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1

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)  

NASA Astrophysics Data System (ADS)

Regional climate models (RCMs) have long been used to downscale global climate simulations. In contrast the ability of RCMs to downscale seasonal climate forecasts has received little attention. The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Does dynamical downscaling using RCMs provide additional useful information for seasonal forecasts made by global models? MRED is using a suite of RCMs to downscale seasonal forecasts produced by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus is on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the usefulness of higher resolution for near-surface fields influenced by high resolution orography. Each RCM covers the conterminous U.S. at approximately 32 km resolution, comparable to the scale of the North American Regional Reanalysis (NARR) which will be used to evaluate the models. The forecast ensemble for each RCM is comprised of 15 members over a period of 22+ years (from 1982 to 2003+) for the forecast period 1 December - 30 April. Each RCM will create a 15-member lagged ensemble by starting on different dates in the preceding November. This results in a 120-member ensemble for each projection (8 RCMs by 15 members per RCM). The RCMs will be continually updated at their lateral boundaries using 6-hourly output from CFS or GEOS5. Hydrometeorological output will be produced in a standard netCDF-based format for a common analysis grid, which simplifies both model intercomparison and the generation of ensembles. MRED will compare individual RCM and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs). Metrics of ensemble spread will also be evaluated. Extensive process-oriented analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will define a strategy for more skillful and useful regional seasonal climate forecasts.

Arritt, R.

2009-04-01

2

The ENSEMBLES Statistical Downscaling Portal  

NASA Astrophysics Data System (ADS)

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

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

2010-05-01

3

Multi-RCM Ensemble Downscaling of NCEP CFS Seasonal Forecasts: Implications for seasonal hydrologic forecast skill  

E-print Network

of California, Santa Barbara, CA #12;Abstract1 We assess the value of dynamical versus statistical downscaling seasonal hydrologic forecasting. Dynamically downscaled CFS forecasts for 014 December ­ 30 April of 1982 forced with dynamically (the MRED forecasts) and10 statistically downscaled CFS forecasts in comparison

Washington at Seattle, University of

4

Use of multi-model ensembles for regional climate downscaling  

NASA Astrophysics Data System (ADS)

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

Reichler, Thomas; Andrade, Marcos; Ohara, Noriaki

2014-05-01

5

Reduction of systematic biases in regional climate downscaling through ensemble forcing  

E-print Network

-to-year variations, indicating the added value of dynamic downscaling. The results suggest that models having betterReduction of systematic biases in regional climate downscaling through ensemble forcing Hongwei is a critical element in RCM simulation or downscaling. Reanalysis data have been popularly used as LB forcing

Wang, Bin

6

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

7

Downscaling a perturbed physics ensemble over the CORDEX Africa domain  

NASA Astrophysics Data System (ADS)

We present here the methodology and the results of 5-member ensemble simulation of the climate of Africa for the period 1950-2100 using climate modelling system PRECIS over the CORDEX Africa domain. The boundary conditions for the regional model simulations were selected from a 17-member perturbed physics ensemble based on the HadCM3 global climate model (Murphy et al. 2007) following the methodology described in McSweeney et al 2012. Such an approach was selected in order to provide a good representation of the overall ensemble spread over a number of sub regions while at the same time avoiding members which have demonstrate particularly unrealistic characteristics in their baseline climate. In the simulations a special attention was given to the representation of some inland water bodies, such as lake Victoria, whose impact on the regional climate was believed to be significant thus allowing for the representation of some regional processes (e.g. land-lake breezes) that were not represented in the global models. In particular the SSTs of the lakes were corrected to better represent the local climatological values. The results suggest that RCM simulations improve the fit to observations of precipitation and temperature in most of the African sub-regions (e.g. North Africa, West Sahel). Also, the range of RCM projections is often different to those from the GCMs in these regions. We discuss the reasons for and links between these results and their implications for use in informing adaptation policy at regional level.

Buontempo, Carlo; Williams, Karina; McSweeney, Carol; Jones, Richard; Mathison, Camilla; Wang, Chang

2014-05-01

8

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

NASA Astrophysics Data System (ADS)

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme.

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

2014-06-01

9

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

NASA Astrophysics Data System (ADS)

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

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

2013-04-01

10

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

11

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">12</div> <div class="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">13</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2005TellA..57..488M"> <span id="translatedtitle">Evaluation of <span class="hlt">downscaled</span> DEMETER multi-model <span class="hlt">ensemble</span> seasonal hindcasts in a northern Italy location by means of a model of wheat growth and soil water balance</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In this paper we explore the new possibilities for early crop yield assessment at the local scale arising from the availability of dynamic crop growth models and of <span class="hlt">downscaled</span> multi-model <span class="hlt">ensemble</span> seasonal forecasts. We compare the use of the latter with other methods, based on crop growth models driven by observed climatic data only. The soil water balance model developed and used at ARPA Emilia-Romagna (CRITERIA) was integrated with crop growth routines from the model WOFOST 7.1. Some validation runs were first carried out and we verified with independent field data that the new integrated model satisfactorily simulated above-ground biomass and leaf area index. The model was then used to test the feasibility of using <span class="hlt">downscaled</span> multi-model <span class="hlt">ensemble</span> seasonal hindcasts, coming from the DEMETER European research project, in order to obtain early (i.e. 90, 60 and 30 d before harvest) yield assessments for winter wheat in northern Italy. For comparison, similar runs with climatology instead of hindcasts were also carried out. For the same purpose, we also produced six simple linear regression models of final crop yields on within season (end of March, April and May) storage organs and above-ground biomass values. Median yields obtained using <span class="hlt">downscaled</span> DEMETER hindcasts always outperformed the simple regression models and were substantially equivalent to the climatology runs, with the exception of the June experiment, where the <span class="hlt">downscaled</span> seasonal hindcasts were clearly better than all other methods in reproducing the winter wheat yields simulated with observed weather data. The crop growth model output dispersion was almost always significantly lower than the dispersion of the <span class="hlt">downscaled</span> <span class="hlt">ensemble</span> seasonal hindcast used as input for crop simulations.</p> <div class="credits"> <p class="dwt_author">Marletto, V.; Zinoni, F.; Criscuolo, L.; Fontana, G.; Marchesi, S.; Morgillo, A.; van Soetendael, M.; Ceotto, E.; Andersen, U.</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">14</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.computingportal.org"> <span id="translatedtitle"><span class="hlt">Ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://nsdl.org/nsdl_dds/services/ddsws1-1/service_explorer.jsp">NSDL National Science Digital Library</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> is a new NSF NSDL Pathways project working to establish a national, distributed digital library for computing education. Our project is building a distributed portal providing access to a broad range of existing educational resources for computing while preserving the collections and their associated curation processes. We want to encourage contribution, use, reuse, review and evaluation of educational materials at multiple levels of granularity and we seek to support the full range of computing education communities including computer science, computer engineering, software engineering, information science, information systems and information technology as well as other areas often called "computing + X" or 'X informatics."</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate"></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/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">16</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 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://hal.archives-ouvertes.fr/docs/00/33/75/26/PDF/Serra08.pdf"> <span id="translatedtitle">Stochastic <span class="hlt">Downscaling</span> Method: Application to Wind Refinement</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">wind: dynamical <span class="hlt">downscaling</span> and statistical <span class="hlt">downscaling</span>. The dynamical <span class="hlt">downscaling</span> consistsStochastic <span class="hlt">Downscaling</span> Method: Application to Wind Refinement September 8, 2008 Stoch. Environ. Res stochastic <span class="hlt">downscaling</span> method: provided a numerical prediction of wind at large scale, we aim to improve</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">18</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2014AtmRe.147...68X"> <span id="translatedtitle">A review on regional dynamical <span class="hlt">downscaling</span> in intraseasonal to seasonal simulation/prediction and major factors that affect <span class="hlt">downscaling</span> ability</span></a>  </p> <div class="result-meta"> <p class="source"><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 developed and extensively applied for dynamically <span class="hlt">downscaling</span> coarse resolution information from different sources, such as general circulation models (GCMs) and reanalyses, for different purposes including past climate simulations and future climate projection. Thus far, the nature, the methods, and a number of crucial issues concerning the use of dynamic <span class="hlt">downscaling</span> are still not well understood. The most important issue is whether, and if so, under what conditions dynamic <span class="hlt">downscaling</span> is really capable of adding more information at different scales compared to the GCM or reanalysis that imposes lateral boundary conditions (LBCs) to the RCMs. There are controversies regarding the <span class="hlt">downscaling</span> ability. In this review paper we present several factors that have consistently demonstrated strong impact on dynamic <span class="hlt">downscaling</span> ability in intraseasonal and seasonal simulations/predictions and future projection. Those factors include setting of the RCM experiment (e.g. imposed LBC quality, domain size and position, LBC coupling, and horizontal resolution); as well as physical processes, mainly convective schemes and vegetation and soil processes that include initializations, vegetation specifications, and planetary boundary layer and surface coupling. These studies indicate that RCMs have <span class="hlt">downscaling</span> ability in some aspects but only under certain conditions. Any significant weaknesses in one of these aspects would cause an RCM to lose its dynamic <span class="hlt">downscaling</span> ability. This paper also briefly presents challenges faced in current RCM dynamic <span class="hlt">downscaling</span> and future prospective, which cover the application of coupled ocean-atmosphere RCMs, <span class="hlt">ensemble</span> applications, and future projections.</p> <div class="credits"> <p class="dwt_author">Xue, Yongkang; Janjic, Zavisa; Dudhia, Jimy; Vasic, Ratko; De Sales, Fernando</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-10-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">19</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://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 " lang="en"> <div class="resultNumber element">20</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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 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 style="font-weight: bold;">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a 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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");' 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_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://adsabs.harvard.edu/abs/2014ThApC.117..257S"> <span id="translatedtitle">Mediterranean climate extremes in synoptic <span class="hlt">downscaling</span> assessments</span></a>  </p> <div class="result-meta"> <p class="source"><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 behaviour of precipitation and maximum temperature extremes in the Mediterranean area under climate change conditions is analysed in the present study. In this context, the ability of synoptic <span class="hlt">downscaling</span> techniques in combination with extreme value statistics for dealing with extremes is investigated. Analyses are based upon a set of long-term station time series in the whole Mediterranean area. At first, a station-specific <span class="hlt">ensemble</span> approach for model validation was developed which includes (1) the <span class="hlt">downscaling</span> of daily precipitation and maximum temperature values from the large-scale atmospheric circulation via analogue method and (2) the fitting of extremes by generalized Pareto distribution (GPD). Model uncertainties are quantified as confidence intervals derived from the <span class="hlt">ensemble</span> distributions of GPD-related return values and described by a new metric called "ratio of overlapping". Model performance for extreme precipitation is highest in winter, whereas the best models for maximum temperature extremes are set up in autumn. Valid models are applied to a 30-year period at the end of the twenty-first century (2070-2099) by means of ECHAM5/MPI-OM general circulation model data for IPCC SRES B1 scenario. The most distinctive future changes are observed in autumn in terms of a strong reduction of precipitation extremes in Northwest Iberia and the Northern Central Mediterranean area as well as a simultaneous distinct increase of maximum temperature extremes in Southwestern Iberia and the Central and Southeastern Mediterranean regions. These signals are checked for changes in the underlying dynamical processes using extreme-related circulation classifications. The most important finding connected to future changes of precipitation extremes in the Northwestern Mediterranean area is a reduction of southerly displaced deep North Atlantic cyclones in 2070-2099 as associated with a strengthened North Atlantic Oscillation. Thus, the here estimated future changes of extreme precipitation are in line with the discourse about the influence of North Atlantic circulation variability on the changing climate in Europe.</p> <div class="credits"> <p class="dwt_author">Seubert, Stefanie; Fernández-Montes, Sonia; Philipp, Andreas; Hertig, Elke; Jacobeit, Jucundus; Vogt, Gernot; Paxian, Andreas; Paeth, Heiko</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">22</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://redwood.cs.ttu.edu/~smohan/Papers/ci12_climateDownscale.pdf"> <span id="translatedtitle">Convolutional Neural Networks for Climate <span class="hlt">Downscaling</span> Ranjini Swaminathan+</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">) predictions. There are two independent <span class="hlt">downscaling</span> pathways: dynamic and statistical. Dynamic <span class="hlt">downscaling</span> uses cli- mate predictions. Although dynamic <span class="hlt">downscaling</span> methods account for regional geographic variationsConvolutional Neural Networks for Climate <span class="hlt">Downscaling</span> Ranjini Swaminathan+ , Mohan Sridharan</p> <div class="credits"> <p class="dwt_author">Gelfond, Michael</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..1610950H"> <span id="translatedtitle">Impact of <span class="hlt">ensemble</span> perturbations provided by convective-scale <span class="hlt">ensemble</span> data assimilation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">How to derive proper initial conditions for convective-scale <span class="hlt">ensemble</span> prediction systems (EPS) is still an open question. One common approach is to generate initial condition perturbations through dynamical <span class="hlt">downscaling</span> of information from lower resolution models. This approach is attractive due to its simplicity and has been showing overall good results. However, by using lower resolution model information, it is not possible to represent the full spectrum of uncertainty in the initial state of the convective-scale EPS. An alternative approach to derive proper high-resolution initial <span class="hlt">ensemble</span> perturbations, is to apply a convective-scale <span class="hlt">ensemble</span> data assimilation system which provides a full analysis <span class="hlt">ensemble</span> in addition to a deterministic analysis. The derived analysis <span class="hlt">ensemble</span>, which gives an estimate of the current theoretical analysis uncertainty, can be used as initial conditions for subsequent <span class="hlt">ensemble</span> forecasts. A kilometer scale <span class="hlt">ensemble</span> data assimilation (KENDA) system for the Consortium for Small-scale Modeling (COSMO) model is currently under development at Deutscher Wetterdienst (DWD). In this study, we investigate the potential benefits of KENDA initial conditions for <span class="hlt">ensemble</span> forecasting. A comparison of COSMO <span class="hlt">ensemble</span> forecasts for the German domain (DE) using initial <span class="hlt">ensemble</span> perturbations provided by KENDA and generated by the <span class="hlt">downscaling</span> approach, highlights the improved representation of uncertainty in <span class="hlt">ensemble</span> forecasts from the KENDA initial conditions. Further, different inflation methods of KENDA <span class="hlt">ensemble</span> perturbations are tested to account for unrepresented error sources.</p> <div class="credits"> <p class="dwt_author">Harnisch, Florian; Keil, Christian; Sommer, Matthias</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://academic.research.microsoft.com/Publication/57465743"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of Precipitation over Vancouver Island using a Synoptic Typing Approach</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A statistical <span class="hlt">downscaling</span> technique is employed to link atmospheric circulation produced by an <span class="hlt">ensemble</span> of global climate model (GCM) simulations over the twenty-first century to precipitation recorded at weather stations on Vancouver Island. Relationships between the different spatial scales are established with synoptic typing, coupled with non-homogeneous Markov models to simulate precipitation intensity and occurrence. Types are generated from daily</p> <div class="credits"> <p class="dwt_author">Stephen R. Sobie; Andrew J. Weaver</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">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/57465283"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of Precipitation over Vancouver Island using a Synoptic Typing Approach</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A statistical <span class="hlt">downscaling</span> technique is employed to link atmospheric circulation produced by an <span class="hlt">ensemble</span> of global climate model (GCM) simulations over the twenty-first century to precipitation recorded at weather stations on Vancouver Island. Relationships between the different spatial scales are established with synoptic typing, coupled with non-homogeneous Markov models to simulate precipitation intensity and occurrence. Types are generated from daily</p> <div class="credits"> <p class="dwt_author">Stephen R. Sobie; Andrew J. Weaver</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">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/2014EGUGA..1611960H"> <span id="translatedtitle">An improved statistical <span class="hlt">downscaling</span> method for seasonal 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 statistical analogue resampling scheme (STARS) is usually applied to generate future climate <span class="hlt">ensembles</span> on a regional scale prescribing an annual mean temperature trend until 2100. The basic idea of this <span class="hlt">downscaling</span> method is, that past weather situations will recur in a similar way in near future. For this purpose, a temporally rearrangement of annual means is done resulting a mapping from dates of a simulation period to dates of the observation period. In order to improve the seasonal representation of the future climate the long-term observations and the prescribed trend taken from the CMIP5 <span class="hlt">ensemble</span> is restricted to a 3-month period for the summer (JJA) and winter (DJF) season, separately. Furthermore, a 30 yr sliding projection shifted by 10 yr has been applied to capture the non-linearity of the mean temperature slope in future. The results reveal a much better characteristic of the seasonal climate change in Germany. Comparisons with dynamical <span class="hlt">ensembles</span> within EURO-CORDEX face the projected distributions of precipitation and temperature extremes. The generally tendency of the statistical <span class="hlt">downscaling</span> approach to a much drier future is reduced within the post-processing by separating dry and wet realisations.</p> <div class="credits"> <p class="dwt_author">Hoffmann, Peter; Lutz, Julia; Menz, Christoph</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">27</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">·Statistical vs dynamical <span class="hlt">downscaling</span> ­ MOS, SST, GCM and RCM comparison ·Precipitation intensity vs frequency ­ compare statistically and dynamically <span class="hlt">downscaled</span> rainy days, dry days, wet and dry spells #12Regional Climate <span class="hlt">Downscaling</span> Intercomparison over the Philippines J.H. Qian, A.W. Robertson, M</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 " 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://engineering.umass.edu/sites/default/files/reu_docs/2010/students/AmyGetchell_REU_SoundBitePresentation.pdf"> <span id="translatedtitle">Assessment of Statistical <span class="hlt">Downscaling</span> of GCM Projections</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">., Lynn, B. and Goldberg, R. (2007). A comparison of statistical and dynamical <span class="hlt">downscaling</span> for surfaceAssessment of Statistical <span class="hlt">Downscaling</span> of GCM Projections for Water Resource Applications Amy to simulate the climate on Earth · What is <span class="hlt">downscaling</span>? A method of processing the coarse information from</p> <div class="credits"> <p class="dwt_author">Mountziaris, T. J.</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">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/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 " 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://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 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%3D20%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://ccr.aos.wisc.edu/resources/publications/pdfs/CCR_1198.pdf"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of Daily Wind Speed Variations MEGAN C. KIRCHMEIER</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 and dynamical. Dynamical <span class="hlt">downscaling</span> typi- cally employs the use of a regional climate model (RCM complete description of RCMs and dynamical <span class="hlt">downscaling</span>. Al- though computationally expensive, dynamicalStatistical <span class="hlt">Downscaling</span> of Daily Wind Speed Variations MEGAN C. KIRCHMEIER Department</p> <div class="credits"> <p class="dwt_author">Wisconsin at Madison, 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">33</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=241249"> <span id="translatedtitle">User's Manual for <span class="hlt">Downscaler</span> Fusion Software</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">Recently, a series of 3 papers has been published in the statistical literature that details the use of <span class="hlt">downscaling</span> to obtain more accurate and precise predictions of air pollution across the conterminous U.S. This <span class="hlt">downscaling</span> approach combines CMAQ gridded numerical model output...</p> <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">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/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 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/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">36</div> <div class="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">37</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://nar.oxfordjournals.org/cgi/screenpdf/37/suppl_1/D690.pdf"> <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://academic.research.microsoft.com/">Microsoft Academic Search </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 fea- turing an integrated set of genome annotation, data- bases, 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 pre- liminary support. New species in the past year include orangutan and</p> <div class="credits"> <p class="dwt_author">Tim J. P. Hubbard; B. L. Aken; Sarah C. Ayling; Benoit Ballester; Kathryn Beal; E. Bragin; S. Brent; Yuan Chen; P. Clapham; Laura Clarke; G. Coates; S. Fairley; S. Fitzgerald; J. Fernandez-banet; L. Gordon; Stefan Gräf; Syed Haider; Martin Hammond; Richard C. G. Holland; Kevin L. Howe; Andrew M. Jenkinson; N. Johnson; Andreas Kähäri; Damian Keefe; S. Keenan; R. Kinsella; Felix Kokocinski; Eugene Kulesha; Daniel Lawson; I. Longden; Karine Megy; Patrick Meidl; B. Overduin; A. Parker; B. Pritchard; D. Rios; M. Schuster; Guy Slater; Damian Smedley; William Spooner; G. Spudich; S. Trevanion; Albert J. Vilella; J. Vogel; S. White; S. Wilder; Arek Zadissa; Ewan Birney; Fiona Cunningham; Val Curwen; Richard Durbin; X. M. Fernandez-suarez; Javier Herrero; Arek Kasprzyk; Glenn Proctor; James Smith; Stephen M. J. Searle; Paul Flicek</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">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/2014ClDy..tmp..275B"> <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">2014-09-01</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/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">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/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 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 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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://web.science.unsw.edu.au/~jasone/publications/mehrotraetal2014.pdf"> <span id="translatedtitle">Evaluation of <span class="hlt">downscaled</span> daily rainfall hindcasts over Sydney, Australia using statistical and dynamical</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 dynamical <span class="hlt">downscaling</span> approaches R. Mehrotra, J. P. Evans, A. Sharma and B. Sivakumar ABSTRACT <span class="hlt">downscaling</span> model based on semi-parametric conditional simulation and a dynamical <span class="hlt">downscaling</span> approach <span class="hlt">downscaling</span> and statistical <span class="hlt">downscaling</span>. Dynamical <span class="hlt">downscaling</span> uses a limited-area high-resolution model (a</p> <div class="credits"> <p class="dwt_author">Evans, Jason</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://web.maths.unsw.edu.au/~jasone/publications/mehrotraetal2013.pdf"> <span id="translatedtitle">Evaluation of <span class="hlt">downscaled</span> daily rainfall hindcasts over Sydney, Australia using statistical and dynamical</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 dynamical <span class="hlt">downscaling</span> approaches R. Mehrotra, J. P. Evans, A. Sharma and B. Sivakumar ABSTRACT <span class="hlt">downscaling</span> model based on semi-parametric conditional simulation and a dynamical <span class="hlt">downscaling</span> approach into two groups: dyna- mical <span class="hlt">downscaling</span> and statistical <span class="hlt">downscaling</span>. Dynamical <span class="hlt">downscaling</span> uses a limited</p> <div class="credits"> <p class="dwt_author">Evans, Jason</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">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/2009AGUFM.A24A..01G"> <span id="translatedtitle">The Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX) (Invited)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX) is a program developed by the Task Force on Regional Climate <span class="hlt">Downscaling</span> of World Climate Research Programme (WCRP). The Task Force’s mandate is to develop a framework to evaluate regional climate <span class="hlt">downscaling</span> techniques; foster an international coordinated effort to develop improved <span class="hlt">downscaling</span> techniques and to provide feedback to the global modelling community; and promote greater interactions between global climate modelers, <span class="hlt">downscalers</span> and end-users. Within this mandate, the primary goal of CORDEX is to extend to a global framework the lessons learned from regional climate <span class="hlt">downscaling</span> programs focused on one continent. The framework includes regional climate models (RCMs) and statistical <span class="hlt">downscaling</span>, with an aim of evaluating the strengths and weaknesses of <span class="hlt">downscaled</span> climate information. CORDEX also provides coordination among existing and emerging <span class="hlt">downscaling</span> programs around the world. CORDEX has defined a set of target domains covering most land areas of the planet, as well as a set of simulation protocols. A primary region of emphasis is Africa, which has received less attention than most other continents in regional climate-change and climate-impacts research. Baseline RCM simulations have started, using the ERA-Interim reanalysis for boundary conditions that will cover 1987-2007. RCMs driven bv global climate models (GCMs) will simulate the period 1950-2100, where the RCMs and driving GCMs will use Representative Concentration Pathway (RCP) greenhouse gas and aerosol scenarios for the future portion, specifically RCP 4.5 and RCP 8.5. Interested groups with limited computing resources will focus on selected 30-year periods of present and future scenario climates. CORDEX has established a preliminary set of archival protocols and targeted variables for output that will be stored in a central, openly accessible repository. Although CORDEX intends to produce simulations and analyses for the IPCC Fifth Assessment Report, the WCRP Task Force views CORDEX as an ongoing program that will extend beyond the IPCC AR5. This talk will outline ways in which interested groups can participate through simulation and analyses.</p> <div class="credits"> <p class="dwt_author">Gutowski, W. J.</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">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/2012amld.book..563R"> <span id="translatedtitle"><span class="hlt">Ensemble</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"><span class="hlt">Ensemble</span> methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “<span class="hlt">ensemble</span>” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. <span class="hlt">Ensembles</span> are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote <span class="hlt">ensemble</span>, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall <span class="hlt">ensemble</span> [158]. In the literature, a plethora of terms other than <span class="hlt">ensembles</span> has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term <span class="hlt">ensemble</span> in its widest meaning, in order to include the whole range of combination methods. Nowadays, <span class="hlt">ensemble</span> methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on <span class="hlt">ensemble</span> methods is witnessed by conferences and workshops specifically devoted to <span class="hlt">ensembles</span>, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been proposed to explain the characteristics and the successful application of <span class="hlt">ensembles</span> to different application domains. For instance, Allwein, Schapire, and Singer interpreted the improved generalization capabilities of <span class="hlt">ensembles</span> of learning machines in the framework of large margin classifiers [4,177], Kleinberg in the context of stochastic discrimination theory [112], and Breiman and Friedman in the light of the bias-variance analysis borrowed from classical statistics [21,70]. Empirical studies showed that both in classification and regression problems, <span class="hlt">ensembles</span> improve on single learning machines, and moreover large experimental studies compared the effectiveness of different <span class="hlt">ensemble</span> methods on benchmark data sets [10,11,49,188]. The interest in this research area is motivated also by the availability of very fast computers and networks of workstations at a relatively low cost that allow the implementation and the experimentation of complex <span class="hlt">ensemble</span> methods using off-the-shelf computer platforms. However, as explained in Section 26.2 there are deeper reasons to use <span class="hlt">ensembles</span> of learning machines, motivated by the intrinsic characteristics of the <span class="hlt">ensemble</span> methods. The main aim of this chapter is to introduce <span class="hlt">ensemble</span> methods and to provide an overview and a bibliography of the main areas of research, without pretending to be exhaustive or to explain the detailed characteristics of each <span class="hlt">ensemble</span> method. The paper is organized as follows. In the next section, the main theoretical and practical reasons for combining multiple learners are introduced. Section 26.3 depicts the main taxonomies on <span class="hlt">ensemble</span> methods proposed in the literature. In Section 26.4 and 26.5, we present an overview of the main supervised <span class="hlt">ensemble</span> methods reported in the literature, adopting a simple taxonomy, originally proposed in Ref. [201]. Applications of <span class="hlt">ensemble</span> methods are only marginally considered, but a specific section on some relevant applications of <span class="hlt">ensemble</span> methods in astronomy and astrophysics has been added (Section 26.6). The conclusion (Section 26.7) ends this pap</p> <div class="credits"> <p class="dwt_author">Re, Matteo; Valentini, Giorgio</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-03-01</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://nzc.iap.ac.cn/uploadpdf/sdglobal.pdf"> <span id="translatedtitle">RESEARCH ARTICLE A statistical <span class="hlt">downscaling</span> scheme to improve global precipitation</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">RESEARCH ARTICLE A statistical <span class="hlt">downscaling</span> scheme to improve global precipitation forecasting a statistical <span class="hlt">downscaling</span> (SD) scheme suitable for global precipitation forecasting. The key idea of this SD indicates that current seasonal operational dynamical prediction has progressed significantly over past two</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">46</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..1412266Y"> <span id="translatedtitle">A hybrid <span class="hlt">downscaling</span> procedure for estimating the vertical distribution of ambient temperature in local scale</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The vertical thermal structure of the atmosphere is defined by a combination of dynamic and radiation transfer processes and plays an important role in describing the meteorological conditions at local scales. The scope of this work is to develop and quantify the predictive ability of a hybrid dynamic-statistical <span class="hlt">downscaling</span> procedure to estimate the vertical profile of ambient temperature at finer spatial scales. The study focuses on the warm period of the year (June - August) and the method is applied to an urban coastal site (Hellinikon), located in eastern Mediterranean. The two-step methodology initially involves the dynamic <span class="hlt">downscaling</span> of coarse resolution climate data via the RegCM4.0 regional climate model and subsequently the statistical <span class="hlt">downscaling</span> of the modeled outputs by developing and training site-specific artificial neural networks (ANN). The 2.5ox2.5o gridded NCEP-DOE Reanalysis 2 dataset is used as initial and boundary conditions for the dynamic <span class="hlt">downscaling</span> element of the methodology, which enhances the regional representivity of the dataset to 20km and provides modeled fields in 18 vertical levels. The regional climate modeling results are compared versus the upper-air Hellinikon radiosonde observations and the mean absolute error (MAE) is calculated between the four grid point values nearest to the station and the ambient temperature at the standard and significant pressure levels. The statistical <span class="hlt">downscaling</span> element of the methodology consists of an <span class="hlt">ensemble</span> of ANN models, one for each pressure level, which are trained separately and employ the regional scale RegCM4.0 output. The ANN models are theoretically capable of estimating any measurable input-output function to any desired degree of accuracy. In this study they are used as non-linear function approximators for identifying the relationship between a number of predictor variables and the ambient temperature at the various vertical levels. An insight of the statistically derived input-output transfer functions is obtained by utilizing the ANN weights method, which quantifies the relative importance of the predictor variables in the estimation procedure. The overall <span class="hlt">downscaling</span> performance evaluation incorporates a set of correlation and statistical measures along with appropriate statistical tests. The hybrid <span class="hlt">downscaling</span> method presented in this work can be extended to various locations by training different site-specific ANN models and the results, depending on the application, can be used for assisting the understanding of the past, present and future climatology. ____________________________ This research has been co-financed by the European Union and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II: Investing in knowledge society through the European Social Fund.</p> <div class="credits"> <p class="dwt_author">Yiannikopoulou, I.; Philippopoulos, K.; Deligiorgi, D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">47</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=235875"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Models</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"><span class="hlt">Ensemble</span> forecasting has been used for operational numerical weather prediction in the United States and Europe since the early 1990s. An <span class="hlt">ensemble</span> of weather or climate forecasts is used to characterize the two main sources of uncertainty in computer models of physical systems: ...</p> <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">48</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.lsta.upmc.fr/BIAU/b4.ps"> <span id="translatedtitle"><span class="hlt">DOWNSCALING</span> OF PRECIPITATION COMBINING KRIGING AND EMPIRICAL ORTHOGONAL FUNCTION ANALYSIS</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">, have been suggested so far to over­ #12; G. BIAU come this scale mismatch (dynamical <span class="hlt">downscalingDOWNSCALING</span> OF PRECIPITATION COMBINING KRIGING AND EMPIRICAL ORTHOGONAL FUNCTION ANALYSIS G. BIAU Montpellier CEDEX 5 Abstract. The term <span class="hlt">downscaling</span> denotes a procedure in which local cli­ matic information</p> <div class="credits"> <p class="dwt_author">Biau, Gérard</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">49</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://sciencepolicy.colorado.edu/admin/publication_files/resource-313-2000.29.pdf"> <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://academic.research.microsoft.com/">Microsoft Academic Search </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 simulations of basin scale</p> <div class="credits"> <p class="dwt_author">Robert L. Wilby; Lauren E. Hay; William J. Gutowski Jr.; Raymond W. Arritt; Eugene S. Takle; Zaitao Pan; George H. Leavesley; Martyn P. Clark</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">50</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013AGUFMGC11F..06G"> <span id="translatedtitle">Precipitation <span class="hlt">Downscaling</span> Products for Hydrologic Applications (Invited)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Hydrologists and engineers require climate data on high-resolution grids (4-12km) for many water resources applications. To get such data from climate models, users have traditionally relied on statistical <span class="hlt">downscaling</span> techniques, with only limited use of dynamic <span class="hlt">downscaling</span> techniques. Statistical techniques utilize a variety of assumptions, data, and methodologies that result in statistical artifacts that may impact hydroclimate representations. These impacts are often pronounced when <span class="hlt">downscaling</span> precipitation. We will discuss four major statistical <span class="hlt">downscaling</span> techniques: Bias Corrected Constructed Analogue (BCCA), Asynchronous Regression (AR), and two forms of Bias Corrected Spatial Disaggregation (BCSD.) The hydroclimate representations within many statistical methods often have too much drizzle, too small extreme events, and an improper representation of spatial scaling characteristics. These scaling problems lead some statistical methods substantially over estimate extreme events at hydrologically important scales (e.g., basin totals.) This can lead to large errors in future hydrologic predictions. In contrast, high-resolution dynamic <span class="hlt">downscaling</span> using the Weather Research and Forecasting model (WRF) provides a better representation of precipitation in many respects, but at a much higher computational cost. This computational constraint prevents the use of high-resolution WRF simulations when examining the range of possible future scenarios generated as part of the Coupled Model Intercomparison Project (CMIP.) Finally, we will present a next generation psuedo-dynamical model that provides dynamic <span class="hlt">downscaling</span> information for a fraction of the computational requirements. This simple weather model uses large scale circulation patterns from a GCM, for example wind, temperature and humidity, but performs advection and microphysical calculations on a high-resolution grid, thus permitting topography to be adequately represented. This model is capable of generating changes in spatial patterns of precipitation related to atmospheric processes in a future climate. The pseudo-dynamical model may provide both the opportunity to better represent precipitation as well as being efficient in application to utilize a range of potential futures in a manner that would support water resources planning and management in the future.</p> <div class="credits"> <p class="dwt_author">Gutmann, E. D.; Pruitt, T.; Liu, C.; Clark, M. P.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Rasmussen, R.</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">51</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/19033362"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2009.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) is a comprehensive genome information system featuring an integrated set of genome annotation, databases, and other information for chordate, selected model organism and disease vector genomes. As of release 51 (November 2008), <span class="hlt">Ensembl</span> fully supports 45 species, and three additional species have preliminary support. New species in the past year include orangutan and six additional low coverage mammalian genomes. Major additions and improvements to <span class="hlt">Ensembl</span> since our previous report include a major redesign of our website; generation of multiple genome alignments and ancestral sequences using the new Enredo-Pecan-Ortheus pipeline and development of our software infrastructure, particularly to support the <span class="hlt">Ensembl</span> Genomes project (http://www.ensemblgenomes.org/). PMID:19033362</p> <div class="credits"> <p class="dwt_author">Hubbard, T J P; Aken, B L; Ayling, S; Ballester, B; Beal, K; Bragin, E; Brent, S; Chen, Y; Clapham, P; Clarke, L; Coates, G; Fairley, S; Fitzgerald, S; Fernandez-Banet, J; Gordon, L; Graf, S; Haider, S; Hammond, M; Holland, R; Howe, K; Jenkinson, A; Johnson, N; Kahari, A; Keefe, D; Keenan, S; Kinsella, R; Kokocinski, F; Kulesha, E; Lawson, D; Longden, I; Megy, K; Meidl, P; Overduin, B; Parker, A; Pritchard, B; Rios, D; Schuster, M; Slater, G; Smedley, D; Spooner, W; Spudich, G; Trevanion, S; Vilella, A; Vogel, J; White, S; Wilder, S; Zadissa, A; Birney, E; Cunningham, F; Curwen, V; Durbin, R; Fernandez-Suarez, X M; Herrero, J; Kasprzyk, A; Proctor, G; Smith, J; Searle, S; Flicek, P</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</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=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">53</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/25352552"> <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=pubmed">PubMed</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">2014-10-28</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://academic.research.microsoft.com/Publication/39997226"> <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://academic.research.microsoft.com/">Microsoft Academic Search </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\\u000a Model (GCM) must be realistic. It is assumed here that features smaller than the GCM resolution are important in determining\\u000a the realism of the large-scale predictors. It is tested whether a three-step method can improve conventional one-step statistical\\u000a <span class="hlt">downscaling</span>. The method uses predictors that</p> <div class="credits"> <p class="dwt_author">Cecilia Hellström; Deliang Chen</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">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/2013JGRD..118.3557S"> <span id="translatedtitle">High-resolution multisite daily rainfall projections in India with statistical <span class="hlt">downscaling</span> for climate change impacts assessment</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Climate change impacts assessment involves <span class="hlt">downscaling</span> of coarse-resolution climate variables simulated by general circulation models (GCMs) using dynamic (physics-based) or statistical (data-driven) approaches. Here we use a statistical <span class="hlt">downscaling</span> technique for projections of all-India monsoon rainfall at a resolution of 0.5° in latitude/longitude. The present statistical <span class="hlt">downscaling</span> model utilizes classification and regression tree, and kernel regression and develops a statistical relationship between large-scale climate variables from reanalysis data and fine-resolution observed rainfall, and then applies the relationship to coarse-resolution GCM outputs. A GCM developed by the Canadian Centre for Climate Modeling and Analysis is employed for this study with its five <span class="hlt">ensemble</span> runs for capturing intramodel uncertainty. The model appears to effectively capture individual station means, the spatial patterns of the standard deviations, and the cross correlation between station rainfalls. Computationally expensive dynamic <span class="hlt">downscaling</span> models have been applied for India. However, our study is the first to attempt statistical <span class="hlt">downscaling</span> for the entire country at a resolution of 0.5°. The <span class="hlt">downscaling</span> model seems to capture the orographic effect on rainfall in mountainous areas of the Western Ghats and northeast India. The model also reveals spatially nonuniform changes in rainfall, with a possible increase for the western coastline and northeastern India (rainfall surplus areas) and a decrease in northern India, western India (rainfall deficit areas), and on the southeastern coastline, highlighting the need for a detailed hydrologic study that includes future projections regarding water availability which may be useful for water resource policy decisions.</p> <div class="credits"> <p class="dwt_author">Salvi, Kaustubh; Kannan, S.; Ghosh, Subimal</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-05-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">56</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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 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://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">58</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=rainfall+interception&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Drainfall%2Binterception"> <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 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://nzc.iap.ac.cn/uploadpdf/SD.pdf"> <span id="translatedtitle">A Statistical <span class="hlt">Downscaling</span> Model for Forecasting Summer Rainfall in China from DEMETER Hindcast Datasets</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 classified into two types: dynamical <span class="hlt">downscaling</span> and statistical <span class="hlt">downscaling</span>. The method of dynamical down, the statistical <span class="hlt">downscaling</span> method tends to be more straightforward than dynamical <span class="hlt">downscaling</span>. And it can alsoA Statistical <span class="hlt">Downscaling</span> Model for Forecasting Summer Rainfall in China from DEMETER Hindcast</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">60</div> <div class="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..1411206B"> <span id="translatedtitle">Uncertainties in <span class="hlt">downscaling</span> of global climate change scenarios. Comparison between dynamical and statistical 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">Changes in monthly temperature and precipitation at stations in two small areas placed in western (Banat Plain) and southwestern (Oltenia Plain) part of Romania for the periods 2021-2050 and 2071-2100 (compared to 1961-1990), under the IPCC A1B scenario, are estimated through two <span class="hlt">downscaling</span> techniques (statistical-SDM and dynamical-RCM). These results were obtained within the SEE project CC-WaterS (www.ccwaters.eu). The statistical <span class="hlt">downscaling</span> technique uses a model based on canonical correlation analysis (CCA). New improvement is achieved in this paper comparing to other previous studies, mainly referring to the combination of the local standardized temperature and precipitation anomalies (11 stations) in a single spatial vector considered as predictand, giving more physical consistence to the results. Various predictors were tested to find the optimum statistical <span class="hlt">downscaling</span> model (SDM): the temperature at 850 hPa (T850), sea level pressure (SLP) and specific humidity at 700 hPa (SH700), either used individually or together. The observed predictand data are based on homogenized dataset. It was found that the T850 is good predictor for all seasons but the combination between the three predictors gives higher skill (in terms of explained variance) for winter and similar skill for other seasons. From physical reasons both versions were retained in order to analyse the uncertainty (similar skill should give similar future climate change signal if the statistical relationship will be also valid in the future and all predictors capture the entire climate change signal). The model was fitted with the data set for the period 1961-1990 and validated over the independent data set 1991-2007.The optimum statistical <span class="hlt">downscaling</span> model, established over the independent data set for each season, has been then applied to predictors from the A1B scenario simulations of the <span class="hlt">ENSEMBLES</span> RCMs (http://ensemblesrt3.dmi.dk), RegCM3 and CNRM, driven by the global models ECHAM5 (run 3) and ARPEGE, respectively. To estimate the uncertainty related to the <span class="hlt">downscaling</span> technique (dynamical or statistical), the results achieved through the statistical <span class="hlt">downscaling</span> model (SDM) applied to the global model ECHAM5 have been compared to those derived directly from 5 RCMs (including RegCM3) with the same driver as well as with those derived from the SDM applied to the two mentioned RCMs. The final <span class="hlt">ensemble</span> achieved from 8 <span class="hlt">ENSEMBLES</span> RCM outputs and SDM outputs has been considered to estimate the uncertainty associated to the climate change signal at the 11 stations. The optimum (most plausible) climate change signal (represented by the <span class="hlt">ensemble</span> average) and the model spread (represented by the standard deviation of the 10 values) have been computed. The uncertainties related to the RCMs/GCM skill in reproducing the predictor variability are analysed in details for the pair RegCM3-ECHAM5.</p> <div class="credits"> <p class="dwt_author">Busuioc, A.; Dumitrescu, A.; Baciu, M.; Cazacioc, L.</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_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" 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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://www.ncbi.nlm.nih.gov/pubmed/17148474"> <span id="translatedtitle"><span class="hlt">Ensembl</span> 2007.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">The <span class="hlt">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org/) project provides a comprehensive and integrated source of annotation of chordate genome sequences. Over the past year the number of genomes available from <span class="hlt">Ensembl</span> has increased from 15 to 33, with the addition of sites for the mammalian genomes of elephant, rabbit, armadillo, tenrec, platypus, pig, cat, bush baby, common shrew, microbat and european hedgehog; the fish genomes of stickleback and medaka and the second example of the genomes of the sea squirt (Ciona savignyi) and the mosquito (Aedes aegypti). Some of the major features added during the year include the first complete gene sets for genomes with low-sequence coverage, the introduction of new strain variation data and the introduction of new orthology/paralog annotations based on gene trees. PMID:17148474</p> <div class="credits"> <p class="dwt_author">Hubbard, T J P; Aken, B L; Beal, K; Ballester, B; Caccamo, M; Chen, Y; Clarke, L; Coates, G; Cunningham, F; Cutts, T; Down, T; Dyer, S C; Fitzgerald, S; Fernandez-Banet, J; Graf, S; Haider, S; Hammond, M; Herrero, J; Holland, R; Howe, K; Howe, K; Johnson, N; Kahari, A; Keefe, D; Kokocinski, F; Kulesha, E; Lawson, D; Longden, I; Melsopp, C; Megy, K; Meidl, P; Ouverdin, B; Parker, A; Prlic, A; Rice, S; Rios, D; Schuster, M; Sealy, I; Severin, J; Slater, G; Smedley, D; Spudich, G; Trevanion, S; Vilella, A; Vogel, J; White, S; Wood, M; Cox, T; Curwen, V; Durbin, R; Fernandez-Suarez, X M; Flicek, P; Kasprzyk, A; Proctor, G; Searle, S; Smith, J; Ureta-Vidal, A; Birney, E</p> <p 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">62</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/1311.7235.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of global solar irradiation in R</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 methodology for <span class="hlt">downscaling</span> solar irradiation from satellite-derived databases is described using R software. Different packages such as raster, parallel, solaR, gstat, sp and rasterVis are considered in this study for improving solar resource estimation in areas with complex topography, in which <span class="hlt">downscaling</span> is a very useful tool for reducing inherent deviations in satellite-derived irradiation databases, which lack of high global spatial resolution. A topographical analysis of horizon blocking and sky-view is developed with a digital elevation model to determine what fraction of hourly solar irradiation reaches the Earth's surface. Eventually, kriging with external drift is applied for a better estimation of solar irradiation throughout the region analyzed. This methodology has been implemented as an example within the region of La Rioja in northern Spain, and the mean absolute error found is a striking 25.5% lower than with the original database.</p> <div class="credits"> <p class="dwt_author">Antonanzas-Torres, F; Antonanzas, J; Perpiñán, O</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">63</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/26364727"> <span id="translatedtitle">Statistical-dynamical <span class="hlt">downscaling</span> of wind climatologies</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-dynamical <span class="hlt">downscaling</span> procedure is applied for an investigation into the availability of wind power over a region of 80 × 87 km which covers flat and hilly terrain. The approach is based on the statistical coupling of a regionally representative wind climate with a numerical atmospheric mesoscale model. The large-scale wind climatology is calculated by a cluster-analysis of a</p> <div class="credits"> <p class="dwt_author">Heinz-Theo Mengelkamp; Hartmut Kapitza; Ulrich Pflüger</p> <p class="dwt_publisher"></p> <p class="publishDate">1997-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">64</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.cerfacs.fr/globc/publication/technicalreport/2009/dsclim_doc.pdf"> <span id="translatedtitle">dsclim: A software package to <span class="hlt">downscale</span> climate scenarios at regional scale using a weather-typing</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">de- veloped to <span class="hlt">downscale</span> climate scenarios. These techniques are either dynamical or statistical based. Dynamical <span class="hlt">downscaling</span> techniques are still expensive and require a significant amount to <span class="hlt">downscale</span> climate scenarios. These techniques are either dy- namical or statistical based. Dynamical</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">65</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFMGC21B0879Z"> <span id="translatedtitle">Joint Variable Spatial <span class="hlt">Downscaling</span> (JVSD): A New <span class="hlt">Downscaling</span> Method with Application to the Southeast US</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Joint Variable Spatial <span class="hlt">Downscaling</span> (JVSD) is a new <span class="hlt">downscaling</span> method developed to produce high resolution gridded hydrological datasets suitable for regional watershed modeling and assessments. JVSD differs from other statistical <span class="hlt">downscaling</span> methods in that multiple climatic variables are <span class="hlt">downscaled</span> simultaneously to produce realistic and consistent climate fields. JVSD includes two major steps: bias correction and spatial <span class="hlt">downscaling</span>. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being <span class="hlt">downscaled</span>. Bias correction is then based on quantile-to-quantile mapping of these stationary frequency distributions probability space. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons with 20th Century Climate in Coupled Models (20C3M) data show that JVSD reproduces the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with the North American regional climate change assessment program (NARCCAP) and other statistical <span class="hlt">downscaling</span> methods over the southeastern US. The results show that JVSD performs favorably. JVSD is applied for all A1B and A2 CMIP3 GCM scenarios in the Apalachicola-Chattahoochee-Flint River Basin (southeast US) with the following general findings: (i) Mean monthly temperature exhibits increasing trends over the ACF basin for all seasons and all A1B and A2 scenarios; Most significant are the A2 temperature increases in the 2050 - 2099 time periods; (ii) In the southern ACF watersheds, mean precipitation generally exhibits a mild decline in early spring and summer and increases in late winter; For the northern ACF watersheds, mean precipitation decreases in summer and increases mildly in winter (as in the south); (iii) In addition to mean trends, the precipitation distributions stretch on both ends with higher highs (floods) and lower lows (droughts). The <span class="hlt">downscaled</span> temperature and precipitation scenarios are the basis of a comprehensive hydrologic and water resources assessment (reported elsewhere) assessing significant water, agricultural, energy, and environmental sector impacts and underscoring the need for mitigation and adaptation measures.</p> <div class="credits"> <p class="dwt_author">Zhang, F.; Georgakakos, A. P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">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/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">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://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">69</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/39436276"> <span id="translatedtitle"><span class="hlt">Downscaling</span> from GCM precipitation: a benchmark for dynamical and statistical <span class="hlt">downscaling</span> methods</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A precipitation <span class="hlt">downscaling</span> method is presented using precipitation from a general circulation model (GCM) as predictor. The method extends a previous method from monthly to daily temporal resolution. The simplest form of the method corrects for biases in wet-day frequency and intensity. A more sophisticated variant also takes account of flow-dependent biases in the GCM. The method is flexible and</p> <div class="credits"> <p class="dwt_author">Jürg Schmidli; Christoph Frei; Pier Luigi Vidale</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">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://academic.research.microsoft.com/Publication/4248013"> <span id="translatedtitle">Detector <span class="hlt">Ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Component-baseddetection methods have demonstrated their promise by integrating a set of part-detectors to deal with large appearance variations of the target. However, an essential and critical issue, i.e., how to handle the im- perfectness of part-detectors in the integration, is not well addressed in the literature. This paper proposes a detec- tor <span class="hlt">ensemble</span> model that consists of a set of</p> <div class="credits"> <p class="dwt_author">Shengyang Dai; Ming Yang; Ying Wu; Aggelos K. Katsaggelos</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">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..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">73</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 " 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/2010AGUFMGC51A0736C"> <span id="translatedtitle">Simulation of an <span class="hlt">ensemble</span> of future climate time series with an hourly weather generator</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous <span class="hlt">downscaling</span> techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic <span class="hlt">downscaling</span> technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the <span class="hlt">downscaling</span> procedure derives distributions of factors of change for several climate statistics from a multi-model <span class="hlt">ensemble</span> of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model <span class="hlt">ensemble</span>. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an <span class="hlt">ensemble</span> of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated <span class="hlt">ensemble</span> of scenarios also accounts for the uncertainty derived from multiple GCMs used in <span class="hlt">downscaling</span>. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ), Detroit (MI), and Firenze (Italy). The stochastic <span class="hlt">downscaling</span> is carried out with eight GCMs from the CMIP3 multi-model dataset (IPCC 4AR, A1B scenario).</p> <div class="credits"> <p class="dwt_author">Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">75</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/00/66/19/73/PDF/IEEE_Lemarie_2010.pdf"> <span id="translatedtitle">The Coupled Multi-scale <span class="hlt">Downscaling</span> Climate System : a decision-making tool for developing</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">There are mainly two techniques to achieve those goals : the statistical <span class="hlt">downscaling</span> and the dynamical <span class="hlt">downscaling</span>The Coupled Multi-scale <span class="hlt">Downscaling</span> Climate System : a decision-making tool for developing a high-resolution (5km) atmospheric <span class="hlt">downscaling</span> system. The NCEP-NCAR Reanalysis and the NCEP</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">76</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2014WRR....50..562B"> <span id="translatedtitle">Stochastic <span class="hlt">downscaling</span> of climate model precipitation outputs in orographically complex regions: 2. <span class="hlt">Downscaling</span> methodology</span></a>  </p> <div class="result-meta"> <p class="source"><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 methodology of stochastic <span class="hlt">downscaling</span> of climate model precipitation outputs to subdaily temporal resolution and in a multisite framework is presented. The methodology is based on the reparameterization for future climate of the Spatiotemporal Neyman-Scott Rectangular Pulses model. The reparameterization is carried out by estimating the model parameters as done for the calibration of the model for the historical climate and using future statistics that are obtained: (i) applying to the daily historical statistics a factor of change computed from the control and future climate model outputs and (ii) by rescaling the altered daily statistics according to the scaling properties exhibited by the historical raw moments, in order to generate the future statistics at the temporal resolutions required by the reparameterization procedure. The <span class="hlt">downscaled</span> scenarios are obtained in a multisite framework accounting for cross correlations among the stations. The methodology represents a robust, efficient, and unique approach to generate multiple series of spatially distributed subdaily precipitation scenarios by Monte Carlo simulation. It presents thus a unique alternative for addressing the internal variability of the precipitation process at high temporal and spatial resolution, as compared to other <span class="hlt">downscaling</span> techniques, which are affected by both computational and resolution problems. The application of the presented approach is demonstrated for a region of complex orography where the model has proved to provide good results, in order to analyze potential changes in such vulnerable areas.</p> <div class="credits"> <p class="dwt_author">Bordoy, R.; Burlando, P.</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">77</div> <div class="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">78</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/~david/Nicholas_and_Battisti.Downscaling_PCP_from_Reanalysis.JAMC.pdf"> <span id="translatedtitle">Generated using version 3.0 of the official AMS LATEX template Empirical <span class="hlt">Downscaling</span> of High-Resolution Regional Precipitation</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">, this technique may be a viable alternative to dynamical <span class="hlt">downscaling</span> of monthly mean precipitation. 1 alternative to empirical methods is dynamical <span class="hlt">downscaling</span>, whereby a mesoscale weather forecasting model, serious drawbacks to the approach. Dynamical <span class="hlt">downscaling</span> is computationally expensive relative</p> <div class="credits"> <p class="dwt_author">Battisti, David</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">79</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">80</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013ClDy...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 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" 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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/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 " lang="en"> <div class="resultNumber element">82</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/915298"> <span id="translatedtitle">Regional <span class="hlt">Downscaling</span> for Air Quality Assessment: A Reasonable Proposition?</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">Assessing future changes in air quality using <span class="hlt">downscaled</span> climate scenarios is a relatively new application of the dynamical <span class="hlt">downscaling</span> technique. Because accurately simulating air quality places higher demands on an accurate meteorological simulation than most previous <span class="hlt">downscaling</span> applications, the ability of regional climate models to adequately generate the requisite variables must be determined. For example, models must generate realistic boundary layer dynamics and the proper frequency of precipitation in addition to precipitation amount and surface temperatures. This article presents two <span class="hlt">downscaled</span> simulations made using the Fifth-Generation Pennsylvania State University–NCAR Mesoscale Model (MM5). One simulation was driven by the NCEP/NCAR Global Reanalysis product and the other by the Goddard Institute for Space Studies global circulation model. Comparisons of the model runs are made against the ventilation and flow properties of the North American Regional Reanalysis (NARR), and also against observed precipitation. The relative dependence of different simulated quantities on regional forcing, model parameterizations, and large scale circulation provides a framework to understand similarities and differences between model simulations. Based on the comparisons, recommendations are made to improve the utility of <span class="hlt">downscaled</span> scenarios for air quality assessment.</p> <div class="credits"> <p class="dwt_author">Gustafson, William I.; Leung, Lai R.</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-08-01</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://climate.agry.purdue.edu/climate/dev/publications/J88.pdf"> <span id="translatedtitle">Roles of atmospheric and land surface data in dynamic regional <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">Roles of atmospheric and land surface data in dynamic regional <span class="hlt">downscaling</span> Deepak K. Ray,1,2 Roger in dynamic regional <span class="hlt">downscaling</span>, J. Geophys. Res., 115, D05102, doi:10.1029/2009JD012218. 1. Introduction [2</p> <div class="credits"> <p class="dwt_author">Niyogi, Dev</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://redwood.cs.ttu.edu/~smohan/Papers/ci12_CNNPoster.pdf"> <span id="translatedtitle">Convolu'onal Neural Networks for Climate <span class="hlt">Downscaling</span> Ranjini Swaminathan*,+, Mohan Sridharan* and Katharine Hayhoe+</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">grid cell) CLIMATE <span class="hlt">DOWNSCALING</span> DATA RESEARCH HYPOTHESIS Deep architectures as predictors of other variables. Ã? Iden'fy best predictors for long-term changesConvolu'onal Neural Networks for Climate <span class="hlt">Downscaling</span> Ranjini Swaminathan</p> <div class="credits"> <p class="dwt_author">Gelfond, Michael</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">85</div> <div class="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">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=http://hal.inria.fr/docs/00/39/04/39/PDF/merlin08_RSEproof.pdf"> <span id="translatedtitle">UNCORRECTEDPROOF 1 Towards deterministic <span class="hlt">downscaling</span> of SMOS soil moisture using MODIS derived soil</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">UNCORRECTEDPROOF 1 Towards deterministic <span class="hlt">downscaling</span> of SMOS soil moisture using MODIS derived soil online xxxx Keywords: <span class="hlt">Downscaling</span> Disaggregation Soil moisture Evaporative fraction NAFE SMOS MODIS 10 11 A deterministic approach for <span class="hlt">downscaling</span> 40 km resolution Soil Moisture and Ocean Salinity (SMOS) 12 observations</p> <div class="credits"> <p class="dwt_author">Boyer, Edmond</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://pielkeclimatesci.wordpress.com/files/2009/11/r-325.pdf"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span>: Assessment of model system dependent retained and added variability for two</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">Dynamical <span class="hlt">downscaling</span>: Assessment of model system dependent retained and added variability for two confirmed that dynamic <span class="hlt">downscaling</span> does not retain (or increase) simulation skill of the large-scale fields be relevant to all applications of dynamic <span class="hlt">downscaling</span> for regional climate simulations. Citation: Rockel, B</p> <div class="credits"> <p class="dwt_author">Pielke, Roger 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">89</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.cgd.ucar.edu/cas/adai/papers/Liang_etal_JGR06.pdf"> <span id="translatedtitle">Regional climate model <span class="hlt">downscaling</span> of the U.S. summer climate and future change</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">- ognized as a valuable dynamic <span class="hlt">downscaling</span> approach to bridge the gap between general circulation modelRegional climate model <span class="hlt">downscaling</span> of the U.S. summer climate and future change Xin-Zhong Liang,1­1995) summer season climate is first compared with observations to study the CMM5's <span class="hlt">downscaling</span> skill</p> <div class="credits"> <p class="dwt_author">Dai, Aiguo</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://www.ce.umn.edu/~foufoula/papers/efg_119.pdf"> <span id="translatedtitle">Sparse regularization for precipitation <span class="hlt">downscaling</span> A. M. Ebtehaj,1,2</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">et al. [2001] in which a precipitation <span class="hlt">downscaling</span> model was dynamically coupled with a coarseSparse regularization for precipitation <span class="hlt">downscaling</span> A. M. Ebtehaj,1,2 E. Foufoula-Georgiou,1 and G 2012. [1] <span class="hlt">Downscaling</span> of remotely sensed precipitation images and outputs of general circulation models</p> <div class="credits"> <p class="dwt_author">Foufoula-Georgiou, Efi</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">91</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://nzc.iap.ac.cn/uploadpdf/fulltext.pdf"> <span id="translatedtitle">REVIEW ARTICLE Prediction of spring precipitation in China using a <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">#12;dynamical <span class="hlt">downscaling</span>. Regional climate models (RCMs) are dynamic models nested in GCMsREVIEW ARTICLE Prediction of spring precipitation in China using a <span class="hlt">downscaling</span> approach Ying Liu of this paper is to use a statistical <span class="hlt">downscaling</span> model to predict spring precipitation over China based</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">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.sscnet.ucla.edu/~yxue/pdf/2013satoCD.pdf"> <span id="translatedtitle">Validating a regional climate model's <span class="hlt">downscaling</span> ability for East Asian summer monsoonal interannual variability</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">research (Dickinson et al. 1989; Giorgi and Bates 1989). Recently, dynamical <span class="hlt">downscaling</span> using regionalValidating a regional climate model's <span class="hlt">downscaling</span> ability for East Asian summer monsoonal of a regional climate model (RCM), WRF, for <span class="hlt">downscaling</span> East Asian summer season climate is investigated based</p> <div class="credits"> <p class="dwt_author">Xue, Yongkang</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://php.indiana.edu/~spryor/Papers_PDF/2005JD005899.pdf"> <span id="translatedtitle">Empirical <span class="hlt">downscaling</span> of wind speed probability distributions S. C. Pryor and J. T. Schoof1</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 wind climatologies. This <span class="hlt">downscaling</span> can be undertaken using either (1) physical/ dynamical methodsEmpirical <span class="hlt">downscaling</span> of wind speed probability distributions S. C. Pryor and J. T. Schoof1] This paper presents a novel approach to developing empirically <span class="hlt">downscaled</span> estimates of near-surface wind</p> <div class="credits"> <p class="dwt_author">Pryor, Sara C.</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://pielkeclimatesci.wordpress.com/files/2009/10/r-250.pdf"> <span id="translatedtitle">1\\RE PRESENT DAY CLIMATE SIMULATIONS ACCURATE ENOUGH FOR RELIABLE REGIONAL <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 combination of dynamical and statisticalmethodsarealsoinuse. Thle motivation for developing <span class="hlt">downscaling</span> tec#12;1\\RE PRESENT DAY CLIMATE SIMULATIONS ACCURATE ENOUGH FOR RELIABLE REGIONAL <span class="hlt">DOWNSCALING</span>? T asdownscaling. The fIrst and most important assumptioncommonto both forms of <span class="hlt">downscaling</span> is that the large scale</p> <div class="credits"> <p class="dwt_author">Pielke, Roger 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">95</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 (BRAN) is <span class="hlt">downscaled</span> for the waters off southeast Australia and the performance assessed against remotely-sensed and ship- board observations. The <span class="hlt">downscaling</span> involves assimilating hydrographic fields</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 " 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://web.maths.unsw.edu.au/~jasone/publications/jietal2011.pdf"> <span id="translatedtitle">Using dynamical <span class="hlt">downscaling</span> to simulate rainfall for East Coast Low events</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">Using dynamical <span class="hlt">downscaling</span> to simulate rainfall for East Coast Low events F. Ji a , J. Evans b be applied to better estimate rainfall amount and its distribution from dynamical <span class="hlt">downscaling</span> of climate http://mssanz.org.au/modsim2011 2733 #12;Ji et al., Using dynamical <span class="hlt">downscaling</span> to simulate rainfall</p> <div class="credits"> <p class="dwt_author">Evans, Jason</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">97</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://pielkeclimatesci.wordpress.com/files/2009/10/r-276.pdf"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span>: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS)</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">Dynamical <span class="hlt">downscaling</span>: Assessment of value retained and added using the Regional Atmospheric by dynamical <span class="hlt">downscaling</span> is quantitatively evaluated by considering the spectral behavior of the Regional. For the particular case considered, dynamical <span class="hlt">downscaling</span> with RAMS in RCM mode does not retain value of the large</p> <div class="credits"> <p class="dwt_author">Pielke, Roger A.</p> <p class="dwt_publisher"></p> <p class="publishDate"></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.ce.umn.edu/~foufoula/papers/efg_134.pdf"> <span id="translatedtitle">On variational <span class="hlt">downscaling</span>, fusion, and assimilation of hydrometeorological states: A unified framework via regularization</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">dynamics. In this paper, we revisit the problems of <span class="hlt">downscaling</span>, data fusion, and data assimi- lationOn variational <span class="hlt">downscaling</span>, fusion, and assimilation of hydrometeorological states: A unified framework that ties together the problems of <span class="hlt">downscaling</span>, data fusion, and data assimilation as ill</p> <div class="credits"> <p class="dwt_author">Foufoula-Georgiou, Efi</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://pielkeclimatesci.wordpress.com/files/2009/10/r-332.pdf"> <span id="translatedtitle">Assessment of three dynamical climate <span class="hlt">downscaling</span> methods using the Weather Research and Forecasting (WRF) model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Assessment of three dynamical climate <span class="hlt">downscaling</span> methods using the Weather Research in dynamical regional climate <span class="hlt">downscaling</span> employs a continuous integration of a limited-area model.S. to dynamically <span class="hlt">downscale</span> the 1-degree NCEP Global Final Analysis (FNL). We perform three types of experiments</p> <div class="credits"> <p class="dwt_author">Pielke, Roger A.</p> <p class="dwt_publisher"></p> <p class="publishDate"></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.geo.utexas.edu/courses/387h/papers/murphy_1999.pdf"> <span id="translatedtitle">An Evaluation of Statistical and Dynamical Techniques for <span class="hlt">Downscaling</span> Local Climate</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">An assessment is made of <span class="hlt">downscaling</span> estimates of screen temperature and precipitation observed at 976 European stations during 1983-94. A statistical <span class="hlt">downscaling</span> technique, in which local values are inferred from observed atmospheric predictor variables, is compared against two dynamical <span class="hlt">downscaling</span> techniques, based on the use of the screen temperature or precipitation simulated at the nearest grid point in integrations of</p> <div class="credits"> <p class="dwt_author">James Murphy</p> <p class="dwt_publisher"></p> <p class="publishDate">1999-01-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_4");' href="#" title="Previous Page"> 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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.sage.wisc.edu/pubs/articles/M-Z/spak/spakJGR2007.pdf"> <span id="translatedtitle">A comparison of statistical and dynamical <span class="hlt">downscaling</span> for surface temperature in North America</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Projections from general circulation model (GCM) simulations must be <span class="hlt">downscaled</span> to the high spatial resolution needed for assessing local and regional impacts of climate change, but uncertainties in the <span class="hlt">downscaling</span> process are difficult to quantify. We employed a multiple linear regression model and the MM5 dynamical model to <span class="hlt">downscale</span> June, July, and August monthly mean surface temperature over eastern North</p> <div class="credits"> <p class="dwt_author">Scott Spak; Tracey Holloway; Barry Lynn; Richard Goldberg</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">102</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://pubs.usgs.gov/of/2011/1238/pdf/ofr20111238.pdf"> <span id="translatedtitle">Dynamically <span class="hlt">Downscaled</span> Climate Simulations over North America: Methods, Evaluation, and Supporting</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">Dynamically <span class="hlt">Downscaled</span> Climate Simulations over North America: Methods, Evaluation, and Supporting;#12;Dynamically <span class="hlt">Downscaled</span> Climate Simulations over North America: Methods, Evaluation, and Supporting://store.usgs.gov. Suggested citation: Hostetler, S.W., Alder, J.R. and Allan, A.M., 2011, Dynamically <span class="hlt">downscaled</span> climate</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">103</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://pubs.giss.nasa.gov/docs/2007/2007_Spak_etal_1.pdf"> <span id="translatedtitle">A comparison of statistical and dynamical <span class="hlt">downscaling</span> for surface temperature in North America</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 comparison of statistical and dynamical <span class="hlt">downscaling</span> for surface temperature in North America. We employed a multiple linear regression model and the MM5 dynamical model to <span class="hlt">downscale</span> June, July significantly to the level of agreement with dynamical <span class="hlt">downscaling</span>. We found that the two methods and all</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://www.geo.utexas.edu/climate/Research/Reprints/New_DynamicDownscaling.pdf"> <span id="translatedtitle">Title page1 An improved dynamical <span class="hlt">downscaling</span> method with GCM bias3</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 Title page1 2 An improved dynamical <span class="hlt">downscaling</span> method with GCM bias3 corrections and its-LaTeX): JCLI-D-12-00005.doc #12;2 ABSTRACT1 An improved dynamical <span class="hlt">downscaling</span> method (IDD) with general climatological means and extreme events relative to traditional dynamical14 <span class="hlt">downscaling</span> approach (TDD</p> <div class="credits"> <p class="dwt_author">Yang, Zong-Liang</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">105</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.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">Functional ANOVA and Regional Climate Experiments: A Statistical Analysis of Dynamic <span class="hlt">Downscaling</span> for dynamic <span class="hlt">downscaling</span> of global models. In this paper, we discuss an initial analysis of a subset dynamic <span class="hlt">downscaling</span> methods and demonstrate that there are significant differences between the two models</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 " lang="en"> <div class="resultNumber element">106</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://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 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/2010AGUFM.H24F..08H"> <span id="translatedtitle">Development of Spatiotemporal Bias-Correction Techniques for <span class="hlt">Downscaling</span> GCM Predictions</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM <span class="hlt">downscaling</span> techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. <span class="hlt">Downscaled</span> GCM results (e.g., CMIP3 <span class="hlt">ensembles</span>) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and <span class="hlt">downscaled</span> CMIP3 data for the central Florida. The hydrologic response of two southwest Florida watersheds to the gridded observation data, the original bias corrected CMIP3 data, and the new spatiotemporally corrected CMIP3 predictions was compared using an integrated surface-subsurface hydrologic model developed by Tampa Bay Water.</p> <div class="credits"> <p class="dwt_author">Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. 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">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/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 odd" lang="en"> <div class="resultNumber element">109</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/19906699"> <span id="translatedtitle"><span class="hlt">Ensembl</span>'s 10th year.</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">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org) integrates genomic information for a comprehensive set of chordate genomes with a particular focus on resources for human, mouse, rat, zebrafish and other high-value sequenced genomes. We provide complete gene annotations for all supported species in addition to specific resources that target genome variation, function and evolution. <span class="hlt">Ensembl</span> data is accessible in a variety of formats including via our genome browser, API and BioMart. This year marks the tenth anniversary of <span class="hlt">Ensembl</span> and in that time the project has grown with advances in genome technology. As of release 56 (September 2009), <span class="hlt">Ensembl</span> supports 51 species including marmoset, pig, zebra finch, lizard, gorilla and wallaby, which were added in the past year. Major additions and improvements to <span class="hlt">Ensembl</span> since our previous report include the incorporation of the human GRCh37 assembly, enhanced visualisation and data-mining options for the <span class="hlt">Ensembl</span> regulatory features and continued development of our software infrastructure. PMID:19906699</p> <div class="credits"> <p class="dwt_author">Flicek, Paul; Aken, Bronwen L; Ballester, Benoit; Beal, Kathryn; Bragin, Eugene; Brent, Simon; Chen, Yuan; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Fernandez-Banet, Julio; Gordon, Leo; Gräf, Stefan; Haider, Syed; Hammond, Martin; Howe, Kerstin; Jenkinson, Andrew; Johnson, Nathan; Kähäri, Andreas; Keefe, Damian; Keenan, Stephen; Kinsella, Rhoda; Kokocinski, Felix; Koscielny, Gautier; Kulesha, Eugene; Lawson, Daniel; Longden, Ian; Massingham, Tim; McLaren, William; Megy, Karine; Overduin, Bert; Pritchard, Bethan; Rios, Daniel; Ruffier, Magali; Schuster, Michael; Slater, Guy; Smedley, Damian; Spudich, Giulietta; Tang, Y Amy; Trevanion, Stephen; Vilella, Albert; Vogel, Jan; White, Simon; Wilder, Steven P; Zadissa, Amonida; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Durbin, Richard; Fernández-Suarez, Xosé M; Herrero, Javier; Hubbard, Tim J P; Parker, Anne; Proctor, Glenn; Smith, James; Searle, Stephen M J</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">110</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/55233542"> <span id="translatedtitle">High precipitating events in Mediterranean regions : a climate <span class="hlt">downscaling</span> approach</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Mediterranean regions are regularly affected by high precipitating events (HPEs) that often lead to devastating flash floods. The evolution of the occurrence and severity of such extreme events in the frame of the global climate change remains an open question. In order to address this question, we have designed a statistico-dynamical <span class="hlt">downscaling</span> method using climate model outputs for an enhanced</p> <div class="credits"> <p class="dwt_author">A.-L. Beaulant; N. Nuissier; B. Joly; V. Ducrocq; A. Joly; S. Somot; F. Sevault; M. Deque</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">111</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/51251015"> <span id="translatedtitle">Improved Regional Climate Change Projections Through 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">The Climate Futures for Tasmania (CFT) project has undertaken a series of dynamical <span class="hlt">downscaling</span> simulations using CSIRO's Conformal Cubic Atmospheric Model (CCAM). These simulations provide high resolution (10 km) output over the Australian state of Tasmania. The simulations use as boundary conditions output from six GCMs and two SRES emission scenarios, giving a total of twelve runs. By modeling the</p> <div class="credits"> <p class="dwt_author">Stuart Corney; Jack Katzfey; John McGregor; Michael Grose; James Bennett; Christopher White; Greg Holz; Nathan Bindoff</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">112</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://web.science.unsw.edu.au/~jasone/publications/evans2011.pdf"> <span id="translatedtitle">CORDEX An international climate <span class="hlt">downscaling</span> J.P. Evans1</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">CORDEX ­ An international climate <span class="hlt">downscaling</span> initiative J.P. Evans1 1 Climate Change Research Centre, University of New South Wales,Sydney, New South Wales Email: jason.evans@unsw.edu.au Abstract, Perth, Australia, 12­16 December 2011 http://mssanz.org.au/modsim2011 2705 #12;Evans, CORDEX</p> <div class="credits"> <p class="dwt_author">Evans, Jason</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">113</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013AGUFMGC43C1063P"> <span id="translatedtitle">Overcoming Spatial Dilution and Frequency Dependent Biases in Statistical <span class="hlt">Downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The impact of climate change on regional hydrology is a key component of adaptation and vulnerability studies. Yet <span class="hlt">downscaling</span> global climate models to provide forcing fields for the necessary hydrological simulations is challenging because the distributed sources and non-linear nature of runoff renders such simulations highly sensitive to inaccuracies in spatial structure and other biases. For this reason bias correction is an integral part of the <span class="hlt">downscaling</span> process. Biases in annual or monthly mean fields of precipitation and temperature have received considerable attention, but other biases remain that produce errors in hydrological applications. One such bias that occurs in some <span class="hlt">downscaling</span> schemes is an increase in the spatial coherence of the daily <span class="hlt">downscaled</span> precipitation. In evaluating this bias, it is useful to quantify the effects of low spatial variability and develop an understanding of how this bias affects hydrological simulations. An accurate representation of spatial precipitation variability is particularly important in simulating flood events; if simulated heavy precipitation is too spatially coherent, the volume of runoff from adjacent portions of a watershed may be artificially high. We illustrate a scale selective constructed analogues technique that ameliorates the spatial dilution found in other analogue schemes. Besides these spatial biases, another <span class="hlt">downscaling</span> issue is that climate models produce substantial biases in frequency space, so that a given model can be deficient in variability in one frequency band yet have too much variability in another. Standard bias-correction methods such as quantile mapping and CDF-t do not address this problem. We show that bias correction in frequency space prior to quantile mapping or CDF-t can reduce these errors, leading to an overall improved agreement with observations.</p> <div class="credits"> <p class="dwt_author">Pierce, D. W.; Cayan, D. 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">114</div> <div class="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..02M"> <span id="translatedtitle"><span class="hlt">Downscaling</span> climate model output for water resources impacts assessment (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">Water agencies in the U.S. and around the globe are beginning to wrap climate change projections into their planning procedures, recognizing that ongoing human-induced changes to hydrology can affect water management in significant ways. Future hydrology changes are derived using global climate model (GCM) projections, though their output is at a spatial scale that is too coarse to meet the needs of those concerned with local and regional impacts. Those investigating local impacts have employed a range of techniques for <span class="hlt">downscaling</span>, the process of translating GCM output to a more locally-relevant spatial scale. Recent projects have produced libraries of publicly-available <span class="hlt">downscaled</span> climate projections, enabling managers, researchers and others to focus on impacts studies, drawing from a shared pool of fine-scale climate data. Besides the obvious advantage to data users, who no longer need to develop expertise in <span class="hlt">downscaling</span> prior to examining impacts, the use of the <span class="hlt">downscaled</span> data by hundreds of people has allowed a crowdsourcing approach to examining the data. The wide variety of applications employed by different users has revealed characteristics not discovered during the initial data set production. This has led to a deeper look at the <span class="hlt">downscaling</span> methods, including the assumptions and effect of bias correction of GCM output. Here new findings are presented related to the assumption of stationarity in the relationships between large- and fine-scale climate, as well as the impact of quantile mapping bias correction on precipitation trends. The validity of these assumptions can influence the interpretations of impacts studies using data derived using these standard statistical methods and help point the way to improved methods.</p> <div class="credits"> <p class="dwt_author">Maurer, E. P.; Pierce, D. W.; Cayan, D. R.</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">115</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">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.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 class="floatContainer result odd" lang="en"> <div class="resultNumber element">117</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.4151V"> <span id="translatedtitle">Comparing climate change impacts on crops in Belgium based on CMIP3 and EU-<span class="hlt">ENSEMBLES</span> multi-model <span class="hlt">ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><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 (GCMs) are sophisticated tools to study the future evolution of the climate. Yet, the coarse scale of GCMs of hundreds of kilometers raises questions about the suitability for agricultural impact assessments. These assessments are often made at field level and require consideration of interactions at sub-GCM grid scale (e.g., elevation-dependent climatic changes). Regional climate models (RCMs) were developed to provide climate projections at a spatial scale of 25-50 km for limited regions, e.g. Europe (Giorgi and Mearns, 1991). Climate projections from GCMs or RCMs are available as multi-model <span class="hlt">ensembles</span>. These <span class="hlt">ensembles</span> are based on large data sets of simulations produced by modelling groups worldwide, who performed a set of coordinated climate experiments in which climate models were run for a common set of experiments and various emissions scenarios (Knutti et al., 2010). The use of multi-model <span class="hlt">ensembles</span> in climate change studies is an important step in quantifying uncertainty in impact predictions, which will underpin more informed decisions for adaptation and mitigation to changing climate (Semenov and Stratonovitch, 2010). The objective of our study was to evaluate the effect of the spatial scale of climate projections on climate change impacts for cereals in Belgium. Climate scenarios were based on two multi-model <span class="hlt">ensembles</span>, one comprising 15 GCMs of the Coupled Model Intercomparison Project phase 3 (CMIP3; Meehl et al., 2007) with spatial resolution of 200-300 km, the other comprising 9 RCMs of the EU-<span class="hlt">ENSEMBLES</span> project (van der Linden and Mitchell, 2009) with spatial resolution of 25 km. To be useful for agricultural impact assessments, the projections of GCMs and RCMs were <span class="hlt">downscaled</span> to the field level. Long series (240 cropping seasons) of local-scale climate scenarios were generated by the LARS-WG weather generator (Semenov et al., 2010) via statistical inference. Crop growth and development were simulated with the AquaCrop model for maize and the Sirius model for winter wheat. Our study showed that for maize significantly different yield changes were predicted for future scenarios based on CMIP3 and EU-<span class="hlt">ENSEMBLES</span> <span class="hlt">ensembles</span>, respectively. Whereas under CMIP3 scenarios the overall impact on maize yield was mostly negative, there was a positive yield impact under <span class="hlt">ENSEMBLES</span> scenarios. In contrast, changes in winter wheat yields were very similar for the two <span class="hlt">ensembles</span>. Our results demonstrated that the use of the EU-<span class="hlt">ENSEMBLES</span> <span class="hlt">ensemble</span> allowed further exploration of uncertainties in agricultural impacts in Belgium, and we hypothesize that even more added value from the use of RCMs could be anticipated in European regions with complex topography where projections from GCMs and RCMs would be significantly different.</p> <div class="credits"> <p class="dwt_author">Vanuytrecht, E.; Raes, D.; Willems, P.; Semenov, M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">118</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">119</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=20110013410&hterms=RCP&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DRCP"> <span id="translatedtitle">The <span class="hlt">Ensemble</span> Canon</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> is an open architecture for the development, integration, and deployment of mission operations software. Fundamentally, it is an adaptation of the Eclipse Rich Client Platform (RCP), a widespread, stable, and supported framework for component-based application development. By capitalizing on the maturity and availability of the Eclipse RCP, <span class="hlt">Ensemble</span> offers a low-risk, politically neutral path towards a tighter integration of operations tools. The <span class="hlt">Ensemble</span> project is a highly successful, ongoing collaboration among NASA Centers. Since 2004, the <span class="hlt">Ensemble</span> project has supported the development of mission operations software for NASA's Exploration Systems, Science, and Space Operations Directorates.</p> <div class="credits"> <p class="dwt_author">MIittman, David S</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">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/2010AGUFMGC42A..06G"> <span id="translatedtitle">The Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX): A Framework for Mitigation and Adaptation Information (Invited)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX) is a program developed by the Task Force on Regional Climate <span class="hlt">Downscaling</span> of World Climate Research Programme (WCRP). The Task Force’s mandate is to develop a framework to evaluate regional climate <span class="hlt">downscaling</span> techniques; foster an international coordinated effort to develop improved <span class="hlt">downscaling</span> techniques and to provide feedback to the global modeling community; and promote greater interactions between global climate modelers, <span class="hlt">downscalers</span> and end-users. Within this mandate, the primary goal of CORDEX is to extend to a global framework the lessons learned from regional climate <span class="hlt">downscaling</span> programs focused on one continent. The framework includes statistical <span class="hlt">downscaling</span> as well as regional climate models (RCMs), with an aim of evaluating the strengths and weaknesses of <span class="hlt">downscaled</span> climate information. CORDEX also provides coordination among existing and emerging <span class="hlt">downscaling</span> programs around the world. This talk will emphasize the statistical <span class="hlt">downscaling</span> component of CORDEX. CORDEX has defined a set of target regions covering most land areas of the planet. A primary region of emphasis is Africa, which has received less attention than most other continents in regional climate-change and climate-impacts research. Baseline <span class="hlt">downscaling</span> efforts by statistical <span class="hlt">downscaling</span> and RCMs have started, focusing on the period covered by the ERA-Interim Reanalysis: 1987-2007. Future work will include <span class="hlt">downscaling</span> GCM output for extended periods in the twentieth and twenty-first centuries, where future projections will be based on Representative Concentration Pathway (RCP) greenhouse gas and aerosol scenarios, specifically RCP 4.5 and RCP 8.5. CORDEX has established a preliminary set of archival protocols and targeted variables for output that will be stored in a central, openly accessible repository. Although CORDEX intends to produce simulations and analyses for the IPCC Fifth Assessment Report, the WCRP Task Force views CORDEX as an ongoing program that will extend beyond the IPCC AR5. This talk will outline ways in which interested groups can participate through simulation and analyses.</p> <div class="credits"> <p class="dwt_author">Gutowski, W. J.; Wcrp Task Force On Regional Climate Downscaling</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_5");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a 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<img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_6");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' href="#">4</a> <a onClick='return showDiv("page_5");' href="#">5</a> <a onClick='return showDiv("page_6");' href="#">6</a> <a style="font-weight: bold;">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_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/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">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/2009EGUGA..11.9000F"> <span id="translatedtitle">Seasonal Predictability and Dynamical <span class="hlt">Downscaling</span> in Spain using ECMWF-System3 and RCA Models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The skill of state of state-of-the-art operational seasonal forecast models (ECMWF System3) in extratropical latitudes (Spain) is assessed using a simple and robust tercile-based statistical methodology, considering both temperature and precipitation forecasts; the only significant average skill is found for dry events in autumn. We also analyze ENSO-conditioned skill, considering only forecasts for El Niña/La Niña years; in this case, skillful seasonal predictions are found in partial agreement with the observed teleconnections derived from historical records, for some variables, seasons and regions, thus providing "windows of opportunity" for operational seasonal forecasts in Europe. Then, we analyze the possibility to enhance the skill of global seasonal predictions using regional climate models. To this aim, the Rossby Centre RCA model was applied to <span class="hlt">downscale</span> the one-month lead time ECMWF System3 seasonal simulations in the European Atlantic domain for the period 1981-2001. We found some preliminary evidence showing that the skill of the regional seasonal predictions is significantly higher than that from the driving global model over large areas. The consideration of the whole <span class="hlt">ensemble</span> (11 members) provided by the System3 forecast system did not overcome the regional model skill found with 5 members.</p> <div class="credits"> <p class="dwt_author">Frias, M. D.; Cofiño, A. S.; Diez, E.; Orfila, B.; Fernandez, J.; Gutierrez, J. M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-04-01</p> </div> </div> </div> </div> <div class="floatContainer result 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=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 " 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://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 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://www.springerlink.com/index/gvu3322m2364r660.pdf"> <span id="translatedtitle">Refinement of dynamically <span class="hlt">downscaled</span> precipitation and temperature scenarios</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A method for adjusting dynamically <span class="hlt">downscaled</span> precipitation and temperature scenarios representing specific sites is presented.\\u000a The method reproduces mean monthly values and standard deviations based on daily observations. The trend obtained in the regional\\u000a climate model both for temperature and precipitation is maintained, and the frequency of modelled and observed rainy days\\u000a shows better agreement. Thus, the method is appropriate</p> <div class="credits"> <p class="dwt_author">Torill Engen-Skaugen</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">126</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/42044550"> <span id="translatedtitle">Impact of nesting strategies in dynamical <span class="hlt">downscaling</span> of reanalysis data</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Coarse-grid global numerical weather simulations or analysis data have to be <span class="hlt">downscaled</span>, e.g., with nested limited-area models (LAMs), for regional interpretation. Here, the impact of different one-way nesting strategies on precipitation simulations over the European Alps with the LAM ALADIN is studied. The LAM is forced by initial and lateral boundary data derived from ERA40 reanalyses with 120 km horizontal</p> <div class="credits"> <p class="dwt_author">A. Beck; B. Ahrens; K. Stadlbacher</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">127</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/48903920"> <span id="translatedtitle">Impact of nesting strategies in dynamical <span class="hlt">downscaling</span> of reanalysis data</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Coarse–grid global numerical weather simulations or analysis data have to be <span class="hlt">downscaled</span>, e.g., with nested limited–area models (LAMs), for regional interpretation. Here, the impact of different one–way nesting strategies on precipitation simulations over the European Alps with the LAM ALADIN is studied. The LAM is forced by initial and lateral boundary data derived from ERA40 reanalyses with 120 km horizontal</p> <div class="credits"> <p class="dwt_author">A. Beck; B. Ahrens; K. Stadlbacher</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">128</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://coast.gkss.de/staff/feser/specnudge.pdf"> <span id="translatedtitle">A Spectral Nudging Technique for Dynamical <span class="hlt">Downscaling</span> Purposes</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 ''spectral nudging'' method imposes time-variable large-scale atmospheric states on a regional atmospheric model. It is based on the idea that regional-scale climate statistics are conditioned by the interplay between continental-scale atmospheric conditions and such regional features as marginal seas and mountain ranges. Following this ''<span class="hlt">downscaling</span>'' idea, the regional model is forced to satisfy not only boundary conditions, possibly in</p> <div class="credits"> <p class="dwt_author">Hans von Storch; Heike Langenberg; Frauke Feser</p> <p class="dwt_publisher"></p> <p class="publishDate">2000-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">129</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/39782355"> <span id="translatedtitle">Evaluation of a WRF dynamical <span class="hlt">downscaling</span> simulation over California</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This paper presents results from a 40 year Weather Research and Forecasting (WRF) based dynamical <span class="hlt">downscaling</span> experiment performed\\u000a at 12 km horizontal grid spacing, centered on the state of California, and forced by a 1° × 1.25° finite-volume current-climate\\u000a Community Climate System Model ver. 3 (CCSM3) simulation. In-depth comparisons between modeled and observed regional-average\\u000a precipitation, 2 m temperature, and snowpack are performed. The</p> <div class="credits"> <p class="dwt_author">Peter Caldwell; Hung-Neng S. Chin; David C. Bader; Govindasamy Bala</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">130</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.springerlink.com/index/x324716801kq373w.pdf"> <span id="translatedtitle">A statistical-dynamical <span class="hlt">downscaling</span> procedure for global climate simulations</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-dynamical <span class="hlt">downscaling</span> procedure for global climate simulations is described. The procedure is based on the assumption that any regional climate is associated with a specific frequency distribution of classified large-scale weather situations. The frequency distributions are derived from multi-year episodes of low resolution global climate simulations. Highly resolved regional distributions of wind and temperature are calculated with a regional</p> <div class="credits"> <p class="dwt_author">F. Frey-Buness; D. Heimann; R. Sausen</p> <p class="dwt_publisher"></p> <p class="publishDate">1995-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">131</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010EGUGA..12.3649H"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of extreme events in the Mediterranean area</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In the context of future projections of climate change, extreme events are considered to be more important than slowly changing mean conditions. In the present study percentile-based indices of extreme temperature and precipitation are derived from station data as well as from high-resolution gridded data for terrestrial areas in the Mediterranean region. As large-scale predictors for statistical <span class="hlt">downscaling</span> models, geopotential heights, thickness of the 1000hPa/500hPa layer, and atmospheric humidity are primarily considered. <span class="hlt">Downscaling</span> from these predictors to Mediterranean extremes indices is done by Multiple Regression and Canonical Correlation Analyses. In order to account for non-stationarities of the models, analyses are realised for different calibration periods and corresponding verification periods. Model performance in the verification periods is assessed by means of correlation coefficients between modelled and observed extremes indices as well as by the reduction of variance being similar to the root mean squared skill score. Output from different coupled global circulation models integrated under A1B- and B1-scenario assumptions is used to assess changes of extreme temperature and precipitation due to enhanced greenhouse warming conditions. Results indicate that the <span class="hlt">downscaling</span> assessments can vary considerably depending on the particular predictors used in the statistical models. Climatic as well as dynamic factors influence extreme conditions and should be considered in a combined manner within <span class="hlt">downscaling</span> models. Regarding temperature extremes, results imply that changes do not follow a simple shift of the whole temperature distribution to higher values since the intra-annual extreme temperature range is indicated to decrease in most parts of the Mediterranean area during the course of the 21st century. This is due to widespread findings that extreme minimum temperatures in winter will increase stronger compared to extreme maximum temperatures in summer. Acknowledgement: Financial support is provided 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">2010-05-01</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://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">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/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">134</div> <div class="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">135</div> <div class="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..16.5768M"> <span id="translatedtitle">New methods for <span class="hlt">downscaling</span> climate information based on a joint empirical-statistical and dynamical <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">The global climate community has produced a wide range of results from atmospheric-ocean general circulation models, which are considered as the primary source of information on the future climate change. However, there are still gaps between the spatial resolution of climate model outputs and the point-scale requirement of most of climate change impact studies. Thus, empirical-statistical <span class="hlt">downscaling</span> (ESD) and dynamical <span class="hlt">downscaling</span> (DD) techniques continue to be used as alternatives and various models have been made available by the scientific community. Several comparative studies have been done during the last decade,dealing with <span class="hlt">downscaling</span> local weather variables such as temperature and precipitation over a region of interest. Accordingly, in this work, new methods and strategies based on merging ESD and DD results will be proposed in order to increase the quality of the local climate projections with a special focus on seasonal and decadal precipitation and temperature based on CMIP3/5 experiments. A new freely available ESD R-package developed by MET Norway is used and will be also presented.</p> <div class="credits"> <p class="dwt_author">Mezghani, Abdelkader; Benestad, Rasmus E.</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">136</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 odd" lang="en"> <div class="resultNumber element">137</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2014EGUGA..16.6242L"> <span id="translatedtitle">Rainfall <span class="hlt">Downscaling</span> Conditional on Upper-air Variables: Assessing Rainfall Statistics in a Changing Climate</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Due to its intermittent and highly variable character, and the modeling parameterizations used, precipitation is one of the least well reproduced hydrologic variables by both Global Climate Models (GCMs) and Regional Climate Models (RCMs). This is especially the case at a regional level (where hydrologic risks are assessed) and at small temporal scales (e.g. daily) used to run hydrologic models. In an effort to remedy those shortcomings and assess the effect of climate change on rainfall statistics at hydrologically relevant scales, Langousis and Kaleris (2013) developed a statistical framework for simulation of daily rainfall intensities conditional on upper air variables. The developed <span class="hlt">downscaling</span> scheme was tested using atmospheric data from the ERA-Interim archive (http://www.ecmwf.int/research/era/do/get/index), and daily rainfall measurements from western Greece, and was proved capable of reproducing several statistical properties of actual rainfall records, at both annual and seasonal levels. This was done solely by conditioning rainfall simulation on a vector of atmospheric predictors, properly selected to reflect the relative influence of upper-air variables on ground-level rainfall statistics. In this study, we apply the developed framework for conditional rainfall simulation using atmospheric data from different GCM/RCM combinations. This is done using atmospheric data from the <span class="hlt">ENSEMBLES</span> project (http://ensembleseu.metoffice.com), and daily rainfall measurements for an intermediate-sized catchment in Italy; i.e. the Flumendosa catchment. Since GCM/RCM products are suited to reproduce the local climatology in a statistical sense (i.e. in terms of relative frequencies), rather than ensuring a one-to-one temporal correspondence between observed and simulated fields (i.e. as is the case for ERA-interim reanalysis data), we proceed in three steps: a) we use statistical tools to establish a linkage between ERA-Interim upper-air atmospheric forecasts and climate model results, b) check and validate the stochastic <span class="hlt">downscaling</span> scheme for the period when precipitation measurements are available, and c) simulate synthetic rainfall series based on future climate projections of upper-air indices. The obtained results shed light to the effects of climate change on the statistical structure of rainfall. Acknowledgments: The research project is implemented within the framework of the Action "Supporting Postdoctoral Researchers" of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State. CRS4 highly acknowledges the contribution of the Sardinian regional authorities.</p> <div class="credits"> <p class="dwt_author">Langousis, Andreas; Deidda, Roberto; Marrocu, Marino; Kaleris, Vassilios</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">138</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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 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://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 " lang="en"> <div class="resultNumber element">140</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/13065134"> <span id="translatedtitle"><span class="hlt">Downscaling</span> temperature and precipitation: a comparison of regression-based methods and artificial neural networks</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 comparison of two statistical <span class="hlt">downscaling</span> methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the <span class="hlt">downscaling</span> are indices of the</p> <div class="credits"> <p class="dwt_author">J. T. Schoof; S. C. Pryor</p> <p class="dwt_publisher"></p> <p class="publishDate">2001-01-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_6");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' 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onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">141</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://hal.archives-ouvertes.fr/docs/00/19/50/54/PDF/herrmann_somot_GRL_2008_inpress_2007GL032442.pdf"> <span id="translatedtitle">GEOPHYSICAL RESEARCH LETTERS, VOL. ???, XXXX, DOI:10.1029/, Relevance of ERA40 dynamical <span class="hlt">downscaling</span> for</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">on this modeling problem, as suggested in those studies. We carried out a spectral dynamical <span class="hlt">downscaling</span> of the ERAGEOPHYSICAL RESEARCH LETTERS, VOL. ???, XXXX, DOI:10.1029/, Relevance of ERA40 dynamical <span class="hlt">downscaling</span> for modeling deep convection in the Mediterranean Sea Marine J. Herrmann Laboratoire d</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">142</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">143</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/42080836"> <span id="translatedtitle">Statistical and dynamical <span class="hlt">downscaling</span> of precipitation over Spain from DEMETER seasonal forecasts</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">Statistical and dynamical <span class="hlt">downscaling</span> methods are tested and compared for <span class="hlt">downscaling</span> seasonal precipitation forecasts over Spain from two DEMETER models: the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Meteorological Office (UKMO). The statistical method considered is a particular implementation of the standard analogue technique, based on close neighbours of the predicted atmospheric geopotential and humidity fields. Dynamical</p> <div class="credits"> <p class="dwt_author">E. Díez; C. Primo; J. A. García-Moya; J. M. Gutiérrez; B. Orfila</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">144</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://vivoni.asu.edu/pdf/Mascaro_et_al_AGU2009.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Satellite Soil Moisture Estimates in the Southern Great Plains through a</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> Satellite Soil Moisture Estimates in the Southern Great Plains through a Calibrated, 2002). · Efficient <span class="hlt">downscaling</span> algorithms are often necessary tools to characterize and reproduce sub have difficulty progressing far below wilting. This skew makes it dynamically difficult From Crow</p> <div class="credits"> <p class="dwt_author">Vivoni, Enrique R.</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://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 " 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://eost.u-strasbg.fr/schmittb/Articles/2011_JGR_Lengline_etal.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of fracture energy during brittle creep experiments O. Lenglin,1</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> of fracture energy during brittle creep experiments O. Lengliné,1 J. Schmittbuhl,1 J. E; accepted 6 June 2011; published 27 August 2011. [1] We present mode 1 brittle creep fracture experiments. MÃ¥løy (2011), <span class="hlt">Downscaling</span> of fracture energy during brittle creep experiments, J. Geophys. Res., 116, B</p> <div class="credits"> <p class="dwt_author">Schmittbuhl, Jean</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">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/2012EGUGA..14.7362B"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of rainfall in Peru using Generalised Linear Models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The assessment of water resources in the Peruvian Andes is particularly important because the Peruvian economy relies heavily on agriculture. Much of the agricultural land is situated near to the coast and relies on large quantities of water for irrigation. The simulation of synthetic rainfall series is thus important to evaluate the reliability of water supplies for current and future scenarios of climate change. In addition to water resources concerns, there is also a need to understand extreme heavy rainfall events, as there was significant flooding in Machu Picchu in 2010. The region exhibits a reduction of rainfall in 1983, associated with El Nino Southern Oscillation (SOI). NCEP Reanalysis 1 data was used to provide weather variable data. Correlations were calculated for several weather variables using raingauge data in the Andes. These were used to evaluate teleconnections and provide suggested covariates for the <span class="hlt">downscaling</span> model. External covariates used in the model include sea level pressure and sea surface temperature over the region of the Humboldt Current. Relative humidity and temperature data over the region are also included. The SOI teleconnection is also used. Covariates are standardised using observations for 1960-1990. The GlimClim <span class="hlt">downscaling</span> model was used to fit a stochastic daily rainfall model to 13 sites in the Peruvian Andes. Results indicate that the model is able to reproduce rainfall statistics well, despite the large area used. Although the correlation between individual rain gauges is generally quite low, all sites are affected by similar weather patterns. This is an assumption of the GlimClim <span class="hlt">downscaling</span> model. Climate change scenarios are considered using several GCM outputs for the A1B scenario. GCM data was corrected for bias using 1960-1990 outputs from the 20C3M scenario. Rainfall statistics for current and future scenarios are compared. The region shows an overall decrease in mean rainfall but with an increase in variance.</p> <div class="credits"> <p class="dwt_author">Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">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/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">149</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ldeo.columbia.edu/~suzana/papers/seth_et_al_clim_dyn.pdf"> <span id="translatedtitle">Abstract To enable <span class="hlt">downscaling</span> of seasonal predic-tion and climate change scenarios, long-term baseline</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 numerous studies for two primary purposes: (1) predictability studies or dynamical <span class="hlt">downscaling</span> of lowAbstract To enable <span class="hlt">downscaling</span> of seasonal predic- tion and climate change scenarios, long, 2006). Further, nearly all the discussion related to technical issues involved in <span class="hlt">downscaling</span>, e</p> <div class="credits"> <p class="dwt_author">Camargo, Suzana 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">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.sscnet.ucla.edu/~yxue/pdf/2013SaleCD.pdf"> <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</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">Dynamic <span class="hlt">downscaling</span> of 22-year CFS winter seasonal hindcasts with the UCLA-ETA regional climate This study evaluates the UCLA-ETA regional model's dynamic <span class="hlt">downscaling</span> ability to improve the National Center % on average compared to observations, respectively. Dynamic <span class="hlt">downscaling</span> improves the precipitation hindcasts</p> <div class="credits"> <p class="dwt_author">Xue, Yongkang</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">151</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://nzc.iap.ac.cn/uploadpdf/WAF_2014_Liu_Li.pdf"> <span id="translatedtitle">Predicting Summer Rainfall over the YangtzeHuai Region Based on Time-Scale Decomposition 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/eprints/">E-print Network</a></p> <p class="result-summary">et al. 2004). Thus, researchers proposed two <span class="hlt">downscaling</span> methods, dynamical and sta- tistical, to resolve the model's resolutions to regional and subgrid scales. For the dynamical <span class="hlt">downscaling</span>, either Statistical <span class="hlt">Downscaling</span> NA LIU Nansen-Zhu International Research Centre, Institute of Atmospheric Physics</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">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.giss.nasa.gov/docs/2012/2012_Racherla_etal_1.pdf"> <span id="translatedtitle">The added value to global model projections of climate change by dynamical <span class="hlt">downscaling</span>: A case study over the continental</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 added value to global model projections of climate change by dynamical <span class="hlt">downscaling</span>: A case; published 27 October 2012. [1] Dynamical <span class="hlt">downscaling</span> is being increasingly used for climate change studies model projections of climate change by dynamical <span class="hlt">downscaling</span>: A case study over the continental U</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">153</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.sscnet.ucla.edu/~yxue/pdf/2011GaoAAS.pdf"> <span id="translatedtitle">ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 28, NO. 5, 2011, 10771098 Assessment of Dynamic <span class="hlt">Downscaling</span> of the Extreme Rainfall</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">ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 28, NO. 5, 2011, 1077­1098 Assessment of Dynamic <span class="hlt">Downscaling</span> This study investigates the capability of the dynamic <span class="hlt">downscaling</span> method (DDM) in an East Asian climate study of dynamic <span class="hlt">downscaling</span> of the extreme rainfall over East Asia using a regional climate model. Adv. Atmos. Sci</p> <div class="credits"> <p class="dwt_author">Xue, Yongkang</p> <p class="dwt_publisher"></p> <p class="publishDate"></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/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 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://www.ncbi.nlm.nih.gov/pubmed/24860045"> <span id="translatedtitle">Hybrid adaptive classifier <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">Traditional random subspace-based classifier <span class="hlt">ensemble</span> approaches (RSCE) have several limitations, such as viewing the same importance for the base classifiers trained in different subspaces, not considering how to find the optimal random subspace set. In this paper, we design a general hybrid adaptive <span class="hlt">ensemble</span> learning framework (HAEL), and apply it to address the limitations of RSCE. As compared with RSCE, HAEL consists of two adaptive processes, i.e., base classifier competition and classifier <span class="hlt">ensemble</span> interaction, so as to adjust the weights of the base classifiers in each <span class="hlt">ensemble</span> and to explore the optimal random subspace set simultaneously. The experiments on the real-world datasets from the KEEL dataset repository for the classification task and the cancer gene expression profiles show that: 1) HAEL works well on both the real-world KEEL datasets and the cancer gene expression profiles and 2) it outperforms most of the state-of-the-art classifier <span class="hlt">ensemble</span> approaches on 28 out of 36 KEEL datasets and 6 out of 6 cancer datasets. PMID:24860045</p> <div class="credits"> <p class="dwt_author">Yu, Zhiwen; Li, Le; Liu, Jiming; Han, Guoqiang</p> <p class="dwt_publisher"></p> <p class="publishDate">2015-02-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">156</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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">157</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://cdsweb.cern.ch/record/1041132"> <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 " 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://ntrs.nasa.gov/search.jsp?R=20020052415&hterms=Relationship+Learners+Field+Dependent&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3DRelationship%2BLearners%253F%2BField%2BDependent"> <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 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.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">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/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 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 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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_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://dspace.mit.edu/handle/1721.1/90185"> <span id="translatedtitle">Beta-<span class="hlt">ensembles</span> with covariance</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">This thesis presents analytic samplers for the [beta]-Wishart and [beta]-MANOVA <span class="hlt">ensembles</span> with diagonal covariance. These generalize the [beta]-<span class="hlt">ensembles</span> of Dumitriu-Edelman, Lippert, Killip-Nenciu, Forrester-Rains, and ...</p> <div class="credits"> <p class="dwt_author">Dubbs, Alexander</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">162</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 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://eric.ed.gov/?q=kovach&pg=3&id=ED254263"> <span id="translatedtitle">Music <span class="hlt">Ensemble</span>: Course Proposal.</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">A proposal is presented for a Music <span class="hlt">Ensemble</span> course to be offered at the Community College of Philadelphia for music students who have had previous vocal or instrumental training. A standardized course proposal cover form is followed by a statement of purpose for the course, a list of major course goals, a course outline, and a bibliography. Next,…</p> <div class="credits"> <p class="dwt_author">Kovach, Brian</p> <p class="dwt_publisher"></p> <p class="publishDate"></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/2013EGUGA..15.5597B"> <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">With model development and cheaper computational resources ocean forecasts are becoming readily available, high resolution coastal forecasting is now a reality. This can only be achieved, however, by <span class="hlt">downscaling</span> global or basin-scale products such as the MyOcean reanalyses and forecasts. These model products have resolution ranging from 1/16th - 1/4 degree, which are often insufficient for coastal scales, but can provide initialisation and boundary data. We present applications of <span class="hlt">downscaling</span> the MyOcean products for use in shelf-seas and the nearshore. We will address the question 'Do coastal predictions improve with higher resolution modelling?' with a few focused examples, while also discussing what is meant by an improved result. Increasing resolution appears to be an obvious route for getting more accurate forecasts in operational coastal models. However, when models resolve finer scales, this may lead to the introduction of high-frequency variability which is not necessarily deterministic. Thus a flow may appear more realistic by generating eddies but the simple statistics like rms error and correlation may become less good because the model variability is not exactly in phase with the observations (Hoffman et al., 1995). By deciding on a specific process to simulate (rather than concentrating on reducing rms error) we can better assess the improvements gained by <span class="hlt">downscaling</span>. In this work we will select two processes which are dominant in our case-study site: Liverpool Bay. Firstly we consider the magnitude and timing of a peak in tide-surge elevations, by separating out the event into timing (or displacement) and intensity (or amplitude) errors. The model can thus be evaluated on how well it predicts the timing and magnitude of the surge. The second important characteristic of Liverpool Bay is the position of the freshwater front. To evaluate model performance in this case, the location, sharpness, and temperature difference across the front will be considered. We will show that by using intelligent metrics designed with a physical process in mind, we can learn more about model performance than by considering 'bulk' statistics alone. R. M. Hoffman and Z. Liu and J-F. Louic and C. Grassotti (1995) 'Distortion Representation of Forecast Errors' Monthly Weather Review 123: 2758-2770</p> <div class="credits"> <p class="dwt_author">Bricheno, Lucy; Wolf, Judith</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">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/2009AGUFM.A33A0225S"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of climate parameters using Active Learning Method (ALM)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">This study is a part of main program RIMAX "risk management of extreme flood events“, which concerns itself of extremes floodwater and damage potential in the Bode river basin in Germany with the variable occurrence of flood events in this area for the past 1000 years. The objective of the project is to produce the local climate time series (climate <span class="hlt">downscaling</span>) as the input for a runoff model in the Bode basin for the last 1000 years on a grid of 5x5 km as well as the estimation of the spatial distributions and temporal variability of the precipitation, the amount of precipitation and further meteorological parameter (temperature, radiation and relative humidity) for this area. A nonlinear <span class="hlt">downscaling</span> based on Fuzzy rules has been used to produce 1000 year climate time series. The global model ECHO from Max Planck institute for Meteorology (MPI) with T30 resolution and 1000 years data has been used as the global model (GCM). The regional model REMO, with 10 km resolution and 20 years data has been used as the regional input. The observations, which include 30 years precipitation, radiation, temperature, wind and relative humidity, have been used as output (predictand). In this study, two set fuzzy rules have been trained to describe the relationship between ECHO/REMO and REMO/Observation. The Fuzzy method used in this work is Active Learning Method (ALM). The heart of calculation of ALM is a fuzzy interpolation and curve fitting which is entitled Ink Drop Spread (IDS). The IDS searches fuzzily for continuous possible paths of interpolated data points on data planes. The ability of ALM to simulate the high values as well as the fluctuation of time series is much better than Takagi-Sugeno models, which have been used for <span class="hlt">downscaling</span> in the last decade. In the next steps, considering predictors from the ECHO time series As well as the predictands from the REMO grid points, some ALM models are developed, which describe the fuzzy rules and the relationship between global and regional scales. These models are verified using checking data and then considering ECHO/REMO models and on the basis of last 1000 years of ECHO, the REMO time series as well as the local data are simulated. These simulated data are used as input-data for the runoff model ARCEGMO.</p> <div class="credits"> <p class="dwt_author">Sodoudi, S.; Reimer, E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " 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/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 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://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 " 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/2013EGUGA..15.5200C"> <span id="translatedtitle">Comparison between different <span class="hlt">downscaling</span> methods by using ERA-40 re-analysis data in central Italy</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Global climate models (GCMs) are the primary tool to assess future climate change. However, most GCMs currently do not provide reliable information on scales below about 100 km and, hence, cannot be used as a direct input of hydrological models for climate change impact assessments. Therefore, a wide range of statistical and dynamical <span class="hlt">downscaling</span> methods, trying to overcome the scale discrepancy between the climatic scenarios and the resolution required for impact assessment, have been developed. In this context, the selection of a suitable <span class="hlt">downscaling</span> method is an important issue. Indeed, the use of different spatial domains, predictor variables, predictands and assessment criteria makes the comparison of the relative performance of different methods difficult to achieve and general rules to a priori select the best <span class="hlt">downscaling</span> method are not available. Additionally, many studies have showed that, depending on the hydrological variables, dynamical and statistical <span class="hlt">downscaling</span> methods significantly contribute to the overall uncertainty related to the hydrological impact assessment studies. Therefore, it is strongly recommended to test different <span class="hlt">downscaling</span> methods by using verification data before applying them to climate model data. The main purpose of this study is the comparison of different statistical <span class="hlt">downscaling</span> approaches (e.g. delta change method, quantile mapping method, local scaling…) applied to rainfall time series. Specifically, the daily rainfall data derived from the ERA-40 re-analysis database (provided by the European Centre for Medium-Range Weather Forecasts, ECMWF, with resolution of about ~120 km), from September 1957 to August 2002, are used for testing the different <span class="hlt">downscaling</span> methods. This dataset is used in place of the scenarios provided by the GCMs with the significant added-value that also the temporal agreement with ground observations can be tested. The ERA-40 re-analysis rainfall data are <span class="hlt">downscaled</span> with different <span class="hlt">downscaling</span> techniques and their ability to reproduce the statistical properties and the temporal pattern of the observed time series is analyzed. For this purpose, high quality rainfall observations obtained by a dense rainfall network in central Italy are used as benchmark. For the evaluation of the <span class="hlt">downscaling</span> methods, a split sample test is applied: the time period 1987-2002 is used for validate the different methods, which are calibrated in the period 1957-1986. The results of this analysis will provide useful guidelines for the selection of the best performing statistical <span class="hlt">downscaling</span> approach applied to rainfall data.</p> <div class="credits"> <p class="dwt_author">Camici, Stefania; Provenzale, Antonello; Brocca, Luca; Moramarco, Tommaso</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">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/52760766"> <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://academic.research.microsoft.com/">Microsoft Academic Search </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</p> <div class="credits"> <p class="dwt_author">Young-Kwon Lim; D. W. Shin; Steven Cocke; T. E. LaRow; Justin T. Schoof; James J. O'Brien; Eric P. Chassignet</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">170</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/48925553"> <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://academic.research.microsoft.com/">Microsoft Academic Search </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</p> <div class="credits"> <p class="dwt_author">Young-Kwon Lim; D. W. Shin; Steven Cocke; T. E. LaRow; Justin T. Schoof; James J. O'Brien; Eric P. Chassignet</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">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/3597681"> <span id="translatedtitle">Predictions of climate change over Europe using statistical and dynamical <span class="hlt">downscaling</span> techniques</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Statistical and dynamical <span class="hlt">downscaling</span> predictions of changes in surface temperature and precipitation for 2080-2100, relative to pre-industrial conditions, are compared at 976 European observing sites, for January and July. Two dynamical <span class="hlt">downscaling</span> methods are considered, involving the use of surface temperature or precipitation simulated at the nearest grid point in a coupled ocean-atmosphere general circulation model (GCM) of resolution 300</p> <div class="credits"> <p class="dwt_author">James Murphy</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">172</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://g-rsm.wikispaces.com/file/view/nh_kanamaru_kanamitsu.pdf"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of global analysis and simulation over the Northern Hemisphere</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 As an extreme demonstration o f regional climate model capability, a dynamical <span class="hlt">downscaling</span>,of NCEP -NCAR reanalysis was successfully performed,over the Northern Hemisphere. Its success is owing to the use of the scale-selective bias-correction scheme, which maintains the large-scale analysis of the driving global reanalysis in the interior of the dom ain where,lateral boundary,forcing has very little control. The <span class="hlt">downscaled</span></p> <div class="credits"> <p class="dwt_author">Hideki Kanamaru; Masao Kanamitsu</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">173</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/nlin/0603075v1"> <span id="translatedtitle">Fast <span class="hlt">Ensemble</span> Smoothing</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">Smoothing is essential to many oceanographic, meteorological and hydrological applications. The interval smoothing problem updates all desired states within a time interval using all available observations. The fixed-lag smoothing problem updates only a fixed number of states prior to the observation at current time. The fixed-lag smoothing problem is, in general, thought to be computationally faster than a fixed-interval smoother, and can be an appropriate approximation for long interval-smoothing problems. In this paper, we use an <span class="hlt">ensemble</span>-based approach to fixed-interval and fixed-lag smoothing, and synthesize two algorithms. The first algorithm produces a linear time solution to the interval smoothing problem with a fixed factor, and the second one produces a fixed-lag solution that is independent of the lag length. Identical-twin experiments conducted with the Lorenz-95 model show that for lag lengths approximately equal to the error doubling time, or for long intervals the proposed methods can provide significant computational savings. These results suggest that <span class="hlt">ensemble</span> methods yield both fixed-interval and fixed-lag smoothing solutions that cost little additional effort over filtering and model propagation, in the sense that in practical <span class="hlt">ensemble</span> application the additional increment is a small fraction of either filtering or model propagation costs. We also show that fixed-interval smoothing can perform as fast as fixed-lag smoothing and may be advantageous when memory is not an issue.</p> <div class="credits"> <p class="dwt_author">S. Ravela; D. McLaughlin</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-03-31</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/2013JHyd..492....1N"> <span id="translatedtitle">Performance assessment of different data mining methods in statistical <span class="hlt">downscaling</span> of daily 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">In this paper, nonlinear Data-Mining (DM) methods have been used to extend the most cited statistical <span class="hlt">downscaling</span> model, SDSM, for <span class="hlt">downscaling</span> of daily precipitation. The proposed model is Nonlinear Data-Mining <span class="hlt">Downscaling</span> Model (NDMDM). The four nonlinear and semi-nonlinear DM methods which are included in NDMDM model are cubic-order Multivariate Adaptive Regression Splines (MARS), Model Tree (MT), k-Nearest Neighbor (kNN) and Genetic Algorithm-optimized Support Vector Machine (GA-SVM). The daily records of 12 rain gauge stations scattered in basins with various climates in Iran are used to compare the performance of NDMDM model with statistical <span class="hlt">downscaling</span> method. Comparison between statistical <span class="hlt">downscaling</span> and NDMDM results in the selected stations indicates that combination of MT and MARS methods can provide daily rain estimations with less mean absolute error and closer monthly standard deviation and skewness values to the historical records for both calibration and validation periods. The results of the future projections of precipitation in the selected rain gauge stations using A2 and B2 SRES scenarios show significant uncertainty of the NDMDM and statistical <span class="hlt">downscaling</span> models.</p> <div class="credits"> <p class="dwt_author">Nasseri, M.; Tavakol-Davani, H.; Zahraie, B.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-06-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">175</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.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">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/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 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://www.springerlink.com/index/00pp40725j42815u.pdf"> <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://academic.research.microsoft.com/">Microsoft Academic Search </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 impacts\\u000a on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion,\\u000a surface hydrology, and wheat and maize yields to two (implicit and explicit) spatial <span class="hlt">downscaling</span> methods used to <span class="hlt">downscale</span>\\u000a the A2a, B2a, and GGa1</p> <div class="credits"> <p class="dwt_author">X-C Zhang</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">178</div> <div class="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 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://adsabs.harvard.edu/abs/2012AGUFM.B53E0725Y"> <span id="translatedtitle">Intercomparison of <span class="hlt">Downscaling</span> Methods on Hydrological Impact for Earth System Model of NE United States</span></a>  </p> <div class="result-meta"> <p class="source"><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 dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be <span class="hlt">downscaled</span> from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this <span class="hlt">downscaling</span> procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several <span class="hlt">downscaling</span> techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four <span class="hlt">downscaling</span> approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial <span class="hlt">downscaling</span> (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic <span class="hlt">Downscaling</span> based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different <span class="hlt">downscaling</span> methods against observational datasets was carried out for assessment of the <span class="hlt">downscaled</span> climate model outputs. The effects of using the different approaches to <span class="hlt">downscale</span> atmospheric variables (specifically air temperature and precipitation) for use as inputs to the Water Balance Model (WBMPlus, Vorosmarty et al., 1998;Wisser et al., 2008) for simulation of daily discharge and monthly stream flow in the Northeast US for a 100-year period in the 21st century were also assessed. Statistical techniques especially monthly bias-corrected spatial disaggregation (M-BCSD) showed potential advantage among other methods for the daily discharge and monthly stream flow simulation. However, Dynamic <span class="hlt">Downscaling</span> will provide important complements to the statistical approaches tested.</p> <div class="credits"> <p class="dwt_author">Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.</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">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/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 id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_8");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span 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</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/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 " 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.computingportal.org/"> <span id="translatedtitle"><span class="hlt">Ensemble</span>: Computing Pathway</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://nsdl.org/nsdl_dds/services/ddsws1-1/service_explorer.jsp">NSDL National Science Digital Library</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> is a NSDL Pathways project working to establish a national, distributed digital library for computing education. The project is building a distributed portal providing access to a broad range of existing educational resources for computing while preserving the collections and their associated curation processes. The developers want to encourage contribution, use, reuse, review and evaluation of educational materials at multiple levels of granularity and seek to support the full range of computing education communities including computer science, computer engineering, software engineering, information science, information systems and information technology as well as other areas often called computing + X, or X informatics.</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate">2011-01-05</p> </div> </div> </div> </div> <div class="floatContainer result 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://arxiv.org/pdf/1007.2491v1"> <span id="translatedtitle"><span class="hlt">Ensemble</span> based quantum metrology</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 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 favourably with the resources employed, such as system size and the time required. Here we consider measurement of magnetic field strength using an <span class="hlt">ensemble</span> of spins, and we identify a third essential resource: the initial system polarisation, i.e. the low entropy of the original state. 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 quantum limit.</p> <div class="credits"> <p class="dwt_author">Marcus Schaffry; Erik M. Gauger; John J. L. Morton; Joseph Fitzsimons; Simon C. Benjamin; Brendon W. Lovett</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-07-15</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/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">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/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">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">186</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=renewable+energy&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3D%2522renewable%2Benergy%2522"> <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 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://ntrs.nasa.gov/search.jsp?R=20140006432&hterms=health&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%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 " 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://www.ncbi.nlm.nih.gov/pubmed/19148764"> <span id="translatedtitle"><span class="hlt">Downscaling</span> drug nanosuspension production: processing aspects and physicochemical characterization.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">In this study, scaling down nanosuspension production to 10 mg of drug compound and evaluation of the nanosuspensions to 1 mg of drug compound per test were investigated. Media milling of seven model drug compounds (cinnarizine-indomethacin-itraconazole-loviride-mebendazole-naproxen-phenytoin) was evaluated in a 96-well plate setup (10, 20, and 30 mg) and a glass-vial-based system in a planetary mill (10, 100, and 1,000 mg). Physicochemical properties evaluated on 1 mg of drug compound were drug content (high-performance liquid chromatography), size [dynamic light scattering (DLS)], morphology (scanning electron microscopy), thermal characteristics (differential scanning calorimetry), and X-ray powder diffraction (XRPD). Scaling down nanosuspension production to 10 mg of drug compound was feasible for the seven model compounds using both designs, the planetary mill design being more robust. Similar results were obtained for both designs upon milling 10 mg of drug compound. Drug content determination was precise and accurate. DLS was the method of choice for size measurements. Morphology evaluation and thermal analysis were feasible, although sample preparation had a big influence on the results. XRPD in capillary mode was successfully performed, both in the suspended state and after freeze-drying in the capillary. Results obtained for the latter were superior. Both the production and the physicochemical evaluation of nanosuspensions can be successfully <span class="hlt">downscaled</span>, enabling nanosuspension screening applications in preclinical development settings. PMID:19148764</p> <div class="credits"> <p class="dwt_author">Van Eerdenbrugh, Bernard; Stuyven, Bernard; Froyen, Ludo; Van Humbeeck, Jan; Martens, Johan A; Augustijns, Patrick; Van den Mooter, Guy</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-01-01</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/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 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://academic.research.microsoft.com/Publication/475333"> <span id="translatedtitle">A New <span class="hlt">Ensemble</span> Diversity Measure Applied to Thinning <span class="hlt">Ensembles</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">We introduce a new way of describing the diversity of an <span class="hlt">ensemble</span> of classifiers, the Percentage Correct Diversity Measure, and compare it against existing methods. We then introduce two new methods for removing classifiers from an <span class="hlt">ensemble</span> based on diversity calculations. Empirical results for twelve datasets from the UC Irvine repository show that diversity is generally modeled by our measure</p> <div class="credits"> <p class="dwt_author">Robert E. Banfield; Lawrence O. Hall; Kevin W. Bowyer; W. Philip Kegelmeyer</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">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.osti.gov/scitech/biblio/22251420"> <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://www.osti.gov/scitech">SciTech Connect</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, E-mail: mshamis@princeton.edu [Department of Mathematics, Princeton University, Princeton New Jersey 08544 (United States) [Department of Mathematics, Princeton University, Princeton New Jersey 08544 (United States); Institute for Advanced Study, Einstein Drive, Princeton, New Jersey 08540 (United States)</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-11-15</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://coaps.fsu.edu/~vmisra/dynamic_downscale.pdf"> <span id="translatedtitle">Dynamic <span class="hlt">Downscaling</span> of Seasonal Simulations over South America</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">In this paper multiple atmospheric global circulation model (AGCM) integrations at T42 spectral truncation and prescribed sea surface temperature were used to drive regional spectral model (RSM) simulations at 80-km resolution for the austral summer season (January-February-March). Relative to the AGCM, the RSM improves the <span class="hlt">ensemble</span> mean simulation of precipitation and the lower- and upper-level tropospheric circulation over both tropical</p> <div class="credits"> <p class="dwt_author">Vasubandhu Misra; Paul A. Dirmeyer; Ben P. Kirtman</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-01-01</p> </div> </div> </div> </div> <div class="floatContainer result 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://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 " 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://climate.snu.ac.kr/2005_new/pub/papers/p108.pdf"> <span id="translatedtitle">Optimal initial perturbations for El Nino <span class="hlt">ensemble</span> prediction with <span class="hlt">ensemble</span> Kalman filter</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">Optimal initial perturbations for El Nino <span class="hlt">ensemble</span> prediction with <span class="hlt">ensemble</span> Kalman filter Yoo of an <span class="hlt">ensemble</span> Kalman filter (EnKF). Among the initial conditions gene- rated by EnKF, <span class="hlt">ensemble</span> members with fast. Keywords <span class="hlt">Ensemble</span> Kalman filter Á Seasonal prediction Á Optimal initial perturbation Á <span class="hlt">Ensemble</span> prediction</p> <div class="credits"> <p class="dwt_author">Kang, In-Sik</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">195</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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 " lang="en"> <div class="resultNumber element">196</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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 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://adsabs.harvard.edu/abs/2013EGUGA..1511726K"> <span id="translatedtitle">Assessing Fire Weather Index using statistical <span class="hlt">downscaling</span> and spatial interpolation techniques in Greece</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Forest fires have always been present in the Mediterranean ecosystems, thus they constitute a major ecological and socio-economic issue. The last few decades though, the number of forest fires has significantly increased, as well as their severity and impact on the environment. Local fire danger projections are often required when dealing with wild fire research. In the present study the application of statistical <span class="hlt">downscaling</span> and spatial interpolation methods was performed to the Canadian Fire Weather Index (FWI), in order to assess forest fire risk in Greece. The FWI is used worldwide (including the Mediterranean basin) to estimate the fire danger in a generalized fuel type, based solely on weather observations. The meteorological inputs to the FWI System are noon values of dry-bulb temperature, air relative humidity, 10m wind speed and precipitation during the previous 24 hours. The statistical <span class="hlt">downscaling</span> methods are based on a statistical model that takes into account empirical relationships between large scale variables (used as predictors) and local scale variables. In the framework of the current study the statistical <span class="hlt">downscaling</span> portal developed by the Santander Meteorology Group (https://www.meteo.unican.es/<span class="hlt">downscaling</span>) in the framework of the EU project CLIMRUN (www.climrun.eu) was used to <span class="hlt">downscale</span> non standard parameters related to forest fire risk. In this study, two different approaches were adopted. Firstly, the analogue <span class="hlt">downscaling</span> technique was directly performed to the FWI index values and secondly the same <span class="hlt">downscaling</span> technique was performed indirectly through the meteorological inputs of the index. In both cases, the statistical <span class="hlt">downscaling</span> portal was used considering the ERA-Interim reanalysis as predictands due to the lack of observations at noon. Additionally, a three-dimensional (3D) interpolation method of position and elevation, based on Thin Plate Splines (TPS) was used, to interpolate the ERA-Interim data used to calculate the index. Results from this method were compared with the statistical <span class="hlt">downscaling</span> results obtained from the portal. Finally, FWI was computed using weather observations obtained from the Hellenic National Meteorological Service, mainly in the south continental part of Greece and a comparison with the previous results was performed.</p> <div class="credits"> <p class="dwt_author">Karali, Anna; Giannakopoulos, Christos; Frias, Maria Dolores; Hatzaki, Maria; Roussos, Anargyros; Casanueva, Ana</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">198</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2002JMP....43.5830D"> <span id="translatedtitle">Matrix models for beta <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">This paper constructs tridiagonal random matrix models for general (?>0) ?-Hermite (Gaussian) and ?-Laguerre (Wishart) <span class="hlt">ensembles</span>. These generalize the well-known Gaussian and Wishart models for ?=1,2,4. Furthermore, in the cases of the ?-Laguerre <span class="hlt">ensembles</span>, we eliminate the exponent quantization present in the previously known models. We further discuss applications for the new matrix models, and present some open problems.</p> <div class="credits"> <p class="dwt_author">Dumitriu, Ioana; Edelman, Alan</p> <p class="dwt_publisher"></p> <p class="publishDate">2002-11-01</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://people.sc.fsu.edu/~navon/pubs/uzunoglu.pdf"> <span id="translatedtitle">Adaptive <span class="hlt">ensemble</span> reduction and inflation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">In this paper we address the question of whether it is possible consistently to reduce the number of <span class="hlt">ensemble</span> members at a late stage in the assimilation cycle. As an extension, we consider the question: given this reduction, is it possible to reintroduce <span class="hlt">ensemble</span> members at a later time, if the accuracy is decreasing significantly? To address these questions, we</p> <div class="credits"> <p class="dwt_author">B. Uzunoglu; S. J. Fletcher; M. Zupanski; I. M. Navon</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">200</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 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://eric.ed.gov/?q=bass&pg=7&id=EJ594156"> <span id="translatedtitle">The Importance of Bass <span class="hlt">Ensemble</span>.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p class="result-summary">States that bass players should be allowed to play chamber music because it is an essential component to all string students' musical development. Expounds that bassists can successfully enjoy chamber music through participation in a bass <span class="hlt">ensemble</span>. Gives suggestions on how to form a bass <span class="hlt">ensemble</span> and on the repertoire of music. (CMK)</p> <div class="credits"> <p class="dwt_author">Bitz, Michael</p> <p class="dwt_publisher"></p> <p class="publishDate">1997-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">202</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://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 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/2014ClDy..tmp..114H"> <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">2014-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">204</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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">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/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">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/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 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://www.legos.obs-mip.fr/~dewitte/bxd_data/fullpaper/Echevinetal2011cd.pdf"> <span id="translatedtitle">Sensitivity of the Humboldt Current system to global warming: a <span class="hlt">downscaling</span> experiment of the IPSL-CM4 model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Sensitivity of the Humboldt Current system to global warming: a <span class="hlt">downscaling</span> experiment of the IPSL on the seasonal variability of the Humboldt Current system ocean dynam- ics is investigated. The IPSL-CM4 large and quadrupling CO2, are <span class="hlt">downscaled</span> using an eddy-resolving regional ocean circu- lation model. The intense</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">208</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/00/70/19/25/PDF/702972_2_merged_1306097147.pdf"> <span id="translatedtitle">JOURNAL OF GEOPHYSICAL RESEARCH, VOL. ???, XXXX, DOI:10.1029/, <span class="hlt">Down-scaling</span> of fracture energy during brittle creep1</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">LENGLIN´E ET AL.: <span class="hlt">DOWN-SCALING</span> DURING BRITTLE CREEP Abstract. We present mode I brittle-creep fractureJOURNAL OF GEOPHYSICAL RESEARCH, VOL. ???, XXXX, DOI:10.1029/, <span class="hlt">Down-scaling</span> of fracture energy during brittle creep1 experiments2 O. Lenglin´e, 1 J. Schmittbuhl, 1 J. E. Elkhoury, 2 J.-P. Ampuero, 2 R</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">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/2013AGUFMGC41C1025A"> <span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of Intensity and Frequency Predictions of Local and Regional 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">The modeling of precipitation at local, regional and global scale remains uncertain, and for many regions, problematic. Further, the <span class="hlt">downscaling</span> of precipitation values from gridded data to single point locations poses many challenges since precipitation in general is not a 'single variable,' but rather a composite response to a variety of atmospheric variables. Multiple linear regression using point gauge measurements (SMHI) as the dependent variable and three different sets of ECMWF reanalysis (model) output variables as independent variables for 5 locations in Sweden suggests that for statistical <span class="hlt">downscaling</span> of modeled precipitation, three additional variables should be included in the calculation: percentage of high cloud cover, mean sea level pressure and top minus surface radiation. We investigate the suitability of usage of these variables in a <span class="hlt">downscaling</span> procedure, develop a predictive model based on these findings, then extend the method to CMIP5 results.</p> <div class="credits"> <p class="dwt_author">Allen, M. R.; Fu, J. S.; Bozdogan, H.</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">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/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 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/2014GMDD....7.7121M"> <span id="translatedtitle">Technical challenges and solutions in representing lakes when using WRF in <span class="hlt">downscaling</span> 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">The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by <span class="hlt">downscaling</span> global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional <span class="hlt">downscaled</span> fields, inland lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for <span class="hlt">downscaling</span> simulations are presented and contrasted.</p> <div class="credits"> <p class="dwt_author">Mallard, M. S.; Nolte, C. G.; Spero, T. L.; Bullock, O. R.; Alapaty, K.; Herwehe, J. A.; Gula, J.; Bowden, J. H.</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">212</div> <div class="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">213</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://nzc.iap.ac.cn/uploadpdf/AOSL10049.pdf"> <span id="translatedtitle">ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2010, VOL. 3, NO. 6, 325329 A Quick Report on a Dynamical <span class="hlt">Downscaling</span> Simulation over China Using</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">describes a dynamical <span class="hlt">downscaling</span> simulation over China using the nested model system, which consists (CAM). Results show that dynamical <span class="hlt">downscaling</span> is of great value in improving the model simulation: dynamical <span class="hlt">downscaling</span>, WRF, CAM Citation: Yu, E.-T., H.-J. Wang, and J.-Q. Sun, 2010: A quick report</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">214</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.sscnet.ucla.edu/~yxue/pdf/2007jcli.pdf"> <span id="translatedtitle">Assessment of Dynamic <span class="hlt">Downscaling</span> of the Continental U.S. Regional Climate Using the Eta/SSiB Regional Climate 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">Assessment of Dynamic <span class="hlt">Downscaling</span> of the Continental U.S. Regional Climate Using the Eta form 12 December 2006) ABSTRACT This study investigates the capability of the dynamic <span class="hlt">downscaling</span>- oped and applied for dynamically <span class="hlt">downscaling</span> GCM Corresponding author address: Yongkang Xue, Department</p> <div class="credits"> <p class="dwt_author">Xue, Yongkang</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">215</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2006TellA..58..159Z"> <span id="translatedtitle">Initiation of <span class="hlt">ensemble</span> data assimilation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The specification of the initial <span class="hlt">ensemble</span> for <span class="hlt">ensemble</span> data assimilation is addressed. The presented work examines the impact of <span class="hlt">ensemble</span> initiation in the Maximum Likelihood <span class="hlt">Ensemble</span> Filter (MLEF) framework, but is also applicable to other <span class="hlt">ensemble</span> data assimilation algorithms. Two methods are considered: the first is based on the use of the Kardar-Parisi-Zhang (KPZ) equation to form sparse random perturbations, followed by spatial smoothing to enforce desired correlation structure, while the second is based on the spatial smoothing of initially uncorrelated random perturbations. Data assimilation experiments are conducted using a global shallow-water model and simulated observations. The two proposed methods are compared to the commonly used method of uncorrelated random perturbations. The results indicate that the impact of the initial correlations in <span class="hlt">ensemble</span> data assimilation is beneficial. The root-mean-square error rate of convergence of the data assimilation is improved, and the positive impact of initial correlations is notable throughout the data assimilation cycles. The sensitivity to the choice of the correlation length scale exists, although it is not very high. The implied computational savings and improvement of the results may be important in future realistic applications of <span class="hlt">ensemble</span> data assimilation.</p> <div class="credits"> <p class="dwt_author">Zupanski, M.; Fletcher, S. J.; Navon, I. M.; Uzunoglu, B.; Heikes, R. P.; Randall, D. A.; Ringler, T. D.; Daescu, D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-03-01</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://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 odd" lang="en"> <div class="resultNumber element">217</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2014PhLA..378..319O"> <span id="translatedtitle">Deformed Ginibre <span class="hlt">ensembles</span> and integrable systems</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We consider three Ginibre <span class="hlt">ensembles</span> (real, complex and quaternion-real) with deformed measures and relate them to known integrable systems by presenting partition functions of these <span class="hlt">ensembles</span> in form of fermionic expectation values. We also introduce double deformed Dyson-Wigner <span class="hlt">ensembles</span> and compare their fermionic representations with those of Ginibre <span class="hlt">ensembles</span>.</p> <div class="credits"> <p class="dwt_author">Orlov, A. Yu.</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">218</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">219</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/1721.1/92863"> <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://www.osti.gov/eprints/">E-print Network</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 ...</p> <div class="credits"> <p class="dwt_author">Dubbs, Alexander</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://cdsweb.cern.ch/record/1707718"> <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://www.osti.gov/eprints/">E-print Network</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. A key requirement is that the sampling process is unbiased and efficient. The main approaches are microcanonical, i.e. they sample graphs that exactly match the enforced constraints. Unfortunately, when applied to strongly heterogeneous networks (including most real-world graphs), 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') method to correctly sample <span class="hlt">ensembles</span> of networks where the constraints are `soft' i.e. they are realized as <span class="hlt">ensemble</span> averages. Being based on exact maximum-entropy distributions, our approach is unbiased by c...</p> <div class="credits"> <p class="dwt_author">Squartini, Tiziano; Garlaschelli, Diego</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-01-01</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_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");' 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showDiv("page_13");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">221</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://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 " 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://adsabs.harvard.edu/abs/2010ems..confE.213M"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of wind fields for wind power applications</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Dynamical <span class="hlt">downscaling</span> of wind fields for wind power applications H.-T. Mengelkamp*,**, S. Huneke**, J, Geyer** *GKSS Research Center Geesthacht GmbH **anemos Gesellschaft für Umweltmeteorologie mbH Investments in wind power require information on the long-term mean wind potential and its temporal variations on daily to annual and decadal time scales. This information is rarely available at specific wind farm sites. Short-term on-site measurements usually are only performed over a 12 months period. These data have to be set into the long-term perspective through correlation to long-term consistent wind data sets. Preliminary wind information is often asked for to select favourable wind sites over regional and country wide scales. Lack of high-quality wind measurements at weather stations was the motivation to start high resolution wind field simulations The simulations are basically a refinement of global scale reanalysis data by means of high resolution simulations with an atmospheric mesoscale model using high-resolution terrain and land-use data. The 3-dimensional representation of the atmospheric state available every six hours at 2.5 degree resolution over the globe, known as NCAR/NCEP reanalysis data, forms the boundary conditions for continuous simulations with the non-hydrostatic atmospheric mesoscale model MM5. MM5 is nested in itself down to a horizontal resolution of 5 x 5 km². The simulation is performed for different European countries and covers the period 2000 to present and is continuously updated. Model variables are stored every 10 minutes for various heights. We have analysed the wind field primarily. The wind data set is consistent in space and time and provides information on the regional distribution of the long-term mean wind potential, the temporal variability of the wind potential, the vertical variation of the wind potential, and the temperature, and pressure distribution (air density). In the context of wind power these data are used • as an initial estimate of wind and energy potential • for the long-term correlation of wind measurements and turbine production data • to provide wind potential maps on a regional to country wide scale • to provide input data sets for simulation models • to determine the spatial correlation of the wind field in portfolio calculations • to calculate the wind turbine energy loss during prescribed downtimes • to provide information on the temporal variations of the wind and wind turbine energy production The time series of wind speed and wind direction are compared to measurements at offshore and onshore locations.</p> <div class="credits"> <p class="dwt_author">Mengelkamp, H.-T.; Huneke, S.; Geyer, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-09-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">223</div> <div class="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">224</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 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.atmo.arizona.edu/~castro/Reviewedpubs/R-17.pdf"> <span id="translatedtitle">Climate change projection of snowfall in the Colorado River Basin using dynamical <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">Climate change projection of snowfall in the Colorado River Basin using dynamical <span class="hlt">downscaling</span>] Recent observations show a decrease in the fraction of precipitation falling as snowfall in the western the simulated spatiotemporal variability of snowfall in the historical period when compared to observations</p> <div class="credits"> <p class="dwt_author">Castro, Christopher L.</p> <p class="dwt_publisher"></p> <p class="publishDate"></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.uib.es/depart/dfs/meteorologia/ROMU/formal/roc_curve/roc_curve.pdf"> <span id="translatedtitle">Impact of the lateral boundary conditions resolution on dynamical <span class="hlt">downscaling</span> of precipitation in mediterranean spain</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 mediterranean spain A. Amengual Ã? R. Romero Ã? V. Homar Ã? C. Ramis Ã? S. Alonso Received: 27 March 2006 / Accepted) horizontal and temporal optimum resolution for dynamical <span class="hlt">downscaling</span> of rainfall in Mediterranean Spain observations. For the whole Mediterranean Spain, model skill is not appreciably improved when using en- hanced</p> <div class="credits"> <p class="dwt_author">Romero, Romu</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=http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=154374"> <span id="translatedtitle"><span class="hlt">DOWNSCALING</span> MONTHLY FORECASTS TO SIMULATE IMPACTS OF CLIMATE CHANGE ON SOIL EROSION AND WHEAT PRODUCTION</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">Climate change can affect agricultural production and soil and water conservation. The objectives of this study were to develop a method for <span class="hlt">downscaling</span> monthly climate forecasts to daily weather series using a climate generator (CLIGEN), and to simulate the potential impacts of projected mean and ...</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">228</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/53400327"> <span id="translatedtitle">Using dynamically <span class="hlt">downscaled</span> GCM outputs in hydrological models: a case study from Tasmania, Australia</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Modelling future runoff by running meteorological projections from global climate models (GCMs) directly through hydrological models presents considerable technical challenges, but promises several advantages over the so-called `perturbation method'. The Climate Futures for Tasmania project has projected water yield in Tasmania, Australia to 2100. This paper describes how the Climate Futures for Tasmania project used dynamically <span class="hlt">downscaled</span> climate projections directly</p> <div class="credits"> <p class="dwt_author">J. Bennett; M. Grose; F. Ling; S. Corney; G. Holz; C. White; B. Graham; D. Post; N. Bindoff</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">229</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/48946505"> <span id="translatedtitle">Errors of Interannual Variability and Trend in Dynamical <span class="hlt">Downscaling</span> of Reanalysis</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 interannual variability of dynamically <span class="hlt">downscaled</span> analysis and its error relative to global coarse resolution analysis is examined in this paper. The regional model error is shown to significantly contaminate the interannual variability of the seasonal mean. The error occupies a significant part of the interannual variability, particularly during the summer season. Accordingly, the leading modes of empirical orthogonal functions</p> <div class="credits"> <p class="dwt_author">Masao Kanamitsu; Kei Yoshimura; Yoo-Bin Yhang; Song-You Hong</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">230</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/42009208"> <span id="translatedtitle">A simple statistical-dynamical <span class="hlt">downscaling</span> scheme based on weather types and conditional resampling</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">A multivariate statistical <span class="hlt">downscaling</span> methodology is implemented to generate local precipitation and temperature series at different sites based on the results from a variable resolution general circulation model. It starts from regional climate properties to establish discriminating weather types for the chosen local variable, precipitation in this case. Intratype variations of the relevant forcing parameters are then taken into account</p> <div class="credits"> <p class="dwt_author">J. Boé; L. Terray; F. Habets; E. Martin</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">231</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/52041216"> <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 of hazardous convective-scale weather. Our basic approach differs from a traditional regional climate model application in that it involves a sequence of daily integrations. We use the weather research and forecasting (WRF) model, with global reanalysis data as initial and boundary conditions.</p> <div class="credits"> <p class="dwt_author">Robert J. Trapp; 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 " 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://academic.research.microsoft.com/Publication/54199986"> <span id="translatedtitle">Impact of the lateral boundary conditions resolution on dynamical <span class="hlt">downscaling</span> of precipitation in mediterranean spain</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">Conclusions on the General Circulation Models (GCMs) horizontal and temporal optimum resolution for dynamical <span class="hlt">downscaling</span> of rainfall in Mediterranean Spain are derived based on the statistical analysis of mesoscale simulations of past events. These events correspond to the 165 heavy rainfall days during 1984 1993, which are simulated with the HIRLAM mesoscale model. The model is nested within the European</p> <div class="credits"> <p class="dwt_author">Do Wan Kim; Wing Kam Liu; Young-Cheol Yoon; Ted Belytschko; Sang-Ho Lee</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">233</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ge-at.iastate.edu/Takle/STATLAM.AMS.pdf"> <span id="translatedtitle">STATISTICAL AND DYNAMICAL <span class="hlt">DOWNSCALING</span> OF GLOBAL MODEL OUTPUT FOR U.S. NATIONAL ASSESSMENT HYDROLOGICAL ANALYSES</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">1. NTRODUCTION Analyses performed for the US National Assessment require accurate projections of climate at scales below those resolved by global General Circulation Models (GCMs). Two techniques have been developed that counter this deficiency: semi- empirical (statistical) <span class="hlt">downscaling</span> (SDS) of GCM outputs, and regional climate models (RCMs) nested within a GCM. To date, few studies have compared SDS and RCM</p> <div class="credits"> <p class="dwt_author">W. J. Gutowski; R. Wilby; L. E. Hay; C. J. Anderson; R. W. Arritt; M. P. Clark; G. H. Leavesley; Z. Pan; R. Silva; E. S. Takle</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://academic.research.microsoft.com/Publication/53468932"> <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 of hazardous convective-scale weather. Our basic approach differs from a traditional regional climate model application in that it involves a sequence of daily integrations. We use the weather research and forecasting (WRF) model, with global reanalysis data as initial and boundary conditions.</p> <div class="credits"> <p class="dwt_author">Robert J. Trapp; Eric D. Robinson; Michael E. Baldwin; Noah S. Diffenbaugh; Benjamin R. J. Schwedler</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">235</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/3203298"> <span id="translatedtitle">Hydrologic Implications of Dynamical and Statistical Approaches to <span class="hlt">Downscaling</span> Climate Model Outputs</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Six approaches for <span class="hlt">downscaling</span> climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the</p> <div class="credits"> <p class="dwt_author">Andrew W. Wood; Lai R. Leung; V. Sridhar; D. P. Lettenmaier</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">236</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/48944841"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span>: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS)</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 value restored and added by dynamical <span class="hlt">downscaling</span> is quantitatively evaluated by considering the spectral behavior of the Regional Atmospheric Modeling System (RAMS) in relation to its domain size and grid spacing. A regional climate model (RCM) simulation is compared with NCEP Reanalysis data regridded to the RAMS grid at each model analysis time for a set of six basic</p> <div class="credits"> <p class="dwt_author">Christopher L. Castro; Roger A. Pielke; Giovanni Leoncini</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">237</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/56462424"> <span id="translatedtitle">Uncertainty and extremes analysis to evaluate dynamical <span class="hlt">downscaling</span> of climate models</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Projections of climate extremes and regional change from global climate models, obtained both with and without dynamical <span class="hlt">downscaling</span> based on regional climate models, are systematically evaluated with a particular emphasis on their ability to support regional preparedness decisions. Global climate models show significant systematic biases at regional scales, some of which can be interpreted based on topographical or other features,</p> <div class="credits"> <p class="dwt_author">D. Das; E. Kodra; K. Steinhaeuser; S. Kao; A. R. Ganguly; M. L. Branstetter; D. J. Erickson; R. Flanery; M. M. Gonzalez; C. Hays; A. W. King; W. Lenhardt; R. Oglesby; R. M. Patton; C. M. Rowe; A. Sorokine; C. Steed</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">238</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/downscaling_2006.pdf"> <span id="translatedtitle">Climate and Tropical Cyclone Activity: A New Model <span class="hlt">Downscaling</span> Approach KERRY EMANUEL</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">Climate and Tropical Cyclone Activity: A New Model <span class="hlt">Downscaling</span> Approach KERRY EMANUEL Program to understand and predict the response of tropical cyclones to climate change, global climate models are at present too coarse to resolve tropical cyclones to the extent necessary to simulate their intensity</p> <div class="credits"> <p class="dwt_author">Emanuel, Kerry 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">239</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://plantecology.syr.edu/fridley/Fridley2009_jamc.pdf"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Climate over Complex Terrain: High Finescale (Ground Temperatures in a Montane Forested Landscape</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">suggests a strong interplay between near-ground heat and water balances and indicates that the influence environments, finescale variance in solar heat transfer due to varying slope angle and ori- entation, shading<span class="hlt">Downscaling</span> Climate over Complex Terrain: High Finescale (Ground</p> <div class="credits"> <p class="dwt_author">Fridley, Jason D.</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">240</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2014JHyd..516..304W"> <span id="translatedtitle">Evaluation of sampling techniques to characterize topographically-dependent variability for soil moisture <span class="hlt">downscaling</span></span></a>  </p> <div class="result-meta"> <p class="source"><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> methods have been proposed to estimate catchment-scale soil moisture patterns from coarser resolution patterns. These methods usually infer the fine-scale variability in soil moisture using variations in ancillary variables like topographic attributes that have relationships to soil moisture. Previously, such relationships have been observed in catchments using soil moisture observations taken on uniform grids at hundreds of locations on multiple dates, but collecting data in this manner limits the applicability of this approach. The objective of this paper is to evaluate the effectiveness of two strategic sampling techniques for characterizing the relationships between topographic attributes and soil moisture for the purpose of constraining <span class="hlt">downscaling</span> methods. The strategic sampling methods are conditioned Latin hypercube sampling (cLHS) and stratified random sampling (SRS). Each sampling method is used to select a limited number of locations or dates for soil moisture monitoring at three catchments with detailed soil moisture datasets. These samples are then used to calibrate two available <span class="hlt">downscaling</span> methods, and the effectiveness of the sampling methods is evaluated by the ability of the <span class="hlt">downscaling</span> methods to reproduce the known soil moisture patterns. cLHS outperforms random sampling in almost every case considered. SRS usually performs better than cLHS when very few locations are sampled, but it can perform worse than random sampling for intermediate and large numbers of locations.</p> <div class="credits"> <p class="dwt_author">Werbylo, Kevin L.; Niemann, Jeffrey D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-08-01</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 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</span> </span> <a id="NextPageLink" onclick='return showDiv("page_14");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">241</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.gi.alaska.edu/~bhatt/publications/zhang_etal_2007b.pdf"> <span id="translatedtitle">Climate <span class="hlt">downscaling</span> for estimating glacier mass balances in northwestern North America: Validation with a USGS</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">Climate <span class="hlt">downscaling</span> for estimating glacier mass balances in northwestern North America: Validation] An atmosphere/glacier modeling system is described for estimating the mass balances of glaciers in both current to force a precipitation- temperature-area-altitude (PTAA) glacier mass balance model with daily maximum</p> <div class="credits"> <p class="dwt_author">Bhatt, Uma</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://ntrs.nasa.gov/search.jsp?R=20140010385&hterms=Climate+change&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3D%2528Climate%2Bchange%2529"> <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">243</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://pubs.er.usgs.gov/publication/70048367"> <span id="translatedtitle">Climate <span class="hlt">downscaling</span> effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p class="result-summary">High-resolution (<span class="hlt">downscaled</span>) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different <span class="hlt">downscaling</span> approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between <span class="hlt">downscaling</span> approaches and that the variation attributable to <span class="hlt">downscaling</span> technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between <span class="hlt">downscaling</span> techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of <span class="hlt">downscaling</span> applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative <span class="hlt">downscaling</span> methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.</p> <div class="credits"> <p class="dwt_author">Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " 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://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 odd" lang="en"> <div class="resultNumber element">245</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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">246</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/70131483"> <span id="translatedtitle">On the <span class="hlt">downscaling</span> of actual evapotranspiration maps based on combination of MODIS and landsat-based actual evapotranspiration estimates</span></a>  </p> <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> is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for <span class="hlt">downscaling</span> MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and <span class="hlt">downscaling</span>. The regression with intercept method does have limitations in <span class="hlt">downscaling</span> if the slope and intercept are computed over a large area. A good agreement was obtained between <span class="hlt">downscaled</span> monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from ?16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and <span class="hlt">downscaled</span> monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between <span class="hlt">downscaled</span> monthly AET using LinZI method and the gridded FLUXNET dataset. The <span class="hlt">downscaled</span> monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed <span class="hlt">downscaling</span> method will be very useful in advancing the application of remotely sensed images in water resources planning and management.</p> <div class="credits"> <p class="dwt_author">Singh, Ramesh K.; Senay, Gabriel; Velpuri, Naga Manohar; Bohms, Stefanie; Verdin, James P.</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">247</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/15020771"> <span id="translatedtitle">Changes in Seasonal and Extreme Hydrologic Conditions of the Georgia Basin/Puget Sound in an <span class="hlt">Ensemble</span> Regional Climate Simulation for the Mid-Century</span></a>  </p> <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">This study examines an <span class="hlt">ensemble</span> of climate change projections simulated by a global climate model (GCM) and <span class="hlt">downscaled</span> with a region climate model (RCM) to 40 km spatial resolution for the western North America. One control and three <span class="hlt">ensemble</span> future climate simulations were produced by the GCM following a business as usual scenario for greenhouse gases and aerosols emissions from 1995 to 2100. The RCM was used to <span class="hlt">downscale</span> the GCM control simulation (1995-2015) and each <span class="hlt">ensemble</span> future GCM climate (2040-2060) simulation. Analyses of the regional climate simulations for the Georgia Basin/Puget Sound showed a warming of 1.5-2oC and statistically insignificant changes in precipitation by the mid-century. Climate change has large impacts on snowpack (about 50% reduction) but relatively smaller impacts on the total runoff for the basin as a whole. However, climate change can strongly affect small watersheds such as those located in the transient snow zone, causing a higher likelihood of winter flooding as a higher percentage of precipitation falls in the form of rain rather than snow, and reduced streamflow in early summer. In addition, there are large changes in the monthly total runoff above the upper 1% threshold (or flood volume) from October through May, and the December flood volume of the future climate is 60% above the maximum monthly flood volume of the control climate. Uncertainty of the climate change projections, as characterized by the spread among the <span class="hlt">ensemble</span> future climate simulations, is relatively small for the basin mean snowpack and runoff, but increases in smaller watersheds, especially in the transient snow zone, and associated with extreme events. This emphasizes the importance of characterizing uncertainty through <span class="hlt">ensemble</span> simulations.</p> <div class="credits"> <p class="dwt_author">Leung, Lai R.; Qian, Yun</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-12-15</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://www.cdc.noaa.gov/people/tom.hamill/HFIP-ensfcst-MWR-hamilletal_rev2.pdf"> <span id="translatedtitle">Global <span class="hlt">Ensemble</span> Predictions of 2009's Tropical Cyclones Initialized with an <span class="hlt">Ensemble</span> Kalman Filter</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Global <span class="hlt">Ensemble</span> Predictions of 2009's Tropical Cyclones Initialized with an <span class="hlt">Ensemble</span> Kalman Filter with the first 20 members of a 60-member <span class="hlt">ensemble</span> Kalman filter (EnKF) using the T382L64 GFS. The GFS</p> <div class="credits"> <p class="dwt_author">Hamill, Tom</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">249</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 " 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://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 odd" lang="en"> <div class="resultNumber element">251</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2615214"> <span id="translatedtitle">Similarity Measures for Protein <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">Analyses of similarities and changes in protein conformation can provide important information regarding protein function and evolution. Many scores, including the commonly used root mean square deviation, have therefore been developed to quantify the similarities of different protein conformations. However, instead of examining individual conformations it is in many cases more relevant to analyse <span class="hlt">ensembles</span> of conformations that have been obtained either through experiments or from methods such as molecular dynamics simulations. We here present three approaches that can be used to compare conformational <span class="hlt">ensembles</span> in the same way as the root mean square deviation is used to compare individual pairs of structures. The methods are based on the estimation of the probability distributions underlying the <span class="hlt">ensembles</span> and subsequent comparison of these distributions. We first validate the methods using a synthetic example from molecular dynamics simulations. We then apply the algorithms to revisit the problem of <span class="hlt">ensemble</span> averaging during structure determination of proteins, and find that an <span class="hlt">ensemble</span> refinement method is able to recover the correct distribution of conformations better than standard single-molecule refinement. PMID:19145244</p> <div class="credits"> <p class="dwt_author">Lindorff-Larsen, Kresten; Ferkinghoff-Borg, Jesper</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">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/2013EGUGA..15.2254J"> <span id="translatedtitle">Bayesianity of <span class="hlt">Ensemble</span> Variational Assimilation</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary"><span class="hlt">Ensemble</span> assimilation methods (EnKF, Particle Filters, <span class="hlt">Ensemble</span> Variational Assimilation) are basically meant to produce a sample of the Bayesian probability distribution of the state of the observed system, conditional to the available data. We present a comparative evaluation of those methods, and in particular of <span class="hlt">Ensemble</span> Variational Assimilation (Ens/4D-Var), considered as Bayesian estimators. <span class="hlt">Ensemble</span> Variational Assimilation achieves bayesianity in the linear and gaussian case. It is implemented here on small dimensional chaotic systems (Lorenz '96, Kuramoto-Sivashinsky) in nonlinear and/non-gaussian situations. The bayesian character of a probability distribution cannot be in general objectively verified, and the weaker property of reliability (statistical consistency between predicted probabilities and observed frequencies of occurrence) is used instead. The general conclusions are, first, that non-gaussianity has no significant impact. Second, that Ens/4D-Var produces reliable and accurate <span class="hlt">ensembles</span>. These conclusions remain valid for long assimilation periods, either through the use of Quasi-Static Variational Assimilation, in which the length of the assimilation window is progressively increased (in the case of a perfect model), or through weak-constraint assimilation (in the case of an imperfect model). Comparison with EnKF and Particle Filters shows that Ens/4D-Var is at least as good a bayesian estimator, although at a higher cost. The pros and cons of Ens/4D-Var are further discussed.</p> <div class="credits"> <p class="dwt_author">Jardak, Mohamed; Talagrand, Olivier</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">253</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">254</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/25240348"> <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.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</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. PMID:25240348</p> <div class="credits"> <p class="dwt_author">Fantoni, Riccardo; Moroni, Saverio</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">255</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.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">256</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 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://academic.research.microsoft.com/Publication/48463980"> <span id="translatedtitle">Influences of climate change on California and Nevada regions revealed by a high-resolution dynamical <span class="hlt">downscaling</span> study</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">In this study, the influence of climate change to California and Nevada regions was investigated through high-resolution (4-km\\u000a grid spacing) dynamical <span class="hlt">downscaling</span> using the WRF (Weather Research & Forecasting) model. The dynamical <span class="hlt">downscaling</span> was performed\\u000a to both the GFS (Global forecast model) reanalysis (called GFS-WRF runs) from 2000–2006 and PCM (Parallel Climate Model) simulations\\u000a (called PCM-WRF runs) from 1997–2006 and</p> <div class="credits"> <p class="dwt_author">Lin-Lin Pan; Shu-Hua Chen; Dan Cayan; Mei-Ying Lin; Quinn Hart; Ming-Hua Zhang; Yubao Liu; Jianzhong Wang</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">258</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2014AdSpR..54..655M"> <span id="translatedtitle">A comparison of different regression models for <span class="hlt">downscaling</span> Landsat and MODIS land surface temperature images over heterogeneous landscape</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (?1000 m) thermal images with high revisiting capability (?1 day) are freely available. To bridge this gap, present study attempts to <span class="hlt">downscale</span> coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression <span class="hlt">downscaling</span> (LMSDS) and (v) Pace regression <span class="hlt">downscaling</span> (PRDS) are applied to <span class="hlt">downscale</span> LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and <span class="hlt">downscaled</span> to 480 m and 240 m resolution. The <span class="hlt">downscale</span> accuracy is achieved using LMSDS and PRDS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data <span class="hlt">downscaled</span> from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMSDS and PRDS models, respectively. The LMSDS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. <span class="hlt">Downscaling</span> model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.</p> <div class="credits"> <p class="dwt_author">Mukherjee, Sandip; Joshi, P. K.; Garg, R. D.</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">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/2008AGUFM.H21E0877R"> <span id="translatedtitle">Estimation of climate change impacts on river flow and catchment hydrological connectivity incorporating uncertainty from multiple climate models, stochastic <span class="hlt">downscaling</span> and hydrological model parameterisation error sources</span></a>  </p> <div class="result-meta"> <p class="source"><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 estimating climate change impacts, there are many sources of uncertainty which must be considered. The main sources of uncertainty arise from the structure and parameterisation of physically based simulation models, <span class="hlt">downscaling</span> methods, stochastic realisations of future weather time series and the underlying emission scenarios. This work focuses on the uncertainties resulting from the use of multiple climate models and the joint impact of the stochastic realisations of future weather time series from a weather generator, EARWIG, and from parameter estimation uncertainty of a hydrological model, CAS-Hydro. These tools have been applied to the River Rye, Yorkshire. A suite of model parameter sets and weather realisations have been used to project likely changes to the hydrological functioning under climate change. Results are presented on the projected changes in flow duration curves and the potential changes in the hydrological connectivity by overland flow within the catchment. The statistical sensitivity of the impact predictions to these sources of uncertainty and the use of a multi-model <span class="hlt">ensemble</span> to enable the production of probabilistic estimates of change is assessed. These estimates of potential changes in flow can then be used to inform the adaptation of water resources design and management.</p> <div class="credits"> <p class="dwt_author">Reaney, S. M.; Fowler, H. J.</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">260</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/0611148v1"> <span id="translatedtitle">Localization of atomic <span class="hlt">ensembles</span> via superfluorescence</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 sub-wavelength localization of an <span class="hlt">ensemble</span> of atoms concentrated to a small volume in space is investigated. The localization relies on the interaction of the <span class="hlt">ensemble</span> with a standing wave laser field. The light scattered in the interaction of standing wave field and atom <span class="hlt">ensemble</span> depends on the position of the <span class="hlt">ensemble</span> relative to the standing wave nodes. This relation can be described by a fluorescence intensity profile, which depends on the standing wave field parameters, the <span class="hlt">ensemble</span> properties, and which is modified due to collective effects in the <span class="hlt">ensemble</span> of nearby particles. We demonstrate that the intensity profile can be tailored to suit different localization setups. Finally, we apply these results to two localization schemes. First, we show how to localize an <span class="hlt">ensemble</span> fixed at a certain position in the standing wave field. Second, we discuss localization of an <span class="hlt">ensemble</span> passing through the standing wave field.</p> <div class="credits"> <p class="dwt_author">M. Macovei; J. Evers; C. H. Keitel; M. S. Zubairy</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-11-14</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_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://www.osti.gov/scitech/biblio/20982392"> <span id="translatedtitle">Localization of atomic <span class="hlt">ensembles</span> via superfluorescence</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 subwavelength localization of an <span class="hlt">ensemble</span> of atoms concentrated to a small volume in space is investigated. The localization relies on the interaction of the <span class="hlt">ensemble</span> with a standing wave laser field. The light scattered in the interaction of the standing wave field and the atom <span class="hlt">ensemble</span> depends on the position of the <span class="hlt">ensemble</span> relative to the standing wave nodes. This relation can be described by a fluorescence intensity profile, which depends on the standing wave field parameters and the <span class="hlt">ensemble</span> properties and which is modified due to collective effects in the <span class="hlt">ensemble</span> of nearby particles. We demonstrate that the intensity profile can be tailored to suit different localization setups. Finally, we apply these results to two localization schemes. First, we show how to localize an <span class="hlt">ensemble</span> fixed at a certain position in the standing wave field. Second, we discuss localization of an <span class="hlt">ensemble</span> passing through the standing wave field.</p> <div class="credits"> <p class="dwt_author">Macovei, Mihai; Evers, Joerg; Keitel, Christoph H.; Zubairy, M. Suhail [Max-Planck-Institute for Nuclear Physics, Saupfercheckweg 1, D-69117 Heidelberg (Germany); Institute for Quantum Studies and Department of Physics, Texas A and M University, College Station, Texas 77843 (United States); Max-Planck-Institute for Nuclear Physics, Saupfercheckweg 1, D-69117 Heidelberg (Germany); Texas A and M University at Qatar, Education City, P. O. Box 23874, Doha (Qatar)</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-03-15</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">262</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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">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.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4041546"> <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=pmc">PubMed Central</a></p> <p class="result-summary">Methods for comparing <span class="hlt">ensembles</span> of biomolecules assess the population overlap between distributions but fail to fully quantify structural similarity. We present a simple and general approach 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 that go undetected using conventional methods 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">2014-01-01</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/2010AGUFMGC51A0745E"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of future changes in European precipitation using Model Output Statistics</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Regional- or local-scale precipitation changes cannot be directly inferred from precipitation simulated by General Circulation Models (GCMs) due to limited GCM spatial resolution. To overcome the problem, one possibility is to estimate regional precipitation through statistical <span class="hlt">downscaling</span>. For climate change studies the statistical links between large and small spatial scales are usually derived from real-world observations and then applied to the output of GCM simulations for the future climate. This approach requires the large-scale predictors from the GCM to be realistically simulated and is therefore known as 'Perfect-Prog(nosis)' (PP) <span class="hlt">downscaling</span>. An alternative approach to statistical <span class="hlt">downscaling</span> is 'Model Output Statistics' (MOS), where empirical corrections for simulated variables are formulated. Although used routinely in numerical weather prediction, application of MOS to climate change simulations is hampered by the fact that standard GCM simulations for historic periods do not represent the temporal evolution of random variability. In order to derive MOS corrections for simulated GCM precipitation we have conducted a simulation for the period 1958-2001 with the ECHAM5 GCM in which key circulation and temperature variables are nudged towards the ERA-40 reanalysis. This simulation thus is consistent with reality with respect to the large-scale weather variability, and MOS corrections that link simulated precipitation with regional observed precipitation can be derived from it. For this approach it is crucial that the simulated precipitation is not nudged towards observations and is calculated purely by the precipitation parameterisations in the GCM. MOS corrections of simulated monthly mean precipitation are shown to offer excellent potential for application in a statistical <span class="hlt">downscaling</span> methodology. Simple local scaling as well as different regression-based MOS <span class="hlt">downscaling</span> methods that use non-local predictors (Maximum Covariance Analysis, PC multiple linear regression) have been fitted and cross-validated using observations from the global GPCC gridded dataset, which has a spatial resolution of 0.5°×0.5°. Cross-validation shows that ECHAM5 precipitation is in many areas a very good predictor for the real precipitation given realistic synoptic-scale atmospheric states. MOS <span class="hlt">downscaling</span> models have been applied to the ECHAM5 simulation for the 21st century used in the IPCC Fourth Assessment Report (AR4) to estimate local mean changes in European seasonal precipitation in 2080-2099 relative to 1980-1999. All <span class="hlt">downscaling</span> models provide high-resolution projections of precipitation, and represent features not captured in the coarse GCM output. Both non-local methods in particular show good skill in resolving precipitation processes in areas of complex topography. Results suggest that local variability can account for considerable differences in projections of precipitation changes in raw and <span class="hlt">downscaled</span> climate change simulations. There is scope to extend this approach to other GCMs and for applying a similar methodology to daily precipitation distributions.</p> <div class="credits"> <p class="dwt_author">Eden, J. M.; Widmann, M.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">265</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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">266</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.math.ntnu.no/~joeid/ure/PreprintShrinkage.pdf"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Kalman Filtering with Shrinkage Regression Techniques</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> Kalman Filtering with Shrinkage Regression Techniques Jon Sætrom & Henning Omre, Norwegian University of Science and Technology; Summary The classical <span class="hlt">Ensemble</span> Kalman Filter (EnKF) is known;Introduction The <span class="hlt">Ensemble</span> Kalman Filter (EnKF) is a Bayesian data assimilation method that in recent years has</p> <div class="credits"> <p class="dwt_author">Eidsvik, Jo</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">267</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://users.math.uni-potsdam.de/~sreich/10_5.pdf"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Kalman and H filters Sebastian Reich</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> Kalman and H filters Sebastian Reich Universität Potsdam, Institut für Mathematik, Am Neuen Palais 10, D-14469 Potsdam Abstract. The <span class="hlt">ensemble</span> Kalman filter has become a popular method for nonlinear data assimilation. Standard <span class="hlt">ensemble</span> Kalman filter implementations need to be modified to avoid</p> <div class="credits"> <p class="dwt_author">Reich, Sebastian</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://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">269</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2001AGUFMNG51C..08B"> <span id="translatedtitle">Thunderstorm-Scale <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">Thunderstorms present an important weather forecast problem. Both the initiation and the general behavior of convection in the atmosphere are challenging to predict. In order to resolve the gross features of individual convective elements in numerical models, grid spacing of a kilometer or less is required, much finer than the current generation of operational numerical weather forecasting models. Sensitivities to small changes in the initial conditions and the physical parameterizations in the models have been shown to have important effects on some occasions at that scale. In order to address the predictability of thunderstorms with numerical models and to experiment with techniques to provide operationally useful information for weather forecasters, an <span class="hlt">ensemble</span> of thunderstorm simulations initalized with conditions from larger scale models has been run experimentally in near-real time since June 2001 at the National Severe Storms Laboratory for use by forecasters from the National Weather Service's Storm Prediction Center. In addition, previous cases run in a non-operational, research mode have been expanded to look at sensitivity to inclusion of ice-phase microphysics. In general, it appears that forecasters find the information from the <span class="hlt">ensemble</span> frequently agrees with their subjective assessment of the thunderstorm potential. This suggests that if computational resources were sufficient, the <span class="hlt">ensemble</span> could play an important role in the thunderstorm forecasting process. The ice-phase microphysics sensitivity tests have shown that, in some cases, the inclusion of ice in the <span class="hlt">ensemble</span> has a profound impact on the estimate of the probable lifetime of storms. The sensitivity due to the changes in model formulation may be as large or larger than the sensitivity due to initial condition uncertainty. Other parameterizations have not been tested in this experiment to date, but it suggests that consideration of both initial condition uncertainty and model formulation may be necessary to develop an <span class="hlt">ensemble</span> that provides reliable guidance about thunderstorms for weather forecasters. >http://www.spc.noaa.gov/exper/Spring_2001/elmore</a></p> <div class="credits"> <p class="dwt_author">Brooks, H. E.; Elmore, K. L.</p> <p class="dwt_publisher"></p> <p class="publishDate">2001-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">270</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.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">271</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/39782523"> <span id="translatedtitle">Hydroclimatological response to dynamically <span class="hlt">downscaled</span> climate change simulations for Korean basins</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 investigated the hydrological response to climate change simulations for three basins in South Korea. To provide fine-scale\\u000a climate information to the PRMS hydrological model, an ECHO-G B2 simulation was dynamically <span class="hlt">downscaled</span> using the RegCM3 double-nested\\u000a system implementing two different convection schemes, namely, the Grell and the MIT-Emanuel (EMU) schemes. The daily minimum\\u000a and maximum temperatures and precipitation from the</p> <div class="credits"> <p class="dwt_author">Eun-Soon Im; Il-Won Jung; Heejun Chang; Deg-Hyo Bae; Won-Tae Kwon</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">272</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/u3v150k85545685n.pdf"> <span id="translatedtitle">Assessment of dynamic <span class="hlt">downscaling</span> of the extreme rainfall over East Asia using a regional climate model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">This study investigates the capability of the dynamic <span class="hlt">downscaling</span> method (DDM) in an East Asian climate study for June 1998\\u000a using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research non-hydrostatic Mesoscale\\u000a Model (MM5). Sensitivity experiments show that MM5 results at upper atmospheric levels cannot match reanalyses data, but the\\u000a results show consistent improvement in simulating moisture transport at low</p> <div class="credits"> <p class="dwt_author">Yanhong Gao; Yongkang Xue; Wen Peng; Hyun-Suk Kang; Duane Waliser</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">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/2014EGUGA..1615487B"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of Tropical Cyclone activity from NCEP/NCAR and ERA 40 reanalysis data sets.</span></a>  </p> <div class="result-meta"> <p class="source"><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 derive climate statistics, long and homogeneous time series are needed. Observational data sets (best track data) of tropical cyclone activity in the western North Pacic basin show strong discrepancies in the long-term trends derived for the last decades. We derive alternative datasets of tropical cyclone (TC) activity, by applying dynamical <span class="hlt">downscaling</span> approach. Two reanalysis data sets: NCEP/NCAR 1 and ERA 40 are <span class="hlt">downscaled</span>, using an atmospheric regional model (CCLM). The reconstructed TC variability (yearly and climate-scale) yields good agreement with the observed one, mainly for the last three decades. Reconstructed and observed long-term trends (1948-2011) of TC frequency differ. Both reanalyses reveal a strong increase of TC activity, while observation-based data sets show rather decadal variability. Additional analysis indicates that the reconstructed long - term (1948-2011) TC activity may suffer from temporal inhomogeneities included in both sets of reanalyses, which were used to drive the regional climate model. For both simulations TC intensity reveals abrupt upward shift in 1978, which coincides with the introduction of satellite-based observations to reanalyses. Moreover, differences between the regional climate model simulations forced by either NCEP/NCAR 1 or ERA 40 point also to uncertainties associated with intrinsic features and quality changes of the reanalyses (e.g. observational data and methods of data assimilation). Therefore the interpretation of dynamically <span class="hlt">downscaled</span> reanalyses should be treated with caution, especially for the pre-satellite period. Study discuss the reliability of the results, derived from <span class="hlt">downscaling</span> the inhomogeneous data set.</p> <div class="credits"> <p class="dwt_author">Barcikowska, Monika; Feser, Frauke; von Storch, Hans</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">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/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 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://coast.gkss.de/staff/storch/pdf/070326.winnie.pdf"> <span id="translatedtitle">A dynamical <span class="hlt">downscaling</span> case study for typhoons in SE Asia using a regional climate model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Abstract This study explores the possibility to reconstruct the weather of SE Asia for the last decades using an atmospheric regional climate model, the Climate version of the Lokal Model (CLM). For this purpose global National Centers for Environmental Prediction - National Center for Atmospheric Research (NCEP-NCAR) reanalyses data were dy- namically <span class="hlt">downscaled</span>,to 50 km,and in a double-nesting approach to</p> <div class="credits"> <p class="dwt_author">Frauke Feser; Hans von Storch</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">276</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/40127257"> <span id="translatedtitle">Precipitation, temperature and wind in Norway: dynamical <span class="hlt">downscaling</span> of ERA40</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 novel <span class="hlt">downscaling</span> approach of the ERA40 (ECMWF 40-years reanalysis) data set has been taken and results for comparison with\\u000a observations in Norway are shown. The method applies a nudging technique in a stretched global model, focused in the Norwegian\\u000a Sea (67°N, 5°W). The effective resolution is three times the one of the ERA40, equivalent to about 30 km grid spacing</p> <div class="credits"> <p class="dwt_author">I. Barstad; A. Sorteberg; F. Flatøy; M. Déqué</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">277</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/48867669"> <span id="translatedtitle">Dynamic <span class="hlt">downscaling</span> of global climate projections for Eastern Europe with a horizontal resolution of 7 km</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Climate change is one of the key factors influencing the quantity and quality of water resources in hydrologically sensitive\\u000a regions. In order to <span class="hlt">downscale</span> global climate simulations from horizontal resolutions of about 125–200 km to about 7 km, a\\u000a double nesting strategy was chosen. The modelling approach was implemented with the Regional Climate Model CCLM (COSMO-Climate\\u000a Local Model) with a first nesting</p> <div class="credits"> <p class="dwt_author">Dirk Pavlik; Dennis Söhl; Thomas Pluntke; Andriy Mykhnovych; Christian Bernhofer</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " 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://academic.research.microsoft.com/Publication/48774053"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of ERA40 in complex terrain using the WRF regional climate model</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Results from a first-time employment of the WRF regional climate model to climatological simulations in Europe are presented.\\u000a The ERA-40 reanalysis (resolution 1°) has been <span class="hlt">downscaled</span> to a horizontal resolution of 30 and 10 km for the period of 1961–1990.\\u000a This model setup includes the whole North Atlantic in the 30 km domain and spectral nudging is used to keep the large</p> <div class="credits"> <p class="dwt_author">U. Heikkilä; A. Sandvik; A. Sorteberg</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">279</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/52522918"> <span id="translatedtitle">Numerical simulation of ice-ocean variability in the Barents Sea region. Towards 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">A dynamic-thermodynamic sea ice model has been coupled to a three-dimensional ocean general circulation model for the purpose of conducting ocean climate dynamical <span class="hlt">downscaling</span> experiments for the Barents Sea region. To assess model performance and suitability for such an application, the coupled model has been used to conduct a hindcast for the period 1990-2002. A comparison with available observations shows</p> <div class="credits"> <p class="dwt_author">W. P. Budgell</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">280</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013AGUFMGC11F..05D"> <span id="translatedtitle">A framework for evaluating statistical <span class="hlt">downscaling</span> performance under changing climatic conditions (Invited)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Statistical <span class="hlt">downscaling</span> (SD) methods may be viewed as generating a value-added product - a refinement of global climate model (GCM) output designed to add finer scale detail and to address GCM shortcomings via a process that gleans information from a combination of observations and GCM-simulated climate change responses. Making use of observational data sets and GCM simulations representing the same historical period, cross-validation techniques allow one to assess how well an SD method meets this goal. However, lacking observations of future, the extent to which a particular SD method's skill might degrade when applied to future climate projections cannot be assessed in the same manner. Here we illustrate and describe extensions to a 'perfect model' experimental design that seeks to quantify aspects of SD method performance both for a historical period (1979-2008) and for late 21st century climate projections. Examples highlighting cases in which <span class="hlt">downscaling</span> performance deteriorates in future climate projections will be discussed. Also, results will be presented showing how synthetic datasets having known statistical properties may be used to further isolate factors responsible for degradations in SD method skill under changing climatic conditions. We will describe a set of input files used to conduct these analyses that are being made available to researchers who wish to utilize this experimental framework to evaluate SD methods they have developed. The gridded data sets cover a region centered on the contiguous 48 United States with a grid spacing of approximately 25km, have daily time resolution (e.g., maximum and minimum near-surface temperature and precipitation), and represent a total of 120 years of model simulations. This effort is consistent with the 2013 National Climate Predictions and Projections Platform Quantitative Evaluation of <span class="hlt">Downscaling</span> Workshop goal of supporting a community approach to promote the informed use of <span class="hlt">downscaled</span> climate projections.</p> <div class="credits"> <p class="dwt_author">Dixon, K. W.; Balaji, V.; Lanzante, J.; Radhakrishnan, A.; Hayhoe, K.; Stoner, A. K.; Gaitan, C. F.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-12-01</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_13");' 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">281</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/15010453"> <span id="translatedtitle">Hydrologic Implications of Dynamical and Statistical Approaches to <span class="hlt">Downscaling</span> Climate Model Outputs</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">Six approaches for <span class="hlt">downscaling</span> climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical <span class="hlt">downscaling</span> methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical <span class="hlt">downscaling</span> via a Regional Climate Model (RCM – at ½-degree spatial resolution), for <span class="hlt">downscaling</span> the climate model outputs to the ?-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.</p> <div class="credits"> <p class="dwt_author">Wood, Andrew W.; Leung, Lai R.; Sridhar, V.; Lettenmaier, D. P.</p> <p class="dwt_publisher"></p> <p class="publishDate">2004-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">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/2014ThApC.tmp..108K"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> and future scenario generation of temperatures for Pakistan Region</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Finer climate change information on spatial scale is required for impact studies than that presently provided by global or regional climate models. It is especially true for regions like South Asia with complex topography, coastal or island locations, and the areas of highly heterogeneous land-cover. To deal with the situation, an inexpensive method (statistical <span class="hlt">downscaling</span>) has been adopted. Statistical <span class="hlt">DownScaling</span> Model (SDSM) employed for <span class="hlt">downscaling</span> of daily minimum and maximum temperature data of 44 national stations for base time (1961-1990) and then the future scenarios generated up to 2099. Observed as well as Predictors (product of National Oceanic and Atmospheric Administration) data were calibrated and tested on individual/multiple basis through linear regression. Future scenario was generated based on HadCM3 daily data for A2 and B2 story lines. The <span class="hlt">downscaled</span> data has been tested, and it has shown a relatively strong relationship with the observed in comparison to ECHAM5 data. Generally, the southern half of the country is considered vulnerable in terms of increasing temperatures, but the results of this study projects that in future, the northern belt in particular would have a possible threat of increasing tendency in air temperature. Especially, the northern areas (hosting the third largest ice reserves after the Polar Regions), an important feeding source for Indus River, are projected to be vulnerable in terms of increasing temperatures. Consequently, not only the hydro-agricultural sector but also the environmental conditions in the area may be at risk, in future.</p> <div class="credits"> <p class="dwt_author">Kazmi, Dildar Hussain; Li, Jianping; Rasul, Ghulam; Tong, Jiang; Ali, Gohar; Cheema, Sohail Babar; Liu, Luliu; Gemmer, Marco; Fischer, Thomas</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">283</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013IJAEO..23...95E"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of thermal images over urban areas using the land surface temperature-impervious percentage relationship</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Intensive expansion and densification of urban areas decreases environmental quality and quality of urban life as exemplified by the urban heat island effect. For this reason, thermal information is becoming an increasingly important data source for integration in urban studies. It is expected that future spaceborne thermal sensors will provide data at appropriate spatial and temporal resolutions for urban studies. Until they become operational, research has to rely on <span class="hlt">downscaling</span> algorithms increasing the spatial resolution of relatively coarse resolution thermal images albeit having a high temporal resolution. Existing <span class="hlt">downscaling</span> algorithms, however, have been developed for sharpening images over rural and natural areas, resulting in large errors when applied to urban areas. The objective of this study is to adapt the DisTrad method for <span class="hlt">downscaling</span> land surface temperature (LST) over urban areas using the relationship between LST and impervious percentage. The proposed approach is evaluated by sharpening aggregated LST derived from Landsat 7 ETM+ imagery collected over the city of Dublin on May 24th 2001. The new approach shows improved <span class="hlt">downscaling</span> results over urban areas for all evaluated resolutions, especially in an environment with mixed land cover. The adapted DisTrad approach was most successful at a resolution of 480 m, resulting in a correlation of R2 = 0.84 with an observed image at the same resolution. Furthermore, sharpening using the adapted DisTrad approach was able to preserve the spatial autocorrelation present in urban environments. The unmixing performance of the adapted DisTrad approach improves with decreasing resolution due to the fact that the functional relationship between LST and impervious percentage was defined at coarse resolutions.</p> <div class="credits"> <p class="dwt_author">Essa, W.; van der Kwast, J.; Verbeiren, B.; Batelaan, O.</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">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.springerlink.com/index/n672045708364524.pdf"> <span id="translatedtitle">Impact of the lateral boundary conditions resolution on dynamical <span class="hlt">downscaling</span> of precipitation in mediterranean spain</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">Conclusions on the General Circulation Models (GCMs) horizontal and temporal optimum resolution for dynamical <span class="hlt">downscaling</span>\\u000a of rainfall in Mediterranean Spain are derived based on the statistical analysis of mesoscale simulations of past events.\\u000a These events correspond to the 165 heavy rainfall days during 1984–1993, which are simulated with the HIRLAM mesoscale model.\\u000a The model is nested within the European Centre</p> <div class="credits"> <p class="dwt_author">A. Amengual; R. Romero; V. Homar; C. Ramis; S. Alonso</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">285</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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">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/2012JSemi..33g5008L"> <span id="translatedtitle">A high-speed mixed-signal <span class="hlt">down-scaling</span> circuit for DAB tuners</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">A high-speed mixed-signal <span class="hlt">down-scaling</span> circuit with low power consumption and low phase noise for use in digital audio broadcasting tuners has been realized and characterized. Some new circuit techniques are adopted to improve its performance. A dual-modulus prescaler (DMP) with low phase noise is realized with a kind of improved source-coupled logic (SCL) D-flip-flop (DFF) in the synchronous divider and a kind of improved complementary metal oxide semiconductor master-slave (CMOS MS)-DFF in the asynchronous divider. A new more accurate wire-load model is used to realize the pulse-swallow counter (PS counter). Fabricated in a 0.18-?m CMOS process, the total chip size is 0.6 × 0.2 mm2. The DMP in the proposed <span class="hlt">down-scaling</span> circuit exhibits a low phase noise of -118.2 dBc/Hz at 10 kHz off the carrier frequency. At a supply voltage of 1.8 V, the power consumption of the <span class="hlt">down-scaling</span> circuit's core part is only 2.7 mW.</p> <div class="credits"> <p class="dwt_author">Lu, Tang; Zhigong, Wang; Jiahui, Xuan; Yang, Yang; Jian, Xu; Yong, Xu</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-07-01</p> </div> </div> </div> </div> <div class="floatContainer result 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/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">288</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 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/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 " 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/2013EGUGA..15.9406B"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of extreme rainfall events in Romania using artificial neural networks</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The main purpose of statistical <span class="hlt">downscaling</span> methods is to model the relationship between large-scale atmospheric circulation and climatic variables on a regional and subregional scale. <span class="hlt">Downscaling</span> is an important area of research as it bridges the gap between predictions of future circulation generated by General Circulation Models (GCMs) and the effects of climate change on smaller areas. In this study we present the first results of a statistical <span class="hlt">downscaling</span> model, using a neural network-based approach by means of multi-layer perceptron networks. As predictands, various indices associated to temperature and precipitation extremes in Romania are used over the entire country (for temperature extremes) and on selected homogenous areas (for precipitation extremes). Several large-scale predictors (sea-level pressure, temperature at 850 / 700 hPa, specific humidity at 850 / 700 hPa) are tested, in order to select the optimum statistical model for each predictand. Predictands are considered separately or in various combinations. This work has been realised within the research project "Changes in climate extremes and associated impact in hydrological events in Romania" (CLIMHYDEX), code PN II-ID-2011-2-0073, financed by the Romanian Executive Agency for Higher Education Research, Development and Innovation Funding (UEFISCDI).</p> <div class="credits"> <p class="dwt_author">Birsan, Marius-Victor; Busuioc, Aristita; Dumitrescu, Alexandru</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">291</div> <div class="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 " 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://adsabs.harvard.edu/abs/2014ClDy...42.2899E"> <span id="translatedtitle">Uncertainty analysis of statistical <span class="hlt">downscaling</span> models using general circulation model over an international wetland</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Regression-based statistical <span class="hlt">downscaling</span> model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely "Shadegan" in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of <span class="hlt">downscaled</span> and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation <span class="hlt">downscaling</span> using 1987-2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.</p> <div class="credits"> <p class="dwt_author">Etemadi, H.; Samadi, S.; Sharifikia, M.</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">293</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">Comet</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-03-28</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://adsabs.harvard.edu/abs/2012AGUFMIN31C1516S"> <span id="translatedtitle">Analysis of Tropical Cyclones and Tropical Waves using the Parallel <span class="hlt">Ensemble</span> Empirical Model Decomposition (EEMD) Method</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In this study, we discuss the parallel implementation in the <span class="hlt">ensemble</span> empirical mode decomposition (EMD, Huang et al., 1998; Wu et al., 2009) and its application to the analysis of tropical waves and tropical cyclones (TCs). Recent studies with high-resolution model simulations and satellite data have shown a potential for improving our understanding of TC formation and thus extending the lead time of TC genesis prediction. It was hypothesized that improved predictability of TC formation can be achieved by improving hierarchical scale interactions of a TC and its environmental flows such as different kind of tropical waves (Shen et al., 2010a,b;2012a,b) and/or MJOs. To verify this hypothesis, it is crucial to quantitatively examine the TC genesis processes that accompany <span class="hlt">downscaling</span> (from large-scale events) and upscaling processes (from small-scale events), and their subsequent non-linear interactions. As these processes are non-linear and non-stationary per se, the original EMD or <span class="hlt">ensemble</span> EMD (EEMD) becomes a natural choice for performing such analyses. The EMD decomposes one set of observation data into the so-called intrinsic mode functions (IMFs). In comparison, the EEMD deals with an <span class="hlt">ensemble</span> of data sets, each of which includes the original observation data and finite amplitude white noise, and then applies an <span class="hlt">ensemble</span> average to obtain the final IMFs. The EEMD was first developed by Wu et al. (2009) to overcome the scale (or mode) mixing problem that may appear in the original EMD. It has been shown that the decomposed mean IMFs stay within the natural filter period windows, significantly reducing the chance of scale mixing while still preserving dyadic property. Depending on the required accuracy of the decomposed IMFs, typical <span class="hlt">ensemble</span> members are about 200~400 or higher. As the required computational resources are linearly proportional to the number of <span class="hlt">ensemble</span> trials, it becomes important to improve the performance of the EEMD algorithm to reduce time to solution. Thus, a hybrid OpenMP-MPI parallelism is implemented into the EEMD. In this talk, we will discuss the design of parallelism and present preliminary results with the parallel EEMD. At the end, we will present how the parallel EEMD will be integrated into the multiscale analysis package (MAP) with three-level parallelism which can efficiently process massive volume of global analysis data.</p> <div class="credits"> <p class="dwt_author">Shen, B.; Wu, Z.; Cheung, S.</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">295</div> <div class="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...40.2687F"> <span id="translatedtitle">Precipitation and temperature space-time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical <span class="hlt">downscaling</span> methods</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">This study evaluates how statistical and dynamical <span class="hlt">downscaling</span> models as well as combined approach perform in retrieving the space-time variability of near-surface temperature and rainfall, as well as their extremes, over the whole Mediterranean region. The dynamical <span class="hlt">downscaling</span> model used in this study is the Weather Research and Forecasting (WRF) model with varying land-surface models and resolutions (20 and 50 km) and the statistical tool is the Cumulative Distribution Function-transform (CDF-t). To achieve a spatially resolved <span class="hlt">downscaling</span> over the Mediterranean basin, the European Climate Assessment and Dataset (ECA&D) gridded dataset is used for calibration and evaluation of the <span class="hlt">downscaling</span> models. In the frame of HyMeX and MED-CORDEX international programs, the <span class="hlt">downscaling</span> is performed on ERA-I reanalysis over the 1989-2008 period. The results show that despite local calibration, CDF-t produces more accurate spatial variability of near-surface temperature and rainfall with respect to ECA&D than WRF which solves the three-dimensional equation of conservation. This first suggests that at 20-50 km resolutions, these three-dimensional processes only weakly contribute to the local value of temperature and precipitation with respect to local one-dimensional processes. Calibration of CDF-t at each individual grid point is thus sufficient to reproduce accurately the spatial pattern. A second explanation is the use of gridded data such as ECA&D which smoothes in part the horizontal variability after data interpolation and damps the added value of dynamical <span class="hlt">downscaling</span>. This explains partly the absence of added-value of the 2-stage <span class="hlt">downscaling</span> approach which combines statistical and dynamical <span class="hlt">downscaling</span> models. The temporal variability of statistically <span class="hlt">downscaled</span> temperature and rainfall is finally strongly driven by the temporal variability of its forcing (here ERA-Interim or WRF simulations). CDF-t is thus efficient as a bias correction tool but does not show any added-value regarding the time variability of the <span class="hlt">downscaled</span> field. Finally, the quality of the reference observation dataset is a key issue. Comparison of CDF-t calibrated with ECA&D dataset and WRF simulations to local measurements from weather stations not assimilated in ECA&D, shows that the temporal variability of the <span class="hlt">downscaled</span> data with respect to the local observations is closer to the local measurements than to ECA&D data. This highlights the strong added-value of dynamical <span class="hlt">downscaling</span> which improves the temporal variability of the atmospheric dynamics with regard to the driving model. This article highlights the benefits and inconveniences emerging from the use of both <span class="hlt">downscaling</span> techniques for climate research. Our goal is to contribute to the discussion on the use of <span class="hlt">downscaling</span> tools to assess the impact of climate change on regional scales.</p> <div class="credits"> <p class="dwt_author">Flaounas, Emmanouil; Drobinski, Philippe; Vrac, Mathieu; Bastin, Sophie; Lebeaupin-Brossier, Cindy; Stéfanon, Marc; Borga, Marco; Calvet, Jean-Christophe</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-06-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">296</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2010AGUFM.H52A..05S"> <span id="translatedtitle">The Future of Land-Use in the United States: <span class="hlt">Downscaling</span> SRES Emission Scenarios</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Scenarios have emerged as useful tools to explore uncertain futures in ecological systems. We describe research being initiated by the U.S. Geological Survey (USGS) to develop a comprehensive portfolio of land-use and land-cover (LULC) scenarios for the United States. The USGS has identified LULC scenarios as a major area of future research and the USGS LandCarbon research project has adopted a scenario-based approach for forecasting changes in LULC that may result in changes to ecosystem carbon flux and greenhouse gas (GHG) emissions. Scenarios generally require knowledge of history and current conditions, and specific understanding about how drivers of change have acted to influence the historical and current condition. We describe a methodology to <span class="hlt">downscale</span> Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) by leveraging three primary sources of information: 1) comprehensive land-use histories developed through remote sensing and survey data, 2) analytical model outputs from global integrated assessment models (IAMs), and 3) expert knowledge. SRES scenarios are organized along two primary dimensions described by economic versus environmental preferences and global versus regional influences. The SRES process identified the primary drivers of GHG emissions and provided narrative storylines describing their unique characteristics within each scenario family. We describe an iterative process to <span class="hlt">downscale</span> SRES storylines to the national and regional scale for the United States. Based on <span class="hlt">downscaled</span> narratives and guided by expert opinion, IAM outputs are used to extend land-use histories into the future. A wide range of qualitative and quantitative products will be developed throughout the <span class="hlt">downscaling</span> process. Qualitative narratives will be developed at national and regional scales and case studies to <span class="hlt">downscale</span> to local scales will be explored. Quantitative data includes scenarios of LULC (rates of change, composition, and conversions) at national and regional scales. Quantitative LULC scenarios are useful inputs for LULC models, which can be used to spatially allocate LULC demand on the landscape, resulting in high-resolution annual maps of LULC consistent with IPCC SRES global scenarios.</p> <div class="credits"> <p class="dwt_author">Sleeter, B. M.; Sohl, T. 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">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/2012EGUGA..1413756M"> <span id="translatedtitle">Assessment of CMIP5 GCM daily predictor variables 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">Assessment of CMIP5 GCM daily predictor variables for statistical <span class="hlt">downscaling</span> To support adaptation to climate change in the water resource sector in South Australia, <span class="hlt">downscaled</span> climate projections are being constructed within the Goyder Institute for Water Research - a 5-year multi-million dollar collaborative research partnership between the Government of South Australia, CSIRO and the university sector. Statistical <span class="hlt">downscaling</span> is a robust approach providing a link between observed (re-analysis) large-scale atmospheric variables (predictors) and local or regional surface climate variables such as daily station rainfall. When applied to outputs of Global Climate Models (GCMs), the credibility of statistically <span class="hlt">downscaled</span> future projections is dependent on the ability of GCMs to reproduce the re-analysis data statistics for the current climate. The main objective of this study is thus to assess daily predictor variables simulated by phase Five of Coupled Model Inter-comparison Project (CMIP5) GCMs, while acknowledging that an optimal measure of overall GCM performance does not exist and the usefulness of any assessment approach varies with the intended application. Here we assess GCMs by comparing cumulative probability density functions of predictor variables against the re-analysis data using the Kolmogorov test metric. Historical daily data simulations from 12 GCMs (BCC-csm1, CanESM2, CSIRO-Mk3.6.0, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC4h, MIROC-ESM-CHEM, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M) for the period 1961-2005 are used. The variables assessed include specific/relative humidity, winds, geopotential heights at different atmospheric levels and sea-level pressure over the Australian region (7-45oS, 100-160oE). We present a summary of results for the South Australia region quantifying the ability of these GCMs in reproducing the mean state and the relative frequency of extremes for these predictors. The complexity and challenges in GCM selection emanating from the inconsistent performance of GCMs across predictor variables will also be discussed. Keywords: Climate change; statistical <span class="hlt">downscaling</span>; GCM performance; water resources; adaptation.</p> <div class="credits"> <p class="dwt_author">Mpelasoka, F. S.; Charles, S.; Chiew, F. H.; Fu, G.; Beecham, S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">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/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">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/2013EGUGA..15.4703K"> <span id="translatedtitle">Future changes in African temperature and precipitation in an <span class="hlt">ensemble</span> of Africa-CORDEX regional climate model simulations</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In this study we investigate possible changes in temperature and precipitation on a regional scale over Africa from 1961 to 2100. We use data from two <span class="hlt">ensembles</span> of climate simulations, one global and one regional, over the Africa-CORDEX domain. The global <span class="hlt">ensemble</span> includes eight coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CNRM-CM5, HadGEM2-ES, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. In the regional <span class="hlt">ensemble</span> all 8 AOGCMs are <span class="hlt">downscaled</span> at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Two forcing scenarios are considered, RCP 4.5 and 8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, we investigate the benefit of the higher horizontal resolution in RCA4 by comparing the results to the coarser driving AOGCM data. The significance of the results is investigated by comparing to i) the model simulated natural variability, and, ii) the biases in the control period. Results dealing with changes in the seasonal cycle of temperature and precipitation are presented. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.</p> <div class="credits"> <p class="dwt_author">Kjellström, Erik; Nikulin, Grigory; Gbobaniyi, Emiola; Jones, Colin</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">300</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://cdsweb.cern.ch/record/1979558"> <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 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" 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showDiv("page_25");' href="#">25</a> </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://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">302</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">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.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3353412"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Modeling of Cancer Metabolism</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 metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the <span class="hlt">Ensemble</span> Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire <span class="hlt">ensemble</span> of models to obtain a sub-set, which is increasingly predictive. EM allows for incorporation of regulatory information and captures the behavior of enzymatic reactions at the molecular level by representing reactions in the elementary reaction form. In this study, a metabolic network consisting of 58 reactions is considered and accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation of key enzymes. Experimentally measured intracellular and extracellular metabolite concentrations are used for developing the <span class="hlt">ensemble</span> of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinyl-CoA ligase (SUCOAS1m) to cause a significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets. PMID:22623918</p> <div class="credits"> <p class="dwt_author">Khazaei, Tahmineh; McGuigan, Alison; Mahadevan, Radhakrishnan</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">304</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/22623918"> <span id="translatedtitle"><span class="hlt">Ensemble</span> modeling of cancer metabolism.</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 metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the <span class="hlt">Ensemble</span> Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire <span class="hlt">ensemble</span> of models to obtain a sub-set, which is increasingly predictive. EM allows for incorporation of regulatory information and captures the behavior of enzymatic reactions at the molecular level by representing reactions in the elementary reaction form. In this study, a metabolic network consisting of 58 reactions is considered and accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation of key enzymes. Experimentally measured intracellular and extracellular metabolite concentrations are used for developing the <span class="hlt">ensemble</span> of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinyl-CoA ligase (SUCOAS1m) to cause a significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets. PMID:22623918</p> <div class="credits"> <p class="dwt_author">Khazaei, Tahmineh; McGuigan, Alison; Mahadevan, Radhakrishnan</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">305</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=Srihari&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DSrihari"> <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 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://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">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.ce.ncsu.edu/research/hydroclimatology/people/sankar/pubs/devineni_etal.pdf"> <span id="translatedtitle">2 Multimodel <span class="hlt">ensembles</span> of streamflow forecasts: Role of predictor 3 state in developing optimal combinations</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">needs 50to apply either dynamical or statistical <span class="hlt">downscaling</span> to 51develop streamflow forecasts. Dynamical <span class="hlt">downscaling</span> nests 52a regional climate model (RCM) with GCM outputs as 53boundary conditions to obtain precipitation and temperature 54at watershed scale (60 Km Ã? 60 Km). The <span class="hlt">downscaled</span> 55precipitation</p> <div class="credits"> <p class="dwt_author">Arumugam, Sankar</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://web.science.unsw.edu.au/~jasone/publications/evansetal2011a.pdf"> <span id="translatedtitle">Evaluating the performance of a WRF physics <span class="hlt">ensemble</span> over South-East Australia</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">prediction system used for operational forecasting, atmospheric research and dynam- ical <span class="hlt">downscaling</span> for the purpose of optimising WRF for dynamical <span class="hlt">downscaling</span> in this region. The East coast of Australia from north the models ability to cap- ture these important events. Dynamical <span class="hlt">downscaling</span> (e.g. Deque et al. 2005; Frei</p> <div class="credits"> <p class="dwt_author">Evans, Jason</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">309</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=276487"> <span id="translatedtitle">Evaluation of a weather generator-based method for statistically <span class="hlt">downscaling</span> non-stationary climate scenarios for impact assessment at a point scale</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p class="result-summary">The non-stationarity is a major concern for statistically <span class="hlt">downscaling</span> climate change scenarios for impact assessment. This study is to evaluate whether a statistical <span class="hlt">downscaling</span> method is fully applicable to generate daily precipitation under non-stationary conditions in a wide range of climatic zo...</p> <div class="credits"> <p class="dwt_author"></p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">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.atmos.washington.edu/~salathe/papers/SalatheMoteWiley_IJOC.pdf"> <span id="translatedtitle">DRAFT: Int. J. Climatology, 2007, in press (MS# IJOC-06-0225) Review of scenario selection and <span class="hlt">downscaling</span> methods for the assessment of climate change impacts</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 <span class="hlt">downscaling</span> methods for the assessment of climate change impacts on hydrology in the United States Pacific and to <span class="hlt">downscale</span> global climate scenarios for the assessment of climate impacts on hydrologic systems research in support of regional resource management. Global climate model scenarios are evaluated</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">311</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">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/2013JGRD..118..520H"> <span id="translatedtitle">A novel approach to statistical <span class="hlt">downscaling</span> considering nonstationarities: application to daily precipitation in the Mediterranean area</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In the present study, nonstationarities in predictor-predictand relationships within the framework of statistical <span class="hlt">downscaling</span> are investigated. In this context, a novel validation approach is introduced in which nonstationarities are explicitly taken into account. The method is based on results from running calibration periods. 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. The specified procedure is demonstrated for mean daily precipitation in the Mediterranean area using the bias to assess model skill. A combined circulation-based and transfer function-based approach is employed as a <span class="hlt">downscaling</span> technique. In this context, large-scale seasonal atmospheric regimes, synoptic-scale daily circulation patterns, and their within-type characteristics, are related to daily station-based precipitation. Results show that nonstationarities are due to varying predictors-precipitation relationships of specific circulation configurations. In this regard, frequency changes of circulation patterns can damp or increase the effects of nonstationary relationships. Within the scope of assessing future precipitation changes under increased greenhouse warming conditions, the identification and analysis of nonstationarities in the predictors-precipitation relationships leads to a substantiated selection of specific statistical <span class="hlt">downscaling</span> models for the future assessments. Using RCP4.5 scenario assumptions, strong increases of daily precipitation become apparent over large parts of the western and northern Mediterranean regions in winter. In spring, summer, and autumn, decreases of precipitation until the end of the 21st century clearly dominate over the entire Mediterranean area.</p> <div class="credits"> <p class="dwt_author">Hertig, E.; Jacobeit, 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">313</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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 " 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/2010EGUGA..12.2389Z"> <span id="translatedtitle">Comparing empirical <span class="hlt">downscaling</span> methods within different kinds of terrain applied on the edge to climate impact 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">We use some statistical <span class="hlt">downscaling</span> techniques to derive local scale scenarios of future daily and monthly temperature and precipitation for the Alpine region. We utilize large scale NCEP/NCAR reanalysis data to establish empirical models and evaluate their performance against long term climate records from Austrian monitoring stations (forest sites, riverside fish population distributions, glaciers or phenological gardens across Europe etc.) for the second half of the 20th century. The performance of different <span class="hlt">downscaling</span> methods (multiple linear regression, canonical correlation analysis, the analog method) is analyzed. These methods are applied to derive transient climate change scenarios from ECHAM4/5 runs. <span class="hlt">Downscaled</span> data have been used in climate risk assessment studies to evaluate the sensitivity of the Austrian forests, fish stocks, phenological occurrence dates etc. to scenarios of climatic change.</p> <div class="credits"> <p class="dwt_author">Zuvela-Aloise, Maja; Matulla, Christoph; Auer, Inge; Böhm, Reinhard; Lexer, Manfred J.; Scheifinger, Helfried</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">315</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/22482774"> <span id="translatedtitle">Potential and limitations of <span class="hlt">ensemble</span> docking.</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 major problem in structure-based virtual screening applications is the appropriate selection of a single or even multiple protein structures to be used in the virtual screening process. A priori it is unknown which protein structure(s) will perform best in a virtual screening experiment. We investigated the performance of <span class="hlt">ensemble</span> docking, as a function of <span class="hlt">ensemble</span> size, for eight targets of pharmaceutical interest. Starting from single protein structure docking results, for each <span class="hlt">ensemble</span> size up to 500,000 combinations of protein structures were generated, and, for each <span class="hlt">ensemble</span>, pose prediction and virtual screening results were derived. Comparison of single to multiple protein structure results suggests improvements when looking at the performance of the worst and the average over all single protein structures to the performance of the worst and average over all protein <span class="hlt">ensembles</span> of size two or greater, respectively. We identified several key factors affecting <span class="hlt">ensemble</span> docking performance, including the sampling accuracy of the docking algorithm, the choice of the scoring function, and the similarity of database ligands to the cocrystallized ligands of ligand-bound protein structures in an <span class="hlt">ensemble</span>. Due to these factors, the prospective selection of optimum <span class="hlt">ensembles</span> is a challenging task, shown by a reassessment of published <span class="hlt">ensemble</span> selection protocols. PMID:22482774</p> <div class="credits"> <p class="dwt_author">Korb, Oliver; Olsson, Tjelvar S G; Bowden, Simon J; Hall, Richard J; Verdonk, Marcel L; Liebeschuetz, John W; Cole, Jason C</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-05-25</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://ntrs.nasa.gov/search.jsp?R=20130001779&hterms=kalman&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dkalman"> <span id="translatedtitle">A Localized <span class="hlt">Ensemble</span> Kalman Smoother</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">Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized <span class="hlt">ensemble</span> Kalman smoother.</p> <div class="credits"> <p class="dwt_author">Butala, Mark D.</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">317</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=19720019483&hterms=altitude+mask&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Daltitude%252Bmask"> <span id="translatedtitle">An altitude chamber rescue <span class="hlt">ensemble</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">Altitude chamber tests accomplished with the astronaut crews in the spacecraft at a simulated altitude of above 200,000 ft requires that a rescue team be provided in the event of an accident in the spacecraft. The rescue crew is stationed in an airlock maintained at an altitude of 18,000 ft. A protective <span class="hlt">ensemble</span> provides the rescue crew with life support capabilities, communications, and protection in the event of an emergency. In the event of an emergency, repressurization of the chamber is initiated; as the chamber descends, the airlock ascends and the two meet at 25,000 ft. This phase of the emergency repressurization takes less than 30 sec.</p> <div class="credits"> <p class="dwt_author">Lloyd, R. P.</p> <p class="dwt_publisher"></p> <p class="publishDate">1972-01-01</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://ntrs.nasa.gov/search.jsp?R=19820026226&hterms=FHD&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3DFHD"> <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">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/2013EGUGA..15.4431B"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Land Surface Temperature in an Urban Area: A Case Study for 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">Land surface temperature (LST) is an important parameter for the urban radiation and heat balance and a boundary condition for the atmospheric urban heat island (UHI). The increase in urban surface temperatures compared to the surrounding area (surface urban heat island, SUHI) has been described and analysed with satellite-based measurements for several decades. Besides continuous progress in the development of new sensors, an operational monitoring is still severely limited by physical constraints regarding the spatial and temporal resolution of the satellite data. Essentially, two measurement concepts must be distinguished: Sensors on geostationary platforms have high temporal (several times per hour) and poor spatial resolution (~ 5 km) while those on low earth orbiters have high spatial (~ 100-1000 m) resolution and a long return period (one day to several weeks). To enable an observation with high temporal and spatial resolution, a <span class="hlt">downscaling</span> scheme for LST from the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard the geostationary meteorological Meteosat 9 to spatial resolutions between 100 and 1000 m was developed and tested for Hamburg in this case study. Therefore, various predictor sets (including parameters derived from multi-temporal thermal data, NDVI, and morphological parameters) were tested. The relationship between predictors and LST was empirically calibrated in the low resolution domain and then transferred to the high resolution domain. The <span class="hlt">downscaling</span> was validated with LST data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) for the same time. Aggregated parameters from multi-temporal thermal data (in particular annual cycle parameters and principal components) proved particularly suitable. The results for the highest resolution of 100 m showed a high explained variance (R² = 0.71) and relatively low root mean square errors (RMSE = 2.2 K). Larger predictor sets resulted in higher errors, because they tended to overfit. As expected the results were better for coarser spatial resolutions (R² = 0.80, RMSE = 1.8 K for 500 m). These results are similar or slightly better than in previous studies, although we are not aware of any study with a comparably large <span class="hlt">downscaling</span> factor. A considerable percentage of the error is systematic due to the different viewing geometry of the sensors (the high resolution LST was overestimated about 1.3 K). The study shows that <span class="hlt">downscaling</span> of SEVIRI LST is possible up to a resolution of 100 m for urban areas and that multi-temporal thermal data are particularly suitable as predictors.</p> <div class="credits"> <p class="dwt_author">Bechtel, Benjamin; Zakšek, Klemen</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">320</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pa.op.dlr.de/climate/pub6.pdf"> <span id="translatedtitle">An Improved Statistical-Dynamical <span class="hlt">Downscaling</span> Scheme and its Application to the Alpine Precipitation Climatology</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   An improved statistical-dynamical <span class="hlt">downscaling</span> method for the regionalization of large-scale climate analyses or simulations\\u000a is introduced. The method is based on the disaggregation of a multi-year time-series of large-scale meteorological data into\\u000a multi-day episodes of quasi-stationary circulation. The episodes are subsequently grouped into a defined number of classes.\\u000a A regional model is used to simulate the evolution of weather</p> <div class="credits"> <p class="dwt_author">U. Fuentes; D. Heimann</p> <p class="dwt_publisher"></p> <p class="publishDate">2000-01-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_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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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 style="font-weight: bold;">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_18");' 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">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.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">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/2013AGUFM.A31E0140A"> <span id="translatedtitle">Reproduction of surface air temperature over South Korea using dynamical <span class="hlt">downscaling</span> and statistical correction</span></a>  </p> <div class="result-meta"> <p class="source"><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 spite of dense meteorological observation conducting over South Korea (The average distance between stations: ~ 12.7km), the detailed topographical effect is not reflected properly due to its mountainous terrains and observation sites mostly situated on low altitudes. A model represents such a topographical effect well, but due to systematic biases in the model, the general temperature distribution is sometimes far different from actual observation. This study attempts to produce a detailed mean temperature distribution for South Korea through a method combining dynamical <span class="hlt">downscaling</span> and statistical correction. For the dynamical <span class="hlt">downscaling</span>, a multi-nesting technique is applied to obtain 3-km resolution data with a focus on the domain for the period of 10 years (1999-2008). For the correction of systematic biases, a perturbation method divided into the mean and the perturbation part was used with a different correction method being applied to each part. The mean was corrected by a weighting function while the perturbation was corrected by the self-organizing maps method. The results with correction agree well with the observed pattern compared to those without correction, improving the spatial and temporal correlations as well as the RMSE. In addition, they represented detailed spatial features of temperature including topographic signals, which cannot be expressed properly by gridded observation. Through comparison with in-situ observation with gridded values after objective analysis, it was found that the detailed structure correctly reflected topographically diverse signals that could not be derived from limited observation data. We expect that the correction method developed in this study can be effectively used for the analyses and projections of climate <span class="hlt">downscaled</span> by using region climate models. Acknowledgements This work was carried out with the support of Korea Meteorological Administration Research and Development Program under Grant CATER 2012-3083 and Rural Development Administration Cooperative Research Program for Agriculture Science and Technology Development under Grant Project No. PJ009353, Republic of Korea. Reference Ahn, J.-B., Lee, J.-L., and Im, E.-S., 2012: The reproducibility of surface air temperature over South Korea using dynamical <span class="hlt">downscaling</span> and statistical correction, J. Meteor. Soc. Japan, 90, 493-507, doi: 10.2151/jmsj.2012-404</p> <div class="credits"> <p class="dwt_author">Ahn, J.; Lee, J.; Shim, K.; Kim, Y.</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">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/2007AGUSM.H53B..03G"> <span id="translatedtitle">Climatological <span class="hlt">Downscaling</span> and Evaluation of AGRMET Precipitation Analyses Over the Continental U.S.</span></a>  </p> <div class="result-meta"> <p class="source"><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 spatially distributed application of a land surface model (LSM) over a region of interest requires the application of similarly distributed precipitation fields that can be derived from various sources, including surface gauge networks, surface-based radar, and orbital platforms. The spatial variability of precipitation influences the spatial organization of soil temperature and moisture states and, consequently, the spatial variability of land- atmosphere fluxes. The accuracy of spatially-distributed precipitation fields can contribute significantly to the uncertainty of model-based hydrological states and fluxes at the land surface. Collaborations between the Air Force Weather Agency (AFWA), NASA, and Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and <span class="hlt">downscaling</span> of precipitation fields. Climatological information included in the LIS-based <span class="hlt">downscaling</span> procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and near-real-time simulations in regions of interest. This work focuses on value added to the AGRMET precipitation product by the inclusion of high-quality climatological information on a monthly time scale. The AGRMET method uses microwave-based satellite precipitation estimates from various polar-orbiting platforms (NOAA POES and DMSP), infrared-based estimates from geostationary platforms (GOES, METEOSAT, etc.), related cloud analysis products, and surface gauge observations in a complex and hierarchical blending process. Results from processing of the legacy AGRMET precipitation products over the U.S. using LIS-based methods for <span class="hlt">downscaling</span>, both with and without climatological factors, are evaluated against high-resolution monthly analyses using the PRISM knowledge- based method (Daly et al. 2002). It is demonstrated that the incorporation of climatological information in a <span class="hlt">downscaling</span> procedure can significantly enhance the accuracy, and potential utility, of AFWA precipitation products for military and civilian customer applications.</p> <div class="credits"> <p class="dwt_author">Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Tian, Y.; Zeng, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2007-05-01</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://adsabs.harvard.edu/abs/2013HESS...17.4481H"> <span id="translatedtitle">Development and comparative evaluation of a stochastic analog method to <span class="hlt">downscale</span> daily GCM 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">There are a number of statistical techniques that <span class="hlt">downscale</span> coarse climate information from general circulation models (GCMs). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data, which is an important factor for predicting hydrologic response to climatic forcing. In this study a new <span class="hlt">downscaling</span> technique (Bias-Correction and Stochastic Analog method; BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve both the spatial autocorrelation structure of observed daily precipitation sequences and the observed temporal frequency distribution of daily rainfall over space. We used the BCSA method to <span class="hlt">downscale</span> 4 different daily GCM precipitation predictions from 1961 to 1999 over the state of Florida, and compared the skill of the method to results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC), and the bias-correction and constructed analog (BCCA) method. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean daily precipitation for both wet and dry seasons while the BCSD, SDBC and BCSA methods accurately reproduced these characteristics, (2) the BCSD and BCCA methods underestimated temporal variability of daily precipitation and thus did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in daily precipitation resulting in underprediction of spatial variance and overprediction of spatial correlation, whereas the new stochastic technique (BCSA) replicated observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a <span class="hlt">downscaling</span> method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be reasonably predicted. For low-relief, rainfall-dominated watersheds, where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.</p> <div class="credits"> <p class="dwt_author">Hwang, S.; Graham, W. D.</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-11-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">325</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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">326</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.osti.gov/scitech/biblio/5206885"> <span id="translatedtitle">Forecast of iceberg <span class="hlt">ensemble</span> drift</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">The objectives of the study are to gain a better understanding of the characteristics of iceberg motion and the factors controlling iceberg drift, and to develop an iceberg <span class="hlt">ensemble</span> drift forecast system to be operated by the Canadian Atmospheric Environment Service. An extensive review of field and theoretical studies on iceberg behaviour, and the factors controlling iceberg motion has been carried out. Long term and short term behaviour of icebergs are critically examined. A quantitative assessment of the effects of the factors controlling iceberg motion is presented. The study indicated that wind and currents are the primary driving forces. Coriolis Force and ocean surface slope also have significant effects. As for waves, only the higher waves have a significant effect. Iceberg drift is also affected by iceberg size characteristics. Based on the findings of the study a comprehensive computerized forecast system to predict the drift of iceberg <span class="hlt">ensembles</span> off Canada's east coast has been designed. The expected accuracy of the forecast system is discussed and recommendations are made for future improvements to the system.</p> <div class="credits"> <p class="dwt_author">El-Tahan, M.S.; El-Tahan, H.W.; Venkatesh, S.</p> <p class="dwt_publisher"></p> <p class="publishDate">1983-05-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">327</div> <div class="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..14.4939H"> <span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of the most recent climate change projections over Africa using REMO</span></a>  </p> <div class="result-meta"> <p class="source"><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 CORDEX initiative a multi-model suite of regionalized climate change information at a horizontal resolution of about 50km will be made available for the first time for the whole of the African continent. The Climate Service Center (CSC) is taking part in this initiative by applying the regional climate model REMO to <span class="hlt">downscale</span> several scenarios of different coupled general circulation models (GCMs) for Africa. So far the CMIP5 projections of the Max-Planck-Institute for Meteorology (MPI-M) Earth System Model for the scenarios RCP2.6, 4.5 and 8.5 have been <span class="hlt">downscaled</span> for the time period from 1950 to 2100. In this study we investigate projected changes in future climate conditions for the three different concentration pathways. Focus is given to projected changes in the hydrological conditions over the major water basins of the African continent. Furthermore, differences to earlier REMO simulations over the region conducted on the basis of the CMIP3 projections of the MPI-M GCM are also examined. This comparison allows to judge on the magnitude of the projected changes in the most recent climate simulations with respect to the findings of IPCC AR4 and subsequent studies.</p> <div class="credits"> <p class="dwt_author">Hänsler, A.; Saeed, F.; Jacob, 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">328</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/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 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://ntrs.nasa.gov/search.jsp?R=20120016072&hterms=topic&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dtopic"> <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">330</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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 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/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">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 " 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://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">333</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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 " 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/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 odd" lang="en"> <div class="resultNumber element">335</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013SPIE.8890E..0ES"> <span id="translatedtitle">Optimum interpolation algorithms for ABI multiple channel radiance <span class="hlt">down-scaling</span> processing</span></a>  </p> <div class="result-meta"> <p class="source"><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 Advanced Baseline Imager (ABI) is the primary instrument onboard GOES-R for imaging Earth's weather, climate, and environment and will be used for a wide range of applications related to weather, oceans, land, climate, and hazards (fires, volcanoes, hurricanes, and storms that spawn tornados). It will provide over 65% of all the mission data products currently defined. ABI views the Earth with 16 different spectral bands, including two visible channels, four nearinfrared channels and ten infrared channels at 0.5, 1, and 2 km spatial resolutions respectively. For most of the operational ABI retrieval algorithms, the collocated/co-registered radiance dataset are at 2 km resolution for all of the bands required. This requires <span class="hlt">down-scaling</span> of the radiance data from 0.5 or 1 km to 2 km for ABI visible and near-IR bands (2 or 1, 3 & 5 respectively), the reference of 2 km is the nominal resolution at the satellite sub-point. In this paper, the spatial resolution characteristic of the ABI fixed grid level1b radiance data is discussed. An optimum interpolation algorithm which has been developed for the ABI multiple channel radiance <span class="hlt">down-scaling</span> processing is present.</p> <div class="credits"> <p class="dwt_author">Sun, Haibing; Wolf, W.; King, T.; Maddy, Eric; Sampson, Shanna</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-10-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">336</div> <div class="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..123H"> <span id="translatedtitle">Comparative validation of statistical and dynamical <span class="hlt">downscaling</span> models on a dense grid in central Europe: temperature</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Minimum and maximum temperature in two regional climate models and five statistical <span class="hlt">downscaling</span> models are validated according to a unified set of criteria that have a potential relevance for impact assessments: persistence (temporal autocorrelations), spatial autocorrelations, extreme quantiles, skewness, kurtosis, and the degree of fit to observed data on both short and long times scales. The validation is conducted on two dense grids in central Europe as follows: (1) a station network and (2) a grid with a resolution of 10 km. The gridded dataset is not contaminated by artifacts of the interpolation procedure; therefore, we claim that using a gridded dataset as a validation base is a valid approach. The fit to observations in short time scales is equally good for the statistical <span class="hlt">downscaling</span> (SDS) models and regional climate models (RCMs) in winter, while it is much better for the SDS models in summer. The reproduction of variability on long time scales, expressed as linear trends, is similarly successful by both SDS models and RCMs. Results for other criteria suggest that there is no justification for preferring dynamical models at the expense of statistical models—and vice versa. The non-linear SDS models do not outperform the linear ones.</p> <div class="credits"> <p class="dwt_author">Huth, Radan; Mikšovský, Ji?í; Št?pánek, Petr; Belda, Michal; Farda, Aleš; Chládová, Zuzana; Pišoft, Petr</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">337</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.mucm.ac.uk/Pages/Downloads/Other_Papers_Reports/JR%20Inference%20in%20Ensemble%20Experiments.pdf"> <span id="translatedtitle">Inference In <span class="hlt">Ensemble</span> Experiments By Jonathan Rougier1</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 experimental design. Keywords: Monte Carlo <span class="hlt">Ensemble</span>, Designed <span class="hlt">Ensemble</span>, Uncertainty, Importance sampling on <span class="hlt">ensembles</span> of climate model evaluations, and contrast the Monte Carlo approach, in which the evaluations, Emulator, Screening, Climate sensitivity 1. Introduction: Monte Carlo integration The raison d</p> <div class="credits"> <p class="dwt_author">Oakley, Jeremy</p> <p class="dwt_publisher"></p> <p class="publishDate"></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.knmi.nl/bibliotheek/knmipubTR/TR274.pdf"> <span id="translatedtitle">Technical Report Implementation and testing of an <span class="hlt">Ensemble</span> Kalman Filter</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">Technical Report Implementation and testing of an <span class="hlt">Ensemble</span> Kalman Filter assimilation system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 5. The <span class="hlt">Ensemble</span> Kalman Filter 11 5.1. Introduction been to implement an <span class="hlt">Ensemble</span> Kalman Filter (EnKF) assim- ilation scheme for comb</p> <div class="credits"> <p class="dwt_author">Stoffelen, Ad</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">339</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 " 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://academic.research.microsoft.com/Publication/50518407"> <span id="translatedtitle">Transductive Methods for Distributed <span class="hlt">Ensemble</span> Classification</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">We consider <span class="hlt">ensemble</span> classification for the case when there is no common labeled training data for designing the function which aggregates individual classifier decisions. We dub this problem distributed <span class="hlt">ensemble</span> classification, addressing e.g. when individual classifiers are trained on different (e.g. proprietary, legacy) databases or operate (perhaps remotely) on different sensing modalities. Typically, fixed, principled (untrained) rules of classifier combination</p> <div class="credits"> <p class="dwt_author">David J. Miller; Siddharth Pal</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</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_16");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return 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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_17");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a onClick='return showDiv("page_3");' href="#">3</a> <a onClick='return showDiv("page_4");' href="#">4</a> <a onClick='return showDiv("page_5");' href="#">5</a> <a onClick='return showDiv("page_6");' href="#">6</a> <a onClick='return showDiv("page_7");' href="#">7</a> <a onClick='return showDiv("page_8");' href="#">8</a> <a onClick='return showDiv("page_9");' href="#">9</a> <a 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 style="font-weight: bold;">18</a> <a onClick='return showDiv("page_19");' href="#">19</a> <a onClick='return showDiv("page_20");' href="#">20</a> <a onClick='return showDiv("page_21");' href="#">21</a> <a onClick='return showDiv("page_22");' href="#">22</a> <a onClick='return showDiv("page_23");' href="#">23</a> <a onClick='return showDiv("page_24");' href="#">24</a> <a onClick='return showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_19");' href="#" title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">341</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://eric.ed.gov/?q=surface+AND+area&pg=4&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 " 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://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">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/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">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.informatik.uni-freiburg.de/~ml/ecmlpkdd/WS-Proceedings/w04/paper21.pdf"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Based on Data Envelopment Analysis</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">There has been much research to evaluate the efficiency of various data fusion\\/<span class="hlt">ensemble</span> approaches. However, when combining individual classifiers for fusion or <span class="hlt">ensemble</span> purposes, typically only misclassification rate has been considered as a performance measure. This might be risky especially when the class distribution is skewed or when the costs associated with both Type I and II errors are significantly</p> <div class="credits"> <p class="dwt_author">So Young Sohn; Hong Choi</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://eric.ed.gov/?q=guitar&pg=7&id=EJ430552"> <span id="translatedtitle">Fine-Tuning Your <span class="hlt">Ensemble</span>'s Jazz Style.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p class="result-summary">Proposes instructional strategies for directors of jazz groups, including guidelines for developing of skills necessary for good performance. Includes effective methods for positive changes in <span class="hlt">ensemble</span> style. Addresses jazz group problems such as beat, tempo, staying in tune, wind power, and solo/<span class="hlt">ensemble</span> lines. Discusses percussionists, bassists,…</p> <div class="credits"> <p class="dwt_author">Garcia, Antonio J.</p> <p class="dwt_publisher"></p> <p class="publishDate">1991-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " 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.ncbi.nlm.nih.gov/pubmed/25201983"> <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=pubmed">PubMed</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">2014-09-23</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://academic.research.microsoft.com/Publication/39293910"> <span id="translatedtitle">Constructing <span class="hlt">Ensemble</span> Classifiers from Expression Trees</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 paper proposes and reviews a family of <span class="hlt">ensemble</span> classifiers constructed from expression trees. Expression trees are induced using gene expression programming and cellular evolutionary algorithm. <span class="hlt">Ensemble</span> classifiers are constructed using several techniques including majority voting, boosting and Dempster-Shafer theory of evidence. Computational experiment results confirm high quality of the proposed classifiers.</p> <div class="credits"> <p class="dwt_author">Joanna Jedrzejowicz; Piotr Jedrzejowicz</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">348</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.cs.bham.ac.uk/~xin/papers/ChandraYao_IDEAL04.pdf"> <span id="translatedtitle">DIVACE: Diverse and Accurate <span class="hlt">Ensemble</span> Learning Algorithm</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">. As far as learning and <span class="hlt">ensemble</span> creation are concerned there are techniques which invovle some manual to learning and creation of <span class="hlt">ensembles</span> automatically was proposed in [7]. #12;2 Abbass [4] proposed the Pareto-frontier Differential Evolution (PDE) method, which is an extension of the Differential Evolution (DE) algorithm</p> <div class="credits"> <p class="dwt_author">Yao, Xin</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.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">350</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/1411.7185v1"> <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.osti.gov/eprints/">E-print Network</a></p> <p class="result-summary">The connection between the non-equilibrium dynamics of isolated quantum many-body systems and statistical mechanics is a fundamental open question. It is generally believed that the unitary quantum evolution of a sufficiently complex system leads to an apparent maximum-entropy state that can be described by thermodynamical <span class="hlt">ensembles</span>. However, conventional <span class="hlt">ensembles</span> fail to describe the large class of systems that exhibit non-trivial conserved quantities. Instead, generalized <span class="hlt">ensembles</span> have been predicted to maximize entropy in these systems. In our experiments we explicitly show 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.</p> <div class="credits"> <p class="dwt_author">Tim Langen; Sebastian Erne; Remi Geiger; Bernhard Rauer; Thomas Schweigler; Maximilian Kuhnert; Wolfgang Rohringer; Igor E. Mazets; Thomas Gasenzer; Jörg Schmiedmayer</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-11-26</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://adsabs.harvard.edu/abs/2008PhRvE..77b1120K"> <span id="translatedtitle">Calculations of canonical averages from the grand canonical <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Grand canonical and canonical <span class="hlt">ensembles</span> become equivalent in the thermodynamic limit, but when the system size is finite the results obtained in the two <span class="hlt">ensembles</span> deviate from each other. In many important cases, the canonical <span class="hlt">ensemble</span> provides an appropriate physical description but it is often much easier to perform the calculations in the corresponding grand canonical <span class="hlt">ensemble</span>. We present a method to compute averages in the canonical <span class="hlt">ensemble</span> based on calculations of the expectation values in the grand canonical <span class="hlt">ensemble</span>. The number of particles, which is fixed in the canonical <span class="hlt">ensemble</span>, is not necessarily the same as the average number of particles in the grand canonical <span class="hlt">ensemble</span>.</p> <div class="credits"> <p class="dwt_author">Kosov, D. S.; Gelin, M. F.; Vdovin, A. I.</p> <p class="dwt_publisher"></p> <p class="publishDate">2008-02-01</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://www.springerlink.com/index/g24044n087712287.pdf"> <span id="translatedtitle">A comparison of statistical <span class="hlt">downscaling</span> and climate change factor methods: impacts on low flows in the River Thames, United Kingdom</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">Strategic-scale assessments of climate change impacts are often undertaken using the change factor (CF) methodology whereby future changes in climate projected by General Circulation Models (GCMs) are applied to a baseline climatology. Alternatively, statistical <span class="hlt">downscaling</span> (SD) methods apply climate variables from GCMs to statistical transfer functions to estimate point-scale meteorological series. This paper explores the relative merits of the CF</p> <div class="credits"> <p class="dwt_author">Jacqueline Diaz-Nieto; Robert L. Wilby</p> <p class="dwt_publisher"></p> <p class="publishDate">2005-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">353</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/40753927"> <span id="translatedtitle">Spatial <span class="hlt">downscaling</span> of global climate model output for site-specific assessment of crop production and soil erosion</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">Spatial and temporal mismatches between coarse resolution projections of global climate models (GCMs) and fine resolution data requirements of ecosystems models are the major obstacles for assessing the site-specific climatic impacts of climate change on natural resources and ecosystems. The objectives of this study were to: (i) develop a simple method for statistically <span class="hlt">downscaling</span> GCM monthly output at the native</p> <div class="credits"> <p class="dwt_author">X.-C. Zhang</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">354</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/45904961"> <span id="translatedtitle">Impact analysis of climate change for an Alpine catchment using high resolution dynamic <span class="hlt">downscaling</span> of ECHAM4 time slices</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">Global climate change affects spatial and temporal patterns of precipitation and so has a major impact on surface and subsurface water balances. While global climate models are designed to describe climate change on global or continental scales, their resolution is too coarse for them to be suitable for describing regional climate change. Therefore, regional climate models are applied to <span class="hlt">downscale</span></p> <div class="credits"> <p class="dwt_author">H. Kunstmann; K. Schneider; R. Forkel; R. Knoche</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">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/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 " lang="en"> <div class="resultNumber element">356</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.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 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://web.gps.caltech.edu/~ampuero/docs/Lenetal11a.pdf"> <span id="translatedtitle">JOURNAL OF GEOPHYSICAL RESEARCH, VOL. ???, XXXX, DOI:10.1029/, <span class="hlt">Down-scaling</span> of fracture energy during brittle creep1</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 mode I brittle-creep fracture experiments along3 fracture surfaces that contain strengthJOURNAL OF GEOPHYSICAL RESEARCH, VOL. ???, XXXX, DOI:10.1029/, <span class="hlt">Down-scaling</span> of fracture energy during brittle creep1 experiments2 O. Lenglin´e, 1 J. Schmittbuhl, 1 J. E. Elkhoury, 2 J.-P. Ampuero, 2 R</p> <div class="credits"> <p class="dwt_author">Ampuero, Jean Paul</p> <p class="dwt_publisher"></p> <p class="publishDate"></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://ntrs.nasa.gov/search.jsp?R=20140011180&hterms=localization+strategy&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dlocalization%2Bstrategy"> <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">359</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/24108493"> <span id="translatedtitle">Feature selection inspired classifier <span class="hlt">ensemble</span> reduction.</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">Classifier <span class="hlt">ensembles</span> constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist in the literature that provide means to construct and aggregate such <span class="hlt">ensembles</span>. However, these <span class="hlt">ensemble</span> systems contain redundant members that, if removed, may further increase group diversity and produce better results. Smaller <span class="hlt">ensembles</span> also relax the memory and storage requirements, reducing system's run-time overhead while improving overall efficiency. This paper extends the ideas developed for feature selection problems to support classifier <span class="hlt">ensemble</span> reduction, by transforming <span class="hlt">ensemble</span> predictions into training samples, and treating classifiers as features. Also, the global heuristic harmony search is used to select a reduced subset of such artificial features, while attempting to maximize the feature subset evaluation. The resulting technique is systematically evaluated using high dimensional and large sized benchmark datasets, showing a superior classification performance against both original, unreduced <span class="hlt">ensembles</span>, and randomly formed subsets. PMID:24108493</p> <div class="credits"> <p class="dwt_author">Diao, Ren; Chao, Fei; Peng, Taoxin; Snooke, Neal; Shen, Qiang</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">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/2011AGUFM.H33D1342H"> <span id="translatedtitle">Hydrologic importance of spatial variability in statistically <span class="hlt">downscaled</span> precipitation predictions from global circulation models for west-central Florida</span></a>  </p> <div class="result-meta"> <p class="source"><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 are a number of statistical techniques that <span class="hlt">downscale</span> coarse climate information from general circulation models (GCM). However, many of them pay little attention to the small-scale spatial variability of precipitation exhibited by the observed meteorological data which can be an important factor for predicting hydrologic response to climatic forcing. In this study a stochastic <span class="hlt">downscaling</span> technique was developed to produce bias-corrected daily GCM precipitation fields that honor the spatial autocorrelation structure of observed daily precipitation sequences. This approach is designed to produce bias-corrected daily GCM results which reproduce observed spatial and temporal variability as well as mean climatology. We used the proposed method to <span class="hlt">downscale</span> 4 GCM precipitation predictions from 1961 to 2000 over west-central Florida and compared the skill of the method to results obtained using the commonly used bias-correction spatial disaggregation (BCSD) approach. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation index, and variograms for wet (June through September) and dry (October through May) season were calculated for each method. Preliminary results showed that the new stochastic technique reproduced observed temporal and spatial variability and features very well for both wet and dry seasons while the interpolation based BCSD approach significantly underestimated spatial variability (i.e., overestimated spatial correlation). The two sets of <span class="hlt">downscaled</span> precipitation scenarios were used with an integrated surface-subsurface hydrologic model to examine hydrologic responses of streamflow and groundwater levels for each climate input scenario for an application in west-central Florida. The results support the hypothesis that accurately representing the spatial variability of precipitation in <span class="hlt">downscaled</span> GCM predictions is important to reproduce observed hydrologic behavior.</p> <div class="credits"> <p class="dwt_author">Hwang, S.; Graham, W. D.; Geurink, J. S.; Adams, A.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_17");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a onClick='return showDiv("page_2");' href="#">2</a> <a 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">361</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=20000012298&hterms=regional+integration&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Dregional%2Bintegration"> <span id="translatedtitle">Regional Climate Simulation with a Variable Resolution Stretched Grid GCM: The Regional <span class="hlt">Down-Scaling</span> Effects</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 results obtained with the variable resolution stretched grid (SG) GEOS GCM (Goddard Earth Observing System General Circulation Models) are discussed, with the emphasis on the regional <span class="hlt">down-scaling</span> effects and their dependence on the stretched grid design and parameters. A variable resolution SG-GCM and SG-DAS using a global stretched grid with fine resolution over an area of interest, is a viable new approach to REGIONAL and subregional CLIMATE studies and applications. The stretched grid approach is an ideal tool for representing regional to global scale interactions. It is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step in regional climate modeling. The GEOS SG-GCM is used for simulations of the anomalous U.S. climate events of 1988 drought and 1993 flood, with enhanced regional resolution. The height low level jet, precipitation and other diagnostic patterns are successfully simulated and show the efficient <span class="hlt">down-scaling</span> over the area of interest the U.S. An imitation of the nested grid approach is performed using the developed SG-DAS (Data Assimilation System) that incorporates the SG-GCM. The SG-DAS is run with withholding data over the area of interest. The design immitates the nested grid framework with boundary conditions provided from analyses. No boundary condition buffer is needed for the case due to the global domain of integration used for the SG-GCM and SG-DAS. The experiments based on the newly developed versions of the GEOS SG-GCM and SG-DAS, with finer 0.5 degree (and higher) regional resolution, are briefly discussed. The major aspects of parallelization of the SG-GCM code are outlined. The KEY OBJECTIVES of the study are: 1) obtaining an efficient <span class="hlt">DOWN-SCALING</span> over the area of interest with fine and very fine resolution; 2) providing CONSISTENT interactions between regional and global scales including the consistent representation of regional ENERGY and WATER BALANCES; 3) providing a high computational efficiency for future SG-GCM and SG-DAS versions using PARALLEL codes.</p> <div class="credits"> <p class="dwt_author">Fox-Rabinovitz, Michael S.; Takacs, Lawrence L.; Suarez, Max; Sawyer, William; Govindaraju, Ravi C.</p> <p class="dwt_publisher"></p> <p class="publishDate">1999-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">362</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2007PhDT.......170B"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of satellite remote sensing data: Application to land cover mapping</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Many satellite images have a spatial resolution coarser than the extent of land cover patterns on the ground, leading to mixed pixels whose composite spectral response consists of responses from multiple land cover classes. Spectral unmixing procedures only determine the fractions of such classes within a coarse pixel without locating them in space. <span class="hlt">Downscaling</span>, also known as super-resolution or sub-pixel mapping, turns these proportions into a fine resolution map of class labels. Sub-pixel mapping is undetermined, in that many different fine resolution maps can lead to an equally good reproduction of the available coarse fractions. Thus, the unknown fine resolution land cover map is regarded as a realization of a random set. Simulated realizations are generated using the geostatistical paradigm of sequential simulation. At any pixel along a path visiting all fine scale pixels, a class label is simulated from a local probability distribution made conditional to: (i) the coarse class fraction data, (ii) any simulated land cover classes at fine pixels previously visited along that path, and (iii) a prior structural model. Two algorithms using different structural model types are proposed for the sequential simulation. The first method proposed is built on block indicator cokriging which allows evaluating the previous local probability distributions by a form of kriging; the structural model is then a series of class labels indicator variograms. The second method is based on the multiple-point simulation algorithm SNESIM where the local probability distributions are read from a training image; the structural function is then that training image which can be seen as an analog image depicting the patterns deemed present at the fine resolution. Two case studies derived from Landsat TM imagery demonstrates the two approaches proposed. The resulting alternative <span class="hlt">downscaled</span> class maps all honor the coarse proportion data, any fine scale data available, and exhibit the spatial patterns called for by the input structural model. When that structural model is incompatible with the sensor data the pattern reproduction is poor. Fine scale data such as water, roads and previously mapped fine scale pixels are shown to be well reproduced in the <span class="hlt">downscaled</span> maps.</p> <div class="credits"> <p class="dwt_author">Boucher, Alexandre</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">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/2013EGUGA..1513442W"> <span id="translatedtitle">Development of incremental dynamical <span class="hlt">downscaling</span> and analysis system for regional scale 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">Regional scale climate change projections play an important role in assessments of influences of global warming and include statistical (SD) and dynamical <span class="hlt">downscaling</span> (DD) approaches. One of DD methods is developed basing on the pseudo-global-warming (PGW) method developed by Kimura and Kitoh (2007) in this study. In general, DD uses regional climate model (RCM) with lateral boundary data. In PGW method, the climatological mean difference estimated by GCMs are added to the objective analysis data (ANAL), and the data are used as the lateral boundary data in the future climate simulations. The ANAL is also used as the lateral boundary conditions of the present climate simulation. One of merits of the PGW method is that influences of biases of GCMs in RCM simulations are reduced. However, the PGW method does not treat climate changes in relative humidity, year-to-year variation, and short-term disturbances. The developing new <span class="hlt">downscaling</span> method is named as the incremental dynamical <span class="hlt">downscaling</span> and analysis system (InDDAS). The InDDAS treat climate changes in relative humidity and year-to-year variations. On the other hand, uncertainties of climate change projections estimated by many GCMs are large and are not negligible. Thus, stochastic regional scale climate change projections are expected for assessments of influences of global warming. Many RCM runs must be performed to make stochastic information. However, the computational costs are huge because grid size of RCM runs should be small to resolve heavy rainfall phenomena. Therefore, the number of runs to make stochastic information must be reduced. In InDDAS, climatological differences added to ANAL become statistically pre-analyzed information. The climatological differences of many GCMs are divided into mean climatological difference (MD) and departures from MD. The departures are analyzed by principal component analysis, and positive and negative perturbations (positive and negative standard deviations multiplied by departure patterns (eigenvectors)) with multi modes are added to MD. Consequently, the most likely future states are calculated with climatological difference of MD. For example, future states in cases that temperature increase is large and small are calculated with MD plus positive and negative perturbations of the first mode.</p> <div class="credits"> <p class="dwt_author">Wakazuki, Yasutaka; Hara, Masayuki; Fujita, Mikiko; Ma, Xieyao; Kimura, Fujio</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">364</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2009EGUGA..11.4752S"> <span id="translatedtitle"><span class="hlt">Downscaling</span> of climate parameters in Bode river basin in Germany using Active Learning Method (ALM)</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">This study is a part of main program RIMAX "risk management of extreme flood events", which concerns itself of "extremes floodwater and damage potential in the Bode river basin in Germany „with the variable occurrence of flood events in this area for the past 1000 years. The objective of the project is to produce the local climate time series (climate <span class="hlt">downscaling</span>) as the input for a runoff model in the Bode basin for the last 1000 years on a grid of 5x5 km as well as the estimation of the spatial distributions and temporal variability of the precipitation, the amount of precipitation and further meteorological parameter (temperature, radiation and relative humidity) for this area. A nonlinear <span class="hlt">downscaling</span> based on Fuzzy rules has been used to produce 1000 year climate time series. The global model ECHO from Max Planck institute for Meteorology (MPI) with T30 resolution and 1000 years data has been used as the global model (GCM). The regional model REMO, with 10 km resolution and 20 years data has been used as the regional input. The observations, which include 30 years precipitation, radiation, temperature, wind and relative humidity, have been used as output (predictand). In this study, two set fuzzy rules have been trained to describe the relationship between ECHO/REMO and REMO/Observation. The Fuzzy method used in this work is Active Learning Method (ALM). The heart of calculation of ALM is a fuzzy interpolation and curve fitting which is entitled Ink Drop Spread (IDS). The IDS searches fuzzily for continuous possible paths of interpolated data points on data planes. The ability of ALM to simulate the high values as well as the fluctuation of time series is much better than Takagi-Sugeno models, which have been used for <span class="hlt">downscaling</span> in the last decade. In the next steps, considering predictors from the ECHO time series and predictands from the REMO grid points, some ALM models are developed, which describe the fuzzy rules and the relationship between global and regional scales. These models are verified using checking data and then considering ECHO/REMO models and on the basis of last 1000 years of ECHO, the REMO time series as well as the local data are simulated. These simulated data are used as input-data for the runoff model ARCEGMO.</p> <div class="credits"> <p class="dwt_author">Sodoudi, S.; Reimer, E.</p> <p class="dwt_publisher"></p> <p class="publishDate">2009-04-01</p> </div> </div> </div> </div> <div class="floatContainer result 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://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">2011-10-13</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">366</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/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 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://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">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/2013SoPh..285..349L"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Modeling of CME Propagation</span></a>  </p> <div class="result-meta"> <p class="source"><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 current progression toward solar maximum provides a unique opportunity to use multi-perspective spacecraft observations together with numerical models to better understand the evolution and propagation of coronal mass ejections (CMEs). Of interest to both the scientific and forecasting communities are the Earth-directed "halo" CMEs, since they typically produce the most geoeffective events. However, determining the actual initial geometries of halo CMEs is a challenge due to the plane-of-sky projection effects. Thus the recent 15 February 2011 halo CME event has been selected for this study. During this event the Solar TErrestrial RElations Observatory (STEREO) A and B spacecraft were fortuitously located ˜ 90° away from the Sun-Earth line such that the CME was viewed as a limb event from these two spacecraft, thereby providing a more reliable constraint on the initial CME geometry. These multi-perspective observations were utilized to provide a simple geometrical description that assumes a cone shape for a CME to calculate its angular width and central position. The event was simulated using the coupled Wang-Sheeley-Arge (WSA)-Enlil 3D numerical solar corona-solar wind model. Daily updated global photospheric magnetic field maps were used to drive the background solar wind. To improve our modeling techniques, the sensitivity of the modeled CME arrival times to the initial input CME geometry was assessed by creating an <span class="hlt">ensemble</span> of numerical simulations based on multiple sets of cone parameters for this event. It was found that the accuracy of the modeled arrival times not only depends on the initial input CME geometry, but also on the accuracy of the modeled solar wind background, which is driven by the input maps of the photospheric field. To improve the modeling of the background solar wind, the recently developed data-assimilated magnetic field synoptic maps produced by the Air Force Data Assimilative Photospheric flux Transport (ADAPT) model were used. The ADAPT maps provide a more instantaneous snapshot of the global photospheric field distribution than that provided by traditional daily updated synoptic maps. Using ADAPT to drive the background solar wind, an <span class="hlt">ensemble</span> set of eight different CME arrival times was generated, where the spread in the predictions was ˜ 13 hours and was nearly centered on the observed CME shock arrival time.</p> <div class="credits"> <p class="dwt_author">Lee, C. O.; Arge, C. N.; Odstr?il, D.; Millward, G.; Pizzo, V.; Quinn, J. M.; Henney, C. J.</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">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/2010ems..confE.576H"> <span id="translatedtitle">Statistical <span class="hlt">downscaling</span> assessments of temperature and precipitation extremes in the Mediterranean area</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The Mediterranean area is regarded as a "climate change hot-spot" (Giorgi 2006) being highly affected by future climate change compared to other regions of the world. This is mostly due to the assessed decrease of precipitation as well as to an increase of the inter-annual precipitation variability, but changes in temperature, especially in its extreme tails, have also to be taken into account. Based on station data of the Mediterranean area as well as on high resolution precipitation and temperature data (0.25° x 0.25° grid for terrestrial areas of Europe, Haylock et al. 2006) percentile-based indices of extreme events are defined. As large-scale predictors for extreme events in the Mediterranean area sea level pressure, geopotential heights, thickness of the 1000hPa/500hPa layer, specific humidity, and relative vorticity are primarily considered. Statistical <span class="hlt">Downscaling</span> is established by relating the Mediterranean extreme events to the large-scale atmospheric circulation. This is done through the application of transfer functions (multiple regression analysis and canonical correlation analysis) as well as through a synoptical <span class="hlt">downscaling</span> approach (cluster analysis). To test the stability of the models the analyses are realised for different calibration periods and corresponding verification periods. Model performance in the verification periods is assessed by means of the correlation coefficients between modelled and observed extremes indices. Additionally the reduction of variance is calculated, being similar to the root mean squared skill score. Output of different coupled global circulation models under A1B- and B1- scenario assumptions is used to assess changes of extreme temperature and precipitation under enhanced greenhouse warming conditions. From the results it becomes evident that the <span class="hlt">downscaling</span> assessment can vary considerably depending on the particular predictor used for the statistical assessment. Climatic as well as dynamic factors influence extreme conditions and should be considered in a combined manner within <span class="hlt">downscaling</span> models. Regarding temperature extremes the results show that the changes do not follow a simple shift of the whole temperature distribution to higher values. More precisely it is indicated that the intra-annual extreme temperature range will decrease in most parts of the Mediterranean area during the course of the 21st century. This is due to the finding that extreme minimum temperatures in winter will increase stronger compared to extreme maximum temperatures in summer. Concerning precipitation extremes the statistical assessment results point to widespread decreases of heavy rainfall events (95th percentile of precipitation) in the Mediterranean area during all seasons of the year. Acknowledgement: Financial support is provided by the DFG (German Research Foundation). Giorgi, F. (2006): Climate change hot- spots, Geophysical Research Letters Vol. 33, L08707, 2006, doi:10.1029/2006GL025734. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008): A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research 113, D20119, doi:10.1029/2008JD010201, 2008</p> <div class="credits"> <p class="dwt_author">Hertig, E.; Jacobeit, J.; Fernandez-Montes, S.</p> <p class="dwt_publisher"></p> <p class="publishDate">2010-09-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">370</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..1412765R"> <span id="translatedtitle">Towards <span class="hlt">downscaling</span> precipitation for Senegal - An approach based on generalized linear models and weather types</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Changes in precipitation patterns with potentially less precipitation and an increasing risk for droughts pose a threat to water resources and agricultural yields in Senegal. Precipitation in this region is dominated by the West-African Monsoon being active from May to October, a seasonal pattern with inter-annual to decadal variability in the 20th century which is likely to be affected by climate change. We built a generalized linear model for a full spatial description of rainfall in Senegal. The model uses season, location, and a discrete set of weather types as predictors and yields a spatially continuous description of precipitation occurrences and intensities. Weather types have been defined on NCEP/NCAR reanalysis using zonal and meridional winds, as well as relative humidity. This model is suitable for <span class="hlt">downscaling</span> precipitation, particularly precipitation occurrences relevant for drough risk mapping.</p> <div class="credits"> <p class="dwt_author">Rust, H. W.; Vrac, M.; Lengaigne, M.; Sultan, B.</p> <p class="dwt_publisher"></p> <p class="publishDate">2012-04-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">371</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.ncbi.nlm.nih.gov/pubmed/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 " lang="en"> <div class="resultNumber element">372</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2012EGUGA..14.8188M"> <span id="translatedtitle">Application of a statistical <span class="hlt">downscaling</span> method to detect inhomogeneities in a temperature time series</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In the context of climate studies, the analysis of long homogeneous time series is of the utmost importance. A homogeneous climate series is defined as a series whose variations are caused only by changes in weather and climate (Conrad and Pollak, 1950). Unfortunately, a time series is often affected by one or more artificial inhomogeneities. Regardless of the type and the effect of inhomogeneities, the analysis of a non-homogeneous series can be misleading. Consequently, it is crucial to determine, assign and adjust any discontinuities in the data, especially in those reference series used in climate change studies. The Twentieth Century Reanalysis (20CR) data can provide an independent estimate of, among other variables, surface temperature. However, the difference in scale affects its potential use as a tool to detect non-climatic inhomogeneities in a local temperature time series. To avoid this limitation, we propose a new approach based on a parsimonious statistical <span class="hlt">downscaling</span> method to bridge the gap between reanalysis data and the local temperature time series. This method was applied to two high-quality international reference stations in the North-East of Spain (present in the ECA database, http://eca.knmi.nl/) whose temperature series are used, for example, in the report of climatic change in Catalonia, Cunillera et al., 2009: Ebre (Tortosa) and Fabra (Barcelona), for the periods 1940-2008 and 1914-2008, respectively. Both series show an anomalous period which is clearly identifiable by visual inspection. The statistical <span class="hlt">downscaling</span> model was calibrated for these stations and independently tested over the reliable periods with good results. The model was then applied to reproduce the doubtful years. The results of the study are in agreement with the metadata: for the Fabra series, the method proposed clearly identifies the artificial inhomogeneity; whilst for the Ebre Observatory, there is no documented change in the station and the suspicious period falls inside the error bands.</p> <div class="credits"> <p class="dwt_author">Marcos, R.; Turco, M.; Llasat, M. C.; Quintana-Seguí, P.</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">373</div> <div class="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">374</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/21962010"> <span id="translatedtitle">The <span class="hlt">ensemble</span> performance index: an improved measure for assessing <span class="hlt">ensemble</span> pose prediction performance.</span></a>  </p> <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 theoretical study on the performance of <span class="hlt">ensemble</span> docking methodologies considering multiple protein structures. We perform a theoretical analysis of pose prediction experiments which is completely unbiased, as we make no assumptions about specific scoring functions, search paradigms, protein structures, or ligand data sets. We introduce a novel interpretable measure, the <span class="hlt">ensemble</span> performance index (EPI), for the assessment of scoring performance in <span class="hlt">ensemble</span> docking, which will be applied to simulated and real data sets. PMID:21962010</p> <div class="credits"> <p class="dwt_author">Korb, Oliver; McCabe, Patrick; Cole, Jason</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-11-28</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">375</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.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">UMASS 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 a Best Collegiate Jazz Choir award by Downbeat magazine. It consists of 8-16 singers and a rhythm section</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">376</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/39308044"> <span id="translatedtitle">An Ant Colony Optimization approach for Stacking <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">An <span class="hlt">ensemble</span> in data mining is the strategy that combines a set of different classifiers together to generate an integrated classification system to classify new instances. In the early research, an <span class="hlt">ensemble</span> outperforms any of its individual components. Stacking is one of the most influential <span class="hlt">ensemble</span> among the proposed <span class="hlt">ensemble</span> schemes. Stacking applies a two-level structure: the base-level classifiers output</p> <div class="credits"> <p class="dwt_author">Yijun Chen; Man Leung Wong</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">377</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.bangor.ac.uk/~mas00a/papers/jrlkcatpami06.pdf"> <span id="translatedtitle">Rotation Forest: A New Classifier <span class="hlt">Ensemble</span> Method</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">to train each base classifier. Using WEKA, we examined the Rotation Forest <span class="hlt">ensemble</span> on a random selection. Bagging with average aggregation is implemented in WEKA and used in the experiments in this paper. Since</p> <div class="credits"> <p class="dwt_author">Kuncheva, Ludmila I.</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">378</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013EGUGA..1513876Z"> <span id="translatedtitle">Hydrological <span class="hlt">ensemble</span> predictions for reservoir inflow management</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Hydrologic forecasting is a topic of special importance for a variety of users with different purposes. It concerns operational hydrologists interested in forecasting hazardous events (eg., floods and droughts) for early warning and prevention, as well as planners and managers searching to optimize the management of water resources systems at different space-time scales. The general aim of this study is to investigate the benefits of using hydrological <span class="hlt">ensemble</span> predictions for reservoir inflow management. <span class="hlt">Ensemble</span> weather forecasts are used as input to a hydrologic forecasting model and daily <span class="hlt">ensemble</span> streamflow forecasts are generated up to a lead time of 7 days. Forecasts are then integrated into a heuristic decision model for reservoir management procedures. Performance is evaluated in terms of potential gain in energy production. The sensitivity of the results to various reservoir characteristics and future streamflow scenarios is assessed. A set of 11 catchments in France is used to illustrate the added value of <span class="hlt">ensemble</span> streamflow forecasts for reservoir management.</p> <div class="credits"> <p class="dwt_author">Zalachori, Ioanna; Ramos, Maria-Helena; Garçon, Rémy; Gailhard, Joel</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">379</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=20070023651&hterms=weight&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Dweight"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Weight Enumerators for Protograph LDPC Codes</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p class="result-summary">Recently LDPC codes with projected graph, or protograph structures have been proposed. In this paper, finite length <span class="hlt">ensemble</span> weight enumerators for LDPC codes with protograph structures are obtained. Asymptotic results are derived as the block size goes to infinity. In particular we are interested in obtaining <span class="hlt">ensemble</span> average weight enumerators for protograph LDPC codes which have minimum distance that grows linearly with block size. As with irregular <span class="hlt">ensembles</span>, linear minimum distance property is sensitive to the proportion of degree-2 variable nodes. In this paper the derived results on <span class="hlt">ensemble</span> weight enumerators show that linear minimum distance condition on degree distribution of unstructured irregular LDPC codes is a sufficient but not a necessary condition for protograph LDPC codes.</p> <div class="credits"> <p class="dwt_author">Divsalar, Dariush</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">380</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 id="filter_results_form" class="filter_results_form floatContainer" style="visibility: visible;"> <div style="width:100%" id="PaginatedNavigation" class="paginatedNavigationElement"> <a id="FirstPageLink" onclick='return showDiv("page_1");' href="#" title="First Page"> <img id="FirstPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.first.18x20.png" alt="First Page" /></a> <a id="PreviousPageLink" onclick='return showDiv("page_18");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" /></a> <span id="PageLinks" class="pageLinks"> <span> <a onClick='return showDiv("page_1");' href="#">1</a> <a 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title="Next Page"> <img id="NextPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.next.18x20.png" alt="Next Page" /></a> <a id="LastPageLink" onclick='return showDiv("page_25.0");' href="#" title="Last Page"> <img id="LastPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.last.18x20.png" alt="Last Page" /></a> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">381</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2014JSP...tmp...64B"> <span id="translatedtitle">Statistical <span class="hlt">Ensembles</span> for Economic Networks</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Economic networks share with other social networks the fundamental property of sparsity. It is well known that the maximum entropy techniques usually employed to estimate or simulate weighted networks produce unrealistic dense topologies. At the same time, strengths should not be neglected, since they are related to core economic variables like supply and demand. To overcome this limitation, the exponential Bosonic model has been previously extended in order to obtain <span class="hlt">ensembles</span> where the average degree and strength sequences are simultaneously fixed (conditional geometric model). In this paper a new exponential model, which is the network equivalent of Boltzmann ideal systems, is introduced and then extended to the case of joint degree-strength constraints (conditional Poisson model). Finally, the fitness of these alternative models is tested against a number of networks. While the conditional geometric model generally provides a better goodness-of-fit in terms of log-likelihoods, the conditional Poisson model could nevertheless be preferred whenever it provides a higher similarity with original data. If we are interested instead only in topological properties, the simple Bernoulli model appears to be preferable to the correlated topologies of the two more complex models.</p> <div class="credits"> <p class="dwt_author">Bargigli, Leonardo</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-03-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">382</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013JPhA...46E5306D"> <span id="translatedtitle">Polarized <span class="hlt">ensembles</span> of random pure 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 family of polarized <span class="hlt">ensembles</span> of random pure states is presented. These <span class="hlt">ensembles</span> are obtained by linear superposition of two random pure states with suitable distributions, and are quite manageable. We will use the obtained results for two purposes: on the one hand we will be able to derive an efficient strategy for sampling states from isopurity manifolds. On the other, we will characterize the deviation of a pure quantum state from separability under the influence of noise.</p> <div class="credits"> <p class="dwt_author">Deelan Cunden, Fabio; Facchi, Paolo; Florio, Giuseppe</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-08-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">383</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3086943"> <span id="translatedtitle">Memory for multiple visual <span class="hlt">ensembles</span> in infancy</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p class="result-summary">The number of individual items that can be maintained in working memory is limited. One solution to this problem is to store representations of <span class="hlt">ensembles</span> that contain summary information about large numbers of items (e.g., the approximate number or cumulative area of a group of many items). Here we explored the developmental origins of <span class="hlt">ensemble</span> representations by asking whether infants represent <span class="hlt">ensembles</span>, and if so, how many at one time. We habituated 9-month old infants to arrays containing 2, 3, or 4 spatially intermixed colored subsets of dots, then asked whether they detected a numerical change to one of the subsets or to the superset of all dots. Experiment Series 1 showed that infants detected a numerical change to one of the subsets when the array contained 2 subsets, but not 3 or 4 subsets. Experiment Series 2 showed that infants detected a change to the superset of all dots no matter how many subsets were presented. Experiment 3 showed that infants represented both the approximate number and the cumulative surface area of these <span class="hlt">ensembles</span>. Our results suggest that infants, like adults (Halberda, Sires & Feigenson, 2006), can store quantitative information about 2 subsets plus the superset: a total of 3 <span class="hlt">ensembles</span>. This converges with the known limit on the number of individual objects infants and adults can store, and suggests that, throughout development, an <span class="hlt">ensemble</span> functions much like an individual object for working memory. PMID:21355663</p> <div class="credits"> <p class="dwt_author">Zosh, Jennifer M.; Halberda, Justin; Feigenson, Lisa</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">384</div> <div class="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..15.4714K"> <span id="translatedtitle">Future changes in European temperature and precipitation in an <span class="hlt">ensemble</span> of Europe-CORDEX regional climate model simulations</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">In this study we investigate possible changes in temperature and precipitation on a regional scale over Europe from 1961 to 2100. We use data from two <span class="hlt">ensembles</span> of climate simulations, one global and one regional, over the Europe-CORDEX domain. The global <span class="hlt">ensemble</span> includes nine coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CNRM-CM5, HadGEM2-ES, IPSL-CM5A-MR, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. In the regional <span class="hlt">ensemble</span> all 9 AOGCMs are <span class="hlt">downscaled</span> at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Two forcing scenarios are considered, RCP 4.5 and 8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, we investigate the benefit of the higher horizontal resolution, in RCA4 by comparing the results to the coarser driving AOGCM data. The significance of the results is investigated by comparing to i) the model simulated natural variability, and, ii) the biases in the control period. Results dealing with changes in the seasonal cycle of temperature and precipitation and their relation to changes in the large-scale atmospheric circulation are presented. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.</p> <div class="credits"> <p class="dwt_author">Kjellström, Erik; Nikulin, Grigory; Jones, Colin</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">385</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://www.berea.edu/convocations/files/2012/10/Stephenson2005-2006.pdf"> <span id="translatedtitle">Robin Cox <span class="hlt">Ensemble</span> A quintet of strings, clarinet, and percussion, Robin Cox <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">. All events are open to the public without charge B E R E A C O L L E G E C O N V O C A T I O N S <span class="hlt">Ensemble</span> Kaboul With traditional instruments and the vocals of Ustada Farida Mahwash, this Afghan <span class="hlt">ensemble</span></p> <div class="credits"> <p class="dwt_author">Baltisberger, Jay H.</p> <p class="dwt_publisher"></p> <p class="publishDate"></p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">386</div> <div class="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.....7.1297K"> <span id="translatedtitle">Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">EURO-CORDEX is an international climate <span class="hlt">downscaling</span> initiative that aims to provide high-resolution climate scenarios for Europe. Here an evaluation of the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) <span class="hlt">ensemble</span> is presented. The study documents the performance of the individual models in representing the basic spatiotemporal patterns of the European climate for the period 1989-2008. Model evaluation focuses on near-surface air temperature and precipitation, and uses the E-OBS data set as observational reference. The <span class="hlt">ensemble</span> consists of 17 simulations carried out by seven different models at grid resolutions of 12 km (nine experiments) and 50 km (eight experiments). Several performance metrics computed from monthly and seasonal mean values are used to assess model performance over eight subdomains of the European continent. Results are compared to those for the ERA40-driven <span class="hlt">ENSEMBLES</span> simulations. The analysis confirms the ability of RCMs to capture the basic features of the European climate, including its variability in space and time. But it also identifies nonnegligible deficiencies of the simulations for selected metrics, regions and seasons. Seasonally and regionally averaged temperature biases are mostly smaller than 1.5 °C, while precipitation biases are typically located in the ±40% range. Some bias characteristics, such as a predominant cold and wet bias in most seasons and over most parts of Europe and a warm and dry summer bias over southern and southeastern Europe reflect common model biases. For seasonal mean quantities averaged over large European subdomains, no clear benefit of an increased spatial resolution (12 vs. 50 km) can be identified. The bias ranges of the EURO-CORDEX <span class="hlt">ensemble</span> mostly correspond to those of the <span class="hlt">ENSEMBLES</span> simulations, but some improvements in model performance can be identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different configurations of one individual model can be of a similar magnitude as the spread across different models, demonstrating a strong influence of the specific choices in physical parameterizations and experimental setup on model performance. Based on a number of simply reproducible metrics, the present study quantifies the currently achievable accuracy of RCMs used for regional climate simulations over Europe and provides a quality standard for future model developments.</p> <div class="credits"> <p class="dwt_author">Kotlarski, S.; Keuler, K.; Christensen, O. B.; Colette, A.; Déqué, M.; Gobiet, A.; Goergen, K.; Jacob, D.; Lüthi, D.; van Meijgaard, E.; Nikulin, G.; Schär, C.; Teichmann, C.; Vautard, R.; Warrach-Sagi, K.; Wulfmeyer, V.</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">387</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFMGC43B0897S"> <span id="translatedtitle">The Future of Land Use in the United States: <span class="hlt">Downscaling</span> SRES Emission Scenarios to Ecoregions and Pixels</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Scenario analysis has emerged as a useful tool for evaluating uncertain futures in ecological systems. We describe research initiated by the U.S. Geological Survey (USGS) to develop a comprehensive portfolio of future land-use and land-cover (LULC) scenarios for the United States. The USGS has identified LULC scenarios as a focal area of future research. Scenarios are used to assist in the understanding of possible future developments in complex systems that typically have high levels of scientific uncertainty. Scenarios generally require knowledge of history and current conditions, and specific understanding about how drivers of change have acted to influence the historical and current condition. We describe methods and results of <span class="hlt">downscaling</span> LULC and associated narrative storylines from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES). The <span class="hlt">downscaling</span> methods leverage three primary sources of information: 1) comprehensive land-use histories developed through remote sensing and survey data, 2) modeled LULC outputs from global integrated assessment models (IAMs), and 3) expert knowledge of regional land change. First, national and ecoregional narrative storylines were derived from the global IPCC framework. Based on the characteristics of <span class="hlt">downscaled</span> narrative storylines, experts used historical data and information on the rates and types of LULC change, in conjunction with coarse-scale IAM projections of land use, to produce future quantitative scenarios. An accounting model was developed to handle all aspects of scenario <span class="hlt">downscaling</span>. Here we present the methods used to construct ecoregion-specific scenarios of LULC change consistent with the IPCC-SRES scenarios, as well as results at multiple geographic scales. The USGS LandCarbon assessment is implementing a scenario-based approach for projecting changes in LULC that may result in changes to ecosystem carbon flux and greenhouse gas (GHG) emissions. Results described here are used in the FOREcasting-SCEnarios model to create spatially explicit annual maps of land use and land cover at a 250-meter pixel resolution for the Conterminous United States.</p> <div class="credits"> <p class="dwt_author">Sleeter, B. M.; Sohl, T. L.; Sayler, K.; Bouchard, M. A.; Reker, R.; Sleeter, R. R.; Zhu, Z.; Auch, R.; Acevedo, W.; Soulard, C. E.; Griffith, G.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">388</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/12214867"> <span id="translatedtitle">Validation of mesoscale low-level winds obtained by dynamical <span class="hlt">downscaling</span> of ERA40 over complex terrain</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">The mesoscale numerical weather prediction model ALADIN has been applied for <span class="hlt">downscaling</span> ERA40 data onto a 10 km grid covering the complex terrain of Slovenia. The modelled wind field is compared with the time-series of observations at 11 stations. In addition to traditional scores (root-mean-square error, mean absolute error, anomaly correlation), a frequency-domain comparison is carried out in order to</p> <div class="credits"> <p class="dwt_author">N. Zagar; M. Zagar; J. Cedilnik; G. Gregoric; J. Rakovec</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-01-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">389</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://academic.research.microsoft.com/Publication/40757078"> <span id="translatedtitle"><span class="hlt">Down-scale</span> analysis for water scarcity in response to soil–water conservation on Loess Plateau of China</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://academic.research.microsoft.com/">Microsoft Academic Search </a></p> <p class="result-summary">Water scarcity is one of the most prominent issues of discussion worldwide concerned with sustainable development, especially in the arid and semi-arid areas. On the Loess Plateau of China, population growth and fast-growing cities and industries have caused ever-increasing competition for water. The present paper shows a <span class="hlt">down-scale</span> analysis on how the region wide mass action of soil–water conservation ecologically</p> <div class="credits"> <p class="dwt_author">He Xiubin; Li Zhanbin; Hao Mingde; Tang Keli; Zheng Fengli</p> <p class="dwt_publisher"></p> <p class="publishDate">2003-01-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">390</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013AdWR...61...42F"> <span id="translatedtitle">Spatial <span class="hlt">downscaling</span> of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">The lack of high resolution precipitation data has posed great challenges to the study and management of extreme rainfall events. Satellite-based rainfall products with large areal coverage provide a potential alternative source of data where in situ measurements are not available. However, the mismatch in scale between these products and model requirements has limited their application and demonstrates that satellite data must be <span class="hlt">downscaled</span> before being used. This study developed a statistical spatial <span class="hlt">downscaling</span> scheme based on the relationships between precipitation and related environmental factors such as local topography and pre-storm meteorological conditions. The method was applied to disaggregate the Tropical Rainfall Measuring Mission (TRMM) 3B42 products, which have a resolution of 0.25° × 0.25°, to 1 × 1 km gridded rainfall fields. The TRMM datasets in accord with six rainstorm events in the Xiao River basin were used to validate the effectiveness of this approach. The <span class="hlt">downscaled</span> precipitation data were compared with ground observations and exhibited good agreement with r2 values ranging from 0.612 to 0.838. In addition, the proposed approach provided better results than the conventional spline and kriging interpolation methods, indicating its promise in the management of extreme rainfall events. The uncertainties in the final results and the implications for further study were discussed, and the needs for additional rigorous investigations of the rainfall physical process prior to institutionalizing the use of satellite data were highlighted.</p> <div class="credits"> <p class="dwt_author">Fang, Jian; Du, Juan; Xu, Wei; Shi, Peijun; Li, Man; Ming, Xiaodong</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-11-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">391</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/16986543"> <span id="translatedtitle">Rotation forest: A new classifier <span class="hlt">ensemble</span> method.</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p class="result-summary">We propose a method for generating classifier <span class="hlt">ensembles</span> based on feature extraction. To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and Principal Component Analysis (PCA) is applied to each subset. All principal components are retained in order to preserve the variability information in the data. Thus, K axis rotations take place to form the new features for a base classifier. The idea of the rotation approach is to encourage simultaneously individual accuracy and diversity within the <span class="hlt">ensemble</span>. Diversity is promoted through the feature extraction for each base classifier. Decision trees were chosen here because they are sensitive to rotation of the feature axes, hence the name "forest." Accuracy is sought by keeping all principal components and also using the whole data set to train each base classifier. Using WEKA, we examined the Rotation Forest <span class="hlt">ensemble</span> on a random selection of 33 benchmark data sets from the UCI repository and compared it with Bagging, AdaBoost, and Random Forest. The results were favorable to Rotation Forest and prompted an investigation into diversity-accuracy landscape of the <span class="hlt">ensemble</span> models. Diversity-error diagrams revealed that Rotation Forest <span class="hlt">ensembles</span> construct individual classifiers which are more accurate than these in AdaBoost and Random Forest, and more diverse than these in Bagging, sometimes more accurate as well. PMID:16986543</p> <div class="credits"> <p class="dwt_author">Rodríguez, Juan J; Kuncheva, Ludmila I; Alonso, Carlos J</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-10-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">392</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 odd" lang="en"> <div class="resultNumber element">393</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 " lang="en"> <div class="resultNumber element">394</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=3932860"> <span id="translatedtitle">Response times from <span class="hlt">ensembles</span> of accumulators</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">Decision-making is explained by psychologists through stochastic accumulator models and by neurophysiologists through the activity of neurons believed to instantiate these models. We investigated an overlooked scaling problem: How does a response time (RT) that can be explained by a single model accumulator arise from numerous, redundant accumulator neurons, each of which individually appears to explain the variability of RT? We explored this scaling problem by developing a unique <span class="hlt">ensemble</span> model of RT, called e pluribus unum, which embodies the well-known dictum “out of many, one.” We used the e pluribus unum model to analyze the RTs produced by <span class="hlt">ensembles</span> of redundant, idiosyncratic stochastic accumulators under various termination mechanisms and accumulation rate correlations in computer simulations of <span class="hlt">ensembles</span> of varying size. We found that predicted RT distributions are largely invariant to <span class="hlt">ensemble</span> size if the accumulators share at least modestly correlated accumulation rates and RT is not governed by the most extreme accumulators. Under these regimes the termination times of individual accumulators was predictive of <span class="hlt">ensemble</span> RT. We also found that the threshold measured on individual accumulators, corresponding to the firing rate of neurons measured at RT, can be invariant with RT but is equivalent to the specified model threshold only when the rate correlation is very high. PMID:24550315</p> <div class="credits"> <p class="dwt_author">Zandbelt, Bram; Purcell, Braden A.; Palmeri, Thomas J.; Logan, Gordon D.; Schall, Jeffrey D.</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">395</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 " lang="en"> <div class="resultNumber element">396</div> <div class="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..16.3087K"> <span id="translatedtitle"><span class="hlt">Downscaling</span> Ocean Conditions: Initial Results using a Quasigeostrophic and Realistic Ocean 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">Previous theoretical work (Henshaw et al, 2003) has shown that the small-scale modes of variability of solutions of the unforced, incompressible Navier-Stokes equation, and Burgers' equation, can be reconstructed with surprisingly high accuracy from the time history of a few of the large-scale modes. Motivated by this theoretical work we first describe a straightforward method for assimilating information on the large scales in order to recover the small scale oceanic variability. The method is based on nudging in specific wavebands and frequencies and is similar to the so-called spectral nudging method that has been used successfully for atmospheric <span class="hlt">downscaling</span> with limited area models (e.g. von Storch et al., 2000). The validity of the method is tested using a quasigestrophic model configured to simulate a double ocean gyre separated by an unstable mid-ocean jet. It is shown that important features of the ocean circulation including the position of the meandering mid-ocean jet and associated pinch-off eddies can indeed be recovered from the time history of a small number of large-scales modes. The benefit of assimilating additional time series of observations from a limited number of locations, that alone are too sparse to significantly improve the recovery of the small scales using traditional assimilation techniques, is also demonstrated using several twin experiments. The final part of the study outlines the application of the approach using a realistic high resolution (1/36 degree) model, based on the NEMO (Nucleus for European Modelling of the Ocean) modeling framework, configured for the Scotian Shelf of the east coast of Canada. The large scale conditions used in this application are obtained from the HYCOM (HYbrid Coordinate Ocean Model) + NCODA (Navy Coupled Ocean Data Assimilation) global 1/12 degree analysis product. Henshaw, W., Kreiss, H.-O., Ystrom, J., 2003. Numerical experiments on the interaction between the larger- and the small-scale motion of the Navier-Stokes equations. Multiscale Modeling and Simulation 1, 119-149. von Storch, H., Langenberg, H., Feser, F., 2000. A spectral nudging technique for dynamical <span class="hlt">downscaling</span> purposes. Monthly Weather Review 128, 3664-3673.</p> <div class="credits"> <p class="dwt_author">Katavouta, Anna; Thompson, Keith</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">397</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2011AGUFMNS43A1502D"> <span id="translatedtitle">Seismic wave propagation in multiphasic complex porous media : upscaling and <span class="hlt">downscaling</span> approaches</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">Seismic wave propagation is often used for subsurface investigation, related either to reservoir issues (oil, gas or CO2 storage) or geotechnical problems (slope stability, water resources, territory management). Indeed, near surface media are rather heterogeneous, complex and partially fluid-filled. These characteristics are more or less sensitive to seismic waves. In order to interpret efficiently seismic attributes in these media, wave propagation may require complex poroelastic theories in multiphasic media as double porosity or patchy saturated cases. Thanks to upscaling techniques, we determine homogeneized parameters leading to a two-phases media described by complex and frequency dependent parameters. An effective generalized Biot-Gassmann theory allows wave propagation in 2D heterogeneous media through a Discontinuous Galerkin finite-element approach. Taking into account the rather complex frequency-dependent rheology of these porous media, wave propagation simulation is simpler in a frequency formulation than in a time formulation, at least for 2D geometries. We illustrate two features essential for an accurate characterization of the medium. On one hand, strong waveforms differences between the complex upscaled media (double porosity, unsaturated, squirt flow models) and the equivalent saturated Biot-Gassmann media (determined by arithmetic and harmonic averages) are underlined on simple examples. This may require the use of these theories for the characterization of these media. In the other hand, after the reconstruction of macro-scale parameters such as velocities and attenuations through seismic attributes (times, amplitudes and so on) using standard visco-elastic interpretation (first step of the <span class="hlt">downscaling</span> procedure), we show that we recover micro-scale parameters (skeleton parameters such as porosity or dry moduli, fluid saturation...) using global search techniques (neighborhood algorithm) with some a priori information (second step of the <span class="hlt">downscaling</span> procedure). We may propose that this two-steps procedure could be used for recovering micro-scale parameters of these complex media. A validation could be performed by direct interpretation of seismograms using the upscaling approach we have elaborated, putting the model description on more robust grounds. In conclusion, we show that a a two-steps procedure could be used for recovering micro-scale parameters and that considering complex poroelastic approaches is crucial to enhance the quantitative seismic imaging of near surface media.</p> <div class="credits"> <p class="dwt_author">Dupuy, B.; Garambois, S.; Virieux, J.</p> <p class="dwt_publisher"></p> <p class="publishDate">2011-12-01</p> </div> </div> </div> </div> <div class="floatContainer result " lang="en"> <div class="resultNumber element">398</div> <div class="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..1612842L"> <span id="translatedtitle">Comparison of three statistical <span class="hlt">downscaling</span> methods for precipitation in the Hérault and Ebro catchments</span></a>  </p> <div class="result-meta"> <p class="source"><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 the GICC project "REMedHE" (http://www.remedhe.org) is to evaluate and compare the evolution of water supply capacity under climatic and anthropogenic changes by 2050 on two Mediterranean catchments: the Hérault (South of France) and the Ebro (North East of Spain) catchments. Indeed, the Mediterranean region has been identified as a "hot spot" of climate change, especially for precipitation which is expected to globally decrease while water needs should continue to increase. To perform such a study, it is then necessary to simulate future water flows with hydrological models fed by high-resolution precipitation data representative of the future climate. To generate high-resolution climate simulations, three different statistical <span class="hlt">downscaling</span> approaches have been applied. The first one consists in a deterministic transfer function based on a Generalized Additive Model (GAM). The second method involves a Stochastic Weather Generator (SWG), simulating local values from probability density functions conditioned by large-scale predictors. The third approach belongs to the "Model Output Statistics" (MOS) family, in bias correcting the large-scale distributions with respect to the local-scale ones, through the Cumulative Distribution Function transform CDFt approach. These statistical <span class="hlt">downscaling</span> models were calibrated and cross-validated using the SAFRAN dataset (for Hérault catchment), a dataset compiled by HydroSciences Montpellier (for Ebro catchment) as local-scale reference and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis outputs as predictors, over two time periods 1959-1984 and 1985-2010. Cross-validation analysis shows that the inter-annual variability of the yearly sum of precipitation from GAM is close to that from SAFRAN. However, daily variability and occurrence frequency are badly represented by GAM. On the opposite, SWG and one version of CDFt allow both the inter-annual and the more high-frequency variabilities to be correctly reproduced. Then, precipitation were simulated over a control time-period (1976-2005) and a future time-period (2036-2065) according to two climate scenarios (RCP4.5 and RCP8.5) and from two General Circulation Models (GCMs): the "Institut Pierre Simon Laplace" (IPSL-CM5A-MR) model and the "Centre National de Recherches Météorologiques" (CNRM-CM5) model. The evolutions of the main statistical properties of precipitation over the two catchments are then analyzed conditionally on the driving GCM and scenario.</p> <div class="credits"> <p class="dwt_author">Lassonde, Sylvain; Vrac, Mathieu; Ruelland, Denis; Dezetter, Alain</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">399</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2014QuIP...13.1583Y"> <span id="translatedtitle">Quantum discord of <span class="hlt">ensemble</span> of quantum 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">We highlight an information-theoretic meaning of quantum discord as the gap between the accessible information and the Holevo bound in the framework of <span class="hlt">ensemble</span> of quantum states. This complementary relationship implies that a large amount of preexisting arguments about the evaluation of quantum discord can be directly applied to the accessible information and vice versa. For an <span class="hlt">ensemble</span> of two pure qubit states, we show that one can avoid the optimization problem with the help of the Koashi-Winter relation. Further, for the general case (two mixed qubit states), we recover the main results presented by Fuchs and Caves (Phys Rev Lett 73:3047, 1994), but totally from the perspective of quantum discord. Following this line of thought, we also investigate the geometric discord as an indicator of quantumness of <span class="hlt">ensembles</span> in detail. Finally, we give an example to elucidate the difference between quantum discord and geometric discord with respect to optimal measurement strategies.</p> <div class="credits"> <p class="dwt_author">Yao, Yao; Huang, Jing-Zheng; Zou, Xu-Bo; Han, Zheng-Fu</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">400</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2013AdAtS..30.1406L"> <span id="translatedtitle">Dynamic analogue initialization for <span class="hlt">ensemble</span> forecasting</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">This paper introduces a new approach for the initialization of <span class="hlt">ensemble</span> numerical forecasting: Dynamic Analogue Initialization (DAI). DAI assumes that the best model state trajectories for the past provide the initial conditions for the best forecasts in the future. As such, DAI performs the <span class="hlt">ensemble</span> forecast using the best analogues from a full size <span class="hlt">ensemble</span>. As a pilot study, the Lorenz63 and Lorenz96 models were used to test DAI's effectiveness independently. Results showed that DAI can improve the forecast significantly. Especially in lower-dimensional systems, DAI can reduce the forecast RMSE by ˜50% compared to the Monte Carlo forecast (MC). This improvement is because DAI is able to recognize the direction of the analysis error through the embedding process and therefore selects those good trajectories with reduced initial error. Meanwhile, a potential improvement of DAI is also proposed, and that is to find the optimal range of embedding time based on the error's growing speed.</p> <div class="credits"> <p class="dwt_author">Li, Shan; Rong, Xingyao; Liu, Yun; Liu, Zhengyu; Fraedrich, Klaus</p> <p class="dwt_publisher"></p> <p class="publishDate">2013-09-01</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_19");' href="#" title="Previous Page"> <img id="PreviousPageLinkImage" class="Icon" src="http://www.science.gov/scigov/images/icon.previous.18x20.png" alt="Previous Page" 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showDiv("page_25");' href="#">25</a> </span> </span> <a id="NextPageLink" onclick='return showDiv("page_22");' 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">401</div> <div class="resultBody element"> <p class="result-title"><a target="resultTitleLink" href="http://science.gov/scigov/link.html?type=RESULT&redirectUrl=http://adsabs.harvard.edu/abs/2014PhRvL.112e0501W"> <span id="translatedtitle">Cavity Cooling of an <span class="hlt">Ensemble</span> Spin System</span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p class="result-summary">We describe how sideband cooling techniques may be applied to large spin <span class="hlt">ensembles</span> in magnetic resonance. Using the Tavis-Cummings model in the presence of a Rabi drive, we solve a Markovian master equation describing the joint spin-cavity dynamics to derive cooling rates as a function of <span class="hlt">ensemble</span> size. Our calculations indicate that the coupled angular momentum subspaces of a spin <span class="hlt">ensemble</span> containing roughly 1011 electron spins may be polarized in a time many orders of magnitude shorter than the typical thermal relaxation time. The described techniques should permit efficient removal of entropy for spin-based quantum information processors and fast polarization of spin samples. The proposed application of a standard technique in quantum optics to magnetic resonance also serves to reinforce the connection between the two fields, which has recently begun to be explored in further detail due to the development of hybrid designs for manufacturing noise-resilient quantum devices.</p> <div class="credits"> <p class="dwt_author">Wood, Christopher J.; Borneman, Troy W.; Cory, David G.</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">402</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/1305.1029v2"> <span id="translatedtitle">Cavity cooling of an <span class="hlt">ensemble</span> spin system</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 describe how sideband cooling techniques may be applied to large spin <span class="hlt">ensembles</span> in magnetic resonance. Using the Tavis-Cummings model in the presence of a Rabi drive, we solve a Markovian master equation describing the joint spin-cavity dynamics to derive cooling rates as a function of <span class="hlt">ensemble</span> size. Our calculations indicate that the coupled angular momentum subspaces of a spin <span class="hlt">ensemble</span> containing roughly $10^{11}$ electron spins may be polarized in a time many orders of magnitude shorter than the typical thermal relaxation time. The described techniques should permit efficient removal of entropy for spin-based quantum information processors and fast polarization of spin samples. The proposed application of a standard technique in quantum optics to magnetic resonance also serves to reinforce the connection between the two fields, which has recently begun to be explored in further detail due to the development of hybrid designs for manufacturing noise-resilient quantum devices.</p> <div class="credits"> <p class="dwt_author">Christopher J. Wood; Troy W. Borneman; David G. Cory</p> <p class="dwt_publisher"></p> <p class="publishDate">2014-02-24</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">403</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=20130013812&hterms=Environment+development&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DEnvironment%2Bdevelopment"> <span id="translatedtitle"><span class="hlt">Ensemble</span> Eclipse: A Process for Prefab Development Environment for the <span class="hlt">Ensemble</span> Project</span></a>  </p> <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 software simplifies the process of having to set up an Eclipse IDE programming environment for the members of the cross-NASA center project, <span class="hlt">Ensemble</span>. It achieves this by assembling all the necessary add-ons and custom tools/preferences. This software is unique in that it allows developers in the <span class="hlt">Ensemble</span> Project (approximately 20 to 40 at any time) across multiple NASA centers to set up a development environment almost instantly and work on <span class="hlt">Ensemble</span> software. The software automatically has the source code repositories and other vital information and settings included. The Eclipse IDE is an open-source development framework. The NASA (<span class="hlt">Ensemble</span>-specific) version of the software includes <span class="hlt">Ensemble</span>-specific plug-ins as well as settings for the <span class="hlt">Ensemble</span> project. This software saves developers the time and hassle of setting up a programming environment, making sure that everything is set up in the correct manner for <span class="hlt">Ensemble</span> development. Existing software (i.e., standard Eclipse) requires an intensive setup process that is both time-consuming and error prone. This software is built once by a single user and tested, allowing other developers to simply download and use the software</p> <div class="credits"> <p class="dwt_author">Wallick, Michael N.; Mittman, David S.; Shams, Khawaja, S.; Bachmann, Andrew G.; Ludowise, Melissa</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">404</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/20857669"> <span id="translatedtitle">Quantum measurement of a mesoscopic spin <span class="hlt">ensemble</span></span></a>  </p> <div class="result-meta"> <p class="source"><a target="_blank" id="logoLink" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p class="result-summary">We describe a method for precise estimation of the polarization of a mesoscopic spin <span class="hlt">ensemble</span> by using its coupling to a single two-level system. Our approach requires a minimal number of measurements on the two-level system for a given measurement precision. We consider the application of this method to the case of nuclear-spin <span class="hlt">ensemble</span> defined by a single electron-charged quantum dot: we show that decreasing the electron spin dephasing due to nuclei and increasing the fidelity of nuclear-spin-based quantum memory could be within the reach of present day experiments.</p> <div class="credits"> <p class="dwt_author">Giedke, G. [Institut fuer Quantenelektronik, ETH Zuerich, Wolfgang-Pauli-Strasse 16, 8093 Zurich (Switzerland); Max-Planck-Institut fuer Quantenoptik, H.-Kopfermann-Str., 85748 Garching (Germany); Taylor, J. M.; Lukin, M. D. [Department of Physics, Harvard University, Cambridge, Massachusetts 02138 (United States); D'Alessandro, D. [Department of Mathematics, Iowa State University, Ames, Iowa 50011 (United States); Imamoglu, A. [Institut fuer Quantenelektronik, ETH Zuerich, Wolfgang-Pauli-Strasse 16, 8093 Zurich (Switzerland)</p> <p class="dwt_publisher"></p> <p class="publishDate">2006-09-15</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">405</div> <div class="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..11913651S"> <span id="translatedtitle">High-resolution surface analysis for extended-range <span class="hlt">downscaling</span> with limited-area atmospheric 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">limited-area model (LAM) simulations are frequently employed to <span class="hlt">downscale</span> coarse-resolution objective analyses over a specified area of the globe using high-resolution computational grids. When LAMs are integrated over extended time frames, from months to years, they are prone to deviations in land surface variables that can be harmful to the quality of the simulated near-surface fields. Nudging of the prognostic surface fields toward a reference-gridded data set is therefore devised in order to prevent the atmospheric model from diverging from the expected values. This paper presents a method to generate high-resolution analyses of land-surface variables, such as surface canopy temperature, soil moisture, and snow conditions, to be used for the relaxation of lower boundary conditions in extended-range LAM simulations. The proposed method is based on performing offline simulations with an external surface model, forced with the near-surface meteorological fields derived from short-range forecast, operational analyses, and observed temperatures and humidity. Results show that the outputs of the surface model obtained in the present study have potential to improve the near-surface atmospheric fields in extended-range LAM integrations.</p> <div class="credits"> <p class="dwt_author">Separovic, Leo; Husain, Syed Zahid; Yu, Wei; Fernig, David</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">406</div> <div class="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..212..348C"> <span id="translatedtitle">The use of weather types and air flow indices for GCM <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">A variety of different methods have been proposed for <span class="hlt">downscaling</span> large-scale General Circulation Model (GCM) output to the time and space scales required for climate impact studies. Using weather types to achieve this goal provides greater understanding of the problems that are involved compared to the many "black box" techniques that have been proposed. Analyses using Lamb weather types, counts of weather fronts and air flow indices over the British Isles show strong relationships with daily rainfall characteristics such as the probability and amount of rainfall. Three versions of a weather type method are described for generating daily rainfall series. Two methods use an objective scheme to classify daily circulation types over the British Isles along the lines of Lamb's subjective classification. The third method is based on user-defined categories of vorticity. Each method categorises rainfall events according to whether the previous day was wet or dry. All three methods successfully reproduce the monthly means, persistence and interannual variability of two daily rainfall time series during a validation period. A significant advantage of using vorticity is that it is a continuous variable and is strongly related to the probability of rainfall and the magnitude of rainfall events. The paper ends with a discussion of the issues relevant to the application of this method to the development of climate scenarios from GCMs.</p> <div class="credits"> <p class="dwt_author">Conway, D.; Jones, P. D.</p> <p class="dwt_publisher"></p> <p class="publishDate">1998-12-01</p> </div> </div> </div> </div> <div class="floatContainer result odd" lang="en"> <div class="resultNumber element">407</div> <div class="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.B31B0389H"> <span id="translatedtitle">Upscaling and <span class="hlt">downscaling</span> of greenhouse gas fluxes in the Alaskan Arctic</span></a>  </p> <div class="result-meta"> <p class="source"><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 effects of climate change in the Arctic are of global concern due to the large stores of carbon in the permafrost soils. Forecasts for these ecosystems contain large uncertainties due in part to the highly heterogeneous landscape, which even impedes current understanding of greenhouse gas fluxes and their controls. Before climate change effects can be predicted accurately, we must have a better understanding of current conditions. Extreme conditions and sparse infrastructure make studying Arctic processes difficult. As a result, much of what we know about Arctic trace gas