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1

Downscaled seasonal forecasts using an ensemble of regional models  

NASA Astrophysics Data System (ADS)

The Multi-Regional climate model Ensemble Downscaling (MRED) project is a multi-institutional effort to evaluate the usefulness of dynamically downscaled global seasonal forecasts. Seven regional climate models have downscaled 10-member ensembles from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) for each winter season (December-April) of 1982-2003. The target region for downscaling is the continental United States. MRED investigators also have developed methods and metrics for analysis of downscaled forecasts. These include an Added Value Index that quantifies skill improvement of a downscaled forecast compared to the corresponding global forecast. Results show that added value from downscaling depends on location, forecast variable, and lead time. Locations with added value are generally in the western United States, and added value tends to be greater for precipitation than for temperature. Downscaled forecasts have greatest skill for seasonal precipitation anomalies in strong El Niño events such as 1982-83 and 1997-98. In most circumstances area averaged seasonal precipitation for the regional models closely tracks the corresponding results for the global model, though with an offset that varies considerably amongst the regional models. There is large spread amongst the 15 CFS ensemble members and this carries through to the corresponding downscaled forecasts. Because of the strong dependence of downscaled results on the global model, future experiments should test the use of multiple global models downscaled by multiple regional models.

Arritt, R.

2012-04-01

2

The ENSEMBLES Statistical Downscaling Portal  

NASA Astrophysics Data System (ADS)

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

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

2010-05-01

3

Multi-model ensembles for regional downscaling by different dynamics in a regional model  

NASA Astrophysics Data System (ADS)

Most of regional dynamics downscaling are using different parameterizations of model physics as its ensemble members, and using different models to do multi-model ensembles. There exists models which can have different model dynamics but the same model physics, such as NCEP Regional Spectral Model (RSM). RSM has hydrostatic and nonhydrostatic options under the same model framework and model physics. Different models are used to be different in dynamics as well as physics. RSM with different dynamics options can be treated as different models with the same model physics, thus, it can be used to investigate how well the different model dynamics used as multi-model ensemble forecast with the same model physics. The experiments from the NOAA project called MRED by using CFS as driven global forecasts for downscaling are used to construct two winters ensemble forecasts, first one is using hydrostatic option of RSM, the second one is using nonhydrostatic option of RSM (the nonhydrostatic option of RSM is used to be called mesoscale spectral model (MSM)), with 15 members ensemble of each. The overall of the results show that RSM and MSM have better scores in terms of precipitation, 2m temperature and 10 m winds. And combining RSM and MSM as the multi-model ensembles has further improvement in scores. The results give us some insights of multi-model ensembles by using only difference in model dynamics but the same in model physics. More details results will be shown in the meeting.

Juang, H. H.; Zhang, Y.

2011-12-01

4

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

Microsoft Academic Search

A prototype early warning system for floods is introduced. For a small headwater catchment, probabilistic streamflow predictions in 24-hourly steps are obtained from downscaling all members of the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System and feeding the resulting precipitation and temperature series into a hydrologic model. We apply “expanded downscaling,” a scheme that was previously used

Gerd Bürger; Dominik Reusser; David Kneis

2009-01-01

5

Testing MOS precipitation downscaling for ENSEMBLES regional climate models over Spain  

NASA Astrophysics Data System (ADS)

Model Output Statistics (MOS) has been recently proposed as an alternative to the standard perfect prognosis statistical downscaling approach for Regional Climate Model (RCM) outputs. In this case, the model output for the variable of interest (e.g. precipitation) is directly downscaled using observations. In this paper we test the performance of a MOS implementation of the popular analog methodology (referred to as MOS analog) applied to downscale daily precipitation outputs over Spain. To this aim, we consider the state-of-the-art ERA40-driven RCMs provided by the EU-funded ENSEMBLES project and the Spain02 gridded observations data set, using the common period 1961-2000. The MOS analog method improves the representation of the mean regimes, the annual cycle, the frequency and the extremes of precipitation for all RCMs, regardless of the region and the model reliability (including relatively low-performing models), while preserving the daily accuracy. The good performance of the method in this complex climatic region suggests its potential transferability to other regions. Furthermore, in order to test the robustness of the method in changing climate conditions, a cross-validation in driest or wettest years was performed. The method improves the RCM results in both cases, especially in the former.

Turco, M.; Quintana-Seguí, P.; Llasat, M. C.; Herrera, S.; GutiéRrez, J. M.

2011-09-01

6

Dynamical downscaling of ECMWF ensemble seasonal forecast over East Africa with RegCM3  

NASA Astrophysics Data System (ADS)

Dynamical downscaling of ECMWF ERA-interim and ENSEMBLE seasonal hindcast is performed with ICTP's regional climate model (RegCM3) over the horn of Africa. The result from the ERA-interim reanalysis ('perfect boundary') condition run indicated that the regional model reproduced both the spatial and inter-annual variability of the regions rainfall. It also captured the teleconnection between ENSO and the regions precipitation pattern well. As these results were encouraging, the ECMWF ENSEMBLE seasonal hindcast experiment run were downscaled from May 1st to October 1st for each year for the period of 1991 - 2000 to produce nine RegCM3 ensemble hindcast. The one month lead ECMWF and RegCM3 ensemble seasonal (JJAS) precipitation hindcasts were assessed in a deterministic and probabilistic mode. The deterministic verification suggested that for most part of the East African domain both RegCM3 and ECMWF hindcasts reproduce the spatial and temporal variability very well, but overestimate the mean and variability over the Arabian peninsula and miss the teleconnection between ENSO and precipitation over the western Indian ocean. This positive bias over the Arabian peninsula and the teleconnection error between ENSO and precipitation anomalies over the western Indian Ocean in RegCM3 are due to the propagation of errors from the driving GCMs to the regional model. The probabilistic assessment (ROCS and RPSS) indicated that both ECMWF and RegCM3 are better than a random forecast and climatology suggesting the potential utility of dynamical forecast over the region. Comparing the skill of ECMWF and RegCM3 probabilistic hindcasts, ECMWF generally performs better on grid point by grid point comparison and at homogeneous zones over Ethiopia but RegCM3 performs better when aggregated on a country scale and when compared against high resolution rain gauge dataset.

Diro, G. T.; Tompkins, A. M.; Bi, X.

2012-04-01

7

Reduction of systematic biases in regional climate downscaling through ensemble forcing  

NASA Astrophysics Data System (ADS)

Simulations of the East Asian summer monsoon for the period of 1979-2001 were carried out using the Weather Research and Forecast (WRF) model forced by three reanalysis datasets (NCEP-R2, ERA-40, and JRA-25). The experiments forced by different reanalysis data exhibited remarkable differences, primarily caused by uncertainties in the lateral boundary (LB) moisture fluxes over the Bay of Bengal and the Philippine Sea. The climatological mean water vapor convergence into the model domain computed from ERA-40 was about 24% higher than that from the NCEP-R2 reanalysis. We demonstrate that using the ensemble mean of NCEP-R2, ERA-40, and JRA-25 as LB forcing considerably reduced the biases in the model simulation. The use of ensemble forcing improved the performance in simulated mean circulation and precipitation, inter-annual variation in seasonal precipitation, and daily precipitation. The model simulated precipitation was superior to that in the reanalysis in both climatology and year-to-year variations, indicating the added value of dynamic downscaling. The results suggest that models having better performance under one set of LB forcing might worsen when another set of reanalysis data is used as LB forcing. Use of ensemble mean LB forcing for assessing regional climate model performance is recommended.

Yang, Hongwei; Wang, Bin; Wang, Bin

2012-02-01

8

Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States  

NASA Astrophysics Data System (ADS)

This study compares two approaches, dynamical and statistical downscaling, for their potential to improve regional seasonal forecasts for the United States (U.S.) during the cold season. In the MultiRCM Ensemble Downscaling (MRED) project, seven regional climate models (RCMs) are used to dynamically downscale the Climate Forecast System (CFS) seasonal prediction over the conterminous U.S. out to 5 months for the period of 1982-2003. The simulations cover December to April of next year with 10 ensemble members from each RCM with different initial and boundary conditions from the corresponding ensemble members. These dynamically downscaled forecasts are compared with statistically downscaled forecasts produced by two bias correction methods applied to both the CFS and RCM forecasts. Results of the comparison suggest that the RCMs add value in seasonal prediction application, but the improvements largely depend on location, forecast lead time, variables, and skill metrics used for evaluation. Generally, more improvements are found over the Northwest and North Central U.S. for the shorter lead times. The comparison results also suggest a hybrid forecast system that combines both dynamical and statistical downscaling methods have the potential to maximize prediction skill.

Yoon, Jin-Ho; Ruby Leung, L.; Correia, James

2011-11-01

9

Comparison of dynamically and statistically downscaled seasonal climate forecasts for the cold season over the United States  

NASA Astrophysics Data System (ADS)

This study compares two approaches, dynamical and statistical downscaling, for their potential to improve regional seasonal forecasts for the United States (U.S.) during the cold season. In the MultiRCM Ensemble Downscaling (MRED) project, seven regional climate models (RCMs) are used to dynamically downscale the Climate Forecast System (CFS) seasonal prediction over the conterminous U.S. out to 5 months for the period of 1982-2003. The simulations cover December to April of next year with 10 ensemble members from each RCM with different initial and boundary conditions from the corresponding ensemble members. These dynamically downscaled forecasts are compared with statistically downscaled forecasts produced by two bias correction methods applied to both the CFS and RCM forecasts. Results of the comparison suggest that the RCMs add value in seasonal prediction application, but the improvements largely depend on location, forecast lead time, variables, and skill metrics used for evaluation. Generally, more improvements are found over the Northwest and North Central U.S. for the shorter lead times. The comparison results also suggest a hybrid forecast system that combines both dynamical and statistical downscaling methods have the potential to maximize prediction skill.

Yoon, Jin-Ho; Ruby Leung, L.; Correia, James, Jr.

2012-11-01

10

Dynamical downscaling of snow trends in Nothern Iberia based on ENSEMBLES regional simulations  

NASA Astrophysics Data System (ADS)

A recent study reported a significant decreasing trend of snow occurrence (-4.6 days/decade) in the Northern Iberian Peninsula since the mid seventies (Pons et al. 2009). This study was based on observations of annual snow frequency (measured as the annual number of snow days) from a network of 33 stations ranging from 60 to 1350 meters. In the present work we analyze the skill of dynamical downscaling methods to reproduce this trend in present climate conditions and also to further project it into the future from A1B-scenario global simulations. In particular, we consider the regional simulation dataset from the ENSEMBLES project, consisting in ten state-of-the-art Regional Climate Models (RCMs) at 25km resolution run with different forcing/boundary conditions. To this aim we first test the regional models with perfect boundaries considering ERA40; it is shown that after correcting the bias, all the RCMs appropriately reproduce the interannual variability and the observed trends (e.g., the ensemble mean presents a trend of -5.8 days/decade). Then we analyze the results for the present climate 20c3m-scenario global simulations. In this case, the results are quite variable with the larger uncertainty being associated with the particular GCM used (ECHAM5, CNRM or HadCM) with trend ranging from -6.7 to -1.8 days/decade. Finally, the trends obtained for the future 2010-2040 A1B runs ranged from -5.7 to -1.4 days/decade, indicating a continuous decreasing of snow frequency in this region. References: Pons, M.R., D. San-Martín, S. Herrera and J.M. Gutiérrez (2009), Snow Trends in Northern Spain. Analysis and simulation with statistical downscaling methods, International Journal of Climatology, DOI. 10.1002/joc.2016A recent study reported a significant decreasing trend of snow occurrence (-4.6 days/decade) in the Northern Iberian Peninsula since the mid seventies (Pons et al. 2009). This study was based on observations of annual snow frequency (measured as the annual number of snow days) from a network of 33 stations ranging from 60 to 1350 meters. In the present work we analyze the skill of dynamical downscaling methods to reproduce this trend in present climate conditions and also to further project it into the future from A1B-scenario global simulations. In particular, we consider the regional simulation dataset from the ENSEMBLES project, consisting in ten state-of-the-art Regional Climate Models (RCMs) at 25km resolution run with different forcing/boundary conditions. To this aim we first test the regional models with perfect boundaries considering ERA40; it is shown that after correcting the bias, all the RCMs appropriately reproduce the interannual variability and the observed trends (e.g., the ensemble mean presents a trend of -5.8 days/decade). Then we analyze the results for the present climate 20c3m-scenario global simulations. In this case, the results are quite variable with the larger uncertainty being associated with the particular GCM used (ECHAM5, CNRM or HadCM) with trends ranging from -6.7 to -1.8 days/decade. Finally, the trends obtained for the future 2010-2040 A1B runs range from -5.7 to -1.4 days/decade, indicating a continuous decrease of snow frequency in this region. References: Pons, M.R., D. San-Martín, S. Herrera and J.M. Gutiérrez (2009), Snow Trends in Northern Spain. Analysis and simulation with statistical downscaling methods, International Journal of Climatology, DOI. 10.1002/joc.2016

Herrera, S.; Pons, M. R.; Sordo, C. M.; Gutiérrez, J. M.

2010-05-01

11

Dynamical downscaling of ECMWF Ensemble seasonal forecasts over East Africa with RegCM3  

NASA Astrophysics Data System (ADS)

Dynamical downscaling of ECMWF ERA-interim reanalysis and an ensemble of May-start ECMWF seasonal hindcasts is performed with ICTP's regional climate model (RegCM3) over the horn of Africa for a ten year period. Using ERA-interim `perfect boundary' conditions the regional model reproduced both the spatial and interannual variability of the region's rainfall and improved the global model's reproduction of year to year rainfall variability, capturing the teleconnection between ENSO and the region's precipitation pattern well. The ensembles of ECMWF seasonal hindcasts and the respective downscaled RegCM3 hindcast suite were then validated in terms of the seasonal climate and deterministic and probabilistic skill scores at a one to four month lead time. Both RegCM3 and ECMWF hindcasts reproduce the spatial and temporal rainfall variability well, but overestimate the mean and variability over the Arabian peninsula and misrepresent the teleconnection between ENSO and precipitation over the western Indian Ocean. The positive bias over the Arabian peninsula in RegCM3 and the teleconnection error between ENSO and precipitation anomalies over the western Indian Ocean are due to the propagation of errors from the driving GCMs to the regional model. Nevertheless, the probabilistic assessment (ROCS and RPSS) indicated that both ECMWF and RegCM3 have significant skill suggesting the potential utility of dynamical forecasts over the region. Comparing the skill of ECMWF and RegCM3 probabilistic hindcasts, ECMWF generally performs better on grid point by grid point comparison and at homogeneous zones over Ethiopia but RegCM3 outperforms when aggregated on a country scale and when compared against high resolution rain gauge data set.

Diro, G. T.; Tompkins, A. M.; Bi, X.

2012-08-01

12

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

NASA Astrophysics Data System (ADS)

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> <div class="credits"> <p class="dwt_author">Sohn, Soo-Jin; Ahn, Joong-Bae; Tam, Chi-Yung</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-02-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">13</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..15.6770S"> <span id="translatedtitle">Six-month lead <span class="hlt">downscaling</span> prediction of winter-spring drought in South Korea based on multi-model <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Given the changing climate, advance information on hydrological extremes such as droughts will help in planning for disaster mitigation and facilitate better decision making for water availability management. A deficit of precipitation for long-term time scales beyond 6 months has impacts on the hydrological sectors such as ground water, streamflow, and reservoir storage. The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center (APCC) 1-Tier multi-model <span class="hlt">ensemble</span> (MME) was <span class="hlt">downscaled</span> for predicting the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both of precipitation and temperature, and can incorporate the impact of global warming on the balance between precipitation and evapotranspiration. It was found that 1-Tier MME has difficulties in capturing the local temperature and rainfall variations over extratropical land areas, and has no skill in predicting SPEI during boreal winter and spring. On the other hand, temperature and precipitation predictions were substantially improved in the <span class="hlt">downscaled</span> MME (DMME). In conjunction with variance inflation, DMME can give reasonably skillful six-month-lead forecasts of SPEI for the winter-to-spring period. The results could potentially improve hydrological extreme predictions using meteorological forecasts for policymaker and stakeholders in water management sector for better climate adaption.</p> <div class="credits"> <p class="dwt_author">Sohn, Soo-Jin; Ahn, Joong-Bae; Tam, Chi-Yung</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">14</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012WRR....4812519Y"> <span id="translatedtitle"><span class="hlt">Downscaling</span> precipitation or bias-correcting streamflow? Some implications for coupled general circulation model (CGCM)-based <span class="hlt">ensemble</span> seasonal hydrologic forecast</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The progress in forecasting seasonal climate by using coupled atmosphere-ocean-land general circulation models (CGCMs) has increased the use of CGCM-based hydrologic forecasting in recent years. A common procedure is to <span class="hlt">downscale</span> the meteorological forcings and use them as inputs to hydrologic models to provide <span class="hlt">ensemble</span> forecasts. Less attention has been paid to bias correcting the hydrologic forecasts directly generated by CGCM. In this study, we show that either <span class="hlt">downscaling</span> precipitation for hydrologic model or directly bias-correcting CGCM streamflow increases the efficiency skill score greatly as compared to the original CGCM streamflow forecast, and bias correcting the streamflow from hydrologic model with <span class="hlt">downscaled</span> precipitation leads to a further skill increase. Bias-correcting CGCM streamflow is more skillful and reliable than <span class="hlt">downscaling</span> precipitation for hydrologic modeling in terms of <span class="hlt">ensemble</span> forecasts, as verified by the ranked probability skill score and the rank histogram. While bias-correcting streamflow from CGCM can provide useful forecasts, combining the <span class="hlt">downscaled</span> CGCM forcings and bias-corrected hydrologic output through the CGCM-hydrology forecasting approach does gain additional skill of accuracy and discrimination.</p> <div class="credits"> <p class="dwt_author">Yuan, Xing; Wood, Eric F.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">15</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2005TellA..57..488M"> <span id="translatedtitle">Evaluation of <span class="hlt">downscaled</span> DEMETER multi-model <span class="hlt">ensemble</span> seasonal hindcasts in a northern Italy location by means of a model of wheat growth and soil water balance</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In this paper we explore the new possibilities for early crop yield assessment at the local scale arising from the availability of dynamic crop growth models and of <span class="hlt">downscaled</span> multi-model <span class="hlt">ensemble</span> seasonal forecasts. We compare the use of the latter with other methods, based on crop growth models driven by observed climatic data only. The soil water balance model developed and used at ARPA Emilia-Romagna (CRITERIA) was integrated with crop growth routines from the model WOFOST 7.1. Some validation runs were first carried out and we verified with independent field data that the new integrated model satisfactorily simulated above-ground biomass and leaf area index. The model was then used to test the feasibility of using <span class="hlt">downscaled</span> multi-model <span class="hlt">ensemble</span> seasonal hindcasts, coming from the DEMETER European research project, in order to obtain early (i.e. 90, 60 and 30 d before harvest) yield assessments for winter wheat in northern Italy. For comparison, similar runs with climatology instead of hindcasts were also carried out. For the same purpose, we also produced six simple linear regression models of final crop yields on within season (end of March, April and May) storage organs and above-ground biomass values. Median yields obtained using <span class="hlt">downscaled</span> DEMETER hindcasts always outperformed the simple regression models and were substantially equivalent to the climatology runs, with the exception of the June experiment, where the <span class="hlt">downscaled</span> seasonal hindcasts were clearly better than all other methods in reproducing the winter wheat yields simulated with observed weather data. The crop growth model output dispersion was almost always significantly lower than the dispersion of the <span class="hlt">downscaled</span> <span class="hlt">ensemble</span> seasonal hindcast used as input for crop simulations.</p> <div class="credits"> <p class="dwt_author">Marletto, V.; Zinoni, F.; Criscuolo, L.; Fontana, G.; Marchesi, S.; Morgillo, A.; van Soetendael, M.; Ceotto, E.; Andersen, U.</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">16</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011NHESS..11.2847H"> <span id="translatedtitle">European extra-tropical storm damage risk from a multi-model <span class="hlt">ensemble</span> of dynamically-<span class="hlt">downscaled</span> global climate models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Uncertainty in the return levels of insured loss from European wind storms was quantified using storms derived from twenty-two 25 km regional climate model runs driven by either the ERA40 reanalyses or one of four coupled atmosphere-ocean global climate models. Storms were identified using a model-dependent storm severity index based on daily maximum 10 m wind speed. The wind speed from each model was calibrated to a set of 7 km historical storm wind fields using the 70 storms with the highest severity index in the period 1961-2000, employing a two stage calibration methodology. First, the 25 km daily maximum wind speed was <span class="hlt">downscaled</span> to the 7 km historical model grid using the 7 km surface roughness length and orography, also adopting an empirical gust parameterisation. Secondly, <span class="hlt">downscaled</span> wind gusts were statistically scaled to the historical storms to match the geographically-dependent cumulative distribution function of wind gust speed. The calibrated wind fields were run through an operational catastrophe reinsurance risk model to determine the return level of loss to a European population density-derived property portfolio. The risk model produced a 50-yr return level of loss of between 0.025% and 0.056% of the total insured value of the portfolio.</p> <div class="credits"> <p class="dwt_author">Haylock, M. R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-10-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">17</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/42080998"> <span id="translatedtitle">Response in extremes of daily precipitation and wind from a <span class="hlt">downscaled</span> multi-model <span class="hlt">ensemble</span> of anthropogenic global climate change scenarios</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Time-slices of eight global climate model (GCM) response simulations of the IPCC IS92a, CMIP2, SRES A2, B2 and A1B greenhouse gas scenarios have been <span class="hlt">downscaled</span> using the HIRHAM atmospheric regional climate model (RCM). The area covers Central and Northern Europe, adjacent sea-areas and Greenland. The GCM data were provided from the Max Planck Institute, Germany (MPI), the Hadley Centre, U.K.</p> <div class="credits"> <p class="dwt_author">Jan Erik Haugen; Trond Iversen</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">18</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012JGRD..117.8107E"> <span id="translatedtitle">Sparse regularization for precipitation <span class="hlt">downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary"><span class="hlt">Downscaling</span> of remotely sensed precipitation images and outputs of general circulation models has been a subject of intense interest in hydrometeorology. The problem of <span class="hlt">downscaling</span> is basically one of resolution enhancement, that is, appropriately adding details or high frequency features onto a low-resolution observation or simulated rainfall field. Invoking the property of rainfall self similarity, this mathematically ill-posed problem has been approached in the past within a stochastic framework resulting in <span class="hlt">ensemble</span> of possible high-resolution realizations. In this work, we recast the rainfall <span class="hlt">downscaling</span> into an ill-posed inverse problem and introduce a class of nonlinear estimators to properly regularize it and obtain the best high-resolution estimate in an optimal sense. This regularization capitalizes on two main observations: (1) precipitation fields are sparse when transformed into an appropriately chosen domain (e.g., wavelet), and (2) small-scale organized precipitation features tend to recur within and across different storm environments. We demonstrate the promise of the proposed methodology through <span class="hlt">downscaling</span> and error analysis of level III precipitation reflectivity snapshots provided by the ground-based next generation Doppler weather radars in a ground validation sites of the Tropical Rainfall Measuring Mission.</p> <div class="credits"> <p class="dwt_author">Ebtehaj, A. M.; Foufoula-Georgiou, E.; Lerman, G.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">19</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013IJAEO..22..106A"> <span id="translatedtitle"><span class="hlt">Downscaling</span> in remote sensing</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary"><span class="hlt">Downscaling</span> has an important role to play in remote sensing. It allows prediction at a finer spatial resolution than that of the input imagery, based on either (i) assumptions or prior knowledge about the character of the target spatial variation coupled with spatial optimisation, (ii) spatial prediction through interpolation or (iii) direct information on the relation between spatial resolutions in the form of a regression model. Two classes of goal can be distinguished based on whether continua are predicted (through <span class="hlt">downscaling</span> or area-to-point prediction) or categories are predicted (super-resolution mapping), in both cases from continuous input data. This paper reviews a range of techniques for both goals, focusing on area-to-point kriging and <span class="hlt">downscaling</span> cokriging in the former case and spatial optimisation techniques and multiple point geostatistics in the latter case. Several issues are discussed including the information content of training data, including training images, the need for model-based uncertainty information to accompany <span class="hlt">downscaling</span> predictions, and the fundamental limits on the representativeness of <span class="hlt">downscaling</span> predictions. The paper ends with a look towards the grand challenge of <span class="hlt">downscaling</span> in the context of time-series image stacks. The challenge here is to use all the available information to produce a <span class="hlt">downscaled</span> series of images that is coherent between images and, thus, which helps to distinguish real changes (signal) from noise.</p> <div class="credits"> <p class="dwt_author">Atkinson, Peter M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-06-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">20</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..1513534D"> <span id="translatedtitle">Multifractal models for space-time rainfall <span class="hlt">downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">It is well know that rainfall fields display fluctuations in space and time that increase as the scale of observation decreases. Multifractal theory represents a solid base to characterize scale-invariance properties observed in rainfall fields as well as to develop <span class="hlt">downscaling</span> models able to reproduce observed statistics. The availability of such <span class="hlt">downscaling</span> tools allows forecasting of floods in small basins by coupling meteorological and hydrological models working on different space-time grid resolution. In this talk multifractal theory will be reviewed highlighting the most relevant aspects for rainfall <span class="hlt">downscaling</span> (e.g., the concept of scale-invariance in rainfall fields displaying space-time self-similarity or self-affinity, the role of orography). The main results of the scale-invariance analysis of rainfall retrieved by remote sensors will be discussed. Finally the application of multifractal models for rainfall <span class="hlt">downscaling</span> will be presented and some new ideas for <span class="hlt">ensemble</span> verification will be argued.</p> <div class="credits"> <p class="dwt_author">Deidda, Roberto</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_1");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" 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showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_3");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">21</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/41997119"> <span id="translatedtitle">A space-time <span class="hlt">downscaling</span> model for rainfall</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Interpretation of the impact of climate change or climate variability on water resources management requires information at scales much smaller than the current resolution of regional climate models. Subgrid-scale variability of precipitation is typically resolved by running nested or variable resolution models or by statistical <span class="hlt">downscaling</span>, the latter being especially attractive in <span class="hlt">ensemble</span> predictions due to its computational efficiency. Most</p> <div class="credits"> <p class="dwt_author">V. Venugopal; Efi Foufoula-Georgiou; Victor Sapozhnikov</p> <p class="dwt_publisher"></p> <p class="publishDate">1999-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">22</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://lw.siena.edu/booker/Research/Progress/Climate/Christensen%20and%20Lettenmaier%202007.pdf"> <span id="translatedtitle">A multimodel <span class="hlt">ensemble</span> approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River basin</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Implications of 21st century climate change on the hydrology and water resources of the Colorado River basin were assessed using a multimodel <span class="hlt">ensemble</span> approach in which <span class="hlt">downscaled</span> and bias corrected output from 11 General Circulation Models (GCMs) was used to drive macroscale hydrology and water resources models. <span class="hlt">Downscaled</span> climate scenarios (<span class="hlt">ensembles</span>) were used as forcings to the Variable Infiltration Capacity</p> <div class="credits"> <p class="dwt_author">N. S. Christensen; D. P. Lettenmaier</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">23</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/1999JGR...10419705V"> <span id="translatedtitle">A space-time <span class="hlt">downscaling</span> model for rainfall</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Interpretation of the impact of climate change or climate variability on water resources management requires information at scales much smaller than the current resolution of regional climate models. Subgrid-scale variability of precipitation is typically resolved by running nested or variable resolution models or by statistical <span class="hlt">downscaling</span>, the latter being especially attractive in <span class="hlt">ensemble</span> predictions due to its computational efficiency. Most existing precipitation <span class="hlt">downscaling</span> schemes are based on spatial disaggregation of rainfall patterns, independently at different times, and do not properly account for the temporal persistence of rainfall at the subgrid spatial scales. Such a temporal persistence in rainfall directly relates to the spatial variability of accumulated local soil moisture and might be important if the <span class="hlt">downscaled</span> values were to be used in a coupled atmospheric-hydrologic model. In this paper we propose a rainfall <span class="hlt">downscaling</span> model which utilizes the presence of dynamic scaling in rainfall [Venugopal et al., 1999] and which in conjunction with a spatial disaggregation scheme preserves both the temporal and spatial correlation structure of rainfall at the subgrid scales.</p> <div class="credits"> <p class="dwt_author">Venugopal, V.; Foufoula-Georgiou, Efi; Sapozhnikov, Victor</p> <p class="dwt_publisher"></p> <p class="publishDate">1999-08-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">24</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFM.A23F..05R"> <span id="translatedtitle">How Useful Are Regional Climate Models For <span class="hlt">Downscaling</span> Seasonal Forecasts?</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A longstanding yet very important question concerns the additional value derived from labor intensive regional climate models (RCMs) nested within GCM seasonal forecast models, over and above simple statistical methods of <span class="hlt">downscaling</span>. This paper compares the two types of <span class="hlt">downscaling</span> of precipitation "hindcasts" over the data-rich region of the Philippines, using observed data from 77 raingauges for the April-June monsoon onset season. Spatial interpolation of RCM and GCM grid box values to station locations is compared with cross-validated regression-based techniques such as canonical correlation analysis. The GCM "hindcasts" are formed from an <span class="hlt">ensemble</span> of simulations from the ECHAM4.5 model at T42 resolution made with observed SSTs prescribed, over the 1977-2004 period. The RegCM3 with 25km resolution is nested within each of a 10-member GCM <span class="hlt">ensemble</span> over the Philippines. To first order, we find that anomaly correlation skill at the station scale for simulations of seasonal total rainfall and monsoon onset date is quite similar using all the techniques considered, including simple spatial interpolation of the GCM values. The RCM has significantly smaller RMS error than the "raw" interpolated GCM, although statistical correction can greatly improve the latter. We examine the role of the availability of sufficiently long records of observed data as a deciding factor, which enters as a means to validate both types of the hindcasts, while being needed in addition to train the more "data hungry" statistical <span class="hlt">downscaling</span> methods.</p> <div class="credits"> <p class="dwt_author">Robertson, A. W.; Qian, J.; Moron, V.; Tippett, M.; Lucero, A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">25</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2005TellA..57..424P"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of DEMETER winter seasonal hindcasts over Northern Italy</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A novel method is applied in order to obtain winter predictions over Northern Italy using state-of-the-art multi-model seasonal <span class="hlt">ensemble</span> hindcasts. The method consists of several stages. In the first stage, the best predictions are computed for a group of eight indices of large-scale circulation variability using the multi-model <span class="hlt">ensemble</span> data set. The predictions are multiple linear regressions of single-model <span class="hlt">ensemble</span> mean hindcasts produced within the European project DEMETER using six different coupled models. The regression is obtained using the method of the best linear unbiased estimate (BLUE). In the second stage, a standard statistical <span class="hlt">downscaling</span> technique of the 'perfect prog' kind is applied in order to predict a group of 12 surface predictands starting from a group of predictors selected between the large-scale indices identified during the first stage. The selection of the predictands is carried out empirically, using those which lead to the best final prediction, while the regression coefficients are defined using observational data only, as in a 'perfect prog' <span class="hlt">downscaling</span> technique. All steps of the prediction computation up to this point are performed in cross-validation mode. Finally, the full high-resolution surface winter predictions are reconstructed using an adequate selection of the forecasted predictands.The predictions obtained have a much higher detail than the DEMETER direct model output predictions and, in parts of the domain, they are characterized by substantially significant skill. The improvement of the skill with respect to single-model <span class="hlt">ensembles</span> is due to the use of the BLUE technique, while the statistical <span class="hlt">downscaling</span> allows us to increase significantly the detail of the prediction. The study includes a discussion on the sensitivity of the results to both the period in years and the number of models used to produce the forecasts, and a comparison with the results obtained using a simple multi-model forecast in which all models are given the same weight.</p> <div class="credits"> <p class="dwt_author">Pavan, V.; Marchesi, S.; Morgillo, A.; Cacciamani, C.; Doblas-Reyes, F. J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">26</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3245178"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2012</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) provides genome resources for chordate genomes with a particular focus on human genome data as well as data for key model organisms such as mouse, rat and zebrafish. Five additional species were added in the last year including gibbon (Nomascus leucogenys) and Tasmanian devil (Sarcophilus harrisii) bringing the total number of supported species to 61 as of <span class="hlt">Ensembl</span> release 64 (September 2011). Of these, 55 species appear on the main <span class="hlt">Ensembl</span> website and six species are provided on the <span class="hlt">Ensembl</span> preview site (Pre!<span class="hlt">Ensembl</span>; http://pre.<span class="hlt">ensembl</span>.org) with preliminary support. The past year has also seen improvements across the project.</p> <div class="credits"> <p class="dwt_author">Flicek, Paul; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; Gordon, Leo; Hendrix, Maurice; Hourlier, Thibaut; Johnson, Nathan; Kahari, Andreas K.; Keefe, Damian; Keenan, Stephen; Kinsella, Rhoda; Komorowska, Monika; Koscielny, Gautier; Kulesha, Eugene; Larsson, Pontus; Longden, Ian; McLaren, William; Muffato, Matthieu; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Riat, Harpreet Singh; Ritchie, Graham R. S.; Ruffier, Magali; Schuster, Michael; Sobral, Daniel; Tang, Y. Amy; Taylor, Kieron; Trevanion, Stephen; Vandrovcova, Jana; White, Simon; Wilson, Mark; Wilder, Steven P.; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Durbin, Richard; Fernandez-Suarez, Xose M.; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J. P.; Parker, Anne; Proctor, Glenn; Spudich, Giulietta; Vogel, Jan; Yates, Andy; Zadissa, Amonida; Searle, Stephen M. J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">27</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/22086963"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2012.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) provides genome resources for chordate genomes with a particular focus on human genome data as well as data for key model organisms such as mouse, rat and zebrafish. Five additional species were added in the last year including gibbon (Nomascus leucogenys) and Tasmanian devil (Sarcophilus harrisii) bringing the total number of supported species to 61 as of <span class="hlt">Ensembl</span> release 64 (September 2011). Of these, 55 species appear on the main <span class="hlt">Ensembl</span> website and six species are provided on the <span class="hlt">Ensembl</span> preview site (Pre!<span class="hlt">Ensembl</span>; http://pre.<span class="hlt">ensembl</span>.org) with preliminary support. The past year has also seen improvements across the project. PMID:22086963</p> <div class="credits"> <p class="dwt_author">Flicek, Paul; Amode, M Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; Gordon, Leo; Hendrix, Maurice; Hourlier, Thibaut; Johnson, Nathan; Kähäri, Andreas K; Keefe, Damian; Keenan, Stephen; Kinsella, Rhoda; Komorowska, Monika; Koscielny, Gautier; Kulesha, Eugene; Larsson, Pontus; Longden, Ian; McLaren, William; Muffato, Matthieu; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Riat, Harpreet Singh; Ritchie, Graham R S; Ruffier, Magali; Schuster, Michael; Sobral, Daniel; Tang, Y Amy; Taylor, Kieron; Trevanion, Stephen; Vandrovcova, Jana; White, Simon; Wilson, Mark; Wilder, Steven P; Aken, Bronwen L; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Durbin, Richard; Fernández-Suarez, Xosé M; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J P; Parker, Anne; Proctor, Glenn; Spudich, Giulietta; Vogel, Jan; Yates, Andy; Zadissa, Amonida; Searle, Stephen M J</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-11-15</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">28</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..1512186H"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> Considering Non-stationarities</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The present study aims to introduce a novel <span class="hlt">downscaling</span> approach which explicitly takes non-stationarities into account. For the illustration of this approach the Mediterranean area is chosen, because it shows a wide range of different climatic characteristics, from humid conditions in the western, northern and eastern Mediterranean regions in winter to arid conditions in the southern and eastern Mediterranean regions in summer. Precipitation in the Mediterranean area is assessed by using a combined circulation- and transfer-function-based approach. Daily station data for the Mediterranean area is used as local precipitation predictand. As large-scale predictors geopotential heights of the 700hPa level in the area 20°N-70°N, 70°W-50°E are selected to include large-scale atmospheric regimes showing inter-annual to decadal variability. To account for daily to inter-annual influences on precipitation 700hPa-geopotential heights are used, again, but now within the scope to obtain circulation patterns within station-specific predictor domains. Furthermore, 700hPa-relative humidity, zonal and meridional wind components of the 700hPa level and convective inhibition are included to describe within-type characteristics of the circulation patterns. At first the statistical models are established using the whole time period available for a particular station. Subsequently, 31-year sub-periods are used to detect non-stationarities in the predictors-predictand-relationships. As a measure of performance the bias and its confidence interval limits are used for error analysis of the distributional mean. The (non-)overlaps of the bootstrap confidence interval of the mean model performance (derived by averaging the performances of all calibration/verification periods) and the bootstrap confidence intervals of the individual model errors are used to identify (non-)stationary model performance. If non-stationarities are detected, the varying predictors-predictand-relationships are analysed for the underlying reasons and statistical model <span class="hlt">ensembles</span> are built to capture the range of observed relationships. In case of the absence of non-stationarities the statistical <span class="hlt">downscaling</span> approach follows a conventional split-sampling approach for verification. Finally the statistical models and model <span class="hlt">ensembles</span> are used to predict mean daily precipitation in the Mediterranean area until the end of the 21st century under increased greenhouse warming conditions. This research project is funded by the German Research Foundation DFG.</p> <div class="credits"> <p class="dwt_author">Hertig, Elke; Jacobeit, Jucundus</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">29</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012MAP...118...79L"> <span id="translatedtitle">Prediction of spring precipitation in China using a <span class="hlt">downscaling</span> approach</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The aim of this paper is to use a statistical <span class="hlt">downscaling</span> model to predict spring precipitation over China based on a large-scale circulation simulation using Development of a European Multi-model <span class="hlt">Ensemble</span> System for Seasonal to Interannual Prediction (DEMETER) General Circulation Models (GCMs) from 1960 to 2001. A singular value decomposition regression analysis was performed to establish the link between the spring precipitation and the large-scale variables, particularly for the geopotential height at 500 hPa and the sea-level pressure. The DEMETER GCM predictors were determined on the basis of their agreement with the reanalysis data for specific domains. This <span class="hlt">downscaling</span> scheme significantly improved the predictability compared with the raw DEMETER GCM output for both the independent hindcast test and the cross-validation test. For the independent hindcast test, multi-year average spatial correlation coefficients (CCs) increased by at least ~30 % compared with the DEMETER GCMs' precipitation output. In addition, the root mean-square errors (RMSEs) decreased more than 35 % compared with the raw DEMETER GCM output. For the cross-validation test, the spatial CCs increased to greater than 0.9 for most of the individual years, and the temporal CCs increased to greater than 0.3 (95 % confidence level) for most regions in China from 1960 to 2001. The RMSEs decreased significantly compared with the raw output. Furthermore, the preceding predictor, the Arctic Oscillation, increased the predicted skill of the <span class="hlt">downscaling</span> scheme during the spring of 1963.</p> <div class="credits"> <p class="dwt_author">Liu, Ying; Fan, Ke</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-10-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">30</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2001AGUFMNG42A0408R"> <span id="translatedtitle">Statistical <span class="hlt">Ensembles</span>, Dynamical <span class="hlt">Ensembles</span> and Hybrid <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">It is now appreciated that information concerning the uncertainty of a forecast should be an intrinsic part of any forecasting product. It is still not clear, however, what the best way of estimating forecast uncertainty is. In the context of ``traditional'' best guess forecasts, one approach is to estimate error statistics using historical forecasts. Statistical <span class="hlt">ensembles</span> can then be generated from these error statistics but such <span class="hlt">ensembles</span> do not capture the state dependence of predictability. Seasonal and state dependence of the error statistics can be incorporated into the error models, but the number of parameters required can then outstrip the amount of data available. In recent years dynamical <span class="hlt">ensembles</span>, i.e. <span class="hlt">ensembles</span> of forecasts, each generated using a NWP model, have become a standard part of operational forecasting procedure. Dynamical <span class="hlt">ensembles</span> attempt to quantify the uncertainty in a forecast due to initial condition error, and also, through stochastic parameterizations and multi-model approaches, the uncertainty due to model error. Dynamical <span class="hlt">ensembles</span> are capable of reflecting state dependence predictability. The primary disadvantages of dynamical <span class="hlt">ensembles</span> is their small size, which is determined by computational expense, and the fact that they do not reflect uncertainty due to residual errors. We present a combined approach. The basic concept is very simple; ``dress'' each member of a dynamical <span class="hlt">ensemble</span> with its own statistical ``daughter'' <span class="hlt">ensemble</span>. This results in ``hyperensembles'' which both capture the state dependent nature of predictability, and contain information on residual errors determined using historical forecasts. The error statistics for the daughter <span class="hlt">ensembles</span> should be the statistics of the errors of the best members of the <span class="hlt">ensembles</span>. While the is idea simple, we show that estimating the best member error statistics for an individual <span class="hlt">ensemble</span> member has pitfalls. Specifically, attempting to identify the best member of an <span class="hlt">ensemble</span> in a space with too low a dimensional will lead to suboptimal results.</p> <div class="credits"> <p class="dwt_author">Roulston, M. S.; Smith, L. A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2001-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">31</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.2842B"> <span id="translatedtitle">Scaling and non-scaling methods for the <span class="hlt">downscaling</span> and disaggregation of temporal rainfall</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Theoretical and observational descriptions of the temporal structure of rainfall as a function of the aggregation scale are important both conceptually and operationally. For example, disaggregation and <span class="hlt">downscaling</span> methods, which preserve large scale means exactly and in the <span class="hlt">ensemble</span> mean respectively, require a detailed knowledge of how rainfall statistics change with the temporal scale. Non-scaling scale relations, which imply a non-power-law form of the second order moment, are reviewed and are applied to a wide data set representative of different rainfall regimes. Scaling approaches to <span class="hlt">downscaling</span> and disaggregation are also explored, based on canonical and microcanonical cascades. Additionally, a 'hybrid' method, which uses a non-scaling method to estimate the second order moment at small scales to be imposed in the calibration of a cascade model, is also developed and implemented. The scaling, non-scaling, and hybrid methods for disaggregation/<span class="hlt">downscaling</span> are comparatively considered and applied to determine their relative merits and performance.</p> <div class="credits"> <p class="dwt_author">Boni, Martino; Zanetti, Stefano; Marani, Marco</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">32</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/51972369"> <span id="translatedtitle">Generation of future and present climates using an hourly weather generator and a stochastic <span class="hlt">downscaling</span> of climate model outputs</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Numerous studies across multiple disciplines search for insights on the effects of climate change at the local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator to <span class="hlt">downscale</span> <span class="hlt">ensembles</span> of climate models outputs and inferring the consequences of climate impacts with an eco-hydrological model. The performance of the hourly weather generator</p> <div class="credits"> <p class="dwt_author">S. Fatichi; V. Y. Ivanov; E. Caporali</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">33</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ntis.gov/search/product.aspx?ABBR=ADA547367"> <span id="translatedtitle"><span class="hlt">Downscale</span> Concept 2.3 User Manual. <span class="hlt">Downscaled</span>, Spatially Distributed Soil Moisture Calculator.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ntis.gov/search/index.aspx">National Technical Information Service (NTIS)</a></p> <p class="result-summary">This technical report is the user manual for a soil moisture <span class="hlt">downscaling</span> tool that is implemented in ArcGIS. The software consists of <span class="hlt">Downscale</span>Concept.exe, an executable file developed from Matlab, and a GIS tool for running <span class="hlt">Downscale</span>Concept, DoConGIS, wh...</p> <div class="credits"> <p class="dwt_author">A. Q. Dozier C. M. Fields J. D. Niemann M. L. Coleman</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">34</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFM.A21G0199C"> <span id="translatedtitle">Analogue <span class="hlt">Downscaling</span> of Seasonal Rainfall Forecasts</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Charles, A. N.; Timbal, B.; Hendon, H.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">35</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/23203987"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2013.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) provides genome information for sequenced chordate genomes with a particular focus on human, mouse, zebrafish and rat. Our resources include evidenced-based gene sets for all supported species; large-scale whole genome multiple species alignments across vertebrates and clade-specific alignments for eutherian mammals, primates, birds and fish; variation data resources for 17 species and regulation annotations based on ENCODE and other data sets. <span class="hlt">Ensembl</span> data are accessible through the genome browser at http://www.<span class="hlt">ensembl</span>.org and through other tools and programmatic interfaces. PMID:23203987</p> <div class="credits"> <p class="dwt_author">Flicek, Paul; Ahmed, Ikhlak; Amode, M Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; García-Girón, Carlos; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah; Juettemann, Thomas; Kähäri, Andreas K; Keenan, Stephen; Komorowska, Monika; Kulesha, Eugene; Longden, Ian; Maurel, Thomas; McLaren, William M; Muffato, Matthieu; Nag, Rishi; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Pritchard, Emily; Riat, Harpreet Singh; Ritchie, Graham R S; Ruffier, Magali; Schuster, Michael; Sheppard, Daniel; Sobral, Daniel; Taylor, Kieron; Thormann, Anja; Trevanion, Stephen; White, Simon; Wilder, Steven P; Aken, Bronwen L; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J P; Johnson, Nathan; Kinsella, Rhoda; Parker, Anne; Spudich, Giulietta; Yates, Andy; Zadissa, Amonida; Searle, Stephen M J</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-11-30</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">36</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3531136"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2013</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) provides genome information for sequenced chordate genomes with a particular focus on human, mouse, zebrafish and rat. Our resources include evidenced-based gene sets for all supported species; large-scale whole genome multiple species alignments across vertebrates and clade-specific alignments for eutherian mammals, primates, birds and fish; variation data resources for 17 species and regulation annotations based on ENCODE and other data sets. <span class="hlt">Ensembl</span> data are accessible through the genome browser at http://www.<span class="hlt">ensembl</span>.org and through other tools and programmatic interfaces.</p> <div class="credits"> <p class="dwt_author">Flicek, Paul; Ahmed, Ikhlak; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; Garcia-Giron, Carlos; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah; Juettemann, Thomas; Kahari, Andreas K.; Keenan, Stephen; Komorowska, Monika; Kulesha, Eugene; Longden, Ian; Maurel, Thomas; McLaren, William M.; Muffato, Matthieu; Nag, Rishi; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Pritchard, Emily; Riat, Harpreet Singh; Ritchie, Graham R. S.; Ruffier, Magali; Schuster, Michael; Sheppard, Daniel; Sobral, Daniel; Taylor, Kieron; Thormann, Anja; Trevanion, Stephen; White, Simon; Wilder, Steven P.; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J. P.; Johnson, Nathan; Kinsella, Rhoda; Parker, Anne; Spudich, Giulietta; Yates, Andy; Zadissa, Amonida; Searle, Stephen M. J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">37</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012MAP...117...87S"> <span id="translatedtitle">A statistical <span class="hlt">downscaling</span> scheme to improve global precipitation forecasting</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Based on hindcasts obtained from the "Development of a European Multimodel <span class="hlt">Ensemble</span> system for seasonal to inTERannual prediction" (DEMETER) project, this study proposes a statistical <span class="hlt">downscaling</span> (SD) scheme suitable for global precipitation forecasting. The key idea of this SD scheme is to select the optimal predictors that are best forecast by coupled general circulation models (CGCMs) and that have the most stable relationships with observed precipitation. Developing the prediction model and further making predictions using these predictors can extract useful information from the CGCMs. Cross-validation and independent sample tests indicate that this SD scheme can significantly improve the prediction capability of CGCMs during the boreal summer (June-August), even over polar regions. The predicted and observed precipitations are significantly correlated, and the root-mean-square-error of the SD scheme-predicted precipitation is largely decreased compared with the raw CGCM predictions. An inter-model comparison shows that the multi-model <span class="hlt">ensemble</span> provides the best prediction performance. This study suggests that combining a multi-model <span class="hlt">ensemble</span> with the SD scheme can improve the prediction skill for precipitation globally, which is valuable for current operational precipitation prediction.</p> <div class="credits"> <p class="dwt_author">Sun, Jianqi; Chen, Huopo</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-08-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">38</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/19033362"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2009.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) is a comprehensive genome information system featuring an integrated set of genome annotation, databases, and other information for chordate, selected model organism and disease vector genomes. As of release 51 (November 2008), <span class="hlt">Ensembl</span> fully supports 45 species, and three additional species have preliminary support. New species in the past year include orangutan and six additional low coverage mammalian genomes. Major additions and improvements to <span class="hlt">Ensembl</span> since our previous report include a major redesign of our website; generation of multiple genome alignments and ancestral sequences using the new Enredo-Pecan-Ortheus pipeline and development of our software infrastructure, particularly to support the <span class="hlt">Ensembl</span> Genomes project (http://www.ensemblgenomes.org/). PMID:19033362</p> <div class="credits"> <p class="dwt_author">Hubbard, T J P; Aken, B L; Ayling, S; Ballester, B; Beal, K; Bragin, E; Brent, S; Chen, Y; Clapham, P; Clarke, L; Coates, G; Fairley, S; Fitzgerald, S; Fernandez-Banet, J; Gordon, L; Graf, S; Haider, S; Hammond, M; Holland, R; Howe, K; Jenkinson, A; Johnson, N; Kahari, A; Keefe, D; Keenan, S; Kinsella, R; Kokocinski, F; Kulesha, E; Lawson, D; Longden, I; Megy, K; Meidl, P; Overduin, B; Parker, A; Pritchard, B; Rios, D; Schuster, M; Slater, G; Smedley, D; Spooner, W; Spudich, G; Trevanion, S; Vilella, A; Vogel, J; White, S; Wilder, S; Zadissa, A; Birney, E; Cunningham, F; Curwen, V; Durbin, R; Fernandez-Suarez, X M; Herrero, J; Kasprzyk, A; Proctor, G; Smith, J; Searle, S; Flicek, P</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-11-25</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">39</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010JGRD..11522102G"> <span id="translatedtitle">SVM-PGSL coupled approach for statistical <span class="hlt">downscaling</span> to predict rainfall from GCM output</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Hydrological impacts of climate change are assessed by <span class="hlt">downscaling</span> the General Circulation Model (GCM) outputs of predictor variables to local or regional scale hydrologic variables (predictand). Support Vector Machine (SVM) is a machine learning technique which is capable of capturing highly nonlinear relationship between predictor and predictand and thus performs better than conventional linear regression in transfer function-based <span class="hlt">downscaling</span> modeling. SVM has certain parameters the values of which need to be fixed appropriately for controlling undertraining and overtraining. In this study, an optimization model is proposed to estimate the values of these parameters. As the optimization model, for selection of parameters, contains SVM as one of its constraints, analytical solution techniques are difficult to use in solving it. Probabilistic Global Search Algorithm (PGSL), a probabilistic search technique, is used to compute the optimum parameters of SVM. With these optimum parameters, training of SVM is performed for statistical <span class="hlt">downscaling</span>. The obtained relationship between large-scale atmospheric variables and local-scale hydrologic variables (e.g., rainfall) is used to compute the hydrologic scenarios for multiple GCMs. The uncertainty resulting from the use of multiple GCMs is further modeled with a modified reliability <span class="hlt">ensemble</span> averaging method. The proposed methodology is demonstrated with the prediction of monsoon rainfall of Assam and Meghalaya meteorological subdivision of northeastern India. The results obtained from the proposed model are compared with earlier developed SVM-based <span class="hlt">downscaling</span> models, and improved performance is observed.</p> <div class="credits"> <p class="dwt_author">Ghosh, Subimal</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-11-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">40</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2004AGUSM.H31A..02R"> <span id="translatedtitle">Rainfall <span class="hlt">Downscaling</span> by a Phase-Conserving, Nonlinearly-Transformed Autoregressive Model: Validation on Radar Precipitation Estimates</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The prediction of the small-scale spatio-temporal pattern of intense rainfall events is crucial for flood risk assessment in small catchments and urban areas. In the absence of a full deterministic modelling of small-scale rainfall, it is common practice to resort to the use of stochastic <span class="hlt">downscaling</span> models to generate <span class="hlt">ensemble</span> rainfall predictions to be used as inputs to rainfall-runoff models. Here we discuss a spatio-temporal <span class="hlt">downscaling</span> procedure that we call the "Rain FARM: Rainfall Filtered AutoRegressive Model," based on a non-linear transformation of a linearly correlated (gaussian) field, and we validate this approach on a set of radar precipitation estimates. The Rain FARM procedure allows for reproducing the scaling properties (if any) of the rainfall pattern and it can be easily linked with meteorological forecasts produced by limited area meteorological models. We believe that this approach represents a significant improvement over commonly available models used for rainfall <span class="hlt">downscaling</span>.</p> <div class="credits"> <p class="dwt_author">Rebora, N.; Ferraris, L.; von Hardenberg, J.; Provenzale, A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-05-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_1");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> 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showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_4");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">41</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..1213747B"> <span id="translatedtitle">Methodology for Air Quality Forecast <span class="hlt">Downscaling</span> from Regional- to Street-Scale</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">42</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/53056276"> <span id="translatedtitle">Rainfall <span class="hlt">Downscaling</span> by a Phase-Conserving, Nonlinearly-Transformed Autoregressive Model: Validation on Radar Precipitation Estimates</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The prediction of the small-scale spatio-temporal pattern of intense rainfall events is crucial for flood risk assessment in small catchments and urban areas. In the absence of a full deterministic modelling of small-scale rainfall, it is common practice to resort to the use of stochastic <span class="hlt">downscaling</span> models to generate <span class="hlt">ensemble</span> rainfall predictions to be used as inputs to rainfall-runoff models.</p> <div class="credits"> <p class="dwt_author">N. Rebora; L. Ferraris; J. von Hardenberg; A. Provenzale</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">43</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3013672"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2011</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) seeks to enable genomic science by providing high quality, integrated annotation on chordate and selected eukaryotic genomes within a consistent and accessible infrastructure. All supported species include comprehensive, evidence-based gene annotations and a selected set of genomes includes additional data focused on variation, comparative, evolutionary, functional and regulatory annotation. The most advanced resources are provided for key species including human, mouse, rat and zebrafish reflecting the popularity and importance of these species in biomedical research. As of <span class="hlt">Ensembl</span> release 59 (August 2010), 56 species are supported of which 5 have been added in the past year. Since our previous report, we have substantially improved the presentation and integration of both data of disease relevance and the regulatory state of different cell types.</p> <div class="credits"> <p class="dwt_author">Flicek, Paul; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Chen, Yuan; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gordon, Leo; Hendrix, Maurice; Hourlier, Thibaut; Johnson, Nathan; Kahari, Andreas; Keefe, Damian; Keenan, Stephen; Kinsella, Rhoda; Kokocinski, Felix; Kulesha, Eugene; Larsson, Pontus; Longden, Ian; McLaren, William; Overduin, Bert; Pritchard, Bethan; Riat, Harpreet Singh; Rios, Daniel; Ritchie, Graham R. S.; Ruffier, Magali; Schuster, Michael; Sobral, Daniel; Spudich, Giulietta; Tang, Y. Amy; Trevanion, Stephen; Vandrovcova, Jana; Vilella, Albert J.; White, Simon; Wilder, Steven P.; Zadissa, Amonida; Zamora, Jorge; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Durbin, Richard; Fernandez-Suarez, Xose M.; Herrero, Javier; Hubbard, Tim J. P.; Parker, Anne; Proctor, Glenn; Vogel, Jan; Searle, Stephen M. J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">44</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..1412266Y"> <span id="translatedtitle">A hybrid <span class="hlt">downscaling</span> procedure for estimating the vertical distribution of ambient temperature in local scale</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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-output transfer functions is obtained by utilizing the ANN weights method, which quantifies the relative importance of the predictor variables in the estimation procedure. The overall <span class="hlt">downscaling</span> performance evaluation incorporates a set of correlation and statistical measures along with appropriate statistical tests. The hybrid <span class="hlt">downscaling</span> method presented in this work can be extended to various locations by training different site-specific ANN models and the results, depending on the application, can be used for assisting the understanding of the past, present and future climatology. ____________________________ This research has been co-financed by the European Union and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II: Investing in knowledge society through the European Social Fund.</p> <div class="credits"> <p class="dwt_author">Yiannikopoulou, I.; Philippopoulos, K.; Deligiorgi, D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">45</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMGC42A..04S"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> for Hydroclimate Applications (Invited)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Statistical <span class="hlt">downscaling</span> and temporal disaggregation methods have been indispensable tools for creating regional climate projections from coarse-scale monthly-mean global climate model output, suitable for climate change assessment. Among other methods, the Bias Correction and Spatial <span class="hlt">Downscaling</span> (BCSD) method has been used extensively in applications over the western United States, especially in developing hydroclimatic scenarios in combination with distributed hydrologic models. However, as climate impacts applications have expanded to address diverse stakeholder planning needs (terrestrial and aquatic ecosystems, water management, human health, energy, etc.), data are required at finer temporal and spatial scales. These advancements have demanded ongoing development of BCSD-based <span class="hlt">downscaling</span> methods. This talk will describe recent projects to develop future hydroclimatic scenarios for large river basins of the western United States (Columbia, Upper Missouri, Colorado) and discuss the interaction between <span class="hlt">downscaling</span> methodology, stakeholder planning, and application requirements. This talk will address methods for increasing spatial resolution and for disaggregating monthly-mean model output to daily time step data. Particular emphasis will be placed on <span class="hlt">downscaling</span> considerations to properly simulated extreme statistics for both high and low flow conditions and on producing consistent results applicable across scales from small watersheds to major river basins.</p> <div class="credits"> <p class="dwt_author">Salathe, E. P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">46</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013JGRD..118.3557S"> <span id="translatedtitle">High-resolution multisite daily rainfall projections in India with statistical <span class="hlt">downscaling</span> for climate change impacts assessment</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Climate change impacts assessment involves <span class="hlt">downscaling</span> of coarse-resolution climate variables simulated by general circulation models (GCMs) using dynamic (physics-based) or statistical (data-driven) approaches. Here we use a statistical <span class="hlt">downscaling</span> technique for projections of all-India monsoon rainfall at a resolution of 0.5° in latitude/longitude. The present statistical <span class="hlt">downscaling</span> model utilizes classification and regression tree, and kernel regression and develops a statistical relationship between large-scale climate variables from reanalysis data and fine-resolution observed rainfall, and then applies the relationship to coarse-resolution GCM outputs. A GCM developed by the Canadian Centre for Climate Modeling and Analysis is employed for this study with its five <span class="hlt">ensemble</span> runs for capturing intramodel uncertainty. The model appears to effectively capture individual station means, the spatial patterns of the standard deviations, and the cross correlation between station rainfalls. Computationally expensive dynamic <span class="hlt">downscaling</span> models have been applied for India. However, our study is the first to attempt statistical <span class="hlt">downscaling</span> for the entire country at a resolution of 0.5°. The <span class="hlt">downscaling</span> model seems to capture the orographic effect on rainfall in mountainous areas of the Western Ghats and northeast India. The model also reveals spatially nonuniform changes in rainfall, with a possible increase for the western coastline and northeastern India (rainfall surplus areas) and a decrease in northern India, western India (rainfall deficit areas), and on the southeastern coastline, highlighting the need for a detailed hydrologic study that includes future projections regarding water availability which may be useful for water resource policy decisions.</p> <div class="credits"> <p class="dwt_author">Salvi, Kaustubh; Kannan, S.; Ghosh, Subimal</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">47</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012MAP...117..121Y"> <span id="translatedtitle">Improve the prediction of summer precipitation in the Southeastern China by a hybrid statistical <span class="hlt">downscaling</span> model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We attempt to apply year-to-year increment prediction to develop an effective statistical <span class="hlt">downscaling</span> scheme for summer (JJA, June-July-August) rainfall prediction at the station-to-station scale in Southeastern China (SEC). The year-to-year increment in a variable was defined as the difference between the current year and the previous year. This difference is related to the quasi-biennial oscillation in interannual variations in precipitation. Three predictors from observations and six from three general circulation models (GCMs) outputs of the development of a European multi-model <span class="hlt">ensemble</span> system for seasonal to interannual prediction (DEMETER) project were used to establish this <span class="hlt">downscaling</span> model. The independent sample test and the cross-validation test show that the <span class="hlt">downscaling</span> scheme yields better predicted skill for summer precipitation at most stations over SEC than the original DEMETER GCM outputs, with greater temporal correlation coefficients and spatial anomaly correlation coefficients, as well as lower root-mean-square errors.</p> <div class="credits"> <p class="dwt_author">Ying, Liu; Ke, Fan</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-08-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">48</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.science.mcmaster.ca/geo/faculty/coulibaly/WRHML/Publications/Publications/Coulibaly_GRL31.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> daily extreme temperatures with genetic programming</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A context-free genetic programming (GP) method is presented that simulated local scale daily extreme (maximum and minimum) temperatures based on large scale atmospheric variables. The method evolves simple and optimal models for <span class="hlt">downscaling</span> daily temperature at a station. The advantage of the context-free GP method is that both the variables and constants of the candidate models are optimized and consequently</p> <div class="credits"> <p class="dwt_author">Paulin Coulibaly</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">49</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2006AGUFM.H14C..02M"> <span id="translatedtitle">Evaluation of Uncertainty in Nested Flood Forecasts by Coupling a Multifractal Precipitation <span class="hlt">Downscaling</span> Model and a Fully-Distributed Hydrological Model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Despite progress in satellite remote sensing, the use of space-borne precipitation fields in hydrological models remains elusive, in particular with respect to the space and time scales appropriate for flood forecasting. A major limitation towards use of coarse satellite products is the lack of a formal framework for <span class="hlt">downscaling</span> precipitation fields in space and time over the catchment of interest. In this work, we develop and test a hydrometeorological forecasting procedure intended to evaluate the uncertainty incorporated into streamflow predictions through the use of <span class="hlt">downscaled</span> precipitation products. Our approach relies on using a space-time multifractal model to <span class="hlt">downscale</span> a coarse precipitation product, such as a satellite observation, and generating an <span class="hlt">ensemble</span> of precipitation fields at high resolution. These synthetic fields are used to force a fully distributed hydrological model known as the TIN-based Real-time Integrated Basin Simulator (tRIBS) for flood prediction at multiple, nested locations. For this study, we first investigate the scaling properties of precipitation derived from the NEXRAD radar network in the Arkansas Red River Basin for the 1997-2003 summer months. We then calibrate a multifractal model based on a log-Poisson generator, exploring the linkages between the model parameters and the large scale meteorological observables. We also evaluate the accuracy of the <span class="hlt">downscaling</span> products relative to the high resolution observed fields. Subsequently, we force tRIBS model with the synthetic <span class="hlt">downscaled</span> precipitation <span class="hlt">ensemble</span> to predict streamflow in the Baron Fork basin in Oklahoma and at nested interior locations. The resulting <span class="hlt">ensembles</span> of synthetic hourly hydrographs and observed streamflow values are then post-processed to verify the forecast procedure. Scalar and non-scalar measures are used to evaluate the forecast reliability, resolution, sharpness and bias. The plausibility of the consistency condition for the <span class="hlt">ensemble</span> forecast procedure is also investigated through the analysis of the verification rank histogram. Furthermore, we identify the catchment scale dependency in the forecast performance through analysis at multiple nested basins. To conclude, we summarize how <span class="hlt">ensemble</span> forecast metrics can be used to quantify propagation of uncertainty from <span class="hlt">downscaled</span> precipitation products to distributed flood forecasts.</p> <div class="credits"> <p class="dwt_author">Mascaro, G.; Vivoni, E. R.; Deidda, R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">50</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMGC51A0736C"> <span id="translatedtitle">Simulation of an <span class="hlt">ensemble</span> of future climate time series with an hourly weather generator</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous <span class="hlt">downscaling</span> techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic <span class="hlt">downscaling</span> technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the <span class="hlt">downscaling</span> procedure derives distributions of factors of change for several climate statistics from a multi-model <span class="hlt">ensemble</span> of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model <span class="hlt">ensemble</span>. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an <span class="hlt">ensemble</span> of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated <span class="hlt">ensemble</span> of scenarios also accounts for the uncertainty derived from multiple GCMs used in <span class="hlt">downscaling</span>. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ), Detroit (MI), and Firenze (Italy). The stochastic <span class="hlt">downscaling</span> is carried out with eight GCMs from the CMIP3 multi-model dataset (IPCC 4AR, A1B scenario).</p> <div class="credits"> <p class="dwt_author">Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">51</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013JHyd..492..254F"> <span id="translatedtitle">Modelling runoff with statistically <span class="hlt">downscaled</span> daily site, gridded and catchment rainfall series</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Statistical <span class="hlt">downscaling</span> has been first time used for catchment and grid daily rainfall.<span class="hlt">Downscaling</span> results from three techniques have been compared.Hydrological implications, rather than rainfall statistics, from statistical <span class="hlt">downscaling</span> results have been explored.</p> <div class="credits"> <p class="dwt_author">Fu, Guobin; Charles, Stephen P.; Chiew, Francis H. S.; Teng, Jin; Zheng, Hongxing; Frost, Andrew J.; Liu, Wenbin; Kirshner, Sergey</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-06-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">52</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/15020655"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Extended Weather Forecasts for Hydrologic Prediction</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">Weather and climate forecasts are critical inputs to hydrologic forecasting systems. The National Center for Environmental Prediction (NCEP) issues 8-15 days outlook daily for the U.S. based on the Medium Range Forecast (MRF) model, which is a global model applied at about 2? spatial resolution. Because of the relatively coarse spatial resolution, weather forecasts produced by the MRF model cannot be applied directly to hydrologic forecasting models that require high spatial resolution to represent land surface hydrology. A mesoscale atmospheric model was used to dynamically <span class="hlt">downscale</span> the 1-8 day extended global weather forecasts to test the feasibility of hydrologic forecasting through this model nesting approach. Atmospheric conditions of each 8-day forecast during the period 1990-2000 were used to provide initial and boundary conditions for the mesoscale model to produce an 8-day atmospheric forecast for the western U.S. at 30 km spatial resolution. To examine the impact of initialization of the land surface state on forecast skill, two sets of simulations were performed with the land surface state initialized based on the global forecasts versus land surface conditions from a continuous mesoscale simulation driven by the NCEP reanalysis. Comparison of the skill of the global and <span class="hlt">downscaled</span> precipitation forecasts in the western U.S. showed higher skill for the <span class="hlt">downscaled</span> forecasts at all precipitation thresholds and increasingly larger differences at the larger thresholds. Analyses of the surface temperature forecasts show that the mesoscale forecasts generally reduced the root-mean-square error by about 1.5 C compared to the global forecasts, because of the much better resolved topography at 30 km spatial resolution. In addition, initialization of the land surface states has large impacts on the temperature forecasts, but not the precipitation forecasts. The improvements in forecast skill using <span class="hlt">downscaling</span> could be potentially significant for improving hydrologic forecasts for managing river basins.</p> <div class="credits"> <p class="dwt_author">Leung, Lai-Yung R.; Qian, Yun</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-03-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">53</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://dx.doi.org/10.1029/1999GL006078"> <span id="translatedtitle">Hydrological responses to dynamically and statistically <span class="hlt">downscaled</span> climate model output</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Wilby, R. L.; Hay, L. E.; Gutowski, Jr. , W. J.; Arritt, R. W.; Takle, E. S.; Pan, Z.; Leavesley, G. H.; Clark, M. P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2000-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">54</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013JHyd..486..479S"> <span id="translatedtitle">Assessment of robustness and significance of climate change signals for an <span class="hlt">ensemble</span> of distribution-based scaled climate projections</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We analyse an <span class="hlt">ensemble</span> of regional climate model projections for Denmark. We compare a delta change (DC) and a distribution-based (DBS) <span class="hlt">downscaling</span> method. Change signals are strongest across models and variables at the end of the century. The DC method is insufficient at recreating projected precipitation regimes. The DBS method better captures temporal dynamics and heavy precipitation events.</p> <div class="credits"> <p class="dwt_author">Seaby, L. P.; Refsgaard, J. C.; Sonnenborg, T. O.; Stisen, S.; Christensen, J. H.; Jensen, K. H.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">55</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFMGC21B0879Z"> <span id="translatedtitle">Joint Variable Spatial <span class="hlt">Downscaling</span> (JVSD): A New <span class="hlt">Downscaling</span> Method with Application to the Southeast US</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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) In the southern ACF watersheds, mean precipitation generally exhibits a mild decline in early spring and summer and increases in late winter; For the northern ACF watersheds, mean precipitation decreases in summer and increases mildly in winter (as in the south); (iii) In addition to mean trends, the precipitation distributions stretch on both ends with higher highs (floods) and lower lows (droughts). The <span class="hlt">downscaled</span> temperature and precipitation scenarios are the basis of a comprehensive hydrologic and water resources assessment (reported elsewhere) assessing significant water, agricultural, energy, and environmental sector impacts and underscoring the need for mitigation and adaptation measures.</p> <div class="credits"> <p class="dwt_author">Zhang, F.; Georgakakos, A. P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">56</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..1212763S"> <span id="translatedtitle">Science Challenges to Produce Skillful and Reliable Hydrologic <span class="hlt">Ensemble</span> Predictions</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> forecast techniques are beginning to be used for hydrological prediction by operational hydrological services throughout the world. These techniques are attractive because they allow effects of a wide range of sources of uncertainty on hydrological forecasts to be accounted for. Not only does <span class="hlt">ensemble</span> prediction in hydrology offer a general approach to probabilistic prediction; it offers a significant new approach to improve hydrological forecast accuracy as well. But, there are many scientific challenges that must be overcome to provide users with high quality hydrologic <span class="hlt">ensemble</span> forecasts. One of these challenges is to re-scale and <span class="hlt">downscale</span> atmospheric forecasts to produce appropriate <span class="hlt">ensemble</span> forcing for hydrologic <span class="hlt">ensemble</span> Streamflow prediction. One criterion for such forcing is that the long term climatology of the forcing <span class="hlt">ensemble</span> members (over many forecasts) must be same as the climatology of the forcing used to calibrate the hydrologic forecast model. Another criterion is that the <span class="hlt">ensemble</span> forcing should preserve both the space-time scale dependent variability of the forcing and the space-time scale dependent uncertainty in this forcing. This is important for at least two main reasons. First, hydrologic processes integrate input forcing over a wide range of space and time scales, depending on the drainage areas above river forecast points. Second, atmospheric forecasts are more skillful at larger space and time scales. Another challenge is that hydrologic <span class="hlt">ensemble</span> forecasts tend to underestimate <span class="hlt">ensemble</span> spread and are affected by systematic hydrologic model biases. A major cause of spread underestimation, especially in short range forecasts, is caused by neglecting uncertainty in initial conditions and neglecting uncertainty in model predictability. Short term forecast errors also may partly be caused by not adjusting model variables to account for recent differences between observed and modeled streamflow. Some of these effects may possibly be reduced by explicitly considering some of their causes as part of the hydrologic <span class="hlt">ensemble</span> modeling process. But some form of post-processing will likely remain an essential step to produce reliable, unbiased hydrologic forecasts.</p> <div class="credits"> <p class="dwt_author">Schaake, John</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">57</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..15.8856G"> <span id="translatedtitle">Validation of a Universal Multifractal <span class="hlt">downscaling</span> process with the help of dense networks of disdrometers</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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). The variability among the generated "virtual" disdrometers is then compared with the observed one. The impact of these results on the comparisons commonly performed between radar and rain gauge / disdrometer rainfall data will finally be briefly discussed.</p> <div class="credits"> <p class="dwt_author">Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Berne, Alexis; Lovejoy, Shaun</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">58</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://eric.ed.gov/?q=%22pan-%22&id=EJ969636"> <span id="translatedtitle">World Music <span class="hlt">Ensemble</span>: Kulintang</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p class="result-summary">|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 <span class="hlt">ensemble</span>…</p> <div class="credits"> <p class="dwt_author">Beegle, Amy C.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">59</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013GMDD....6.5117E"> <span id="translatedtitle">A regional climate modelling projection <span class="hlt">ensemble</span> experiment - NARCliM</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of <span class="hlt">ensembles</span> members that can be simulated such that choices must be made concerning which Global Climate Models (GCMs) to <span class="hlt">downscale</span> from, and which Regional Climate Models (RCMs) to <span class="hlt">downscale</span> with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCMs and RCMs, as well as spanning the range of future climate projections present in the full GCM <span class="hlt">ensemble</span>. The created <span class="hlt">ensemble</span> provides a more robust view of future regional climate changes.</p> <div class="credits"> <p class="dwt_author">Evans, J. P.; Ji, F.; Lee, C.; Smith, P.; Argüeso, D.; Fita, L.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-09-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">60</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/881929"> <span id="translatedtitle">Physically Based Global <span class="hlt">Downscaling</span>: Regional Evaluation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Ghan, Steven J.; Shippert, Timothy R.; Fox, Jared</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-02-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_2");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a 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<img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">61</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2005ThApC..82..119B"> <span id="translatedtitle">Fuzzy rule-based <span class="hlt">downscaling</span> of precipitation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A fuzzy rule-based methodology for <span class="hlt">downscaling</span> local hydrological variables from large-scale atmospheric circulation is presented. The method is used to estimate the frequency distribution of daily precipitation conditioned on daily geopotential fields. The task is accomplished in two steps. First, the exceedence probabilities corresponding to selected precipitation thresholds are estimated by fuzzy rules defined between geopotential fields (premises) and exceedence events (response). Then a continuous probability distribution is constructed from the discrete exceedence probabilities and the observed behaviour of precipitation. The methodology is applied to precipitation measured at Essen, a location in the Ruhr catchment, Germany. Ten years of precipitation data (1970 1979) were used for training and another ten years (1980 1989) for validation. The 700 hPa geopotential fields are used to characterise large-scale circulation. The application example demonstrates that this direct <span class="hlt">downscaling</span> method is able to capture the relationship between premises and the response; namely both the estimated exceedence probabilities and the frequency distribution reproduce the empirical data observed in the validation period.</p> <div class="credits"> <p class="dwt_author">Bardossy, A.; Bogardi, I.; Matyasovszky, I.</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-08-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">62</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFM.A33H..07D"> <span id="translatedtitle">Assessment of add-value of dynamical <span class="hlt">downscaling</span> in Japan</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Assessment of add-value of dynamical <span class="hlt">downscaling</span> in Japan Climate change caused by human activities will continue for centuries. It will need at least several decades until mitigation will take effect. It is necessary to put adaptation together immediately. The impacts and potential applications of interest to the stakeholders are mostly at regional and local scales. Users of climate scenarios produced by global climate models with coarse grid-spacing have been dissatisfied with the inadequate mismatch of spatial scale. <span class="hlt">Downscaling</span> technique is used to obtain the regional climate scenarios, especially in regions of complex topography, coastlines, and in regions with highly heterogeneous land surface covers where those results are highly sensitive to fine spatial scale climate processes. Dynamical and statistical <span class="hlt">downscaling</span> techniques available for generating regional climate information have the respective strengths and weaknesses. We quantified the confidence and uncertainties of Type-2, Type-3, and Type-4 dynamical <span class="hlt">downscaling</span> where the lateral and bottom boundary conditions were obtained from Japanese 25-year ReAnalysis (JRA-25), AGCM (AMIP run) and CGCM (CMIP run) respectively. We assessed the value (skill) added by the <span class="hlt">downscaling</span> to a climate simulation in Japan. Based on the lesson learning from the multi-<span class="hlt">downscaling</span> project in Japan (S5-3) and the Research Program on Climate Change Adaptation (RECCA), "added value" and "predictability" of <span class="hlt">downscaling</span> to provide scientific knowledge for sustainable development by adapting to climate change is discussed.</p> <div class="credits"> <p class="dwt_author">Dairaku, K.; Pielke, R. A.; Beltran-Przekurat, A.; Iizuka, S.; Sasaki, W.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">63</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/626843"> <span id="translatedtitle"><span class="hlt">Ensembling</span> MML Causal Discovery</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This paper presents an <span class="hlt">ensemble</span> MML approach for the dis- covery of causal models. The component learners are formed based on the MML causal induction methods. Six dierent <span class="hlt">ensemble</span> causal induc- tion algorithms are proposed. Our experiential results reveal that (1) the <span class="hlt">ensemble</span> MML causal induction approach has achieved an improved re- sult compared with any single learner in terms</p> <div class="credits"> <p class="dwt_author">Honghua Dai; Gang Li; Zhi-hua Zhou</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">64</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009EGUGA..1112640C"> <span id="translatedtitle">High resolution probabilistic precipitation forecast over Spain combining the statistical <span class="hlt">downscaling</span> tool PROMETEO and the AEMET short range EPS system (AEMET/SREPS)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The Short-Range <span class="hlt">Ensemble</span> Prediction System (SREPS) is a multi-LAM (UM, HIRLAM, MM5, LM and HRM) multi analysis/boundary conditions (ECMWF, UKMetOffice, DWD and GFS) run twice a day by AEMET (72 hours lead time) over a European domain, with a total of 5 (LAMs) x 4 (GCMs) = 20 members. One of the main goals of this project is analyzing the impact of models and boundary conditions in the short-range high-resolution forecasted precipitation. A previous validation of this method has been done considering a set of climate networks in Spain, France and Germany, by interpolating the prediction to the gauge locations (SREPS, 2008). In this work we compare these results with those obtained by using a statistical <span class="hlt">downscaling</span> method to post-process the global predictions, obtaining an "advanced interpolation" for the local precipitation using climate network precipitation observations. In particular, we apply the PROMETEO <span class="hlt">downscaling</span> system based on analogs and compare the SREPS <span class="hlt">ensemble</span> of 20 members with the PROMETEO statistical <span class="hlt">ensemble</span> of 5 (analog <span class="hlt">ensemble</span>) x 4 (GCMs) = 20 members. Moreover, we will also compare the performance of a combined approach post-processing the SREPS outputs using the PROMETEO system. References: SREPS 2008. 2008 EWGLAM-SRNWP Meeting (http://www.aemet.es/documentos/va/divulgacion/conferencias/prediccion/Ewglam/PRED_CSantos.pdf)</p> <div class="credits"> <p class="dwt_author">Cofino, A. S.; Santos, C.; Garcia-Moya, J. A.; Gutierrez, J. M.; Orfila, B.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">65</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..1511304T"> <span id="translatedtitle">AMIC Project: Comparison of WRF High Resolution Dynamical <span class="hlt">Downscaling</span> of ERA-Interim and EC-Earth for Azores Islands</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Project AMIC integrates the Portuguese members of the new EC-Earth climate modeling consortium. The aim is to contribute to the IPCC fifth report with a significant set of simulations with a state of the art model, while giving the group timely access to the complete <span class="hlt">ensemble</span> of simulations for diagnostic studies, and regional <span class="hlt">downscaling</span>. Additionally, Project AMIC will produce a new set of high resolution simulations of the Portuguese islands climate, using a state of the art model (WRF) at 6km horizontal resolution, with boundary conditions from the new ERA-Interim reanalysis (1989-2009) and from the EC-Earth decadal (20 year) runs. These simulations will allow for validation of the <span class="hlt">downscaling</span> methodology, and will characterize both the current and near future climate. This study aims to compare two present day climate high resolution dynamical <span class="hlt">downscaling</span> WRF simulations for the Portuguese islands of Azores using the ECMWF ERA-Interim reanalysis and the EC-Earth v2.3 boundary conditions for the period 1989-2010. In small volcanic islands the local scale climate is influenced by the regional scale climate and by the orography and orientation of air masses over the islands. In these environments the climatological conditions are a vital importance for the local agriculture and water management. With this study we aim to see how well the dynamical <span class="hlt">downscaling</span> using EC-Earth v2.3 behaves when put against to the ERA-Interim reanalysis. To achieve this goal results from both simulations are compared against with the available observation network in both islands. This study results will show us what kind of deviations we can expect for the future scenarios runs using EC-Earth boundaries currently being made in IDL.</p> <div class="credits"> <p class="dwt_author">Tomé, Ricardo; Miranda, Pedro; Azevedo, Eduardo; Santo, Fátima</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">66</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012ClDy...38..391J"> <span id="translatedtitle">High-resolution precipitation and temperature <span class="hlt">downscaling</span> for glacier models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The spatial resolution gap between global or regional climate models and the requirements for local impact studies motivates the need for climate <span class="hlt">downscaling</span>. For impact studies that involve glacier modelling, the sparsity or complete absence of climate monitoring activities within the regions of interest presents a substantial additional challenge. <span class="hlt">Downscaling</span> methods for this application must be independent of climate observations and cannot rely on tuning to station data. We present new, computationally-efficient methods for <span class="hlt">downscaling</span> precipitation and temperature to the high spatial resolutions required to force mountain glacier models. Our precipitation <span class="hlt">downscaling</span> is based on an existing linear theory for orographic precipitation, which we modify for large study regions by including moist air tracking. Temperature is <span class="hlt">downscaled</span> using an interpolation scheme that reconstructs the vertical temperature structure to estimate surface temperatures from upper air data. Both methods are able to produce output on km to sub-km spatial resolution, yet do not require tuning to station measurements. By comparing our <span class="hlt">downscaled</span> precipitation (1 km resolution) and temperature (200 m resolution) fields to station measurements in southern British Columbia, we evaluate their performance regionally and through the annual cycle. Precipitation is improved by as much as 30% (median relative error) over the input reanalysis data and temperature is reconstructed with a mean bias of 0.5°C at locations with high vertical relief. Both methods perform best in mountainous terrain, where glaciers tend to be concentrated.</p> <div class="credits"> <p class="dwt_author">Jarosch, Alexander H.; Anslow, Faron S.; Clarke, Garry K. C.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">67</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012CG.....41..119M"> <span id="translatedtitle">A general method for <span class="hlt">downscaling</span> earth resource information</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A programme scripted for use in an R programming environment called dissever is presented. This programme was designed to facilitate a generalised method for <span class="hlt">downscaling</span> coarsely resolved earth resource information using available finely gridded covariate data. Under the assumption that the relationship between the target variable being <span class="hlt">downscaled</span> and the available covariates can be nonlinear, dissever uses weighted generalised additive models (GAMs) to drive the empirical function. An iterative algorithm of GAM fitting and adjustment attempts to optimise the <span class="hlt">downscaling</span> to ensure that the target variable value given for each coarse grid cell equals the average of all target variable values at the fine scale in each coarse grid cell. A number of outputs needed for mapping results and diagnostic purposes are automatically generated from dissever. We demonstrate the programs' functionality by <span class="hlt">downscaling</span> a soil organic carbon (SOC) map with 1-km by 1-km grid resolution down to a 90-m by 90-m grid resolution using available covariate information derived from a digital elevation model, Landsat ETM+ data, and airborne gamma radiometric data. dissever produced high quality results as indicated by a low weighted root mean square error between averaged 90-m SOC predictions within their corresponding 1-km grid cell (0.82 kg m-3). Additionally, from a concordance between the <span class="hlt">downscaled</span> map and another map created using digital soil mapping methods there was a strong agreement (0.94). Future versioning of dissever will investigate quantifying the uncertainty of the <span class="hlt">downscaled</span> outputs.</p> <div class="credits"> <p class="dwt_author">Malone, Brendan P.; McBratney, Alex B.; Minasny, Budiman; Wheeler, Ichsani</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">68</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ACPD...13.1179O"> <span id="translatedtitle">Extreme winds over Europe in the <span class="hlt">ENSEMBLES</span> regional climate models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate predictions of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model <span class="hlt">downscalings</span> over Europe from the "<span class="hlt">ENSEMBLE</span>-based Predictions of Climate Changes and their Impacts" project (<span class="hlt">ENSEMBLES</span>), and investigates the predicted changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the Generalised Pareto Distribution. The models show that for much of Europe the 50 yr return wind is projected to change by less than 2 m s-1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s-1 between two different <span class="hlt">downscalings</span>.</p> <div class="credits"> <p class="dwt_author">Outten, S. D.; Esau, I.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">69</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ACP....13.5163O"> <span id="translatedtitle">Extreme winds over Europe in the <span class="hlt">ENSEMBLES</span> regional climate models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate projections of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model <span class="hlt">downscalings</span> over Europe following the SRES A1B scenario from the "<span class="hlt">ENSEMBLE</span>-based Predictions of Climate Changes and their Impacts" project (<span class="hlt">ENSEMBLES</span>). It investigates the projected changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the generalised Pareto distribution. The models show that, for much of Europe, the 50 yr return wind is projected to change by less than 2 m s-1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s-1 between two different <span class="hlt">downscalings</span>.</p> <div class="credits"> <p class="dwt_author">Outten, S. D.; Esau, I.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">70</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.9475M"> <span id="translatedtitle">South America <span class="hlt">downscaling</span>: using spatial artificial neural network</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The mathematical models used to simulate the present climate and project future climate with forcing by greenhouse gases and aerosols are generally referred to as General Circulation Models or Global Climate Models (GCMs). However, the spatial resolution of GCMs remains quite coarse, in the order of 300 x 300 km, and at scale, the regional and local details of the climate which are influenced by spatial heterogeneities in the regional physiography are lost. Therefore, there is the need to convert the GCM outputs into a reliable data set with higher spatial resolution, with daily rainfall and temperature time series at the scale of the watershed or a region to which the climate impact is going to be investigated. The methods used to convert GCM outputs into local meteorological variables required for reliable climate modeling are usually referred to as <span class="hlt">downscaling</span> techniques. There are a variety of <span class="hlt">downscaling</span> techniques in the literature, but two major approaches can be identified at the moment, namely, dynamic <span class="hlt">downscaling</span> and empirical (statistical) <span class="hlt">downscaling</span>. The most widely used empirical <span class="hlt">downscaling</span> methods are the multiple linear regression and stochastic weather generation. However, the interest in nonlinear regression methods, namely, artificial neural network (ANN), is nowadays increasing because of their high potential for complex, nonlinear and time-varying input-output mapping. The main aim of this work is to develop and test a novel type of statistical <span class="hlt">downscaling</span> technique based on the Artificial Neural Network (ANN), applied of the climate change. This work analyses the performance of the IPCC models in simulate the present and future climate using ANN. The ANN used here are based on a feed forward configuration of the multilayer perception that has been used by a growing number of authors. To carry out statistical <span class="hlt">downscaling</span> for each meteorological date (grid point), the predictors and predictands were supplied to the models (ANN) and spatial interpolation.</p> <div class="credits"> <p class="dwt_author">Mendes, David; Marengo, José</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">71</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.7545V"> <span id="translatedtitle">Fitting asymmetrical copulas for rainfall <span class="hlt">downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In recent work, copulas have been applied in <span class="hlt">downscaling</span> as a method to describe the observed scale dependence in rainfall. Although this approach was found to be effective, asymmetries are observed in the resulting copulas, i.e. behaviour where the margins are no longer exchangeable. This behaviour can be modelled using a variety of techniques, e.g. Khoudraji's device. Although a number of papers exist on asymmetric copulas, it was found that the practical use, such as fitting and testing, of these copulas has not yet been discussed much. In this study we propose and test a method for fitting asymmetrical copulas. Furthermore, the construction of such copulas using a generalisation of Khoudraji's device (E. Liebscher, 2008, Construction of asymmetrical multivariate copulas, Journal of Multivariate Analysis) is discussed as the proposed fitting method is based on this method. Our method is based on a piecewise estimation of the problem, rather than attempting to solve the entire problem at once. Moreover, by applying an iterative approach, the problem becomes tractable and well-fitting copulas can be retrieved.</p> <div class="credits"> <p class="dwt_author">van den Berg, M. J.; Verhoest, N. E. C.; De Baets, B.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">72</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AdG....23...65Y"> <span id="translatedtitle"><span class="hlt">Downscaling</span>, parameterization, decomposition, compression: a perspective from the multiresolution analysis</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Geophysical models in general, and atmospheric models more specifically, are always limited in spatial resolutions. Due to this limitation, we face with two different needs. The first is a need for knowing (or "<span class="hlt">downscaling</span>") more spatial details (e.g., precipitation distribution) than having model simulations for practical applications, such as hydrological modelling. The second is a need for "parameterizing" the subgrid-scale physical processes in order to represent the feedbacks of these processes on to the resolved scales (e.g., the convective heating rate). The present article begins by remarking that it is essential to consider the <span class="hlt">downscaling</span> and parametrization as an "inverse" of each other: <span class="hlt">downscaling</span> seeks a detail of the subgrid-scale processes, then the parameterization seeks an integrated effect of the former into the resolved scales. A consideration on why those two closely-related operations are traditionally treated separately, gives insights of the fundamental limitations of the current <span class="hlt">downscalings</span> and parameterizations. The multiresolution analysis (such as those based on wavelet) provides an important conceptual framework for developing a unified formulation for the <span class="hlt">downscaling</span> and parameterization. In the vocabulary of multiresolution analysis, these two operations may be considered as types of decompression and compression. A new type of a subgrid-scale representation scheme, NAM-SCA (nonhydrostatic anelastic model with segmentally-constant approximation), is introduced under this framework.</p> <div class="credits"> <p class="dwt_author">Yano, J.-I.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-06-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">73</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012GMDD....5..425C"> <span id="translatedtitle"><span class="hlt">Downscaling</span> the climate change for oceans around Australia</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">At present, global climate models used to project changes in climate do not resolve mesoscale ocean features such as boundary currents and eddies. These missing features may be important to realistically project the marine impacts of climate change. Here we present a framework for dynamically <span class="hlt">downscaling</span> coarse climate change projections utilising a global ocean model that resolves these features in the Australian region. The <span class="hlt">downscaling</span> model used here is ocean-only. The ocean feedback on the air-sea fluxes is explored by restoring to surface temperature and salinity, as well as a calculated feedback to wind stress. These feedback approximations do not replace the need for fully coupled models, but they allow us to assess the sensitivity of the ocean in <span class="hlt">downscaled</span> climate change simulations. Significant differences are found in sea surface temperature, salinity, stratification and transport between the <span class="hlt">downscaled</span> projections and those of the climate model. While the magnitude of the climate change differences may vary with the feedback parameterisation used, the patterns of the climate change differences are consistent and develop rapidly indicating they are mostly independent of feedback that ocean differences may have on the air-sea fluxes. Until such a time when it is feasible to regularly run a global climate model with eddy resolution, our framework for ocean climate change <span class="hlt">downscaling</span> provides an attractive way to explore how climate change may affect the mesoscale ocean environment.</p> <div class="credits"> <p class="dwt_author">Chamberlain, M. A.; Sun, C.; Matear, R. J.; Feng, M.; Phipps, S. J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-02-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">74</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.dri.edu/images/stories/divisions/dhs/dhsfaculty/Justin-Huntington/Mejia_et_al_2012.pdf"> <span id="translatedtitle">Linking Global Climate Models to an Integrated Hydrologic Model: Using an Individual Station <span class="hlt">Downscaling</span> Approach</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This paper implements an individual station-based <span class="hlt">downscaled</span> approach based on a quantil- quantile bias correction mapping to further <span class="hlt">downscale</span> regional gridded simulated output into individual station locations. We describe and propose a framework to optimize the usefulness of this <span class="hlt">downscaling</span> approach over small watersheds in mountain regions, where <span class="hlt">downscale</span> gridded data (~10km) is still too coarse for use in sub-regional</p> <div class="credits"> <p class="dwt_author">John F. Mejia; Justin Huntington; Benjamin Hatchett; Darko Koracin; Richard G. Niswonger</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">75</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..15.5800S"> <span id="translatedtitle">Regional climate change projections over South America based on the CLARIS-LPB RCM <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">CLARIS-LPB was an EU FP7 financed Europe-South America Network for Climate Change Assessment and Impact Studies in La Plata Basin. CLARIS-LPB has created the first <span class="hlt">ensemble</span> ever of RCM <span class="hlt">downscalings</span> over South America. Here we present the climate change scenarios for a near future period (2011-2040) and for a far future period (2071-2100). The <span class="hlt">ensemble</span> is based on seven RCMs driven by three CMIP3 GCMs for emission scenario SRES A1B. The RCM model domains cover all of South America, with a horizontal resolution of approximately 50 km, but project focus has been on results over the La Plata Basin. The <span class="hlt">ensemble</span> mean for temperature change shows more warming over tropical South America than over the southern part of the continent. During summer (DJF) the Low-Parana and Uruguay regions show less warming than the surrounding regions. For the <span class="hlt">ensemble</span> mean of precipitation changes the patterns are almost the same for near and far future but with larger values for far future. Thus overall trends do not change with time. The near future shows in general small changes over large areas (less than ±10%). For JJA a dry tendency is seen over eastern Brazil that becomes stronger and extends geographically with time. In near future most models show a drying trend over this area. In far future almost all models agree on the drying. For DJF a wet tendency is seen over the La Plata basin area which becomes stronger with time. In near future almost all <span class="hlt">downscalings</span> agree on this wet tendency and in far future all <span class="hlt">downscalings</span> agree on the sign. The RCM <span class="hlt">ensemble</span> is unbalanced with respect to forcing GCMs. 6 out of 11(10) simulations use ECHAM5 for the near(far) future period while 4(3) use HadCM3 and only one IPSL. Thus, all <span class="hlt">ensemble</span> mean values will be tilted towards ECHAM5. It is of course possible to compensate for this imbalance among GCMs by some weighting but no such weighting has been applied for the current analysis. The north-south gradient in warming is in general stronger in the ECHAM5 <span class="hlt">downscalings</span> and is also more evident during JJA than during DJF. The HadCM3 and IPSL <span class="hlt">downscalings</span> give larger warming in near future than ECHAM5 <span class="hlt">downscalings</span>. This tendency is still present in far future but differences connected to GCMs are then much less evident. For precipitation the spread in trends and amounts of changes between different <span class="hlt">downscalings</span> are much larger than for temperature. In contrast to temperature the precipitation patterns are in general more similar for the same RCM than for the same GCM. Thus, the results are sensitive for how precipitation processes are parameterized and/or for how local surface-atmosphere feedback mechanisms are simulated. Looking at a certain RCM and period the patterns for near and far futures are similar but stronger for the far future period.</p> <div class="credits"> <p class="dwt_author">Samuelsson, Patrick; Solman, Silvina; Sanchez, Enrique; Rocha, Rosmeri; Li, Laurent; Marengo, José; Remedio, Armelle; Berbery, Hugo</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">76</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/51972756"> <span id="translatedtitle">A standardized framework for evaluating the skill of regional climate <span class="hlt">downscaling</span> techniques</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Regional climate impact assessments require high-resolution projections to resolve local factors that modify the impact of global-scale forcing. To generate these projections, global climate model simulations are commonly <span class="hlt">downscaled</span> using a variety of statistical and dynamical techniques. Despite the essential role of <span class="hlt">downscaling</span> in regional assessments, there is no standard approach to evaluating various <span class="hlt">downscaling</span> methods. Hence, impact communities often</p> <div class="credits"> <p class="dwt_author">Katharine Anne Hayhoe</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">77</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.geo.utexas.edu/courses/387h/papers/murphy_1999.pdf"> <span id="translatedtitle">An Evaluation of Statistical and Dynamical Techniques for <span class="hlt">Downscaling</span> Local Climate</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">An assessment is made of <span class="hlt">downscaling</span> estimates of screen temperature and precipitation observed at 976 European stations during 1983-94. A statistical <span class="hlt">downscaling</span> technique, in which local values are inferred from observed atmospheric predictor variables, is compared against two dynamical <span class="hlt">downscaling</span> techniques, based on the use of the screen temperature or precipitation simulated at the nearest grid point in integrations of</p> <div class="credits"> <p class="dwt_author">James Murphy</p> <p class="dwt_publisher"></p> <p class="publishDate">1999-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">78</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009GeoRL..3611708M"> <span id="translatedtitle">Probabilistic <span class="hlt">downscaling</span> approaches: Application to wind cumulative distribution functions</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A statistical method is developed to generate local cumulative distribution functions (CDFs) of surface climate variables from large-scale fields. Contrary to most <span class="hlt">downscaling</span> methods producing continuous time series, our “probabilistic <span class="hlt">downscaling</span> methods” (PDMs), named “CDF-transform”, is designed to deal with and provide local-scale CDFs through a transformation applied to large-scale CDFs. First, our PDM is compared to a reference method (Quantile-matching), and validated on a historical time period by <span class="hlt">downscaling</span> CDFs of wind intensity anomalies over France, for reanalyses and simulations from a general circulation model (GCM). Then, CDF-transform is applied to GCM output fields to project changes in wind intensity anomalies for the 21st century under A2 scenario. Results show a decrease in wind anomalies for most weather stations, ranging from less than 1% (in the South) to nearly 9% (in the North), with a maximum in the Brittany region.</p> <div class="credits"> <p class="dwt_author">Michelangeli, P.-A.; Vrac, M.; Loukos, H.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-06-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">79</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..1511666H"> <span id="translatedtitle">Addressing deterministic and stochastic variance in statistical <span class="hlt">downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary"><span class="hlt">Downscaling</span> seeks to add appropriate temporal and spatial variance to low resolution GCM predictor fields. In doing so, there is a deterministic component that is conditioned by the GCM, and a residual component which may be considered as undetermined and/or stochastic variance. For application in many sectors, such as hydrology, extreme events, or multi-year drought, a <span class="hlt">downscaling</span> method that ignores aspects of the sources of variance risks providing significantly misleading results. Different statistical <span class="hlt">downscaling</span> approaches deal with this situation in different ways. Analogue pattern-perturbation approaches inherently accommodate the range of temporal and spatial variance, although are vulnerable to spatial stationarity issues. Transfer function based <span class="hlt">downscaling</span> is very good at capturing the deterministic component but may lose the high-frequency stochastic variance. Weather generator approaches are excellent at the capturing the spectrum of variance, but may require special approaches to handle the low-frequency deterministic variance, and their weather generator parameters may be particularly vulnerable to stationarity. Thus in practice, most statistical <span class="hlt">downscaling</span> methods (should) include some explicit treatment to accommodate the spectrum of variance on different time scales, and that includes both the deterministic and stochastic components. We present a method that uses the daily observed data as a sample set spanning the continuum of possibilities in an n-dimensional predictor-space. The nature of the distribution of predictand response values (the <span class="hlt">downscaling</span> target variable, e.g. precipitation) within the local domain of a position within the predictor space describes the balance between deterministic and stochastic variability. A response distribution within a local domain of the predictor-space with high variance reflects a dominance of stochastic variability within that region of the predictor space. In contrast, a response distribution with low variance reflects a dominance of deterministic variability. By explicitly using both information aspects within the predictor space a <span class="hlt">downscaled</span> response time series may be created that captures the continuum of variance on different time scales. Because the method explicitly determines the mean deterministic and the stochastic components within the predictor space, the method allows for disaggregating the balance of variance as a function of predictor state, geographic place, and time. Examples of each of these disaggregations are presented, leading to a mapping of the mean ratio between the deterministic and stochastic variance across the Africa CORDEX region. The results show that the ratio has notable spatial and temporal dependencies, and highlights regional issues of <span class="hlt">downscaling</span> robustness.</p> <div class="credits"> <p class="dwt_author">Hewitson, Bruce; Jack, Christopher; Coop, Lisa</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">80</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ClDy..tmp...90W"> <span id="translatedtitle">On regional dynamical <span class="hlt">downscaling</span> for the assessment and projection of temperature and precipitation extremes across Tasmania, Australia</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The ability of an <span class="hlt">ensemble</span> of six GCMs, <span class="hlt">downscaled</span> to a 0.1° lat/lon grid using the Conformal Cubic Atmospheric Model over Tasmania, Australia, to simulate observed extreme temperature and precipitation climatologies and statewide trends is assessed for 1961-2009 using a suite of extreme indices. The <span class="hlt">downscaled</span> simulations have high skill in reproducing extreme temperatures, with the majority of models reproducing the statewide averaged sign and magnitude of recent observed trends of increasing warm days and warm nights and decreasing frost days. The warm spell duration index is however underestimated, while variance is generally overrepresented in the extreme temperature range across most regions. The simulations show a lower level of skill in modelling the amplitude of the extreme precipitation indices such as very wet days, but simulate the observed spatial patterns and variability. In general, simulations of dry extreme precipitation indices are underestimated in dryer areas and wet extremes indices are underestimated in wetter areas. Using two SRES emissions scenarios, the simulations indicate a significant increase in warm nights compared to a slightly more moderate increase in warm days, and an increase in maximum 1- and 5-day precipitation intensities interspersed with longer consecutive dry spells across Tasmania during the twenty-first century.</p> <div class="credits"> <p class="dwt_author">White, Christopher J.; McInnes, Kathleen L.; Cechet, Robert P.; Corney, Stuart P.; Grose, Michael R.; Holz, Gregory K.; Katzfey, Jack J.; Bindoff, Nathaniel L.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-03-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" 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onClick='return showDiv("page_20");' href="#">20</a> <a onClick='return showDiv("page_21");' href="#">21</a> <a onClick='return showDiv("page_22");' href="#">22</a> <a onClick='return showDiv("page_23");' href="#">23</a> <a onClick='return showDiv("page_24");' href="#">24</a> <a onClick='return showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_6");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">81</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=479121"> <span id="translatedtitle">An Overview of <span class="hlt">Ensembl</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary"><span class="hlt">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org/) is a bioinformatics project to organize biological information around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of individual genomes, and of the synteny and orthology relationships between them. It is also a framework for integration of any biological data that can be mapped onto features derived from the genomic sequence. <span class="hlt">Ensembl</span> is available as an interactive Web site, a set of flat files, and as a complete, portable open source software system for handling genomes. All data are provided without restriction, and code is freely available. <span class="hlt">Ensembl</span>'s aims are to continue to “widen” this biological integration to include other model organisms relevant to understanding human biology as they become available; to “deepen” this integration to provide an ever more seamless linkage between equivalent components in different species; and to provide further classification of functional elements in the genome that have been previously elusive.</p> <div class="credits"> <p class="dwt_author">Birney, Ewan; Andrews, T. Daniel; Bevan, Paul; Caccamo, Mario; Chen, Yuan; Clarke, Laura; Coates, Guy; Cuff, James; Curwen, Val; Cutts, Tim; Down, Thomas; Eyras, Eduardo; Fernandez-Suarez, Xose M.; Gane, Paul; Gibbins, Brian; Gilbert, James; Hammond, Martin; Hotz, Hans-Rudolf; Iyer, Vivek; Jekosch, Kerstin; Kahari, Andreas; Kasprzyk, Arek; Keefe, Damian; Keenan, Stephen; Lehvaslaiho, Heikki; McVicker, Graham; Melsopp, Craig; Meidl, Patrick; Mongin, Emmanuel; Pettett, Roger; Potter, Simon; Proctor, Glenn; Rae, Mark; Searle, Steve; Slater, Guy; Smedley, Damian; Smith, James; Spooner, Will; Stabenau, Arne; Stalker, James; Storey, Roy; Ureta-Vidal, Abel; Woodwark, K. Cara; Cameron, Graham; Durbin, Richard; Cox, Anthony; Hubbard, Tim; Clamp, Michele</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">82</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.diku.dk/OLD/undervisning/2002e/156/Lectures/Lecture11/ensembles-of-learning-machines.pdf"> <span id="translatedtitle"><span class="hlt">Ensembles</span> of learning machines</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary"><span class="hlt">Ensembles</span> of learning machines constitute one of the main current directions in machine learning research, and have been applied\\u000a to a wide range of real problems. Despite of the absence of an unified theory on <span class="hlt">ensembles</span>, there are many theoretical reasons\\u000a for combining multiple learners, and an empirical evidence of the effectiveness of this approach. In this paper we present</p> <div class="credits"> <p class="dwt_author">Giorgio Valentini; Francesco Masulli</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">83</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2894800"> <span id="translatedtitle"><span class="hlt">Ensembl</span> variation resources</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">Background The maturing field of genomics is rapidly increasing the number of sequenced genomes and producing more information from those previously sequenced. Much of this additional information is variation data derived from sampling multiple individuals of a given species with the goal of discovering new variants and characterising the population frequencies of the variants that are already known. These data have immense value for many studies, including those designed to understand evolution and connect genotype to phenotype. Maximising the utility of the data requires that it be stored in an accessible manner that facilitates the integration of variation data with other genome resources such as gene annotation and comparative genomics. Description The <span class="hlt">Ensembl</span> project provides comprehensive and integrated variation resources for a wide variety of chordate genomes. This paper provides a detailed description of the sources of data and the methods for creating the <span class="hlt">Ensembl</span> variation databases. It also explores the utility of the information by explaining the range of query options available, from using interactive web displays, to online data mining tools and connecting directly to the data servers programmatically. It gives a good overview of the variation resources and future plans for expanding the variation data within <span class="hlt">Ensembl</span>. Conclusions Variation data is an important key to understanding the functional and phenotypic differences between individuals. The development of new sequencing and genotyping technologies is greatly increasing the amount of variation data known for almost all genomes. The <span class="hlt">Ensembl</span> variation resources are integrated into the <span class="hlt">Ensembl</span> genome browser and provide a comprehensive way to access this data in the context of a widely used genome bioinformatics system. All <span class="hlt">Ensembl</span> data is freely available at http://www.<span class="hlt">ensembl</span>.org and from the public MySQL database server at ensembldb.<span class="hlt">ensembl</span>.org.</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">84</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013AdAtS..30.1085F"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of summer temperature extremes in northern China</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Two approaches of statistical <span class="hlt">downscaling</span> were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960-2008. One was to <span class="hlt">downscale</span> daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly <span class="hlt">downscale</span> the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross-validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical <span class="hlt">downscaling</span> to Hadley Centre Coupled Model Version 3 (HadCM3) simulations under the Representative Concentration Pathways 4.5 (RCP 4.5) scenario of the Fifth Coupled Model Inter-comparison Project (CMIP5). The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5°C, and the 10th of daily minimum temperatures will increase by about 2°C during the period 2011-35 relative to 1980-99.</p> <div class="credits"> <p class="dwt_author">Fan, Lijun; Chen, Deliang; Fu, Congbin; Yan, Zhongwei</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-07-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">85</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/48895563"> <span id="translatedtitle">Stochastic <span class="hlt">downscaling</span> of precipitation: From dry events to heavy rainfalls</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary"><span class="hlt">Downscaling</span> precipitation is a difficult challenge for the climate community. We propose and study a new stochastic weather typing approach to perform such a task. In addition to providing accurate small and medium precipitation, our procedure possesses built-in features that allow us to model adequately extreme precipitation distributions. First, we propose a new distribution for local precipitation via a probability</p> <div class="credits"> <p class="dwt_author">M. Vrac; P. Naveau</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">86</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/26358400"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of hydrometeorological variables using general circulation model output</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Empirical relationships between daily hydrometeorological variables for a catchment in Nagano prefecture, Japan and three indices of regional atmospheric circulation are examined with a view to assessing their use in General Circulation Model (GCM) <span class="hlt">downscaling</span>. The indices (vorticity, flow strength and angular direction of airflow) were calculated by using daily grid-point sea-level pressure data derived from: (a) the National Centers</p> <div class="credits"> <p class="dwt_author">Robert L. Wilby; Hany Hassan; Keisuke Hanaki</p> <p class="dwt_publisher"></p> <p class="publishDate">1998-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">87</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009pcms.confE.194B"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of daily precipitation over Llobregat river basin in Catalonia (Spain) using three <span class="hlt">downscaling</span> methods.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Any long-term change in the patterns of average weather in a global or regional scale is called climate change. It may cause a progressive increase of atmospheric temperature and consequently may change the amount, frequency and intensity of precipitation. All these changes of meteorological parameters may modify the water cycle: run-off, infiltration, aquifer recharge, etc. Recent studies in Catalonia foresee changes in hydrological systems caused by climate change. This will lead to alterations in the hydrological cycle that could impact in land use, in the regimen of water extractions, in the hydrological characteristics of the territory and reduced groundwater recharge. Besides, can expect a loss of flow in rivers. In addition to possible increases in the frequency of extreme rainfall, being necessary to modify the design of infrastructure. Because this, it work focuses on studying the impacts of climate change in one of the most important basins in Catalonia, the Llobregat River Basin. The basin is the hub of the province of Barcelona. It is a highly populated and urbanized catchment, where water resources are used for different purposes, as drinking water production, agricultural irrigation, industry and hydro-electrical energy production. In consequence, many companies and communities depend on these resources. To study the impact of climate change in the Llobregat basin, storms (frequency, intensity) mainly, we will need regional climate change information. 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. <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> <div class="credits"> <p class="dwt_author">Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-09-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">88</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2006JPhA...3910891P"> <span id="translatedtitle">'Lazy' quantum <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We compare different strategies aimed to prepare an <span class="hlt">ensemble</span> with a given density matrix ?. Preparing the <span class="hlt">ensemble</span> of eigenstates of ? with appropriate probabilities can be treated as 'generous' strategy: it provides maximal accessible information about the state. Another extremity is the so-called 'Scrooge' <span class="hlt">ensemble</span>, which is mostly stingy in sharing the information. We introduce 'lazy' <span class="hlt">ensembles</span> which require minimal effort to prepare the density matrix by selecting pure states with respect to completely random choice. We consider two parties, Alice and Bob, playing a kind of game. Bob wishes to guess which pure state is prepared by Alice. His null hypothesis, based on the lack of any information about Alice's intention, is that Alice prepares any pure state with equal probability. Then, the average quantum state measured by Bob turns out to be ?, and he has to make a new hypothesis about Alice's intention solely based on the information that the observed density matrix is ?. The arising 'lazy' <span class="hlt">ensemble</span> is shown to be the alternative hypothesis which minimizes type I error.</p> <div class="credits"> <p class="dwt_author">Parfionov, George; Zapatrin, Romàn</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-08-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">89</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009EGUGA..11.9000F"> <span id="translatedtitle">Seasonal Predictability and Dynamical <span class="hlt">Downscaling</span> in Spain using ECMWF-System3 and RCA Models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The skill of state of state-of-the-art operational seasonal forecast models (ECMWF System3) in extratropical latitudes (Spain) is assessed using a simple and robust tercile-based statistical methodology, considering both temperature and precipitation forecasts; the only significant average skill is found for dry events in autumn. We also analyze ENSO-conditioned skill, considering only forecasts for El Niña/La Niña years; in this case, skillful seasonal predictions are found in partial agreement with the observed teleconnections derived from historical records, for some variables, seasons and regions, thus providing "windows of opportunity" for operational seasonal forecasts in Europe. Then, we analyze the possibility to enhance the skill of global seasonal predictions using regional climate models. To this aim, the Rossby Centre RCA model was applied to <span class="hlt">downscale</span> the one-month lead time ECMWF System3 seasonal simulations in the European Atlantic domain for the period 1981-2001. We found some preliminary evidence showing that the skill of the regional seasonal predictions is significantly higher than that from the driving global model over large areas. The consideration of the whole <span class="hlt">ensemble</span> (11 members) provided by the System3 forecast system did not overcome the regional model skill found with 5 members.</p> <div class="credits"> <p class="dwt_author">Frias, M. D.; Cofiño, A. S.; Diez, E.; Orfila, B.; Fernandez, J.; Gutierrez, J. M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">90</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.3465R"> <span id="translatedtitle">Sensitivity and dependence of mesoscale <span class="hlt">downscaled</span> prediction results on different parameterizations of convection and cloud microphysics</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Remesan, R.; Bellerby, T.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">91</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011WRR....4710502C"> <span id="translatedtitle">Stochastic <span class="hlt">downscaling</span> of precipitation with neural network conditional mixture models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We present a new class of stochastic <span class="hlt">downscaling</span> models, the conditional mixture models (CMMs), which builds on neural network models. CMMs are mixture models whose parameters are functions of predictor variables. These functions are implemented with a one-layer feed-forward neural network. By combining the approximation capabilities of mixtures and neural networks, CMMs can, in principle, represent arbitrary conditional distributions. We evaluate the CMMs at <span class="hlt">downscaling</span> precipitation data at three stations in the French Mediterranean region. A discrete (Dirac) component is included in the mixture to handle the "no-rain" events. Positive rainfall is modeled with a mixture of continuous densities, which can be either Gaussian, log-normal, or hybrid Pareto (an extension of the generalized Pareto). CMMs are stochastic weather generators in the sense that they provide a model for the conditional density of local variables given large-scale information. In this study, we did not look for the most appropriate set of predictors, and we settled for a decent set as the basis to compare the <span class="hlt">downscaling</span> models. The set of predictors includes the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalyses sea level pressure fields on a 6 × 6 grid cell region surrounding the stations plus three date variables. We compare the three distribution families of CMMs with a simpler benchmark model, which is more common in the <span class="hlt">downscaling</span> community. The difference between the benchmark model and CMMs is that positive rainfall is modeled with a single Gamma distribution. The results show that CMM with hybrid Pareto components outperforms both the CMM with Gaussian components and the benchmark model in terms of log-likelihood. However, there is no significant difference with the log-normal CMM. In general, the additional flexibility of mixture models, as opposed to using a single distribution, allows us to better represent the distribution of rainfall, both in the central part and in the upper tail.</p> <div class="credits"> <p class="dwt_author">Carreau, Julie; Vrac, Mathieu</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-10-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">92</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/51666993"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of wind fields for wind power applications</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Dynamical <span class="hlt">downscaling</span> of wind fields for wind power applications H.-T. Mengelkamp*,**, S. Huneke**, J, Geyer** *GKSS Research Center Geesthacht GmbH **anemos Gesellschaft für Umweltmeteorologie mbH Investments in wind power require information on the long-term mean wind potential and its temporal variations on daily to annual and decadal time scales. This information is rarely available at specific wind farm sites. Short-term</p> <div class="credits"> <p class="dwt_author">H.-T. Mengelkamp; S. Huneke; J. Geyer</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">93</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012GMD.....5.1177C"> <span id="translatedtitle"><span class="hlt">Downscaling</span> the climate change for oceans around Australia</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">At present, global climate models used to project changes in climate poorly resolve mesoscale ocean features such as boundary currents and eddies. These missing features may be important to realistically project the marine impacts of climate change. Here we present a framework for dynamically <span class="hlt">downscaling</span> coarse climate change projections utilising a near-global ocean model that resolves these features in the Australasian region, with coarser resolution elsewhere. A time-slice projection for a 2060s ocean was obtained by adding climate change anomalies to initial conditions and surface fluxes of a near-global eddy-resolving ocean model. Climate change anomalies are derived from the differences between present and projected climates from a coarse global climate model. These anomalies are added to observed fields, thereby reducing the effect of model bias from the climate model. The <span class="hlt">downscaling</span> model used here is ocean-only and does not include the effects that changes in the ocean state will have on the atmosphere and air-sea fluxes. We use restoring of the sea surface temperature and salinity to approximate real-ocean feedback on heat flux and to keep the salinity stable. Extra experiments with different feedback parameterisations are run to test the sensitivity of the projection. Consistent spatial differences emerge in sea surface temperature, salinity, stratification and transport between the <span class="hlt">downscaled</span> projections and those of the climate model. Also, the spatial differences become established rapidly (< 3 yr), indicating the importance of mesoscale resolution. However, the differences in the magnitude of the difference between experiments show that feedback of the ocean onto the air-sea fluxes is still important in determining the state of the ocean in these projections. Until such a time when it is feasible to regularly run a global climate model with eddy resolution, our framework for ocean climate change <span class="hlt">downscaling</span> provides an attractive way to explore the response of mesoscale ocean features with climate change and their effect on the broader ocean.</p> <div class="credits"> <p class="dwt_author">Chamberlain, M. A.; Sun, C.; Matear, R. J.; Feng, M.; Phipps, S. J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-09-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">94</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.springerlink.com/index/d8h28096371231r1.pdf"> <span id="translatedtitle">Geostatistical <span class="hlt">downscaling</span> of fracture surface topography accounting for local roughness</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This study proposes a new geostatistical methodology that accounts for roughness characteristics when <span class="hlt">downscaling</span> fracture\\u000a surface topography. In the proposed approach, the small-scale fracture surface roughness is described using a “local roughness\\u000a pattern” that indicates the relative height of a location compared to its surrounding locations, while the large-scale roughness\\u000a is considered using the surface semivariogram. By accounting for both</p> <div class="credits"> <p class="dwt_author">Hirotaka Saito; Giovanni Grasselli</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">95</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/55445313"> <span id="translatedtitle">Possible Impacts of Climate Change on Wind Gust under <span class="hlt">Downscaled</span> Future Climate Conditions over Ontario, Canada</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The overarching purpose of this study was to project changes in the occurrence frequency and magnitude of future wind gust events under <span class="hlt">downscaled</span> future climate conditions over Ontario, Canada. Wind gust factors were employed to simulate hourly\\/daily wind gust based on hourly\\/daily wind speed. Regression-based <span class="hlt">downscaling</span> methods were used to <span class="hlt">downscale</span> future hourly\\/daily wind speed to each of the 14</p> <div class="credits"> <p class="dwt_author">Chad Shouquan Cheng; Guilong Li</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">96</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2004IJCli..24..161T"> <span id="translatedtitle">A statistical <span class="hlt">downscaling</span> method for monthly total precipitation over Turkey</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Researchers are aware of certain types of problems that arise when modelling interconnections between general circulation and regional processes, such as prediction of regional, local-scale climate variables from large-scale processes, e.g. by means of general circulation model (GCM) outputs. The problem solution is called <span class="hlt">downscaling</span>. In this paper, a statistical <span class="hlt">downscaling</span> approach to monthly total precipitation over Turkey, which is an integral part of system identification for analysis of local-scale climate variables, is investigated. Based on perfect prognosis, a new computationally effective working method is introduced by the proper predictors selected from the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis data sets, which are simulated as perfectly as possible by GCMs during the period of 1961-98. The Sampson correlation ratio is used to determine the relationships between the monthly total precipitation series and the set of large-scale processes (namely 500 hPa geopotential heights, 700 hPa geopotential heights, sea-level pressures, 500 hPa vertical pressure velocities and 500-1000 hPa geopotential thicknesses). In the study, statistical preprocessing is implemented by independent component analysis rather than principal component analysis or principal factor analysis. The proposed <span class="hlt">downscaling</span> method originates from a recurrent neural network model of Jordan that uses not only large-scale predictors, but also the previous states of the relevant local-scale variables. Finally, some possible improvements and suggestions for further study are mentioned.</p> <div class="credits"> <p class="dwt_author">Tatli, Hasan; Nüzhet Dalfes, H.; Mente, Sibel</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-02-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">97</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.5852M"> <span id="translatedtitle">Utility of Coarse and <span class="hlt">Downscaled</span> Soil Moisture Observations at C- and L-Band in Hydrological Simulations</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Current microwave soil moisture (?) products, including AMSR-E, are based on sensors operating at C-band. Retrieval performance at this frequency degrades as vegetation increases. New satellite missions specifically dedicated to ? sensing, including SMOS and SMAP, are expected to produce more accurate estimates, as they utilize L-band sensors that are less sensitive to vegetation. Assessing the enhancement of L-band ? products in terms of their utility for hydrologic forecasting is thus important to support new spaceborne missions. In this study, we pursue this objective by using ? data from the SMEX04 experiment in Sonora (Mexico), including: L- and C-band data from airborne sensors in a 75 x 50 km2 area (800-m resolution) and ground data from an elevation transect in the Sierra Los Locos (SLL) basin. We first calibrate a multifractal <span class="hlt">downscaling</span> model in two frameworks mimicking disaggregation of: (1) AMSR-E (from 25.6 to 0.8 km), and (2) SMAP (from 12.8 to 0.8 km) products using C- and L-band aircraft ? data, respectively. We show that, due to the higher accuracy of the L-band sensor, the <span class="hlt">ensemble</span> of ? fields disaggregated in the SMAP framework is able to reproduce, with significant improvement, the ? variability (a) within the satellite footprint; (b) at basin scale, and (c) along the transect. The utility of C- and L-band ? products for hydrological simulations is then tested through simple data assimilation experiments using a distributed model focused on the SLL basin. Results reveal that the model prognostic capability is considerably enhanced when L-band ? fields are assimilated. The advantages of ingesting an <span class="hlt">ensemble</span> of <span class="hlt">downscaled</span> ? data consist of: (i) the capability for the model to simulate soil moisture in distributed fashion, which is prevented by assimilating the single coarse satellite estimate; and (ii) the possibility to produce an <span class="hlt">ensemble</span> of hydrological simulations accounting for predictive uncertainty. This study yields insights into the added value of new satellite missions based on L-band sensors.</p> <div class="credits"> <p class="dwt_author">Mascaro, G.; Vivoni, E. R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">98</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ClDy..tmp..358R"> <span id="translatedtitle">Performance assessment of three convective parameterization schemes in WRF for <span class="hlt">downscaling</span> summer rainfall over South Africa</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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 (ENSO) and subtropical dipole modes. The correlations between the simulated area averaged rainfalls over South Africa and Nino3.4 index were -0.66, -0.69 and -0.49 with KF, BMJ and GDE scheme respectively as compared to the observed correlation of -0.57. The model could reproduce the observed ENSO-South Africa rainfall relationship and could successfully simulate three wet (dry) years that are associated with La Niña (El Niño) and the BMJ scheme is closest to the observed variability. Also, the model showed good skill in simulating the excess rainfall over South Africa that is associated with positive subtropical Indian Ocean Dipole for the DJF season 2005/2006.</p> <div class="credits"> <p class="dwt_author">Ratna, Satyaban B.; Ratnam, J. V.; Behera, S. K.; Rautenbach, C. J. deW.; Ndarana, T.; Takahashi, K.; Yamagata, T.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-08-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">99</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMGC51I0847T"> <span id="translatedtitle">Developing Regionally <span class="hlt">Downscaled</span> Probabilistic Climate Change Projections for the Southeast Regional Assessment Project</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The Southeast US contains the highest levels of biodiversity in North America outside of the tropics. This is partly due to the climate over the last few millennia, characterized by abundant precipitation, mild temperatures, and low climatic variability. Recently, the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) concluded that it is very likely that humans are largely responsible for increasing the global average surface temperature by 1.0 oC in the 20th century through the release of greenhouse gasses (GHG) such as CO2 into the atmosphere. This warming is expected to continue well into the future and is projected to cause sizeable impacts on managed and unmanaged ecosystems. Thus, mitigation of, and adaptation to the impacts of climate change on ecosystems in the Southeast will likely be the key challenge confronting natural resource managers in the coming decades. Central to this is how to best implement an adaptive management strategy given the large uncertainty associated with climate change projections. This requires a careful treatment of this uncertainty as well as methods to <span class="hlt">downscale</span> climate projections to the scale of ecosystem processes because of the coarse spatial resolution of the models. To date, most studies use the range of GCM output to represent the full range of projection uncertainty; thus increasing the risk of underestimating structural and parametric uncertainty associated with these projections. This underestimation will then propagate through associated integrated assessments that use climate change projections, leading to overconfident predictions. As a result, decision-makers may insufficiently hedge against the risks associated with extreme climatic events that have a low probability of occurrence, but are high impact events. We address this by developing a suite of regional probabilistic climate change projections for the Southeast Regional Assessment Project (SERAP). Two core climatic datasets are used for base projections: (1) GCM simulations from the IPCC AR4 for fully coupled global-scale climate simulations; and (2) an Earth Model of Intermediate Complexity (EMIC) to sample the parametric uncertainty of key climate system variables such as ocean diffusivity. These datasets are further post-processed through: (1) Bayesian <span class="hlt">ensemble</span> dressing methods to estimate structural uncertainty and the accuracy of the GCMs; and (2) statistically <span class="hlt">downscaled</span> simulations forced by boundary conditions from the GCM and EMIC runs. The probabilistic projections generated through these methods form the basis for projecting ecosystem changes in the Southeast over the next century.</p> <div class="credits"> <p class="dwt_author">Terando, A. J.; Bhat, S.; Haran, M.; Hayhoe, K.; Keller, K.; Tonkonojenkov, R.; Urban, N.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">100</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..15.6035T"> <span id="translatedtitle">Large-Scale Weather Generator for <span class="hlt">Downscaling</span> Precipitation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Well parametrized distributed precipitation-runoff models are able to correctly quantify hydrological state variables (e.g. streamflow, soil moisture, among others) for the past decades. In order to estimate future risks associated with hydrometeorological extremes, it is necessary to incorporate information about the future weather and climate. A common approach is to <span class="hlt">downscale</span> Regional Climate Model (RCM) projections. Therefore, various statistical <span class="hlt">downscaling</span> schemes, utilizing diverse mathematical methods, have been developed. One kind of statistical <span class="hlt">downscaling</span> technique is the so called Weather Generator (WG). These algorithms provide meteorological time series as the realization of a stochastic process. First, single- and multi-site models were developed. Recently, however WG at sub-daily scales and on gridded spatial resolution have captured the interest because of the new development in distributed hydrological modelling. A standard approach for a multi-site WG is to sample a multivariate normal process for all locations. Doing so, it is necessary to calculate the Cholesky factor of the cross-covariance matrix to guarantee a spatially consistent sampling. In general, gridded WGs are an extension of multi-site WGs to larger domains (i.e. >10000 grid cells). On these large grids, it is not possible to accurately determine the Cholesky factor and further enhancements are required. In this work, a framework for a WG is proposed, which provides meteorological time-series on a large scale grid, e.g. 4 km grid of Germany. It employs a sequential Gaussian simulation method, conditioning the value of a grid cell only on a neighborhood, not on the whole field. This methodology is incorporated into a multi-scale <span class="hlt">downscaling</span> scheme, which is able to provide precipitation data sets at different spatial and temporal resolutions, ranging from 4 km to 32 km, and from days to months, respectively. This framework uses a copula approach for spatial <span class="hlt">downscaling</span>, exploiting the strong dependence between different spatial scales, and a multiplicative cascade approach for the temporal disaggregation. This study incorporates a gridded, daily data set for the domain of Germany at a 4 km resolution. The data set was interpolated by external drift kriging of station data from the German Weather Service (DWD) and spans over the time period from 1961 to 2000. The data set was aggregated to the different spatio-temporal scales resolutions investigated in this study. The proposed methodology provides precipitation time series at the resolution and grid sizes required by large hydrological application (at national level). First results indicate that the framework is able to consistently preserve precipitation statistics including variability at multiple spatio-temporal resolutions. Nevertheless, it has to be investigated, whether rainfall extremes are correctly represented.</p> <div class="credits"> <p class="dwt_author">Thober, Stephan; Samaniego, Luis; Bardossy, Andras</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_4");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return 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showDiv("page_7");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">101</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/48276300"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Color Image Segmentation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">\\u000a The subject of this chapter is <span class="hlt">ensemble</span> color image segmentation. This is an image fusion application in which combine several\\u000a simple image segmentation algorithms to obtain a state-of-the-art image segmentation algorithm. The goal of image segmentation\\u000a is to decompose the input image into a set of meaningful or spatially coherent regions sharing similar attributes. The algorithm\\u000a is often a crucial</p> <div class="credits"> <p class="dwt_author">H. B. Mitchell</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">102</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009AGUFM.U13B0061H"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of Minimum Surface Temperature in the Semi-arid Great Basin Region and Implications for Bio-geophysical Processes</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">This study addresses <span class="hlt">downscaling</span> methodology for monthly surface air temperature from global climate model (GCM) horizontal grid resolutions (> 100 km) to regional scales (< 10 km) appropriate for climate impact studies. Preliminary hindcast analysis for the period 1950-2008 indicated that the minimum temperatures extracted from the GCMs at 46 individual stations in Nevada show correct seasonal trends, but the monthly mean minima are significantly underestimated compared to three observational networks (Western Regional Climate Center (WRCC), DRI), National Climate Data Center (NCDC), and Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate data sets. The daily mean surface air temperature, from the three GCMs (NCAR-CCSM3, ECHAM5, and CSIRO-Mk3.5) and a regional climate model (RCM) using the Weather Research and Forecasting (WRF) model forced by the CCSM3 outputs, is generally under-predicted with root-mean-square errors as large as 6 K on an annual scale. The underlying premise of this study is that changes in minimum temperature are manifested on the landscape via changes in hydrological parameters viz., runoff timing and evapotranspiration rates, ecological parameters viz., rates of invasion of exotic species and fire hazards, and socio-economic parameters viz., urban energy use. The systematic error or bias in surface minimum temperature simulated by the GCMs and their <span class="hlt">ensembles</span> under designated Intergovernmental Panel on Climate Change (IPCC) climate change scenarios (A1B, A2, and B1) is investigated to assess and substantiate this argument. The present study employs the <span class="hlt">downscaling</span> technique of bias correction and spatial disaggregation (BCSD) to improve GCM representation of monthly minimum temperature characteristics at local and regional scales which are critical to properly quantify for ecologic, hydrologic, and socio-economic forecasting under future climate change scenarios.</p> <div class="credits"> <p class="dwt_author">Hatchett, B. J.; Vellore, R.; Koracin, D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">103</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.dpri.kyoto-u.ac.jp/dat/nenpo/no47/47b0/a47b0t21.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Spatial Rainfall Field from Global Scale to Local Scale Using Improved Multiplicative Random Cascade Method</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Synopsis Non-homogenous multiplicative random cascade method <span class="hlt">downscales</span> spatial rainfall field from a coarse scale into a finer one. Currently, this kind of <span class="hlt">downscaling</span> is less reliable even though it correctly produces a long term average spatial pattern. It fails reproducing the patterns in repeated trials; and there is a higher chance of magnitude fluctuation. These drawbacks are needed to overcome.</p> <div class="credits"> <p class="dwt_author">Roshan K. SHRESTHA; Yasuto TACHIKAWA; Kaoru TAKARA</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">104</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/991999"> <span id="translatedtitle"><span class="hlt">Downscaling</span> socioeconomic and emissions scenarios for global environmental change research:a review</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">Abstract: Global change research encompasses global to local scale analysis. Impacts analysis in particular often requires spatial <span class="hlt">downscaling</span>, whereby socio-economic and emissions variables specified at relatively large spatial scales are translated to values at a country or grid level. The methods used for spatial <span class="hlt">downscaling</span> are reviewed, classified, and current applications discussed.</p> <div class="credits"> <p class="dwt_author">Van Vuuren, Detlet; Smith, Steven J.; Riahi, Keywan</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">105</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ufa.cas.cz/html/climaero/kysely/2001_jc_Huth_Kysely_Dubrovsky.pdf"> <span id="translatedtitle">Time Structure of Observed, GCM-Simulated, <span class="hlt">Downscaled</span>, and Stochastically Generated Daily Temperature Series</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The time structure of simulated daily maximum and minimum temperature series, produced by several different methods, is compared with observations at six stations in central Europe. The methods are statistical <span class="hlt">downscaling</span>, stochastic weather generator, and general circulation models (GCMs). Outputs from control runs of two GCMs are examined: ECHAM3 and CCCM2. Four time series are constructed by statistical <span class="hlt">downscaling</span> using</p> <div class="credits"> <p class="dwt_author">Radan Huth; Jan Kyselý; Martin Dubrovský</p> <p class="dwt_publisher"></p> <p class="publishDate">2001-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">106</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/48949858"> <span id="translatedtitle">SVM-PGSL coupled approach for statistical <span class="hlt">downscaling</span> to predict rainfall from GCM output</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Hydrological impacts of climate change are assessed by <span class="hlt">downscaling</span> the General Circulation Model (GCM) outputs of predictor variables to local or regional scale hydrologic variables (predictand). Support Vector Machine (SVM) is a machine learning technique which is capable of capturing highly nonlinear relationship between predictor and predictand and thus performs better than conventional linear regression in transfer function-based <span class="hlt">downscaling</span> modeling.</p> <div class="credits"> <p class="dwt_author">Subimal Ghosh</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">107</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.7362B"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of rainfall in Peru using Generalised Linear Models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">108</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.2934W"> <span id="translatedtitle">Physical Consistency of Multi-Parameter Error-Correction and <span class="hlt">Downscaling</span> of Regional Climate Models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Regional climate models (RCMs) proofed to have skill in simulating past and present climate, but they still feature systematic errors and often lack the quality to be used directly as input for climate change impact studies. Statistical post-processing methods, like Quantile Mapping (QM) are one way to tailor RCM output for impact research. QM adapts modeled time series by adjusting the modeled to the observed empirical cumulative frequency distributions. Thus, additionally to the error correction, QM <span class="hlt">downscales</span> the RCM simulation to the point-scale. In this study we show the applicability of QM to relative humidity, global radiation, wind speed and surface air pressure on the daily scale, as QM's applicability for daily temperature and precipitation has already been shown in previous studies. RCM data were taken from the <span class="hlt">ENSEMBLES</span> data-set. The error-correction is performed for study regions within Austria and Switzerland, defined within the Austrian climate research fund project "CC-Snow" and the EU FP 7 project "Assessing Climate Impacts on the Quantity and Quality of Water" (ACQWA) based on observational station records. A controversial topic is the physical consistency of the error-corrected meteorological parameters, as each parameter is treated separately by QM. We investigate the correlation of the above mentioned parameters before and after the correction process and compare it with observed corellations. The hypothesis is that we conserve the state of physical consistency of RCMs on average. The correlation is evaluated for the control run (1971 - 2010) and scenario run (2011 - 2050), keeping in mind the applicability on impact studies on future climate.</p> <div class="credits"> <p class="dwt_author">Wilcke, R. A. I.; Mendlik, T. M.; Gobiet, A. G.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">109</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.computingportal.org/"> <span id="translatedtitle"><span class="hlt">Ensemble</span>: Computing Pathway</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://nsdl.org/nsdl_dds/services/ddsws1-1/service_explorer.jsp">NSDL National Science Digital Library</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> is a NSDL Pathways project working to establish a national, distributed digital library for computing education. The project is building a distributed portal providing access to a broad range of existing educational resources for computing while preserving the collections and their associated curation processes. The developers want to encourage contribution, use, reuse, review and evaluation of educational materials at multiple levels of granularity and seek to support the full range of computing education communities including computer science, computer engineering, software engineering, information science, information systems and information technology as well as other areas often called computing + X, or X informatics.</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate">2011-01-05</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">110</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009ems..confE.387D"> <span id="translatedtitle">Moroccan precipitation in a regional climate change simulation, evaluating a statistical <span class="hlt">downscaling</span> approach</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Morocco is located at the extreme north-west of Africa between 20 and 37° N. According to the aridity index of De Martonne classification, Moroccan climate varies from sub-humid in the north to arid in the south. The country has experienced several drought events which have had marked impacts on socio-economic sectors and national economy (1940-1945, 1980-1985, 1994-1995 …). During a dry year, the deficit of rainfall can exceed 60% of the climatological value. Rainfall amounts registered show a negative trend at national and regional scales. The drought seems to become more persistent over time. At the same time, the total number of wet days shows a negative trend revealing an increase in the annual dry day number. Many regions became more arid (According to the aridity index of De Martonne) between 1961 and 2008: namely Oujda, Taza, Kenitra, Rabat, Meknès. In order to evaluate climate change impacts on Moroccan winter precipitation, future climate conditions in Morocco under the SRES scenario A1B, are studied by using two 30-year time-slice simulations performed by the variable resolution configuration of the GCM ARPEGE-Climat. The spatial resolution ranges between 50 and 60 km over the country. This high resolution scenarios exhibit for the period 2021-2050 a change in the precipitation distribution and in extreme events. In particular, the precipitation amounts and the occurrence frequency of wet days will decrease in the scenario on all the fourteen stations considered. In terms of extreme events, the maximum dry spell length increases in nearly all the stations and the frequency of high precipitation events is projected to decrease. The evolution of highest percentiles of precipitation distribution does not go, however, in the same sense everywhere. Assessment of a range of uncertainties due to climate modelling has been done by using present-day and SRES scenario A1B data issued from 10 <span class="hlt">ENSEMBLES</span>-RCMs. This shows that ARPEGE-Climate results are in the 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> <div class="credits"> <p class="dwt_author">Driouech, F.; Déqué, M.; Sánchez-Gómez, E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">111</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMGC51A0729P"> <span id="translatedtitle">New Daily <span class="hlt">Downscaled</span> Information at the "Bias-Corrected <span class="hlt">Downscaled</span> WCRP CMIP3 Climate Projections" online archive</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Recent efforts have generated a new empirical <span class="hlt">downscaling</span> technique that is well-positioned to inform climate change vulnerability assessments for ecosystems as well as studies on future storm and flood frequency. The technique combines bias-correction (BC) of general circulation model (GCM) outputs with a constructed analogs approach (CA) for spatially <span class="hlt">downscale</span> the daily solutions from GCM simulations. These combined steps are referred to as BCCA. A recent methods intercomparison (Maurer et al. 2010, HESS, 14:1125-1139) shows that BCCA outperforms CA and the archive's current underlying methodology (BCSD, Wood et al. 2002) when applied to NCEP/NCAR Reanalysis. Given how BCCA is designed to translate daily sequences from GCM simulations, it offers the opportunity to provide <span class="hlt">downscaled</span> projection information on diurnal temperature range (relevant to ecohydrological investigations) and interarrival frequencies of daily to multi-day precipitation events. The information on diurnal temperature range also has significance to watershed hydrologic studies in arid to semi-arid regions, where evapotranspiration (ET) is the dominant fate of precipitation and simulation of ET processes is sensitive to diurnal temperature range. Recognizing these benefits, archive collaborators initiated an effort to develop a daily BCCA CMIP3 data archive that complements the archive's existing monthly BCSD CMIP3 dataset. The two datasets' have the following attributes: -- Space: BCSD coverage = NLDAS domain), resolution = 1/8°; BCCA has same attributes -- Time: BCSD period = GCM-simulated 1950-2099, BCCA has three nested periods based on common availability of daily GCM outputs at PCMDI (1961-2000, 2045-2064, and 2080-2099) -- Variables: BCSD has been performed for monthly mean temperature and precipitation; BCCA has been performed for daily minimum and maximum temperature and precipitation. Presentation highlights BCCA implementation for archive expansions, illustrates key differences in BCCA and BCSD data products and highlights the archive collaborators' future hydrologic assessment plans informed by the BCCA products.</p> <div class="credits"> <p class="dwt_author">Pruitt, T.; Thrasher, B.; Das, T.; Maurer, E. P.; Duffy, P.; Long, J.; Brekke, L. D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">112</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2004GeoRL..3119101B"> <span id="translatedtitle">Impact of nesting strategies in dynamical <span class="hlt">downscaling</span> of reanalysis data</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Coarse-grid global numerical weather simulations or analysis data have to be <span class="hlt">downscaled</span>, e.g., with nested limited-area models (LAMs), for regional interpretation. Here, the impact of different one-way nesting strategies on precipitation simulations over the European Alps with the LAM ALADIN is studied. The LAM is forced by initial and lateral boundary data derived from ERA40 reanalyses with 120 km horizontal gridspacing and 6 h update interval. The nesting strategies considered include relaxation-based techniques with direct nesting of the high-resolution LAM (horizontal gridspacing ?x = 12 km; domain size 2800 × 2500 km2) or double nesting with an intermediate-resolution nest (?x = 50 km). Additionally, the impact of a spectral initialization technique is investigated. Results indicate that the considered nesting strategies are comparably successful in terms of precipitation simulation, despite the large resolution jump (120 to 12 km) involved. Thus, the cheapest method in terms of computational resources, i.e., direct nesting, seems to be the most adequate for dynamical <span class="hlt">downscaling</span> of reanalysis data over complex terrain.</p> <div class="credits"> <p class="dwt_author">Beck, A.; Ahrens, B.; Stadlbacher, K.</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-10-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">113</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/49934672"> <span id="translatedtitle">Switch<span class="hlt">Ensemble</span> [music program</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Switch<span class="hlt">Ensemble</span> is a music performance program written for the Apple IIGS. It provides a large variety of age-appropriate activities for students with a broad range of physical and cognitive abilities. Switch<span class="hlt">Ensemble</span> makes full use of the exceptional graphics and sound capabilities of the IIGS and it supports a wide variety of input devices</p> <div class="credits"> <p class="dwt_author">J. Adams</p> <p class="dwt_publisher"></p> <p class="publishDate">1992-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">114</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/750076"> <span id="translatedtitle">The <span class="hlt">Ensembl</span> genome database project</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The <span class="hlt">Ensembl</span> (http:\\/\\/www.<span class="hlt">ensembl</span>.org\\/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also</p> <div class="credits"> <p class="dwt_author">Tim J. P. Hubbard; Daniel Barker; Ewan Birney; Graham Cameron; Yuan Chen; Laura Clarke; Tony Cox; James A. Cuff; Val Curwen; Thomas Down; Richard Durbin; Eduardo Eyras; James Gilbert; Martin Hammond; Lukasz Huminiecki; Arek Kasprzyk; Heikki Lehväslaiho; Philip Lijnzaad; Craig Melsopp; Emmanuel Mongin; Roger Pettett; Matthew R. Pocock; Simon C. Potter; Alastair Rust; Esther Schmidt; Stephen M. J. Searle; Guy Slater; James Smith; William Spooner; Arne Stabenau; Jim Stalker; Elia Stupka; Abel Ureta-vidal; Imre Vastrik; Michele E. Clamp</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">115</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013CG.....55...44M"> <span id="translatedtitle">Resampling the <span class="hlt">ensemble</span> Kalman filter</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> Kalman filters (EnKF) based on a small <span class="hlt">ensemble</span> tend to provide collapse of the <span class="hlt">ensemble</span> over time. It is demonstrated that this collapse is caused by positive coupling of the <span class="hlt">ensemble</span> members due to use of the estimated Kalman gain for the update of all <span class="hlt">ensemble</span> members at each time step. This coupling can be avoided by resampling the Kalman gain from its sampling distribution in the conditioning step. In the analytically tractable Gauss-linear model finite sample distributions for all covariance matrix estimates involved in the Kalman gain estimate are known and hence exact Kalman gain resampling can be done. For the general nonlinear case we introduce the resampling <span class="hlt">ensemble</span> Kalman filter (ResEnKF) algorithm. The resampling strategy in the algorithm is based on bootstrapping of the <span class="hlt">ensemble</span> and Monte Carlo simulation of the likelihood model. We also define a semi-parametric and parametric version of the resampling <span class="hlt">ensemble</span> Kalman filter algorithm. An empirical study demonstrates that ResEnKF provides more reliable prediction intervals than traditional EnKF, on the cost of somewhat less accuracy in the point predictions.</p> <div class="credits"> <p class="dwt_author">Myrseth, Inge; Sætrom, Jon; Omre, Henning</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-06-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">116</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2599852"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Modeling of Metabolic Networks</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">Complete modeling of metabolic networks is desirable, but it is difficult to accomplish because of the lack of kinetics. As a step toward this goal, we have developed an approach to build an <span class="hlt">ensemble</span> of dynamic models that reach the same steady state. The models in the <span class="hlt">ensemble</span> are based on the same mechanistic framework at the elementary reaction level, including known regulations, and span the space of all kinetics allowable by thermodynamics. This <span class="hlt">ensemble</span> allows for the examination of possible phenotypes of the network upon perturbations, such as changes in enzyme expression levels. The size of the <span class="hlt">ensemble</span> is reduced by acquiring data for such perturbation phenotypes. If the mechanistic framework is approximately accurate, the <span class="hlt">ensemble</span> converges to a smaller set of models and becomes more predictive. This approach bypasses the need for detailed characterization of kinetic parameters and arrives at a set of models that describes relevant phenotypes upon enzyme perturbations.</p> <div class="credits"> <p class="dwt_author">Tran, Linh M.; Rizk, Matthew L.; Liao, James C.</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">117</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013JSP...153...10H"> <span id="translatedtitle">The Polyanalytic Ginibre <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">For integers n, q=1,2,3,… , let Pol n, q denote the -linear space of polynomials in z and , of degree ? n-1 in z and of degree ? q-1 in . We supply Pol n, q with the inner product structure of the resulting Hilbert space is denoted by Pol m, n, q . Here, it is assumed that m is a positive real. We let K m, n, q denote the reproducing kernel of Pol m, n, q , and study the associated determinantal process, in the limit as m, n?+? while n= m+O(1); the number q, the degree of polyanalyticity, is kept fixed. We call these processes polyanalytic Ginibre <span class="hlt">ensembles</span>, because they generalize the Ginibre <span class="hlt">ensemble</span>—the eigenvalue process of random (normal) matrices with Gaussian weight. There is a physical interpretation in terms of a system of free fermions in a uniform magnetic field so that a fixed number of the first Landau levels have been filled. We consider local blow-ups of the polyanalytic Ginibre <span class="hlt">ensembles</span> around points in the spectral droplet, which is here the closed unit disk . We obtain asymptotics for the blow-up process, using a blow-up to characteristic distance m -1/2; the typical distance is the same both for interior and for boundary points of . This amounts to obtaining the asymptotical behavior of the generating kernel K m, n, q . Following (Ameur et al. in Commun. Pure Appl. Math. 63(12):1533-1584, 2010), the asymptotics of the K m, n, q are rather conveniently expressed in terms of the Berezin measure (and density) [Equation not available: see fulltext.] For interior points | z|<1, we obtain that in the weak-star sense, where ? z denotes the unit point mass at z. Moreover, if we blow up to the scale of m -1/2 around z, we get convergence to a measure which is Gaussian for q=1, but exhibits more complicated Fresnel zone behavior for q>1. In contrast, for exterior points | z|>1, we have instead that , where is the harmonic measure at z with respect to the exterior disk . For boundary points, | z|=1, the Berezin measure converges to the unit point mass at z, as with interior points, but the blow-up to the scale m -1/2 exhibits quite different behavior at boundary points compared with interior points. We also obtain the asymptotic boundary behavior of the 1-point function at the coarser local scale q 1/2 m -1/2.</p> <div class="credits"> <p class="dwt_author">Haimi, Antti; Hedenmalm, Haakan</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-10-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">118</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009AGUFM.A33A0225S"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of climate parameters using Active Learning Method (ALM)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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 global and regional scales. These models are verified using checking data and then considering ECHO/REMO models and on the basis of last 1000 years of ECHO, the REMO time series as well as the local data are simulated. These simulated data are used as input-data for the runoff model ARCEGMO.</p> <div class="credits"> <p class="dwt_author">Sodoudi, S.; Reimer, E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">119</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.2774V"> <span id="translatedtitle">A copula-based approach for <span class="hlt">downscaling</span> rainfall fields</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Proper precipitation forcing is paramount to hydrological modelling. However, the available observations often do not meet the scale requirements of the model. To mitigate this, many authors have attempted to use <span class="hlt">downscaling</span> techniques. These techniques often assume that the field at a scale smaller than the observed pixels follows a scaled distribution of the coarse scale field. If this assumption is incorrect, the estimated reliability of the model output is likely to be wrong as well. In this presentation we show that although this assumption is correct if the entire field is considered, it is not valid for the distribution of the rainfall field within a single pixel. It is found that the scaling behaviour is a function of the intensity of the rainfall field. In order to model the subpixel variability, a copula-based methodology was developed which outperforms the classical approaches.</p> <div class="credits"> <p class="dwt_author">van den Berg, Martinus; Vandenberghe, Sander; de Baets, Bernard; Verhoest, Niko</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">120</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..1411047F"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> inter-comparison for high resolution climate reconstruction</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In the scope of the project: "High-resolution Rainfall EroSivity analysis and fORecasTing - RESORT", an evaluation of various methods of dynamic <span class="hlt">downscaling</span> is presented. The methods evaluated range from the classic method of nesting a regional model results in a global model, in this case the ECMWF reanalysis, to more recently proposed methods, which consist in using Newtonian relaxation methods in order to nudge the results of the regional model to the reanalysis. The method with better results involves using a system of variational data assimilation to incorporate observational data with results from the regional model. The climatology of a simulation of 5 years using this method is tested against observations on mainland Portugal and the ocean in the area of the Portuguese Continental Shelf, which shows that the method developed is suitable for the reconstruction of high resolution climate over continental Portugal.</p> <div class="credits"> <p class="dwt_author">Ferreira, J.; Rocha, A.; Castanheira, J. M.; Carvalho, A. C.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_5");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' 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onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">121</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012JSCHE..67.I355F"> <span id="translatedtitle">PHYSICAL-BASED <span class="hlt">DOWNSCALING</span> INCLUDING CHARACTERISTICS OF URBAN WEATHER</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">CReSiBUC consists of CReSS and SiBUC. This model is able to consider land surface conditions in detail. Especially, the characteristics of urban conditions such as artificial heat and geometry of building height can be considered. In this study, the effect of the physical-based <span class="hlt">downscaling</span> is investigated by using CReSiBUC. Simulations are carried out around Tokyo Metropolitan Area during 5 summer seasons (from 2003 to 2007). Temperatures at 3 a.m. and Temperatures at 3 p.m. are investigated. It is found that outputs of CReSiBUC are more accurate than temperatures of MANAL. This result suggests the importance of considering urban conditions in detail.</p> <div class="credits"> <p class="dwt_author">Fujii, Takahiro; Tanaka, Kenji; Souma, Kazuyoshi; Kojiiri, Toshiharu</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">122</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ClDy...40.1141B"> <span id="translatedtitle"><span class="hlt">Downscaling</span> large-scale climate variability using a regional climate model: the case of ENSO over Southern Africa</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">This study documents methodological issues arising when <span class="hlt">downscaling</span> modes of large-scale atmospheric variability with a regional climate model, over a remote region that is yet under their influence. The retained case study is El Niño Southern Oscillation and its impacts on Southern Africa and the South West Indian Ocean. Regional simulations are performed with WRF model, driven laterally by ERA40 reanalyses over the 1971-1998 period. We document the sensitivity of simulated climate variability to the model physics, the constraint of relaxing the model solutions towards reanalyses, the size of the relaxation buffer zone towards the lateral forcings and the forcing fields through ERA-Interim driven simulations. The model's internal variability is quantified using 15-member <span class="hlt">ensemble</span> simulations for seasons of interest, single 30-year integrations appearing as inappropriate to investigate the simulated interannual variability properly. The incidence of SST prescription is also assessed through additional integrations using a simple ocean mixed-layer model. Results show a limited skill of the model to reproduce the seasonal droughts associated with El Niño conditions. The model deficiencies are found to result from biased atmospheric forcings and/or biased response to these forcings, whatever the physical package retained. In contrast, regional SST forcing over adjacent oceans favor realistic rainfall anomalies over the continent, although their amplitude remains too weak. These results confirm the significant contribution of nearby ocean SST to the regional effects of ENSO, but also illustrate that regionalizing large-scale climate variability can be a demanding exercise.</p> <div class="credits"> <p class="dwt_author">Boulard, Damien; Pohl, Benjamin; Crétat, Julien; Vigaud, Nicolas; Pham-Xuan, Thanh</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-03-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">123</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFM.H43A1174S"> <span id="translatedtitle">Evaluation of a WRF dynamic <span class="hlt">downscaling</span> simulation over Western Montana</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Dynamic <span class="hlt">downscaling</span> of global circulation models (GCMs) provides a mean to interpret large-scale data at a resolution more relevant to policy and locality. Regional climate models (RCMs), such as the Weather and Research Forecasting (WRF) model, can take advantage of finer resolution topography, water-land boundaries, and land use delineation to provide a better estimate of precipitation and temperature at the regional scale. Higher resolution atmospheric information is necessary to accurately predict the ecologic and hydrologic impacts of climate change in various geographic locations. High resolution WRF simulations for climate studies in the Western U.S. have primarily focused in areas largely influenced by coastal characteristics such as California and the Pacific Northwest. These areas are largely affected by sea surface temperatures, coastal weather patterns, and unique moisture fluxes around land and sea boundaries. Since model performance may vary with geographic location it is important to evaluate the model in a variety of areas and topographic terrain. In this study, we present the results of a <span class="hlt">downscaled</span> reanalysis of climate over Western Montana for the years 2000 - 2006. We used the WRF model to resolve one-degree Global Forecast System (GFS) data to 4-km grid spacing. To evaluate the model we compared average precipitation and temperature data for winter and summer months to observational analysis datasets from PRISM (Parameter-elevation Regressions on Independent Slopes Model). The simulation for Western Montana was also compared to similar studies completed in coastal regions of the Western U.S. to explore trends that may be specific to geographic location as well as boundary and initial conditions from the GFS. The output from this study will be used for future experiments focused on eco-hydro-climatic conditions of Western Montana under climate change scenarios.</p> <div class="credits"> <p class="dwt_author">Silverman, N.; Maneta, M. P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">124</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://people.uleth.ca/~sarah.boon/Gardneretal_JClimate2009.pdf"> <span id="translatedtitle">Near-Surface Temperature Lapse Rates over Arctic Glaciers and Their Implications for Temperature <span class="hlt">Downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Distributed glacier surface melt models are often forced using air temperature fields that are either <span class="hlt">downscaled</span> from climate models or reanalysis, or extrapolated from station measurements. Typically, the <span class="hlt">downscaling</span> and\\/or extrapolation are performed using a constanttemperaturelapserate, which is often taken to be the free-air moist adiabatic lapse rate (MALR: 68-78 Ck m 21). To explore the validity of this approach,</p> <div class="credits"> <p class="dwt_author">Alex S. Gardner; Martin J. Sharp; Roy M. Koerner; Claude Labine; Sarah Boon; Shawn J. Marshall; David O. Burgess; David Lewis</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">125</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/6079008"> <span id="translatedtitle">Incremental construction of classifier and discriminant <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">We discuss approaches to incrementally construct an <span class="hlt">ensemble</span>. The first constructs an <span class="hlt">ensemble</span> of classifiers choosing a subset from a larger set, and the second constructs an <span class="hlt">ensemble</span> of discriminants, where a classifier is used for some classes only. We investigate criteria including accuracy, significant improvement, diversity, correlation, and the role of search direction. For discriminant <span class="hlt">ensembles</span>, we test subset</p> <div class="credits"> <p class="dwt_author">Aydin Ulas; Murat Semerci; Olcay Taner Yildiz; Ethem Alpaydin</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">126</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://bfgp.oxfordjournals.org/cgi/reprint/elm025v1.pdf"> <span id="translatedtitle">Genome browsing with <span class="hlt">Ensembl</span>: a practical overview</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A wealth of gene information is accruing in public databases. Genome browsers such as <span class="hlt">Ensembl</span> are needed to organize and depict this information in the context of the genome. <span class="hlt">Ensembl</span> provides an open source gene set based on experimental evidence for over 30 species, the majority of which are vertebrates. Genes and annotation are accessible through the <span class="hlt">Ensembl</span> browser (http:\\/\\/www.<span class="hlt">ensembl</span>.org),</p> <div class="credits"> <p class="dwt_author">Giulietta Spudich; Ewan Birney</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">127</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.csse.monash.edu/~webb/Files/FraymanRolfeWebb02.pdf"> <span id="translatedtitle">Solving Regression Problems Using Competitive <span class="hlt">Ensemble</span> Models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The use of <span class="hlt">ensemble</span> models in many problem domains has increased significantly in the last few years. The <span class="hlt">ensemble</span> modeling, in particularly boosting, has shown a great promise in improving predictive performance of a model. Com- bining the <span class="hlt">ensemble</span> members is normally done in a co-operative fashion where each of the <span class="hlt">ensemble</span> members performs the same task and their predictions</p> <div class="credits"> <p class="dwt_author">Yakov Frayman; Bernard F. Rolfe; Geoffrey I. Webb</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">128</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/750094"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2002: accommodating comparative genomics</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">ABSTRACT The,<span class="hlt">Ensembl,(http:\\/\\/www.ensembl</span>.org\\/) database project,provides,a,bioinformatics,framework,to organise,biology,around,the,sequences,of large genomes.,It is a comprehensive,source,of stable automatic annotation of human, mouse and other genome sequences, available as either an interactive web,site or as,flat files. <span class="hlt">Ensembl</span>,also,integrates manually,annotated,gene,structures,from,external sources,where,available. As well as being,one,of the leading sources of genome annotation, <span class="hlt">Ensembl</span> is an,open,source,software,engineering,project,to develop,a portable,system,able to handle,very large genomes,and associated,requirements.,These range from,sequence,analysis,to data storage,and,visua- lisation and,installations,exist around,the world,in</p> <div class="credits"> <p class="dwt_author">Michele E. Clamp; T. Daniel Andrews; Daniel Barker; Paul Bevan; Graham Cameron; Yuan Chen; Laura Clarke; Tony Cox; James A. Cuff; Val Curwen; Thomas Down; Richard Durbin; Eduardo Eyras; James Gilbert; Martin Hammond; Tim J. P. Hubbard; Arek Kasprzyk; Damian Keefe; Heikki Lehväslaiho; V. Iyer; Craig Melsopp; Emmanuel Mongin; Roger Pettett; Simon C. Potter; Alastair Rust; Esther Schmidt; Stephen M. J. Searle; Guy Slater; James Smith; William Spooner; Arne Stabenau; Jim Stalker; Elia Stupka; Abel Ureta-vidal; Imre Vastrik; Ewan Birney</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">129</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/22371429"> <span id="translatedtitle"><span class="hlt">Ensemble</span> manifold regularization.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">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. PMID:22371429</p> <div class="credits"> <p class="dwt_author">Geng, Bo; Tao, Dacheng; Xu, Chao; Yang, Linjun; Hua, Xian-Sheng</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-06-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">130</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=479128"> <span id="translatedtitle">The <span class="hlt">Ensembl</span> Computing Architecture</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary"><span class="hlt">Ensembl</span> is a software project to automatically annotate large eukaryotic genomes and release them freely into the public domain. The project currently automatically annotates 10 complete genomes. This makes very large demands on compute resources, due to the vast number of sequence comparisons that need to be executed. To circumvent the financial outlay often associated with classical supercomputing environments, farms of multiple, lower-cost machines have now become the norm and have been deployed successfully with this project. The architecture and design of farms containing hundreds of compute nodes is complex and nontrivial to implement. This study will define and explain some of the essential elements to consider when designing such systems. Server architecture and network infrastructure are discussed with a particular emphasis on solutions that worked and those that did not (often with fairly spectacular consequences). The aim of the study is to give the reader, who may be implementing a large-scale biocompute project, an insight into some of the pitfalls that may be waiting ahead.</p> <div class="credits"> <p class="dwt_author">Cuff, James A.; Coates, Guy M.P.; Cutts, Tim J.R.; Rae, Mark</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">131</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ThApC.tmp..232O"> <span id="translatedtitle">Evaluating climate change effects on runoff by statistical <span class="hlt">downscaling</span> and hydrological model GR2M</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The main purpose of this study is to evaluate the impacts of climate change on Izmir-Tahtali freshwater basin, which is located in the Aegean Region of Turkey. For this purpose, a developed strategy involving statistical <span class="hlt">downscaling</span> and hydrological modeling is illustrated through its application to the basin. Prior to statistical <span class="hlt">downscaling</span> of precipitation and temperature, the explanatory variables are obtained from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data set. All possible regression approach is used to establish the most parsimonious relationship between precipitation, temperature, and climatic variables. Selected predictors have been used in training of artificial neural networks-based <span class="hlt">downscaling</span> models and the trained models with the obtained relationships have been operated to produce scenario precipitation and temperature from the simulations of third Generation Coupled Climate Model. Biases from <span class="hlt">downscaled</span> outputs have been reduced after <span class="hlt">downscaling</span> process. Finally, the corrected <span class="hlt">downscaled</span> outputs have been transformed to runoff by means of a monthly parametric hydrological model GR2M to assess the probable impacts of temperature and precipitation changes on runoff. According to the A1B climate scenario results, statistically significant trends are foreseen for precipitation, temperature, and runoff in the study basin.</p> <div class="credits"> <p class="dwt_author">Okkan, Umut; Fistikoglu, Okan</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-09-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">132</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2006JGRD..11123106B"> <span id="translatedtitle">A simple statistical-dynamical <span class="hlt">downscaling</span> scheme based on weather types and conditional resampling</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A multivariate statistical <span class="hlt">downscaling</span> methodology is implemented to generate local precipitation and temperature series at different sites based on the results from a variable resolution general circulation model. It starts from regional climate properties to establish discriminating weather types for the chosen local variable, precipitation in this case. Intratype variations of the relevant forcing parameters are then taken into account by multivariate regression using the distances of a given day to the different weather types as predictors. The final step consists of conditional resampling. The methodology is evaluated in the Seine basin in France. Using reanalysis fields as predictors, satisfying results are obtained at daily timescale and concerning low-frequency variations, both for temperature and precipitation. The use of model results as predictors gives a realistic representation of regional climate properties. Nevertheless, as the validation of a statistical <span class="hlt">downscaling</span> algorithm for present day climate conditions does not necessarily imply the validity of its climate change projections, the plausibility of the <span class="hlt">downscaled</span> climate projections is assessed by verifying the consistency between spatially averaged <span class="hlt">downscaled</span> results and direct model outputs for two climate change scenarios. Despite some discrepancies for precipitation with the more extreme scenario, the consistency is good for both local variables. This result reinforces the confidence in the use of the <span class="hlt">downscaling</span> scheme in altered climates. Finally, it is shown that the intertype variations of the atmospheric circulation represent only a fraction of the climate change signal for the local variables. Thus a <span class="hlt">downscaling</span> methodology based on weather typing should incorporate information concerning intratype modifications.</p> <div class="credits"> <p class="dwt_author">Boé, J.; Terray, L.; Habets, F.; Martin, E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">133</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFM.A21G0201O"> <span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> of NASA/GISS ModelE: Continuous, Multi-Year WRF Simulations</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The WRF Model is being used at the U.S. EPA for dynamical <span class="hlt">downscaling</span> of the NASA/GISS ModelE fields to assess regional impacts of climate change in the United States. The WRF model has been successfully linked to the ModelE fields in their raw hybrid vertical coordinate, and continuous, multi-year WRF <span class="hlt">downscaling</span> simulations have been performed. WRF will be used to <span class="hlt">downscale</span> decadal time slices of ModelE for recent past, current, and future climate as the simulations being conducted for the IPCC Fifth Assessment Report become available. This presentation will focus on the sensitivity to interior nudging within the RCM. The use of interior nudging for <span class="hlt">downscaled</span> regional climate simulations has been somewhat controversial over the past several years but has been recently attracting attention. Several recent studies that have used reanalysis (i.e., verifiable) fields as a proxy for GCM input have shown that interior nudging can be beneficial toward achieving the desired <span class="hlt">downscaled</span> fields. In this study, the value of nudging will be shown using fields from ModelE that are <span class="hlt">downscaled</span> using WRF. Several different methods of nudging are explored, and it will be shown that the method of nudging and the choices made with respect to how nudging is used in WRF are critical to balance the constraint of ModelE against the freedom of WRF to develop its own fields.</p> <div class="credits"> <p class="dwt_author">Otte, T.; Bowden, J. H.; Nolte, C. G.; Otte, M. J.; Herwehe, J. A.; Faluvegi, G.; Shindell, D. T.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">134</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/servlets/purl/1043326"> <span id="translatedtitle">Sub-daily Statistical <span class="hlt">Downscaling</span> of Meteorological Variables Using Neural Networks</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Kumar, Jitendra [ORNL; Brooks, Bjørn-Gustaf J. [University of Illinois, Urbana-Champaign; Thornton, Peter E [ORNL; Dietze, Michael [University of Illinois, Urbana-Champaign</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">135</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/1773184"> <span id="translatedtitle">Self-poised <span class="hlt">Ensemble</span> Learning</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">\\u000a This paper proposes a new approach to train <span class="hlt">ensembles</span> of learning machines in a regression context. At each iteration a new\\u000a learner is added to compensate the error made by the previous learner in the prediction of its training patterns. The algorithm\\u000a operates directly over values to be predicted by the next machine to retain the <span class="hlt">ensemble</span> in the target</p> <div class="credits"> <p class="dwt_author">Ricardo Ñanculef; Carlos Valle; Héctor Allende; Claudio Moraga</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">136</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=200778"> <span id="translatedtitle">USING <span class="hlt">ENSEMBLE</span> PREDICTIONS TO SIMULATE FIELD-SCALE SOIL WATER TIME SERIES WITH UPSCALED AND <span class="hlt">DOWNSCALED</span> SOIL HYDRAULIC PROPERTIES</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p class="result-summary">Simulations of soil water flow require measurements of soil hydraulic properties which are particularly difficult at field scale. Laboratory measurements provide hydraulic properties at scales finer than the field scale, whereas pedotransfer functions (PTFs) integrate information on hydraulic prope...</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">137</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010PhDT.......137H"> <span id="translatedtitle">A standardized framework for evaluating the skill of regional climate <span class="hlt">downscaling</span> techniques</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Regional climate impact assessments require high-resolution projections to resolve local factors that modify the impact of global-scale forcing. To generate these projections, global climate model simulations are commonly <span class="hlt">downscaled</span> using a variety of statistical and dynamical techniques. Despite the essential role of <span class="hlt">downscaling</span> in regional assessments, there is no standard approach to evaluating various <span class="hlt">downscaling</span> methods. Hence, impact communities often have little awareness of limitations and uncertainties associated with <span class="hlt">downscaled</span> projections. To develop a standardized framework for evaluating and comparing <span class="hlt">downscaling</span> approaches, I first identify three primary characteristics of a distribution directly relevant to impact analyses that can be used to evaluate a simulated variable such as temperature or precipitation at a given location: (1) annual, seasonal, and monthly mean values; (2) thresholds, extreme values, and accumulated quantities such as 24h precipitation or degree-days; and (3) persistence, reflecting multi-day events such as heat waves, cold spells, and wet periods. Based on a survey of the literature and solicitation of expert opinion, I select a set of ten statistical tests to evaluate these characteristics, including measures of error, skill, and correlation. I apply this framework to evaluate the skill of four <span class="hlt">downscaling</span> methods, from a simple delta approach to a complex asynchronous quantile regression, in simulating daily temperature at twenty stations across North America. Identical global model fields force each <span class="hlt">downscaling</span> method, and the historical observational record at each location is randomly divided by year into two equal parts, such that each statistical method is trained on one set of historical observations, and evaluated on an entirely independent set of observations. Biases relative to observations are calculated for the historical evaluation period, and differences between projections for the future. Application of the framework to this broad range of <span class="hlt">downscaling</span> methods and locations is successful in that: (1) the <span class="hlt">downscaling</span> method used is identified as a more important determinant of data quality than station location or GCM; and (2) key differences between <span class="hlt">downscaling</span> methods are made apparent. For tests focusing on the general distribution of the variable, all methods except bias correction are relatively successful in simulating observed climate, suggesting that if an impact is most sensitive to changes in the mean, even a relatively simple <span class="hlt">downscaling</span> approach such as "delta" will significantly improve simulation of local-scale climate. For tests that focus on the tails of the distribution, however, differences do arise between simple vs. quantile-based <span class="hlt">downscaling</span> methods. Specifically, the latter appears less sensitive to location and more consistently able to reproduce observed climate. In terms of future projections, the most notable differences between <span class="hlt">downscaling</span> methods becomes apparent at the right-hand tail of the distribution, where simple methods tend to simulate much greater increases (up to double the extreme heat days, for some locations) than more complex <span class="hlt">downscaling</span> methods. I conclude by discussing how a standardized evaluation framework may advance our understanding of regional climate impact studies in understanding biases and limitations in results, as well as providing critical input into the selection of <span class="hlt">downscaling</span> methods for future assessments. Given the potential exhibited by this initial test, I explore how this evaluation framework could be expanded in the future to make it even more useful: to the regional scale, for example, by including tests for spatial correlations and forcing relationships; or across variables, to capture interactions directly relevant to impact studies, such as heat waves (a function of temperature and humidity, affecting human health, energy demand, and agriculture) or snow amounts (a function of precipitation and temperature, affecting infrastructure and ecosystems); or to evaluate a broader selection of clim</p> <div class="credits"> <p class="dwt_author">Hayhoe, Katharine Anne</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">138</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..1413531W"> <span id="translatedtitle">Modelling climate impact on floods under future emission scenarios using an <span class="hlt">ensemble</span> of climate model projections</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Evidence provided by modelled assessments of climate change impact on flooding is fundamental to water resource and flood risk decision making. Impact models usually rely on climate projections from Global and Regional Climate Models, and there is no doubt that these provide a useful assessment of future climate change. However, cascading <span class="hlt">ensembles</span> of climate projections into impact models is not straightforward because of problems of coarse resolution in Global and Regional Climate Models (GCM/RCM) and the deficiencies in modelling high-intensity precipitation events. Thus decisions must be made on how to appropriately pre-process the meteorological variables from GCM/RCMs, such as selection of <span class="hlt">downscaling</span> methods and application of Model Output Statistics (MOS). In this paper a grand <span class="hlt">ensemble</span> of projections from several GCM/RCM are used to drive a hydrological model and analyse the resulting future flood projections for the Upper Severn, UK. The impact and implications of applying MOS techniques to precipitation as well as hydrological model parameter uncertainty is taken into account. The resultant grand <span class="hlt">ensemble</span> of future river discharge projections from the RCM/GCM-hydrological model chain is evaluated against a response surface technique combined with a perturbed physics experiment creating a probabilisic <span class="hlt">ensemble</span> climate model outputs. The <span class="hlt">ensemble</span> distribution of results show that future risk of flooding in the Upper Severn increases compared to present conditions, however, the study highlights that the uncertainties are large and that strong assumptions were made in using Model Output Statistics to produce the estimates of future discharge. The importance of analysing on a seasonal basis rather than just annual is highlighted. The inability of the RCMs (and GCMs) to produce realistic precipitation patterns, even in present conditions, is a major caveat of local climate impact studies on flooding, and this should be a focus for future development.</p> <div class="credits"> <p class="dwt_author">Wetterhall, F.; Cloke, H. L.; He, Y.; Freer, J.; Pappenberger, F.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">139</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/51235239"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A statistical <span class="hlt">downscaling</span> method (SDSM) was evaluated by simultaneously <span class="hlt">downscaling</span> air temperature, evaporation, and precipitation in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP\\/NCAR reanalysis data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for <span class="hlt">downscaling</span> were measured daily mean</p> <div class="credits"> <p class="dwt_author">J. T. Chu; J. Xia; C.-Y. Xu; V. P. Singh</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">140</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/39664256"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A statistical <span class="hlt">downscaling</span> method (SDSM) was evaluated by simultaneously <span class="hlt">downscaling</span> air temperature, evaporation, and precipitation\\u000a in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP\\/NCAR reanalysis\\u000a data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for\\u000a <span class="hlt">downscaling</span> were measured daily mean</p> <div class="credits"> <p class="dwt_author">J. T. Chu; J. Xia; C.-Y. Xu; V. P. Singh</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_6");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' 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onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">141</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012AtmRe.103..119S"> <span id="translatedtitle">A comparison of different regional climate models and statistical <span class="hlt">downscaling</span> methods for extreme rainfall estimation under climate change</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In most cases climate change projections from General Circulation Models (GCM) and Regional Climate Models (RCM) cannot be directly applied to climate change impact studies, and <span class="hlt">downscaling</span> is therefore needed. A large number of statistical <span class="hlt">downscaling</span> methods exist but no clear recommendations exist of which methods are more appropriate, depending on the application. This paper compares five statistical <span class="hlt">downscaling</span> methods based on a common change factor methodology using results from four different RCMs driven by different GCMs. Precipitation time series for a future scenario are generated for a location north of Copenhagen for the period 2071-2100 under climate change projections by the scenario A1B. Special focus is given to the changes of extreme events since <span class="hlt">downscaling</span> methods mainly differ in the way extreme events are generated. There is a significant uncertainty in the <span class="hlt">downscaled</span> projected changes of the mean, standard deviation, skewness and probability of dry days. Large uncertainties are also observed in the <span class="hlt">downscaled</span> changes in extreme event statistics. However, three of the four RCMs analysed show an increase in the extreme precipitation events in the future. The uncertainties are partly due to the variability of the RCM projections and partly due to the variability of the statistical <span class="hlt">downscaling</span> methods. The paper highlights the importance of acknowledging the limitations and advantages of different statistical <span class="hlt">downscaling</span> methods as well as the uncertainties in <span class="hlt">downscaling</span> climate change projections for use in hydrological models.</p> <div class="credits"> <p class="dwt_author">Sunyer, M. A.; Madsen, H.; Ang, P. H.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">142</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.4176H"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> Based on Spartan Spatial Random Fields</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Stochastic methods of space-time interpolation and conditional simulation have been used in statistical <span class="hlt">downscaling</span> approaches to increase the resolution of measured fields. One of the popular interpolation methods in geostatistics is kriging, also known as optimal interpolation in data assimilation. Kriging is a stochastic, linear interpolator which incorporates time/space variability by means of the variogram function. However, estimation of the variogram from data involves various assumptions and simplifications. At the same time, the high numerical complexity of kriging makes it difficult to use for very large data sets. We present a different approach based on the so-called Spartan Spatial Random Fields (SSRFs). SSRFs were motivated from classical field theories of statistical physics [1]. The SSRFs provide a different approach of parametrizing spatial dependence based on 'effective interactions,' which can be formulated based on general statistical principles or even incorporate physical constraints. This framework leads to a broad family of covariance functions [2], and it provides new perspectives in covariance parameter estimation and interpolation [3]. A significant advantage offered by SSRFs is reduced numerical complexity, which can lead to much faster codes for spatial interpolation and conditional simulation. In addition, on grids composed of rectangular cells, the SSRF representation leads to an explicit expression for the precision matrix (the inverse covariance). Therefore SSRFs could provide useful models of error covariance for data assimilation methods. We use simulated and real data to demonstrate SSRF properties and <span class="hlt">downscaled</span> fields. keywords: interpolation, conditional simulation, precision matrix References [1] Hristopulos, D.T., 2003. Spartan Gibbs random field models for geostatistical applications, SIAM Journal in Scientific Computation, 24, 2125-2162. [2] Hristopulos, D.T., Elogne, S. N. 2007. Analytic properties and covariance functions of a new class of generalized Gibbs random fields, IEEE Transactions on Information Theory, 53(12), 4667-4679. [3] Hristopulos, D.T., Elogne, S. N. 2009. Computationally efficient spatial interpolators based on Spartan Spatial random fields, IEEE Transactions on Signal Processing, 57(9), 3475-3487.</p> <div class="credits"> <p class="dwt_author">Hristopulos, Dionissios</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">143</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://coaps.fsu.edu/~vmisra/dynamic_downscale.pdf"> <span id="translatedtitle">Dynamic <span class="hlt">Downscaling</span> of Seasonal Simulations over South America</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">In this paper multiple atmospheric global circulation model (AGCM) integrations at T42 spectral truncation and prescribed sea surface temperature were used to drive regional spectral model (RSM) simulations at 80-km resolution for the austral summer season (January-February-March). Relative to the AGCM, the RSM improves the <span class="hlt">ensemble</span> mean simulation of precipitation and the lower- and upper-level tropospheric circulation over both tropical</p> <div class="credits"> <p class="dwt_author">Vasubandhu Misra; Paul A. Dirmeyer; Ben P. Kirtman</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">144</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/22093453"> <span id="translatedtitle">Estimating preselected and postselected <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Massar, Serge [Laboratoire d'Information Quantique, C.P. 225, Universite libre de Bruxelles (U.L.B.), Av. F. D. Rooselvelt 50, B-1050 Bruxelles (Belgium); Popescu, Sandu [H. H. Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL (United Kingdom); Hewlett-Packard Laboratories, Stoke Gifford, Bristol BS12 6QZ (United Kingdom)</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-11-15</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">145</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/19148764"> <span id="translatedtitle"><span class="hlt">Downscaling</span> drug nanosuspension production: processing aspects and physicochemical characterization.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">In this study, scaling down nanosuspension production to 10 mg of drug compound and evaluation of the nanosuspensions to 1 mg of drug compound per test were investigated. Media milling of seven model drug compounds (cinnarizine-indomethacin-itraconazole-loviride-mebendazole-naproxen-phenytoin) was evaluated in a 96-well plate setup (10, 20, and 30 mg) and a glass-vial-based system in a planetary mill (10, 100, and 1,000 mg). Physicochemical properties evaluated on 1 mg of drug compound were drug content (high-performance liquid chromatography), size [dynamic light scattering (DLS)], morphology (scanning electron microscopy), thermal characteristics (differential scanning calorimetry), and X-ray powder diffraction (XRPD). Scaling down nanosuspension production to 10 mg of drug compound was feasible for the seven model compounds using both designs, the planetary mill design being more robust. Similar results were obtained for both designs upon milling 10 mg of drug compound. Drug content determination was precise and accurate. DLS was the method of choice for size measurements. Morphology evaluation and thermal analysis were feasible, although sample preparation had a big influence on the results. XRPD in capillary mode was successfully performed, both in the suspended state and after freeze-drying in the capillary. Results obtained for the latter were superior. Both the production and the physicochemical evaluation of nanosuspensions can be successfully <span class="hlt">downscaled</span>, enabling nanosuspension screening applications in preclinical development settings. PMID:19148764</p> <div class="credits"> <p class="dwt_author">Van Eerdenbrugh, Bernard; Stuyven, Bernard; Froyen, Ludo; Van Humbeeck, Jan; Martens, Johan A; Augustijns, Patrick; Van den Mooter, Guy</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-16</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">146</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010ems..confE.373F"> <span id="translatedtitle">Evolution of the Canadian regional <span class="hlt">ensemble</span> prediction system</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A regional <span class="hlt">ensemble</span> prediction system (REPS) over North America is expected to become operational at the Canadian Meteorological Centre (CMC) in late 2010 or early 2011. Different configurations of the REPS have already been tested and verified at different locations and time periods. The system was used during the Beijing 2008 summer Olympics and for the North American domain with a focus over southern British Columbia, Canada, during the 2010 Vancouver Olympics. It will also provide forecasts for tropical storms and hurricanes for the Haïti area during the summer and autumn of 2010. The Canadian Global Environmental Multiscale (GEM) model has been designed with the possibility to be run as a limited area model (GEM-LAM). The Canadian REPS is composed of 20 members running the GEM-LAM at a near 33 km grid spacing and with the same physical parameterizations as those present in the operational global deterministic prediction system at CMC. Two initial perturbation strategies (moist targeted singular vectors [SV] and the <span class="hlt">ensemble</span> Kalman filter [EnKF]), as well as two stochastic methods for perturbations of parameterizations were verified against surface and upper air (rawinsondes) observations during summer and winter periods to determine which system has the best forecast abilities. For the SV-based REPS, 20 initial conditions (IC) are generated using a targeted SV perturbation method. These ICs are then used to run 20 global GEMs that will provide the lateral boundary conditions (LBCs) for each GEM-LAM. For the EnKF-based REPS, the 20 LBCs are built by <span class="hlt">downscaling</span> the 20 members of the Canadian global <span class="hlt">ensemble</span> prediction system (GEPS) to the resolution of the REPS. Verifications indicate that the EnKF approach gives better skill for summer and winter periods. The skill difference between the two systems comes mainly from the reliability attribute (smaller bias and reduced under-dispersion). Stochastic perturbations on model physical tendencies and on physical parameters were both tested. These two perturbation methods show a significant improvement in the reliability skill but tend to slightly degrade the resolution. Nevertheless, both systems show an overall improvement in the skill. The physical tendencies perturbation method showed the best scores and was chosen. Research to improve the system using surface parameter perturbations is presently ongoing. Initial results show improved skill for surface during the summer season when perturbations are done on fields related to the land surface scheme such as the albedo, soil temperature and moisture.</p> <div class="credits"> <p class="dwt_author">Frenette, R.; Charron, M.; Li, X.; Gagnon, N.; Lavaysse, C.; Belair, S.; Carrera, M.; Yau, P.; Candille, G.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">147</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/21450714"> <span id="translatedtitle">Quantum metrology with molecular <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Schaffry, Marcus; Gauger, Erik M. [Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH (United Kingdom); Morton, John J. L. [CAESR, Clarendon Laboratory, Department of Physics, University of Oxford, OX1 3PU (United Kingdom); Fitzsimons, Joseph [Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH (United Kingdom); Institute for Quantum Computing, University of Waterloo, Waterloo, Ontario (Canada); Benjamin, Simon C. [Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH (United Kingdom); Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543 (Singapore); Lovett, Brendon W. [Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH (United Kingdom); School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS (United Kingdom)</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-10-15</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">148</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ClDy...40..839P"> <span id="translatedtitle">Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical <span class="hlt">downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">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 class="dwt_publisher"></p> <p class="publishDate">2013-02-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">149</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=277355"> <span id="translatedtitle">Assessment of the scale effect on statistical <span class="hlt">downscaling</span> quality at a station scale using a weather generator-based model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">150</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.springerlink.com/index/ph103p8348h24255.pdf"> <span id="translatedtitle">Canonical <span class="hlt">Ensemble</span> with Temperature Limitation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Ideal Bose and Fermi systems are studied on the basis of a canonical <span class="hlt">ensemble</span>, subject to the condition that their temperature is less than a given temperature Tmax. A single new parameter (the tau-parameter, ?) is needed to keep account of the new constraint. The parameter ? is shown to be the exponential of a pseudo-chemical potential that is linearly</p> <div class="credits"> <p class="dwt_author">Viorel Badescu; Peter T. Landsberg</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">151</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/60690198"> <span id="translatedtitle">Quantum metrology with molecular <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">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:</p> <div class="credits"> <p class="dwt_author">Marcus Schaffry; Erik M. Gauger; John J. L. Morton; Joseph Fitzsimons; Simon C. Benjamin; Brendon W. Lovett</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">152</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/16173235"> <span id="translatedtitle">Demography and the canonical <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The Gibbs canonical <span class="hlt">ensemble</span> of statistical mechanics is used to describe the probability distribution of the age classes of mothers of new-borns in an age-structured population. The Malthusian parameter emerges as a Lagrange multiplier corresponding to a generation time constraint, while a new perturbation parameter appears as the Lagrange multiplier corresponding to a maternity constraint. Classical Lotka stability reduces to</p> <div class="credits"> <p class="dwt_author">Jonathan D. H. Smith</p> <p class="dwt_publisher"></p> <p class="publishDate">1998-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">153</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://enkf.nersc.no/Publications/mit00a.pdf"> <span id="translatedtitle">An Adaptive <span class="hlt">Ensemble</span> Kalman Filter</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">To the extent that model error is nonnegligible in numerical models of the atmosphere, it must be accounted for in 4D atmospheric data assimilation systems. In this study, a method of estimating and accounting for model error in the context of an <span class="hlt">ensemble</span> Kalman filter technique is developed. The method involves parameterizing the model error and using innovations to estimate</p> <div class="credits"> <p class="dwt_author">Herschel L. Mitchell; P. L. Houtekamer</p> <p class="dwt_publisher"></p> <p class="publishDate">2000-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">154</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/19732901"> <span id="translatedtitle">Development of a <span class="hlt">downscale</span> sedimentation field flow fractionation device for biological event monitoring.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">Classically described as a macroscale size-density based method, Sedimentation field flow fractionation (SdFFF) has been successfully used for cell sorting. The goal of this study was to develop a new SdFFF device for <span class="hlt">downscale</span> applications, in particular for oncology research to rapidly monitor chemical biological event induction in a cell line. The development of a <span class="hlt">downscale</span> SdFFF device required reduction of the separation channel volume. Taking advantage of a newly laboratory designed apparatus, channel volume was successfully decreased by reducing both length and breadth. To validate the apparatus and method, we used the well-known model of diosgenin dose-dependent induction of apoptosis or megakaryocytic differentiation in HEL cells. After a minute scale acquisition of a reference profile, the <span class="hlt">downscale</span> device was able to perform fast, early, significant and reproducible monitoring of apoptosis and differentiation, two important biological mechanisms in the field of cancer research. PMID:19732901</p> <div class="credits"> <p class="dwt_author">Bégaud-Grimaud, G; Battu, S; Liagre, B; Beneytout, J L; Jauberteau, M O; Cardot, P J P</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-08-21</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">155</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFM.H52E..04T"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Alkaline Phosphatase Activity in a Subtropical Reservoir</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Tseng, Y.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">156</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009AGUSM.H24A..04S"> <span id="translatedtitle">A Simple <span class="hlt">Downscaling</span> Algorithm for Remotely Sensed Land Surface Temperature</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The method is illustrated using a combination of MODIS NDVI data with a spatial resolution of 250m and 3 Km Meteosat Second Generation SEVIRI LST data. Geostationary Earth Observation data carry a large potential for assessment of surface state variables. Not the least the European Meteosat Second Generation platform with its SEVIRI sensor is well suited for studies of the dynamics of land surfaces due to its high temporal frequency (15 minutes) and its red, Near Infrared (NIR) channels that provides vegetation indices, and its two split window channels in the thermal infrared for assessment of Land Surface Temperature (LST). For some applications the spatial resolution in geostationary data is too coarse. Due to the low statial resolution of 4.8 km at nadir for the SEVIRI sensor, a means of providing sub pixel information is sought for. By combining and properly scaling two types of satellite images, namely data from the MODIS sensor onboard the polar orbiting platforms TERRA and AQUA and the coarse resolution MSG-SEVIRI, we exploit the best from two worlds. The vegetation index/surface temperature space has been used in a vast number of studies for assessment of air temperature, soil moisture, dryness indices, evapotranspiration and for studies of land use change. In this paper, we present an improved method to derive a finer resolution Land Surface Temperature (LST). A new, deterministic scaling method has been applied, and is compared to existing deterministic <span class="hlt">downscaling</span> methods based on LST and NDVI. We also compare our results from in situ measurements of LST from the Dahra test site in West Africa.</p> <div class="credits"> <p class="dwt_author">Sandholt, I.; Nielsen, C.; Stisen, S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">157</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://eric.ed.gov/?q=music+AND+choir&pg=2&id=EJ640050"> <span id="translatedtitle">Is It Curtains for Traditional <span class="hlt">Ensembles</span>?</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p class="result-summary">|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…</p> <div class="credits"> <p class="dwt_author">Van Zandt, Kathryn</p> <p class="dwt_publisher"></p> <p class="publishDate">2001-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">158</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/2527965"> <span id="translatedtitle">Improved customer choice predictions using <span class="hlt">ensemble</span> methods</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">In this paper various <span class="hlt">ensemble</span> learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of <span class="hlt">ensemble</span> learning usually improves the prediction quality of exible models like decision trees and thus leads to improved predictions. We give experimental results for two real-life marketing datasets using decision trees, <span class="hlt">ensemble</span> versions of decision</p> <div class="credits"> <p class="dwt_author">Michiel C. Van Wezel; Rob Potharst</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">159</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/626847"> <span id="translatedtitle">Compact Dual <span class="hlt">Ensembles</span> for Active Learning</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Generic <span class="hlt">ensemble</span> methods can achieve excellent learning performance, but are not good candidates for active learning because of their difierent design purposes. We investigate how to use diversity of the member classiflers of an <span class="hlt">ensemble</span> for e-cient active learning. We empirically show, using benchmark data sets, that (1) to achieve a good (stable) <span class="hlt">ensemble</span>, the number of classiflers needed in</p> <div class="credits"> <p class="dwt_author">Amit Mandvikar; Huan Liu; Hiroshi Motoda</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">160</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://eric.ed.gov/?q=%22pan-%22&pg=3&id=EJ631682"> <span id="translatedtitle">African Drum and Steel Pan <span class="hlt">Ensembles</span>.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p class="result-summary">|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> <div class="credits"> <p class="dwt_author">Sunkett, Mark E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2000-01-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_7");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return 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showDiv("page_10");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">161</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012HESSD...9.9847G"> <span id="translatedtitle">Comparing dynamical, stochastic and combined <span class="hlt">downscaling</span> approaches - lessons from a case study in the Mediterranean region</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Various <span class="hlt">downscaling</span> techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Such techniques may be grouped into two <span class="hlt">downscaling</span> approaches: the deterministic dynamical <span class="hlt">downscaling</span> (DD) and the stochastic statistical <span class="hlt">downscaling</span> (SD). Although SD has been traditionally seen as an alternative to DD, recent works on statistical <span class="hlt">downscaling</span> have aimed to combine the benefits of these two approaches. The overall objective of this study is to examine the relative benefits of each <span class="hlt">downscaling</span> approach and their combination in making the GCM scenarios suitable for basin scale hydrological applications. The case study presented here focuses on the Apulia region (South East of Italy, surface area about 20 000 km2), characterized by a typical Mediterranean climate; the monthly cumulated precipitation and monthly mean of daily minimum and maximum temperature distribution were examined for the period 1953-2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile transform. The SD resulted efficient in reducing the mean bias in the spatial distribution at both annual and seasonal scales, but it was not able to correct the miss-modeled non-stationary components of the GCM dynamics. The DD provided a partial correction by enhancing the trend spatial heterogeneity and time evolution predicted by the GCM, although the comparison with observations resulted still underperforming. The best results were obtained through the combination of both DD and SD approaches.</p> <div class="credits"> <p class="dwt_author">Guyennon, N.; Romano, E.; Portoghese, I.; Salerno, F.; Calmanti, S.; Petrangeli, A. B.; Tartari, G.; Copetti, D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-08-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">162</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.2522S"> <span id="translatedtitle">Possible Impacts of Climate Change on Wind Gust under <span class="hlt">Downscaled</span> Future Climate Conditions over Ontario, Canada</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The overarching purpose of this study was to project changes in the occurrence frequency and magnitude of future wind gust events under <span class="hlt">downscaled</span> future climate conditions over Ontario, Canada. Wind gust factors were employed to simulate hourly/daily wind gust based on hourly/daily wind speed. Regression-based <span class="hlt">downscaling</span> methods were used to <span class="hlt">downscale</span> future hourly/daily wind speed to each of the 14 selected cities in Ontario for eight GCM models with IPCC SRES A2 and B1 scenarios. The wind gust simulation models were then applied using <span class="hlt">downscaled</span> future GCM wind speed data to project changes in occurrence frequency and intensity of the future hourly/daily wind gust events. <span class="hlt">Downscaling</span> transfer functions and wind gust simulation models were validated using a cross-validation scheme and comparing data distributions and extreme-event frequencies derived from <span class="hlt">downscaled</span> GCM control runs and observations over a comparative time period 1961-2000. The results showed that the models for all variables used in the study performed well. By comparing the current-past averaged conditions, the occurrence frequency and intensity of future wind gust events in the study area are projected to increase. The modeled results from this study found that the frequency and intensity of future wind gust events are projected to significantly increase under a changing climate in this century. This talk will introduce the research project and outline the modeling exercise and verification process. The major findings on future wind gust projections from the study will be summarized in the presentation as well. One of the major conclusions from the study is that the procedures used in the study are useful for climate change impact analysis on future wind gusts. The implication of the significant increases in future wind gust risks would be useful to be considered when revising engineering infrastructure design standards and developing adaptation strategies and policies.</p> <div class="credits"> <p class="dwt_author">Shouquan Cheng, Chad; Li, Guilong</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">163</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.4235D"> <span id="translatedtitle">A statistical model to <span class="hlt">downscale</span> GCM output to wind speeds at turbine rotor height</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">During the last thirty years surface wind speed observations of the northern hemisphere show a declining trend of 10%. It is however unclear whether such a trend would continue in future, but if a reduction of this magnitude would continue over 50 years it would affect wind-power generation (McVicar et al, 2010). Global circulation models are useful tools to assess this trend, but their horizontal and vertical resolution is coarse and output parameters are of varying quality. Therefore we use a statistical model to <span class="hlt">downscale</span> GCM output to the height of the wind turbine rotor. The statistical <span class="hlt">downscaling</span> technique is based on a lineair relationship between rotor-height windspeed observations on the one hand and GCM modeled atmospheric variables on the other hand. Unlike most statistical <span class="hlt">downscaling</span> techniques, the predictors and predictands in the regression model are not the absolute values of the atmospheric variables, but the parameters of their distribution. This allows us to <span class="hlt">downscale</span> the entire wind speed distribution. The <span class="hlt">downscaling</span> technique, which was build up for a site in the Netherlands (Cabauw), is calibrated with ERA-interim reanalysis data, tested on a control period and applied to ECHAM5 present data and future scenario's. The ECHAM5 data has first been evaluated using ERA-interim reanalysis data and only the parameters that are adequately represented by the GCM were taken into account. This technique of <span class="hlt">downscaling</span> wind speed distributions parameters proves to be a appropriate method to study the average wind climate and the extremes in wind speed. Therefore it offers valuable information to wind energy industries. McVicar, T., Roderick, M., 2010, Winds of change, Nature Geoscience, Vol 3, 747-748</p> <div class="credits"> <p class="dwt_author">Devis, A.; Demuzere, M.; van Lipzig, N. P. M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">164</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMGC51A0740P"> <span id="translatedtitle">Weather Typing Statistical <span class="hlt">downscaling</span> with dsclim: diagnostics, and uncertainties in data provision for the impact community</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Recently, an innovative statistical methodology has been developed to <span class="hlt">downscale</span> climate simulations in France using a weather-typing approach (Boé et al., 2006), and further developed (Pagé et al., 2009). It has been used to <span class="hlt">downscale</span> 15 CMIP3 models as well as 7 Météo-France ARPEGE climate numerical model (Salas et al., 2005) simulations. In the framework of the ANR-SCAMPEI project, dsclim has been carefully configured to be able to <span class="hlt">downscale</span> climate simulations over France mountainous areas. Some new diagnostics have been developed to analyze the performance of the methodology and its configuration. In parallel, several projects to make these <span class="hlt">downscaled</span> climate scenarios available to the impact community are going on, notably GICC-DRIAS and EU-IS-ENES. In the context of IS-ENES, several national Use Cases have been developed to formalize the steps needed to provide climate scenarios suitable for the impact community starting from the global climate scenarios data, and also taking into account the uncertainties. References Pagé, C., L. Terray et J. Boé, 2009: dsclim: A software package to <span class="hlt">downscale</span> climate scenarios at regional scale using a weather-typing based statistical methodology. Technical Report TR/CMGC/09/21, CERFACS, Toulouse, France. Boé, J., L. Terray, F. Habets, et E. Martin, 2006: A simple statistical-dynamical <span class="hlt">downscaling</span> scheme based on weather types and conditional resampling. J. Geophys. Res., 111, D21106. Salas y Mélia, D., F. Chauvin, M. Déqué, H. Douville, J.-F. Guérémy, P. Marquet, S. Planton, J.-F. Royer, and S. Tyteca, 2005: Description and validation of CNRM-CM3 global coupled climate model. Technical report, Centre national de recherches météorologiques, Groupe de Météorologie de Grande Echelle et Climat, Météo-France.</p> <div class="credits"> <p class="dwt_author">Page, C.; Sanchez, E.; Terray, L.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">165</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..1213118B"> <span id="translatedtitle">Long-range Prediction of climatic Change in the Eastern Seaboard of Thailand over the 21st Century using various <span class="hlt">Downscaling</span> Approaches</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Triggered by a long drought, a huge water supply crisis took place at the Eastern Seaboard of Thailand (east of the Gulf of Thailand) in 2005. In that year no rainfall occurred for four months after the beginning of the rainy season which led to the situation that the industrial estates of the Eastern Seaboard were not able to fully operate. Normally, most of the urban and industrial water used in this coastal region along east of the Gulf of Thailand, which is part of the Pacific Ocean, is surface water stored in a large-scale reservoir-network across the main watershed in the region. Thus the three major reservoirs usually gather water from monsoon storms that blow from the South and provide accumulative 80% of the annual rainfall during the 6 months of the rainy season which normally lasts from May-October. During the dry season (November - April) the winds are blowing from northern Indo-China land mass and rain drops only a few days in a month. Because of this typical tropical climate system, surface water resources across most of the southeastern Asia-Pacific region and the Eastern Seaboard of Thailand, in particular, rely on the annual occurrence of the monsoon season. There is now sufficient evidence that the named extreme weather conditions of 2005 occurring in that part of Thailand are not a singularity, but might be another signal of recent ongoing climate change in that country as a whole. Because of this imminent threat to the water resources of the region, and for the set-up of appropriate water management schemes for the near future, a climate impact study is proposed here. More specifically, the water budget of the Khlong Yai basin, which is the main watershed of the Eastern Seaboard, is modeled using the distributed hydrological model SWAT. To that avail the watershed model is coupled sequentially to a global climate model (GCM), whereby the latter provides the input forcing parameters (e.g. precipitation and temperature) to the former. Because of 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 validated for years 2001-2006 at selected meteorological stations in the Khlong Yai basin, assuming t</p> <div class="credits"> <p class="dwt_author">Bejranonda, Werapol; Koch, Manfred; Koontanakulvong, Sucharit</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">166</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFM.H43A1168F"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of Gridded Rainfall and Their Impacts on Hydrological Response Analysis</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Water resource management and planning increasingly need to incorporate the effects of global climate change on regional climate variability in order to accurately assess future water supplies. Therefore future climate projections, particularly of rainfall, are of utmost interest to water resource management and water-users. General circulation models (GCMs) are the primary tool used to simulate present climate and project future climate. The outputs of GCMs are useful in understanding how future global climate responds to prescribed greenhouse gases emission scenarios. However GCMs do not provide realistic daily rainfall at scales below about 200 km, at which hydrological processes are typically assessed. Statistical <span class="hlt">downscaling</span> techniques have been developed to resolve the scale discrepancy between GCM climate change scenarios and the resolution required for hydrological impact assessment, based on the assumption that large-scale atmospheric conditions have a strong influence on local-scale weather. Gridded rainfall is important for a variety of scientific and engineering applications, including climate change detection, the evaluation of climate models, the parameterization of stochastic weather generators, as well as assessment of climate change impacts on regional hydrological regimes and water availability, whereas statistical <span class="hlt">downscaling</span> has predominantly provided daily rainfall series at the site (point) scale. The first part of the study explores the application of statistical <span class="hlt">downscaling</span> to gridded rainfall datasets using three methods: 1) statistically <span class="hlt">downscaling</span> to sites and then post-processing to interpolate to gridded rainfall; 2) treating each grid cell as an "observed" site for statistical <span class="hlt">downscaling</span> directly; and 3) treating each sub-catchment as an "observed" site and statistically <span class="hlt">downscaling</span> to sub-catchment averaged rainfall. The statistical <span class="hlt">downscaling</span> Nonhomogeneous Hidden Markov Model (NHMM), which models multi-site patterns of daily rainfall as a finite number of 'hidden' (i.e. unobserved) weather states, is used for a study region comprising several catchments of the southern Murray-Darling Basin (MDB) in south-eastern Australia, which until this year has been experiencing a decade long drought. The second part of the study investigates the impacts of different gridded rainfall on the hydrological response analysis by inputting them in to the calibrated hydrological model. These research results can be used as reference for application of statistical <span class="hlt">downscaling</span> method to generate gridded daily rainfall to quantify the hydrological responses to climatic change for long-term water management strategies.</p> <div class="credits"> <p class="dwt_author">Fu, G.; Charles, S. P.; Chiew, F. H.; Teng, J.; Frost, A. J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">167</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..1512144H"> <span id="translatedtitle">Evaluating dry and wet period changes using an <span class="hlt">ensemble</span> of GCMs, <span class="hlt">ENSEMBLES</span> RCMs and additional higher resolved RCMs</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Global and regional climate projections are connected with a multitude of uncertainties. Deriving robust information on future regional climate change, e.g., to develop climate change adaptation measures therefore is highly challenging. Within the German joint research project REGKLAM (development and implementation of a regional climate adaptation program for the model region Dresden) five regional climate models are applied - subsequently called REGKLAM models. They encompass dynamical and statistical <span class="hlt">downscaling</span> approaches, yet all approaches are based on the same GCM (ECHAM5 / MPI-OM). More GCMs were analysed in comparison to ECHAM5 to put the REGKLAM results in perspective. The <span class="hlt">ensemble</span> was even broadened by including RCMs from the <span class="hlt">ENSEMBLES</span> project. A total of 26 models (9 GCMS, 12 <span class="hlt">ENSEMBLES</span> RCMs and 5 REGKLAM RCMs) are included in the analysis, some of them with several realizations. Analyses focus on the SRES scenario A1B. The study area - model region of Dresden and surroundings - covers approximately 150 x 150 km. Depending on their spatial resolution, two to 15 GCM grid points were considered in the analysis. 30 grid points were analysed for the <span class="hlt">ENSEMBLES</span> RCMs, while the REGKLAM RCMs have an even better spatial resolution yielding in a number of 91 to 324 data points. Indicators were calculated separately for each grid point. The resulting change signals were then averaged for the study area. Daily time series of all models were analysed to explore future changes in dry and wet period duration. A wet/dry period is defined as a period of consecutive days with precipitation of above/less than or equal 1 mm. The whole collective of occurring wet and dry phases - ranging from durations of one day up to several weeks - is analysed applying a threshold exceedance probability analysis, whereby different durations of wet and dry periods were examined as threshold. The models were validated against observational data for 1961-2000. Those analyses show that most models underestimate the number of dry days and accordingly the duration of dry periods. Model validation furthermore showed that some models had major difficulties in representing the seasonal precipitation cycle of the study area. Projected changes for the 21st century are analysed for two periods (2021-2050 and 2071-2100) in comparison to the respective reference period 1961-1990. This approach of focusing on the model internal change signals helps dealing with individual model biases. The GCM ECHAM5 results, the basis for regionalisations and the development of adaptation measures within the REGKLAM project, lies in the midrange of all GCMs for wet period results, whereas ECHAM5 shows the strongest drying trends in summer. Generally the entire <span class="hlt">ensemble</span> shows trends towards wetter winters and drier summers that match the already observed trends. These are much more pronounced for the late 21st century, whereas the bandwidth for the mid 21st century is very wide. Long lasting dry periods show a strong increase in summer over a wide range of models, while there is a tendency for more wet days and longer wet periods in winter.</p> <div class="credits"> <p class="dwt_author">Hänsel, Stephanie; Mehler, Susann; Matschullat, Jörg</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">168</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/24051840"> <span id="translatedtitle">Coupled <span class="hlt">ensemble</span> flow line advection and analysis.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> run simulations are becoming increasingly widespread. In this work, we couple particle advection with pathline analysis to visualize and reveal the differences among the flow fields of <span class="hlt">ensemble</span> runs. Our method first constructs a variation field using a Lagrangian-based distance metric. The variation field characterizes the variation between vector fields of the <span class="hlt">ensemble</span> runs, by extracting and visualizing the variation of pathlines within <span class="hlt">ensemble</span>. Parallelism in a MapReduce style is leveraged to handle data processing and computing at scale. Using our prototype system, we demonstrate how scientists can effectively explore and investigate differences within <span class="hlt">ensemble</span> simulations. PMID:24051840</p> <div class="credits"> <p class="dwt_author">Guo, Hanqi; Yuan, Xiaoru; Huang, Jian; Zhu, Xiaomin</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">169</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.7194S"> <span id="translatedtitle">Development of new <span class="hlt">ensemble</span> methods based on the performace skills of regional climate models over South Korea</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">It is well known that multi-model <span class="hlt">ensembles</span> can reduce the uncertainties of the model results and increase the reliability of the model results. In this paper, the prediction skills for temperature and precipitation of five <span class="hlt">ensemble</span> methods were discussed by using the 20 years simulation results (from 1989 to 2008) by four regional climate models (RCMs : SNURCM, WRF, RegCM4, and RSM) driven by NCEP-DOE and ERA-interim boundary conditions. The simulation domain is CORDEX (COordinated Regional climate <span class="hlt">Downscaling</span> Experiment) East Asia and the number of grids is 197 x 233 grids with a 50-km horizontal resolution. The new three <span class="hlt">ensemble</span> methods, PEA_BRC, PEA_RAC and PEA_ROC, developed in this study, are performance based <span class="hlt">ensemble</span> averaging methods by using bias, RMSE (root mean square errors) and correlation, RMSE and absolute correlation, and RMSE and original correlation, respectively. The other two <span class="hlt">ensemble</span> methods are equal weighted averaging (EWA) and multivariate linear regression (Mul_Reg). Fifteen years and five years data from 20-year simulation data were used to derive the weighting coefficients and cross-validate the prediction skills of five <span class="hlt">ensemble</span> methods. The total number of training and evaluation is 20 times through a cyclic method from 20 years data. The Mul_Reg (EWA) method among the five <span class="hlt">ensemble</span> methods shows the best (worst) skill without regard to seasons and variables during the training period. And the PEA_RAC and PEA_ROC show very similar skills with Mul_Reg for all variables and seasons during training period. However, the skills and stabilities of Mul_Reg are drastically reduced when it applied to prediction regardless of variables and seasons. However, the skills and stabilities of PEA_RAC are slightly reduced. As a result, the PEA_RAC shows the best skill without regard to the seasons and variables during the prediction period. This result confirms that the new <span class="hlt">ensemble</span> methods developed in this study, the PEA_RAC, can be used for the prediction of regional climate without regard to the variables and averaging time scale. In addition, the simplicity of deriving process of weighting coefficients and applications are also the strong points of the <span class="hlt">ensemble</span> method, PEA_RAC.</p> <div class="credits"> <p class="dwt_author">Suh, M. S.; Oh, S. G.; Lee, D. K.; Cha, D. H.; Choi, S. J.; Hong, S. Y.; Kang, H. S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">170</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010GeoRL..37.8804E"> <span id="translatedtitle">Precursory signals in analysis <span class="hlt">ensemble</span> spread</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Time evolution of the <span class="hlt">ensemble</span> spread of an experimental <span class="hlt">ensemble</span> atmospheric reanalysis ALERA is examined in relation to various meteorological phenomena. The analysis <span class="hlt">ensemble</span> spread increases about two days prior to westerly bursts in the eastern Indian Ocean. Precursory signals are also found in the monsoon onset. The analysis <span class="hlt">ensemble</span> spread is large at the leading edge of the Somali jet and it grows as the jet extends eastward. Over Vietnam analysis <span class="hlt">ensemble</span> spread is maximized several weeks before the maximum of the monsoon westerlies. In the stratosphere the analysis <span class="hlt">ensemble</span> spread takes the maximum value a few days prior to a sudden warming. Our findings indicate that the <span class="hlt">ensemble</span> analysis contains additional information on atmospheric uncertainty of scientific interest, which may also have practical value.</p> <div class="credits"> <p class="dwt_author">Enomoto, Takeshi; Hattori, Miki; Miyoshi, Takemasa; Yamane, Shozo</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">171</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009AGUFM.A33A0210G"> <span id="translatedtitle">Performance of dynamical <span class="hlt">downscaling</span> for Colorado River basin</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The ongoing 2000s western U.S. drought has focused attention on drought susceptibility of the Colorado River basin. There is a concern that many climate models predict permanently drier conditions for the next century over the Colorado basin, however interpretation of these projections is complicated by their coarse spatial resolution which does not resolve the role of the relatively small mountain headwaters area that is the source of much of the basin’s runoff. Regional climate models (RCMs) are able to resolve these spatial scales, and for this reason arguably should be a preferred source of information about the future hydrology of the Colorado basin. We use the Advanced Research version of the Weather Research and Forecasting (WRF/ARW) regional climate model to explore the effects of climate change on the hydrology of the basin. Initially, we selected three years -- 1993 (wet), 2002 (dry), and 1980 (normal) as test cases, with boundary conditions from the NCEP/DOE reanalysis. For these years, we evaluated the impact of domain size through comparison with WRF runs performed for the North American Regional Climate Change Assessment Program (NARCCAP) Phase I, with particular attention to the Colorado River basin. We also tested spatial resolutions of 16 km and 25 km in addition to the NARCCAP 50 km spatial resolution. We then performed an 11-year current climate run for the period 1980-1990 with boundary conditions from the NCEP/DOE reanalysis at 50 km spatial resolution and compared spatial patterns of simulated winter precipitation and snow water equivalent (SWE) with the 1/8-degree historical North American Land Data Assimilation System (NLDAS) data set. Subsequently, we evaluated the impacts of projected future climate change on changes in the spatial distribution of winter precipitation and SWE using 10-year runs with boundary conditions taken from the CCSM General Circulation Model for current and mid-21st century boundary conditions. We also compared the RCM results for current and future climate with inferred changes taken directly from the GCM via statistical <span class="hlt">downscaling</span>.</p> <div class="credits"> <p class="dwt_author">Gao, Y.; Zhu, C.; Lettenmaier, D. P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">172</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2006ChJAS...6b.157R"> <span id="translatedtitle">Algorithm of <span class="hlt">Ensemble</span> Pulsar Time</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">An algorithm of the <span class="hlt">ensemble</span> pulsar time based on the Wiener filtration method has been constructed. This algorithm has allowed the separation of the contributions of an atomic clock and a pulsar itself to the post-fit pulsar timing residuals. The method has been applied to the timing data of the millisecond pulsars PSR B1855+09 and PSR B1937+21 and allowed to filter out the atomic scale component from the pulsar phase variations. Direct comparison of the terrestrial time TT(BIPM96) and the <span class="hlt">ensemble</span> pulsar time PTens has displayed that the difference TT(BIPM96) - PTens is within ±0.4 ?s range. A new limit of gravitational wave background based on the difference TT(BIPM96) - PTens was established to be ?_g {h}^2˜ 10-10.</p> <div class="credits"> <p class="dwt_author">Rodin, Alexander E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">173</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/9825637"> <span id="translatedtitle">Demography and the canonical <span class="hlt">ensemble</span>.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">The Gibbs canonical <span class="hlt">ensemble</span> of statistical mechanics is used to describe the probability distribution of the age classes of mothers of new-borns in an age-structured population. The Malthusian parameter emerges as a Lagrange multiplier corresponding to a generation time constraint, while a new perturbation parameter appears as the Lagrange multiplier corresponding to a maternity constraint. Classical Lotka stability reduces to the unperturbed case of the more general canonical <span class="hlt">ensemble</span> model. The model is used in a case study of the female (peninsular) Malaysian population of 1970. The Malthusian parameter and perturbation are calculated easily by linear regression. Use of the model identifies an anomaly in the population due to the effects of World War II. PMID:9825637</p> <div class="credits"> <p class="dwt_author">Smith, J D</p> <p class="dwt_publisher"></p> <p class="publishDate">1998-11-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">174</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010PhDT........87S"> <span id="translatedtitle">Cavity QED with atomic <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Cavity Quantum Electrodynamics has long been a proving grounds for the study of the interaction of light with matter. Historically the objective has typically been to couple one atom to one photon as strongly as possible. While this endeavor has yielded a variety of beautiful and groundbreaking results, we take a different approach. Inspired by the quantum repeater scheme of Duan, Lukin, Cirac and Zoller, we have built a cavity-<span class="hlt">ensemble</span> experiment, where the strong coupling between the light and the matter is achieved via the combination of the resonant enhancement of a cavity and a collective enhancement of an <span class="hlt">ensemble</span>. We investigate the capabilities and limitations of such an approach through a number of experiments. The first experiment we describe is a very-high-quality source of photon pairs of opposite polarization, but otherwise nearly-identical spectral properties. We proceed to a high-fidelity single photon source, and carefully investigate the decoherence mechanisms that limit the performance of such a system. Next we present the cavity-mediated transfer of a single collective excitation between atomic <span class="hlt">ensembles</span>, and deterministic entanglement generation. Lastly, we present a heralded, polarization preserving quantum memory. All of these experiments depend critically on the strong light-matter coupling afforded by the cavity-<span class="hlt">ensemble</span> interaction, and require increasingly more sophisticated state control of the atoms. Finally, we describe our new apparatus, combining a relatively long, high-finesse optical resonator with a 2microm dipole trap. We focus on the technical details of stabilizing the narrow resonator, and discuss briefly a proposal for high efficiency Quantum Non-Demolition photon detection. We conclude with preliminary data demonstrating single-atom detection.</p> <div class="credits"> <p class="dwt_author">Simon, Jonathan</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">175</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/23288332"> <span id="translatedtitle"><span class="hlt">Ensemble</span> learning incorporating uncertain registration.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalized analysis. This is achieved by incorporating registration uncertainty into an <span class="hlt">ensemble</span> learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalized feature data, e.g., grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an <span class="hlt">ensemble</span> learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel-based morphometry and deformation tensor-based morphometry over bootstrap aggregating, a common <span class="hlt">ensemble</span> learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important. PMID:23288332</p> <div class="credits"> <p class="dwt_author">Simpson, Ivor J A; Woolrich, Mark W; Andersson, Jesper L R; Groves, Adrian R; Schnabel, Julia A</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-12-27</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">176</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://pubs.er.usgs.gov/publication/70048367"> <span id="translatedtitle">Climate <span class="hlt">downscaling</span> effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">177</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/6370870"> <span id="translatedtitle">Generalization of a statistical <span class="hlt">downscaling</span> model to provide local climate change projections for Australia</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Climate change information required for impact studies is of a much finer spatial scale than climate models can directly provide. Statistical <span class="hlt">downscaling</span> models (SDMs) are commonly used to fill this scale gap. SDMs are based on the view that the regional climate is conditioned by two factors: (1) the large- scale climatic state and (2) local physiographic features. An SDM</p> <div class="credits"> <p class="dwt_author">B. Timbal; E. Fernandez; Z. Li</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">178</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=https://webspace.utexas.edu/err449/IEEE_Kaheil_Rosero_2007.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> and Forecasting of Evapotranspiration Using a Synthetic Model of Wavelets and Support Vector Machines</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Providing reliable forecasts of evapotranspiration (ET) at farm level is a key element toward efficient water manage- ment in irrigated basins. This paper presents an algorithm that provides a means to <span class="hlt">downscale</span> and forecast dependent variables such as ET images. Using the discrete wavelet transform (DWT) and support vector machines (SVMs), the algorithm finds multiple relationships between inputs and outputs</p> <div class="credits"> <p class="dwt_author">Yasir H. Kaheil; Enrique Rosero; M. Kashif Gill; Mac McKee; Luis A. Bastidas</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">179</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/60587079"> <span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> of GCM Simulations: Toward the Improvement of Forecast Bias over California</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">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</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">180</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFMGC21A0865K"> <span id="translatedtitle">Evaluation of Added Value to Precipitation Predictions using Regional <span class="hlt">Downscaling</span> in the Western United States</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In order to investigate regional predictions of future climate for the Western United States, predictions of precipitation from dynamically <span class="hlt">downscaled</span> regional climate model products were compared with the goal of determining how each method represents current and future spatial precipitation patterns. In particular, interannual precipitation patterns related to El Niño-Southern Oscillation (ENSO) forcing will be investigated for the Global Climate Models (GCMs) from the WCRP CMIP3 multi-model dataset for the SRES-A2 emissions scenario, the North American Regional Climate Change Assessment Program (NARCCAP) dynamically <span class="hlt">downscaled</span> projections, and the dynamically <span class="hlt">downscaled</span> simulations using the Desert Research Institute's Regional Climate Model (DRI-RCM) based on the Weather and Research Forecasting Model (WRFV3.2.1). DRI-RCM output consists of 36 and 12 km resolution products for the periods 1971-2000 and 2041-2070 driven with output from the Community Climate System Model 3.0 (CCSM). NARCCAP predictions provide 50 km resolution products using four different GCMs (emissions scenario SRES-A2) and a number of regional climate models (RCMs) over most of North America. Precipitation patterns were calculated for both central Pacific ENSO (CPAC) events and eastern Pacific ENSO (EPAC). Using the EPAC and CPAC indices to characterize ENSO events, composite maps of precipitation were created to analyze spatial precipitation patterns and examine the added value of using regional <span class="hlt">downscaling</span> methods.</p> <div class="credits"> <p class="dwt_author">King, K. C.; Mejia, J. F.; Koracin, D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_8");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' 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class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_9");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' href="#">4</a> <a onClick='return showDiv("page_5");' href="#">5</a> <a onClick='return showDiv("page_6");' href="#">6</a> <a onClick='return showDiv("page_7");' href="#">7</a> <a onClick='return showDiv("page_8");' href="#">8</a> <a onClick='return showDiv("page_9");' href="#">9</a> <a style="font-weight: bold;">10</a> <a onClick='return showDiv("page_11");' href="#">11</a> <a onClick='return showDiv("page_12");' href="#">12</a> <a onClick='return showDiv("page_13");' href="#">13</a> <a onClick='return showDiv("page_14");' href="#">14</a> <a onClick='return showDiv("page_15");' href="#">15</a> <a onClick='return showDiv("page_16");' href="#">16</a> <a onClick='return showDiv("page_17");' href="#">17</a> <a onClick='return showDiv("page_18");' href="#">18</a> <a onClick='return showDiv("page_19");' href="#">19</a> <a onClick='return showDiv("page_20");' href="#">20</a> <a onClick='return showDiv("page_21");' href="#">21</a> <a onClick='return showDiv("page_22");' href="#">22</a> <a onClick='return showDiv("page_23");' href="#">23</a> <a onClick='return showDiv("page_24");' href="#">24</a> <a onClick='return showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_11");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">181</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/56462424"> <span id="translatedtitle">Uncertainty and extremes analysis to evaluate dynamical <span class="hlt">downscaling</span> of climate models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Projections of climate extremes and regional change from global climate models, obtained both with and without dynamical <span class="hlt">downscaling</span> based on regional climate models, are systematically evaluated with a particular emphasis on their ability to support regional preparedness decisions. Global climate models show significant systematic biases at regional scales, some of which can be interpreted based on topographical or other features,</p> <div class="credits"> <p class="dwt_author">D. Das; E. Kodra; K. Steinhaeuser; S. Kao; A. R. Ganguly; M. L. Branstetter; D. J. Erickson; R. Flanery; M. M. Gonzalez; C. Hays; A. W. King; W. Lenhardt; R. Oglesby; R. M. Patton; C. M. Rowe; A. Sorokine; C. Steed</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">182</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012JGRD..11712118L"> <span id="translatedtitle">California reanalysis <span class="hlt">downscaling</span> at 10 km using an ocean-atmosphere coupled regional model system</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A fully coupled regional <span class="hlt">downscaling</span> system based on the Regional Spectral Model (RSM) for atmosphere and the Regional Ocean Modeling System (ROMS) for the ocean was developed for the purpose of <span class="hlt">downscaling</span> observed analysis or global model outputs. The two models share the same grid and resolution with efficient parallelization through the use of dual message passing interfaces. Coupled <span class="hlt">downscaling</span> was performed using historical Simple Ocean Data Assimilation (SODA) oceanic reanalysis and NCEP/DOE (R-2) atmospheric reanalysis in order to study the impact of coupling on the regional scale atmospheric analysis. The results were subsequently compared with the uncoupled <span class="hlt">downscaling</span> forced by the prescribed observed sea surface temperature (SST). The coupled experiment yielded the SST and ocean current with realistic small-scale oceanic features that are almost absent in the oceanic reanalysis. Upwelling over the California coast is well resolved and comparable to findings obtained from high-resolution observations. The coupling impact on the atmospheric circulation mainly modulates the near surface atmospheric variables when compared to the simulation conducted without coupling. The duration of the Catalina Eddy detected in the coupled experiment increased by about 6.5% when compared to that in the uncoupled experiment. The offshore land breeze is enhanced by about 10%, whereas the change in the onshore sea breeze is very small during the summer.</p> <div class="credits"> <p class="dwt_author">Li, Haiqin; Kanamitsu, Masao; Hong, Song-You</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-06-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">183</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..1510642B"> <span id="translatedtitle">A comparison of two classification based approaches for <span class="hlt">downscaling</span> of monthly PM10 concentrations</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Circulation type classifications may be utilised for the <span class="hlt">downscaling</span> of local climatic and environmental target variables in different methodological settings. In this contribution we apply and compare two different classification based approaches for <span class="hlt">downscaling</span> of monthly indices of PM10 concentrations (monthly mean and number of days exceeding a certain threshold) at different stations in Bavaria (Germany) during the period 1979 to 2010. The first approach uses monthly frequencies of circulation types as predictors in multiple linear regression models (stepwise regression) to estimate monthly predictand values (monthly PM10 indices). The second approach utilizes type specific mean values of the target variable - determined for a calibration period - to estimate predictand values in the validation period. Both approaches are run using varying circulation classifications. This comprises different methodological concepts for circulation classification (e.g. threshold based methods, leader algorithms, cluster analysis) and as well different temporal (1-day or multiple day sequences) and spatial domains (synoptic to continental scale). All models are applied to multiple calibration and validation samples and different skill scores (e.g. reduction of variance, Pearson R) are estimated for each of the validation samples in order to quantify model performance. As main preliminary findings we may state that: - the regression based <span class="hlt">downscaling</span> approach in most cases clearly outperforms the approach that uses type specific mean values (reference forecasting), - best skill is reached in winter (DJF) and spring (MAM), - comparable model skill is reached for the <span class="hlt">downscaling</span> of monthly means and extremes indicators (number of days exceeding a certain threshold).</p> <div class="credits"> <p class="dwt_author">Beck, Christoph; Weitnauer, Claudia; Jacobeit, Jucundus</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">184</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.hydrol-earth-syst-sci-discuss.net/3/1145/2006/hessd-3-1145-2006.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of daily precipitation with a stochastic weather generator for the subtropical region in South China</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Daily precipitation series at station or local scales is a critical input for rainfall-runoff modelling which, in turn, plays a vital role in the assessment of climate change impact on hydrologic processes and many other water resource studies. Future climate projected by General Circulation Models (GCMs) presents averaged values in large scales. Therefore, <span class="hlt">downscaling</span> techniques are usually needed to transfer</p> <div class="credits"> <p class="dwt_author">Y. D. Chen; X. Chen; C.-Y. Xu; Q. Shao</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">185</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/54420097"> <span id="translatedtitle">Assessing impacts of climate change in a semi arid watershed using <span class="hlt">downscaled</span> IPCC climate output</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This presentation discusses our research aimed at helping water managers at Salt River Project (SRP), Phoenix, assess long term climate change impacts for the Salt and Verde River basins, and make informed policy decisions. Our goal was to assess the future 100 year water balance by development, application and testing of a physically based distributed hydrologic model forced by <span class="hlt">downscaled</span></p> <div class="credits"> <p class="dwt_author">S. Rajagopal; F. Dominguez; H. V. Gupta; P. A. Troch; C. L. Castro</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">186</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.scmo.ca/Ao/articles/v360301.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> the hydrological cycle in the Mackenzie basin with the Canadian regional climate model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The Canadian Regional Climate Model (CRCM) has been nested within the Canadian Centre for Climate Modelling and Analysis ‘ second generation General Circulation Model (GCM), for a single month simulation over the Mackenzie River Basin and environs. The purpose of the study is to assess the ability of the higher resolution CRCM to <span class="hlt">downscale</span> the hydrological cycle of the nesting</p> <div class="credits"> <p class="dwt_author">Murray D. MacKay; Ronald E. Stewart; Guy Bergeron</p> <p class="dwt_publisher"></p> <p class="publishDate">1998-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">187</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.lsce.ipsl.fr/Phocea/file.php?class=pisp&reload=1244811993&file=mvrac/files/58/58_17_.pdf"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of near-surface wind over complex terrain in southern France</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Summary Accurate and rapid determination of near-surface wind fields in a complex area (orography, inhomogeneous surface properties) is a challenge for applications like the evalua- tion of wind energy production, the prediction of pollution transport and hazardous conditions for aeronautics and ship navigation, or the estimation of damage to farm plantations, among others. This paper presents a statistical <span class="hlt">downscaling</span> approach</p> <div class="credits"> <p class="dwt_author">T. Salameh; P. Drobinski; M. Vrac; P. Naveau</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">188</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013HESSD..10.7857T"> <span id="translatedtitle">Influence of <span class="hlt">downscaling</span> methods in projecting climate change impact on hydrological extremes of upper Blue Nile basin</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Methods from two statistical <span class="hlt">downscaling</span> categories were used to investigate the impact of climate change on high rainfall and flow extremes of the upper Blue Nile basin. The main <span class="hlt">downscaling</span> differences considered were on the rainfall variable while a generally similar method was applied for temperature. The applied <span class="hlt">downscaling</span> methods are a stochastic weather generator, LARS-WG, and an advanced change factor method, the Quantile Perturbation Method (QPM). These were applied on 10 GCM runs and two emission scenarios (A1B and B1). The <span class="hlt">downscaled</span> rainfall and evapotranspiration were input into a calibrated and validated lumped conceptual model. The future simulations were conducted for 2050s and 2090s horizon and were compared with 1980-2000 control period. From the results all <span class="hlt">downscaling</span> methods agree in projecting increase in temperature for both periods. Nevertheless, the change signal on the rainfall was dependent on the climate model and the <span class="hlt">downscaling</span> method applied. LARS weather generator was good for monthly statistics although caution has to be taken when it is applied for impact analysis dealing with extremes, as it showed a deviation from the extreme value distribution's tail shape. Contrary, the QPM method was good for extreme cases but only for good quality daily climate model data. The study showed the choice of <span class="hlt">downscaling</span> method is an important factor to be considered and results based on one <span class="hlt">downscaling</span> method may not give the full picture. Regardless, the projections on the extreme high flows and the mean main rainy season flow mostly showed a decreasing change signal for both periods. This is either by decreasing rainfall or increasing evapotranspiration depending on the <span class="hlt">downscaling</span> method.</p> <div class="credits"> <p class="dwt_author">Taye, M. T.; Willems, P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-06-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">189</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/22319486"> <span id="translatedtitle">A statistical description of neural <span class="hlt">ensemble</span> dynamics.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an <span class="hlt">ensemble</span>. Inferring these connections is often intractable because the set of possible interactions grows exponentially with <span class="hlt">ensemble</span> size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and <span class="hlt">ensemble</span> data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the <span class="hlt">ensemble</span> toward analyzing changes in the dynamics of the <span class="hlt">ensemble</span> as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of <span class="hlt">ensemble</span> data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available <span class="hlt">ensemble</span> data, and use an adaptive quantization technique to aggregate poorly estimated regions of the <span class="hlt">ensemble</span> data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and <span class="hlt">ensemble</span> sizes. Lastly, the performance of this method on both simulated and real <span class="hlt">ensemble</span> data is used to demonstrate its utility. PMID:22319486</p> <div class="credits"> <p class="dwt_author">Long, John D; Carmena, Jose M</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-11-28</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">190</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2006JHyd..330..621T"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of precipitation for climate change scenarios: A support vector machine approach</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be <span class="hlt">downscaled</span> to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical <span class="hlt">downscaling</span> of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based <span class="hlt">downscaling</span> model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional <span class="hlt">downscaling</span> using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical <span class="hlt">downscaling</span>, and are suitable for conducting climate impact studies.</p> <div class="credits"> <p class="dwt_author">Tripathi, Shivam; Srinivas, V. V.; Nanjundiah, Ravi S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-11-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">191</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMGC42A..03B"> <span id="translatedtitle">Precipitation <span class="hlt">downscaling</span> for hydrological applications using regional climate model outputs (Invited)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The purpose of this contribution is to provide hydrologically reasonable <span class="hlt">downscaled</span> precipitation series for climate change scenarios. Instead of using traditional statistical <span class="hlt">downscaling</span> methods which assume a stationary relationship between global variables and local precipitation we assume that changes in these relations are well modelled by regional climate models (RCMs). Because the RCMs estimate the rainfall over the blocks with bias (annual mean and extrema) we need to find ways of linking the information by <span class="hlt">downscaling</span> the rainfall distributions corresponding to the circulation patterns. The method used is to find the quantile-quantile (Q-Q) link between modeled values and observed data for each CP during a calibration period. This transformation is then used for RCM based future precipitation distributions. The methodology is used for the German part of the Rhine catchment. Three different RCMs are considered. First the CP based wetness indices (mean precipitation for days with given CP divided by the climatological mean) are calculated both for the observations and the RCM control period runs. These show a remarkable similarity, indicating a reasonable skill. Using a ten year validation period it turns out that the CP-based method delivers not only improved means but also impoved extremes compared to the RCM raw data. Because the correction was done for each 25 km block individually, the corrected values are good in detail and also for events (simultaneous occurrence of high or low quantiles). We then take the output of future scenarios (2021 to 2050) of the RCMs, which we call `modelled future', and use the Q-Q transform obtained from the control runs using the CP-based <span class="hlt">downscaling</span> approach during the 30 years of observations (1961 to 1991), to obtain the `<span class="hlt">downscaled</span> future'.</p> <div class="credits"> <p class="dwt_author">Bardossy, A.; Pegram, G. G.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">192</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010APJAS..46..425H"> <span id="translatedtitle">Future climate change scenarios over Korea using a multi-nested <span class="hlt">downscaling</span> system: A pilot study</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">This study examines a scenario of future summer climate change for the Korean peninsula using a multi-nested regional climate system. The global-scale scenario from the ECHAM5, which has a 200 km grid, was <span class="hlt">downscaled</span> to a 50 km grid over Asia using the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM). This allowed us to obtain large-scale forcing information for a one-way, double-nested Weather and Research Forecasting (WRF) model that consists of a 12 km grid over Korea and a 3 km grid near Seoul. As a pilot study prior to the multi-year simulation work the years 1995 and 2055 were selected for the present and future summers. This RSM-WRF multi-nested <span class="hlt">downscaling</span> system was evaluated by examining a <span class="hlt">downscaled</span> climatology in 1995 with the largescale forcing from the NCEP/Department of Energy (DOE) reanalysis. The changes in monsoonal flows over East Asia and the associated precipitation change scenario over Korea are highlighted. It is found that the RSM-WRF system is capable of reproducing large-scale features associated with the East-Asian summer monsoon (EASM) and its associated hydro-climate when it is nested by the NCEP/DOE reanalysis. The ECHAM5-based <span class="hlt">downscaled</span> climate for the present (1995) summer is found to suffer from a weakening of the low-level jet and sub-tropical high when compared the reanalysis-based climate. Predicted changes in summer monsoon circulations between 1995 and 2055 include a strengthened subtropical high and an intensified mid-level trough. The resulting projected summer precipitation is doubled over much of South Korea, accompanied by a pronounced surface warming with a maximum of about 2 K. It is suggested that <span class="hlt">downscaling</span> strategy of this study, with its cloud-resolving scale, makes it suitable for providing high-resolution meteorological data with which to derive hydrology or air pollution models.</p> <div class="credits"> <p class="dwt_author">Hong, Song-You; Moon, Nan-Kyoung; Lim, Kyo-Sun Sunny; Kim, Jong-Won</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-11-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">193</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/13294186"> <span id="translatedtitle"><span class="hlt">Ensemble</span> clustering using semidefinite programming with applications</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">In this paper, we study the <span class="hlt">ensemble</span> clustering problem, where the input is in the form of multiple clustering solutions.\\u000a The goal of <span class="hlt">ensemble</span> clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in\\u000a the input <span class="hlt">ensemble</span>. We obtain several new results for this problem. Specifically, we show that the notion of agreement under\\u000a such</p> <div class="credits"> <p class="dwt_author">Vikas Singh; Lopamudra Mukherjee; Jiming Peng; Jinhui Xu</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">194</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/4445944"> <span id="translatedtitle">Text Classification by Relearning and <span class="hlt">Ensemble</span> Computation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The k-nearest neighbor(k-NN) is improved by applying the distance functions with relearning and <span class="hlt">ensemble</span> computations to classify\\u000a text data with the higher accuracy values. The proposed relearning and combining <span class="hlt">ensemble</span> computations are an effective technique\\u000a for improving accuracy. We develop a new approach to combine kNN classifier based on weighted distance function with relearning\\u000a and <span class="hlt">ensemble</span> computations. The combining algorithm</p> <div class="credits"> <p class="dwt_author">Naohiro Ishii; Takahiro Yamada; Yongguang Bao</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">195</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AdWR...34..990P"> <span id="translatedtitle">A stochastic framework for <span class="hlt">downscaling</span> processes of spatial averages based on the property of spectral multiscaling and its statistical diagnosis on spatio-temporal rainfall fields</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Spectral multi-scaling postulates a power-law type of scaling of spectral distribution functions of stationary processes of spatial averages, over nested and geometrically similar sub-regions of the spatial parameter space of a given spatio-temporal random field. Presently a new framework is formulated for <span class="hlt">down-scaling</span> processes of spatial averages, following naturally from the postulate of spectral multi-scaling, and key ingredients required for its implementation are described. Moreover, results from an extensive diagnostic study are presented, seeking statistical evidence supportive of spectral multi-scaling. Such evidence emerges from two sources of data. One is a 13 year long historical record of radar observations of rainfall in southeastern UK (Chenies radar), with high spatial (2 km) and temporal (5 min) resolution. The other is an <span class="hlt">ensemble</span> of rain rate fields simulated by a spatio-temporal random pulse model fitted to the historical data. The results are consistent between historical and simulated rainfall data, indicating frequency-dependent scaling relationships interpreted as evidence of spectral multi-scaling across a range of spatial scales.</p> <div class="credits"> <p class="dwt_author">Pavlopoulos, Harry</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-08-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">196</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012FoPh...42.1239S"> <span id="translatedtitle">A Real <span class="hlt">Ensemble</span> Interpretation of Quantum Mechanics</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A new <span class="hlt">ensemble</span> interpretation of quantum mechanics is proposed according to which the <span class="hlt">ensemble</span> associated to a quantum state really exists: it is the <span class="hlt">ensemble</span> of all the systems in the same quantum state in the universe. Individual systems within the <span class="hlt">ensemble</span> have microscopic states, described by beables. The probabilities of quantum theory turn out to be just ordinary relative frequencies probabilities in these <span class="hlt">ensembles</span>. Laws for the evolution of the beables of individual systems are given such that their <span class="hlt">ensemble</span> relative frequencies evolve in a way that reproduces the predictions of quantum mechanics. These laws are highly non-local and involve a new kind of interaction between the members of an <span class="hlt">ensemble</span> that define a quantum state. These include a stochastic process by which individual systems copy the beables of other systems in the <span class="hlt">ensembles</span> of which they are a member. The probabilities for these copy processes do not depend on where the systems are in space, but do depend on the distribution of beables in the <span class="hlt">ensemble</span>. Macroscopic systems then are distinguished by being large and complex enough that they have no copies in the universe. They then cannot evolve by the copy law, and hence do not evolve stochastically according to quantum dynamics. This implies novel departures from quantum mechanics for systems in quantum states that can be expected to have few copies in the universe. At the same time, we are able to argue that the center of masses of large macroscopic systems do satisfy Newton's laws.</p> <div class="credits"> <p class="dwt_author">Smolin, Lee</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-10-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">197</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/21076261"> <span id="translatedtitle">Extended Gibbs <span class="hlt">ensembles</span> with flow</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Ison, M. J. [Departamento de Fisica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428 (Argentina); LPC Caen, ENSICAEN, Universite de Caen, CNRS/IN2P3, Caen (France); Gulminelli, F. [LPC Caen, ENSICAEN, Universite de Caen, CNRS/IN2P3, Caen (France); Dorso, C. O. [Departamento de Fisica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428 (Argentina)</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-11-15</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">198</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=479122"> <span id="translatedtitle">The <span class="hlt">Ensembl</span> Core Software Libraries</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">Systems for managing genomic data must store a vast quantity of information. <span class="hlt">Ensembl</span> stores these data in several MySQL databases. The core software libraries provide a practical and effective means for programmers to access these data. By encapsulating the underlying database structure, the libraries present end users with a simple, abstract interface to a complex data model. Programs that use the libraries rather than SQL to access the data are unaffected by most schema changes. The architecture of the core software libraries, the schema, and the factors influencing their design are described. All code and data are freely available.</p> <div class="credits"> <p class="dwt_author">Stabenau, Arne; McVicker, Graham; Melsopp, Craig; Proctor, Glenn; Clamp, Michele; Birney, Ewan</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">199</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..1213322M"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of global climate scenarios for the 21-st century to estimate hydrological extreme events in the Danube basin</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The goal of this study is to emphasize the changes in hydro meteorological variables simulated by global climate models, variables that represent good predictors for the Danube discharges. There were processed the following GCMs: CNRM - CM3, ECHAM5 - MPI, EGMAM and IPSL - CM4 by considering the A1B scenario within stream1 experiment in the <span class="hlt">ENSEMBLES</span> project, available from (http://cera-www.dkrz.de/). Daily values from March, April and May (MAM) for 42 years for: the pressure at sea level (SLP) over Europe (30N-65N; 0-40E), precipitation at 10 stations from the middle and lower Danube basin and discharges at Or?ova (situated in the Danube lower basin but representative for the middle basin) were analysed. The observation data are for the period 1958-1999 (web-sites ECMWF and ECA&D). In the 21-st century 2 periods (2009-2050) and (2051-2092) were considered in order to realise comparisons with the observations. For all simulations both the pressure and precipitation values were corrected of bias related to the reference period (1958-1999). In the pressure field, the predictors from three key zones were selected as being significant in the precipitation behaviour. These zones are centred on the points (45N; 12.5E), (42.5N; 17.5E) and (40N; 25E). The precipitation values are <span class="hlt">downscaled</span> by means of non homogeneous hidden Markov model (NHMM) with 7 states in which the three indices of sea level pressure: the laplacian values, the mean pressure values and the WE gradient values in the key zones are considered as predictors. After <span class="hlt">downscaling</span> the daily precipitation with NHMM, a simulation was done on 100 realizations each with 42 years and 90 days for each year. Then a fitting was done to GEV and GP distributions estimating in this way precipitation amounts corresponding to a return period of 100-years and probability distributions for 2 periods in the 21-st century (2009-2050) and (2051-2092). The results are slightly different for the two basins. For instance, it was observed that for the middle basin the models estimate an increasing of the return level compared with the observations while for the lower basin, the models indicate generally a decreasing of the return level corresponding to the return period of 100 years. Taking into account the link between local scale (Orsova discharges) and atmospheric circulation (SLP in the key zone obtained for the observed data), the estimations of the states of the atmospheric circulation in the 21-st century is achieved, by means of the simulations provided by the 4-GCMs. The results lead to the conclusions that an increase of the extreme hydrological events occurrence is expected especially in the second part of the 21- st century. In addition to the above description, daily values of precipitation, minimum and maximum temperature at 10 stations situated in the Danube middle and lower basin were analyzed from the point of view of occurred changes in the climate extremes indices (CEI) in 21- st century compared to the 20-th century, considering values simulated with the 2 global models CNRM and IPSL for 100 years. From the 27 CEI analyses for the extreme temperatures in the 21-st century comparative with the 20-th century, the most significant results show a significant increasing of the tropical nights number and of summer days, a decreasing of intervals with cold days and days with frost. Concerning the precipitation both the indices which put in evidence the dry periods and very wet intervals present a light trend of increase in the 21-st century in comparison with 20-th, namely there is an increase trend of the extreme events in the precipitation in this century in the comparison with the last century.</p> <div class="credits"> <p class="dwt_author">Mares, Ileana; Mares, Constantin; Stanciu, Petre; Mihailescu, Mihaela</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">200</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://eric.ed.gov/?q=community+AND+culture&pg=7&id=EJ971454"> <span id="translatedtitle">Joys of Community <span class="hlt">Ensemble</span> Playing: The Case of the Happy Roll Elastic <span class="hlt">Ensemble</span> in Taiwan</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p class="result-summary">|The Happy Roll Elastic <span class="hlt">Ensemble</span> (HREE) is a community music <span class="hlt">ensemble</span> supported by Tainan Culture Centre in Taiwan. With enjoyment and friendship as its primary goals, it aims to facilitate the joys of <span class="hlt">ensemble</span> playing and the spirit of social networking. This article highlights the key aspects of HREE's development in its first two years…</p> <div class="credits"> <p class="dwt_author">Hsieh, Yuan-Mei; Kao, Kai-Chi</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-01-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_9");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' 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showDiv("page_12");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">201</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/53945769"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River of China</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The applicability of the Statistical <span class="hlt">DownScaling</span> Method (SDSM) in the Haihe River basin of China was evaluated, and its strengths and weaknesses in simultaneously <span class="hlt">downscaling</span> air temperature, evaporation and precipitation were discussed. The used large scale atmospheric data were daily NCEP\\/NCAR reanalysis data and the daily emissions scenarios A2 and B2 of the HadCM3 model. Measured daily mean air temperature,</p> <div class="credits"> <p class="dwt_author">J. Chu</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">202</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/5206885"> <span id="translatedtitle">Forecast of iceberg <span class="hlt">ensemble</span> drift</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">El-Tahan, M.S.; El-Tahan, H.W.; Venkatesh, S.</p> <p class="dwt_publisher"></p> <p class="publishDate">1983-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">203</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013JMP....54h3507D"> <span id="translatedtitle">The beta-Wishart <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We introduce a ``broken-arrow'' matrix model for the ?-Wishart <span class="hlt">ensemble</span>, which improves on the traditional bidiagonal model by generalizing to non-identity covariance parameters. We prove that its joint eigenvalue density involves the correct hypergeometric function of two matrix arguments, and a continuous parameter ? > 0. If we choose ? = 1, 2, 4, we recover the classical Wishart <span class="hlt">ensembles</span> of general covariance over the reals, complexes, and quaternions. Jack polynomials are often defined as the eigenfunctions of the Laplace-Beltrami operator. We prove that Jack polynomials are in addition eigenfunctions of an integral operator defined as an average over a ?-dependent measure on the sphere. When combined with an identity due to Stanley, we derive a definition of Jack polynomials. An efficient numerical algorithm is also presented for simulations. The algorithm makes use of secular equation software for broken arrow matrices currently unavailable in the popular technical computing languages. The simulations are matched against the cdfs for the extreme eigenvalues. The techniques here suggest that arrow and broken arrow matrices can play an important role in theoretical and computational random matrix theory including the study of corners processes. We provide a number of simulations illustrating the extreme eigenvalue distributions that are likely to be useful for applications. We also compare the n --> ? answer for all ? with the free-probability prediction.</p> <div class="credits"> <p class="dwt_author">Dubbs, Alexander; Edelman, Alan; Koev, Plamen; Venkataramana, Praveen</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-08-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">204</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013SPIE.8743E..05M"> <span id="translatedtitle">Image change detection via <span class="hlt">ensemble</span> learning</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Martin, Benjamin W.; Vatsavai, Ranga R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">205</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/26685721"> <span id="translatedtitle">Performance enhancement of <span class="hlt">ensemble</span> empirical mode decomposition</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary"><span class="hlt">Ensemble</span> empirical mode decomposition (EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the amplitude of added white noise and the number of <span class="hlt">ensemble</span> trials. A test signal with mode mixing</p> <div class="credits"> <p class="dwt_author">Jian Zhang; Ruqiang Yan; Robert X. Gao; Zhihua Feng</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">206</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/48893998"> <span id="translatedtitle">A multisite seasonal <span class="hlt">ensemble</span> streamflow forecasting technique</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">We present a technique for providing seasonal <span class="hlt">ensemble</span> streamflow forecasts at several locations simultaneously on a river network. The framework is an integration of two recent approaches: the nonparametric multimodel <span class="hlt">ensemble</span> forecast technique and the nonparametric space-time disaggregation technique. The four main components of the proposed framework are as follows: (1) an index gauge streamflow is constructed as the sum</p> <div class="credits"> <p class="dwt_author">Cameron Bracken; Balaji Rajagopalan; James Prairie</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">207</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ucrel.lancs.ac.uk/acl/W/W02/W02-1029.pdf"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Methods for Automatic Thesaurus Extraction</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary"><span class="hlt">Ensemble</span> methods are state of the art for many NLP tasks. Recent work by Banko and Brill (2001) suggests that this would not necessarily be true if very large training corpora were available. However, their results are limited by the simplic- ity of their evaluation task and individual classiers. Our work explores <span class="hlt">ensemble</span> efcac y for the more complex task</p> <div class="credits"> <p class="dwt_author">James R. Curran</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">208</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://eric.ed.gov/?q=%22Wind+Power%22&pg=3&id=EJ430552"> <span id="translatedtitle">Fine-Tuning Your <span class="hlt">Ensemble</span>'s Jazz Style.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Garcia, Antonio J.</p> <p class="dwt_publisher"></p> <p class="publishDate">1991-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">209</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/6035801"> <span id="translatedtitle">Neural network <span class="hlt">ensembles</span> for time series forecasting</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This work provides an analysis of using the evolutionary al- gorithm EPNet to create <span class="hlt">ensembles</span> of artificial neural net- works to solve a range of forecasting tasks. Several previous studies have tested the EPNet algorithm in the classifica- tion field, taking the best individuals to solve the problem and creating <span class="hlt">ensembles</span> to improve the performance. But no studies have analyzed</p> <div class="credits"> <p class="dwt_author">Victor M. Landassuri-moreno; John A. Bullinaria</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">210</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012PhRvA..86a2310B"> <span id="translatedtitle">Optimizing inhomogeneous spin <span class="hlt">ensembles</span> for quantum memory</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We propose a method to maximize the fidelity of quantum memory implemented by a spectrally inhomogeneous spin <span class="hlt">ensemble</span>. The method is based on preselecting the optimal spectral portion of the <span class="hlt">ensemble</span> by judiciously designed pulses. This leads to significant improvement of the transfer and storage of quantum information encoded in the microwave or optical field.</p> <div class="credits"> <p class="dwt_author">Bensky, Guy; Petrosyan, David; Majer, Johannes; Schmiedmayer, Jörg; Kurizki, Gershon</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-07-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">211</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/323992"> <span id="translatedtitle">Analysis Scheme in the <span class="hlt">Ensemble</span> Kalman Filter</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This paper discusses an important issue related to the implementation and interpretation of the analysis scheme in the <span class="hlt">ensemble</span> Kalman filter. It is shown that the observations must be treated as random variables at the analysis steps. That is, one should add random perturbations with the correct statistics to the observations and generate an <span class="hlt">ensemble</span> of observations that then is</p> <div class="credits"> <p class="dwt_author">Gerrit Burgers; Peter Jan van Leeuwen; Geir Evensen</p> <p class="dwt_publisher"></p> <p class="publishDate">1998-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">212</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/50512237"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Weight Enumerators for Protograph LDPC Codes</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Recently, LDPC codes with projected graph, or protograph structures have been proposed. In this paper, finite length <span class="hlt">ensemble</span> weight enumerators for LDPC codes with protograph structures are obtained. Asymptotic results are derived as the block size goes to infinity. In particular, we are interested in obtaining <span class="hlt">ensemble</span> average weight enumerators for protograph LDPC codes which have typical minimum distance that</p> <div class="credits"> <p class="dwt_author">Dariush Divsalar</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">213</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ThApC.114..253R"> <span id="translatedtitle">Description and validation of a two-step analogue/regression <span class="hlt">downscaling</span> method</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">This study describes a two-step analogue statistical <span class="hlt">downscaling</span> method for daily temperature and precipitation. The first step is an analogue approach: the " n" days most similar to the day to be <span class="hlt">downscaled</span> are selected. In the second step, a multiple regression analysis using the " n" most analogous days is performed for temperature, whereas for precipitation, the probability distribution of the " n" analogous days is used to define the amount of precipitation. Verification of this method has been carried out for the Spanish Iberian Peninsula and the Balearic Islands. Results show good performance for temperature (BIAS close to 0.1 °C and mean absolute errors around 1.9 °C) and an acceptable skill for precipitation (reasonably low BIAS except in autumn with a mean of -18 %, mean absolute error lower than for a reference simulation, i.e. persistence and a well-simulated probability distribution according to two non-parametric tests of similarity).</p> <div class="credits"> <p class="dwt_author">Ribalaygua, J.; Torres, L.; Pórtoles, J.; Monjo, R.; Gaitán, E.; Pino, M. R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-10-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">214</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3349308"> <span id="translatedtitle">Temporal <span class="hlt">Downscaling</span> of Crop Coefficient and Crop Water Requirement from Growing Stage to Substage Scales</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">Crop water requirement is essential for agricultural water management, which is usually available for crop growing stages. However, crop water requirement values of monthly or weekly scales are more useful for water management. A method was proposed to <span class="hlt">downscale</span> crop coefficient and water requirement from growing stage to substage scales, which is based on the interpolation of accumulated crop and reference evapotranspiration calculated from their values in growing stages. The proposed method was compared with two straightforward methods, that is, direct interpolation of crop evapotranspiration and crop coefficient by assuming that stage average values occurred in the middle of the stage. These methods were tested with a simulated daily crop evapotranspiration series. Results indicate that the proposed method is more reliable, showing that the <span class="hlt">downscaled</span> crop evapotranspiration series is very close to the simulated ones.</p> <div class="credits"> <p class="dwt_author">Shang, Songhao</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">215</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMGC51A0745E"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of future changes in European precipitation using Model Output Statistics</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Regional- or local-scale precipitation changes cannot be directly inferred from precipitation simulated by General Circulation Models (GCMs) due to limited GCM spatial resolution. To overcome the problem, one possibility is to estimate regional precipitation through statistical <span class="hlt">downscaling</span>. For climate change studies the statistical links between large and small spatial scales are usually derived from real-world observations and then applied to the output of GCM simulations for the future climate. This approach requires the large-scale predictors from the GCM to be realistically simulated and is therefore known as 'Perfect-Prog(nosis)' (PP) <span class="hlt">downscaling</span>. An alternative approach to statistical <span class="hlt">downscaling</span> is 'Model Output Statistics' (MOS), where empirical corrections for simulated variables are formulated. Although used routinely in numerical weather prediction, application of MOS to climate change simulations is hampered by the fact that standard GCM simulations for historic periods do not represent the temporal evolution of random variability. In order to derive MOS corrections for simulated GCM precipitation we have conducted a simulation for the period 1958-2001 with the ECHAM5 GCM in which key circulation and temperature variables are nudged towards the ERA-40 reanalysis. This simulation thus is consistent with reality with respect to the large-scale weather variability, and MOS corrections that link simulated precipitation with regional observed precipitation can be derived from it. For this approach it is crucial that the simulated precipitation is not nudged towards observations and is calculated purely by the precipitation parameterisations in the GCM. MOS corrections of simulated monthly mean precipitation are shown to offer excellent potential for application in a statistical <span class="hlt">downscaling</span> methodology. Simple local scaling as well as different regression-based MOS <span class="hlt">downscaling</span> methods that use non-local predictors (Maximum Covariance Analysis, PC multiple linear regression) have been fitted and cross-validated using observations from the global GPCC gridded dataset, which has a spatial resolution of 0.5°×0.5°. Cross-validation shows that ECHAM5 precipitation is in many areas a very good predictor for the real precipitation given realistic synoptic-scale atmospheric states. MOS <span class="hlt">downscaling</span> models have been applied to the ECHAM5 simulation for the 21st century used in the IPCC Fourth Assessment Report (AR4) to estimate local mean changes in European seasonal precipitation in 2080-2099 relative to 1980-1999. All <span class="hlt">downscaling</span> models provide high-resolution projections of precipitation, and represent features not captured in the coarse GCM output. Both non-local methods in particular show good skill in resolving precipitation processes in areas of complex topography. Results suggest that local variability can account for considerable differences in projections of precipitation changes in raw and <span class="hlt">downscaled</span> climate change simulations. There is scope to extend this approach to other GCMs and for applying a similar methodology to daily precipitation distributions.</p> <div class="credits"> <p class="dwt_author">Eden, J. M.; Widmann, M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">216</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/48933548"> <span id="translatedtitle">Regional climate model <span class="hlt">downscaling</span> of the U.S. summer climate and future change</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A mesoscale model (MM5)–based regional climate model (CMM5) integration driven by the Parallel Climate Model (PCM), a fully coupled atmosphere-ocean-land-ice general circulation model (GCM), for the present (1986–1995) summer season climate is first compared with observations to study the CMM5's <span class="hlt">downscaling</span> skill and uncertainty over the United States. The results indicate that the CMM5, with its finer resolution (30 km)</p> <div class="credits"> <p class="dwt_author">Xin-Zhong Liang; Jianping Pan; Jinhong Zhu; Kenneth E. Kunkel; Julian X. L. Wang; Aiguo Dai</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">217</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/42008851"> <span id="translatedtitle">Regional climate model <span class="hlt">downscaling</span> of the U.S. summer climate and future change</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A mesoscale model (MM5)-based regional climate model (CMM5) integration driven by the Parallel Climate Model (PCM), a fully coupled atmosphere-ocean-land-ice general circulation model (GCM), for the present (1986-1995) summer season climate is first compared with observations to study the CMM5's <span class="hlt">downscaling</span> skill and uncertainty over the United States. The results indicate that the CMM5, with its finer resolution (30 km)</p> <div class="credits"> <p class="dwt_author">Xin-Zhong Liang; Jianping Pan; Jinhong Zhu; Kenneth E. Kunkel; Julian X. L. Wang; Aiguo Dai</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">218</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..15.9429S"> <span id="translatedtitle">Sensitivity of Hydrological Model Simulations to Underling Assumptions in a Stochastic <span class="hlt">Downscaling</span> method</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Climate Change Impacts Studies (CCIS) for Water Resources Management (WRM) are of crucial importance for the human community and especially for water scarce Mediterranean- like regions, where the available water is expected to decrease due to climate change. General Circulation Models (GCM) are one of the most valuable tools available to perform CCIS. However, they cannot be directly applied to water resources evaluations due to their coarse spatial resolution and bias in their simulation of certain outputs, especially precipitation. <span class="hlt">Downscaling</span> methods have been developed to address this problem, by defining statistical relationships between the variables simulated by GCMs and local observations. Once these relationships are defined and tested via post evaluation during a control period, the relationship is used to generate synthetic time series for the future, based on the different future climate scenarios simulated by the GCMs. For CCIS in WRM, synthetic time series of precipitation and temperature are applied as input variables to run hydrological models and obtain future projections of hydrological response. The main drawbacks of this procedure are: (1) inevitably we have to assume time stationary in the <span class="hlt">downscaling</span> parameters (which in principle can vary with climate change), and (2) The <span class="hlt">downscaling</span> parameterizations are another source of model uncertainties that must be quantified and communicated. Here, we evaluate the sensitivity of hydrological model simulations to assumptions underlying a <span class="hlt">downscaling</span> method based on a Stochastic Rainfall Generating process (SRGP). The method is used to demonstrate that exact daily rainfall sequences are not necessary for climate impacts assessment, and that the "stochastically equivalent" rainfall sequence simulations provided by the model are both sufficient, and provide important added value in terms of realistic assessments of uncertainty. The method also establishes which parameters of the rainfall generating process are primary controllers of the impacts caused by climate variability/change, and which must therefore be given special consideration during long-term climate simulations.</p> <div class="credits"> <p class="dwt_author">Sapriza, Gonzalo; Jodar, Jorge; Carrera, Jesús; Gupta, Hoshin V.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">219</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/15010453"> <span id="translatedtitle">Hydrologic Implications of Dynamical and Statistical Approaches to <span class="hlt">Downscaling</span> Climate Model Outputs</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">Six approaches for <span class="hlt">downscaling</span> climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical <span class="hlt">downscaling</span> methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical <span class="hlt">downscaling</span> via a Regional Climate Model (RCM – at ½-degree spatial resolution), for <span class="hlt">downscaling</span> the climate model outputs to the ?-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.</p> <div class="credits"> <p class="dwt_author">Wood, Andrew W.; Leung, Lai R.; Sridhar, V.; Lettenmaier, D. P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">220</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2008JGRD..113.9112L"> <span id="translatedtitle">Assessment of three dynamical climate <span class="hlt">downscaling</span> methods using the Weather Research and Forecasting (WRF) model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The common methodology in dynamical regional climate <span class="hlt">downscaling</span> employs a continuous integration of a limited-area model with a single initialization of the atmospheric fields and frequent updates of lateral boundary conditions based on general circulation model outputs or reanalysis data sets. This study suggests alternative methods that can be more skillful than the traditional one in obtaining high-resolution climate information. We use the Weather Research and Forecasting (WRF) model with a grid spacing at 36 km over the conterminous U.S. to dynamically <span class="hlt">downscale</span> the 1-degree NCEP Global Final Analysis (FNL). We perform three types of experiments for the entire year of 2000: (1) continuous integrations with a single initialization as usually done, (2) consecutive integrations with frequent re-initializations, and (3) as (1) but with a 3-D nudging being applied. The simulations are evaluated in a high temporal scale (6-hourly) by comparison with the 32-km NCEP North American Regional Reanalysis (NARR). Compared to NARR, the <span class="hlt">downscaling</span> simulation using the 3-D nudging shows the highest skill, and the continuous run produces the lowest skill. While the re-initialization runs give an intermediate skill, a run with a more frequent (e.g., weekly) re-initialization outperforms that with the less frequent re-initialization (e.g., monthly). Dynamical <span class="hlt">downscaling</span> outperforms bi-linear interpolation, especially for meteorological fields near the surface over the mountainous regions. The 3-D nudging generates realistic regional-scale patterns that are not resolved by simply updating the lateral boundary conditions as done traditionally, therefore significantly improving the accuracy of generating regional climate information.</p> <div class="credits"> <p class="dwt_author">Lo, Jeff Chun-Fung; Yang, Zong-Liang; Pielke, Roger A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-05-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_10");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" 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onClick='return showDiv("page_6");' href="#">6</a> <a onClick='return showDiv("page_7");' href="#">7</a> <a onClick='return showDiv("page_8");' href="#">8</a> <a onClick='return showDiv("page_9");' href="#">9</a> <a onClick='return showDiv("page_10");' href="#">10</a> <a onClick='return showDiv("page_11");' href="#">11</a> <a style="font-weight: bold;">12</a> <a onClick='return showDiv("page_13");' href="#">13</a> <a onClick='return showDiv("page_14");' href="#">14</a> <a onClick='return showDiv("page_15");' href="#">15</a> <a onClick='return showDiv("page_16");' href="#">16</a> <a onClick='return showDiv("page_17");' href="#">17</a> <a onClick='return showDiv("page_18");' href="#">18</a> <a onClick='return showDiv("page_19");' href="#">19</a> <a onClick='return showDiv("page_20");' href="#">20</a> <a onClick='return showDiv("page_21");' href="#">21</a> <a onClick='return showDiv("page_22");' href="#">22</a> <a onClick='return showDiv("page_23");' href="#">23</a> <a onClick='return showDiv("page_24");' href="#">24</a> <a onClick='return showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_13");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">221</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/52167255"> <span id="translatedtitle">Development of Spatiotemporal Bias-Correction Techniques for <span class="hlt">Downscaling</span> GCM Predictions</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM <span class="hlt">downscaling</span> techniques focus on preserving only observed temporal variability on point by point basis,</p> <div class="credits"> <p class="dwt_author">S. Hwang; W. D. Graham; J. Geurink; A. Adams; C. J. Martinez</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">222</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.springerlink.com/index/y51t869245045546.pdf"> <span id="translatedtitle">Dynamic <span class="hlt">downscaling</span> of global climate projections for Eastern Europe with a horizontal resolution of 7 km</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Climate change is one of the key factors influencing the quantity and quality of water resources in hydrologically sensitive\\u000a regions. In order to <span class="hlt">downscale</span> global climate simulations from horizontal resolutions of about 125–200 km to about 7 km, a\\u000a double nesting strategy was chosen. The modelling approach was implemented with the Regional Climate Model CCLM (COSMO-Climate\\u000a Local Model) with a first nesting</p> <div class="credits"> <p class="dwt_author">Dirk Pavlik; Dennis Söhl; Thomas Pluntke; Andriy Mykhnovych; Christian Bernhofer</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">223</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/51291931"> <span id="translatedtitle">Prospects for <span class="hlt">downscaling</span> seasonal precipitation variability using conditioned weather generator parameters</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This paper explores the use of synoptic-scale predictor variables to <span class="hlt">downscale</span> both high- and low-frequency components of daily precipitation at sites across the British Isles. Part I investigates seasonal and inter-annual variations in three weather generator parameters with respect to concurrent variations in a North Atlantic Oscillation (NAO) index and area-average sea surface temperature (SST) anomalies. Marked spatial gradients were</p> <div class="credits"> <p class="dwt_author">R. L. Wilby; D. Conway; P. D. Jones</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">224</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFM.H31B0994H"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image <span class="hlt">downscaling</span> has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform <span class="hlt">downscaling</span> of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for <span class="hlt">downscaling</span> problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the <span class="hlt">downscaled</span> 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.</p> <div class="credits"> <p class="dwt_author">Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">225</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.3130G"> <span id="translatedtitle">Use of a random cascade approach for <span class="hlt">downscaling</span> of GCMs projections of future precipitation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We present a Stochastic Space Random Cascade (SSRC) approach to <span class="hlt">downscale</span> precipitation from a GCM for the purpose of water resources projection under climate change scenarios for a meso-scale Italian Alpine watersheds, Oglio river (1440 km2). The snow fed Oglio river displays complex physiography and statistical <span class="hlt">downscaling</span> methods are required for hydrological projections, according to the Intergovernmental Panel on Climate Change (IPCC). First, a back cast analysis is carried out to evaluate the most representative within a set of four available GCMs (R30, ECHAM4, NCAR_PCM, HADCM3). Monthly precipitation for the window 1990-2000 from 270 gauging stations (one every 25 km2) in Northern Italy is used and scores from objective indicators are calculated. The SSRC model is locally tuned upon Oglio river for spatial <span class="hlt">downscaling</span> (approx. 2 km) of daily precipitation from NCAR_PCM, giving more accurate results for the area according to our preliminary findings. We use a 10 years (1990-1999) series of observed daily precipitation data from 25 rain gages. Scale Recursive Estimation coupled with Expectation Maximization algorithm is used for model estimation. Seasonal parameters of the multiplicative cascade are accommodated by statistical distributions conditioned upon climatic forcing, based on regression analysis. Main advantage of the SSRC is to reproduce spatial clustering, intermittency, self-similarity of precipitation fields and their spatial correlation structure, with relatively low computational burden. <span class="hlt">Downscaling</span> of projected future precipitation scenarios (A2 scenario from NCAR_PCM) is carried out, necessary for water budget pending climate change, and some preliminary conclusions are drawn.</p> <div class="credits"> <p class="dwt_author">Groppelli, Bibiana; Bocchiola, Daniele; Rosso, Renzo</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">226</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://horizon.ucsd.edu/maltmn/sasha/Hydromet%202000.pdf"> <span id="translatedtitle">Predicting and <span class="hlt">Downscaling</span> ENSO Impacts on Intraseasonal Precipitation Statistics in California: The 1997\\/98 Event</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Three long-range forecasting methods have been evaluated for prediction and <span class="hlt">downscaling</span> of seasonal and intraseasonal precipitation statistics in California. Full-statistical, hybrid-dynamical-statistical and full-dynamical approaches have been used to forecast El Nino-Southern Oscillation (ENSO)-related total precipitation, daily precipitation frequency, and average intensity anomalies during the January-March season. For El Nino winters, the hybrid approach emerges as the best performer, while La</p> <div class="credits"> <p class="dwt_author">Alexander Gershunov; Tim P. Barnett; Daniel R. Cayan; Tony Tubbs; Lisa Goddard</p> <p class="dwt_publisher"></p> <p class="publishDate">2000-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">227</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009JGRD..11412108N"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of short-term climate fluctuations: On the benefits of precipitation assimilation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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 was not assimilated.</p> <div class="credits"> <p class="dwt_author">Nunes, Ana M. B.; Roads, John O.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-06-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">228</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFM.H21E1083Z"> <span id="translatedtitle">Assimilation of precipitation-affected microwave radiances in a cloud-resolving WRF <span class="hlt">ensemble</span> data assimilation system</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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 observable innovations in radiance space are examined. The evaluation of background error covariance, in particular the cross-covariance between microphysical and dynamical variables will also be discussed.</p> <div class="credits"> <p class="dwt_author">Zhang, S. Q.; Zupanski, M.; Hou, A. Y.; Lin, X.; Cheung, S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">229</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012JSemi..33g5008L"> <span id="translatedtitle">A high-speed mixed-signal <span class="hlt">down-scaling</span> circuit for DAB tuners</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A high-speed mixed-signal <span class="hlt">down-scaling</span> circuit with low power consumption and low phase noise for use in digital audio broadcasting tuners has been realized and characterized. Some new circuit techniques are adopted to improve its performance. A dual-modulus prescaler (DMP) with low phase noise is realized with a kind of improved source-coupled logic (SCL) D-flip-flop (DFF) in the synchronous divider and a kind of improved complementary metal oxide semiconductor master-slave (CMOS MS)-DFF in the asynchronous divider. A new more accurate wire-load model is used to realize the pulse-swallow counter (PS counter). Fabricated in a 0.18-?m CMOS process, the total chip size is 0.6 × 0.2 mm2. The DMP in the proposed <span class="hlt">down-scaling</span> circuit exhibits a low phase noise of -118.2 dBc/Hz at 10 kHz off the carrier frequency. At a supply voltage of 1.8 V, the power consumption of the <span class="hlt">down-scaling</span> circuit's core part is only 2.7 mW.</p> <div class="credits"> <p class="dwt_author">Lu, Tang; Zhigong, Wang; Jiahui, Xuan; Yang, Yang; Jian, Xu; Yong, Xu</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-07-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">230</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009EGUGA..11.7642V"> <span id="translatedtitle">Test of a dynamical <span class="hlt">downscaling</span> chain for assessing climate at regional scale.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Vargiu, A.; Peneva, E.; Marrocu, M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">231</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012JPhA...45W4006H"> <span id="translatedtitle">Percolation in the canonical <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We study the bond percolation problem under the constraint that the total number of occupied bonds is fixed, so that the canonical <span class="hlt">ensemble</span> applies. We show via an analytical approach that at criticality, the constraint can induce new finite-size corrections with exponent ycan = 2yt - d both in energy-like and magnetic quantities, where yt = 1/? is the thermal renormalization exponent and d is the spatial dimension. Furthermore, we find that while most of the universal parameters remain unchanged, some universal amplitudes, like the excess cluster number, can be modified and become non-universal. We confirm these predictions by extensive Monte Carlo simulations of the two-dimensional percolation problem which has ycan = -1/2. This article is part of ‘Lattice models and integrability’, a special issue of Journal of Physics A: Mathematical and Theoretical in honour of F Y Wu's 80th birthday.</p> <div class="credits"> <p class="dwt_author">Hu, Hao; Blöte, Henk W. J.; Deng, Youjin</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">232</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012ClDy..tmp..303F"> <span id="translatedtitle">Precipitation and temperature space-time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical <span class="hlt">downscaling</span> methods</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">This study evaluates how statistical and dynamical <span class="hlt">downscaling</span> models as well as combined approach perform in retrieving the space-time variability of near-surface temperature and rainfall, as well as their extremes, over the whole Mediterranean region. The dynamical <span class="hlt">downscaling</span> model used in this study is the Weather Research and Forecasting (WRF) model with varying land-surface models and resolutions (20 and 50 km) and the statistical tool is the Cumulative Distribution Function-transform (CDF-t). To achieve a spatially resolved <span class="hlt">downscaling</span> over the Mediterranean basin, the European Climate Assessment and Dataset (ECA&D) gridded dataset is used for calibration and evaluation of the <span class="hlt">downscaling</span> models. In the frame of HyMeX and MED-CORDEX international programs, the <span class="hlt">downscaling</span> is performed on ERA-I reanalysis over the 1989-2008 period. The results show that despite local calibration, CDF-t produces more accurate spatial variability of near-surface temperature and rainfall with respect to ECA&D than WRF which solves the three-dimensional equation of conservation. This first suggests that at 20-50 km resolutions, these three-dimensional processes only weakly contribute to the local value of temperature and precipitation with respect to local one-dimensional processes. Calibration of CDF-t at each individual grid point is thus sufficient to reproduce accurately the spatial pattern. A second explanation is the use of gridded data such as ECA&D which smoothes in part the horizontal variability after data interpolation and damps the added value of dynamical <span class="hlt">downscaling</span>. This explains partly the absence of added-value of the 2-stage <span class="hlt">downscaling</span> approach which combines statistical and dynamical <span class="hlt">downscaling</span> models. The temporal variability of statistically <span class="hlt">downscaled</span> temperature and rainfall is finally strongly driven by the temporal variability of its forcing (here ERA-Interim or WRF simulations). CDF-t is thus efficient as a bias correction tool but does not show any added-value regarding the time variability of the <span class="hlt">downscaled</span> field. Finally, the quality of the reference observation dataset is a key issue. Comparison of CDF-t calibrated with ECA&D dataset and WRF simulations to local measurements from weather stations not assimilated in ECA&D, shows that the temporal variability of the <span class="hlt">downscaled</span> data with respect to the local observations is closer to the local measurements than to ECA&D data. This highlights the strong added-value of dynamical <span class="hlt">downscaling</span> which improves the temporal variability of the atmospheric dynamics with regard to the driving model. This article highlights the benefits and inconveniences emerging from the use of both <span class="hlt">downscaling</span> techniques for climate research. Our goal is to contribute to the discussion on the use of <span class="hlt">downscaling</span> tools to assess the impact of climate change on regional scales.</p> <div class="credits"> <p class="dwt_author">Flaounas, Emmanouil; Drobinski, Philippe; Vrac, Mathieu; Bastin, Sophie; Lebeaupin-Brossier, Cindy; Stéfanon, Marc; Borga, Marco; Calvet, Jean-Christophe</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-10-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">233</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/323739"> <span id="translatedtitle">Verification of GCM-generated regional seasonal precipitation for current climate and of statistical <span class="hlt">downscaling</span> estimates under changing climate conditions</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">Empirical <span class="hlt">downscaling</span> procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a <span class="hlt">downscaling</span> technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical <span class="hlt">downscaling</span> procedures in climate change applications. The case considered is regional seasonal precipitation in Romania. The <span class="hlt">downscaling</span> model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). The climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in time-slice mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050. Generally, applications of statistical <span class="hlt">downscaling</span> to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 x CO{sub 2} GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical <span class="hlt">downscaling</span>. Since the skill of the GCMs in regional terms is already established, it is concluded that the <span class="hlt">downscaling</span> technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.</p> <div class="credits"> <p class="dwt_author">Busuioc, A. [National Inst. of Meteorology and Hydrology, Bucharest (Romania); Storch, H. von; Schnur, R. [GKSS Research Center, Geesthacht (Germany). Inst. of Hydrophysics</p> <p class="dwt_publisher"></p> <p class="publishDate">1999-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">234</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010ems..confE..73B"> <span id="translatedtitle">Using dynamically <span class="hlt">downscaled</span> GCM outputs in hydrological models: a case study from Tasmania, Australia</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Modelling future runoff by running meteorological projections from global climate models (GCMs) directly through hydrological models presents considerable technical challenges, but promises several advantages over the so-called ‘perturbation method'. The Climate Futures for Tasmania project has projected water yield in Tasmania, Australia to 2100. This paper describes how the Climate Futures for Tasmania project used dynamically <span class="hlt">downscaled</span> climate projections directly in hydrological models to produce useable information for water managers and industry. Tasmania is a difficult region for climate change hydrology studies. Tasmanian rainfall is generated by complex regional weather systems such as atmospheric blocking that are not always well-represented in GCM-scale projections. Further, the spatial resolution of GCMs is too coarse to represent the complex distribution of Tasmanian rainfall. Rainfall changes caused by changes in these regional weather systems may not be predicted by GCMs. Previous studies of climate change impacts on Tasmanian rivers have used the ‘perturbation method', where historical rainfall and evaporation data are modified to reflect changes predicted by GCMs. In this method rainfall events occur exactly as often as in the historical record - only the magnitude of events changes. This can mask long-term effects on runoff caused by changes in the timing or duration of rainfall events due to climate change. We avoided this problem by dynamically <span class="hlt">downscaling</span> six GCMs with the regional climate model CCAM to a spatial resolution of 0.1 degrees under the A2 SRES emissions scenario. Dynamical <span class="hlt">downscaling</span> is computing-intensive, but can simulate changes to rain-bearing weather systems (e.g. increases in convective storms). <span class="hlt">Downscaled</span> hindcasts generally showed excellent spatial and temporal agreement with climate observations. However, some spatial biases were still evident. To account for these biases, modelled rainfall and evaporation were bias-adjusted by percentile to observations for 1961-2007. A premise of bias-adjustment is that discrepancies between observed and modelled data are constant through time. A leave-one-decade-out test was devised to demonstrate that the biases were constant through time. Existing statewide hydrologic models were adapted to accept the bias-adjusted dynamically <span class="hlt">downscaled</span> GCM projections. Five runoff models were available: AWBM, Ihacres, Sacramento, Simhyd, and SMARG. Each of these models reacts differently to climate inputs. The uncertainty in projected changes to runoff due to the choice of hydrological model was assessed for one GCM. <span class="hlt">Downscaled</span> projections from all six GCMs were run through the Simhyd model to produce runoff at a 0.05 degree statewide grid. Runoff was aggregated to river basins, and human activities such as irrigation and hydropower generation were accounted for. Model hindcasts of river flows showed very good agreement with observed flows. Impacts on future runoff were highly spatially heterogeneous, demonstrating the value of high resolution <span class="hlt">downscaling</span> for hydrologic projections. There was some evidence that changes in rain-bearing systems - such as the incidence of convective storms - will influence water yields in certain catchments. The result is one of the most detailed regional climate change hydrology studies in Australia. The hydrologic projections have been tailored to the needs of water managers and industry, ensuring the research will be understandable and useable.</p> <div class="credits"> <p class="dwt_author">Bennett, J.; Grose, M.; Ling, F.; Corney, S.; Holz, G.; White, C.; Graham, B.; Post, D.; Bindoff, N.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">235</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2008PhyA..387.3384L"> <span id="translatedtitle">The ?n statistic for the ?-Hermite <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The fluctuation ?n of the nth unfolded eigenvalue was recently characterized for the classical Gaussian <span class="hlt">ensembles</span> of N×N random matrices (GOE, GUE, GSE). It is investigated here for the ?-Hermite <span class="hlt">ensemble</span> as a function of the reciprocal of the temperature ? by Monte Carlo simulations. The <span class="hlt">ensemble</span>-averaged fluctuation <?n2> and the autocorrelation function vary logarithmically with n for any ?>0 (1?n?N). The simple logarithmic behavior of the higher-order moments of ?n, reported in the literature for the GOE (?=1) and the GUE (?=2), holds for any ?>0 and is accounted for by Gaussian distributions whose variances depend linearly on lnn.</p> <div class="credits"> <p class="dwt_author">Le Caër, G.; Male, C.; Delannay, R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-06-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">236</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFM.A21G0197W"> <span id="translatedtitle">A method to treat climate changes of year-to-year variations in the pseudo-global-warming method as a dynamical <span class="hlt">downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The pseudo-global-warming (PGW) method is a time-slice dynamical <span class="hlt">downscaling</span> one developed by Kimura and Kitoh (2007) to obtain regional climate change information with finer resolutions. In the present climate experiment, regional climate model (RCM) experiment is carried out with objective analysis data (ANAL) as the lateral boundary conditions. On the other hand, in the future climate experiment, the lateral boundary conditions are the sum of the ANAL and the difference between the present and future climates by an atmosphere-ocean general circulation model (AOGCM). The advantage of this method is that the influences of biases of the AOGCMs are reduced. In addition, the number of <span class="hlt">downscaling</span> experiments could be reduced in multi-model problems, namely, a RCM experiment result with the boundary conditions created by a multi-model <span class="hlt">ensemble</span> mean of AOGCMs seems to be similar to the average of the results of RCMs with their AOGCMs. However, PGW method involves some problems. One of them is that climate changes in year-to-year variations are ignored. To overcome this problems, a new method is introduced. In the new method, a mean climatological difference of a AOGCM is added to ANAL in future climate experiment, which is the same as PGW method. Next, the year-to-year variation term of ANAL, AOGCM in the present climate (GCM-P), and that in the future climate (GCM-F) are normalized in each level and element of ANAL to X’a, X’p, and X’f, respectively. The eigenvector of X’a (Va) is extracted by Principal Component Analysis (PCA). Only Va is trusted among the variations of ANAL, GCM-P, and GCP-F. Thus, the coefficients (Ta, Tp, and Tf) of the Multiple Regression Analysis (MRA) with Va are examined, and a coefficient (Tw) for Va are newly estimated as the variation term of boundary conditions of RCM in the future climate (X’w). To create Tw, three-step calculations are included in the estimation. First, a matrix operator, that the covariance of coefficients matrix of GCM-P is changed into that of GCM-P, is calculated using the lower triangle matrix made by the Cholesky decomposition. The operation is carried out for Ta, and Tw1 and its covariance Cw1 are estimated. Second, the reliability of Tw1 and Cw1 are investigated. The Cw1 is modified using a weight matrix W into Cw2. The details are omitted, but the unreliable variation mode is mostly replaced by that of the ANAL. Tw2 are calculated from Cw2. Third, The total amplitude of the variations is adjusted. The total amplitude of Cw2 (Pw), that is a trace of orthogonalized Cw2, is somewhat small, because all variations of Tw could not be expressed by Va. To amplify Pw, Cw is defined as Qw/Pw*Cw2. Here, Qw=Qf/Qp*Qa. Qa (Qp, Qf) is a trace of covariance of coefficient, which is estimated by a PCA and a MRA with ANAL (GCM-P, GCM-F). The final coefficient Tw is calculated by Cw. Consequently, a suitable year-to-year variation pattern is created for the boundary conditions of PGW experiment in the future climate. Here, differences of the boundary conditions between the present and future climate are constant in the normal PGW method. However, these vary with year in the new method. In the multi-model <span class="hlt">downscaling</span> problem, the statistical operations such as the average are carried out for Cw.</p> <div class="credits"> <p class="dwt_author">Wakazuki, Y.; Hara, M.; Kimura, F.; Regional Climate Modeling Research Team</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">237</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.5790S"> <span id="translatedtitle">A chaotic-dynamic approach for <span class="hlt">downscaling</span> of global climate model (GCM) outputs: A case study for the Korean peninsula</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Together with population explosion and other socio-economic factors, natural hydroclimatic extremes (e.g. floods, droughts) crucially influence the planning and management of our water resources and environment. The vital role of these extremes in water and environmental disasters, diseases, and associated losses is abundantly clear, as 900 million people still lack access to safe drinking water, 2.5 billion people lack access to proper sanitation, millions of people die and billions of dollars are lost every year from water-related disasters and diseases. Global climate change, which is anticipated to result in more frequent and more intense hydroclimatic extremes, will most likely make our future water situation far more challenging. An important step, at the current time, in the assessment of impacts of climate change on regional and local water resources is the ‘<span class="hlt">downscaling</span>' of coarse-scale outputs from global climate models (GCMs) to catchment-scale hydroclimatic variables (especially rainfall) for use in hydrologic models. The existing <span class="hlt">downscaling</span> techniques may be grouped under two broad categories: statistical <span class="hlt">downscaling</span> and dynamical <span class="hlt">downscaling</span>. Although both these techniques generally provide reasonable results, they do not explicitly take into account and adequately represent the inherent nonlinear, and in particular chaotic, dynamic nature of the climate system and the associated processes. To this end, the present study proposes a chaotic dynamic-based <span class="hlt">downscaling</span> approach. The suitability and effectiveness of this approach are tested through its application for the Korean peninsula.</p> <div class="credits"> <p class="dwt_author">Sivakumar, Bellie; Kyoung, Minsoo; Kim, Hungsoo</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">238</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009pcms.confE..42G"> <span id="translatedtitle">Spatial <span class="hlt">downscaling</span> of precipitation from AOGCMs for climate change projections using random cascades: a case study in Italy</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We present a Stochastic Space Random Cascade (SSRC) approach to <span class="hlt">downscale</span> precipitation from a GCMs for the purpose of water resources projection under climate change scenarios for a meso-scale Italian Alpine watersheds, Oglio rivers (1600 km2). Snow fed Oglio river displays complex physiography and high environmental gradient, and statistical <span class="hlt">downscaling</span> methods are required for climate change assessment, according to the Intergovernmental Panel on Climate Change (IPCC). The SSRC model is locally tuned upon Oglio river for spatial <span class="hlt">downscaling</span> (approx. 1 km) of daily precipitation from NCAR_PCM, giving more accurate results for the area according to our preliminary findings. We use a 10 years (1990-1999) series of observed daily precipitation data from 25 rain gages. Scale Recursive Estimation coupled with Expectation Maximization algorithm is used for model estimation. Seasonal parameters of the multiplicative cascade are accommodated by statistical distributions conditioned upon climatic forcing, based on regression analysis. Main advantage of the SSRC is to reproduce spatial clustering, intermittency, self-similarity of precipitation fields and their spatial correlation structure, with relatively low computational burden. Projections of future <span class="hlt">downscaled</span> precipitation scenarios (A2 scenario from NCAR_PCM) are given, necessary for water budget pending climate change, and some preliminary conclusions are drawn. Key words: climate change; precipitation; AOGCMs; statistical <span class="hlt">downscaling</span>.</p> <div class="credits"> <p class="dwt_author">Groppelli, B.; Bocchiola, D.; Rosso, R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">239</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/50993673"> <span id="translatedtitle">Genetic Algorithm Based Selective <span class="hlt">Ensemble</span> with Multiset Representation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Recently, it has been shown that, in <span class="hlt">ensemble</span> learning, it may be preferable to <span class="hlt">ensemble</span> some instead of all the classifiers. Various selective <span class="hlt">ensemble</span> approaches are then designed, where optimization algorithms like genetic algorithm (GA) are used to evolve weights of component classifiers and classifiers with weights greater than a threshold are selected. This paper proposes a novel selective <span class="hlt">ensemble</span></p> <div class="credits"> <p class="dwt_author">Gang Wang; Xinshun Xu; Liang Peng</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">240</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013MPLB...2730019R"> <span id="translatedtitle">Quantum Dynamics with AN <span class="hlt">Ensemble</span> of Hamiltonians</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We review recent progress in the nonequilibrium dynamics of thermally isolated many-body quantum systems, evolving with an <span class="hlt">ensemble</span> of Hamiltonians as opposed to deterministic evolution with a single time-dependent Hamiltonian. Such questions arise in (i) quantum dynamics of disordered systems, where different realizations of disorder give rise to an <span class="hlt">ensemble</span> of real-time quantum evolutions, (ii) quantum evolution with noisy Hamiltonians (temporal disorder), which leads to stochastic Schrödinger equations, and, (iii) in the broader context of quantum optimal control, where one needs to analyze an <span class="hlt">ensemble</span> of permissible protocols in order to find one that optimizes a given figure of merit. The theme of <span class="hlt">ensemble</span> quantum evolution appears in several emerging new directions in noneqilibrium quantum dynamics of thermally isolated many-body systems, which include many-body localization, noise-driven systems, and shortcuts to adiabaticity.</p> <div class="credits"> <p class="dwt_author">Rahmani, Armin</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-10-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_11");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" 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showDiv("page_24");' href="#">24</a> <a onClick='return showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_14");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">241</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ntis.gov/search/product.aspx?ABBR=ADA268338"> <span id="translatedtitle">Soldier Integrated Protective <span class="hlt">Ensemble</span>: The Soldiers' Perspective.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ntis.gov/search/index.aspx">National Technical Information Service (NTIS)</a></p> <p class="result-summary">The field portion of the Soldier Integrated Protective <span class="hlt">Ensemble</span> (SIPE) Advanced Technology Demonstration (ATD) was conducted at Fort Benning, Georgia, from September through November 1992. Individual task performance-data were collected by the Test and Ex...</p> <div class="credits"> <p class="dwt_author">M. S. Salter</p> <p class="dwt_publisher"></p> <p class="publishDate">1993-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">242</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ntis.gov/search/product.aspx?ABBR=ADA289407"> <span id="translatedtitle">Reactivation of Hippocampal <span class="hlt">Ensemble</span> Memories During Sleep.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ntis.gov/search/index.aspx">National Technical Information Service (NTIS)</a></p> <p class="result-summary">Simultaneous recordings were made from large <span class="hlt">ensembles</span> of hippocampal place cells in three rats during spatial behavioral tasks and in slow-wave sleep preceding and following these behaviors. Cells that fired together when the animal occupied particular l...</p> <div class="credits"> <p class="dwt_author">M. A. Wilson B. L. McNaughton</p> <p class="dwt_publisher"></p> <p class="publishDate">1994-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">243</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2039884"> <span id="translatedtitle"><span class="hlt">Ensemble</span> refinement of protein crystal structures</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">Summary X-ray crystallography typically uses a single set of coordinates and B-factors to describe macromolecular conformations. Refinement of multiple copies of the entire structure has been previously used in specific cases as an alternative means of representing structural flexibility. Here, we systematically validate this method using simulated diffraction data, and find <span class="hlt">ensemble</span> refinement produces better representations of the distributions of atomic positions in the simulated structures than single conformer refinements. Comparison of principal components calculated from the refined <span class="hlt">ensembles</span> and simulations shows that concerted motions are captured locally, but correlations dissipate over long distances. <span class="hlt">Ensemble</span> refinement is also used on 50 experimental structures of varying resolution, and leads to decreases in R-free, implying that improvements in the representation of flexibility observed for the simulated structures may apply to real structures. These gains are essentially independent of resolution or data-to-parameter ratio, suggesting even structures at moderate resolution can benefit from <span class="hlt">ensemble</span> refinement.</p> <div class="credits"> <p class="dwt_author">Levin, Elena J.; Kondrashov, Dmitry A.; Wesenberg, Gary E.; Phillips, George N.</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">244</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://eric.ed.gov/?q=Brechts+AND+Verfremdung&id=EJ092927"> <span id="translatedtitle">"Verfremdung" in Action at the Berliner <span class="hlt">Ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p class="result-summary">|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> <div class="credits"> <p class="dwt_author">Brown, Thomas K.</p> <p class="dwt_publisher"></p> <p class="publishDate">1973-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">245</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011JPhCS.327a2049C"> <span id="translatedtitle">Atomic clock <span class="hlt">ensemble</span> in space</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Atomic Clock <span class="hlt">Ensemble</span> in Space (ACES) is a mission using high-performance clocks and links to test fundamental laws of physics in space. Operated in the microgravity environment of the International Space Station, the ACES clocks, PHARAO and SHM, will generate a frequency reference reaching instability and inaccuracy at the 1 · 10-16 level. A link in the microwave domain (MWL) and an optical link (ELT) will make the ACES clock signal available to ground laboratories equipped with atomic clocks. Space-to-ground and ground-to-ground comparisons of atomic frequency standards will be used to test Einstein's theory of general relativity including a precision measurement of the gravitational red-shift, a search for time variations of fundamental constants, and Lorentz Invariance tests. Applications in geodesy, optical time transfer, and ranging will also be supported. ACES has now reached an advanced technology maturity, with engineering models completed and successfully tested and flight hardware under development. This paper presents the ACES mission concept and the status of its main instruments.</p> <div class="credits"> <p class="dwt_author">Cacciapuoti, L.; Salomon, C.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">246</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://hrg.snu.ac.kr/research/paper50.pdf"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Streamflow Prediction Using Climate Forecast Information</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Probabilistic approaches are useful for the long-term hydrologic forecasting because uncertainties are relatively large. This study applied <span class="hlt">Ensemble</span> Streamflow Prediction (ESP), a probabilistic streamflow forecasting method of the US NWS, to making 1-month ahead inflow forecasts at the Chungju Dam in Korea. ESP runs a rainfall-runoff model with meteorological inputs to generate an <span class="hlt">ensemble</span> of possible streamflow hydrographs. To improve</p> <div class="credits"> <p class="dwt_author">Young-Oh Kim</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">247</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/499812"> <span id="translatedtitle">SVM binary classifier <span class="hlt">ensembles</span> for image classification</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">We study how the SVM-based binary classifiers can be effectively combined to tackle the multi-class image classification problem. We study several <span class="hlt">ensemble</span> schemes, including OPC (one per class), PWC (pairwise coupling), and ECOC (error-correction output coding), that aim to achieve good error correction capability through redundancy. To enhance these <span class="hlt">ensemble</span> schemes' accuracy, we propose methods that on the one hand</p> <div class="credits"> <p class="dwt_author">King-Shy Goh; Edward Y. Chang; Kwang-Ting Cheng</p> <p class="dwt_publisher"></p> <p class="publishDate">2001-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">248</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/4287504"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Subsurface Modeling Using Grid Computing Technology</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary"><span class="hlt">Ensemble</span> Kalman filter (EnKF) uses a randomized <span class="hlt">ensemble</span> of subsurface models for error and uncertainty estimation. However, the complexity of geological models and the requirement of a large number of simulation runs make routine applications extremely difficult due to expensive computation cost. Grid computing technologies provide a cost-efficient way to combine geographically distributed computing resources to solve large-scale data and</p> <div class="credits"> <p class="dwt_author">Xin Li; Zhou Lei; Christopher D White; Gabrielle Allen; Guan Qin; F. T.-C. Tsai</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">249</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.springerlink.com/index/7cjc6735xejlkwmh.pdf"> <span id="translatedtitle">Lithology Recognition by Neural Network <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This paper investigates the advantages of methods based on Neural Network Classifier <span class="hlt">Ensembles</span> - sets of neural networks working\\u000a in a cooperative way to achieve a consensus decision- in the solution of the lithology recognition problem, a common task\\u000a found in the petroleum exploration field. Classifier <span class="hlt">ensembles</span> (Committees) are developed here in two stages: first, by applying\\u000a procedures for creating</p> <div class="credits"> <p class="dwt_author">Rafael Valle Dos Santos; Fredy Artola; Sérgio Da Fontoura; Marley B. R. Vellasco</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">250</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/12624043"> <span id="translatedtitle">Duality in random matrix <span class="hlt">ensembles</span> for all ?</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Gaussian and Chiral ?-<span class="hlt">Ensembles</span>, which generalise well-known orthogonal (?=1), unitary (?=2), and symplectic (?=4) <span class="hlt">ensembles</span> of random Hermitian matrices, are considered. Averages are shown to satisfy duality relations like {?,N,n}?{4\\/?,n,N} for all ?>0, where N and n respectively denote the number of eigenvalues and products of characteristic polynomials. At the edge of the spectrum, matrix integrals of the Airy (Kontsevich)</p> <div class="credits"> <p class="dwt_author">Patrick Desrosiers</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">251</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/20636903"> <span id="translatedtitle">Kinetics of <span class="hlt">ensembles</span> with variable charges</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">Kinetics of particle <span class="hlt">ensembles</span> with variable charges is investigated. It is shown that the energy of such <span class="hlt">ensembles</span> is not conserved in the interparticle collisions. The case when the equilibrium charge depends on the particle coordinate is studied, and the collision integral describing the momentum and energy transfer in collisions is derived. Solution of the resulting kinetic equation shows that the system is unstable - the mean thermal energy exhibits explosion-like growth, diverging at a finite time.</p> <div class="credits"> <p class="dwt_author">Ivlev, A.V.; Zhdanov, S.K.; Klumov, B.A.; Morfill, G.E. [Centre for Interdisciplinary Plasma Science, Max-Planck-Institut fuer Extraterrestrische Physik, D-85741 Garching (Germany); Tsytovich, V.N. [General Physics Institute, Russian Academy of Sciences, 117942 Moscow (Russian Federation); De Angelis, U. [Department of Physical Sciences, University of Naples 'Federico II', I-80126 Naples (Italy)</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">252</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFMGC13C..05A"> <span id="translatedtitle">Weather extremes in very large, high-resolution <span class="hlt">ensembles</span>: the weatherathome experiment</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">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 class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">253</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3086943"> <span id="translatedtitle">Memory for multiple visual <span class="hlt">ensembles</span> in infancy</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">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 <span class="hlt">ensemble</span> representations by asking whether infants represent <span class="hlt">ensembles</span>, and if so, how many at one time. We habituated 9-month old infants to arrays containing 2, 3, or 4 spatially intermixed colored subsets of dots, then asked whether they detected a numerical change to one of the subsets or to the superset of all dots. Experiment Series 1 showed that infants detected a numerical change to one of the subsets when the array contained 2 subsets, but not 3 or 4 subsets. Experiment Series 2 showed that infants detected a change to the superset of all dots no matter how many subsets were presented. Experiment 3 showed that infants represented both the approximate number and the cumulative surface area of these <span class="hlt">ensembles</span>. Our results suggest that infants, like adults (Halberda, Sires & Feigenson, 2006), can store quantitative information about 2 subsets plus the superset: a total of 3 <span class="hlt">ensembles</span>. This converges with the known limit on the number of individual objects infants and adults can store, and suggests that, throughout development, an <span class="hlt">ensemble</span> functions much like an individual object for working memory.</p> <div class="credits"> <p class="dwt_author">Zosh, Jennifer M.; Halberda, Justin; Feigenson, Lisa</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">254</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/20465522"> <span id="translatedtitle">Structural <span class="hlt">ensemble</span> in computational drug screening.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">Importance of the field: Structure-based in silico drug screening is now widely used in drug development projects. Structure-based in silico drug screening is generally performed using a protein-compound docking program and docking scoring function. Many docking programs have been developed over the last 2 decades, but their prediction accuracy remains insufficient. Areas covered in this review: This review highlights the recent progress of the post-processing of protein-compound complexes after docking. What the reader will gain: These methods utilize <span class="hlt">ensembles</span> of docking poses of compounds to improve the prediction accuracy for the ligand-docking pose and screening results. While the individual docking poses are not reliable, the free energy surface or the most probable docking pose can be estimated from the <span class="hlt">ensemble</span> of docking poses. Take home message: The protein-compound docking program provides an arbitral rather than a canonical <span class="hlt">ensemble</span> of docking poses. When the <span class="hlt">ensemble</span> of docking poses satisfies the canonical <span class="hlt">ensemble</span>, we can discuss how these post-docking analysis methods work and fail. Thus, improvements to the docking software will be needed in order to generate well-defined <span class="hlt">ensembles</span> of docking poses. PMID:20465522</p> <div class="credits"> <p class="dwt_author">Fukunishi, Yoshifumi</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-07-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">255</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=276487"> <span id="translatedtitle">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> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">256</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2002ClDy...18..627D"> <span id="translatedtitle"><span class="hlt">Downscaling</span> ability of one-way nested regional climate models: the Big-Brother Experiment</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A methodology is developed for testing the <span class="hlt">downscaling</span> ability of nested regional climate models (RCMs). The proposed methodology, nick-named the Big-Brother Experiment (BBE), is based on a "perfect-prognosis" approach and hence does not suffer from model errors nor from limitations in observed climatologies. The BBE consists in first establishing a reference climate by performing a large-domain high-resolution RCM simulation: this simulation is called the Big Brother. This reference simulation is then degraded by filtering short scales that are unresolved in today's global objective analyses (OA) and/or global climate models (GCMs) when integrated for climate projections. This filtered reference is then used to drive the same nested RCM (called the Little Brother), integrated at the same high-resolution as the Big Brother, but over a smaller domain that is embedded in the Big-Brother domain. The climate statistics of the Little Brother are then compared with those of the Big Brother over the Little-Brother domain. Differences can thus be attributed unambiguously to errors associated with the nesting and <span class="hlt">downscaling</span> technique, and not to model errors nor to observation limitations. The results of the BBE applied to a one-winter-month simulation over eastern North America at 45-km grid-spacing resolution show that the one-way nesting strategy has skill in <span class="hlt">downscaling</span> large-scale information to the regional scales. The time mean and variability of fine-scale features in a number of fields, such as sea level pressure, 975-hPa temperature and precipitation are successfully reproduced, particularly over regions where small-scale surface forcings are strong. Over other regions such as the ocean and away from the surface, the small-scale reproducibility is more difficult to achieve.</p> <div class="credits"> <p class="dwt_author">Denis, B.; Laprise, R.; Caya, D.; Côté, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-03-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">257</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ClDy...41.1419V"> <span id="translatedtitle">Climate variability and trends in <span class="hlt">downscaled</span> high-resolution simulations and projections over Metropolitan France</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In order to fulfill the society demand for climate information at the spatial scale allowing impact studies, long-term high-resolution climate simulations are produced, over an area covering metropolitan France. One of the major goals of this article is to investigate whether such simulations appropriately simulate the spatial and temporal variability of the current climate, using two simulation chains. These start from the global IPSL-CM4 climate model, using two regional models (LMDz and MM5) at moderate resolution (15-20 km), followed with a statistical <span class="hlt">downscaling</span> method in order to reach a target resolution of 8 km. The statistical <span class="hlt">downscaling</span> technique includes a non-parametric method that corrects the distribution by using high-resolution analyses over France. First the uncorrected simulations are evaluated against a set of high-resolution analyses, with a focus on temperature and precipitation. Uncorrected <span class="hlt">downscaled</span> temperatures suffer from a cold bias that is present in the global model as well. Precipitations biases have a season- and model-dependent behavior. Dynamical models overestimate rainfall but with different patterns and amplitude, but both have underestimations in the South-Eastern area (Cevennes mountains) in winter. A variance decomposition shows that uncorrected simulations fairly well capture observed variances from inter-annual to high-frequency intra-seasonal time scales. After correction, distributions match with analyses by construction, but it is shown that spatial coherence, persistence properties of warm, cold and dry episodes also match to a certain extent. Another aim of the article is to describe the changes for future climate obtained using these simulations under Scenario A1B. Results are presented on the changes between current and mid-term future (2021-2050) averages and variability over France. Interestingly, even though the same global climate model is used at the boundaries, regional climate change responses from the two models significantly differ.</p> <div class="credits"> <p class="dwt_author">Vautard, Robert; Noël, Thomas; Li, Laurent; Vrac, Mathieu; Martin, Eric; Dandin, Philippe; Cattiaux, Julien; Joussaume, Sylvie</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-09-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">258</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..1411806V"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical <span class="hlt">downscaling</span> methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical <span class="hlt">downscaling</span>, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were <span class="hlt">downscaled</span> dynamically by application of the DWD model chain GME ? COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.</p> <div class="credits"> <p class="dwt_author">Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">259</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010TCD.....4.2233L"> <span id="translatedtitle">Present and LGM permafrost from climate simulations: contribution of statistical <span class="hlt">downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variability between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical <span class="hlt">downscaling</span> methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of surface air temperature (SAT). Then, we define permafrost distribution over Eurasia by SAT conditions. In a first validation step on present climate (CTRL period), GAM shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical <span class="hlt">downscaling</span> based on a stochastic generator approach through a Multinomial Logistic Regression (MLR), which directly models the probabilities of local permafrost indices. The obtained permafrost distributions appear in a better agreement with data. In both cases, the provided local information reduces the inter-variability between climate models. Nevertheless, this also proves that a simple relationship between permafrost and the SAT only is not always sufficient to represent local permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. Our SDMs do not significantly improve permafrost distribution and do not reduce the inter-variability between climate models, at this period. We show that LGM permafrost distribution from climate models strongly depends on large-scale SAT. The differences with LGM data, larger than in the CTRL period, reduce the contribution of <span class="hlt">downscaling</span> and depend on several factors deserving further studies.</p> <div class="credits"> <p class="dwt_author">Levavasseur, G.; Vrac, M.; Roche, D. M.; Paillard, D.; Martin, A.; Vandenberghe, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-10-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">260</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMGC51A0742S"> <span id="translatedtitle">Statistical versus dynamical <span class="hlt">downscaling</span> over the mountainous regions in France: a performance evaluation and comparison of several scenarios</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Over the mountainous areas, several mesoscale features and precipitation processes are combined with a complex orography that makes it difficult to evaluate the consequences of global warming. Precipitation over these areas is also an important component of the hydrological cycle, since it influences the water resources, agriculture, forestry, floods, land management, etc. The future climate scenarios provided directly by the General Circulation Models (GCMs) are inadequate to evaluate the impacts of global warming over the mountainous areas, since they operate at very coarse horizontal resolution that cannot resolve mesoscale processes. Consequently, several techniques have been developed to <span class="hlt">downscale</span> the GCMs' information to regional scales. In this work, two <span class="hlt">downscaling</span> methods have been implemented to study climate change over the mountainous areas in France (Alps, Pyrenees, Corsica). The first method consists of dynamical <span class="hlt">downscaling</span> carried out by the Météo-France Regional Climate Model (RCM) ALADIN (Radu et al. 2008) using a 12 km grid-mesh over France. The second method consists of a statistical <span class="hlt">downscaling</span> model that combines the weather regimes and an analogues approach (Boé and Terray, 2008). The statistical <span class="hlt">downscaling</span> provides outputs over the entire France at an 8 km resolution. These two methods are first compared over the present climate for the period 1961-1999. Then, three different SRES scenarios (A1B, B1, A2) have been <span class="hlt">downscaled</span> using both dynamical and statistical methods. A comparison of the methodologies will be shown, accompanied by an evaluation of some uncertainties aspects of climate change over different parameters. Boé, J. and L. Terray, 2008: A Weather-Type Approach to Analyzing Winter Precipitation in France: Twentieth-Century Trends and the Role of Anthropogenic Forcing. J. Climate, 21 (13), 3118. Radu R., Déqué M. and Somot S., 2008: Spectral nudging in a spectral regional climate model, Tellus A, 60, 898-910.</p> <div class="credits"> <p class="dwt_author">Sanchezgomez, E.; Page, C.; Deque, M.; Terray, L.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_12");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> 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showDiv("page_15");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">261</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010ClDy...35..449L"> <span id="translatedtitle"><span class="hlt">Downscaling</span> large-scale NCEP CFS to resolve fine-scale seasonal precipitation and extremes for the crop growing seasons over the southeastern United States</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Seasonally predicted precipitation at a resolution of 2.5° was statistically <span class="hlt">downscaled</span> to a fine spatial scale of ~20 km over the southeastern United States. The <span class="hlt">downscaling</span> was conducted for spring and summer, when the fine-scale prediction of precipitation is typically very challenging in this region. We obtained the global model precipitation for <span class="hlt">downscaling</span> from the National Center for Environmental Prediction/Climate Forecast System (NCEP/CFS) retrospective forecasts. Ten member integration data with time-lagged initial conditions centered on mid- or late February each year were used for <span class="hlt">downscaling</span>, covering the period from 1987 to 2005. The primary techniques involved in <span class="hlt">downscaling</span> are Cyclostationary Empirical Orthogonal Function (CSEOF) analysis, multiple regression, and stochastic time series generation. Trained with observations and CFS data, CSEOF and multiple regression facilitated the identification of the statistical relationship between coarse-scale and fine-scale climate variability, leading to improved prediction of climate at a fine resolution. <span class="hlt">Downscaled</span> precipitation produced seasonal and annual patterns that closely resemble the fine resolution observations. Prediction of long-term variation within two decades was improved by the <span class="hlt">downscaling</span> in terms of variance, root mean square error, and correlation. Relative to the coarsely resolved unskillful CFS forecasts, the proposed <span class="hlt">downscaling</span> drove a significant reduction in wet biases, and correlation increased by 0.1-0.5. Categorical predictability of seasonal precipitation and extremes (frequency of heavy rainfall days), measured with the Heidke skill score (HSS), was also improved by the <span class="hlt">downscaling</span>. For instance, domain averaged HSS for two category predictability by the <span class="hlt">downscaling</span> are at least 0.20, while the scores by the CFS are near zero and never exceed 0.1. On the other hand, prediction of the frequency of subseasonal dry spells showed limited improvement over half of the Georgia and Alabama region.</p> <div class="credits"> <p class="dwt_author">Lim, Young-Kwon; Cocke, Steven; Shin, D. W.; Schoof, Justin T.; Larow, Timothy E.; O'Brien, James J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-08-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">262</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011GMD.....4..759M"> <span id="translatedtitle">A pragmatic approach for the <span class="hlt">downscaling</span> and bias correction of regional climate simulations: evaluation in hydrological modeling</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The present study investigates a statistical approach for the <span class="hlt">downscaling</span> of climate simulations focusing on those meteorological parameters most commonly required as input for climate change impact models (temperature, precipitation, air humidity and wind speed), including the option to correct biases in the climate model simulations. The approach is evaluated by the utilization of a hydrometeorological model chain consisting of (i) the regional climate model MM5 (driven by reanalysis data at the boundaries of the model domain), (ii) the <span class="hlt">downscaling</span> and model interface SCALMET, and (iii) the physically based hydrological model PROMET. The results of different hydrological model runs set up for the historical period 1971-2000 are compared to discharge recordings at the gauge of the Upper Danube Watershed (Central Europe) on a daily time basis. To avoid "in-sample" evaluation, a cross-validation approach is followed splitting the period in two halves of 15 yr. While one half is utilized to derive the <span class="hlt">downscaling</span> functions based on spatially distributed observations (e.g. 1971-1985), the other is used for the application of the <span class="hlt">downscaling</span> functions within the hydrometeorological model chain (e.g. 1986-2000). By alternately using both parts for the generation and the application of the <span class="hlt">downscaling</span> functions, discharge simulations are generated for the whole period 1971-2000. The comparison of discharge simulations and observations reveals that the presented approaches allow for a more accurate simulation of discharge in the catchment of the Upper Danube Watershed and the considered gauge at the outlet in Achleiten. The correction for subgrid-scale variability is shown to reduce biases in simulated discharge compared to the utilization of bilinear interpolation. Further enhancements in model performance could be achieved by a correction of biases in the RCM data within the <span class="hlt">downscaling</span> process. These findings apply to the cross-validation experiment as well as to an "in-sample" application, where the whole period 1971-2000 is used for the generation and the application of the <span class="hlt">downscaling</span> functions. Although the presented <span class="hlt">downscaling</span> approach strongly improves the performance of the hydrological model, deviations from the observed discharge conditions persist that are not found when driving the hydrological model with spatially distributed meteorological observations.</p> <div class="credits"> <p class="dwt_author">Marke, T.; Mauser, W.; Pfeiffer, A.; Zängl, G.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">263</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2006AGUFM.A41E0090T"> <span id="translatedtitle">Relationship Between <span class="hlt">Ensemble</span> Mean Square and <span class="hlt">Ensemble</span> Mean Skill in Four Climate Models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In this study, we investigated the uncertainty of <span class="hlt">ensemble</span> prediction of the ENSO (El Niño and Southern Oscillation) and the AO (Arctic Oscillation) variability, using four different climate models and various <span class="hlt">ensemble</span> schemes. Several important issues related to climate predictability including uncertainty measures and dominant precursors that control the uncertainty were addressed. It was found that the <span class="hlt">ensemble</span> mean (EM) square is a useful measure for the uncertainty of both the ENSO and the AO dynamical prediction. The relationship between EM2 and the prediction skill depends on the measure of skill. When correlation- based measures are used, the prediction skill is likely to be a linear function of EM2, i.e., the larger the EM2 the higher skill the prediction; whereas when RMSE-based (root mean square of error) metrics are used, a "triangular relationship" could be suggested between them, namely that when EM2 is large, the prediction is likely to be reliable whereas when EM2 is small, the prediction skill is much variable. In contrast to <span class="hlt">ensemble</span> numerical weather predictions (NWP), the <span class="hlt">ensemble</span> spread in the <span class="hlt">ensemble</span> prediction of these climate models was found to have little connection with the prediction skill. This is probably due to a small variation of <span class="hlt">ensemble</span> spread in the climate models, which may be associated with the intrinsic nature of <span class="hlt">ensemble</span> climate predictions (ECP).</p> <div class="credits"> <p class="dwt_author">Tang, Y.; Lin, H.; Moore, A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">264</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..1215134S"> <span id="translatedtitle">Skill of <span class="hlt">Ensemble</span> Seasonal Probability Forecasts</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In operational forecasting, the computational complexity of large simulation models is, ideally, justified by enhanced performance over simpler models. We will consider probability forecasts and contrast the skill of <span class="hlt">ENSEMBLES</span>-based seasonal probability forecasts of interest to the finance sector (specifically temperature forecasts for Nino 3.4 and the Atlantic Main Development Region (MDR)). The <span class="hlt">ENSEMBLES</span> model simulations will be contrasted against forecasts from statistical models based on the observations (climatological distributions) and empirical dynamics based on the observations but conditioned on the current state (dynamical climatology). For some start dates, individual <span class="hlt">ENSEMBLES</span> models yield significant skill even at a lead-time of 14 months. The nature of this skill is discussed, and chances of application are noted. Questions surrounding the interpretation of probability forecasts based on these multi-model <span class="hlt">ensemble</span> simulations are then considered; the distributions considered are formed by kernel dressing the <span class="hlt">ensemble</span> and blending with the climatology. The sources of apparent (RMS) skill in distributions based on multi-model simulations is discussed, and it is demonstrated that the inclusion of "zero-skill" models in the long range can improve Root-Mean-Square-Error scores, casting some doubt on the common justification for the claim that all models should be included in forming an operational probability forecast. It is argued that the rational response varies with lead time.</p> <div class="credits"> <p class="dwt_author">Smith, Leonard A.; Binter, Roman; Du, Hailiang; Niehoerster, Falk</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">265</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010pcms.confE..30F"> <span id="translatedtitle">Orographic Signature on Multiscale Statistics of Extreme Rainfall: Conditional <span class="hlt">downscaling</span> with emphasis on extremes</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Foufoula-Georgiou, E.; Ebtehaj, M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-09-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">266</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMGC41H..04L"> <span id="translatedtitle">Assessing the future of crop yield variability in the United States with <span class="hlt">downscaled</span> climate projections (Invited)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">One aspect of climate change of particular concern to farmers and food markets is the potential for increased year-to-year variability in crop yields. Recent episodes of food price increases following the Australian drought or Russian heat wave have heightened this concern. <span class="hlt">Downscaled</span> climate projections that properly capture the magnitude of daily and interannual variability of weather can be useful for projecting future yield variability. Here we examine the potential magnitude and cause of changes in variability of corn yields in the United States up to 2050. Using <span class="hlt">downscaled</span> climate projections from multiple models, we estimate a distribution of changes in mean and variability of growing season average temperature and precipitation. These projections are then fed into a model of maize yield that explicitly factors in the effect of extremely warm days. Changes in yield variability can result from a shift in mean temperatures coupled with a nonlinear crop response, a shift in climate variability, or a combination of the two. The results are decomposed into these different causes, with implications for future research to reduce uncertainties in projections of future yield variability.</p> <div class="credits"> <p class="dwt_author">Lobell, D. B.; Urban, D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">267</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMGC43F1023W"> <span id="translatedtitle">Climate Change Projections using Dynamical <span class="hlt">Downscaling</span> for the Colorado River Basin</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In this study we use dynamical <span class="hlt">downscaling</span> of the UKMO-HadCM3 model A2 emission scenario to evaluate a future climate scenario for the Colorado River basin for the continuous period 1968-2079. The focus is on the winter season and the effects of temperature and precipitation variability on snow trends for the Colorado River Basin. We subdivided the region into altitudinal and latitudinal bands to evaluate and characterize the snow trends and their driving mechanisms for each geographical subregion. The trends of three hydrologic variables were tested by Mann-Kendall method and the results showed that 1) temperature has a statistically significant increasing trend for all subregions, 2) a statistically significant decreasing trend in snow was detected widely except for the highest elevations and latitudes, and 3) precipitation showed slightly increasing trend overall without statistical significance. Correlation analysis indicated that precipitation has a strong positive relation with snow at high elevation. On the other hand, temperature is relatively highly correlated with snow at middle elevations and this resulted in steeply decreasing linear trend in snow for mid-elevation regions. This dynamically <span class="hlt">downscaled</span> climate change scenario shows that in the future widespread declines in snow are likely to occur in the Colorado River basin especially in middle elevations mainly due to significant increases in temperature.</p> <div class="credits"> <p class="dwt_author">Wi, S.; Dominguez, F.; Durcik, M.; Valdes, J. B.; Diaz, H. F.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">268</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..15.5889T"> <span id="translatedtitle">Empirical-statistical <span class="hlt">downscaling</span> and error correction of extreme precipitation from regional climate models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Significant effort has been made over the past few years toward a characterization of future climatic changes and the estimation of related impacts using different greenhouse gas emissions scenarios. According to different regional climate models, warming climate will presumable result in seasonal shifts and possibly an increase in extreme precipitation events. Climate projection information originates from Regional Climate Models (RCMs). Since even high resolution RCMs are still too coarse for direct application in local climate change impact studies and since they are known to feature considerable errors, particularly regarding the precipitation and their extremes, error correction is needed to provide accurate climate information. Here, statistical <span class="hlt">downscaling</span> techniques will play a vital role between the RCM output and the impacts analysis. Such an empirical statistical error correction forces RCM outputs in direction of observations, thus correct them assuming that the observations is an error free reference. The main objective of this paper is to describe a methodology for empirical statistical <span class="hlt">downscaling</span> and error correction of extremes precipitation at local scale under climate change. Detailed evaluation procedures and methodological development for the application to higher quantiles of daily precipitation will be described. These investigations extend the analyses by looking at the entire distributions' tails, which will enable to identify suitable improved extrapolation methods to provide qualitative localized climate change signal for extreme precipitation events also in future scenarios.</p> <div class="credits"> <p class="dwt_author">Tani, Satyanarayana; Themeßl, Matthias Jacob; Gobiet, Andreas</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">269</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013OcMod..65...11C"> <span id="translatedtitle">Assessing the impact of <span class="hlt">downscaled</span> winds on a regional ocean model simulation of the Humboldt system</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Simulating the oceanic circulation in Eastern Boundary Upwelling Systems (EBUS) is a challenging issue due to the paucity of wind stress products of a sufficiently high spatial resolution to simulate the observed upwelling dynamics. In this study, we present the results of regional simulations of the Humboldt current system (Peru and Chile coasts) to assess the value of a statistical <span class="hlt">downscaling</span> model of surface forcing. Twin experiments that differ only from the momentum flux forcing are carried out over the 1992-2000 period that encompasses the major 1997/98 El Niño/La Niña event. It is shown that the mean biases of the oceanic circulation can be drastically reduced simply substituting the mean wind field of NCEP reanalysis by a higher resolution mean product (QuikSCAT). The statistical <span class="hlt">downscaling</span> model improves further the simulations allowing more realistic intraseasonal and interannual coastal undercurrent variability, which is notoriously strong off Central Peru and Central Chile. Despite some limitations, our results suggest that the statistical approach may be useful to regional oceanic studies of present and future climates.</p> <div class="credits"> <p class="dwt_author">Cambon, Gildas; Goubanova, Katerina; Marchesiello, Patrick; Dewitte, Boris; Illig, Séréna; Echevin, Vincent</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">270</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3099348"> <span id="translatedtitle">Disease and Phenotype Data at <span class="hlt">Ensembl</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">Biological databases are an important resource for the life sciences community. Accessing the hundreds of databases supporting molecular biology and related fields is a daunting and time-consuming task. Integrating this information into one access point is a necessity for the life sciences community, which includes researchers focusing on human disease. Here we discuss the <span class="hlt">Ensembl</span> genome browser, which acts as a single entry point with Graphical User Interface to data from multiple projects, including OMIM, dbSNP, and the NHGRI GWAS catalog. <span class="hlt">Ensembl</span> provides a comprehensive source of annotation for the human genome, along with other species of biomedical interest. In this unit, we explore how to use the <span class="hlt">Ensembl</span> genome browser in example queries related to human genetic diseases. Support protocols demonstrate quick sequence export using the BioMart tool.</p> <div class="credits"> <p class="dwt_author">Spudich, Giulietta M.; Fernandez-Suarez, Xose M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">271</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2002EGSGA..27..766L"> <span id="translatedtitle">Spatiotemporal Stochastic Forcing In <span class="hlt">Ensemble</span> Systems</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In 1998, the ECMWF introduced in the operational <span class="hlt">Ensemble</span> Prediction System (EPS) a new scheme to simulate random model errors due to parameterized phys- ical processes (Buizza et al., 1999). This scheme is based on the notion that this randomness is coherent between the different parameterization modules and has a certain coherence on the space and time scales represented by the model. Following this idea, we have perturbed with a spatiotemporal correlated noise of the Ornstein- Uhlenbeck type both, a diffusively coupled one-dimensional array of Lorenz chaotic cells (Lorenzo and Pérez-Muñuzuri, 1999, 2001), and a simplified atmospheric global circulation model, PUMA (Portable University Model of the Atmosphere) (Frisius et al., 1998). In both cases, forcing increases the spread of the <span class="hlt">ensemble</span> for a certain value of the correlation time where the predictability also attains a critical value. On the other hand, for increasing correlation length ( fixed) the numerical results suggest a nonmonotonous behavior of the <span class="hlt">ensemble</span> spread. The influence of noise amplitude, as well as the effect of a multiplicative or additive contribution of the noise is also shown. Finally, the impact of model resolution and <span class="hlt">ensemble</span> size on the performance of the <span class="hlt">ensemble</span> forecast has been analyzed numerically. newline [1] Buizza, R., Miller, M. and Palmer, T.N. (1999) Stochastic representation of model uncertainties in the ECMWF <span class="hlt">Ensemble</span> Prediction System. Q.J.R. Meteorol. Soc. 125, 2887-2908. [2] Frisius, T., Lunkeit, F., Fraedrich, K. and James, I.N. (1998) Storm-track orga- nization and variability in a simplified atmospheric global circulation model. Q.J.R. Meteorol. Soc. 124, 1019-1043. [3] Lorenzo, M.N. and Pérez-Muñuzuri, V. (1999) Colored noise-induced chaotic ar- ray synchronization. Phys. Rev. E 60 2779-2787. [4] Lorenzo, M.N. and Pérez-Muñuzuri, V. (2001) Influence of low intensity noise on assemblies of diffusively coupled chaotic cells. Chaos 11, 371-376.</p> <div class="credits"> <p class="dwt_author">Lorenzo, M. N.; Montero, P.; Pérez-Muñuzuri, V.</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">272</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFMNG22A..01B"> <span id="translatedtitle">Accounting for <span class="hlt">ensemble</span> variance inaccuracy with Hybrid <span class="hlt">Ensemble</span> 4D-VAR</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Inevitably, neither <span class="hlt">ensemble</span> covariances nor static covariance models are equal to the true error covariance matrix given past observations. To better understand the distribution of true error covariances given a single imperfect <span class="hlt">ensemble</span> covariance, we begin by considering an idealized univariate model in which Bayes' theorem can be used to derive the distribution of true error variances given an imperfect <span class="hlt">ensemble</span> variance. The equation for the mean of this distribution shows that a Hybrid error variance formulation is more accurate than either formulations based solely on <span class="hlt">ensemble</span> variances or formulations based solely on static climatological variances. We show how this Hybrid best estimate of error variance may be derived from a large number of realizations of (innovation, <span class="hlt">ensemble</span>-variance) pairs. The approach assumes that the climatological distribution of true error variances is an inverse-gamma distribution and that the distribution of <span class="hlt">ensemble</span> variances given a single true error variance is a gamma distribution. To help explain and justify this approach we use a "replicate Earth" paradigm and an <span class="hlt">Ensemble</span> Kalman filter applied to Lorenz's (2005) simple model 1. We then apply these theoretically derived weights to the newly built Navy-Hybrid-4DVAR scheme. The forecast performance using the theoretical weights was found to be as good as that from weights obtained from a much more computationally expensive brute force tuning method. Thus, the new theory provided a justification for the Hybrid plus tools to facilitate its implementation.</p> <div class="credits"> <p class="dwt_author">Bishop, C. H.; Kuhl, D. D.; Satterfield, E.; Tom, R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">273</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFM.H41C1049F"> <span id="translatedtitle">Developing a regional retrospective <span class="hlt">ensemble</span> precipitation dataset for watershed hydrology modeling, Idaho, USA</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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> precipitation realizations are conditioned on hourly observations from reporting gages and then conditioned again on the Parameter-elevation Regressions on Independent Slopes Model (PRISM) at the monthly timescale to reflect orographic precipitation trends common to watersheds of the Western US. While this methodology potentially introduces cross-pollination of errors due to the re-use of precipitation gage data, it nevertheless achieves an <span class="hlt">ensemble</span>-based precipitation estimate and appropriate measures of uncertainty at a spatiotemporal resolution appropriate for watershed modeling.</p> <div class="credits"> <p class="dwt_author">Flores, A. N.; Smith, K.; LaPorte, P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">274</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011PhRvL.107n3005S"> <span id="translatedtitle">Optomechanical Cavity Cooling of an Atomic <span class="hlt">Ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We demonstrate cavity sideband cooling of a single collective motional mode of an atomic <span class="hlt">ensemble</span> down to a mean phonon occupation number ?n?min?=2.0-0.3+0.9. Both ?n?min? and the observed cooling rate are in good agreement with an optomechanical model. The cooling rate constant is proportional to the total photon scattering rate by the <span class="hlt">ensemble</span>, demonstrating the cooperative character of the light-emission-induced cooling process. We deduce fundamental limits to cavity cooling either the collective mode or, sympathetically, the single-atom degrees of freedom.</p> <div class="credits"> <p class="dwt_author">Schleier-Smith, Monika H.; Leroux, Ian D.; Zhang, Hao; van Camp, Mackenzie A.; Vuleti?, Vladan</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">275</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009EGUGA..11..728C"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River of China</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The applicability of the Statistical <span class="hlt">DownScaling</span> Method (SDSM) in the Haihe River basin of China was evaluated, and its strengths and weaknesses in simultaneously <span class="hlt">downscaling</span> air temperature, evaporation and precipitation were discussed. The used large scale atmospheric data were daily NCEP/NCAR reanalysis data and the daily emissions scenarios A2 and B2 of the HadCM3 model. Measured daily mean air temperature, pan evaporation and precipitation data (1961-2000) from 11 weather stations in the Haihe River basin were selected as climate variables to be <span class="hlt">downscaled</span>. The results showed that: (1) the amount and change pattern of the climate variables could be reasonably simulated; the determination coefficients between observed and <span class="hlt">downscaled</span> mean temperature, pan evaporation and precipitation were 99%, 93% and 73%, respectively; (2) there were some systematic errors in simulating extreme events, but the results were considered to be acceptable for practical use; and (3) in the future 2011~2040, the mean air temperature would increase about 0.6°C; there were no obvious changes in pan evaporation, and the total annual precipitation would decrease by about 4?. It was concluded that in the future 30 years, the climate would be warmer and drier, extreme events could be more intense, and the autumn might be the most distinct season for all of these changes.</p> <div class="credits"> <p class="dwt_author">Chu, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">276</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010ThApC..99..149C"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A statistical <span class="hlt">downscaling</span> method (SDSM) was evaluated by simultaneously <span class="hlt">downscaling</span> air temperature, evaporation, and precipitation in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for <span class="hlt">downscaling</span> were measured daily mean air temperature, pan evaporation, and precipitation data (1961-2000) from 11 weather stations in the Haihe River basin. The results obtained from SDSM showed that: (1) the pattern of change in and numerical values of the climate variables can be reasonably simulated, with the coefficients of determination between observed and <span class="hlt">downscaled</span> mean temperature, pan evaporation, and precipitation being 99%, 93%, and 73%, respectively; (2) systematic errors existed in simulating extreme events, but the results were acceptable for practical applications; and (3) the mean air temperature would increase by about 0.7°C during 2011~2040; the total annual precipitation would decrease by about 7% in A2 scenario but increase by about 4% in B2 scenario; and there were no apparent changes in pan evaporation. It was concluded that in the next 30 years, climate would be warmer and drier, extreme events could be more intense, and autumn might be the most distinct season among all the changes.</p> <div class="credits"> <p class="dwt_author">Chu, J. T.; Xia, J.; Xu, C.-Y.; Singh, V. P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">277</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/26358709"> <span id="translatedtitle">A comparison of <span class="hlt">downscaled</span> and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The fundamental rationale for statistical <span class="hlt">downscaling</span> is that the raw outputs of climate change experiments from General Circulation Models (GCMs) are an inadequate basis for assessing the effects of climate change on land-surface processes at regional scales. This is because the spatial resolution of GCMs is too coarse to resolve important sub-grid scale processes (most notably those pertaining to the</p> <div class="credits"> <p class="dwt_author">R. L. Wilby; L. E. Hay; G. H. Leavesley</p> <p class="dwt_publisher"></p> <p class="publishDate">1999-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">278</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011GMDD....4...45M"> <span id="translatedtitle">A pragmatic approach for the <span class="hlt">downscaling</span> and bias correction of regional climate simulations - evaluation in hydrological modeling</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The present study investigates a statistical approach for the <span class="hlt">downscaling</span> of climate simulations focusing on those meteorological parameters most commonly required as input for climate change impact models (temperature, precipitation, air humidity and wind speed), including the option to correct biases in the climate model simulations. The approach is evaluated by the utilization of a hydrometeorological model chain consisting of (i) the regional climate model MM5 (driven by reanalysis data at the boundaries of the model domain), (ii) the <span class="hlt">downscaling</span> and model interface SCALMET, and (iii) the hydrological model PROMET. The results of four hydrological model runs are compared to discharge recordings at the gauge of the Upper Danube Watershed (Central Europe) for the historical period of 1972-2000 on a daily time basis. The comparison reveals that the presented approaches allow for a more accurate simulation of discharge for the catchment of the Upper Danube Watershed and the considered gauge at the outlet in Achleiten. The correction for subgrid-scale variability is shown to reduce biases in simulated discharge compared to the utilization of bilinear interpolation. Further enhancements in model performance could be achieved by a correction of biases in the RCM data within the <span class="hlt">downscaling</span> process. Although the presented <span class="hlt">downscaling</span> approach strongly improves the performance of the hydrological model, deviations from the observed discharge conditions persist that are not found when driving the hydrological model with spatially distributed meteorological observations.</p> <div class="credits"> <p class="dwt_author">Marke, T.; Mauser, W.; Pfeiffer, A.; Zängl, G.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">279</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/56257321"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of Seasonal Forecasts and Climate Change Scenarios using Generalized Linear Modeling Approach for Stochastic Weather Generators</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Climate forecasts and climate change scenarios are typically provided in the form of monthly or seasonally aggregated totals or means. But time series of daily weather (e.g., precipitation amount, minimum and maximum temperature) are commonly required for use in agricultural decision-making. Stochastic weather generators constitute one technique to temporally <span class="hlt">downscale</span> such climate information. The recently introduced approach for stochastic weather</p> <div class="credits"> <p class="dwt_author">Y. Kim; R. W. Katz; B. Rajagopalan; G. P. Podesta</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">280</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011WRR....47.3519G"> <span id="translatedtitle">Spatial <span class="hlt">downscaling</span> of precipitation from GCMs for climate change projections using random cascades: A case study in Italy</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We present a Stochastic Space Random Cascade (SSRC) approach to <span class="hlt">downscale</span> precipitation from a General Circulation Models (GCMs), developed for the assessment of water resources under climate change scenarios for the Oglio river (1440 km2), in the Italian Alps. The snow-fed Oglio river displays complex physiography and high environmental gradient and statistical <span class="hlt">downscaling</span> methods are required for climate change assessment. First, a back cast analysis is carried out to evaluate the most representative within a set of four available GCMs (R30, ECHAM4, PCM, HadCM3). Monthly precipitation for the window 1990-2000 from 270 gauging stations (one every 25 km2) in northern Italy is used and scores from objective indicators are calculated. The SSRC model is then tuned upon the Oglio river catchment for spatial <span class="hlt">downscaling</span> (2 km2) of daily precipitation from the NCAR Parallel Climate Model, giving the comparatively best results for the area. Scale Recursive Estimation coupled with the Expectation Maximization algorithm is used for model estimation. The seasonal parameters of the multiplicative cascade are accommodated by statistical distributions conditioned upon the climatic forcing, based on a regression analysis. The SSRC approach reproduces well the spatial clustering, intermittency, self-similarity, and spatial correlation structure of precipitation fields, with relatively low computational burden. <span class="hlt">Downscaling</span> of future precipitation scenarios (A2 scenario from the Parallel Climate Model) is then carried out and some preliminary conclusions are drawn.</p> <div class="credits"> <p class="dwt_author">Groppelli, B.; Bocchiola, D.; Rosso, R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-03-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" 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onClick='return showDiv("page_22");' href="#">22</a> <a onClick='return showDiv("page_23");' href="#">23</a> <a onClick='return showDiv("page_24");' href="#">24</a> <a onClick='return showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_16");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">281</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=192252"> <span id="translatedtitle">A COMPARISON OF EXPLICIT AND IMPLICIT SPATIAL <span class="hlt">DOWNSCALING</span> OF GCM OUTPUT FOR SOIL EROSION AND CROP PRODUCTION ASSESSMENTS</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p class="result-summary">Spatial <span class="hlt">downscaling</span> of climate change scenarios can be a significant source of uncertainty in simulating climatic impact on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to t...</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">282</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013HESS...17..705G"> <span id="translatedtitle">Benefits from using combined dynamical-statistical <span class="hlt">downscaling</span> approaches - lessons from a case study in the Mediterranean region</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Various <span class="hlt">downscaling</span> techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Such techniques may be grouped into two <span class="hlt">downscaling</span> approaches: the deterministic dynamical <span class="hlt">downscaling</span> (DD) and the statistical <span class="hlt">downscaling</span> (SD). Although SD has been traditionally seen as an alternative to DD, recent works on statistical <span class="hlt">downscaling</span> have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD permits to obtain more suitable climate scenarios for basin scale hydrological applications starting from GCM simulations. The case study presented here focuses on the Apulia region (South East of Italy, surface area about 20 000 km2), characterised by a typical Mediterranean climate; the monthly cumulated precipitation and monthly mean of daily minimum and maximum temperature distribution were examined for the period 1953-2000. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction. The SD resulted efficient in reducing the mean bias in the spatial distribution at both annual and seasonal scales, but it was not able to correct the miss-modelled non-stationary components of the GCM dynamics. The DD provided a partial correction by enhancing the spatial heterogeneity of trends and the long-term time evolution predicted by the GCM. The best results were obtained through the combination of both DD and SD approaches.</p> <div class="credits"> <p class="dwt_author">Guyennon, N.; Romano, E.; Portoghese, I.; Salerno, F.; Calmanti, S.; Petrangeli, A. B.; Tartari, G.; Copetti, D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-02-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">283</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013TCD.....7.3163G"> <span id="translatedtitle">The Greenland ice sheet: modelling the surface mass balance from GCM output with a new statistical <span class="hlt">downscaling</span> technique</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The aim of this study is to derive a realistic estimation of the Surface Mass Balance (SMB) of the Greenland ice sheet (GrIS) through statistical <span class="hlt">downscaling</span> of Global Coupled Model (GCM) outputs. To this end, climate simulations performed with the CNRM-CM5.1 Atmosphere-Ocean GCM within the CMIP5 (Coupled Model Intercomparison Project phase 5) framework are used for the period 1850-2300. From the year 2006, two different emission scenarios are considered (RCP4.5 and RCP8.5). Simulations of SMB performed with the detailed snowpack model Crocus driven by CNRM-CM5.1 surface atmospheric forcings serve as a reference. On the basis of these simulations, statistical relationships between total precipitation, snow-ratio, snowmelt, sublimation and near-surface air temperature are established. This leads to the formulation of SMB variation as a function of temperature variation. Based on this function, a <span class="hlt">downscaling</span> technique is proposed in order to refine 150 km horizontal resolution SMB output from CNRM-CM5.1 to a 15 km resolution grid. This leads to a much better estimation of SMB along the GrIS margins, where steep topography gradients are not correctly represented at low-resolution. For the recent past (1989-2008), the integrated SMB over the GrIS is respectively 309 and 243 Gt yr-1 for raw and <span class="hlt">downscaled</span> CNRM-CM5.1. In comparison, the Crocus snowpack model forced with ERA-Interim yields a value of 245 Gt yr-1. The major part of the remaining discrepancy between Crocus and <span class="hlt">downscaled</span> CNRM-CM5.1 SMB is due to the different snow albedo representation. The difference between the raw and the <span class="hlt">downscaled</span> SMB tends to increase with near-surface air temperature via an increase in snowmelt.</p> <div class="credits"> <p class="dwt_author">Geyer, M.; Salas Y Melia, D.; Brun, E.; Dumont, M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-06-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">284</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011CliPa...7.1225L"> <span id="translatedtitle">Present and LGM permafrost from climate simulations: contribution of statistical <span class="hlt">downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the variability between their results. Studying a heterogeneous variable such as permafrost implies conducting analysis at a smaller spatial scale compared with climate models resolution. Our approach consists of applying statistical <span class="hlt">downscaling</span> methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systematic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical <span class="hlt">downscaling</span> based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with CTRL data. In average for the nine PMIP2 models, we measure a global agreement with CTRL permafrost data that is better when using ML-GAM than when applying the GAM method with air temperature conditions. In both cases, the provided local information reduces the variability between climate models results. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs (GAM and ML-GAM) is not significantly in better agreement with LGM permafrost data than large-scale fields. At the LGM, both methods do not reduce the variability between climate models results. We show that LGM permafrost distribution from climate models strongly depends on large-scale air temperature at the surface. LGM simulations from climate models lead to larger differences with LGM data than in the CTRL period. These differences reduce the contribution of <span class="hlt">downscaling</span>.</p> <div class="credits"> <p class="dwt_author">Levavasseur, G.; Vrac, M.; Roche, D. M.; Paillard, D.; Martin, A.; Vandenberghe, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-11-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">285</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.7496S"> <span id="translatedtitle">Future risk of global drought from <span class="hlt">downscaled</span>, bias corrected climate projections</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Understanding how changes in drought conditions will develop in the 21st century, including changes in severity, extent, and duration, is of great importance to many sectors such as water resources management and agricultural activity. There may also be profound implications for the occurrence of wildfires and heat waves that are associated with dry conditions. Recent severe droughts in the Western U.S., southeast Australia, Eastern Africa, Europe and northern China are testament to the impacts that large scale drought can have and are perhaps indicators of things to come. The direct use of climate model outputs for analysis of future drought however is problematic because of known model biases, particularly model simulated precipitation and temperature fields that have first order impact on droughts. Here we present a comprehensive statistical analysis of future drought conditions globally in a multi-model, multi-scenario based framework. The analysis is based on recently completed simulations using the Variable Infiltration Capacity land surface model (LSM), forced by <span class="hlt">downscaled</span>, bias corrected climate projections using a newly developed equidistant quantile matching method. This improves upon traditional quantile matching methods by taking into account changes in the future projection climate distribution and better represents extreme years that are most associated with the development of drought. We apply this to a suite of climate models for monthly precipitation and temperature but show how this can be extended to radiation, humidity and windspeed to capture associated changes and interplay among these associated drivers, although this is limited to a small set of climate models with available data. Further enhancements include improved temporal <span class="hlt">downscaling</span> to account for changes in, for example, storm intensities and diurnal temperature range. The bias corrected and <span class="hlt">downscaled</span> climate forcings are used to drive the LSM to generate future projections of the terrestrial water and energy cycles. These outputs are then analyzed to understand the propagation of projected drought, including frequency and severity, and to compare these projections with analyses based on 20th C observations. Individual drought events are identified using a cluster based tracking algorithm, which follows drought development through time and space and identifies the severest events based on severity-area-duration analysis. This work improves on previous future global drought analyses based directly on climate model output, by removing the biases associated with climate model simulations, focuses on higher spatial resolution to better represent topographic and vegetation heterogeneity and uses a comprehensive land surface model as the foundation of the analyzed information.</p> <div class="credits"> <p class="dwt_author">Sheffield, Justin; Li, Haibin; Wood, Eric</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">286</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2007PhDT.......170B"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of satellite remote sensing data: Application to land cover mapping</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Many satellite images have a spatial resolution coarser than the extent of land cover patterns on the ground, leading to mixed pixels whose composite spectral response consists of responses from multiple land cover classes. Spectral unmixing procedures only determine the fractions of such classes within a coarse pixel without locating them in space. <span class="hlt">Downscaling</span>, also known as super-resolution or sub-pixel mapping, turns these proportions into a fine resolution map of class labels. Sub-pixel mapping is undetermined, in that many different fine resolution maps can lead to an equally good reproduction of the available coarse fractions. Thus, the unknown fine resolution land cover map is regarded as a realization of a random set. Simulated realizations are generated using the geostatistical paradigm of sequential simulation. At any pixel along a path visiting all fine scale pixels, a class label is simulated from a local probability distribution made conditional to: (i) the coarse class fraction data, (ii) any simulated land cover classes at fine pixels previously visited along that path, and (iii) a prior structural model. Two algorithms using different structural model types are proposed for the sequential simulation. The first method proposed is built on block indicator cokriging which allows evaluating the previous local probability distributions by a form of kriging; the structural model is then a series of class labels indicator variograms. The second method is based on the multiple-point simulation algorithm SNESIM where the local probability distributions are read from a training image; the structural function is then that training image which can be seen as an analog image depicting the patterns deemed present at the fine resolution. Two case studies derived from Landsat TM imagery demonstrates the two approaches proposed. The resulting alternative <span class="hlt">downscaled</span> class maps all honor the coarse proportion data, any fine scale data available, and exhibit the spatial patterns called for by the input structural model. When that structural model is incompatible with the sensor data the pattern reproduction is poor. Fine scale data such as water, roads and previously mapped fine scale pixels are shown to be well reproduced in the <span class="hlt">downscaled</span> maps.</p> <div class="credits"> <p class="dwt_author">Boucher, Alexandre</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">287</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009EGUGA..11.4752S"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of climate parameters in Bode river basin in Germany using Active Learning Method (ALM)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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 regional scales. These models are verified using checking data and then considering ECHO/REMO models and on the basis of last 1000 years of ECHO, the REMO time series as well as the local data are simulated. These simulated data are used as input-data for the runoff model ARCEGMO.</p> <div class="credits"> <p class="dwt_author">Sodoudi, S.; Reimer, E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">288</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/55089736"> <span id="translatedtitle">Quantum Storage in Solid State Atomic <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Reversible and coherent mapping of quantum information between light and matter is an important experimental challenge in quantum information science. In particular such quantum memories are necessary for the implementation of quantum repeaters that would extend the range of quantum communication. In recent years, atomic <span class="hlt">ensembles</span> have proven to be a promising system in order to implement such a task.</p> <div class="credits"> <p class="dwt_author">Hugues de Riedmatten</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">289</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://webird.tcd.ie/bitstream/2262/2418/1/TCD-CS-2006-48.pdf"> <span id="translatedtitle">Ecient <span class="hlt">Ensemble</span> Methods for Document Clustering</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Recent <span class="hlt">ensemble</span> clustering techniques have been shown to be eective in improving the accuracy and stability of standard clus- tering algorithms. However, an inherent drawback of these techniques is the computational cost of generating and combining multiple clusterings of the data. In this paper, we present an ecient kernel-based ensem- ble clustering method suitable for application to large, high-dimensional datasets</p> <div class="credits"> <p class="dwt_author">Derek Greene; Padraig Cunningham</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">290</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ntis.gov/search/product.aspx?ABBR=ADA188401"> <span id="translatedtitle">Chemical Defense Flight Glove <span class="hlt">Ensemble</span> Evaluation.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ntis.gov/search/index.aspx">National Technical Information Service (NTIS)</a></p> <p class="result-summary">Four chemical defense flight glove <span class="hlt">ensembles</span> were evaluated for their effect on manual dexterity. Two- and three-layer combinations included in the study were: cotton liner/7 mil butyl/Nomex; cotton liner/12.5 mil epichlorohydron butyl/Nomex; Nomex/7 mil ...</p> <div class="credits"> <p class="dwt_author">J. Ross C. Ervin</p> <p class="dwt_publisher"></p> <p class="publishDate">1987-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">291</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/1807507"> <span id="translatedtitle"><span class="hlt">Ensembles</span> of Partitions via Data Resampling</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The combination of multiple clusterings is a difficult problem in the practice of distributed data mining. Both the cluster generation mechanism and the partition integration process influence the quality of the combinations. In this paper we propose a data resampling approach for building cluster <span class="hlt">ensembles</span> that are both robust and stable. In particular, we investigate the effectiveness of a bootstrapping</p> <div class="credits"> <p class="dwt_author">Behrouz Minaei-bidgoli; Alexander P. Topchy; William F. Punch</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">292</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/19508095"> <span id="translatedtitle">Level Spacing Distributions of Random Matrix <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A formalism of nearest-neighbor spacing distribution is presented for the random matrix <span class="hlt">ensembles</span> related to general orthogonal polynomials. We show that the spacing distributions are derived from certain integral equations written in terms of the corresponding orthogonal or skew orthogonal polynomials. By using the formalism the universality of the spacing distribution is proved in special cases related to classical orthogonal</p> <div class="credits"> <p class="dwt_author">Masahiro Shiroishi; Taro Nagao; Miki Wadati</p> <p class="dwt_publisher"></p> <p class="publishDate">1993-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">293</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/2443429"> <span id="translatedtitle">Integrated Debugging of Large Modular Robot <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Creatively misquoting Thomas Hobbes, the pro- cess of software debugging is nasty, brutish, and all too long. This holds all the more true in robotics, which frequently involves concurrency, extensive nondeterminisism, event-driven components, complex state machines, and difficult platform limitations. Inspired by the challenges we have encountered while attempting to debug software on simulated <span class="hlt">ensembles</span> of tens of thousands of</p> <div class="credits"> <p class="dwt_author">Benjamin D. Rister; Jason Campbell; Padmanabhan Pillai; Todd C. Mowry</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">294</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/18859385"> <span id="translatedtitle">Preparable <span class="hlt">ensembles</span> for remote state preparation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">We determine all the <span class="hlt">ensembles</span> that can be remotely prepared in a deterministic and oblivious way using a nonmaximally entangled resource with minimum classical communication cost when Bob's unitary transformations form a cyclic group. We show that the classical communication has maximal informational entropy. We also give a constraint for the shared entangled resource that can be used for remote</p> <div class="credits"> <p class="dwt_author">Z. Kurucz; P. Adam</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">295</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/4721828"> <span id="translatedtitle"><span class="hlt">Ensemble</span> based 3D human motion classification</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Due to the rapid development of motion capture technology, more and more human motion databases appear. In order to effectively and efficiently manage human motion database, human motion classification is necessary. In this paper, we propose an <span class="hlt">ensemble</span> based human motion classification approach (EHMCA). Specifically, EHMCA first extracts the descriptors from human motion sequences. Then, singular value decomposition (SVD) is</p> <div class="credits"> <p class="dwt_author">Zhiwen Yu; Xing Wang; Hau-san Wong</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">296</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/12243906"> <span id="translatedtitle">Entanglement of individual photon and atomic <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Here we present an experimentally feasible scheme to entangle flying qubit (individual photon with polarization modes) and stationary qubit (atomic <span class="hlt">ensembles</span> with long-lived collective excitations). This entanglement integrate two different species can act as a critical element for the coherent transform of quantum information between flying and stationary qubits. The entanglement degree can be also adjusted expediently with linear optics.</p> <div class="credits"> <p class="dwt_author">Guo-Ping Guo; Guang-Can Guo</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">297</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/1688714"> <span id="translatedtitle">Neural network <span class="hlt">ensembles</span>: evaluation of aggregation algorithms</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary"><span class="hlt">Ensembles</span> of artificial neural networks show improved generalization capabilities that outperform those of single networks. However, for aggregation to be effective, the individual networks must be as accurate and diverse as possible. An important problem is, then, how to tune the aggregate members in order to have an optimal compromise between these two conflicting conditions. We present here an extensive</p> <div class="credits"> <p class="dwt_author">Pablo M. Granitto; Pablo F. Verdes; H. Alejandro Ceccatto</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">298</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/813993"> <span id="translatedtitle">A Learning Algorithm For Neural Network <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The performance of a single regressor\\/classifier can be improved by combining the outputs of several predictors. This is true provided the combined predictors are accurate and diverse enough, which posses the problem of generating suitable aggregate members in order to have optimal generalization capabilities. We propose here a new method for selecting members of regression\\/classification <span class="hlt">ensembles</span>. In particular, using artificial</p> <div class="credits"> <p class="dwt_author">Hugo D. Navone; Pablo M. Granitto; Pablo F. Verdes; H. Alejandro Ceccatto</p> <p class="dwt_publisher"></p> <p class="publishDate">2001-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">299</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://anish.softcomputing.net/jikm-ac.pdf"> <span id="translatedtitle">Decision Support Systems Using <span class="hlt">Ensemble</span> Genetic Programming</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This paper proposes a decision support sys- tem for tactical air combat environment using a combina- tion of unsupervised learning for clustering the data and an <span class="hlt">ensemble</span> of three well-known genetic programming tech- niques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic pro- gramming (LGP), Multi-Expression Programming (MEP) and Gene Expression Programming (GEP).</p> <div class="credits"> <p class="dwt_author">Ajith Abraham; Crina Grosan</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">300</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/21388839"> <span id="translatedtitle">Exploration of nonlocalities in <span class="hlt">ensembles</span> consisting of bipartite quantum states</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">It is revealed that <span class="hlt">ensembles</span> consisting of multipartite quantum states can exhibit different kinds of nonlocalities. An operational measure is introduced to quantify nonlocalities in <span class="hlt">ensembles</span> consisting of bipartite quantum states. Various upper and lower bounds for the measure are estimated and the exact values for <span class="hlt">ensembles</span> consisting of mutually orthogonal maximally entangled bipartite states are evaluated.</p> <div class="credits"> <p class="dwt_author">Ye Mingyong [Department of Physics and Center of Theoretical and Computational Physics, University of Hong Kong, Pokfulam Road (Hong Kong); School of Physics and Optoelectronics Technology, Fujian Normal University, Fuzhou 350007 (China); Bai Yankui [College of Physical Science and Information Engineering and Hebei Advance Thin Films Laboratory, Hebei Normal University, Shijiazhuang, Hebei 050016 (China); Lin Xiumin [School of Physics and Optoelectronics Technology, Fujian Normal University, Fuzhou 350007 (China); Wang, Z. D. [Department of Physics and Center of Theoretical and Computational Physics, University of Hong Kong, Pokfulam Road (Hong Kong)</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-15</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_14");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return 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class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_15");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' href="#">4</a> <a onClick='return showDiv("page_5");' href="#">5</a> <a onClick='return showDiv("page_6");' href="#">6</a> <a onClick='return showDiv("page_7");' href="#">7</a> <a onClick='return showDiv("page_8");' href="#">8</a> <a onClick='return 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title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">301</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/31009"> <span id="translatedtitle">Sparse Regression <span class="hlt">Ensembles</span> in Infinite and Finite Hypothesis Spaces</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">We examine methods for constructing regression <span class="hlt">ensembles</span> based on alinear program (LP). The <span class="hlt">ensemble</span> regression function consists of linear combinationsof base hypotheses generated by some boosting-type base learning algorithm.Unlike the classification case, for regression the set of possible hypotheses producibleby the base learning algorithm may be infinite. We explicitly tackle the issue of howto define and solve <span class="hlt">ensemble</span> regression when</p> <div class="credits"> <p class="dwt_author">Gunnar Rätsch; Ayhan Demiriz</p> <p class="dwt_publisher"></p> <p class="publishDate">2000-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">302</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/56055403"> <span id="translatedtitle"><span class="hlt">Ensemble</span> stream flow predictions, a way towards better hydrological forecasting</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The hydrological forecasting division at SMHI has been using hydrological EPS and hydrological probabilities forecasts operationally since some years ago. The inputs to the hydrological model HBV are the EPS forecasts from ECMWF. From the <span class="hlt">ensemble</span>, non-exceedance probabilities are estimated and final correction of the <span class="hlt">ensemble</span> spread, based on evaluation is done. <span class="hlt">Ensemble</span> stream flow predictions are done for about</p> <div class="credits"> <p class="dwt_author">C. Edlund</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">303</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/64522"> <span id="translatedtitle"><span class="hlt">Ensembling</span> neural networks: Many could be better than all</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Neural network <span class="hlt">ensemble</span> is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the relationship between the <span class="hlt">ensemble</span> and its component neural networks is analyzed from the context of both regression and classification, which reveals that it may be better to <span class="hlt">ensemble</span> many instead of all of the neural networks at hand.</p> <div class="credits"> <p class="dwt_author">Zhi-hua Zhou; Jianxin Wu; Wei Tang</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">304</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/4736653"> <span id="translatedtitle">Construction of surrogate model <span class="hlt">ensembles</span> with sparse data</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Construction of neural network <span class="hlt">ensembles</span> (NNE) with sparse data requires comprehensive performance measure, multi-stage validation and usually a large member size. This paper presents a hybrid method which takes a selective optimization approach and is characterized with several novel features. First, candidate <span class="hlt">ensembles</span> are widely explored using a multi-objective genetic algorithm. Secondly, the best local <span class="hlt">ensembles</span> registered with each distinct</p> <div class="credits"> <p class="dwt_author">Dingding Chen; Allan Zhong; John Gano; Syed Hamid; Orlando De Jesus; Stan Stephenson</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">305</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/59178862"> <span id="translatedtitle">A survey of selected percussion <span class="hlt">ensemble</span> literature: 1930-1985</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The purpose of this study was to identify the beginning of percussion <span class="hlt">ensemble</span> literature; selected percussion <span class="hlt">ensemble</span> literature performance practices and rehearsal techniques; and to identify selected percussion <span class="hlt">ensemble</span> literature from 1930 to 1985. ^ The content of this research was based upon the following: books, dissertations, articles, and World Wide Web sites. An introduction to the research was presented</p> <div class="credits"> <p class="dwt_author">Thomas Lee Spann</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">306</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.springerlink.com/index/j46266183hu82175.pdf"> <span id="translatedtitle">Ergonomic comparison of a chem\\/bio prototype firefighter <span class="hlt">ensemble</span> and a standard <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Firefighter turnout gear and equipment protect the wearer against external hazards but, unfortunately, restrict mobility.\\u000a The aim of this study was to determine the ease of mobility and comfort while wearing a new prototype firefighter <span class="hlt">ensemble</span>\\u000a (PE) with additional chemical\\/biological hazard protection compared to a standard <span class="hlt">ensemble</span> (SE) by measuring static and dynamic\\u000a range of motion (ROM), job-related tasks, and</p> <div class="credits"> <p class="dwt_author">Aitor Coca; R. Roberge; A. Shepherd; J. B. Powell; J. O. Stull; W. J. Williams</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">307</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/4599071"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Size, Balance, and Model-Error Representation in an <span class="hlt">Ensemble</span> Kalman Filter</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The <span class="hlt">ensemble</span> Kalman filter (EnKF) has been proposed for operational atmospheric data assimilation. Some outstanding issues relate to the required <span class="hlt">ensemble</span> size, the impact of localization methods on balance, and the representation of model error. To investigate these issues, a sequential EnKF has been used to assimilate simulated radiosonde, satellite thickness, and aircraft reports into a dry, global, primitive-equation model.</p> <div class="credits"> <p class="dwt_author">Herschel L. Mitchell; P. L. Houtekamer; Gérard Pellerin</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">308</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3021968"> <span id="translatedtitle">Identifying <span class="hlt">Ensembles</span> of Signal Transduction Models using Pareto Optimal <span class="hlt">Ensemble</span> Techniques (POETs)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model <span class="hlt">ensembles</span> using multiobjective optimization. In this study, we used Pareto Optimal <span class="hlt">Ensemble</span> Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass action kinetics within an ordinary differential equation (ODE) framework (64-ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an <span class="hlt">ensemble</span> of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the <span class="hlt">ensemble</span> following the addition of extracellular ligand. Also, the <span class="hlt">ensemble</span> recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model <span class="hlt">ensembles</span> could capture qualitatively important network features without exact parameter information.</p> <div class="credits"> <p class="dwt_author">Song, Sang Ok; Chakrabarti, Anirikh; Varner, Jeffrey D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">309</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14..698C"> <span id="translatedtitle">Development of a Dynamic <span class="hlt">Downscaling</span> strategy for Ganga Basin and Investigation of the Hydrological Pattern</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The interaction between climate and hydrology is highly complex and non-linear. In India, the synoptic scale atmospheric flow, diversity of local topography, vegetation, climatic conditions, and high population density, etc., interact with one another to give a unique weather distribution. The interaction between the large scale climate and local scale hydrologic cycle is very important in regional scale hydrological modelling. The Weather Research and Forecasting (WRF) model is a numerical weather prediction and atmospheric simulation system designed to resolve this interaction at regional scale. WRF has been used earlier to investigate the <span class="hlt">downscaling</span> methodology over the United States (Lo et al., 2008). We study the impact of climatic condition on Ganga basin hydrologic cycle using WRF. A single domain with a resolution of 25 km was used to cover the whole of India and the region of interest and validation is the entire Ganga basin. We performed the <span class="hlt">downscaling</span> for the year 2010 with five configurations: (1) one continuous time integration with single initialization, (2) time integration with monthly reinitialization, (3) single initialization but with 3-D nudging without relaxation of PBL (4) same as 3 but with relaxation of PBL and (5) same as 4 but with spectral nudging relaxation. The results are compared against the synoptic observations taken over the Ganga basin. The 5th method has the best skill, followed by 4th, 3rd , 2nd and 1st . The results show that the nudging generates realistic regional climatic pattern which cannot be achieved simply by updating the boundary conditions. To find out the Hydrological interaction, trend and pattern over the Ganga Basin, the Hydrological fields of the best model (Spectral Nudging) are analysed. The rainfall patterns are compared with TRMM 3B42 daily data. The precipitation, surface temperature, and the regional wind pattern is reasonably simulated. The study reveals the power of WRF in resolving the climatic and hydrological interactions and also shows that the WRF can be used in making an accurate forecast. The rainfall distribution shows some degree of correlation with the TRMM at the middle Indo-Gangetic plane, along the foothills of Himalaya, and over some portion of Tibetian Plateau. The seasonality index of Hydrologic variables like Rainfall, Surface runoff and Soil moisture show a level of seasonal pattern over the Indo-Gangetic plane but the degree of seasonality pattern is weak at the foothills of Himalaya. The hydrological fields like surface run off, base flow, soil moisture distribution and soil temperature show the expected regional variations and seasonal patterns. The dynamical <span class="hlt">downscaling</span> outperforms the interpolation of climatic variables over space and time. This implies the suitability of WRF to study the hydrological cycle over a data sparse region and, probably, to study the effect of potential climate change on it. Reference: Jeff Chun-Fung Lo, Zong-Liang Yang, and Roger A. Pielke Sr., 2008, Assessment of three dynamical climate <span class="hlt">downscaling</span> methods using the Weather Research and Forecasting (WRF) model, Journal of Geophysical Research, Vol 113, D09112</p> <div class="credits"> <p class="dwt_author">Chaudhuri, C.; Srivastava, R.; Tripathi, S. N.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">310</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/23940670"> <span id="translatedtitle"><span class="hlt">Downscaling</span> the analysis of complex transmembrane signaling cascades to closed attoliter volumes.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">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> <div class="credits"> <p class="dwt_author">Grasso, Luigino; Wyss, Romain; Piguet, Joachim; Werner, Michael; Hassaïne, Ghérici; Hovius, Ruud; Vogel, Horst</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-08-05</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">311</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..1412765R"> <span id="translatedtitle">Towards <span class="hlt">downscaling</span> precipitation for Senegal - An approach based on generalized linear models and weather types</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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 inter-annual to decadal variability in the 20th century which is likely to be affected by climate change. We built a generalized linear model for a full spatial description of rainfall in Senegal. The model uses season, location, and a discrete set of weather types as predictors and yields a spatially continuous description of precipitation occurrences and intensities. Weather types have been defined on NCEP/NCAR reanalysis using zonal and meridional winds, as well as relative humidity. This model is suitable for <span class="hlt">downscaling</span> precipitation, particularly precipitation occurrences relevant for drough risk mapping.</p> <div class="credits"> <p class="dwt_author">Rust, H. W.; Vrac, M.; Lengaigne, M.; Sultan, B.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">312</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..1510686L"> <span id="translatedtitle">A comparison of dynamical and statistical <span class="hlt">downscaling</span> methods for regional wave climate projections along French coastlines.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">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 110 coastal locations along the French coast. The model, based on the BAJ parameterization of the source terms (Bidlot et al, 2007) was calibrated against ten years of GlobWave altimeter observations (2000-2009) and validated through deep and shallow water buoy observations. The dynamical <span class="hlt">downscaling</span> method has been performed with the same numerical wave model TOMAWAC used for building ANEMOC-2. Forecast simulations are forced by the 10m wind fields of ARPEGE-CLIMAT (A1B, A2, B1) from 2010 to 2100. The model covers the Atlantic Ocean and uses a spatial resolution along the French and European coast of 10 and 20 km respectively. The results of the model are stored with a time resolution of one hour. References: Benoit M., Marcos F., and F. Becq, (1996). Development of a third generation shallow-water wave model with unstructured spatial meshing. Proc. 25th Int. Conf. on Coastal Eng., (ICCE'1996), Orlando (Florida, USA), pp 465-478. Bidlot J-R, Janssen P. and Adballa S., (2007). A revised formulation of ocean wave dissipation and its model impact, technical memorandum ECMWF n°509. Menendez, M., Mendez, F.J., Izaguirre,C., Camus, P., Espejo, A., Canovas, V., Minguez, R., Losada, I.J., Medina, R. (2011). Statistical <span class="hlt">Downscaling</span> of Multivariate Wave Climate Using a Weather Type Approach, 12th International Workshop on Wave Hindcasting and Forecasting and 3rd Coastal Hazard Symposium, Kona (Hawaii).</p> <div class="credits"> <p class="dwt_author">Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Mendez, Fernando</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">313</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010ems..confE.576H"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> assessments of temperature and precipitation extremes in the Mediterranean area</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The Mediterranean area is regarded as a "climate change hot-spot" (Giorgi 2006) being highly affected by future climate change compared to other regions of the world. This is mostly due to the assessed decrease of precipitation as well as to an increase of the inter-annual precipitation variability, but changes in temperature, especially in its extreme tails, have also to be taken into account. Based on station data of the Mediterranean area as well as on high resolution precipitation and temperature data (0.25° x 0.25° grid for terrestrial areas of Europe, Haylock et al. 2006) percentile-based indices of extreme events are defined. As large-scale predictors for extreme events in the Mediterranean area sea level pressure, geopotential heights, thickness of the 1000hPa/500hPa layer, specific humidity, and relative vorticity are primarily considered. Statistical <span class="hlt">Downscaling</span> is established by relating the Mediterranean extreme events to the large-scale atmospheric circulation. This is done through the application of transfer functions (multiple regression analysis and canonical correlation analysis) as well as through a synoptical <span class="hlt">downscaling</span> approach (cluster analysis). To test the stability of the models the analyses are realised for different calibration periods and corresponding verification periods. Model performance in the verification periods is assessed by means of the correlation coefficients between modelled and observed extremes indices. Additionally the reduction of variance is calculated, being similar to the root mean squared skill score. Output of different coupled global circulation models under A1B- and B1- scenario assumptions is used to assess changes of extreme temperature and precipitation under enhanced greenhouse warming conditions. From the results it becomes evident that the <span class="hlt">downscaling</span> assessment can vary considerably depending on the particular predictor used for the statistical assessment. Climatic as well as dynamic factors influence extreme conditions and should be considered in a combined manner within <span class="hlt">downscaling</span> models. Regarding temperature extremes the results show that the changes do not follow a simple shift of the whole temperature distribution to higher values. More precisely it is indicated that the intra-annual extreme temperature range will decrease in most parts of the Mediterranean area during the course of the 21st century. This is due to the finding that extreme minimum temperatures in winter will increase stronger compared to extreme maximum temperatures in summer. Concerning precipitation extremes the statistical assessment results point to widespread decreases of heavy rainfall events (95th percentile of precipitation) in the Mediterranean area during all seasons of the year. Acknowledgement: Financial support is provided by the DFG (German Research Foundation). Giorgi, F. (2006): Climate change hot- spots, Geophysical Research Letters Vol. 33, L08707, 2006, doi:10.1029/2006GL025734. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008): A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research 113, D20119, doi:10.1029/2008JD010201, 2008</p> <div class="credits"> <p class="dwt_author">Hertig, E.; Jacobeit, J.; Fernandez-Montes, S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-09-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">314</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/15246577"> <span id="translatedtitle"><span class="hlt">Downscaling</span> climate change scenarios in an urban land use change model.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">The objective of this paper is to describe the process through which climate change scenarios were <span class="hlt">downscaled</span> in an urban land use model and the results of this experimentation. The land use models (Urban Growth Model [UGM] and the Land Cover Deltatron Model [LCDM]) utilized in the project are part of the SLEUTH program which uses a probabilistic cellular automata protocol. The land use change scenario experiments were developed for the 31-county New York Metropolitan Region (NYMR) of the US Mid-Atlantic Region. The Intergovernmental Panel on Climate Change (IPCC), regional greenhouse gas (GHG) emissions scenarios (Special Report on Emissions Scenarios (SRES) A2 and B2 scenarios) were used to define the narrative scenario conditions of future land use change. The specific research objectives of the land use modeling work involving the SLEUTH program were threefold: (1) Define the projected conversion probabilities and the amount of rural-to-urban land use change for the NYMR as derived by the UGM and LCDM for the years 2020 and 2050, as defined by the pattern of growth for the years 1960-1990; (2) <span class="hlt">Down-scale</span> the IPCC SRES A2 and B2 scenarios as a narrative that could be translated into alternative growth projections; and, (3) Create two alternative future growth scenarios: A2 scenario which will be associated with more rapid land conversion than found in initial projections, and a B2 scenario which will be associated with a slower level of land conversion. The results of the modeling experiments successfully illustrate the spectrum of possible land use/land cover change scenarios for the years 2020 and 2050. The application of these results into the broader scale climate and health impact study is discussed, as is the general role of land use/land cover change models in climate change studies and associated environmental management strategies. PMID:15246577</p> <div class="credits"> <p class="dwt_author">Solecki, William D; Oliveri, Charles</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-08-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">315</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AdAtS..28.1077G"> <span id="translatedtitle">Assessment of dynamic <span class="hlt">downscaling</span> of the extreme rainfall over East Asia using a regional climate model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">This study investigates the capability of the dynamic <span class="hlt">downscaling</span> method (DDM) in an East Asian climate study for June 1998 using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research non-hydrostatic Mesoscale Model (MM5). Sensitivity experiments show that MM5 results at upper atmospheric levels cannot match reanalyses data, but the results show consistent improvement in simulating moisture transport at low levels. The <span class="hlt">downscaling</span> ability for precipitation is regionally dependent. During the monsoon season over the Yangtze River basin and the pre-monsoon season over North China, the DDM cannot match observed precipitation. Over Northwest China and the Tibetan Plateau (TP), where there is high topography, the DDM shows better performance than reanalyses. Simulated monsoon evolution processes over East Asia, however, are much closer to observational data than reanalyses. The convection scheme has a substantial impact on extreme rainfall over the Yangtze River basin and the pre-monsoon over North China, but only a marginal contribution for Northwest China and the TP. Land surface parameterizations affect the locations and pattern of rainfall bands. The 10-day re-initialization in this study shows some improvement in simulated precipitation over some sub-regions but with no obvious improvement in circulation. The setting of the location of lateral boundaries (LLB) westward improves performance of the DDM. Including the entire TP in the western model domain improves the DDM performance in simulating precipitation in most sub-regions. In addition, a seasonal simulation demonstrates that the DDM can also obtain consistent results, as in the June case, even when another two months consist of no strong climate/weather events.</p> <div class="credits"> <p class="dwt_author">Gao, Yanhong; Xue, Yongkang; Peng, Wen; Kang, Hyun-Suk; Waliser, Duane</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-09-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">316</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFM.H41I..05C"> <span id="translatedtitle">Stochastic Weather Generator Based <span class="hlt">Ensemble</span> Streamflow Forecasting</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Efficient water resources management owes considerably to skillful basin wide streamflow forecasts at both short (1-2 weeks) and long (seasonal and longer) time scales. The skillful projection of the streamflow probability density function (PDF) is especially of interest. Presently, the <span class="hlt">Ensemble</span> Streamflow Prediction (ESP) approach is used by River Forecasting Centers such as the Colorado Basin River Forecasting Center (CBRFC) with their hydrologic model to produce <span class="hlt">ensembles</span> and thus the PDF. The main drawback of this is that the number of <span class="hlt">ensembles</span> is limited to the number of years of the historical data, which is often quite small. CBRFC currently maintains a 30 year calibration period. Furthermore, if seasonal forecast information is included through a use of a subset of these years, the <span class="hlt">ensemble</span> size decreases substantially, further degrading the resolution of the estimated PDF. To improve on this, we propose a stochastic weather generator based approach coupled to the hydrologic modeling system. The weather generator uses a Markov Chain to simulate the precipitation state of a day (wet or dry) and a K-nearest neighbor (K-NN) resampling approach to simulate the daily weather vector. This stochastic weather generator can also produce daily weather sequences conditioned on seasonal categorical climate forecasts such as those issued by NOAA/CPC, as well as sequences at multiple locations across the basin. Daily weather sequences for a desired time horizon (1-2 weeks or seasonal) are produced using the K-NN weather generator; these are then driven through the hydrologic model to produce an <span class="hlt">ensemble</span> forecast of streamflow. The weather generator's ability to produce a rich variety of daily weather sequences enables increased resolution and more accurate estimation of the streamflow PDF. We demonstrate this approach to San Juan River Basin and present preliminary findings. First, results from the stochastic weather generator are presented showing that the generated sequences capture the historic variability across multiple locations in the basin quite well. We also show that the weather sequences the PDF of the weather attributes appropriately based on seasonal climate forecast. CBRFC's new Community Hydrologic Prediction System (CHPS) was used in conjunction with the generated weather sequences to produce <span class="hlt">ensembles</span> of streamflow. The skills from these simulations are compared with the existing ESP forecasting approach.</p> <div class="credits"> <p class="dwt_author">Caraway, N.; Werner, K.; Rajagopalan, B.; Wood, A. W.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">317</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009SPIE.7306E..36M"> <span id="translatedtitle"><span class="hlt">Ensemble</span> training to improve recognition using 2D ear</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The ear has gained popularity as a biometric feature due to the robustness of the shape over time and across emotional expression. Popular methods of ear biometrics analyze the ear as a whole, leaving these methods vulnerable to error due to occlusion. Many researchers explore ear recognition using an <span class="hlt">ensemble</span>, but none present a method for designing the individual parts that comprise the <span class="hlt">ensemble</span>. In this work, we introduce a method of modifying the <span class="hlt">ensemble</span> shapes to improve performance. We determine how different properties of an <span class="hlt">ensemble</span> training system can affect overall performance. We show that <span class="hlt">ensembles</span> built from small parts will outperform <span class="hlt">ensembles</span> built with larger parts, and that incorporating a large number of parts improves the performance of the <span class="hlt">ensemble</span>.</p> <div class="credits"> <p class="dwt_author">Middendorff, Christopher; Bowyer, Kevin W.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">318</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010MSSP...24.2104Z"> <span id="translatedtitle">Performance enhancement of <span class="hlt">ensemble</span> empirical mode decomposition</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> empirical mode decomposition (EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the amplitude of added white noise and the number of <span class="hlt">ensemble</span> trials. A test signal with mode mixing that mimics realistic bearing vibration signals measured on a bearing test bed was developed to enable quantitative evaluation of the EEMD and provide guidance on how to choose the two parameters appropriately for bearing signal decomposition. Subsequently, a modified EEMD (MEEMD) method is proposed to reduce the computational cost of the original EEMD method as well as improving its performance. Numerical evaluation and systematic study using vibration data measured on an experimental bearing test bed verified the effectiveness and computational efficiency of the proposed MEEMD method for bearing defect diagnosis.</p> <div class="credits"> <p class="dwt_author">Zhang, Jian; Yan, Ruqiang; Gao, Robert X.; Feng, Zhihua</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-10-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">319</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013APS..MARC20011M"> <span id="translatedtitle"><span class="hlt">Ensemble</span> brightening in size purified silicon nanocrystals</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We report on the quantum yield, photoluminescence (PL) lifetime and <span class="hlt">ensemble</span> photoluminescent stability of monodisperse plasma-synthesized silicon nanocrystals (SiNCs) prepared though density-gradient ultracentrifugation in mixed organic solvents. Improved size uniformity leads to a reduction in PL linewidth, band alignment, and the emergence of entropic order in dry nanocrystal films. We find a significant PL enhancement in thin solid films assembled from the fractions, and we use a combination of measurement, simulation and modeling to link this brightening to a temporally enhanced quantum yield arising from SiNC interactions in ordered <span class="hlt">ensembles</span> of monodisperse nanocrystals. Using an appropriate excitation scheme, we exploit this enhancement to achieve photostable emission.</p> <div class="credits"> <p class="dwt_author">Miller, Joseph B.; Vansickle, Austin R.; Anthony, Rebecca J.; Kroll, Daniel M.; Kortshagen, Uwe R.; Hobbie, Erik K.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-03-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">320</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/16381079"> <span id="translatedtitle">Multibaric-multithermal <span class="hlt">ensemble</span> molecular dynamics simulations.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">We present new generalized-<span class="hlt">ensemble</span> molecular dynamics simulation algorithms, which we refer to as the multibaric-multithermal molecular dynamics. We describe three algorithms based on (1) the Nosé thermostat and the Andersen barostat, (2) the Nosé-Poincaré thermostat and the Andersen barostat, and (3) the Gaussian thermostat and the Andersen barostat. The multibaric-multithermal simulations perform random walks widely both in the potential-energy space and in the volume space. Therefore, one can calculate isobaric-isothermal <span class="hlt">ensemble</span> averages in wide ranges of temperature and pressure from only one simulation run. We test the effectiveness of the multibaric-multithermal algorithm by applying it to a Lennard-Jones 12-6 potential system. PMID:16381079</p> <div class="credits"> <p class="dwt_author">Okumura, Hisashi; Okamoto, Yuko</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-02-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_15");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a 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src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">321</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2002EGSGA..27.6455E"> <span id="translatedtitle">The <span class="hlt">Ensemble</span> Kalman Filter: Implementation Issues</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">There is now a large number of <span class="hlt">Ensemble</span> Kalman Filter (EnKF) implementations in the litterature. These have taken somewhat different approaches when it comes to the practical implementation of the analysis scheme. This paper will among others provide a new interpretaion of the EnKF which has allowed us to better understand the filter's success in a range of applications. It will among others discuss an "optimal" and generic implementation strategy which allows for assimilation of very large data sets to a minimal numerical cost. It is also showed that a common approach of filtering long range spurious correlations which are attributed a too small <span class="hlt">ensemble</span>, actually introduces a non-balanced analysis. In fact, the EnKF in its correct implementation will provide a dynamically balanced analysis with no need for reinitialization.</p> <div class="credits"> <p class="dwt_author">Evensen, G.</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">322</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012JHEP...11..111K"> <span id="translatedtitle">ABCD of beta <span class="hlt">ensembles</span> and topological strings</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We study ?-<span class="hlt">ensembles</span> with B N , C N , and D N eigenvalue measure and their relation with refined topological strings. Our results generalize the familiar connections between local topological strings and matrix models leading to A N measure, and illustrate that all those classical eigenvalue <span class="hlt">ensembles</span>, and their topological string counterparts, are elated one to another via various deformations and specializations, quantum shifts and discrete quotients. We review the solution of the Gaussian models via Macdonald identities, and interpret them as conifold theories. The interpolation between the various models is plainly apparent in this case. For general polynomial potential, we calculate the partition function in the multi-cut phase in a perturbative fashion, beyond tree-level in the large- N limit. The relation to refined topological string orientifolds on the corresponding local geometry is discussed along the way.</p> <div class="credits"> <p class="dwt_author">Krefl, Daniel; Walcher, Johannes</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-11-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">323</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/6768103"> <span id="translatedtitle">Microcanonical <span class="hlt">ensemble</span> formulation of lattice gauge theory</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">A new formulation of lattice gauge theory without explicit path integrals or sums is obtained by using the microcanonical <span class="hlt">ensemble</span> of statistical mechanics. Expectation values in the new formalism are calculated by solving a large set of coupled, nonlinear, ordinary differential equations. The average plaquette for compact electrodynamics calculated in this fashion agrees with standard Monte Carlo results. Possible advantages of the microcanonical method in applications to fermionic systems are discussed.</p> <div class="credits"> <p class="dwt_author">Callaway, D.J.E.; Rahman, A.</p> <p class="dwt_publisher"></p> <p class="publishDate">1982-08-30</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">324</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/50506742"> <span id="translatedtitle">Trapping Sets in Irregular LDPC Code <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Trapping sets represent subgraphs in the Tanner graph of a code that, for certain classes of channels, exhibit a strong influence on the height and point of onset of the error-floor. We compute the asymptotic normalized distributions of trapping sets in random, irregular, binary low-density parity-check (LDPC) code <span class="hlt">ensembles</span>. Our derivations rely on techniques from large deviation theory and statistical</p> <div class="credits"> <p class="dwt_author">Olgica Milenkovic; Emina Soljanin; Philip Whiting</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">325</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.1712G"> <span id="translatedtitle">Incorporation of seasonal climate forecasts in the <span class="hlt">ensemble</span> streamflow prediction system</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A technique for incorporating 0-3 months lead temperature and precipitation forecasts from two Canadian numerical weather prediction (NWP) models into the <span class="hlt">ensemble</span> streamflow prediction (ESP) system is presented. The technique involves <span class="hlt">downscaling</span> monthly NWP forecast outputs to station locations using the model output statistics (MOS) approach and then temporally disaggregating the monthly forecasts into daily input weather data suitable for driving a hydrologic model. The daily weather sequence for a desired month is generated by a nearest neighbor re-sampling of one of the years in the historical record, and then modifying the daily weather data for the same month of the re-sampled year so as to reproduce the MOS-based monthly forecast value. Streamflow forecasts from the MOS-based scheme are compared to pre-ESP and post-ESP re-sampling schemes without seasonal climate forecast guidance. In the pre-ESP scheme, daily weather inputs for the hydrologic model were conditionally re-sampled from historical records. In the post-ESP scheme, streamflow traces produced by the climatic ESP system were conditionally re-sampled. The three schemes were applied to the Bow and Castle rivers, both located in the headwaters of the South Saskatchewan River basin in the province of Alberta, Canada. Correlations between the MOS-based median forecast and observed flow for the Castle River were consistently higher than those based on the pre-ESP and post-ESP schemes. Other skill measures showed mixed results, with the MOS-based forecasts being more skillful in some cases and less skillful in others. All three schemes exhibited better skill for above-normal flow categories than for below-normal categories. It is also shown that considerable improvement in the ESP forecast skill could be achieved through more accurate simulation of streamflow, particularly for forecast issue dates late in the water year.</p> <div class="credits"> <p class="dwt_author">Gan, T. Y.; Gobena, A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">326</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/23454721"> <span id="translatedtitle">Complementary <span class="hlt">ensemble</span> clustering of biomedical data.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">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. PMID:23454721</p> <div class="credits"> <p class="dwt_author">Fodeh, Samah Jamal; Brandt, Cynthia; Luong, Thai Binh; Haddad, Ali; Schultz, Martin; Murphy, Terrence; Krauthammer, Michael</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-02-27</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">327</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3174015"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Clustering using Semidefinite Programming with Applications</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">In this paper, we study the <span class="hlt">ensemble</span> clustering problem, where the input is in the form of multiple clustering solutions. The goal of <span class="hlt">ensemble</span> clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input <span class="hlt">ensemble</span>. We obtain several new results for this problem. Specifically, we show that the notion of agreement under such circumstances can be better captured using a 2D string encoding rather than a voting strategy, which is common among existing approaches. Our optimization proceeds by first constructing a non-linear objective function which is then transformed into a 0–1 Semidefinite program (SDP) using novel convexification techniques. This model can be subsequently relaxed to a polynomial time solvable SDP. In addition to the theoretical contributions, our experimental results on standard machine learning and synthetic datasets show that this approach leads to improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. In addition, we identify several new application scenarios for this problem. These include combining multiple image segmentations and generating tissue maps from multiple-channel Diffusion Tensor brain images to identify the underlying structure of the brain.</p> <div class="credits"> <p class="dwt_author">Singh, Vikas; Mukherjee, Lopamudra; Peng, Jiming; Xu, Jinhui</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">328</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/23377041"> <span id="translatedtitle">Modeling polydispersive <span class="hlt">ensembles</span> of diamond nanoparticles.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">While significant progress has been made toward production of monodispersed samples of a variety of nanoparticles, in cases such as diamond nanoparticles (nanodiamonds) a significant degree of polydispersivity persists, so scaling-up of laboratory applications to industrial levels has its challenges. In many cases, however, monodispersivity is not essential for reliable application, provided that the inevitable uncertainties are just as predictable as the functional properties. As computational methods of materials design are becoming more widespread, there is a growing need for robust methods for modeling <span class="hlt">ensembles</span> of nanoparticles, that capture the structural complexity characteristic of real specimens. In this paper we present a simple statistical approach to modeling of <span class="hlt">ensembles</span> of nanoparticles, and apply it to nanodiamond, based on sets of individual simulations that have been carefully selected to describe specific structural sources that are responsible for scattering of fundamental properties, and that are typically difficult to eliminate experimentally. For the purposes of demonstration we show how scattering in the Fermi energy and the electronic band gap are related to different structural variations (sources), and how these results can be combined strategically to yield statistically significant predictions of the properties of an entire <span class="hlt">ensemble</span> of nanodiamonds, rather than merely one individual 'model' particle or a non-representative sub-set. PMID:23377041</p> <div class="credits"> <p class="dwt_author">Barnard, Amanda S</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-02-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">329</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013Nanot..24h5703B"> <span id="translatedtitle">Modeling polydispersive <span class="hlt">ensembles</span> of diamond nanoparticles</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">While significant progress has been made toward production of monodispersed samples of a variety of nanoparticles, in cases such as diamond nanoparticles (nanodiamonds) a significant degree of polydispersivity persists, so scaling-up of laboratory applications to industrial levels has its challenges. In many cases, however, monodispersivity is not essential for reliable application, provided that the inevitable uncertainties are just as predictable as the functional properties. As computational methods of materials design are becoming more widespread, there is a growing need for robust methods for modeling <span class="hlt">ensembles</span> of nanoparticles, that capture the structural complexity characteristic of real specimens. In this paper we present a simple statistical approach to modeling of <span class="hlt">ensembles</span> of nanoparticles, and apply it to nanodiamond, based on sets of individual simulations that have been carefully selected to describe specific structural sources that are responsible for scattering of fundamental properties, and that are typically difficult to eliminate experimentally. For the purposes of demonstration we show how scattering in the Fermi energy and the electronic band gap are related to different structural variations (sources), and how these results can be combined strategically to yield statistically significant predictions of the properties of an entire <span class="hlt">ensemble</span> of nanodiamonds, rather than merely one individual ‘model’ particle or a non-representative sub-set.</p> <div class="credits"> <p class="dwt_author">Barnard, Amanda S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-03-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">330</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013PhyD..243..128G"> <span id="translatedtitle"><span class="hlt">Ensemble</span> data assimilation for hyperbolic systems</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> based methods are now widely used in applications such as weather prediction, but there are few rigorous results regarding their application. The broad goal of this paper is to provide some theoretical evidence of their applicability in the computational study of dynamical systems in some idealized, yet interesting setting. The specific goal of this paper is to investigate a data assimilation procedure (DAP), an <span class="hlt">ensemble</span> Kalman filter (EKF), in the context of hyperbolic systems. We show that with appropriate assumptions on observations, for every trajectory on an attractor, the predictions produced by the DAP remain close to the truth for all time provided the <span class="hlt">ensemble</span> is properly initialized, making the DAP reliable. We deal with the case of a one-dimensional unstable direction first, and later extend to higher dimensional unstable spaces. A feature of this approach is that no model linearizations are involved, making it efficient and potentially of interest for applications in high dimensional systems. Lyapunov exponents are also investigated.</p> <div class="credits"> <p class="dwt_author">González-Tokman, Cecilia; Hunt, Brian R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">331</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2654620"> <span id="translatedtitle">A Scalable Framework For Cluster <span class="hlt">Ensembles</span> *</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">An <span class="hlt">ensemble</span> of clustering solutions or partitions may be generated for a number of reasons. If the data set is very large, clustering may be done on tractable size disjoint subsets. The data may be distributed at different sites for which a distributed clustering solution with a final merging of partitions is a natural fit. In this paper, two new approaches to combining partitions, represented by sets of cluster centers, are introduced. The advantage of these approaches is that they provide a final partition of data that is comparable to the best existing approaches, yet scale to extremely large data sets. They can be 100,000 times faster while using much less memory. The new algorithms are compared against the best existing cluster <span class="hlt">ensemble</span> merging approaches, clustering all the data at once and a clustering algorithm designed for very large data sets. The comparison is done for fuzzy and hard k-means based clustering algorithms. It is shown that the centroid-based <span class="hlt">ensemble</span> merging algorithms presented here generate partitions of quality comparable to the best label vector approach or clustering all the data at once, while providing very large speedups.</p> <div class="credits"> <p class="dwt_author">Hore, Prodip; Hall, Lawrence O.; Goldgof, Dmitry B.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">332</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010JMP....51i3302D"> <span id="translatedtitle">Tridiagonal realization of the antisymmetric Gaussian ?-<span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The Householder reduction of a member of the antisymmetric Gaussian unitary <span class="hlt">ensemble</span> gives an antisymmetric tridiagonal matrix with all independent elements. The random variables permit the introduction of a positive parameter ?, and the eigenvalue probability density function of the corresponding random matrices can be computed explicitly, as can the distribution of {qi}, the first components of the eigenvectors. Three proofs are given. One involves an inductive construction based on bordering of a family of random matrices which are shown to have the same distributions as the antisymmetric tridiagonal matrices. This proof uses the Dixon-Anderson integral from Selberg integral theory. A second proof involves the explicit computation of the Jacobian for the change of variables between real antisymmetric tridiagonal matrices, its eigenvalues, and {qi}. The third proof maps matrices from the antisymmetric Gaussian ?-<span class="hlt">ensemble</span> to those realizing particular examples of the Laguerre ?-<span class="hlt">ensemble</span>. In addition to these proofs, we note some simple properties of the shooting eigenvector and associated Prüfer phases of the random matrices.</p> <div class="credits"> <p class="dwt_author">Dumitriu, Ioana; Forrester, Peter J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">333</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2004AGUFM.H22A..07H"> <span id="translatedtitle">A Bayesian Methodology for <span class="hlt">Ensemble</span> Forecasting</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A Bayesian methodology for probabilistic forecasting of a future river stage time series in terms of an <span class="hlt">ensemble</span> is presented. The methodology derives from the theory of the Bayesian Forecasting System (BFS). Within the BFS, uncertainty due to future precipitation is quantified in a precipitation uncertainty processor independently of other uncertainties. The other uncertainties are aggregated and quantified in a hydrologic uncertainty processor. Then the precipitation uncertainty and the hydrologic uncertainty are integrated together in an integrator. The resultant probabilistic forecast quantifies the total uncertainty. This paper presents two algorithms for generating an <span class="hlt">ensemble</span> forecast using the BFS. The first algorithm uses output distributions from the analytic-numerical BFS to recursively generate a river stage time series; this algorithm is suited to headwater basins. The second algorithm implements the precipitation uncertainty processor, hydrologic uncertainty processor, and integrator as Monte Carlo generators, which sequentially process a precipitation time series into a river stage time series; this algorithm is suited to complex river basins. The algorithms are illustrated with numerical examples of Bayesian <span class="hlt">ensemble</span> forecasts for several different forecasting scenarios. Properties and advantages of the Bayesian forecasts for decision making are highlighted. Sample sizes required for correct representation of uncertainty are examined.</p> <div class="credits"> <p class="dwt_author">Herr, H. D.; Krzysztofowicz, R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">334</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012AtmRe.111...90X"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> precipitation over Southwest Asia: Impacts of radiance data assimilation on the forecasts of the WRF-ARW model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Based on the dynamical <span class="hlt">downscaling</span> with the Advanced Research Weather (WRF-ARW) mesoscale model, the accuracy of the precipitation forecasts in Southwest Asia has been assessed. Results show that the accuracy of the 24-h and 48-h forecasts for precipitation is closely related to the complex topography of the mountain areas. To understand the impacts of the initial condition uncertainties on accuracy of the dynamical <span class="hlt">downscaling</span>, a series of data assimilation experiments has been performed. The Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) radiance observations and a data assimilation system named the Gridpoint Statistical Interpolation (GSI), developed by the National Centers for Environmental Prediction (NCEP), were used in this study. The results show that the satellite data provides beneficial information for improving the initial conditions for the dynamical model system and the "forecast" errors are reduced for most locations within the 24-h hindcasts.</p> <div class="credits"> <p class="dwt_author">Xu, Jianjun; Powell, , Alfred M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-07-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">335</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFM.H23G1367N"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Satellite-based Passive Microwave Observations Using the Principle of Relevant Information and Auxiliary High Resolution Remote Sensing Products</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Hydrometeorological models simulate the atmospheric and hydrological processes at scales of 1- 10 km that are significantly influenced by the local and regional availability of soil moisture. Microwave observations at frequencies < 10 GHz are highly sensitive to changes in near-surface moisture and have been widely used to retrieve soil moisture information. While satellite-based active microwave observations are available at spatial resolutions of hundreds of meters, with temporal resolutions of several weeks, passive observations are obtained only at tens of kilometers with temporal resolutions of sub daily to 2-3 days. The European Space Agency-Soil Moisture and Ocean Salinity (ESA-SMOS) and the near-future NASA-Soil Moisture Active Passive (SMAP) missions will provide unprecedented passive microwave observations of brightness temperatures (TB) at the L-band frequency of 1.4 GHz. These products will be available at spatial resolutions of about 40-50 km and need to be <span class="hlt">downscaled</span> to 1 km to merge them with models for data assimilation and to study the effects of land surface heterogeneity such as dynamic vegetation conditions. Very few studies have directly <span class="hlt">downscaled</span> coarse-resolution TB observations to match model scales. Since <span class="hlt">downscaling</span> is an ill-posed problem, additional information is required at the fine scales and some studies have leveraged auxiliary high-resolution remote sensing (RS) products in <span class="hlt">downscaling</span> TB. Most of the above studies involve a) physical models that are computationally intensive when extended to global scales, or b) multi-scale algorithms that impose hierarchical models on TB assuming spatial homogeneity, or c) statistical algorithms that are based on second-order statistics such as variances and correlations. These approaches are therefore sub-optimal when applied to the real data or extended to regional/global scales. Optimal <span class="hlt">downscaling</span> requires computationally-efficient algorithms that retain information from higher-order moments, especially under heterogeneous land surface conditions. Novel transformation functions leveraging physical relationships and recent advances in signal processing techniques can be used to transform information from high-resolution RS products into TB. In this study, a <span class="hlt">downscaling</span> methodology was developed using the Principle of Relevant Information (PRI) to <span class="hlt">downscale</span> observations of TB from 50 km to 200 m using observations of land surface temperature, leaf area index, and land cover at 200 m. The PRI provides a hierarchical decomposition of image data that is optimal in terms of the transfer of information across scales and is therefore a better alternative to methods that use second-order statistics only. Non-parametric probability density functions and Bayes' rule was used to transform information from the RS products into TB. An Observing System Simulation Experiment was developed under heterogeneous and dynamic vegetation conditions to generate synthetic observations at 200m to evaluate the <span class="hlt">downscaling</span> methodology and the transformation functions.</p> <div class="credits"> <p class="dwt_author">Nagarajan, K.; Judge, J.; Principe, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">336</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.springerlink.com/index/2p464u6531854q84.pdf"> <span id="translatedtitle">Sensitivity of the Humboldt Current system to global warming: a <span class="hlt">downscaling</span> experiment of the IPSL-CM4 model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The impact of climate warming on the seasonal variability of the Humboldt Current system ocean dynamics is investigated. The\\u000a IPSL-CM4 large scale ocean circulation resulting from two contrasted climate scenarios, the so-called Preindustrial and quadrupling\\u000a CO2, are <span class="hlt">downscaled</span> using an eddy-resolving regional ocean circulation model. The intense surface heating by the atmosphere in\\u000a the quadrupling CO2 scenario leads to a</p> <div class="credits"> <p class="dwt_author">Vincent Echevin; Katerina Goubanova; Ali Belmadani; Boris Dewitte</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">337</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/12214867"> <span id="translatedtitle">Validation of mesoscale low-level winds obtained by dynamical <span class="hlt">downscaling</span> of ERA40 over complex terrain</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The mesoscale numerical weather prediction model ALADIN has been applied for <span class="hlt">downscaling</span> ERA40 data onto a 10 km grid covering the complex terrain of Slovenia. The modelled wind field is compared with the time-series of observations at 11 stations. In addition to traditional scores (root-mean-square error, mean absolute error, anomaly correlation), a frequency-domain comparison is carried out in order to</p> <div class="credits"> <p class="dwt_author">N. Zagar; M. Zagar; J. Cedilnik; G. Gregoric; J. Rakovec</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">338</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.agu.org/journals/wr/wr1008/2009WR008855/2009WR008855.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> soil moisture in the southern Great Plains through a calibrated multifractal model for land surface modeling applications</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Accounting for small-scale spatial heterogeneity of soil moisture ($\\\\theta$) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a <span class="hlt">downscaling</span> model based on multifractal theory using aircraft-based (800 m) $\\\\theta$ estimates collected during the southern Great Plains experiment in 1997 (SGP97). We first</p> <div class="credits"> <p class="dwt_author">Giuseppe Mascaro; Enrique R. Vivoni; Roberto Deidda</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">339</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/40757078"> <span id="translatedtitle"><span class="hlt">Down-scale</span> analysis for water scarcity in response to soil–water conservation on Loess Plateau of China</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Water scarcity is one of the most prominent issues of discussion worldwide concerned with sustainable development, especially in the arid and semi-arid areas. On the Loess Plateau of China, population growth and fast-growing cities and industries have caused ever-increasing competition for water. The present paper shows a <span class="hlt">down-scale</span> analysis on how the region wide mass action of soil–water conservation ecologically</p> <div class="credits"> <p class="dwt_author">He Xiubin; Li Zhanbin; Hao Mingde; Tang Keli; Zheng Fengli</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">340</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.2499Z"> <span id="translatedtitle">Tempo-spatial <span class="hlt">downscaling</span> of multiple GCMs projections for soil erosion risk analysis at El Reno, Oklahoma, USA</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Proper spatial and temporal treatments of climate change scenarios projected by General Circulation Models (GCMs) are critical to accurate assessment of climatic impacts on natural resources and ecosystems. For accurate prediction of soil erosion risk at a particular farm or field under climate change, climate change scenarios projected by General Circulation Models (GCMs) must be appropriately <span class="hlt">downscaled</span> to the target location. The objective of this study was to evaluate site-specific impacts of climate change on soil erosion and surface hydrology at El Reno, Oklahoma in U.S.A. using the Water Erosion Prediction Project (WEPP) model. Climate change scenarios during 2010-2039 projected by four GCMs (CCSR/NIES, CGCM2, CSIRO-Mk2 and HadCM3) under three emission scenarios (A2, B2 and GGa) were used. Monthly projections at the GCMs grid scales were tempo-spatially <span class="hlt">downscaled</span> to daily weather data at the El Reno location. Univariate transfer functions were derived by matching probability distributions between location-measured and GCM-projected monthly precipitation and temperature for the 1957-2006 period. The derived functions were used to spatially <span class="hlt">downscale</span> the GCMs monthly projections of 2010-2039 to the El Reno unit watershed. The <span class="hlt">downscaled</span> monthly data were further disaggregated to daily weather series using a stochastic weather generator (CLIGEN). Potential changes in soil erosion risk or uncertainty at the study location will be evaluated using soil erosion rates predicted using the WEPP model for the climate change scenarios projected by the four GCMs. The effectiveness of conservation tillage under future climate change will also be explored.</p> <div class="credits"> <p class="dwt_author">Zhang, X.-C. John</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_16");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' 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showDiv("page_12");' href="#">12</a> <a onClick='return showDiv("page_13");' href="#">13</a> <a onClick='return showDiv("page_14");' href="#">14</a> <a onClick='return showDiv("page_15");' href="#">15</a> <a onClick='return showDiv("page_16");' href="#">16</a> <a onClick='return showDiv("page_17");' href="#">17</a> <a style="font-weight: bold;">18</a> <a onClick='return showDiv("page_19");' href="#">19</a> <a onClick='return showDiv("page_20");' href="#">20</a> <a onClick='return showDiv("page_21");' href="#">21</a> <a onClick='return showDiv("page_22");' href="#">22</a> <a onClick='return showDiv("page_23");' href="#">23</a> <a onClick='return showDiv("page_24");' href="#">24</a> <a onClick='return showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_19");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">341</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.springerlink.com/index/y752277tv5020937.pdf"> <span id="translatedtitle">Temporal <span class="hlt">downscale</span> for hourly rainfall time series using correlated Neyman-Scott rectangular pulse point rainfall model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A stochastic point rainfall model which can represent the inter-relation between rain-cell intensity and duration is used\\u000a to investigate the possibility of temporal <span class="hlt">downscale</span> for hourly rainfall time series. The long-term simulation result appears\\u000a to regenerate the observed data well in terms of statistics, but, there are still problems such as the underestimation of\\u000a maximum rainfall depths and overestimation of</p> <div class="credits"> <p class="dwt_author">Suhee Han; Hyun Suk Shin; Sangdan Kim</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">342</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFMGC43B0897S"> <span id="translatedtitle">The Future of Land Use in the United States: <span class="hlt">Downscaling</span> SRES Emission Scenarios to Ecoregions and Pixels</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Scenario analysis has emerged as a useful tool for evaluating uncertain futures in ecological systems. We describe research initiated by the U.S. Geological Survey (USGS) to develop a comprehensive portfolio of future land-use and land-cover (LULC) scenarios for the United States. The USGS has identified LULC scenarios as a focal area of future research. Scenarios are used to assist in the understanding of possible future developments in complex systems that typically have high levels of scientific uncertainty. Scenarios generally require knowledge of history and current conditions, and specific understanding about how drivers of change have acted to influence the historical and current condition. We describe methods and results of <span class="hlt">downscaling</span> LULC and associated narrative storylines from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES). The <span class="hlt">downscaling</span> methods leverage three primary sources of information: 1) comprehensive land-use histories developed through remote sensing and survey data, 2) modeled LULC outputs from global integrated assessment models (IAMs), and 3) expert knowledge of regional land change. First, national and ecoregional narrative storylines were derived from the global IPCC framework. Based on the characteristics of <span class="hlt">downscaled</span> narrative storylines, experts used historical data and information on the rates and types of LULC change, in conjunction with coarse-scale IAM projections of land use, to produce future quantitative scenarios. An accounting model was developed to handle all aspects of scenario <span class="hlt">downscaling</span>. Here we present the methods used to construct ecoregion-specific scenarios of LULC change consistent with the IPCC-SRES scenarios, as well as results at multiple geographic scales. The USGS LandCarbon assessment is implementing a scenario-based approach for projecting changes in LULC that may result in changes to ecosystem carbon flux and greenhouse gas (GHG) emissions. Results described here are used in the FOREcasting-SCEnarios model to create spatially explicit annual maps of land use and land cover at a 250-meter pixel resolution for the Conterminous United States.</p> <div class="credits"> <p class="dwt_author">Sleeter, B. M.; Sohl, T. L.; Sayler, K.; Bouchard, M. A.; Reker, R.; Sleeter, R. R.; Zhu, Z.; Auch, R.; Acevedo, W.; Soulard, C. E.; Griffith, G.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">343</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2005APS..DMP.L1002W"> <span id="translatedtitle">The Preferred <span class="hlt">Ensemble</span> Fact with Applications to Quantum Feedback Control</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">It is well known that there are infinitely many different <span class="hlt">ensembles</span> of pure states that are equivalent to any given mixed quantum state. The preferred <span class="hlt">ensemble</span> fallacy [1] is that any particular <span class="hlt">ensemble</span> should be used in the interpretation of an experiment involving a quantum system in a mixed state. Notwithstanding this, for open quantum systems obeying a master equation that has a mixed steady state, there is a preferred <span class="hlt">ensemble</span> fact: only some <span class="hlt">ensembles</span> are physically realizable. By this we mean that it is only some <span class="hlt">ensembles</span> for which *an observer can know at all times which pure state member of the <span class="hlt">ensemble</span> the system is in; and *the weight of that state in the <span class="hlt">ensemble</span> is the proportion of time the system spends in that state. The preferred <span class="hlt">ensemble</span> fact has applications in quantum feedback control in LQG (linear quadratic gaussian) systems [3], which has recently been implemented experimentally in a number of systems such as spin-squeezing and nanomechanical devices. Specifically, the existence of preferred <span class="hlt">ensembles</span> determines the quantum limit to how well certain control goals can be achieved. I will illustrate these ideas with an example from quantum optics. [1] P. Kok and S.L. Braunstein, Phys. Rev. A 61, 042304 (2000). [2] H.M. Wiseman and J.A. Vaccaro, Phys. Rev. Lett. 87, 240402 (2001). [3] H. M. Wiseman, and A. C. Doherty, Phys. Rev. Lett. To appear (quant-ph/0408099)</p> <div class="credits"> <p class="dwt_author">Wiseman, Howard</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">344</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013AdWR...61...42F"> <span id="translatedtitle">Spatial <span class="hlt">downscaling</span> of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The lack of high resolution precipitation data has posed great challenges to the study and management of extreme rainfall events. Satellite-based rainfall products with large areal coverage provide a potential alternative source of data where in situ measurements are not available. However, the mismatch in scale between these products and model requirements has limited their application and demonstrates that satellite data must be <span class="hlt">downscaled</span> before being used. This study developed a statistical spatial <span class="hlt">downscaling</span> scheme based on the relationships between precipitation and related environmental factors such as local topography and pre-storm meteorological conditions. The method was applied to disaggregate the Tropical Rainfall Measuring Mission (TRMM) 3B42 products, which have a resolution of 0.25° × 0.25°, to 1 × 1 km gridded rainfall fields. The TRMM datasets in accord with six rainstorm events in the Xiao River basin were used to validate the effectiveness of this approach. The <span class="hlt">downscaled</span> precipitation data were compared with ground observations and exhibited good agreement with r2 values ranging from 0.612 to 0.838. In addition, the proposed approach provided better results than the conventional spline and kriging interpolation methods, indicating its promise in the management of extreme rainfall events. The uncertainties in the final results and the implications for further study were discussed, and the needs for additional rigorous investigations of the rainfall physical process prior to institutionalizing the use of satellite data were highlighted.</p> <div class="credits"> <p class="dwt_author">Fang, Jian; Du, Juan; Xu, Wei; Shi, Peijun; Li, Man; Ming, Xiaodong</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-11-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">345</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ClDy...40..601D"> <span id="translatedtitle">Potential for small scale added value of RCM's <span class="hlt">downscaled</span> climate change signal</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In recent decades, the need of future climate information at local scales have pushed the climate modelling community to perform increasingly higher resolution simulations and to develop alternative approaches to obtain fine-scale climatic information. In this article, various nested regional climate model (RCM) simulations have been used to try to identify regions across North America where high-resolution <span class="hlt">downscaling</span> generates fine-scale details in the climate projection derived using the "delta method". Two necessary conditions were identified for an RCM to produce added value (AV) over lower resolution atmosphere-ocean general circulation models in the fine-scale component of the climate change (CC) signal. First, the RCM-derived CC signal must contain some non-negligible fine-scale information—independently of the RCM ability to produce AV in the present climate. Second, the uncertainty related with the estimation of this fine-scale information should be relatively small compared with the information itself in order to suggest that RCMs are able to simulate robust fine-scale features in the CC signal. Clearly, considering necessary (but not sufficient) conditions means that we are studying the "potential" of RCMs to add value instead of the AV, which preempts and avoids any discussion of the actual skill and hence the need for hindcast comparisons. The analysis concentrates on the CC signal obtained from the seasonal-averaged temperature and precipitation fields and shows that the fine-scale variability of the CC signal is generally small compared to its large-scale component, suggesting that little AV can be expected for the time-averaged fields. For the temperature variable, the largest potential for fine-scale added value appears in coastal regions mainly related with differential warming in land and oceanic surfaces. Fine-scale features can account for nearly 60 % of the total CC signal in some coastal regions although for most regions the fine scale contributions to the total CC signal are of around ˜5 %. For the precipitation variable, fine scales contribute to a change of generally less than 15 % of the seasonal-averaged precipitation in present climate with a continental North American average of ˜5 % in both summer and winter seasons. In the case of precipitation, uncertainty due to sampling issues may further dilute the information present in the <span class="hlt">downscaled</span> fine scales. These results suggest that users of RCM simulations for climate change studies in a delta method framework have little high-resolution information to gain from RCMs at least if they limit themselves to the study of first-order statistical moments. Other possible benefits arising from the use of RCMs—such as in the large scale of the <span class="hlt">downscaled</span> fields- were not explored in this research.</p> <div class="credits"> <p class="dwt_author">Di Luca, Alejandro; de Elía, Ramón; Laprise, René</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-02-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">346</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ClDy...41.1871L"> <span id="translatedtitle">A regional climate model <span class="hlt">downscaling</span> projection of China future climate change</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Climate changes over China from the present (1990-1999) to future (2046-2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 <span class="hlt">downscaling</span> corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 <span class="hlt">downscaling</span> produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.</p> <div class="credits"> <p class="dwt_author">Liu, Shuyan; Gao, Wei; Liang, Xin-Zhong</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-10-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">347</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.7856H"> <span id="translatedtitle"><span class="hlt">Downscaling</span> reanalysis data to high-resolution variables above a glacier surface (Cordillera Blanca, Peru)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Recently initiated observation networks in the Cordillera Blanca provide temporally high-resolution, yet short-term atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical <span class="hlt">downscaling</span> (ESD) model that links 6-hourly NCEP/NCAR reanalysis data to the local target variables, measured at the tropical glacier Artesonraju (Northern Cordillera Blanca). The approach is particular in the context of ESD for two reasons. First, the observational time series for model calibration are short (only about two years). Second, unlike most ESD studies in climate research, we focus on variables at a high temporal resolution (i.e., six-hourly values). Our target variables are two important drivers in the surface energy balance of tropical glaciers; air temperature and specific humidity. The selection of predictor fields from the reanalysis data is based on regression analyses and climatologic considerations. The ESD modelling procedure includes combined empirical orthogonal function and multiple regression analyses. Principal component screening is based on cross-validation using the Akaike Information Criterion as model selection criterion. Double cross-validation is applied for model evaluation. Potential autocorrelation in the time series is considered by defining the block length in the resampling procedure. Apart from the selection of predictor fields, the modelling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice by using both single- and mixed-field predictors of the variables air temperature (1000 hPa), specific humidity (1000 hPa), and zonal wind speed (500 hPa). The chosen <span class="hlt">downscaling</span> domain ranges from 80 to 50 degrees west and from 0 to 20 degrees south. Statistical transfer functions are derived individually for different months and times of day (month/hour-models). The forecast skill of the month/hour-models largely depends on month and time of day, ranging from 0 to 0.8, but the mixed-field predictors generally perform better than the single-field predictors. At all time scales, the ESD model shows added value against two simple reference models; (i) the direct use of reanalysis grid point values, and (ii) mean diurnal and seasonal cycles over the calibration period. The ESD model forecast 1960 to 2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation, but is sensitive to the chosen predictor type. So far, we have not assessed the performance of NCEP/NCAR reanalysis data against other reanalysis products. The developed ESD model is computationally cheap and applicable wherever measurements are available for model calibration.</p> <div class="credits"> <p class="dwt_author">Hofer, Marlis; Mölg, Thomas; Marzeion, Ben; Kaser, Georg</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">348</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFMNS43A1502D"> <span id="translatedtitle">Seismic wave propagation in multiphasic complex porous media : upscaling and <span class="hlt">downscaling</span> approaches</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Seismic wave propagation is often used for subsurface investigation, related either to reservoir issues (oil, gas or CO2 storage) or geotechnical problems (slope stability, water resources, territory management). Indeed, near surface media are rather heterogeneous, complex and partially fluid-filled. These characteristics are more or less sensitive to seismic waves. In order to interpret efficiently seismic attributes in these media, wave propagation may require complex poroelastic theories in multiphasic media as double porosity or patchy saturated cases. Thanks to upscaling techniques, we determine homogeneized parameters leading to a two-phases media described by complex and frequency dependent parameters. An effective generalized Biot-Gassmann theory allows wave propagation in 2D heterogeneous media through a Discontinuous Galerkin finite-element approach. Taking into account the rather complex frequency-dependent rheology of these porous media, wave propagation simulation is simpler in a frequency formulation than in a time formulation, at least for 2D geometries. We illustrate two features essential for an accurate characterization of the medium. On one hand, strong waveforms differences between the complex upscaled media (double porosity, unsaturated, squirt flow models) and the equivalent saturated Biot-Gassmann media (determined by arithmetic and harmonic averages) are underlined on simple examples. This may require the use of these theories for the characterization of these media. In the other hand, after the reconstruction of macro-scale parameters such as velocities and attenuations through seismic attributes (times, amplitudes and so on) using standard visco-elastic interpretation (first step of the <span class="hlt">downscaling</span> procedure), we show that we recover micro-scale parameters (skeleton parameters such as porosity or dry moduli, fluid saturation...) using global search techniques (neighborhood algorithm) with some a priori information (second step of the <span class="hlt">downscaling</span> procedure). We may propose that this two-steps procedure could be used for recovering micro-scale parameters of these complex media. A validation could be performed by direct interpretation of seismograms using the upscaling approach we have elaborated, putting the model description on more robust grounds. In conclusion, we show that a a two-steps procedure could be used for recovering micro-scale parameters and that considering complex poroelastic approaches is crucial to enhance the quantitative seismic imaging of near surface media.</p> <div class="credits"> <p class="dwt_author">Dupuy, B.; Garambois, S.; Virieux, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">349</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2006HyPr...20.1385P"> <span id="translatedtitle">A <span class="hlt">downscaling</span> method of topographic index distribution for matching the scales of model application and parameter identification</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Higher resolution topographic information contained in the topographic index of TOPMODEL is lost when digital elevation models (DEMs) with a coarse grid resolution are used; thus, the topographic index is scale dependent, demonstrating identified model parameter values to be dependent on DEM resolution. This makes it difficult to use model parameter values identified through a different resolution of TOPMODEL. The inconsistency is the result of the difference between the scale at which the model parameters are identified and the scale at which the model is applied. To overcome this problem, scale laws that govern the relationship between the resolution of digital elevation data and geomorphometric parameters of the topographic index were analysed and a method to <span class="hlt">downscale</span> the topographic index distribution developed to account for the difference in scales between model application and parameter identification. The method to <span class="hlt">downscale</span> the topographic index is composed of two ideas: one involves introducing a resolution factor to account for the scale effect in upslope catchment area per unit contour length in the topographic index; the other utilizes a fractal method through steepest slope scaling to account for the scale effect on slopes. This method successfully derived a topographic index distribution of a fine-resolution DEM by using only a coarse-resolution DEM. The method has been applied successfully to the Kamishiiba catchment (210 km2) in Japan and has demonstrated that the <span class="hlt">downscaled</span> topographic index distribution derived using a 1000 m grid DEM is very similar to the topographic index distribution derived via fine-target-resolution DEMs. The method is then coupled with a TOPMODEL simulation to match the scales of model application and parameter identification. It is shown that the simulated runoff from the <span class="hlt">downscaled</span> TOPMODEL applied at 1000 m resolution of the Kamishiiba catchment, with the same set of effective parameter values derived from 50 m resolution DEM, matched the simulated runoff in the 50 m DEM resolution TOPMODEL. It was also shown that TOPMODEL coupled with the <span class="hlt">downscaling</span> method of the topographic index accurately simulated runoff for different rainfall events in the catchment without recalibration.</p> <div class="credits"> <p class="dwt_author">Pradhan, N. R.; Tachikawa, Y.; Takara, K.</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">350</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ClDy..tmp..109Y"> <span id="translatedtitle">Reliability and importance of structural diversity of climate model <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We investigate the performance of the newest generation multi-model <span class="hlt">ensemble</span> (MME) from the Coupled Model Intercomparison Project (CMIP5). We compare the <span class="hlt">ensemble</span> to the previous generation models (CMIP3) as well as several single model <span class="hlt">ensembles</span> (SMEs), which are constructed by varying components of single models. These SMEs range from <span class="hlt">ensembles</span> where parameter uncertainties are sampled (perturbed physics <span class="hlt">ensembles</span>) through to an <span class="hlt">ensemble</span> where a number of the physical schemes are switched (multi-physics <span class="hlt">ensemble</span>). We focus on assessing reliability against present-day climatology with rank histograms, but also investigate the effective degrees of freedom (EDoF) of the fields of variables which makes the statistical test of reliability more rigorous, and consider the distances between the observation and <span class="hlt">ensemble</span> members. We find that the features of the CMIP5 rank histograms, of general reliability on broad scales, are consistent with those of CMIP3, suggesting a similar level of performance for present-day climatology. The spread of MMEs tends towards being "over-dispersed" rather than "under-dispersed". In general, the SMEs examined tend towards insufficient dispersion and the rank histogram analysis identifies them as being statistically distinguishable from many of the observations. The EDoFs of the MMEs are generally greater than those of SMEs, suggesting that structural changes lead to a characteristically richer range of model behaviours than is obtained with parametric/physical-scheme-switching <span class="hlt">ensembles</span>. For distance measures, the observations and models <span class="hlt">ensemble</span> members are similarly spaced from each other for MMEs, whereas for the SMEs, the observations are generally well outside the <span class="hlt">ensemble</span>. We suggest that multi-model <span class="hlt">ensembles</span> should represent an important component of uncertainty analysis.</p> <div class="credits"> <p class="dwt_author">Yokohata, Tokuta; Annan, James D.; Collins, Matthew; Jackson, Charles S.; Shiogama, Hideo; Watanabe, Masahiro; Emori, Seita; Yoshimori, Masakazu; Abe, Manabu; Webb, Mark J.; Hargreaves, Julia C.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">351</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/48409421"> <span id="translatedtitle">Is the density matrix description of statistical <span class="hlt">ensembles</span> exhaustive?</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Summary  As is well known, statistical <span class="hlt">ensembles</span> of quantum systems, prepared through completely different physical procedures can\\u000a correspond to the same density matrix and therefore cannot be distinguished by means of the average values on the <span class="hlt">ensemble</span>\\u000a of observables of the system. In this paper we introduce quantities which can be determined provided the <span class="hlt">ensemble</span> can be identically\\u000a reproduced and whose</p> <div class="credits"> <p class="dwt_author">G. C. Ghirardi; A. Rimini; T. Weber</p> <p class="dwt_publisher"></p> <p class="publishDate">1975-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">352</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2006slft.confE.188M"> <span id="translatedtitle">Production and properties of 2+1 flavor DWF <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The RBC and UKQCD collaborations have generated 2+1 flavor <span class="hlt">ensembles</span> with domain wall fermions (DWF) using the QCDOC computers. These configurations are produced with the Ra- tional Hybrid Monte Carlo (RHMC), which has been refined and improved during this last year's running to speed up production by about a factor of six. This talk deals with the details of the <span class="hlt">ensemble</span> production, tuning of the RHMC for DWF, the evolution of topology and other founda- tional aspects of this <span class="hlt">ensemble</span>.</p> <div class="credits"> <p class="dwt_author">Mawhinney, Robert</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">353</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.springerlink.com/index/k5600643552u0326.pdf"> <span id="translatedtitle">Apparent evaporative resistance at critical conditions for five clothing <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A limiting factor for clothing <span class="hlt">ensembles</span> inherent during heat stress exposures is the evaporative resistance, which can be\\u000a used to compare candidate <span class="hlt">ensembles</span> and in rational models of heat exchange. In this study, the apparent total evaporative\\u000a resistance of five clothing <span class="hlt">ensembles</span> (cotton work clothes, cotton coveralls, and coveralls made of Tyvek® 1424 and 1427, NexGen® and Tychem QC®) was</p> <div class="credits"> <p class="dwt_author">Victor Caravello; Elizabeth A. McCullough; Candi D. Ashley; Thomas E. Bernard</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">354</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/50178455"> <span id="translatedtitle">Hydrogen maser <span class="hlt">ensemble</span> performance and characterization of frequency standards</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The performance of a post-processed clock <span class="hlt">ensemble</span> dominated by active, cavity-tuned, hydrogen masers has been evaluated. The <span class="hlt">ensemble</span> has an average frequency drift of less than ±3×10-15 per year, and an Allan deviation less than 1×10-15 for ? between 4 hours and 100 days. This <span class="hlt">ensemble</span> has been used to help characterize the frequency stability of a number of commercially</p> <div class="credits"> <p class="dwt_author">Thomas E. Parker</p> <p class="dwt_publisher"></p> <p class="publishDate">1999-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">355</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013PhRvL.111l0601C"> <span id="translatedtitle">Nonequilibrium Microcanonical and Canonical <span class="hlt">Ensembles</span> and Their Equivalence</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Generalizations of the microcanonical and canonical <span class="hlt">ensembles</span> for paths of Markov processes have been proposed recently to describe the statistical properties of nonequilibrium systems driven in steady states. Here, we propose a theory of these <span class="hlt">ensembles</span> that unifies and generalizes earlier results and show how it is fundamentally related to the large deviation properties of nonequilibrium systems. Using this theory, we provide conditions for the equivalence of nonequilibrium <span class="hlt">ensembles</span>, generalizing those found for equilibrium systems, construct driven physical processes that generate these <span class="hlt">ensembles</span>, and rederive in a simple way known and new product rules for their transition rates. A nonequilibrium diffusion model is used to illustrate these results.</p> <div class="credits"> <p class="dwt_author">Chetrite, Raphaël; Touchette, Hugo</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-09-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">356</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/21811801"> <span id="translatedtitle">Design <span class="hlt">ensemble</span> machine learning model for breast cancer diagnosis.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, <span class="hlt">ensemble</span> ones have been developed for classifications. In addition, a combined <span class="hlt">ensemble</span> model with these three schemes has been constructed for further validations. The experimental results indicate that the <span class="hlt">ensemble</span> learning performs better than individual single ones. Moreover, the combined <span class="hlt">ensemble</span> model illustrates the highest accuracy of classifications for the breast cancer among all models. PMID:21811801</p> <div class="credits"> <p class="dwt_author">Hsieh, Sheau-Ling; Hsieh, Sung-Huai; Cheng, Po-Hsun; Chen, Chi-Huang; Hsu, Kai-Ping; Lee, I-Shun; Wang, Zhenyu; Lai, Feipei</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-08-03</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">357</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2760036"> <span id="translatedtitle">A Model-Based <span class="hlt">Ensembling</span> Approach for Developing QSARs</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> methods have become popular for QSAR modeling, but most studies have assumed balanced data consisting of approximately equal numbers of active and inactive compounds. Cheminformatics data is often far from being balanced. We extend the application of <span class="hlt">ensemble</span> methods to include cases of imbalance of class membership and to more adequately assess model output. Based on the extension, we propose an <span class="hlt">ensemble</span> method called MBEnsemble that automatically determines the appropriate tuning parameters to provide reliable predictions and maximize the F-measure. Results from multiple datasets demonstrate that the proposed <span class="hlt">ensemble</span> technique works well on imbalanced data.</p> <div class="credits"> <p class="dwt_author">Zhang, Qianyi; Hughes-Oliver, Jacqueline M.; Ng, Raymond T.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">358</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.8744T"> <span id="translatedtitle">Regional climate simulations over Africa using WRF Model: Sensitivity to the dynamical <span class="hlt">downscaling</span> methods.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The main objective of the CORDEX program (COordinated Regional climate <span class="hlt">Downscaling</span> Experiment) [1] is the production of regional climate change scenarios at a global scale, creating a contribution to the IPCC (Intergovernmental Panel on Climate Change) AR5 (5th Assessment Report). Inside this project, Africa is the key region due to the lack of data at this moment. In this work, a sensitivity study is performed over the CORDEX-AFRICA domain with the same physical parameterizations and using five different WRF configurations: a long-term continuous run, a monthly re-initialized run, a monthly re-initialized run with soil variables fixed, a long-term continuous run with analysis nudging over the planet boundary layer (PBL) and a long-term continuous run with analysis nudging at the whole atmospheric column. These simulations, driven by ERA-Interim data [2] as initial and lateral boundary conditions and with a 50 km spatial resolution, were performed over the 5-year period between December 1990 and December 1995. In order to assess theperformance of the simulations several statistics, such as correlation coefficient (r), bias, root mean square (RMS) and a defined skill score (SS), based on the difference between areas of the probability density functions (PDFs) associated to study parameters [3], were applied using ERA-Interim, CRU-TS 3.1 and University of Delaware database as validation data for some variables, such as near-surface temperature, precipitation and moisture fluxes.</p> <div class="credits"> <p class="dwt_author">Taima-Hernández, D.; Enríquez, A.; Pérez, J. C.; Díaz, J. P.; González, A.; Expósito, F. J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">359</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3785125"> <span id="translatedtitle">Optimisation of <span class="hlt">Downscaled</span> Tandem Affinity Purifications to Identify Core Protein Complexes</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">In this study we show that via stable, retroviral-expression of tagged EGFR del (L747-S752 deletion mutant) in the PC9 lung cancer cell line and stable doxycycline-inducible expression of tagged Grb2 using a Flp-mediated recombination HEK293 cell system, the SH-TAP can be <span class="hlt">downscaled</span> to 5 to 12.5 mg total protein input (equivalent to 0.5 - 1 × 15 cm culture plate or 4 - 8 × 106 cells). The major constituents of the EGFR del complex (USB3B, GRB2, ERRFI, HSP7C, GRP78, HSP71) and the Grb2 complex (ARHG5, SOS1, ARG35, CBL, CBLB, PTPRA, SOS2, DYN2, WIPF2, IRS4) were identified. Adjustment of the quantity of digested protein injected into the mass spectrometer reveals that optimisation is required as high quantities of material led to a decrease in protein sequence coverage and the loss of some interacting proteins. This investigation should aid other researchers in performing tandem affinity purifications in general, and in particular, from low quantities of input material.</p> <div class="credits"> <p class="dwt_author">Haura, Eric B.; Sacco, Roberto; Li, Jiannong; Muller, Andre C.; Grebien, Florian; Superti-Furga, Giulio; Bennett, Keiryn L.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">360</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFMOS54B..07J"> <span id="translatedtitle">Deep convection in the Labrador Sea, as captured by a global ocean reanalysis and regional <span class="hlt">downscalings</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Labrador Sea Water is formed by deep convection caused by strong surface cooling. The convection patch reaches as deep as 2000~m, but is limited to areas where adequat preconditionning occurs. Indeed, baroclinic eddies that form along the West Greenland boundary current enable the restratification throughout the column. The recent Global Eddy Permitting Ocean Reanalysis (GLORYS1, Ferry et al., 2010) shows a good ability to capture the interannual variability of deep convection in the Labrador Sea. Temperature increments from the data assimilation are used to describe how the ocean model is corrected in the reanalysis. It is shown that data assimilation has the same effects as the expected effects of heat transport by baroclinic eddies that form along the West Greenland boundary current. Most of the ocean models at resolution coarser than ~ 1/15° fail to capture deep convection in the Labrador Sea because eddies are not resolved, and existing eddy parameterizations have not setlled the problem. Yet, the formation of Labrador deep water in a climate change scenario, i.e. simulated by relatively coarse ocean models, is a major concern. Therefore, regional <span class="hlt">downscalings</span> within GLORYS1 are built up using the ocean model NEMO (Madec, 2008), without data assimlilation, and at a resolution of 1/4°. The information form GLORYS data assimilation is used to improve the representation of deep convection in the Labrador Sea.</p> <div class="credits"> <p class="dwt_author">Jourdain, N. C.; Barnier, B.; Molines, J.; Chanut, J.; Ferry, N.; Garric, G.; Parent, L.; Mercator-Ocean Team</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_17");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a 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href="#">10</a> <a onClick='return showDiv("page_11");' href="#">11</a> <a onClick='return showDiv("page_12");' href="#">12</a> <a onClick='return showDiv("page_13");' href="#">13</a> <a onClick='return showDiv("page_14");' href="#">14</a> <a onClick='return showDiv("page_15");' href="#">15</a> <a onClick='return showDiv("page_16");' href="#">16</a> <a onClick='return showDiv("page_17");' href="#">17</a> <a onClick='return showDiv("page_18");' href="#">18</a> <a style="font-weight: bold;">19</a> <a onClick='return showDiv("page_20");' href="#">20</a> <a onClick='return showDiv("page_21");' href="#">21</a> <a onClick='return showDiv("page_22");' href="#">22</a> <a onClick='return showDiv("page_23");' href="#">23</a> <a onClick='return showDiv("page_24");' href="#">24</a> <a onClick='return showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_20");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">361</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..1411917K"> <span id="translatedtitle">Extreme Wind Gusts within European Winter Storms estimated from Dynamical and Statistical <span class="hlt">Downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Extreme winter wind storms are major natural catastrophes leading to enormous socio-economic impacts in Europe. The impact of a single events depends on the severity and extent of the event itself but also on the region hit by the storm, combined with its specific exposure of values and vulnerability. The spatial distribution of exposed values and their vulnerability is highly heterogeneous. Therefore, it is necessary to analyze extremes of surface wind speeds within winter wind storms with high spatial resolution. This study analyzes if rather simple linear regression methods are suitable for estimating extreme surface wind gusts of high spatial resolution, using different coarse resolution predictors. The statistical relationships between coarse resolution predictors from ECMWF reanalysis data and high resolution (~7km x 7km) predictands, i.e. the maximum gusts, are derived from dynamical simulations of extreme historical events performed with the German Weather Service (DWD) model chain GME—COSMO-EU. Validation of the results of the statistical <span class="hlt">downscaling</span> confirms the high skill of linear regressions for different European sub-regions. Hence, the application of these methods to more extensive datasets in order to estimate extreme wind gusts and their exceedance probabilities or return periods is justified.</p> <div class="credits"> <p class="dwt_author">Kruschke, T.; Lorenz, P.; Osinski, R.; Voigt, M.; Leckebusch, G. C.; Ulbrich, U.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">362</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013OcMod..69...50M"> <span id="translatedtitle">A <span class="hlt">downscaling</span> method for simulating deep current interactions with topography – Application to the Sigsbee Escarpment</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A nesting approach is applied to dynamically <span class="hlt">downscale</span> the deep circulation from a basin-scale model in regions of complicated topography where deep dynamics may be poorly resolved. The method is applied to nest a high vertical and horizontal resolution Navy Coastal Ocean Model (NCOM) domain covering the north-central Gulf of Mexico within a HYbrid Coordinate Ocean Model (HYCOM) Gulf of Mexico domain. The northwestern Gulf of Mexico has a very steep topographic feature, the Sigsbee Escarpment, over which localized bottom-intensified currents with short cross-isobath length scales have been observed in water depths between 1500 m and 3000 m. It has been hypothesized that these intense currents are related to the presence of the Loop Current or Loop Current Eddies, strong upper ocean mesoscale circulation features in the Gulf. A modeling system is required that can resolve the short length scales of topography and the currents, resolve the vertical trapping of the currents, and realistically simulate the mesoscale upper and deep ocean circulation features. The multi-model nesting approach described here simulates these intense currents with characteristics very similar to observations, and demonstrates the connectivity to the larger scale ocean circulation features.</p> <div class="credits"> <p class="dwt_author">Morey, Steven L.; Dukhovskoy, Dmitry S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">363</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2773294"> <span id="translatedtitle"><span class="hlt">Downscaling</span> limits and confinement effects in the miniaturization of porous polymer monoliths in narrow bore capillaries</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">Monolithic poly(butyl methacrylate-co-ethylene dimethacrylate) columns have been prepared in capillaries ranging in inner diameter from 5 to 75 ?m using thermally initiated free-radical polymerization of a mixture of butyl methacrylate, ethylene dimethacrylate and porogens at different temperatures. Scanning electron microscopy and the measurement of hydrodynamic properties reveal that the downward scalability of the monolithic columns is greatly affected by the confinement effect of the capillary wall resulting from the decreased volume-to-surface ratio as the capillary diameter is decreased. The <span class="hlt">downscaling</span> process is affected most by the polymerization temperature, the diffusion of the propagating radicals, and the density of coverage of polymerizable groups on the inner walls of the capillary. Optimization of all these factors enables the preparation of monolithic structures in capillaries with inner diameters as low as 5 ?m while retaining the desirable properties of monoliths prepared in much larger capillaries. Under these conditions, the formation of undesired dense polymer layers attached to the capillary wall was minimized. The chromatographic performance of 10, 25 and 50 ?m capillaries evaluated in the reversed phase gradient separation of three proteins showed no change in elution times at identical flow velocities and gradient times while peak elution width was the smallest with the narrowest capillary.</p> <div class="credits"> <p class="dwt_author">Nischang, Ivo; Svec, Frantisek; Frechet, Jean M.J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">364</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013AdAtS..30.1287K"> <span id="translatedtitle">Effect of doubling the <span class="hlt">ensemble</span> size on the performance of <span class="hlt">ensemble</span> prediction in the warm season using MOGREPS implemented at the KMA</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Using the Met Office Global and Regional <span class="hlt">Ensemble</span> Prediction System (MOGREPS) implemented at the Korea Meteorological Administration (KMA), the effect of doubling the <span class="hlt">ensemble</span> size on the performance of <span class="hlt">ensemble</span> prediction in the warm season was evaluated. Because a finite <span class="hlt">ensemble</span> size causes sampling error in the full forecast probability distribution function (PDF), <span class="hlt">ensemble</span> size is closely related to the efficiency of the <span class="hlt">ensemble</span> prediction system. Prediction capability according to doubling the <span class="hlt">ensemble</span> size was evaluated by increasing the number of <span class="hlt">ensembles</span> from 24 to 48 in MOGREPS implemented at the KMA. The initial analysis perturbations generated by the <span class="hlt">Ensemble</span> Transform Kalman Filter (ETKF) were integrated for 10 days from 22 May to 23 June 2009. Several statistical verification scores were used to measure the accuracy, reliability, and resolution of <span class="hlt">ensemble</span> probabilistic forecasts for 24 and 48 <span class="hlt">ensemble</span> member forecasts. Even though the results were not significant, the accuracy of <span class="hlt">ensemble</span> prediction improved slightly as <span class="hlt">ensemble</span> size increased, especially for longer forecast times in the Northern Hemisphere. While increasing the number of <span class="hlt">ensemble</span> members resulted in a slight improvement in resolution as forecast time increased, inconsistent results were obtained for the scores assessing the reliability of <span class="hlt">ensemble</span> prediction. The overall performance of <span class="hlt">ensemble</span> prediction in terms of accuracy, resolution, and reliability increased slightly with <span class="hlt">ensemble</span> size, especially for longer forecast times.</p> <div class="credits"> <p class="dwt_author">Kay, Jun Kyung; Kim, Hyun Mee; Park, Young-Youn; Son, Joohyung</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">365</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/21039134"> <span id="translatedtitle">Charged black hole in a canonical <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">We consider the thermodynamics of a charged black hole enclosed in a cavity. The charge in the cavity and the temperature at the walls are fixed, yielding a canonical <span class="hlt">ensemble</span>. We derive the phase structure and stability of black hole equilibrium states. We compare our results to that of other work which uses asymptotically anti-de Sitter (AdS) boundary conditions to define the thermodynamics. The thermodynamic properties have extensive similarities which suggest that the idea of AdS holography is more dependent on the existence of the boundary than on the exact details of asymptotically AdS metrics.</p> <div class="credits"> <p class="dwt_author">Lundgren, Andrew P. [Department of Physics, Cornell University, Ithaca, New York 14853 (United States)</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-02-15</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">366</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010PhRvE..82c1111M"> <span id="translatedtitle">Temperature for a dynamic spin <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In molecular dynamics simulations, temperature is evaluated, via the equipartition principle, by computing the mean kinetic energy of atoms. There is no similar recipe yet for evaluating temperature of a dynamic system of interacting spins. By solving semiclassical Langevin spin-dynamics equations, and applying the fluctuation-dissipation theorem, we derive an equation for the temperature of a spin <span class="hlt">ensemble</span>, expressed in terms of dynamic spin variables. The fact that definitions for the kinetic and spin temperatures are fully consistent is illustrated using large-scale spin dynamics and spin-lattice dynamics simulations.</p> <div class="credits"> <p class="dwt_author">Ma, Pui-Wai; Dudarev, S. L.; Semenov, A. A.; Woo, C. H.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">367</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2828896"> <span id="translatedtitle">CARON - Average RMSD of NMR structure <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">The NMR protein structures are often deposited in the Protein Data Bank as <span class="hlt">ensembles</span> of models that agree with the experimental restraints. Information about stereochemical variability and the molecular flexibility can be obtained by systematic comparison of all models. Here we describe CARON, a software that allows the computation of the root-mean-square-distances between equivalent atoms and residues in all models and introduces these values into the occupancy and the B-factor fields of PDB-formatted files. This tool allows the user to both get a quantitative estimation of the conformational homogeneity of the models and to exploit this information in common computer graphics programs.</p> <div class="credits"> <p class="dwt_author">Sikic, Kresimir; Carugo, Oliviero</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">368</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013JHyd..502...77B"> <span id="translatedtitle">Bias-corrected short-range Member-to-Member <span class="hlt">ensemble</span> forecasts of reservoir inflow</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We develop a short-term <span class="hlt">ensemble</span> reservoir inflow forecasting system.The <span class="hlt">ensemble</span> attempts to sample all sources of uncertainty in the modeling chain.Increasing the diversity of the <span class="hlt">ensemble</span> greatly improves <span class="hlt">ensemble</span> quality.Bias correction of <span class="hlt">ensemble</span> members offers significant additional improvement.For the flashy case study basin, only recent errors are important in bias correction.</p> <div class="credits"> <p class="dwt_author">Bourdin, Dominique R.; Stull, Roland B.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-10-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">369</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/15659602"> <span id="translatedtitle"><span class="hlt">Ensemble</span> coding of vocal control in birdsong.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">Zebra finch song is represented in the high-level motor control nucleus high vocal center (HVC) (Reiner et al., 2004) as a sparse sequence of spike bursts. In contrast, the vocal organ is driven continuously by smoothly varying muscle control signals. To investigate how the sparse HVC code is transformed into continuous vocal patterns, we recorded in the singing zebra finch from populations of neurons in the robust nucleus of arcopallium (RA), a premotor area intermediate between HVC and the motor neurons. We found that highly similar song elements are typically produced by different RA <span class="hlt">ensembles</span>. Furthermore, although the song is modulated on a wide range of time scales (10-100 ms), patterns of neural activity in RA change only on a short time scale (5-10 ms). We suggest that song is driven by a dynamic circuit that operates on a single underlying clock, and that the large convergence of RA neurons to vocal control muscles results in a many-to-one mapping of RA activity to song structure. This permits rapidly changing RA <span class="hlt">ensembles</span> to drive both fast and slow acoustic modulations, thereby transforming the sparse HVC code into a continuous vocal pattern. PMID:15659602</p> <div class="credits"> <p class="dwt_author">Leonardo, Anthony; Fee, Michale S</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-01-19</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">370</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/22131302"> <span id="translatedtitle">The S(2)-<span class="hlt">Ensemble</span> Fusion Algorithm.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">This paper presents a novel model for performing classification and visualization of high-dimensional data by means of combining two enhancing techniques. The first is a semi-supervised learning, an extension of the supervised learning used to incorporate unlabeled information to the learning process. The second is an <span class="hlt">ensemble</span> learning to replicate the analysis performed, followed by a fusion mechanism that yields as a combined result of previously performed analysis in order to improve the result of a single model. The proposed learning schema, termed S(2)-<span class="hlt">Ensemble</span>, is applied to several unsupervised learning algorithms within the family of topology maps, such as the Self-Organizing Maps and the Neural Gas. This study also includes a thorough research of the characteristics of these novel schemes, by means quality measures, which allow a complete analysis of the resultant classifiers from the viewpoint of various perspectives over the different ways that these classifiers are used. The study conducts empirical evaluations and comparisons on various real-world datasets from the UCI repository, which exhibit different characteristics, so to enable an extensive selection of situations where the presented new algorithms can be applied. PMID:22131302</p> <div class="credits"> <p class="dwt_author">Baruque, Bruno; Corchado, Emilio; Yin, Hujun</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">371</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/22267160"> <span id="translatedtitle">Activity recall in a visual cortical <span class="hlt">ensemble</span>.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">Cue-triggered recall of learned temporal sequences is an important cognitive function that has been attributed to higher brain areas. Here recordings in both anesthetized and awake rats demonstrate that after repeated stimulation with a moving spot that evoked sequential firing of an <span class="hlt">ensemble</span> of primary visual cortex (V1) neurons, just a brief flash at the starting point of the motion path was sufficient to evoke a sequential firing pattern that reproduced the activation order evoked by the moving spot. The speed of recalled spike sequences may reflect the internal dynamics of the network rather than the motion speed. In awake rats, such recall was observed during a synchronized ('quiet wakeful') brain state having large-amplitude, low-frequency local field potential (LFP) but not in a desynchronized ('active') state having low-amplitude, high-frequency LFP. Such conditioning-enhanced, cue-evoked sequential spiking of a V1 <span class="hlt">ensemble</span> may contribute to experience-based perceptual inference in a brain state-dependent manner. PMID:22267160</p> <div class="credits"> <p class="dwt_author">Xu, Shengjin; Jiang, Wanchen; Poo, Mu-Ming; Dan, Yang</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-01-22</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">372</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013CoPhC.184.1426F"> <span id="translatedtitle">Equivalence between microcanonical <span class="hlt">ensembles</span> for lattice models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The development of reliable methods for estimating microcanonical averages constitutes an important issue in statistical mechanics. One possibility consists of calculating a given microcanonical quantity by means of typical relations in the grand-canonical <span class="hlt">ensemble</span>. But given that distinct <span class="hlt">ensembles</span> are equivalent only at the thermodynamic limit, a natural question is if finite size effects would prevent such a procedure. In this work we investigate thoroughly this query in different systems yielding first- and second-order phase transitions. Our study is carried out from the direct comparison with the thermodynamic relation (?s?e), where the entropy s is obtained from the density of states and e is the energy per site. A systematic analysis for finite sizes is undertaken. We find that, although results become inequivalent for extremely low system sizes, the equivalence holds true for rather small L’s. Therefore direct, simple (when compared with other well established approaches) and very precise microcanonical quantities can be obtained from the proposed method.</p> <div class="credits"> <p class="dwt_author">Fiore, Carlos E.; DaSilva, Cláudio J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">373</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/23955761"> <span id="translatedtitle">Face recognition using <span class="hlt">ensemble</span> string matching.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">In this paper, we present a syntactic string matching approach to solve the frontal face recognition problem. String matching is a powerful partial matching technique, but is not suitable for frontal face recognition due to its requirement of globally sequential representation and the complex nature of human faces, containing discontinuous and non-sequential features. Here, we build a compact syntactic Stringface representation, which is an <span class="hlt">ensemble</span> of strings. A novel <span class="hlt">ensemble</span> string matching approach that can perform non-sequential string matching between two Stringfaces is proposed. It is invariant to the sequential order of strings and the direction of each string. The embedded partial matching mechanism enables our method to automatically use every piece of non-occluded region, regardless of shape, in the recognition process. The encouraging results demonstrate the feasibility and effectiveness of using syntactic methods for face recognition from a single exemplar image per person, breaking the barrier that prevents string matching techniques from being used for addressing complex image recognition problems. The proposed method not only achieved significantly better performance in recognizing partially occluded faces, but also showed its ability to perform direct matching between sketch faces and photo faces. PMID:23955761</p> <div class="credits"> <p class="dwt_author">Chen, Weiping; Gao, Yongsheng</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-08-15</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">374</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2000PhDT........10E"> <span id="translatedtitle"><span class="hlt">Ensemble</span> cloud model applications to forecasting thunderstorms</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A cloud model <span class="hlt">ensemble</span> forecasting approach is developed to create forecasts which describe the range and distribution of thunderstorm lifetimes that may be expected to occur on a particular day. Such forecasts are crucial for both anticipating severe weather and ensuring the smooth flow of air traffic at busy, hub airports. Storm lifetime is an important characteristic to examine because long-lasting storms tend to produce more significant weather, and have a greater impact on air traffic, than do storms with brief lifetimes. Eighteen days distributed over two warm seasons are examined. Soundings valid at 1800 UTC, 2100 UTC and 0000 UTC, provided by the 0300 UTC run of the operational Mesoeta model from the National Centers for Environmental Prediction, are used to provide initial conditions for the cloud model <span class="hlt">ensemble</span>. These soundings are from a 160 x 160 km square centered over the location of interest and are shown to represent a likely range of atmospheric states. A minimum threshold value for maximum vertical velocity within the cloud model domain is used to estimate storm lifetime. Forecast storm lifetimes are verified against observed storm lifetimes, as derived from the Storm Cell Identification and Tracking algorithm applied to WSR-88D radar data from the National Weather Service (NWS). When kernel density estimates are applied to the pooled data set consisting of all 18 days, a vertical velocity threshold of 8 m s-1results in a forecast probability density function (pdf) of storm lifetime which is closest to the observed pdf. Model results from all 18 days also reveal that the storm lifetime resulting from a given input sounding cannot be determined by analyzing the bulk sounding parameters, such as convective available potential energy, bulk Richardson number (BRN), BRN shear, or storm relative helicity. Standard 2 x 2 contingency statistics reveal that under certain conditions, the <span class="hlt">ensemble</span> model displays some skill locating where convection is most likely to occur. Contingency statistics also show that when storm lifetimes of at least 60 min are used as a proxy for severe weather, the <span class="hlt">ensemble</span> shows considerable skill at identifying days that are likely to produce severe weather. Because the <span class="hlt">ensemble</span> model appears to have skill in predicting the range and distribution of storm lifetimes on a daily basis, the forecast pdf of storm lifetime is used directly to create probabilistic forecasts of storm lifetime, given the current age of a storm. Such a product could furnish useful information to Air Traffic controllers by providing guidance about how soon a storm is likely to affect (or cease to affect) air traffic at a specific location. Similarly, this product could provide NWS forecasters with guidance about how likely it is that a particular cell will affect a given community.</p> <div class="credits"> <p class="dwt_author">Elmore, Kimberly Laurence</p> <p class="dwt_publisher"></p> <p class="publishDate">2000-08-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">375</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/24051841"> <span id="translatedtitle">Comparative Visual Analysis of Lagrangian Transport in CFD <span class="hlt">Ensembles</span>.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">Sets of simulation runs based on parameter and model variation, so-called <span class="hlt">ensembles</span>, are increasingly used to model physical behaviors whose parameter space is too large or complex to be explored automatically. Visualization plays a key role in conveying important properties in <span class="hlt">ensembles</span>, such as the degree to which members of the <span class="hlt">ensemble</span> agree or disagree in their behavior. For <span class="hlt">ensembles</span> of time-varying vector fields, there are numerous challenges for providing an expressive comparative visualization, among which is the requirement to relate the effect of individual flow divergence to joint transport characteristics of the <span class="hlt">ensemble</span>. Yet, techniques developed for scalar <span class="hlt">ensembles</span> are of little use in this context, as the notion of transport induced by a vector field cannot be modeled using such tools. We develop a Lagrangian framework for the comparison of flow fields in an <span class="hlt">ensemble</span>. Our techniques evaluate individual and joint transport variance and introduce a classification space that facilitates incorporation of these properties into a common <span class="hlt">ensemble</span> visualization. Variances of Lagrangian neighborhoods are computed using pathline integration and Principal Components Analysis. This allows for an inclusion of uncertainty measurements into the visualization and analysis approach. Our results demonstrate the usefulness and expressiveness of the presented method on several practical examples. PMID:24051841</p> <div class="credits"> <p class="dwt_author">Hummel, Mathias; Obermaier, Harald; Garth, Christoph; Joy, Kenneth I</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">376</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://eric.ed.gov/?q=music+AND+gender&pg=2&id=EJ878595"> <span id="translatedtitle">Gender and Attraction: Predicting Middle School Performance <span class="hlt">Ensemble</span> Participation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p class="result-summary">|This study was designed to predict middle school sixth graders' group membership in band (n = 81), chorus (n = 45), and as non-participants in music performance <span class="hlt">ensembles</span> (n = 127), as determined by gender and factors on the Attraction Toward School Performance <span class="hlt">Ensemble</span> (ATSPE) scale (alpha = 0.88). Students completed the ATSPE as elementary…</p> <div class="credits"> <p class="dwt_author">Warnock, Emery C.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">377</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://files.eric.ed.gov/fulltext/EJ996057.pdf"> <span id="translatedtitle">Preferences of and Attitudes toward Treble Choral <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p class="result-summary">|In choral <span class="hlt">ensembles</span>, a pursuit where females far outnumber males, concern exists that females are being devalued. Attitudes of female choral singers may be negatively affected by the gender imbalance that exists in mixed choirs and by the placement of the mixed choir as the most select <span class="hlt">ensemble</span> in a program. The purpose of this research was to…</p> <div class="credits"> <p class="dwt_author">Wilson, Jill M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">378</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.atmos.umd.edu/~ekalnay/TothKalnay97.pdf"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Forecasting at NCEP and the Breeding Method</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The breeding method has been used to generate perturbations for <span class="hlt">ensemble</span> forecasting at the National Centers for Environmental Prediction (formerly known as the National Meteorological Center) since December 1992. At that time a single breeding cycle with a pair of bred forecasts was implemented. In March 1994, the <span class="hlt">ensemble</span> was expanded to seven independent breeding cycles on the Cray C90</p> <div class="credits"> <p class="dwt_author">Zoltan Toth; Eugenia Kalnay</p> <p class="dwt_publisher"></p> <p class="publishDate">1997-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">379</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/13983678"> <span id="translatedtitle"><span class="hlt">Ensemble</span> empirical mode decomposition based ECG noise filtering method</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Electrocardiogram is often corrupted by various noises, such as high-frequency muscle contraction. In this study, <span class="hlt">ensemble</span> empirical mode decomposition (EEMD) was used for ECG noise reduction. Gaussian noise was applied and the average (<span class="hlt">ensemble</span>) intrinsic mode function (IMF) was used for ECG reconstruction. Three high frequency ECG noises; muscle contraction, 50 Hz power line interferences and Gaussian noise were examined.</p> <div class="credits"> <p class="dwt_author">Kang-Ming Chang</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">380</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/709125"> <span id="translatedtitle">Sparse Regression <span class="hlt">Ensembles</span> in Infinite and Finite Hypothesis Spaces</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">We examine methods for constructing regression <span class="hlt">ensembles</span> based on a linear program (LP). The <span class="hlt">ensemble</span> regression function consists of linear combinations of base hypotheses generated by some boosting- type base learning algorithm. Unlike the classification case, for regression the set of possible hypotheses producible by the base learning algorithm may be infinite. We explicitly tackle the issue of how to</p> <div class="credits"> <p class="dwt_author">Gunnar Rätsch; Ayhan Demiriz; Kristin P. Bennett</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-01-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_18");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' 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onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">381</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/90851"> <span id="translatedtitle">An Empirical Comparison of Pruning Methods for <span class="hlt">Ensemble</span> Classifiers</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Many researchers have shown that <span class="hlt">ensemble</span> methods suchas Boosting and Bagging improve the accuracy of classication. Boostingand Bagging perform well with unstable learning algorithms suchas neural networks or decision trees. Pruning decision tree classiersis intended to make trees simpler and more comprehensible and avoidover-tting. However it is known that pruning individual classiers ofan <span class="hlt">ensemble</span> does not necessarily lead to improved</p> <div class="credits"> <p class="dwt_author">Terry Windeatt; Gholamreza Ardeshir</p> <p class="dwt_publisher"></p> <p class="publishDate">2001-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">382</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/50869047"> <span id="translatedtitle">Study of <span class="hlt">ensemble</span> learning-based fusion prognostics</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">In this paper we explore the effectiveness of <span class="hlt">ensemble</span> learning in the failure prognosis field by MLP neural network. An effective <span class="hlt">ensemble</span> should consist of a set of learners that are both accurate and diverse. In the training stage, we use the Adaboost. R2 technique to train several weak learners (multi layer perceptron network-MLP) to increase the diversity of the</p> <div class="credits"> <p class="dwt_author">Sun Jianzhong; Zuo Hongfu; Yang Haibin; Michael Pecht</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">383</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/52211802"> <span id="translatedtitle">Using CPC long lead climate outlooks for <span class="hlt">ensemble</span> streamflow forecasting</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">We describe a method for implementing the NWS NCEP Climate Prediction Center's probabilistic climate outlooks within an experimental <span class="hlt">ensemble</span> hydrologic forecast system for the western US. The CPC 3-month average probability of exceedence (POE) statistical climate outlooks are transformed into a 30-member <span class="hlt">ensemble</span> of precipitation and temperature sequences using a resampling technique called the Schaake Shuffle. Subsequently, these traces are</p> <div class="credits"> <p class="dwt_author">A. W. Wood; D. P. Lettenmaier</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">384</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/1715724"> <span id="translatedtitle">Evolving hybrid <span class="hlt">ensembles</span> of learning machines for better generalisation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search