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1

The relative value of MRED winter seasonal forecasts vs. statistically downscaled CFS forecasts for seasonal hydrological forecasting  

NASA Astrophysics Data System (ADS)

The relative value of dynamical vs. statistical downscaling of Climate Forecast System (CFS) forecasts for seasonal hydrologic forecasting is assessed. The dynamically downscaled retrospective climate forecasts were produced by the MRED ("Multi-RCM Ensemble Downscaling of NCEP CFS Seasonal Forecasts") project. In MRED, multiple Regional Climate Models (RCMs) were used to downscale CFS wintertime seasonal forecast from original spatial resolution of 2.5 degree to 0.375 degree (dynamical downscaling). We used 10 common ensemble members among all RCMs (initialization dates at 0000 UTC Nov. 21-25, Nov 29-Dec. 3) for the forecast period of Dec. 1-Apr. over 22-year period (1982-2003). The spatial domain is the Conterminous United States (CONUS). We assessed the value of the MRED forecasts in comparison with a much simpler bias correction and spatial downscaling (BCSD) (statistical downscaling); specifically in terms of the resultant seasonal forecast skill of hydrologic variables such as Runoff (RO), Snow Water Equivalent (SWE) and Soil Moisture (SM). At first, a probability mapping approach was applied both to dynamically downscaled and the CFS (at its native resolution), precipitation, Tmax and Tmin forecasts to correct the forecasts bias relative to the statistics of a gridded observation data set. Both sets of forecasts were then spatially downscaled from their original spatial resolutions to the spatial resolution of the Variable Infiltration Capacity (VIC) hydrologic model (0.125 degree) using a resampling approach. We conducted three separate experiments with both dynamically and statistically downscaled forecasts, and ESP forecasts (where precipitation and temperature data were randomly sampled from the observed climatology) to generate reforecasts of RO, SWE and SM. The initial hydrologic state (IHS) and a "synthetic truth" data set of RO, SWE and SM were derived by a control simulation over a long term, where the VIC model was forced using the gridded observation data set. We estimated Root Mean Square Error (RMSE) and correlation-based skill scores for each experiment for lead times (1-5 months) by comparing forecasts of monthly values of RO, SWE, and SM at each lead times with their respective values obtained from the "synthetic truth" data set. Based on the RMSE score and correlation values we estimated the value of dynamically vs. statistically downscaled CFS forecasts and identified the regions across CONUS and lead times when dynamical downscaling of CFS forecasts results in to some or no improvement of hydrological forecast skill relative to statistically downscaled forecasts.

Shukla, S.; Livneh, B.; Lettenmaier, D.

2012-04-01

2

Multi-RCM ensemble downscaling of NCEP CFS winter season forecasts: Implications for seasonal hydrologic forecast skill  

NASA Astrophysics Data System (ADS)

assess the value of dynamical versus statistical downscaling of National Centers for Environmental Prediction's (NCEP) Climate Forecast System (CFS) winter season forecasts for seasonal hydrologic forecasting. Dynamically downscaled CFS forecasts for 1 December to 30 April of 1982-2003 were obtained from the Multi-RCM Ensemble Downscaling (MRED) project that used multiple Regional Climate Models (RCMs) to downscale CFS forecasts. Statistical downscaling of CFS forecasts was achieved by a much simpler bias correction and spatial downscaling method. We evaluate forecast accuracy of runoff (RO), soil moisture (SM), and snow water equivalent produced by a hydrology model forced with dynamically (the MRED forecasts) and statistically downscaled CFS forecasts in comparison with predictions of those variables produced by forcing the same hydrology model with gridded observations (reference data set). Our results show that the MRED forecasts produce modest skill beyond what results from statistical downscaling of CFS. Although the improvement in hydrologic forecast skill associated with the ensemble average of the MRED forecasts (Multimodel) relative to statistical downscaled CFS forecasts is field significant for RO and SM forecasts with up to 3 months lead, the region of improvement is mainly limited to parts of the northwest and north central U.S. In general, one or more RCMs outperform the other RCMs as well as the Multimodel. Hence, we argue that careful selection of RCMs (based on their hindcast skill over any given region) is critical to improving hydrologic forecast skill using dynamical downscaling.

Shukla, Shraddhanand; Lettenmaier, Dennis P.

2013-10-01

3

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

4

Precipitation intensity in an ensemble of downscaled seasonal forecasts  

NASA Astrophysics Data System (ADS)

Dynamical downscaling using regional climate models (RCMs) has the potential to produce improved representation of precipitation intensity and frequency in seasonal forecasts, owing to improved spatial resolution of dynamical processes and topography compared with global models. Ensemble forecasts also can improve on single-model forecasts by accounting for uncertainties in initial conditions and model formulation. This study examines results from an ensemble of downscaled seasonal forecasts, focusing on monthly and seasonal statistics of the frequency and intensity of precipitation. We present results from seven RCMs run as part of the Multi-Regional climate model Ensemble Downscaling (MRED) project. These RCMs downscaled forecasts produced by the National Centers for Environmental Prediction's (NCEP) Climate Forecast System (CFS) version 1 from 1983-2003 for the winter season (January through April) over the continental United States. Preliminary comparisons show a general overforecast in frequency and intensity of extreme precipitation (50 mm per day or greater) in the RCMs compared to observations, particularly in January and April. When comparing frequency and intensity of precipitation between months, some RCMs exhibit variations from month to month. For instance, the MM5 regional model had more frequent, intense precipitation than the other RCMs and observations in January, but by April was closer to observations. Two versions of the Regional Spectral Model (RSM) tended toward more frequent, intense precipitation by April. It is likely that the choice of the model configuration, such as representation of moist physics, plays a significant role in these differences. While the CFS global model performs reasonably well for lighter precipitation events compared to observations, it does not forecast many extreme precipitation events. The RCMs simulate these extreme precipitation events, albeit sometimes too intense or too frequent.

Ansorge, A. J.; Arritt, R. W.

2012-04-01

5

Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts  

NASA Astrophysics Data System (ADS)

Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the variance-enhanced products, compared to the bi-linear interpolation, which is a decisive advantage. The disaggregation technique of Perica and Foufoula-Georgiou (1996) hence represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. References Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I. 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48 (3): RG3003, [np]. Doi: 10.1029/2009RG000314. Perica, S., and Foufoula-Georgiou, E. 1996. Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions. Journal Of Geophysical Research, 101(D21): 26347-26361. Ruiz, J., Saulo, C. and Kalnay, E. 2009. Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America. Weather and forecasting, 24: 319-336. DOI: 10.1175/2008WAF2007098.1 This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.

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

2013-04-01

6

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

NASA Astrophysics Data System (ADS)

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

Duan, Kai; Mei, Yadong

2014-05-01

7

WRF ensemble downscaling seasonal forecasts of China winter precipitation during 1982-2008  

NASA Astrophysics Data System (ADS)

The non-hydrostatic Weather Research and Forecasting model (WRF) was nested into NCEP's operational seasonal forecast model Climate Forecast System (CFS) to downscale seasonal prediction of winter precipitation over continental China. Using the same initial conditions, 16 ensemble downscaling forecasts configured with two alternative schemes of microphysics, cumulus, land surface and radiation in WRF were conducted at 30 km for 27-cold seasons (December-February) during 1982-2008. On average, WRF downscaling forecasts reduced wet bias of seasonal mean precipitation from CFS prediction by 25-71%, decreased errors by up to 33%, and increased equitable threat score by 0.1 for low threshold. With appropriate physical configurations, WRF could improve interannual variations over the region where CFS has correct anomaly signal. The spatial distribution of daily precipitation characteristics such as rainy frequency and extremes highlighted the sensitivity of downscaling forecasts to physical configurations, and the dominant uncertainties were introduced by land surface and radiation schemes. The differences in convective and resolved rainfall between alternative land surface and radiation schemes were consistent with differences of surface downwelling shortwave and longwave radiation through cloud-radiation feedback. Such feedback was strengthened in the land surface sensitivity experiments due to different parameterizations of surface albedo. As compared with CFS ensemble predictions with different initial conditions, the WRF ensemble downscaling forecasts with various physical schemes had larger spread, and some schemes could complement each other in different regions that provided a promising opportunity to enhance the prediction through optimization. The optimized WRF reduced error from the optimized CFS by 30% and increased pattern correlation by 0.12. Moreover, WRF physical configuration ensemble increased percentage of skillful probabilistic forecasts from CFS initial condition ensemble.

Yuan, Xing; Liang, Xin-Zhong; Wood, Eric F.

2012-10-01

8

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

NASA Astrophysics Data System (ADS)

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

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

2014-12-01

9

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

10

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

11

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

12

Precipitation Intensity in Downscaled Seasonal Forecasts  

NASA Astrophysics Data System (ADS)

We examine precipitation intensity in downscaled seasonal forecasts over the continental United States produced using six regional climate models (RCMs). The RCMs downscaled winter seasonal forecasts from the global NCEP Climate Forecast System (CFS) version 1 from 1982-2003 as part of the Multi-RCM Ensemble Downscaling (MRED) project. We assess model performance for the January-February-March (JFM) and February-March-April (FMA) seasons over the Interior West, Southwest, Gulf Coast, and Ohio Valley regions. The latter three regions have well-known influences from El Niño-Southern Oscillation (ENSO). We examine model skill for the seasonal mean as well as daily precipitation frequency and intensity. The statistical gamma distribution was used to concisely summarize the precipitation intensity distribution through two fitted parameters, commonly termed the shape and scale parameters. Downscaling is found to improve the representation of winter precipitation intensity, especially in the higher part of the range. For the Southwest, Gulf Coast, and Ohio Valley, changes in the seasonal mean generally result from an increase or decrease in both the shape and scale parameters. This implies that changes in the mean are accompanied by consistent changes in the nature of the intensity distribution. In contrast, the shape and scale parameters vary inversely in the Interior West. We find significant inter-model spread in both the seasonal mean and the daily intensity distribution amongst the RCMs, despite the fact that they all use the same large-scale boundary conditions from the CFS. We show that models having the same skill in the mean can differ in their ability to predict observed precipitation intensity. The gamma distribution provides a compact approach for identifying such cases.

Arritt, R. W.; Ansorge, A.

2012-12-01

13

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 " 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/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 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/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 " 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/2010AGUFM.H51A0874B"> <span id="translatedtitle">Optimal selection of MULTI-model <span class="hlt">downscaled</span> <span class="hlt">ensembles</span> for interannual and seasonal climate prediction in the eastern seaboard of Thailand</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">Because of the imminent threat of the water resources of the eastern seaboard of Thailand, a climate impact study has been carried out there. To that avail, a hydrological watershed model is being used to simulate the future water availability in the wake of possible climate change in the region. The hydrological model is forced by predictions from global climate models (GCMs) that are to be <span class="hlt">downscaled</span> in an appropriate manner. The challenge at that stage of the climate impact analysis lies then the in the choice of the best GCM and the (statistical) <span class="hlt">downscaling</span> method. In this study the selection of coarse grid resolution output of the GCMs, transferring information to the fine grid of local climate-hydrology is achieved by cross-correlation and multiple linear regression using meteorological data in the eastern seaboard of Thailand observed between 1970-1999. The grids of 20 atmosphere/ocean global climate models (AOGCM), covering latitude 12.5-15.0 N and longitude 100.0-102.5 E were examined using the Climate-Change Scenario Generator (SCENGEN). With that tool the model efficiency of the prediction of daily precipitation and mean temperature was calculated by comparing the 1980-1999 ECMWF reanalysis predictions with the observed data during that time period. The root means square errors of the predictions were considered and ranked to select the top 5 models, namely, BCCR-BCM2.0, GISS-ER, ECHO-G, ECHAM5/MPI-OM and PCM. The daily time-series of 338 predictors in 9 runs of the 5 selected models were gathered from the CMIP3 multi-model database. Monthly time-serial cross-correlations between the climate predictors and the meteorological measurements from 25 rainfall, 4 minimum and maximum temperature, 4 humidity and 2 solar radiation stations in the study area were then computed and ranked. Using the ranked predictors, a multiple-linear regression model (<span class="hlt">downscaling</span> transfer model) to forecast the local climate was set up. To improve the prediction power of this GCM <span class="hlt">downscaling</span> approach, the regression equations were considered as a dynamic regression model that can alter the predictor by seasonal variation. The possible seasonal effect was examined for the 1974-1999 period which was equally divided into a calibration and verification sub-period. The calibrated model using the whole observed time-series was compared with the models separated into 2 seasons; dry and wet, 3 seasons; winter, summer and rainy, and 4 seasons; dry, pre-monsoon, first monsoon and second monsoon. The verification power of the various model variants was measured considering Akaike's information criterion (AIC) and the Nash-Sutcliffe coefficient of the corresponding model fit. The results show that the 4-seasons-variation prediction works best. The highest efficiency for the prediction of rainfall is achieved for the dry season, Oct-Mar, whereas the smallest efficiency is obtained in the monsoon seasons. The overall number of predictor giving top efficiency lies between 3 and 20 in the regression models. In the next, still ongoing stage of the climate impact study the predictions from this new, seasonally optimized <span class="hlt">downscaling</span> transfer model are being used in the simulations of the future hydrological water budget in that region of Thailand.</p> <div class="credits"> <p class="dwt_author">Bejranonda, W.; Koch, M.</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">17</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ntrs.nasa.gov/search.jsp?R=20080040695&hterms=cross+fit&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3D%2528cross%2Bfit%2529"> <span id="translatedtitle">Simulation of SEU Cross-sections using <span class="hlt">MRED</span> under Conditions of Limited Device Information</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">This viewgraph presentation reviews the simulation of Single Event Upset (SEU) cross sections using the membrane electrode assembly (MEA) resistance and electrode diffusion (<span class="hlt">MRED</span>) tool using "Best guess" assumptions about the process and geometry, and direct ionization, low-energy beam test results. This work will also simulate SEU cross-sections including angular and high energy responses and compare the simulated results with beam test data for the validation of the model. Using <span class="hlt">MRED</span>, we produced a reasonably accurate upset response model of a low-critical charge SRAM without detailed information about the circuit, device geometry, or fabrication process</p> <div class="credits"> <p class="dwt_author">Lauenstein, J. M.; Reed, R. A.; Weller, R. A.; Mendenhall, M. H.; Warren, K. M.; Pellish, J. A.; Schrimpf, R. D.; Sierawski, B. D.; Massengill, L. W.; Dodd, P. E.; Shaneyfelt, M. R.; Felix, J. A.; Schwank, J. R.</p> <p 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">18</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.personal.leeds.ac.uk/~stapdb/research/rain_paper.pdf"> <span id="translatedtitle">The <span class="hlt">Downscaling</span> of Rain Gauge Time-Series By Multiplicative Beta Cascade</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">1 The <span class="hlt">Downscaling</span> of Rain Gauge Time-Series By Multiplicative Beta Cascade Kevin S. Paulson: <span class="hlt">Downscaling</span> Rain Gauge Time-Series. Key Words: rain, rain gauge, <span class="hlt">downscaling</span>, multifractal, multiplicative cascade #12;2 Abstract This paper develops a <span class="hlt">downscaling</span> algorithm capable of producing <span class="hlt">ensembles</span> of rain</p> <div class="credits"> <p class="dwt_author">Baxter, Paul D.</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">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/2013AGUFMGC43C1069M"> <span id="translatedtitle">New statistical <span class="hlt">downscaling</span> for 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">This poster will document the production of a set of statistically <span class="hlt">downscaled</span> future climate projections for Canada based on the latest available RCM and GCM simulations - the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2007) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). The main stages of the project included (1) <span class="hlt">downscaling</span> method evaluation, (2) scenarios selection, (3) production of statistically <span class="hlt">downscaled</span> results, and (4) applications of results. We build upon a previous <span class="hlt">downscaling</span> evaluation project (Bürger et al. 2012, Bürger et al. 2013) in which a quantile-based method (Bias Correction/Spatial Disaggregation - BCSD; Werner 2011) provided high skill compared with four other methods representing the majority of types of <span class="hlt">downscaling</span> used in Canada. Additional quantile-based methods (Bias-Correction/Constructed Analogues; Maurer et al. 2010 and Bias-Correction/Climate Imprint ; Hunter and Meentemeyer 2005) were evaluated. A subset of 12 CMIP5 simulations was chosen based on an objective set of selection criteria. This included hemispheric skill assessment based on the CLIMDEX indices (Sillmann et al. 2013), historical criteria used previously at the Pacific Climate Impacts Consortium (Werner 2011), and refinement based on a modified clustering algorithm (Houle et al. 2012; Katsavounidis et al. 1994). Statistical <span class="hlt">downscaling</span> was carried out on the NARCCAP <span class="hlt">ensemble</span> and a subset of the CMIP5 <span class="hlt">ensemble</span>. We produced <span class="hlt">downscaled</span> scenarios over Canada at a daily time resolution and 300 arc second (~10 km) spatial resolution from historical runs for 1951-2005 and from RCP 2.6, 4.5, and 8.5 projections for 2006-2100. The ANUSPLIN gridded daily dataset (McKenney et al. 2011) was used as a target. It has national coverage, spans the historical period of interest 1951-2005, and has daily time resolution. It uses interpolation of station data based on thin-plate splines. This type of method has been shown to have superior skill in interpolating RCM data over North America (McGinnis et al. 2012). An early application of the new dataset was to provide projections of climate extremes for adaptation planning by the British Columbia Ministry of Transportation and Infrastructure. Recently, certain stretches of highway have experienced extreme precipitation events resulting in substantial damage to infrastructure. As part of the planning process to refurbish or replace components of these highways, information about the magnitude and frequency of future extreme events are needed to inform the infrastructure design. The increased resolution provided by <span class="hlt">downscaling</span> improves the representation of topographic features, particularly valley temperature and precipitation effects. A range of extreme values, from simple daily maxima and minima to complex multi-day and threshold-based climate indices were computed and analyzed from the <span class="hlt">downscaled</span> output. Selected results from this process and how the projections of precipitation extremes are being used in the context of highway infrastructure planning in British Columbia will be presented.</p> <div class="credits"> <p class="dwt_author">Murdock, T. Q.; Cannon, A. J.; Sobie, S.</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">20</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=https://www.meted.ucar.edu/training_module.php?id=794"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of NWP Data</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">Forecasters utilize <span class="hlt">downscaled</span> NWP products when producing forecasts of predictable features, such as terrain-related and coastal features, at finer resolution than provided by most NWP models directly. This module is designed to help the forecaster determine which <span class="hlt">downscaled</span> products are most appropriate for a given forecast situation and the types of further corrections the forecaster will have to create. This module engages the learner through interactive case examples illustrating and comparing the major capabilities and limitations of some commonly-used <span class="hlt">downscaled</span> products for 2-m temperatures and 10-m winds. Products covered include Gridded MOS, PRISM, NCEP <span class="hlt">downscaling</span> for NAM and for NAEFS, <span class="hlt">downscaling</span> in the AWIPS Graphical Forecast Editor, and the use of high-resolution models to perform <span class="hlt">downscaling</span>.</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate">2014-09-14</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" 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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_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://www.agu.org/journals/jd/jd0709/2006JD007333/2006JD007333.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of rain gauge time series by multiplicative beta cascade</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 develops a <span class="hlt">downscaling</span> algorithm capable of producing <span class="hlt">ensembles</span> of rain rate time series, with integration times as short as 10 s, consistent with a time series of rain rates with integration times as long as 6 hours. The algorithm is based on a stochastic multiplicative cascade using beta distributions as the random generator. The statistics of these cascades</p> <div class="credits"> <p class="dwt_author">Kevin S. Paulson; Paul D. Baxter</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">22</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.clemson.edu/caah/pdp/real-estate-development/pdfs/program%20related/MREDStudent_Data_2014.pdf"> <span id="translatedtitle">161 <span class="hlt">MRED</span> Students from 29 States and 73 Undergrad Institutions Founded in 2004, the two-year, full-time, 57-credit professional Master of Real Estate Development</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">-year, full-time, 57-credit professional Master of Real Estate Development (<span class="hlt">MRED</span>) program is jointly offered, Architecture, City and Regional Planning, and Real Estate Development. The program is highly competitive of prior real estate experience. #12;2 WE WANT OUR STUDENTS TO BE GREAT PLACEMAKERS, NOT JUST BUILDERS</p> <div class="credits"> <p class="dwt_author">Duchowski, Andrew T.</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">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/2014EGUGA..1611668A"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of inundation extents</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 Global Inundation Extent from Multi-Satellite (GIEMS) provides multi-year monthly variations of the global surface water extent at about 25 kmx25 km resolution, from 1993 to 2007. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. This paper present three approaches to do <span class="hlt">downscale</span> GIEMS: The first one is based on a image-processing technique using neighborhood constraints. The third approach uses a PCA representation to perform an algebraic inversion. The PCA-representation is also very convenient to perform temporal and spatial interpolation of complexe inundation fields. The third <span class="hlt">downscaling</span> method uses topography information from Hydroshed Digital Elevation Model (DEM). Information such as the elevation, distance to river and flow accumulation are used to define a ``flood ability index'' that is used by the <span class="hlt">downscaling</span>. Three basins will be considered for illustrative purposes: Amazon, Niger and Mekong. Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and <span class="hlt">downscaling</span> of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeorology, in press, 2014. Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, <span class="hlt">downscaled</span> from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett., 39(L08403), 2012.</p> <div class="credits"> <p class="dwt_author">Aires, Filipe; Prigent, Catherine; Papa, Fabrice</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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/2014AtmRe.143...17L"> <span id="translatedtitle">An application of hybrid <span class="hlt">downscaling</span> model to forecast summer precipitation at stations in 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 pattern prediction hybrid <span class="hlt">downscaling</span> method was applied to predict summer (June-July-August) precipitation at China 160 stations. The predicted precipitation from the <span class="hlt">downscaling</span> scheme is available one month before. Four predictors were chosen to establish the hybrid <span class="hlt">downscaling</span> scheme. The 500-hPa geopotential height (GH5) and 850-hPa specific humidity (q85) were from the skillful predicted output of three DEMETER (Development of a European Multi-model <span class="hlt">Ensemble</span> System for Seasonal to Interannual Prediction) general circulation models (GCMs). The 700-hPa geopotential height (GH7) and sea level pressure (SLP) were from reanalysis datasets. The hybrid <span class="hlt">downscaling</span> scheme (HD-4P) has better prediction skill than a conventional statistical <span class="hlt">downscaling</span> model (SD-2P) which contains two predictors derived from the output of GCMs, although two <span class="hlt">downscaling</span> schemes were performed to improve the seasonal prediction of summer rainfall in comparison with the original output of the DEMETER GCMs. In particular, HD-4P <span class="hlt">downscaling</span> predictions showed lower root mean square errors than those based on the SD-2P model. Furthermore, the HD-4P <span class="hlt">downscaling</span> model reproduced the China summer precipitation anomaly centers more accurately than the scenario of the SD-2P model in 1998. A hybrid <span class="hlt">downscaling</span> prediction should be effective to improve the prediction skill of summer rainfall at stations in China.</p> <div class="credits"> <p class="dwt_author">Liu, Ying; Fan, Ke</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-06-01</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://academic.research.microsoft.com/Publication/55332589"> <span id="translatedtitle">Climate <span class="hlt">Downscaling</span> over Nordeste, Brazil, Using the NCEP RSM97</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 NCEP Regional Spectral Model (RSM), with horizontal resolution of 60 km, was used to <span class="hlt">downscale</span> the ECHAM4.5 AGCM (T42) simulations forced with observed SSTs over northeast Brazil. An <span class="hlt">ensemble</span> of 10 runs for the period January-June 1971-2000 was used in this study. The RSM can resolve the spatial patterns of observed seasonal precipitation and capture the interannual variability of</p> <div class="credits"> <p class="dwt_author">Liqiang Sun; David Ferran Moncunill; Huilan Li; Antonio Divino Moura; Francisco de Assis de Souza Filho</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">26</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/2007WRR....43.9415M"> <span id="translatedtitle"><span class="hlt">Downscaling</span> rainfall temporal variability</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 realistic description of land surface/atmosphere interactions in climate and hydrologic studies requires the specification of the rainfall forcing at aggregation scales of 1 hour or less. This is in contrast with the wide availability of daily rainfall observations and with the typically coarse output resolution of climate and numerical weather forecast models. Several methods have been devised to generate hourly or subhourly data from daily or monthly values, which usually rely on statistical regressions determined under the current climate conditions. Here we present a new method for <span class="hlt">downscaling</span> rainfall in time using theoretically based estimates of rainfall variability at the hourly scale from daily statistics. The method is validated on a wide data set representative of different rainfall regimes and produces approximately unbiased estimates of rainfall variance at the hourly scale when a power law-tailed autocorrelation is assumed for the rainfall process. We further demonstrate how the <span class="hlt">downscaling</span> method together with a Bartlett-Lewis rainfall stochastic model may be used to generate hourly rainfall sequences that reproduce the observed small-scale variability uniquely from daily statistics. Conclusions of a somewhat general nature are also drawn on the capability of finite memory stochastic models to reproduce the observed rainfall variability at different aggregation scales.</p> <div class="credits"> <p class="dwt_author">Marani, Marco; Zanetti, Stefano</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-09-01</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://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 " 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/2014HESS...18.2899S"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of seasonal soil moisture forecasts using satellite 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">A new approach to <span class="hlt">downscaling</span> soil moisture forecasts from the seasonal <span class="hlt">ensemble</span> prediction forecasting system of the ECMWF (European Centre for Medium-Range Weather Forecasts) is presented in this study. Soil moisture forecasts from this system are rarely used nowadays, although they could provide valuable information. Weaknesses of the model soil scheme in forecasting soil water content and the low spatial resolution of the seasonal forecasts are the main reason why soil water information has hardly been used so far. The basic idea to overcome some of these problems is the application of additional information provided by two satellite sensors (ASCAT and Envisat ASAR) to improve the forecast quality, mainly to reduce model bias and increase the spatial resolution. Seasonal forecasts from 2011 and 2012 have been compared to in situ measurement sites in Kenya to test this two-step approach. Results confirm that this <span class="hlt">downscaling</span> is adding skill to the seasonal forecasts.</p> <div class="credits"> <p class="dwt_author">Schneider, S.; Jann, A.; Schellander-Gorgas, T.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-08-01</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/2012EGUGA..14.5859D"> <span id="translatedtitle"><span class="hlt">Downscaling</span> precipitation extremes in a complex Alpine catchment</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 is expected to have significant effects on the frequency and intensity of heavy precipitation events. Assessing the impacts of climate change on precipitation extremes is a challenging task. On the one hand, the output of Regional Climate Models (RCMs) is subjected to systematic biases in the case of precipitation, especially in a complex mountain topography, and on the other hand, yet only a few statistical <span class="hlt">downscaling</span> techniques are known to <span class="hlt">downscale</span> precipitation extremes reliably. In this investigation two statistical <span class="hlt">downscaling</span> approaches were applied to simulate precipitation extremes in the Alpine part of the Lech catchment. The first one, Expanded <span class="hlt">Downscaling</span> (EDS), is a perfect prognosis approach that is based on regression. EDS has been calibrated and validated using large-scale predictor variables derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset and local station data. The EDS model was then applied to <span class="hlt">downscale</span> the output of two GCMs (ECHAM5, HadGEM2) for current (1971-2000) and future (2071-2100) time horizons, forced with the SRES A1B emission scenario. The second approach is the Long Ashton Research Station Weather Generator (LARS-WG) which can be characterized as a change factor conditioned weather generator. LARS-WG was calibrated on local station data only and then applied to <span class="hlt">downscale</span> the output of five different GCM-RCM combinations to meteorological stations. The RCMs have a horizontal resolution of ~25 km and were obtained from the <span class="hlt">ENSEMBLES</span> project of the European Union. In order to assess precipitation extremes with higher return values, a generalized extreme value distribution was applied to the data. Confidence intervals were calculated by using the non-parametric bootstrapping technique. The results show that both <span class="hlt">downscaling</span> approaches reproduce observed precipitation extremes fairly well. Even for very extreme precipitation events such as the 20-year event a good agreement between observation and simulation is obtained. When studying the effects of climate change on precipitation extremes, a wide range of results with both projected increases and decreases in precipitation extremes can be found. However, the number of climate simulations which indicates statistically significant increases is by far larger than that showing decreases. Very robust signals can be found for spring and autumn, in which almost all climate scenarios indicate increases in the intensity of precipitation extremes.</p> <div class="credits"> <p class="dwt_author">Dobler, C.</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">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/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">31</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ntrs.nasa.gov/search.jsp?R=20140006440&hterms=Climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DClimate"> <span id="translatedtitle">Evaluating <span class="hlt">Downscaling</span> Methods for Seasonal Climate Forecasts over East Africa</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">The U.S. National Multi-Model <span class="hlt">Ensemble</span> seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of <span class="hlt">downscaling</span> methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially <span class="hlt">downscaled</span> and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available <span class="hlt">downscaling</span> methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.</p> <div class="credits"> <p class="dwt_author">Robertson, Franklin R.; Roberts, J. Brent; Bosilovich, Michael; Lyon, Bradfield</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">32</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/2013AGUFMGC43C1055R"> <span id="translatedtitle">Evaluating <span class="hlt">Downscaling</span> Methods for Seasonal Climate Forecasts over East 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">The U.S. National Multi-Model <span class="hlt">Ensemble</span> seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of <span class="hlt">downscaling</span> methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially <span class="hlt">downscaled</span> and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available <span class="hlt">downscaling</span> methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.</p> <div class="credits"> <p class="dwt_author">Roberts, J. B.; Robertson, F. R.; Bosilovich, M. G.; Lyon, B.</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">33</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ntrs.nasa.gov/search.jsp?R=20140006513&hterms=africa+statistics&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dafrica%2Bstatistics"> <span id="translatedtitle">Evaluating <span class="hlt">Downscaling</span> Methods for Seasonal Climate Forecasts over East Africa</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">The U.S. National Multi-Model <span class="hlt">Ensemble</span> seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of <span class="hlt">downscaling</span> methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially <span class="hlt">downscaled</span> and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available <span class="hlt">downscaling</span> methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period</p> <div class="credits"> <p class="dwt_author">Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris</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">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/2013HESS...17.4189R"> <span id="translatedtitle">Optimising predictor domains for spatially coherent 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">Statistical <span class="hlt">downscaling</span> is widely used to overcome the scale gap between predictors from numerical weather prediction models or global circulation models and predictands like local precipitation, required for example for medium-term operational forecasts or climate change impact studies. The predictors are considered over a given spatial domain which is rarely optimised with respect to the target predictand location. In this study, an extended version of the growing rectangular domain algorithm is proposed to provide an <span class="hlt">ensemble</span> of near-optimum predictor domains for a statistical <span class="hlt">downscaling</span> method. This algorithm is applied to find five-member <span class="hlt">ensembles</span> of near-optimum geopotential predictor domains for an analogue <span class="hlt">downscaling</span> method for 608 individual target zones covering France. Results first show that very similar <span class="hlt">downscaling</span> performances based on the continuous ranked probability score (CRPS) can be achieved by different predictor domains for any specific target zone, demonstrating the need for considering alternative domains in this context of high equifinality. A second result is the large diversity of optimised predictor domains over the country that questions the commonly made hypothesis of a common predictor domain for large areas. The domain centres are mainly distributed following the geographical location of the target location, but there are apparent differences between the windward and the lee side of mountain ridges. Moreover, domains for target zones located in southeastern France are centred more east and south than the ones for target locations on the same longitude. The size of the optimised domains tends to be larger in the southeastern part of the country, while domains with a very small meridional extent can be found in an east-west band around 47° N. Sensitivity experiments finally show that results are rather insensitive to the starting point of the optimisation algorithm except for zones located in the transition area north of this east-west band. Results also appear generally robust with respect to the archive length considered for the analogue method, except for zones with high interannual variability like in the Cévennes area. This study paves the way for defining regions with homogeneous geopotential predictor domains for precipitation <span class="hlt">downscaling</span> over France, and therefore de facto ensuring the spatial coherence required for hydrological applications.</p> <div class="credits"> <p class="dwt_author">Radanovics, S.; Vidal, J.-P.; Sauquet, E.; Ben Daoud, A.; Bontron, G.</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">35</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/2008AGUFM.H13F0987D"> <span id="translatedtitle">A new Multi-Scale Data Assimilation Algorithm to <span class="hlt">Downscale</span> Satellite-Based Soil Moisture 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">The study focuses on <span class="hlt">downscaling</span> of soil moisture from coarse remote sensing footprints to finer scales. Two approaches are proposed for soil moisture <span class="hlt">downscaling</span>. The first approach provides the probability distribution functions at the finer scales with no information about the spatial organization of soil moisture fields. The second approach implements a multiscale <span class="hlt">ensemble</span> Kalman filter (EnKF) that assimilates remotely sensed coarse scale soil moisture footprint, attributes of fine scale geophysical parameters/variables (i.e., soil texture: %sand, vegetation: NDVI, topography: slope, and precipitation) and coarse/fine scale simulation into a spatial characterization of soil moisture evolution at the finer scales. To <span class="hlt">downscale</span> the remotely sensed coarse scale soil moisture to another spatial scale, the multiscale EnKF uses a bridging model. The bridging model infers the pixel-specific scaling coefficient from the compatible geophysical parameters/variables that influence the soil moisture evolution process at that particular spatial scale. Data from diverse hydroclimatic regions from the semiarid Arizona, the agricultural landscape of Iowa, and the grassland/rangeland of Oklahoma are used in the study to implement the multiscale <span class="hlt">downscaling</span> algorithm. The results demonstrate that the bridging model of multiscale EnKF helps to characterize the evolution of soil moisture within the remotely sensed footprint. Validation conducted at the finest scale also shows reasonable agreement between the measured field data and the simulated <span class="hlt">downscaled</span> soil moisture evolution.</p> <div class="credits"> <p class="dwt_author">Das, N. N.; Mohanty, B. P.; Efendiev, Y.</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-12-01</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=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. 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">2012-01-01</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/2010ems..confE.188T"> <span id="translatedtitle">Hydrological <span class="hlt">Ensemble</span> Prediction System (HEPS)</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">Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Following on the success of the use of <span class="hlt">ensembles</span> for weather forecasting, the hydrological community now moves increasingly towards Hydrological <span class="hlt">Ensemble</span> Prediction Systems (HEPS) for improved flood forecasting using operationally available NWP products as inputs. However, these products are often generated on relatively coarse scales compared to hydrologically relevant basin units and suffer systematic biases that may have considerable impact when passed through the non-linear hydrological filters. Therefore, a better understanding on how best to produce, communicate and use hydrologic <span class="hlt">ensemble</span> forecasts in hydrological short-, medium- und long term prediction of hydrological processes is necessary. The "Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment" (HEPEX), is an international initiative consisting of hydrologists, meteorologist and end-users to advance probabilistic hydrologic forecast techniques for flood, drought and water management applications. Different aspects of the hydrological <span class="hlt">ensemble</span> processor are being addressed including • Production of useful meteorological products relevant for hydrological applications, ranging from nowcasting products to seasonal forecasts. The importance of hindcasts that are consistent with the operational weather forecasts will be discussed to support bias correction and <span class="hlt">downscaling</span>, statistically meaningful verification of HEPS, and the development and testing of operating rules; • Need for <span class="hlt">downscaling</span> and post-processing of weather <span class="hlt">ensembles</span> to reduce bias before entering hydrological applications; • Hydrological model and parameter uncertainty and how to correct and quantify them; • Post-processing of hydrological outputs to assess the total uncertainty as well as the different contributions of the errors - including input errors - cascading through the highly non-linear filter that a river basin represents; • Assessment of skill of probabilistic hydrological forecasting; • Communication of probabilistic results to different end-users for improved decision making. Here an overview of the state of HEPEX is given with particular emphasis on the need of meteorological products and their adaptation to hydrological applications. In particular, the conclusions of the last topical workshop on "Post-Processing and <span class="hlt">Downscaling</span> Atmospheric Forecasts for Hydrologic Applications" held at Meteo-France, Toulouse, in June 2009 is presented.</p> <div class="credits"> <p class="dwt_author">Thielen-Del Pozo, J.; Schaake, J.; Martin, E.; Pailleux, J.; Pappenberger, F.</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">38</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/2011ClDy...37.2087T"> <span id="translatedtitle">Evaluation of different <span class="hlt">downscaling</span> techniques for hydrological climate-change impact studies at the catchment 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">Hydrological modeling for climate-change impact assessment implies using meteorological variables simulated by global climate models (GCMs). Due to mismatching scales, coarse-resolution GCM output cannot be used directly for hydrological impact studies but rather needs to be <span class="hlt">downscaled</span>. In this study, we investigated the variability of seasonal streamflow and flood-peak projections caused by the use of three statistical approaches to <span class="hlt">downscale</span> precipitation from two GCMs for a meso-scale catchment in southeastern Sweden: (1) an analog method (AM), (2) a multi-objective fuzzy-rule-based classification (MOFRBC) and (3) the Statistical <span class="hlt">DownScaling</span> Model (SDSM). The obtained higher-resolution precipitation values were then used to simulate daily streamflow for a control period (1961-1990) and for two future emission scenarios (2071-2100) with the precipitation-streamflow model HBV. The choice of <span class="hlt">downscaled</span> precipitation time series had a major impact on the streamflow simulations, which was directly related to the ability of the <span class="hlt">downscaling</span> approaches to reproduce observed precipitation. Although SDSM was considered to be most suitable for <span class="hlt">downscaling</span> precipitation in the studied river basin, we highlighted the importance of an <span class="hlt">ensemble</span> approach. The climate and streamflow change signals indicated that the current flow regime with a snowmelt-driven spring flood in April will likely change to a flow regime that is rather dominated by large winter streamflows. Spring flood events are expected to decrease considerably and occur earlier, whereas autumn flood peaks are projected to increase slightly. The simulations demonstrated that projections of future streamflow regimes are highly variable and can even partly point towards different directions.</p> <div class="credits"> <p class="dwt_author">Teutschbein, Claudia; Wetterhall, Fredrik; Seibert, Jan</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">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/2014HESS...18.5077S"> <span id="translatedtitle">Satellite-driven <span class="hlt">downscaling</span> of global reanalysis precipitation products for hydrological applications</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">Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be <span class="hlt">downscaled</span> to a spatial and temporal resolution applicable for flood modeling. This study evaluates such <span class="hlt">downscaling</span> and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation data set and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002-2011) in the Susquehanna River basin located in the northeastern United States. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity, and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall data set, while runoff simulations forced with the stage IV precipitation data set were considered as the runoff reference. Results show that the generated rainfall <span class="hlt">ensembles</span> from the <span class="hlt">downscaled</span> reanalysis product encapsulate the reference rainfall. The statistical analysis consists of frequency and quantile plots plus mean relative error and root-mean-square error statistics. The results demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven <span class="hlt">downscaled</span> reanalysis data set compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the <span class="hlt">downscaled</span> product has 3 times lower mean relative error than the original product; this ratio increases to 4 times for the simulated runoff values. The proposed <span class="hlt">downscaling</span> scheme is modular in design and can be applied on any gridded satellite and reanalysis data set.</p> <div class="credits"> <p class="dwt_author">Seyyedi, H.; Anagnostou, E. N.; Beighley, E.; McCollum, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-12-01</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/2014HESSD..11.9067S"> <span id="translatedtitle">Satellite-driven <span class="hlt">downscaling</span> of global reanalysis precipitation products for hydrological applications</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">Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be <span class="hlt">downscaled</span> to a spatial and temporal resolution applicable for flood modeling. This study evaluates such <span class="hlt">downscaling</span> and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation dataset and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002-2011) in the Susquehanna River basin located in the northeast US. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall dataset, while runoff simulations forced with the stage IV precipitation dataset were considered as the runoff reference. Results show that the generated rainfall <span class="hlt">ensembles</span> from the <span class="hlt">downscaled</span> reanalysis products encapsulate the reference rainfall. The statistical analysis, including frequency and quantile plots plus mean relative error and root mean square error statistics, demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven <span class="hlt">downscaled</span> reanalysis dataset compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the <span class="hlt">downscaled</span> product has three times lower mean relative error than the original product; this ratio increases to four times for the simulated runoff values. The proposed <span class="hlt">downscaling</span> scheme is modular in design and can be applied on gridded satellite and reanalysis dataset.</p> <div class="credits"> <p class="dwt_author">Seyyedi, H.; Anagnostou, E. N.; Beighley, E.; McCollum, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-07-01</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> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a style="font-weight: bold;">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' 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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 onClick='return showDiv("page_2");' href="#">2</a> <a style="font-weight: bold;">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 onClick='return showDiv("page_10");' 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 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_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://www.atmos.washington.edu/~salathe/papers/Salathe_Downscale_IJOC2004.pdf"> <span id="translatedtitle">Preprint <span class="hlt">Downscaling</span> Climate Change Salath 02/02/2005 <span class="hlt">Downscaling</span> Simulations of future Global Climate with Application to Hydrologic</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Preprint <span class="hlt">Downscaling</span> Climate Change ­ Salathé 02/02/2005 <span class="hlt">Downscaling</span> Simulations of future Global approaches the problem of <span class="hlt">downscaling</span> global climate model simulations with an emphasis on validating statistics to streamflow computed from the observed data. <span class="hlt">Downscaled</span> climate-change scenarios from</p> <div class="credits"> <p class="dwt_author">Salathé Jr., Eric P.</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://adsabs.harvard.edu/abs/2013AGUFM.H21A1005Z"> <span id="translatedtitle">Atmospheric <span class="hlt">Downscaling</span> using Genetic Programming</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 coupling of models for the different components of the soil-vegetation-atmosphere system is required to understand component interactions and feedback processes. The Transregional Collaborative Research Center 32 (TR 32) has developed a coupled modeling platform, TerrSysMP, consisting of the atmospheric model COSMO, the land-surface model CLM, and the hydrological model ParFlow. These component models are usually operated at different resolutions in space and time owing to the dominant processes. These different scales should also be considered in the coupling mode, because it is for instance unfeasible to run the computationally quite expensive atmospheric models at the usually much higher spatial resolution required by hydrological models. Thus up- and <span class="hlt">downscaling</span> procedures are required at the interface between atmospheric model and land-surface/subsurface models. Here we present an advanced atmospheric <span class="hlt">downscaling</span> scheme, that creates realistic fine-scale fields (e.g. 400 m resolution) of the atmospheric state variables from the coarse atmospheric model output (e.g. 2.8 km resolution). The mixed physical/statistical scheme is developed from a training data set of high-resolution atmospheric model runs covering a range different weather conditions using Genetic Programming (GP). GP originates from machine learning: From a set of functions (arithmetic expressions, IF-statements, etc.) and terminals (constants or variables) GP generates potential solutions to a given problem while minimizing a fitness or cost function. We use a multi-objective approach that aims at fitting spatial structures, spatially distributed variance and spatio-temporal correlation of the fields. We account for the spatio-temporal nature of the data in two ways. On the one hand we offer GP potential predictors, which are based on our physical understanding of the atmospheric processes involved (spatial and temporal gradients, etc.). On the other hand we include functions operating on spatial fields, which are partly adapted from image classification. Our preliminary results show that realistic fine-scale structures can be retrieved from the coarse scale input, which constitutes a major advancement compared to the usually applied interpolations methods. Example for <span class="hlt">downscaling</span> of near-surface temperature during an almost clear-sky night. Colorbar values are given in Kelvin.</p> <div class="credits"> <p class="dwt_author">Zerenner, T.; Venema, V.; Simmer, C.</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">43</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/2015ClDy...44.2097B"> <span id="translatedtitle">An <span class="hlt">ensemble</span> climate projection for 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">The Met Office Hadley Centre's PRECIS regional climate modelling system has been used to generate a five member <span class="hlt">ensemble</span> of climate projections for Africa over the 50 km resolution Coordinated Regional climate <span class="hlt">Downscaling</span> Experiment-Africa domain. The <span class="hlt">ensemble</span> comprises the <span class="hlt">downscaling</span> of a subset of the Hadley Centre's perturbed physics global climate model (GCM) <span class="hlt">ensemble</span> chosen to exclude <span class="hlt">ensemble</span> members unable to represent the African climate realistically and then to capture the spread in outcomes from the projections of the remaining models. The PRECIS simulations were run from December 1949 to December 2100. The regional climate model (RCM) <span class="hlt">ensemble</span> captures the annual cycle of temperatures well both for Africa as a whole and the sub-regions. It slightly overestimates precipitation over Africa as a whole and captures the annual cycle of rainfall for most of the African regions. The RCM <span class="hlt">ensemble</span> substantially improve the patterns and magnitude of precipitation simulation compared to their driving GCM which is particularly noticeable in the Sahel for both the magnitude and timing of the wet season. Present-day simulations of the RCM <span class="hlt">ensemble</span> are more similar to each other than those of the driving GCM <span class="hlt">ensemble</span> which indicates that their climatologies are influenced significantly by the RCM formulation and less so by their driving GCMs. Consistent with this, the spread and magnitudes of the large-scale responses of the RCMs are often different than the driving GCMs and arguably more credible given the improved performance of the RCM. This also suggests that local climate forcing will be a significant driver of the regional response to climate change over Africa.</p> <div class="credits"> <p class="dwt_author">Buontempo, Carlo; Mathison, Camilla; Jones, Richard; Williams, Karina; Wang, Changgui; McSweeney, Carol</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-04-01</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/2013AGUFM.A11L..02D"> <span id="translatedtitle">Future hub-height wind speed distributions from statistically <span class="hlt">downscaled</span> CMIP5 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">In order to realistically estimate wind-power yields, we need to know the hub-height wind speed under future climate conditions. Climate conditions of the upper atmosphere are commonly simulated using general circulation models (GCMs). However their typical resolutions are too coarse to assess the climate at the height of a wind turbine. This study simulates the hub-height wind speed probability distributions (PDFs) over Europe under future climate conditions. The analysis is based on the simulations of the CMIP5 earth system models, which are the latest development of GCMs. They include more components and feedbacks and their runs are performed at higher resolutions. In a first step, the <span class="hlt">ensemble</span> of GCMs is evaluated on their representation of the wind speed PDFs in the lower atmosphere using ERA-Interim data. The evaluation indicates that GCMs are skillful down to their lowest model levels apart for the south of Europe, which is affected by a large scale winter bias and for certain coastal and orographical regions. Secondly, a statistical approach is developed which <span class="hlt">downscales</span> the GCM output to the wind speed PDF at the height of the wind turbine hub. Since the evaluation analysis shows that GCMs are also skillful at the lower model levels, the statistical <span class="hlt">downscaling</span> uses GCM variables describing the lower atmosphere, instead of the commonly used large scale circulation variables of the upper atmosphere. By doing so less uncertainty will be added trough the <span class="hlt">downscaling</span> implementation. The <span class="hlt">downscaling</span> methodology is developed for an observational site in the Netherlands, using hub-height wind speed observations and ERA-Interim data for the period 1989-2009. The statistical approach is based on a regression analysis of the parameters of the PDFs. Results indicate that the predictor selection is very much defined by the stability conditions of the atmospheric boundary layer. During convective summer-day conditions, the observed hub-height wind speed can skillfully be modeled using the GCM wind speed as the only predictor. In contrast, during summer-nights the stable boundary layer is much shallower and the statistical model indicates that the simulation of hub-height wind speed PDFs is substantially improved when temperature information is included in the statistical <span class="hlt">downscaling</span> model. In a final step the statistical <span class="hlt">downscaling</span> model will be applied on simulations of the <span class="hlt">ensemble</span> of CMIP5 GCMs for present and future climate conditions. The <span class="hlt">downscaled</span> hub-height wind speed PDFs will be used to analyze potential wind-power curves.</p> <div class="credits"> <p class="dwt_author">Devis, A.; Demuzere, M.; van Lipzig, N.</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">45</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. 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">2013-01-01</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://academic.research.microsoft.com/Publication/52788982"> <span id="translatedtitle">Consensus between GCM climate change projections with empirical <span class="hlt">downscaling</span>: precipitation <span class="hlt">downscaling</span> over South Africa</span></a>  </p> <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 issues that surround the development of empirical <span class="hlt">downscaling</span> techniques as context for presenting a new approach based on self-organizing maps (SOMs). The technique is applied to the <span class="hlt">downscaling</span> of daily precipitation over South Africa. SOMs are used to characterize the state of the atmosphere on a localized domain surrounding each target location on the basis of NCEP</p> <div class="credits"> <p class="dwt_author">B. C. Hewitson; R. G. Crane</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">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/2003AdAtS..20..951H"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> based on dynamically <span class="hlt">downscaled</span> predictors: Application to monthly precipitation in Sweden</span></a>  </p> <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 prerequisite of a successful statistical <span class="hlt">downscaling</span> is that large-scale predictors simulated by the General Circulation Model (GCM) must be realistic. It is assumed here that features smaller than the GCM resolution are important in determining the realism of the large-scale predictors. It is tested whether a three-step method can improve conventional one-step statistical <span class="hlt">downscaling</span>. The method uses predictors that are upscaled from a dynamical <span class="hlt">downscaling</span> instead of predictors taken directly from a GCM simulation. The method is applied to <span class="hlt">downscaling</span> of monthly precipitation in Sweden. The statistical model used is a multiple regression model that uses indices of large-scale atmospheric circulation and 850-hPa specific humidity as predictors. Data from two GCMs (HadCM2 and ECHAM4) and two RCM experiments of the Rossby Centre model (RCA1) driven by the GCMs are used. It is found that upscaled RCA1 predictors capture the seasonal cycle better than those from the GCMs, and hence increase the reliability of the <span class="hlt">downscaled</span> precipitation. However, there are only slight improvements in the simulation of the seasonal cycle of <span class="hlt">downscaled</span> precipitation. Due to the cost of the method and the limited improvements in the <span class="hlt">downscaling</span> results, the three-step method is not justified to replace the one-step method for <span class="hlt">downscaling</span> of Swedish precipitation.</p> <div class="credits"> <p class="dwt_author">Hellström, Cecilia; Chen, Deliang</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-11-01</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://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 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/2012AtmRe.118..346W"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of climate forecast system seasonal predictions for the Southeastern Mediterranean</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">Most of the annual rainfall in the Southeastern Mediterranean falls in the wet season from November to March. It is associated with Mediterranean cyclones, and is sensitive to climate variability. Predicting the wet season precipitation with a few months advance is highly valuable for water resource planning and climate-associated risk management in this semi-arid region. The regional water resource managements and climate-sensitive economic activities have relied on seasonal forecasts from global climate prediction centers. However due to their coarse resolutions, global seasonal forecasts lack regional and local scale information required by regional and local water resource managements. In this study, an analog statistical-<span class="hlt">downscaling</span> algorithm, k-nearest neighbors (KNN), was introduced to bridge the gap between the coarse forecasts from global models and the needed fine-scale information for the Southeastern Mediterranean. The algorithm, driven by the NCEP Climate Forecast System (CFS) operational forecast and the NCEP/DOE reanalysis, provides monthly precipitations at 2-4 months of lead-time at 18 stations within the major regional hydrological basins. Large-scale predictors for KNN were objectively determined by the correlations between the station historic daily precipitation and variables in reanalysis and CFS reforecast. Besides a single deterministic forecast, this study constructed sixty <span class="hlt">ensemble</span> members for probabilistic estimates. The KNN algorithm demonstrated its robustness when validated with NCEP/DOE reanalysis from 1981 to 2009 as hindcasts before applied to <span class="hlt">downscale</span> CFS forecasts. The <span class="hlt">downscaled</span> predictions show fine-scale information, such as station-to-station variability. The verification against observations shows improved skills of this <span class="hlt">downscaling</span> utility relative to the CFS model. The KNN-based <span class="hlt">downscaling</span> system has been in operation for the Israel Water Authority predicting precipitation and driving hydrologic models estimating river flow and aquifer charge for water supply.</p> <div class="credits"> <p class="dwt_author">Wu, Wanli; Liu, Yubao; Ge, Ming; Rostkier-Edelstein, Dorita; Descombes, Gael; Kunin, Pavel; Warner, Thomas; Swerdlin, Scott; Givati, Amir; Hopson, Thomas; Yates, David</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-11-01</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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2686571"> <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=pmc">PubMed Central</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">2009-01-01</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/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 " 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.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4383879"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2015</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 genomic interpretation system providing the most up-to-date annotations, querying tools and access methods for chordates and key model organisms. This year we released updated annotation (gene models, comparative genomics, regulatory regions and variation) on the new human assembly, GRCh38, although we continue to support researchers using the GRCh37.p13 assembly through a dedicated site (http://grch37.<span class="hlt">ensembl</span>.org). Our Regulatory Build has been revamped to identify regulatory regions of interest and to efficiently highlight their activity across disparate epigenetic data sets. A number of new interfaces allow users to perform large-scale comparisons of their data against our annotations. The REST server (http://rest.<span class="hlt">ensembl</span>.org), which allows programs written in any language to query our databases, has moved to a full service alongside our upgraded website tools. Our online Variant Effect Predictor tool has been updated to process more variants and calculate summary statistics. Lastly, the WiggleTools package enables users to summarize large collections of data sets and view them as single tracks in <span class="hlt">Ensembl</span>. The <span class="hlt">Ensembl</span> code base itself is more accessible: it is now hosted on our GitHub organization page (https://github.com/<span class="hlt">Ensembl</span>) under an Apache 2.0 open source license. PMID:25352552</p> <div class="credits"> <p class="dwt_author">Cunningham, Fiona; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Billis, Konstantinos; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.; Janacek, Sophie H.; Johnson, Nathan; Juettemann, Thomas; Kähäri, Andreas K.; Keenan, Stephen; Martin, Fergal J.; Maurel, Thomas; McLaren, William; Murphy, Daniel N.; Nag, Rishi; Overduin, Bert; Parker, Anne; Patricio, Mateus; Perry, Emily; Pignatelli, Miguel; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P.; Zadissa, Amonida; Aken, Bronwen L.; Birney, Ewan; Harrow, Jennifer; Kinsella, Rhoda; Muffato, Matthieu; Ruffier, Magali; Searle, Stephen M.J.; Spudich, Giulietta; Trevanion, Stephen J.; Yates, Andy; Zerbino, Daniel R.; Flicek, Paul</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-01-01</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://adsabs.harvard.edu/abs/2014EGUGA..1611854A"> <span id="translatedtitle">Improving GEFS Weather Forecasts for Indian Monsoon with Statistical <span class="hlt">Downscaling</span></span></a>  </p> <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">Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global <span class="hlt">Ensemble</span> Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical <span class="hlt">downscaling</span> technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is established between the principal components and observed rainfall over training period and predictions are obtained for testing period. The validations show high improvements in correlation coefficient between observed and predicted data (0.25 to 0.55). The results speak in favour of statistical <span class="hlt">downscaling</span> methodology which shows the capability to reduce the gap between observed data and predictions. A detailed study is required to be carried out by applying different <span class="hlt">downscaling</span> techniques to quantify the improvements in predictions.</p> <div class="credits"> <p class="dwt_author">Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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/2013AGUFMIN23A1414R"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Climate Data from Distributed Archives</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">Model refinement -- numerical estimates of climate change at higher resolution than climate models are currently capable of producing -- is an essential weapon in the arsenal of decision makers and researchers in climate change. We describe here steps toward a general-purpose system for model refinement. We envision a system wherein multiple climate models, alone or in combination, can be used as predictors; multiple refinement methods, alone or in combination, can be deployed and trained, including evaluation within a perfect-model framework, described below; time periods and locations of training can be chosen at will; and providing all of these options as standard web services within the Earth System Grid Federation (ESGF) global data infrastructure for the distribution of climate model output. The perfect-model framework for systematic testing of model refinement using empirical-statistical <span class="hlt">downscaling</span> (ESD) schemes is being developed at NOAA/GFDL under the National Climate Predictions and Projections Platform (NCPP) project. It uses the approach that Laprise and collaborators call the "big-brother" framework for evaluating dynamical <span class="hlt">downscaling</span>. High-resolution model output is used as a "nature run" and used in place of observations to train the ESD scheme under testing. The data is interpolated to a coarse grid (the "little brother") and the ESD scheme attempts to <span class="hlt">downscale</span> and bias-correct the "future", i.e beyond the period of training. The output of ESD can then be rigorously compared to the original nature run on a chosen list of metrics. Initial work was performed in collaboration with Texas Tech University: the high-resolution time-slice models that GFDL submitted to CMIP5 are used as training sets for the <span class="hlt">downscaling</span> methods developed by Katharine Hayhoe and collaborators. The approach is being extended to using other <span class="hlt">downscaling</span> schemes, such as BCSD, Delta, quantile mapping, constructed analogs, and machine learning algorithms; and in future to using other model output for training datasets as well. Initial results were first presented at the Quantitative Evaluation of <span class="hlt">Downscaling</span> 2013 Workshop (QED-2013). We will describe a software infrastructure wherein: a) any CMIP5 high-resolution model output can be used as a training set; b) any ESD scheme can be deployed using a standard template or API developed under the ExArch project; c) the outputs of <span class="hlt">downscaling</span> will also conform to CMIP5 standards and be capable of being analyzed on the same footing as any CMIP5 output; d) analysis services computing the chosen metrics can be run on the <span class="hlt">downscaled</span> output; e) the infrastructure can be deployed "in-house" by the ESD group, or potentially run as a web service on any ESGF node.</p> <div class="credits"> <p class="dwt_author">Radhakrishnan, A.; Guentchev, G.; Cinquini, L.; Schweitzer, R.; Nikonov, S.; Balaji, V.</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">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/2010EGUGA..1213219B"> <span id="translatedtitle">Transient climate rainfall <span class="hlt">downscaling</span> using a combined dynamic-stochastic methodology</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">Managers of water resource systems need <span class="hlt">downscaled</span> climate change projections that are relevant at the catchment scale and at a range of future time horizons. However, the uncertainty in future climate projections and the natural variability of the climate system affect the robustness of their decisions. Dynamic <span class="hlt">downscaling</span> of discrete future time-slices also limits the analysis of the temporal development of climate change impacts, as only steady state scenarios are widely available. Addressing these issues a new transient (i.e. temporally non-stationary) rainfall simulation methodology has been developed which combines dynamical and statistical <span class="hlt">downscaling</span> to generate a multi-model <span class="hlt">ensemble</span> of transient daily point-scale rainfall timeseries. Each timeseries is sampled from a continuous stochastic simulation of the control-future time period and exhibits climatic non-stationarity in accordance with GCM/RCM projections. The <span class="hlt">ensemble</span> as a whole represents aspects of both climate model uncertainty and natural variability and provides a basis for probabilistic time-horizon analyses such as when a particular impact will occur or when a particular threshold will be reached. The methodology is demonstrated for a case study raingauge located near the Brévilles spring in Northern France. Thirteen RCM projections from the PRUDENCE project for both control (1961-1990) and future (2071-2100) time-slices were obtained to form the basis of a multi-model representation of climate change. Each dynamically <span class="hlt">downscales</span> the climate from either the ECHAM4/OPYC or the HadCM3 GCM. Multiplicative ‘change factors' were evaluated for a set of statistics of daily rainfall for each RCM. These quantify the future value of each statistic as a multiple of the control value for each calendar month in turn. Multiplying the case study raingauge statistics by the change factors provides future projections with an implicit correction for biases in the RCM control runs and a representation of the variability exhibited between the RCMs. In the absence of transient RCM projections a ‘scale factor' approach was adopted to estimate climate change throughout the transient period. Future changes were assumed to occur in proportion to global annual average temperature change. Scale factors were evaluated for four 30-year time-slice integrations of the GCMs, for which global average temperatures were available. These were linearly interpolated for the intervening years. Transient change factors were then estimated in proportion to the scale factors. Applying these to the observed rainfall statistics gave a transient projection of the daily rainfall statistics for the case study location, for each RCM for a continuous period from 1997 to 2085. A new transient formulation of the Neyman-Scott Rectangular Pulses (NSRP) stochastic rainfall model has been developed. This model was used for stochastic <span class="hlt">downscaling</span> to the point scale and to model natural rainfall variability at the daily scale. A piecewise smoothly varying transient NSRP parameterization was obtained by fitting to the transient projected rainfall statistics. Transient NSRP simulations then produced continuous daily rainfall time series which exhibit climatic non-stationarity. The simulation was realized 100 times to generate an <span class="hlt">ensemble</span>, which models natural climate variability. Repeating for each RCM in turn generates a multi-model <span class="hlt">ensemble</span> of 1300 transient <span class="hlt">downscaled</span> daily rainfall timeseries. The <span class="hlt">ensemble</span> improves on RCM simulations of the present-day climate and exhibits a time varying decrease in annual and summer rainfall and a time varying increase in winter rainfall amounts. The 10-year return period daily extreme rainfall is also likely to increase over the simulated period.</p> <div class="credits"> <p class="dwt_author">Burton, Aidan; Blenkinsop, Stephen; Fowler, Hayley J.; Kilsby, Chris G.</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">56</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://iri.columbia.edu/~jqian/phil_downscaling_27oct2009_JQ.pdf"> <span id="translatedtitle">Regional Climate <span class="hlt">Downscaling</span> Intercomparison over the Philippines</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Regional Climate <span class="hlt">Downscaling</span> Intercomparison over the Philippines J.H. Qian, A.W. Robertson, M: PAGASA, the Philippines #12;#12;#12;#12;Analysis of r a i n f a l l fluctuations in the Philippines 237 Figure 1 Climatological map (after "Philippines Water Resources", 1976). Vigan, Legaspi, Zamboanga</p> <div class="credits"> <p class="dwt_author">Qian, Jian-Hua "Joshua"</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">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/2010AGUFMGC23C0926S"> <span id="translatedtitle">Assessing the performance of dynamical and statistical <span class="hlt">downscaling</span> techniques to simulate crop yield in West 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">Global circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. It therefore requires the use of <span class="hlt">downscaling</span> methods. This study analyzes the performance of both dynamical and statistical <span class="hlt">downscaling</span> techniques in simulating crop yield at local scale. A detailed case study is conducted using historical weather data for Senegal, applied to the crop model SARRAH for simulating several tropical cereals (sorghum, millet, maize) at local scale. This control simulation is used as a benchmark to evaluate a set of Regional Climate Models (RCM) simulations, forced by ERA-Interim, from the <span class="hlt">ENSEMBLES</span> project and a statistical <span class="hlt">downscaling</span> method, the CDF-Transform, used to correct biases in RCM outputs. We first evaluate each climate variable that drives the simulated yield in the control simulation (radiation, rainfall, temperatures). We then simulate crop yields with RCM outputs (with or without applying the CDG-Transform) and evaluate the performance of each RCM in regards to crop yield simulations.</p> <div class="credits"> <p class="dwt_author">Sultan, B.; Oettli, P.; Vrac, M.; Baron, C.</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">58</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. PMID:21045057</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; Kähäri, 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; Fernández-Suarez, Xosé 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 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://www.osti.gov/scitech/servlets/purl/974391"> <span id="translatedtitle">Accounting for Global Climate Model Projection Uncertainty in Modern Statistical <span class="hlt">Downscaling</span></span></a>  </p> <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">Future climate change has emerged as a national and a global security threat. To carry out the needed adaptation and mitigation steps, a quantification of the expected level of climate change is needed, both at the global and the regional scale; in the end, the impact of climate change is felt at the local/regional level. An important part of such climate change assessment is uncertainty quantification. Decision and policy makers are not only interested in 'best guesses' of expected climate change, but rather probabilistic quantification (e.g., Rougier, 2007). For example, consider the following question: What is the probability that the average summer temperature will increase by at least 4 C in region R if global CO{sub 2} emission increases by P% from current levels by time T? It is a simple question, but one that remains very difficult to answer. It is answering these kind of questions that is the focus of this effort. The uncertainty associated with future climate change can be attributed to three major factors: (1) Uncertainty about future emission of green house gasses (GHG). (2) Given a future GHG emission scenario, what is its impact on the global climate? (3) Given a particular evolution of the global climate, what does it mean for a particular location/region? In what follows, we assume a particular GHG emission scenario has been selected. Given the GHG emission scenario, the current batch of the state-of-the-art global climate models (GCMs) is used to simulate future climate under this scenario, yielding an <span class="hlt">ensemble</span> of future climate projections (which reflect, to some degree our uncertainty of being able to simulate future climate give a particular GHG scenario). Due to the coarse-resolution nature of the GCM projections, they need to be spatially <span class="hlt">downscaled</span> for regional impact assessments. To <span class="hlt">downscale</span> a given GCM projection, two methods have emerged: dynamical <span class="hlt">downscaling</span> and statistical (empirical) <span class="hlt">downscaling</span> (SDS). Dynamic <span class="hlt">downscaling</span> involves configuring and running a regional climate model (RCM) nested within a given GCM projection (i.e., the GCM provides bounder conditions for the RCM). On the other hand, statistical <span class="hlt">downscaling</span> aims at establishing a statistical relationship between observed local/regional climate variables of interest and synoptic (GCM-scale) climate predictors. The resulting empirical relationship is then applied to future GCM projections. A comparison of the pros and cons of dynamical versus statistical <span class="hlt">downscaling</span> is outside the scope of this effort, but has been extensively studied and the reader is referred to Wilby et al. (1998); Murphy (1999); Wood et al. (2004); Benestad et al. (2007); Fowler et al. (2007), and references within those. The scope of this effort is to study methodology, a statistical framework, to propagate and account for GCM uncertainty in regional statistical <span class="hlt">downscaling</span> assessment. In particular, we will explore how to leverage an <span class="hlt">ensemble</span> of GCM projections to quantify the impact of the GCM uncertainty in such an assessment. There are three main component to this effort: (1) gather the necessary climate-related data for a regional SDS study, including multiple GCM projections, (2) carry out SDS, and (3) assess the uncertainty. The first step is carried out using tools written in the Python programming language, while analysis tools were developed in the statistical programming language R; see Figure 1.</p> <div class="credits"> <p class="dwt_author">Johannesson, G</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-03-17</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://ntrs.nasa.gov/search.jsp?R=20140009212&hterms=africa+statistics&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dafrica%2Bstatistics"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Reanalysis over Continental Africa with a Regional Model: NCEP Versus ERA Interim Forcing</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">Five annual climate cycles (1998-2002) are simulated for continental Africa and adjacent oceans by a regional atmospheric model (RM3). RM3 horizontal grid spacing is 0.44deg at 28 vertical levels. Each of 2 simulation <span class="hlt">ensembles</span> is driven by lateral boundary conditions from each of 2 alternative reanalysis data sets. One simulation downs cales National Center for Environmental Prediction reanalysis 2 (NCPR2) and the other the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I). NCPR2 data are archived at 2.5deg grid spacing, while a recent version of ERA-I provides data at 0.75deg spacing. ERA-I-forced simulations are recomrp. ended by the Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX). Comparisons of the 2 sets of simulations with each other and with observational evidence assess the relative performance of each <span class="hlt">downscaling</span> system. A third simulation also uses ERA-I forcing, but degraded to the same horizontal resolution as NCPR2. RM3-simulated pentad and monthly mean precipitation data are compared to Tropical Rainfall Measuring Mission (TRMM) data, gridded at 0.5deg, and RM3-simulated circulation is compared to both reanalyses. Results suggest that each <span class="hlt">downscaling</span> system provides advantages and disadvantages relative to the other. The RM3/NCPR2 achieves a more realistic northward advance of summer monsoon rains over West Africa, but RM3/ERA-I creates the more realistic monsoon circulation. Both systems recreate some features of JulySeptember 1999 minus 2002 precipitation differences. Degrading the resolution of ERA-I driving data unrealistically slows the monsoon circulation and considerably diminishes summer rainfall rates over West Africa. The high resolution of ERA-I data, therefore, contributes to the quality of the <span class="hlt">downscaling</span>, but NCPR2laterai boundary conditions nevertheless produce better simulations of some features.</p> <div class="credits"> <p class="dwt_author">Druyan, Leonard M.; Fulakeza, Matthew B.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-01-01</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_2");' 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_5");' 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">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/2011ClDy...37..835G"> <span id="translatedtitle">Climate variability and projected change in the western United States: regional <span class="hlt">downscaling</span> and drought 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">Climate change in the twenty-first century, projected by a large <span class="hlt">ensemble</span> average of global coupled models forced by a mid-range (A1B) radiative forcing scenario, is <span class="hlt">downscaled</span> to Climate Divisions across the western United States. A simple empirical <span class="hlt">downscaling</span> technique is employed, involving model-projected linear trends in temperature or precipitation superimposed onto a repetition of observed twentieth century interannual variability. This procedure allows the projected trends to be assessed in terms of historical climate variability. The linear trend assumption provides a very close approximation to the time evolution of the <span class="hlt">ensemble</span>-average climate change, while the imposition of repeated interannual variability is probably conservative. These assumptions are very transparent, so the scenario is simple to understand and can provide a useful baseline assumption for other scenarios that may incorporate more sophisticated empirical or dynamical <span class="hlt">downscaling</span> techniques. Projected temperature trends in some areas of the western US extend beyond the twentieth century historical range of variability (HRV) of seasonal averages, especially in summer, whereas precipitation trends are relatively much smaller, remaining within the HRV. Temperature and precipitation scenarios are used to generate Division-scale projections of the monthly palmer drought severity index (PDSI) across the western US through the twenty-first century, using the twentieth century as a baseline. The PDSI is a commonly used metric designed to describe drought in terms of the local surface water balance. Consistent with previous studies, the PDSI trends imply that the higher evaporation rates associated with positive temperature trends exacerbate the severity and extent of drought in the semi-arid West. Comparison of twentieth century historical droughts with projected twenty-first century droughts (based on the prescribed repetition of twentieth century interannual variability) shows that the projected trend toward warmer temperatures inhibits recovery from droughts caused by decade-scale precipitation deficits.</p> <div class="credits"> <p class="dwt_author">Gutzler, David S.; Robbins, Tessia O.</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">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/2003AGUFMNG51A0824B"> <span id="translatedtitle">Multifractal <span class="hlt">downscaling</span> of a GCM rainfield</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 get a more efficient production management of reservoirs, it would be helpful to apply long-term meteorological forecasts to hydrological models. Unfortunately, the explicit scales of present GCM's are quite larger (e.g. 243kmx243kmx32 days) than those of hydrological models (e.g. 1 kmx1kmx1day). Therefore it is indispensable to proceed to a <span class="hlt">downscaling</span> of the output of the former in order to obtain an input for the latter. In this paper, we present a multifractal <span class="hlt">downscaling</span> procedure. The site of the study is the area of Doubs river, with the help of a dense local hydrological network, but in order to get a larger spatial scale ratio we extend our multifractal analysis to France, with the help of Météo-France PRECIP data base. We first argue that it is indispensable to consider a multifractal <span class="hlt">downscaling</span> procedure in order to respect the scaling properties of the hydro-meteorological fields. We performed time, scale and time-space multifractal analysis of the available data and evaluate the corresponding universal exponents, as well as the anisotropy/dynamical exponent of the time-space generalized scale. We show that these exponents are quite robust. We compare our analysis to similar works, but restricted to the use of Log-Poison cascade and space-time isotropy. We show both theoretically and empirically that these restrictions are untenable, in particular with respect to the extremes. We also show simulations should be done with the help of continuous (in scale) and causal cascade models, not with ad-hoc time-space cascades, and present the corresponding numerical simulations. of space-time <span class="hlt">downscaling</span> of (meso-scale) GCM data down to (micro-scale) hydrological scales. We greatly acknowledge the financial support from Electricité de France, as well as Météo-France for providing access to its PRECIP data base.</p> <div class="credits"> <p class="dwt_author">Biaou, A.; Hubert, P.; Schertzer, D.; Hendrickx, F.; Tchiguirinskaia, I.</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-12-01</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://adsabs.harvard.edu/abs/2004EOSTr..85..417B"> <span id="translatedtitle">Empirical-Statistical <span class="hlt">Downscaling</span> in Climate 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">Research into possible impacts of a climate change requires descriptions of local and regional descriptions of climate. For instance, the local and regional aspect of a climate change is stressed in the U.S. Strategic Plan for the Climate Change Science Program (CCSP) (http://www.climatescience.gov/Library/stratplan2003/default.htm). Global climate models (GCMs) are important tools for studying climate change and making projections for the future. Although GCMs provide realistic representations of large-scale aspects of climate, they generally do not give good descriptions of the local and regional scales. It is nevertheless possible to relate large-scale climatic features to smaller spatial scales. There are two main approaches for deriving information on local or regional scales from the global climate scenarios generated by GCMs: (1) numerical <span class="hlt">downscaling</span> (also known as ``dynamical <span class="hlt">downscaling</span>'') involving a nested regional climate model (RCM) or (2) empirical-statistical <span class="hlt">downscaling</span> employing statistical relationships between the large-scale climatic state and local variations derived from historical data records.</p> <div class="credits"> <p class="dwt_author">Benestad, R. E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-10-01</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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3964975"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2014</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) creates tools and data resources to facilitate genomic analysis in chordate species with an emphasis on human, major vertebrate model organisms and farm animals. Over the past year we have increased the number of species that we support to 77 and expanded our genome browser with a new scrollable overview and improved variation and phenotype views. We also report updates to our core datasets and improvements to our gene homology relationships from the addition of new species. Our REST service has been extended with additional support for comparative genomics and ontology information. Finally, we provide updated information about our methods for data access and resources for user training. PMID:24316576</p> <div class="credits"> <p class="dwt_author">Flicek, Paul; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Billis, Konstantinos; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah; Johnson, Nathan; Juettemann, Thomas; Kähäri, Andreas K.; Keenan, Stephen; Kulesha, Eugene; Martin, Fergal J.; Maurel, Thomas; McLaren, William M.; Murphy, Daniel N.; Nag, Rishi; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Pritchard, Emily; Riat, Harpreet S.; Ruffier, Magali; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Trevanion, Stephen J.; Vullo, Alessandro; Wilder, Steven P.; Wilson, Mark; Zadissa, Amonida; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J.P.; Kinsella, Rhoda; Muffato, Matthieu; Parker, Anne; Spudich, Giulietta; Yates, Andy; Zerbino, Daniel R.; Searle, Stephen M.J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-01-01</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://pubs.er.usgs.gov/publication/70022063"> <span id="translatedtitle">Hydrological responses to dynamically and statistically <span class="hlt">downscaled</span> climate model output</span></a>  </p> <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, W.J., Jr.; 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">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/2012EGUGA..14.9817S"> <span id="translatedtitle">Estimating 2m Temperature Boundaries from <span class="hlt">Ensemble</span> 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">The aim of this work was, to develop a method that forecasts confidence intervals for the 2m temperature with a high spatial resolution. Using GFS <span class="hlt">ensemble</span> prediction data, the MetGIS <span class="hlt">downscaling</span> tool and temperature observations from the Greater Alpine Region, a verification experiment was realized to evaluate the forecast spread. All <span class="hlt">ensemble</span> forecasts, the control run and the deterministic forecast, which make up the forecast spread, were <span class="hlt">downscaled</span> to station locations and verified, resulting in various forecast errors for each forecast time and each station. The best forecast for each verification point and time, which is the one with the smallest absolute error, was sorted out, leading to a sample of minimum absolute forecast errors. Although it is not possible to determine which <span class="hlt">ensemble</span> member will perform best beforehand, information about the maximum deviations of these best forecasts is important, because the forecast spread has to be enlarged by this margin. Analysis of these errors showed that the deviations of the best forecasts are bigger at the start of the forecast range and decrease with longer forecast lead times. This means, that the forecast spread at the start of the forecast is not wide enough to take analysis, forecast and model errors into account, and this margin has to be considered in the <span class="hlt">downscaling</span> process. In order to do so, the 90% percentile of the absolute errors derived from the most accurate forecasts at each time was estimated and found to be spatially quite constant. This minimum error was added to the <span class="hlt">downscaled</span> forecast spread, yielding a temperature range for which the observed temperature lies outside in only 10% of cases. Further verification proofed that this is true in a statistical sense, and a comparison with climate data showed that these temperature range forecasts can outdo climatological forecasts up to 13 days.</p> <div class="credits"> <p class="dwt_author">Sperka, S.; Spreitzhofer, 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">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/2014ClDy...43.3201G"> <span id="translatedtitle">Comparison of statistically <span class="hlt">downscaled</span> precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada</span></a>  </p> <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 coarse resolution of global climate models, <span class="hlt">downscaling</span> techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical <span class="hlt">downscaling</span> experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically <span class="hlt">downscaled</span> daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and <span class="hlt">ensembles</span>, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971-2000) and A2 (2041-2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree <span class="hlt">ensembles</span> outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of <span class="hlt">downscaling</span> models deteriorated in future climate.</p> <div class="credits"> <p class="dwt_author">Gaitan, Carlos F.; Hsieh, William W.; Cannon, Alex J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-12-01</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/2014EGUGA..16.6323P"> <span id="translatedtitle">Statistical-dynamical <span class="hlt">downscaling</span> for wind energy potentials: Evaluation and applications to decadal hindcasts and climate change projections</span></a>  </p> <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-dynamical <span class="hlt">downscaling</span> (SDD) approach for the regionalisation of wind energy output (Eout) over Europe with special focus on Germany is proposed. SDD uses an extended circulation weather type (CWT) analysis on global daily MSLP fields with the central point being located over Germany. 77 weather classes based on the associated circulation weather type and the intensity of the geostrophic flow are identified. Representatives of these classes are dynamical <span class="hlt">downscaled</span> with the regional climate model COSMO-CLM. By using weather class frequencies of different datasets the simulated representatives are recombined to probability density functions (PDFs) of near-surface wind speed and finally to Eout of a sample wind turbine for present and future climate. This is performed for reanalysis, decadal hindcasts and long-term future projections. For evaluation purposes results of SDD are compared to wind observations and to simulated Eout of purely dynamical <span class="hlt">downscaling</span> (DD) methods. For the present climate SDD is able to simulate realistic PDFs of 10m-wind speed for most stations in Germany. The resulting spatial Eout patterns are similar to DD simulated Eout. In terms of decadal hindcasts results of SDD are similar to DD simulated Eout over Germany, Poland, Czech Republic, and Benelux, for which high correlations between annual Eout timeseries of SDD and DD are detected for selected hindcasts. Lower correlation is found for other European countries. It is demonstrated that SDD can be used to <span class="hlt">downscale</span> the full <span class="hlt">ensemble</span> of the MPI-ESM decadal prediction system. Long-term climate change projections in SRES scenarios of ECHAM5/MPI-OM as obtained by SDD agree well to results of other studies using DD methods, with increasing Eout over Northern Europe and a negative trend over Southern Europe. Despite some biases it is concluded that SDD is an adequate tool to assess regional wind energy changes in large model <span class="hlt">ensembles</span>.</p> <div class="credits"> <p class="dwt_author">Pinto, Joaquim G.; Reyers, Mark; Mömken, Julia</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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/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">70</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://oaspub.epa.gov/eims/eimsapi.dispdetail?deid=280783"> <span id="translatedtitle">"Going the Extra Mile in <span class="hlt">Downscaling</span>: Why <span class="hlt">Downscaling</span> is not jut "Plug-and-Play"</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p class="result-summary">This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of <span class="hlt">downscaling</span> the Comm...</p> <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">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/2011JGRD..116.5113K"> <span id="translatedtitle">Nonstationary probabilistic <span class="hlt">downscaling</span> of extreme 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">Reanalysis data and general circulation model outputs typically provide information at a coarse spatial resolution, which cannot directly be used for local impact studies. <span class="hlt">Downscaling</span> methods have been developed to overcome this problem, and to obtain local-scale information from large-scale atmospheric variables. The deduction of local-scale extremes still is a challenge. Here a probabilistic <span class="hlt">downscaling</span> approach is presented where the cumulative distribution functions (CDFs) of large- and local-scale extremes are linked by means of a transfer function. In this way, the CDF of the local-scale extremes is obtained for a projection period, and statistical characteristics, like return levels, are inferred. The input series are assumed to be distributed according to an extreme value distribution, the Generalized Pareto distribution (GPD). The GPD parameters are linked to further explanatory variables, hence defining a nonstationary model. The methodology (XCDF-t) results in a parametric CDF, which is as well a GPD. Realizations generated from this CDF provide confidence bands. The approach is applied to <span class="hlt">downscale</span> National Centers for Environmental Prediction reanalysis precipitation in winter. Daily local precipitation at five stations in southern France is obtained. The calibration period 1951-1985 is used to infer precipitation over the validation period 1986-1999. The applicability of the approach is verified by using observations, quantile-quantile plots, and the continuous ranked probability score. The stationary XCDF-t approach shows good results and outperforms the nonparametric CDF-t approach or quantile mapping for some stations. The inclusion of covariate information improves results only sometimes; therefore, covariates have to be chosen with care.</p> <div class="credits"> <p class="dwt_author">Kallache, M.; Vrac, M.; Naveau, P.; Michelangeli, P.-A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-03-01</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/2010EGUGA..12.2136V"> <span id="translatedtitle"><span class="hlt">Ensemble</span> forecast post-processing over Belgium: Comparison of deterministic-like and <span class="hlt">ensemble</span> regression 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">A comparison of the benefits of post-processing ECMWF <span class="hlt">ensemble</span> forecasts based on a deterministic-like and a regression technique is performed for Belgium. The former is a Linear Model Output Statistics technique (EVMOS) recently developed to allow for providing an appropriate <span class="hlt">ensemble</span> variability at all lead times (Vannitsem 2009) and the latter is the Non-homogeneous Gaussian Regression, NGR, (Gneiting et al, 2005). The training of the post-processing techniques is based on the reforecast dataset of ECMWF which covers a period from 1991 to 2007. The EVMOS approach is mainly providing a correction of the systematic error and does not enhance substantially the spread of the <span class="hlt">ensemble</span>. The application of the NGR method provides an <span class="hlt">ensemble</span> which encompasses the observations, unlike the EVMOS scheme. However, by taking into account the observational error, the analysis suggests that the <span class="hlt">ensemble</span> based on the EVMOS post-processing scheme is also found to be consistent. This apparent contradiction is clarified and it turns out that both schemes are valuable depending on the specific purpose, the evaluation of the uncertainty of large scale flows or the <span class="hlt">downscaling</span> of the temperature uncertainty at the level of the local observations.</p> <div class="credits"> <p class="dwt_author">Vannitsem, Stéphane; Hagedorn, Renate</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">73</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ntrs.nasa.gov/search.jsp?R=19950024819&hterms=personal+values&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dpersonal%2Bvalues"> <span id="translatedtitle">The Personal Software Process: <span class="hlt">Downscaling</span> the factory</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">It is argued that the next wave of software process improvement (SPI) activities will be based on a people-centered paradigm. The most promising such paradigm, Watts Humphrey's personal software process (PSP), is summarized and its advantages are listed. The concepts of the PSP are shown also to fit a <span class="hlt">down-scaled</span> version of Basili's experience factory. The author's data and lessons learned while practicing the PSP are presented along with personal experience, observations, and advice from the perspective of a consultant and teacher for the personal software process.</p> <div class="credits"> <p class="dwt_author">Roy, Daniel M.</p> <p class="dwt_publisher"></p> <p class="publishDate">1994-01-01</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://adsabs.harvard.edu/abs/2010EGUGA..1214308V"> <span id="translatedtitle">Wave model <span class="hlt">downscaling</span> for coastal applications</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> is a suitable technique for obtaining high-resolution estimates from relatively coarse-resolution global models. Dynamical and statistical <span class="hlt">downscaling</span> has been applied to the multidecadal simulations of ocean waves. Even as large-scale variability might be plausibly estimated from these simulations, their value for the small scale applications such as design of coastal protection structures and coastal risk assessment is limited due to their relatively coarse spatial and temporal resolutions. Another advantage of the high resolution wave modeling is that it accounts for shallow water effects. Therefore, it can be used for both wave forecasting at specific coastal locations and engineering applications that require knowledge about extreme wave statistics at or near the coastal facilities. In the present study <span class="hlt">downscaling</span> is applied to both ECMWF and NCEP/NCAR global reanalysis of atmospheric pressure over the Black Sea with 2.5 degrees spatial resolution. A simplified regional atmospheric model is employed for calculation of the surface wind field at 0.5 degrees resolution that serves as forcing for the wave models. Further, a high-resolution nested WAM/SWAN wave model suite of nested wave models is applied for spatial <span class="hlt">downscaling</span>. It aims at resolving the wave conditions in a limited area at the close proximity to the shore. The pilot site is located in the northern part the Bulgarian Black Sea shore. The system involves the WAM wave model adapted for basin scale simulation at 0.5 degrees spatial resolution. The WAM output for significant wave height, mean wave period and mean angle of wave approach is used in terms of external boundary conditions for the SWAN wave model, which is set up for the western Black Sea shelf at 4km resolution. The same model set up on about 400m resolution is nested to the first SWAN run. In this case the SWAN 2D spectral output provides boundary conditions for the high-resolution model run. The models are implemented for a couple of storms occurred in 2009 as well as for a reconstructed past extreme storm. The system is validated against ADCP-born wave directional measurements. The SWAN model correlates well with measurements but slightly underestimates the wave height mostly due to coarse resolution of wind forcing. Presently, the results obtained for the study site feed up morphological models used for estimation of morphological changes such as sea bed and beach erosion. The system is targeted at regions where local wave growth and transformation rate differ from the offshore locations often used to estimate the near shore wave parameters. This includes areas with complicated bathymetry such as bays that endure a greater extent of human impact.</p> <div class="credits"> <p class="dwt_author">Valchev, Nikolay; Davidan, Georgi; Trifonova, Ekaterina; Andreeva, Nataliya</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">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/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 " 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://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 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://eric.ed.gov/?q=INSTRUMENTATION&pg=3&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 " 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/2013AGUFM.H43G1551C"> <span id="translatedtitle">Assimilation of <span class="hlt">Downscaled</span> SMOS Soil Moisture for Quantifying Drought Impacts on Crop Yield in Agricultural Regions in Brazil</span></a>  </p> <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">Reliable soil moisture (SM) information in the root zone (RZSM) is critical for quantification of agricultural drought impacts on crop yields and for recommending management and adaptation strategies for crop management, commodity trading and food security.The recently launched European Space Agency-Soil Moisture and Ocean Salinity (ESA-SMOS) and the near-future National Aeronautics and Space Administration-Soil Moisture Active Passive (NASA-SMAP) missions provide SM at unprecedented spatial resolutions of 10-25 km, but these resolutions are still too coarse for agricultural applications in heterogeneous landscapes, making <span class="hlt">downscaling</span> a necessity. This <span class="hlt">downscaled</span> near-surface SM can be merged with crop growth models in a data assimilation framework to provide optimal estimates of RZSM and crop yield. The objectives of the study include: 1) to implement a novel downscalingalgorithm based on the Information theoretical learning principlesto <span class="hlt">downscale</span> SMOS soil moisture at 25 km to 1km in the Brazilian La Plata Basin region and2) to assimilate the 1km-soil moisture in the crop model for a normal and a drought year to understand the impact on crop yield. In this study, a novel <span class="hlt">downscaling</span> algorithm based on the Principle of Relevant Information (PRI) was applied to in-situ and remotely sensed precipitation, SM, land surface temperature and leaf area index in the Brazilian Lower La Plata region in South America. An <span class="hlt">Ensemble</span> Kalman Filter (EnKF) based assimilation algorithm was used to assimilate the <span class="hlt">downscaled</span> soil moisture to update both states and parameters. The <span class="hlt">downscaled</span> soil moisture for two growing seasons in2010-2011 and 2011-2012 was assimilated into the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model over 161 km2 rain-fed region in the Brazilian LPB regionto improve the estimates of soybean yield. The first season experienced normal precipitation, while the second season was impacted by drought. Assimilation improved yield during both the seasons compared to the open-loop and matched well with the published yield statistics in the region.</p> <div class="credits"> <p class="dwt_author">Chakrabarti, S.; Bongiovanni, T. E.; Judge, J.; Principe, J. C.; Fraisse, C.</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">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/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">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/2009AGUFM.H13D1013F"> <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://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</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 is discussed first. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from the high-frequency extremes, to the low-frequency interannual variability. It is designed to reproduce a range of variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The use of the weather generator for the simulation of future climate scenarios, as inferred from global climate models, is discussed next. Using a Bayesian approach, the stochastic <span class="hlt">downscaling</span> procedure derives the distributions of factors of change for several climate statistics from a multi-model <span class="hlt">ensemble</span> of outputs of Global Circulation Models (GCM). The factors of change are subsequently applied to the statistics derived observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator is assumed to consistently reproduce future realizations of climate for a specific location at the hourly scale. The presentation further discusses how generated scenarios serve as input to an eco-hydrological model. Uncertainties present in the climate model realizations and the multi-model <span class="hlt">ensemble</span> predictions are discussed. An application of the weather generator tool in reproducing present (1961-2000) and future (2081-2100) climates is presented for the Tucson airport (AZ) location. The stochastic <span class="hlt">downscaling</span> is carried out with eight General Circulation Models from the CMIP3 multi-model dataset, A1B scenario is used. The weather generator performance for several variable climate statistics and aggregation periods is highly satisfactory. An increase of 3.5-4.5 degree of air temperature and a general reduction in precipitation is detected in the generated future scenario, as inferred from the <span class="hlt">downscaled</span> GCM outputs.</p> <div class="credits"> <p class="dwt_author">Fatichi, S.; Ivanov, V. Y.; Caporali, E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-12-01</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_3");' 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 style="font-weight: bold;">4</a> 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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_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://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 " 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://adsabs.harvard.edu/abs/2013EGUGA..15.7445D"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of RCM outputs for representative catchments in the Mediterranean region, for the 1951-2100 time-frame</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">Within the activities of the EU FP7 CLIMB project (www.climb-fp7.eu), we developed <span class="hlt">downscaling</span> procedures to reliably assess climate forcing at hydrologically relevant scales, and applied them to six representative hydrological basins located in the Mediterranean region: Riu Mannu and Noce in Italy, Chiba in Tunisia, Kocaeli in Turkey, Thau in France, and Gaza in Palestine. As a first step towards this aim, we used daily precipitation and temperature data from the gridded E-OBS project (www.ecad.eu/dailydata), as reference fields, to rank 14 Regional Climate Model (RCM) outputs from the <span class="hlt">ENSEMBLES</span> project (http://<span class="hlt">ensembles</span>-eu.metoffice.com). The four best performing model outputs were selected, with the additional constraint of maintaining 2 outputs obtained from running different RCMs driven by the same GCM, and 2 runs from the same RCM driven by different GCMs. For these four RCM-GCM model combinations, a set of <span class="hlt">downscaling</span> techniques were developed and applied, for the period 1951-2100, to variables used in hydrological modeling (i.e. precipitation; mean, maximum and minimum daily temperatures; direct solar radiation, relative humidity, magnitude and direction of surface winds). The quality of the final products is discussed, together with the results obtained after applying a bias reduction procedure to daily temperature and precipitation fields.</p> <div class="credits"> <p class="dwt_author">Deidda, Roberto; Marrocu, Marino; Pusceddu, Gabriella; Langousis, Andreas; Mascaro, Giuseppe; Caroletti, Giulio</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">83</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/2013AGUFM.S51A2313Y"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of slip distribution for strong earthquakes</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 intend to develop a <span class="hlt">downscaling</span> model to enhance the earthquake slip distribution resolution. Slip distributions have been obtained by other researchers using various inversion methods. As a <span class="hlt">downscaling</span> model, we are discussing fractal models that include mono-fractal models (fractional Brownian motion, fBm; fractional Lévy motion, fLm) and multi-fractal models as candidates. Log - log-linearity of k (wave number) versus E (k) (power spectrum) is the necessary condition for fractality: the slip distribution is expected to satisfy log - log-linearity described above if we can apply fractal model to a slip distribution as a <span class="hlt">downscaling</span> model. Therefore, we conducted spectrum analyses using slip distributions of 11 earthquakes as explained below. 1) Spectrum analyses using one-dimensional slip distributions (strike direction) were conducted. 2) Averaging of some results of power spectrum (dip direction) was conducted. Results show that, from the viewpoint of log - log-linearity, applying a fractal model to slip distributions can be inferred as valid. We adopt the filtering method after Lavallée (2008) to generate fBm/ fLm. In that method, generated white noises (random numbers) are filtered using a power law type filter (log - log-linearity of the spectrum). Lavallée (2008) described that Lévy white noise that generates fLm is more appropriate than the Gaussian white noise which generates fBm. In addition, if the 'alpha' parameter of the Lévy law, which governs the degree of attenuation of tails of the probability distribution, is 2.0, then the Lévy distribution is equivalent to the Gauss distribution. We analyzed slip distributions of 11 earthquakes: the Tohoku earthquake (Wei et al., 2011), Haiti earthquake (Sladen, 2010), Simeulue earthquake (Sladen, 2008), eastern Sichuan earthquake (Sladen, 2008), Peru earthquake (Konca, 2007), Tocopilla earthquake (Sladen, 2007), Kuril earthquake (Sladen, 2007), Benkulu earthquake (Konca, 2007), and southern Java earthquake (Konca, 2006)). We obtained the following results. 1) Log - log-linearity (slope of the linear relationship is ' - ?') of k versus E(k) holds for all earthquakes. 2) For example, ? = 3.70 and ? = 1.96 for the Tohoku earthquake (2011) and ? = 4.16 and ? = 2.00 for the Haiti earthquake (2010). For these cases, the Gauss' law is appropriate because alpha is almost 2.00. 3) However, ? = 5.25 and ? = 1.25 for the Peru earthquake (2007) and ? = 2.24 and ? = 1.57 for the Simeulue earthquake (2008). For these earthquakes, the Lévy law is more appropriate because ? is far from 2.0. 4) Although Lavallée (2003, 2008) concluded that the Lévy law is more appropriate than the Gauss' law for white noise, which is later filtered, our results show that the Gauss law is appropriate for some earthquakes. Lavallée and Archuleta, 2003, Stochastic modeling of slip spatial complexities for the 1979 Imperial Valley, California, earthquake, GEOPHYSICAL RESEARCH LETTERS, 30(5). Lavallée, 2008, On the random nature of earthquake source and ground motion: A unified theory, ADVANCES IN GEOPHYSICS, 50, Chap 16.</p> <div class="credits"> <p class="dwt_author">Yoshida, T.; Oya, S.; Kuzuha, Y.</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">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/2014EGUGA..1616461K"> <span id="translatedtitle">To <span class="hlt">Downscale</span> or not to <span class="hlt">Downscale</span>? That's the question. A flood forecasting perspective.</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 a growing body of literature investigating the subject of rainfall <span class="hlt">downscaling</span>. The research subject has been sparked by the need to link the predictions of climate models, that are typically ran on tens of kilometer grids, to distributed watershed models, that typically require input at the sub-kilometer scale. This obvious disparity seems to imply that techniques and algorithms need to be developed to scale down the coarse grid information keeping as much of physical reality of the reconstructed fine grid fields. However, the benefits or <span class="hlt">downscaling</span> rainfall may be less important than previously expected. Our group has been developing and testing multiscale distributed watershed models for flood predictions for several years and we consistently find that finer resolution rainfall may not imply better flood prediction capabilities. At the heart of this issue is the existence of the self-similar network that aggregates flows in the landscape and that ultimately determines the occurrence of floods in a particular basin outlet. We present examples of how rainfall inputs with different resolution impact our flood prediction accuracy across multiple spatial scales. We show for example, using precipitation fields on a daily 12 km grid and a 5 minute 500 m grid, that basins larger than 1000 km2, are insensitive to the resolution of the input product. We show that the sensitivity to the input product is largely determined by the equations that describe the rainfall runoff transformation (linear vs nonlinear). However, we also show that prediction accuracy, with different input grids, increases with increasing scale of the basin (e.g. 30,000 km2). The answer to the question for <span class="hlt">downscaling</span> or not in flood prediction becomes, "what size is your basin?"</p> <div class="credits"> <p class="dwt_author">Krajewski, Witold F.; Mantilla, Ricardo; Ayalew, Tibebu B.; Small, Scott J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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://www.osti.gov/scitech/servlets/purl/1160288"> <span id="translatedtitle">The ultimate <span class="hlt">downscaling</span> limit of FETs.</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 created a highly efficient, universal 3D quant um transport simulator. We demonstrated that the simulator scales linearly - both with the problem size (N) and number of CPUs, which presents an important break-through in the field of computational nanoelectronics. It allowed us, for the first time, to accurately simulate and optim ize a large number of realistic nanodevices in a much shorter time, when compared to other methods/codes such as RGF[~N 2.333 ]/KNIT, KWANT, and QTBM[~N 3 ]/NEMO5. In order to determine the best-in-class for different beyond-CMOS paradigms, we performed rigorous device optimization for high-performance logic devices at 6-, 5- and 4-nm gate lengths. We have discovered that there exists a fundamental <span class="hlt">down-scaling</span> limit for CMOS technology and other Field-Effect Transistors (FETs). We have found that, at room temperatures, all FETs, irre spective of their channel material, will start experiencing unacceptable level of thermally induced errors around 5-nm gate lengths.</p> <div class="credits"> <p class="dwt_author">Mamaluy, Denis; Gao, Xujiao; Tierney, Brian David</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-10-01</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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3278305"> <span id="translatedtitle">Exploring <span class="hlt">Ensemble</span> Visualization</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> is a collection of related datasets. Each dataset, or member, of an <span class="hlt">ensemble</span> is normally large, multidimensional, and spatio-temporal. <span class="hlt">Ensembles</span> are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an <span class="hlt">ensemble</span> to see how parameter choices affect the simulation. To draw inferences from an <span class="hlt">ensemble</span>, scientists need to compare data both within and between <span class="hlt">ensemble</span> members. We propose two techniques to support <span class="hlt">ensemble</span> exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of <span class="hlt">ensemble</span> data. PMID:22347540</p> <div class="credits"> <p class="dwt_author">Phadke, Madhura N.; Pinto, Lifford; Alabi, Femi; Harter, Jonathan; Taylor, Russell M.; Wu, Xunlei; Petersen, Hannah; Bass, Steffen A.; Healey, Christopher G.</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">87</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=https://www.meted.ucar.edu/training_module.php?id=170"> <span id="translatedtitle">Introduction to <span class="hlt">Ensemble</span> Prediction</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">This webcast is a shorter companion to the <span class="hlt">Ensemble</span> Prediction Explained module, focusing more directly on immediate operational needs. Introductory content includes the role of <span class="hlt">ensemble</span> forecasts, presentation of basic <span class="hlt">ensemble</span> forecasting terms, and discussion of how <span class="hlt">ensemble</span> prediction systems (EPSs) are created. The largest section is focused on common <span class="hlt">ensemble</span> forecast products, including how they differ from traditional NWP products, how we interpret <span class="hlt">ensemble</span> forecast products, the advantages and limitations of each product, how EPS products are verified, and how to use <span class="hlt">ensemble</span> products in conjunction with one another to increase your understanding of forecast uncertainty. Finally, three brief cases from cold and warm seasons illustrate the use of <span class="hlt">ensemble</span> products in the forecast process.</p> <div class="credits"> <p class="dwt_author">COMET</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-06-27</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/2013ClDy...41..255D"> <span id="translatedtitle">Dynamic <span class="hlt">downscaling</span> of 22-year CFS winter seasonal hindcasts with the UCLA-ETA regional climate model over the United States</span></a>  </p> <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 the UCLA-ETA regional model's dynamic <span class="hlt">downscaling</span> ability to improve the National Center for Environmental Prediction Climate Forecast System (NCEP CFS), winter season predictions over the contiguous United States (US). Spatial distributions and temporal variations of seasonal and monthly precipitation are the main focus. A multi-member <span class="hlt">ensemble</span> means of 22 winters from 1982 through 2004 are included in the study. CFS over-predicts the precipitation in eastern and western US by as much as 45 and 90 % on average compared to observations, respectively. Dynamic <span class="hlt">downscaling</span> improves the precipitation hindcasts across the domain, except in the southern States, by substantially reducing the excessive precipitation produced by the CFS. Average precipitation root-mean-square error for CFS and UCLA-ETA are 1.5 and 0.9 mm day-1, respectively. In addition, <span class="hlt">downscaling</span> improves the simulation of spatial distribution of snow water equivalent and land surface heat fluxes. Despite these large improvements, the UCLA-ETA's ability to improve the inter-annual and intra-seasonal precipitation variability is not clear, probably because of the imposed CFS' lateral boundary conditions. Preliminary analysis of the cause for the large precipitation differences between the models reveals that the CFS appears to underestimate the moisture flux convergence despite producing excessive precipitation amounts. Additionally, the comparison of modeled monthly surface sensible and latent heat fluxes with Global Land Data Assimilation System land data set shows that the CFS incorrectly partitioned most of surface energy into evaporation, unlike the UCLA-ETA. These findings suggest that the <span class="hlt">downscaling</span> improvements are mostly due to a better representation of land-surface processes by the UCLA-ETA. Sensitivity tests also reveal that higher-resolution topography only played a secondary role in the dynamic <span class="hlt">downscaling</span> improvement.</p> <div class="credits"> <p class="dwt_author">De Sales, Fernando; Xue, Yongkang</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">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/2003EAEJA.....6980H"> <span id="translatedtitle">Predicting seasonal fluctuations with multi-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">Seasonal-timescale climate predictions are now made routinely at a number of operational meteorological centres around the world, using comprehensive coupled models of the atmosphere, oceans, and land surface. Known limitations for producing perfect deterministic forecasts are uncertainties in initial data and model formulation. To account for these uncertainties, a multi-model <span class="hlt">ensemble</span> prediction system has been developed in a joint European project known as DEMETER (Development of a European <span class="hlt">Ensemble</span> Prediction System for Seasonal to Inter-annual Prediction). The model system consists of seven different global coupled atmosphere-ocean models and runs from sets of initial conditions, each slightly different from one another, but consistent with available observations.To assess the potential skill of the DEMETER multi-model system, an extensive set of hindcast <span class="hlt">ensemble</span> integrations over about 40 years (1960-2000) is being run. These hindcasts of seasonal climate variables are verified comprehensively in terms of deterministic (<span class="hlt">ensemble</span> mean) as well as probabilistic performance. The evaluation of the results clearly demonstrates the success of the multi-model <span class="hlt">ensemble</span> concept in the sense that the multi-model performance is generally superior to individual-model performance. Furthermore, an innovative method for supplying seasonal forecast information into agriculture and health models has been developed and tested. The strategy deals successfully with important problems as communication across disciplines, <span class="hlt">downscaling</span> of climate simulations, and use of probabilistic information. These examples of seasonal prediction applications illustrate the existence of both potential and real economic value for society.</p> <div class="credits"> <p class="dwt_author">Hagedorn, R.; Doblas-Reyes, F. J.; Palmer, T. N.</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-04-01</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/2013EGUGA..1512041M"> <span id="translatedtitle">VALUE - Validating and Integrating <span class="hlt">Downscaling</span> Methods for Climate Change Research</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">Our understanding of global climate change is mainly based on General Circulation Models (GCMs) with a relatively coarse resolution. Since climate change impacts are mainly experienced on regional scales, high-resolution climate change scenarios need to be derived from GCM simulations by <span class="hlt">downscaling</span>. Several projects have been carried out over the last years to validate the performance of statistical and dynamical <span class="hlt">downscaling</span>, yet several aspects have not been systematically addressed: variability on sub-daily, decadal and longer time-scales, extreme events, spatial variability and inter-variable relationships. Different <span class="hlt">downscaling</span> approaches such as dynamical <span class="hlt">downscaling</span>, statistical <span class="hlt">downscaling</span> and bias correction approaches have not been systematically compared. Furthermore, collaboration between different communities, in particular regional climate modellers, statistical <span class="hlt">downscalers</span> and statisticians has been limited. To address these gaps, the EU Cooperation in Science and Technology (COST) action VALUE (www.value-cost.eu) has been brought into life. VALUE is a research network with participants from currently 23 European countries running from 2012 to 2015. Its main aim is to systematically validate and develop <span class="hlt">downscaling</span> methods for climate change research in order to improve regional climate change scenarios for use in climate impact studies. Inspired by the co-design idea of the international research initiative "future earth", stakeholders of climate change information have been involved in the definition of research questions to be addressed and are actively participating in the network. The key idea of VALUE is to identify the relevant weather and climate characteristics required as input for a wide range of impact models and to define an open framework to systematically validate these characteristics. Based on a range of benchmark data sets, in principle every <span class="hlt">downscaling</span> method can be validated and compared with competing methods. The results of this exercise will directly provide end users with important information about the uncertainty of regional climate scenarios, and will furthermore provide the basis for further developing <span class="hlt">downscaling</span> methods. This presentation will provide background information on VALUE and discuss the identified characteristics and the validation framework.</p> <div class="credits"> <p class="dwt_author">Maraun, Douglas; Widmann, Martin; Benestad, Rasmus; Kotlarski, Sven; Huth, Radan; Hertig, Elke; Wibig, Joanna; Gutierrez, Jose</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">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/2012JGRD..11717116H"> <span id="translatedtitle">Predictor selection for <span class="hlt">downscaling</span> GCM data with LASSO</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 last 10 years, <span class="hlt">downscaling</span> techniques, including both dynamical (i.e., the regional climate model) and statistical methods, have been widely developed to provide climate change information at a finer resolution than that provided by global climate models (GCMs). Because one of the major aims of <span class="hlt">downscaling</span> techniques is to provide the most accurate information possible, data analysts have tried a number of approaches to improve predictor selection, which is one of the most important steps in <span class="hlt">downscaling</span> techniques. Classical methods such as regression techniques, particularly stepwise regression (SWR), have been employed for <span class="hlt">downscaling</span>. However, SWR presents some limits, such as deficiencies in dealing with collinearity problems, while also providing overly complex models. Thus, the least absolute shrinkage and selection operator (LASSO) technique, which is a penalized regression method, is presented as another alternative for predictor selection in <span class="hlt">downscaling</span> GCM data. It may allow for more accurate and clear models that can properly deal with collinearity problems. Therefore, the objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Québec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters for comparison.</p> <div class="credits"> <p class="dwt_author">Hammami, Dorra; Lee, Tae Sam; Ouarda, Taha B. M. J.; Lee, Jonghyun</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">92</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.nrcse.washington.edu/pdf/trs21_markov.pdf"> <span id="translatedtitle">A hidden Markov model for <span class="hlt">downscaling</span> synoptic atmospheric patterns to precipitation amounts</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">A hidden Markov model for <span class="hlt">downscaling</span> synoptic atmospheric patterns to precipitation amounts Enrica;A hidden Markov model for <span class="hlt">downscaling</span> synoptic atmospheric patterns to precipitation amounts Enrica. Abstract Nonhomogeneous hidden Markov models (NHMMs) provide a relatively simple framework for simulating</p> <div class="credits"> <p class="dwt_author">Washington at Seattle, University of</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">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/2014EGUGA..16.5020S"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Precipitation via Meiyu-like 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">This study identifies daily Meiyu-like East Asian Summer Monsoon patterns that are linked to precipitation observations in the Poyang lake catchment. This analysis provides insight into the dynamics of strong, local precipitation events and has the potential to improve projections of precipitation from coarse-grid numerical simulations. Precipitation observations between 1960 and 1999 are taken from 13 rain gauges located in the Poyang lake catchment, which is a sub-catchment of the Yangtze River. The analysis shows, that the observations are linked to daily patterns of relative vorticity at 850 hPa (Vo850) and vertical velocity at 500 hPa (W500) taken from the ERA-40 reanalysis data set. The patterns are derived by two approaches: (a) empirical orthogonal function (EOF) analysis and (b) rotated EOF analysis. Vo850 and W500 refer to geostrophic and ageostrophic processes, respectively. A logistic regression connects the large-scale dynamics to the local observations, whereby a forward regression selects the patterns best suited as predictors for the probability of exceeding thresholds of 24h accumulated rainfall at the gauges. The regression model is verified by cross-validation. The spatial structure of the detected patterns can be interpreted in terms of well-known meso-?-scale disturbances called Southwest vortices. Overall, the proposed EOF and rotated EOF patterns are both related to physical processes and have the potential to work as predictors for exceedance rates of local precipitation in the Poyang catchment. References T. Simon, A. Hense, B. Su, T. Jiang, C. Simmer, and C. Ohlwein, 2013: Pattern-based statistical <span class="hlt">downscaling</span> of East Asian Summer Monsoon precipitation. Tellus A. 65. http://dx.doi.org/10.3402/tellusa.v65i0.19749</p> <div class="credits"> <p class="dwt_author">Simon, Thorsten; Hense, Andreas; Jiang, Tong; Simmer, Clemens; Ohlwein, Christian</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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.ncbi.nlm.nih.gov/pubmed/24701932"> <span id="translatedtitle"><span class="hlt">Downscaling</span> the chemical oxygen demand test.</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 usefulness of the standard chemical oxygen demand (COD) test for water characterization is offset to some extent by its requirement for highly toxic or expensive Cr, Ag, and Hg species. In addition, oxidation of the target samples by chromate requires a 2-3 h heating step. We have <span class="hlt">downscaled</span> this method to obtain a reduction of up to ca. 80% in the use and generation of toxic residues and a time reduction of up to ca. 67%. This also translates into considerable energy savings by reducing the time required for heating as well as costly labour time. Such reductions can be especially important for analytical laboratories with heavy loads of COD analyses. Numerical results obtained with the standard COD method for laboratory KHP samples (potassium hydrogen phthalate) show an average relative error of 1.41% vs. an average of 2.14% obtained with the downsized or small-scale version. The average % standard deviation when using the former is 2.16% vs. 3.24% obtained with the latter. When analysing municipal wastewater samples, the relative error is smaller for the proposed small-scale method than for the standard method (0.05 vs. 0.58, respectively), and the % std. dev. is 1.25% vs. 1.06%. The results obtained with various industrial wastewaters show good agreement with those obtained using the standard method. Chloride ions do not interfere at concentrations below 2000 mg Nacl/L. This highly encouraging proof-of-concept offers a potentially alternative greener approach to COD analysis. PMID:24701932</p> <div class="credits"> <p class="dwt_author">Carbajal-Palacios, Patricia; Balderas-Hernandez, Patricia; Ibanez, Jorge G; Roa-Morales, Gabriela</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-01-01</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://adsabs.harvard.edu/abs/2013ClDy...40..805Z"> <span id="translatedtitle">Development of climate change projections for small watersheds using multi-model <span class="hlt">ensemble</span> simulation and stochastic weather generation</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) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step <span class="hlt">downscaling</span> method to generate climate change projections for small watersheds through combining a weighted multi-RCM <span class="hlt">ensemble</span> and a stochastic weather generator. The <span class="hlt">ensemble</span> was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The <span class="hlt">ensemble</span> led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The <span class="hlt">ensemble</span>-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical <span class="hlt">downscaling</span> and statistical <span class="hlt">downscaling</span> can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.</p> <div class="credits"> <p class="dwt_author">Zhang, Hua; Huang, Guo H.</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">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/2012EGUGA..14.8308W"> <span id="translatedtitle">Probability-weighted historical patterns for <span class="hlt">downscaling</span> seasonal forecasts using minimum relative entropy</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">Seasonal forecasts based on large scale circulation patterns provide weak predictive power for climatic deviations with lead times of several months. For example, the ENSO and PDO indices from November to February contain some information about the climate in the Pacific Northwest of the USA from April to August. These forecasts are probabilistic and large scale, often concerning regional averages over several months. To be useful in planning for, for example, management of hydropower reservoirs, these forecasts need to be <span class="hlt">downscaled</span> to finer space and time resolution, providing reasonable realizations of spatial and temporal variability and correlations. Especially for planning of multiple-reservoir systems, which are interconnected through both the hydrological network and the power-grid, it is important to have correct statistical properties at smaller scales, while still benefitting from the information about the large scales. One method to achieve this is to use the forecast to obtain a probability weighted <span class="hlt">ensemble</span> of historical patterns, e.g. fields or time series of observations. These <span class="hlt">ensembles</span>, having non-uniform weights, can be used in weighted Monte-Carlo simulations for risk analysis and planning of reservoir operations. We present a method to determine the weights based on moments of the large-scale forecast, while ensuring the deviations from uniform weights do not introduce more information than is warranted by the forecast. This is ensured by minimizing the relative entropy from the original uniform distribution of weights to the new weights, while simultaneously satisfying the constraints posed by the moments of the large-scale forecast information. We analyze the results of the minimum relative entropy update and compare it to other methods for obtaining weighted <span class="hlt">ensemble</span> forecasts. This analysis from an information-theoretical perspective shows how the new method balances information and lost uncertainty, while other methods may not use all information or create false certainty.</p> <div class="credits"> <p class="dwt_author">Weijs, S. V.; van de Giesen, N.</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">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/2014OcMod..84...35L"> <span id="translatedtitle">Wave climate projections along the French coastline: Dynamical versus statistical <span class="hlt">downscaling</span> methods</span></a>  </p> <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 estimation of possible impacts related to climate change on the wave climate is subject to several levels of uncertainty. In this work, we focus on the uncertainties inherent in the method applied to project the wave climate using atmospheric simulations. Two approaches are commonly used to obtain the regional wave climate: dynamical and statistical <span class="hlt">downscaling</span> from atmospheric data. We apply both approaches based on the outputs of a global climate model (GCM), ARPEGE-CLIMAT, under three possible future scenarios (B1, A1B and A2) of the Fourth Assessment Report, AR4 (IPCC, 2007), along the French coast and evaluate their results for the wave climate with a high level of precision. The performance of the dynamical and the statistical methods is determined through a comparative analysis of the estimated means, standard deviations and monthly quantile distributions of significant wave heights, the joint probability distributions of wave parameters and seasonal and interannual variability. Analysis of the results shows that the statistical projections are able to reproduce the wave climatology as well as the dynamical projections, with some deficiencies being observed in the summer and for the upper tail of the significant wave height. In addition, with its low computational time requirements, the statistical <span class="hlt">downscaling</span> method allows an <span class="hlt">ensemble</span> of simulations to be calculated faster than the dynamical method. It then becomes possible to quantify the uncertainties associated with the choice of the GCM or the socio-economic scenarios, which will improve estimates of the impact of wave climate change along the French coast.</p> <div class="credits"> <p class="dwt_author">Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Méndez, Fernando</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-12-01</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://www.engr.colostate.edu/~ramirez/ce_old/projects/Kang-Ramirez-Downscaling.pdf"> <span id="translatedtitle">A coupled stochastic spacetime intermittent random cascade model for rainfall <span class="hlt">downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">A coupled stochastic spacetime intermittent random cascade model for rainfall <span class="hlt">downscaling</span> Boosik consistent with this composite character of rainfall variability. The new <span class="hlt">downscaling</span> model is a composite cascade model for rainfall <span class="hlt">downscaling</span>, Water Resour. Res., 46, W10534, doi:10.1029/2008WR007692. 1</p> <div class="credits"> <p class="dwt_author">Ramírez, Jorge A.</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">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/2014JESS..123.1603A"> <span id="translatedtitle">Assessment of climate change impacts on rainfall using large scale climate variables and <span class="hlt">downscaling</span> models - A case study</span></a>  </p> <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 of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and <span class="hlt">downscaling</span> numerical weather <span class="hlt">ensemble</span> forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash-Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical <span class="hlt">downscaling</span> model (SDSM) as a popular <span class="hlt">downscaling</span> tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.</p> <div class="credits"> <p class="dwt_author">Ahmadi, Azadeh; Moridi, Ali; Lafdani, Elham Kakaei; Kianpisheh, Ghasem</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-10-01</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://www.umass.edu/music/auditions/JazzEnsembleAuditions/JazzEnsembleAuditionsFall2014web-v3.pdf"> <span id="translatedtitle">FALL '14 JAZZ <span class="hlt">ENSEMBLE</span> AUDITIONS: AUDITIONS FOR: LARGE JAZZ <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/eprints/">E-print Network</a></p> <p class="result-summary">FALL '14 JAZZ <span class="hlt">ENSEMBLE</span> AUDITIONS: AUDITIONS FOR: LARGE JAZZ <span class="hlt">ENSEMBLES</span>: Jazz <span class="hlt">Ensemble</span> I, Chapel Jazz as scheduled & Jazz Lab <span class="hlt">Ensemble</span> Jazz Lab meets T & Th 6:30 ­ 8:15 Mandatory rehearsals & Performances as scheduled PLUS CHAMBER JAZZ <span class="hlt">ENSEMBLES</span> (combos) typically meet weekday evenings/late afternoon Mandatory</p> <div class="credits"> <p class="dwt_author">Massachusetts at Amherst, University of</p> <p class="dwt_publisher"></p> <p class="publishDate"></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 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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_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://www.umass.edu/music/auditions/JazzEnsembleAuditionsFall2013web.pdf"> <span id="translatedtitle">FALL `13 JAZZ <span class="hlt">ENSEMBLE</span> AUDITIONS: AUDITIONS FOR: LARGE JAZZ <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/eprints/">E-print Network</a></p> <p class="result-summary">FALL `13 JAZZ <span class="hlt">ENSEMBLE</span> AUDITIONS: AUDITIONS FOR: LARGE JAZZ <span class="hlt">ENSEMBLES</span>: Jazz <span class="hlt">Ensemble</span> I, Chapel Jazz as scheduled & Jazz Lab <span class="hlt">Ensemble</span> Jazz Lab meets T & Th 6:30 ­ 8:15 Mandatory rehearsals & Performances as scheduled PLUS CHAMBER JAZZ <span class="hlt">ENSEMBLES</span> (combos) typically meet weekday evenings/late afternoon Mandatory</p> <div class="credits"> <p class="dwt_author">Massachusetts at Amherst, University of</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://euler.nmt.edu/~brian/downscaling.pdf"> <span id="translatedtitle"><span class="hlt">DOWN-SCALING</span> OF SEBAL DERIVED EVAPOTRANSPIRATION MAPS1 FROM MODIS (250m) TO LANDSAT (30m) SCALE2</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">1 <span class="hlt">DOWN-SCALING</span> OF SEBAL DERIVED EVAPOTRANSPIRATION MAPS1 FROM MODIS (250m) TO LANDSAT (30m) SCALE2). This paper15 considers the feasibility of applying various <span class="hlt">down-scaling</span> methods to combine MODIS and16 Landsat 7 and MODIS images. Two <span class="hlt">down-scaling</span> procedures were evaluated:19 input <span class="hlt">down-scaling</span> and output</p> <div class="credits"> <p class="dwt_author">Borchers, Brian</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">103</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://oaspub.epa.gov/eims/eimsapi.dispdetail?deid=282623"> <span id="translatedtitle">Using a Coupled Lake Model with WRF for Dynamical <span class="hlt">Downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p class="result-summary">The Weather Research and Forecasting (WRF) model is used to <span class="hlt">downscale</span> a coarse reanalysis (National Centers for Environmental Prediction?Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine...</p> <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">104</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/1998JHyd..205....1W"> <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://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</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 for Environmental Prediction, NCEP Re-analysis data set (1979-1995); and (b) the UK Meteorological Office, Hadley Centre coupled ocean-atmosphere model (HadCM2SUL) for two periods indicative of present (1980-1999) and future greenhouse gas plus sulfate aerosol forcing (2080-2099). Statistical models of the surface variables were then "forced" by using the three airflow indices obtained from HadCM2SUL. The differences between the NCEP and HadCM2SUL "present" <span class="hlt">downscaled</span> variables were generally greater than those arising between the <span class="hlt">downscaling</span> of the two GCM airflow scenarios. The lack of change in <span class="hlt">downscaled</span> surface variables between the 1980-1999 and 2080-2099 data was attributed to the low sensitivity of atmospheric circulation patterns in HadCM2SUL to greenhouse gas forcing.</p> <div class="credits"> <p class="dwt_author">Wilby, Robert L.; Hassan, Hany; Hanaki, Keisuke</p> <p class="dwt_publisher"></p> <p class="publishDate">1998-02-01</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://adsabs.harvard.edu/abs/2013ClDy...41.3145W"> <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">2013-12-01</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=https://www.meted.ucar.edu/training_module.php?id=1073"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Applications in Winter</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">This lesson provides an introduction to <span class="hlt">ensemble</span> forecast systems using an operational case study of the Blizzard of 2013 in Southern Ontario. The module uses models available to forecasters in the Meteorological Service of Canada, including Canadian and U.S. global and regional <span class="hlt">ensembles</span>. After briefly discussing the rationale for <span class="hlt">ensemble</span> forecasting, the module presents small lessons on probabilistic <span class="hlt">ensemble</span> products useful in winter weather forecasting, immediately followed by forecast applications to a southern Ontario case. The learner makes forecasts for the Ontario Storm Prediction Center area and, in the short range, for the Toronto metropolitan area. An additional section applies a probabilistic aviation product to forecasts for Toronto Pearson International Airport.</p> <div class="credits"> <p class="dwt_author">COMET</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-04-22</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/2014EGUGA..16.7437R"> <span id="translatedtitle">Stepwise analogue <span class="hlt">downscaling</span> for hydrology (SANDHY): validation experiments over 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">Statistical <span class="hlt">downscaling</span> aims at finding relationships between local precipitation (predictand) and large-scale predictor fields, in various contexts, from medium-term forecasting to climate change impact studies. One of the challenges of statistical <span class="hlt">downscaling</span> in a climate change context is that the predictor-predictand relationship should still be valid under climate change conditions. A minimum requirement is therefore to test the performance of the <span class="hlt">downscaling</span> method on independent data under current climate conditions. The <span class="hlt">downscaling</span> method considered is the Stepwise ANalog <span class="hlt">Downscaling</span> method for HYdrology (SANDHY). ERA-40 reanalysis data are used as large scale predictors and daily precipitation from the French near surface reanalysis (Safran) as predictand. Two 20-year periods have been selected from the common archive period of the two data sources: 1958-1978 ('early') and 1982-2002 ('late'). SANDHY has been optimised over the late period in terms of geopotential predictor domains individually for 608 target zones covering France. The validation setup consists of 4 experiments, that all use the parameters as optimised for the late period and that are compared in terms of continous ranked probability skill score (CRPSS) with climatology as reference: Reference simulation. A simulation of the late period is performed using the late period as an archive for searching the analogue dates, thus representing the best possible case. The CRPSS shows a spatial distribution similar to the one of the mean precipitation. Out-of-sample validation. The early period is simulated using the late period as an archive for searching the analogue dates. The idea is to simulate a period whose local data is not 'known' by the model as it would be the case in any application. The average skill loss compared to the reference simulation is reasonable with some more skill loss in the northern part of the country and no loss in the southeastern part. Alternative archive. The late period is simulated using the early period as an archive for the analogue search. Using the alternative archive leads to small and spatially uniform skill loss compared to the reference simulation. Imperfect predictor domains. The early period is simulated using the early period as an archive for the analogue search. The results are very similar to the out-of-sample validation in terms of mean skill loss and spatial distribution. The results of experiment 2 indicate that SANDHY is quite robust at most locations. Experiment 3 shows that both archives are suitable for <span class="hlt">downscaling</span>. Experiment 4 shows that the skill loss observed in experiment 2 stems rather from the imperfect predictor domains than from the imperfect archive. Overall the results increase the confidence in applying SANDHY for <span class="hlt">downscaling</span> in various contexts over France.</p> <div class="credits"> <p class="dwt_author">Radanovics, Sabine; Vidal, Jean-Philippe; Sauquet, Eric; Ben Daoud, Aurélien; Bontron, Guillaume</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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/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 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=https://www.meted.ucar.edu/training_module.php?id=156"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Forecasting Explained</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">This module, the latest in our series on Numerical Weather Prediction, covers the theory and use of <span class="hlt">ensemble</span> prediction systems (EPSs). The module will help forecasters develop an understanding of the basis for EPSs, the skills to interpret <span class="hlt">ensemble</span> products, and strategies for their use in the forecast process. It contains six sections: an Introduction that briefly presents background theory; Generation, which describes how <span class="hlt">ensemble</span> systems are constructed; Statistical Concepts, which provides a brief refresher on knowledge required for <span class="hlt">ensemble</span> product interpretation; Summarizing Data, which describes common <span class="hlt">ensemble</span> forecast products; Verification, which discusses how EPSs performance is assessed and documented; and Case Applications, which provides links to a number of forecast cases illustrating the use of EPSs in the forecast process. Questions and Exercises are offered throughout to help you test your learning and provide practical examples. The module also includes a pre-assessment and module final quiz.</p> <div class="credits"> <p class="dwt_author">COMET</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-09-27</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/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">A Member-to-Member <span class="hlt">ensemble</span> forecasting system is developed for inflows to hydroelectric reservoirs that incorporates multiple numerical weather prediction models and multiple distributed hydrological models linked by a variety of <span class="hlt">downscaling</span> schemes. Each hydrological model uses multiple differently-optimized parameter sets and begins each daily forecast from several different initial conditions. The <span class="hlt">ensemble</span> thereby attempts to sample all sources of error in the modeling chain. The importance of sampling all sources of error is illustrated by comparing this <span class="hlt">ensemble</span> with an <span class="hlt">ensemble</span> comprised of single 'best' parameterization for each hydrological model. Degree-of-mass-balance bias correction schemes trained using data windows of varying lengths are applied to the individual <span class="hlt">ensemble</span> members. Based on examination of various verification metrics, we determine that a bias corrector that uses a linearly-weighted combination of past errors calculated over a three-day moving window is able to significantly improve forecast quality for the flashy case study watershed in southwestern British Columbia, Canada. Incorporation of all sources of modeling uncertainty is found to greatly improve <span class="hlt">ensemble</span> resolution and discrimination. The full potential for these improvements using <span class="hlt">ensembles</span> is only realized after removal of bias.</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">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/2013AGUFM.H43I1585P"> <span id="translatedtitle">Stochastic Cascade Dynamical <span class="hlt">Downscaling</span> of Precipitation over Complex Terrain</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 Climate Models (GCMs) suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called <span class="hlt">downscaling</span> techniques are used to bridge the spatial and temporal resolution gaps between what climate modelers are currently able to provide and what decision-makers require. Among the most important impacts of regional-scale prediction of climate change is to assess how food production and security will be affected. Regional scale precipitation and temperature simulations are crucial to understand how global warming will affect fresh water storage and the ability to grow agricultural crops. Precipitation and temperature <span class="hlt">downscaling</span> improve the coarse resolution and poor local representation of global climate models and help decision-makers to assess the likely hydrological impacts of climate change, and it would also help crop modelers to generate more realistic climatic-change scenarios. Thus, a spatial <span class="hlt">downscaling</span> method was developed based on the multiplicative random cascade disaggregation theory, considering a ?-lognormal model describing the rainfall precipitation distribution and using the Mandelbrot-Kahane-Peyriere (MKP) function. In this paper, gridded 15 km resolution rainfall data over a 220 x 220 km section of the Andean Plateau and surroundings, generated by the Weather Research and Forecasting model (WRF), were <span class="hlt">downscaled</span> to gridded 1 km layers with the Multifractal <span class="hlt">downscaling</span> technique, complemented by a local heterogeneity filter. The process was tested for daily data over a period of five years (01/01/2001 - 12/31/2005). Specifically, The model parameters were estimated from 5 years of observed daily rainfall data from 18 rain gauges located in the region. A detailed testing of the model was undertaken on the basis of a comparison of the statistical characteristics of the spatial and temporal variability of rainfall between the rainfall fields obtained from the rain gauge network and those generated by the simulation model. The potential advantages of this methodology are discussed.Stochastic Cascade Dynamical <span class="hlt">Downscaling</span> of Precipitation over Complex Terrain</p> <div class="credits"> <p class="dwt_author">Posadas, A.; Duffaut, L. E.; Jones, C.; Carvalho, L. V.; Carbajal, M.; Heidinger, H.; Quiroz, R.</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">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/2013AGUFMGC43C1054A"> <span id="translatedtitle">Validation of WRF <span class="hlt">Downscaling</span> Capabilities Over Western Australia to Detect Rainfall and Temperature 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">When evaluating the merits of regional climate simulations, one of the most compelling arguments for this high resolution, dynamical <span class="hlt">downscaling</span> approach is its ability to simulate the extremes of temperature and precipitation with greater skill than lower resolution models. A historical (1970-2000), <span class="hlt">ensemble</span> regional climate simulation using WRF was performed over Western Australia at a 50km, 10km and 5km resolution in order to evaluate the effectiveness of the model in simulating annually extreme climate events as defined by the core climate indices of the CCI/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). Five temperature and five precipitation indices were chosen and the capacity of the simulation to detect the temporal and spatial structure of these indices was assessed. Validation took place through comparisons to observational CSIRO Australia Water Availability Project (AWAP) daily gridded minimum and maximum temperature and precipitation data and RCM simulations driven by ERA-Interim lateral boundary conditions over the same area. The study is part one of a two part project to examine future changes in extreme temperature and precipitation in the region and the influence of land cover change and anthropogenic greenhouse gases on these changes.</p> <div class="credits"> <p class="dwt_author">Andrys, J.; Lyons, T.; Kala, J.</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">113</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://wind.mit.edu/~hansen/papers/LawrenceHansenMWR2005.pdf.gz"> <span id="translatedtitle">A Transformed Lagged <span class="hlt">Ensemble</span> Forecasting Technique for Increasing <span class="hlt">Ensemble</span> Size</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">A Transformed Lagged <span class="hlt">Ensemble</span> Forecasting Technique for Increasing <span class="hlt">Ensemble</span> Size Andrew. R.Lawrence@ecmwf.int #12;Abstract An <span class="hlt">ensemble</span>-based data assimilation approach is used to transform old en- semble. The impact of the transformations are propagated for- ward in time over the <span class="hlt">ensemble</span>'s forecast period</p> <div class="credits"> <p class="dwt_author">Hansens, Jim</p> <p class="dwt_publisher"></p> <p class="publishDate"></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=https://www.meted.ucar.edu/training_module.php?id=1104"> <span id="translatedtitle">An Introduction to the <span class="hlt">Downscaled</span> Climate and Hydrology Projections Website</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">These two videos serve as an introduction to the <span class="hlt">Downscaled</span> Climate and Hydrology Projections website. This website, the result of a collaboration between several federal and non-federal partners, provides access to <span class="hlt">downscaled</span> climate and hydrology projections for the contiguous United States and parts of Canada and Mexico, derived from contemporary global climate models. In the first video, Dr. Subhrendu Gangopadhyay, hydrologic engineer at the Bureau of Reclamation's Technical Service Center in Denver, introduces the website and provides an overview of the MetEd lesson Preparing Hydro-climate Inputs for Climate Change in Water Resources Planning. This lesson provides necessary background information needed to use the projections site effectively to retrieve climate and hydrology projections data for impacts analysis. In the second video, Dr. Gangopadhyay steps through the process of retrieving projections data using the website. This resource, produced in cooperation between the Bureau of Reclamation and The COMET® Program, is hosted on COMET's YouTube Channel.</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate">2014-09-14</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://academic.research.microsoft.com/Publication/44475746"> <span id="translatedtitle">Dynamic <span class="hlt">Downscaling</span> of Seasonal Climate Predictions over Brazil</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 projections for March-April-May (MAM) 1985 and 1997 made with the NASA Goddard Institute for Space Studies (GISS) GCM over South America on a 4° latitude by 5° longitude grid are `<span class="hlt">downscaled</span>' to 0.5° grid spacing. This is accomplished by interpolating the GCM simulation product in time and space to create lateral boundary conditions (LBCs) for synchronous nested simulations by</p> <div class="credits"> <p class="dwt_author">Leonard M. Druyan; Matthew Fulakeza; Patrick Lonergan</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">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=4066535"> <span id="translatedtitle">Evaluating the utility of dynamical <span class="hlt">downscaling</span> in agricultural impacts projections</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">Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical <span class="hlt">downscaling</span>—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn <span class="hlt">downscaled</span> by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically <span class="hlt">downscaling</span> raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections. PMID:24872455</p> <div class="credits"> <p class="dwt_author">Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-01-01</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://ntrs.nasa.gov/search.jsp?R=20060015642&hterms=DATA+MINING&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DDATA%2BMINING"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Data Mining Methods</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an <span class="hlt">ensemble</span> is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in <span class="hlt">ensemble</span> methods has largely revolved around designing <span class="hlt">ensembles</span> consisting of competent yet complementary models.</p> <div class="credits"> <p class="dwt_author">Oza, Nikunj C.</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">118</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. PMID:20459805</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 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/2014JHyd..519.2978G"> <span id="translatedtitle">Evaluation of real-time hydrometeorological <span class="hlt">ensemble</span> prediction on hydrologic scales in Northern California</span></a>  </p> <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 paper presents an evaluation of real time <span class="hlt">ensemble</span> forecasts produced during 2010-2012 by the demonstration project INFORM (Integrated Forecast and Reservoir Management) in Northern California. In addition, the innovative elements of the forecast component of the INFORM project are highlighted. The forecast component is designed to dynamically <span class="hlt">downscale</span> operational multi-lead <span class="hlt">ensemble</span> forecasts from the Global <span class="hlt">Ensemble</span> Forecast System (GEFS) and the Climate Forecast system (CFS) of the National Centers of Environmental Prediction (NCEP), and to use adaptations of the operational hydrologic models of the US National Weather Service California Nevada River Forecast Center to provide <span class="hlt">ensemble</span> reservoir inflow forecasts in real time. A full-physics 10-km resolution (10 km on the side) mesoscale model was implemented for the <span class="hlt">ensemble</span> prediction of surface precipitation and temperature over the domain of Northern California with lead times out to 16 days with 6-hourly temporal resolution. An intermediate complexity regional model with a 10 km resolution was implemented to <span class="hlt">downscale</span> the NCEP CFS <span class="hlt">ensemble</span> forecasts for lead times out to 41.5 days. Methodologies for precipitation and temperature model forecast adjustment to comply with the corresponding observations were formulated and tested as regards their effectiveness for improving the <span class="hlt">ensemble</span> predictions of these two variables and also for improving reservoir inflow forecasts. The evaluation is done using the real time databases of INFORM and concerns the snow accumulation and melt seasons. Performance is measured by metrics that range from those that use forecast means to those that use the entire forecast <span class="hlt">ensemble</span>. The results show very good skill in forecasting precipitation and temperature over the subcatchments of the INFORM domain out to a week in advance for all basins, models and seasons. For temperature, in some cases, non-negligible skill has been obtained out to four weeks for the melt season. Reservoir inflow forecasts exhibit also good skill for the shorter lead-times out to a week or so, and provide a good quantitative basis in support of reservoir management decisions pertaining to objectives with a short term horizon (e.g., flood control and energy production). For the northernmost basin of Trinity reservoir inflow forecasts exhibit good skill for lead times longer than 3 weeks in the snow melt season. Bias correction of the <span class="hlt">ensemble</span> precipitation and temperature forecasts with fixed bias factors over the range of lead times improves forecast performance for almost all leads for precipitation and temperature and for the shorter lead times for reservoir inflow. The results constitute a first look at the performance of operational coupled hydrometeorological <span class="hlt">ensemble</span> forecasts in support of reservoir management.</p> <div class="credits"> <p class="dwt_author">Georgakakos, Konstantine P.; Graham, Nicholas E.; Modrick, Theresa M.; Murphy, Michael J.; Shamir, Eylon; Spencer, Cristopher R.; Sperfslage, Jason A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-11-01</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/2015JGRD..120.1023D"> <span id="translatedtitle">Transferability in the future climate of a statistical <span class="hlt">downscaling</span> method for precipitation in 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">A statistical <span class="hlt">downscaling</span> approach for precipitation in France based on the analog method and its evaluation for different combinations of predictors is described, with focus on the transferability of the method to the future climate. First, the realism of <span class="hlt">downscaled</span> present-day precipitation climatology and interannual variability for different combinations of predictors from four reanalyses is assessed. Satisfactory results are obtained, but elaborated predictors do not lead to major and consistent across-reanalyses improvements. The <span class="hlt">downscaling</span> method is then evaluated on its capacity to capture precipitation trends in the last decades. As uncertainties in <span class="hlt">downscaled</span> trends due to the choice of the reanalysis are large and observed trends are weak, this analysis does not lead to strong conclusions on the applicability of the method to a changing climate. The temporal transferability is then assessed thanks to a perfect model framework. The statistical <span class="hlt">downscaling</span> relationship is built using present-day predictors and precipitation simulated by 12 regional climate models. The entire projections are then <span class="hlt">downscaled</span>, and future <span class="hlt">downscaled</span> and simulated precipitation changes are compared. A good temporal transferability is obtained only with a specific combination of predictors. Finally, the regional climate models are <span class="hlt">downscaled</span>, thanks to the relationship built with reanalyses and observations, for the best combination of predictors. Results are similar to the changes simulated by the models, which reinforces our confidence in the realism of the models and of the <span class="hlt">downscaling</span> method. Uncertainties in precipitation change due to reanalyses are found to be limited compared to those due to regional simulations.</p> <div class="credits"> <p class="dwt_author">Dayon, G.; Boé, J.; Martin, E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-02-01</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_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 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showDiv("page_8");' 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">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/2010JHyd..381...18B"> <span id="translatedtitle"><span class="hlt">Downscaling</span> transient climate change using a Neyman-Scott Rectangular Pulses stochastic rainfall model</span></a>  </p> <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">SummaryThe future management of hydrological systems must be informed by climate change projections at relevant time horizons and at appropriate spatial scales. Furthermore, the robustness of such management decisions is dependent on both the uncertainty inherent in future climate change scenarios and the natural climate system. Addressing these needs, we present a new transient rainfall simulation methodology which combines dynamical and statistical <span class="hlt">downscaling</span> techniques to produce transient (i.e. temporally non-stationary) climate change scenarios. This is used to generate a transient multi-model <span class="hlt">ensemble</span> of simulated point-scale rainfall time series for 1997-2085 for the polluted Brévilles spring in Northern France. The recovery of this previously potable source may be affected by climatic changes and variability over the next few decades. The provision of locally-relevant transient climate change scenarios for use as input to hydrological models of both water quality and quantity will ultimately provide a valuable resource for planning and decision making. Observed rainfall from 1988-2006 was characterised in terms of a set of statistics for each calendar month: the daily mean, variance, probability dry, lag-1 autocorrelation and skew, and the monthly variance. The Neyman-Scott Rectangular Pulses (NSRP) stochastic rainfall model was fitted to these observed statistics and correctly simulated both monthly statistics and extreme rainfall properties. Multiplicative change factors which quantify the change in each statistic between the periods 1961-1990 and 2071-2100 were estimated for each month and for each of 13 Regional Climate Models (RCMs) from the PRUDENCE <span class="hlt">ensemble</span>. To produce transient climate change scenarios, pattern scaling factors were estimated and interpolated from four time-slice integrations of two General Circulation Models which condition the RCMs, ECHAM4/OPYC and HadCM3. Applying both factors to the observed statistics provided projected transient rainfall statistics (PTRS) to which piece-wise smoothly varying transient rainfall model parameterizations were fitted. These fits provided good representations of the PTRS for each RCM. An <span class="hlt">ensemble</span> of 100 continuous daily rainfall time series, with steadily varying stochastic properties which model these projections of transient climate change, was then simulated using a new transient NSRP simulation methodology for each RCM. Together the <span class="hlt">ensembles</span> form a 1300 member transient multi-model <span class="hlt">ensemble</span> of rainfall time series. The simulated transient <span class="hlt">ensemble</span> properties were investigated, identifying RCMs giving rise to unusual behaviour. For the Brévilles, annual rainfall is projected to decrease until 2085 but the change is highly sensitive to General Circulation Model forcing; ECHAM4-driven RCMs project larger annual decreases than HadCM3/HadAM3H/P driven RCMs. All RCMs project an increase in winter rainfall and a larger summer decrease. An increase of ˜10% in the 10-year return period annual maximum rainfall is projected by 2085, however both strong increasing trends and a slight decreasing trend are found for individual RCMs. Compared with transient RCMs, the new methodology provides a number of advantages: reduced biases, point scale scenarios relevant for local-scale impact studies, improved representation of natural variability and improved representation of extremes.</p> <div class="credits"> <p class="dwt_author">Burton, A.; Fowler, H. J.; Blenkinsop, S.; Kilsby, C. G.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-02-01</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/2013AGUFMGC23B0922M"> <span id="translatedtitle">Precipitation Prediction in North Africa Based on 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">Although Global Climate Models (GCM) outputs should not be used directly to predict precipitation variability and change at the local scale, GCM projections of large-scale features in ocean and atmosphere can be applied to infer future statistical properties of climate at finer resolutions through empirical statistical <span class="hlt">downscaling</span> techniques. A number of such <span class="hlt">downscaling</span> methods have been proposed in the literature, and although all of them have advantages and limitations depending on the specific <span class="hlt">downscaling</span> problem, most of them have been developed and tested in developed countries. In this research, we explore the use of statistical <span class="hlt">downscaling</span> to generate future local precipitation scenarios in different locations in Northern Africa, where available data is sparse and missing values are frequently observed in the historical records. The presence of arid and semiarid regions in North African countries and the persistence of long periods with no rain pose challenges to the <span class="hlt">downscaling</span> exercise since normality assumptions may be a serious limitation in the application of traditional linear regression methods. In our work, the development of monthly statistical relationships between the local precipitation and the large-scale predictors considers common Empirical Orthogonal Functions (EOFs) from different NCAR/Reanalysis climate fields (e.g., Sea Level Pressure (SLP) and Global Precipitation). GCM/CMIP5 data is considered in the predictor data set to analyze the future local precipitation. Both parametric (e.g., Generalized Linear Models (GLM)) and nonparametric (e,g,, Bootstrapping) approaches are considered in the regression analysis, and different spatial windows in the predictor fields are tested in the prediction experiments. In the latter, seasonal spatial cross-covariance between predictant and predictors is estimated by means of a teleconnections algorithm which was implemented to define the regions in the predictor domain that better captures the variability of the observed local process. Also, a split-window approach is used in the cross-validation stage for comparison purposes of the monthly regression schemes, and different pre-processing alternatives of the precipitation records are implemented to reduce the strong skewness observed in the periodic distribution functions. Preliminary results show that bootstrapping approaches like those based on K-Nearest Neighbors (K-NN) resampling improves the preservation of the historical variability, for which the GLM methods exhibit important limitations. It has been also observed the important role that plays both the teleconnections analysis and the normalization pre-processing in the prediction skill. It is expected that the methodologies from this research can be extrapolated to other regions and time scales for the study of climate change impact and water resources management.</p> <div class="credits"> <p class="dwt_author">Molina, J. M.; Zaitchik, B.</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">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/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 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://adsabs.harvard.edu/abs/2012EGUGA..14.5772N"> <span id="translatedtitle">Stochastic <span class="hlt">downscaling</span> of numerically simulated spatial rain and cloud fields using a transient multifractal approach</span></a>  </p> <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">Atmospheric fields can be extremely variable over wide ranges of spatial scales, with a scale ratio of 109-1010 between largest (planetary) and smallest (viscous dissipation) scale. Furthermore atmospheric fields with strong variability over wide ranges in scale most likely should not be artificially split apart into large and small scales, as in reality there is no scale separation between resolved and unresolved motions. Usually the effects of the unresolved scales are modeled by a deterministic bulk formula representing an <span class="hlt">ensemble</span> of incoherent subgrid processes on the resolved flow. This is a pragmatic approach to the problem and not the complete solution to it. These models are expected to underrepresent the small-scale spatial variability of both dynamical and scalar fields due to implicit and explicit numerical diffusion as well as physically based subgrid scale turbulent mixing, resulting in smoother and less intermittent fields as compared to observations. Thus, a fundamental change in the way we formulate our models is required. Stochastic approaches equipped with a possible realization of subgrid processes and potentially coupled to the resolved scales over the range of significant scale interactions range provide one alternative to address the problem. Stochastic multifractal models based on the cascade phenomenology of the atmosphere and its governing equations in particular are the focus of this research. Previous results have shown that rain and cloud fields resulting from both idealized and realistic numerical simulations display multifractal behavior in the resolved scales. This result is observed even in the absence of scaling in the initial conditions or terrain forcing, suggesting that multiscaling is a general property of the nonlinear solutions of the Navier-Stokes equations governing atmospheric dynamics. Our results also show that the corresponding multiscaling parameters for rain and cloud fields exhibit complex nonlinear behavior depending on large scale parameters such as terrain forcing and mean atmospheric conditions at each location, particularly mean wind speed and moist stability. A particularly robust behavior found is the transition of the multiscaling parameters between stable and unstable cases, which has a clear physical correspondence to the transition from stratiform to organized (banded) convective regime. Thus multifractal diagnostics of moist processes are fundamentally transient and should provide a physically robust basis for the <span class="hlt">downscaling</span> and sub-grid scale parameterizations of moist processes. Here, we investigate the possibility of using a simplified computationally efficient multifractal <span class="hlt">downscaling</span> methodology based on turbulent cascades to produce statistically consistent fields at scales higher than the ones resolved by the model. Specifically, we are interested in producing rainfall and cloud fields at spatial resolutions necessary for effective flash flood and earth flows forecasting. The results are examined by comparing <span class="hlt">downscaled</span> field against observations, and tendency error budgets are used to diagnose the evolution of transient errors in the numerical model prediction which can be attributed to aliasing.</p> <div class="credits"> <p class="dwt_author">Nogueira, M.; Barros, A. P.; Miranda, P. M.</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">125</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..1212998R"> <span id="translatedtitle">What we can learn from probabilistic verification scores in the context of hydrological <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">The acknowledgement that hydro-meteorological forecasts may be uncertain, has resulted in an increasing use of probabilistic forecasting techniques in operational forecasting systems, often through the use of <span class="hlt">ensemble</span> forecasting. Rather than providing a single value forecast, these <span class="hlt">ensemble</span> forecasts provide a probability of a future state, e.g. precipitation, water level or discharge. Although these probabilistic forecasts provide valuable information on the range of possible future states, the reliability, sharpness and resolution of these probability forecasts should be evaluated through a process of forecast verification. In this paper we present results of an extensive verification of forecasts made using two meteorological <span class="hlt">ensemble</span> forecast products; the global scale ECMWF-EPS and the dynamically <span class="hlt">downscaled</span> COSMO-LEPS. These are used to provide input forcing to the operational forecasting system of the Rhine basin, used by both the German Federal Institute of Hydrology, and the Dutch Centre for Water Management for predicting water levels and discharges at key forecasting locations. <span class="hlt">Ensemble</span> flow forecasts are generated by forcing a calibrated hydrological model (HBV), using either of the two meteorological <span class="hlt">ensemble</span> products. Verification of a large set of hindcast runs shows that when compared to climatology, positive skill scores are found at all river gauges considered for lead times of up to 9 days. This shows that the medium-range flow forecasts obtained to be useful. However, the comparison between the low resolution ECMWF and the high resolution COSMO-LEPS model shows that the <span class="hlt">downscaled</span> forecasts provide better representation of the variability. Higher skills are found across all catchment sizes, particularly for shorter lead time forecasts. The <span class="hlt">downscaling</span> of the <span class="hlt">ensemble</span> forecasts to a scale commensurate with the sub-basin scale in the hydrological model is thus recommended. Additionally we demonstrate the use of the probabilistic verification scores established for (i) the forecasting system developer, and (ii) the operational forecaster. On the one hand the developer aims to compare and improve forecasts, and thus make decisions on data to be used in the system. Suitable measures are threshold based binary verification methods such as Reliability diagrams and the Brier score and multicategorical methods such as Rank histogram and the Ranked Probability Score. For the forecaster, on the other hand, verification statistics that can help judge the forecast at hand and provide guidance on the decision to be taken. For this situation we discuss the use of the Reliability diagram and the Relative Operating Characteristic.</p> <div class="credits"> <p class="dwt_author">Renner, Maik; Werner, Micha</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">126</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..14.4754R"> <span id="translatedtitle">Optimising predictor domains for spatially coherent 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">Relationships between local precipitation (predictands) and large-scale circulation (predictors) are used for statistical <span class="hlt">downscaling</span> purposes in various contexts, from medium-term forecasting to climate change impact studies. For hydrological purposes like flood forecasting, the <span class="hlt">downscaled</span> precipitation spatial fields have furthermore to be coherent over possibly large basins. This thus first requires to know what predictor domain can be associated to the precipitation over each part of the studied basin. This study addresses this issue by identifying the optimum predictor domains over the whole of France, for a specific <span class="hlt">downscaling</span> method based on a analogue approach and developed by Ben Daoud et al. (2011). The <span class="hlt">downscaling</span> method used here is based on analogies on different variables: temperature, relative humidity, vertical velocity and geopotentials. The optimum predictor domain has been found to consist of the nearest grid cell for all variables except geopotentials (Ben Daoud et al., 2011). Moreover, geopotential domains have been found to be sensitive to the target location by Obled et al. (2002), and the present study thus focuses on optimizing the domains of this specific predictor over France. The predictor domains for geopotential at 500 hPa and 1000 hPa are optimised for 608 climatologically homogeneous zones in France using the ERA-40 reanalysis data for the large-scale predictors and local precipitation from the Safran near-surface atmospheric reanalysis (Vidal et al., 2010). The similarity of geopotential fields is measured by the Teweles and Wobus shape criterion. The predictive skill of different predictor domains for the different regions is tested with the Continuous Ranked Probability Score (CRPS) for the 25 best analogue days found with the statistical <span class="hlt">downscaling</span> method. Rectangular predictor domains of different sizes, shapes and locations are tested, and the one that leads to the smallest CRPS for the zone in question is retained. The resulting optimised domains are analysed for defining regions where neighbouring zones have equal or similar predictor domains and identifying which French river basins contain zones associated with different predictor domains, i.e. are exposed to different meteorological influences. The above analysis will be used (1) to extend the statistical <span class="hlt">downscaling</span> method of Ben Daoud et al. (2011) to the whole of France and (2) to develop it further in order to achieve spatially coherent forecasts while preserving the predictive skill on the local scale. Ben Daoud, A., Sauquet, E., Lang, M., Bontron, G., and Obled, C. (2011). Precipitation forecasting through an analog sorting technique: a comparative study. Advances in Geosciences, 29:103-107. doi: 10.5194/adgeo-29-103-2011 Obled, C., Bontron, G., and Garçon, R. (2002). Quantitative precipitation forecasts: a statistical adaptation of model outputs through an analogues sorting approach. Atmospheric Research, 63(3-4):303-324. doi: 10.1016/S0169-8095(02)00038-8 Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M. (2010) A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology, 30:1627-1644. doi: 10.1002/joc.2003</p> <div class="credits"> <p class="dwt_author">Radanovics, S.; Vidal, J.-P.; Sauquet, E.; Ben Daoud, A.; Bontron, 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">127</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/2013AGUFMGC43C1062O"> <span id="translatedtitle">"Uncertainty in <span class="hlt">downscaling</span> using high-resolution observational datasets"</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 bridge the gap between the resolution of global climate modeling efforts and the scale that decision-makers work at statistical <span class="hlt">downscaling</span> is often employed. The performance of any statistical <span class="hlt">downscaling</span> is dependant on the quality of the observational data at the location(s) of <span class="hlt">downscaling</span> (whether gridded or point-source). However, discussions of the assumptions made during statistical <span class="hlt">downscaling</span>, such as the stationariness of the relationships between predictor(s) and predictand, normally do not acknowledge the uncertainty introduced by the observational dataset. Many observational datasets do not have the erroneous temporal discontinuities caused by non-climatic biases, such as instrument changes or station relocations, diminished by a homogenization process. Moreover stations included within the underlying networks of high-resolution gridded datasets are typically not required to meet high standards of quality. Hence we evaluated three popular observational climate datasets, of the high-resolution gridded type, for their depiction of temperature values over the span of the datasets and the continental U.S. This was done using the homogenized United States Historical Climatology Network (USHCN) dataset version 2.0. The summer average temperatures at selected stations within the USHCN were compared to those created by interpolating gridpoints to the locations of those stations. The relationships these datasets have with more traditional climate datasets (e.g. the GISS, CRU, USHCN) have not formally been evaluated. The comparisons were not to judge which dataset was closest aligned with the USHCN dataset, but rather to discuss the common features (across datasets) of the residuals (i.e. differences with the USHCN dataset). We found that the lack of homogenization was a primary cause of the residuals, but that proxies for the non-climatic biases were not as well related to the residuals as expected. This was due in part to the gridding process that spatially extends the effects of non-climatic biases. Results suggest that while the residuals were statistically significant at the continental, regional and sub-regional spatial scales; they were particularly large at the smaller scales. The residuals also varied through time, and thus the trends in these datasets were often inaccurate and the temporal average residuals depended on the time period of focus. An evaluation of these datasets' ability to bias correct and spatially disaggregate as a function of grid box size, is being undertaken and will be presented. This is facilitated by calculating per grid box the average residual and the intra-grid variance of the residuals. This uncertainty within the observational data is likely existent in any <span class="hlt">downscaling</span> product using these datasets to facilitate the <span class="hlt">downscaling</span>. Thus this is an uncertainty not often discussed and even less quantified.</p> <div class="credits"> <p class="dwt_author">Oswald, E.; Rood, R. B.</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">128</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://arxiv.org/pdf/0705.3693.pdf"> <span id="translatedtitle">Morphing <span class="hlt">Ensemble</span> Kalman Filters</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">A new type of <span class="hlt">ensemble</span> filter is proposed, which combines an <span class="hlt">ensemble</span> Kalman filter (EnKF) with the ideas of morphing and registration from image processing. This results in filters suitable for nonlinear problems whose solutions exhibit moving coherent features, such as thin interfaces in wildfire modeling. The <span class="hlt">ensemble</span> members are represented as the composition of one common state with a spatial transformation, called registration mapping, plus a residual. A fully automatic registration method is used that requires only gridded data, so the features in the model state do not need to be identified by the user. The morphing EnKF operates on a transformed state consisting of the registration mapping and the residual. Essentially, the morphing EnKF uses intermediate states obtained by morphing instead of linear combinations of the states.</p> <div class="credits"> <p class="dwt_author">Beezley, Jonathan D</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">129</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ntrs.nasa.gov/search.jsp?R=20020052415&hterms=active+passive+sonar&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dactive%2Bpassive%2Bsonar"> <span id="translatedtitle">Input Decimated <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">Using an <span class="hlt">ensemble</span> of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated <span class="hlt">ensembles</span> (IDEs) outperform <span class="hlt">ensembles</span> whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.</p> <div class="credits"> <p class="dwt_author">Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)</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">130</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/2014JGRD..119.2131M"> <span id="translatedtitle">Genetic particle filter application to land surface temperature <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">Thermal infrared data are widely used for surface flux estimation giving the possibility to assess water and energy budgets through land surface temperature (LST). Many applications require both high spatial resolution (HSR) and high temporal resolution (HTR), which are not presently available from space. It is therefore necessary to develop methodologies to use the coarse spatial/high temporal resolutions LST remote-sensing products for a better monitoring of fluxes at appropriate scales. For that purpose, a data assimilation method was developed to <span class="hlt">downscale</span> LST based on particle filtering. The basic tenet of our approach is to constrain LST dynamics simulated at both HSR and HTR, through the optimization of aggregated temperatures at the coarse observation scale. Thus, a genetic particle filter (GPF) data assimilation scheme was implemented and applied to a land surface model which simulates prior subpixel temperatures. First, the GPF <span class="hlt">downscaling</span> scheme was tested on pseudoobservations generated in the framework of the study area landscape (Crau-Camargue, France) and climate for the year 2006. The GPF performances were evaluated against observation errors and temporal sampling. Results show that GPF outperforms prior model estimations. Finally, the GPF method was applied on Spinning Enhanced Visible and InfraRed Imager time series and evaluated against HSR data provided by an Advanced Spaceborne Thermal Emission and Reflection Radiometer image acquired on 26 July 2006. The temperatures of seven land cover classes present in the study area were estimated with root-mean-square errors less than 2.4 K which is a very promising result for <span class="hlt">downscaling</span> LST satellite products.</p> <div class="credits"> <p class="dwt_author">Mechri, Rihab; Ottlé, Catherine; Pannekoucke, Olivier; Kallel, Abdelaziz</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-03-01</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://academic.research.microsoft.com/Publication/42501614"> <span id="translatedtitle"><span class="hlt">Downscaling</span> climate models and environmental policy: From global to regional politics</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">High resolution climate models of regions, or <span class="hlt">downscaling</span>, promises to be at the forefront of future climate policy research. However, most research in this area is in the natural sciences, and the policy community has not taken full notice of this trend at their doorstep. <span class="hlt">Downscaling</span> provides more concrete information about local impacts of climate change. This raises several important</p> <div class="credits"> <p class="dwt_author">Peter Jacques</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">132</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/42011025"> <span id="translatedtitle">Statistical and dynamical <span class="hlt">downscaling</span> of precipitation: An evaluation and comparison of scenarios for the European Alps</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 compares six statistical <span class="hlt">downscaling</span> models (SDMs) and three regional climate models (RCMs) in their ability to <span class="hlt">downscale</span> daily precipitation statistics in a region of complex topography. The six SDMs include regression methods, weather typing methods, a conditional weather generator, and a bias correction and spatial disaggregation approach. The comparison is carried out over the European Alps for current</p> <div class="credits"> <p class="dwt_author">J. Schmidli; C. M. Goodess; C. Frei; M. R. Haylock; Y. Hundecha; J. Ribalaygua; T. Schmith</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">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/2015ClDy..tmp...57Z"> <span id="translatedtitle">A new statistical precipitation <span class="hlt">downscaling</span> method with Bayesian model averaging: a case study in China</span></a>  </p> <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 statistical <span class="hlt">downscaling</span> method was developed and applied to <span class="hlt">downscale</span> monthly total precipitation from 583 stations in China. Generally, there are two steps involved in statistical <span class="hlt">downscaling</span>: first, the predictors are selected (large-scale variables) and transformed; and second, a model between the predictors and the predictand (in this case, precipitation) is established. In the first step, a selection process of the predictor domain, called the optimum correlation method (OCM), was developed to transform the predictors. The transformed series obtained by the OCM showed much better correlation with the predictand than those obtained by the traditional transform method for the same predictor. Moreover, the method combining OCM and linear regression obtained better <span class="hlt">downscaling</span> results than the traditional linear regression method, suggesting that the OCM could be used to improve the results of statistical <span class="hlt">downscaling</span>. In the second step, Bayesian model averaging (BMA) was adopted as an alternative to linear regression. The method combining the OCM and BMA showed much better performance than the method combining the OCM and linear regression. Thus, BMA could be used as an alternative to linear regression in the second step of statistical <span class="hlt">downscaling</span>. In conclusion, the <span class="hlt">downscaling</span> method combining OCM and BMA produces more accurate results than the multiple linear regression method when used to statistically <span class="hlt">downscale</span> large-scale variables.</p> <div class="credits"> <p class="dwt_author">Zhang, Xianliang; Yan, Xiaodong</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-01-01</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://iri.columbia.edu/~awr/papers/SamuelsEtAl_IJoC%20submitted_Jan09.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Middle East Rainfall using a Support Vector Machine and Hidden Markov Model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">and Environmental Engineering Keywords: hidden markov model, <span class="hlt">downscaling</span>, precipitation, Middle East http Rainfall using a Support Vector Machine and Hidden Markov Model Rana Samuels1,2 , Andrew W. Robertson3 with a non-homogeneous hidden Markov model (NHMM) to <span class="hlt">downscale</span> daily station rainfall sequences over Israel</p> <div class="credits"> <p class="dwt_author">Robertson, Andrew W.</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">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/48897043"> <span id="translatedtitle">Constraining uncertainty in regional hydrologic impacts of climate change: Nonstationarity in <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">Regional impacts of climate change remain subject to large uncertainties accumulating from various sources, including those due to choice of general circulation models (GCMs), scenarios, and <span class="hlt">downscaling</span> methods. Objective constraints to reduce the uncertainty in regional predictions have proven elusive. In most studies to date the nature of the <span class="hlt">downscaling</span> relationship (DSR) used for such regional predictions has been assumed</p> <div class="credits"> <p class="dwt_author">Deepashree Raje; P. P. Mujumdar</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">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.atmos.washington.edu/~salathe/papers/downscale/yakima.pdf"> <span id="translatedtitle">Comparison of Various Precipitation <span class="hlt">Downscaling</span> Methods for the Simulation of Streamflow in a Rainshadow River Basin*</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Comparison of Various Precipitation <span class="hlt">Downscaling</span> Methods for the Simulation of Streamflow simulations of precipitation from climate models lack sufficient resolution and contain large biases that make, the effectiveness of several methods to <span class="hlt">downscale</span> large-scale precipitation is examined. To facilitate comparisons</p> <div class="credits"> <p class="dwt_author">Salathé Jr., Eric P.</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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4271150"> <span id="translatedtitle">The <span class="hlt">Ensembl</span> REST API: <span class="hlt">Ensembl</span> Data for Any Language</span></a>  </p> <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">Motivation: We present a Web service to access <span class="hlt">Ensembl</span> data using Representational State Transfer (REST). The <span class="hlt">Ensembl</span> REST server enables the easy retrieval of a wide range of <span class="hlt">Ensembl</span> data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular <span class="hlt">Ensembl</span> Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. Availability and implementation: The <span class="hlt">Ensembl</span> REST API can be accessed at http://rest.<span class="hlt">ensembl</span>.org and source code is freely available under an Apache 2.0 license from http://github.com/<span class="hlt">Ensembl/ensembl</span>-rest. Contact: ayates@ebi.ac.uk or flicek@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25236461</p> <div class="credits"> <p class="dwt_author">Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R. S.; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-01-01</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://arxiv.org/pdf/1501.01848v2"> <span id="translatedtitle">Spherical Matrix <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/eprints/">E-print Network</a></p> <p class="result-summary">The spherical orthogonal, unitary, and symplectic <span class="hlt">ensembles</span> (SOE/SUE/SSE) $S_\\beta(N,r)$ consist of $N \\times N$ real symmetric, complex hermitian, and quaternionic self-adjoint matrices of Frobenius norm $r$, made into a probability space with the uniform measure on the sphere. For each of these <span class="hlt">ensembles</span>, we determine the joint eigenvalue distribution for each $N$, and we prove the empirical spectral measures rapidly converge to the semicircular distribution as $N \\to \\infty$. In the unitary case ($\\beta=2$), we also find an explicit formula for the empirical spectral density for each $N$.</p> <div class="credits"> <p class="dwt_author">Gene S. Kopp; Steven J. Miller</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-02-02</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://adsabs.harvard.edu/abs/2013AGUFM.A11F0122D"> <span id="translatedtitle">Comparing the skill of precipitation forecasts from high resolution simulations and statistically <span class="hlt">downscaled</span> products in the Australian Snowy Mountains</span></a>  </p> <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">Statistically significant improvements to a 'Poor Man's <span class="hlt">Ensemble</span>' (PME) of coarse-resolution numeral precipitation forecast for the Australian Snowy Mountains can be achieved using a clustering algorithm. Daily upwind soundings are classified according to one of four clusters, which are employed to adjust the precipitation forecasts using a linear regression. This approach is a type of 'statistical <span class="hlt">downscaling</span>', in that it relies on a historical relationship between observed and forecast precipitation amounts, and is a computationally cheap and fast way to improve forecast skill. For the 'wettest' class, the root-mean-square error for the one-day forecast was reduced from 26.98 to 17.08 mm, and for the second 'wet' class the improvement was from 8.43 to 5.57 mm. Regressions performed for the two 'dry' classes were not shown to significantly improve the forecast. Statistical measures of the probability of precipitation and the quantitative precipitation forecast were evaluated for the whole of the 2011 winter (May-September). With a 'hit rate' (fraction of correctly-forecast rain days) of 0.9, and a 'false alarm rate' (fraction of forecast rain days which did not occur) of 0.16 the PME forecast performs well in identifying rain days. The precipitation amount is, however systematically under-predicted, with a mean bias of -5.76 mm and RMSE of 12.86 mm for rain days during the 2011 winter. To compare the statistically <span class="hlt">downscaled</span> results with the capabilities of a state of the art numerical prediction system, the WRF model was run at 4 km resolution over the Australian Alpine region for the same period, and precipitation forecasts analysed in a similar manner. It had a hit rate of 0.955 and RMSE of 5.16 mm for rain days. The main reason for the improved performance relative to the PME is that the high resolution of the simulations better captures the orographic forcing due to the terrain, and consequently resolves the precipitation processes more realistically, but case studies of individual events also showed that the choice microphysical parameterisation was very important to precipitation amounts. The WRF model is capable of reasonably good forecasts of the sounding 'class' for Wagga Wagga, with an accuracy of 80% for the first day and 65% for the third day of the forecast, facilitating the use of the PME <span class="hlt">downscaling</span> for a number of forecast days instead of only the day of the sounding.</p> <div class="credits"> <p class="dwt_author">Dai, J.; Chubb, T.; Manton, M.; Siems, S. T.</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">140</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..14.2551I"> <span id="translatedtitle">A new project on development and application of comprehensive <span class="hlt">downscaling</span> methods over Hokkaido.</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 project on development and application of comprehensive <span class="hlt">downscaling</span> methods over Hokkaido started as one of the branches of "Research Program on climate change adaptation" funded by Ministry of Education, Sports, Culture, Science, and Technology of Japan in 2010. Our group will develop two new <span class="hlt">downscaling</span> algorithms in order to get more information on the uncertainty of high/low temperatures or heavy rainfall. Both of the algorithms called "sampling <span class="hlt">downscaling</span>" and "hybrid <span class="hlt">downscaling</span>" are based upon the mixed use of statistical and dynamical <span class="hlt">downscaling</span> ideas. Another point of the project is to evaluate the effect of land-use changes in Hokkaido, where the major pioneering began only about a century ago. Scientific outcomes on climate changes in Hokkaido from the project will be provided to not only public sectors in Hokkaido but also people who live in Hokkaido through a graphical-user-interface system just like a weather forecast system in a forecast-center's webpage.</p> <div class="credits"> <p class="dwt_author">Inatsu, M.; Yamada, T. J.; Sato, T.; Nakamura, K.; Matsuoka, N.; Komatsu, A.; Pokhrel, Y. N.; Sugimoto, S.; Miyazaki, S.</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_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 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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_9");' 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">141</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/800401"> <span id="translatedtitle">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">Several means for improving the performance and training of neural networks for classification are proposed. Crossvalidation is used as a tool for optimizing network parameters and architecture. It is shown that the remaining residual generalization error can be reduced by invoking <span class="hlt">ensembles</span> of similar networks</p> <div class="credits"> <p class="dwt_author">Lars Kai Hansen; Peter Salamon</p> <p class="dwt_publisher"></p> <p class="publishDate">1990-01-01</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/2011AGUFMGC41D0853V"> <span id="translatedtitle">Toward Robust and Efficient Climate <span class="hlt">Downscaling</span> for Wind Energy</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 presentation describes a more accurate and economical (less time, money and effort) wind resource assessment technique for the renewable energy industry, that incorporates innovative statistical techniques and new global mesoscale reanalyzes. The technique judiciously selects a collection of "case days" that accurately represent the full range of wind conditions observed at a given site over a 10-year period, in order to estimate the long-term energy yield. We will demonstrate that this new technique provides a very accurate and statistically reliable estimate of the 10-year record of the wind resource by intelligently choosing a sample of ±120 case days. This means that the expense of <span class="hlt">downscaling</span> to quantify the wind resource at a prospective wind farm can be cut by two thirds from the current industry practice of <span class="hlt">downscaling</span> a randomly chosen 365-day sample to represent winds over a "typical" year. This new estimate of the long-term energy yield at a prospective wind farm also has far less statistical uncertainty than the current industry standard approach. This key finding has the potential to reduce significantly market barriers to both onshore and offshore wind farm development, since insurers and financiers charge prohibitive premiums on investments that are deemed to be high risk. Lower uncertainty directly translates to lower perceived risk, and therefore far more attractive financing terms could be offered to wind farm developers who employ this new technique.</p> <div class="credits"> <p class="dwt_author">Vanvyve, E.; Rife, D.; Pinto, J. O.; Monaghan, A. J.; Davis, C. A.</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">143</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/2010BoLMe.135..161D"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of Wind Variability from Meteorological 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">Measurements show that on numerous occasions the low-level wind is highly variable across a large portion of south-eastern Australia. Under such conditions the risk of a large rapid change in total wind power is increased. While variability tends to increase with mean wind speed, a large component of wind variability is not explained by wind speed alone. In this work, reanalysis fields from the US National Centers for Environmental Prediction (NCEP) are statistically <span class="hlt">downscaled</span> to model wind variability at a coastal location in Victoria, Australia. In order to reduce the dimensionality of the problem, the NCEP fields are each decomposed using empirical orthogonal function (EOF) techniques. The <span class="hlt">downscaling</span> technique is applied to two periods in the seasonal cycle, namely (i) winter to early spring, and (ii) summer. In each case, data representing 2 years are used to form a model that is then validated using independent data from another year. The EOFs that best predict wind variability are examined. To allow for non-linearity and complex interaction between variables, all empirical models are built using random forests. Quantitatively, the model compares favourably with a simple regression of wind variability against wind speed, as well as multiple linear regression models.</p> <div class="credits"> <p class="dwt_author">Davy, Robert J.; Woods, Milton J.; Russell, Christopher J.; Coppin, Peter A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-04-01</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-users.cs.umn.edu/~banerjee/papers/09/sdm09-ensemble.pdf"> <span id="translatedtitle">Bayesian Cluster <span class="hlt">Ensembles</span> Hongjun Wang</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Bayesian Cluster <span class="hlt">Ensembles</span> Hongjun Wang Hanhuai Shan Arindam Banerjee Abstract Cluster <span class="hlt">ensembles</span> provide a framework for combining mul- tiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of the basic cluster <span class="hlt">ensemble</span> problem, notably including</p> <div class="credits"> <p class="dwt_author">Banerjee, Arindam</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">145</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/2014ClDy..tmp..230D"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of CMIP5 global circulation models over CORDEX-Africa with COSMO-CLM: evaluation over the present climate and analysis of the added value</span></a>  </p> <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 work we present the results of the application of the consortium for small-scale modeling (COSMO) regional climate model (COSMO-CLM, hereafter, CCLM) over Africa in the context of the coordinated regional climate <span class="hlt">downscaling</span> experiment. An <span class="hlt">ensemble</span> of climate change projections has been created by <span class="hlt">downscaling</span> the simulations of four global climate models (GCM), namely: MPI-ESM-LR, HadGEM2-ES, CNRM-CM5, and EC-Earth. Here we compare the results of CCLM to those of the driving GCMs over the present climate, in order to investigate whether RCMs are effectively able to add value, at regional scale, to the performances of GCMs. It is found that, in general, the geographical distribution of mean sea level pressure, surface temperature and seasonal precipitation is strongly affected by the boundary conditions (i.e. driving GCMs), and seasonal statistics are not always improved by the <span class="hlt">downscaling</span>. However, CCLM is generally able to better represent the annual cycle of precipitation, in particular over Southern Africa and the West Africa monsoon (WAM) area. By performing a singular spectrum analysis it is found that CCLM is able to reproduce satisfactorily the annual and sub-annual principal components of the precipitation time series over the Guinea Gulf, whereas the GCMs are in general not able to simulate the bimodal distribution due to the passage of the WAM and show a unimodal precipitation annual cycle. Furthermore, it is shown that CCLM is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet and dry days, and the frequency of heavy rain events.</p> <div class="credits"> <p class="dwt_author">Dosio, Alessandro; Panitz, Hans-Jürgen; Schubert-Frisius, Martina; Lüthi, Daniel</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-07-01</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/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">147</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/2014GMD.....7..621E"> <span id="translatedtitle">Design of 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">ensemble</span> members that can be simulated such that choices must be made concerning which global climate models (GCMs) to <span class="hlt">downscale</span> from, and which regional climate models (RCMs) to <span class="hlt">downscale</span> with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM <span class="hlt">ensembles</span>, as well as spanning the range of future climate projections present in the GCM <span class="hlt">ensemble</span>. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created <span class="hlt">ensemble</span> provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process.</p> <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">2014-04-01</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/2006AGUSM.A33A..09L"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of the FSUGSM Temperature and Precipitation over the Southeast 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">A new statistical <span class="hlt">downscaling</span> method for the regionalization of global model simulations is developed in this study. The basis of this method is that clearer separation of prominent local climate signals (e.g., seasonal cycle, dominant intraseasonal or interannual oscillations) over the training period leads to better prediction of local climate scenario from the large-scale simulations. To this end, 1) CSEOF (Cyclostationary EOF) analysis is conducted on both observation and FSUGSM (Florida State University Global Spectral Model) runs over the training period, followed by 2) regression between lower modes of observation and GSM runs. 3) CSEOF PC time series for prediction domain is subsequently generated based on relationship identified from the first two steps. 4) The local scale data for the prediction domain are constructed from the generated PC time series and the eigenfunctions obtained from training. This procedure is repeated by withholding a particular year as a prediction domain for the sake of cross-validation. Daily precipitation and temperature (Tmax and Tmin) data obtained from FSUGSM (~1.8° lon.-lat., T63) seasonal forecast run have been <span class="hlt">downscaled</span> to local spatial scale of 0.2°×0.2° (~20 km) for the southeast US region, covering Florida, Georgia, and Alabama. <span class="hlt">Downscaled</span> results, FSUGSM, and bias-corrected FSURSM (FSU regional spectral model) are compared with observations. RMSE, correlation, and other skill scores reveal that statistical <span class="hlt">downscaling</span> successfully produces the local climate scenario from coarsely resolved large-scale simulations. In addition, biases unveiled from the FSUGSM have been significantly reduced by this <span class="hlt">downscaling</span> technique. Comparison in predictability with dynamical <span class="hlt">downscaling</span> (FSURSM) shows that this statistical <span class="hlt">downscaling</span> is moderately better than RSM. However, local surface temperature in Florida is better captured by RSM than statistical <span class="hlt">downscaling</span>. The new <span class="hlt">downscaling</span> method successfully provides the near- surface local climate predictions which could be associated with urban impacts, agriculture and hydrology, and other vegetation characteristics.</p> <div class="credits"> <p class="dwt_author">Lim, Y.; Shin, D.; Cocke, S.; Larow, T. E.; O'Brien, J. J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-05-01</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://pubs.er.usgs.gov/publication/ofr20141190"> <span id="translatedtitle"><span class="hlt">Downscaled</span> climate projections for the Southeast United States: evaluation and use for ecological applications</span></a>  </p> <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">Climate change is likely to have many effects on natural ecosystems in the Southeast U.S. The National Climate Assessment Southeast Technical Report (SETR) indicates that natural ecosystems in the Southeast are likely to be affected by warming temperatures, ocean acidification, sea-level rise, and changes in rainfall and evapotranspiration. To better assess these how climate changes could affect multiple sectors, including ecosystems, climatologists have created several <span class="hlt">downscaled</span> climate projections (or <span class="hlt">downscaled</span> datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these <span class="hlt">downscaled</span> datasets, known as <span class="hlt">downscaling</span>, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for <span class="hlt">downscaling</span> and the number of <span class="hlt">downscaled</span> datasets produced in recent years present many challenges for scientists and decisionmakers in assessing the impact or vulnerability of a given species or ecosystem to climate change. Given the number of available <span class="hlt">downscaled</span> datasets, how do these model outputs compare to each other? Which variables are available, and are certain <span class="hlt">downscaled</span> datasets more appropriate for assessing vulnerability of a particular species? Given the desire to use these datasets for impact and vulnerability assessments and the lack of comparison between these datasets, the goal of this report is to synthesize the information available in these <span class="hlt">downscaled</span> datasets and provide guidance to scientists and natural resource managers with specific interests in ecological modeling and conservation planning related to climate change in the Southeast U.S. This report enables the Southeast Climate Science Center (SECSC) to address an important strategic goal of providing scientific information and guidance that will enable resource managers and other participants in Landscape Conservation Cooperatives to make science-based climate change adaptation decisions.</p> <div class="credits"> <p class="dwt_author">Wooten, Adrienne; Smith, Kara; Boyles, Ryan; Terando, Adam J.; Stefanova, Lydia; Misra, Vasru; Smith, Tom; Blodgett, David L.; Semazzi, Fredrick</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-01-01</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.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=479123"> <span id="translatedtitle">The <span class="hlt">Ensembl</span> Analysis Pipeline</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> pipeline is an extension to the <span class="hlt">Ensembl</span> system which allows automated annotation of genomic sequence. The software comprises two parts. First, there is a set of Perl modules (“Runnables” and “RunnableDBs”) which are `wrappers' for a variety of commonly used analysis tools. These retrieve sequence data from a relational database, run the analysis, and write the results back to the database. They inherit from a common interface, which simplifies the writing of new wrapper modules. On top of this sits a job submission system (the “RuleManager”) which allows efficient and reliable submission of large numbers of jobs to a compute farm. Here we describe the fundamental software components of the pipeline, and we also highlight some features of the Sanger installation which were necessary to enable the pipeline to scale to whole-genome analysis. PMID:15123589</p> <div class="credits"> <p class="dwt_author">Potter, Simon C.; Clarke, Laura; Curwen, Val; Keenan, Stephen; Mongin, Emmanuel; Searle, Stephen M.J.; Stabenau, Arne; Storey, Roy; Clamp, Michele</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">151</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/2014JHyd..517..120P"> <span id="translatedtitle">Projections of the Ganges-Brahmaputra precipitation-<span class="hlt">Downscaled</span> from GCM predictors</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> Global Climate Model (GCM) projections of future climate is critical for impact studies. <span class="hlt">Downscaling</span> enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical <span class="hlt">Downscaling</span> Model (SDSM) to <span class="hlt">downscale</span> 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia-the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in <span class="hlt">downscaling</span> the precipitation. <span class="hlt">Downscaling</span> was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the <span class="hlt">downscaled</span> precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 <span class="hlt">downscaled</span> precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change. Precipitation during and after the monsoon is likely to increase in both basins under the A1B and A2 emission scenarios; whereas, the pre-monsoon precipitation is likely to decrease. Peak monsoon precipitation is likely to shift from July to August, and may impact the livelihoods of large rural populations linked to subsistence agriculture in the basins. Uncertainty analysis of the <span class="hlt">downscaled</span> precipitation indicated that the uncertainty in the <span class="hlt">downscaled</span> precipitation was less than the uncertainty in the original CGCM3.1 precipitation; hence, the CGCM3.1 <span class="hlt">downscaled</span> precipitation was a better input for the regional hydrological impact studies. However, <span class="hlt">downscaled</span> precipitation from multiple GCMs is suggested for comprehensive impact studies.</p> <div class="credits"> <p class="dwt_author">Pervez, Md Shahriar; Henebry, Geoffrey M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-09-01</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://pubs.er.usgs.gov/publication/70124278"> <span id="translatedtitle">Projections of the Ganges-Brahmaputra precipitation: <span class="hlt">downscaled</span> from GCM predictors</span></a>  </p> <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"><span class="hlt">Downscaling</span> Global Climate Model (GCM) projections of future climate is critical for impact studies. <span class="hlt">Downscaling</span> enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical <span class="hlt">Downscaling</span> Model (SDSM) to <span class="hlt">downscale</span> 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia—the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in <span class="hlt">downscaling</span> the precipitation. <span class="hlt">Downscaling</span> was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the <span class="hlt">downscaled</span> precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 <span class="hlt">downscaled</span> precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change. Precipitation during and after the monsoon is likely to increase in both basins under the A1B and A2 emission scenarios; whereas, the pre-monsoon precipitation is likely to decrease. Peak monsoon precipitation is likely to shift from July to August, and may impact the livelihoods of large rural populations linked to subsistence agriculture in the basins. Uncertainty analysis of the <span class="hlt">downscaled</span> precipitation indicated that the uncertainty in the <span class="hlt">downscaled</span> precipitation was less than the uncertainty in the original CGCM3.1 precipitation; hence, the CGCM3.1 <span class="hlt">downscaled</span> precipitation was a better input for the regional hydrological impact studies. However, <span class="hlt">downscaled</span> precipitation from multiple GCMs is suggested for comprehensive impact studies.</p> <div class="credits"> <p class="dwt_author">Pervez, Md Shahriar; Henebry, Geoffrey M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-01-01</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://adsabs.harvard.edu/abs/2012AGUFM.A34E..04L"> <span id="translatedtitle">Dynamically <span class="hlt">Downscaled</span> Future Climate Change over East Asia</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 assesses the future climate change over East Asia using the Global/Regional Integrated Model System (GRIMs) - Regional Model Program (RMP). The RMP is forced by two types of future climate scenarios produced by the Hadley Center Global Environmental Model version 2; the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios of Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). Analyses for the current (1980-2005) climate are performed to evaluate the RMP's ability to reproduce precipitation and temperature. Two different future (2006-2050) simulations are compared with the current climatology to investigate the climatic change. The RMP reproduces the observed seasonal mean and variation of precipitation and temperature satisfactorily. The spatial distribution of the simulated climatology is generally worse in RMP than those from the HG2, but the distributions of monsoonal precipitation are adequately captured. Furthermore, the RMP shows higher reproducibility of climate extreme accompanying excessive heat wave and precipitation. In the future, the strong warming is distinct with intensified monsoonal precipitation. In particular, extreme weather conditions are increased and intensified over South Korea. The heat wave is increased by twice with decreased variability. In RCP 8.5 <span class="hlt">downscaling</span>, frequency and variability of heavy rainfall are increased by 24% and 31.5%, while they are similar to current climate in RCP 4.5 <span class="hlt">downscaling</span>. This study indicates that future climate projection accompanying climate extreme and its variability over East Asia can be adequately addressed on the RMP test-bed, and the climatic change progressed without stabilization increases occurrence and intensity of extreme weather conditions.</p> <div class="credits"> <p class="dwt_author">Lee, J.; Hong, S.; Chang, E.; Suh, M.; Kang, H.</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">154</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/2013AGUFMGC41E..07W"> <span id="translatedtitle">SDSM-DC: A smarter approach to <span class="hlt">downscaling</span> for decision-making? (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">General Circulation Model (GCM) output has been used for <span class="hlt">downscaling</span> and impact assessments for at least 25 years. <span class="hlt">Downscaling</span> methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' <span class="hlt">downscaling</span> typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use <span class="hlt">downscaling</span> tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical <span class="hlt">DownScaling</span> Model (SDSM) over the last decade. This sample offers insights to <span class="hlt">downscaling</span> practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new <span class="hlt">downscaling</span> tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by <span class="hlt">downscaling</span> multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.</p> <div class="credits"> <p class="dwt_author">Wilby, R. L.; Dawson, C. W.</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">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/2011AGUFMGC41E..07W"> <span id="translatedtitle">SDSM-DC: A smarter approach to <span class="hlt">downscaling</span> for decision-making? (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">General Circulation Model (GCM) output has been used for <span class="hlt">downscaling</span> and impact assessments for at least 25 years. <span class="hlt">Downscaling</span> methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' <span class="hlt">downscaling</span> typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use <span class="hlt">downscaling</span> tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical <span class="hlt">DownScaling</span> Model (SDSM) over the last decade. This sample offers insights to <span class="hlt">downscaling</span> practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new <span class="hlt">downscaling</span> tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by <span class="hlt">downscaling</span> multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.</p> <div class="credits"> <p class="dwt_author">Wilby, R. L.; Dawson, C. W.</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://academic.research.microsoft.com/Publication/52699852"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> with a weather typing approach: adapting the methodology to France mountainous areas</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, an innovative statistical methodology has been developed to <span class="hlt">downscale</span> climate simulations using a weather-typing approach (Boé et al., 2006), aimed at first for the France Seine river basin, and extended\\/adapted to the whole metropolitan France thereafter. It has been used to <span class="hlt">downscale</span> 15 CMIP3 models as well as several Météo-France ARPEGE climate numerical model (Salas et al., 2005) simulations.</p> <div class="credits"> <p class="dwt_author">L. Terray; C. Pagé; E. Sanchez</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">157</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/2005JHyd..307..145D"> <span id="translatedtitle">Hydrologic impact of climate change in the Saguenay watershed: comparison of <span class="hlt">downscaling</span> methods and hydrologic 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">Changes in global climate will have significant impact on local and regional hydrological regimes, which will in turn affect ecological, social and economical systems. However, climate-change impact studies on hydrologic regime have been relatively rare until recently, mainly because Global Circulation Models, which are widely used to simulate future climate scenarios, do not provide hourly or daily rainfall reliable enough for hydrological modeling. Nevertheless, more reliable rainfall series corresponding to future climate scenarios can be derived from GCM outputs using the so called '<span class="hlt">downscaling</span> techniques'. This study applies two types of statistical (a stochastic and a regression based) <span class="hlt">downscaling</span> techniques to generate the possible future values of local meteorological variables such as precipitation and temperature in the Chute-du-Diable sub-basin of the Saguenay watershed in northern Québec, Canada. The <span class="hlt">downscaled</span> data is used as input to two different hydrologic models to simulate the corresponding future flow regime in the catchment. In addition to assessing the relative potential of the <span class="hlt">downscaling</span> methods, the paper also provides comparative study results of the possible impact of climate change on river flow and total reservoir inflow in the Chute-du-Diable basin. Although the two <span class="hlt">downscaling</span> techniques do not provide identical results, the time series generated by both methods indicates a general increasing trend in the mean daily temperature values. While the regression based <span class="hlt">downscaling</span> technique resulted in an increasing trend in the mean and variability of daily precipitation values, such a trend is not obvious in the case of precipitation time series <span class="hlt">downscaled</span> with the stochastic weather generator. Moreover, the hydrologic impact analysis made with the <span class="hlt">downscaled</span> precipitation and temperature time series as input to the two hydrological models suggest an overall increasing trend in mean annual river flow and reservoir inflow as well as earlier spring peak flows in the basin.</p> <div class="credits"> <p class="dwt_author">Dibike, Yonas B.; Coulibaly, Paulin</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-06-01</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://adsabs.harvard.edu/abs/2015EaFut...3....1M"> <span id="translatedtitle">VALUE: A framework to validate <span class="hlt">downscaling</span> approaches for climate change studies</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">VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary <span class="hlt">downscaling</span> community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical <span class="hlt">downscaling</span> methods. In this paper, we present the key ingredients of this framework. VALUE's main approach to validation is user- focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the <span class="hlt">downscaling</span> procedure where problems may occur: what is the isolated <span class="hlt">downscaling</span> skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open <span class="hlt">downscaling</span> intercomparison study, but is intended also to provide general guidance for other validation studies.</p> <div class="credits"> <p class="dwt_author">Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilcke, Renate A. I.</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-01-01</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://www.osti.gov/scitech/biblio/1043326"> <span id="translatedtitle">Sub-daily Statistical <span class="hlt">Downscaling</span> of Meteorological Variables Using Neural Networks</span></a>  </p> <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] [ORNL; Brooks, Bjørn-Gustaf J. [University of Illinois, Urbana-Champaign] [University of Illinois, Urbana-Champaign; Thornton, Peter E [ORNL] [ORNL; Dietze, Michael [University of Illinois, Urbana-Champaign] [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 " 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://adsabs.harvard.edu/abs/2014ThApC.117..343O"> <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">2014-07-01</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" 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id="NextPageLink" onclick='return 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/2013AGUFMGC43C1070A"> <span id="translatedtitle">Applying <span class="hlt">downscaled</span> climate data to wildlife areas in Washington State, 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">Conservation and natural resource managers require information about potential climate change effects for the species and ecosystems they manage. We evaluated potential future climate and bioclimate changes for wildlife areas in Washington State (USA) using five climate simulations for the 21st century from the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset run under the A2 greenhouse gases emissions scenario. These data were <span class="hlt">downscaled</span> to a 30-arc-second (~1-km) grid encompassing the state of Washington by calculating and interpolating future climate anomalies, and then applying the interpolated data to observed historical climate data. This climate data <span class="hlt">downscaling</span> technique (also referred to as the 'delta' method) is relatively simple and makes a number of assumptions that affect how the <span class="hlt">downscaled</span> data can be used and interpreted. We used the <span class="hlt">downscaled</span> climate data to calculate bioclimatic variables (e.g., growing degree days) that represent important physiological and environmental limits for Washington species and habitats of management concern. Multivariate descriptive plots and maps were used to evaluate the direction, magnitude, and spatial patterns of projected future climate and bioclimatic changes. The results indicate which managed areas experience the largest climate and bioclimatic changes under each of the potential future climate simulations. We discuss these changes while accounting for some of the limitations of our <span class="hlt">downscaling</span> technique and the uncertainties associated with using these <span class="hlt">downscaled</span> data for conservation and natural resource management applications.</p> <div class="credits"> <p class="dwt_author">Allan, A.; Shafer, S. L.; Bartlein, P. J.; Helbrecht, L.; Pelltier, R.; Thompson, B.</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">162</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/27782203"> <span id="translatedtitle">Orthogonal polynomial <span class="hlt">ensembles</span> in probability theory</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 survey a number of models from physics, statistical mechanics, probability theory and combinatorics, which are each described in terms of an orthogonal polynomial <span class="hlt">ensemble</span>. The most prominent example is apparently the Hermite <span class="hlt">ensemble</span>, the eigenvalue distribution of the Gaussian Unitary <span class="hlt">Ensemble</span> (GUE), and other well-known <span class="hlt">ensembles</span> known in random matrix theory like the Laguerre <span class="hlt">ensemble</span> for the spectrum of</p> <div class="credits"> <p class="dwt_author">Wolfgang Koenig</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">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/2013JMP....54k3505S"> <span id="translatedtitle">Density of states for Gaussian unitary <span class="hlt">ensemble</span>, Gaussian orthogonal <span class="hlt">ensemble</span>, and interpolating <span class="hlt">ensembles</span> through supersymmetric approach</span></a>  </p> <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 use the supersymmetric formalism to derive an integral formula for the density of states of the Gaussian Orthogonal <span class="hlt">Ensemble</span>, and then apply saddle-point analysis to give a new derivation of the 1/N-correction to Wigner's law. This extends the work of Disertori on the Gaussian Unitary <span class="hlt">Ensemble</span>. We also apply our method to the interpolating <span class="hlt">ensembles</span> of Mehta-Pandey.</p> <div class="credits"> <p class="dwt_author">Shamis, Mira</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-11-01</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/2006JGRD..111.5307D"> <span id="translatedtitle">Ozone <span class="hlt">ensemble</span> forecasts: 1. A new <span class="hlt">ensemble</span> design</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 Ozone <span class="hlt">Ensemble</span> Forecast System (OEFS) is tested as a technique to improve the accuracy of real-time photochemical air quality modeling. The performance of 12 different forecasts along with their <span class="hlt">ensemble</span> mean is tested against the observations during 11-15 August 2004, over five monitoring stations in the Lower Fraser Valley, British Columbia, Canada, a population center in a complex coastal mountain setting. The 12 <span class="hlt">ensemble</span> members are obtained by driving the U.S. Environmental Protection Agency (EPA) Models-3/Community Multiscale Air Quality Model (CMAQ) with two mesoscale meteorological models, each run at two resolutions (12- and 4-km): the Mesoscale Compressible Community (MC2) model and the Penn State/NCAR mesoscale (MM5) model. Moreover, CMAQ is run for three emission scenarios: a control run, a run with 50% more NOx emissions, and a run with 50% fewer. For the locations and days used to test this new OEFS, the <span class="hlt">ensemble</span> mean is the best forecast if ranked using correlation, gross error, and root mean square error and has average performance when evaluated with the unpaired peak prediction accuracy. <span class="hlt">Ensemble</span> averaging removes part of the unpredictable components of the physical and chemical processes involved in the ozone fate, resulting in a more skilful forecast when compared to any deterministic <span class="hlt">ensemble</span> member. There is not one of the 12 individual forecasts that clearly outperforms the others on the basis of the four statistical parameters considered here. A lagged-averaged OEFS is also tested as follows. The 12-member OEFS is expanded to an 18-member OEFS by adding the second day from the six 12-km "yesterday" forecasts to the "today" <span class="hlt">ensemble</span> forecast. The 18-member <span class="hlt">ensemble</span> does not improve the <span class="hlt">ensemble</span> mean forecast skill. Neither correlation nor a relationship between <span class="hlt">ensemble</span> spread and forecast error is evident.</p> <div class="credits"> <p class="dwt_author">Delle Monache, Luca; Deng, Xingxiu; Zhou, Yongmei; Stull, Roland</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-03-01</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/2013AGUFM.H31F1248S"> <span id="translatedtitle">Radar-guided radiometer <span class="hlt">downscaling</span> for combined soil moisture retrieval</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">Combining the advantages of both active and passive microwave measurements in a soil moisture-retrieval can dramatically increase resolution and sensitivity. Simultaneous remote sensing observations of the normalized radar cross section (?0) and emissivity (?) will be jointly used to ultimately achieve an improved soil moisture-retrieval algorithm. The ?0 values are derived from the Precipitation Radar (PR) from TRMM (product 2A21 V7) while the ? values are derived from the brightness temperatures (BTs) measured by the passive microwave radiometric system TRMM Microwave Imager (TMI) (product 1B11 V7). Emissivity values are used instead of BTs because they are more directly related to water content. The coarse-resolution passive measurements (TMI) are first <span class="hlt">downscaled</span> to match the finer resolution of the active ones (PR) via a Kalman filter, with which the error of the TMI instrument in terms of emissivity is parameterized so that different weights will be given to the PR and TMI measurements. The <span class="hlt">downscaling</span> is performed over the state of Oklahoma, for 'no-rain' conditions (indicated by PR), for high PR incidence angles, in order to obtain simultaneous measurements of the two instruments (because of different scanning geometries, synchronized measurements of both instruments can only be achieved at high PR incidence angles), for each TMI channel separately (not including the two high-resolution ones and the 21.3 GHz), for the early morning hours only (active and passive sensors retrieve information on soil moisture at different depths and this discrepancy becomes even greater in the late afternoon hours of the day, therefore selecting only the early-morning overpasses will mitigate this effect), and for different regions within Oklahoma. The regions are selected based on land class. Regions with homogeneous vegetation cover are examined separately from regions characterized by heterogeneous vegetation cover. Oklahoma was selected as the area of study, because of its variety of land classes and the availability of ground-based validation soil moisture data. Is there a correlation between radar backscatter and emissivity values?</p> <div class="credits"> <p class="dwt_author">Stampoulis, D.; Haddad, Z. S.; Anagnostou, E. N.</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">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/2013AGUFM.A23F0373F"> <span id="translatedtitle">Improving dynamical <span class="hlt">downscaling</span> of thunderstorms in New England</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 aims to quantify the variability of wind speed and precipitation during summer storms events in New England by using standard verification metrics along with the Method For Object-Based Diagnostic Evaluation technique (MODE). Using WRF-ARW to dynamically <span class="hlt">downscale</span> a set of storm events, the first approach investigates potential errors propagated from global analysis products used as initial and boundary conditions. The second approach evaluates the significance of applying a topographic wind parametrization scheme in order to obtain more realistic wind speeds. This fundamental study is born out of the necessity of developing a model for power outage prediction caused by severe storms. In New England, a densely forested region of the US, severe winds and precipitation are key weather factors that cause vulnerability in the power grid infrastructure. During storms, trees are uprooted and branches break, resulting in significant interruptions to electricity distribution. The power outage prediction framework utilizes simulated values of meteorological parameters from storms that have caused outages in the past; and the geographic coordinates of the trouble spots recorded by local utilities during these storms. These two components are used as input for a generalized multi-linear regression that estimate the coefficients for these meteorological parameters, which are then applied to weather forecasts of potential hazardous events, providing an estimate of the number and spatial distribution of power outages over the region for the approaching weather system. Given that the count and location of the predicted outages rely on the weather description of past events, the accuracy of spatial patterns and intensity of meteorological fields are crucial to developing an unbiased database for the regression. With that in mind, it is important to quantify the influence that a particular global analysis product can impose to the dynamical <span class="hlt">downscaling</span> of precipitation and wind speed over the studied region. Additionally, a topographic wind parametrization scheme that includes enhanced drag coefficients and steep terrain corrections is used to quantify potential improvements in the wind speed fields over New England terrain. The comparisons are performed using standard verification metrics, along with the MODE object-based verification technique. The latter technique offering advantages over traditional approaches because it considers structural attributes of distributed events (area, centroid, axis angle, and intensity) instead of strictly point-wise comparisons, which are the main interest of our study into regionally-distributed likelihoods of power failure.</p> <div class="credits"> <p class="dwt_author">Frediani, M. E.; Anagnostou, E. N.; Hopson, T. M.; Hacker, J.</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">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/2014EGUGA..16.2865R"> <span id="translatedtitle">Deterministic and probabilistic optimization of analogs and weather-regimes <span class="hlt">downscaling</span> algorithms for seasonal 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 two statistical-<span class="hlt">downscaling</span> methods to estimate seasonal precipitation at predetermined stations that rely on global forecasts, using a technique to find past-analog synoptic-weather patterns and their connection to local precipitation. One of the methods utilizes a classification of the large-scale weather patterns into regimes (weather-regimes <span class="hlt">downscaling</span>) and the other is based on the identification of closest past analogs without grouping the weather events into defined regimes (a "pure analogs" approach, analogs <span class="hlt">downscaling</span>). Determining the closest past synoptic pattern requires the definition of a distance between the present and past states. We have chosen to work with a general definition of distance following the Minkowski metric of order p (p-norm distance). In an attempt to explain the uncertainty associated with the determination of past analogs, not only the closest state to the actual event is identified, but also the following ones, up to n, and their contributions are weighted in inverse proportion to their squared distances. The sensitivity to n and p was objectively analyzed using deterministic and probabilistic verification procedures with the aim of optimizing the algorithms. Two types of information are relevant to the end users in this study: (1) the absolute seasonal precipitation amount and (2) whether a given precipitation threshold of the climatology distribution is exceeded or not. We analyzed the ability of the <span class="hlt">downscaling</span> algorithms to reproduce the seasonal amount by deterministically evaluating the linear relationship between the <span class="hlt">downscaled</span> and observed seasonal precipitation amounts. Next, we checked the improvement by the <span class="hlt">downscaling</span> method over an existing reference forecast in providing threshold exceedance information. In the absence of a <span class="hlt">downscaling</span> algorithm, the only gauge-specific available seasonal forecast was the seasonal climatological mean of the precipitation at the site. The skill of the <span class="hlt">downscaling</span> estimations was assessed in terms of four attributes relevant to the end user: accuracy, reliability, resolution, and discrimination relative to the observed climatological mean. To analyze these attributes we calculated Brier skill scores, their decomposition into reliability and resolution terms, and the area under the relative-operating curve. These are calculated for the probability of exceeding the 66th percentile and of not reaching the 33rd percentile. Results show that skill full deterministic and probabilistic estimates of seasonal precipitation at each site are obtained with our <span class="hlt">downscaling</span> methods. Our analysis shows that weighting n=2,3 analogs/weather regimes results in significant improvement as compared to relying on the closest past state only, but no further improvement is attained for n>3. The sensitivity of the <span class="hlt">downscaling</span> algorithm to p-norm is different whether the algorithm is verified deterministically or probabilistically.</p> <div class="credits"> <p class="dwt_author">Rostkier-Edelstein, Dorita; Kunin, Pavel</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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://adsabs.harvard.edu/abs/2008AGUFMOS52B..01H"> <span id="translatedtitle">Ocean Prediction via <span class="hlt">downscaling</span> of large-scale ocean circulation 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 Hybrid Coordinate Ocean Model (HYCOM) is used to forecast the three-dimensional structure in various parts of the world ocean (the Gulf of Mexico, the northern Gulf of Mexico, the Persian Gulf, the Gulf of California, and the Hawaii region). The horizontal resolution varies between them, but the coarsest is ~3.5 km. Vertical resolution varies from 20 to 32 layers. Lateral boundary forcing is supplied by global or basin- scale versions of HYCOM, and surface wind and heat flux forcing from the Navy Operational Global Atmospheric Prediction System (NOGAPS). The lateral boundary conditions use a "buffer area" for the baroclinic mode where the fine-grid solution is relaxed towards the outer coarse grid solution. The method of Characteristics is used for the barotropic mode. Most of the models assimilate ocean observations via the Navy Coupled Ocean Data Assimilation (NCODA) system. The primary observations include satellite-derived sea surface height and temperature as well as Argo profile data. The NCODA configuration used here is based on multi-variant optimal interpolation and uses the Cooper-Haines (1983) technique for downward projection of surface observations. The forecast length varies but is typically between 3-7 days. The value-added of <span class="hlt">downscaling</span> to higher resolution is demonstrated through various model-data comparisons, particularly data that was withheld from the data assimilation system. In the Gulf of Mexico, Loop Current Rings and (some) cyclonic rings compare favorably to independently derived thermal fronts measured with multi-channel SST's (MCSST). The northern Gulf of Mexico domain, which represents a triple- nested system, is used to generate <span class="hlt">ensembles</span> to examine the variance associated with errors in the initial state, surface wind forcing, etc. Near-surface current patterns in the Persian Gulf are compared to drouged drifters. Several of the circulation features in the Hawaii area are compared to observations collected during a recent Navy exercise. The sea level height in the Gulf of California agrees very well with the height measured by coastal tide guage stations. This domain was also used to exhaustively investigate the sensitivity of the lateral boundary condition parameters. The general circulation features in these regions are discussed, as well as technical aspects of the assimilation and validation.</p> <div class="credits"> <p class="dwt_author">Hogan, P. J.; Smedstad, O.; Wallcraft, A. J.; Zamudio, L.; Thoppil, P. G.</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-12-01</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://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 " 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://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">171</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/56087501"> <span id="translatedtitle">Local seasonal forecasts over France: what can we expect from statistical <span class="hlt">downscaling</span> ? Results with the DEMETER and <span class="hlt">ENSEMBLES</span> systems</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 management of the power generation system at the scale of a country is a very complex problem which involves in particular climatic variables at different spatial and time scales. Air temperature and precipitation are among the most important ones, as they explain respectively an important part of the demand variability and the hydro power production capacity. Direct GCMs forecasts</p> <div class="credits"> <p class="dwt_author">Z. Qu; L. Dubus; J. M. Gutiérrez</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">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/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 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://adsabs.harvard.edu/abs/2012AGUFM.A34E..03S"> <span id="translatedtitle">Mid-Century Warming in the Los Angeles Region and its Uncertainty using Dynamical and Statistical <span class="hlt">Downscaling</span></span></a>  </p> <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 a combination of dynamical and statistical <span class="hlt">downscaling</span> techniques, we projected mid-21st century warming in the Los Angeles region at 2-km resolution. To account for uncertainty associated with the trajectory of future greenhouse gas emissions, we examined projections for both "business-as-usual" (RCP8.5) and "mitigation" (RCP2.6) emissions scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5). To account for the considerable uncertainty associated with choice of global climate model, we <span class="hlt">downscaled</span> results for all available global climate models in CMIP5. For the business-as-usual scenario, we find that by the mid-21st century, the most likely warming is roughly 2.6°C averaged over the region's land areas, with a 95% confidence that the warming lies between 0.9 and 4.2°C. The high resolution of the projections reveals a pronounced spatial pattern in the warming: High elevations and inland areas separated from the coast by at least one mountain complex warm 20 to 50% more than the areas near the coast or within the Los Angeles basin. This warming pattern is especially apparent in summertime. The summertime warming contrast between the inland and coastal zones has a large effect on the most likely expected number of extremely hot days per year. Coastal locations and areas within the Los Angeles basin see roughly two to three times the number of extremely hot days, while high elevations and inland areas typically experience approximately three to five times the number of extremely hot days. Under the mitigation emissions scenario, the most likely warming and increase in heat extremes are somewhat smaller. However, the majority of the warming seen in the business-as-usual scenario still occurs at all locations in the most likely case under the mitigation scenario, and heat extremes still increase significantly. This warming study is the first part of a series studies of our project. More climate change impacts on the Santa Ana wind, rainfall, snowfall and snowmelt, cloud and surface hydrology are forthcoming and could be found in www.atmos.ucla.edu/csrl.he <span class="hlt">ensemble</span>-mean, annual-mean surface air temperature change and its uncertainty from the available CMIP5 GCMs under the RCP8.5 (left) and RCP2.6 (right) emissions scenarios, unit: °C.</p> <div class="credits"> <p class="dwt_author">Sun, F.; Hall, A. D.; Walton, D.; Capps, S. B.; Qu, X.; Huang, H. J.; Berg, N.; Jousse, A.; Schwartz, M.; Nakamura, M.; Cerezo-Mota, R.</p> <p 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">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/2007JGRD..11224102L"> <span id="translatedtitle">Dynamically and statistically <span class="hlt">downscaled</span> seasonal simulations of maximum surface air temperature 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">Coarsely resolved surface air temperature (2 m height) seasonal integrations from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) (~1.8° lon.-lat. (T63)) for the period of 1994 to 2002 (March through September each year) are <span class="hlt">downscaled</span> to a fine spatial scale of ~20 km. Dynamical and statistical <span class="hlt">downscaling</span> methods are applied for the southeastern United States region, covering Florida, Georgia, and Alabama. Dynamical <span class="hlt">downscaling</span> is conducted by running the FSU/COAPS Nested Regional Spectral Model (NRSM), which is nested into the domain of the FSU/COAPS GSM. We additionally present a new statistical <span class="hlt">downscaling</span> method. The rationale for the statistical approach is that clearer separation of prominent climate signals (e.g., seasonal cycle, intraseasonal, or interannual oscillations) in observation and GSM, respectively, over the training period can facilitate the identification of the statistical relationship in climate variability between two data sets. Cyclostationary Empirical Orthogonal Function (CSEOF) analysis and multiple regressions are trained with those data sets to extract their statistical relationship, which eventually leads to better prediction of regional climate from the large-scale simulations. <span class="hlt">Downscaled</span> temperatures are compared with the FSU/COAPS GSM fields and observations. <span class="hlt">Downscaled</span> seasonal anomalies exhibit strong agreement with observations and a reduction in bias relative to the direct GSM simulations. Interannual temperature change is also reasonably simulated at local grid points. A series of evaluations including mean absolute errors, anomaly correlations, frequency of extreme events, and categorical predictability reveal that both <span class="hlt">downscaling</span> techniques can be reliably used for numerous seasonal climate applications.</p> <div class="credits"> <p class="dwt_author">Lim, Young-Kwon; Shin, D. W.; Cocke, Steven; LaRow, T. E.; Schoof, Justin T.; O'Brien, James J.; Chassignet, Eric P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-12-01</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://adsabs.harvard.edu/abs/2013AGUFMGC43C1053X"> <span id="translatedtitle">A Dynamical <span class="hlt">Downscaling</span> Approach with GCM Bias Corrections and Spectral Nudging</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">To reduce the biases in the regional climate <span class="hlt">downscaling</span> simulations, a dynamical <span class="hlt">downscaling</span> approach with GCM bias corrections and spectral nudging is developed and assessed over North America. Regional climate simulations are performed with the Weather Research and Forecasting (WRF) model embedded in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). To reduce the GCM biases, the GCM climatological means and the variances of interannual variations are adjusted based on the National Centers for Environmental Prediction-NCAR global reanalysis products (NNRP) before using them to drive WRF which is the same as our previous method. In this study, we further introduce spectral nudging to reduce the RCM-based biases. Two sets of WRF experiments are performed with and without spectral nudging. All WRF experiments are identical except that the initial and lateral boundary conditions are derived from the NNRP, the original GCM output, and the bias corrected GCM output, respectively. The GCM-driven RCM simulations with bias corrections and spectral nudging (IDDng) are compared with those without spectral nudging (IDD) and North American Regional Reanalysis (NARR) data to assess the additional reduction in RCM biases relative to the IDD approach. The results show that the spectral nudging introduces the effect of GCM bias correction into the RCM domain, thereby minimizing the climate drift resulting from the RCM biases. The GCM bias corrections and spectral nudging significantly improve the <span class="hlt">downscaled</span> mean climate and extreme temperature simulations. Our results suggest that both GCM bias corrections or spectral nudging are necessary to reduce the error of <span class="hlt">downscaled</span> climate. Only one of them does not guarantee better <span class="hlt">downscaling</span> simulation. The new dynamical <span class="hlt">downscaling</span> method can be applied to regional projection of future climate or <span class="hlt">downscaling</span> of GCM sensitivity simulations. Annual mean RMSEs. The RMSEs are computed over the verification region by monthly mean data over 1981-2010. Experimental design</p> <div class="credits"> <p class="dwt_author">Xu, Z.; Yang, Z.</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">176</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/2014EGUGA..1614315G"> <span id="translatedtitle">Comparison among different <span class="hlt">downscaling</span> approaches in building water scarcity scenarios in an Alpine basin.</span></a>  </p> <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. Although statistical <span class="hlt">downscaling</span> (SD) has been traditionally seen as an alternative to dynamical <span class="hlt">downscaling</span> (DD), recent works on statistical <span class="hlt">downscaling</span> have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD is able to provide more reliable climate forcing for crop water demand models. The case study presented here focuses on the Maggiore Lake (Alpine region), with a watershed of approximately 4750 km2 and whose waters are mainly used for irrigation purposes in the Lombardia and Piemonte regions. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction of the precipitation data collected in the period 1950-2012 by the 19 rainfall gauges located in the watershed area (some of them operating not continuously during the study period). The relationship between the precipitation regime and the inflow to the reservoir is obtained through a simple multilinear regression model, validated using both precipitation data and inflow measurements to the lake in the period 1996-2012 then, the same relation has been applied to the control (20c) and scenario (a1b) simulations <span class="hlt">downscaled</span> by means of the different <span class="hlt">downscaling</span> approaches (DD, SD and combined DD-SD). The resulting forcing has been used as input to a daily water balance model taking into account the inflow to the lake, the demand for irrigation and the reservoir management policies. The impact of the different <span class="hlt">downscaling</span> approaches on the water budget scenarios has been evaluated in terms of occurrence, duration and intensity of water scarcity periods.</p> <div class="credits"> <p class="dwt_author">Guyennon, Nicolas; Romano, Emanuele; Mariani, Davide; Bruna Petrangeli, Anna; Portoghese, Ivan</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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://adsabs.harvard.edu/abs/2014JGRD..119.7193M"> <span id="translatedtitle">Using a coupled lake model with WRF for 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 Weather Research and Forecasting (WRF) model is used to <span class="hlt">downscale</span> a coarse reanalysis (National Centers for Environmental Prediction-Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine the consequences of using different methods for setting lake temperatures and ice on predicted 2 m temperature and precipitation in the Great Lakes region. A control simulation is performed where lake surface temperatures and ice coverage are interpolated from the GCM proxy. Because the R2 represents the five Great Lakes with only three grid points, ice formation is poorly represented, with large, deep lakes freezing abruptly. Unrealistic temperature gradients appear in areas where the coarse-scale fields have no inland water points nearby and lake temperatures on the finer grid are set using oceanic points from the GCM proxy. Using WRF coupled with the Freshwater Lake (FLake) model reduces errors in lake temperatures and significantly improves the timing and extent of ice coverage. Overall, WRF-FLake increases the accuracy of 2 m temperature compared to the control simulation where lake variables are interpolated from R2. However, the decreased error in FLake-simulated lake temperatures exacerbates an existing wet bias in monthly precipitation relative to the control run because the erroneously cool lake temperatures interpolated from R2 in the control run tend to suppress overactive precipitation.</p> <div class="credits"> <p class="dwt_author">Mallard, Megan S.; Nolte, Christopher G.; Bullock, O. Russell; Spero, Tanya L.; Gula, Jonathan</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-06-01</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=http://ntrs.nasa.gov/search.jsp?R=20140006432&hterms=health&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dhealth"> <span id="translatedtitle"><span class="hlt">Downscaling</span> MODIS Land Surface Temperature for Urban Public Health Applications</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for <span class="hlt">downscaling</span> the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.</p> <div class="credits"> <p class="dwt_author">Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel</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">179</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ntrs.nasa.gov/search.jsp?R=20110011613&hterms=map&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dmap"> <span id="translatedtitle"><span class="hlt">Downscaling</span> NASA Climatological Data to Produce Detailed Climate Zone Maps</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">The design of energy efficient sustainable buildings is heavily dependent on accurate long-term and near real-time local weather data. To varying degrees the current meteorological networks over the globe have been used to provide these data albeit often from sites far removed from the desired location. The national need is for access to weather and solar resource data accurate enough to use to develop preliminary building designs within a short proposal time limit, usually within 60 days. The NASA Prediction Of Worldwide Energy Resource (POWER) project was established by NASA to provide industry friendly access to globally distributed solar and meteorological data. As a result, the POWER web site (power.larc.nasa.gov) now provides global information on many renewable energy parameters and several buildings-related items but at a relatively coarse resolution. This paper describes a method of <span class="hlt">downscaling</span> NASA atmospheric assimilation model results to higher resolution and maps those parameters to produce building climate zone maps using estimates of temperature and precipitation. The distribution of climate zones for North America with an emphasis on the Pacific Northwest for just one year shows very good correspondence to the currently defined distribution. The method has the potential to provide a consistent procedure for deriving climate zone information on a global basis that can be assessed for variability and updated more regularly.</p> <div class="credits"> <p class="dwt_author">Chandler, William S.; Hoell, James M.; Westberg, David J.; Whitlock, Charles H.; Zhang, Taiping; Stackhouse, P. W.</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">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/2014CSR....87....7B"> <span id="translatedtitle">Impacts of high resolution model <span class="hlt">downscaling</span> in coastal regions</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 issue of appropriate resolution of coastal models is addressed in this paper. The quality of coastal predictions from three different spatial resolutions of a coastal ocean model is assessed in the context of simulation of the freshwater front in Liverpool Bay. Model performance is examined during the study period February 2008 using a 3-D baroclinic hydrodynamic model. Some characteristic lengthscales and non-dimensional numbers are introduced to describe the coastal plume and freshwater front. Metrics based on these lengthscales and the governing physical processes are used to assess model performance and these metrics have been calculated for the suite of <span class="hlt">downscaled</span> models and compared with observations. Increased model resolution was found to better capture the position and strength of the freshwater front. However, instabilities along the front such as the tidal excursion led to large temporal and spatial variability in its position in the highest resolution model. By examining the spatial structure of the baroclinic Rossby radius in each model we identify which lengthscales are being resolved at different resolutions. In this dynamic environment it is more valuable to represent the governing time and space scales, rather than relying on strict point by point tests when evaluating model skill.</p> <div class="credits"> <p class="dwt_author">Bricheno, Lucy M.; Wolf, Judith M.; Brown, Jennifer M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-09-01</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" 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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://adsabs.harvard.edu/abs/2013AGUFMGC53A1041A"> <span id="translatedtitle"><span class="hlt">Downscaling</span> MODIS Land Surface Temperature for Urban Public Health Applications</span></a>  </p> <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 part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heat-related mortality data. The current HWWS do not take into account intra-urban spatial variations in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with land surface temperature (LST) estimates derived from thermal remote sensing data. In order to further improve the assessment of intra-urban variations in risk from extreme heat, we developed and evaluated a number of spatial statistical techniques for <span class="hlt">downscaling</span> the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. We will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.</p> <div class="credits"> <p class="dwt_author">Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G., Jr.; Estes, S. M.; Quattrochi, D. A.; Johnson, D.</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">182</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/24988779"> <span id="translatedtitle"><span class="hlt">Downscaling</span> the environmental associations and spatial patterns of species richness.</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 introduce a method that enables the estimation of species richness environment association and prediction of geographic patterns of species richness at grains finer than the original grain of observation. The method is based on a hierarchical model that uses coarse-grain values of species richness and fine-grain environmental data as input. In the model, the (unobserved) fine-grain species richness is linked to the observed fine-grain environment and upscaled using a simple species-area relationship (SAR). The upscaled values are then stochastically linked to the observed coarse-grain species richness. We tested the method on Southern African Bird Atlas data by <span class="hlt">downscaling</span> richness from 2 degrees to 0.25 degrees (-250 km to -30 km) resolution. When prior knowledge of the SAR slope (average species turnover within coarse-grain cells) was available, the method predicted the fine-grain relationship between richness and environment and provided fine-grain predictions of richness that closely resembled results from native fine-grain models. Without the SAR knowledge the method still accurately quantified the richness-environment relationship, but accurately predicted only relative (rank) values of richness. The approach can be easily extended and it is a powerful path for cross-scale statistical modeling of richness-environment relationships, and for the provision of high-resolution maps for basic science and conservation. PMID:24988779</p> <div class="credits"> <p class="dwt_author">Keil, Petr; Jetz, Walter</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-06-01</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/2014EGUGA..16.5258A"> <span id="translatedtitle">Future changes in the West African Monsoon: A COSMO-CLM and RCA4 multimodel <span class="hlt">ensemble</span> study</span></a>  </p> <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 multi-model multi-<span class="hlt">ensemble</span> study, we intercompare results from two regional climate simulation <span class="hlt">ensembles</span> to see how well they reproduce the known main features of the West African Monsoon (WAM). Each <span class="hlt">ensemble</span> was created under the ongoing CORDEX-Africa activities by using the regional climate models (RCA4 and COSMO-CLM) to <span class="hlt">downscale</span> four coupled atmosphere ocean general circulation models (AOGCMs), namely, CNRM-CM5, HadGEM2-ES, EC-EARTH, and MPI-ESM-LR. Spatial resolution of the driving AOGCMs varies from about 1° to 3° while all regional simulations are at the same 0.44° resolution. Future climate projections from the RCP8.5 scenario are analyzed and inter-compared for both <span class="hlt">ensembles</span> in order to assess deviations and uncertainties. The main focus in our analysis is on the projected WAM rainy season statistics. We look at projected changes in onset and cessation, total precipitation and temperature toward the end of the century (2071-2100) for different time scales spanning seasonal climatologies, annual cycles and interannual variability, and a number of spatial scales covering the Sahel, the Gulf of Guinea and the entire West Africa. Differences in the <span class="hlt">ensemble</span> projections are linked to the parameterizations employed in both regional models and the influence of this is discussed.</p> <div class="credits"> <p class="dwt_author">Anders, Ivonne; Gbobaniyi, Emiola</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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://adsabs.harvard.edu/abs/2009AGUSM.H12B..04S"> <span id="translatedtitle">Comparison of <span class="hlt">Downscaled</span> RCM and GCM Data for Hydrologic Impact 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">From observations of increases in global average air and ocean temperatures, widespread melting of snow and ice, and significant increases in net anthropogenic radiative forcing, it is clear that our global climate system is undergoing substantial warming (IPCC, 2008). A key area of concern for hydrologists and engineers alike is to determine how this warming will affect various hydrologic processes. To date, climate change impact studies have generally involved the <span class="hlt">downscaling</span> of large-scale atmospheric predictors with the result then being input into a hydrological model to see how the flow regimes in a river/basin will change under various future climate change scenarios. Although many studies have been completed using large scale global climate model (GCM) simulations, few studies have shown the benefits of regional climate models (RCM). In this work, comparisons are made between the effectiveness of using CRCM4.2 vs. CGCM3.1 data as well as using statistically <span class="hlt">downscaled</span> inputs in a climate change impact study. The study area is the Chute-du-Diable sub-basin located within the Saguenay-Lac-Saint-Jean Watershed in Quebec, Canada. <span class="hlt">Downscaled</span> results are compared with observed meteorological data for the years 1961-1990 at the Chute- des-Passes (CDP) and Chute-du-Diable (CDD) weather stations; and flow is simulated in the Mistassibi River and the Chute-du-Diable reservoir. A statistical based regression technique (SDSM) and a dynamic artificial neural network model (Time lagged feed-forward neural network (TLFN)) are used for <span class="hlt">downscaling</span> both the CRCM4.2 and CGCM3.1 data, and the HBV2005 hydrological modeling system is used for simulating flows in the watershed. For the base-line period (1961-1990), <span class="hlt">downscaling</span> results revealed that <span class="hlt">downscaled</span> CRCM4.2 precipitation and temperature series are closer to observed meteorological data at both CDD and CDP stations than <span class="hlt">downscaled</span> CGCM3.1 series. The Wilcoxon Rank-Sum test and Levene test revealed that although TLFN and SDSM are capable of capturing the monthly means for precipitation and temperature accurately while SDSM is much better at capturing the degree of variability than TLFN. Statistical analysis also revealed that TLFN is best for <span class="hlt">downscaling</span> temperature while SDSM is best for <span class="hlt">downscaling</span> precipitation. With respect to the future (SRES A2) climate scenario both SDSM and TLFN revealed an 8-30% increase in precipitation and 1.8-5oC increase in mean temperature by 2050s with CGCM3.1 showing a larger increase than the CRCM4.2 model. Moreover, hydrologic simulations based on both statistically and dynamically <span class="hlt">downscaled</span> precipitation and temperature inputs show increases in river flow and reservoir inflow throughout all seasons except for the summer where reduction of flow is observed. Annually, mean flow increases by about 16% to 45% in the 2050s with CRCM4.2 showing smaller increases than CGCM3.1. The study results also revealed that employing <span class="hlt">downscaled</span> RCM data can yield better results than raw RCM and <span class="hlt">downscaled</span> GCM data. The study results suggest that statistically <span class="hlt">downscaling</span> RCM data could improve hydrologic impact assessment results at the catchment scale.</p> <div class="credits"> <p class="dwt_author">Sharma, M.; Coulibaly, P.; Dibike, Y.</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">185</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/2012AGUFMGC11B1004B"> <span id="translatedtitle">A Comprehensive Framework for Quantitative Evaluation of <span class="hlt">Downscaled</span> Climate Predictions and 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">The variety of methods used for <span class="hlt">downscaling</span> climate predictions and projections is large and growing larger. Comparative studies of <span class="hlt">downscaling</span> techniques to date are often initiated in relation to specific projects, are focused on limited sets of <span class="hlt">downscaling</span> techniques, and hence do not allow for easy comparison of outcomes. In addition, existing information about the quality of <span class="hlt">downscaled</span> datasets is not available in digital form. There is a strong need for systematic evaluation of <span class="hlt">downscaling</span> methods using standard protocols which will allow for a fair comparison of their advantages and disadvantages with respect to specific user needs. The National Climate Predictions and Projections platform, with the contributions of NCPP's Climate Science Advisory Team, is developing community-based standards and a prototype framework for the quantitative evaluation of <span class="hlt">downscaling</span> techniques and datasets. Certain principles guide the development of this framework. We want the evaluation procedures to be reproducible and transparent, simple to understand, and straightforward to implement. To this end we propose a set of open standards that will include the use of specific data sets, time periods of analysis, evaluation protocols, evaluation tests and metrics. Secondly, we want the framework to be flexible and extensible to <span class="hlt">downscaling</span> techniques which may be developed in the future, to high-resolution global models, and to evaluations that are meaningful for additional applications and sectors. Collaboration among practitioners who will be using the <span class="hlt">downscaled</span> data and climate scientists who develop <span class="hlt">downscaling</span> methods will therefore be essential to the development of this framework. The proposed framework consists of three analysis protocols, along with two tiers of specific metrics and indices that are to be calculated. The protocols describe the following types of evaluation that can be performed: 1) comparison to observations, 2) comparison to a "perfect model" simulation at high resolution, and 3) idealized comparisons where an analytic solution is known. Each of these protocols addresses different questions about the data, and defines different needs for evaluation datasets. For each protocol we identify individual pathways that may depend on the particular details of a given <span class="hlt">downscaling</span> method or the goals of the validation. For example, whether the comparison is made to gridded observational data or to a set of station observations. Complementing the protocols are two tiers of metrics -- measures of performance of the methods in many dimensions. Tier 1 aims at a general statistical evaluation of the <span class="hlt">downscaled</span> data. Tier 1 metrics will be primarily determined in collaboration with developers of <span class="hlt">downscaling</span> methods, and can provide direct feedback into their further development. It is envisioned that Tier 2 consists of a flexible and extensible collection of metrics that will be developed in close collaboration with climate impacts modelers and those who use <span class="hlt">downscaled</span> data for addressing real-world problems.</p> <div class="credits"> <p class="dwt_author">Barsugli, J. J.; Guentchev, G.</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">186</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/2013AGUFM.H44C..05C"> <span id="translatedtitle">Meteorological Drought Prediction Using a Multi-Model <span class="hlt">Ensemble</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">In the United States, drought is among the costliest natural hazards, with an annual average of 6 billion dollars in damage. Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Started in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the National Multi-Model <span class="hlt">Ensemble</span> (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the meteorological drought predictability using the retrospective NMME forecasts for the period from 1982 to 2010. Before predicting SPI, monthly-mean precipitation (P) forecasts from each model were bias corrected and spatially <span class="hlt">downscaled</span> (BCSD) to regional grids of 0.5-degree resolution over the contiguous United States based on the probability distribution functions derived from the hindcasts. The corrected P forecasts were then appended to the CPC Unified Precipitation Analysis to form a P time series for computing 3-month and 6-month SPIs. The <span class="hlt">ensemble</span> SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation and root-mean-square errors against the observations, are used to evaluate forecast skill. For P forecasts, errors vary among models and skill generally is low after the second month. All model P forecasts have higher skill in winter and lower skill in summer. In wintertime, BCSD improves both P and SPI forecast skill. Most improvements are over the western mountainous regions and along the Great Lake. Overall, SPI predictive skill is regionally and seasonally dependent. The six-month SPI forecasts are skillful out to four months. For shorter lead months, the <span class="hlt">ensemble</span> SPI forecast skill is comparable to that based on persistence. The spread of SPI forecasts among models is small, and the predictive skill comes from the observations appended to the P forecasts. For longer lead months, model forecasts contribute to the meteorological drought predictability. The <span class="hlt">ensemble</span> SPI forecasts have higher skill than those based on persistence and individual models.</p> <div class="credits"> <p class="dwt_author">Chen, L.; Mo, K. C.; Zhang, Q.; Huang, J.</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">187</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/2015ClDy...44..529H"> <span id="translatedtitle"><span class="hlt">Downscaling</span> near-surface wind over complex terrain using a physically-based statistical modeling 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">A physically-based statistical modeling approach to <span class="hlt">downscale</span> coarse resolution reanalysis near-surface winds over a region of complex terrain is developed and tested in this study. Our approach is guided by physical variables and meteorological relationships that are important for determining near-surface wind flow. Preliminary fine scale winds are estimated by correcting the course-to-fine grid resolution mismatch in roughness length. Guided by the physics shaping near-surface winds, we then formulate a multivariable linear regression model which uses near-surface micrometeorological variables and the preliminary estimates as predictors to calculate the final wind products. The coarse-to-fine grid resolution ratio is approximately 10-1 for our study region of southern California. A validated 3-km resolution dynamically-<span class="hlt">downscaled</span> wind dataset is used to train and validate our method. Winds from our statistical modeling approach accurately reproduce the dynamically-<span class="hlt">downscaled</span> near-surface wind field with wind speed magnitude and wind direction errors of <1.5 ms-1 and 30°, respectively. This approach can greatly accelerate the production of near-surface wind fields that are much more accurate than reanalysis data, while limiting the amount of computational and time intensive dynamical <span class="hlt">downscaling</span>. Future studies will evaluate the ability of this approach to <span class="hlt">downscale</span> other reanalysis data and climate model outputs with varying coarse-to-fine grid resolutions and domains of interest.</p> <div class="credits"> <p class="dwt_author">Huang, Hsin-Yuan; Capps, Scott B.; Huang, Shao-Ching; Hall, Alex</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-01-01</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/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">189</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..72..231K"> <span id="translatedtitle"><span class="hlt">Downscaling</span> ocean conditions: Experiments with a quasi-geostrophic 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 predictability of small-scale ocean variability, given the time history of the associated large-scales, is investigated using a quasi-geostrophic model of two wind-driven gyres separated by an unstable, mid-ocean jet. Motivated by the recent theoretical study of Henshaw et al. (2003), we propose a straightforward method for assimilating information on the large-scale in order to recover the small-scale details of the quasi-geostrophic circulation. The similarity of this method to the spectral nudging of limited area atmospheric models is discussed. Results from the spectral nudging of the quasi-geostrophic model, and an independent multivariate regression-based approach, show that important features of the ocean circulation, including the position of the meandering mid-ocean jet and the associated pinch-off eddies, can be recovered from the time history of a small number of large-scale modes. We next propose a hybrid approach for assimilating both the large-scales and additional observed time series from a limited number of locations that alone are too sparse to recover the small scales using traditional assimilation techniques. The hybrid approach improved significantly the recovery of the small-scales. The results highlight the importance of the coupling between length scales in <span class="hlt">downscaling</span> applications, and the value of assimilating limited point observations after the large-scales have been set correctly. The application of the hybrid and spectral nudging to practical ocean forecasting, and projecting changes in ocean conditions on climate time scales, is discussed briefly.</p> <div class="credits"> <p class="dwt_author">Katavouta, A.; Thompson, K. R.</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">190</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 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://www.me.utexas.edu/~ferreira/docs/nanotechnology-2012.pdf"> <span id="translatedtitle">Effect of <span class="hlt">downscaling</span> nano-copper interconnects on the microstructure revealed by high resolution TEM-orientation-mapping</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Effect of <span class="hlt">downscaling</span> nano-copper interconnects on the microstructure revealed by high resolution Nanotechnology 23 (2012) 135702 (7pp) doi:10.1088/0957-4484/23/13/135702 Effect of <span class="hlt">downscaling</span> nano-copper@mail.utexas.edu Received 14 December 2011 Published 14 March 2012 Online at stacks.iop.org/Nano/23/135702 Abstract</p> <div class="credits"> <p class="dwt_author">Ferreira, Paulo J.</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://www.ncbi.nlm.nih.gov/pubmed/24705474"> <span id="translatedtitle">Measuring similarity between dynamic <span class="hlt">ensembles</span> of biomolecules.</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 a simple and general approach termed REsemble for quantifying population overlap and structural similarity between <span class="hlt">ensembles</span>. This approach captures improvements in the quality of <span class="hlt">ensembles</span> determined using increasing input experimental data--improvements that go undetected when conventional methods for comparing <span class="hlt">ensembles</span> are used--and reveals unexpected similarities between RNA <span class="hlt">ensembles</span> determined using NMR and molecular dynamics simulations. PMID:24705474</p> <div class="credits"> <p class="dwt_author">Yang, Shan; Salmon, Loïc; Al-Hashimi, Hashim M</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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/3537073"> <span id="translatedtitle">ORTHOGONAL POLYNOMIAL <span class="hlt">ENSEMBLES</span> IN PROBABILITY THEORY</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 survey a number of models from physics, statistical mechanics, probability theory and combinatorics, which are each described in terms of an orthogonal polynomial en- semble. The most prominent example is apparently the Hermite <span class="hlt">ensemble</span>, the eigenvalue dis- tribution of the Gaussian Unitary <span class="hlt">Ensemble</span> (GUE), and other well-known <span class="hlt">ensembles</span> known in random matrix theory like the Laguerre <span class="hlt">ensemble</span> for the</p> <div class="credits"> <p class="dwt_author">Wolfgang Konig</p> <p class="dwt_publisher"></p> <p class="publishDate"></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/4906449"> <span id="translatedtitle"><span class="hlt">Ensembles</span> of jittered association rule 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">Abstract The <span class="hlt">ensembling</span> of classifiers tends to improve predictive accuracy. To obtain an <span class="hlt">ensemble</span> with N classifiers, one typically needs to run N learning processes. In this paper we explore the path of Model Jittering <span class="hlt">Ensembling</span>, where one single model is perturbed in order to obtain variants that can be used as an <span class="hlt">ensemble</span>. We use as base classifiers sets</p> <div class="credits"> <p class="dwt_author">Paulo J. Azevedo; Alípio Mário Jorge</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">195</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.esrl.noaa.gov/psd/people/tom.hamill/ensrf_mwr.pdf"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Data Assimilation without Perturbed Observations</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) is a data assimilation scheme based on the traditional Kalman filter update equation. An <span class="hlt">ensemble</span> of forecasts are used to estimate the background-error covariances needed to compute the Kalman gain. It is known that if the same observations and the same gain are used to update each member of the <span class="hlt">ensemble</span>, the <span class="hlt">ensemble</span> will systematically</p> <div class="credits"> <p class="dwt_author">Jeffrey S. Whitaker; Thomas M. Hamill</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">196</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://faculty.arts.ubc.ca/afisher/EME/EME_syllabus_2012W.pdf"> <span id="translatedtitle">Music 157A, 557: Early Music <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/eprints/">E-print Network</a></p> <p class="result-summary">Music 157A, 557: Early Music <span class="hlt">Ensemble</span> 2012 Early Music <span class="hlt">Ensemble</span> is a mixed instrumental/vocal <span class="hlt">ensemble</span> specializing in the performance of music's musical strengths and to help assign each student to an appropriate <span class="hlt">ensemble</span>. We will ask each student</p> <div class="credits"> <p class="dwt_author">Pulfrey, David L.</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">197</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 " 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://arxiv.org/pdf/1102.3713.pdf"> <span id="translatedtitle">Optimal Control of Inhomogeneous <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/eprints/">E-print Network</a></p> <p class="result-summary">Inhomogeneity, in its many forms, appears frequently in practical physical systems. Readily apparent in quantum systems, inhomogeneity is caused by hardware imperfections, measurement inaccuracies, and environmental variations, and subsequently limits the performance and efficiency achievable in current experiments. In this paper, we provide a systematic methodology to mathematically characterize and optimally manipulate inhomogeneous <span class="hlt">ensembles</span> with concepts taken from <span class="hlt">ensemble</span> control. In particular, we develop a computational method to solve practical quantum pulse design problems cast as optimal <span class="hlt">ensemble</span> control problems, based on multidimensional pseudospectral approximations. We motivate the utility of this method by designing pulses for both standard and novel applications. We also show the convergence of the pseudospectral method for optimal <span class="hlt">ensemble</span> control. The concepts developed here are applicable beyond quantum control, such as to neuron systems, and furthermore to systems with by parameter uncert...</p> <div class="credits"> <p class="dwt_author">Ruths, Justin</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">199</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. PMID:15123594</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 " 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://adsabs.harvard.edu/abs/2014JHyd..517.1145T"> <span id="translatedtitle">Prediction of design flood discharge by statistical <span class="hlt">downscaling</span> and General Circulation 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 global warming and the climate change have caused an observed change in the hydrological data; therefore, forecasters need re-calculated scenarios in many situations. <span class="hlt">Downscaling</span>, which is reduction of time and space dimensions in climate models, will most probably be the future of climate change research. However, it may not be possible to redesign an existing dam but at least precaution parameters can be taken for the worse scenarios of flood in the downstream of the dam location. The purpose of this study is to develop a new approach for predicting the peak monthly discharges from statistical <span class="hlt">downscaling</span> using linear genetic programming (LGP). Attempts were made to evaluate the impacts of the global warming and climate change on determining of the flood discharge by considering different scenarios of General Circulation Models. Reasonable results were achieved in <span class="hlt">downscaling</span> the peak monthly discharges directly from daily surface weather variables (NCEP and CGCM3) without involving any rainfall-runoff models.</p> <div class="credits"> <p class="dwt_author">Tofiq, F. A.; Guven, A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-09-01</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");' 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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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_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://arxiv.org/pdf/1408.5640v1"> <span id="translatedtitle">Quantum Gibbs <span class="hlt">ensemble</span> Monte Carlo</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">We present a path integral Monte Carlo method which is the full quantum analogue of the Gibbs <span class="hlt">ensemble</span> Monte Carlo method of Panagiotopoulos to study the gas-liquid coexistence line of a classical fluid. Unlike previous extensions of Gibbs <span class="hlt">ensemble</span> Monte Carlo to include quantum effects, our scheme is viable even for systems with strong quantum delocalization in the degenerate regime of temperature. This is demonstrated by an illustrative application to the gas-superfluid transition of $^4$He in two dimensions.</p> <div class="credits"> <p class="dwt_author">Riccardo Fantoni; Saverio Moroni</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-08-24</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://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">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/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 " 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://pubs.er.usgs.gov/publication/70007520"> <span id="translatedtitle"><span class="hlt">Downscaling</span> future climate scenarios to fine scales for hydrologic and ecological modeling and analysis</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">The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the <span class="hlt">downscaling</span> while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale <span class="hlt">downscaling</span> to analyses of ecological processes influenced by topographic complexity.</p> <div class="credits"> <p class="dwt_author">Flint, Lorraine E.; Flint, Alan L.</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">205</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 " 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://adsabs.harvard.edu/abs/2012ClDy...39..185I"> <span id="translatedtitle">Validation of precipitation over Japan during 1985-2004 simulated by three regional climate models and two multi-model <span class="hlt">ensemble</span> means</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 dynamically <span class="hlt">downscaled</span> Japanese reanalysis data (JRA-25) for 60 regions of Japan using three regional climate models (RCMs): the Non-Hydrostatic Regional Climate Model (NHRCM), modified RAMS version 4.3 (NRAMS), and modified Weather Research and Forecasting model (TWRF). We validated their simulations of the precipitation climatology and interannual variations of summer and winter precipitation. We also validated precipitation for two multi-model <span class="hlt">ensemble</span> means: the arithmetic <span class="hlt">ensemble</span> mean (AEM) and an <span class="hlt">ensemble</span> mean weighted according to model reliability. In the 60 regions NRAMS simulated both the winter and summer climatological precipitation better than JRA-25, and NHRCM simulated the wintertime precipitation better than JRA-25. TWRF, however, overestimated precipitation in the 60 regions in both the winter and summer, and NHRCM overestimated precipitation in the summer. The three RCMs simulated interannual variations, particularly summer precipitation, better than JRA-25. AEM simulated both climatological precipitation and interannual variations during the two seasons more realistically than JRA-25 and the three RCMs overall, but the best RCM was often superior to the AEM result. In contrast, the weighted <span class="hlt">ensemble</span> mean skills were usually superior to those of the best RCM. Thus, both RCMs and multi-model <span class="hlt">ensemble</span> means, especially multi-model <span class="hlt">ensemble</span> means weighted according to model reliability, are powerful tools for simulating seasonal and interannual variability of precipitation in Japan under the current climate.</p> <div class="credits"> <p class="dwt_author">Ishizaki, Yasuhiro; Nakaegawa, Toshiyuki; Takayabu, Izuru</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">207</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/2014EGUGA..1611713F"> <span id="translatedtitle">NARCliM regional <span class="hlt">downscaling</span> project in Australia: Long-term climatological analysis of the control period</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">NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modeling project for the Australian area. It will provide a comprehensive dynamically <span class="hlt">downscaled</span> climate dataset for the CORDEX-AustralAsia region at 50km, and South-East Australia at a resolution of 10km. NARCliM data will be used by state governments to design their climate change adaptation plans. It runs an <span class="hlt">ensemble</span> of WRF simulations using three different physical configurations and four different GCMs for the present and future periods along three different time-windows (1990-2010, 2020-2040 and 2060-2080). We will present the validation of the control period (1950-2009) using the NNRP re-analysis. Simulated climatologies are compared with observed ones from a gridded data-set (AWAP) comparing observed and simulated seasonal climatologies and long-term series based on the climatological sensitivity to different climate indices (representing modes of variability including ENSO, the Indian Ocean Dipole, and the Southern Annular Mode which affect the Australia climate). Results show that the performance of the simulated climate presents a regional (from tropical to desert areas), seasonal and variable (precipitation and minimum/maximum daily temperatures) sensitivity without a clear outperforming physical configuration. Long-term analysis (mostly by means of correlations with the time-series of the indices) shows that increasing spatial resolution has a positive impact on how the model represents the continental climate response to the large scale and improves the results from the data providing the boundary conditions (NNRP) taking the response of the observations as the reference.</p> <div class="credits"> <p class="dwt_author">Fita, Lluís; Argüeso, Daniel; Evans, Jason P.; King, Andrew D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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://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 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://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">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/2012AGUFM.A41H0062U"> <span id="translatedtitle">Diamond-NICAM-SPRINTARS: <span class="hlt">downscaling</span> and simulation results</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">As a part of initiative "Research Program on Climate Change Adaptation" (RECCA) which investigates how predicted large-scale climate change may affect a local weather, and further examines possible atmospheric hazards that cities may encounter due to such a climate change, thus to guide policy makers on implementing new environmental measures, a "Development of Seamless Chemical AssimiLation System and its Application for Atmospheric Environmental Materials" (SALSA) project is funded by the Japanese Ministry of Education, Culture, Sports, Science and Technology and is focused on creating a regional (local) scale assimilation system that can accurately recreate and predict a transport of carbon dioxide and other air pollutants. In this study, a regional model of the next generation global cloud-resolving model NICAM (Non-hydrostatic ICosahedral Atmospheric Model) (Tomita and Satoh, 2004) is used and ran together with a transport model SPRINTARS (Spectral Radiation Transport Model for Aerosol Species) (Takemura et al, 2000) and a chemical transport model CHASER (Sudo et al, 2002) to simulate aerosols across urban cities (over a Kanto region including metropolitan Tokyo). The presentation will mainly be on a "Diamond-NICAM" (Figure 1), a regional climate model version of the global climate model NICAM, and its dynamical <span class="hlt">downscaling</span> methodologies. Originally, a global NICAM can be described as twenty identical equilateral triangular-shaped panels covering the entire globe where grid points are at the corners of those panels, and to increase a resolution (called a "global-level" in NICAM), additional points are added at the middle of existing two adjacent points, so a number of panels increases by fourfold with an increment of one global-level. On the other hand, a Diamond-NICAM only uses two of those initial triangular-shaped panels, thus only covers part of the globe. In addition, NICAM uses an adaptive mesh scheme and its grid size can gradually decrease, as the grid points get closer to the point of focus. This "stretching" strength of grid points is called a "stretching ratio", and allows a Diamond-NICAM to adjust its domain size. Also, this eliminates a need for the multiple nesting as a grid size gradually and smoothly changes in the global NICAM. Area of Diamond-NICAM-SPRINTARS when g-level is set at 7 with a stretching ratio of 100.</p> <div class="credits"> <p class="dwt_author">Uchida, J.</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">211</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/2012PhDT.......191S"> <span id="translatedtitle">Climate Modeling & <span class="hlt">Downscaling</span> for Semi-Arid Regions</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 performs numerical modeling for the climate of semi-arid regions by running a high-resolution atmospheric model constrained by large-scale climatic boundary conditions, a practice commonly called climate <span class="hlt">downscaling</span>. These investigations focus especially on precipitation and temperature, quantities that are critical to life in semi-arid regions. Using the Weather Research and Forecast (WRF) model, a non-hydrostatic geophysical fluid dynamical model with a full suite of physical parameterization, a series of numerical sensitivity experiments are conducted to test how the intensity and spatial/temporal distribution of precipitation change with grid resolution, time step size, the resolution of lower boundary topography and surface characteristics. Two regions, Arizona in U.S. and Aral Sea region in Central Asia, are chosen as the test-beds for the numerical experiments: The former for its complex terrain and the latter for the dramatic man-made changes in its lower boundary conditions (the shrinkage of Aral Sea). Sensitivity tests show that the parameterization schemes for rainfall are not resolution-independent, thus a refinement of resolution is no guarantee of a better result. But, simulations (at all resolutions) do capture the inter-annual variability of rainfall over Arizona. Nevertheless, temperature is simulated more accurately with refinement in resolution. Results show that both seasonal mean rainfall and frequency of extreme rainfall events increase with resolution. For Aral Sea, sensitivity tests indicate that while the shrinkage of Aral Sea has a dramatic impact on the precipitation over the confine of (former) Aral Sea itself, its effect on the precipitation over greater Central Asia is not necessarily greater than the inter-annual variability induced by the lateral boundary conditions in the model and large scale warming in the region. The numerical simulations in the study are cross validated with observations to address the realism of the regional climate model. The findings of this sensitivity study are useful for water resource management in semi-arid regions. Such high spatio-temporal resolution gridded-data can be used as an input for hydrological models for regions such as Arizona with complex terrain and sparse observations. Results from simulations of Aral Sea region are expected to contribute to ecosystems management for Central Asia.</p> <div class="credits"> <p class="dwt_author">Sharma, Ashish</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2990198"> <span id="translatedtitle">A Spatio-Temporal <span class="hlt">Downscaler</span> for Output From Numerical Models</span></a>  </p> <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">Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this article we propose a simple, fully model-based strategy to <span class="hlt">downscale</span> the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially-varying coefficients which are specified through a correlated spatial Gaussian process. As an example, we apply our method to ozone concentration data for the eastern U.S. and compare it to Bayesian melding (Fuentes and Raftery 2005) and ordinary kriging (Cressie 1993; Chilès and Delfiner 1999). Our results show that our method outperforms Bayesian melding in terms of computing speed and it is superior to both Bayesian melding and ordinary kriging in terms of predictive performance; predictions obtained with our method are better calibrated and predictive intervals have empirical coverage closer to the nominal values. Moreover, our model can be easily extended to accommodate for the temporal dimension. In this regard, we consider several spatio-temporal versions of the static model. We compare them using out-of-sample predictions of ozone concentration for the eastern U.S. for the period May 1–October 15, 2001. For the best choice, we present a summary of the analysis. Supplemental material, including color versions of Figures 4, 5, 6, 7, and 8, and MCMC diagnostic plots, are available online. PMID:21113385</p> <div class="credits"> <p class="dwt_author">Berrocal, Veronica J.; Gelfand, Alan E.; Holland, David M.</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">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/2009EGUGA..11.8181B"> <span id="translatedtitle">A <span class="hlt">downscaling</span> method for the assessment of local 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">The use of complimentary models is necessary to study the impact of climate change scenarios on the hydrological response at different space-time scales. However, the structure of GCMs is such that their space resolution (hundreds of kilometres) is too coarse and not adequate to describe the variability of extreme events at basin scale (Burlando and Rosso, 2002). To bridge the space-time gap between the climate scenarios and the usual scale of the inputs for hydrological prediction models is a fundamental requisite for the evaluation of climate change impacts on water resources. Since models operate a simplification of a complex reality, their results cannot be expected to fit with climate observations. Identifying local climate scenarios for impact analysis implies the definition of more detailed local scenario by <span class="hlt">downscaling</span> GCMs or RCMs results. Among the output correction methods we consider the statistical approach by Déqué (2007) reported as a ‘Variable correction method' in which the correction of model outputs is obtained by a function build with the observation dataset and operating a quantile-quantile transformation (Q-Q transform). However, in the case of daily precipitation fields the Q-Q transform is not able to correct the temporal property of the model output concerning the dry-wet lacunarity process. An alternative correction method is proposed based on a stochastic description of the arrival-duration-intensity processes in coherence with the Poissonian Rectangular Pulse scheme (PRP) (Eagleson, 1972). In this proposed approach, the Q-Q transform is applied to the PRP variables derived from the daily rainfall datasets. Consequently the corrected PRP parameters are used for the synthetic generation of statistically homogeneous rainfall time series that mimic the persistency of daily observations for the reference period. Then the PRP parameters are forced through the GCM scenarios to generate local scale rainfall records for the 21st century. The statistical parameters characterizing daily storm occurrence, storm intensity and duration needed to apply the PRP scheme are considered among STARDEX collection of extreme indices.</p> <div class="credits"> <p class="dwt_author">Bruno, E.; Portoghese, I.; Vurro, 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">214</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://dspace.mit.edu/handle/1721.1/47844"> <span id="translatedtitle"><span class="hlt">Ensemble</span> regression : using <span class="hlt">ensemble</span> model output for atmospheric dynamics and prediction</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> regression (ER) is a linear inversion technique that uses <span class="hlt">ensemble</span> statistics from atmospheric model output to make dynamical inferences and forecasts. ER defines a multivariate regression operator using <span class="hlt">ensemble</span> ...</p> <div class="credits"> <p class="dwt_author">Gombos, Daniel (Daniel Lawrence)</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">215</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=3614370"> <span id="translatedtitle">Comparative Visualization of <span class="hlt">Ensembles</span> Using <span class="hlt">Ensemble</span> Surface Slicing</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">By definition, an <span class="hlt">ensemble</span> is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an <span class="hlt">ensemble</span> is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call <span class="hlt">Ensemble</span> Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators. PMID:23560167</p> <div class="credits"> <p class="dwt_author">Alabi, Oluwafemi S.; Wu, Xunlei; Harter, Jonathan M.; Phadke, Madhura; Pinto, Lifford; Petersen, Hannah; Bass, Steffen; Keifer, Michael; Zhong, Sharon; Healey, Chris; Taylor, Russell M.</p> <p 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">216</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/21475662"> <span id="translatedtitle">Algorithms on <span class="hlt">ensemble</span> quantum computers.</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 <span class="hlt">ensemble</span> (or bulk) quantum computation, all computations are performed on an <span class="hlt">ensemble</span> of computers rather than on a single computer. Measurements of qubits in an individual computer cannot be performed; instead, only expectation values (over the complete <span class="hlt">ensemble</span> of computers) can be measured. As a result of this limitation on the model of computation, many algorithms cannot be processed directly on such computers, and must be modified, as the common strategy of delaying the measurements usually does not resolve this <span class="hlt">ensemble</span>-measurement problem. Here we present several new strategies for resolving this problem. Based on these strategies we provide new versions of some of the most important quantum algorithms, versions that are suitable for implementing on <span class="hlt">ensemble</span> quantum computers, e.g., on liquid NMR quantum computers. These algorithms are Shor's factorization algorithm, Grover's search algorithm (with several marked items), and an algorithm for quantum fault-tolerant computation. The first two algorithms are simply modified using a randomizing and a sorting strategies. For the last algorithm, we develop a classical-quantum hybrid strategy for removing measurements. We use it to present a novel quantum fault-tolerant scheme. More explicitly, we present schemes for fault-tolerant measurement-free implementation of Toffoli and ?(z)(¼) as these operations cannot be implemented "bitwise", and their standard fault-tolerant implementations require measurement. PMID:21475662</p> <div class="credits"> <p class="dwt_author">Boykin, P Oscar; Mor, Tal; Roychowdhury, Vwani; Vatan, Farrokh</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">217</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=https://www.meted.ucar.edu/training_module.php?id=201"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Prediction System Matrix: Characteristics of Operational <span class="hlt">Ensemble</span> Prediction Systems (EPS)</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">This one-stop <span class="hlt">Ensemble</span> Model Matrix provides information on the configurations of the NCEP Short-Range <span class="hlt">Ensemble</span> Forecast (SREF) and Medium-Range <span class="hlt">Ensemble</span> Forecast (MREF) systems. Information on <span class="hlt">ensemble</span> perturbation methods; NWP model resolution, dynamics, physics (precipitation, radiation, land surface and turbulence); and <span class="hlt">ensemble</span> post-processing and verification links are provided. As the <span class="hlt">ensemble</span> prediction systems (EPSs) are improved, the information in the <span class="hlt">Ensemble</span> Model Matrix will be updated. Additionally, as new EPSs are added to AWIPS, we will add new columns to the <span class="hlt">Ensemble</span> Model Matrix.</p> <div class="credits"> <p class="dwt_author">COMET</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-04-05</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://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=308425"> <span id="translatedtitle">A method to <span class="hlt">downscale</span> soil moisture to fine-resolutions using topographic, vegetation, and soil data</span></a>  </p> <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">Soil moisture can be estimated over large regions with spatial resolutions greater than 500 m, but many applications require finer resolutions (10 – 100 m grid cells). Several methods use topographic data to <span class="hlt">downscale</span>, but vegetation and soil patterns can also be important. In this paper, a downsc...</p> <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">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.science.mcmaster.ca/geo/faculty/coulibaly/WRHML/Publications/Publications/ANN03_06.pdf"> <span id="translatedtitle"><span class="hlt">DOWNSCALING</span> GLOBAL CLIMATE MODEL OUTPUTS TO STUDY THE HYDROLOGIC IMPACT OF CLIMATE 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">To correctly model the flood regime of a catchment, continuous rainfall-runoff simulation at hourly or at least at daily time steps is necessary. Such daily rainfall series at a catchment corresponding to future climate scenarios can be derived from Global Climate Model (GCM) outputs using the so called '<span class="hlt">downscaling</span> techniques'. These conversion methods can provide future daily rainfall scenarios relevant</p> <div class="credits"> <p class="dwt_author">YONAS B. DIBIKE; PAULIN COULIBALY</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://www.pet.hw.ac.uk/documents/Couples_NERC_CDT_2014.pdf"> <span id="translatedtitle">Oil and Gas CDT <span class="hlt">Downscaling</span>-Upscaling: A Robust Method of Linking</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">are then used as inputs to flow (or other) simulation, resulting in the calculation of bulk, effectiveOil and Gas CDT <span class="hlt">Downscaling</span>-Upscaling: A Robust Method of Linking Geomechanical Simulations and Fluid Flow Simulations Heriot-Watt University, Institute of Petroleum Engineering Supervisory Team Prof</p> <div class="credits"> <p class="dwt_author">Henderson, Gideon</p> <p class="dwt_publisher"></p> <p class="publishDate"></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" 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">221</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ece.uvic.ca/~wslu/Publications/Lu-Conference/C06-7.pdf"> <span id="translatedtitle">Adaptive <span class="hlt">Down-Scaling</span> Techniques for JPEG-Based Low Bit-Rate Image Coding</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Adaptive <span class="hlt">Down-Scaling</span> Techniques for JPEG-Based Low Bit-Rate Image Coding Ana-Maria Sevcenco and Wu ways: either adaptive in terms of the rate of the down-sampling or in terms of the quality factor techniques. Keywords ­ Image coding, adaptive down-sampling, local image features and statistics, quality</p> <div class="credits"> <p class="dwt_author">Lu, Wu-Sheng</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://academic.research.microsoft.com/Publication/51896417"> <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://academic.research.microsoft.com/">Microsoft Academic Search </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</p> <div class="credits"> <p class="dwt_author">D. B. Lobell; D. Urban</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">223</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=213342"> <span id="translatedtitle">Reductions in seasonal climate forecast dependability as a result of <span class="hlt">downscaling</span></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">This research determines whether NOAA/CPC seasonal climate forecasts are skillful enough to retain utility after they have been <span class="hlt">downscaled</span> for use in crop models. Utility is assessed using net dependability, the product of the large-scale 3-month forecast dependability and a factor accounting for l...</p> <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">224</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/47619640"> <span id="translatedtitle">An estimate of future climate change for western France using a statistical <span class="hlt">downscaling</span> 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">A statistical <span class="hlt">downscaling</span> procedure based on an analogue technique is used to determine projections for future climate change in western France. Three ocean and atmosphere coupled models are used as the starting point of the regionalization technique. Models' climatology and day to day variability are found to reproduce the broad main characteristics seen in the reanalyses. The response of the</p> <div class="credits"> <p class="dwt_author">B. Timbal; A. Dufour; B. McAvaney</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">225</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">226</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.image.ucar.edu/~ssain/publications/narccap_gfdl.pdf"> <span id="translatedtitle">Functional ANOVA and Regional Climate Experiments: A Statistical Analysis of Dynamic <span class="hlt">Downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">of the United States to a few hundred grid points. Even the large western states are discretized to just a few for dynamic <span class="hlt">downscaling</span> of global models. In this paper, we discuss an initial analysis of a subset, and it is a grand scientific challenge to model the dynamics of this system over time and in response to external</p> <div class="credits"> <p class="dwt_author">Sain, Steve</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">227</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 " 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://www.maths.unsw.edu.au/~mbaird/downscale_submit.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> an eddy-resolving global ocean model for the continental shelf off southeast</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary"><span class="hlt">Downscaling</span> an eddy-resolving global ocean model for the continental shelf off southeast Australia from Oceans Flagship Program, Hobart, Tasmania, Australia Abstract An eddy-resolving global ocean model-flowing5 western boundary current, and the mesoscale eddies that are spawned at its6 separation (Cresswell</p> <div class="credits"> <p class="dwt_author">Baird, Mark</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">229</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=ftp://texmex.mit.edu/pub/emanuel/PAPERS/Emanuel_PNAS_2013.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> CMIP5 climate models shows increased tropical cyclone activity over the 21st century</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">of global warming on tropical cyclones. climate change | natural hazards Some 90 tropical cyclones develop of these additional factors to global climate change generally results in a reduction of the global frequency of tropical cyclones as the climate warms, seen in many explicit and <span class="hlt">downscaled</span> simulations using global</p> <div class="credits"> <p class="dwt_author">Rothman, Daniel</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://ntrs.nasa.gov/search.jsp?R=20140010385&hterms=climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dclimate%2Bchange"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">Statistical <span class="hlt">downscaling</span> can be used to efficiently <span class="hlt">downscale</span> a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically <span class="hlt">downscales</span> (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical <span class="hlt">Downscaling</span> and Bias Correction (SDBC) approach. Based on these <span class="hlt">downscaled</span> data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical <span class="hlt">downscaling</span> as an intermediate step does not lead to considerable differences in the results of statistical <span class="hlt">downscaling</span> for the study domain.</p> <div class="credits"> <p class="dwt_author">Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard</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">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/2014OcDyn..64..927S"> <span id="translatedtitle"><span class="hlt">Downscaling</span> IPCC control run and future scenario with focus on the Barents Sea</span></a>  </p> <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 atmosphere-ocean general circulation models are the tool by which projections for climate changes due to radiative forcing scenarios have been produced. Further, regional atmospheric <span class="hlt">downscaling</span> of the global models may be applied in order to evaluate the details in, e.g., temperature and precipitation patterns. Similarly, detailed regional information is needed in order to assess the implications of future climate change for the marine ecosystems. However, regional results for climate change in the ocean are sparse. We present the results for the circulation and hydrography of the Barents Sea from the ocean component of two global models and from a corresponding pair of regional model configurations. The global models used are the GISS AOM and the NCAR CCSM3. The ROMS ocean model is used for the regional <span class="hlt">downscaling</span> of these results (ROMS-G and ROMS-N configurations, respectively). This investigation was undertaken in order to shed light on two questions that are essential in the context of regional ocean projections: (1) How should a regional model be set up in order to take advantage of the results from global projections; (2) What limits to quality in the results of regional models are imposed by the quality of global models? We approached the first question by initializing the ocean model in the control simulation by a realistic ocean analysis and specifying air-sea fluxes according to the results from the global models. For the projection simulation, the global models' oceanic anomalies from their control simulation results were added upon initialization. Regarding the second question, the present set of simulations includes regional <span class="hlt">downscalings</span> of the present-day climate as well as projected climate change. Thus, we study separately how <span class="hlt">downscaling</span> changes the results in the control climate case, and how scenario results are changed. For the present-day climate, we find that <span class="hlt">downscaling</span> reduces the differences in the Barents Sea between the original global models. Furthermore, the <span class="hlt">downscaled</span> results are closer to observations. On the other hand, the <span class="hlt">downscaled</span> results from the scenario simulations are significantly different: while the heat transport into the Barents Sea and the salinity distribution change modestly from control to scenario with ROMS-G, in ROMS-N the heat transport is much larger in the scenario simulation, and the water masses become much less saline. The lack of robustness in the results from the scenario simulations leads us to conclude that the results for the regional oceanic response to changes in the radiative forcing depend on the choice of AOGCM and is not settled. Consequently, the effect of climate change on the marine ecosystem of the Barents Sea is anything but certain.</p> <div class="credits"> <p class="dwt_author">Sandø, Anne Britt; Melsom, Arne; Budgell, William Paul</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-07-01</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/2010EGUGA..1214100T"> <span id="translatedtitle">Spatiotemporal statistical <span class="hlt">downscaling</span> method with uncertainty for climate change impact assessment on droughts</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 impact assessment requires spatial and temporal scales at which impacts take place. The outputs of the current general circulation models (GCMs) and regional climatic models (RCMs) cannot be used for analysis at local extent due to their coarse resolution. <span class="hlt">Downscaling</span> methods and spatial interpolation techniques are employed in this study to <span class="hlt">downscale</span> mean monthly precipitation over the region Thessaly, Greece. The outputs of Global Circulation Models CGCMa2 and the ECHAM5 were applied for two socioeconomic scenarios, namely, SRES A2 and SRES B2 for the assessment of climate change impact on droughts. Observations from 79 precipitation stations for the period October 1960 to September 2002 were used. Ordinary kriging was employed for the spatial distribution of precipitation data into 128 grids of 10 x 10 km. K-means cluster analysis was performed to the historical data for the formation of six clusters for precipitation. The <span class="hlt">downscaling</span> methodology is based on a generalized multiple regression (GMLR) of GCM predictor variables with observed cluster precipitation and the application of stochastic timeseries models for the treatment of the residuals (white noise) in clusters. The GMLR models used large-scale predictor parameters of GCMs output such as minimum Surface Temperature (STmin) and Geopotential Thickness 500-1000 hpa (GZ500-1000). The accuracy of precipitation <span class="hlt">downscaling</span> for the base period (1960-1990) was quite low and the regression coefficient between <span class="hlt">downscaled</span> and observed grid precipitation was between 0,29 and 0,58. A MPAR(2) multivariate autoregressive model was developed for the simulation of the residuals between GMLR projections and observed data. Using the time series models 100 synthetic timeseries of precipitation were reproduced in each grid. Various statistics (Mean Average Error (MAE), Root Mean Square Error (RMSE), Coefficient of Efficiency (CI), Index of Agreement (IA) and Persistence Index (PI)) were used for the comparison of <span class="hlt">downscaled</span> with the observed grid precipitation and the calculated drought Standardized Precipitation Index (SPI) for the development base period (1960-1990) and the validation period (1990-2002). The comparison analysis for the two period indicated the accuracy, reliability of the <span class="hlt">downscaling</span> method and the uncertainty introduced in climate change studies.</p> <div class="credits"> <p class="dwt_author">Tzabiras, John; Loukas, Athanasios; Vasiliades, Lampros</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">233</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/2013AGUFMGC43C1056M"> <span id="translatedtitle">Addressing impacts of different statistical <span class="hlt">downscaling</span> methods on large scale hydrologic 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">Many hydrologic assessments, such as evaluations of climate change impacts on water resources, require <span class="hlt">downscaled</span> climate model outputs to force hydrologic simulations at a spatial resolution finer than the climate models' native scale. Statistical <span class="hlt">downscaling</span> is an attractive alternative to dynamical <span class="hlt">downscaling</span> methods for continental scale hydrologic applications because of its lower computational cost. The goal of this study is to illustrate and compare how the errors in precipitation and temperature produced by different statistical <span class="hlt">downscaling</span> methods propagate into hydrologic simulations. Multi-decadal hydrologic simulations were performed with three process-based hydrologic models (CLM, VIC, and PRMS) forced by multiple climate datasets over the contiguous United States. The forcing datasets include climate data derived from gauge observations (M02) as well as climate data <span class="hlt">downscaled</span> from the NCEP-NCAR reanalysis using 4 statistical <span class="hlt">downscaling</span> methods for a domain with 12-km grid spacing: two forms of Bias Corrected Spatially Disaggregated methods (BCSD-monthly and BCSD-daily), Bias Corrected Constructed Analogue (BCCA), and Asynchronous Regression (AR). Our results show that both BCCA and BCSD-daily underestimate extreme precipitation events while AR produces these correctly at the scale at which the simulations were run but does not scale them up appropriately to larger basin scales like HUC-4 and HUC-2. These artifacts lead to a poor representation of flooding events when hydrologic models are forced by these methods over a range of spatial scales. We also illustrate that errors in precipitation depths dominate impacts on runoff depth estimations, and that errors in wet day frequency have a larger effect on shortwave radiation estimations than do the errors in temperatures; this error subsequently affects the partitioning of precipitation into evaporation and runoff as we show over mountainous areas of the upper Colorado River. Finally we show the inter-model differences across our simulations are generally lower than than inter-forcing data differences. We conclude with preliminary guidance on sound methodological choices for future climate impact studies using these methods. Comparison of annual precipitation between statistically <span class="hlt">downscaled</span> data and observation (M02) and illustration of how these differences propagate into hydrologic simulations with two models. Figure shows the simulations over the western United States.</p> <div class="credits"> <p class="dwt_author">Mizukami, N.; Clark, M. P.; Gutmann, E. D.; Mendoza, P. A.; Brekke, L. D.; Arnold, J.; Raff, D. A.</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">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/2012EGUGA..14.2728P"> <span id="translatedtitle">Multiscale spatial recorrelation of RCM precipitation <span class="hlt">downscaling</span> to correct predictions over large areas and small</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 <span class="hlt">downscaling</span> Regional Circulation Model (RCM) rainfall estimates, it is common to concentrate on the local statistics. To ignore the spatial dependence at large scales may lead to problems in hydrological applications, particularly in estimating/modelling extreme runoff from large areas, due to the wrongly modelled clustering behaviour of storms. In Bárdossy and Pegram [2011], <span class="hlt">downscaling</span> of RCM rainfall marginal distributions, dependent on Circulation Patterns (CPs), was successfully achieved over 172 blocks of the Rhine basin at 25 km scale. Uneasy about the spatial statistics of the <span class="hlt">downscaled</span> RCM rainfall, we calculated the spatial cross correlation coefficients (cccs) of daily rainfalls of the same set. We found that the cccs of the RCM precipitations were significantly lower than those of the observations, especially for large areas aggregated from the elemental block estimates. CP based <span class="hlt">downscaling</span> led to a slight increase of the cccs but their values remained below those of the observed cccs. We therefore decided to perform a recorrelation treatment to correct the cccs of the RCM estimates back to the observed set, before undertaking the final quantile-quantile (Q-Q) transform. In this presentation we use a matrix method of recorrelation which was successful in that it recaptured the observed cccs almost exactly. In addition, it was demonstrated that the method coped with problems presented by the high proportion of dry days, when applied to five moderately large and climatologically different South African regions (10 000 to 14 000 sq km) in addition to the large German Rhine basin (108 000 sq km). After recorrelation, the appropriate Q-Q transforms are used to recover the appropriate distributions, and it is demonstrated that the spatial coherence of precipitation over large areas is recovered well enough to closely match that of the gauge-based observations. Bárdossy, A., and G. Pegram (2011), <span class="hlt">Downscaling</span> precipitation using regional climate models and circulation patterns toward hydrology, Water Resources Research, 47(W04505), doi:10.1029/2010WR009,689.</p> <div class="credits"> <p class="dwt_author">Pegram, G. G. S.; Bárdossy, A.</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">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/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 " 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/2014GMD.....7..387F"> <span id="translatedtitle">TopoSCALE v.1.0: <span class="hlt">downscaling</span> gridded climate data in complex terrain</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">Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by, for example, topography, and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to <span class="hlt">downscale</span> coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM). The method makes use of an interpolation of pressure-level data according to topographic height of the subgrid. An elevation and topography correction is used to <span class="hlt">downscale</span> short-wave radiation. Long-wave radiation is <span class="hlt">downscaled</span> by deriving a cloud-component of all-sky emissivity at grid level and using <span class="hlt">downscaled</span> temperature and relative humidity fields to describe variability with elevation. Precipitation is <span class="hlt">downscaled</span> with a simple non-linear lapse and optionally disaggregated using a climatology approach. We test the method in comparison with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure levels: air temperature, relative humidity, wind speed and incoming long-wave radiation. This method may be of use in improving inputs to numerical simulations in heterogeneous and/or remote terrain, especially when statistical methods are not possible, due to lack of observations (i.e. remote areas or future periods).</p> <div class="credits"> <p class="dwt_author">Fiddes, J.; Gruber, S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-02-01</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://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">238</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/22308400"> <span id="translatedtitle">Quantum Gibbs <span class="hlt">ensemble</span> Monte Carlo</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 present a path integral Monte Carlo method which is the full quantum analogue of the Gibbs <span class="hlt">ensemble</span> Monte Carlo method of Panagiotopoulos to study the gas-liquid coexistence line of a classical fluid. Unlike previous extensions of Gibbs <span class="hlt">ensemble</span> Monte Carlo to include quantum effects, our scheme is viable even for systems with strong quantum delocalization in the degenerate regime of temperature. This is demonstrated by an illustrative application to the gas-superfluid transition of {sup 4}He in two dimensions.</p> <div class="credits"> <p class="dwt_author">Fantoni, Riccardo, E-mail: rfantoni@ts.infn.it [Dipartimento di Scienze Molecolari e Nanosistemi, Università Ca’ Foscari Venezia, Calle Larga S. Marta DD2137, I-30123 Venezia (Italy); Moroni, Saverio, E-mail: moroni@democritos.it [DEMOCRITOS National Simulation Center, Istituto Officina dei Materiali del CNR and SISSA Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, I-34136 Trieste (Italy)</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-09-21</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://arxiv.org/pdf/1008.4362v1"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Averages when ?is a Square Integer</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">We give a hyperpfaffian formulation of partition functions and <span class="hlt">ensemble</span> averages for Hermitian and circular <span class="hlt">ensembles</span> when L is an arbitrary integer and \\beta=L^2 and when L is an odd integer and \\beta=L^2 +1.</p> <div class="credits"> <p class="dwt_author">Christopher D. Sinclair</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-08-25</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://www.ma.utexas.edu/mp_arc/mp_arc/c/08/08-176.pdf"> <span id="translatedtitle">Topological quantization of <span class="hlt">ensemble</span> averages</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 define the current of a quantum observable and, under well-defined conditions, we connect its <span class="hlt">ensemble</span> average to the index of a Fredholm operator. The present work builds on a formalism developed by Kellendonk and Schulz-Baldes (2004 J. Funct. Anal. 209 388) to study the quantization of edge currents for continuous magnetic Schrödinger operators. The generalization given here may be</p> <div class="credits"> <p class="dwt_author">Emil Prodan</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</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_11");' 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">241</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://dolecki.perso.math.cnrs.fr/Chapter_1.pdf"> <span id="translatedtitle">Thorie des <span class="hlt">ensembles</span> 1. Motivation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">des <span class="hlt">ensembles</span>. David Hilbert (1862-1943) écrivait de cette contribution «Que personne ne puisse nous Bologne en 1928 du langage formel de Peano, Hilbert disait que c'était un outil essentiel pour sa théorie</p> <div class="credits"> <p class="dwt_author">Dolecki, Szymon</p> <p class="dwt_publisher"></p> <p class="publishDate"></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.ncbi.nlm.nih.gov/pubmed/24802114"> <span id="translatedtitle">A double pruning scheme for boosting <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"><span class="hlt">Ensemble</span> learning consists of generating a collection of classifiers whose predictions are then combined to yield a single unified decision. <span class="hlt">Ensembles</span> of complementary classifiers provide accurate and robust predictions, which are often better than the predictions of the individual classifiers in the <span class="hlt">ensemble</span>. Nevertheless, <span class="hlt">ensembles</span> also have some drawbacks: typically, all classifiers are queried to compute the final <span class="hlt">ensemble</span> prediction. Therefore, all the classifiers need to be accessible to address potential queries. This entails larger storage requirements and slower predictions than a single classifier. <span class="hlt">Ensemble</span> pruning techniques are useful to alleviate these drawbacks. Static pruning techniques reduce the <span class="hlt">ensemble</span> size by selecting a sub-<span class="hlt">ensemble</span> of classifiers from the original <span class="hlt">ensemble</span>. In dynamic pruning, the querying process is halted when the partial <span class="hlt">ensemble</span> prediction is sufficient to reach a stable final decision with a reasonable amount of confidence. In this paper, we present the results of a comprehensive analysis of static and dynamic pruning techniques applied to Adaboost <span class="hlt">ensembles</span>. These <span class="hlt">ensemble</span> pruning techniques are evaluated on a wide range of classification problems. From this analysis, one concludes that the combination of static and dynamic pruning techniques provides a notable reduction in the memory requirements and an improvement in the classification time without a significant loss of prediction accuracy. PMID:24802114</p> <div class="credits"> <p class="dwt_author">Soto, Víctor; García-Moratilla, Sergio; Martínez-Muñoz, Gonzalo; Hernández-Lobato, Daniel; Suárez, Alberto</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-12-01</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://t2.physik.tu-dortmund.de/de/mitglieder/anders/arbeiten/Thesis_Jovchev.pdf"> <span id="translatedtitle">Spindephasierung und kohrente Kontrolle eines <span class="hlt">Ensembles</span> von</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Spindephasierung und kohärente Kontrolle eines <span class="hlt">Ensembles</span> von Quantenpunkten Spindephasing and coherent control of an <span class="hlt">ensemble</span> of quantum dots Master-Thesis von Andre Jovchev April 2012 Institut für Festkörperphysik AG Grewe #12;Spindephasierung und kohärente Kontrolle eines <span class="hlt">Ensembles</span> von Quantenpunkten</p> <div class="credits"> <p class="dwt_author">Anders, Frithjof</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://ti.arc.nasa.gov/m/profile/oza/files/oza01.pdf"> <span id="translatedtitle">Online <span class="hlt">Ensemble</span> Learning Nikunj Chandrakant Oza</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Date Date Date University of California at Berkeley 2001 #12;Online <span class="hlt">Ensemble</span> Learning Copyright 2001Online <span class="hlt">Ensemble</span> Learning by Nikunj Chandrakant Oza B.S. (Massachusetts Institute of Technology by Nikunj Chandrakant Oza #12;1 Abstract Online <span class="hlt">Ensemble</span> Learning by Nikunj Chandrakant Oza Doctor</p> <div class="credits"> <p class="dwt_author">Oza, Nikunj C.</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">245</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=steel&pg=5&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 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://adsabs.harvard.edu/abs/2015NJPh...17b3052S"> <span id="translatedtitle">Unbiased sampling of network <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">Sampling random graphs with given properties is a key step in the analysis of networks, as random <span class="hlt">ensembles</span> represent basic null models required to identify patterns such as communities and motifs. An important requirement is that the sampling process is unbiased and efficient. The main approaches are microcanonical, i.e. they sample graphs that match the enforced constraints exactly. Unfortunately, when applied to strongly heterogeneous networks (like most real-world examples), the majority of these approaches become biased and/or time-consuming. Moreover, the algorithms defined in the simplest cases, such as binary graphs with given degrees, are not easily generalizable to more complicated <span class="hlt">ensembles</span>. Here we propose a solution to the problem via the introduction of a ‘Maximize and Sample’ (‘Max & Sam’ for short) method to correctly sample <span class="hlt">ensembles</span> of networks where the constraints are ‘soft’, i.e. realized as <span class="hlt">ensemble</span> averages. Our method is based on exact maximum-entropy distributions and is therefore unbiased by construction, even for strongly heterogeneous networks. It is also more computationally efficient than most microcanonical alternatives. Finally, it works for both binary and weighted networks with a variety of constraints, including combined degree-strength sequences and full reciprocity structure, for which no alternative method exists. Our canonical approach can in principle be turned into an unbiased microcanonical one, via a restriction to the relevant subset. Importantly, the analysis of the fluctuations of the constraints suggests that the microcanonical and canonical versions of all the <span class="hlt">ensembles</span> considered here are not equivalent. We show various real-world applications and provide a code implementing all our algorithms.</p> <div class="credits"> <p class="dwt_author">Squartini, Tiziano; Mastrandrea, Rossana; Garlaschelli, Diego</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-02-01</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://oaspub.epa.gov/eims/eimsapi.dispdetail?deid=269553"> <span id="translatedtitle">An Observation-base investigation of nudging in WRF for <span class="hlt">downscaling</span> surface climate information to 12-km Grid Spacing</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p class="result-summary">Previous research has demonstrated the ability to use the Weather Research and Forecast (WRF) model and contemporary dynamical <span class="hlt">downscaling</span> methods to refine global climate modeling results to a horizontal resolution of 36 km. Environmental managers and urban planners have expre...</p> <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">248</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.A33A0222M"> <span id="translatedtitle">High-resolution climate simulations for Central Europe: An assessment of dynamical and statistical <span class="hlt">downscaling</span> techniques</span></a>  </p> <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">To bridge the resolution gap between the outputs of global climate models (GCMs) and finer-scale data needed for studies of the climate change impacts, two approaches are widely used: dynamical <span class="hlt">downscaling</span>, based on application of regional climate models (RCMs) embedded into the domain of the GCM simulation, and statistical <span class="hlt">downscaling</span> (SDS), using empirical transfer functions between the large-scale data generated by the GCM and local measurements. In our contribution, we compare the performance of different variants of both techniques for the region of Central Europe. The dynamical <span class="hlt">downscaling</span> is represented by the outputs of two regional models run in the 10 km horizontal grid, ALADIN-CLIMATE/CZ (co-developed by the Czech Hydrometeorological Institute and Meteo-France) and RegCM3 (developed by the Abdus Salam Centre for Theoretical Physics). The applied statistical methods were based on multiple linear regression, as well as on several of its nonlinear alternatives, including techniques employing artificial neural networks. Validation of the <span class="hlt">downscaling</span> outputs was carried out using measured data, gathered from weather stations in the Czech Republic, Slovakia, Austria and Hungary for the end of the 20th century; series of daily values of maximum and minimum temperature, precipitation and relative humidity were analyzed. None of the regional models or statistical <span class="hlt">downscaling</span> techniques could be identified as the universally best one. For instance, while most statistical methods misrepresented the shape of the statistical distribution of the target variables (especially in the more challenging cases such as estimation of daily precipitation), RCM-generated data often suffered from severe biases. It is also shown that further enhancement of the simulated fields of climate variables can be achieved through a combination of dynamical <span class="hlt">downscaling</span> and statistical postprocessing. This can not only be used to reduce biases and other systematic flaws in the generated time series, but also to further localize the RCM outputs beyond the resolution of their original grid. The resulting data then provide a suitable input for subsequent studies of the local climate and its change in the target region.</p> <div class="credits"> <p class="dwt_author">Miksovsky, J.; Huth, R.; Halenka, T.; Belda, M.; Farda, A.; Skalak, P.; Stepanek, P.</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">249</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/2012AGUFM.A41H0065B"> <span id="translatedtitle">Changes in water and wind resources across the central and northeastern U.S. 2060-2010 in 24 km WRF <span class="hlt">downscale</span> climate 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">GCM <span class="hlt">ensembles</span> for the IPCC AR4 indicate that by 2060 water and wind resources will change appreciably over the central and northeastern U.S. In order to investigate these possible changes on a scale relevant for agriculture and offshore wind-power planners, we produced 24 km <span class="hlt">downscale</span> simulations using the Weather Research and Forecasting (WRF) model. Our simulations span the years 2006-2010 and 2056-2060 with boundary conditions supplied by CCSM4 (IPCC emissions scenario RCP 8.5). By calculating the difference between the simulated time periods we find: 1) ~10% decrease in total annual precipitation across the southern half of the Ogallala aquifer in the central U.S., and ~10% increase across the northeastern states; and 2) Minimal change in annual-average 10-meter wind strength across the study areas, but with significant changes seasonal values. Interrogation of the simulation results is ongoing, and a complete synthesis will be presented at the annual meeting.</p> <div class="credits"> <p class="dwt_author">Birkel, S. D.; Maasch, K. A.; Oglesby, R. J.; Fulginiti, L.; Trindade, F.; Hays, C.</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">250</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 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=https://www.meted.ucar.edu/training_module.php?id=1029"> <span id="translatedtitle">Introduction to <span class="hlt">Ensembles</span>: Forecasting Hurricane Sandy</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">This module provides an introduction to <span class="hlt">ensemble</span> forecast systems with an operational case study of Hurricane Sandy. The module concentrates on models from NCEP and FNMOC available to forecasters in the U.S. Navy, including NAEFS (North American <span class="hlt">Ensemble</span> Forecast System), and NUOPC (National Unified Operational Prediction Capability). Probabilistic forecasts of winds and waves developed from these <span class="hlt">ensemble</span> forecast systems are applied to a ship transit and coastal resource protection. Lessons integrated in the case study provide information on <span class="hlt">ensemble</span> statistics, products, bias correction and verification. Additional lessons address multimodel <span class="hlt">ensembles</span>, extreme events, and automated forecasting.</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate">2014-09-14</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/2013AGUFMGC12C..05G"> <span id="translatedtitle">Lessons learned from the National Climate Predictions and Projections (NCPP) platform Workshop on Quantitative Evaluation of <span class="hlt">Downscaling</span> 2013</span></a>  </p> <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 mission of NCPP is to accelerate the provision of climate information on regional and local scale for use in adaptation planning and decision making through collaborative participation of a community of scientists and practitioners. A major focus is the development of a capability for objective and quantitative evaluation of <span class="hlt">downscaled</span> climate information in support of applications. NCPP recognizes the importance of focusing this evaluation effort on real-world applications and the necessity to work closely with the user community to deliver usable evaluations and guidance. This summer NCPP organized our first workshop on quantitative evaluation of <span class="hlt">downscaled</span> climate datasets (http://earthsystemcog.org/projects/<span class="hlt">downscaling</span>-2013/). Workshop participants included representatives from <span class="hlt">downscaling</span> efforts, applications partners from the health, ecological, agriculture and water resources impacts communities, and people working on data infrastructure, metadata, and standards development. The workshop exemplifies NCPP's approach of collaborative and participatory problem-solving where scientists are working together with practitioners to develop applications related evaluation. The set of observed and <span class="hlt">downscaled</span> datasets included for evaluation in the workshop were assessed using a variety of metrics to elucidate the statistical characteristics of temperature and precipitation time series. In addition, the <span class="hlt">downscaled</span> datasets were evaluated in terms of their representation of indices relevant to the participating applications working groups, more specifically related to human health and ecological impacts. The presentation will focus on sharing the lessons we learned from our workshop.</p> <div class="credits"> <p class="dwt_author">Guentchev, G.</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">253</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ntrs.nasa.gov/search.jsp?R=20020008664&hterms=indel&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dindel"> <span id="translatedtitle">Statistical <span class="hlt">Ensemble</span> of Large Eddy Simulations</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">A statistical <span class="hlt">ensemble</span> of large eddy simulations (LES) is run simultaneously for the same flow. The information provided by the different large scale velocity fields is used to propose an <span class="hlt">ensemble</span> averaged version of the dynamic model. This produces local model parameters that only depend on the statistical properties of the flow. An important property of the <span class="hlt">ensemble</span> averaged dynamic procedure is that it does not require any spatial averaging and can thus be used in fully inhomogeneous flows. Also, the <span class="hlt">ensemble</span> of LES's provides statistics of the large scale velocity that can be used for building new models for the subgrid-scale stress tensor. The <span class="hlt">ensemble</span> averaged dynamic procedure has been implemented with various models for three flows: decaying isotropic turbulence, forced isotropic turbulence, and the time developing plane wake. It is found that the results are almost independent of the number of LES's in the statistical <span class="hlt">ensemble</span> provided that the <span class="hlt">ensemble</span> contains at least 16 realizations.</p> <div class="credits"> <p class="dwt_author">Carati, Daniele; Rogers, Michael M.; Wray, Alan A.; Mansour, Nagi N. (Technical Monitor)</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">254</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">255</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/2014ClDy...43.1731J"> <span id="translatedtitle">Rainfall anomaly prediction using statistical <span class="hlt">downscaling</span> in a multimodel superensemble over tropical South America</span></a>  </p> <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 the predictability of rainfall variations over South America and the Amazon basin. A primary factor leading to model inaccuracy in precipitation forecasts is the coarse resolution data utilized by coupled models during the training phase. By using MERRA reanalysis and statistical <span class="hlt">downscaling</span> along with the superensemble methodology, it is possible to obtain more precise forecast of rainfall anomalies over tropical South America during austral fall. Selective inclusion (and exclusion) of member models also allows for increased accuracy of superensemble forecasts. The use of coupled atmospheric-ocean numerical models to predict the rainfall anomalies has had mixed results. Improvement in individual member models is also possible on smaller spatial scales and in regions where substantial topographical changes were not handled well under original model initial conditions. The combination of <span class="hlt">downscaling</span> and superensemble methodologies with other research methods presents the potential opportunity for increased accuracy not only in seasonal forecasts but on shorter temporal scales as well.</p> <div class="credits"> <p class="dwt_author">Johnson, Bradford; Kumar, Vinay; Krishnamurti, T. N.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-10-01</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://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. PMID:22619572</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">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/2014JHyd..519.3163S"> <span id="translatedtitle">Comparing statistically <span class="hlt">downscaled</span> simulations of Indian monsoon at different spatial resolutions</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">Impacts of climate change are typically assessed with fairly coarse resolution General Circulation Models (GCMs), which are unable to resolve local scale features that are critical to precipitation variability. GCM simulations must be <span class="hlt">downscaled</span> to finer resolutions, through statistical or dynamic modelling for further use in hydrologic analysis. In this study, we use a linear regression based statistical <span class="hlt">downscaling</span> method for obtaining monthly Indian Summer Monsoon Rainfall (ISMR) projections at multiple spatial resolutions, viz., 0.05°, 0.25° and 0.50°, and compare them. We use 19 GCMs of Coupled Model Intercomparison Project Phase 5 (CMIP5) suite and combine them with multi model averaging and Bayesian model averaging. We find spatially non-uniform changes in projections at all resolutions for both combinations of projections. Our results show that the changes in the mean for future time periods (2020s, 2050s, and 2080s) at different resolutions, viz., 0.05°, 0.25° and 0.5°, obtained with both Multi-Model Average (MMA) and Bayesian Multi-Model Average (BMA) are comparable. We also find that the model uncertainty decreases with projection times into the future for all resolutions. We compute Signal to Noise Ratio (SNR), which represents the climate change signal in simulations with respect to the noise arising from multi-model uncertainty. This appears to be almost similar at different resolutions. The present study highlight that, a mere increase in resolution by a way of computationally more expensive statistical <span class="hlt">downscaling</span> does not necessarily contribute towards improving the signal strength. Denser data networks and finer resolution GCMs may be essential for producing usable rainfall and hydrologic information at finer resolutions in the context of statistical <span class="hlt">downscaling</span>.</p> <div class="credits"> <p class="dwt_author">Shashikanth, K.; Madhusoodhanan, C. G.; Ghosh, Subimal; Eldho, T. I.; Rajendran, K.; Murtugudde, Raghu</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-11-01</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://academic.research.microsoft.com/Publication/40127064"> <span id="translatedtitle">Influence of similarity measures on the performance of the analog method for <span class="hlt">downscaling</span> daily precipitation</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 examines the performance of the analog method for <span class="hlt">downscaling</span> daily precipitation. The evaluation is performed\\u000a for (1) a number of similarity measures for searching analogs, (2) various ways to include the past atmospheric evolution,\\u000a and (3) different truncations in EOF space. It is carried out for two regions with complex topographic structures, and with\\u000a distinct climatic characteristics, namely,</p> <div class="credits"> <p class="dwt_author">C. Matulla; X. Zhang; X. L. Wang; J. Wang; E. Zorita; S. Wagner; H. von Storch</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">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/2012HESSD...910719D"> <span id="translatedtitle">Evaluation of areal precipitation estimates based on <span class="hlt">downscaled</span> reanalysis and station data by hydrological modelling</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 data sparse regions, as in many mountainous catchments, it is a challenge to generate suitable precipitation input fields for hydrological modelling, as station data do not provide enough information to derive areal precipitation estimates. This study presents a method using the spatial variation of precipitation from <span class="hlt">downscaled</span> reanalysis data for the interpolation of gauge observations. The second aim of this study is the evaluation of different precipitation estimates by hydrological modelling. Study area is the Karadarya catchment in Central Asia (11 700 km2). ERA-40 reanalysis data are <span class="hlt">downscaled</span> with the regional climate model Weather Research and Forecasting Model (WRF). Precipitation data from gauge observations are interpolated (i) using monthly accumulated WRF precipitation data, (ii) using monthly fields from multiple linear regression against topographical variables and (iii) with the inverse distance approach. These precipitation data sets are also compared to (iv) the direct use of the precipitation output from the WRF <span class="hlt">downscaled</span> ERA-40 data and (v) precipitation from the APHRODITE data set. Our study suggests that using monthly fields from <span class="hlt">downscaled</span> reanalysis data can be a good approach for the interpolation of station data in data sparse mountainous regions. Compared to mean annual precipitation from continental and global scale gridded data sets our precipitation estimates for the study area are considerably higher. The introduction of a calibrated precipitation bias factor for the comparison of different precipitation estimates by hydrological modelling allows for a more informed differentiation with regard to the temporal dynamics, on the one hand, and the overall bias, on the other hand. Uncertainty and sensitivity analyses suggest that our results are robust against uncertainties in the calibration parameters, other model parameters and inputs, and the selected calibration period.</p> <div class="credits"> <p class="dwt_author">Duethmann, D.; Zimmer, J.; Gafurov, A.; Güntner, A.; Merz, B.; Vorogushyn, S.</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">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/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 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> <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">261</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/10596934"> <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)\\u000a is a challenge for applications like the evaluation of wind energy production, the prediction of pollution transport and hazardous\\u000a conditions for aeronautics and ship navigation, or the estimation of damage to farm plantations, among others. This paper\\u000a presents a statistical <span class="hlt">downscaling</span> approach based on</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">2009-01-01</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://www.springerlink.com/index/w650885516033675.pdf"> <span id="translatedtitle">Regional climate of hazardous convective weather through high-resolution dynamical <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">We explore the use of high-resolution dynamical <span class="hlt">downscaling</span> as a means to simulate the regional climatology and variability\\u000a of hazardous convective-scale weather. Our basic approach differs from a traditional regional climate model application in\\u000a that it involves a sequence of daily integrations. We use the weather research and forecasting (WRF) model, with global reanalysis\\u000a data as initial and boundary conditions.</p> <div class="credits"> <p class="dwt_author">Robert J. TrappEric; Eric D. Robinson; Michael E. Baldwin; Noah S. Diffenbaugh; Benjamin R. J. Schwedler</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">263</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">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/2014FrP.....2...20M"> <span id="translatedtitle">Statistical Analysis of Protein <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">As 3D protein-configuration data is piling up, there is an ever-increasing need for well-defined, mathematically rigorous analysis approaches, especially that the vast majority of the currently available methods rely heavily on heuristics. We propose an analysis framework which stems from topology, the field of mathematics which studies properties preserved under continuous deformations. First, we calculate a barcode representation of the molecules employing computational topology algorithms. Bars in this barcode represent different topological features. Molecules are compared through their barcodes by statistically determining the difference in the set of their topological features. As a proof-of-principle application, we analyze a dataset compiled of <span class="hlt">ensembles</span> of different proteins, obtained from the <span class="hlt">Ensemble</span> Protein Database. We demonstrate that our approach correctly detects the different protein groupings.</p> <div class="credits"> <p class="dwt_author">Máté, Gabriell; Heermann, Dieter</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-04-01</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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3855309"> <span id="translatedtitle">EASER: <span class="hlt">Ensembl</span> Easy Sequence Retriever</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 rapid advances in genome sequencing technologies have increased the pace at which biological sequence databases are becoming available to the broad scientific community. Thus, obtaining and preparing an appropriate sequence dataset is a crucial first step for all types of genomic analyses. Here, we present a script that can widely facilitate the easy, fast, and effortless downloading and preparation of a proper biological sequence dataset for various genomics studies. This script retrieves <span class="hlt">Ensembl</span> defined genomic features, associated with a given <span class="hlt">Ensembl</span> identifier. Coding (CDS) and genomic sequences can be easily retrieved based on a selected relationship from a set of relationship types, either considering all available organisms or a user specified subset of organisms. The script is very user-friendly and by default starts with an interactive mode if no command-line options are specified. PMID:24324324</p> <div class="credits"> <p class="dwt_author">Maldonado, Emanuel; Khan, Imran; Philip, Siby; Vasconcelos, Vítor; Antunes, Agostinho</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">266</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://arxiv.org/pdf/1501.00219.pdf"> <span id="translatedtitle">Spectral diagonal <span class="hlt">ensemble</span> Kalman filters</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">A new type of <span class="hlt">ensemble</span> Kalman filter is developed, which is based on replacing the sample covariance in the analysis step by its diagonal in a spectral basis. It is proved that this technique improves the aproximation of the covariance when the covariance itself is diagonal in the spectral basis, as is the case, e.g., for a second-order stationary random field and the Fourier basis. The method is extended by wavelets to the case when the state variables are random fields, which are not spatially homogeneous. Efficient implementations by the fast Fourier transform (FFT) and discrete wavelet transform (DWT) are presented for several types of observations, including high-dimensional data given on a part of the domain, such as radar and satellite images. Computational experiments confirm that the method performs well on the Lorenz 96 problem and the shallow water equations with very small <span class="hlt">ensembles</span> and over multiple analysis cycles.</p> <div class="credits"> <p class="dwt_author">Kasanický, Ivan; Vejmelka, Martin</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-01-01</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/2015NPGD....2..115K"> <span id="translatedtitle">Spectral diagonal <span class="hlt">ensemble</span> Kalman filters</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 type of <span class="hlt">ensemble</span> Kalman filter is developed, which is based on replacing the sample covariance in the analysis step by its diagonal in a spectral basis. It is proved that this technique improves the aproximation of the covariance when the covariance itself is diagonal in the spectral basis, as is the case, e.g., for a second-order stationary random field and the Fourier basis. The method is extended by wavelets to the case when the state variables are random fields which are not spatially homogeneous. Efficient implementations by the fast Fourier transform (FFT) and discrete wavelet transform (DWT) are presented for several types of observations, including high-dimensional data given on a part of the domain, such as radar and satellite images. Computational experiments confirm that the method performs well on the Lorenz 96 problem and the shallow water equations with very small <span class="hlt">ensembles</span> and over multiple analysis cycles.</p> <div class="credits"> <p class="dwt_author">Kasanický, I.; Mandel, J.; Vejmelka, M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-01-01</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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4301745"> <span id="translatedtitle">Triticeae Resources in <span class="hlt">Ensembl</span> Plants</span></a>  </p> <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">Recent developments in DNA sequencing have enabled the large and complex genomes of many crop species to be determined for the first time, even those previously intractable due to their polyploid nature. Indeed, over the course of the last 2 years, the genome sequences of several commercially important cereals, notably barley and bread wheat, have become available, as well as those of related wild species. While still incomplete, comparison with other, more completely assembled species suggests that coverage of genic regions is likely to be high. <span class="hlt">Ensembl</span> Plants (http://plants.<span class="hlt">ensembl</span>.org) is an integrative resource organizing, analyzing and visualizing genome-scale information for important crop and model plants. Available data include reference genome sequence, variant loci, gene models and functional annotation. For variant loci, individual and population genotypes, linkage information and, where available, phenotypic information are shown. Comparative analyses are performed on DNA and protein sequence alignments. The resulting genome alignments and gene trees, representing the implied evolutionary history of the gene family, are made available for visualization and analysis. Driven by the case of bread wheat, specific extensions to the analysis pipelines and web interface have recently been developed to support polyploid genomes. Data in <span class="hlt">Ensembl</span> Plants is accessible through a genome browser incorporating various specialist interfaces for different data types, and through a variety of additional methods for programmatic access and data mining. These interfaces are consistent with those offered through the <span class="hlt">Ensembl</span> interface for the genomes of non-plant species, including those of plant pathogens, pests and pollinators, facilitating the study of the plant in its environment. PMID:25432969</p> <div class="credits"> <p class="dwt_author">Bolser, Dan M.; Kerhornou, Arnaud; Walts, Brandon; Kersey, Paul</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-01-01</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://arxiv.org/pdf/cond-mat/0201332v1"> <span id="translatedtitle">Stiff polymer in monomer <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/eprints/">E-print Network</a></p> <p class="result-summary">We make use of the previously developed formalism for a monomer <span class="hlt">ensemble</span> and include angular dependence of the segments of the polymer chains thus described. In particular we show how to deal with stiffness when the polymer chain is confined to certain regions. We investigate the stiffness from the perspectives of a differential equation, integral equations, or recursive relations for both continuum and lattice models. Exact analytical solutions are presented for two cases, whereas numerical results are shown for a third case.</p> <div class="credits"> <p class="dwt_author">K. K. Muller-Nedebock; H. L. Frisch; J. K. Percus</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-01-18</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://adsabs.harvard.edu/abs/2014NPGD....1..615D"> <span id="translatedtitle">Non-parametric Bayesian mixture of sparse regressions with application towards feature selection for 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">Climate projections simulated by Global Climate Models (GCM) are often used for assessing the impacts of climate change. However, the relatively coarse resolutions of GCM outputs often precludes their application towards accurately assessing the effects of climate change on finer regional scale phenomena. <span class="hlt">Downscaling</span> of climate variables from coarser to finer regional scales using statistical methods are often performed for regional climate projections. Statistical <span class="hlt">downscaling</span> (SD) is based on the understanding that the regional climate is influenced by two factors - the large scale climatic state and the regional or local features. A transfer function approach of SD involves learning a regression model which relates these features (predictors) to a climatic variable of interest (predictand) based on the past observations. However, often a single regression model is not sufficient to describe complex dynamic relationships between the predictors and predictand. We focus on the covariate selection part of the transfer function approach and propose a nonparametric Bayesian mixture of sparse regression models based on Dirichlet Process (DP), for simultaneous clustering and discovery of covariates within the clusters while automatically finding the number of clusters. Sparse linear models are parsimonious and hence relatively more generalizable than non-sparse alternatives, and lends to domain relevant interpretation. Applications to synthetic data demonstrate the value of the new approach and preliminary results related to feature selection for statistical <span class="hlt">downscaling</span> shows our method can lead to new insights.</p> <div class="credits"> <p class="dwt_author">Das, D.; Dy, J.; Ross, J.; Obradovic, Z.; Ganguly, A. R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-04-01</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/2014NPGeo..21.1145D"> <span id="translatedtitle">Non-parametric Bayesian mixture of sparse regressions with application towards feature selection for 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">Climate projections simulated by Global Climate Models (GCMs) are often used for assessing the impacts of climate change. However, the relatively coarse resolutions of GCM outputs often preclude their application to accurately assessing the effects of climate change on finer regional-scale phenomena. <span class="hlt">Downscaling</span> of climate variables from coarser to finer regional scales using statistical methods is often performed for regional climate projections. Statistical <span class="hlt">downscaling</span> (SD) is based on the understanding that the regional climate is influenced by two factors - the large-scale climatic state and the regional or local features. A transfer function approach of SD involves learning a regression model that relates these features (predictors) to a climatic variable of interest (predictand) based on the past observations. However, often a single regression model is not sufficient to describe complex dynamic relationships between the predictors and predictand. We focus on the covariate selection part of the transfer function approach and propose a nonparametric Bayesian mixture of sparse regression models based on Dirichlet process (DP) for simultaneous clustering and discovery of covariates within the clusters while automatically finding the number of clusters. Sparse linear models are parsimonious and hence more generalizable than non-sparse alternatives, and lend themselves to domain relevant interpretation. Applications to synthetic data demonstrate the value of the new approach and preliminary results related to feature selection for statistical <span class="hlt">downscaling</span> show that our method can lead to new insights.</p> <div class="credits"> <p class="dwt_author">Das, D.; Dy, J.; Ross, J.; Obradovic, Z.; Ganguly, A. R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-12-01</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/2013AGUFMGC43C1052P"> <span id="translatedtitle">Weather Typing Statistical <span class="hlt">Downscaling</span> with dsclim: Diagnostic methodology and configuration sensitivity</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 8-km output of the statistical <span class="hlt">downscaling</span> methodology dsclim has been used since a few years to perform impacts and adaptation studies in France. The dsclim method is resampling the Météo-France SAFRAN observation mesoscale analysis. Since then, the SAFRAN observation period has been extended from 1981-2005 to 1958-2012. At the same time, there are strong needs of cross-national impact studies, hence the required use of an European observation dataset in the methodology. In this context, a diagnostic package has been developed to properly evaluate the <span class="hlt">downscaling</span> methodology and its performance: it enables to evaluate the sensitivity and the impacts of the changes in its configuration, taking also properly into account stochastic aspects. In this study we evaluated the impacts on the results with respect to the extension of the learning period from 1981-2005 to 1958-2012, as well as the comparison on the use of the EOBS dataset instead of SAFRAN, having the objective of running dsclim over a larger region within the EU FP7 SPECS project and the EU COST Action VALUE <span class="hlt">downscaling</span> methods intercomparison. This study was funded by the EU project SPECS funded by the European Commission's Seventh Framework Research Programme under the grant agreement 243964.</p> <div class="credits"> <p class="dwt_author">Page, C.; Albertus, G.</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">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/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 " 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/2012EGUGA..1410829G"> <span id="translatedtitle">Application of statistical <span class="hlt">downscaling</span> technique for the production of wine grapes (Vitis vinifera L.) in Spain</span></a>  </p> <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 and soil are two of the most important limiting factors for agricultural production. Nowadays climate change has been documented in many geographical locations affecting different cropping systems. The General Circulation Models (GCM) has become important tools to simulate the more relevant aspects of the climate expected for the XXI century in the frame of climatic change. These models are able to reproduce the general features of the atmospheric dynamic but their low resolution (about 200 Km) avoids a proper simulation of lower scale meteorological effects. <span class="hlt">Downscaling</span> techniques allow overcoming this problem by adapting the model outcomes to local scale. In this context, FIC (Fundación para la Investigación del Clima) has developed a statistical <span class="hlt">downscaling</span> technique based on a two step analogue methods. This methodology has been broadly tested on national and international environments leading to excellent results on future climate models. In a collaboration project, this statistical <span class="hlt">downscaling</span> technique was applied to predict future scenarios for the grape growing systems in Spain. The application of such model is very important to predict expected climate for the different growing crops, mainly for grape, where the success of different varieties are highly related to climate and soil. The model allowed the implementation of agricultural conservation practices in the crop production, detecting highly sensible areas to negative impacts produced by any modification of climate in the different regions, mainly those protected with protected designation of origin, and the definition of new production areas with optimal edaphoclimatic conditions for the different varieties.</p> <div class="credits"> <p class="dwt_author">Gaitán Fernández, E.; García Moreno, R.; Pino Otín, M. R.; Ribalaygua Batalla, 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">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/2014ClDy...42..701L"> <span id="translatedtitle">A high-resolution ocean-atmosphere coupled <span class="hlt">downscaling</span> of the present climate over California</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 ocean-atmosphere model system that consists of the regional spectral model and the regional ocean modeling system for atmosphere and ocean components, respectively, is applied to <span class="hlt">downscale</span> the present climate (1985-1994) over California from a global simulation of the Community Climate System Model 3.0 (CCSM3). The horizontal resolution of the regional coupled modeling system is 10 km, while that of the CCSM3 is at a spectral truncation of T85 (approximately 1.4°). The effects of the coupling along the California coast in the boreal summer and winter are highlighted. Evaluation of the sea surface temperature (SST) and 2-m air temperature climatology shows that alleviation of the warm bias along the California coast in the global model output is clear in the regional coupled model run. The 10-m wind is also improved by reducing the northwesterly winds along the coast. The higher resolution coupling effect on the temperature and specific humidity is the largest near the surface, while the significant impact on the wind magnitude appears at a height of approximately 850-hPa heights. The frequency of the Catalina Eddy and its duration are increased by more than 60 % in the coupled <span class="hlt">downscaling</span>, which is attributed to enhanced offshore sea-breeze. Our study indicates that coupling is vital to regional climate <span class="hlt">downscaling</span> of mesoscale phenomena over coastal areas.</p> <div class="credits"> <p class="dwt_author">Li, Haiqin; Kanamitsu, Masao; Hong, Song-You; Yoshimura, Kei; Cayan, Daniel R.; Misra, Vasubandhu</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-02-01</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/2008TellA..60..451A"> <span id="translatedtitle">Marine <span class="hlt">downscaling</span> of a future climate scenario for the North Sea</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 SRES A1B scenario for the period 2072-2097 with the Bergen Climate Model (BCM) has been <span class="hlt">downscaled</span> for the marine climate in the North Sea using the Regional Ocean Model System (ROMS). The results are compared to the 20C3M run for the period 1972-1997. The results show a warming of the North Sea, with a volume average of 1.4 °C, and a mean SST change of 1.7 °C. The warming is strongest in May-June. Geographically the strongest warming in the North Sea is found towards Skagerrak-Kattegat in the surface waters and in the central North Sea at 50 m depth. The <span class="hlt">downscaling</span> show a weak increase in the Atlantic inflow to the North Sea. The BCM scenario has a change in the wind stress pattern in the Faeroe Island region. This strengthens the branch of Atlantic Water flowing west of the Faeroes and weakens the flux through the Faeroe-Shetland Channel. As a result both BCM and the <span class="hlt">downscaling</span> show large changes in the temperature in this area, with weak warming and sometimes cooling south of the Faeroes and strong warming on the north side.</p> <div class="credits"> <p class="dwt_author">Ådlandsvik, Bjørn</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-05-01</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://pubs.er.usgs.gov/publication/70095788"> <span id="translatedtitle">Applying <span class="hlt">downscaled</span> global climate model data to a hydrodynamic surface-water and groundwater model</span></a>  </p> <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">Precipitation data from Global Climate Models have been <span class="hlt">downscaled</span> to smaller regions. Adapting this <span class="hlt">downscaled</span> precipitation data to a coupled hydrodynamic surface-water/groundwater model of southern Florida allows an examination of future conditions and their effect on groundwater levels, inundation patterns, surface-water stage and flows, and salinity. The <span class="hlt">downscaled</span> rainfall data include the 1996-2001 time series from the European Center for Medium-Range Weather Forecasting ERA-40 simulation and both the 1996-1999 and 2038-2057 time series from two global climate models: the Community Climate System Model (CCSM) and the Geophysical Fluid Dynamic Laboratory (GFDL). Synthesized surface-water inflow datasets were developed for the 2038-2057 simulations. The resulting hydrologic simulations, with and without a 30-cm sea-level rise, were compared with each other and field data to analyze a range of projected conditions. Simulations predicted generally higher future stage and groundwater levels and surface-water flows, with sea-level rise inducing higher coastal salinities. A coincident rise in sea level, precipitation and surface-water flows resulted in a narrower inland saline/fresh transition zone. The inland areas were affected more by the rainfall difference than the sea-level rise, and the rainfall differences make little difference in coastal inundation, but a larger difference in coastal salinities.</p> <div class="credits"> <p class="dwt_author">Swain, Eric; Stefanova, Lydia; Smith, Thomas</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-01-01</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/2011AGUFM.A23C0185O"> <span id="translatedtitle">Regional Climate Change across the Continental U.S. Projected from <span class="hlt">Downscaling</span> IPCC AR5 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">Projecting climate change scenarios to local scales is important for understanding and mitigating the effects of climate change on society and the environment. Many of the general circulation models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture local changes in temperature and precipitation extremes. We seek to project the GCM's large-scale climate change signal to the local scale using a regional climate model (RCM) by applying dynamical <span class="hlt">downscaling</span> techniques. The RCM will be used to better understand the local changes of temperature and precipitation extremes that may result from a changing climate. Preliminary results from <span class="hlt">downscaling</span> NASA/GISS ModelE simulations of the IPCC AR5 Representative Concentration Pathway (RCP) scenario 6.0 will be shown. The Weather Research and Forecasting (WRF) model will be used as the RCM to <span class="hlt">downscale</span> decadal time slices for ca. 2000 and ca. 2030 and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0.</p> <div class="credits"> <p class="dwt_author">Otte, T. L.; Nolte, C. G.; Otte, M. J.; Pinder, R. W.; Faluvegi, G.; Shindell, D. T.</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">279</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.432B"> <span id="translatedtitle">Assessment of Future Storm Losses in Germany: Probabilistic Extension of the Statistical-Dynamical <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">Future loss estimation is an important planning tool for insurance companies. In particular, good estimates of ranges of uncertainty are necessary for the assessment of climate change impacts and its implications. In this study, the probabilistic aspect of loss estimation is considered by prediction of loss distributions instead of best estimates for average values. For this purpose, <span class="hlt">downscaling</span> of global climate model data is combined with regional modelling and a probabilistic loss function, which describes the relation between wind speeds and losses. The statistical-dynamical <span class="hlt">downscaling</span> (SDD) approach is applied to reanalysis data and ECHAM5 climate scenarios for 1960-2100. The SDD consists of a cluster classification of storm relevant weather episodes, referred to as weather types (WT), dynamical <span class="hlt">downscaling</span> for WT episodes and a recombination of wind speed distributions on the regional scale using frequencies of WT occurrences. Changes in wind distributions for different time periods are divided into external changes due to variability of WT frequencies and internal changes due to wind speed distributions within WT classes. The losses are estimated using generalized loss functions, which fit wind speeds locally to observed loss frequencies via quantile regression. The results corroborate earlier findings, which describe an enhancement of loss potentials for Germany associated with winter storms under future climate conditions. In addition, uncertainty ranges in terms of quantile functions allow for a discussion of loss potential changes with respect to the relative sizes of events.</p> <div class="credits"> <p class="dwt_author">Born, K.; Karremann, M. K.; Ludwig, P.; Pinto, J.</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">280</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ntrs.nasa.gov/search.jsp?R=20000102382&hterms=handwriting&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dhandwriting"> <span id="translatedtitle">Dimensionality Reduction Through Classifier <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in <span class="hlt">ensembles</span> of classifiers, yielding results superior to single classifiers, <span class="hlt">ensembles</span> that use the full set of features, and <span class="hlt">ensembles</span> based on principal component analysis on both real and synthetic datasets.</p> <div class="credits"> <p class="dwt_author">Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)</p> <p class="dwt_publisher"></p> <p class="publishDate">1999-01-01</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 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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 onClick='return showDiv("page_10");' 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 style="font-weight: bold;">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_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://arxiv.org/pdf/1208.0399v3"> <span id="translatedtitle">Thermodynamic curvature and <span class="hlt">ensemble</span> nonequivalence</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">In this work we consider thermodynamic geometries defined as Hessians of different potentials and derive some useful formulae that show their complementary role in the description of thermodynamic systems with two degrees of freedom that show <span class="hlt">ensemble</span> nonequivalence. From the expressions derived for the metrics, we can obtain the curvature scalars in a very simple and compact form. We explain here the reason why each curvature scalar diverges over the line of divergence of one of the specific heats. This application is of special interest in the study of changes of stability in black holes as defined by Davies. From these results we are able to prove on a general footing a conjecture first formulated by Liu, L\\"u, Luo and Shao stating that different Hessian metrics can correspond to different behaviors in the various <span class="hlt">ensembles</span>. We study the case of two thermodynamic dimensions. Moreover, comparing our result with the more standard turning point method developed by Poincar\\'e, we obtain that the divergence of the scalar curvature of the Hessian metric of one potential exactly matches the change of stability in the corresponding <span class="hlt">ensemble</span>.</p> <div class="credits"> <p class="dwt_author">Alessandro Bravetti; Francisco Nettel</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-07-22</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/2014PhRvE..90c2117K"> <span id="translatedtitle">Heat fluctuations and initial <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">Time-integrated quantities such as work and heat increase incessantly in time during nonequilibrium processes near steady states. In the long-time limit, the average values of work and heat become asymptotically equivalent to each other, since they only differ by a finite energy change in average. However, the fluctuation theorem (FT) for the heat is found not to hold with the equilibrium initial <span class="hlt">ensemble</span>, while the FT for the work holds. This reveals an intriguing effect of everlasting initial memory stored in rare events. We revisit the problem of a Brownian particle in a harmonic potential dragged with a constant velocity, which is in contact with a thermal reservoir. The heat and work fluctuations are investigated with initial Boltzmann <span class="hlt">ensembles</span> at temperatures generally different from the reservoir temperature. We find that, in the infinite-time limit, the FT for the work is fully recovered for arbitrary initial temperatures, while the heat fluctuations significantly deviate from the FT characteristics except for the infinite initial-temperature limit (a uniform initial <span class="hlt">ensemble</span>). Furthermore, we succeed in calculating finite-time corrections to the heat and work distributions analytically, using the modified saddle point integral method recently developed by us. Interestingly, we find noncommutativity between the infinite-time limit and the infinite-initial-temperature limit for the probability distribution function (PDF) of the heat.</p> <div class="credits"> <p class="dwt_author">Kim, Kwangmoo; Kwon, Chulan; Park, Hyunggyu</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-09-01</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://www.ncbi.nlm.nih.gov/pubmed/25314405"> <span id="translatedtitle">Heat fluctuations and initial <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">Time-integrated quantities such as work and heat increase incessantly in time during nonequilibrium processes near steady states. In the long-time limit, the average values of work and heat become asymptotically equivalent to each other, since they only differ by a finite energy change in average. However, the fluctuation theorem (FT) for the heat is found not to hold with the equilibrium initial <span class="hlt">ensemble</span>, while the FT for the work holds. This reveals an intriguing effect of everlasting initial memory stored in rare events. We revisit the problem of a Brownian particle in a harmonic potential dragged with a constant velocity, which is in contact with a thermal reservoir. The heat and work fluctuations are investigated with initial Boltzmann <span class="hlt">ensembles</span> at temperatures generally different from the reservoir temperature. We find that, in the infinite-time limit, the FT for the work is fully recovered for arbitrary initial temperatures, while the heat fluctuations significantly deviate from the FT characteristics except for the infinite initial-temperature limit (a uniform initial <span class="hlt">ensemble</span>). Furthermore, we succeed in calculating finite-time corrections to the heat and work distributions analytically, using the modified saddle point integral method recently developed by us. Interestingly, we find noncommutativity between the infinite-time limit and the infinite-initial-temperature limit for the probability distribution function (PDF) of the heat. PMID:25314405</p> <div class="credits"> <p class="dwt_author">Kim, Kwangmoo; Kwon, Chulan; Park, Hyunggyu</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-09-01</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://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">2013-04-01</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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3226070"> <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=pmc">PubMed Central</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">2011-01-01</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/2013AGUFMGC41E..04D"> <span id="translatedtitle">Cluster analysis of explicitly and <span class="hlt">downscaled</span> simulated North Atlantic tropical cyclone tracks</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 response of tropical cyclone (TC) activity to climate change is a question of major interest. In order to address this crucial issue, several types of models have been developed in the past, such as Global Climate Models (GCMs). However, the horizontal resolution of those models usually leads to some difficulties in resolving the inner core of TCs and then to properly simulate TC activity. In order to avoid this problem, an alternative tool has been developed by Emanuel (2005). This <span class="hlt">downscaling</span> technique uses tracks that are initiated by randomly seeding large areas of the tropics with weak vortices. Then the survival of the tracks is based on large-scale environmental conditions produced by GCMs in our case. Here we compare the statistics of TC tracks simulated explicitly in four GCMs to the results of the <span class="hlt">downscaling</span> technique driven by the four same GCMs in the present and future climates over the North Atlantic basin. Simulated tracks are objectively separated into four groups using a cluster technique (Kossin et al. 2010). The four clusters form zonal and meridional separations of tracks as shown in Figure 1. The meridional separation largely captures the separation between hybrid or baroclinic storms (clusters 1 and 2) and deep tropical systems (clusters 3 and 4), while the zonal separation segregates Gulf of Mexico and Cape Verde storms. Except for the seasonality, the <span class="hlt">downscaled</span> simulations better capture the general characteristics of the clusters (mean duration of the tracks, intensity...) compared with the explicit simulations, which present strong biases. In the second part of this study, we use three different scenarios to examine the possible future changes of the clusters from the <span class="hlt">downscaled</span> simulations. We explored the role of a warming of the SST, an increase in carbon dioxide and a combination of both ones. The results show that the response to each scenario is highly varying depending on the simulation examined. References - Kossin, J. P., S. J. Camargo, and M. Sitkowski, 2010: Climate modulation of North Atlantic hurricane tracks. Journal of Climate, 23, 3057-3076, DOI: 10.1175/2010JCLI3497.1. - Emanuel, K., 2005: Climate and Tropical Cyclone activity: A new <span class="hlt">downscaling</span> approach. Journal of Climate, 19, 4797-4802.</p> <div class="credits"> <p class="dwt_author">Daloz, A.; Camargo, S. J.; Kossin, J. P.; Emanuel, K.</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">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/2006AGUFM.A41E0091L"> <span id="translatedtitle">Comparison of Predictive Skill Between the Statistically and the Dynamically <span class="hlt">Downscaled</span> Temperature and Precipitation Over the Southeast 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">Global model simulations from the FSUGSM (Florida State University Global Spectral Model) are statistically <span class="hlt">downscaled</span> to construct the local climate scenario over the southeast US, covering Florida, Georgia, and Alabama. The basis of this <span class="hlt">downscaling</span> method is that clearer separation of prominent local climate signals (e.g., seasonal cycle, dominant intraseasonal or interannual oscillations) over the training period leads to better prediction of local climate scenario from the large-scale simulations. To this end, 1) CSEOF (Cyclostationary EOF) analysis is conducted on both observation and FSUGSM runs over the training period, followed by 2) the multiple regression between lower modes of observation and GSM runs. 3) CSEOF PC time series for prediction domain is subsequently generated based on relationship identified from the first two steps. 4) The local scale data for the prediction domain are constructed from the generated PC time series and the eigenfunctions obtained from training. This procedure is repeated by withholding a particular year as a prediction domain for the sake of cross-validation. Daily precipitation and temperature (Tmax and Tmin) obtained from FSUGSM (~1.8° lon.-lat., T63) seasonal forecast run have been <span class="hlt">downscaled</span> to local spatial scale of 0.2°×0.2° (~20 km) for the southeast US region. Correlation, error variance, and other skill scores reveal that statistical <span class="hlt">downscaling</span> successfully produces the seasonal local climate scenario from coarsely resolved large-scale simulations. Biases unveiled from the FSUGSM have been significantly reduced by this <span class="hlt">downscaling</span> technique. Comparison in predictability with dynamical <span class="hlt">downscaling</span> (FSUNRSM (FSU nested regional spectral model)) shows that their skills are comparable to each other. Their capability that captures the event of above/below normal temperature and rainfall/no rainfall is faithful, specifically for the maximum temperature. However, observed variance of the variables is partly reduced by statistical <span class="hlt">downscaling</span>, while RSM hardly loses the observed variance.</p> <div class="credits"> <p class="dwt_author">Lim, Y.; Shin, D.; Cocke, S.; Larow, T. E.</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">288</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/24830256"> <span id="translatedtitle">[Evaluating the performance of the UCLA method for spatially <span class="hlt">downscaling</span> soil moisture products using three Ts/VI indices].</span></a>  </p> <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">Soil moisture products derived from microwave remote sensing data are commonly used in the studies of large-scale water resources or climate change. However, the spatial resolutions of these products are usually too coarse to be used in regional- or watershed-scale studies. Therefore, it is necessary to spatially <span class="hlt">downscale</span> the coarse-resolution soil moisture products for use in regional- or watershed-scale studies. The UCLA method is one of the methods for spatially <span class="hlt">downscaling</span> soil moisture products. In this method, the spatial indices (Ts/VI indices) calculated from land surface temperature and vegetation index are used as auxiliary variables for spatial <span class="hlt">downscaling</span>. In this paper, we compared the performance of the UCLA method for spatially <span class="hlt">downscaling</span> the coarse-resolution AMSR-E soil moisture products, using three Ts/VI indices as auxiliary variables, i. e., the soil wetness index (SW), temperature vegetation dryness index (TVDI), and vegetation temperature condition index (VTCI). These auxiliary variables were calculated from the products of MODIS land surface temperature (MYD11A1) and MODIS vegetation index (MYD13A2). The <span class="hlt">downscaled</span> results using the three Ts/VI indices were all reasonable. However, the <span class="hlt">downscaled</span> results using TVDI and VTCI were better than using SW. Therefore, we concluded that TVDI and VTCI are more suitable than SW to be used as the auxiliary variable when applying the UCLA method for <span class="hlt">downscaling</span> soil moisture products. Finally, we discussed the error sources of applying the UCLA method, such as measurement errors of coarse resolution soil products, calculation errors from spatial indices, and errors from the UCLA method itself, and we also discussed the potential improvements of future research. PMID:24830256</p> <div class="credits"> <p class="dwt_author">Ling, Zi-Wei; He, Long-Bin; Zeng, Hui</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-02-01</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://adsabs.harvard.edu/abs/2012AcMeS..26...52D"> <span id="translatedtitle">A comparison of breeding and <span class="hlt">ensemble</span> transform vectors for global <span class="hlt">ensemble</span> generation</span></a>  </p> <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">To compare the initial perturbation techniques using breeding vectors and <span class="hlt">ensemble</span> transform vectors, three <span class="hlt">ensemble</span> prediction systems using both initial perturbation methods but with different <span class="hlt">ensemble</span> member sizes based on the spectral model T213/L31 are constructed at the National Meteorological Center, China Meteorological Administration (NMC/CMA). A series of <span class="hlt">ensemble</span> verification scores such as forecast skill of the <span class="hlt">ensemble</span> mean, <span class="hlt">ensemble</span> resolution, and <span class="hlt">ensemble</span> reliability are introduced to identify the most important attributes of <span class="hlt">ensemble</span> forecast systems. The results indicate that the <span class="hlt">ensemble</span> transform technique is superior to the breeding vector method in light of the evaluation of anomaly correlation coefficient (ACC), which is a deterministic character of the <span class="hlt">ensemble</span> mean, the root-mean-square error (RMSE) and spread, which are of probabilistic attributes, and the continuous ranked probability score (CRPS) and its decomposition. The advantage of the <span class="hlt">ensemble</span> transform approach is attributed to its orthogonality among <span class="hlt">ensemble</span> perturbations as well as its consistence with the data assimilation system. Therefore, this study may serve as a reference for configuration of the best <span class="hlt">ensemble</span> prediction system to be used in operation.</p> <div class="credits"> <p class="dwt_author">Deng, Guo; Tian, Hua; Li, Xiaoli; Chen, Jing; Gong, Jiandong; Jiao, Meiyan</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">290</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/2014EGUGA..1614240H"> <span id="translatedtitle">Multivariate <span class="hlt">Ensemble</span> Sensitivity with Localization</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">So far in the literature, covariance localization (tapering) has not been applied when performing <span class="hlt">ensemble</span> sensitivity analysis. Sampling error in computing the sensitivities via lagged covariances leads to an over-estimation of the impact of a perturbation. Most commonly when computing sensitivities, the analysis covariance is approximated with the corresponding diagonal matrix. Two consequences follow: (1) the multi-variate sensitivity is approximated by a univariate sensitivity, and (2) sampling error in off-diagonal elements are obviated. It is unknown, however, how much information is lost by ignoring the off-diagonal elements in the full covariance. When forecasts depend on many details of the previous analysis, it is reasonable to expect that the diagonal approximation is too severe. The purpose of this presentation is to clarify the effects of the diagonal approximation, and investigate the need for localization when off-diagonal elements are considered. Motivated by examples arising from sensitivities estimated within a cycling mesoscale <span class="hlt">ensemble</span> data assimilation system, for easier interpretation we turn to the two-scale model first presented by Lorenz in 2005. We show that for most problems, an efficient matrix inversion is possible by finding a minimum-norm solution, and employing appropriate matrix factorization. Comparing the full inversion with off-diagonal elements, the fine-scale sensitivity estimates can be substantially different from those arising when the diagonal approximation is used. Localization on the sensitivity can be handled by an off-line empirical or Bayesian estimation technique. Because the sensitivity estimated from the full inversion is subject to sampling error, it is sensitive to the localization. The results show that compared to typical practices, more complete <span class="hlt">ensemble</span> sensitivity formulations may be needed to draw robust inferences in general.</p> <div class="credits"> <p class="dwt_author">Hacker, Joshua; Lei, Lili</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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://arxiv.org/pdf/quant-ph/0201128v2"> <span id="translatedtitle">Entangling many atomic <span class="hlt">ensembles</span> through laser manipulation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">We propose an experimentally feasible scheme to generate Greenberger-Horne-Zeilinger (GHZ) type of maximal entanglement between many atomic <span class="hlt">ensembles</span> based on laser manipulation and single-photon detection. The scheme, with inherent fault tolerance to the dominant noise and efficient scaling of the efficiency with the number of <span class="hlt">ensembles</span>, allows to maximally entangle many atomic <span class="hlt">ensemble</span> within the reach of current technology. Such a maximum entanglement of many <span class="hlt">ensembles</span> has wide applications in demonstration of quantum nonlocality, high-precision spectroscopy, and quantum information processing.</p> <div class="credits"> <p class="dwt_author">L. -M. Duan</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-04-24</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://ntrs.nasa.gov/search.jsp?R=19820026226&hterms=acoustic+parameter+change+age&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dacoustic%2Bparameter%2Bchange%2Bage"> <span id="translatedtitle"><span class="hlt">Ensemble</span> averaging of acoustic data</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">A computer program called <span class="hlt">Ensemble</span> Averaging of Acoustic Data is documented. The program samples analog data, analyzes the data, and displays them in the time and frequency domains. Hard copies of the displays are the program's output. The documentation includes a description of the program and detailed user instructions for the program. This software was developed for use on the Ames 40- by 80-Foot Wind Tunnel's Dynamic Analysis System consisting of a PDP-11/45 computer, two RK05 disk drives, a tektronix 611 keyboard/display terminal, and FPE-4 Fourier Processing Element, and an analog-to-digital converter.</p> <div class="credits"> <p class="dwt_author">Stefanski, P. K.</p> <p class="dwt_publisher"></p> <p class="publishDate">1982-01-01</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://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.154.5576&rank=5&rep=rep1&type=pdf"> <span id="translatedtitle">Entity extraction via <span class="hlt">ensemble</span> semantics</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Combining information extraction systems yields significantly higher quality resources than each system in isolation. In this paper, we generalize such a mixing of sources and features in a framework called <span class="hlt">Ensemble</span> Semantics. We show very large gains in entity extraction by combining state-of-the-art distributional and patternbased systems with a large set of features from a webcrawl, query logs, and Wikipedia. Experimental results on a webscale extraction of actors, athletes and musicians show significantly higher mean average precision scores (29 % gain) compared with the current state of the art. 1</p> <div class="credits"> <p class="dwt_author">Marco Pennacchiotti; Patrick Pantel</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">294</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=ELASTIC&pg=3&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 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://www.ensemble.cms.vt.edu/user-group/2012-03-22-presentation.pdf"> <span id="translatedtitle"><span class="hlt">Ensemble</span> CMS Meeting<span class="hlt">Ensemble</span> CMS Meeting Thursday, March 22, 2012</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary"><span class="hlt">Ensemble</span>? #12;Survey numbersy How does <span class="hlt">Ensemble</span> compare? #12;Survey numbersy Other systems? #12;CMS survey for flexibility · Full survey report posted to <span class="hlt">Ensemble</span> sitesite #12;About the speed ... · Upgrade in the works Information · Track usage via Google Analytics· Track usage via Google Analytics · Looking at awstats · Local</p> <div class="credits"> <p class="dwt_author">Buehrer, R. Michael</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://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">297</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">298</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/2014EGUGA..1615300E"> <span id="translatedtitle"><span class="hlt">Downscaling</span> for extreme and non-extreme daily precipitation using GCM 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">Understanding long-term changes in daily precipitation characteristics, particularly those associated with extreme events, is an important component of climate change science and impact assessment. The limited spatial resolution of General Circulation Models (GCMs) makes direct estimates of future daily precipitation unrealistic and higher-resolution estimates are often made using GCM-driven Regional Climate Models (RCMs). Whilst able to simulate precipitation characteristics at smaller scales, RCMs do not represent local variables and remain limited by systematic errors and biases. Previous work has demonstrated that it is possible to <span class="hlt">downscale</span> medium-to-heavy precipitation simulated by GCMs using stochastic bias correction, also known as model output statistics (MOS). Here, we extend upon this approach and apply a stochastic MOS correction for <span class="hlt">downscaling</span> the full distribution of European precipitation (extreme and non-extreme) simulated by two GCMs. A mixture model, combining gamma and generalised Pareto distributions, is used to represent the complete precipitation distribution. This is combined with a logistic regression model and a vector generalised linear model (VGLM) in order to estimate the precipitation distribution based on simulated precipitation. GCM-MOS models are fitted using simulations of ECHAM5 and HadGEM3 nudged to ERA-interim for the period 1979-2010. Preliminary findings based on cross-validation and appropriate skill scores suggest that the stochastic MOS method performs favourably compared to stationary models and particularly so in estimating high quantiles. Additionally, we will present <span class="hlt">downscaled</span> scenarios from each GCM for European precipitation characteristics over the twenty-first century.</p> <div class="credits"> <p class="dwt_author">Eden, Jonathan; Widmann, Martin; Maraun, Douglas; Vrac, Mathieu</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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://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">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.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 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 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</span> </span> <a id="NextPageLink" onclick='return showDiv("page_17");' 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">301</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://hal.archives-ouvertes.fr/docs/00/96/17/87/PDF/2013-004_post-print.pdf"> <span id="translatedtitle">Regional climate <span class="hlt">downscaling</span> with prior statistical correction of the global climate1 A. Colette (1), R. Vautard (2), M. Vrac (2)3</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">1 Regional climate <span class="hlt">downscaling</span> with prior statistical correction of the global climate1 forcing2 A Risques, INERIS, Verneuil-en-4 Halatte, France5 2. Laboratoire des Sciences du Climat et de l Corresponding author address : augustin.colette@ineris.fr8 Abstract9 A novel climate <span class="hlt">downscaling</span> methodology</p> <div class="credits"> <p class="dwt_author">Paris-Sud XI, Université de</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://hal.archives-ouvertes.fr/docs/00/30/45/95/PDF/hess-5-201-2001.pdf"> <span id="translatedtitle">A geostatistiical approach to multisensor rain field reconstruction and <span class="hlt">downscaling</span> Hydrology and Earth System Sciences, 5(2), 201213 (2001) EGS</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">A geostatistiical approach to multisensor rain field reconstruction and <span class="hlt">downscaling</span> 201 Hydrology and Earth System Sciences, 5(2), 201­213 (2001) © EGS A geostatistical approach to multisensor rain field for corresponding author: luca@diam.unige.it Abstract A rain field reconstruction and <span class="hlt">downscaling</span> methodology</p> <div class="credits"> <p class="dwt_author">Paris-Sud XI, Université de</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">303</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/945745"> <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://www.osti.gov/scitech">SciTech Connect</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 have become popular methods for filling the gap between global and local-to-regional climate applications. Recent inter-comparison studies of these <span class="hlt">downscaling</span> techniques show that both <span class="hlt">downscaling</span> methods have similar skill in simulating the mean and variability of present climate conditions while they show significant differences for future climate conditions (Leung et al., 2003). One difficulty with the SD method is that it relies on predictor-predict and relationships, which may not hold in future climate conditions. In addition, it is now commonly accepted that the dynamical <span class="hlt">downscaling</span> with the regional climate model (RCM) is more skillful at the resolving orographic climate effect than the driving coarser-grid GCM simulations. To assess the possible societal impacts of climate changes, many RCMs have been developed and used to provide a better projection of future regional-scale climates for guiding policies in economy, ecosystem, water supply, agriculture, human health, and air quality (Giorgi et al., 1994; Leung and Ghan, 1999; Leung et al., 2003; Liang et al., 2004; Kim, 2004; Duffy et al., 2006). Although many regional climate features, such as seasonal mean and extreme precipitation have been successfully captured in these RCMs, obvious biases of simulated precipitation remain, particularly the winter wet bias commonly seen in mountain regions of the Western United States. The importance of regional climate research over California is not only because California has the largest population in the nation, but California has one of the most sophisticated water collection and distribution systems in the world. Therefore, adapting California's water management system to climate change presents significant challenges. Besides, the strong scale interaction between atmospheric circulation and topography in this region provides a challenging testbed for RCMs. Thus, the success of California winter precipitation forecast over mountains would greatly help develop a reliable water management system to adapt to climate change.</p> <div class="credits"> <p class="dwt_author">Chin, H S</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-09-24</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://eric.ed.gov/?q=surface+AND+area&pg=5&id=EJ934197"> <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.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</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>…</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 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/5775"> <span id="translatedtitle"><span class="hlt">Ensemble</span>-based Human Communication Recognition</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 propose a novel architecture for systems that target the recognition of hu- man communication - Distributed <span class="hlt">Ensembles</span>. Distributed <span class="hlt">Ensembles</span> results from the observation that in many different fields hard problems are handled by employing multi- ple computational entities that cooperate to solve a problem. Even though these solutions share this common trait, the goals in each field for employing</p> <div class="credits"> <p class="dwt_author">P. Barthelmess</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://arxiv.org/pdf/0801.4219v1"> <span id="translatedtitle">Statistical <span class="hlt">Ensembles</span> with Fluctuating Extensive Quantities</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">We suggest an extension of the standard concept of statistical <span class="hlt">ensembles</span>. Namely, we introduce a class of <span class="hlt">ensembles</span> with extensive quantities fluctuating according to an externally given distribution. As an example the influence of energy fluctuations on multiplicity fluctuations in limited segments of momentum space for a classical ultra-relativistic gas is considered.</p> <div class="credits"> <p class="dwt_author">M. I. Gorenstein; M. Hauer</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-01-28</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://www.ma.utexas.edu/mp_arc/c/08/08-176.pdf"> <span id="translatedtitle">Topological quantization of <span class="hlt">ensemble</span> averages Emil Prodan</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Topological quantization of <span class="hlt">ensemble</span> averages Emil Prodan Department of Physics, Yeshiva University looking for novel manifestations of the topological quantization. As a new application, we show the formalism can be used to probe the existence of edge states. #12;Topological quantization of <span class="hlt">ensemble</span></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">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=4183303"> <span id="translatedtitle">Visual stimuli recruit intrinsically generated cortical <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 cortical microcircuit is built with recurrent excitatory connections, and it has long been suggested that the purpose of this design is to enable intrinsically driven reverberating activity. To understand the dynamics of neocortical intrinsic activity better, we performed two-photon calcium imaging of populations of neurons from the primary visual cortex of awake mice during visual stimulation and spontaneous activity. In both conditions, cortical activity is dominated by coactive groups of neurons, forming <span class="hlt">ensembles</span> whose activation cannot be explained by the independent firing properties of their contributing neurons, considered in isolation. Moreover, individual neurons flexibly join multiple <span class="hlt">ensembles</span>, vastly expanding the encoding potential of the circuit. Intriguingly, the same coactive <span class="hlt">ensembles</span> can repeat spontaneously and in response to visual stimuli, indicating that stimulus-evoked responses arise from activating these intrinsic building blocks. Although the spatial properties of stimulus-driven and spontaneous <span class="hlt">ensembles</span> are similar, spontaneous <span class="hlt">ensembles</span> are active at random intervals, whereas visually evoked <span class="hlt">ensembles</span> are time-locked to stimuli. We conclude that neuronal <span class="hlt">ensembles</span>, built by the coactivation of flexible groups of neurons, are emergent functional units of cortical activity and propose that visual stimuli recruit intrinsically generated <span class="hlt">ensembles</span> to represent visual attributes. PMID:25201983</p> <div class="credits"> <p class="dwt_author">Miller, Jae-eun Kang; Ayzenshtat, Inbal; Carrillo-Reid, Luis; Yuste, Rafael</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-01-01</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://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">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.osti.gov/scitech/biblio/1093136"> <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://www.osti.gov/scitech">SciTech Connect</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 [ORNL] [ORNL; Vatsavai, Raju [ORNL] [ORNL</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">311</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=4109431"> <span id="translatedtitle">Conductor gestures influence evaluations of <span class="hlt">ensemble</span> performance</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">Previous research has found that listener evaluations of <span class="hlt">ensemble</span> performances vary depending on the expressivity of the conductor’s gestures, even when performances are otherwise identical. It was the purpose of the present study to test whether this effect of visual information was evident in the evaluation of specific aspects of <span class="hlt">ensemble</span> performance: articulation and dynamics. We constructed a set of 32 music performances that combined auditory and visual information and were designed to feature a high degree of contrast along one of two target characteristics: articulation and dynamics. We paired each of four music excerpts recorded by a chamber <span class="hlt">ensemble</span> in both a high- and low-contrast condition with video of four conductors demonstrating high- and low-contrast gesture specifically appropriate to either articulation or dynamics. Using one of two equivalent test forms, college music majors and non-majors (N = 285) viewed sixteen 30 s performances and evaluated the quality of the <span class="hlt">ensemble’s</span> articulation, dynamics, technique, and tempo along with overall expressivity. Results showed significantly higher evaluations for performances featuring high rather than low conducting expressivity regardless of the <span class="hlt">ensemble’s</span> performance quality. Evaluations for both articulation and dynamics were strongly and positively correlated with evaluations of overall <span class="hlt">ensemble</span> expressivity. PMID:25104944</p> <div class="credits"> <p class="dwt_author">Morrison, Steven J.; Price, Harry E.; Smedley, Eric M.; Meals, Cory D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-01-01</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/2003PhRvE..67a1801M"> <span id="translatedtitle">Stiff polymer in monomer <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 employ an ordered monomer <span class="hlt">ensemble</span> formalism in order to develop techniques to investigate a stiff polymer chain which is confined to a certain region. In particular, we calculate the segment density for a given location and segment orientation distribution within the confining geometry. With this method the role of the stiffness can be examined by means of differential equations, integral equations, or recursive relations for both continuum and lattice models. A suitable choice of lattice model permits an exact analytical solution for the segment location and orientation density for a chain between two parallel plates. For the stiff polymer in a spherical cavity we develop an integral equation formalism which is treated numerically, and in the same spherical geometry, a different model of the polymer displays a solution of a differential equation.</p> <div class="credits"> <p class="dwt_author">Müller-Nedebock, K. K.; Frisch, H. L.; Percus, J. K.</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">313</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/12636521"> <span id="translatedtitle">Stiff polymer in monomer <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">We employ an ordered monomer <span class="hlt">ensemble</span> formalism in order to develop techniques to investigate a stiff polymer chain which is confined to a certain region. In particular, we calculate the segment density for a given location and segment orientation distribution within the confining geometry. With this method the role of the stiffness can be examined by means of differential equations, integral equations, or recursive relations for both continuum and lattice models. A suitable choice of lattice model permits an exact analytical solution for the segment location and orientation density for a chain between two parallel plates. For the stiff polymer in a spherical cavity we develop an integral equation formalism which is treated numerically, and in the same spherical geometry, a different model of the polymer displays a solution of a differential equation. PMID:12636521</p> <div class="credits"> <p class="dwt_author">Müller-Nedebock, K K; Frisch, H L; Percus, J K</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">314</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/2013AGUFM.H14G..07O"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Future Climate Change Projections for Water Resource Applications: A Case Study for Mesoamerica (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">Mesoamerica is a region that is potentially at severe risk due to future climate change. This is especially true for the water resources required for agriculture, human consumption, and hydroelectric power generation. Yet global climate models cannot properly resolve surface climate in the region, due to it's complex topography and nearness to oceans. Precipitation in particular is poorly handled. Further, Mesoamerica is hardly the only region worldwide for which these issues exist. To address this deficiency, a series of high-resolution (4-12 km) dynamical <span class="hlt">downscaling</span> simulations of future climate change between now and 2060 have been made for Mesoamerica and the Caribbean. We used the Weather Research and Forecasting (WRF) regional climate model to <span class="hlt">downscale</span> results from the NCAR CCSM4 CMIP5 RCP8.5 global simulation. The entire region is covered at 12 km horizontal spatial resolution, with as much as possible (especially in mountainous regions) at 4 km. We compare a control period (2006-2010) with 50 years into the future (2056-2060). Basic results for surface climate will be presented, as well as a developing strategy for explicitly employing these results in projecting the implications for water resources in the region. Connections will also be made to other regions around the globe that could benefit from this type of integrated modeling and analysis.</p> <div class="credits"> <p class="dwt_author">Oglesby, R. J.; Rowe, C. M.; Munoz-Arriola, F.</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">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/2014ClDy...43..375K"> <span id="translatedtitle">Development of sampling <span class="hlt">downscaling</span>: a case for wintertime precipitation in Hokkaido</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 has developed sampling <span class="hlt">downscaling</span> (SmDS), in which dynamical <span class="hlt">downscaling</span> (DDS) is executed for a few of period selected from a long-term integration by general circulation model based on an observed statistical relationship between large-scale climate and regional-scale precipitation. SmDS expectedly produces climatology and frequency distribution of precipitation over a nested region with reducing computational cost, if a global-scale climate pattern mostly controls regional-scale weather statistics. Here SmDS was attempted for wintertime precipitation over Hokkaido, Japan, because a linkage between snowfall and sea-level pressure patterns has been known by Japanese synopticians and it can be detected by singular value decomposition (SVD) analysis on wintertime inter-annual variability during the period from 1980/1981 to 2009/2010 for precipitation over Hokkaido and moisture flux convergence around there. DDS for the full period over the same domain was also performed for comparison with SmDS. SmDS selected two winters from the top and two winters from the bottom of the projection onto the first SVD mode. It was found that, comparing with the full DDS, SmDS indeed provided unbiased statistics for average but exaggerated extreme statistics such as heavy rainfall frequency. It was also shown that the sampling in the SmDS method was much more effective than the random sampling.</p> <div class="credits"> <p class="dwt_author">Kuno, Ryusuke; Inatsu, Masaru</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-07-01</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/2014EGUGA..1616395P"> <span id="translatedtitle">Combining artificial neural networks and circulation type classification: does it improve <span class="hlt">downscaling</span> 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">Circulation type classifications may be used for <span class="hlt">downscaling</span> in so called reference class forecasting (RCF), i.e. to to assign atmospheric circulation predictors to a certain type of a circulation type classification and use the value for the target variable associated with this type in the past as a model value. Doing so often already leads to useful statistical assessment models. However a generally superior method is that of artificial neural networks (NNW). Using adequate configuration, the latter are able to outperform the RCF method in virtually all cases. However the adequate configuration of NNWs is often not easy to decide and the training of the network weights may be an extensive and slow process while RCF is relatively fast. In the context of a starting project dealing with alpine climate change studies (Virtual Alpine Observatory II, VAO2), this study evaluates if a combination of both statistical approaches (called neural networks of classification types, NNC) may lead to an improvement for statistical <span class="hlt">downscaling</span>. Preliminary results suggest that the gain in skill and the computational speed for the network training largely depends on the configuration of both: the circulation type classification and the network configuration regarding, topology, learning rate, predictors and so on. In this context it is important to consider the evolution of the learning process, where sometimes the NNW is superior and sometimes the NNC.</p> <div class="credits"> <p class="dwt_author">Philipp, Andreas; Beck, Christoph; Kaspar, Severin; Jacobeit, Jucundus</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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/2014ThApC.tmp..190D"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of temperature using three techniques in the Tons River basin in Central India</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, <span class="hlt">downscaling</span> models were developed for the projections of monthly maximum and minimum air temperature for three stations, namely, Allahabad, Satna, and Rewa in Tons River basin, which is a sub-basin of the Ganges River in Central India. The three <span class="hlt">downscaling</span> techniques, namely, multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LS-SVM), were used for the development of models, and best identified model was used for simulations of future predictand (temperature) using third-generation Canadian Coupled Global Climate Model (CGCM3) simulation of A2 emission scenario for the period 2001-2100. The performance of the models was evaluated based on four statistical performance indicators. To reduce the bias in monthly projected temperature series, bias correction technique was employed. The results show that all the models are able to simulate temperature; however, LS-SVM models perform slightly better than ANN and MLR. The best identified LS-SVM models are then employed to project future temperature. The results of future projections show the increasing trends in maximum and minimum temperature for A2 scenario. Further, it is observed that minimum temperature will increase at greater rate than maximum temperature.</p> <div class="credits"> <p class="dwt_author">Duhan, Darshana; Pandey, Ashish</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-08-01</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/2014JGRC..119.3497S"> <span id="translatedtitle">Climate change projection in the Northwest Pacific marginal seas through dynamic <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">This study presents future climate change projections in the Northwest Pacific (NWP) marginal seas using dynamic <span class="hlt">downscaling</span> from global climate models (GCMs). A regional climate model (RCM) for the Northwest Pacific Ocean was setup and integrated over the period from 2001 to 2100. The model used forcing fields from three different GCM simulations to <span class="hlt">downscale</span> the effect of global climate change. MIROC, ECHAM, and HADCM were selected to provide climate change signals for the RCM. These signals were calculated from the GCMs using Cyclostationary Empirical Orthogonal Function analysis and added to the present lateral open boundary and the surface forcing. The RCM was validated by comparing hindcast result with the observation. It was able to project detailed regional climate change processes that GCMs were not able to resolve. A relatively large increases of water temperature were found in the marginal seas. However, only a marginal change was found along the Kuroshio path. Heat supply to the atmosphere decreases in most study areas due to a slower warming of the sea surface compared to the atmosphere. The RCM projection suggests that the temperature of the Yellow Sea Bottom Cold Water will gradually increase by 2100. Volume transports through major straits except the Taiwan Strait in the marginal seas are projected to increase slightly in future. Increased northeasterly wind stress in the East China Sea may also result in the transport change.</p> <div class="credits"> <p class="dwt_author">Seo, Gwang-Ho; Cho, Yang-Ki; Choi, Byoung-Ju; Kim, Kwang-Yul; Kim, Bong-guk; Tak, Yong-jin</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-06-01</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/2015JGRC..120..346K"> <span id="translatedtitle">Upscale and <span class="hlt">downscale</span> energy transfer over the tropical Pacific revealed by scatterometer winds</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">direction of the energy cascade in the mesoscales of atmospheric turbulence is investigated using near-surface winds over the tropical Pacific measured by satellite scatterometers SeaWinds (QuikSCAT) and ASCAT (MetOp-A). The tropical Pacific was subdivided into nine regions, classified as rainy or dry. Longitudinal third-order along-track structure functions DLLLa and skewness SLa were calculated as a function of separation r for each region and month during the period November 2008 to October 2009. We find that the results support both <span class="hlt">downscale</span> and upscale interpretations, depending on region and month. The results indicate that normally energy cascades <span class="hlt">downscale</span>, but cascades upscale over the cold tongue in the cold season and over the west Pacific in summer months. An explanation is offered based on the heating or cooling of the air by the underlying sea surface temperature. It is also found that the signature of intermittent small-scale (<100 km) events could be identified in graphs of SLa, implying that this diagnostic may be useful in the studies of tropical disturbances.</p> <div class="credits"> <p class="dwt_author">King, Gregory P.; Vogelzang, Jur; Stoffelen, Ad</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-01-01</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://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 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 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">321</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/23836646"> <span id="translatedtitle"><span class="hlt">Downscaling</span> CMIP5 climate models shows increased tropical cyclone activity over the 21st century.</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">A recently developed technique for simulating large [O(10(4))] numbers of tropical cyclones in climate states described by global gridded data is applied to simulations of historical and future climate states simulated by six Coupled Model Intercomparison Project 5 (CMIP5) global climate models. Tropical cyclones <span class="hlt">downscaled</span> from the climate of the period 1950-2005 are compared with those of the 21st century in simulations that stipulate that the radiative forcing from greenhouse gases increases by over preindustrial values. In contrast to storms that appear explicitly in most global models, the frequency of <span class="hlt">downscaled</span> tropical cyclones increases during the 21st century in most locations. The intensity of such storms, as measured by their maximum wind speeds, also increases, in agreement with previous results. Increases in tropical cyclone activity are most prominent in the western North Pacific, but are evident in other regions except for the southwestern Pacific. The increased frequency of events is consistent with increases in a genesis potential index based on monthly mean global model output. These results are compared and contrasted with other inferences concerning the effect of global warming on tropical cyclones. PMID:23836646</p> <div class="credits"> <p class="dwt_author">Emanuel, Kerry A</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-07-23</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/2015APJAS..51...77J"> <span id="translatedtitle">Projected change in East Asian summer monsoon by dynamic <span class="hlt">downscaling</span>: Moisture budget 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">The summer monsoon considerably affects water resource and natural hazards including flood and drought in East Asia, one of the world's most densely populated area. In this study, we investigate future changes in summer precipitation over East Asia induced by global warming through dynamical <span class="hlt">downscaling</span> with the Weather Research and Forecast model. We have selected a global model from the Coupled Model Intercomparison Project Phase 5 based on an objective evaluation for East Asian summer monsoon and applied its climate change under Representative Concentration Pathway 4.5 scenario to a pseudo global warming method. Unlike the previous studies that focused on a qualitative description of projected precipitation changes over East Asia, this study tried to identify the physical causes of the precipitation changes by analyzing a local moisture budget. Projected changes in precipitation over the eastern foothills area of Tibetan Plateau including Sichuan Basin and Yangtze River displayed a contrasting pattern: a decrease in its northern area and an increase in its southern area. A local moisture budget analysis indicated the precipitation increase over the southern area can be mainly attributed to an increase in horizontal wind convergence and surface evaporation. On the other hand, the precipitation decrease over the northern area can be largely explained by horizontal advection of dry air from the northern continent and by divergent wind flow. Regional changes in future precipitation in East Asia are likely to be attributed to different mechanisms which can be better resolved by regional dynamical <span class="hlt">downscaling</span>.</p> <div class="credits"> <p class="dwt_author">Jung, Chun-Yong; Shin, Ho-Jeong; Jang, Chan Joo; Kim, Hyung-Jin</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-02-01</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://adsabs.harvard.edu/abs/2013AGUSM.H43B..03F"> <span id="translatedtitle">Estimating Precipitation from Space: new directions in variational <span class="hlt">downscaling</span> and data fusion 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"><span class="hlt">Downscaling</span>, data fusion, and data assimilation of non-Gaussian fields are problems of fundamental importance in the atmospheric, hydrometeorologic, and oceanic sciences. The increasing availability of satellite data, e.g. precipitation from TRMM and the forthcoming GPM mission as well as soil moisture from SMAP, at multiple resolutions and accuracies has fueled renewed interest in these problems towards the development of estimation frameworks that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying fields. In this paper, we present a new and unifying formalism for statistical estimation (<span class="hlt">downscaling</span> and data fusion) of multi-sensor, multi-scale precipitation measurements. The formalism is constructed to explicitly allow the preservation of some key geometrical and statistical properties of precipitation, such as extreme gradients (indicative of the presence of rainbands and multi-cellular spatial patterns) and non-Gaussian statistics. While we restrict our presentation and examples in the spatial domain, extension to time, and/or space-time can be obtained. The proposed framework draws upon: (1) recent observations that precipitation fields exhibit "sparsity" in a gradient or wavelet domain and a probability distribution well approximated by a Generalized Gaussian, and (2) new theoretical developments in the signal processing and optimization communities for non-linear, non-smooth data recovery from noisy, blurred and downsampled signals via regularized estimation.</p> <div class="credits"> <p class="dwt_author">Foufoula, E.; Ebtehaj, M.</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">324</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=3725040"> <span id="translatedtitle"><span class="hlt">Downscaling</span> CMIP5 climate models shows increased tropical cyclone activity over the 21st century</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">A recently developed technique for simulating large [O(104)] numbers of tropical cyclones in climate states described by global gridded data is applied to simulations of historical and future climate states simulated by six Coupled Model Intercomparison Project 5 (CMIP5) global climate models. Tropical cyclones <span class="hlt">downscaled</span> from the climate of the period 1950–2005 are compared with those of the 21st century in simulations that stipulate that the radiative forcing from greenhouse gases increases by over preindustrial values. In contrast to storms that appear explicitly in most global models, the frequency of <span class="hlt">downscaled</span> tropical cyclones increases during the 21st century in most locations. The intensity of such storms, as measured by their maximum wind speeds, also increases, in agreement with previous results. Increases in tropical cyclone activity are most prominent in the western North Pacific, but are evident in other regions except for the southwestern Pacific. The increased frequency of events is consistent with increases in a genesis potential index based on monthly mean global model output. These results are compared and contrasted with other inferences concerning the effect of global warming on tropical cyclones. PMID:23836646</p> <div class="credits"> <p class="dwt_author">Emanuel, Kerry A.</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">325</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ntrs.nasa.gov/search.jsp?R=20120016072&hterms=soil&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dsoil"> <span id="translatedtitle">Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation <span class="hlt">Downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the <span class="hlt">downscaling</span> of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation <span class="hlt">downscaling</span> is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.</p> <div class="credits"> <p class="dwt_author">Koster, Randal; Walker, Greg; Mahanama, Sarith; Reichle, Rolf</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">326</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://ntrs.nasa.gov/search.jsp?R=20140011180&hterms=so-called&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dso-called"> <span id="translatedtitle">Hybrid Data Assimilation without <span class="hlt">Ensemble</span> Filtering</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the <span class="hlt">ensemble</span> is generated using a square-root <span class="hlt">ensemble</span> Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member <span class="hlt">ensemble</span> solution close to the variational solution; we also found it necessary to re-center the members of the <span class="hlt">ensemble</span> about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the <span class="hlt">ensemble</span>. This led us to consider a hybrid strategy in which the members of the <span class="hlt">ensemble</span> are generated by simply converting the variational analysis to the resolution of the <span class="hlt">ensemble</span> and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure.</p> <div class="credits"> <p class="dwt_author">Todling, Ricardo; Akkraoui, Amal El</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-01-01</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://arxiv.org/pdf/0908.3041v3"> <span id="translatedtitle">de Sitter thermodynamics 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://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">The existing thermodynamics of the cosmological horizon in de-Sitter spacetime is established in the micro-canonical <span class="hlt">ensemble</span>, while thermodynamics of black hole horizons are established in the canonical <span class="hlt">ensemble</span>. Generally in the ordinary thermodynamics and statistical mechanics, both of the micro-canonical and canonical <span class="hlt">ensembles</span> yield the same equation of state for any thermodynamic system. This implies the existence of a formulation of de-Sitter thermodynamics based on the canonical <span class="hlt">ensemble</span>. This paper reproduces the de-Sitter thermodynamics in the canonical <span class="hlt">ensemble</span>. The procedure is as follows: We put a spherical wall at the center of de-Sitter spacetime, whose mass is negligible and perfectly reflects the Hawking radiation coming from the cosmological horizon. Then the region enclosed by the wall and horizon settles down to a thermal equilibrium state, for which the Euclidean action is evaluated and the partition function is obtained. The integration constant (subtraction term) of Euclidean action is determined to reproduce the equation of state (e.g. entropy-area law) verified already in the micro-canonical <span class="hlt">ensemble</span>. Our de-Sitter canonical <span class="hlt">ensemble</span> is well-defined to preserve the "thermodynamic consistency", which means that the state variables satisfy not only the four laws of thermodynamics but also the appropriate differential relations with thermodynamic functions; e.g. partial derivatives of the free energy give the entropy, pressure, and so on. The special role of cosmological constant in de-Sitter thermodynamics is also revealed.</p> <div class="credits"> <p class="dwt_author">Hiromi Saida</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-11-04</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://adsabs.harvard.edu/abs/2003PhLA..309...24B"> <span id="translatedtitle">A general scheme for <span class="hlt">ensemble</span> purification</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 exhibit a general procedure to purify any given <span class="hlt">ensemble</span> by identifying an appropriate interaction between the physical system S of the <span class="hlt">ensemble</span> and the reference system K. We show that the interaction can be chosen in such a way to lead to a spatial separation of the pair S- K. As a consequence, one can use it to prepare at a distance different equivalent <span class="hlt">ensembles</span>. The argument associates a physically precise procedure to the purely formal and fictitious process usually considered in the literature. We conclude with an illuminating example taken from quantum computational theory.</p> <div class="credits"> <p class="dwt_author">Bassi, Angelo; Ghirardi, GianCarlo</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-03-01</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://www.osti.gov/scitech/servlets/purl/1175481"> <span id="translatedtitle">Creating <span class="hlt">ensembles</span> of decision trees through sampling</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 system for decision tree <span class="hlt">ensembles</span> that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in <span class="hlt">ensembles</span>. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in <span class="hlt">ensembles</span>.</p> <div class="credits"> <p class="dwt_author">Kamath, Chandrika; Cantu-Paz, Erick</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-08-30</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://arxiv.org/pdf/1110.3296v2"> <span id="translatedtitle">Time as a parameter of statistical <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/eprints/">E-print Network</a></p> <p class="result-summary">The notion of time is derived as a parameter of statistical <span class="hlt">ensemble</span> representing the underlying system. Varying population numbers of microstates in statistical <span class="hlt">ensemble</span> result in different expectation values corresponding to different times. We show a single parameter which equates to the notion of time is logarithm of the total number of microstates in statistical <span class="hlt">ensemble</span>. We discuss the implications of proposed model for some topics of modern physics: Poincar\\'e recurrence theorem vs. Second Law of Thermodynamics, matter vs. anti-matter asymmetry of the universe, expansion of the universe, Big Bang.</p> <div class="credits"> <p class="dwt_author">Sergei Viznyuk</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-11-26</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://adsabs.harvard.edu/abs/2011AGUFM.U21A0001H"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Exigent Forecasting of Critical Weather Events</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">To improve the forecasting of and society's preparedness for "worst-case" weather damage scenarios, we have developed <span class="hlt">ensemble</span> exigent analysis. Exigent analysis determines worst cast scenarios and associated probability quantiles from the joint spatial properties of multivariate damaging weather events. Using the <span class="hlt">ensemble</span>-estimated forecast covariance, we (1) identify the forecast exigent analysis perturbation (ExAP) and (2) find the contemporaneous and antecedent meteorological conditions that are most likely to coexist with or to evolve into the ExAP at the forecast time. Here we focus on the first objective, the ExAP identification problem. The ExAP is the perturbation wrt to the <span class="hlt">ensemble</span> mean at the forecast time that maximizes the damage in the subspace of the <span class="hlt">ensemble</span> with respect to a user-defined damage metric (i.e. maximizes the sum of the damage perturbation over the domain of interest) and to a user-specified <span class="hlt">ensemble</span> probability quantile (EPQ) defined in terms of the Mahalanobis distance of the perturbation to the <span class="hlt">ensemble</span> mean. Making use of a universal relationship (for Gaussian <span class="hlt">ensembles</span>) between the quantile of the damage functional and the EPQ, we explain the ExAP using topological arguments. Then, we formally define the ExAP by making use of the <span class="hlt">ensemble</span>-estimated covariance of the damage <span class="hlt">ensemble</span> in a Lagrangian minimization technique according to an exigent analysis theorem. Two case studies with varying complexities and expected accuracies are used to illustrate <span class="hlt">ensemble</span> exigent analysis. The first case study employs the gridded forecast number of heating degree days (HDD) to analyze forecast heating demand over a large portion of the United Sates for a cold event on 9 January 2010. The second case uses <span class="hlt">ensemble</span> forecasts of 2-meter temperature and estimates of the spatial distribution of citrus trees to define the damage functional as the percentage of Florida citrus trees damaged by the 11 January 2010 Florida freeze event. The ExAP of this damage functional, which equals a map of the forecast worst-case freeze-damage, estimates that the exigent condition at the 90th EPQ results in 4.2 times more damaged trees than the <span class="hlt">ensemble</span> mean.</p> <div class="credits"> <p class="dwt_author">Hoffman, R. N.; Gombos, D.</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">332</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://arxiv.org/pdf/1409.5005v1"> <span id="translatedtitle">Diffusion for 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://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Two <span class="hlt">ensembles</span> of standard maps are studied analytically and numerically. In particular the diffusion coefficient is calculated. For one type of <span class="hlt">ensemble</span> the chaotic parameter is chosen at random from a Gaussian distribution and is then kept fixed, while for the other type it varies from step to step. The effect of averaging out the details is evaluated and in particular it is found to be much more effective in the process of the second type. The work may shed light on the possible properties of different <span class="hlt">ensembles</span> of mixed systems.</p> <div class="credits"> <p class="dwt_author">Or Alus; Shmuel Fishman</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-09-17</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://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">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/2013JHyd..488..136J"> <span id="translatedtitle">Databased comparison of Sparse Bayesian Learning and Multiple Linear Regression for statistical <span class="hlt">downscaling</span> of low flow indices</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">SummaryThis study attempts to compare the performance of two statistical <span class="hlt">downscaling</span> frameworks in <span class="hlt">downscaling</span> hydrological indices (descriptive statistics) characterizing the low flow regimes of three rivers in Eastern Canada - Moisie, Romaine and Ouelle. The statistical models selected are Relevance Vector Machine (RVM), an implementation of Sparse Bayesian Learning, and the Automated Statistical <span class="hlt">Downscaling</span> tool (ASD), an implementation of Multiple Linear Regression. Inputs to both frameworks involve climate variables significantly (? = 0.05) correlated with the indices. These variables were processed using Canonical Correlation Analysis and the resulting canonical variates scores were used as input to RVM to estimate the selected low flow indices. In ASD, the significantly correlated climate variables were subjected to backward stepwise predictor selection and the selected predictors were subsequently used to estimate the selected low flow indices using Multiple Linear Regression. With respect to the correlation between climate variables and the selected low flow indices, it was observed that all indices are influenced, primarily, by wind components (Vertical, Zonal and Meridonal) and humidity variables (Specific and Relative Humidity). The <span class="hlt">downscaling</span> performance of the framework involving RVM was found to be better than ASD in terms of Relative Root Mean Square Error, Relative Mean Absolute Bias and Coefficient of Determination. In all cases, the former resulted in less variability of the performance indices between calibration and validation sets, implying better generalization ability than for the latter.</p> <div class="credits"> <p class="dwt_author">Joshi, Deepti; St-Hilaire, André; Daigle, Anik; Ouarda, Taha B. M. J.</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">335</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.civenv.unimelb.edu.au/~jwalker/papers/cahmda04-5.pdf"> <span id="translatedtitle">Manju Hemakumara, Jetse Kalma, Jeffrey Walker, and Garry Willgoose (2004), <span class="hlt">Downscaling</span> of low resolution passive microwave soil moisture</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">-surface soil moisture data from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E attributes. Second, the paper reports on <span class="hlt">downscaling</span> of the low resolution AMSR-E near-surface soil moisture of the Advanced Mi- crowave Scanning Radiometer for the Earth observing systems (AMSR-E) soil moisture product</p> <div class="credits"> <p class="dwt_author">Walker, Jeff</p> <p class="dwt_publisher"></p> <p class="publishDate"></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.legos.obs-mip.fr/papa/2014_Aires_etal_JHM.pdf"> <span id="translatedtitle">Characterization and SpaceTime <span class="hlt">Downscaling</span> of the Inundation Extent over the Inner Niger Delta Using GIEMS and MODIS Data</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Characterization and Space­Time <span class="hlt">Downscaling</span> of the Inundation Extent over the Inner Niger Delta the Moderate Resolution Imaging Spectroradiometer (MODIS). The study concentrates on the Inner Niger Delta this analysis for the Inner Niger Delta. The methods are very general and may be applied to many basins</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">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/57540796"> <span id="translatedtitle">Marine <span class="hlt">downscaling</span> of a future climate scenario in the North Sea and possible effects on dinoflagellate harmful algal blooms</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">Two hydrodynamic and ecological models were used to investigate the effects of climate change—according to the IPCC A1b emission scenario – on the primary productivity of the North Sea and on harmful algal blooms. Both models were forced with atmospheric fields from a regional <span class="hlt">downscaling</span> of General Circulation Models to compare two sets of 20-year simulations representative of present climate</p> <div class="credits"> <p class="dwt_author">Y. F. Friocourt; M. Skogen; W. Stolte; J. Albretsen</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">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.ciesin.org/datasets/downscaled/htmls/Guidance_Paper.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> and geo-spatial gridding of socio-economic projections from the IPCC Special Report on Emissions Scenarios (SRES)</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 database has been developed containing <span class="hlt">downscaled</span> socio-economic scenarios of future population and GDP at country level and on a geo-referenced gridscale. It builds on the recent Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES), but has been created independently of that report. The SRES scenarios are derived from projected data on economic, demographic, technological and</p> <div class="credits"> <p class="dwt_author">Stuart R. Gaffin; Cynthia R. Rosenzweig; Xiaoshi Xing; Greg Yetman</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">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/54733983"> <span id="translatedtitle">Comparison of statistical and dynamical <span class="hlt">downscaling</span> of extreme precipitations over France in present-day and future 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">We present a comparison of two <span class="hlt">downscaling</span> methods of extreme precipitations over France at a climatic time scale : a dynamical one performed with the Regional Climate Model ALADIN-Climate used at a resolution of 12 km, and a statistical one based on the weather regime approach and using the analog methodology to reconstruct daily fields of precipitations at a 8</p> <div class="credits"> <p class="dwt_author">Jeanne Colin; Michel Déqué; Emila Sanchez Gomez; Samuel Somot</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">340</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.unh.edu/erg/sites/www.unh.edu.erg/files/ray_et_al_rse_2010_2.pdf"> <span id="translatedtitle">Landslide susceptibility mapping using <span class="hlt">downscaled</span> AMSR-E soil moisture: A case study from Cleveland Corral, California, US</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Landslide susceptibility mapping using <span class="hlt">downscaled</span> AMSR-E soil moisture: A case study from Cleveland in revised form 28 April 2010 Accepted 31 May 2010 Keywords: AMSR-E Remote sensing VIC-3L Landslide Soil moisture data can provide routine updates of slope conditions necessary for landslide predictions</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate"></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_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://adsabs.harvard.edu/abs/2012AGUFM.H41A1151S"> <span id="translatedtitle">Long-lead multi-model <span class="hlt">ensemble</span> prediction of a drought index sensitive to global warming</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, whereas there are difficulties in predicting such long-lead precipitation anomalies especially over in-land extratropical areas, even using state-of-the-art multiple coupled model <span class="hlt">ensembles</span>. The potential of prediction of long-lead hydrological variations based on climatic water balance with multi-coupled model statistics has been investigated. Multi-scalar hydrological index based on both of precipitation and temperature, i.e. newly proposed standardized precipitation evapotranspiration index (SPEI), is used not only to appropriately define the hydrological extremes but also to consider hydrological balance between precipitation and evapotranspiration. Further, since it includes the role of temperature, it becomes sensitive to any linear trend, such as the global warming, and can properly respond its consequent extremes, unlike standardized precipitation index (SPI). To predict long-lead district level multi-model <span class="hlt">ensemble</span> (MME)-based hydrological extremes, six-month <span class="hlt">downscaled</span> MME (DMME) prediction system is developed for 60 stations in South Korea. DMME, in conjunction with variance inflation, can give predictions of hydrological extremes with reasonable skills in terms of SPI and SPEI. 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, S.; Ahn, J.; Tam, C. F.</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">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/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">343</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/19011220"> <span id="translatedtitle"><span class="hlt">Ensemble</span> properties in quantum steady-state nonequilibrium theories</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> properties of nonequilibrium theories for quantum steady-state processes, introduced by Caroli et al. and Feuchtwang, are investigated. Starting from an uncoupled state of separate grand-canonical <span class="hlt">ensembles</span> of two leads and a microcanonical <span class="hlt">ensemble</span> of an isolated intermediate region, it is shown rigorously how these formalisms lead to the correct coupled state of a joint <span class="hlt">ensemble</span> whose physical properties cease</p> <div class="credits"> <p class="dwt_author">Carsten Heide; Nikolai F. Schwabe</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">344</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.umass.edu/music/auditions/VocalJazzEnsemble-Fall2013v2.pdf"> <span id="translatedtitle">UMASS VOCAL JAZZ <span class="hlt">ENSEMBLE</span> Dr. Catherine Jensen-Hole-Director</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">be emailed as a p.d.f. file) 3. Sight reading jazz rhythms Please sign up for audition time outside room 149FUMASS VOCAL JAZZ <span class="hlt">ENSEMBLE</span> Dr. Catherine Jensen-Hole-Director cathyhole@hotmail.com 413 577 2459 The UMass Vocal Jazz <span class="hlt">Ensemble</span> is the primary <span class="hlt">ensemble</span> for vocal jazz studies. The <span class="hlt">ensemble</span> has received</p> <div class="credits"> <p class="dwt_author">Massachusetts at Amherst, University of</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">345</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://reports-archive.adm.cs.cmu.edu/anon/2014/CMU-CS-14-100.pdf"> <span id="translatedtitle">Anytime Prediction: Efficient <span class="hlt">Ensemble</span> Methods for Any</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Anytime Prediction: Efficient <span class="hlt">Ensemble</span> Methods for Any Computational Budget Alexander Grubb January for the degree of Doctor of Philosophy c 2014 Alexander Grubb This work supported by ONR MURI grant N00014</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">346</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/20698690"> <span id="translatedtitle">Particle number fluctuations in the microcanonical <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">Particle number fluctuations are studied in the microcanonical <span class="hlt">ensemble</span>. For the Boltzmann statistics we deduce exact analytical formulas for the microcanonical partition functions in the case of noninteracting massless neutral particles and charged particles with zero net charge. The particle number fluctuations are calculated and we find that in the microcanonical <span class="hlt">ensemble</span> they are suppressed in comparison to the fluctuations in the canonical and grand canonical <span class="hlt">ensembles</span>. This remains valid in the thermodynamic limit too, so that the well-known equivalence of all statistical <span class="hlt">ensembles</span> refers to average quantities, but does not apply to fluctuations. In the thermodynamic limit we are able to calculate the particle number fluctuations in the system of massive bosons and fermions when the exact conservation laws of both the energy and charge are taken into account.</p> <div class="credits"> <p class="dwt_author">Begun, V.V. [Bogolyubov Institute for Theoretical Physics, Kiev (Ukraine); Gorenstein, M.I.; Kostyuk, A.P. [Bogolyubov Institute for Theoretical Physics, Kiev (Ukraine); Institut fuer Theoretische Physik, Universitaet Frankfurt (Germany); Frankfurt Institute for Advanced Studies, Frankfurt (Germany); Zozulya, O.S. [Bogolyubov Institute for Theoretical Physics, Kiev (Ukraine); Utrecht University, Utrecht (Netherlands)</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-05-01</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://www.ncbi.nlm.nih.gov/pubmed/25859041"> <span id="translatedtitle">Experimental observation of a generalized Gibbs <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 description of the non-equilibrium dynamics of isolated quantum many-body systems within the framework of statistical mechanics is a fundamental open question. Conventional thermodynamical <span class="hlt">ensembles</span> fail to describe the large class of systems that exhibit nontrivial conserved quantities, and generalized <span class="hlt">ensembles</span> have been predicted to maximize entropy in these systems. We show experimentally that a degenerate one-dimensional Bose gas relaxes to a state that can be described by such a generalized <span class="hlt">ensemble</span>. This is verified through a detailed study of correlation functions up to 10th order. The applicability of the generalized <span class="hlt">ensemble</span> description for isolated quantum many-body systems points to a natural emergence of classical statistical properties from the microscopic unitary quantum evolution. PMID:25859041</p> <div class="credits"> <p class="dwt_author">Langen, Tim; Erne, Sebastian; Geiger, Remi; Rauer, Bernhard; Schweigler, Thomas; Kuhnert, Maximilian; Rohringer, Wolfgang; Mazets, Igor E; Gasenzer, Thomas; Schmiedmayer, Jörg</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-04-10</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://as2.c.u-tokyo.ac.jp/archive/kek2012.03.pdf"> <span id="translatedtitle">Statistical Mechanics without <span class="hlt">Ensembles</span> Akira Shimizu</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Statistical Mechanics without <span class="hlt">Ensembles</span> Akira Shimizu Department of Basic Science, University. Introduction: Principles of statistical mechanics revisited. 2. Thermal Pure Quantum states (TPQs) 3. Formulation of statistical mechanics with TPQs (a) Construction of a new class of TPQs (b) Genuine</p> <div class="credits"> <p class="dwt_author">Shimizu, Akira</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">349</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.info.ucl.ac.be/~pdupont/pdupont/pdf/prib11.pdf"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Logistic Regression for Feature Selection</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> Logistic Regression for Feature Selection Roman Zakharov and Pierre Dupont Machine algorithm em- bedded into logistic regression. It specifically addresses high dimensional data with few relevance is treated as a feature sampling prob- ability and a multivariate logistic regression</p> <div class="credits"> <p class="dwt_author">Dupont, Pierre</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://kuscholarworks.ku.edu/handle/1808/9849"> <span id="translatedtitle">Symphony No. 1 for Wind <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/eprints/">E-print Network</a></p> <p class="result-summary">Symphony No. 1 for Wind <span class="hlt">Ensemble</span> is a three-movement work lasting twenty to twenty-two minutes. While symphonies by Paul Hindemith, Vincent Persichetti, Frank Ticheli, John Corigliano, James Barnes, and David Maslanka are ...</p> <div class="credits"> <p class="dwt_author">Woodhouse, Ryan</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-05-31</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://pubs.er.usgs.gov/publication/70022771"> <span id="translatedtitle">A comparison of delta change and <span class="hlt">downscaled</span> GCM scenarios for three mountainous basins in the 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">Simulated daily precipitation, temperature, and runoff time series were compared in three mountainous basins in the United States: (1) the Animas River basin in Colorado, (2) the East Fork of the Carson River basin in Nevada and California, and (3) the Cle Elum River basin in Washington State. Two methods of climate scenario generation were compared: delta change and statistical <span class="hlt">downscaling</span>. The delta change method uses differences between simulated current and future climate conditions from the Hadley Centre for Climate Prediction and Research (HadCM2) General Circulation Model (GCM) added to observed time series of climate variables. A statistical <span class="hlt">downscaling</span> (SDS) model was developed for each basin using station data and output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis regridded to the scale of HadCM2. The SDS model was then used to simulate local climate variables using HadCM2 output for current and future conditions. Surface climate variables from each scenario were used in a precipitation-runoff model. Results from this study show that, in the basins tested, a precipitation-runoff model can simulate realistic runoff series for current conditions using statistically <span class="hlt">downscaled</span> NCEP output. But, use of <span class="hlt">downscaled</span> HadCM2 output for current or future climate assessments are questionable because the GCM does not produce accurate estimates of the surface variables needed for runoff in these regions. Given the uncertainties in the GCMs ability to simulate current conditions based on either the delta change or <span class="hlt">downscaling</span> approaches, future climate assessments based on either of these approaches must be treated with caution.</p> <div class="credits"> <p class="dwt_author">Hay, L.E.; Wilby, R.L.; Leavesley, G.H.</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">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/2008AGUFM.A21E0242G"> <span id="translatedtitle">Dynamical and Statistical Wind <span class="hlt">Downscaling</span> in the Northeast of the Iberian 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">Estimations of possible changes of wind variability at the regional scale as a response to the evolution of large scale climate entail relevant economic and ecological implications for society, as for instance, the assessment of the variations and sustainability in wind energy resources. Not only in this context but also from a meteorological point of view, the evaluation of surface wind variability involves many interesting aspects that are worth to be analyzed. The limited reliability of the general circulation models at the regional/local scale requires the use of <span class="hlt">downscaling</span> techniques to derive regional climate variability from the large scale circulation. Dynamical, statistical or a combination or both approaches can be applied to the <span class="hlt">downscaling</span> problem to explore the wind field behavior in the region of interest. In this work, the potential predictability of the wind speed is evaluated by means of its relationship with the atmospheric circulation over the North Atlantic area using different methodologies. For this aim, wind speed observations from the region of Navarra, Northeast of the Iberian Peninsula, are employed; the data span a 14 years period, from 1992 to 2005. A dynamical <span class="hlt">downscaling</span> using the Weather Research and Forecast (WRF) model is used to analyze the wind variability at daily time scales. The spatial wind variability is analyzed by dividing the region into various subregions by means of cluster analysis. The temporal variability is addressed by classifying the wind fields into weather types (wind circulation types) with similar spatial structure. The model is skillful in identifying the observed subregions and in reproducing the temporal wind variability at most of them. In addition, the spatial structure of the wind circulation types is generally reproduced by the simulation, with a tendency to underestimate the spatial wind speed variability. The statistical methodology explores the variability of wind speed and also wind power production at monthly timescales and consists in a linear technique which isolates optimal correlated modes of variability between the synoptic fields over the North Atlantic and the observed wind velocity (Canonical Correlation Analysis). Results evidence the existence of wind predictability in the region of study at monthly timescales. An assessment of the sensitivity of the methodology is performed as a first step in the evaluation of the potential sources of uncertainty affecting the regional estimations of the wind field. The statistical relationship found during the period of available observations is used to perform a climatological reconstruction of the surface wind field within the last five centuries using reanalysis, observational and reconstruction data sources. This evaluation of past wind variability could have relevant applications for the study of regional wind predictability over the 21th century.</p> <div class="credits"> <p class="dwt_author">Gonzalez-Rouco, J.; Jimenez, P. A.; Bustamante, E. G.; Navarro, J.; Montavez, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-12-01</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://adsabs.harvard.edu/abs/2010ems..confE.433H"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of daily precipitation: A two-step probabilistic 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"><span class="hlt">Downscaling</span> of climate data is an important issue in order to obtain high-resolution data desired for most applications in meteorology and hydrology and to gain a better understanding of local climate variability. Statistical <span class="hlt">downscaling</span> transforms data from large to local scale by relating punctual climate observations, climate model outputs and high-resolution surface data. In this study, a probabilistic <span class="hlt">downscaling</span> approach is applied on precipitation data from the subtropical mountain environment of the High Atlas in Morocco. The observations were collected within the GLOWA project IMPETUS West Africa. The considered area is characterized by strong NW-SE gradients both of altitude and of precipitation. The method consists of two steps. In order to interpolate between observational sites, the first step applies Multiple Linear Regression (MLR) on observed data taking local topographic information into account. The dependent variable (predictand) is estimated using different explanatory variables (predictors): height, latitude, longitude, slope, aspect, or gradients of height in zonal and meridional direction. For a predictand like temperature, which follows approximately a normal distribution, this method is appropriate. The development of transfer functions for precipitation is challenging, because the empirical distribution is heavily skewed due to many days with marginal or zero amounts and few extreme events. Because an application of MLR on observed values yields partly negative rainfall amounts, a probabilistic approach is utilized. At this, MLR is applied on parameters of a theoretical distribution (e.g. Weibull), which is fit to empirical distributions of precipitation amounts. In the second step, a transfer function between distributions of large-scale predictors, e.g. climate model or reanalysis data, and of local observations is derived. This is achieved by an equal probability mapping between cumulative distributions functions (CDFs) of large-scale data for recent and future climate conditions and the CDF of observed data. This enables the derivation of a transformation equation to forecast the CDF of the future period at the stations. By combining both parts a future prediction at every point of the investigation area is achieved.</p> <div class="credits"> <p class="dwt_author">Haas, R.; Born, K.</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">354</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/2003JMP....44.4807L"> <span id="translatedtitle">A matrix model for the ?-Jacobi <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">This note presents a random matrix model for general (?>0) ?-Jacobi <span class="hlt">ensembles</span>. This generalizes the well-known MANOVA models for ?=1,2,4 and eliminates the quantization of ? (and other parameters) present in the previously known models. This model is a partial answer to an open problem presented by Dumitriu and Edelman, where they also presented models for the ?-Laguerre and ?-Hermite <span class="hlt">ensembles</span>.</p> <div class="credits"> <p class="dwt_author">Lippert, Ross A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-10-01</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/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">356</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">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.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 " 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://www.ncbi.nlm.nih.gov/pubmed/24368510"> <span id="translatedtitle">Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical <span class="hlt">downscaling</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">There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5??m in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial-temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical <span class="hlt">downscaling</span> approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical <span class="hlt">downscaling</span> assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003-2005. Via cross-validation experiments, our model had an out-of-sample prediction R(2) of 0.78 and a root mean-squared error (RMSE) of 3.61??g/m(3) between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial-temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined. PMID:24368510</p> <div class="credits"> <p class="dwt_author">Chang, Howard H; Hu, Xuefei; Liu, Yang</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-07-01</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://adsabs.harvard.edu/abs/2011AGUFMGC21E..07H"> <span id="translatedtitle">Statistical-dynamical <span class="hlt">downscaling</span> for the future urban climate of Hamburg, Germany</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 framework of the interdisciplinary project KLIMZUG-NORD, adaptation measures to climate change are developed for the Metropolitan Region of Hamburg. For the development of these measures it is crucial to know how the urban climate of Hamburg, a city with a population of 1.8 Mio, will alter due to climate change. Regional climate models provide climate projections on a horizontal resolution of up to 10 km, which is still too coarse to sufficiently simulate urban related phenomena such as the urban heat island (UHI). Therefore, these climate projections have to be <span class="hlt">downscaled</span>. Since the computational amount increases rapidly with increasing horizontal resolution, a statistical-dynamical method for the UHI was developed. As a first step of the <span class="hlt">downscaling</span> method, synoptic situations which are relevant for the UHI are determined. This is done combining objective weather type classification of ERA-40 reanalysis data using k-means-based cluster analysis and a regression-based statistical model for the observed UHI of Hamburg. The meteorological variables and domain used for the weather type classification are chosen to explain the variability of the UHI as best as possible. The second step is the simulation of the resulting synoptic situations with the mesoscale meteorological model METRAS providing a horizontal resolution of 1 km. To get the average UHI for a certain period, the simulation results are statistically recombined according to the frequency of the synoptic weather types. This is done for present and future climate simulations for the A1B scenario conducted with the regional climate models REMO and CLM and for the A2 scenario conducted with the regional climate model CCAM to identify changes in Hamburg's UHI. In this presentation the method will be presented with focus on the weather type classification and on the simulation results for the summer season.</p> <div class="credits"> <p class="dwt_author">Hoffmann, P.; Flagg, D. D.; Grawe, D.; Katzfey, J. J.; Kirschner, P.; Linde, M.; Schlünzen, K. H.; Schoetter, R.</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">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/2010EGUGA..12.1496W"> <span id="translatedtitle">Modelling Extreme Events in a Changing Climate using Regional Dynamically-<span class="hlt">Downscaled</span> 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">The ability of regional dynamically-<span class="hlt">downscaled</span> global circulation models (GCMs) to assess changes to future extreme climatic events and the likely impacts was investigated. A collaborative research initiative generated projections on a high-resolution 0.1° (~14km) grid across Tasmania, an island state of Australia, using the CSIRO Conformal Cubic Atmospheric Model (CCAM). Two future emission scenarios and multiple GCMs were used for the period 1961-2100. Extreme value methods were employed for the analysis of temperature and precipitation extremes and a bias-adjustment procedure was developed to correct extreme magnitudes against observed data. Changes to the magnitude, intensity, frequency and duration of extreme events were modelled and analysed using a suite of indices to demonstrate evolving changes to extremes. Estimates of precipitation return periods were calculated using events fitted to a Generalized Pareto distribution through a robust extreme value threshold selection procedure developed for gridded precipitation datasets. Results were correlated against mean trends, both seasonally and annually, and compared to station and gridded observations. Future trends in individual and multi-model projections were compared with existing Australia-wide and global scale results calculated from GCMs. Increases in both daily maxima and minima temperature associated with climate change were noted, resulting in fewer cold nights, more heat waves and increased bushfire weather occurrences. Projections of future precipitation extremes were found to correlate closely with changes to regional climate drivers and spatial variance was also found across the state that closely matched observations. Results demonstrate that dynamical <span class="hlt">downscaling</span> captures regional climate variability (particularly relevant for precipitation) and displays significant ability in modelling future changes to extreme events at the local scale for use in adaptation and emergency planning applications.</p> <div class="credits"> <p class="dwt_author">White, Christopher J.; Corney, Stuart; Grose, Michael; Holz, Greg; Bennett, James; Bindoff, Nathaniel L.</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" 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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_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/2012AGUFM.H41J..03B"> <span id="translatedtitle"><span class="hlt">Ensemble</span> postprocessing for probabilistic quantitative precipitation 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">Precipitation is one of the most difficult weather variables to predict in hydrometeorological applications. In order to assess the uncertainty inherent in deterministic numerical weather prediction (NWP), meteorological services around the globe develop <span class="hlt">ensemble</span> prediction systems (EPS) based on high-resolution NWP systems. With non-hydrostatic model dynamics and without parameterization of deep moist convection, high-resolution NWP models are able to describe convective processes in more detail and provide more realistic mesoscale structures. However, precipitation forecasts are still affected by displacement errors, systematic biases and fast error growth on small scales. Probabilistic guidance can be achieved from an <span class="hlt">ensemble</span> setup which accounts for model error and uncertainty of initial and boundary conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) provides such an <span class="hlt">ensemble</span> system based on the German-focused limited-area model COSMO-DE. With a horizontal grid-spacing of 2.8 km, COSMO-DE is the convection-permitting high-resolution part of the operational model chain at DWD. The COSMO-DE-EPS consists of 20 realizations of COSMO-DE, driven by initial and boundary conditions derived from 4 global models and 5 perturbations of model physics. <span class="hlt">Ensemble</span> systems like COSMO-DE-EPS are often limited with respect to <span class="hlt">ensemble</span> size due to the immense computational costs. As a consequence, they can be biased and exhibit insufficient <span class="hlt">ensemble</span> spread, and probabilistic forecasts may be not well calibrated. In this study, probabilistic quantitative precipitation forecasts are derived from COSMO-DE-EPS and evaluated at more than 1000 rain gauges located all over Germany. COSMO-DE-EPS is a frequently updated <span class="hlt">ensemble</span> system, initialized 8 times a day. We use the time-lagged approach to inexpensively increase <span class="hlt">ensemble</span> spread, which results in more reliable forecasts especially for extreme precipitation events. Moreover, we will show that statistical postprocessing can compensate deficiencies in calibration of biased and underdispersive <span class="hlt">ensemble</span> forecasts and should be considered as an integral part of an <span class="hlt">ensemble</span> prediction system. The relative gain in predictive skill is evaluated for logistic regression which provides well calibrated forecast for the probability of precipitation and threshold exceedance. Quantile regression is used to obtain skillfull probabilistic forecasts of extreme precipitation events. The selection of predictive covariates is done by penalized regression based on the least absolute shrinkage and selection operator (LASSO).</p> <div class="credits"> <p class="dwt_author">Bentzien, S.; Friederichs, P.</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">362</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://crd.lbl.gov/~dhbailey/dhbpapers/Ensemble_TechReport.pdf"> <span id="translatedtitle">Dimension Reduction Using Rule <span class="hlt">Ensemble</span> Machine Learning Methods: A Numerical Study of Three <span class="hlt">Ensemble</span> Methods</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">learners." While <span class="hlt">ensembles</span> offer computationally efficient models that have good predictive capability an <span class="hlt">ensemble</span> technique that returns a model of ranked rules. The model accurately predicts class labels and has and with complex behavior. In addition, through automated parameter tuning, it is possible to grow powerful models</p> <div class="credits"> <p class="dwt_author">Bailey, David H.</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">363</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/2014EGUGA..1611466A"> <span id="translatedtitle">Inundation <span class="hlt">downscaling</span> for the development of a long-term and global inundation database compatible to SWOT mission</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 Global Inundation Extent from Multi-Satellite (GIEMS) provides multi-year monthly variations of the global surface water extent at about 25 kmx25 km resolution, from 1993 to 2007. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. Even for climate applications, the GIEMS resolution might be limited given recent results on the key importance of the smallest ponds in the emission of CH4, as compared to the largest ones. If the inundation extent is combined to altimetry measurements to obtain water volume changes, and finally river discharge to the ocean (Frappart et al. 2011), then a better resolved inundation extent will also improve the accuracy of these estimates. In the context of the SWOT mission, the <span class="hlt">downscaling</span> of GIEMS has multiple applications uses but a major one will be to use the SWOT retrievals to develop a <span class="hlt">downscaling</span> of GIEMS. This SWOT-compatible <span class="hlt">downscaling</span> could then be used to built a SWOT-compatible high-resolution database back in time from 1993 to the SWOT launch date. This extension of SWOT record is necessary to perform climate studies related to climate change. This paper present three approaches to do <span class="hlt">downscale</span> GIEMS. Two basins will be considered for illustrative purpose, Amazon, Niger and Mekhong. - Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and <span class="hlt">downscaling</span> of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeorology, in press, 2014. - Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, <span class="hlt">downscaled</span> from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. - Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett., 39(L08403), 2012. - Frappart, F.; F. Papa, A. Guntner, S. Werth, J. Santos da Silva, J. Tomasella, F. Seyler, C. Prigent, W.B. Rossow, S. Calmant, and M.-P. Bonnet. Satellite-based estimates of groundwater storage variations in large drainage basins with extensive floodplains. Remote Sens. Environ., 115 :1588-1594, 2011.</p> <div class="credits"> <p class="dwt_author">Aires, Filipe; Prigent, Catherine; Papa, Fabrice</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-05-01</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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3721968"> <span id="translatedtitle">Multiscale Macromolecular Simulation: Role of Evolving <span class="hlt">Ensembles</span></span></a>  </p> <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">Multiscale analysis provides an algorithm for the efficient simulation of macromolecular assemblies. This algorithm involves the coevolution of a quasiequilibrium probability density of atomic configurations and the Langevin dynamics of spatial coarse-grained variables denoted order parameters (OPs) characterizing nanoscale system features. In practice, implementation of the probability density involves the generation of constant OP <span class="hlt">ensembles</span> of atomic configurations. Such <span class="hlt">ensembles</span> are used to construct thermal forces and diffusion factors that mediate the stochastic OP dynamics. Generation of all-atom <span class="hlt">ensembles</span> at every Langevin timestep is computationally expensive. Here, multiscale computation for macromolecular systems is made more efficient by a method that self-consistently folds in <span class="hlt">ensembles</span> of all-atom configurations constructed in an earlier step, history, of the Langevin evolution. This procedure accounts for the temporal evolution of these <span class="hlt">ensembles</span>, accurately providing thermal forces and diffusions. It is shown that efficiency and accuracy of the OP-based simulations is increased via the integration of this historical information. Accuracy improves with the square root of the number of historical timesteps included in the calculation. As a result, CPU usage can be decreased by a factor of 3-8 without loss of accuracy. The algorithm is implemented into our existing force-field based multiscale simulation platform and demonstrated via the structural dynamics of viral capsomers. PMID:22978601</p> <div class="credits"> <p class="dwt_author">Singharoy, A.; Joshi, H.; Ortoleva, P.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">365</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/2014PhRvA..89d2321Q"> <span id="translatedtitle">Coupling spin <span class="hlt">ensembles</span> via superconducting flux qubits</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 a hybrid quantum system consisting of spin <span class="hlt">ensembles</span> and superconducting flux qubits, where each spin <span class="hlt">ensemble</span> is realized using the nitrogen-vacancy centers in a diamond crystal and the nearest-neighbor spin <span class="hlt">ensembles</span> are effectively coupled via a flux qubit. We show that the coupling strengths between flux qubits and spin <span class="hlt">ensembles</span> can reach the strong and even ultrastrong coupling regimes by either engineering the hybrid structure in advance or tuning the excitation frequencies of spin <span class="hlt">ensembles</span> via external magnetic fields. When extending the hybrid structure to an array with equal coupling strengths, we find that in the strong-coupling regime, the hybrid array is reduced to a tight-binding model of a one-dimensional bosonic lattice. In the ultrastrong-coupling regime, it exhibits quasiparticle excitations separated from the ground state by an energy gap. Moreover, these quasiparticle excitations and the ground state are stable under a certain condition that is tunable via the external magnetic field. This may provide an experimentally accessible method to probe the instability of the system.</p> <div class="credits"> <p class="dwt_author">Qiu, Yueyin; Xiong, Wei; Tian, Lin; You, J. Q.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-04-01</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://pubs.er.usgs.gov/publication/70035550"> <span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species</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"><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.</p> <div class="credits"> <p class="dwt_author">Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.</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">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.ncbi.nlm.nih.gov/pubmed/20136746"> <span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species.</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> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis. PMID:20136746</p> <div class="credits"> <p class="dwt_author">Stohlgren, Thomas J; Ma, Peter; Kumar, Sunil; Rocca, Monique; Morisette, Jeffrey T; Jarnevich, Catherine S; Benson, Nate</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-02-01</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/2012AGUFM.H43A1313W"> <span id="translatedtitle">The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment (HEPEX)</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 Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF). With support from the US National Weather Service (NWS) and the European Commission (EC), the HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological <span class="hlt">ensemble</span> forecasts for decision support in emergency management and water resources sectors. The strategy to meet this goal includes meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. HEPEX has organized about a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Today, the HEPEX mission is to demonstrate the added value of hydrological <span class="hlt">ensemble</span> prediction systems (HEPS) for emergency management and water resources sectors to make decisions that have important consequences for economy, public health, safety, and the environment. HEPEX is now organised around six major themes that represent core elements of a hydrologic <span class="hlt">ensemble</span> prediction enterprise: input and pre-processing, <span class="hlt">ensemble</span> techniques, data assimilation, post-processing, verification, and communication and use in decision making. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.</p> <div class="credits"> <p class="dwt_author">Wood, A. W.; Thielen, J.; Pappenberger, F.; Schaake, J. C.; Hartman, R. K.</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">369</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/2013NRL.....8..142S"> <span id="translatedtitle">Optimized gold nanoshell <span class="hlt">ensembles</span> for biomedical applications</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 theoretically study the properties of the optimal size distribution in the <span class="hlt">ensemble</span> of hollow gold nanoshells (HGNs) that exhibits the best performance at in vivo biomedical applications. For the first time, to the best of our knowledge, we analyze the dependence of the optimal geometric means of the nanoshells' thicknesses and core radii on the excitation wavelength and the type of human tissue, while assuming lognormal fit to the size distribution in a real HGN <span class="hlt">ensemble</span>. Regardless of the tissue type, short-wavelength, near-infrared lasers are found to be the most effective in both absorption- and scattering-based applications. We derive approximate analytical expressions enabling one to readily estimate the parameters of optimal distribution for which an HGN <span class="hlt">ensemble</span> exhibits the maximum efficiency of absorption or scattering inside a human tissue irradiated by a near-infrared laser.</p> <div class="credits"> <p class="dwt_author">Sikdar, Debabrata; Rukhlenko, Ivan D.; Cheng, Wenlong; Premaratne, Malin</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">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/23537206"> <span id="translatedtitle">Optimized gold nanoshell <span class="hlt">ensembles</span> for biomedical 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=pubmed">PubMed</a></p> <p class="result-summary">: We theoretically study the properties of the optimal size distribution in the <span class="hlt">ensemble</span> of hollow gold nanoshells (HGNs) that exhibits the best performance at in vivo biomedical applications. For the first time, to the best of our knowledge, we analyze the dependence of the optimal geometric means of the nanoshells' thickness