from an <span class="hlt">ensemble</span> average and the subtraction technique are also presented. In the subtraction technique two simulations, one with the other without the ``driver'' are conducted with the same random number generator seed and the results are subtracted. This work is supported by DOE, NSF, and ENSC (France).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015NPGD....2..833R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015NPGD....2..833R"><span>Multivariate localization methods for <span class="hlt">ensemble</span> Kalman filtering</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.</p> <p>2015-05-01</p> <p>In <span class="hlt">ensemble</span> Kalman filtering (EnKF), the small number of <span class="hlt">ensemble</span> members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the <span class="hlt">ensemble</span>-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015NPGeo..22..723R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015NPGeo..22..723R"><span>Multivariate localization methods for <span class="hlt">ensemble</span> Kalman filtering</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.</p> <p>2015-12-01</p> <p>In <span class="hlt">ensemble</span> Kalman filtering (EnKF), the small number of <span class="hlt">ensemble</span> members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the <span class="hlt">ensemble</span>-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4393075','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4393075"><span>Hierarchical <span class="hlt">Ensemble</span> Methods for Protein Function Prediction</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p></p> <p>2014-01-01</p> <p>Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on <span class="hlt">ensembles</span> of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” <span class="hlt">ensemble</span> decision, taking into account the hierarchical relationships between classes. The main hierarchical <span class="hlt">ensemble</span> methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.osti.gov/scitech/biblio/22365877','SCIGOV-STC'); return false;" href="https://www.osti.gov/scitech/biblio/22365877"><span>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://adsabs.harvard.edu/abs/2015APS..MART20004C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015APS..MART20004C"><span>Cavity Cooling for <span class="hlt">Ensemble</span> Spin Systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cory, David</p> <p>2015-03-01</p> <p>Recently there has been a surge of interest in exploring thermodynamics in quantum systems where dissipative effects can be exploited to perform useful work. One such example is quantum state engineering where a quantum state of high purity may be prepared by dissipative coupling through a cold thermal bath. This has been used to great effect in many quantum systems where cavity cooling has been used to cool mechanical modes to their quantum ground state through coupling to the resolved sidebands of a high-Q resonator. In this talk we explore how these techniques may be applied to an <span class="hlt">ensemble</span> spin system. This is an attractive process as it potentially allows for parallel remove of entropy from a large number of quantum systems, enabling an <span class="hlt">ensemble</span> to achieve a polarization greater than thermal equilibrium, and potentially on a time scale much shorter than thermal relaxation processes. This is achieved by the coupled angular momentum subspaces of the <span class="hlt">ensemble</span> behaving as larger effective spins, overcoming the weak individual coupling of individual spins to a microwave resonator. Cavity cooling is shown to cool each of these subspaces to their respective ground state, however an additional algorithmic step or dissipative process is required to couple between these subspaces and enable cooling to the full ground state of the joint system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A33E0289O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A33E0289O"><span>Regional <span class="hlt">downscaling</span> of temporal resolution in near-surface wind from statistically <span class="hlt">downscaled</span> Global Climate Models (GCMs) for use in San Francisco Bay coastal flood modeling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>O'Neill, A.; Erikson, L. H.; Barnard, P.</p> <p>2013-12-01</p> <p>While Global Climate Models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically <span class="hlt">downscaled</span> GCM projections from Multivariate Adaptive Constructed Analogues (MACA) provide daily near-surface winds at an appropriate spatial resolution for wave modeling within San Francisco Bay. Using 30 years (1975-2004) of climatological data from four representative stations around San Francisco Bay, a library of example daily wind conditions for four corresponding over-water sub-regions is constructed. Empirical cumulative distribution functions (ECDFs) of station conditions are compared to MACA GFDL hindcasts to create correction factors, which are then applied to 21st century MACA wind projections. For each projection day, a best match example is identified via least squares error among all stations from the library. The best match's daily variation in velocity components (u/v) is used as an analogue of representative wind variation and is applied at 3-hour increments about the corresponding sub-region's projected u/v values. High temporal resolution reconstructions using this methodology on hindcast MACA fields from 1975-2004 accurately recreate extreme wind values within the San Francisco Bay, and because these extremes in wind forcing are of key importance in wave and subsequent coastal flood modeling, this represents a valuable method of generating near-surface wind vectors for use in coastal flood modeling.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.1103H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.1103H"><span>Partitioning internal variability and model uncertainty components in a multireplicate multimodel <span class="hlt">ensemble</span> of hydrometeorological future projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hingray, Benoit; Saïd, Mériem; Lafaysse, Matthieu; Gailhlard, Joël; Mezghani, Abdelkader</p> <p>2014-05-01</p> <p>A simple and robust framework was proposed by Hingray and Mériem (2013) for the partitioning of the different components of internal variability and model uncertainty in a multireplicate multimodel <span class="hlt">ensemble</span> (MRMME) of climate projections obtained for a suite of statistical <span class="hlt">downscaling</span> models (SDMs) and global climate models (GCMs). It is based on the quasi-ergodic assumption for transient climate simulations. Model uncertainty components are estimated from the noise-free signals of each modeling chain using a two-way ANOVA framework. The residuals from the noise-free signal are used to estimate the large and small scale internal variability (IV) components associated with each considered GCM/SDM configuration. This framework makes it possible to take into account all runs and replicates available from any climate <span class="hlt">ensemble</span> of opportunity. This quasi-ergodic ANOVA framework was applied to the MRMME of hydrometeorological simulations produced for the Upper Durance River basin (French Alps) over the 1860-2100 period within the RIWER2030 research project (http://www.lthe.fr/RIWER2030/). The different uncertainty sources were quantified as a function of lead time for projected changes in temperature, precipitation, evaporation losses, snow cover and discharges (Lafaysse et al., 2013). For temperature, GCM uncertainty prevails and, as opposed to IV, SDM uncertainty is non-negligible. Significant warming and in turn significant changes are predicted for evaporation, snow cover and seasonality of discharges. For precipitation, GCM and SDM uncertainty components are of the same order. Despite high model uncertainty, the non-zero climate change response of simulation chains is significant and annual precipitation is expected to decrease. However, high values are obtained for the large and small scale components of IV, inherited respectively from the GCMs and the different replicates of a given SDM. The same applies for annual discharge. The uncertainty in values that could</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=314310&keyword=Weather+AND+forecasting&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=90684389&CFTOKEN=76431295','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=314310&keyword=Weather+AND+forecasting&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=90684389&CFTOKEN=76431295"><span>Evaluation of near surface ozone and particulate matter in air quality simulations driven by dynamically <span class="hlt">downscaled</span> historical meteorological fields</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, techniques typically used for future air quality projections are applied to a historical 11-year period to assess the performance of the modeling system when the driving meteorological conditions are obtained using dynamical <span class="hlt">downscaling</span> of coarse-scale fields witho...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=311081&keyword=nasa&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=90803258&CFTOKEN=93825959','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=311081&keyword=nasa&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=90803258&CFTOKEN=93825959"><span>Refinement of horizontal resolution in dynamical <span class="hlt">downscaling</span> of climate information using WRF: Costs, benefits, and lessons learned</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>Dynamical <span class="hlt">downscaling</span> techniques have previously been developed by the U.S. Environmental Protection Agency (EPA) using a nested WRF at 108- and 36-km. Subsequent work extended one-way nesting down to 12-km resolution. Recently, the EPA Office of Research and Development used com...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC34B..01K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC34B..01K"><span>Evaluation of <span class="hlt">Downscaled</span> CMIP5 Model Skill in Simulating Daily Maximum Temperature Over the Southeastern 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>Keellings, D.</p> <p>2015-12-01</p> <p><span class="hlt">Downscaled</span> CMIP5 climate projections of maximum daily temperature from the <span class="hlt">Downscaled</span> CMIP3 and CMIP5 Climate and Hydrology Projections archive are examined regionally over the southeastern U.S. Three measures of model skill (means-based, distribution-based, extreme-based) are utilized to assess the ability of 15 <span class="hlt">downscaled</span> models to simulate daily maximum temperature observations. A new test is proposed to determine statistical significance of the probability density function based skill measures. Skill scores are found to be generally high for all three measures throughout the study region, but lower scores are present in coastal and mountainous areas. Application of the significance test shows that while the skill scores may be high they are not significantly higher than could be expected at random in some areas. The distribution-based skill scores are not significant in much of Florida and the Appalachians. The extreme-based skill scores are not significant in more than 90% of the region for all models investigated. The findings suggest that although the <span class="hlt">downscaled</span> models have simulated observed means well and are a good match to the entire distribution of observations, they are not simulating the occurrence of extreme (above 90th percentile) maximum daily temperatures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011280','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011280"><span><span class="hlt">Ensemble</span> Data Assimilation Without <span class="hlt">Ensembles</span>: Methodology and Application to Ocean Data Assimilation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Keppenne, Christian L.; Rienecker, Michele M.; Kovach, Robin M.; Vernieres, Guillaume</p> <p>2013-01-01</p> <p>Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the <span class="hlt">ensemble</span> Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an <span class="hlt">ensemble</span> in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an <span class="hlt">ensemble</span> sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012NHESS..12..651L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012NHESS..12..651L"><span>Statistical <span class="hlt">downscaling</span> of the French Mediterranean climate: assessment for present and projection in an anthropogenic scenario</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lavaysse, C.; Vrac, M.; Drobinski, P.; Lengaigne, M.; Vischel, T.</p> <p>2012-03-01</p> <p>The Mediterranean basin is a particularly vulnerable region to climate change, featuring a sharply contrasted climate between the North and South and governed by a semi-enclosed sea with pronounced surrounding topography covering parts of the Europe, Africa and Asia regions. The physiographic specificities contribute to produce mesoscale atmospheric features that can evolve to high-impact weather systems such as heavy precipitation, wind storms, heat waves and droughts. The evolution of these meteorological extremes in the context of global warming is still an open question, partly because of the large uncertainty associated with existing estimates produced by global climate models (GCM) with coarse horizontal resolution (~200 km). <span class="hlt">Downscaling</span> climatic information at a local scale is, thus, needed to improve the climate extreme prediction and to provide relevant information for vulnerability and adaptation studies. In this study, we investigate wind, temperature and precipitation distributions for past recent climate and future scenarios at eight meteorological stations in the French Mediterranean region using one statistical <span class="hlt">downscaling</span> model, referred as the "Cumulative Distribution Function transform" (CDF-t) approach. A thorough analysis of the uncertainty associated with statistical <span class="hlt">downscaling</span> and bi-linear interpolation of large-scale wind speed, temperature and rainfall from reanalyses (ERA-40) and three GCM historical simulations, has been conducted and quantified in terms of Kolmogorov-Smirnov scores. CDF-t produces a more accurate and reliable local wind speed, temperature and rainfall. Generally, wind speed, temperature and rainfall CDF obtained with CDF-t are significantly similar with the observed CDF, even though CDF-t performance may vary from one station to another due to the sensitivity of the driving large-scale fields or local impact. CDF-t has then been applied to climate simulations of the 21st century under B1 and A2 scenarios for the three</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017HESS...21.1693V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017HESS...21.1693V"><span>A combined statistical bias correction and stochastic <span class="hlt">downscaling</span> method 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>Volosciuk, Claudia; Maraun, Douglas; Vrac, Mathieu; Widmann, Martin</p> <p>2017-03-01</p> <p>Much of our knowledge about future changes in precipitation relies on global (GCMs) and/or regional climate models (RCMs) that have resolutions which are much coarser than typical spatial scales of precipitation, particularly extremes. The major problems with these projections are both climate model biases and the gap between gridbox and point scale. Wong et al. (2014) developed a model to jointly bias correct and <span class="hlt">downscale</span> precipitation at daily scales. This approach, however, relied on pairwise correspondence between predictor and predictand for calibration, and, thus, on nudged simulations which are rarely available. Here we present 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 GCMs/RCMs. In a first step, we bias correct RCM-simulated precipitation against gridded observations at the same scale using a parametric quantile mapping (QMgrid) approach. In a second step, we bridge the scale gap: we predict local variance employing a regression-based model with coarse-scale precipitation as a predictor. The regression model is calibrated between gridded and point-scale (station) observations. For this concept we present one specific implementation, although the optimal model may differ for each studied location. To correct the whole 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 the first step. For the second step a vector generalized linear gamma model is employed. For evaluation we adopt the perfect predictor experimental setup of VALUE. We also compare our method to the classical QM as it is usually applied, i.e., between RCM and point scale (QMpoint). Precipitation is in most cases improved by (parts of) our method across different European climates. The method generally performs better in summer than in winter and in winter best in the</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..11.4752S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..11.4752S"><span><span class="hlt">Downscaling</span> of climate parameters in Bode river basin in Germany using Active Learning Method (ALM)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sodoudi, S.; Reimer, E.</p> <p>2009-04-01</p> <p>This study is a part of main program RIMAX "risk management of extreme flood events", which concerns itself of "extremes floodwater and damage potential in the Bode river basin in Germany „with the variable occurrence of flood events in this area for the past 1000 years. The objective of the project is to produce the local climate time series (climate <span class="hlt">downscaling</span>) as the input for a runoff model in the Bode basin for the last 1000 years on a grid of 5x5 km as well as the estimation of the spatial distributions and temporal variability of the precipitation, the amount of precipitation and further meteorological parameter (temperature, radiation and relative humidity) for this area. A nonlinear <span class="hlt">downscaling</span> based on Fuzzy rules has been used to produce 1000 year climate time series. The global model ECHO from Max Planck institute for Meteorology (MPI) with T30 resolution and 1000 years data has been used as the global model (GCM). The regional model REMO, with 10 km resolution and 20 years data has been used as the regional input. The observations, which include 30 years precipitation, radiation, temperature, wind and relative humidity, have been used as output (predictand). In this study, two set fuzzy rules have been trained to describe the relationship between ECHO/REMO and REMO/Observation. The Fuzzy method used in this work is Active Learning Method (ALM). The heart of calculation of ALM is a fuzzy interpolation and curve fitting which is entitled Ink Drop Spread (IDS). The IDS searches fuzzily for continuous possible paths of interpolated data points on data planes. The ability of ALM to simulate the high values as well as the fluctuation of time series is much better than Takagi-Sugeno models, which have been used for <span class="hlt">downscaling</span> in the last decade. In the next steps, considering predictors from the ECHO time series and predictands from the REMO grid points, some ALM models are developed, which describe the fuzzy rules and the relationship between global and</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20000012298&hterms=regional+climate&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dregional%2Bclimate','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20000012298&hterms=regional+climate&qs=N%3D0%26Ntk%3DTitle%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dregional%2Bclimate"><span>Regional Climate Simulation with a Variable Resolution Stretched Grid GCM: The Regional <span class="hlt">Down-Scaling</span> Effects</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Fox-Rabinovitz, Michael S.; Takacs, Lawrence L.; Suarez, Max; Sawyer, William; Govindaraju, Ravi C.</p> <p>1999-01-01</p> <p>The results obtained with the variable resolution stretched grid (SG) GEOS GCM (Goddard Earth Observing System General Circulation Models) are discussed, with the emphasis on the regional <span class="hlt">down-scaling</span> effects and their dependence on the stretched grid design and parameters. A variable resolution SG-GCM and SG-DAS using a global stretched grid with fine resolution over an area of interest, is a viable new approach to REGIONAL and subregional CLIMATE studies and applications. The stretched grid approach is an ideal tool for representing regional to global scale interactions. It is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step in regional climate modeling. The GEOS SG-GCM is used for simulations of the anomalous U.S. climate events of 1988 drought and 1993 flood, with enhanced regional resolution. The height low level jet, precipitation and other diagnostic patterns are successfully simulated and show the efficient <span class="hlt">down-scaling</span> over the area of interest the U.S. An imitation of the nested grid approach is performed using the developed SG-DAS (Data Assimilation System) that incorporates the SG-GCM. The SG-DAS is run with withholding data over the area of interest. The design immitates the nested grid framework with boundary conditions provided from analyses. No boundary condition buffer is needed for the case due to the global domain of integration used for the SG-GCM and SG-DAS. The experiments based on the newly developed versions of the GEOS SG-GCM and SG-DAS, with finer 0.5 degree (and higher) regional resolution, are briefly discussed. The major aspects of parallelization of the SG-GCM code are outlined. The KEY OBJECTIVES of the study are: 1) obtaining an efficient <span class="hlt">DOWN-SCALING</span> over the area of interest with fine and very fine resolution; 2) providing CONSISTENT interactions between regional and global scales including the consistent representation of regional ENERGY and WATER BALANCES; 3) providing a high</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MNRAS.455..438A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MNRAS.455..438A"><span>The MIP <span class="hlt">ensemble</span> simulation: local <span class="hlt">ensemble</span> statistics in the Cosmic Web</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Aragon-Calvo, M. A.</p> <p>2016-01-01</p> <p>We present a new technique that allows us to compute <span class="hlt">ensemble</span> statistics on a local basis, directly relating halo properties to their local environment. This is achieved by the use of a correlated <span class="hlt">ensemble</span> in which the large-scale structure (LSS) is common to all realizations while having each an independent halo population. The correlated <span class="hlt">ensemble</span> can be stacked, effectively increasing the halo number density by an arbitrary factor, thus breaking the fundamental limit in the halo number density given by the halo mass function. This technique allows us to compute local <span class="hlt">ensemble</span> statistics of the matter/halo distribution at any position in the simulation box, while removing the intrinsic stochasticity in the halo formation process and directly relating halo properties to their environment. We introduce the Multum In Parvo correlated <span class="hlt">ensemble</span> simulation consisting of 220 realizations on a 32 h-1 Mpc box with 2563 particles each. This is equivalent in terms of effective volume and number of particles to a box of ˜193 h-1 Mpc of side with ˜15403 particles containing ˜5 × 106 haloes with a minimum mass of 3.25 × 109 h-1 M⊙. The potential of the technique presented here is illustrated by computing the local <span class="hlt">ensemble</span> statistics of the halo ellipticity and halo shape-LSS alignment. We show that, while there are general trends in the ellipticity and alignment of haloes with their LSS, there are also significant spatial variations which has important implications for observational studies of galaxy shape and alignment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27069595','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27069595"><span><span class="hlt">Downscaling</span> land-use data to provide global 30″ estimates of five land-use classes.</span></a></p> <p><a target="_blank" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hoskins, Andrew J; Bush, Alex; Gilmore, James; Harwood, Tom; Hudson, Lawrence N; Ware, Chris; Williams, Kristen J; Ferrier, Simon</p> <p>2016-05-01</p> <p>Land-use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land-use mapping. Assessments of land-use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically <span class="hlt">downscaling</span> mapped land-use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land-use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km(2)) estimates of five land-use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio-realms (unique combinations of biome and biogeographical realm) and <span class="hlt">downscaled</span> using relationships with fine-grained climate, land cover, landform, and anthropogenic influence layers. The <span class="hlt">downscaled</span> land-use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio-realms produced global fine-grained layers from the 2005 time step of the Land-use Harmonization dataset. Coarse-scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R (2): 0.68 ± 0.19). Validation with the PREDICTS database showed the new <span class="hlt">downscaled</span> land-use layers improved discrimination of all five classes at PREDICTS sites (P < 0.0001 in all cases). Additional validation of the <span class="hlt">downscaled</span> cropping layer with the geoWiki layer showed an R (2) improvement of 0.12 compared with the Land-use Harmonization data. The <span class="hlt">downscaling</span> method presented here produced the first global land-use dataset at a spatial grain relevant to ecological processes that drive changes in biodiversity over space and time. Integrating these data with biodiversity measures</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMNG31A3791A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMNG31A3791A"><span>A Modified Artifitial Neural Network <span class="hlt">Ensemble</span> Framework for Drought Estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alobaidi, M. H.; Marpu, P. R.; Ouarda, T.</p> <p>2014-12-01</p> <p>Drought estimation at ungauged sites is a difficult task due to various challenges such as scale and limited availability and information about hydrologic neighborhoods. <span class="hlt">Ensemble</span> regression has been recently utilized in modeling various hydrologic systems and showed advantage over classical regression approaches to such studies. A challenging task in <span class="hlt">ensemble</span> modeling is the proper training of the <span class="hlt">ensemble</span>'s individual learners and the <span class="hlt">ensemble</span> combiners. In this work, an <span class="hlt">ensemble</span> framework is proposed to enhance the generalization ability of the sub-<span class="hlt">ensemble</span> models and its combiner. Information mixtures between the subsamples are introduced. Such measure is dedicated to the <span class="hlt">ensemble</span> members and <span class="hlt">ensemble</span> combiners. Controlled homogeneity magnitudes are then stimulated and induced in the proposed model via a two-stage resampling algorithm. Artificial neural networks (ANNs) were used as <span class="hlt">ensemble</span> members in addition to different <span class="hlt">ensemble</span> integration plans. The model provided superior results when compared to previous models applied to the case study in this work. The root mean squared error (RMSE) in the testing phase for the drought quantiles improved by 67% - 76%. The bias error (BIAS) also showed 61% - 95% improvement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..18.1868R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..18.1868R"><span>On the <span class="hlt">ensemble</span> Rauch-Tung-Striebel smoother and its equivalence to the <span class="hlt">ensemble</span> Kalman smoother</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Raanes, Patrick</p> <p>2016-04-01</p> <p>The Rauch-Tung-Striebel (RTS) smoother is a linear-Gaussian smoothing algorithm that is popular in the engineering community. This is a study of its <span class="hlt">ensemble</span> formulation (EnRTS). An on-line expression is derived and discussed. In particular, it is used to show that the EnRTS is equivalent to the <span class="hlt">ensemble</span> Kalman smoother (EnKS), even in the non-linear, non-Gaussian case. The theory is revisited under practical considerations and equability is illustrated by numerical experiments, even though equivalence is broken by inflation and localisation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMGC41H..04S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMGC41H..04S"><span>Understanding the Propagation of GCM and <span class="hlt">Downscaling</span> Uncertainty for Projecting Crop Yield: A Nationwide Analysis over India</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sharma, T.; Murari, H. V.; H, V.; Karmakar, S.; Ghosh, S.; Soora, N. K.</p> <p>2015-12-01</p> <p>General Circulation Models (GCM) play an important role in assessing the impacts of climate change at global scale; however, coarser resolution limits their direct application at regional scale. To understand the climate variability at regional scale, different <span class="hlt">downscaling</span> techniques (such as dynamical and statistical) have been developed which use the GCM outputs as boundary condition to produce finer resolution climate projections. Although, both dynamical and statistical <span class="hlt">downscaling</span> techniques have proven to be able to capture the climate variability at regional scale; there are certain uncertainties lying in their projections especially for a region like India which have complex terrain and climatic pattern. Here, the uncertainties, resulting from the use of multiple GCM and <span class="hlt">downscaling</span> models, are quantified with the assessment of impacts on regional crop yield. Two crop models with different complexity-Decision Support System for Agro-technology Transfer (DSSAT) and Infocrop, are used, forced by dynamically (CORDEX, COordinated Regional climate <span class="hlt">Downscaling</span> EXperiment) and statistically (Kannan and Ghosh, 2011; Salvi et al., 2013) <span class="hlt">downscaled</span> data derived from multiple GCM's. Advantage of these crop models is their ability to capture complexity of Indian condition. Yields of major crops in India, such as, rice, wheat and maize have been considered in the crop model and the impacts of climate change are assessed on their yields. The uncertainties in projected crop yields are also quantified, which must be incorporated for deriving vulnerability and risk maps for crop-climate assessments. This may further help to determine different crop management practices in order to reduce adverse impacts of climate change in future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRD..120...92T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..120...92T"><span>Statistical <span class="hlt">downscaling</span> of rainfall changes in Hawai`i based on the CMIP5 global model projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Timm, Oliver Elison; Giambelluca, Thomas W.; Diaz, Henry F.</p> <p>2015-01-01</p> <p>mean rainfall projections for Hawai`i are given based on statistical <span class="hlt">downscaling</span> of the latest Coupled Model Intercomparison Project phase 5 (CMIP5) global model results for two future representative concentration pathways (RCP4.5 and RCP8.5). The spatial information content of our statistical <span class="hlt">downscaling</span> method is improved over previous efforts through the inclusion of spatially extensive, high-quality monthly rainfall data set and the use of improved large-scale climate predictor information. Predictor variables include moisture transport in the middle atmosphere (700 hPa), vertical temperature gradients, and geopotential height fields of the 1000 and 500 hPa layers. The results allow for the first time to derive a spatially interpolated map with future rainfall change estimates for the main Hawaiian Islands. The statistical <span class="hlt">downscaling</span> was applied to project wet (November-April) and dry (May-October) season rainfall anomalies for the middle and late 21st century. Overall, the statistical <span class="hlt">downscaling</span> gives more reliable results for the wet season than for the dry season. The wet-season results indicate a pronounced dipole structure between windward facing mountain slopes and the leeward side of most of the islands. The climatically wet regions on the windward slopes of the mountain regions are expected to become wetter or remain stable in their seasonal precipitation amounts. On the climatically dry leeward sides of Kaua`i, O`ahu, Maui, and Hawai`i Island, future precipitation exhibits the strongest drying trends. The projected future rainfall anomaly pattern is associated with a circulation anomaly that resembles a shift in the position or strength of the subtropical high and the average location of extratropical troughs. These new results suggest that a negative trend dominates the area-averaged changes in the statistical <span class="hlt">downscaling</span> over the Hawaiian Islands. However, the islands are expected to experience a greater contrast between the wet and dry regions in</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20160013848','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20160013848"><span>Comparison Of <span class="hlt">Downscaled</span> CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Milesi, Cristina; Costa-Cabral, Mariza; Rath, John; Mills, William; Roy, Sujoy; Thrasher, Bridget; Wang, Weile; Chiang, Felicia; Loewenstein, Max; Podolske, James</p> <p>2014-01-01</p> <p>Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution <span class="hlt">downscaled</span> daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of <span class="hlt">downscaling</span> method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series <span class="hlt">downscaled</span> from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset <span class="hlt">downscaled</span> to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method <span class="hlt">downscaled</span> to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005HESSD...2..543T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005HESSD...2..543T"><span><span class="hlt">Downscaled</span> Rainfall Prediction Model (DRPM) using a Unit Disaggregation Curve (UDC)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tantanee, S.; Patamatamakul, S.; Oki, T.; Sriboonlue, V.; Prempree, T.</p> <p>2005-04-01</p> <p>This study was undertaken to identify the process for generating finer time scaled rainfall from higher time scaled data. The <span class="hlt">Downscaled</span> Rainfall Prediction Model (DRPM) using the technique of unit disaggregation curve (UDC) was developed under the concept of coupling the stochastic autoregressive (AR) model with a wavelet filter and disaggregation model. Sequences of the number of rainy days and monthly rainfall were simulated from 52-year rainfall records at 4 stations in the northeastern part of Thailand. Compared with actual rainfall sequences, the 30 year generated sequences provided R-square values of 0.47-0.60. The model was applied to forecast the number of rainy days and monthly rainfall for the year of 2002. When compared with actual records the prediction model provided R-square values of 0.50 to 0.79.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011CG.....37.1277C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011CG.....37.1277C"><span>Quantile regression neural networks: Implementation in R and application to precipitation <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cannon, Alex J.</p> <p>2011-09-01</p> <p>The qrnn package for R implements the quantile regression neural network, which is an artificial neural network extension of linear quantile regression. The model formulation follows from previous work on the estimation of censored regression quantiles. The result is a nonparametric, nonlinear model suitable for making probabilistic predictions of mixed discrete-continuous variables like precipitation amounts, wind speeds, or pollutant concentrations, as well as continuous variables. A differentiable approximation to the quantile regression error function is adopted so that gradient-based optimization algorithms can be used to estimate model parameters. Weight penalty and bootstrap aggregation methods are used to avoid overfitting. For convenience, functions for quantile-based probability density, cumulative distribution, and inverse cumulative distribution functions are also provided. Package functions are demonstrated on a simple precipitation <span class="hlt">downscaling</span> task.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1325486','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1325486"><span>Processing and Monthly Summaries of <span class="hlt">Downscaled</span> Climate Data for Knoxville, Tennessee and Surrounding Region</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Sylvester, Linda; Omitaomu, Olufemi A.; Parish, Esther S.; Allen, Melissa</p> <p>2016-09-01</p> <p>Oak Ridge National Laboratory (ORNL) and the City of Knoxville, Tennessee have partnered to work on a Laboratory Directed Research and Development (LDRD) project towards investigating climate change, mitigation, and adaptation measures in mid-sized cities. ORNL has statistically and dynamically <span class="hlt">downscaled</span> ten Global Climate Models (GCMs) to both 1 km and 4 km resolutions. The processing and summary of those ten gridded datasets for use in a web-based tool is described. The summaries of each model are shown individually to assist in determining the similarities and differences between the model scenarios. The variables of minimum and maximum daily temperature and total monthly precipitation are summarized for the area of Knoxville, Tennessee for the periods of 1980-2005 and 2025-2050.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3733713','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3733713"><span><span class="hlt">Downscaling</span> the Analysis of Complex Transmembrane Signaling Cascades to Closed Attoliter Volumes</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Grasso, Luigino; Wyss, Romain; Piguet, Joachim; Werner, Michael; Hassaïne, Ghérici; Hovius, Ruud; Vogel, Horst</p> <p>2013-01-01</p> <p>Cellular signaling is classically investigated by measuring optical or electrical properties of single or populations of living cells. Here we show that ligand binding to cell surface receptors and subsequent activation of signaling cascades can be monitored in single, (sub-)micrometer sized native vesicles with single-molecule sensitivity. The vesicles are derived from live mammalian cells using chemicals or optical tweezers. They comprise parts of a cell’s plasma membrane and cytosol and represent the smallest autonomous containers performing cellular signaling reactions thus functioning like minimized cells. Using fluorescence microscopies, we measured in individual vesicles the different steps of G-protein-coupled receptor mediated signaling like ligand binding to receptors, subsequent G-protein activation and finally arrestin translocation indicating receptor deactivation. Observing cellular signaling reactions in individual vesicles opens the door for <span class="hlt">downscaling</span> bioanalysis of cellular functions to the attoliter range, multiplexing single cell analysis, and investigating receptor mediated signaling in multiarray format. PMID:23940670</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/2013EGUGA..1510686L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1510686L"><span>A comparison of dynamical and statistical <span class="hlt">downscaling</span> methods for regional wave climate projections along French coastlines.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Mendez, Fernando</p> <p>2013-04-01</p> <p>Wave climate forecasting is a major issue for numerous marine and coastal related activities, such as offshore industries, flooding risks assessment and wave energy resource evaluation, among others. Generally, there are two main ways to predict the impacts of the climate change on the wave climate at regional scale: the dynamical and the statistical <span class="hlt">downscaling</span> of GCM (Global Climate Model). In this study, both methods have been applied on the French coast (Atlantic , English Channel and North Sea shoreline) under three climate change scenarios (A1B, A2, B1) simulated with the GCM ARPEGE-CLIMAT, from Météo-France (AR4, IPCC). The aim of the work is to characterise the wave climatology of the 21st century and compare the statistical and dynamical methods pointing out advantages and disadvantages of each approach. The statistical <span class="hlt">downscaling</span> method proposed by the Environmental Hydraulics Institute of Cantabria (Spain) has been applied (Menendez et al., 2011). At a particular location, the sea-state climate (Predictand Y) is defined as a function, Y=f(X), of several atmospheric circulation patterns (Predictor X). Assuming these climate associations between predictor and predictand are stationary, the statistical approach has been used to project the future wave conditions with reference to the GCM. The statistical relations between predictor and predictand have been established over 31 years, from 1979 to 2009. The predictor is built as the 3-days-averaged squared sea level pressure gradient from the hourly CFSR database (Climate Forecast System Reanalysis, http://cfs.ncep.noaa.gov/cfsr/). The predictand has been extracted from the 31-years hindcast sea-state database ANEMOC-2 performed with the 3G spectral wave model TOMAWAC (Benoit et al., 1996), developed at EDF R&D LNHE and Saint-Venant Laboratory for Hydraulics and forced by the CFSR 10m wind field. Significant wave height, peak period and mean wave direction have been extracted with an hourly-resolution at</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1412765R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1412765R"><span>Towards <span class="hlt">downscaling</span> precipitation for Senegal - An approach based on generalized linear models and weather types</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rust, H. W.; Vrac, M.; Lengaigne, M.; Sultan, B.</p> <p>2012-04-01</p> <p>Changes in precipitation patterns with potentially less precipitation and an increasing risk for droughts pose a threat to water resources and agricultural yields in Senegal. Precipitation in this region is dominated by the West-African Monsoon being active from May to October, a seasonal pattern with