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Sample records for ensemble downscaling mred

  1. The ENSEMBLES Statistical Downscaling Portal

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

  2. Comparison of data-driven methods for downscaling ensemble weather forecasts

    NASA Astrophysics Data System (ADS)

    Liu, X.; Coulibaly, P.; Evora, N.

    2007-02-01

    This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. Given the coarse resolution (about 200-km grid spacing) of the MRF model, an optimal use of the weather forecasts at the local or watershed scale, requires appropriate downscaling techniques. The selected methods are applied for downscaling ensemble daily precipitation and temperature series for the Chute-du-Diable basin located in northeastern Canada. The downscaling results show that the TLFN and EPR have similar performance in downscaling ensemble daily precipitation as well as daily maximum and minimum temperature series whatever the season. Both the TLFN and EPR are more efficient downscaling techniques than SDSM for both the ensemble daily precipitation and temperature.

  3. Comparison of data-driven methods for downscaling ensemble weather forecasts

    NASA Astrophysics Data System (ADS)

    Liu, Xiaoli; Coulibaly, P.; Evora, N.

    2008-03-01

    This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. Given the coarse resolution (about 200-km grid spacing) of the MRF model, an optimal use of the weather forecasts at the local or watershed scale, requires appropriate downscaling techniques. The selected methods are applied for downscaling ensemble daily precipitation and temperature series for the Chute-du-Diable basin located in northeastern Canada. The downscaling results show that the TLFN and EPR have similar performance in downscaling ensemble daily precipitation as well as daily maximum and minimum temperature series whatever the season. Both the TLFN and EPR are more efficient downscaling techniques than SDSM for both the ensemble daily precipitation and temperature.

  4. Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

    Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the 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.

  5. Downscaling medium-range ensemble forecasts using a neural network approach

    NASA Astrophysics Data System (ADS)

    Ohba, M.; Kadokura, S.; Yoshida, Y.; Nohara, D.; Toyoda, Y.

    2015-06-01

    In this study, we present an application of self-organizing maps (SOMs) to downscaling weekly ensemble forecasts for probabilistic prediction of local precipitation in Japan. SOM is simultaneously employed on four elemental variables derived from the JRA55 reanalysis over area of study (Southwestern Japan), whereby a two-dimensional lattice of weather patterns (WPs) dominated during the 1958-2008 period is obtained. Downscaling weekly ensemble forecasts to local precipitation are conducted by using the obtained SOM lattice based on the WPs of the global model ensemble forecast. A probabilistic local precipitation is easily and quickly obtained from the ensemble forecast. The predictability skill of the ensemble forecasts for the precipitation is significantly improved under the downscaling technique.

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

    NASA Astrophysics Data System (ADS)

    Duan, Kai; Mei, Yadong

    2014-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

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

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

    PubMed Central

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

    2014-01-01

    Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind “noise,” which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical “downscaling” of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Key Points Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations PMID:26213518

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

    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.

  10. An evaluation of the seasonal added value of downscaling over the United States using new verification measures

    NASA Astrophysics Data System (ADS)

    De Haan, Laurel L.; Kanamitsu, Masao; De Sales, Fernando; Sun, Liqiang

    2015-10-01

    Two separate dynamically downscaled ensembles are used to assess the added value of downscaling over the continental United States on a seasonal timescale. One data set is from a 55-year continuous run forced with observed sea surface temperatures. The other data set has downscaling results from seven regional models for 21 winters forced from a single coupled global model. The second data set, known as the Multi-RCM Ensemble Downscaling (MRED) project was used as a collection of individual models as well as a multi-model ensemble. The data was first tested for the potential loss of small-scale details due to averaging, and it was found that the number of small-scale details is not reduced when averaging over several models or several years. The added value of the downscaling was then calculated by standard measures, including climatology and correlation, as well as two newer measures: the added value index (AVI) and fraction skill score (FSS). The additional verification measures provided more information about the added value than was found with the standard measures. In general, more added value was found with the multi-model ensemble than with individual models. While it was clear that the added value was dependent on the forcing model, regional model, season, variable, and region, there were some areas where the downscale consistently added value, particularly near the coast and in topographically interesting areas.

  11. Generation of Daily Rainfall Scenario Based on Nonstationary Spatial-Temporal Downscaling Techniques with Multimodel Ensemble of Different GCMs

    NASA Astrophysics Data System (ADS)

    Kim, T. J.; Kwon, H. H.

    2014-12-01

    Recently, extreme weather occurrences associated with climate change are gradually increasing in frequency, causing unprecedented major weather-related disasters. General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the discrepancy between the spatio-temporal scale at which the models deliver output and the scales that are generally required for applied studies has led to the development of various downscaling methods. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a multimodel ensemble of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. In particular, this study uses MMEs from the APEC Climate Center (APCC) as a predictor. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management. Acknowledgement: This research was supported by a grant (13SCIPA01) from Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and the Korea Agency for Infrastructure Technology Advancement (KAIA). Keywords: Climate Change, GCM, Hidden Markov Chain Model, Multi-Model Ensemble

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

    NASA Astrophysics Data System (ADS)

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

    2013-02-01

    Abstract The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center one-tier multimodel <span class="hlt">ensemble</span> (MME) was <span class="hlt">downscaled</span> for predicting the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both precipitation and temperature, and can incorporate the effect of global warming on the balance between precipitation and evapotranspiration. It was found that the one-tier MME has difficulty in capturing the local temperature and rainfall variations over extratropical land areas, and has no skill in predicting SPEI during boreal winter and spring. On the other hand, temperature and precipitation predictions were substantially improved in the <span class="hlt">downscaled</span> MME. In conjunction with variance inflation, <span class="hlt">downscaled</span> MME can give reasonably skillful 6 month-lead forecasts of SPEI for the winter to spring period. Our results could lead to more reliable hydrological extreme predictions for policymakers and stakeholders in the water management sector, and for better mitigation and climate adaptations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.299H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.299H"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of the ERA-40 reanalysis and ARPEGE GCM with the WRF regional climate model in complex terrain in Norway - comparison with <span class="hlt">ENSEMBLES</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heikkilä, U.; Sandvik, A. D.; Sorteberg, A.</p> <p>2010-09-01</p> <p>We present a supplement to the recently finished EU-project <span class="hlt">ENSEMBLES</span> project employing the WRF regional climate model (www.wrf-model.org). Results are presented from a dynamical <span class="hlt">downscaling</span> of the ERA-40 reanalysis to 30 km and 10 km resolution as well as the ARPEGE global model simulations in Europe for the 30-year period from 1961 to 1990. In addition some preliminary results from a WRF <span class="hlt">downscaling</span> of the ARPEGE R1b future prediction (2020-2050) will be shown. A relatively weak spectral nudging is used in all experiments. The model evaluation focuses on complex terrain in Norway. The results are evaluated against daily mean observations of precipitation, 2-meter temperature and 10-meter wind speed for the 30-year period. We find that the WRF <span class="hlt">downscaling</span> of the ERA-40 reanalysis is reproducing the distributions of the observed daily mean parameters reasonably well. Also the frequency of wet days as well as the occurrence of extreme events are improved in the <span class="hlt">downscaled</span> data set. A significant improvement of the extreme events as well as the distributions is found when the horizontal resolution is further refined from 30 km to 10 km. The spectral nudging procedure is not found to suppress the extreme events but to significantly improve the phase of precipitation. Model intercomparison with some of the regional model runs of the <span class="hlt">ENSEMBLES</span> project reveals that the WRF <span class="hlt">downscaling</span> ranks high within the individual models. The <span class="hlt">ENSEMBLES</span> mean is producing the best results in most cases.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMOS53B1046B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMOS53B1046B"><span id="translatedtitle">High Resolution Sea Surface Temperature Projections using Statistical <span class="hlt">Downscaling</span> of General Circulation Model <span class="hlt">Ensembles</span> in the North Pacific.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Beltran, F. M.; Sansó, B.</p> <p>2014-12-01</p> <p>In this work we develop a general methodology to obtain high-resolution spatial-temporal forecasts of Sea Surface Temperature (SST) using <span class="hlt">ensembles</span> of general circulation model (GCM) output and historical records as the major driving force. As a case study, we consider SST in the North Pacific Ocean. We use two <span class="hlt">ensembles</span> of different GCM simulation output, made available in the 4th Assessment Report of the Intergovernmental Panel on Climate Change. One corresponds to 20th century forcing conditions and the other corresponds to the emissions scenario A1B for the 21st century. Given a representation of the SST spatio-temporal fields based on a common set of empirical orthogonal functions (EOFs), we use a hierarchical Bayesian model for the EOF coefficients to estimate a baseline and a set of model discrepancies. These components are all time-varying. The model enables us to extract relevant temporal patterns of variability from both the observations and simulations as well as obtain common patterns from all GCM simulations. This is used to obtain unified 21st century forecasts of relevant oceanic indexes as well as whole fields of forecast North Pacific SST. The unified forecast captures large longterm oceanic behavior, however the coarse resolution prevents us from capturing coastal behaviors. We use the unified forecast to model high resolution SST by establishing a link between large and small scale variability using statistical <span class="hlt">downscaling</span> techniques. Using a combination of a discrete process convolution and a dynamic linear model, we obtain a smooth high-resolution forecast of SST fields off the coast of California. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AdAtS..25..867Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AdAtS..25..867Z"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> for multi-model <span class="hlt">ensemble</span> prediction of summer monsoon rainfall in the Asia-Pacific region using geopotential height field</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhu, Congwen; Park, Chung-Kyu; Lee, Woo-Sung; Yun, Won-Tae</p> <p>2008-09-01</p> <p>The 21-yr <span class="hlt">ensemble</span> predictions of model precipitation and circulation in the East Asian and western North Pacific (Asia-Pacific) summer monsoon region (0° 50°N, 100° 150°E) were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center (APCC) multi-model <span class="hlt">ensemble</span> seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model <span class="hlt">ensemble</span> predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index (WNPMI) defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height (GPH) can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to <span class="hlt">downscale</span> the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition (SVD). In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to <span class="hlt">downscale</span> the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this <span class="hlt">downscaling</span> scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient (ACC) has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model <span class="hlt">ensemble</span> (MME) forecast.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080040695','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080040695"><span id="translatedtitle">Simulation of SEU Cross-sections using <span class="hlt">MRED</span> under Conditions of Limited Device Information</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lauenstein, J. M.; Reed, R. A.; Weller, R. A.; Mendenhall, M. H.; Warren, K. M.; Pellish, J. A.; Schrimpf, R. D.; Sierawski, B. D.; Massengill, L. W.; Dodd, P. E.; Shaneyfelt, M. R.; Felix, J. A.; Schwank, J. R.</p> <p>2007-01-01</p> <p>This viewgraph presentation reviews the simulation of Single Event Upset (SEU) cross sections using the membrane electrode assembly (MEA) resistance and electrode diffusion (<span class="hlt">MRED</span>) tool using "Best guess" assumptions about the process and geometry, and direct ionization, low-energy beam test results. This work will also simulate SEU cross-sections including angular and high energy responses and compare the simulated results with beam test data for the validation of the model. Using <span class="hlt">MRED</span>, we produced a reasonably accurate upset response model of a low-critical charge SRAM without detailed information about the circuit, device geometry, or fabrication process</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4607420','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4607420"><span id="translatedtitle">MreC and <span class="hlt">MreD</span> Proteins Are Not Required for Growth of Staphylococcus aureus</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tavares, Andreia C.; Fernandes, Pedro B.; Carballido-López, Rut; Pinho, Mariana G.</p> <p>2015-01-01</p> <p>The transmembrane proteins MreC and <span class="hlt">MreD</span> are present in a wide variety of bacteria and are thought to be involved in cell shape determination. Together with the actin homologue MreB and other morphological elements, they play an essential role in the synthesis of the lateral cell wall in rod-shaped bacteria. In ovococcus, which lack MreB homologues, mreCD are also essential and have been implicated in peripheral cell wall synthesis. In this work we addressed the possible roles of MreC and <span class="hlt">MreD</span> in the spherical pathogen Staphylococcus aureus. We show that MreC and <span class="hlt">MreD</span> are not essential for cell viability and do not seem to affect cell morphology, cell volume or cell cycle control. MreC and <span class="hlt">MreD</span> localize preferentially to the division septa, but do not appear to influence peptidoglycan composition, nor the susceptibility to different antibiotics and to oxidative and osmotic stress agents. Our results suggest that the function of MreCD in S. aureus is not critical for cell division and cell shape determination. PMID:26470021</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.A33A0217L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A33A0217L"><span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> NCEP Global Climate Forecast System (CFS) Seasonal Predictions Using Regional Atmospheric Modeling System (RAMS)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, L.; Zheng, Y.; Pielke, R. A.</p> <p>2009-12-01</p> <p>As part of the NOAA CPPA-sponsored <span class="hlt">MRED</span> project, the state-of-the-art Regional Atmospheric Modeling System (RAMS) version 6.0 is used to dynamically and progressively <span class="hlt">downscale</span> NCEP global Climate Forecast System (CFS, at 100s-km grid increment) seasonal predictions to a regional domain that covers the conterminous United States at 30-km grid increment. The first set of RCM prediction experiment focuses on the winter seasons, during which the precipitation is largely dependent on synoptic-scale mid-latitude storms and orographic dominant mesoscale processes. Our first suite of numerical experiment includes one <span class="hlt">ensemble</span> member for each year from 1982 through 2008, with all the simulations starting on December 1 and ending on April 30. Driven by the same atmospheric and SST forcings, RAMS will be compared with other RCMs, and evaluated against observations and reanalysis (NARR) to see if the simulations capture the climatology and interannual variability of temperature and precipitation distributions. The overall strengths and weaknesses of the modeling systems will be identified, as well as the consistent model biases. In addition, we will analyze the changes in kinetic energy spectra before and after the spectral nudging algorithm is implemented. The results show that with the spectral nudging scheme, RAMS can better preserve large-scale kinetic energy than standard boundary forcing method, and allow more large-scale energy to cascade to smaller scales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJBm...60..307S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJBm...60..307S"><span id="translatedtitle">Future projections of labor hours based on WBGT for Tokyo and Osaka, Japan, using multi-period <span class="hlt">ensemble</span> dynamical <span class="hlt">downscale</span> simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Suzuki-Parker, Asuka; Kusaka, Hiroyuki</p> <p>2016-02-01</p> <p>Following the heatstroke prevention guideline by the Ministry of Health, Labor, and Welfare of Japan, "safe hours" for heavy and light labor are estimated based on hourly wet-bulb globe temperature (WBGT) obtained from the three-member <span class="hlt">ensemble</span> multi-period (the 2000s, 2030s, 2050s, 2070s, and 2090s) climate projections using dynamical <span class="hlt">downscaling</span> approach. Our target cities are Tokyo and Osaka, Japan. The results show that most of the current climate daytime hours are "light labor safe,", but these hours are projected to decrease by 30-40 % by the end of the twenty-first century. A 60-80 % reduction is projected for heavy labor hours, resulting in less than 2 hours available for safe performance of heavy labor. The number of "heavy labor restricted days" (days with minimum daytime WBGT exceeding the safe level threshold for heavy labor) is projected to increase from ~5 days in the 2000s to nearly two-thirds of the days in August in the 2090s.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25935576','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25935576"><span id="translatedtitle">Future projections of labor hours based on WBGT for Tokyo and Osaka, Japan, using multi-period <span class="hlt">ensemble</span> dynamical <span class="hlt">downscale</span> simulations.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Suzuki-Parker, Asuka; Kusaka, Hiroyuki</p> <p>2016-02-01</p> <p>Following the heatstroke prevention guideline by the Ministry of Health, Labor, and Welfare of Japan, "safe hours" for heavy and light labor are estimated based on hourly wet-bulb globe temperature (WBGT) obtained from the three-member <span class="hlt">ensemble</span> multi-period (the 2000s, 2030s, 2050s, 2070s, and 2090s) climate projections using dynamical <span class="hlt">downscaling</span> approach. Our target cities are Tokyo and Osaka, Japan. The results show that most of the current climate daytime hours are "light labor safe,", but these hours are projected to decrease by 30-40 % by the end of the twenty-first century. A 60-80 % reduction is projected for heavy labor hours, resulting in less than 2 hours available for safe performance of heavy labor. The number of "heavy labor restricted days" (days with minimum daytime WBGT exceeding the safe level threshold for heavy labor) is projected to increase from ~5 days in the 2000s to nearly two-thirds of the days in August in the 2090s. PMID:25935576</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li class="active"><span>1</span></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_1 --> <div id="page_2" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="21"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015IJBm..tmp...44S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015IJBm..tmp...44S"><span id="translatedtitle">Future projections of labor hours based on WBGT for Tokyo and Osaka, Japan, using multi-period <span class="hlt">ensemble</span> dynamical <span class="hlt">downscale</span> simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Suzuki-Parker, Asuka; Kusaka, Hiroyuki</p> <p>2015-05-01</p> <p>Following the heatstroke prevention guideline by the Ministry of Health, Labor, and Welfare of Japan, "safe hours" for heavy and light labor are estimated based on hourly wet-bulb globe temperature (WBGT) obtained from the three-member <span class="hlt">ensemble</span> multi-period (the 2000s, 2030s, 2050s, 2070s, and 2090s) climate projections using dynamical <span class="hlt">downscaling</span> approach. Our target cities are Tokyo and Osaka, Japan. The results show that most of the current climate daytime hours are "light labor safe,", but these hours are projected to decrease by 30-40 % by the end of the twenty-first century. A 60-80 % reduction is projected for heavy labor hours, resulting in less than 2 hours available for safe performance of heavy labor. The number of "heavy labor restricted days" (days with minimum daytime WBGT exceeding the safe level threshold for heavy labor) is projected to increase from ~5 days in the 2000s to nearly two-thirds of the days in August in the 2090s.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H41E0866E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H41E0866E"><span id="translatedtitle">A Novel approach for monitoring cyanobacterial blooms using an <span class="hlt">ensemble</span> based system from MODIS imagery <span class="hlt">downscaled</span> to 250 metres spatial resolution</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>El Alem, A.; Chokmani, K.; Laurion, I.; El-Adlouni, S. E.</p> <p>2014-12-01</p> <p>In reason of inland freshwaters sensitivity to Harmful algae blooms (HAB) development and the limits coverage of standards monitoring programs, remote sensing data have become increasingly used for monitoring HAB extension. Usually, HAB monitoring using remote sensing data is based on empirical and semi-empirical models. Development of such models requires a great number of continuous in situ measurements to reach an acceptable accuracy. However, Ministries and water management organizations often use two thresholds, established by the World Health Organization, to determine water quality. Consequently, the available data are ordinal «semi-qualitative» and they are mostly unexploited. Use of such databases with remote sensing data and statistical classification algorithms can produce hazard management maps linked to the presence of cyanobacteria. Unlike standard classification algorithms, which are generally unstable, classifiers based on <span class="hlt">ensemble</span> systems are more general and stable. In the present study, an <span class="hlt">ensemble</span> based classifier was developed and compared to a standard classification method called CART (Classification and Regression Tree) in a context of HAB monitoring in freshwaters using MODIS images <span class="hlt">downscaled</span> to 250 spatial resolution and ordinal in situ data. Calibration and validation data on cyanobacteria densities were collected by the Ministère du Développement durable, de l'Environnement et de la Lutte contre les changements climatiques on 22 waters bodies between 2000 and 2010. These data comprise three density classes: waters poorly (< 20,000 cells mL-1), moderately (20,000 - 100,000 cells mL-1), and highly (> 100,000 cells mL-1) loaded in cyanobacteria. Results were very interesting and highlighted that inland waters exhibit different spectral response allowing them to be classified into the three above classes for water quality monitoring. On the other, even if the accuracy (Kappa-index = 0.86) of the proposed approach is relatively lower than that of the CART algorithm (Kappa-index = 0.87), but its robustness is higher with a standard-deviation of 0.05 versus 0.06, specifically when applied on MODIS images. A new accurate, robust, and quick approach is thus proposed for a daily near real-time monitoring of HAB in southern Quebec freshwaters.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005WRR....41.2024G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005WRR....41.2024G"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> using K-nearest neighbors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gangopadhyay, Subhrendu; Clark, Martyn; Rajagopalan, Balaji</p> <p>2005-02-01</p> <p>Statistical <span class="hlt">downscaling</span> provides a technique for deriving local-scale information of precipitation and temperature from numerical weather prediction model output. The K-nearest neighbor (K-nn) is a new analog-type approach that is used in this paper to <span class="hlt">downscale</span> the National Centers for Environmental Prediction 1998 medium-range forecast model output. The K-nn algorithm queries days similar to a given feature vector in this archive and using empirical orthogonal function analysis identifies a subset of days (K) similar to the feature day. These K days are then weighted using a bisquare weight function and randomly sampled to generate <span class="hlt">ensembles</span>. A set of 15 medium-range forecast runs was used, and seven <span class="hlt">ensemble</span> members were generated from each run. The <span class="hlt">ensemble</span> of 105 members was then used to select the local-scale precipitation and temperature values in four diverse basins across the contiguous United States. These <span class="hlt">downscaled</span> precipitation and temperature estimates were subsequently analyzed to test the performance of this <span class="hlt">downscaling</span> approach. The <span class="hlt">downscaled</span> <span class="hlt">ensembles</span> were evaluated in terms of bias, the ranked probability skill score as a measure of forecast skill, spatial covariability between stations, temporal persistence, consistency between variables, and conditional bias and to develop spread-skill relationships. Though this approach does not explicitly model the space-time variability of the weather fields at each individual station, the above statistics were extremely well captured. The K-nn method was also compared with a multiple-linear-regression-based <span class="hlt">downscaling</span> model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC31D..02F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC31D..02F"><span id="translatedtitle">Regional <span class="hlt">downscaling</span> of decadal predictions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Feldmann, H.</p> <p>2014-12-01</p> <p>During the last years the research field of decadal predictions gained increased attention. Its intention is to exploit the predictability derived from slowly varying components of the climate system on inter-annual to decadal time-scales. Such predictions are mostly performed using <span class="hlt">ensembles</span> of global earth system models. The prediction systems are able to achieve a relatively high predictive skill over some oceanic regions, like the North Atlantic sector. But potential users of decadal predictions are often interested in forecasts over land areas and require a higher resolution, too. Therefore, the German research program MiKlip develops a decadal <span class="hlt">ensemble</span> predictions system with regional <span class="hlt">downscaling</span> as an additional option. Dynamical <span class="hlt">downscaling</span> and a statistical-dynamical <span class="hlt">downscaling</span> approach are applied within the MiKlip regionalization module. The global prediction system consists of the MPI-ESM model. Different RCMs are used for the <span class="hlt">downscaling</span>, e.g. CCLM and REMO. The focus regions are Europe and Western Africa. Hindcast experiments for the period 1960 - 2013 were performed to assess the general skill of the prediction system. Of special interest is the value added by the regional <span class="hlt">downscaling</span>. For mean quantities, like annual mean temperature and precipitation, the predictive skill is comparable between the global and the <span class="hlt">downscaled</span> systems. For extremes on the other hand there seems to be an improvement by the RCM <span class="hlt">ensemble</span>. The skill strongly varies on sub-continental regions and with the season. The lead time up to which a positive predictive skill can be achieved depends on the parameter and season, too. A further goal is to assess the potential for valuable information, which can be derived from predicting long-term variations of the European climate. The leading mode of decadal variability in the European/Atlantic sector is the Atlantic Multidecadal Variation (AMV). The potential predictability from AMV teleconnections especially for extreme value anomalies over Europe is explored using re-analysis driven RCM simulations. It is evaluated how such teleconnections are represented in the RCM. For instance, the multi-year mean soil water content is correlated to the AMV index in Europe. This provides the potential to predict drought tendencies, which is relevant for agricultural applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1069M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1069M"><span id="translatedtitle">New statistical <span class="hlt">downscaling</span> for Canada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Murdock, T. Q.; Cannon, A. J.; Sobie, S.</p> <p>2013-12-01</p> <p>This poster will document the production of a set of statistically <span class="hlt">downscaled</span> future climate projections for Canada based on the latest available RCM and GCM simulations - the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2007) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). The main stages of the project included (1) <span class="hlt">downscaling</span> method evaluation, (2) scenarios selection, (3) production of statistically <span class="hlt">downscaled</span> results, and (4) applications of results. We build upon a previous <span class="hlt">downscaling</span> evaluation project (Bürger et al. 2012, Bürger et al. 2013) in which a quantile-based method (Bias Correction/Spatial Disaggregation - BCSD; Werner 2011) provided high skill compared with four other methods representing the majority of types of <span class="hlt">downscaling</span> used in Canada. Additional quantile-based methods (Bias-Correction/Constructed Analogues; Maurer et al. 2010 and Bias-Correction/Climate Imprint ; Hunter and Meentemeyer 2005) were evaluated. A subset of 12 CMIP5 simulations was chosen based on an objective set of selection criteria. This included hemispheric skill assessment based on the CLIMDEX indices (Sillmann et al. 2013), historical criteria used previously at the Pacific Climate Impacts Consortium (Werner 2011), and refinement based on a modified clustering algorithm (Houle et al. 2012; Katsavounidis et al. 1994). Statistical <span class="hlt">downscaling</span> was carried out on the NARCCAP <span class="hlt">ensemble</span> and a subset of the CMIP5 <span class="hlt">ensemble</span>. We produced <span class="hlt">downscaled</span> scenarios over Canada at a daily time resolution and 300 arc second (~10 km) spatial resolution from historical runs for 1951-2005 and from RCP 2.6, 4.5, and 8.5 projections for 2006-2100. The ANUSPLIN gridded daily dataset (McKenney et al. 2011) was used as a target. It has national coverage, spans the historical period of interest 1951-2005, and has daily time resolution. It uses interpolation of station data based on thin-plate splines. This type of method has been shown to have 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.A21G0178L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A21G0178L"><span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> NCEP Global Climate Forecast System (CFS) Seasonal Predictions Using Regional Atmospheric Modeling System (RAMS) - Evaluation with North American Regional Reanalysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, L.; Zheng, Y.; Pielke, R. A.; Dynamical Downscaling Using Rams</p> <p>2010-12-01</p> <p>As part of the NOAA CPPA-sponsored <span class="hlt">MRED</span> project, the state-of-the-art Regional Atmospheric Modeling System (RAMS) version 6.0 is used to dynamically and progressively <span class="hlt">downscale</span> NCEP global Climate Forecast System (CFS, at 100s-km grid increment) seasonal predictions to a regional domain that covers the conterminous United States at 30-km grid increment. The project’s first stage focuses on the winter seasons, during which the precipitation is largely dependent on synoptic-scale mid-latitude storms and orographic dominant mesoscale processes. Ten <span class="hlt">ensemble</span> experiments for each year from 1982 through 2003 have been performed, starting on November 21 through 25, and November 29 through December 3 respectively, and ending on April 30. Driven by the CFS atmospheric and SST forcings, RAMS is evaluated against observations and North American Regional Reanalysis (NARR) to see if the simulations capture the climatology and interannual variability of temperature and precipitation distributions, as well as the energy and water cycles. The results show that large interannual variations exist in each of the <span class="hlt">downscaled</span> variables, and there are also pronounced differences between each of these variables. The spatial correlation coefficients between NARR and RAMS simulations are high for surface air temperature and specific humidity, surface pressure, geopotential heights, surface downwelling short- and long- wave radiation, latent heat fluxes, but low for precipitation and sensible heat fluxes. Meridional and zonal moisture fluxes and geopotential height at 850 hPa have the largest interannual variability. The annual-mean domain-averaged surface temperature, surface specific humidity, and precipitation shows that RAMS simulated a warmer and drier near surface climate, and rained more for all the years compare to NARR products. RAMS-simulated spatial patterns of 200 mb geopotential height, surface specific humidity, and surface air temperature mostly resemble NARR product, while the warmer temperature bias is a direct result of overestimation of surface temperature around Gulf of Mexico and southeast of the model domain. In addition, we analyzed the changes in kinetic energy spectra before and after the spectral nudging algorithm was implemented. The results show that with the spectral nudging scheme, RAMS can better preserve large-scale kinetic energy than standard boundary forcing method, and allow more large-scale energy to cascade to smaller scales. The extent that the spectral nudging constrains the evolution of the smaller scale features, and preserve the large-scale forcing is quantified by comparing the results with and without the nudging scheme.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1064W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1064W"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span>: Lessons Learned</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Walton, D.; Hall, A. D.; Sun, F.</p> <p>2013-12-01</p> <p>In this study, we examine ways to improve statistical <span class="hlt">downscaling</span> of general circulation model (GCM) output. Why do we <span class="hlt">downscale</span> GCM output? GCMs have low resolution, so they cannot represent local dynamics and topographic effects that cause spatial heterogeneity in the regional climate change signal. Statistical <span class="hlt">downscaling</span> recovers fine-scale information by utilizing relationships between the large-scale and fine-scale signals to bridge this gap. In theory, the <span class="hlt">downscaled</span> climate change signal is more credible and accurate than its GCM counterpart, but in practice, there may be little improvement. Here, we tackle the practical problems that arise in statistical <span class="hlt">downscaling</span>, using temperature change over the Los Angeles region as a test case. This region is an ideal place to apply <span class="hlt">downscaling</span> since its complex topography and shoreline are poorly simulated by GCMs. By comparing two popular statistical <span class="hlt">downscaling</span> methods and one dynamical <span class="hlt">downscaling</span> method, we identify issues with statistically <span class="hlt">downscaled</span> climate change signals and develop ways to fix them. We focus on scale mismatch, domain of influence, and other problems - many of which users may be unaware of - and discuss practical solutions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1611668A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1611668A"><span id="translatedtitle"><span class="hlt">Downscaling</span> of inundation extents</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Aires, Filipe; Prigent, Catherine; Papa, Fabrice</p> <p>2014-05-01</p> <p>The Global Inundation Extent from Multi-Satellite (GIEMS) provides multi-year monthly variations of the global surface water extent at about 25 kmx25 km resolution, from 1993 to 2007. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. This paper present three approaches to do <span class="hlt">downscale</span> GIEMS: The first one is based on a image-processing technique using neighborhood constraints. The third approach uses a PCA representation to perform an algebraic inversion. The PCA-representation is also very convenient to perform temporal and spatial interpolation of complexe inundation fields. The third <span class="hlt">downscaling</span> method uses topography information from Hydroshed Digital Elevation Model (DEM). Information such as the elevation, distance to river and flow accumulation are used to define a ``flood ability index'' that is used by the <span class="hlt">downscaling</span>. Three basins will be considered for illustrative purposes: Amazon, Niger and Mekong. Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and <span class="hlt">downscaling</span> of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeorology, in press, 2014. Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, <span class="hlt">downscaled</span> from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett., 39(L08403), 2012.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AtmRe.143...17L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AtmRe.143...17L"><span id="translatedtitle">An application of hybrid <span class="hlt">downscaling</span> model to forecast summer precipitation at stations in China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Ying; Fan, Ke</p> <p>2014-06-01</p> <p>A pattern prediction hybrid <span class="hlt">downscaling</span> method was applied to predict summer (June-July-August) precipitation at China 160 stations. The predicted precipitation from the <span class="hlt">downscaling</span> scheme is available one month before. Four predictors were chosen to establish the hybrid <span class="hlt">downscaling</span> scheme. The 500-hPa geopotential height (GH5) and 850-hPa specific humidity (q85) were from the skillful predicted output of three DEMETER (Development of a European Multi-model <span class="hlt">Ensemble</span> System for Seasonal to Interannual Prediction) general circulation models (GCMs). The 700-hPa geopotential height (GH7) and sea level pressure (SLP) were from reanalysis datasets. The hybrid <span class="hlt">downscaling</span> scheme (HD-4P) has better prediction skill than a conventional statistical <span class="hlt">downscaling</span> model (SD-2P) which contains two predictors derived from the output of GCMs, although two <span class="hlt">downscaling</span> schemes were performed to improve the seasonal prediction of summer rainfall in comparison with the original output of the DEMETER GCMs. In particular, HD-4P <span class="hlt">downscaling</span> predictions showed lower root mean square errors than those based on the SD-2P model. Furthermore, the HD-4P <span class="hlt">downscaling</span> model reproduced the China summer precipitation anomaly centers more accurately than the scenario of the SD-2P model in 1998. A hybrid <span class="hlt">downscaling</span> prediction should be effective to improve the prediction skill of summer rainfall at stations in China.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..525..286D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..525..286D"><span id="translatedtitle">Dynamic coupling of support vector machine and K-nearest neighbour for <span class="hlt">downscaling</span> daily rainfall</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Devak, Manjula; Dhanya, C. T.; Gosain, A. K.</p> <p>2015-06-01</p> <p>Climate change impact assessment studies in water resources section demand the simulations of climatic variables at coarser scales from dynamic General Circulation Models (GCMs) to be mapped to even finer scales. Related studies in this area have mostly been relying on statistical techniques for <span class="hlt">downscaling</span> variables to finer resolution. This demands a careful selection of a suitable <span class="hlt">downscaling</span> model, to alleviate the <span class="hlt">downscaling</span> uncertainty. In this study, it is proposed to develop a dynamic framework for <span class="hlt">downscaling</span> purpose by integrating the frequently used techniques, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). In order to give flexibility in future predictors-predictand relationships and to account the sensitivity in model parameters, it is also proposed to generate an <span class="hlt">ensemble</span> of outputs by identifying various plausible model parameter combinations. The performance of this framework for <span class="hlt">downscaling</span> daily precipitation values at different locations is compared with simple KNN and SVM models. The proposed hybrid model is found to be better in capturing various characteristics of daily precipitation than individual models, especially in simulating the extremes, both in magnitude and duration. The mean <span class="hlt">ensemble</span> is found to be efficient than single best simulation with optimum parameter combinations. The efficacy of hybrid SVM-KNN <span class="hlt">ensemble</span> <span class="hlt">downscaling</span> model is established through detailed investigations. The future <span class="hlt">downscaled</span> projection for mid-century and late century employing this hybrid model indicates an increased variability in future precipitation, though the intensity varies for various locations. The developed methodology hence ensures lesser <span class="hlt">downscaling</span> uncertainty and also eliminates the inherent assumption of relationship stationarity considered in many <span class="hlt">downscaling</span> models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.A21G0181C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.A21G0181C"><span id="translatedtitle">Approaches for Assessing <span class="hlt">Downscaled</span> Climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, L.; Fan, X.; Ma, Z.</p> <p>2010-12-01</p> <p>Most of the global modeling outputs, including the IPCC projections, global reanalyses such as the European 40-year reanalysis and NCEP/NCAR reanalysis project, are at very coarse resolution (>100 km) for the purpose of a global coverage within the existing computational capability. However, most of natural and ecological resource management activities need climate data to be at ecologically and hydrologically relevant regional scales. To match this requirement, <span class="hlt">downscaling</span> of climate model output to regional scale is necessary. In addition to statistical <span class="hlt">downscaling</span>, dynamical <span class="hlt">downscaling</span> of climate has been conducted at various institutions in order to obtain a full set of dynamically consistent regional climate. Although utilization of nudging techniques in regional climate simulation techniques have been demonstrated to be able to keep the simulated states to the driving state at large scales while generating small-scale features, questions about the assessment and evaluation of the <span class="hlt">downscaled</span> climate arise as more and more institutions and individuals are involved in climate <span class="hlt">downscaling</span> and more and more <span class="hlt">downscaled</span> climate datasets becomes available. What are the confidence levels at which a <span class="hlt">downscaled</span> climate can be a real <span class="hlt">downscaled</span> climate? How much freedom should the regional climate model have to deviate from the large-scale driving field? Does the <span class="hlt">downscaled</span> climate retains all large scale features at original coarse resolution, while it adds valuable subscale information but not too noisy? This study investigates and suggests approaches that can quantitatively evaluate <span class="hlt">downscaled</span> climate from different configurations and/or from different modeling systems. The methods are used here to evaluate three <span class="hlt">downscaled</span> climates of NCEP/NCAR reanalyses (NNRP), using the Weather Research and Forecasting (WRF) model at 108-36-12km. The three types of <span class="hlt">downscaling</span> experiments were performed for a total of one month. The first type is serving as a base whereas the large scale information is communicated through lateral boundary conditions only; the second is using an internal nudging to grid analysis which is also called four-dimensional data assimilation (FDDA); and the third is using spectral nudging, which constrains internal model states to large scale waves. The evaluation methods proposed and examined in this study provide an objective measure of how a <span class="hlt">downscaling</span> approach is performing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/22086963','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/22086963"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2012.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Flicek, Paul; Amode, M Ridwan; Barrell, Daniel; Beal, Kathryn; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fairley, Susan; Fitzgerald, Stephen; Gil, Laurent; Gordon, Leo; Hendrix, Maurice; Hourlier, Thibaut; Johnson, Nathan; Kähäri, Andreas K; Keefe, Damian; Keenan, Stephen; Kinsella, Rhoda; Komorowska, Monika; Koscielny, Gautier; Kulesha, Eugene; Larsson, Pontus; Longden, Ian; McLaren, William; Muffato, Matthieu; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Riat, Harpreet Singh; Ritchie, Graham R S; Ruffier, Magali; Schuster, Michael; Sobral, Daniel; Tang, Y Amy; Taylor, Kieron; Trevanion, Stephen; Vandrovcova, Jana; White, Simon; Wilson, Mark; Wilder, Steven P; Aken, Bronwen L; Birney, Ewan; Cunningham, Fiona; Dunham, Ian; Durbin, Richard; Fernández-Suarez, Xosé M; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J P; Parker, Anne; Proctor, Glenn; Spudich, Giulietta; Vogel, Jan; Yates, Andy; Zadissa, Amonida; Searle, Stephen M J</p> <p>2012-01-01</p> <p>The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) provides genome resources for chordate genomes with a particular focus on human genome data as well as data for key model organisms such as mouse, rat and zebrafish. Five additional species were added in the last year including gibbon (Nomascus leucogenys) and Tasmanian devil (Sarcophilus harrisii) bringing the total number of supported species to 61 as of <span class="hlt">Ensembl</span> release 64 (September 2011). Of these, 55 species appear on the main <span class="hlt">Ensembl</span> website and six species are provided on the <span class="hlt">Ensembl</span> preview site (Pre!<span class="hlt">Ensembl</span>; http://pre.<span class="hlt">ensembl</span>.org) with preliminary support. The past year has also seen improvements across the project. PMID:22086963</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1611960H','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hoffmann, Peter; Lutz, Julia; Menz, Christoph</p> <p>2014-05-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25833698','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25833698"><span id="translatedtitle"><span class="hlt">Downscaled</span> projections of Caribbean coral bleaching that can inform conservation planning.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>van Hooidonk, Ruben; Maynard, Jeffrey Allen; Liu, Yanyun; Lee, Sang-Ki</p> <p>2015-09-01</p> <p>Projections of climate change impacts on coral reefs produced at the coarse resolution (~1°) of Global Climate Models (GCMs) have informed debate but have not helped target local management actions. Here, projections of the onset of annual coral bleaching conditions in the Caribbean under Representative Concentration Pathway (RCP) 8.5 are produced using an <span class="hlt">ensemble</span> of 33 Coupled Model Intercomparison Project phase-5 models and via dynamical and statistical <span class="hlt">downscaling</span>. A high-resolution (~11 km) regional ocean model (MOM4.1) is used for the dynamical <span class="hlt">downscaling</span>. For statistical <span class="hlt">downscaling</span>, sea surface temperature (SST) means and annual cycles in all the GCMs are replaced with observed data from the ~4-km NOAA Pathfinder SST dataset. Spatial patterns in all three projections are broadly similar; the average year for the onset of annual severe bleaching is 2040-2043 for all projections. However, <span class="hlt">downscaled</span> projections show many locations where the onset of annual severe bleaching (ASB) varies 10 or more years within a single GCM grid cell. Managers in locations where this applies (e.g., Florida, Turks and Caicos, Puerto Rico, and the Dominican Republic, among others) can identify locations that represent relative albeit temporary refugia. Both <span class="hlt">downscaled</span> projections are different for the Bahamas compared to the GCM projections. The dynamically <span class="hlt">downscaled</span> projections suggest an earlier onset of ASB linked to projected changes in regional currents, a feature not resolved in GCMs. This result demonstrates the value of dynamical <span class="hlt">downscaling</span> for this application and means statistically <span class="hlt">downscaled</span> projections have to be interpreted with caution. However, aside from west of Andros Island, the projections for the two types of <span class="hlt">downscaling</span> are mostly aligned; projected onset of ASB is within ±10 years for 72% of the reef locations. PMID:25833698</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3147673','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3147673"><span id="translatedtitle">The Requirement for Pneumococcal MreC and <span class="hlt">MreD</span> Is Relieved by Inactivation of the Gene Encoding PBP1a ?†</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Land, Adrian D.; Winkler, Malcolm E.</p> <p>2011-01-01</p> <p>MreC and <span class="hlt">MreD</span>, along with the actin homologue MreB, are required to maintain the shape of rod-shaped bacteria. The depletion of MreCD in rod-shaped bacteria leads to the formation of spherical cells and the accumulation of suppressor mutations. Ovococcus bacteria, such as Streptococcus pneumoniae, lack MreB homologues, and the functions of the S. pneumoniae MreCD (MreCDSpn) proteins are unknown. mreCD are located upstream from the pcsB cell division gene in most Streptococcus species, but we found that mreCD and pcsB are transcribed independently. Similarly to rod-shaped bacteria, we show that mreCD are essential in the virulent serotype 2 D39 strain of S. pneumoniae, and the depletion of MreCD results in cell rounding and lysis. In contrast, laboratory strain R6 contains suppressors that allow the growth of ?mreCD mutants, and bypass suppressors accumulate in D39 ?mreCD mutants. One class of suppressors eliminates the function of class A penicillin binding protein 1a (PBP1a). Unencapsulated ?pbp1a D39 mutants have smaller diameters than their pbp1a+ parent or ?pbp2a and ?pbp1b mutants, which lack other class A PBPs and do not show the suppression of ?mreCD mutations. Suppressed ?mreCD ?pbp1a double mutants form aberrantly shaped cells, some with misplaced peptidoglycan (PG) biosynthesis compared to that of single ?pbp1a mutants. Quantitative Western blotting showed that MreCSpn is abundant (?8,500 dimers per cell), and immunofluorescent microscopy (IFM) located MreCDSpn to the equators and septa of dividing cells, similarly to the PBPs and PG pentapeptides indicative of PG synthesis. These combined results are consistent with a model in which MreCDSpn direct peripheral PG synthesis and control PBP1a localization or activity. PMID:21685290</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.5859D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.5859D"><span id="translatedtitle"><span class="hlt">Downscaling</span> precipitation extremes in a complex Alpine catchment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dobler, C.</p> <p>2012-04-01</p> <p>Climate change is expected to have significant effects on the frequency and intensity of heavy precipitation events. Assessing the impacts of climate change on precipitation extremes is a challenging task. On the one hand, the output of Regional Climate Models (RCMs) is subjected to systematic biases in the case of precipitation, especially in a complex mountain topography, and on the other hand, yet only a few statistical <span class="hlt">downscaling</span> techniques are known to <span class="hlt">downscale</span> precipitation extremes reliably. In this investigation two statistical <span class="hlt">downscaling</span> approaches were applied to simulate precipitation extremes in the Alpine part of the Lech catchment. The first one, Expanded <span class="hlt">Downscaling</span> (EDS), is a perfect prognosis approach that is based on regression. EDS has been calibrated and validated using large-scale predictor variables derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset and local station data. The EDS model was then applied to <span class="hlt">downscale</span> the output of two GCMs (ECHAM5, HadGEM2) for current (1971-2000) and future (2071-2100) time horizons, forced with the SRES A1B emission scenario. The second approach is the Long Ashton Research Station Weather Generator (LARS-WG) which can be characterized as a change factor conditioned weather generator. LARS-WG was calibrated on local station data only and then applied to <span class="hlt">downscale</span> the output of five different GCM-RCM combinations to meteorological stations. The RCMs have a horizontal resolution of ~25 km and were obtained from the <span class="hlt">ENSEMBLES</span> project of the European Union. In order to assess precipitation extremes with higher return values, a generalized extreme value distribution was applied to the data. Confidence intervals were calculated by using the non-parametric bootstrapping technique. The results show that both <span class="hlt">downscaling</span> approaches reproduce observed precipitation extremes fairly well. Even for very extreme precipitation events such as the 20-year event a good agreement between observation and simulation is obtained. When studying the effects of climate change on precipitation extremes, a wide range of results with both projected increases and decreases in precipitation extremes can be found. However, the number of climate simulations which indicates statistically significant increases is by far larger than that showing decreases. Very robust signals can be found for spring and autumn, in which almost all climate scenarios indicate increases in the intensity of precipitation extremes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.3380Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.3380Z"><span id="translatedtitle">Atmospheric <span class="hlt">Downscaling</span> using Genetic Programming</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zerenner, Tanja; Venema, Victor; Simmer, Clemens</p> <p>2013-04-01</p> <p>Coupling models for the different components of the Soil-Vegetation-Atmosphere-System requires up-and <span class="hlt">downscaling</span> procedures. Subject of our work is the <span class="hlt">downscaling</span> scheme used to derive high resolution forcing data for land-surface and subsurface models from coarser atmospheric model output. The current <span class="hlt">downscaling</span> scheme [Schomburg et. al. 2010, 2012] combines a bi-quadratic spline interpolation, deterministic rules and autoregressive noise. For the development of the scheme, training and validation data sets have been created by carrying out high-resolution runs of the atmospheric model. The deterministic rules in this scheme are partly based on known physical relations and partly determined by an automated search for linear relationships between the high resolution fields of the atmospheric model output and high resolution data on surface characteristics. Up to now deterministic rules are available for <span class="hlt">downscaling</span> surface pressure and partially, depending on the prevailing weather conditions, for near surface temperature and radiation. Aim of our work is to improve those rules and to find deterministic rules for the remaining variables, which require <span class="hlt">downscaling</span>, e.g. precipitation or near surface specifc humidity. To accomplish that, we broaden the search by allowing for interdependencies between different atmospheric parameters, non-linear relations, non-local and time-lagged relations. To cope with the vast number of possible solutions, we use genetic programming, a method from machine learning, which is based on the principles of natural evolution. We are currently working with GPLAB, a Genetic Programming toolbox for Matlab. At first we have tested the GP system to retrieve the known physical rule for <span class="hlt">downscaling</span> surface pressure, i.e. the hydrostatic equation, from our training data. We have found this to be a simple task to the GP system. Furthermore we have improved accuracy and efficiency of the GP solution by implementing constant variation and optimization as genetic operators. Next we have worked on an improvement of the <span class="hlt">downscaling</span> rule for the two-meter-temperature. We have added an if-function with four input arguments to the function set. Since this has shown to increase bloat we have additionally modified our fitness function by including penalty terms for both the size of the solutions and the number intron nodes, i.e program parts that are never evaluated. Starting from the known <span class="hlt">downscaling</span> rule for the two-meter temperature, which linearly exploits the orography anomalies allowed or disallowed by a certain temperature gradient, our GP system has been able to find an improvement. The rule produced by the GP clearly shows a better performance concerning the reproduced small-scale variability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012amld.book..563R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012amld.book..563R"><span id="translatedtitle"><span class="hlt">Ensemble</span> Methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Re, Matteo; Valentini, Giorgio</p> <p>2012-03-01</p> <p><span class="hlt">Ensemble</span> methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. <span class="hlt">Ensembles</span> are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote <span class="hlt">ensemble</span>, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall <span class="hlt">ensemble</span> [158]. In the literature, a plethora of terms other than <span class="hlt">ensembles</span> has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term <span class="hlt">ensemble</span> in its widest meaning, in order to include the whole range of combination methods. Nowadays, <span class="hlt">ensemble</span> methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on <span class="hlt">ensemble</span> methods is witnessed by conferences and workshops specifically devoted to <span class="hlt">ensembles</span>, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been 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 paper and lists some issues not covered in this work.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=235875&keyword=Kalman&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55685676&CFTOKEN=96258174','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=235875&keyword=Kalman&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55685676&CFTOKEN=96258174"><span id="translatedtitle"><span class="hlt">Ensemble</span> Models</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p><span class="hlt">Ensemble</span> forecasting has been used for operational numerical weather prediction in the United States and Europe since the early 1990s. An <span class="hlt">ensemble</span> of weather or climate forecasts is used to characterize the two main sources of uncertainty in computer models of physical systems: ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1616101E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1616101E"><span id="translatedtitle"><span class="hlt">Downscaling</span> GCM-simulated precipitation for the last millennium</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Eden, Jonathan; Widmann, Martin; Smith, Richard</p> <p>2014-05-01</p> <p>Climate variability in the pre-instrumental period can be estimated either from climate proxy data or from numerical simulations. Both approaches still have considerable uncertainties and consistency tests are crucial for identifying robust features. One of the problems when comparing simulations with proxy-based reconstructions are potential scale mismatches. If the proxy-based reconstructions represent regional climate a direct comparison with simulated variables from global climate models, which in palaeoclimate applications are run with coarse resolutions, can lead to misleading results for two reasons: (i) the climate model might be biased even on large spatial scales, and (ii) small-scale spatial variability cannot be represented by the climate model. This problem can be expected to be particularly relevant for precipitation because of its high spatial variability. One way of addressing this problem is by applying <span class="hlt">downscaling</span> techniques to the simulations. We have applied a statistical <span class="hlt">downscaling</span> and correction method to precipitation from a simulation for the last millennium with the MPI for Meteorology Earth System Model, which uses ECHAM5-T31 as the atmosphere component. Our <span class="hlt">downscaling</span> method, which is based on model output statistics (MOS), has been shown to outperform more standard (so-called perfect-prog) statistical <span class="hlt">downscaling</span> methods when applied to simulated precipitation from the second half of the twentieth century, but it has not yet been applied to palaeoclimate simulations. Our aim is two-fold: to assess (a) whether <span class="hlt">downscaling</span> using MOS yields additional information about long-term changes in regional climate and (b) to what extent the <span class="hlt">downscaled</span> simulations may be in greater agreement with proxy-based reconstructions than raw model output. Two MOS <span class="hlt">downscaling</span> methods, based on local scaling and principal component regression, are calibrated 'event-wise' (i.e. between contemporaneous sequences of simulated and observed events) using precipitation from a simulation of ECHAM5 (nudged to ERA-40) and gridded observations. Both methods are then applied to simulated precipitation for the last millennium. Our findings show that, under cross-validation for the period 1958-2001, <span class="hlt">downscaling</span> with MOS from the T31 resolution to a 0.5° x 0.5° target grid produces precipitation estimates that generally match the temporal variability of the observed record in large parts of Europe. MOS also shows good skill in estimating monthly precipitation amounts at small scales that are more realistic than raw model output. In comparison with a multi-proxy gridded reconstruction (Pauling et al., 2006) it is shown that reconstructed precipitation falls within the range of the <span class="hlt">downscaled</span> <span class="hlt">ensemble</span> spread in some parts of Europe. However, in many areas MOS fails to produce <span class="hlt">downscaled</span> estimates that are in agreement with either the temporal evolution or magnitude indicated by the proxy record. Ultimately, this inconsistency limits the potential for such a comparison to be used as a validation tool except in individual cases.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li class="active"><span>2</span></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_2 --> <div id="page_3" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="41"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1055R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1055R"><span id="translatedtitle">Evaluating <span class="hlt">Downscaling</span> Methods for Seasonal Climate Forecasts over East Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roberts, J. B.; Robertson, F. R.; Bosilovich, M. G.; Lyon, B.</p> <p>2013-12-01</p> <p>The U.S. National Multi-Model <span class="hlt">Ensemble</span> seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of <span class="hlt">downscaling</span> methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially <span class="hlt">downscaled</span> and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available <span class="hlt">downscaling</span> methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006513','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006513"><span id="translatedtitle">Evaluating <span class="hlt">Downscaling</span> Methods for Seasonal Climate Forecasts over East Africa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris</p> <p>2013-01-01</p> <p>The U.S. National Multi-Model <span class="hlt">Ensemble</span> seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of <span class="hlt">downscaling</span> methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially <span class="hlt">downscaled</span> and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available <span class="hlt">downscaling</span> methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006440','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006440"><span id="translatedtitle">Evaluating <span class="hlt">Downscaling</span> Methods for Seasonal Climate Forecasts over East Africa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Robertson, Franklin R.; Roberts, J. Brent; Bosilovich, Michael; Lyon, Bradfield</p> <p>2013-01-01</p> <p>The U.S. National Multi-Model <span class="hlt">Ensemble</span> seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of <span class="hlt">downscaling</span> methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially <span class="hlt">downscaled</span> and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available <span class="hlt">downscaling</span> methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4383879','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4383879"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2015</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Cunningham, Fiona; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Billis, Konstantinos; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.; Janacek, Sophie H.; Johnson, Nathan; Juettemann, Thomas; Kähäri, Andreas K.; Keenan, Stephen; Martin, Fergal J.; Maurel, Thomas; McLaren, William; Murphy, Daniel N.; Nag, Rishi; Overduin, Bert; Parker, Anne; Patricio, Mateus; Perry, Emily; Pignatelli, Miguel; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P.; Zadissa, Amonida; Aken, Bronwen L.; Birney, Ewan; Harrow, Jennifer; Kinsella, Rhoda; Muffato, Matthieu; Ruffier, Magali; Searle, Stephen M.J.; Spudich, Giulietta; Trevanion, Stephen J.; Yates, Andy; Zerbino, Daniel R.; Flicek, Paul</p> <p>2015-01-01</p> <p><span class="hlt">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org) is a genomic interpretation system providing the most up-to-date annotations, querying tools and access methods for chordates and key model organisms. This year we released updated annotation (gene models, comparative genomics, regulatory regions and variation) on the new human assembly, GRCh38, although we continue to support researchers using the GRCh37.p13 assembly through a dedicated site (http://grch37.<span class="hlt">ensembl</span>.org). Our Regulatory Build has been revamped to identify regulatory regions of interest and to efficiently highlight their activity across disparate epigenetic data sets. A number of new interfaces allow users to perform large-scale comparisons of their data against our annotations. The REST server (http://rest.<span class="hlt">ensembl</span>.org), which allows programs written in any language to query our databases, has moved to a full service alongside our upgraded website tools. Our online Variant Effect Predictor tool has been updated to process more variants and calculate summary statistics. Lastly, the WiggleTools package enables users to summarize large collections of data sets and view them as single tracks in <span class="hlt">Ensembl</span>. The <span class="hlt">Ensembl</span> code base itself is more accessible: it is now hosted on our GitHub organization page (https://github.com/<span class="hlt">Ensembl</span>) under an Apache 2.0 open source license. PMID:25352552</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25352552','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25352552"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2015.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Cunningham, Fiona; Amode, M Ridwan; Barrell, Daniel; Beal, Kathryn; Billis, Konstantinos; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E; Janacek, Sophie H; Johnson, Nathan; Juettemann, Thomas; Kähäri, Andreas K; Keenan, Stephen; Martin, Fergal J; Maurel, Thomas; McLaren, William; Murphy, Daniel N; Nag, Rishi; Overduin, Bert; Parker, Anne; Patricio, Mateus; Perry, Emily; Pignatelli, Miguel; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P; Zadissa, Amonida; Aken, Bronwen L; Birney, Ewan; Harrow, Jennifer; Kinsella, Rhoda; Muffato, Matthieu; Ruffier, Magali; Searle, Stephen M J; Spudich, Giulietta; Trevanion, Stephen J; Yates, Andy; Zerbino, Daniel R; Flicek, Paul</p> <p>2015-01-01</p> <p><span class="hlt">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org) is a genomic interpretation system providing the most up-to-date annotations, querying tools and access methods for chordates and key model organisms. This year we released updated annotation (gene models, comparative genomics, regulatory regions and variation) on the new human assembly, GRCh38, although we continue to support researchers using the GRCh37.p13 assembly through a dedicated site (http://grch37.<span class="hlt">ensembl</span>.org). Our Regulatory Build has been revamped to identify regulatory regions of interest and to efficiently highlight their activity across disparate epigenetic data sets. A number of new interfaces allow users to perform large-scale comparisons of their data against our annotations. The REST server (http://rest.<span class="hlt">ensembl</span>.org), which allows programs written in any language to query our databases, has moved to a full service alongside our upgraded website tools. Our online Variant Effect Predictor tool has been updated to process more variants and calculate summary statistics. Lastly, the WiggleTools package enables users to summarize large collections of data sets and view them as single tracks in <span class="hlt">Ensembl</span>. The <span class="hlt">Ensembl</span> code base itself is more accessible: it is now hosted on our GitHub organization page (https://github.com/<span class="hlt">Ensembl</span>) under an Apache 2.0 open source license. PMID:25352552</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15..446H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15..446H"><span id="translatedtitle">A combined statistical and dynamical approach for <span class="hlt">downscaling</span> large-scale footprints of European windstorms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Haas, Rabea; Pinto, Joaquim G.</p> <p>2013-04-01</p> <p>The occurrence of mid-latitude windstorms is related to strong socio-economic effects. For detailed and reliable regional impact studies, large datasets of high-resolution wind fields are required. In this study, a statistical <span class="hlt">downscaling</span> approach in combination with dynamical <span class="hlt">downscaling</span> is introduced to derive storm related gust speeds on a high-resolution grid over Europe. Multiple linear regression models are trained using reanalysis data and wind gusts from regional climate model simulations for a sample of 100 top ranking windstorm events. The method is computationally inexpensive and reproduces individual windstorm footprints adequately. Compared to observations, the results for Germany are at least as good as pure dynamical <span class="hlt">downscaling</span>. This new tool can be easily applied to large <span class="hlt">ensembles</span> of general circulation model simulations and thus contribute to a better understanding of the regional impact of windstorms based on decadal and climate change projections.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..529.1407N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..529.1407N"><span id="translatedtitle">Transient stochastic <span class="hlt">downscaling</span> of quantitative precipitation estimates for hydrological applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nogueira, M.; Barros, A. P.</p> <p>2015-10-01</p> <p>Rainfall fields are heavily thresholded and highly intermittent resulting in large areas of zero values. This deforms their stochastic spatial scale-invariant behavior, introducing scaling breaks and curvature in the spatial scale spectrum. To address this problem, spatial scaling analysis was performed inside continuous rainfall features (CRFs) delineated via cluster analysis. The results show that CRFs from single realizations of hourly rainfall display ubiquitous multifractal behavior that holds over a wide range of scales (from ≈1 km up to 100's km). The results further show that the aggregate scaling behavior of rainfall fields is intrinsically transient with the scaling parameters explicitly dependent on the atmospheric environment. These findings provide a framework for robust stochastic <span class="hlt">downscaling</span>, bridging the gap between spatial scales of observed and simulated rainfall fields and the high-resolution requirements of hydrometeorological and hydrological studies. Here, a fractal <span class="hlt">downscaling</span> algorithm adapted to CRFs is presented and applied to generate stochastically <span class="hlt">downscaled</span> hourly rainfall products from radar derived Stage IV (∼4 km grid resolution) quantitative precipitation estimates (QPE) over the Integrated Precipitation and Hydrology Experiment (IPHEx) domain in the southeast USA. The methodology can produce large <span class="hlt">ensembles</span> of statistically robust high-resolution fields without additional data or any calibration requirements, conserving the coarse resolution information and generating coherent small-scale variability and field statistics, hence adding value to the original fields. Moreover, it is computationally inexpensive enabling fast production of high-resolution rainfall realizations with latency adequate for forecasting applications. When the transient nature of the scaling behavior is considered, the results show a better ability to reproduce the statistical structure of observed rainfall compared to using fixed scaling parameters derived from <span class="hlt">ensemble</span> mean analysis. A 7-year data set of 50 hourly realizations of <span class="hlt">downscaled</span> Stage IV rainfall fields at 1 km resolution for the IPHEx domain is publicly available from http://www.iphex.pratt.duke.edu. The value of the <span class="hlt">downscaled</span> products is demonstrated through hydrological simulations of two distinct storm events in the Southern Appalachians, a winter storm that caused multiple landslides and a summer tropical event that caused flashfloods. The simulations are forced by the entire span of plausible fractally <span class="hlt">downscaled</span> rainfall fields at two distinct resolutions (1 km and 250 m). The results show very good skill against the observed streamflow, especially with regard to the timing and peak discharge of the hydrograph, and the accuracy is enhanced by increasing the target <span class="hlt">downscaling</span> resolution from 1 km to 250 m. Probabilistic simulations of both events capture the observed behavior indicating that the proposed CRF-based stochastic fractal interpolation provides a generalized framework for producing fast and reliable probabilistic forecasts and their associated uncertainty for extreme events and risk management of hydrometeorological hazards, as well as long-term hydrologic modeling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26845558','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26845558"><span id="translatedtitle"><span class="hlt">Ensemble</span> Tractography.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Takemura, Hiromasa; Caiafa, Cesar F; Wandell, Brian A; Pestilli, Franco</p> <p>2016-02-01</p> <p>Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using <span class="hlt">ensemble</span> methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an <span class="hlt">ensemble</span> of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The <span class="hlt">ensemble</span> approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the <span class="hlt">ensemble</span> approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic <span class="hlt">ensemble</span> tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles. PMID:26845558</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4742469','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4742469"><span id="translatedtitle"><span class="hlt">Ensemble</span> Tractography</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Wandell, Brian A.</p> <p>2016-01-01</p> <p>Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using <span class="hlt">ensemble</span> methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an <span class="hlt">ensemble</span> of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The <span class="hlt">ensemble</span> approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the <span class="hlt">ensemble</span> approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic <span class="hlt">ensemble</span> tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles. PMID:26845558</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC44B..06P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC44B..06P"><span id="translatedtitle">The Influence of <span class="hlt">Downscaling</span> Models and Observations on Future Hydrochemistry Reponses of Forest Watersheds</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pourmokhtarian, A.; Driscoll, C. T.; Campbell, J. L.; Hayhoe, K.; Stoner, A. M. K.</p> <p>2014-12-01</p> <p>Most projections of climate change impacts on ecosystems rely on multiple climate model projections, but utilize only one <span class="hlt">downscaling</span> approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate <span class="hlt">downscaling</span> method and training observations used in the complex mountainous terrain of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three different <span class="hlt">downscaling</span> methods: the monthly delta method (or the "change factor method"); monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD); and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs, from four AOGCMs (CCSM3, HadCM3, PCM, and GFDL-CM2) driven by higher (A1fi) and lower (B1) future emission scenarios, on two sets of observations (1/8th degree resolution grid vs. individual weather station) to generate the high-resolution climate input for the hydrochemical model PnET-BGC (<span class="hlt">ensemble</span> of 48 runs). The choice of <span class="hlt">downscaling</span> approach and spatial resolution of the observations used to train the <span class="hlt">downscaling</span> model both had a major impact on modeled soil moisture and streamflow which in turn affected forest growth, net nitrification and stream chemistry. Specifically, the delta method, the simplest <span class="hlt">downscaling</span> approach evaluated, was highly sensitive to the observations used, resulting in projections that were significantly different than those produced with the BCSD and ARRM methods. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce poor results in model applications run at higher temporal and/or spatial resolutions. These results underscore the importance of carefully considering the observations and <span class="hlt">downscaling</span> method used to generate climate change projections for smaller scale modeling studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/17148474','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/17148474"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2007.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hubbard, T J P; Aken, B L; Beal, K; Ballester, B; Caccamo, M; Chen, Y; Clarke, L; Coates, G; Cunningham, F; Cutts, T; Down, T; Dyer, S C; Fitzgerald, S; Fernandez-Banet, J; Graf, S; Haider, S; Hammond, M; Herrero, J; Holland, R; Howe, K; Howe, K; Johnson, N; Kahari, A; Keefe, D; Kokocinski, F; Kulesha, E; Lawson, D; Longden, I; Melsopp, C; Megy, K; Meidl, P; Ouverdin, B; Parker, A; Prlic, A; Rice, S; Rios, D; Schuster, M; Sealy, I; Severin, J; Slater, G; Smedley, D; Spudich, G; Trevanion, S; Vilella, A; Vogel, J; White, S; Wood, M; Cox, T; Curwen, V; Durbin, R; Fernandez-Suarez, X M; Flicek, P; Kasprzyk, A; Proctor, G; Searle, S; Smith, J; Ureta-Vidal, A; Birney, E</p> <p>2007-01-01</p> <p>The <span class="hlt">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org/) project provides a comprehensive and integrated source of annotation of chordate genome sequences. Over the past year the number of genomes available from <span class="hlt">Ensembl</span> has increased from 15 to 33, with the addition of sites for the mammalian genomes of elephant, rabbit, armadillo, tenrec, platypus, pig, cat, bush baby, common shrew, microbat and european hedgehog; the fish genomes of stickleback and medaka and the second example of the genomes of the sea squirt (Ciona savignyi) and the mosquito (Aedes aegypti). Some of the major features added during the year include the first complete gene sets for genomes with low-sequence coverage, the introduction of new strain variation data and the introduction of new orthology/paralog annotations based on gene trees. PMID:17148474</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.7619V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.7619V"><span id="translatedtitle">A Combined Bias Correction and Stochastic <span class="hlt">Downscaling</span> Method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Volosciuk, Claudia; Maraun, Douglas; Vrac, Mathieu</p> <p>2015-04-01</p> <p>Precipitation is highly variable in space and time, especially its extremes. Much of our knowledge about future changes in precipitation relies on global (GCM) and/or regional climate models (RCM) that have resolutions which are much coarser than typical spatial scales of extreme precipitation. The major problems with these projections are both GCM/RCM-biases in simulated precipitation and the scale gap between grid box and point scale. In particular, traditional bias correction methods (e.g., quantile mapping) cannot bridge this scale gap, and empirical statistical <span class="hlt">downscaling</span> methods have a very limited ability to correct biases. Wong et al. presented a first attempt to jointly bias correct and <span class="hlt">downscale</span> precipitation at daily scales. However, this approach relies on spectrally nudged RCM simulations, which are rarely available. Here we present a combined statistical bias correction and stochastic <span class="hlt">downscaling</span> method, with the aim of combining their respective advantages, that in principle also works for free running RCMs, such as those available from <span class="hlt">ENSEMBLES</span> or CORDEX. Thereby, we separate bias correction from <span class="hlt">downscaling</span>. In a first step, we bias correct the RCMs (EURO-CORDEX) against gridded observational datasets (e.g., E-OBS) at the same scale using a quantile mapping approach that relies on distribution transformation. To correct the whole precipitation distribution including extreme tails we apply a mixture distribution of a gamma distribution for the precipitation mass and a generalized Pareto distribution for the extreme tail. In a second step, we bridge the scale gap: we add small scale variability to the bias corrected precipitation time series using a vector generalized linear gamma model (VGLM gamma). To calibrate the VGLM gamma model we determine the statistical relationship between precipitation observations on different scales, i.e. between gridded (e.g., E-OBS) and station (ECA&D) observations. We apply this combined bias correction and <span class="hlt">downscaling</span> method to example stations and present an initial validation against, e.g., discrete quantile mapping and the raw RCM output. Furthermore, we present an example application of our method to future projections with CORDEX-RCMs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702834','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702834"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2016</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yates, Andrew; Akanni, Wasiu; Amode, M. Ridwan; Barrell, Daniel; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.; Janacek, Sophie H.; Johnson, Nathan; Juettemann, Thomas; Keenan, Stephen; Lavidas, Ilias; Martin, Fergal J.; Maurel, Thomas; McLaren, William; Murphy, Daniel N.; Nag, Rishi; Nuhn, Michael; Parker, Anne; Patricio, Mateus; Pignatelli, Miguel; Rahtz, Matthew; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P.; Zadissa, Amonida; Birney, Ewan; Harrow, Jennifer; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Spudich, Giulietta; Trevanion, Stephen J.; Cunningham, Fiona; Aken, Bronwen L.; Zerbino, Daniel R.; Flicek, Paul</p> <p>2016-01-01</p> <p>The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) is a system for genome annotation, analysis, storage and dissemination designed to facilitate the access of genomic annotation from chordates and key model organisms. It provides access to data from 87 species across our main and early access Pre! websites. This year we introduced three newly annotated species and released numerous updates across our supported species with a concentration on data for the latest genome assemblies of human, mouse, zebrafish and rat. We also provided two data updates for the previous human assembly, GRCh37, through a dedicated website (http://grch37.<span class="hlt">ensembl</span>.org). Our tools, in particular the VEP, have been improved significantly through integration of additional third party data. REST is now capable of larger-scale analysis and our regulatory data BioMart can deliver faster results. The website is now capable of displaying long-range interactions such as those found in cis-regulated datasets. Finally we have launched a website optimized for mobile devices providing views of genes, variants and phenotypes. Our data is made available without restriction and all code is available from our GitHub organization site (http://github.com/<span class="hlt">Ensembl</span>) under an Apache 2.0 license. PMID:26687719</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26687719','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26687719"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2016.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yates, Andrew; Akanni, Wasiu; Amode, M Ridwan; Barrell, Daniel; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E; Janacek, Sophie H; Johnson, Nathan; Juettemann, Thomas; Keenan, Stephen; Lavidas, Ilias; Martin, Fergal J; Maurel, Thomas; McLaren, William; Murphy, Daniel N; Nag, Rishi; Nuhn, Michael; Parker, Anne; Patricio, Mateus; Pignatelli, Miguel; Rahtz, Matthew; Riat, Harpreet Singh; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Wilder, Steven P; Zadissa, Amonida; Birney, Ewan; Harrow, Jennifer; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Spudich, Giulietta; Trevanion, Stephen J; Cunningham, Fiona; Aken, Bronwen L; Zerbino, Daniel R; Flicek, Paul</p> <p>2016-01-01</p> <p>The <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org) is a system for genome annotation, analysis, storage and dissemination designed to facilitate the access of genomic annotation from chordates and key model organisms. It provides access to data from 87 species across our main and early access Pre! websites. This year we introduced three newly annotated species and released numerous updates across our supported species with a concentration on data for the latest genome assemblies of human, mouse, zebrafish and rat. We also provided two data updates for the previous human assembly, GRCh37, through a dedicated website (http://grch37.<span class="hlt">ensembl</span>.org). Our tools, in particular the VEP, have been improved significantly through integration of additional third party data. REST is now capable of larger-scale analysis and our regulatory data BioMart can deliver faster results. The website is now capable of displaying long-range interactions such as those found in cis-regulated datasets. Finally we have launched a website optimized for mobile devices providing views of genes, variants and phenotypes. Our data is made available without restriction and all code is available from our GitHub organization site (http://github.com/<span class="hlt">Ensembl</span>) under an Apache 2.0 license. PMID:26687719</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241249&keyword=Matlab&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55726329&CFTOKEN=79569204','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241249&keyword=Matlab&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55726329&CFTOKEN=79569204"><span id="translatedtitle">User's Manual for <span class="hlt">Downscaler</span> Fusion Software</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Recently, a series of 3 papers has been published in the statistical literature that details the use of <span class="hlt">downscaling</span> to obtain more accurate and precise predictions of air pollution across the conterminous U.S. This <span class="hlt">downscaling</span> approach combines CMAQ gridded numerical model output...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5667B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5667B"><span id="translatedtitle">Using satellite products to evaluate statistical <span class="hlt">downscaling</span> with generalised linear models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bergin, Emma; Buytaert, Wouter; Kwok-Pan, Chun; Turner, Andrew; Chawla, Ila; Mujumdar, Pradeep</p> <p>2015-04-01</p> <p>Generalised linear models (GLMs) have been around for some time and are routinely used for statistical <span class="hlt">downscaling</span> of rainfall data. However, in many regions it is difficult to evaluate them because of a lack of in situ data. <span class="hlt">Downscaling</span> models are frequently fitted using data from rain gauges. Therefore the validation of models using the same data can result in over-confidence of the model. One such region is northern India owing to the complexity of the monsoon system and relative lack of availability of raw raingauge data. Here we present a method to evaluate GLM-based <span class="hlt">downscaling</span> using satellite products. We fit a multi-site <span class="hlt">downscaling</span> model using generalised linear models for a case study region in the Upper Ganges, using data from 32 daily rain gauges from the Indian Meteorological Department for our study. The Asian monsoon is one of the largest manifestations of the annual cycle in the Earth System And given its importance for water resources in northern India, the analysis and projection of rainfall series in the Upper Ganges basin is of great significance for the region. We use correlations analyses to select physically meaningful predictors for the monsoon season for JJAS. Our GLM is fitted using rain gauge data for the period 1951-1999 using separate regressions for rainfall occurrence and amount. For the amounts model, we use sea surface temperature predictors from the Niño-3 region, moisture flux across the zonal plane at 850hPa over the Arabian Sea, specific humidity at 850hPa and air temperature at 2m over the Ganges basin. For the occurrence model we use air temperature at 2m over the Ganges basin. Additional predictors were trialled but were not significant. Our model is validated using a split-sample test for 1999-2005 using rain gauge data and independent satellite and reanalysis rainfall products. We use the TRMM 3B42 v7a and APHRODITE satellite rainfall products and the Princeton <span class="hlt">downscaled</span> NCEP reanalysis rainfall to form an <span class="hlt">ensemble</span> of rainfall observations. We compare the uncertainty of the observations with 100 realisations from GLM simulations. We find that our <span class="hlt">ensemble</span> of observations falls within the envelope of uncertainty from the GLM simulations during the monsoon season. <span class="hlt">Downscaling</span> models are frequently evaluated only for their performance using average statistics. More detailed analyses of daily rainfall plots therefore give increased confidence that <span class="hlt">downscaling</span> models may also have potential for use over shorter time scales. Our findings suggest that in data-sparse and remote regions, satellite and reanalysis products can provide an important independent verification to <span class="hlt">downscaling</span> models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3964975','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3964975"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2014</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Flicek, Paul; Amode, M. Ridwan; Barrell, Daniel; Beal, Kathryn; Billis, Konstantinos; Brent, Simon; Carvalho-Silva, Denise; Clapham, Peter; Coates, Guy; Fitzgerald, Stephen; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah; Johnson, Nathan; Juettemann, Thomas; Kähäri, Andreas K.; Keenan, Stephen; Kulesha, Eugene; Martin, Fergal J.; Maurel, Thomas; McLaren, William M.; Murphy, Daniel N.; Nag, Rishi; Overduin, Bert; Pignatelli, Miguel; Pritchard, Bethan; Pritchard, Emily; Riat, Harpreet S.; Ruffier, Magali; Sheppard, Daniel; Taylor, Kieron; Thormann, Anja; Trevanion, Stephen J.; Vullo, Alessandro; Wilder, Steven P.; Wilson, Mark; Zadissa, Amonida; Aken, Bronwen L.; Birney, Ewan; Cunningham, Fiona; Harrow, Jennifer; Herrero, Javier; Hubbard, Tim J.P.; Kinsella, Rhoda; Muffato, Matthieu; Parker, Anne; Spudich, Giulietta; Yates, Andy; Zerbino, Daniel R.; Searle, Stephen M.J.</p> <p>2014-01-01</p> <p><span class="hlt">Ensembl</span> (http://www.<span class="hlt">ensembl</span>.org) creates tools and data resources to facilitate genomic analysis in chordate species with an emphasis on human, major vertebrate model organisms and farm animals. Over the past year we have increased the number of species that we support to 77 and expanded our genome browser with a new scrollable overview and improved variation and phenotype views. We also report updates to our core datasets and improvements to our gene homology relationships from the addition of new species. Our REST service has been extended with additional support for comparative genomics and ontology information. Finally, we provide updated information about our methods for data access and resources for user training. PMID:24316576</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H11G0959D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H11G0959D"><span id="translatedtitle">Multivariate <span class="hlt">Downscaling</span> of Decadal Climate Change Projections over the Sunbelt</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>DAS Bhowmik, R.; Arumugam, S.; Sinha, T.; Mahinthakumar, K.</p> <p>2014-12-01</p> <p>Bias Correction and Statistical <span class="hlt">downscaling</span> (BCSD) of precipitation and temperature are commonly required to bring the large scale variables available from GCMs to a finer grid-scale for ingesting them into watershed models. Most of the currently employed procedures on BCSD primarily consider a univariate approach by developing a statistical relationship between large-scale precipitation/temperature with the local-scale precipitation/temperature ignoring the interdependency between the two variables. In this study, an asynchronous Canonical Correlation Analysis (CCA) approach is proposed for <span class="hlt">downscaling</span> multiple climatic variables by preserving the temporal correlations among them. The method was first applied on historical runs of climate model inter-comparison project-5 (CMIP5) for the period 1951-1999 and compared with bias corrected dataset using univariate approach from Bureau of Reclamation. Further, the method was applied on decadal runs of CMIP5 models and compared with univariate asynchronous regression results. A metric, fraction bias was defined, and distribution of fraction bias from <span class="hlt">ensemble</span> was considered for comparing with univariate approach. CCA relatively performs better in preserving the cross-correlation at grids where observed cross correlations are significant, while reducing fraction biases in mean and standard deviation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESS...19.1827S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESS...19.1827S"><span id="translatedtitle">Inter-comparison of statistical <span class="hlt">downscaling</span> methods for projection of extreme precipitation in Europe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sunyer, M. A.; Hundecha, Y.; Lawrence, D.; Madsen, H.; Willems, P.; Martinkova, M.; Vormoor, K.; Bürger, G.; Hanel, M.; Kriau?i?nien?, J.; Loukas, A.; Osuch, M.; Yücel, I.</p> <p>2015-04-01</p> <p>Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical <span class="hlt">downscaling</span> is necessary to address climate change impacts at the catchment scale. This study compares eight statistical <span class="hlt">downscaling</span> methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to <span class="hlt">downscale</span> precipitation output from 15 regional climate models (RCMs) from the <span class="hlt">ENSEMBLES</span> project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the <span class="hlt">downscaled</span> time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the <span class="hlt">ensemble</span> of RCMs and SDMs indicates that at least 30% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an <span class="hlt">ensemble</span> of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010JGRD..11522102G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010JGRD..11522102G"><span id="translatedtitle">SVM-PGSL coupled approach for statistical <span class="hlt">downscaling</span> to predict rainfall from GCM output</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ghosh, Subimal</p> <p>2010-11-01</p> <p>Hydrological impacts of climate change are assessed by <span class="hlt">downscaling</span> the General Circulation Model (GCM) outputs of predictor variables to local or regional scale hydrologic variables (predictand). Support Vector Machine (SVM) is a machine learning technique which is capable of capturing highly nonlinear relationship between predictor and predictand and thus performs better than conventional linear regression in transfer function-based <span class="hlt">downscaling</span> modeling. SVM has certain parameters the values of which need to be fixed appropriately for controlling undertraining and overtraining. In this study, an optimization model is proposed to estimate the values of these parameters. As the optimization model, for selection of parameters, contains SVM as one of its constraints, analytical solution techniques are difficult to use in solving it. Probabilistic Global Search Algorithm (PGSL), a probabilistic search technique, is used to compute the optimum parameters of SVM. With these optimum parameters, training of SVM is performed for statistical <span class="hlt">downscaling</span>. The obtained relationship between large-scale atmospheric variables and local-scale hydrologic variables (e.g., rainfall) is used to compute the hydrologic scenarios for multiple GCMs. The uncertainty resulting from the use of multiple GCMs is further modeled with a modified reliability <span class="hlt">ensemble</span> averaging method. The proposed methodology is demonstrated with the prediction of monsoon rainfall of Assam and Meghalaya meteorological subdivision of northeastern India. The results obtained from the proposed model are compared with earlier developed SVM-based <span class="hlt">downscaling</span> models, and improved performance is observed.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUSM.H31A..02R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUSM.H31A..02R"><span id="translatedtitle">Rainfall <span class="hlt">Downscaling</span> by a Phase-Conserving, Nonlinearly-Transformed Autoregressive Model: Validation on Radar Precipitation Estimates</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rebora, N.; Ferraris, L.; von Hardenberg, J.; Provenzale, A.</p> <p>2004-05-01</p> <p>The prediction of the small-scale spatio-temporal pattern of intense rainfall events is crucial for flood risk assessment in small catchments and urban areas. In the absence of a full deterministic modelling of small-scale rainfall, it is common practice to resort to the use of stochastic <span class="hlt">downscaling</span> models to generate <span class="hlt">ensemble</span> rainfall predictions to be used as inputs to rainfall-runoff models. Here we discuss a spatio-temporal <span class="hlt">downscaling</span> procedure that we call the "Rain FARM: Rainfall Filtered AutoRegressive Model," based on a non-linear transformation of a linearly correlated (gaussian) field, and we validate this approach on a set of radar precipitation estimates. The Rain FARM procedure allows for reproducing the scaling properties (if any) of the rainfall pattern and it can be easily linked with meteorological forecasts produced by limited area meteorological models. We believe that this approach represents a significant improvement over commonly available models used for rainfall <span class="hlt">downscaling</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC54A..01H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC54A..01H"><span id="translatedtitle">Recent Developments in Statistical <span class="hlt">Downscaling</span> of Extremes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hertig, E.</p> <p>2014-12-01</p> <p>Based on the output of general circulation models (GCMs) regionalization techniques are usually applied to obtain fine-scale climate change information. Different types of regionalization techniques have been developed which comprise regional climate models and statistical <span class="hlt">downscaling</span> approaches such as conditional weather generators, artificial neural networks, synoptic studies, and transfer functions. In the scope of climate variability and climate change the variations and changes of extremes are of special importance. Extreme events are not only of scientific interest but also have a profound impact on society. For the statistical <span class="hlt">downscaling</span> of extremes, promising approaches have been introduced and/or developed further in the last few years. Aspects of recent developments in the scope of statistical <span class="hlt">downscaling</span> of extremes will be presented. In this context, various approaches to <span class="hlt">downscale</span> extremes, particularly those associated with extreme precipitation events, will be discussed. Key problems related to statistical <span class="hlt">downscaling</span> of extremes will be addressed. Furthermore, information on Working Group 4 "Extremes" of the EU COST action VALUE (www.value-cost.eu) will be provided. VALUE systematically validates and develops <span class="hlt">downscaling</span> methods for climate change research in order to improve regional climate change scenarios for use in climate impact studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AdAtS..20..951H','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hellström, Cecilia; Chen, Deliang</p> <p>2003-11-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.tmp..234G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.tmp..234G"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of meteorological time series and climatic projections in a watershed in Turkey</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Göncü, S.; Albek, E.</p> <p>2015-07-01</p> <p>In this study, meteorological time series from five meteorological stations in and around a watershed in Turkey were used in the statistical <span class="hlt">downscaling</span> of global climate model results to be used for future projections. Two general circulation models (GCMs), Canadian Climate Center (CGCM3.1(T63)) and Met Office Hadley Centre (2012) (HadCM3) models, were used with three Special Report Emission Scenarios, A1B, A2, and B2. The statistical <span class="hlt">downscaling</span> model SDSM was used for the <span class="hlt">downscaling</span>. The <span class="hlt">downscaled</span> <span class="hlt">ensembles</span> were put to validation with GCM predictors against observations using nonparametric statistical tests. The two most important meteorological variables, temperature and precipitation, passed validation statistics, and partial validation was achieved with other time series relevant in hydrological studies, namely, cloudiness, relative humidity, and wind velocity. Heat waves, number of dry days, length of dry and wet spells, and maximum precipitation were derived from the primary time series as annual series. The change in monthly predictor sets used in constructing the multiple regression equations for <span class="hlt">downscaling</span> was examined over the watershed and over the months in a year. Projections between 1962 and 2100 showed that temperatures and dryness indicators show increasing trends while precipitation, relative humidity, and cloudiness tend to decrease. The spatial changes over the watershed and monthly temporal changes revealed that the western parts of the watershed where water is produced for subsequent downstream use will get drier than the rest and the precipitation distribution over the year will shift. Temperatures showed increasing trends over the whole watershed unparalleled with another period in history. The results emphasize the necessity of mitigation efforts to combat climate change on local and global scales and the introduction of adaptation strategies for the region under study which was shown to be vulnerable to climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1213747B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1213747B"><span id="translatedtitle">Methodology for Air Quality Forecast <span class="hlt">Downscaling</span> from Regional- to Street-Scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob</p> <p>2010-05-01</p> <p>The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how <span class="hlt">downscaling</span> from the European MACC <span class="hlt">ensemble</span> to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of <span class="hlt">downscaling</span> from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of <span class="hlt">downscaling</span> according to the proposed methodology are presented. The potential for <span class="hlt">downscaling</span> of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" <span class="hlt">downscaling</span> of European air-quality forecasts to the city and street levels with different approaches will be formulated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8020K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8020K"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of AGCM Precipitation Output with a Formatted Regression Frame</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, Sunmin; Tachikawa, Yasoto; Nakakita, Eiichi</p> <p>2015-04-01</p> <p>The <span class="hlt">downscaling</span> issue has been taking an important role to bridge research in climate change and impact assessment. Especially, the SDS (statistical <span class="hlt">downscaling</span>) issue has a long history of research and development in the field of hydrology and several types of SDS methods are already successful in other applications. The main advantage of SDS compared to DDS (dynamic <span class="hlt">downscaling</span>) is that it does not take high computing resources, and can easily apply to any place with a minimum of observation data available. However, SDS also has limitations. Some statistical relationships between model variables are not strong enough to build a stable SDS model. Most critically, we cannot sure whether the statistical relationship developed with the present climate data can simulate the statistical relationship of the future climate. We have been developing a SDS method that can avoid the critical issue of the conventional SDS method, and take as many advantages of DDS as possible, based on analyzing two different spatial resolutions of AGCM outputs, 20-km and 60-km. By establishing a statistical relationship between the 60-km and 20-km output for both present and future separately, and by applying the relationship to the <span class="hlt">ensemble</span> output of 60-km AGCM, it is able to produce <span class="hlt">ensemble</span> output at 20-km spatial resolution with the independent statistical relationship for the present and future climates. In details, the <span class="hlt">downscaling</span> target is 60-km resolution of daily precipitation for 20-km resolution data. We have considered a window having (3x60-km)x(3x60-km) of area, and the <span class="hlt">downscaling</span> target is the 3x3 of 20-km resolution grids in the center of the <span class="hlt">downscaling</span> window. For the evaluation of the proposed method, we have prepared 15 years (1979-1993) of observation data, and identify the parameters with the square root information filter scheme. We optimize the parameters on a monthly basis, and apply the regression model to 10 more years of testing period (1994-2004). The proposed regression model provides very effective and efficient results with a certain level of estimation error.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1611854A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1611854A"><span id="translatedtitle">Improving GEFS Weather Forecasts for Indian Monsoon with Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal</p> <p>2014-05-01</p> <p>Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global <span class="hlt">Ensemble</span> Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical <span class="hlt">downscaling</span> technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is established between the principal components and observed rainfall over training period and predictions are obtained for testing period. The validations show high improvements in correlation coefficient between observed and predicted data (0.25 to 0.55). The results speak in favour of statistical <span class="hlt">downscaling</span> methodology which shows the capability to reduce the gap between observed data and predictions. A detailed study is required to be carried out by applying different <span class="hlt">downscaling</span> techniques to quantify the improvements in predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1412266Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1412266Y"><span id="translatedtitle">A hybrid <span class="hlt">downscaling</span> procedure for estimating the vertical distribution of ambient temperature in local scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yiannikopoulou, I.; Philippopoulos, K.; Deligiorgi, D.</p> <p>2012-04-01</p> <p>The vertical thermal structure of the atmosphere is defined by a combination of dynamic and radiation transfer processes and plays an important role in describing the meteorological conditions at local scales. The scope of this work is to develop and quantify the predictive ability of a hybrid dynamic-statistical <span class="hlt">downscaling</span> procedure to estimate the vertical profile of ambient temperature at finer spatial scales. The study focuses on the warm period of the year (June - August) and the method is applied to an urban coastal site (Hellinikon), located in eastern Mediterranean. The two-step methodology initially involves the dynamic <span class="hlt">downscaling</span> of coarse resolution climate data via the RegCM4.0 regional climate model and subsequently the statistical <span class="hlt">downscaling</span> of the modeled outputs by developing and training site-specific artificial neural networks (ANN). The 2.5ox2.5o gridded NCEP-DOE Reanalysis 2 dataset is used as initial and boundary conditions for the dynamic <span class="hlt">downscaling</span> element of the methodology, which enhances the regional representivity of the dataset to 20km and provides modeled fields in 18 vertical levels. The regional climate modeling results are compared versus the upper-air Hellinikon radiosonde observations and the mean absolute error (MAE) is calculated between the four grid point values nearest to the station and the ambient temperature at the standard and significant pressure levels. The statistical <span class="hlt">downscaling</span> element of the methodology consists of an <span class="hlt">ensemble</span> of ANN models, one for each pressure level, which are trained separately and employ the regional scale RegCM4.0 output. The ANN models are theoretically capable of estimating any measurable input-output function to any desired degree of accuracy. In this study they are used as non-linear function approximators for identifying the relationship between a number of predictor variables and the ambient temperature at the various vertical levels. An insight of the statistically derived input-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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC11F..06G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC11F..06G"><span id="translatedtitle">Precipitation <span class="hlt">Downscaling</span> Products for Hydrologic Applications (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutmann, E. D.; Pruitt, T.; Liu, C.; Clark, M. P.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Rasmussen, R.</p> <p>2013-12-01</p> <p>Hydrologists and engineers require climate data on high-resolution grids (4-12km) for many water resources applications. To get such data from climate models, users have traditionally relied on statistical <span class="hlt">downscaling</span> techniques, with only limited use of dynamic <span class="hlt">downscaling</span> techniques. Statistical techniques utilize a variety of assumptions, data, and methodologies that result in statistical artifacts that may impact hydroclimate representations. These impacts are often pronounced when <span class="hlt">downscaling</span> precipitation. We will discuss four major statistical <span class="hlt">downscaling</span> techniques: Bias Corrected Constructed Analogue (BCCA), Asynchronous Regression (AR), and two forms of Bias Corrected Spatial Disaggregation (BCSD.) The hydroclimate representations within many statistical methods often have too much drizzle, too small extreme events, and an improper representation of spatial scaling characteristics. These scaling problems lead some statistical methods substantially over estimate extreme events at hydrologically important scales (e.g., basin totals.) This can lead to large errors in future hydrologic predictions. In contrast, high-resolution dynamic <span class="hlt">downscaling</span> using the Weather Research and Forecasting model (WRF) provides a better representation of precipitation in many respects, but at a much higher computational cost. This computational constraint prevents the use of high-resolution WRF simulations when examining the range of possible future scenarios generated as part of the Coupled Model Intercomparison Project (CMIP.) Finally, we will present a next generation psuedo-dynamical model that provides dynamic <span class="hlt">downscaling</span> information for a fraction of the computational requirements. This simple weather model uses large scale circulation patterns from a GCM, for example wind, temperature and humidity, but performs advection and microphysical calculations on a high-resolution grid, thus permitting topography to be adequately represented. This model is capable of generating 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5462V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5462V"><span id="translatedtitle">Selecting <span class="hlt">downscaled</span> climate projections for water resource impacts and adaptation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vidal, Jean-Philippe; Hingray, Benoît</p> <p>2015-04-01</p> <p>Increasingly large <span class="hlt">ensembles</span> of global and regional climate projections are being produced and delivered to the climate impact community. However, such an enormous amount of information can hardly been dealt with by some impact models due to computational constraints. Strategies for transparently selecting climate projections are therefore urgently needed for informing small-scale impact and adaptation studies and preventing potential pitfalls in interpreting <span class="hlt">ensemble</span> results from impact models. This work proposes results from a selection approach implemented for an integrated water resource impact and adaptation study in the Durance river basin (Southern French Alps). A large <span class="hlt">ensemble</span> of 3000 daily transient gridded climate projections was made available for this study. It was built from different runs of 4 <span class="hlt">ENSEMBLES</span> Stream2 GCMs, statistically <span class="hlt">downscaled</span> by 3 probabilistic methods based on the K-nearest neighbours resampling approach (Lafaysse et al., 2014). The selection approach considered here exemplifies one of the multiple possible approaches described in a framework for identifying tailored subsets of climate projections for impact and adaptation studies proposed by Vidal & Hingray (2014). It was chosen based on the specificities of both the study objectives and the characteristics of the projection dataset. This selection approach aims at propagating as far as possible the relative contributions of the four different sources of uncertainties considered, namely GCM structure, large-scale natural variability, structure of the <span class="hlt">downscaling</span> method, and catchment-scale natural variability. Moreover, it took the form of a hierarchical structure to deal with the specific constraints of several types of impact models (hydrological models, irrigation demand models and reservoir management models). The implemented 3-layer selection approach is therefore mainly based on conditioned Latin Hypercube sampling (Christierson et al., 2012). The choice of conditioning variables - climate change signal in temporally and spatially integrated variables - has been carefully made with respect their relevance for water resource management. This work proposes a twofold assessment of this selection approach. First, a climate validation allows checking the selection response of more extreme climate variables critical for hydrological impacts as well as spatially distributed ones. Second, a hydrological validation allows checking the selection response of streamflow variables relevant for water resource management. Findings highlight that such validations may critically help preventing misinterpretations and misuses of impact model <span class="hlt">ensemble</span> outputs for integrated adaptation purposes. This work is part of the GICC R2D2-2050 project (Risk, water Resources and sustainable Development of the Durance catchment in 2050) and the EU FP7 COMPLEX project (Knowledge Based Climate Mitigation Systems for a Low Carbon Economy). Christierson, B. v., Vidal, J.-P., & Wade, S. D. (2012) Using UKCP09 probabilistic climate information for UK water resource planning}. J. Hydrol., {424-425}, 48-67. doi: 10.1016/j.jhydrol.2011.12.020} Lafaysse, M.; Hingray, B.; Terray, L.; Mezghani, A. & Gailhard, J. (2014) Internal variability and model uncertainty components in future hydrometeorological projections: The Alpine Durance basin. Water Resour. Res., {50}, 3317-3341. doi: 10.1002/2013WR014897 Vidal, J.-P. & Hingray, B. (2014) A framework for identifying tailored subsets of climate projections for impact and adaptation studies. EGU2014-7851</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=philippines&pg=5&id=EJ969636','ERIC'); return false;" href="http://eric.ed.gov/?q=philippines&pg=5&id=EJ969636"><span id="translatedtitle">World Music <span class="hlt">Ensemble</span>: Kulintang</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Beegle, Amy C.</p> <p>2012-01-01</p> <p>As instrumental world music <span class="hlt">ensembles</span> such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music <span class="hlt">ensembles</span> just starting to gain popularity in particular parts of the United States. The kulintang <span class="hlt">ensemble</span>, a drum and gong <span class="hlt">ensemble</span>…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMIN23A1414R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMIN23A1414R"><span id="translatedtitle"><span class="hlt">Downscaling</span> Climate Data from Distributed Archives</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Radhakrishnan, A.; Guentchev, G.; Cinquini, L.; Schweitzer, R.; Nikonov, S.; Balaji, V.</p> <p>2013-12-01</p> <p>Model refinement -- numerical estimates of climate change at higher resolution than climate models are currently capable of producing -- is an essential weapon in the arsenal of decision makers and researchers in climate change. We describe here steps toward a general-purpose system for model refinement. We envision a system wherein multiple climate models, alone or in combination, can be used as predictors; multiple refinement methods, alone or in combination, can be deployed and trained, including evaluation within a perfect-model framework, described below; time periods and locations of training can be chosen at will; and providing all of these options as standard web services within the Earth System Grid Federation (ESGF) global data infrastructure for the distribution of climate model output. The perfect-model framework for systematic testing of model refinement using empirical-statistical <span class="hlt">downscaling</span> (ESD) schemes is being developed at NOAA/GFDL under the National Climate Predictions and Projections Platform (NCPP) project. It uses the approach that Laprise and collaborators call the "big-brother" framework for evaluating dynamical <span class="hlt">downscaling</span>. High-resolution model output is used as a "nature run" and used in place of observations to train the ESD scheme under testing. The data is interpolated to a coarse grid (the "little brother") and the ESD scheme attempts to <span class="hlt">downscale</span> and bias-correct the "future", i.e beyond the period of training. The output of ESD can then be rigorously compared to the original nature run on a chosen list of metrics. Initial work was performed in collaboration with Texas Tech University: the high-resolution time-slice models that GFDL submitted to CMIP5 are used as training sets for the <span class="hlt">downscaling</span> methods developed by Katharine Hayhoe and collaborators. The approach is being extended to using other <span class="hlt">downscaling</span> schemes, such as BCSD, Delta, quantile mapping, constructed analogs, and machine learning algorithms; and in future to using other model output for training datasets as well. Initial results were first presented at the Quantitative Evaluation of <span class="hlt">Downscaling</span> 2013 Workshop (QED-2013). We will describe a software infrastructure wherein: a) any CMIP5 high-resolution model output can be used as a training set; b) any ESD scheme can be deployed using a standard template or API developed under the ExArch project; c) the outputs of <span class="hlt">downscaling</span> will also conform to CMIP5 standards and be capable of being analyzed on the same footing as any CMIP5 output; d) analysis services computing the chosen metrics can be run on the <span class="hlt">downscaled</span> output; e) the infrastructure can be deployed "in-house" by the ESD group, or potentially run as a web service on any ESGF node.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUSM.H53A..03S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUSM.H53A..03S"><span id="translatedtitle"><span class="hlt">Downscaling</span> GCM Output with Genetic Programming Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shi, X.; Dibike, Y. B.; Coulibaly, P.</p> <p>2004-05-01</p> <p>Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called '<span class="hlt">downscaling</span> techniques'. This study applies Genetic Programming (GP) based technique to <span class="hlt">downscale</span> daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP <span class="hlt">downscaling</span> technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation <span class="hlt">downscaling</span>, in addition to the mean daily precipitation and daily precipitation variability for each month, monthly average dry and wet-spell lengths are also considered as performance criteria. For the cases of Tmax and Tmin, means and variances of these variables corresponding to each month were considered as performance criteria. The GP <span class="hlt">downscaling</span> results show satisfactory agreement between the observed daily temperature (Tmax and Tmin) and the simulated temperature. However, the <span class="hlt">downscaling</span> results for the daily precipitation still require some improvement - suggesting further investigation of other grammars. KEY WORDS: Climate change; GP <span class="hlt">downscaling</span>; GCM.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC23C0926S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC23C0926S"><span 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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sultan, B.; Oettli, P.; Vrac, M.; Baron, C.</p> <p>2010-12-01</p> <p>Global circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. It therefore requires the use of <span class="hlt">downscaling</span> methods. This study analyzes the performance of both dynamical and statistical <span class="hlt">downscaling</span> techniques in simulating crop yield at local scale. A detailed case study is conducted using historical weather data for Senegal, applied to the crop model SARRAH for simulating several tropical cereals (sorghum, millet, maize) at local scale. This control simulation is used as a benchmark to evaluate a set of Regional Climate Models (RCM) simulations, forced by ERA-Interim, from the <span class="hlt">ENSEMBLES</span> project and a statistical <span class="hlt">downscaling</span> method, the CDF-Transform, used to correct biases in RCM outputs. We first evaluate each climate variable that drives the simulated yield in the control simulation (radiation, rainfall, temperatures). We then simulate crop yields with RCM outputs (with or without applying the CDG-Transform) and evaluate the performance of each RCM in regards to crop yield simulations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GeoRL..4210847W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GeoRL..4210847W"><span id="translatedtitle">Incremental dynamical <span class="hlt">downscaling</span> for probabilistic analysis based on multiple GCM projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wakazuki, Y.; Rasmussen, R.</p> <p>2015-12-01</p> <p>A dynamical <span class="hlt">downscaling</span> method for probabilistic regional-scale climate change projections was developed to cover the inherent uncertainty associated with multiple general circulation model (GCM) climate simulations. The climatological increments estimated by GCM results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the <span class="hlt">ensemble</span> mean with the magnitudes of their standard deviations were extracted and added to the <span class="hlt">ensemble</span> mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding them to reanalysis data. The incremental handling of GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. For the probabilistic analysis, three values of a climatological variable simulated by RCMs for a mode were analyzed under an assumption of linear response to the multiple modal perturbations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26293893','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26293893"><span id="translatedtitle">Climate change effects on extreme flows of water supply area in Istanbul: utility of regional climate models and <span class="hlt">downscaling</span> method.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kara, Fatih; Yucel, Ismail</p> <p>2015-09-01</p> <p>This study investigates the climate change impact on the changes of mean and extreme flows under current and future climate conditions in the Omerli Basin of Istanbul, Turkey. The 15 regional climate model output from the EU-<span class="hlt">ENSEMBLES</span> project and a <span class="hlt">downscaling</span> method based on local implications from geophysical variables were used for the comparative analyses. Automated calibration algorithm is used to optimize the parameters of Hydrologiska Byråns Vattenbalansavdel-ning (HBV) model for the study catchment using observed daily temperature and precipitation. The calibrated HBV model was implemented to simulate daily flows using precipitation and temperature data from climate models with and without <span class="hlt">downscaling</span> method for reference (1960-1990) and scenario (2071-2100) periods. Flood indices were derived from daily flows, and their changes throughout the four seasons and year were evaluated by comparing their values derived from simulations corresponding to the current and future climate. All climate models strongly underestimate precipitation while <span class="hlt">downscaling</span> improves their underestimation feature particularly for extreme events. Depending on precipitation input from climate models with and without <span class="hlt">downscaling</span> the HBV also significantly underestimates daily mean and extreme flows through all seasons. However, this underestimation feature is importantly improved for all seasons especially for spring and winter through the use of <span class="hlt">downscaled</span> inputs. Changes in extreme flows from reference to future increased for the winter and spring and decreased for the fall and summer seasons. These changes were more significant with <span class="hlt">downscaling</span> inputs. With respect to current time, higher flow magnitudes for given return periods will be experienced in the future and hence, in the planning of the Omerli reservoir, the effective storage and water use should be sustained. PMID:26293893</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/974391','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/974391"><span id="translatedtitle">Accounting for Global Climate Model Projection Uncertainty in Modern Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Johannesson, G</p> <p>2010-03-17</p> <p>Future climate change has emerged as a national and a global security threat. To carry out the needed adaptation and mitigation steps, a quantification of the expected level of climate change is needed, both at the global and the regional scale; in the end, the impact of climate change is felt at the local/regional level. An important part of such climate change assessment is uncertainty quantification. Decision and policy makers are not only interested in 'best guesses' of expected climate change, but rather probabilistic quantification (e.g., Rougier, 2007). For example, consider the following question: What is the probability that the average summer temperature will increase by at least 4 C in region R if global CO{sub 2} emission increases by P% from current levels by time T? It is a simple question, but one that remains very difficult to answer. It is answering these kind of questions that is the focus of this effort. The uncertainty associated with future climate change can be attributed to three major factors: (1) Uncertainty about future emission of green house gasses (GHG). (2) Given a future GHG emission scenario, what is its impact on the global climate? (3) Given a particular evolution of the global climate, what does it mean for a particular location/region? In what follows, we assume a particular GHG emission scenario has been selected. Given the GHG emission scenario, the current batch of the state-of-the-art global climate models (GCMs) is used to simulate future climate under this scenario, yielding an <span class="hlt">ensemble</span> of future climate projections (which reflect, to some degree our uncertainty of being able to simulate future climate give a particular GHG scenario). Due to the coarse-resolution nature of the GCM projections, they need to be spatially <span class="hlt">downscaled</span> for regional impact assessments. To <span class="hlt">downscale</span> a given GCM projection, two methods have emerged: dynamical <span class="hlt">downscaling</span> and statistical (empirical) <span class="hlt">downscaling</span> (SDS). Dynamic <span class="hlt">downscaling</span> involves 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1411206B','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Busuioc, A.; Dumitrescu, A.; Baciu, M.; Cazacioc, L.</p> <p>2012-04-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140009212','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140009212"><span id="translatedtitle"><span class="hlt">Downscaling</span> Reanalysis over Continental Africa with a Regional Model: NCEP Versus ERA Interim Forcing</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Druyan, Leonard M.; Fulakeza, Matthew B.</p> <p>2013-01-01</p> <p>Five annual climate cycles (1998-2002) are simulated for continental Africa and adjacent oceans by a regional atmospheric model (RM3). RM3 horizontal grid spacing is 0.44deg at 28 vertical levels. Each of 2 simulation <span class="hlt">ensembles</span> is driven by lateral boundary conditions from each of 2 alternative reanalysis data sets. One simulation downs cales National Center for Environmental Prediction reanalysis 2 (NCPR2) and the other the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I). NCPR2 data are archived at 2.5deg grid spacing, while a recent version of ERA-I provides data at 0.75deg spacing. ERA-I-forced simulations are recomrp. ended by the Coordinated Regional <span class="hlt">Downscaling</span> Experiment (CORDEX). Comparisons of the 2 sets of simulations with each other and with observational evidence assess the relative performance of each <span class="hlt">downscaling</span> system. A third simulation also uses ERA-I forcing, but degraded to the same horizontal resolution as NCPR2. RM3-simulated pentad and monthly mean precipitation data are compared to Tropical Rainfall Measuring Mission (TRMM) data, gridded at 0.5deg, and RM3-simulated circulation is compared to both reanalyses. Results suggest that each <span class="hlt">downscaling</span> system provides advantages and disadvantages relative to the other. The RM3/NCPR2 achieves a more realistic northward advance of summer monsoon rains over West Africa, but RM3/ERA-I creates the more realistic monsoon circulation. Both systems recreate some features of JulySeptember 1999 minus 2002 precipitation differences. Degrading the resolution of ERA-I driving data unrealistically slows the monsoon circulation and considerably diminishes summer rainfall rates over West Africa. The high resolution of ERA-I data, therefore, contributes to the quality of the <span class="hlt">downscaling</span>, but NCPR2laterai boundary conditions nevertheless produce better simulations of some features.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3278305','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3278305"><span id="translatedtitle">Exploring <span class="hlt">Ensemble</span> Visualization</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Phadke, Madhura N.; Pinto, Lifford; Alabi, Femi; Harter, Jonathan; Taylor, Russell M.; Wu, Xunlei; Petersen, Hannah; Bass, Steffen A.; Healey, Christopher G.</p> <p>2012-01-01</p> <p>An <span class="hlt">ensemble</span> is a collection of related datasets. Each dataset, or member, of an <span class="hlt">ensemble</span> is normally large, multidimensional, and spatio-temporal. <span class="hlt">Ensembles</span> are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an <span class="hlt">ensemble</span> to see how parameter choices affect the simulation. To draw inferences from an <span class="hlt">ensemble</span>, scientists need to compare data both within and between <span class="hlt">ensemble</span> members. We propose two techniques to support <span class="hlt">ensemble</span> exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of <span class="hlt">ensemble</span> data. PMID:22347540</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H14C..05P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H14C..05P"><span id="translatedtitle">Data Assimilation Methods for Hydrologic <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pan, M.; Wood, E. F.; Luo, L.</p> <p>2012-12-01</p> <p>Data assimilation techniques have been among the most useful tools in Earth sciences. As for their applications in hydrology, significant efforts have been devoted to improving the predictions of dynamic models, e.g., catchment hydrologic models, land surface models (LSM), and ultimately general circulation models (GCM), using various types of observational data, e.g. remotely sensed surface parameters. Here we focus on the applications to a fundamentally important but less explored category of problems - estimating hydrologic quantities of interest across different spatial and temporal scales, and the primarily problem is <span class="hlt">downscaling</span> in space and time (since upscaling is in most cases trivial). <span class="hlt">Downscaling</span> plays a vital role in bridging the scale gaps between various types of modeling and observation systems, for example, from the relatively coarse GCM to LSM, and to catchment scale models, and from coarse resolution remote sensors (long wavelength or gravitational) to fine resolution sensors (visible/infrared). Through <span class="hlt">downscaling</span>, fine scale applications (e.g. catchment hydrologic models, local geo-chemical and geo-biological models) can make use of predictions from coarse scale models (e.g. weather/climate models) or coarse resolution remote sensing measurements. Our <span class="hlt">downscaling</span> approach will rely on both (a) the physical models to parameterize the related cross-scale physical processes and to link hydrologic variables defined at one scale to another, and (b) the mathematical tools to properly handle the uncertainties during the estimation and as well as to help quantify those cross-scale relationships too difficult for the physical models. We showcase the <span class="hlt">downscaling</span> of two hydrologic variables: (1) deriving spatial fields of land surface runoff from river streamflow measurements and (2) creating fine resolution soil moisture data from coarse resolution remote sensing retrievals or dynamic models. In the runoff case, all the measurements are collected in the form of river streamflow, which is an integrated response to the spatial field of runoff in time. A routing model captures this integration process in space and time, and the <span class="hlt">downscaling</span> is essentially to invert such a routing process (i.e. to disaggregate streamflow in time and space) using data assimilation techniques and background estimates of the runoff field. In the soil moisture case, the redistribution of soil moisture at fine scales is controlled by factors like topography and soil/vegetation properties. Some of these processes are well captured by the topographic index-based TOPMODEL and other more difficult scaling relationships can be lumped into a multi-scale statistical model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011ClDy...37..835G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011ClDy...37..835G"><span id="translatedtitle">Climate variability and projected change in the western United States: regional <span class="hlt">downscaling</span> and drought statistics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutzler, David S.; Robbins, Tessia O.</p> <p>2011-09-01</p> <p>Climate change in the twenty-first century, projected by a large <span class="hlt">ensemble</span> average of global coupled models forced by a mid-range (A1B) radiative forcing scenario, is <span class="hlt">downscaled</span> to Climate Divisions across the western United States. A simple empirical <span class="hlt">downscaling</span> technique is employed, involving model-projected linear trends in temperature or precipitation superimposed onto a repetition of observed twentieth century interannual variability. This procedure allows the projected trends to be assessed in terms of historical climate variability. The linear trend assumption provides a very close approximation to the time evolution of the <span class="hlt">ensemble</span>-average climate change, while the imposition of repeated interannual variability is probably conservative. These assumptions are very transparent, so the scenario is simple to understand and can provide a useful baseline assumption for other scenarios that may incorporate more sophisticated empirical or dynamical <span class="hlt">downscaling</span> techniques. Projected temperature trends in some areas of the western US extend beyond the twentieth century historical range of variability (HRV) of seasonal averages, especially in summer, whereas precipitation trends are relatively much smaller, remaining within the HRV. Temperature and precipitation scenarios are used to generate Division-scale projections of the monthly palmer drought severity index (PDSI) across the western US through the twenty-first century, using the twentieth century as a baseline. The PDSI is a commonly used metric designed to describe drought in terms of the local surface water balance. Consistent with previous studies, the PDSI trends imply that the higher evaporation rates associated with positive temperature trends exacerbate the severity and extent of drought in the semi-arid West. Comparison of twentieth century historical droughts with projected twenty-first century droughts (based on the prescribed repetition of twentieth century interannual variability) shows that the projected trend toward warmer temperatures inhibits recovery from droughts caused by decade-scale precipitation deficits.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=steel&pg=2&id=EJ969636','ERIC'); return false;" href="http://eric.ed.gov/?q=steel&pg=2&id=EJ969636"><span id="translatedtitle">World Music <span class="hlt">Ensemble</span>: Kulintang</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Beegle, Amy C.</p> <p>2012-01-01</p> <p>As instrumental world music <span class="hlt">ensembles</span> such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music <span class="hlt">ensembles</span> just starting to gain popularity in particular parts of the United States. The kulintang <span class="hlt">ensemble</span>, a drum and gong ensemble…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC51A0736C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC51A0736C"><span id="translatedtitle">Simulation of an <span class="hlt">ensemble</span> of future climate time series with an hourly weather generator</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.</p> <p>2010-12-01</p> <p>There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous <span class="hlt">downscaling</span> techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic <span class="hlt">downscaling</span> technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the <span class="hlt">downscaling</span> procedure derives distributions of factors of change for several climate statistics from a multi-model <span class="hlt">ensemble</span> of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model <span class="hlt">ensemble</span>. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an <span class="hlt">ensemble</span> of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated <span class="hlt">ensemble</span> of scenarios also accounts for the uncertainty derived from multiple GCMs used in <span class="hlt">downscaling</span>. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ), 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004EOSTr..85..417B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004EOSTr..85..417B"><span id="translatedtitle">Empirical-Statistical <span class="hlt">Downscaling</span> in Climate Modeling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Benestad, R. E.</p> <p>2004-10-01</p> <p>Research into possible impacts of a climate change requires descriptions of local and regional descriptions of climate. For instance, the local and regional aspect of a climate change is stressed in the U.S. Strategic Plan for the Climate Change Science Program (CCSP) (http://www.climatescience.gov/Library/stratplan2003/default.htm). Global climate models (GCMs) are important tools for studying climate change and making projections for the future. Although GCMs provide realistic representations of large-scale aspects of climate, they generally do not give good descriptions of the local and regional scales. It is nevertheless possible to relate large-scale climatic features to smaller spatial scales. There are two main approaches for deriving information on local or regional scales from the global climate scenarios generated by GCMs: (1) numerical <span class="hlt">downscaling</span> (also known as ``dynamical <span class="hlt">downscaling</span>'') involving a nested regional climate model (RCM) or (2) empirical-statistical <span class="hlt">downscaling</span> employing statistical relationships between the large-scale climatic state and local variations derived from historical data records.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PESS....2...42S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PESS....2...42S"><span id="translatedtitle"><span class="hlt">Ensemble</span> experiments using a nested LETKF system to reproduce intense vortices associated with tornadoes of 6 May 2012 in Japan</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Seko, Hiromu; Kunii, Masaru; Yokota, Sho; Tsuyuki, Tadashi; Miyoshi, Takemasa</p> <p>2015-12-01</p> <p>Experiments simulating intense vortices associated with tornadoes that occurred on 6 May 2012 on the Kanto Plain, Japan, were performed with a nested local <span class="hlt">ensemble</span> transform Kalman filter (LETKF) system. Intense vortices were reproduced by <span class="hlt">downscale</span> experiments with a 12-member <span class="hlt">ensemble</span> in which the initial conditions were obtained from the nested LETKF system analyses. The <span class="hlt">downscale</span> experiments successfully generated intense vortices in three regions similar to the observed vortices, whereas only one tornado was reproduced by a deterministic forecast. The intense vorticity of the strongest tornado, which was observed in the southernmost region, was successfully reproduced by 10 of the 12 <span class="hlt">ensemble</span> members. An examination of the results of the <span class="hlt">ensemble</span> <span class="hlt">downscale</span> experiments showed that the duration of intense vorticities tended to be longer when the vertical shear of the horizontal wind was larger and the lower airflow was more humid. Overall, the study results show that <span class="hlt">ensemble</span> forecasts have the following merits: (1) probabilistic forecasts of the outbreak of intense vortices associated with tornadoes are possible; (2) the miss rate of outbreaks should decrease; and (3) environmental factors favoring outbreaks can be obtained by comparing the multiple possible scenarios of the <span class="hlt">ensemble</span> forecasts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC21B0879Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC21B0879Z"><span id="translatedtitle">Joint Variable Spatial <span class="hlt">Downscaling</span> (JVSD): A New <span class="hlt">Downscaling</span> Method with Application to the Southeast US</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, F.; Georgakakos, A. P.</p> <p>2011-12-01</p> <p>Joint Variable Spatial <span class="hlt">Downscaling</span> (JVSD) is a new <span class="hlt">downscaling</span> method developed to produce high resolution gridded hydrological datasets suitable for regional watershed modeling and assessments. JVSD differs from other statistical <span class="hlt">downscaling</span> methods in that multiple climatic variables are <span class="hlt">downscaled</span> simultaneously to produce realistic and consistent climate fields. JVSD includes two major steps: bias correction and spatial <span class="hlt">downscaling</span>. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being <span class="hlt">downscaled</span>. Bias correction is then based on quantile-to-quantile mapping of these stationary frequency distributions probability space. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons with 20th Century Climate in Coupled Models (20C3M) data show that JVSD reproduces the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with the North American regional climate change assessment program (NARCCAP) and other statistical <span class="hlt">downscaling</span> methods over the southeastern US. The results show that JVSD performs favorably. JVSD is applied for all A1B and A2 CMIP3 GCM scenarios in the Apalachicola-Chattahoochee-Flint River Basin (southeast US) with the following general findings: (i) Mean monthly temperature exhibits increasing trends over the ACF basin for all seasons and all A1B and A2 scenarios; Most significant are the A2 temperature increases in the 2050 - 2099 time periods; (ii) 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMOS51A0962C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMOS51A0962C"><span id="translatedtitle">Comparison of Statistical <span class="hlt">Downscaling</span> Methods for Seasonal Precipitation Prediction: An Application Toward a Fire and Haze Early Warning System for Southeast Asia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cho, J.; Lee, H.; Lee, E.; Field, R. D.; Hameed, S. N.; Foo, K. K.; Albar, I.; Sopaheluwakan, A.</p> <p>2014-12-01</p> <p>Smoke haze from forest fires is among Southeast Asia's most serious environmental problems and there is a clear need for a long-lead fire and haze early warning system (EWS) for the regions. The seasonal forecast supplied by the APEC Climate Center (APCC) is one of available information can be used to predict drought conditions triggering forest fires in the region. The objective of this study is to assess the skill of the current and <span class="hlt">downscaled</span> products of APCC's seasonal forecast of 6-month lead-time for predicting ASO precipitation over the fire-prone regions. First, seasonal forecast skill by six individual models (MSC_CANCM3, MSC_CANCM4, NASA, NCEP, PNU, POAMA) and simple composite model (SCM) <span class="hlt">ensemble</span> was assessed by considering available each <span class="hlt">ensemble</span> members. Second, three different statistical <span class="hlt">downscaling</span> methods including simple bias-correction (SBC), moving window regression (MWReg), and climate index regression (CIReg) were applied and the forecast sill were compared. Both current and <span class="hlt">downscaled</span> seasonal forecast showed higher predictability over Sumatra regions compared to the Kalimantan regions. Statistical <span class="hlt">downscaling</span> of forecasts showed the skill improvement over the Kalimantan region where current APCC's forecast shows low predictability. Study also shows that temporal correlation coefficient (TCC) between observed and forecasted ASO precipitation increases as lead-time decrease.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4785R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4785R"><span id="translatedtitle">Statistical-dynamical <span class="hlt">downscaling</span> for wind energy potentials: Evaluation and applications to decadal hindcasts and climate change projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reyers, Mark; Pinto, Joaquim G.; Moemken, Julia</p> <p>2015-04-01</p> <p>A statistical-dynamical <span class="hlt">downscaling</span> (SDD) approach for the regionalisation of wind energy output (Eout) over Europe with special focus on Germany is proposed. SDD uses an extended circulation weather type (CWT) analysis on global daily MSLP fields with the central point being located over Germany. 77 weather classes based on the associated circulation weather type and the intensity of the geostrophic flow are identified. Representatives of these classes are dynamical <span class="hlt">downscaled</span> with the regional climate model COSMO-CLM. By using weather class frequencies of different datasets the simulated representatives are recombined to probability density functions (PDFs) of near-surface wind speed and finally to Eout of a sample wind turbine for present and future climate. This is performed for reanalysis, decadal hindcasts and long-term future projections. For evaluation purposes results of SDD are compared to wind observations and to simulated Eout of purely dynamical <span class="hlt">downscaling</span> (DD) methods. For the present climate SDD is able to simulate realistic PDFs of 10m-wind speed for most stations in Germany. The resulting spatial Eout patterns are similar to DD simulated Eout. In terms of decadal hindcasts results of SDD are similar to DD simulated Eout over Germany, Poland, Czech Republic, and Benelux, for which high correlations between annual Eout timeseries of SDD and DD are detected for selected hindcasts. Lower correlation is found for other European countries. It is demonstrated that SDD can be used to <span class="hlt">downscale</span> the full <span class="hlt">ensemble</span> of the MPI-ESM decadal prediction system. Long-term climate change projections in SRES scenarios of ECHAM5/MPI-OM as obtained by SDD agree well to results of other studies using DD methods, with increasing Eout over Northern Europe and a negative trend over Southern Europe. Despite some biases it is concluded that SDD is an adequate tool to assess regional wind energy changes in large model <span class="hlt">ensembles</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ClDy...43.3201G','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gaitan, Carlos F.; Hsieh, William W.; Cannon, Alex J.</p> <p>2014-12-01</p> <p>Given the coarse resolution of global climate models, <span class="hlt">downscaling</span> techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical <span class="hlt">downscaling</span> experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically <span class="hlt">downscaled</span> daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and <span class="hlt">ensembles</span>, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971-2000) and A2 (2041-2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree <span class="hlt">ensembles</span> outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of <span class="hlt">downscaling</span> models deteriorated in future climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.8856G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.8856G"><span id="translatedtitle">Validation of a Universal Multifractal <span class="hlt">downscaling</span> process with the help of dense networks of disdrometers</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Berne, Alexis; Lovejoy, Shaun</p> <p>2013-04-01</p> <p>The resolution of the rainfall data usually provided by operational C-band radar networks of Western European meteorological services is 1 km in space and 5 min in time. It has been shown that higher resolutions are needed for various applications, notably in the field of urban hydrology. A way of dealing with this unmeasured small scale rainfall variability is to input stochastically <span class="hlt">downscaled</span> rainfall fields to urban hydrological models and simulate not a single response for the studied catchment but an <span class="hlt">ensemble</span>. In this paper we suggest to discuss a <span class="hlt">downscaling</span> procedure for the rainfall field. It relies on the Universal Multifractals which have been extensively used to model and simulate geophysical fields extremely variable over a wide range of spatio-temporal scales such as rainfall. Here this standard framework of multiplicative cascades has been modified in a discrete case to better take into account the numerous zeros of the rainfall field (i.e. a pixel with no rainfall recorded). More precisely the zeros are introduced at each scale within the cascade process in a probabilistic scale invariant way. The <span class="hlt">downscaling</span> suggested here consists in retrieving the scaling properties of the rainfall field on the available range of scales and stochastically continuing the underlying process below the scale of observation. Rainfall data coming from a dense network of 16 optical disdrometers (Particle Size and Velocity, PARSIVEL, 1st generation) that was deployed for 16 month over an area of approximately 1 km2 in the campus of Ecole Polytechnique Federale de Lausanne (Switzerland) will be used to validate this <span class="hlt">downscaling</span> procedure. Preliminary results with a network of second generation PARSIVEL currently under construction in Ecole des Ponts ParisTech (France) will also be shown. The methodology implemented consists in <span class="hlt">downscaling</span> a rainfall field with a resolution of 1 km and 5 min to a resolution comparable with the disdrometers' one (few tens of cm and 1 min). The variability among the generated "virtual" disdrometers is then compared with the observed one. The impact of these results on the comparisons commonly performed between radar and rain gauge / disdrometer rainfall data will finally be briefly discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC41E..08G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41E..08G"><span id="translatedtitle">Producing information for Vulnerability, Impacts and Adaptation work: The COordinated Regional <span class="hlt">Downscaling</span> EXperiment (CORDEX) (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Giorgi, F.</p> <p>2013-12-01</p> <p>Regional climate information is needed for use in Vulnerability, Impacts and Adaptation (VIA) studies. This information can be obtained either from Global Climate Model (GCM) simulations or from different <span class="hlt">downscaling</span> techniques that regionally enhance the GCM fields to produce fine scale climate information. <span class="hlt">Downscaling</span> techniques include both dynamical (i.e. Regional Climate Models, or RCMs) and statistical methods, and can be applied in a variety of contexts, such as process studies and regional to local climate change projections. One of the key issues in producing climate information for VIA application is that of suitably characterizing underlying uncertainties. In fact, there are several sources of uncertainty in climate projections: limitations and systematic errors in GCMs and <span class="hlt">downscaling</span> tools, greenhouse gas (GHG) emission and concentration scenarios, response of different models (physics and configurations) to GHG forcing, internal decadal to multidecadal variability of the climate system. In order to characterize these uncertainties, large <span class="hlt">ensembles</span> of model projections are needed, a task that is best approached in a mullti-model, multi-laboratory international context. These premises have lead to the inception of the COordinated Regional <span class="hlt">Downscaling</span> EXperiment (CORDEX), under the auspices of the World Climate Research program (WGRP). The purpose of CORDEX is threefold: 1) to evaluate and possibly improve regional <span class="hlt">downscaling</span> techniques (both dynamical and statistical); 2) to produce a new generation of regional climate change projections for regions worldwide based on a multi-model approach; 3) to foster the interactions across the climate and VIA research communites. The CORDEX Phase I framework has been designed and implemented, and related activities have been strongly growing in the last 1-2 years with a wide international participation. This paper will review the status of CORDEX, especially drawing from the results of a major pan-CORDEX conference taking place on 4-7 November 2013 in Brussels. In particular, the paper will summarize lessons learned from the CORDEX Phase I activities and discuss future directions and areas in need of strengthening in view of the development of the CORDEX Phase II framework.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280783&keyword=earth&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55697005&CFTOKEN=47271020','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280783&keyword=earth&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55697005&CFTOKEN=47271020"><span id="translatedtitle">"Going the Extra Mile in <span class="hlt">Downscaling</span>: Why <span class="hlt">Downscaling</span> is not jut "Plug-and-Play"</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of <span class="hlt">downscaling</span> the Comm...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110013410','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110013410"><span id="translatedtitle">The <span class="hlt">Ensemble</span> Canon</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>MIittman, David S</p> <p>2011-01-01</p> <p><span class="hlt">Ensemble</span> is an open architecture for the development, integration, and deployment of mission operations software. Fundamentally, it is an adaptation of the Eclipse Rich Client Platform (RCP), a widespread, stable, and supported framework for component-based application development. By capitalizing on the maturity and availability of the Eclipse RCP, <span class="hlt">Ensemble</span> offers a low-risk, politically neutral path towards a tighter integration of operations tools. The <span class="hlt">Ensemble</span> project is a highly successful, ongoing collaboration among NASA Centers. Since 2004, the <span class="hlt">Ensemble</span> project has supported the development of mission operations software for NASA's Exploration Systems, Science, and Space Operations Directorates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EOSTr..94R.131S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EOSTr..94R.131S"><span id="translatedtitle">A geostatistical approach to <span class="hlt">downscaling</span> climate forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schultz, Colin</p> <p>2013-03-01</p> <p>Though global general circulation models are the tool of choice for forecasting the effects of climate change, their spatial resolutions are too broad for the needs of regional planners. To provide locally relevant information, modelers typically employ one of two techniques: producing a new forecast using a regional dynamic model or statistically <span class="hlt">downscaling</span> the projections of the larger model. As a subset of the statistical approach, Jha et al. propose a geostatistical technique to translate climate-modeling results to a smaller spatial scale.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950024819','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950024819"><span id="translatedtitle">The Personal Software Process: <span class="hlt">Downscaling</span> the factory</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Roy, Daniel M.</p> <p>1994-01-01</p> <p>It is argued that the next wave of software process improvement (SPI) activities will be based on a people-centered paradigm. The most promising such paradigm, Watts Humphrey's personal software process (PSP), is summarized and its advantages are listed. The concepts of the PSP are shown also to fit a <span class="hlt">down-scaled</span> version of Basili's experience factory. The author's data and lessons learned while practicing the PSP are presented along with personal experience, observations, and advice from the perspective of a consultant and teacher for the personal software process.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26888907','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26888907"><span id="translatedtitle"><span class="hlt">Ensembl</span> regulation resources.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zerbino, Daniel R; Johnson, Nathan; Juetteman, Thomas; Sheppard, Dan; Wilder, Steven P; Lavidas, Ilias; Nuhn, Michael; Perry, Emily; Raffaillac-Desfosses, Quentin; Sobral, Daniel; Keefe, Damian; Gräf, Stefan; Ahmed, Ikhlak; Kinsella, Rhoda; Pritchard, Bethan; Brent, Simon; Amode, Ridwan; Parker, Anne; Trevanion, Steven; Birney, Ewan; Dunham, Ian; Flicek, Paul</p> <p>2016-01-01</p> <p>New experimental techniques in epigenomics allow researchers to assay a diversity of highly dynamic features such as histone marks, DNA modifications or chromatin structure. The study of their fluctuations should provide insights into gene expression regulation, cell differentiation and disease. The <span class="hlt">Ensembl</span> project collects and maintains the <span class="hlt">Ensembl</span> regulation data resources on epigenetic marks, transcription factor binding and DNA methylation for human and mouse, as well as microarray probe mappings and annotations for a variety of chordate genomes. From this data, we produce a functional annotation of the regulatory elements along the human and mouse genomes with plans to expand to other species as data becomes available. Starting from well-studied cell lines, we will progressively expand our library of measurements to a greater variety of samples. <span class="hlt">Ensembl</span>'s regulation resources provide a central and easy-to-query repository for reference epigenomes. As with all <span class="hlt">Ensembl</span> data, it is freely available at http://www.<span class="hlt">ensembl</span>.org, from the Perl and REST APIs and from the public <span class="hlt">Ensembl</span> MySQL database server at ensembldb.<span class="hlt">ensembl</span>.org.Database URL: http://www.<span class="hlt">ensembl</span>.org. PMID:26888907</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8278P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8278P"><span id="translatedtitle">Statistical Testing of Dynamically <span class="hlt">Downscaled</span> Rainfall Data for the East Coast of Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Parana Manage, Nadeeka; Lockart, Natalie; Willgoose, Garry; Kuczera, George</p> <p>2015-04-01</p> <p>This study performs a validation of statistical properties of <span class="hlt">downscaled</span> climate data, concentrating on the rainfall which is required for hydrology predictions used in reservoir simulations. The data sets used in this study have been produced by the NARCliM (NSW/ACT Regional Climate Modelling) project which provides a dynamically <span class="hlt">downscaled</span> climate dataset for South-East Australia at 10km resolution. NARCliM has used three configurations of the Weather Research Forecasting Regional Climate Model and four different GCMs (MIROC-medres 3.2, ECHAM5, CCCMA 3.1 and CSIRO mk3.0) from CMIP3 to perform twelve <span class="hlt">ensembles</span> of simulations for current and future climates. Additionally to the GCM-driven simulations, three control run simulations driven by the NCEP/NCAR reanalysis for the entire period of 1950-2009 has also been performed by the project. The validation has been performed in the Upper Hunter region of Australia which is a semi-arid to arid region 200 kilometres North-West of Sydney. The analysis used the time series of <span class="hlt">downscaled</span> rainfall data and ground based measurements for selected Bureau of Meteorology rainfall stations within the study area. The initial testing of the gridded rainfall was focused on the autoregressive characteristics of time series because the reservoir performance depends on long-term average runoffs. A correlation analysis was performed for fortnightly, monthly and annual averaged time resolutions showing a good statistical match between reanalysis and ground truth. The spatial variation of the statistics of gridded rainfall series were calculated and plotted at the catchment scale. The spatial correlation analysis shows a poor agreement between NARCliM data and ground truth at each time resolution. However, the spatial variability plots show a strong link between the statistics and orography at the catchment scale.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5019R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5019R"><span id="translatedtitle">Defining predictand areas with homogeneous predictors for spatially coherent precipitation <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Radanovics, Sabine; Vidal, Jean-Philippe; Sauquet, Eric; Ben Daoud, Aurélien; Bontron, Guillaume</p> <p>2013-04-01</p> <p>Statistical <span class="hlt">downscaling</span> aims at finding relationships between local precipitation (predictand) and large-scale predictor fields, in various contexts, from medium-term forecasting to climate change impact studies. For distributed hydrological modelling the <span class="hlt">downscaled</span> precipitation spatial fields have furthermore to be coherent over possibly large river basins. This study addresses this issue by grouping coherent predictand areas in terms of optimised predictor domains over the whole of France, for an analogue <span class="hlt">downscaling</span> method developed by Ben Daoud et al. (2011). This <span class="hlt">downscaling</span> method is based on analogies on different variables: temperature, relative humidity, vertical velocity and geopotentials. These predictor variables are taken from ERA40 at 2.5 degree resolution and local precipitation over 608 climatologically homogeneous zones in France are taken from the Safran near-surface atmospheric reanalysis (Vidal et al., 2010). The predictor domains for each zone consist of the nearest grid cell for all variables except geopotentials for which the optimum domain is sensitive to the predictand location. For large catchments with diverse meteorological influences it is thus beneficial to optimise the predictor domains individually for areas with different influences (e.g. Timbal et al., 2003). The drawback is that different predictor domains may provide inconsistent values between elementary zones. This study therefore aims at reducing the number of different predictor domains by grouping the predictand areas that may use the same predictor domain. The geopotential predictor domains were first optimised for each of the 608 zones in the Safran data separately. The predictive skill of different predictor domains is evaluated with the Continuous Ranked Probability Skill Score (CRPSS) for the 25 best analogue days found with the statistical <span class="hlt">downscaling</span> method averaged over 20 years. Rectangular predictor domains of different sizes, shapes and locations are tested, and the 5 ones that lead to the highest CRPSS for the zone in question are retained. The 5 retained domains were found to be equally skillfull with a maximum difference of around 1% of CRPSS on average, and are thus all candidates for clustering predictand zones. An objective procedure has then been implemented for clustering zones together, based on their sharing a common predictor domain inside their 5 near-optimal domain <span class="hlt">ensemble</span>. For zones sharing several near-optimal predictor domains, the aim was to minimise the number of disjoint predictand areas. Furthermore solutions that lead to more similar sized areas were preferred. This procedure defines areas with natural spatial coherence and reduces the number of different predictor domains using a procedure based on objective rules, unlike most of studies where this is done either subjectively or arbitrarily. It allowed to reduce significantly the number of independent zones and to identify large homogeneous areas encompassing relatively large river basins. Further developments will address the issue of spatial coherent <span class="hlt">downscaling</span> for predictand areas that do not share any near-optimal predictor domains. Ben Daoud, A., Sauquet, E., Lang, M., Bontron, G., and Obled, C. (2011). Precipitation forecasting through an analog sorting technique: a comparative study. Advances in Geosciences, 29:103-107. doi: 10.5194/adgeo-29-103-2011 Timbal, B., Dufour, A., and McAvaney, B. (2003). An estimate of future climate change for western France using a statistical <span class="hlt">downscaling</span> technique. Climate Dynamics, 20(7-8):807-823. doi: 10.1007/s00382-002-0298-9 Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M. (2010) A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology, 30:1627-1644. doi: 10.1002/joc.2003</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUSM.H31B..02T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUSM.H31B..02T"><span id="translatedtitle">Bayesian Processor of <span class="hlt">Ensemble</span> for Precipitation Forecasting: A Development Plan</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Toth, Z.; Krzysztofowicz, R.</p> <p>2006-05-01</p> <p>The Bayesian Processor of <span class="hlt">Ensemble</span> (BPE) is a new, theoretically-based technique for probabilistic forecasting of weather variates. It is a generalization of the Bayesian Processor of Output (BPO) developed by Krzysztofowicz and Maranzano for processing single values of multiple predictors into a posterior distribution function of a predictand. The BPE processes an <span class="hlt">ensemble</span> of a predictand generated by multiple integrations of a numerical weather prediction (NWP) model, and optimally fuses the <span class="hlt">ensemble</span> with climatic data in order to quantify uncertainty about the predictand. As is well known, Bayes theorem provides the optimal theoretical framework for fusing information from different sources and for obtaining the posterior distribution function of a predictand. Using a family of such distribution functions, a given raw <span class="hlt">ensemble</span> can be mapped into a posterior <span class="hlt">ensemble</span>, which is well calibrated, has maximum informativeness, and preserves the spatio-temporal and cross-variate dependence structure of the NWP output fields. The challenge is to develop and test the BPE suitable for operational forecasting. This talk will present the basic design components of the BPE, along with a discussion of the climatic and training data to be used in its potential application at the National Centers for Environmental Prediction (NCEP). The technique will be tested first on quasi-normally distributed variates and next on precipitation variates. For reasons of economy, the BPE will be applied on the relatively coarse resolution grid corresponding to the <span class="hlt">ensemble</span> output, and then the posterior <span class="hlt">ensemble</span> will be <span class="hlt">downscaled</span> to finer grids such as that of the National Digital Forecast Database (NDFD).</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4756621','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4756621"><span id="translatedtitle"><span class="hlt">Ensembl</span> regulation resources</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Zerbino, Daniel R.; Johnson, Nathan; Juetteman, Thomas; Sheppard, Dan; Wilder, Steven P.; Lavidas, Ilias; Nuhn, Michael; Perry, Emily; Raffaillac-Desfosses, Quentin; Sobral, Daniel; Keefe, Damian; Gräf, Stefan; Ahmed, Ikhlak; Kinsella, Rhoda; Pritchard, Bethan; Brent, Simon; Amode, Ridwan; Parker, Anne; Trevanion, Steven; Birney, Ewan; Dunham, Ian; Flicek, Paul</p> <p>2016-01-01</p> <p>New experimental techniques in epigenomics allow researchers to assay a diversity of highly dynamic features such as histone marks, DNA modifications or chromatin structure. The study of their fluctuations should provide insights into gene expression regulation, cell differentiation and disease. The <span class="hlt">Ensembl</span> project collects and maintains the <span class="hlt">Ensembl</span> regulation data resources on epigenetic marks, transcription factor binding and DNA methylation for human and mouse, as well as microarray probe mappings and annotations for a variety of chordate genomes. From this data, we produce a functional annotation of the regulatory elements along the human and mouse genomes with plans to expand to other species as data becomes available. Starting from well-studied cell lines, we will progressively expand our library of measurements to a greater variety of samples. Ensembl’s regulation resources provide a central and easy-to-query repository for reference epigenomes. As with all <span class="hlt">Ensembl</span> data, it is freely available at http://www.<span class="hlt">ensembl</span>.org, from the Perl and REST APIs and from the public <span class="hlt">Ensembl</span> MySQL database server at ensembldb.<span class="hlt">ensembl</span>.org. Database URL: http://www.<span class="hlt">ensembl</span>.org PMID:26888907</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFM.A32B..02G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFM.A32B..02G"><span id="translatedtitle">Reducing Uncertainties in Regional Climate Scenarios: the <span class="hlt">ENSEMBLES</span> Strategy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Goodess, C. M.</p> <p>2006-12-01</p> <p>One of the major objectives of the European-Union (EU) funded <span class="hlt">ENSEMBLES</span> project (2004-2009) is to develop an <span class="hlt">ensemble</span> prediction system for climate change based on the principal state-of-the-art, high resolution, global and regional Earth System models developed in Europe, validated against quality controlled, high resolution gridded datasets for Europe, to produce for the first time, an objective probabilistic estimate of uncertainty in future climate at the seasonal to decadal and longer timescales. <span class="hlt">ENSEMBLES</span> is also working to quantify and reduce the uncertainty in the representation of physical, chemical, biological and human-related feedbacks in the Earth System (including water resource, land use, and air quality issues, and carbon cycle feedbacks). This presentation focuses on how such process-based studies can inform the construction of regional climate scenarios. <span class="hlt">ENSEMBLES</span> follows on from the recently-completed PRUDENCE and STARDEX EU projects which have clearly demonstrated the importance of driving model (i.e., GCM) uncertainty, together with the need to take a multi-model approach to regional scenario construction, whether using statistical and/or dynamical methods for <span class="hlt">downscaling</span> to higher spatial and temporal scales. For <span class="hlt">ENSEMBLES</span>, one of the main scientific challenges with respect to the construction of probabilistic regional climate scenarios is how to make best use of information about the physical processes underlying GCM/RCM performance in order to devise (i) optimal strategies for pairing GCMs and RCMs in an <span class="hlt">ensemble</span> prediction system, and (ii) appropriate weighting schemes for probabilistic <span class="hlt">downscaled</span> scenarios. Preliminary work on these issues will be presented. The extent to which model performance on seasonal-to-decadal timescales can be used qualitatively and/or quantitatively to constrain predictions on climate change timescales will also be considered. <span class="hlt">ENSEMBLES</span> also aims to demonstrate end-to-end applications of its outputs, i.e., any scenario results must be relevant to impacts scientists and stakeholders. Thus the presentation will also address the extent to which progress on these scientific questions is guided or constrained by user demands (e.g., for information at higher spatial and temporal scales, and for user-friendly tools).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25887522','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25887522"><span id="translatedtitle">The <span class="hlt">ensembl</span> regulatory build.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zerbino, Daniel R; Wilder, Steven P; Johnson, Nathan; Juettemann, Thomas; Flicek, Paul R</p> <p>2015-01-01</p> <p>Most genomic variants associated with phenotypic traits or disease do not fall within gene coding regions, but in regulatory regions, rendering their interpretation difficult. We collected public data on epigenetic marks and transcription factor binding in human cell types and used it to construct an intuitive summary of regulatory regions in the human genome. We verified it against independent assays for sensitivity. The <span class="hlt">Ensembl</span> Regulatory Build will be progressively enriched when more data is made available. It is freely available on the <span class="hlt">Ensembl</span> browser, from the <span class="hlt">Ensembl</span> Regulation MySQL database server and in a dedicated track hub. PMID:25887522</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/16881400','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/16881400"><span id="translatedtitle"><span class="hlt">Downscaling</span> climate information for local disease mapping.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bernardi, M; Gommes, R; Grieser, J</p> <p>2006-06-01</p> <p>The study of the impacts of climate on human health requires the interdisciplinary efforts of health professionals, climatologists, biologists, and social scientists to analyze the relationships among physical, biological, ecological, and social systems. As the disease dynamics respond to variations in regional and local climate, climate variability affects every region of the world and the diseases are not necessarily limited to specific regions, so that vectors may become endemic in other regions. Climate data at local level are thus essential to evaluate the dynamics of vector-borne disease through health-climate models and most of the times the climatological databases are not adequate. Climate data at high spatial resolution can be derived by statistical <span class="hlt">downscaling</span> using historical observations but the method is limited by the availability of historical data at local level. Since the 90s', the statistical interpolation of climate data has been an important priority of the Agrometeorology Group of the Food and Agriculture Organization of the United Nations (FAO), as they are required for agricultural planning and operational activities at the local level. Since 1995, date of the first FAO spatial interpolation software for climate data, more advanced applications have been developed such as SEDI (Satellite Enhanced Data Interpolation) for the <span class="hlt">downscaling</span> of climate data, LOCCLIM (Local Climate Estimator) and the NEW_LOCCLIM in collaboration with the Deutscher Wetterdienst (German Weather Service) to estimate climatic conditions at locations for which no observations are available. In parallel, an important effort has been made to improve the FAO climate database including at present more than 30,000 stations worldwide and expanding the database from developing countries coverage to global coverage. PMID:16881400</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/881929','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/881929"><span id="translatedtitle">Physically Based Global <span class="hlt">Downscaling</span>: Regional Evaluation</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Ghan, Steven J.; Shippert, Timothy R.; Fox, Jared</p> <p>2006-02-01</p> <p>The climate simulated by a global atmosphere/land model with a physically-based subgrid orography scheme is evaluated in ten selected regions. Climate variables simulated for each of multiple elevation classes within each grid cell are mapped according the high-resolution distribution of surface elevation in each region. Comparison of the simulated annual mean climate with gridded observations leads to the following conclusions. At low to moderate elevations the <span class="hlt">downscaling</span> scheme correctly simulates increasing precipitation, decreasing temperature, and increasing snow with increasing elevation within regions smaller than 100 km. At high elevations the <span class="hlt">downscaling</span> scheme correctly simulates a decrease in precipitation with increasing elevation. Too little precipitation is simulated on the windward side of mountain ranges and too much precipitation is simulated on the lee side. The simulated sensitivity of surface air temperature to surface elevation is too strong, particularly in valleys influenced by drainage circulations. Observations show little evidence of a “snow shadow”, so the neglect of the subgrid rainshadow does not produce an unrealistic simulation of the snow distribution. Summertime snow area, which is a proxy for land ice, is much larger than observed. Summertime snow water equivalent is far less than the observed thickness of glaciers because a 1 m upper bound on snow water is applied to the simulations and because snow transport by slides is neglected. The 1 m upper bound on snow water equivalent also causes an underestimate of seasonal snow water during late winter, compared with gridded station measurements. Potential solutions to these problems are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20060015642','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20060015642"><span id="translatedtitle"><span class="hlt">Ensemble</span> Data Mining Methods</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oza, Nikunj C.</p> <p>2004-01-01</p> <p><span class="hlt">Ensemble</span> Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an <span class="hlt">ensemble</span> is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in <span class="hlt">ensemble</span> methods has largely revolved around designing <span class="hlt">ensembles</span> consisting of competent yet complementary models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015CliPD..11.4425C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015CliPD..11.4425C"><span id="translatedtitle">Probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caillouet, L.; Vidal, J.-P.; Sauquet, E.; Graff, B.</p> <p>2015-09-01</p> <p>This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871-2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late 19th century onwards. The SANDHY (Stepwise ANalogue <span class="hlt">Downscaling</span> method for HYdrology) statistical <span class="hlt">downscaling</span> method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the SAFRAN high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily <span class="hlt">ensemble</span> of 125 analogues dates over the 1871-2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping unchanged the structure of the SANDHY method while reducing those seasonal biases. The calendar selection keeps the closest analogue dates in the year for each target date. The stepwise selection applies two new analogy steps based on similarity of the Sea Surface Temperature (SST) and the large-scale Two-metre Temperature (T2m). Comparisons to the SAFRAN reanalysis over 1959-2007 and to homogenized series over the whole 20th century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. The probabilistic <span class="hlt">downscaling</span> of 20CR over the period 1871-2012 with the SANDHY probabilistic <span class="hlt">downscaling</span> method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a~historical perspective.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5500I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5500I"><span id="translatedtitle">Enhancing Local Climate Projections of Precipitation: Assets and Limitations of Quantile Mapping Techniques for Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ivanov, Martin; Kotlarski, Sven; Schär, Christoph</p> <p>2015-04-01</p> <p>The Swiss CH2011 scenarios provide a portfolio of climate change scenarios for the region of Switzerland, specifically tailored for use in climate impact research. Although widely applied by a variety of end-users, these scenarios are subject to several limitations related to the underlying delta change methodology. Examples are difficulties to appropriately account for changes in the spatio-temporal variability of meteorological fields and for changes in extreme events. The recently launched ELAPSE project (Enhancing local and regional climate change projections for Switzerland) is connected to the EU COST Action VALUE (www.value-cost.eu) and aims at complementing CH2011 by further scenario products, including a bias-corrected version of daily scenarios at the site scale. For this purpose the well-established empirical quantile mapping (QM) methodology is employed. Here, daily temperature and precipitation output of 15 GCM-RCM model chains of the <span class="hlt">ENSEMBLES</span> project is <span class="hlt">downscaled</span> and bias-corrected to match observations at weather stations in Switzerland. We consider established QM techniques based on all empirical quantiles or linear interpolation between the empirical percentiles. In an attempt to improve the <span class="hlt">downscaling</span> of extreme precipitation events, we also apply a parametric approximation of the daily precipitation distribution by a dynamically weighted mixture of a Gamma distribution for the bulk and a Pareto distribution for the right tail for the first time in the context of QM. All techniques are evaluated and intercompared in a cross-validation framework. The statistical <span class="hlt">downscaling</span> substantially improves virtually all considered distributional and temporal characteristics as well as their spatial distribution. The empirical methods have in general very similar performances. The parametric method does not show an improvement over the empirical ones. Critical sites and seasons are highlighted and discussed. Special emphasis is placed on investigating the effect of bias correction on the mutual dependency between daily temperature and precipitation. The <span class="hlt">downscaling</span> substantially improves the bivariate distribution of the two variables and does not change their temporal dependence as indicated by the Fourier co-spectrum analysis. This contribution will advise on the assets and limitations of the related scenario products for use in climate impact research in the alpine environment of Switzerland.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H43G1551C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H43G1551C"><span id="translatedtitle">Assimilation of <span class="hlt">Downscaled</span> SMOS Soil Moisture for Quantifying Drought Impacts on Crop Yield in Agricultural Regions in Brazil</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chakrabarti, S.; Bongiovanni, T. E.; Judge, J.; Principe, J. C.; Fraisse, C.</p> <p>2013-12-01</p> <p>Reliable soil moisture (SM) information in the root zone (RZSM) is critical for quantification of agricultural drought impacts on crop yields and for recommending management and adaptation strategies for crop management, commodity trading and food security.The recently launched European Space Agency-Soil Moisture and Ocean Salinity (ESA-SMOS) and the near-future National Aeronautics and Space Administration-Soil Moisture Active Passive (NASA-SMAP) missions provide SM at unprecedented spatial resolutions of 10-25 km, but these resolutions are still too coarse for agricultural applications in heterogeneous landscapes, making <span class="hlt">downscaling</span> a necessity. This <span class="hlt">downscaled</span> near-surface SM can be merged with crop growth models in a data assimilation framework to provide optimal estimates of RZSM and crop yield. The objectives of the study include: 1) to implement a novel downscalingalgorithm based on the Information theoretical learning principlesto <span class="hlt">downscale</span> SMOS soil moisture at 25 km to 1km in the Brazilian La Plata Basin region and2) to assimilate the 1km-soil moisture in the crop model for a normal and a drought year to understand the impact on crop yield. In this study, a novel <span class="hlt">downscaling</span> algorithm based on the Principle of Relevant Information (PRI) was applied to in-situ and remotely sensed precipitation, SM, land surface temperature and leaf area index in the Brazilian Lower La Plata region in South America. An <span class="hlt">Ensemble</span> Kalman Filter (EnKF) based assimilation algorithm was used to assimilate the <span class="hlt">downscaled</span> soil moisture to update both states and parameters. The <span class="hlt">downscaled</span> soil moisture for two growing seasons in2010-2011 and 2011-2012 was assimilated into the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model over 161 km2 rain-fed region in the Brazilian LPB regionto improve the estimates of soybean yield. The first season experienced normal precipitation, while the second season was impacted by drought. Assimilation improved yield during both the seasons compared to the open-loop and matched well with the published yield statistics in the region.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..1210559L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..1210559L"><span id="translatedtitle">Improved large-scale hydrological modelling through the assimilation of streamflow and <span class="hlt">downscaled</span> satellite soil moisture observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lopez Lopez, P.; Wanders, N.; Schellekens, J.; Renzullo, L. J.; Sutanudjaja, E. H.; Bierkens, M. F. P.</p> <p>2015-10-01</p> <p>The coarse spatial resolution of global hydrological models (typically > 0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tuned river models. A possible solution to the problem may be to drive the coarse resolution models with locally available high spatial resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee river basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with <span class="hlt">downscaled</span> global meteorological data (from 0.5° <span class="hlt">downscaled</span> to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high resolution gauging station based gridded dataset (0.05°). <span class="hlt">Downscaled</span> satellite derived soil moisture (from approx. 0.5° <span class="hlt">downscaled</span> to 0.08° resolution) from AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an <span class="hlt">ensemble</span> Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and <span class="hlt">downscaled</span> soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse resolution meteorological data with assimilation of <span class="hlt">downscaled</span> spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can help to advance this research.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8627L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8627L"><span id="translatedtitle">Improved large-scale hydrological modelling through the assimilation of streamflow and <span class="hlt">downscaled</span> satellite soil moisture observations.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>López López, Patricia; Wanders, Niko; Sutanudjaja, Edwin; Renzullo, Luigi; Sterk, Geert; Schellekens, Jaap; Bierkens, Marc</p> <p>2015-04-01</p> <p>The coarse spatial resolution of global hydrological models (typically > 0.25o) often limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tunes river models. A possible solution to the problem may be to drive the coarse resolution models with high-resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the modelling resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigated the impact that assimilating streamflow and satellite soil moisture observations have on global hydrological model estimation, driven by coarse- and high-resolution meteorological observations, for the Murrumbidgee river basin in Australia. The PCR-GLOBWB global hydrological model is forced with <span class="hlt">downscaled</span> global climatological data (from 0.5o <span class="hlt">downscaled</span> to 0.1o resolution) obtained from the WATCH Forcing Data (WFDEI) and local high resolution gauging station based gridded datasets (0.05o), sourced from the Australian Bureau of Meteorology. <span class="hlt">Downscaled</span> satellite derived soil moisture (from 0.5o <span class="hlt">downscaled</span> to 0.1o resolution) from AMSR-E and streamflow observations collected from 25 gauging stations are assimilated using an <span class="hlt">ensemble</span> Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global climatological data. Results show that the assimilation of streamflow observations result in the largest improvement of the model estimates. The joint assimilation of both streamflow and <span class="hlt">downscaled</span> soil moisture observations leads to further improved in streamflow simulations (10% reduction in RMSE), mainly in the headwater catchments (up to 10,000 km2). Results also show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. This study demonstrates that it is possible to improve hydrological simulations forced by coarse resolution meteorological data with <span class="hlt">downscaled</span> satellite soil moisture and streamflow observations and bring them closer to a hydrological model forced with local climatological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can significantly help to advance this research.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1511304T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1511304T"><span id="translatedtitle">AMIC Project: Comparison of WRF High Resolution Dynamical <span class="hlt">Downscaling</span> of ERA-Interim and EC-Earth for Azores Islands</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tomé, Ricardo; Miranda, Pedro; Azevedo, Eduardo; Santo, Fátima</p> <p>2013-04-01</p> <p>Project AMIC integrates the Portuguese members of the new EC-Earth climate modeling consortium. The aim is to contribute to the IPCC fifth report with a significant set of simulations with a state of the art model, while giving the group timely access to the complete <span class="hlt">ensemble</span> of simulations for diagnostic studies, and regional <span class="hlt">downscaling</span>. Additionally, Project AMIC will produce a new set of high resolution simulations of the Portuguese islands climate, using a state of the art model (WRF) at 6km horizontal resolution, with boundary conditions from the new ERA-Interim reanalysis (1989-2009) and from the EC-Earth decadal (20 year) runs. These simulations will allow for validation of the <span class="hlt">downscaling</span> methodology, and will characterize both the current and near future climate. This study aims to compare two present day climate high resolution dynamical <span class="hlt">downscaling</span> WRF simulations for the Portuguese islands of Azores using the ECMWF ERA-Interim reanalysis and the EC-Earth v2.3 boundary conditions for the period 1989-2010. In small volcanic islands the local scale climate is influenced by the regional scale climate and by the orography and orientation of air masses over the islands. In these environments the climatological conditions are a vital importance for the local agriculture and water management. With this study we aim to see how well the dynamical <span class="hlt">downscaling</span> using EC-Earth v2.3 behaves when put against to the ERA-Interim reanalysis. To achieve this goal results from both simulations are compared against with the available observation network in both islands. This study results will show us what kind of deviations we can expect for the future scenarios runs using EC-Earth boundaries currently being made in IDL.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016WRR....52..471L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016WRR....52..471L"><span id="translatedtitle">Assessing the relative effectiveness of statistical <span class="hlt">downscaling</span> and distribution mapping in reproducing rainfall statistics based on climate model results</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Langousis, Andreas; Mamalakis, Antonios; Deidda, Roberto; Marrocu, Marino</p> <p>2016-01-01</p> <p>To improve the level skill of climate models (CMs) in reproducing the statistics of daily rainfall at a basin level, two types of statistical approaches have been suggested. One is statistical correction of CM rainfall outputs based on historical series of precipitation. The other, usually referred to as statistical rainfall <span class="hlt">downscaling</span>, is the use of stochastic models to conditionally simulate rainfall series, based on large-scale atmospheric forcing from CMs. While promising, the latter approach attracted reduced attention in recent years, since the developed <span class="hlt">downscaling</span> schemes involved complex weather identification procedures, while demonstrating limited success in reproducing several statistical features of rainfall. In a recent effort, Langousis and Kaleris (<link href="#wrcr21852-bib-0084"/>) developed a statistical framework for simulation of daily rainfall intensities conditional on upper-air variables, which is simpler to implement and more accurately reproduces several statistical properties of actual rainfall records. Here we study the relative performance of: (a) direct statistical correction of CM rainfall outputs using nonparametric distribution mapping, and (b) the statistical <span class="hlt">downscaling</span> scheme of Langousis and Kaleris (<link href="#wrcr21852-bib-0084"/>), in reproducing the historical rainfall statistics, including rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e., the Flumendosa catchment, using rainfall and atmospheric data from four CMs of the <span class="hlt">ENSEMBLES</span> project. The obtained results are promising, since the proposed <span class="hlt">downscaling</span> scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the CM used and the characteristics of the calibration period. This is particularly the case for yearly rainfall maxima.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015NPGeo..22..383P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015NPGeo..22..383P"><span id="translatedtitle">Spatial random <span class="hlt">downscaling</span> of rainfall signals in Andean heterogeneous terrain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Posadas, A.; Duffaut Espinosa, L. A.; Yarlequé, C.; Carbajal, M.; Heidinger, H.; Carvalho, L.; Jones, C.; Quiroz, R.</p> <p>2015-07-01</p> <p>Remotely sensed data are often used as proxies for indirect precipitation measures over data-scarce and complex-terrain areas such as the Peruvian Andes. Although this information might be appropriate for some research requirements, the extent at which local sites could be related to such information is very limited because of the resolution of the available satellite data. <span class="hlt">Downscaling</span> techniques are used to bridge the gap between what climate modelers (global and regional) are able to provide and what decision-makers require (local). Precipitation <span class="hlt">downscaling</span> improves the poor local representation of satellite data and helps end-users acquire more accurate estimates of water availability. Thus, a multifractal <span class="hlt">downscaling</span> technique complemented by a heterogeneity filter was applied to TRMM (Tropical Rainfall Measuring Mission) 3B42 gridded data (spatial resolution ~ 28 km) from the Peruvian Andean high plateau or Altiplano to generate <span class="hlt">downscaled</span> rainfall fields that are relevant at an agricultural scale (spatial resolution ~ 1 km).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.S51A2313Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.S51A2313Y"><span id="translatedtitle"><span class="hlt">Downscaling</span> of slip distribution for strong earthquakes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yoshida, T.; Oya, S.; Kuzuha, Y.</p> <p>2013-12-01</p> <p>We intend to develop a <span class="hlt">downscaling</span> model to enhance the earthquake slip distribution resolution. Slip distributions have been obtained by other researchers using various inversion methods. As a <span class="hlt">downscaling</span> model, we are discussing fractal models that include mono-fractal models (fractional Brownian motion, fBm; fractional Lévy motion, fLm) and multi-fractal models as candidates. Log - log-linearity of k (wave number) versus E (k) (power spectrum) is the necessary condition for fractality: the slip distribution is expected to satisfy log - log-linearity described above if we can apply fractal model to a slip distribution as a <span class="hlt">downscaling</span> model. Therefore, we conducted spectrum analyses using slip distributions of 11 earthquakes as explained below. 1) Spectrum analyses using one-dimensional slip distributions (strike direction) were conducted. 2) Averaging of some results of power spectrum (dip direction) was conducted. Results show that, from the viewpoint of log - log-linearity, applying a fractal model to slip distributions can be inferred as valid. We adopt the filtering method after Lavallée (2008) to generate fBm/ fLm. In that method, generated white noises (random numbers) are filtered using a power law type filter (log - log-linearity of the spectrum). Lavallée (2008) described that Lévy white noise that generates fLm is more appropriate than the Gaussian white noise which generates fBm. In addition, if the 'alpha' parameter of the Lévy law, which governs the degree of attenuation of tails of the probability distribution, is 2.0, then the Lévy distribution is equivalent to the Gauss distribution. We analyzed slip distributions of 11 earthquakes: the Tohoku earthquake (Wei et al., 2011), Haiti earthquake (Sladen, 2010), Simeulue earthquake (Sladen, 2008), eastern Sichuan earthquake (Sladen, 2008), Peru earthquake (Konca, 2007), Tocopilla earthquake (Sladen, 2007), Kuril earthquake (Sladen, 2007), Benkulu earthquake (Konca, 2007), and southern Java 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4761110','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4761110"><span id="translatedtitle"><span class="hlt">Ensembl</span> comparative genomics resources</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Muffato, Matthieu; Beal, Kathryn; Fitzgerald, Stephen; Gordon, Leo; Pignatelli, Miguel; Vilella, Albert J.; Searle, Stephen M. J.; Amode, Ridwan; Brent, Simon; Spooner, William; Kulesha, Eugene; Yates, Andrew; Flicek, Paul</p> <p>2016-01-01</p> <p>Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The <span class="hlt">Ensembl</span> comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. <span class="hlt">Ensembl</span> computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define <span class="hlt">Ensembl</span> Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The <span class="hlt">Ensembl</span> comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including <span class="hlt">Ensembl</span> Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes <span class="hlt">Ensembl</span> an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available. Database URL: http://www.<span class="hlt">ensembl</span>.org. PMID:26896847</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26896847','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26896847"><span id="translatedtitle"><span class="hlt">Ensembl</span> comparative genomics resources.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Herrero, Javier; Muffato, Matthieu; Beal, Kathryn; Fitzgerald, Stephen; Gordon, Leo; Pignatelli, Miguel; Vilella, Albert J; Searle, Stephen M J; Amode, Ridwan; Brent, Simon; Spooner, William; Kulesha, Eugene; Yates, Andrew; Flicek, Paul</p> <p>2016-01-01</p> <p>Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The <span class="hlt">Ensembl</span> comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. <span class="hlt">Ensembl</span> computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define <span class="hlt">Ensembl</span> Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The <span class="hlt">Ensembl</span> comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including <span class="hlt">Ensembl</span> Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes <span class="hlt">Ensembl</span> an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available.Database URL: http://www.<span class="hlt">ensembl</span>.org. PMID:26896847</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012CG.....41..119M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012CG.....41..119M"><span id="translatedtitle">A general method for <span class="hlt">downscaling</span> earth resource information</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Malone, Brendan P.; McBratney, Alex B.; Minasny, Budiman; Wheeler, Ichsani</p> <p>2012-04-01</p> <p>A programme scripted for use in an R programming environment called dissever is presented. This programme was designed to facilitate a generalised method for <span class="hlt">downscaling</span> coarsely resolved earth resource information using available finely gridded covariate data. Under the assumption that the relationship between the target variable being <span class="hlt">downscaled</span> and the available covariates can be nonlinear, dissever uses weighted generalised additive models (GAMs) to drive the empirical function. An iterative algorithm of GAM fitting and adjustment attempts to optimise the <span class="hlt">downscaling</span> to ensure that the target variable value given for each coarse grid cell equals the average of all target variable values at the fine scale in each coarse grid cell. A number of outputs needed for mapping results and diagnostic purposes are automatically generated from dissever. We demonstrate the programs' functionality by <span class="hlt">downscaling</span> a soil organic carbon (SOC) map with 1-km by 1-km grid resolution down to a 90-m by 90-m grid resolution using available covariate information derived from a digital elevation model, Landsat ETM+ data, and airborne gamma radiometric data. dissever produced high quality results as indicated by a low weighted root mean square error between averaged 90-m SOC predictions within their corresponding 1-km grid cell (0.82 kg m-3). Additionally, from a concordance between the <span class="hlt">downscaled</span> map and another map created using digital soil mapping methods there was a strong agreement (0.94). Future versioning of dissever will investigate quantifying the uncertainty of the <span class="hlt">downscaled</span> outputs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011PhDT.......163P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011PhDT.......163P"><span id="translatedtitle">Complex System <span class="hlt">Ensemble</span> Analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pearson, Carl A.</p> <p></p> <p>A new measure for interaction network <span class="hlt">ensembles</span> and their dynamics is presented: the <span class="hlt">ensemble</span> transition matrix, T, the proportions of networks in an <span class="hlt">ensemble</span> that support particular transitions. That presentation begins with generation of the <span class="hlt">ensemble</span> and application of constraint perturbations to compute T, including estimating alternatives to accommodate cases where the problem size becomes computationally intractable. Then, T is used to predict <span class="hlt">ensemble</span> dynamics properties in expected-value like calculations. Finally, analyses from the two complementary assumptions about T - that it represents uncertainty about a unique system or that it represents stochasticity around a unique constraint - are presented: entropy-based experiment selection and generalized potentials/heat dissipation of the system, respectively. Extension of these techniques to more general graph models is described, but not demonstrated. Future directions for research using T are proposed in the summary chapter. Throughout this work, the presentation of various calculations involving T are motivated by the Budding Yeast Cell Cycle example, with argument for the generality of the approaches presented by the results of their application to a database of pseudo-randomly generated dynamic constraints.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1160288','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1160288"><span id="translatedtitle">The ultimate <span class="hlt">downscaling</span> limit of FETs.</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Mamaluy, Denis; Gao, Xujiao; Tierney, Brian David</p> <p>2014-10-01</p> <p>We created a highly efficient, universal 3D quant um transport simulator. We demonstrated that the simulator scales linearly - both with the problem size (N) and number of CPUs, which presents an important break-through in the field of computational nanoelectronics. It allowed us, for the first time, to accurately simulate and optim ize a large number of realistic nanodevices in a much shorter time, when compared to other methods/codes such as RGF[~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> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1710270C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1710270C"><span id="translatedtitle">Comparison between dynamical and stochastic <span class="hlt">downscaling</span> methods in central Italy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Camici, Stefania; Palazzi, Elisa; Pieri, Alexandre; Brocca, Luca; Moramarco, Tommaso; Provenzale, Antonello</p> <p>2015-04-01</p> <p>Global climate models (GCMs) are the primary tool to assess future climate change. However, most GCMs currently do not provide reliable information on scales below about 100 km and, hence, cannot be used as a direct input of hydrological models for climate change impact assessments. Therefore, a wide range of statistical and dynamical <span class="hlt">downscaling</span> methods have been developed to overcome the scale discrepancy between the GCM climatic scenarios and the resolution required for hydrological applications and impact studies. In this context, the selection of a suitable <span class="hlt">downscaling</span> method is an important issue. The use of different spatial domains, predictor variables, predictands and assessment criteria makes the relative performance of different methods difficult to achieve and general rules to select a priori the best <span class="hlt">downscaling</span> method do not exist. Additionally, many studies have shown that, depending on the hydrological variable, each <span class="hlt">downscaling</span> method significantly contributes to the overall uncertainty of the final hydrological response. Therefore, it is strongly recommended to test/evaluate different <span class="hlt">downscaling</span> methods by using ground-based data before applying them to climate model data. In this study, the daily rainfall data from the ERA-Interim re-analysis database (provided by the European Centre for Medium-Range Weather Forecasts, ECMWF) for the period 1979-2008 and with a resolution of about 80 km, are <span class="hlt">downscaled</span> using both dynamical and statistical methods. In the first case, the Weather Research and Forecasting (WRF) model was nested into the ERA-Interim re-analysis system to achieve a spatial resolution of about 4 km; in the second one, the stochastic rainfall <span class="hlt">downscaling</span> method called RainFARM was applied to the ERA-Interim data to obtain one stochastic realization of the rainfall field with a resolution of ~1 km. The <span class="hlt">downscaled</span> rainfall data obtained with the two methods are then used to force a continuous rainfall-runoff model in order to obtain a hydrological response in terms of discharge output. Preliminary results show that both <span class="hlt">downscaling</span> methods are able to reproduce the statistical properties and temporal pattern of rainfall observations while the results in terms of discharge will be shown at the conference session. This analysis will provide useful guidelines for the selection of the best performing <span class="hlt">downscaling</span> approach applied to rainfall data in this particular study area.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JHyd..502...77B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JHyd..502...77B"><span id="translatedtitle">Bias-corrected short-range Member-to-Member <span class="hlt">ensemble</span> forecasts of reservoir inflow</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bourdin, Dominique R.; Stull, Roland B.</p> <p>2013-10-01</p> <p>A Member-to-Member <span class="hlt">ensemble</span> forecasting system is developed for inflows to hydroelectric reservoirs that incorporates multiple numerical weather prediction models and multiple distributed hydrological models linked by a variety of <span class="hlt">downscaling</span> schemes. Each hydrological model uses multiple differently-optimized parameter sets and begins each daily forecast from several different initial conditions. The <span class="hlt">ensemble</span> thereby attempts to sample all sources of error in the modeling chain. The importance of sampling all sources of error is illustrated by comparing this <span class="hlt">ensemble</span> with an <span class="hlt">ensemble</span> comprised of single 'best' parameterization for each hydrological model. Degree-of-mass-balance bias correction schemes trained using data windows of varying lengths are applied to the individual <span class="hlt">ensemble</span> members. Based on examination of various verification metrics, we determine that a bias corrector that uses a linearly-weighted combination of past errors calculated over a three-day moving window is able to significantly improve forecast quality for the flashy case study watershed in southwestern British Columbia, Canada. Incorporation of all sources of modeling uncertainty is found to greatly improve <span class="hlt">ensemble</span> resolution and discrimination. The full potential for these improvements using <span class="hlt">ensembles</span> is only realized after removal of bias.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1231194','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1231194"><span id="translatedtitle">Matlab Cluster <span class="hlt">Ensemble</span> Toolbox</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Sapio, Vincent De; Kegelmeyer, Philip</p> <p>2009-04-27</p> <p>This is a Matlab toolbox for investigating the application of cluster <span class="hlt">ensembles</span> to data classification, with the objective of improving the accuracy and/or speed of clustering. The toolbox divides the cluster <span class="hlt">ensemble</span> problem into four areas, providing functionality for each. These include, (1) synthetic data generation, (2) clustering to generate individual data partitions and similarity matrices, (3) consensus function generation and final clustering to generate <span class="hlt">ensemble</span> data partitioning, and (4) implementation of accuracy metrics. With regard to data generation, Gaussian data of arbitrary dimension can be generated. The kcenters algorithm can then be used to generate individual data partitions by either, (a) subsampling the data and clustering each subsample, or by (b) randomly initializing the algorithm and generating a clustering for each initialization. In either case an overall similarity matrix can be computed using a consensus function operating on the individual similarity matrices. A final clustering can be performed and performance metrics are provided for evaluation purposes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020052415','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020052415"><span id="translatedtitle">Input Decimated <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)</p> <p>2001-01-01</p> <p>Using an <span class="hlt">ensemble</span> of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated <span class="hlt">ensembles</span> (IDEs) outperform <span class="hlt">ensembles</span> whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015OcMod..90...57M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015OcMod..90...57M"><span id="translatedtitle"><span class="hlt">Downscaling</span> biogeochemistry in the Benguela eastern boundary current</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Machu, E.; Goubanova, K.; Le Vu, B.; Gutknecht, E.; Garçon, V.</p> <p>2015-06-01</p> <p>Dynamical <span class="hlt">downscaling</span> is developed to better predict the regional impact of global changes in the framework of scenarios. As an intermediary step towards this objective we used the Regional Ocean Modeling System (ROMS) to <span class="hlt">downscale</span> a low resolution coupled atmosphere-ocean global circulation model (AOGCM; IPSL-CM4) for simulating the recent-past dynamics and biogeochemistry of the Benguela eastern boundary current. Both physical and biogeochemical improvements are discussed over the present climate scenario (1980-1999) under the light of <span class="hlt">downscaling</span>. Despite biases introduced through boundary conditions (atmospheric and oceanic), the physical and biogeochemical processes in the Benguela Upwelling System (BUS) have been improved by the ROMS model, relative to the IPSL-CM4 simulation. Nevertheless, using coarse-resolution AOGCM daily atmospheric forcing interpolated on ROMS grids resulted in a shifted SST seasonality in the southern BUS, a deterioration of the northern Benguela region and a very shallow mixed layer depth over the whole regional domain. We then investigated the effect of wind <span class="hlt">downscaling</span> on ROMS solution. Together with a finer resolution of dynamical processes and of bathymetric features (continental shelf and Walvis Ridge), wind <span class="hlt">downscaling</span> allowed correction of the seasonality, the mixed layer depth, and provided a better circulation over the domain and substantial modifications of subsurface biogeochemical properties. It has also changed the structure of the lower trophic levels by shifting large offshore areas from autotrophic to heterotrophic regimes with potential important consequences on ecosystem functioning. The regional <span class="hlt">downscaling</span> also improved the phytoplankton distribution and the southward extension of low oxygen waters in the Northern Benguela. It allowed simulating low oxygen events in the northern BUS and highlighted a potential upscaling effect related to the nitrogen irrigation from the productive BUS towards the tropical/subtropical South Atlantic basin. This study shows that forcing a <span class="hlt">downscaled</span> ocean model with higher resolution winds than those issued from an AOGCM, results in improved representation of physical and biogeochemical processes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4271150','PMC'); return false;" href="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> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R. S.; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul</p> <p>2015-01-01</p> <p>Motivation: We present a Web service to access <span class="hlt">Ensembl</span> data using Representational State Transfer (REST). The <span class="hlt">Ensembl</span> REST server enables the easy retrieval of a wide range of <span class="hlt">Ensembl</span> data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular <span class="hlt">Ensembl</span> Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. Availability and implementation: The <span class="hlt">Ensembl</span> REST API can be accessed at http://rest.<span class="hlt">ensembl</span>.org and source code is freely available under an Apache 2.0 license from http://github.com/<span class="hlt">Ensembl/ensembl</span>-rest. Contact: ayates@ebi.ac.uk or flicek@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25236461</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013PhDT........96T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013PhDT........96T"><span id="translatedtitle">High resolution <span class="hlt">ensemble</span> error growth and dimensionality in tropical cyclone genesis environments</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thatcher, Levi Sterling</p> <p></p> <p>Over the last several decades, <span class="hlt">ensemble</span> forecasts of atmospheric phenomena have become increasingly popular, not only because they provide an improved mean forecast of various events, but also because they render an estimate of the accompanying forecast uncertainty. Research into high-resolution <span class="hlt">ensembles</span> based in the Tropics and in terms of tropical cyclone (TC) genesis mechanisms has been relatively sparse, even though such disturbances are notoriously difficult to forecast. In this study, we couple several popular <span class="hlt">ensemble</span> perturbation methods to the mesoscale Weather Research and Forecasting (WRF) model at high resolution to examine the predictability of genesis, error growth characteristics, and underdispersion issues in forecasts of Hurricane Ernesto (2006) and Typhoon Nuri (2008). In order to examine the effects of model resolution on TC genesis forecasts, a <span class="hlt">downscaled</span> 5-km resolution regional control <span class="hlt">ensemble</span>, based on a <span class="hlt">downscaling</span> of the National Centers for Environmental Prediction's Global <span class="hlt">Ensemble</span> Forecast System (GEFS), is compared against the standard GEFS simulations. To analyze the effect of the various perturbation methods on genesis and forecast characteristics, we compare results from the regional GEFS-based simulation to several implementations of the breeding of growing modes (BGM), wherein we vary the variables perturbed, cycling period durations, and boundary conditions. While the global GEFS forecast failed to predict a well-developed Ernesto in any of its members, the high-resolution GEFS-based <span class="hlt">ensemble</span> contained several intense TCs by actual genesis time. Based on a sample of 154 <span class="hlt">ensemble</span> member forecasts, the impact of environmental precursors on TC genesis likelihood is investigated. Despite the large number of easterly waves that do not develop into TCs and the large amount of water vapor in the summer Tropics, we find that the strength of the preexisting wave and initial 850 hPa water vapor are significant determining factors for TC genesis. Finally, we create several <span class="hlt">ensemble</span> forecasts of Ernesto using the stochastic kinetic-energy backscatter scheme (SKEBS) and find that the standard SKEBS <span class="hlt">ensemble</span> has more dispersion per unit error compared with both the BGM and GEFS-based <span class="hlt">ensembles</span>. In addition, SKEBS shows notably lower vapor bias and larger theta bias compared with the initial condition-based <span class="hlt">ensembles</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.5800S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.5800S"><span id="translatedtitle">Regional climate change projections over South America based on the CLARIS-LPB RCM <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Samuelsson, Patrick; Solman, Silvina; Sanchez, Enrique; Rocha, Rosmeri; Li, Laurent; Marengo, José; Remedio, Armelle; Berbery, Hugo</p> <p>2013-04-01</p> <p>CLARIS-LPB was an EU FP7 financed Europe-South America Network for Climate Change Assessment and Impact Studies in La Plata Basin. CLARIS-LPB has created the first <span class="hlt">ensemble</span> ever of RCM <span class="hlt">downscalings</span> over South America. Here we present the climate change scenarios for a near future period (2011-2040) and for a far future period (2071-2100). The <span class="hlt">ensemble</span> is based on seven RCMs driven by three CMIP3 GCMs for emission scenario SRES A1B. The RCM model domains cover all of South America, with a horizontal resolution of approximately 50 km, but project focus has been on results over the La Plata Basin. The <span class="hlt">ensemble</span> mean for temperature change shows more warming over tropical South America than over the southern part of the continent. During summer (DJF) the Low-Parana and Uruguay regions show less warming than the surrounding regions. For the <span class="hlt">ensemble</span> mean of precipitation changes the patterns are almost the same for near and far future but with larger values for far future. Thus overall trends do not change with time. The near future shows in general small changes over large areas (less than ±10%). For JJA a dry tendency is seen over eastern Brazil that becomes stronger and extends geographically with time. In near future most models show a drying trend over this area. In far future almost all models agree on the drying. For DJF a wet tendency is seen over the La Plata basin area which becomes stronger with time. In near future almost all <span class="hlt">downscalings</span> agree on this wet tendency and in far future all <span class="hlt">downscalings</span> agree on the sign. The RCM <span class="hlt">ensemble</span> is unbalanced with respect to forcing GCMs. 6 out of 11(10) simulations use ECHAM5 for the near(far) future period while 4(3) use HadCM3 and only one IPSL. Thus, all <span class="hlt">ensemble</span> mean values will be tilted towards ECHAM5. It is of course possible to compensate for this imbalance among GCMs by some weighting but no such weighting has been applied for the current analysis. The north-south gradient in warming is in general stronger in the ECHAM5 <span class="hlt">downscalings</span> and is also more evident during JJA than during DJF. The HadCM3 and IPSL <span class="hlt">downscalings</span> give larger warming in near future than ECHAM5 <span class="hlt">downscalings</span>. This tendency is still present in far future but differences connected to GCMs are then much less evident. For precipitation the spread in trends and amounts of changes between different <span class="hlt">downscalings</span> are much larger than for temperature. In contrast to temperature the precipitation patterns are in general more similar for the same RCM than for the same GCM. Thus, the results are sensitive for how precipitation processes are parameterized and/or for how local surface-atmosphere feedback mechanisms are simulated. Looking at a certain RCM and period the patterns for near and far futures are similar but stronger for the far future period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17..986C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17..986C"><span id="translatedtitle">Representative meteorological <span class="hlt">ensembles</span> of change climate change in the Araucanía Region, Chile.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cepeda, Javier; Vargas, Ximena</p> <p>2015-04-01</p> <p>One of the main uncertainties in hydrologic modeling is attributed to meteorological inputs. When climate change impact analysis is performed, uncertainty increases due to that meteorological time series are obtained through Global Circulation Models (GCM) for a specific climate change scenario. The Intergovernmental Panel on Climate Change (IPCC) in their last report (AR5, 2013 ) recommend the Representative Concentration Pathway. RCP scenarios, developed under the Coupled Model Intercomparison Project Phase 5 (CMIP5). Pathways for stabilization of radiative forcing by 2100 characterize these scenarios being a radiative forcing of 8.5 w/m2, the highest future condition considered. In order to reduce the meteorological uncertainties, we study the behavior of the daily precipitation series I three meteorological stations in the valley of the Araucanía region, in southern Chile, using ten <span class="hlt">ensembles</span> from CGM MK-3.6 model for RCP 8.5. The main hypothesis is that good transformer functions between the observations and data obtained from the model is essential to have suitable future projections. To obtain these functions, statistical <span class="hlt">downscaling</span> is performed; first spatial <span class="hlt">downscaling</span> is carried out, and then a temporal <span class="hlt">downscaling</span> of the daily precipitation data for each month is made. <span class="hlt">Ensembles</span> whit transfer functions without discontinuities or those with the least were preferred. From this analysis we selected four <span class="hlt">ensembles</span>. For the three gage stations we apply the transfer's functions during the observed period and compared the average seasonal variation curve, the duration curve of daily, monthly and annually precipitation and average number of rainy days. Finally, based on qualitative analysis and quantitative criteria we suggest which <span class="hlt">ensemble</span> are the most representative historical conditions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24949961','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24949961"><span id="translatedtitle">Assembling cell <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Spruston, Nelson</p> <p>2014-06-19</p> <p>The way the hippocampus processes information and encodes memories in the form of "cell assemblies" is likely determined in part by how its circuits are wired up during development. In this issue, Xu et al. now provide new insight into how neurons arising from a single common precursor migrate to their final destination and form functionally synchronous <span class="hlt">ensembles</span>. PMID:24949961</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=written+AND+proposal+AND+skill&pg=5&id=ED254263','ERIC'); return false;" href="http://eric.ed.gov/?q=written+AND+proposal+AND+skill&pg=5&id=ED254263"><span id="translatedtitle">Music <span class="hlt">Ensemble</span>: Course Proposal.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Kovach, Brian</p> <p></p> <p>A proposal is presented for a Music <span class="hlt">Ensemble</span> course to be offered at the Community College of Philadelphia for music students who have had previous vocal or instrumental training. A standardized course proposal cover form is followed by a statement of purpose for the course, a list of major course goals, a course outline, and a bibliography. Next,…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=19810000222&hterms=PROTECTIVE+CLOTHING&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DPROTECTIVE%2BCLOTHING','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19810000222&hterms=PROTECTIVE+CLOTHING&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DPROTECTIVE%2BCLOTHING"><span id="translatedtitle">Protective Garment <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wakefield, M. E.</p> <p>1982-01-01</p> <p>Protective garment <span class="hlt">ensemble</span> with internally-mounted environmental- control unit contains its own air supply. Alternatively, a remote-environmental control unit or an air line is attached at the umbilical quick disconnect. Unit uses liquid air that is vaporized to provide both breathing air and cooling. Totally enclosed garment protects against toxic substances.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17..776S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17..776S"><span id="translatedtitle">Assessing the Uncertainty in <span class="hlt">Downscaling</span> Approaches using Hydrological Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sharma, Tarul; Chhabra, Surbhi; Karmakar, Subhankar; Ghosh, Subimal; Salvi, Kaustubh</p> <p>2015-04-01</p> <p>General Circulation Models (GCMs) play an important role in defining the climate change impacts at a global scale, but its coarser resolution limits its direct application at regional scale. To understand the meteorological variability at regional scale, regional climate models have been developed which use the GCM outputs as boundary condition to <span class="hlt">downscale</span> them at finer scale. Two broad classes of <span class="hlt">downscaling</span> are dynamical, which involve developing physics based regional model and statistical, which involves establishing statistical relationship between coarse scale climate variables and fine resolution variable of interest. The two approaches perform well in their own domain, however, comparing the results, obtained using two approaches with different basis leads to a source of uncertainty, associated with the approach. Here, we quantify the uncertainty associated with approach in terms of hydrologic variables that are simulated separately using dynamically and statistically <span class="hlt">downscaled</span> climate forcings. GCM model named EC-Earth has been statistically <span class="hlt">downscaled</span> (SD) using multi-site kernel regression method and it has been compared with dynamically <span class="hlt">downscaled</span> CORDEX outputs of the same GCM. For this, period from 1981 to 2005 has been considered as baseline period and period from 2016 to 2040 has been considered as future period. Since, these meteorological outputs affect the regional hydrological components such as runoff, Evapo-Transpiration (ET), soil moisture, and baseflow; simulated outputs from a meso-scale hydrological model named Variable Infiltration Capacity (VIC) model has been used to compare these <span class="hlt">downscaled</span> variables. Advantage of this model is that it considers the effect of Land Use/Land Cover (LULC), vegetative properties, and soil properties at sub-grid level; which plays an important role in the hydrology of a region. Comparatively more future increase in all the hydrological variables over major part of India was simulated using SD outputs, then CORDEX outputs. Also, <span class="hlt">downscaling</span> uncertainty showed decrease in minimum and maximum temperature derived from SD model as compared to CORDEX data. Partial correlation of each hydrological variable with meteorological data showed future change in precipitation and maximum temperature as the most affecting variables which will influence the change in hydrological parameters as compared to wind and minimum temperature. However, CORDEX results showed change in precipitation and maximum temperature as the major parameters that will affect ET, runoff and soil moisture; whereas statistically <span class="hlt">downscaled</span> results showed only change in precipitation as the most influential variable. Keywords: Dynamical and Statistical <span class="hlt">Downscaling</span>, Hydrological model, Uncertainty analysis</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..12.6179W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..12.6179W"><span id="translatedtitle">Hydrologic extremes - an intercomparison of multiple gridded statistical <span class="hlt">downscaling</span> methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Werner, A. T.; Cannon, A. J.</p> <p>2015-06-01</p> <p>Gridded statistical <span class="hlt">downscaling</span> methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate <span class="hlt">downscaling</span> methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, <span class="hlt">downscaling</span> comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded <span class="hlt">downscaling</span> models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven <span class="hlt">downscaling</span> methods - bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) - are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as <span class="hlt">downscaling</span> target data. The skill of the <span class="hlt">downscaling</span> methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis datasets complicated the evaluation of <span class="hlt">downscaling</span> performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from <span class="hlt">downscaled</span> reanalyses and gridded observations was useful for diagnosing the reliability of the various historical datasets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1512041M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1512041M"><span id="translatedtitle">VALUE - Validating and Integrating <span class="hlt">Downscaling</span> Methods for Climate Change Research</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maraun, Douglas; Widmann, Martin; Benestad, Rasmus; Kotlarski, Sven; Huth, Radan; Hertig, Elke; Wibig, Joanna; Gutierrez, Jose</p> <p>2013-04-01</p> <p>Our understanding of global climate change is mainly based on General Circulation Models (GCMs) with a relatively coarse resolution. Since climate change impacts are mainly experienced on regional scales, high-resolution climate change scenarios need to be derived from GCM simulations by <span class="hlt">downscaling</span>. Several projects have been carried out over the last years to validate the performance of statistical and dynamical <span class="hlt">downscaling</span>, yet several aspects have not been systematically addressed: variability on sub-daily, decadal and longer time-scales, extreme events, spatial variability and inter-variable relationships. Different <span class="hlt">downscaling</span> approaches such as dynamical <span class="hlt">downscaling</span>, statistical <span class="hlt">downscaling</span> and bias correction approaches have not been systematically compared. Furthermore, collaboration between different communities, in particular regional climate modellers, statistical <span class="hlt">downscalers</span> and statisticians has been limited. To address these gaps, the EU Cooperation in Science and Technology (COST) action VALUE (www.value-cost.eu) has been brought into life. VALUE is a research network with participants from currently 23 European countries running from 2012 to 2015. Its main aim is to systematically validate and develop <span class="hlt">downscaling</span> methods for climate change research in order to improve regional climate change scenarios for use in climate impact studies. Inspired by the co-design idea of the international research initiative "future earth", stakeholders of climate change information have been involved in the definition of research questions to be addressed and are actively participating in the network. The key idea of VALUE is to identify the relevant weather and climate characteristics required as input for a wide range of impact models and to define an open framework to systematically validate these characteristics. Based on a range of benchmark data sets, in principle every <span class="hlt">downscaling</span> method can be validated and compared with competing methods. The results of 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016ClDy...46.1991S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016ClDy...46.1991S"><span id="translatedtitle">Credibility of statistical <span class="hlt">downscaling</span> under nonstationary climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salvi, Kaustubh; Ghosh, Subimal; Ganguly, Auroop R.</p> <p>2016-03-01</p> <p>Statistical <span class="hlt">downscaling</span> (SD) establishes empirical relationships between coarse-resolution climate model simulations with higher-resolution climate variables of interest to stakeholders. These statistical relations are estimated based on historical observations at the finer resolutions and used for future projections. The implicit assumption is that the SD relations, extracted from data are stationary or remain unaltered, despite non-stationary change in climate. The validity of this assumption relates directly to the credibility of SD. Falsifiability of climate projections is a challenging proposition. Calibration and verification, while necessary for SD, are unlikely to be able to reproduce the full range of behavior that could manifest at decadal to century scale lead times. We propose a design-of-experiments (DOE) strategy to assess SD performance under nonstationary climate and evaluate the strategy via a transfer-function based SD approach. The strategy relies on selection of calibration and validation periods such that they represent contrasting climatic conditions like hot-versus-cold and ENSO-versus-non-ENSO years. The underlying assumption is that conditions such as warming or predominance of El Niño may be more prevalent under climate change. In addition, two different historical time periods are identified, which resemble pre-industrial and the most severe future emissions scenarios. The ability of the empirical relations to generalize under these proxy conditions is considered an indicator of their performance under future nonstationarity. Case studies over two climatologically disjoint study regions, specifically India and Northeast United States, reveal robustness of DOE in identifying the locations where nonstationarity prevails as well as the role of effective predictor selection under nonstationarity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy..tmp..219S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy..tmp..219S"><span id="translatedtitle">Credibility of statistical <span class="hlt">downscaling</span> under nonstationary climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salvi, Kaustubh; Ghosh, Subimal; Ganguly, Auroop R.</p> <p>2015-06-01</p> <p>Statistical <span class="hlt">downscaling</span> (SD) establishes empirical relationships between coarse-resolution climate model simulations with higher-resolution climate variables of interest to stakeholders. These statistical relations are estimated based on historical observations at the finer resolutions and used for future projections. The implicit assumption is that the SD relations, extracted from data are stationary or remain unaltered, despite non-stationary change in climate. The validity of this assumption relates directly to the credibility of SD. Falsifiability of climate projections is a challenging proposition. Calibration and verification, while necessary for SD, are unlikely to be able to reproduce the full range of behavior that could manifest at decadal to century scale lead times. We propose a design-of-experiments (DOE) strategy to assess SD performance under nonstationary climate and evaluate the strategy via a transfer-function based SD approach. The strategy relies on selection of calibration and validation periods such that they represent contrasting climatic conditions like hot-versus-cold and ENSO-versus-non-ENSO years. The underlying assumption is that conditions such as warming or predominance of El Niño may be more prevalent under climate change. In addition, two different historical time periods are identified, which resemble pre-industrial and the most severe future emissions scenarios. The ability of the empirical relations to generalize under these proxy conditions is considered an indicator of their performance under future nonstationarity. Case studies over two climatologically disjoint study regions, specifically India and Northeast United States, reveal robustness of DOE in identifying the locations where nonstationarity prevails as well as the role of effective predictor selection under nonstationarity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014OcMod..84...35L','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Méndez, Fernando</p> <p>2014-12-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ACPD...12.1191L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ACPD...12.1191L"><span id="translatedtitle">Differences between <span class="hlt">downscaling</span> with spectral and grid nudging using WRF</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, P.; Tsimpidi, A. P.; Hu, Y.; Stone, B.; Russell, A. G.; Nenes, A.</p> <p>2012-01-01</p> <p>Dynamical <span class="hlt">downscaling</span> has been extensively used to study regional climate forced by large-scale global climate models. During the <span class="hlt">downscaling</span> process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the <span class="hlt">downscaling</span> of NCEP/NCAR data using Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ACP....12.3601L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ACP....12.3601L"><span id="translatedtitle">Differences between <span class="hlt">downscaling</span> with spectral and grid nudging using WRF</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, P.; Tsimpidi, A. P.; Hu, Y.; Stone, B.; Russell, A. G.; Nenes, A.</p> <p>2012-04-01</p> <p>Dynamical <span class="hlt">downscaling</span> has been extensively used to study regional climate forced by large-scale global climate models. During the <span class="hlt">downscaling</span> process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the <span class="hlt">downscaling</span> of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H24F..08H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H24F..08H"><span id="translatedtitle">Development of Spatiotemporal Bias-Correction Techniques for <span class="hlt">Downscaling</span> GCM Predictions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.</p> <p>2010-12-01</p> <p>Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM <span class="hlt">downscaling</span> techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. <span class="hlt">Downscaled</span> GCM results (e.g., CMIP3 <span class="hlt">ensembles</span>) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and <span class="hlt">downscaled</span> CMIP3 data for the 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..519.2978G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..519.2978G"><span id="translatedtitle">Evaluation of real-time hydrometeorological <span class="hlt">ensemble</span> prediction on hydrologic scales in Northern California</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Georgakakos, Konstantine P.; Graham, Nicholas E.; Modrick, Theresa M.; Murphy, Michael J.; Shamir, Eylon; Spencer, Cristopher R.; Sperfslage, Jason A.</p> <p>2014-11-01</p> <p>The paper presents an evaluation of real time <span class="hlt">ensemble</span> forecasts produced during 2010-2012 by the demonstration project INFORM (Integrated Forecast and Reservoir Management) in Northern California. In addition, the innovative elements of the forecast component of the INFORM project are highlighted. The forecast component is designed to dynamically <span class="hlt">downscale</span> operational multi-lead <span class="hlt">ensemble</span> forecasts from the Global <span class="hlt">Ensemble</span> Forecast System (GEFS) and the Climate Forecast system (CFS) of the National Centers of Environmental Prediction (NCEP), and to use adaptations of the operational hydrologic models of the US National Weather Service California Nevada River Forecast Center to provide <span class="hlt">ensemble</span> reservoir inflow forecasts in real time. A full-physics 10-km resolution (10 km on the side) mesoscale model was implemented for the <span class="hlt">ensemble</span> prediction of surface precipitation and temperature over the domain of Northern California with lead times out to 16 days with 6-hourly temporal resolution. An intermediate complexity regional model with a 10 km resolution was implemented to <span class="hlt">downscale</span> the NCEP CFS <span class="hlt">ensemble</span> forecasts for lead times out to 41.5 days. Methodologies for precipitation and temperature model forecast adjustment to comply with the corresponding observations were formulated and tested as regards their effectiveness for improving the <span class="hlt">ensemble</span> predictions of these two variables and also for improving reservoir inflow forecasts. The evaluation is done using the real time databases of INFORM and concerns the snow accumulation and melt seasons. Performance is measured by metrics that range from those that use forecast means to those that use the entire forecast <span class="hlt">ensemble</span>. The results show very good skill in forecasting precipitation and temperature over the subcatchments of the INFORM domain out to a week in advance for all basins, models and seasons. For temperature, in some cases, non-negligible skill has been obtained out to four weeks for the melt season. 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H21H1286F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H21H1286F"><span id="translatedtitle">Passive Microwave Soil Moisture <span class="hlt">Downscaling</span> Using Vegetation and Surface Temperatures</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fang, B.; Lakshmi, V.</p> <p>2012-12-01</p> <p>Soil moisture satellite estimates are available from a variety of passive microwave satellite missions, but their resolution is frequently too large for use by land managers and action agencies. In this article, a soil moisture <span class="hlt">downscaling</span> algorithm based on look-up curves between daily temperature change and daily average soil moisture is presented and developed to bridge the scale. The algorithm was derived from 1/8o spatial resolution North American Land Data Assimilation System (NLDAS-2) surface temperature and soil moisture data, and also used 5 km Advanced Very High Resolution Radiometer (AVHRR) and 1km Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) as look-up dataset for different vegetation and surface temperature conditions. The differences between 1km MODIS temperature <span class="hlt">downscaled</span> soil moisture values and Advanced Microwave Scanning Radiometer - EOS (AMSR-E) soil moisture values were used to modify AMSR-E soil moistures. The 1km <span class="hlt">downscaled</span> soil moisture maps display greater details on the spatial pattern of soil moisture distribution. Two sets of ground-based measurements, the Oklahoma Mesonet and the Little Washita Micronet were used to validate the algorithm. The Root Mean Square Error (RMSE) of the 1km <span class="hlt">downscaled</span> soil moisture versus Oklahoma Mesonet observations for clear days ranges from 0.119~0.168, whereas the RMSE of 1km <span class="hlt">downscaled</span> soil moisture versus the Little Washita Watershed observations ranges from 0.022~0.077. The results demonstrate that the 1 km <span class="hlt">downscaled</span> soil moisture has better agreement with watershed in situ data compared to the other sources of soil moisture.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/15123589','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/15123589"><span id="translatedtitle">The <span class="hlt">Ensembl</span> analysis pipeline.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Potter, Simon C; Clarke, Laura; Curwen, Val; Keenan, Stephen; Mongin, Emmanuel; Searle, Stephen M J; Stabenau, Arne; Storey, Roy; Clamp, Michele</p> <p>2004-05-01</p> <p>The <span class="hlt">Ensembl</span> pipeline is an extension to the <span class="hlt">Ensembl</span> system which allows automated annotation of genomic sequence. The software comprises two parts. First, there is a set of Perl modules ("Runnables" and "RunnableDBs") which are 'wrappers' for a variety of commonly used analysis tools. These retrieve sequence data from a relational database, run the analysis, and write the results back to the database. They inherit from a common interface, which simplifies the writing of new wrapper modules. On top of this sits a job submission system (the "RuleManager") which allows efficient and reliable submission of large numbers of jobs to a compute farm. Here we describe the fundamental software components of the pipeline, and we also highlight some features of the Sanger installation which were necessary to enable the pipeline to scale to whole-genome analysis. PMID:15123589</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26387108','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26387108"><span id="translatedtitle">The Protein <span class="hlt">Ensemble</span> Database.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Varadi, Mihaly; Tompa, Peter</p> <p>2015-01-01</p> <p>The scientific community's major conceptual notion of structural biology has recently shifted in emphasis from the classical structure-function paradigm due to the emergence of intrinsically disordered proteins (IDPs). As opposed to their folded cousins, these proteins are defined by the lack of a stable 3D fold and a high degree of inherent structural heterogeneity that is closely tied to their function. Due to their flexible nature, solution techniques such as small-angle X-ray scattering (SAXS), nuclear magnetic resonance (NMR) spectroscopy and fluorescence resonance energy transfer (FRET) are particularly well-suited for characterizing their biophysical properties. Computationally derived structural <span class="hlt">ensembles</span> based on such experimental measurements provide models of the conformational sampling displayed by these proteins, and they may offer valuable insights into the functional consequences of inherent flexibility. The Protein <span class="hlt">Ensemble</span> Database (http://pedb.vib.be) is the first openly accessible, manually curated online resource storing the <span class="hlt">ensemble</span> models, protocols used during the calculation procedure, and underlying primary experimental data derived from SAXS and/or NMR measurements. By making this previously inaccessible data freely available to researchers, this novel resource is expected to promote the development of more advanced modelling methodologies, facilitate the design of standardized calculation protocols, and consequently lead to a better understanding of how function arises from the disordered state. PMID:26387108</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1231194-matlab-cluster-ensemble-toolbox','SCIGOV-ESTSC'); return false;" href="http://www.osti.gov/scitech/biblio/1231194-matlab-cluster-ensemble-toolbox"><span id="translatedtitle">Matlab Cluster <span class="hlt">Ensemble</span> Toolbox</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech/">Energy Science and Technology Software Center (ESTSC)</a></p> <p></p> <p>2009-04-27</p> <p>This is a Matlab toolbox for investigating the application of cluster <span class="hlt">ensembles</span> to data classification, with the objective of improving the accuracy and/or speed of clustering. The toolbox divides the cluster <span class="hlt">ensemble</span> problem into four areas, providing functionality for each. These include, (1) synthetic data generation, (2) clustering to generate individual data partitions and similarity matrices, (3) consensus function generation and final clustering to generate <span class="hlt">ensemble</span> data partitioning, and (4) implementation of accuracy metrics. Withmore » regard to data generation, Gaussian data of arbitrary dimension can be generated. The kcenters algorithm can then be used to generate individual data partitions by either, (a) subsampling the data and clustering each subsample, or by (b) randomly initializing the algorithm and generating a clustering for each initialization. In either case an overall similarity matrix can be computed using a consensus function operating on the individual similarity matrices. A final clustering can be performed and performance metrics are provided for evaluation purposes.« less</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26529728','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26529728"><span id="translatedtitle">Effective Visualization of Temporal <span class="hlt">Ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hao, Lihua; Healey, Christopher G; Bass, Steffen A</p> <p>2016-01-01</p> <p>An <span class="hlt">ensemble</span> is a collection of related datasets, called members, built from a series of runs of a simulation or an experiment. <span class="hlt">Ensembles</span> are large, temporal, multidimensional, and multivariate, making them difficult to analyze. Another important challenge is visualizing <span class="hlt">ensembles</span> that vary both in space and time. Initial visualization techniques displayed <span class="hlt">ensembles</span> with a small number of members, or presented an overview of an entire <span class="hlt">ensemble</span>, but without potentially important details. Recently, researchers have suggested combining these two directions, allowing users to choose subsets of members to visualization. This manual selection process places the burden on the user to identify which members to explore. We first introduce a static <span class="hlt">ensemble</span> visualization system that automatically helps users locate interesting subsets of members to visualize. We next extend the system to support analysis and visualization of temporal <span class="hlt">ensembles</span>. We employ 3D shape comparison, cluster tree visualization, and glyph based visualization to represent different levels of detail within an <span class="hlt">ensemble</span>. This strategy is used to provide two approaches for temporal <span class="hlt">ensemble</span> analysis: (1) segment based <span class="hlt">ensemble</span> analysis, to capture important shape transition time-steps, clusters groups of similar members, and identify common shape changes over time across multiple members; and (2) time-step based <span class="hlt">ensemble</span> analysis, which assumes <span class="hlt">ensemble</span> members are aligned in time by combining similar shapes at common time-steps. Both approaches enable users to interactively visualize and analyze a temporal <span class="hlt">ensemble</span> from different perspectives at different levels of detail. We demonstrate our techniques on an <span class="hlt">ensemble</span> studying matter transition from hadronic gas to quark-gluon plasma during gold-on-gold particle collisions. PMID:26529728</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.7513L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.7513L"><span id="translatedtitle">Rainfall <span class="hlt">Downscaling</span> Conditional on Upper-air Atmospheric Predictors: Improved Assessment of Rainfall Statistics in a Changing Climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Langousis, Andreas; Mamalakis, Antonis; Deidda, Roberto; Marrocu, Marino</p> <p>2015-04-01</p> <p>To improve the level skill of Global Climate Models (GCMs) and Regional Climate Models (RCMs) in reproducing the statistics of rainfall at a basin level and at hydrologically relevant temporal scales (e.g. daily), two types of statistical approaches have been suggested. One is the statistical correction of climate model rainfall outputs using historical series of precipitation. The other is the use of stochastic models of rainfall to conditionally simulate precipitation series, based on large-scale atmospheric predictors produced by climate models (e.g. geopotential height, relative vorticity, divergence, mean sea level pressure). The latter approach, usually referred to as statistical rainfall <span class="hlt">downscaling</span>, aims at reproducing the statistical character of rainfall, while accounting for the effects of large-scale atmospheric circulation (and, therefore, climate forcing) on rainfall statistics. While promising, statistical rainfall <span class="hlt">downscaling</span> has not attracted much attention in recent years, since the suggested approaches involved complex (i.e. subjective or computationally intense) identification procedures of the local weather, in addition to demonstrating limited success in reproducing several statistical features of rainfall, such as seasonal variations, the distributions of dry and wet spell lengths, the distribution of the mean rainfall intensity inside wet periods, and the distribution of rainfall extremes. In an effort to remedy those shortcomings, Langousis and Kaleris (2014) developed a statistical framework for simulation of daily rainfall intensities conditional on upper air variables, which accurately reproduces the statistical character of rainfall at multiple time-scales. Here, we study the relative performance of: a) quantile-quantile (Q-Q) correction of climate model rainfall products, and b) the statistical <span class="hlt">downscaling</span> scheme of Langousis and Kaleris (2014), in reproducing the statistical structure of rainfall, as well as rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e. the Flumendosa catchment, using climate model rainfall and atmospheric data from the <span class="hlt">ENSEMBLES</span> project (http://ensembleseu.metoffice.com). In doing so, we split the historical rainfall record of mean areal precipitation (MAP) in 15-year calibration and 45-year validation periods, and compare the historical rainfall statistics to those obtained from: a) Q-Q corrected climate model rainfall products, and b) synthetic rainfall series generated by the suggested <span class="hlt">downscaling</span> scheme. To our knowledge, this is the first time that climate model rainfall and statistically <span class="hlt">downscaled</span> precipitation are compared to catchment-averaged MAP at a daily resolution. The obtained results are promising, since the proposed <span class="hlt">downscaling</span> scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the climate model used and the length of the calibration period. This is particularly the case for the yearly rainfall maxima, where direct statistical correction of climate model rainfall outputs shows increased sensitivity to the length of the calibration period and the climate model used. The robustness of the suggested <span class="hlt">downscaling</span> scheme in modeling rainfall extremes at a daily resolution, is a notable feature that can effectively be used to assess hydrologic risk at a regional level under changing climatic conditions. Acknowledgments The research project is implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State. CRS4 highly acknowledges the contribution of the Sardinian regional authorities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JESS..124..843S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JESS..124..843S"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> and projection of future temperature and precipitation change in middle catchment of Sutlej River Basin, India</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Singh, Dharmaveer; Jain, Sanjay K.; Gupta, R. D.</p> <p>2015-06-01</p> <p><span class="hlt">Ensembles</span> of two Global Climate Models (GCMs), CGCM3 and HadCM3, are used to project future maximum temperature ( T Max), minimum temperature ( T Min) and precipitation in a part of Sutlej River Basin, northwestern Himalayan region, India. Large scale atmospheric variables of CGCM3 and HadCM3 under different emission scenarios and the National Centre for Environmental Prediction/National Centre for Atmospheric Research reanalysis datasets are <span class="hlt">downscaled</span> using Statistical <span class="hlt">Downscaling</span> Model (SDSM). Variability and changes in T Max, T Min and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model are presented for future periods: 2020s, 2050s and 2080s. The study reveals rise in annual average T Max, T Min and precipitation under scenarios A1B and A2 for CGCM3 model as well as under A2 and B2 scenarios for HadCM3 model in 2020s, 2050s and 2080s. Increase in mean monthly T Min is also observed for all months of the year under all scenarios of both the models. This is followed by decrease in T Max during June, July August and September. However, the model projects rise in precipitation in months of July, August and September under A1B and A2 scenarios of CGCM3 model and A2 and B2 of HadCM3 model for future periods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.6616N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.6616N"><span id="translatedtitle">Predicting future wind power generation and power demand in France using statistical <span class="hlt">downscaling</span> methods developed for hydropower applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Najac, Julien</p> <p>2014-05-01</p> <p>For many applications in the energy sector, it is crucial to dispose of <span class="hlt">downscaling</span> methods that enable to conserve space-time dependences at very fine spatial and temporal scales between variables affecting electricity production and consumption. For climate change impact studies, this is an extremely difficult task, particularly as reliable climate information is usually found at regional and monthly scales at best, although many industry oriented applications need further refined information (hydropower production model, wind energy production model, power demand model, power balance model…). Here we thus propose to investigate the question of how to predict and quantify the influence of climate change on climate-related energies and the energy demand. To do so, statistical <span class="hlt">downscaling</span> methods originally developed for studying climate change impacts on hydrological cycles in France (and which have been used to compute hydropower production in France), have been applied for predicting wind power generation in France and an air temperature indicator commonly used for predicting power demand in France. We show that those methods provide satisfactory results over the recent past and apply this methodology to several climate model runs from the <span class="hlt">ENSEMBLES</span> project.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007WRR....43.7402V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007WRR....43.7402V"><span id="translatedtitle">Stochastic <span class="hlt">downscaling</span> of precipitation: From dry events to heavy rainfalls</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vrac, M.; Naveau, P.</p> <p>2007-07-01</p> <p><span class="hlt">Downscaling</span> precipitation is a difficult challenge for the climate community. We propose and study a new stochastic weather typing approach to perform such a task. In addition to providing accurate small and medium precipitation, our procedure possesses built-in features that allow us to model adequately extreme precipitation distributions. First, we propose a new distribution for local precipitation via a probability mixture model of Gamma and Generalized Pareto (GP) distributions. The latter one stems from Extreme Value Theory (EVT). The performance of this mixture is tested on real and simulated data, and also compared to classical rainfall densities. Then our <span class="hlt">downscaling</span> method, extending the recently developed nonhomogeneous stochastic weather typing approach, is presented. It can be summarized as a three-step program. First, regional weather precipitation patterns are constructed through a hierarchical ascending clustering method. Second, daily transitions among our precipitation patterns are represented by a nonhomogeneous Markov model influenced by large-scale atmospheric variables like NCEP reanalyses. Third, conditionally on these regional patterns, precipitation occurrence and intensity distributions are modeled as statistical mixtures. Precipitation amplitudes are assumed to follow our mixture of Gamma and GP densities. The proposed <span class="hlt">downscaling</span> approach is applied to 37 weather stations in Illinois and compared to various possible parameterizations and to a direct modeling. Model selection procedures show that choosing one GP distribution shape parameter per pattern for all stations provides the best rainfall representation amongst all tested models. This work highlights the importance of EVT distributions to improve the modeling and <span class="hlt">downscaling</span> of local extreme precipitations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC41E..05G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41E..05G"><span id="translatedtitle">Evaluating the utility of dynamical <span class="hlt">downscaling</span> in agricultural impacts projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Glotter, M.; Elliott, J. W.; McInerney, D. J.; Moyer, E. J.</p> <p>2013-12-01</p> <p>The need to understand the future impacts of climate change has driven the increasing use of dynamical <span class="hlt">downscaling</span> to produce fine-spatial-scale climate projections for impacts models. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield. Our results suggest that it does not. We simulate U.S. maize yields under current and future CO2 concentrations with the widely-used DSSAT crop model, driven by a variety of climate inputs including two general circulation models (GCMs), each in turn <span class="hlt">downscaled</span> by two regional climate models (RCMs). We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven yields are essentially indistinguishable in all scenarios (<10% discrepancy in national yield, equivalent to error from observations). While RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kms) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the added value of dynamically <span class="hlt">downscaling</span> raw GCM output for impacts assessments may not justify its computational demands, and that some rethinking of <span class="hlt">downscaling</span> methods is warranted.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=282623&keyword=climate+AND+proxy&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=62458260&CFTOKEN=39293634','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=282623&keyword=climate+AND+proxy&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=62458260&CFTOKEN=39293634"><span id="translatedtitle">Using a Coupled Lake Model with WRF for Dynamical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The Weather Research and Forecasting (WRF) model is used to <span class="hlt">downscale</span> a coarse reanalysis (National Centers for Environmental Prediction–Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=282623&keyword=proxy&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55711826&CFTOKEN=55273921','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=282623&keyword=proxy&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55711826&CFTOKEN=55273921"><span id="translatedtitle">Using a Coupled Lake Model with WRF for Dynamical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The Weather Research and Forecasting (WRF) model is used to <span class="hlt">downscale</span> a coarse reanalysis (National Centers for Environmental Prediction–Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009pcms.confE.194B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009pcms.confE.194B"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of daily precipitation over Llobregat river basin in Catalonia (Spain) using three <span class="hlt">downscaling</span> methods.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.</p> <p>2009-09-01</p> <p>Any long-term change in the patterns of average weather in a global or regional scale is called climate change. It may cause a progressive increase of atmospheric temperature and consequently may change the amount, frequency and intensity of precipitation. All these changes of meteorological parameters may modify the water cycle: run-off, infiltration, aquifer recharge, etc. Recent studies in Catalonia foresee changes in hydrological systems caused by climate change. This will lead to alterations in the hydrological cycle that could impact in land use, in the regimen of water extractions, in the hydrological characteristics of the territory and reduced groundwater recharge. Besides, can expect a loss of flow in rivers. In addition to possible increases in the frequency of extreme rainfall, being necessary to modify the design of infrastructure. Because this, it work focuses on studying the impacts of climate change in one of the most important basins in Catalonia, the Llobregat River Basin. The basin is the hub of the province of Barcelona. It is a highly populated and urbanized catchment, where water resources are used for different purposes, as drinking water production, agricultural irrigation, industry and hydro-electrical energy production. In consequence, many companies and communities depend on these resources. To study the impact of climate change in the Llobregat basin, storms (frequency, intensity) mainly, we will need regional climate change information. A regional climate is determined by interactions at large, regional and local scales. The general circulation models (GCMs) are run at too coarse resolution to permit accurate description of these regional and local interactions. So far, they have been unable to provide consistent estimates of climate change on a local scale. Several regionalization techniques have been developed to bridge the gap between the large-scale information provided by GCMs and fine spatial scales required for regional and environmental impact studies. <span class="hlt">Downscaling</span> methods to assess the effect of large-scale circulations on local parameters have. Statistical <span class="hlt">downscaling</span> methods are based on the view that regional climate can be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or "predictors" for which GCMs are trustable to regional or local surface "predictands" for which models are less skilful. The main advantage of these methods is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. Three statistical <span class="hlt">downscaling</span> methods are applied: Analogue method, Delta Change and Direct Forcing. These methods have been used to determine daily precipitation projections at rain gauge location to study the intensity, frequency and variability of storms in a context of climate change in the Llobregat River Basin in Catalonia, Spain. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change adaptation. Two stakeholders involved in the project provided the historical time series: Catalan Water Agency (ACA) and the State Meteorological Agency (AEMET).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.191R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.191R"><span id="translatedtitle">Climate change at local level : let's look around <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ravenel, H.; Jan, J.; Moisselin, J. M.; Pagé, C.</p> <p>2009-09-01</p> <p>Weather services and climatologists in research centre are overwhelmed by requests from local authorities about climate change in their regions. Most of the times local authorities want initially a level of precision in terms of time and space scale which far beyond the scientific knowledge we have for the time being. The communication will build upon several experiences of such requests and show the importance of building common language and confidence between the different actors that are to be involved in <span class="hlt">downscaling</span> exercise. The goal is to bridge the gap between initial requests by decision makers and existing scientific knowledge. UNDP (United Nations Development Program) set up recently a unit called ClimSAT to help regions (sub national authorities) to establish mitigation and adaptation action plans. ClimSAT already initiated such plans in Uruguay, Albania, Uganda, Senegal, Morocco, … Météo-France takes part to ClimSAT for instance by explaining the importance of data rescue, providing with latest information about climate change impacts and stressing the interests to involve national weather services in regional climate change action plans, … In Basse Normandie, Bretagne and Pays de Loire, Météo-France has been involved in several processes aiming ultimately at building local climate change action plans. For the time being, no real dynamical or statistical <span class="hlt">downscaling</span> exercise have been launched : For impacts on precipitation pattern, IPCC models do not really agree on this zone, so <span class="hlt">downscaling</span> is not really pertinent. For temperature, the climate change signal is clearer, but <span class="hlt">downscaling</span> won't give much more information. Of course on other meteorogical parameters or on other variable that are linked to meteorological parameters, <span class="hlt">downscaling</span> could be of interest and will probably be necessary. With or without <span class="hlt">downscaling</span>, the stake is to build, at a local level, mechanisms which are similar to IPCC and UNFCCC. In that context, <span class="hlt">downscaling</span> could either be helpful or create a kind of black box effect which will hamper real dialogues between stakeholders.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PIAHS.369..147H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PIAHS.369..147H"><span id="translatedtitle"><span class="hlt">Downscaling</span> approach to develop future sub-daily IDF relations for Canberra Airport Region, Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Herath, H. M. S. M.; Sarukkalige, P. R.; Nguyen, V. T. V.</p> <p>2015-06-01</p> <p><span class="hlt">Downscaling</span> of climate projections is the most adopted method to assess the impacts of climate change at regional and local scale. In the last decade, <span class="hlt">downscaling</span> techniques which provide reasonable improvement to resolution of General Circulation Models' (GCMs) output are developed in notable manner. Most of these techniques are limited to spatial <span class="hlt">downscaling</span> of GCMs' output and still there is a high demand to develop temporal <span class="hlt">downscaling</span> approaches. As the main objective of this study, combined approach of spatial and temporal <span class="hlt">downscaling</span> is developed to improve the resolution of rainfall predicted by GCMs. Canberra airport region is subjected to this study and the applicability of proposed <span class="hlt">downscaling</span> approach is evaluated for Sydney, Melbourne, Brisbane, Adelaide, Perth and Darwin regions. Statistical <span class="hlt">Downscaling</span> Model (SDSM) is used to spatial <span class="hlt">downscaling</span> and numerical model based on scaling invariant concept is used to temporal <span class="hlt">downscaling</span> of rainfalls. National Centre of Environmental Prediction (NCEP) data is used in SDSM model calibration and validation. Regression based bias correction function is used to improve the accuracy of <span class="hlt">downscaled</span> annual maximum rainfalls using HadCM3-A2. By analysing the non-central moments of observed rainfalls, single time regime (from 30 min to 24 h) is identified which exist scaling behaviour and it is used to estimate the sub daily extreme rainfall depths from daily <span class="hlt">downscaled</span> rainfalls. Finally, as the major output of this study, Intensity Duration Frequency (IDF) relations are developed for the future periods of 2020s, 2050s and 2080s in the context of climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/799409','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/799409"><span id="translatedtitle"><span class="hlt">Ensemble</span> Atmospheric Dispersion Modeling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Addis, R.P.</p> <p>2002-06-24</p> <p>Prognostic atmospheric dispersion models are used to generate consequence assessments, which assist decision-makers in the event of a release from a nuclear facility. Differences in the forecast wind fields generated by various meteorological agencies, differences in the transport and diffusion models, as well as differences in the way these models treat the release source term, result in differences in the resulting plumes. Even dispersion models using the same wind fields may produce substantially different plumes. This talk will address how <span class="hlt">ensemble</span> techniques may be used to enable atmospheric modelers to provide decision-makers with a more realistic understanding of how both the atmosphere and the models behave.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ThApC.112..447H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ThApC.112..447H"><span id="translatedtitle"><span class="hlt">Downscaling</span> daily precipitation over the Yellow River source region in China: a comparison of three statistical <span class="hlt">downscaling</span> methods</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hu, Yurong; Maskey, Shreedhar; Uhlenbrook, Stefan</p> <p>2013-05-01</p> <p>Three statistical <span class="hlt">downscaling</span> methods are compared with regard to their ability to <span class="hlt">downscale</span> summer (June-September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical <span class="hlt">Downscaling</span> Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046-2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three <span class="hlt">downscaling</span> methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one <span class="hlt">downscaling</span> method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1054A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1054A"><span id="translatedtitle">Validation of WRF <span class="hlt">Downscaling</span> Capabilities Over Western Australia to Detect Rainfall and Temperature Extremes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Andrys, J.; Lyons, T.; Kala, J.</p> <p>2013-12-01</p> <p>When evaluating the merits of regional climate simulations, one of the most compelling arguments for this high resolution, dynamical <span class="hlt">downscaling</span> approach is its ability to simulate the extremes of temperature and precipitation with greater skill than lower resolution models. A historical (1970-2000), <span class="hlt">ensemble</span> regional climate simulation using WRF was performed over Western Australia at a 50km, 10km and 5km resolution in order to evaluate the effectiveness of the model in simulating annually extreme climate events as defined by the core climate indices of the CCI/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). Five temperature and five precipitation indices were chosen and the capacity of the simulation to detect the temporal and spatial structure of these indices was assessed. Validation took place through comparisons to observational CSIRO Australia Water Availability Project (AWAP) daily gridded minimum and maximum temperature and precipitation data and RCM simulations driven by ERA-Interim lateral boundary conditions over the same area. The study is part one of a two part project to examine future changes in extreme temperature and precipitation in the region and the influence of land cover change and anthropogenic greenhouse gases on these changes.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFMOS52B..02R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMOS52B..02R"><span id="translatedtitle"><span class="hlt">Downscaling</span> an Eddy-Resolving Global Model for the Continental Shelf off South Eastern Australia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Roughan, M.; Baird, M.; MacDonald, H.; Oke, P.</p> <p>2008-12-01</p> <p>The Australian Bluelink collaboration between CSIRO, the Bureau of Meteorology and the Royal Australian Navy has made available to the research community the output of BODAS (Bluelink ocean data assimilation system), an <span class="hlt">ensemble</span> optimal interpolation reanalysis system with ~10 km resolution around Australia. Within the Bluelink project, BODAS fields are assimilated into a dynamic ocean model of the same resolution to produce BRAN (BlueLink ReANalysis, a hindcast of water properties around Australia from 1992 to 2004). In this study, BODAS hydrographic fields are assimilated into a ~ 3 km resolution Princeton Ocean Model (POM) configuration of the coastal ocean off SE Australia. Experiments were undertaken to establish the optimal strength and duration of the assimilation of BODAS fields into the 3 km resolution POM configuration for the purpose of producing hindcasts of ocean state. It is shown that the resultant <span class="hlt">downscaling</span> of Bluelink products is better able to reproduce coastal features, particularly velocities and hydrography over the continental shelf off south eastern Australia. The BODAS-POM modelling system is used to provide a high-resolution simulation of the East Australian Current over the period 1992 to 2004. One of the applications that we will present is an investigation of the seasonal and inter-annual variability in the dispersion of passive particles in the East Australian Current. The practical outcome is an estimate of the connectivity of estuaries along the coast of southeast Australia, which is relevant for the dispersion of marine pests.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AdAtS..32..680Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AdAtS..32..680Y"><span id="translatedtitle">Seasonal prediction of June rainfall over South China: Model assessment and statistical <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ye, Kun-Hui; Tam, Chi-Yung; Zhou, Wen; Sohn, Soo-Jin</p> <p>2015-05-01</p> <p>The performances of various dynamical models from the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model <span class="hlt">ensemble</span> (MME) in predicting station-scale rainfall in South China (SC) in June were evaluated. It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain. This could be related to the MME's ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region. Further assessment of station-scale June rainfall prediction based on direct model output (DMO) over 97 stations in SC revealed that the MME mean outperforms each individual model. However, poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models. In order to improve the performance at those stations with poor DMO prediction skill, a station-based statistical <span class="hlt">downscaling</span> scheme was constructed and applied to the individual and MME mean hindcast runs. For several models, this scheme can outperform DMO at more than 30 stations, because it can tap into the abilities of the models in capturing the anomalous Indo-Pacific circulation to which SC rainfall is considerably sensitive. Therefore, enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22251869','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22251869"><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> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Shamis, Mira</p> <p>2013-11-15</p> <p>We use the supersymmetric formalism to derive an integral formula for the density of states of the Gaussian Orthogonal <span class="hlt">Ensemble</span>, and then apply saddle-point analysis to give a new derivation of the 1/N-correction to Wigner's law. This extends the work of Disertori on the Gaussian Unitary <span class="hlt">Ensemble</span>. We also apply our method to the interpolating <span class="hlt">ensembles</span> of Mehta–Pandey.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H43I1585P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H43I1585P"><span id="translatedtitle">Stochastic Cascade Dynamical <span class="hlt">Downscaling</span> of Precipitation over Complex Terrain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Posadas, A.; Duffaut, L. E.; Jones, C.; Carvalho, L. V.; Carbajal, M.; Heidinger, H.; Quiroz, R.</p> <p>2013-12-01</p> <p>Global Climate Models (GCMs) suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called <span class="hlt">downscaling</span> techniques are used to bridge the spatial and temporal resolution gaps between what climate modelers are currently able to provide and what decision-makers require. Among the most important impacts of regional-scale prediction of climate change is to assess how food production and security will be affected. Regional scale precipitation and temperature simulations are crucial to understand how global warming will affect fresh water storage and the ability to grow agricultural crops. Precipitation and temperature <span class="hlt">downscaling</span> improve the coarse resolution and poor local representation of global climate models and help decision-makers to assess the likely hydrological impacts of climate change, and it would also help crop modelers to generate more realistic climatic-change scenarios. Thus, a spatial <span class="hlt">downscaling</span> method was developed based on the multiplicative random cascade disaggregation theory, considering a ?-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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006JGRD..111.5307D','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Delle Monache, Luca; Deng, Xingxiu; Zhou, Yongmei; Stull, Roland</p> <p>2006-03-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JSP...153...10H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JSP...153...10H"><span id="translatedtitle">The Polyanalytic Ginibre <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Haimi, Antti; Hedenmalm, Haakan</p> <p>2013-10-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhRvD..92j5006B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvD..92j5006B"><span id="translatedtitle">Critical behavior in topological <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bulycheva, K.; Gorsky, A.; Nechaev, S.</p> <p>2015-11-01</p> <p>We consider the relation between three physical problems: 2D directed lattice random walks, <span class="hlt">ensembles</span> of Tn ,n +1 torus knots, and instanton <span class="hlt">ensembles</span> in 5D Super QED with one compact dimension in ? -background and with 5D Chern-Simons term at the level one. All these <span class="hlt">ensembles</span> exhibit the critical behavior typical for the "area+length+corners " statistics of grand <span class="hlt">ensembles</span> of 2D directed paths. Using the combinatorial description, we obtain an explicit expression of the generating function for q -Narayana numbers which amounts to the new critical behavior in the <span class="hlt">ensemble</span> of Tn ,n +1 torus knots and in the <span class="hlt">ensemble</span> of instantons in 5D SQED. Depending on the number of the nontrivial fugacities, we get either the critical point, or cascade of critical lines and critical surfaces. In the 5D gauge theory the phase transition is of the third order, while in the <span class="hlt">ensemble</span> of paths and <span class="hlt">ensemble</span> of knots it is typically of the first order. We also discuss the relation with the integrable models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22399060','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22399060"><span id="translatedtitle">The fundamental <span class="hlt">downscaling</span> limit of field effect transistors</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Mamaluy, Denis Gao, Xujiao</p> <p>2015-05-11</p> <p>We predict that within next 15 years a fundamental <span class="hlt">down-scaling</span> limit for CMOS technology and other Field-Effect Transistors (FETs) will be reached. Specifically, we show that at room temperatures all FETs, irrespective of their channel material, will start experiencing unacceptable level of thermally induced errors around 5-nm gate lengths. These findings were confirmed by performing quantum mechanical transport simulations for a variety of 6-, 5-, and 4-nm gate length Si devices, optimized to satisfy high-performance logic specifications by ITRS. Different channel materials and wafer/channel orientations have also been studied; it is found that altering channel-source-drain materials achieves only insignificant increase in switching energy, which overall cannot sufficiently delay the approaching <span class="hlt">downscaling</span> limit. Alternative possibilities are discussed to continue the increase of logic element densities for room temperature operation below the said limit.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ApPhL.106s3503M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ApPhL.106s3503M"><span id="translatedtitle">The fundamental <span class="hlt">downscaling</span> limit of field effect transistors</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mamaluy, Denis; Gao, Xujiao</p> <p>2015-05-01</p> <p>We predict that within next 15 years a fundamental <span class="hlt">down-scaling</span> limit for CMOS technology and other Field-Effect Transistors (FETs) will be reached. Specifically, we show that at room temperatures all FETs, irrespective of their channel material, will start experiencing unacceptable level of thermally induced errors around 5-nm gate lengths. These findings were confirmed by performing quantum mechanical transport simulations for a variety of 6-, 5-, and 4-nm gate length Si devices, optimized to satisfy high-performance logic specifications by ITRS. Different channel materials and wafer/channel orientations have also been studied; it is found that altering channel-source-drain materials achieves only insignificant increase in switching energy, which overall cannot sufficiently delay the approaching <span class="hlt">downscaling</span> limit. Alternative possibilities are discussed to continue the increase of logic element densities for room temperature operation below the said limit.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=165530','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=165530"><span id="translatedtitle"><span class="hlt">Ensembl</span> 2002: accommodating comparative genomics</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Clamp, M.; Andrews, D.; Barker, D.; Bevan, P.; Cameron, G.; Chen, Y.; Clark, L.; Cox, T.; Cuff, J.; Curwen, V.; Down, T.; Durbin, R.; Eyras, E.; Gilbert, J.; Hammond, M.; Hubbard, T.; Kasprzyk, A.; Keefe, D.; Lehvaslaiho, H.; Iyer, V.; Melsopp, C.; Mongin, E.; Pettett, R.; Potter, S.; Rust, A.; Schmidt, E.; Searle, S.; Slater, G.; Smith, J.; Spooner, W.; Stabenau, A.; Stalker, J.; Stupka, E.; Ureta-Vidal, A.; Vastrik, I.; Birney, E.</p> <p>2003-01-01</p> <p>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 human, mouse and other genome sequences, available as either an interactive web site or as flat files. <span class="hlt">Ensembl</span> also integrates manually annotated gene structures from external sources where available. As well as being one of the leading sources of genome annotation, <span class="hlt">Ensembl</span> is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. These range from sequence analysis to data storage and visualisation and installations exist around the world in both companies and at academic sites. With both human and mouse genome sequences available and more vertebrate sequences to follow, many of the recent developments in <span class="hlt">Ensembl</span> have focusing on developing automatic comparative genome analysis and visualisation. PMID:12519943</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DClimate','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Climate&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DClimate"><span id="translatedtitle"><span class="hlt">Downscaling</span> GISS ModelE Boreal Summer Climate over Africa</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Druyan, Leonard M.; Fulakeza, Matthew</p> <p>2015-01-01</p> <p>The study examines the perceived added value of <span class="hlt">downscaling</span> atmosphere-ocean global climate model simulations over Africa and adjacent oceans by a nested regional climate model. NASA/Goddard Institute for Space Studies (GISS) coupled ModelE simulations for June- September 1998-2002 are used to form lateral boundary conditions for synchronous simulations by the GISS RM3 regional climate model. The ModelE computational grid spacing is 2deg latitude by 2.5deg longitude and the RM3 grid spacing is 0.44deg. ModelE precipitation climatology for June-September 1998-2002 is shown to be a good proxy for 30-year means so results based on the 5-year sample are presumed to be generally representative. Comparison with observational evidence shows several discrepancies in ModelE configuration of the boreal summer inter-tropical convergence zone (ITCZ). One glaring shortcoming is that ModelE simulations do not advance the West African rain band northward during the summer to represent monsoon precipitation onset over the Sahel. Results for 1998-2002 show that onset simulation is an important added value produced by <span class="hlt">downscaling</span> with RM3. ModelE Eastern South Atlantic Ocean computed sea-surface temperatures (SST) are some 4 K warmer than reanalysis, contributing to large positive biases in overlying surface air temperatures (Tsfc). ModelE Tsfc are also too warm over most of Africa. RM3 <span class="hlt">downscaling</span> somewhat mitigates the magnitude of Tsfc biases over the African continent, it eliminates the ModelE double ITCZ over the Atlantic and it produces more realistic orographic precipitation maxima. Parallel ModelE and RM3 simulations with observed SST forcing (in place of the predicted ocean) lower Tsfc errors but have mixed impacts on circulation and precipitation biases. <span class="hlt">Downscaling</span> improvements of the meridional movement of the rain band over West Africa and the configuration of orographic precipitation maxima are realized irrespective of the SST biases.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy..tmp..429D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy..tmp..429D"><span id="translatedtitle"><span class="hlt">Downscaling</span> GISS ModelE boreal summer climate over Africa</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Druyan, Leonard M.; Fulakeza, Matthew</p> <p>2015-11-01</p> <p>The study examines the perceived added value of <span class="hlt">downscaling</span> atmosphere-ocean global climate model simulations over Africa and adjacent oceans by a nested regional climate model. NASA/Goddard Institute for Space Studies (GISS) coupled ModelE simulations for June-September 1998-2002 are used to form lateral boundary conditions for synchronous simulations by the GISS RM3 regional climate model. The ModelE computational grid spacing is 2° latitude by 2.5° longitude and the RM3 grid spacing is 0.44°. ModelE precipitation climatology for June-September 1998-2002 is shown to be a good proxy for 30-year means so results based on the 5-year sample are presumed to be generally representative. Comparison with observational evidence shows several discrepancies in ModelE configuration of the boreal summer inter-tropical convergence zone (ITCZ). One glaring shortcoming is that ModelE simulations do not advance the West African rain band northward during the summer to represent monsoon precipitation onset over the Sahel. Results for 1998-2002 show that onset simulation is an important added value produced by <span class="hlt">downscaling</span> with RM3. ModelE Eastern South Atlantic Ocean computed sea-surface temperatures (SST) are some 4 K warmer than reanalysis, contributing to large positive biases in overlying surface air temperatures (Tsfc). ModelE Tsfc are also too warm over most of Africa. RM3 <span class="hlt">downscaling</span> somewhat mitigates the magnitude of Tsfc biases over the African continent, it eliminates the ModelE double ITCZ over the Atlantic and it produces more realistic orographic precipitation maxima. Parallel ModelE and RM3 simulations with observed SST forcing (in place of the predicted ocean) lower Tsfc errors but have mixed impacts on circulation and precipitation biases. <span class="hlt">Downscaling</span> improvements of the meridional movement of the rain band over West Africa and the configuration of orographic precipitation maxima are realized irrespective of the SST biases.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1071M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1071M"><span id="translatedtitle">Characterizing Uncertainties in Hydrologic Extremes: Statistical vs. Dynamical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mauger, G. S.; Salathe, E. P., Jr.</p> <p>2013-12-01</p> <p>Numerous agencies are now charged with considering the impacts of climate change in management decisions, both from the standpoint of adapting to changing conditions and minimizing emissions of greenhouse gases. These decisions require robust projections of change and defensible estimates of their uncertainty. We present work that is specifically focused on characterizing the uncertainty in projections of hydrologic extremes. Much recent work has been devoted to characterizing the uncertainty in hydrologic projections due to differences in <span class="hlt">downscaling</span> methodology (e.g., Abatzoglou and Brown, 2012; Bürger et al., 2012; Rasmussen et al., 2011; Wetterhall et al., 2012) and among hydrologic models (e.g., Bennett et al., 2012; Clark et al., 2008; Fenicia et al., 2008; Smith and Marshall, 2010; Vano et al., 2012). These have established a basis for such analyses, but have generally focused on the implications for monthly and annual flows rather than flow extremes. In addition, few among these have been focused within the Pacific Northwest. In this work we assess the uncertainty in projected changes to hydrologic extremes associated with dynamical vs. statistical <span class="hlt">downscaling</span>. The analysis is focused on 3 distinct watersheds within the Pacific Northwest - the Skagit, Green, and Willamette river basins. Results highlight the sensitivity of flood projections to <span class="hlt">downscaling</span> approach and hydrologic model assumptions. Sensitivities are characterized as a function of geographic location, hydrologic regime, and climate - identifying circumstances under which projections are reliable and others in which answers differ markedly based on methodology. For example, one notable result is that dynamically <span class="hlt">downscaled</span> projections appear to refute the assumed relationship between watershed type (snow-dominant vs. rain-dominant) and projected changes to flood risk - currently considered a key indicator of future flood risk. Results presented here provide key information for decision-making as well as for prioritizing future impacts research.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H43A1300R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H43A1300R"><span id="translatedtitle">Application of Quantile Regression for Statistical <span class="hlt">Downscaling</span> of Daily Precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rasmussen, P.; Tareghian, R.</p> <p>2012-12-01</p> <p>Statistical <span class="hlt">downscaling</span> is often used in climate change studies to bridge the gap between the resolution of global climate models and the resolution required in applications, as well as to resolve issues with model biases. Conventional linear regression models have been extensively used for this purpose. In the context of statistical <span class="hlt">downscaling</span>, it involves the development of relationships between for example daily precipitation and large-scale variables that are presumably well represented in global climate models. However, linear regression models have a number of potential shortcomings. For example, the best prediction of high, low, and medium precipitation may require use of different subsets of predictor variables, something that cannot be accomplished with traditional regression models. The error distribution may not be Gaussian, even after some transformation of variables, and the error variance may not be independent of predictors. We address these shortcomings through the use of linear quantile regression. While traditional regression models predict the mean value in the conditional distribution, quantile regression predicts user-selected quantiles in the conditional distribution. By developing quantile regression models for a range of quantile levels, one can obtain an accurate representation of the conditional distribution corresponding to given values of the predictors, and a <span class="hlt">downscaled</span> daily precipitation value can be obtained by sampling from the conditional distribution established in this way. The issue of selecting predictor variables for quantile regression is not as straightforward as for traditional regression models. We address this issue through Bayesian model averaging, implemented using the Gibbs sampler combined with stochastic search techniques. The suitability of the approach is evaluated and compared to the traditional regression model, using climate station data from Manitoba and data from the NCEP/NCAR Global Reanalysis. While in some cases quantile regression produces results that are fairly similar to those obtained from conventional linear regression, there are a number of instances where <span class="hlt">downscaling</span> based on quantile regression outperforms the traditional method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.tmp..247V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.tmp..247V"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> rainfall using artificial neural network: significantly wetter Bangkok?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vu, Minh Tue; Aribarg, Thannob; Supratid, Siriporn; Raghavan, Srivatsan V.; Liong, Shie-Yui</p> <p>2015-08-01</p> <p>Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically <span class="hlt">downscaling</span> global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses <span class="hlt">downscaled</span> results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final <span class="hlt">downscaled</span> results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC41D0594M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC41D0594M"><span id="translatedtitle">Developing Climate-Informed <span class="hlt">Ensemble</span> Streamflow Forecasts over the Colorado River Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miller, W. P.; Lhotak, J.; Werner, K.; Stokes, M.</p> <p>2014-12-01</p> <p>As climate change is realized, the assumption of hydrometeorologic stationarity embedded within many hydrologic models is no longer valid over the Colorado River Basin. As such, resource managers have begun to request more information to support decisions, specifically with regards to the incorporation of climate change information and operational risk. To this end, <span class="hlt">ensemble</span> methodologies have become increasingly popular among the scientific and forecasting communities, and resource managers have begun to incorporate this information into decision support tools and operational models. Over the Colorado River Basin, reservoir operations are determined, in large part, by forecasts issued by the Colorado Basin River Forecast Center (CBRFC). The CBRFC produces both single value and <span class="hlt">ensemble</span> forecasts for use by resource managers in their operational decision-making process. These <span class="hlt">ensemble</span> forecasts are currently driven by a combination of daily updating model states used as initial conditions and weather forecasts plus historical meteorological information used to generate forecasts with the assumption that past hydroclimatological conditions are representative of future hydroclimatology. Recent efforts have produced updated bias-corrected and spatially <span class="hlt">downscaled</span> projections of future climate over the Colorado River Basin. In this study, the historical climatology used as input to the CBRFC forecast model is adjusted to represent future projections of climate based on data developed by the updated projections of future climate data. <span class="hlt">Ensemble</span> streamflow forecasts reflecting the impacts of climate change are then developed. These forecasts are subsequently compared to non-informed <span class="hlt">ensemble</span> streamflow forecasts to evaluate the changing range of streamflow forecasts and risk over the Colorado River Basin. <span class="hlt">Ensemble</span> forecasts may be compared through the use of a reservoir operations planning model, providing resource managers with <span class="hlt">ensemble</span> information regarding changing future water supply, availability, and reservoir management. Further efforts seek to combine the utility of hydrologic models with a dynamic evapotranspiration component to evaluate impacts due to changes in evapotranspiration rates or develop unique climate patterns with the use of a stochastic weather generator.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H33F0879F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H33F0879F"><span id="translatedtitle">Developing High-Resolution Inundation Estimates through a <span class="hlt">Downscaling</span> of Brightness Temperature Measurements</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fisher, C. K.; Wood, E. F.</p> <p>2014-12-01</p> <p>There is currently a large demand for high-resolution estimates of inundation extent and flooding for applications in water management, risk assessment and hydrologic modeling. In many regions of the world it is possible to examine the extent of past inundation from visible and infrared imagery provided by sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS); however, this is not possible in regions that are densely vegetated or are under persistent cloud cover. As a result of this, there is a need for alternative methodologies that make use of other remotely sensed data sources to inform high-resolution estimates of inundation. One such data source is the AMSR-E/Aqua 37 GHz vertically and horizontally polarized brightness temperature measurements, which have been used in previous studies to estimate the extent of inundated areas and which can make observations in vegetated or cloudy regions. The objective of this work was to develop a decision tree classifier based <span class="hlt">downscaling</span> methodology by which inundation extent can be obtained at a high resolution, based on microwave brightness temperature measurements and high resolution topographic information. Using a random forest classifier that combined the AMSR-E 37GHz brightness temperatures (~12km mean spatial resolution) and a number of high-resolution topographic indices derived from the National Elevation Dataset for the United States (30m spatial resolution), a high-resolution estimate of inundation was created. A case study of this work is presented for the severe flooding that occurred in Iowa during the summer of 2008. Training and validation data for the random forest classifier were derived from an <span class="hlt">ensemble</span> of previously existing estimates of inundation from sources such as MODIS imagery, as well as simulated inundation extents generated from a hydrologic routing model. Results of this work suggest that the decision tree based <span class="hlt">downscaling</span> has skill in producing high-resolution estimates of inundation when informed by the brightness temperature measurements along with high quality training data and can be used to estimate the likelihood of inundation for the region of interest.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.6242L','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Langousis, Andreas; Deidda, Roberto; Marrocu, Marino; Kaleris, Vassilios</p> <p>2014-05-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC23B0922M','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Molina, J. M.; Zaitchik, B.</p> <p>2013-12-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.5768M','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mezghani, Abdelkader; Benestad, Rasmus E.</p> <p>2014-05-01</p> <p>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> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=479128','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=479128"><span id="translatedtitle">The <span class="hlt">Ensembl</span> Computing Architecture</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Cuff, James A.; Coates, Guy M.P.; Cutts, Tim J.R.; Rae, Mark</p> <p>2004-01-01</p> <p><span class="hlt">Ensembl</span> is a software project to automatically annotate large eukaryotic genomes and release them freely into the public domain. The project currently automatically annotates 10 complete genomes. This makes very large demands on compute resources, due to the vast number of sequence comparisons that need to be executed. To circumvent the financial outlay often associated with classical supercomputing environments, farms of multiple, lower-cost machines have now become the norm and have been deployed successfully with this project. The architecture and design of farms containing hundreds of compute nodes is complex and nontrivial to implement. This study will define and explain some of the essential elements to consider when designing such systems. Server architecture and network infrastructure are discussed with a particular emphasis on solutions that worked and those that did not (often with fairly spectacular consequences). The aim of the study is to give the reader, who may be implementing a large-scale biocompute project, an insight into some of the pitfalls that may be waiting ahead. PMID:15123594</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy..tmp..394P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy..tmp..394P"><span id="translatedtitle"><span class="hlt">Downscaling</span> humidity with Localized Constructed Analogs (LOCA) over the conterminous United States</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pierce, D. W.; Cayan, D. R.</p> <p>2015-09-01</p> <p>Humidity is important to climate impacts in hydrology, agriculture, ecology, energy demand, and human health and comfort. Nonetheless humidity is not available in some widely-used archives of statistically <span class="hlt">downscaled</span> climate projections for the western U.S. In this work the Localized Constructed Analogs (LOCA) statistical <span class="hlt">downscaling</span> method is used to <span class="hlt">downscale</span> specific humidity to a 1°/16° grid over the conterminous U.S. and the results compared to observations. LOCA reproduces observed monthly climatological values with a mean error of ~0.5 % and RMS error of ~2 %. Extreme (1-day in 1- and 20-years) maximum values (relevant to human health and energy demand) are within ~5 % of observed, while extreme minimum values (relevant to agriculture and wildfire) are within ~15 %. The asymmetry between extreme maximum and minimum errors is largely due to residual errors in the bias correction of extreme minimum values. The temporal standard deviations of <span class="hlt">downscaled</span> daily specific humidity values have a mean error of ~1 % and RMS error of ~3 %. LOCA increases spatial coherence in the final <span class="hlt">downscaled</span> field by ~13 %, but the <span class="hlt">downscaled</span> coherence depends on the spatial coherence in the data being <span class="hlt">downscaled</span>, which is not addressed by bias correction. Temporal correlations between daily, monthly, and annual time series of the original and <span class="hlt">downscaled</span> data typically yield values >0.98. LOCA captures the observed correlations between temperature and specific humidity even when the two are <span class="hlt">downscaled</span> independently.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5044C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5044C"><span id="translatedtitle">Probabilistic precipitation and temperature <span class="hlt">downscaling</span> of the Twentieth Century Reanalysis over France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin</p> <p>2015-04-01</p> <p>This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the last century built on the NOAA 20th century global extended atmospheric reanalysis (20CR, Compo et al., 2011). It aims at delivering appropriate meteorological forcings for continuous distributed hydrological modelling over the last 140 years. The longer term objective is to improve our knowledge of major historical hydrometeorological events having occurred outside of the last 50-year period, over which comprehensive reconstructions and observations are available. It would constitute a perfect framework for assessing the recent observed events but also future events projected by climate change impact studies. The Sandhy (Stepwise ANalogue <span class="hlt">Downscaling</span> method for Hydrology) statistical <span class="hlt">downscaling</span> method (Radanovics et al., 2013), initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between 20CR predictors - temperature, geopotential shape, vertical velocity and relative humidity - and local predictands - precipitation and temperature - relevant for catchment-scale hydrology. Multiple predictor domains for geopotential shape are retained from a local optimisation over France using the Safran near-surface reanalysis (Vidal et al., 2010). Sandhy gives an <span class="hlt">ensemble</span> of 125 equally plausible gridded precipitation and temperature time series over the whole 1871-2012 period. Previous studies showed that Sandhy precipitation outputs are very slightly biased at the annual time scale. Nevertheless, the seasonal precipitation signal for areas with a high interannual variability is not well simulated. Moreover, winter and summer temperatures are respectively over- and underestimated. Reliable seasonal precipitation and temperature signals are however necessary for hydrological modelling, especially for evapotranspiration and snow accumulation/snowmelt processes. Two different post-processing methods are considered to correct monthly precipitation and temperature time series. The first one applies two new analogy steps, using the sea surface temperature (SST) and the large-scale two-meter temperature. The second method is a calendar selection that keeps the closest analogue dates in the year for each target date. A sensitivity study has been performed to assess the final number of analogues dates to retain for each method. A comparison to Safran over 1958-2010 shows that biases on the interannual cycle of precipitation and temperature are strongly reduced with both methods. Using two supplementary analogy levels moreover leads to a large improvement of correlation in seasonal temperature time series. These two methods have also been validated before 1958 thanks to both raw observations and homogenized time series. The two post-processing methods come with some advantages and drawbacks. The calendar selection allows to slightly better correct for seasonal biases in precipitation and is therefore adapted in a forecasting context. The selection with two supplementary analogy levels would allow for possible season shifts and SST trends and is therefore better suited for climate reconstruction and climate change studies. Compo, G. P. et al. (2011). The Twentieth Century Reanalysis Project. Quarterly Journal of the Royal Meteorological Society, 137:1-28. doi: 10.1002/qj.776 Radanovics, S., Vidal, J.-P., Sauquet, E., Ben Daoud, A., and Bontron, G. (2013). Optimising predictor domains for spatially coherent precipitation <span class="hlt">downscaling</span>. Hydrology and Earth System Sciences, 17:4189-4208. doi:10.5194/hess-17-4189-2013 Vidal, J.-P ., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M. (2010). A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology, 30:1627-1644. doi:10.1002/joc.2003</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.297F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.297F"><span id="translatedtitle">Definition of <span class="hlt">Ensemble</span> Error Statistics for Optimal <span class="hlt">Ensemble</span> Data Assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frehlich, R.</p> <p>2009-09-01</p> <p>Next generation data assimilation methods must include the state dependent observation errors, i.e., the spatial and temporal variations produced by the atmospheric turbulent field. A rigorous analysis of optimal data assimilation algorithms and <span class="hlt">ensemble</span> forecast systems requires a definition of model "truth" or perfect measurement which then defines the total observation error and forecast error. Truth is defined as the spatial average of the continuous atmospheric state variables centered on the model grid locations. To be consistent with the climatology of turbulence, the spatial average is chosen as the effective spatial filter of the numerical model. The observation errors then consist of two independent components: an instrument error and an observation sampling error which describes the mismatch of the spatial average of the observation and the spatial average of the perfect measurement or "truth". The observation sampling error is related to the "error of representativeness" but is defined only in terms of the local statistics of the atmosphere and the sampling pattern of the observation. Optimal data assimilation requires an estimate of the local background error correlation as well as the local observation error correlation. Both of these local correlations can be estimated from <span class="hlt">ensemble</span> assimilation techniques where each member of the <span class="hlt">ensemble</span> are produced by generating and assimilating random observations consistent with the estimates of the local sampling errors based on estimates of the local turbulent statistics. A rigorous evaluation of these optimal <span class="hlt">ensemble</span> data assimilation techniques requires a definition of the <span class="hlt">ensemble</span> members and the <span class="hlt">ensemble</span> average that describes the error correlations. A new formulation is presented that is consistent with the climatology of atmospheric turbulence and the implications of this formulation for <span class="hlt">ensemble</span> forecast systems is discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1062O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1062O"><span id="translatedtitle">"Uncertainty in <span class="hlt">downscaling</span> using high-resolution observational datasets"</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Oswald, E.; Rood, R. B.</p> <p>2013-12-01</p> <p>In order to bridge the gap between the resolution of global climate modeling efforts and the scale that decision-makers work at statistical <span class="hlt">downscaling</span> is often employed. The performance of any statistical <span class="hlt">downscaling</span> is dependant on the quality of the observational data at the location(s) of <span class="hlt">downscaling</span> (whether gridded or point-source). However, discussions of the assumptions made during statistical <span class="hlt">downscaling</span>, such as the stationariness of the relationships between predictor(s) and predictand, normally do not acknowledge the uncertainty introduced by the observational dataset. Many observational datasets do not have the erroneous temporal discontinuities caused by non-climatic biases, such as instrument changes or station relocations, diminished by a homogenization process. Moreover stations included within the underlying networks of high-resolution gridded datasets are typically not required to meet high standards of quality. Hence we evaluated three popular observational climate datasets, of the high-resolution gridded type, for their depiction of temperature values over the span of the datasets and the continental U.S. This was done using the homogenized United States Historical Climatology Network (USHCN) dataset version 2.0. The summer average temperatures at selected stations within the USHCN were compared to those created by interpolating gridpoints to the locations of those stations. The relationships these datasets have with more traditional climate datasets (e.g. the GISS, CRU, USHCN) have not formally been evaluated. The comparisons were not to judge which dataset was closest aligned with the USHCN dataset, but rather to discuss the common features (across datasets) of the residuals (i.e. differences with the USHCN dataset). We found that the lack of homogenization was a primary cause of the residuals, but that proxies for the non-climatic biases were not as well related to the residuals as expected. This was due in part to the gridding process that spatially extends the effects of non-climatic biases. Results suggest that while the residuals were statistically significant at the continental, regional and sub-regional spatial scales; they were particularly large at the smaller scales. The residuals also varied through time, and thus the trends in these datasets were often inaccurate and the temporal average residuals depended on the time period of focus. An evaluation of these datasets' ability to bias correct and spatially disaggregate as a function of grid box size, is being undertaken and will be presented. This is facilitated by calculating per grid box the average residual and the intra-grid variance of the residuals. This uncertainty within the observational data is likely existent in any <span class="hlt">downscaling</span> product using these datasets to facilitate the <span class="hlt">downscaling</span>. Thus this is an uncertainty not often discussed and even less quantified.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.7362B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.7362B"><span id="translatedtitle"><span class="hlt">Downscaling</span> of rainfall in Peru using Generalised Linear Models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.</p> <p>2012-04-01</p> <p>The assessment of water resources in the Peruvian Andes is particularly important because the Peruvian economy relies heavily on agriculture. Much of the agricultural land is situated near to the coast and relies on large quantities of water for irrigation. The simulation of synthetic rainfall series is thus important to evaluate the reliability of water supplies for current and future scenarios of climate change. In addition to water resources concerns, there is also a need to understand extreme heavy rainfall events, as there was significant flooding in Machu Picchu in 2010. The region exhibits a reduction of rainfall in 1983, associated with El Nino Southern Oscillation (SOI). NCEP Reanalysis 1 data was used to provide weather variable data. Correlations were calculated for several weather variables using raingauge data in the Andes. These were used to evaluate teleconnections and provide suggested covariates for the <span class="hlt">downscaling</span> model. External covariates used in the model include sea level pressure and sea surface temperature over the region of the Humboldt Current. Relative humidity and temperature data over the region are also included. The SOI teleconnection is also used. Covariates are standardised using observations for 1960-1990. The GlimClim <span class="hlt">downscaling</span> model was used to fit a stochastic daily rainfall model to 13 sites in the Peruvian Andes. Results indicate that the model is able to reproduce rainfall statistics well, despite the large area used. Although the correlation between individual rain gauges is generally quite low, all sites are affected by similar weather patterns. This is an assumption of the GlimClim <span class="hlt">downscaling</span> model. Climate change scenarios are considered using several GCM outputs for the A1B scenario. GCM data was corrected for bias using 1960-1990 outputs from the 20C3M scenario. Rainfall statistics for current and future scenarios are compared. The region shows an overall decrease in mean rainfall but with an increase in variance.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H21A1013M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H21A1013M"><span id="translatedtitle">Developing Climate-Informed <span class="hlt">Ensemble</span> Streamflow Forecasts over the Colorado River Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miller, W. P.; Werner, K.; Stokes, M.</p> <p>2013-12-01</p> <p>As the impacts of climate change are realized, the assumption of hydrometeorologic stationarity embedded within many hydrologic models is likely no longer valid over the Colorado River Basin. As such, resource managers in the region have begun to request increasingly more information to support decisions, specifically with regards to the incorporation of climate change information and operational risk. To this end, <span class="hlt">ensemble</span> methodologies have become increasingly popular among the scientific and forecasting communities, and resource managers have begun to incorporate this information into decision support tools and operational models. Over the Colorado River Basin, reservoir operations are determined, in part, by forecasts issued by the Colorado Basin River Forecast Center (CBRFC). Traditionally, the CBRFC produces both single value and <span class="hlt">ensemble</span> forecasts for use by resource managers in their operational decision-making process. These <span class="hlt">ensemble</span> forecasts are currently driven by a combination of daily updating model states used as initial conditions and weather forecasts plus historical meteorological information used to generate forecasts with the assumption that past hydroclimatological conditions are representative of future hydroclimatology. Recent efforts have produced future projections of climate information over the Colorado River Basin. In this study, the historical climatology typically used as input to the CBRFC forecast model is adjusted to represent future projections of climate based on data developed by the Coupled Model Intercomparison Project 5 that has been bias-corrected and spatially <span class="hlt">downscaled</span>. <span class="hlt">Ensemble</span> streamflow forecasts reflecting the impacts of climate change are then developed. This <span class="hlt">ensemble</span> forecast may then be input into a reservoir operations planning model, providing resource managers with <span class="hlt">ensemble</span> information regarding future water supply, availability, and reservoir management aiding in the determination of possible implications for resource management.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4857R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4857R"><span id="translatedtitle">Future changes of wind energy potentials over Europe in a large CMIP5 multi-model <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reyers, Mark; Moemken, Julia; Pinto, Joaquim G.</p> <p>2015-04-01</p> <p>A statistical-dynamical <span class="hlt">downscaling</span> method is used to estimate future changes of wind energy output (Eout) of an idealized wind turbine across Europe at the regional scale. With this aim, 22 GCMs of the CMIP5 <span class="hlt">ensemble</span> are considered. The <span class="hlt">downscaling</span> method uses circulation weather types and regional climate modelling with the COSMO-CLM model. Future projections are computed for two time periods (2021-2060 and 2061-2100) following two scenarios (RCP4.5 and RCP8.5). The CMIP5 <span class="hlt">ensemble</span> mean response reveal a more likely than not increase of mean annual Eout over Northern and Central Europe and a likely decrease over Southern Europe. There is some uncertainty with respect to the magnitude and the sign of the changes. Higher robustness in future changes is observed for specific seasons. Except from the Mediterranean area, an <span class="hlt">ensemble</span> mean increase of Eout is simulated for winter and a decreasing for the summer season, resulting in a strong increase of the intra-annual variability for most of Europe. The latter is in particular likely during the 2nd half of the 21st century under the RCP8.5 scenario. In general, signals are stronger for 2061-2100 compared to 2021-2060 and for RCP8.5 compared to RCP4.5. Regarding changes of the inter-annual variability of Eout for Central Europe, the future projections strongly vary between individual models and also between future periods and scenarios within single models. This study showed for an <span class="hlt">ensemble</span> of 22 CMIP5 models that changes in the wind energy potentials over Europe may take place in future decades. However, due to the uncertainties detected in this research, further investigations with multi-model <span class="hlt">ensembles</span> are needed to provide a better quantification and understanding of the future changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A11F0122D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A11F0122D"><span id="translatedtitle">Comparing the skill of precipitation forecasts from high resolution simulations and statistically <span class="hlt">downscaled</span> products in the Australian Snowy Mountains</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dai, J.; Chubb, T.; Manton, M.; Siems, S. T.</p> <p>2013-12-01</p> <p>Statistically significant improvements to a 'Poor Man's <span class="hlt">Ensemble</span>' (PME) of coarse-resolution numeral precipitation forecast for the Australian Snowy Mountains can be achieved using a clustering algorithm. Daily upwind soundings are classified according to one of four clusters, which are employed to adjust the precipitation forecasts using a linear regression. This approach is a type of 'statistical <span class="hlt">downscaling</span>', in that it relies on a historical relationship between observed and forecast precipitation amounts, and is a computationally cheap and fast way to improve forecast skill. For the 'wettest' class, the root-mean-square error for the one-day forecast was reduced from 26.98 to 17.08 mm, and for the second 'wet' class the improvement was from 8.43 to 5.57 mm. Regressions performed for the two 'dry' classes were not shown to significantly improve the forecast. Statistical measures of the probability of precipitation and the quantitative precipitation forecast were evaluated for the whole of the 2011 winter (May-September). With a 'hit rate' (fraction of correctly-forecast rain days) of 0.9, and a 'false alarm rate' (fraction of forecast rain days which did not occur) of 0.16 the PME forecast performs well in identifying rain days. The precipitation amount is, however systematically under-predicted, with a mean bias of -5.76 mm and RMSE of 12.86 mm for rain days during the 2011 winter. To compare the statistically <span class="hlt">downscaled</span> results with the capabilities of a state of the art numerical prediction system, the WRF model was run at 4 km resolution over the Australian Alpine region for the same period, and precipitation forecasts analysed in a similar manner. It had a hit rate of 0.955 and RMSE of 5.16 mm for rain days. The main reason for the improved performance relative to the PME is that the high resolution of the simulations better captures the orographic forcing due to the terrain, and consequently resolves the precipitation processes more realistically, but case studies of individual events also showed that the choice microphysical parameterisation was very important to precipitation amounts. The WRF model is capable of reasonably good forecasts of the sounding 'class' for Wagga Wagga, with an accuracy of 80% for the first day and 65% for the third day of the forecast, facilitating the use of the PME <span class="hlt">downscaling</span> for a number of forecast days instead of only the day of the sounding.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.8005G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.8005G"><span id="translatedtitle">Looking for added value in Australian <span class="hlt">downscaling</span> for climate change studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grose, Michael</p> <p>2015-04-01</p> <p><span class="hlt">Downscaling</span> gives the prospect of added value in the regional pattern and temporal nature of rainfall change with a warmer climate. However, such value is not guaranteed and the use of <span class="hlt">downscaling</span> can raise rather than diminish uncertainties. Validation of <span class="hlt">downscaling</span> methods tends to focus on the ability to simulate current climate statistics, rather than the robustness of simulated future climate change. Here we compare the future climate change signal in average rainfall from various dynamical and statistical <span class="hlt">downscaling</span> outputs used for all of Australia and in regional climate change studies over smaller domains. We show that <span class="hlt">downscaling</span> can generate different regional patterns of projected change compared to the global climate models used as input, indicating the potential for added value in projections. These differences often make physical sense in regions of complex topography such as in southeast Australia, the eastern seaboard and Tasmania. However, results from different methods are not always consistent. In addition, <span class="hlt">downscaling</span> can produce projected changes that are not clearly related to finer resolution and are difficult to interpret. In some cases, each <span class="hlt">downscaling</span> method gives a different range of results and different messages about projected rainfall change for a region. This shows that <span class="hlt">downscaling</span> has the potential to add value to projections, but also brings the potential for uncertain or contradictory messages. We conclude that each method has strengths and weaknesses, and these should be clearly communicated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy...45.2541Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy...45.2541Z"><span id="translatedtitle">A new statistical precipitation <span class="hlt">downscaling</span> method with Bayesian model averaging: a case study in China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhang, Xianliang; Yan, Xiaodong</p> <p>2015-11-01</p> <p>A new statistical <span class="hlt">downscaling</span> method was developed and applied to <span class="hlt">downscale</span> monthly total precipitation from 583 stations in China. Generally, there are two steps involved in statistical <span class="hlt">downscaling</span>: first, the predictors are selected (large-scale variables) and transformed; and second, a model between the predictors and the predictand (in this case, precipitation) is established. In the first step, a selection process of the predictor domain, called the optimum correlation method (OCM), was developed to transform the predictors. The transformed series obtained by the OCM showed much better correlation with the predictand than those obtained by the traditional transform method for the same predictor. Moreover, the method combining OCM and linear regression obtained better <span class="hlt">downscaling</span> results than the traditional linear regression method, suggesting that the OCM could be used to improve the results of statistical <span class="hlt">downscaling</span>. In the second step, Bayesian model averaging (BMA) was adopted as an alternative to linear regression. The method combining the OCM and BMA showed much better performance than the method combining the OCM and linear regression. Thus, BMA could be used as an alternative to linear regression in the second step of statistical <span class="hlt">downscaling</span>. In conclusion, the <span class="hlt">downscaling</span> method combining OCM and BMA produces more accurate results than the multiple linear regression method when used to statistically <span class="hlt">downscale</span> large-scale variables.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=311127&keyword=water+AND+pollution&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55735888&CFTOKEN=87057520','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=311127&keyword=water+AND+pollution&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55735888&CFTOKEN=87057520"><span id="translatedtitle">Assessing the Added Value of Dynamical <span class="hlt">Downscaling</span> Using the Standardized Precipitation Index</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>In this study, the Standardized Precipitation Index (SPI) is used to ascertain the added value of dynamical <span class="hlt">downscaling</span> over the contiguous United States. WRF is used as a regional climate model (RCM) to dynamically <span class="hlt">downscale</span> reanalysis fields to compare values of SPI over drough...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.387D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.387D"><span id="translatedtitle">Moroccan precipitation in a regional climate change simulation, evaluating a statistical <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Driouech, F.; Déqué, M.; Sánchez-Gómez, E.</p> <p>2009-09-01</p> <p>Morocco is located at the extreme north-west of Africa between 20 and 37° N. According to the aridity index of De Martonne classification, Moroccan climate varies from sub-humid in the north to arid in the south. The country has experienced several drought events which have had marked impacts on socio-economic sectors and national economy (1940-1945, 1980-1985, 1994-1995 …). During a dry year, the deficit of rainfall can exceed 60% of the climatological value. Rainfall amounts registered show a negative trend at national and regional scales. The drought seems to become more persistent over time. At the same time, the total number of wet days shows a negative trend revealing an increase in the annual dry day number. Many regions became more arid (According to the aridity index of De Martonne) between 1961 and 2008: namely Oujda, Taza, Kenitra, Rabat, Meknès. In order to evaluate climate change impacts on Moroccan winter precipitation, future climate conditions in Morocco under the SRES scenario A1B, are studied by using two 30-year time-slice simulations performed by the variable resolution configuration of the GCM ARPEGE-Climat. The spatial resolution ranges between 50 and 60 km over the country. This high resolution scenarios exhibit for the period 2021-2050 a change in the precipitation distribution and in extreme events. In particular, the precipitation amounts and the occurrence frequency of wet days will decrease in the scenario on all the fourteen stations considered. In terms of extreme events, the maximum dry spell length increases in nearly all the stations and the frequency of high precipitation events is projected to decrease. The evolution of highest percentiles of precipitation distribution does not go, however, in the same sense everywhere. Assessment of a range of uncertainties due to climate modelling has been done by using present-day and SRES scenario A1B data issued from 10 <span class="hlt">ENSEMBLES</span>-RCMs. This shows that ARPEGE-Climate results are in the range covered by these RCMs for all the climate indices considered. In order to validate, in the case of Moroccan winter precipitation, a statistical <span class="hlt">downscaling</span> approach that uses large scale fields to construct local scenarios of future climate change, the link between north Atlantic weather regimes and Moroccan local precipitation has been investigated, in terms of precipitation average, and the frequencies of occurrence of wet and intense precipitation days. The robustness of the statistical approach considered is evaluated using the outputs of ARPEGE-Climate and also those of the 10 <span class="hlt">ENSEMBLES</span>-RCMs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC13D1122Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC13D1122Z"><span id="translatedtitle">Assessing Climate change impacts on river basins in New Zealand using model based <span class="hlt">downscaling</span>, statistical <span class="hlt">downscaling</span> and regional climate modelling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zammit, C.; Diettrich, J.; Sood, A.</p> <p>2013-12-01</p> <p>Spatial resolution of General Circulation Models (GCMs) is too coarse to represent regional climate variations at the scales required for environmental impact assessments in New Zealand. <span class="hlt">Downscaling</span> is necessary for climate change impact analyses that seek to constrain regional climate by information from global climate models. It is particularly important in the New Zealand context, as given maritime, topographic and convective climate processes. As a result local to regional scale variability is not always well represented by the broader global scale features simulated by GCMs. Three techniques are available to generate climate change information that can be used as input of environmental models: i) <span class="hlt">Downscaling</span> to the New Zealand Virtual Climate Station Network grid (Tait et al, 2006); ii) Semi-empirical (statistical) <span class="hlt">downscaling</span> (SDS) of GCM outputs; and iii) Regional climate models (RCMs) nested within a GCM. In this study, we compare the downstream impact of the three techniques for three different emission scenarios as characterised in the IPCC Fourth Assessment (B1-low emission, A1B- middle of the road, and A2-high emission scenario) and two of the 12 GCM models used in New Zealand (UKMO_HADCM3 and MPI_ECHAM5). Our study will focus on surface water hydrological responses (ie discharge, infiltration, evaporation, snow storage) for a number of river basins across the North and South Island of New Zealand. The analysis will compare the current situation (1980-1999) with two future time periods (2030-2049 and 2080-2099) and will draw recommendation regarding climate change impact uncertainty and its communication to decision makers.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3614370','PMC'); return false;" href="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> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Alabi, Oluwafemi S.; Wu, Xunlei; Harter, Jonathan M.; Phadke, Madhura; Pinto, Lifford; Petersen, Hannah; Bass, Steffen; Keifer, Michael; Zhong, Sharon; Healey, Chris; Taylor, Russell M.</p> <p>2012-01-01</p> <p>By definition, an <span class="hlt">ensemble</span> is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an <span class="hlt">ensemble</span> is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call <span class="hlt">Ensemble</span> Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators. PMID:23560167</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012SPIE.8294E..0UA','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012SPIE.8294E..0UA"><span id="translatedtitle">Comparative visualization of <span class="hlt">ensembles</span> using <span class="hlt">ensemble</span> surface slicing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alabi, Oluwafemi S.; Wu, Xunlei; Harter, Jonathan M.; Phadke, Madhura; Pinto, Lifford; Petersen, Hannah; Bass, Steffen; Keifer, Michael; Zhong, Sharon; Healey, Chris; Taylor, Russell M., II</p> <p>2012-01-01</p> <p>By definition, an <span class="hlt">ensemble</span> is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an <span class="hlt">ensemble</span> is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call <span class="hlt">Ensemble</span> Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.2551I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.2551I"><span id="translatedtitle">A new project on development and application of comprehensive <span class="hlt">downscaling</span> methods over Hokkaido.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Inatsu, M.; Yamada, T. J.; Sato, T.; Nakamura, K.; Matsuoka, N.; Komatsu, A.; Pokhrel, Y. N.; Sugimoto, S.; Miyazaki, S.</p> <p>2012-04-01</p> <p>A new project on development and application of comprehensive <span class="hlt">downscaling</span> methods over Hokkaido started as one of the branches of "Research Program on climate change adaptation" funded by Ministry of Education, Sports, Culture, Science, and Technology of Japan in 2010. Our group will develop two new <span class="hlt">downscaling</span> algorithms in order to get more information on the uncertainty of high/low temperatures or heavy rainfall. Both of the algorithms called "sampling <span class="hlt">downscaling</span>" and "hybrid <span class="hlt">downscaling</span>" are based upon the mixed use of statistical and dynamical <span class="hlt">downscaling</span> ideas. Another point of the project is to evaluate the effect of land-use changes in Hokkaido, where the major pioneering began only about a century ago. Scientific outcomes on climate changes in Hokkaido from the project will be provided to not only public sectors in Hokkaido but also people who live in Hokkaido through a graphical-user-interface system just like a weather forecast system in a forecast-center's webpage.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/21475662','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/21475662"><span id="translatedtitle">Algorithms on <span class="hlt">ensemble</span> quantum computers.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Boykin, P Oscar; Mor, Tal; Roychowdhury, Vwani; Vatan, Farrokh</p> <p>2010-06-01</p> <p>In <span class="hlt">ensemble</span> (or bulk) quantum computation, all computations are performed on an <span class="hlt">ensemble</span> of computers rather than on a single computer. Measurements of qubits in an individual computer cannot be performed; instead, only expectation values (over the complete <span class="hlt">ensemble</span> of computers) can be measured. As a result of this limitation on the model of computation, many algorithms cannot be processed directly on such computers, and must be modified, as the common strategy of delaying the measurements usually does not resolve this <span class="hlt">ensemble</span>-measurement problem. Here we present several new strategies for resolving this problem. Based on these strategies we provide new versions of some of the most important quantum algorithms, versions that are suitable for implementing on <span class="hlt">ensemble</span> quantum computers, e.g., on liquid NMR quantum computers. These algorithms are Shor's factorization algorithm, Grover's search algorithm (with several marked items), and an algorithm for quantum fault-tolerant computation. The first two algorithms are simply modified using a randomizing and a sorting strategies. For the last algorithm, we develop a classical-quantum hybrid strategy for removing measurements. We use it to present a novel quantum fault-tolerant scheme. More explicitly, we present schemes for fault-tolerant measurement-free implementation of Toffoli and ?(z)(¼) as these operations cannot be implemented "bitwise", and their standard fault-tolerant implementations require measurement. PMID:21475662</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMSH53A4205P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMSH53A4205P"><span id="translatedtitle">CME <span class="hlt">Ensemble</span> Forecasting - A Primer</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pizzo, V. J.; de Koning, C. A.; Cash, M. D.; Millward, G. H.; Biesecker, D. A.; Codrescu, M.; Puga, L.; Odstrcil, D.</p> <p>2014-12-01</p> <p>SWPC has been evaluating various approaches for <span class="hlt">ensemble</span> forecasting of Earth-directed CMEs. We have developed the software infrastructure needed to support broad-ranging CME <span class="hlt">ensemble</span> modeling, including composing, interpreting, and making intelligent use of <span class="hlt">ensemble</span> simulations. The first step is to determine whether the physics of the interplanetary propagation of CMEs is better described as chaotic (like terrestrial weather) or deterministic (as in tsunami propagation). This is important, since different <span class="hlt">ensemble</span> strategies are to be pursued under the two scenarios. We present the findings of a comprehensive study of CME <span class="hlt">ensembles</span> in uniform and structured backgrounds that reveals systematic relationships between input cone parameters and ambient flow states and resulting transit times and velocity/density amplitudes at Earth. These results clearly indicate that the propagation of single CMEs to 1 AU is a deterministic process. Thus, the accuracy with which one can forecast the gross properties (such as arrival time) of CMEs at 1 AU is determined primarily by the accuracy of the inputs. This is no tautology - it means specifically that efforts to improve forecast accuracy should focus upon obtaining better inputs, as opposed to developing better propagation models. In a companion paper (deKoning et al., this conference), we compare in situ solar wind data with forecast events in the SWPC operational archive to show how the qualitative and quantitative findings presented here are entirely consistent with the observations and may lead to improved forecasts of arrival time at Earth.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012PhRvE..85e6122P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012PhRvE..85e6122P"><span id="translatedtitle">Entropy of stochastic blockmodel <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peixoto, Tiago P.</p> <p>2012-05-01</p> <p>Stochastic blockmodels are generative network models where the vertices are separated into discrete groups, and the probability of an edge existing between two vertices is determined solely by their group membership. In this paper, we derive expressions for the entropy of stochastic blockmodel <span class="hlt">ensembles</span>. We consider several <span class="hlt">ensemble</span> variants, including the traditional model as well as the newly introduced degree-corrected version [Karrer , Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.83.016107 83, 016107 (2011)], which imposes a degree sequence on the vertices, in addition to the block structure. The imposed degree sequence is implemented both as “soft” constraints, where only the expected degrees are imposed, and as “hard” constraints, where they are required to be the same on all samples of the <span class="hlt">ensemble</span>. We also consider generalizations to multigraphs and directed graphs. We illustrate one of many applications of this measure by directly deriving a log-likelihood function from the entropy expression, and using it to infer latent block structure in observed data. Due to the general nature of the <span class="hlt">ensembles</span> considered, the method works well for <span class="hlt">ensembles</span> with intrinsic degree correlations (i.e., with entropic origin) as well as extrinsic degree correlations, which go beyond the block structure.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22093453','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22093453"><span id="translatedtitle">Estimating preselected and postselected <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Massar, Serge; Popescu, Sandu</p> <p>2011-11-15</p> <p>In analogy with the usual quantum state-estimation problem, we introduce the problem of state estimation for a pre- and postselected <span class="hlt">ensemble</span>. The problem has fundamental physical significance since, as argued by Y. Aharonov and collaborators, pre- and postselected <span class="hlt">ensembles</span> are the most basic quantum <span class="hlt">ensembles</span>. Two new features are shown to appear: (1) information is flowing to the measuring device both from the past and from the future; (2) because of the postselection, certain measurement outcomes can be forced never to occur. Due to these features, state estimation in such <span class="hlt">ensembles</span> is dramatically different from the case of ordinary, preselected-only <span class="hlt">ensembles</span>. We develop a general theoretical framework for studying this problem and illustrate it through several examples. We also prove general theorems establishing that information flowing from the future is closely related to, and in some cases equivalent to, the complex conjugate information flowing from the past. Finally, we illustrate our approach on examples involving covariant measurements on spin-1/2 particles. We emphasize that all state-estimation problems can be extended to the pre- and postselected situation. The present work thus lays the foundations of a much more general theory of quantum state estimation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/18632380','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/18632380"><span id="translatedtitle"><span class="hlt">Ensemble</span> algorithms in reinforcement learning.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wiering, Marco A; van Hasselt, Hado</p> <p>2008-08-01</p> <p>This paper describes several <span class="hlt">ensemble</span> methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different <span class="hlt">ensemble</span> methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed <span class="hlt">ensemble</span> methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where <span class="hlt">ensemble</span> methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity; the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV <span class="hlt">ensembles</span> significantly outperform the single RL algorithms. PMID:18632380</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC41D0853V','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vanvyve, E.; Rife, D.; Pinto, J. O.; Monaghan, A. J.; Davis, C. A.</p> <p>2011-12-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4066535','PMC'); return false;" href="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> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J.</p> <p>2014-01-01</p> <p>Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn <span class="hlt">downscaled</span> by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically <span class="hlt">downscaling</span> raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections. PMID:24872455</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=200778','TEKTRAN'); return false;" href="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> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..522..645K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..522..645K"><span id="translatedtitle">A holistic, multi-scale dynamic <span class="hlt">downscaling</span> framework for climate impact assessments and challenges of addressing finer-scale watershed dynamics</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kim, Jongho; Ivanov, Valeriy Y.</p> <p>2015-03-01</p> <p>We present a state-of-the-art holistic, multi-scale dynamic <span class="hlt">downscaling</span> approach suited to address climate change impacts on hydrologic metrics and hydraulic regime of surface flow at the "scale of human decisions" in ungauged basins. The framework rests on stochastic and physical <span class="hlt">downscaling</span> techniques that permit one-way crossing 106-100 m scales, with a specific emphasis on 'nesting' hydraulic assessments within a coarser-scale hydrologic model. Future climate projections for the location of Manchester watershed (MI) are obtained from an <span class="hlt">ensemble</span> of General Circulation Models of the 3rd phase of the Coupled Model Intercomparison Project database and <span class="hlt">downscaled</span> to a "point" scale using a weather generator. To represent the natural variability of historic and future climates, we generated continuous time series of 300 years for the locations of 3 meteorological stations located in the vicinity of the ungauged basin. To make such a multi-scale approach computationally feasible, we identified the months of May and August as the periods of specific interest based on ecohydrologic considerations. Analyses of historic and future simulation results for the identified periods show that the same median rainfall obtained by accounting for climate natural variability triggers hydrologically-mediated non-uniqueness in flow variables resolved at the hydraulic scale. An emerging challenge is that uncertainty initiated at the hydrologic scale is not necessarily preserved at smaller-scale flow variables, because of non-linearity of underlying physical processes, which ultimately can mask climate uncertainty. We stress the necessity of augmenting climate-level uncertainties of emission scenario, multi-model, and natural variability with uncertainties arising due to non-linearities in smaller-scale processes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy...44.2637D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy...44.2637D"><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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dosio, Alessandro; Panitz, Hans-Jürgen; Schubert-Frisius, Martina; Lüthi, Daniel</p> <p>2015-05-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.5258A','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Anders, Ivonne; Gbobaniyi, Emiola</p> <p>2014-05-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20020020435&hterms=ECC&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DECC','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20020020435&hterms=ECC&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DECC"><span id="translatedtitle"><span class="hlt">Ensemble</span> Cannonical Correlation Prediction of Seasonal Precipitation Over the US</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lau, William K. M.; Kim, Kyu-Myong; Shen, Samuel; Einaudi, Franco (Technical Monitor)</p> <p>2001-01-01</p> <p>This paper presents preliminary results of an <span class="hlt">ensemble</span> cannonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate <span class="hlt">downscaling</span> studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into nonoverlapping sectors. The cannonical SST modes for each sector are used as the predictor for the <span class="hlt">ensemble</span> hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all regions of the US and for all seasonal compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible for enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduced the spring predictability barrier over all regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and regional regional data. Moreover, the ECC forecasts can be applied to other climate subsystems and, in conjunction with further diagnostic or model studies will enables a better understanding of the dynamic links between climate variations and precipitation, not only for the US, but also for other regions of the world.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/21450714','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/21450714"><span id="translatedtitle">Quantum metrology with molecular <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Schaffry, Marcus; Gauger, Erik M.; Morton, John J. L.; Fitzsimons, Joseph; Benjamin, Simon C.; Lovett, Brendon W.</p> <p>2010-10-15</p> <p>The field of quantum metrology promises measurement devices that are fundamentally superior to conventional technologies. Specifically, when quantum entanglement is harnessed, the precision achieved is supposed to scale more favorably with the resources employed, such as system size and time required. Here, we consider measurement of magnetic-field strength using an <span class="hlt">ensemble</span> of spin-active molecules. We identify a third essential resource: the change in <span class="hlt">ensemble</span> polarization (entropy increase) during the metrology experiment. We find that performance depends crucially on the form of decoherence present; for a plausible dephasing model, we describe a quantum strategy, which can indeed beat the standard strategy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22308400','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22308400"><span id="translatedtitle">Quantum Gibbs <span class="hlt">ensemble</span> Monte Carlo</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Fantoni, Riccardo; Moroni, Saverio</p> <p>2014-09-21</p> <p>We present a path integral Monte Carlo method which is the full quantum analogue of the Gibbs <span class="hlt">ensemble</span> Monte Carlo method of Panagiotopoulos to study the gas-liquid coexistence line of a classical fluid. Unlike previous extensions of Gibbs <span class="hlt">ensemble</span> Monte Carlo to include quantum effects, our scheme is viable even for systems with strong quantum delocalization in the degenerate regime of temperature. This is demonstrated by an illustrative application to the gas-superfluid transition of {sup 4}He in two dimensions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.5215F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.5215F"><span id="translatedtitle">Extremes of European temperature in <span class="hlt">ENSEMBLES</span> regional climate models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frias, M. Dolores; Minguez, Roberto; Gutierrez, Jose Manuel; Mendez, Fernando J.</p> <p>2010-05-01</p> <p>In recent years, there has been an increasing interest in studying the impacts of climate extremes in different sectors (agriculture, energy, insurance, etc.). In particular, extreme temperatures and heat waves have had a big impact in European socioeconomic activities during the last years (e.g. the 2003 heat wave in France); moreover, climate change has the potential to alter the prevalence and severity of extremes thus given rise to more severe impacts with unpredictable consequences. Regional climate models offer the opportunity to analyze and project in different future scenarios the variability of extremes at regional time scales. In the present work, we estimate changes of maximum temperatures in Europe using two state-of-the-art regional circulation models from the EU <span class="hlt">ENSEMBLES</span> project. Regional climate models are used as dynamical <span class="hlt">downscaling</span> tools to provide simulations on smaller scales than those represented for global climate models. Extremes are expressed in terms of return values derived from a time-dependent generalized extreme value (GEV) model for monthly maxima. The study focuses on the end of the 20th century (1961-2000), used as a calibration/validation period, and analyzes the changes projected for the period 2020-2050 considering the A1B emission scenario.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1412222S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1412222S"><span id="translatedtitle">Stochastic <span class="hlt">Downscaling</span> for Hydrodynamic and Ecological Modeling of Lakes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schlabing, D.; Eder, M.; Frassl, M.; Rinke, K.; Bárdossy, A.</p> <p>2012-04-01</p> <p>Weather generators are of interest in climate impact studies, because they allow different modi operandi: (1) More realizations of the past, (2) possible futures as defined by the modeler and (3) possible futures according to the combination of greenhouse gas emission scenarios and their Global Circulation Model (GCM) consequences. Climate modeling has huge inherently unquantifiable uncertainties, yet the results present themselves as single point values without any measure of uncertainty. Given this reduction of risk-relevant information, stochastic <span class="hlt">downscaling</span> offers itself as a tool to recover the variability present in local measurements. One should bear in mind that the lake models that are fed with <span class="hlt">downscaling</span> results are themselves deterministic and single runs may prove to be misleading. Especially population dynamics simulated by ecological models are sensitive to very particular events in the input data. A way to handle this sensitivity is to perform Monte Carlo studies with varying meteorological driving forces using a weather generator. For these studies, the Vector-Autoregressive Weather generator (VG), which was first presented at the EGU 2011, was developed further. VG generates daily air temperature, humidity, long- and shortwave radiance and wind. Wind and shortwave radiation is subsequently disaggregated to hourly values, because their short term variability has proven important for the application. Changes relative to the long-term values are modeled as disturbances that act during the autoregressive generation of the synthetic time series. The method preserves the dependence structure between the variables, as changes in the disturbed variable, say temperature, are propagated to the other variables. The approach is flexible because the disturbances can be chosen freely. Changes in mean can be represented as constant disturbance, changes in variability as episodes of certain length and amplitude. The disturbances can also be extracted from GCMs with the help of QQ-<span class="hlt">downscaled</span> time series. Results of water-quality and ecological modeling using data from VG is contributed by Marieke Anna Frassl under the title "Simulating the effect of meteorological variability on a lake ecosystem". Maria Magdalena Eder contributes three dimensional hydrodynamic lake simulations using VG data in a poster entitled "Advances in estimating the climate sensibility of a large lake using scenario simulations". Both posters can be found in the Session "Lakes and Inland Seas" (HS10.1).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1712462P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1712462P"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of summer precipitation over northwestern South America</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Palomino Lemus, Reiner; Córdoba Machado, Samir; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda; Jesús Esteban Parra, María</p> <p>2015-04-01</p> <p>In this study a statistical <span class="hlt">downscaling</span> (SD) model using Principal Component Regression (PCR) for simulating summer precipitation in Colombia during the period 1950-2005, has been developed, and climate projections during the 2071-2100 period by applying the obtained SD model have been obtained. For these ends the Principal Components (PCs) of the SLP reanalysis data from NCEP were used as predictor variables, while the observed gridded summer precipitation was the predictand variable. Period 1950-1993 was utilized for calibration and 1994-2010 for validation. The Bootstrap with replacement was applied to provide estimations of the statistical errors. All models perform reasonably well at regional scales, and the spatial distribution of the correlation coefficients between predicted and observed gridded precipitation values show high values (between 0.5 and 0.93) along Andes range, north and north Pacific of Colombia. Additionally, the ability of the MIROC5 GCM to simulate the summer precipitation in Colombia, for present climate (1971-2005), has been analyzed by calculating the differences between the simulated and observed precipitation values. The simulation obtained by this GCM strongly overestimates the precipitation along a horizontal sector through the center of Colombia, especially important at the east and west of this country. However, the SD model applied to the SLP of the GCM shows its ability to faithfully reproduce the rainfall field. Finally, in order to get summer precipitation projections in Colombia for the period 1971-2100, the <span class="hlt">downscaled</span> model, recalibrated for the total period 1950-2010, has been applied to the SLP output from MIROC5 model under the RCP2.6, RCP4.5 and RCP8.5 scenarios. The changes estimated by the SD models are not significant under the RCP2.6 scenario, while for the RCP4.5 and RCP8.5 scenarios a significant increase of precipitation appears regard to the present values in all the regions, reaching around the 27% in northern Colombia region under the RCP8.5 scenario. Keywords: Statistical <span class="hlt">downscaling</span>, precipitation, Principal Component Regression, climate change, Colombia. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/ofr20141190','USGSPUBS'); return false;" href="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> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Wootten, Adrienne; Smith, Kara; Boyles, Ryan; Terando, Adam J.; Stefanova, Lydia; Misra, Vasru; Smith, Tom; Blodgett, David L.; Semazzi, Fredrick</p> <p>2014-01-01</p> <p>Climate change is likely to have many effects on natural ecosystems in the Southeast U.S. The National Climate Assessment Southeast Technical Report (SETR) indicates that natural ecosystems in the Southeast are likely to be affected by warming temperatures, ocean acidification, sea-level rise, and changes in rainfall and evapotranspiration. To better assess these how climate changes could affect multiple sectors, including ecosystems, climatologists have created several <span class="hlt">downscaled</span> climate projections (or <span class="hlt">downscaled</span> datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these <span class="hlt">downscaled</span> datasets, known as <span class="hlt">downscaling</span>, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for <span class="hlt">downscaling</span> and the number of <span class="hlt">downscaled</span> datasets produced in recent years present many challenges for scientists and decisionmakers in assessing the impact or vulnerability of a given species or ecosystem to climate change. Given the number of available <span class="hlt">downscaled</span> datasets, how do these model outputs compare to each other? Which variables are available, and are certain <span class="hlt">downscaled</span> datasets more appropriate for assessing vulnerability of a particular species? Given the desire to use these datasets for impact and vulnerability assessments and the lack of comparison between these datasets, the goal of this report is to synthesize the information available in these <span class="hlt">downscaled</span> datasets and provide guidance to scientists and natural resource managers with specific interests in ecological modeling and conservation planning related to climate change in the Southeast U.S. This report enables the Southeast Climate Science Center (SECSC) to address an important strategic goal of providing scientific information and guidance that will enable resource managers and other participants in Landscape Conservation Cooperatives to make science-based climate change adaptation decisions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/20982392','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/20982392"><span id="translatedtitle">Localization of atomic <span class="hlt">ensembles</span> via superfluorescence</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Macovei, Mihai; Evers, Joerg; Keitel, Christoph H.; Zubairy, M. Suhail</p> <p>2007-03-15</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015NJPh...17b3052S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015NJPh...17b3052S"><span id="translatedtitle">Unbiased sampling of network <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Squartini, Tiziano; Mastrandrea, Rossana; Garlaschelli, Diego</p> <p>2015-02-01</p> <p>Sampling random graphs with given properties is a key step in the analysis of networks, as random <span class="hlt">ensembles</span> represent basic null models required to identify patterns such as communities and motifs. An important requirement is that the sampling process is unbiased and efficient. The main approaches are microcanonical, i.e. they sample graphs that match the enforced constraints exactly. Unfortunately, when applied to strongly heterogeneous networks (like most real-world examples), the majority of these approaches become biased and/or time-consuming. Moreover, the algorithms defined in the simplest cases, such as binary graphs with given degrees, are not easily generalizable to more complicated <span class="hlt">ensembles</span>. Here we propose a solution to the problem via the introduction of a ‘Maximize and Sample’ (‘Max & Sam’ for short) method to correctly sample <span class="hlt">ensembles</span> of networks where the constraints are ‘soft’, i.e. realized as <span class="hlt">ensemble</span> averages. Our method is based on exact maximum-entropy distributions and is therefore unbiased by construction, even for strongly heterogeneous networks. It is also more computationally efficient than most microcanonical alternatives. Finally, it works for both binary and weighted networks with a variety of constraints, including combined degree-strength sequences and full reciprocity structure, for which no alternative method exists. Our canonical approach can in principle be turned into an unbiased microcanonical one, via a restriction to the relevant subset. Importantly, the analysis of the fluctuations of the constraints suggests that the microcanonical and canonical versions of all the <span class="hlt">ensembles</span> considered here are not equivalent. We show various real-world applications and provide a code implementing all our algorithms.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRD..120.8227B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..120.8227B"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> simulation and future projection of precipitation over China</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bao, Jiawei; Feng, Jinming; Wang, Yongli</p> <p>2015-08-01</p> <p>This study assesses present-day and future precipitation changes over China by using the Weather Research and Forecasting (WRF) model version 3.5.1. The WRF model was driven by the Geophysical Fluid Dynamics Laboratory Earth System Model with the Generalized Ocean Layer Dynamics component (GFDL-ESM2G) output over China at the resolution of 30 km for the present day (1976-2005) and near future (2031-2050) under the Representative Concentration Pathways 4.5 (RCP4.5) scenario. The results demonstrate that with improved resolution and better representation of finer-scale physical process, dynamical <span class="hlt">downscaling</span> adds value to the regional precipitation simulation. WRF <span class="hlt">downscaling</span> generally simulates more reliable spatial distributions of total precipitation and extreme precipitation in China with higher spatial pattern correlations and closer magnitude. It is able to successfully eliminate the artificial precipitation maximum area simulated by GFDL-ESM2G over the west of the Sichuan Basin, along the eastern edge of the Tibetan Plateau in both summer and winter. Besides, the regional annual cycle and frequencies of precipitation intensity are also well depicted by WRF. In the future projections, under the RCP4.5 scenario, both models project that summer precipitation over most parts of China will increase, especially in western and northern China, and that precipitation over some southern regions is projected to decrease. The projected increase of future extreme precipitation makes great contributions to the total precipitation increase. In southern regions, the projected larger extreme precipitation amounts accompanied with fewer extreme precipitation frequencies suggest that future daily extreme precipitation intensity is likely to increase in these regions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1710540J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1710540J"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> for winter streamflow in Douro River</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jesús Esteban Parra, María; Hidalgo Muñoz, José Manuel; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda</p> <p>2015-04-01</p> <p>In this paper we have obtained climate change projections for winter flow of the Douro River in the period 2071-2100 by applying the technique of Partial Regression and various General Circulation Models of CMIP5. The streamflow data base used has been provided by the Center for Studies and Experimentation of Public Works, CEDEX. Series from gauing stations and reservoirs with less than 10% of missing data (filled by regression with well correlated neighboring stations) have been considered. The homogeneity of these series has been evaluated through the Pettit test and degree of human alteration by the Common Area Index. The application of these criteria led to the selection of 42 streamflow time series homogeneously distributed over the basin, covering the period 1951-2011. For these streamflow data, winter seasonal values were obtained by averaging the monthly values from January to March. Statistical <span class="hlt">downscaling</span> models for the streamflow have been fitted using as predictors the main atmospheric modes of variability over the North Atlantic region. These modes have been obtained using winter sea level pressure data of the NCEP reanalysis, averaged for the months from December to February. Period 1951-1995 was used for calibration, while 1996-2011 period was used in validating the adjusted models. In general, these models are able to reproduce about 70% of the variability of the winter streamflow of the Douro River. Finally, the obtained statistical models have been applied to obtain projections for 2071-2100 period, using outputs from different CMIP5 models under the RPC8.5 scenario. The results for the end of the century show modest declines of winter streamflow in this river for most of the models. Keywords: Statistical <span class="hlt">downscaling</span>, streamflow, Douro River, climate change. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=steels&pg=6&id=EJ631682','ERIC'); return false;" href="http://eric.ed.gov/?q=steels&pg=6&id=EJ631682"><span id="translatedtitle">African Drum and Steel Pan <span class="hlt">Ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Sunkett, Mark E.</p> <p>2000-01-01</p> <p>Discusses how to develop both African drum and steel pan <span class="hlt">ensembles</span> providing information on teacher preparation, instrument choice, beginning the <span class="hlt">ensemble</span>, and lesson planning. Includes additional information for the drum <span class="hlt">ensembles</span>. Lists references and instructional materials, sources of drums and pans, and common note layout/range for steel pan…</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=326566','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=326566"><span id="translatedtitle"><span class="hlt">Ensembl</span> genomes 2016: more genomes, more complexity</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p><span class="hlt">Ensembl</span> Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org). Together, the two resources provide a consistent...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=steel&pg=6&id=EJ631682','ERIC'); return false;" href="http://eric.ed.gov/?q=steel&pg=6&id=EJ631682"><span id="translatedtitle">African Drum and Steel Pan <span class="hlt">Ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Sunkett, Mark E.</p> <p>2000-01-01</p> <p>Discusses how to develop both African drum and steel pan <span class="hlt">ensembles</span> providing information on teacher preparation, instrument choice, beginning the <span class="hlt">ensemble</span>, and lesson planning. Includes additional information for the drum <span class="hlt">ensembles</span>. Lists references and instructional materials, sources of drums and pans, and common note layout/range for steel pan…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.373F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.373F"><span id="translatedtitle">Evolution of the Canadian regional <span class="hlt">ensemble</span> prediction system</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frenette, R.; Charron, M.; Li, X.; Gagnon, N.; Lavaysse, C.; Belair, S.; Carrera, M.; Yau, P.; Candille, G.</p> <p>2010-09-01</p> <p>A regional <span class="hlt">ensemble</span> prediction system (REPS) over North America is expected to become operational at the Canadian Meteorological Centre (CMC) in late 2010 or early 2011. Different configurations of the REPS have already been tested and verified at different locations and time periods. The system was used during the Beijing 2008 summer Olympics and for the North American domain with a focus over southern British Columbia, Canada, during the 2010 Vancouver Olympics. It will also provide forecasts for tropical storms and hurricanes for the Haïti area during the summer and autumn of 2010. The Canadian Global Environmental Multiscale (GEM) model has been designed with the possibility to be run as a limited area model (GEM-LAM). The Canadian REPS is composed of 20 members running the GEM-LAM at a near 33 km grid spacing and with the same physical parameterizations as those present in the operational global deterministic prediction system at CMC. Two initial perturbation strategies (moist targeted singular vectors [SV] and the <span class="hlt">ensemble</span> Kalman filter [EnKF]), as well as two stochastic methods for perturbations of parameterizations were verified against surface and upper air (rawinsondes) observations during summer and winter periods to determine which system has the best forecast abilities. For the SV-based REPS, 20 initial conditions (IC) are generated using a targeted SV perturbation method. These ICs are then used to run 20 global GEMs that will provide the lateral boundary conditions (LBCs) for each GEM-LAM. For the EnKF-based REPS, the 20 LBCs are built by <span class="hlt">downscaling</span> the 20 members of the Canadian global <span class="hlt">ensemble</span> prediction system (GEPS) to the resolution of the REPS. Verifications indicate that the EnKF approach gives better skill for summer and winter periods. The skill difference between the two systems comes mainly from the reliability attribute (smaller bias and reduced under-dispersion). Stochastic perturbations on model physical tendencies and on physical parameters were both tested. These two perturbation methods show a significant improvement in the reliability skill but tend to slightly degrade the resolution. Nevertheless, both systems show an overall improvement in the skill. The physical tendencies perturbation method showed the best scores and was chosen. Research to improve the system using surface parameter perturbations is presently ongoing. Initial results show improved skill for surface during the summer season when perturbations are done on fields related to the land surface scheme such as the albedo, soil temperature and moisture.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24051840','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24051840"><span id="translatedtitle">Coupled <span class="hlt">ensemble</span> flow line advection and analysis.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Guo, Hanqi; Yuan, Xiaoru; Huang, Jian; Zhu, Xiaomin</p> <p>2013-12-01</p> <p><span class="hlt">Ensemble</span> run simulations are becoming increasingly widespread. In this work, we couple particle advection with pathline analysis to visualize and reveal the differences among the flow fields of <span class="hlt">ensemble</span> runs. Our method first constructs a variation field using a Lagrangian-based distance metric. The variation field characterizes the variation between vector fields of the <span class="hlt">ensemble</span> runs, by extracting and visualizing the variation of pathlines within <span class="hlt">ensemble</span>. Parallelism in a MapReduce style is leveraged to handle data processing and computing at scale. Using our prototype system, we demonstrate how scientists can effectively explore and investigate differences within <span class="hlt">ensemble</span> simulations. PMID:24051840</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AdWR...76...81R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AdWR...76...81R"><span id="translatedtitle">A method to <span class="hlt">downscale</span> soil moisture to fine resolutions using topographic, vegetation, and soil data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ranney, Kayla J.; Niemann, Jeffrey D.; Lehman, Brandon M.; Green, Timothy R.; Jones, Andrew S.</p> <p>2015-02-01</p> <p>Soil moisture can be estimated over large regions with spatial resolutions greater than 500 m, but many applications require finer resolutions (10-100 m). Several methods use topographic data to <span class="hlt">downscale</span>, but vegetation and soil patterns can also be important. In this paper, a <span class="hlt">downscaling</span> model that uses fine-resolution topographic, vegetation, and soil data is presented. The method is tested at the Cache la Poudre catchment where detailed vegetation and soil data were collected. Additional testing is performed at the Tarrawarra and Nerrigundah catchments where limited soil data are available. <span class="hlt">Downscaled</span> soil moisture patterns at Cache la Poudre improve when vegetation and soil data are used, and model performance is similar to an EOF method. Using interpolated soil data at Tarrawarra and Nerrigundah decreases model performance and results in worse performance than an EOF method, suggesting that soil data needs greater spatial detail and accuracy to be useful for <span class="hlt">downscaling</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308919&keyword=water+AND+problem&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55736581&CFTOKEN=75799787','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308919&keyword=water+AND+problem&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55736581&CFTOKEN=75799787"><span id="translatedtitle">Examining Projected Changes in Weather & Air Quality Extremes Between 2000 & 2030 using Dynamical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Climate change may alter regional weather extremes resulting in a range of environmental impacts including changes in air quality, water quality and availability, energy demands, agriculture, and ecology. Dynamical <span class="hlt">downscaling</span> simulations were conducted with the Weather Research...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014FrP.....2...20M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014FrP.....2...20M"><span id="translatedtitle">Statistical Analysis of Protein <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Máté, Gabriell; Heermann, Dieter</p> <p>2014-04-01</p> <p>As 3D protein-configuration data is piling up, there is an ever-increasing need for well-defined, mathematically rigorous analysis approaches, especially that the vast majority of the currently available methods rely heavily on heuristics. We propose an analysis framework which stems from topology, the field of mathematics which studies properties preserved under continuous deformations. First, we calculate a barcode representation of the molecules employing computational topology algorithms. Bars in this barcode represent different topological features. Molecules are compared through their barcodes by statistically determining the difference in the set of their topological features. As a proof-of-principle application, we analyze a dataset compiled of <span class="hlt">ensembles</span> of different proteins, obtained from the <span class="hlt">Ensemble</span> Protein Database. We demonstrate that our approach correctly detects the different protein groupings.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015NPGeo..22..485K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015NPGeo..22..485K"><span id="translatedtitle">Spectral diagonal <span class="hlt">ensemble</span> Kalman filters</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kasanický, I.; Mandel, J.; Vejmelka, M.</p> <p>2015-08-01</p> <p>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 approximation 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016IJTP..tmp...35K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016IJTP..tmp...35K"><span id="translatedtitle">State <span class="hlt">Ensembles</span> and Quantum Entropy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kak, Subhash</p> <p>2016-01-01</p> <p>This paper considers quantum communication involving an <span class="hlt">ensemble</span> of states. Apart from the von Neumann entropy, it considers other measures one of which may be useful in obtaining information about an unknown pure state and another that may be useful in quantum games. It is shown that under certain conditions in a two-party quantum game, the receiver of the states can increase the entropy by adding another pure state.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4301745','PMC'); return false;" href="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> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Bolser, Dan M.; Kerhornou, Arnaud; Walts, Brandon; Kersey, Paul</p> <p>2015-01-01</p> <p>Recent developments in DNA sequencing have enabled the large and complex genomes of many crop species to be determined for the first time, even those previously intractable due to their polyploid nature. Indeed, over the course of the last 2 years, the genome sequences of several commercially important cereals, notably barley and bread wheat, have become available, as well as those of related wild species. While still incomplete, comparison with other, more completely assembled species suggests that coverage of genic regions is likely to be high. <span class="hlt">Ensembl</span> Plants (http://plants.<span class="hlt">ensembl</span>.org) is an integrative resource organizing, analyzing and visualizing genome-scale information for important crop and model plants. Available data include reference genome sequence, variant loci, gene models and functional annotation. For variant loci, individual and population genotypes, linkage information and, where available, phenotypic information are shown. Comparative analyses are performed on DNA and protein sequence alignments. The resulting genome alignments and gene trees, representing the implied evolutionary history of the gene family, are made available for visualization and analysis. Driven by the case of bread wheat, specific extensions to the analysis pipelines and web interface have recently been developed to support polyploid genomes. Data in <span class="hlt">Ensembl</span> Plants is accessible through a genome browser incorporating various specialist interfaces for different data types, and through a variety of additional methods for programmatic access and data mining. These interfaces are consistent with those offered through the <span class="hlt">Ensembl</span> interface for the genomes of non-plant species, including those of plant pathogens, pests and pollinators, facilitating the study of the plant in its environment. PMID:25432969</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20000102382','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20000102382"><span id="translatedtitle">Dimensionality Reduction Through Classifier <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)</p> <p>1999-01-01</p> <p>In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in <span class="hlt">ensembles</span> of classifiers, yielding results superior to single classifiers, <span class="hlt">ensembles</span> that use the full set of features, and <span class="hlt">ensembles</span> based on principal component analysis on both real and synthetic datasets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC41E..07W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC41E..07W"><span id="translatedtitle">SDSM-DC: A smarter approach to <span class="hlt">downscaling</span> for decision-making? (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wilby, R. L.; Dawson, C. W.</p> <p>2011-12-01</p> <p>General Circulation Model (GCM) output has been used for <span class="hlt">downscaling</span> and impact assessments for at least 25 years. <span class="hlt">Downscaling</span> methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' <span class="hlt">downscaling</span> typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use <span class="hlt">downscaling</span> tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical <span class="hlt">DownScaling</span> Model (SDSM) over the last decade. This sample offers insights to <span class="hlt">downscaling</span> practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new <span class="hlt">downscaling</span> tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by <span class="hlt">downscaling</span> multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC41E..07W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41E..07W"><span id="translatedtitle">SDSM-DC: A smarter approach to <span class="hlt">downscaling</span> for decision-making? (Invited)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wilby, R. L.; Dawson, C. W.</p> <p>2013-12-01</p> <p>General Circulation Model (GCM) output has been used for <span class="hlt">downscaling</span> and impact assessments for at least 25 years. <span class="hlt">Downscaling</span> methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' <span class="hlt">downscaling</span> typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use <span class="hlt">downscaling</span> tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical <span class="hlt">DownScaling</span> Model (SDSM) over the last decade. This sample offers insights to <span class="hlt">downscaling</span> practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new <span class="hlt">downscaling</span> tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by <span class="hlt">downscaling</span> multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..1210067G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..1210067G"><span id="translatedtitle">Sensitivity analysis of runoff modeling to statistical <span class="hlt">downscaling</span> models in the western Mediterranean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grouillet, B.; Ruelland, D.; Ayar, P. V.; Vrac, M.</p> <p>2015-10-01</p> <p>This paper analyzes the sensitivity of a hydrological model to different methods to statistically <span class="hlt">downscale</span> climate precipitation and temperature over four western Mediterranean basins illustrative of different hydro-meteorological situations. The comparison was conducted over a common 20 year period (1986-2005) to capture different climatic conditions in the basins. Streamflow was simulated using the GR4j conceptual model. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically <span class="hlt">downscaled</span> datasets to reproduce various aspects of the hydrograph. Three different statistical <span class="hlt">downscaling</span> models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the "cumulative distribution function - transform" approach (CDFt). We used the models to <span class="hlt">downscale</span> precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two GCMs (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various <span class="hlt">downscaled</span> data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution <span class="hlt">downscaled</span> climate values leads to a major improvement of runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical <span class="hlt">downscaling</span> models to be used in climate change impact studies to minimize the range of uncertainty associated with such <span class="hlt">downscaling</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016HESS...20.1031G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016HESS...20.1031G"><span id="translatedtitle">Sensitivity analysis of runoff modeling to statistical <span class="hlt">downscaling</span> models in the western Mediterranean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Grouillet, Benjamin; Ruelland, Denis; Vaittinada Ayar, Pradeebane; Vrac, Mathieu</p> <p>2016-03-01</p> <p>This paper analyzes the sensitivity of a hydrological model to different methods to statistically <span class="hlt">downscale</span> climate precipitation and temperature over four western Mediterranean basins illustrative of different hydro-meteorological situations. The comparison was conducted over a common 20-year period (1986&ndsh;2005) to capture different climatic conditions in the basins. The daily GR4j conceptual model was used to simulate streamflow that was eventually evaluated at a 10-day time step. Cross-validation showed that this model is able to correctly reproduce runoff in both dry and wet years when high-resolution observed climate forcings are used as inputs. These simulations can thus be used as a benchmark to test the ability of different statistically <span class="hlt">downscaled</span> data sets to reproduce various aspects of the hydrograph. Three different statistical <span class="hlt">downscaling</span> models were tested: an analog method (ANALOG), a stochastic weather generator (SWG) and the cumulative distribution function-transform approach (CDFt). We used the models to <span class="hlt">downscale</span> precipitation and temperature data from NCEP/NCAR reanalyses as well as outputs from two general circulation models (GCMs) (CNRM-CM5 and IPSL-CM5A-MR) over the reference period. We then analyzed the sensitivity of the hydrological model to the various <span class="hlt">downscaled</span> data via five hydrological indicators representing the main features of the hydrograph. Our results confirm that using high-resolution <span class="hlt">downscaled</span> climate values leads to a major improvement in runoff simulations in comparison to the use of low-resolution raw inputs from reanalyses or climate models. The results also demonstrate that the ANALOG and CDFt methods generally perform much better than SWG in reproducing mean seasonal streamflow, interannual runoff volumes as well as low/high flow distribution. More generally, our approach provides a guideline to help choose the appropriate statistical <span class="hlt">downscaling</span> models to be used in climate change impact studies to minimize the range of uncertainty associated with such <span class="hlt">downscaling</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020008664','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020008664"><span id="translatedtitle">Statistical <span class="hlt">Ensemble</span> of Large Eddy Simulations</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Carati, Daniele; Rogers, Michael M.; Wray, Alan A.; Mansour, Nagi N. (Technical Monitor)</p> <p>2001-01-01</p> <p>A statistical <span class="hlt">ensemble</span> of large eddy simulations (LES) is run simultaneously for the same flow. The information provided by the different large scale velocity fields is used to propose an <span class="hlt">ensemble</span> averaged version of the dynamic model. This produces local model parameters that only depend on the statistical properties of the flow. An important property of the <span class="hlt">ensemble</span> averaged dynamic procedure is that it does not require any spatial averaging and can thus be used in fully inhomogeneous flows. Also, the <span class="hlt">ensemble</span> of LES's provides statistics of the large scale velocity that can be used for building new models for the subgrid-scale stress tensor. The <span class="hlt">ensemble</span> averaged dynamic procedure has been implemented with various models for three flows: decaying isotropic turbulence, forced isotropic turbulence, and the time developing plane wake. It is found that the results are almost independent of the number of LES's in the statistical <span class="hlt">ensemble</span> provided that the <span class="hlt">ensemble</span> contains at least 16 realizations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ThApC.117..343O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ThApC.117..343O"><span id="translatedtitle">Evaluating climate change effects on runoff by statistical <span class="hlt">downscaling</span> and hydrological model GR2M</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Okkan, Umut; Fistikoglu, Okan</p> <p>2014-07-01</p> <p>The main purpose of this study is to evaluate the impacts of climate change on Izmir-Tahtali freshwater basin, which is located in the Aegean Region of Turkey. For this purpose, a developed strategy involving statistical <span class="hlt">downscaling</span> and hydrological modeling is illustrated through its application to the basin. Prior to statistical <span class="hlt">downscaling</span> of precipitation and temperature, the explanatory variables are obtained from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data set. All possible regression approach is used to establish the most parsimonious relationship between precipitation, temperature, and climatic variables. Selected predictors have been used in training of artificial neural networks-based <span class="hlt">downscaling</span> models and the trained models with the obtained relationships have been operated to produce scenario precipitation and temperature from the simulations of third Generation Coupled Climate Model. Biases from <span class="hlt">downscaled</span> outputs have been reduced after <span class="hlt">downscaling</span> process. Finally, the corrected <span class="hlt">downscaled</span> outputs have been transformed to runoff by means of a monthly parametric hydrological model GR2M to assess the probable impacts of temperature and precipitation changes on runoff. According to the A1B climate scenario results, statistically significant trends are foreseen for precipitation, temperature, and runoff in the study basin.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70168924','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70168924"><span id="translatedtitle">Evaluation of <span class="hlt">downscaled</span>, gridded climate data for the conterminous United States</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Robert J. Behnke; Stephen J. Vavrus; Andrew Allstadt; Thomas P. Albright; Thogmartin, Wayne E.; Volker C. Radeloff</p> <p>2016-01-01</p> <p>Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous <span class="hlt">downscaled</span> weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are <span class="hlt">downscaled</span>, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used <span class="hlt">downscaled</span> data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between <span class="hlt">downscaled</span> and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect <span class="hlt">downscaled</span> weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a <span class="hlt">downscaled</span> weather data set for a given ecological application.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010JHyd..385...13C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010JHyd..385...13C"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of daily precipitation using support vector machines and multivariate analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Shien-Tsung; Yu, Pao-Shan; Tang, Yi-Hsuan</p> <p>2010-05-01</p> <p>Summary<span class="hlt">Downscaling</span> local daily precipitation from large-scale weather variables is often necessary when studying how climate change impacts hydrology. This study proposes a two-step statistical <span class="hlt">downscaling</span> method for projection of daily precipitation. The first step is classification to determine whether the day is dry or wet, and the second is regression to estimate the amount of precipitation conditional on the occurrence of a wet day. Predictors of classification and regression models are selected from large-scale weather variables in NECP reanalysis data based on statistical tests. The proposed statistical <span class="hlt">downscaling</span> method is developed according to two methodologies. One methodology is support vector machine (SVM), including support vector classification (SVC) and support vector regression (SVR), and the other is multivariate analysis, including discriminant analysis (for classification) and multiple regression. The popular statistical <span class="hlt">downscaling</span> model (SDSM) is analyzed for comparison. A comparison of <span class="hlt">downscaling</span> results in the Shih-Men Reservoir basin in Taiwan reveals that overall, the SVM reproduces most reasonable daily precipitation properties, although the SDMS performs better than other models in small daily precipitation (less than about 10 mm). Finally, projection of local daily precipitation is performed, and future work to advance the <span class="hlt">downscaling</span> method is proposed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003IJCli..23..887S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003IJCli..23..887S"><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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salathé, Eric P., Jr.</p> <p>2003-06-01</p> <p>Global simulations of precipitation from climate models lack sufficient resolution and contain large biases that make them unsuitable for regional studies, such as forcing hydrologic simulations. In this study, the effectiveness of several methods to <span class="hlt">downscale</span> large-scale precipitation is examined. To facilitate comparisons with observations and to remove uncertainties in other fields, large-scale predictor fields to be <span class="hlt">downscaled</span> are taken from the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalyses. Three <span class="hlt">downscaling</span> methods are used: (1): a local scaling of the simulated large-scale precipitation; (2) a modified scaling of simulated precipitation that takes into account the large-scale wind field; and (3) an analogue method with 1000 hPa heights as predictor.A hydrologic model of the Yakima River in central Washington state, USA, is then forced by the three <span class="hlt">downscaled</span> precipitation datasets. Simulations with the raw large-scale precipitation and gridded observations are also made. Comparisons among these simulated flows reveal the effectiveness of the <span class="hlt">downscaling</span> methods. The local scaling of the simulated large-scale precipitation is shown to be quite successful and simple to implement. Furthermore, the tuning of the <span class="hlt">downscaling</span> methods is valid across phases of the Pacific decadal oscillation, suggesting that the methods are applicable to climate-change studies.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1070A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1070A"><span id="translatedtitle">Applying <span class="hlt">downscaled</span> climate data to wildlife areas in Washington State, USA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Allan, A.; Shafer, S. L.; Bartlein, P. J.; Helbrecht, L.; Pelltier, R.; Thompson, B.</p> <p>2013-12-01</p> <p>Conservation and natural resource managers require information about potential climate change effects for the species and ecosystems they manage. We evaluated potential future climate and bioclimate changes for wildlife areas in Washington State (USA) using five climate simulations for the 21st century from the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset run under the A2 greenhouse gases emissions scenario. These data were <span class="hlt">downscaled</span> to a 30-arc-second (~1-km) grid encompassing the state of Washington by calculating and interpolating future climate anomalies, and then applying the interpolated data to observed historical climate data. This climate data <span class="hlt">downscaling</span> technique (also referred to as the 'delta' method) is relatively simple and makes a number of assumptions that affect how the <span class="hlt">downscaled</span> data can be used and interpreted. We used the <span class="hlt">downscaled</span> climate data to calculate bioclimatic variables (e.g., growing degree days) that represent important physiological and environmental limits for Washington species and habitats of management concern. Multivariate descriptive plots and maps were used to evaluate the direction, magnitude, and spatial patterns of projected future climate and bioclimatic changes. The results indicate which managed areas experience the largest climate and bioclimatic changes under each of the potential future climate simulations. We discuss these changes while accounting for some of the limitations of our <span class="hlt">downscaling</span> technique and the uncertainties associated with using these <span class="hlt">downscaled</span> data for conservation and natural resource management applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1712011M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1712011M"><span id="translatedtitle">VALUE - A Framework to Validate <span class="hlt">Downscaling</span> Approaches for Climate Change Studies</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilke, Renate A. I.</p> <p>2015-04-01</p> <p>VALUE is an open European network to validate and compare <span class="hlt">downscaling</span> methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary <span class="hlt">downscaling</span> community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical <span class="hlt">downscaling</span> methods. Here, we present the key ingredients of this framework. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the <span class="hlt">downscaling</span> procedure where problems may occur: what is the isolated <span class="hlt">downscaling</span> skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open <span class="hlt">downscaling</span> intercomparison study, but is intended also to provide general guidance for other validation studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1043326','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/1043326"><span id="translatedtitle">Sub-daily Statistical <span class="hlt">Downscaling</span> of Meteorological Variables Using Neural Networks</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kumar, Jitendra; Brooks, Bjørn-Gustaf J.; Thornton, Peter E; Dietze, Michael</p> <p>2012-01-01</p> <p>A new open source neural network temporal <span class="hlt">downscaling</span> model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We <span class="hlt">downscaled</span> multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between <span class="hlt">downscaled</span> output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates that this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by <span class="hlt">downscaling</span> multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the <span class="hlt">downscaled</span> data as in the training data with probabilities that differed by no more than 6%. Our <span class="hlt">downscaling</span> approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JHyd..492....1N','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nasseri, M.; Tavakol-Davani, H.; Zahraie, B.</p> <p>2013-06-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016OcMod.100...20V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcMod.100...20V"><span id="translatedtitle"><span class="hlt">Downscaling</span> and extrapolating dynamic seasonal marine forecasts for coastal ocean users</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vanhatalo, Jarno; Hobday, Alistair J.; Little, L. Richard; Spillman, Claire M.</p> <p>2016-04-01</p> <p>Marine weather and climate forecasts are essential in planning strategies and activities on a range of temporal and spatial scales. However, seasonal dynamical forecast models, that provide forecasts in monthly scale, often have low offshore resolution and limited information for inshore coastal areas. Hence, there is increasing demand for methods capable of fine scale seasonal forecasts covering coastal waters. Here, we have developed a method to combine observational data with dynamical forecasts from POAMA (Predictive Ocean Atmosphere Model for Australia; Australian Bureau of Meteorology) in order to produce seasonal <span class="hlt">downscaled</span>, corrected forecasts, extrapolated to include inshore regions that POAMA does not cover. We demonstrate the method in forecasting the monthly sea surface temperature anomalies in the Great Australian Bight (GAB) region. The resolution of POAMA in the GAB is approximately 2° × 1° (lon. × lat.) and the resolution of our <span class="hlt">downscaled</span> forecast is approximately 1° × 0.25°. We use data and model hindcasts for the period 1994-2010 for forecast validation. The predictive performance of our statistical <span class="hlt">downscaling</span> model improves on the original POAMA forecast. Additionally, this statistical <span class="hlt">downscaling</span> model extrapolates forecasts to coastal regions not covered by POAMA and its forecasts are probabilistic which allows straightforward assessment of uncertainty in <span class="hlt">downscaling</span> and prediction. A range of marine users will benefit from access to <span class="hlt">downscaled</span> and nearshore forecasts at seasonal timescales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011TellA..63..158J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011TellA..63..158J"><span id="translatedtitle">Future climate impact on spruce bark beetle life cycle in relation to uncertainties in regional climate model data <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jönsson, Anna Maria; Bärring, Lars</p> <p>2011-01-01</p> <p>In this study, we quantify the effect of uncertainties in climate projections on an impact model (IPS) that describes the temperature-dependent swarming and development of Ips typographus. Three forcing climate data sets (<span class="hlt">ensembles</span>) were used: (1) E-Obs gridded observations, (2) ERA-40 reanalysis data <span class="hlt">downscaled</span> by eight regional climate models (RCMs) and (3) regional scenarios from one RCM forced by seven GCM simulations representing SRES-A1B, for the period of 1961-2097. The IPS_RCM_ERA40 <span class="hlt">ensemble</span> members, including IPS_RC3_ERA40, were generally within the IPS_E-Obs confidence limits. The IPS model is however sensitive to the warming during spring and cooling during autumn, and deviations in simulated swarming were related to known climate model biases. The variation between the IPS_RCA3_GCM <span class="hlt">ensemble</span> members was particularly high in regions where warmer summers (temperature increase from +2 °C to +4 °C) will induce an additional generation per year, for example a shift from one to two generations per year in south Scandinavia, and an increased frequency of three generations per year in central Europe. Impact assessments based on an <span class="hlt">ensemble</span> of climate data gives more robust decision support than a single climate model approach because it allows a probabilistic assessment of the geographical areas experiencing a transition in biological response.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007AGUFM.H23F1682V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007AGUFM.H23F1682V"><span id="translatedtitle">Verification of a <span class="hlt">Downscaling</span> Sequence Applied to Medium Range Meteorological Predictions for Global Flood Prediction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Voisin, N.; Wood, A. W.; Lettenmaier, D. P.</p> <p>2007-12-01</p> <p>We describe a prototype system for medium range (up to two week lead) flood prediction in large rivers, which is intended for global implementation - particularly in river basins having limited in situ meteorological observations. The procedure draws from the experimental North American Land Data Assimilation System (NLDAS) and the University of Washington West-wide Seasonal Hydrologic Forecast System for streamflow prediction. Meteorological forecasts based on a numerical weather prediction model serve both as the forcing for hydrologic model initialization and forecasts for lead times up to fifteen days. The hydrologic component of the system is the Variable Infiltration Capacity (VIC) macroscale hydrology model. In the prototype, VIC is spun up for forecast initialization using daily ERA-40 precipitation, wind, and surface air temperature. In hindcast mode, VIC is driven by global NCEP <span class="hlt">ensemble</span> 15-day re-forecasts (NOAA/ESRL) that are bias corrected with respect to ERA- 40 and spatially disaggregated using two higher spatial resolution satellite products: Global Precipitation Climatology Project (GPCP) 1DD daily precipitation and Tropical Rainfall Measuring System (TRMM) 3B42 precipitation are used to spatially disaggregate NCEP re-forecasts precipitation during the 15-day forecast period. The use of forecast models and satellite remote sensing data in this procedure reduces the need for in situ precipitation and other observations in parts of the world where surface networks are critically deficient, but where a global hydrologic forecast capability arguably would have the greatest value. The prototype system was implemented at one-half degree spatial resolution and tested during the 1979-August 2002 period. For the Mississippi R. Basin (where ample data for model evaluation exist) we evaluate the spatial disaggregation step in which observed precipitation products (NARR) are first aggregated to a coarser resolution (for the sole purpose of the evaluation) and then used in the spatial disaggregation step. The output of this procedure is compared to the original high resolution data. We also compare our disaggregation scheme with the analog technique of Hamill and Whitaker. Finally, we verify forecast error statistic¬s resulting from the application of the entire <span class="hlt">downscaling</span> sequence.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..14.8417C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..14.8417C"><span id="translatedtitle">Dynamically <span class="hlt">Downscaling</span> Precipitation from Extra-Tropical Cyclones</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Champion, A.; Hodges, K.; Bengtsson, L.</p> <p>2012-04-01</p> <p>Recent flooding events experienced by the UK and Western Europe have highlighted the potential disruption caused by precipitation associated with extra-tropical cyclones. The question as to the effect of a warming climate on these events also needs to be addressed to determine whether such events will become more frequent or more intense in the future. The changes in precipitation can be addressed through the use of Global Climate Models (GCMs), however the resolution of GCMs are often too coarse to drive hydrological models, required to investigate any flooding that may be associated with the precipitation. The changes to the precipitation associated with extra-tropical cyclones are investigated by tracking cyclones in two resolutions of the ECHAM5 GCM, T213 and T319 for 20th and 21st century climate simulations. It is shown that the intensity of extreme precipitation associated with extra-tropical cyclones is predicted to increase in a warmer climate at both resolutions. It was also found that the increase in resolution shows an increase in the number of extreme events for several fields, including precipitation; however it is also seen that the magnitude of the response is not uniform across the seasons. The tails of the distributions are investigated using Extreme Value Theory (EVT) using a Generalised Pareto Distribution (GPD) with a Peaks over Threshold (POT) method, calculating return periods for given return levels. From the cyclones identified in the T213 resolution of the GCM a small number of cyclones were selected that pass over the UK, travelling from the South-West to the North-East. These are cyclones that are more likely to have large amounts of moisture associated with them and therefore potentially being associated with large precipitation intensities. Four cyclones from each climate were then selected to drive a Limited Area Model (LAM), to gain a more realistic representation of the precipitation associated with each extra-tropical cyclone. The suitability of the LAM for <span class="hlt">downscaling</span> was evaluated by running the LAM for the events of June and July 2007 (UK floods) and comparing the output to observations. The results from this comparison provide confidence that the model is able of reproducing realistic intensities for extreme precipitation events. Whilst this method does not allow for a robust comparison between the climates it does for allow for an analysis of the method, and whether dynamically <span class="hlt">downscaling</span> individual events is suitable. It was found that by nesting the LAM within the GCM, large increases in the precipitation intensities were seen, as well as gaining a greater temporal resolution. Analysis of more events will allow a more robust comparison between climates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A23F0373F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A23F0373F"><span id="translatedtitle">Improving dynamical <span class="hlt">downscaling</span> of thunderstorms in New England</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frediani, M. E.; Anagnostou, E. N.; Hopson, T. M.; Hacker, J.</p> <p>2013-12-01</p> <p>This study aims to quantify the variability of wind speed and precipitation during summer storms events in New England by using standard verification metrics along with the Method For Object-Based Diagnostic Evaluation technique (MODE). Using WRF-ARW to dynamically <span class="hlt">downscale</span> a set of storm events, the first approach investigates potential errors propagated from global analysis products used as initial and boundary conditions. The second approach evaluates the significance of applying a topographic wind parametrization scheme in order to obtain more realistic wind speeds. This fundamental study is born out of the necessity of developing a model for power outage prediction caused by severe storms. In New England, a densely forested region of the US, severe winds and precipitation are key weather factors that cause vulnerability in the power grid infrastructure. During storms, trees are uprooted and branches break, resulting in significant interruptions to electricity distribution. The power outage prediction framework utilizes simulated values of meteorological parameters from storms that have caused outages in the past; and the geographic coordinates of the trouble spots recorded by local utilities during these storms. These two components are used as input for a generalized multi-linear regression that estimate the coefficients for these meteorological parameters, which are then applied to weather forecasts of potential hazardous events, providing an estimate of the number and spatial distribution of power outages over the region for the approaching weather system. Given that the count and location of the predicted outages rely on the weather description of past events, the accuracy of spatial patterns and intensity of meteorological fields are crucial to developing an unbiased database for the regression. With that in mind, it is important to quantify the influence that a particular global analysis product can impose to the dynamical <span class="hlt">downscaling</span> of precipitation and wind speed over the studied region. Additionally, a topographic wind parametrization scheme that includes enhanced drag coefficients and steep terrain corrections is used to quantify potential improvements in the wind speed fields over New England terrain. The comparisons are performed using standard verification metrics, along with the MODE object-based verification technique. The latter technique offering advantages over traditional approaches because it considers structural attributes of distributed events (area, centroid, axis angle, and intensity) instead of strictly point-wise comparisons, which are the main interest of our study into regionally-distributed likelihoods of power failure.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.4176H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.4176H"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> Based on Spartan Spatial Random Fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hristopulos, Dionissios</p> <p>2010-05-01</p> <p>Stochastic methods of space-time interpolation and conditional simulation have been used in statistical <span class="hlt">downscaling</span> approaches to increase the resolution of measured fields. One of the popular interpolation methods in geostatistics is kriging, also known as optimal interpolation in data assimilation. Kriging is a stochastic, linear interpolator which incorporates time/space variability by means of the variogram function. However, estimation of the variogram from data involves various assumptions and simplifications. At the same time, the high numerical complexity of kriging makes it difficult to use for very large data sets. We present a different approach based on the so-called Spartan Spatial Random Fields (SSRFs). SSRFs were motivated from classical field theories of statistical physics [1]. The SSRFs provide a different approach of parametrizing spatial dependence based on 'effective interactions,' which can be formulated based on general statistical principles or even incorporate physical constraints. This framework leads to a broad family of covariance functions [2], and it provides new perspectives in covariance parameter estimation and interpolation [3]. A significant advantage offered by SSRFs is reduced numerical complexity, which can lead to much faster codes for spatial interpolation and conditional simulation. In addition, on grids composed of rectangular cells, the SSRF representation leads to an explicit expression for the precision matrix (the inverse covariance). Therefore SSRFs could provide useful models of error covariance for data assimilation methods. We use simulated and real data to demonstrate SSRF properties and <span class="hlt">downscaled</span> fields. keywords: interpolation, conditional simulation, precision matrix References [1] Hristopulos, D.T., 2003. Spartan Gibbs random field models for geostatistical applications, SIAM Journal in Scientific Computation, 24, 2125-2162. [2] Hristopulos, D.T., Elogne, S. N. 2007. Analytic properties and covariance functions of a new class of generalized Gibbs random fields, IEEE Transactions on Information Theory, 53(12), 4667-4679. [3] Hristopulos, D.T., Elogne, S. N. 2009. Computationally efficient spatial interpolators based on Spartan Spatial random fields, IEEE Transactions on Signal Processing, 57(9), 3475-3487.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H31F1248S','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Stampoulis, D.; Haddad, Z. S.; Anagnostou, E. N.</p> <p>2013-12-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4552S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4552S"><span id="translatedtitle">Estimating climate change for Southeast Europe: a dynamical <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sotiropoulou, Rafaella-Eleni P.; Tagaris, Efthimios; Sotiropoulos, Andreas; Spanos, Ioannis; Milonas, Panagiotis; Michaelakis, Antonios</p> <p>2015-04-01</p> <p>Mediterranean region is considered to be the most prominent climate response Hot-Spot since it is located in a transition zone between the arid climate of northern Africa and the wet climate of central Europe. Even a minor change in large scale climatic factors might impose large impacts on the climatic conditions of different Mediterranean areas. Furthermore, the complex topography and the vast coastlines suggest a fine scale spatial variability of the climatic conditions. Because of these, there is an increasing interest for this area. The objective of this study is to estimate the changes in climatic parameters (such as temperature and precipitation) over southeast Europe in the near future at a very fine grid resolution. The NASA GISS GCM ModelE is used to simulate current and future climate at a horizontal resolution of 2° × 2.5° latitude by longitude. The model accounts for both the seasonal and the diurnal solar cycles in its temperature calculations. It simulates the emissions, transport, chemical transformation and deposition of several chemical tracers. Sea surface temperatures (SST) are calculated using model-derived surface energy fluxes and specified ocean heat transports. The simulations cover the period from 1880 to 2061. Greenhouse gas concentrations up to 2008 are prescribed using ice-core measurements, while for the period 2009-2061 the GHG levels are supplied from the IPCC A1B emissions scenario. Since the outputs from the GCM are relatively coarse for applications to regional and local scales, the Weather Research and Forecasting (WRF version 3.4.1) model is used to dynamically <span class="hlt">downscale</span> GCM simulations. The domain covers the south - southeast Europe in 273 x 161 horizontal grids of 9 km x 9 km, with 28 vertical layers. Because of the time needed for the <span class="hlt">downscaling</span> procedure meteorological conditions are presented, here, for five current (i.e., 2008 - 2012) and five future (i.e., 2058-2062) years. Annual temperature is estimated to be higher in the future all over the domain. Annual precipitation is estimated to be lower in the major part of the land at the south east and south west of the domain. Seasonal analysis suggests that precipitation change varies locally. Acknowledgement: This work was supported by the EU co-funded LIFE-CONOPS project through grand agreement LIFE12 ENV/GR/000466.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..1213118B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..1213118B"><span id="translatedtitle">Long-range Prediction of climatic Change in the Eastern Seaboard of Thailand over the 21st Century using various <span class="hlt">Downscaling</span> Approaches</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bejranonda, Werapol; Koch, Manfred; Koontanakulvong, Sucharit</p> <p>2010-05-01</p> <p>Triggered by a long drought, a huge water supply crisis took place at the Eastern Seaboard of Thailand (east of the Gulf of Thailand) in 2005. In that year no rainfall occurred for four months after the beginning of the rainy season which led to the situation that the industrial estates of the Eastern Seaboard were not able to fully operate. Normally, most of the urban and industrial water used in this coastal region along east of the Gulf of Thailand, which is part of the Pacific Ocean, is surface water stored in a large-scale reservoir-network across the main watershed in the region. Thus the three major reservoirs usually gather water from monsoon storms that blow from the South and provide accumulative 80% of the annual rainfall during the 6 months of the rainy season which normally lasts from May-October. During the dry season (November - April) the winds are blowing from northern Indo-China land mass and rain drops only a few days in a month. Because of this typical tropical climate system, surface water resources across most of the southeastern Asia-Pacific region and the Eastern Seaboard of Thailand, in particular, rely on the annual occurrence of the monsoon season. There is now sufficient evidence that the named extreme weather conditions of 2005 occurring in that part of Thailand are not a singularity, but might be another signal of recent ongoing climate change in that country as a whole. Because of this imminent threat to the water resources of the region, and for the set-up of appropriate water management schemes for the near future, a climate impact study is proposed here. More specifically, the water budget of the Khlong Yai basin, which is the main watershed of the Eastern Seaboard, is modeled using the distributed hydrological model SWAT. To that avail the watershed model is coupled sequentially to a global climate model (GCM), whereby the latter provides the input forcing parameters (e.g. precipitation and temperature) to the former. Because of the different scales of the hydrological (local to regional) and of the GCM (global), one is faced with the problem of '<span class="hlt">downscaling</span>' the coarse grid resolution output of the GCM to the fine grid of the hydrological model. Although there have been numerous <span class="hlt">downscaling</span> approaches proposed to that regard over the last decade, the jury is still out about the best method to use in a particular application. The focus here is on the <span class="hlt">downscaling</span> part of the investigation, i.e. the proper preparation of the GCM's output to serve as input, i.e. the driving force, to the hydrological model (which is not further discussed here). Daily <span class="hlt">ensembles</span> of climate variables computed by means of the CGCM3 model of the Canadian Climate Center which has a horizontal grid resolution of approximately the size of the whole study basin are used here, indicating clearly the need for <span class="hlt">downscaling</span>. Daily observations of local climate variables available since 1971 are used as additional input to the various <span class="hlt">downscaling</span> tools proposed which are, namely, the stochastic weather generator (LARS-WG), the statistical <span class="hlt">downscaling</span> model (SDSM), and a multiple linear regression model between the observed variables and the CGCM3 predictors. Both the 2D and the 3D versions of the CGCM3 model are employed to predict, 100 years ahead up to year 2100, the monthly rainfall and temperatures, based on the past calibration period (training period) 1971-2000. To investigate the prediction performance, multiple linear regression, autoregressive (AR) and autoregressive integrated moving average (ARIMA) models are applied to the time series of the observation data which are aggregated into monthly time steps to be able compare them with the <span class="hlt">downscaling</span> results above. Likewise, multiple linear regression and ARIMA models also executed on the CGCM3 predictors and the Pacific / Indian oceans indices as external regressors to predict short-term local climate variations. The results of the various <span class="hlt">downscaling</span> method are validated for years 2001-2006 at selected meteorological stations in the Khlong Yai basin, assuming the IPCC's A1B and A2 emission scenarios. The performance of the monthly climate prediction has been evaluated by comparison with observed data using the Nash-Sutcliffe model efficiency measure. Among the statistical/stochastical <span class="hlt">downscaling</span> and the forecasting methods used, the climate prediction by the ARIMA model with ocean indices and CGCM predictor included as external regressors are the most reliable. Thus for the verification period 2001-2006 Nash-Sutcliffe coefficient of 0.84, 0.47 and 0.50 are obtained for the minimum and maximum temperatures and the rainfall, respectively, whereas the corresponding 1-year ahead predictions are 0.77, 0.43 and 0.48, respectively. The best external regressor for the prediction of the minimum temperatures in the basin is, surprisingly, the El Niño 1.2 SST anomaly time series; for the prediction of the maximum temperatures, the minimum surface air temperature predictor in CGCM3 (tasmin); and for the prediction of the rainfall, the 850hPa eastward-wind predictor in CGCM3 (p8_uas). Based on year 2000, the <span class="hlt">downscaling</span> results show that the average minimum temperature will be higher by 0.4 to 5.9 ° C by year 2100, while the average maximum temperature will be rather stable, with only little change between -0.2 to +0.3° C. As for the rainfall at year 2100, a possible change from +0.2 to 12.0 mm/month is obtained. These climate prediction results mean that, although there will be more rainfall in the future, the much higher temperature will lead to more evapotranspiration, i.e. more agricultural water demand. Besides, the increasing rainfall will most likely lead to unexpected flood events in the future that will require precautionary planning at the watershed-scale. This will be further analyzed during the course of the ongoing study using the SWAT hydrological model mentioned above.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMIN33A3761A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMIN33A3761A"><span id="translatedtitle">A Modified <span class="hlt">Ensemble</span> Framework for Drought Estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alobaidi, M. H.; Marpu, P. R.; Ouarda, T.</p> <p>2014-12-01</p> <p>Drought estimation at ungauged sites is a difficult task due to various challenges such as scale and limited availability and information about hydrologic neighborhoods. <span class="hlt">Ensemble</span> regression has been recently utilized in modeling various hydrologic systems and showed advantage over classical regression approaches to such studies. A challenging task in <span class="hlt">ensemble</span> modeling is the proper training of the <span class="hlt">ensemble</span>'s individual learners and the <span class="hlt">ensemble</span> combiners. In this work, an <span class="hlt">ensemble</span> framework is proposed to enhance the generalization ability of the sub-<span class="hlt">ensemble</span> models and its combiner. Information mixtures between the subsamples are introduced. Such measure is dedicated to the <span class="hlt">ensemble</span> members and <span class="hlt">ensemble</span> combiners. Controlled homogeneity magnitudes are then stimulated and induced in the proposed model via a two-stage resampling algorithm. Artificial neural networks (ANNs) were used as <span class="hlt">ensemble</span> members in addition to different <span class="hlt">ensemble</span> integration plans. The model provided superior results when compared to previous models applied to the case study in this work. The root mean squared error (RMSE) in the testing phase for the drought quantiles improved by 67% - 76%. The bias error (BIAS) also showed 61% - 95% improvement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3226070','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3226070"><span id="translatedtitle">A Statistical Description of Neural <span class="hlt">Ensemble</span> Dynamics</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Long, John D.; Carmena, Jose M.</p> <p>2011-01-01</p> <p>The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an <span class="hlt">ensemble</span>. Inferring these connections is often intractable because the set of possible interactions grows exponentially with <span class="hlt">ensemble</span> size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and <span class="hlt">ensemble</span> data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the <span class="hlt">ensemble</span> toward analyzing changes in the dynamics of the <span class="hlt">ensemble</span> as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of <span class="hlt">ensemble</span> data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available <span class="hlt">ensemble</span> data, and use an adaptive quantization technique to aggregate poorly estimated regions of the <span class="hlt">ensemble</span> data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and <span class="hlt">ensemble</span> sizes. Lastly, the performance of this method on both simulated and real <span class="hlt">ensemble</span> data is used to demonstrate its utility. PMID:22319486</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/15020771','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/15020771"><span id="translatedtitle">Changes in Seasonal and Extreme Hydrologic Conditions of the Georgia Basin/Puget Sound in an <span class="hlt">Ensemble</span> Regional Climate Simulation for the Mid-Century</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Leung, Lai R.; Qian, Yun</p> <p>2003-12-15</p> <p>This study examines an <span class="hlt">ensemble</span> of climate change projections simulated by a global climate model (GCM) and <span class="hlt">downscaled</span> with a region climate model (RCM) to 40 km spatial resolution for the western North America. One control and three <span class="hlt">ensemble</span> future climate simulations were produced by the GCM following a business as usual scenario for greenhouse gases and aerosols emissions from 1995 to 2100. The RCM was used to <span class="hlt">downscale</span> the GCM control simulation (1995-2015) and each <span class="hlt">ensemble</span> future GCM climate (2040-2060) simulation. Analyses of the regional climate simulations for the Georgia Basin/Puget Sound showed a warming of 1.5-2oC and statistically insignificant changes in precipitation by the mid-century. Climate change has large impacts on snowpack (about 50% reduction) but relatively smaller impacts on the total runoff for the basin as a whole. However, climate change can strongly affect small watersheds such as those located in the transient snow zone, causing a higher likelihood of winter flooding as a higher percentage of precipitation falls in the form of rain rather than snow, and reduced streamflow in early summer. In addition, there are large changes in the monthly total runoff above the upper 1% threshold (or flood volume) from October through May, and the December flood volume of the future climate is 60% above the maximum monthly flood volume of the control climate. Uncertainty of the climate change projections, as characterized by the spread among the <span class="hlt">ensemble</span> future climate simulations, is relatively small for the basin mean snowpack and runoff, but increases in smaller watersheds, especially in the transient snow zone, and associated with extreme events. This emphasizes the importance of characterizing uncertainty through <span class="hlt">ensemble</span> simulations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110011613','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110011613"><span id="translatedtitle"><span class="hlt">Downscaling</span> NASA Climatological Data to Produce Detailed Climate Zone Maps</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chandler, William S.; Hoell, James M.; Westberg, David J.; Whitlock, Charles H.; Zhang, Taiping; Stackhouse, P. W.</p> <p>2011-01-01</p> <p>The design of energy efficient sustainable buildings is heavily dependent on accurate long-term and near real-time local weather data. To varying degrees the current meteorological networks over the globe have been used to provide these data albeit often from sites far removed from the desired location. The national need is for access to weather and solar resource data accurate enough to use to develop preliminary building designs within a short proposal time limit, usually within 60 days. The NASA Prediction Of Worldwide Energy Resource (POWER) project was established by NASA to provide industry friendly access to globally distributed solar and meteorological data. As a result, the POWER web site (power.larc.nasa.gov) now provides global information on many renewable energy parameters and several buildings-related items but at a relatively coarse resolution. This paper describes a method of <span class="hlt">downscaling</span> NASA atmospheric assimilation model results to higher resolution and maps those parameters to produce building climate zone maps using estimates of temperature and precipitation. The distribution of climate zones for North America with an emphasis on the Pacific Northwest for just one year shows very good correspondence to the currently defined distribution. The method has the potential to provide a consistent procedure for deriving climate zone information on a global basis that can be assessed for variability and updated more regularly.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140006517','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140006517"><span id="translatedtitle"><span class="hlt">Downscaling</span> MODIS Land Surface Temperature for Urban Public Health Applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice, Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel</p> <p>2013-01-01</p> <p>This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for <span class="hlt">downscaling</span> the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014CSR....87....7B','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bricheno, Lucy M.; Wolf, Judith M.; Brown, Jennifer M.</p> <p>2014-09-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24988779','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24988779"><span id="translatedtitle"><span class="hlt">Downscaling</span> the environmental associations and spatial patterns of species richness.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Keil, Petr; Jetz, Walter</p> <p>2014-06-01</p> <p>We introduce a method that enables the estimation of species richness environment association and prediction of geographic patterns of species richness at grains finer than the original grain of observation. The method is based on a hierarchical model that uses coarse-grain values of species richness and fine-grain environmental data as input. In the model, the (unobserved) fine-grain species richness is linked to the observed fine-grain environment and upscaled using a simple species-area relationship (SAR). The upscaled values are then stochastically linked to the observed coarse-grain species richness. We tested the method on Southern African Bird Atlas data by <span class="hlt">downscaling</span> richness from 2 degrees to 0.25 degrees (-250 km to -30 km) resolution. When prior knowledge of the SAR slope (average species turnover within coarse-grain cells) was available, the method predicted the fine-grain relationship between richness and environment and provided fine-grain predictions of richness that closely resembled results from native fine-grain models. Without the SAR knowledge the method still accurately quantified the richness-environment relationship, but accurately predicted only relative (rank) values of richness. The approach can be easily extended and it is a powerful path for cross-scale statistical modeling of richness-environment relationships, and for the provision of high-resolution maps for basic science and conservation. PMID:24988779</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC53A1041A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC53A1041A"><span id="translatedtitle"><span class="hlt">Downscaling</span> MODIS Land Surface Temperature for Urban Public Health Applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G., Jr.; Estes, S. M.; Quattrochi, D. A.; Johnson, D.</p> <p>2013-12-01</p> <p>This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heat-related mortality data. The current HWWS do not take into account intra-urban spatial variations in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with land surface temperature (LST) estimates derived from thermal remote sensing data. In order to further improve the assessment of intra-urban variations in risk from extreme heat, we developed and evaluated a number of spatial statistical techniques for <span class="hlt">downscaling</span> the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. We will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JPRS...66..337P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JPRS...66..337P"><span id="translatedtitle">Image fusion by spatially adaptive filtering using <span class="hlt">downscaling</span> cokriging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pardo-Iguzquiza, E.; Rodríguez-Galiano, V. F.; Chica-Olmo, M.; Atkinson, Peter M.</p> <p></p> <p>The aim of this paper was to extend the method of <span class="hlt">downscaling</span> cokriging for image fusion by making the method spatially adaptive in that the filter parameters (cokriging weights) can change across the image. The method can adapt itself to the usual statistical non-homogeneity (spatially variable mean, variance and correlation length) of a satellite sensor image that covers an area with different spatial patterns of geographical objects or different terrain types. The solution adopted was to estimate the models of covariances and cross-covariances (or semivariograms and cross-semivariograms) by the same procedure as described in Pardo-Iguzquiza et al. (2006) but with the method applied locally instead of globally. The correct implementation of this local estimation is the key for computational feasibility and prediction efficiency. Two parameters to be taken into account are the grid of locations on which a moving window is centred (local modelling is performed inside this window) and the size of this moving window. With respect to the latter parameter, there is a trade-off between a size small enough to make the procedure locally adaptive and large enough to produce reliable statistical estimates. The computational burden will impose limits to the distance between grid points on which the local moving window is centred. A case study with Landsat ETM+ images was used to show the implementation of the method and the result was evaluated using several statistics widely used for assessing the quality of a fused image, apart from its visual appearance.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A34E..03S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A34E..03S"><span id="translatedtitle">Mid-Century Warming in the Los Angeles Region and its Uncertainty using Dynamical and Statistical <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sun, F.; Hall, A. D.; Walton, D.; Capps, S. B.; Qu, X.; Huang, H. J.; Berg, N.; Jousse, A.; Schwartz, M.; Nakamura, M.; Cerezo-Mota, R.</p> <p>2012-12-01</p> <p>Using a combination of dynamical and statistical <span class="hlt">downscaling</span> techniques, we projected mid-21st century warming in the Los Angeles region at 2-km resolution. To account for uncertainty associated with the trajectory of future greenhouse gas emissions, we examined projections for both "business-as-usual" (RCP8.5) and "mitigation" (RCP2.6) emissions scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5). To account for the considerable uncertainty associated with choice of global climate model, we <span class="hlt">downscaled</span> results for all available global climate models in CMIP5. For the business-as-usual scenario, we find that by the mid-21st century, the most likely warming is roughly 2.6°C averaged over the region's land areas, with a 95% confidence that the warming lies between 0.9 and 4.2°C. The high resolution of the projections reveals a pronounced spatial pattern in the warming: High elevations and inland areas separated from the coast by at least one mountain complex warm 20 to 50% more than the areas near the coast or within the Los Angeles basin. This warming pattern is especially apparent in summertime. The summertime warming contrast between the inland and coastal zones has a large effect on the most likely expected number of extremely hot days per year. Coastal locations and areas within the Los Angeles basin see roughly two to three times the number of extremely hot days, while high elevations and inland areas typically experience approximately three to five times the number of extremely hot days. Under the mitigation emissions scenario, the most likely warming and increase in heat extremes are somewhat smaller. However, the majority of the warming seen in the business-as-usual scenario still occurs at all locations in the most likely case under the mitigation scenario, and heat extremes still increase significantly. This warming study is the first part of a series studies of our project. More climate change impacts on the Santa Ana wind, rainfall, snowfall and snowmelt, cloud and surface hydrology are forthcoming and could be found in www.atmos.ucla.edu/csrl.he <span class="hlt">ensemble</span>-mean, annual-mean surface air temperature change and its uncertainty from the available CMIP5 GCMs under the RCP8.5 (left) and RCP2.6 (right) emissions scenarios, unit: °C.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy..tmp..302S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy..tmp..302S"><span id="translatedtitle">Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sohn, Soo-Jin; Tam, Chi-Yung</p> <p>2015-07-01</p> <p>Capturing climatic variations in boreal winter to spring (December-May) is essential for properly predicting droughts in South Korea. This study investigates the variability and predictability of the South Korean climate during this extended season, based on observations from 60 station locations and multi-model <span class="hlt">ensemble</span> (MME) hindcast experiments (1983/1984-2005/2006) archived at the APEC Climate Center (APCC). Multivariate empirical orthogonal function (EOF) analysis results based on observations show that the first two leading modes of winter-to-spring precipitation and temperature variability, which together account for ~80 % of the total variance, are characterized by regional-scale anomalies covering the whole South Korean territory. These modes were also closely related to some of the recurrent large-scale circulation changes in the northern hemisphere during the same season. Consistent with the above, examination of the standardized precipitation evapotranspiration index (SPEI) indicates that drought conditions in South Korea tend to be accompanied by regional-to-continental-scale circulation anomalies over East Asia to the western north Pacific. Motivated by the aforementioned findings on the spatial-temporal coherence among station-scale precipitation and temperature anomalies, a new bivariate and pattern-based <span class="hlt">downscaling</span> method was developed. The novelty of this method is that precipitation and temperature data were first filtered using multivariate EOFs to enhance their spatial-temporal coherence, before being linked to large-scale circulation variables using canonical correlation analysis (CCA). To test its applicability and to investigate its related potential predictability, a perfect empirical model was first constructed with observed datasets as predictors. Next, a model output statistics (MOS)-type hybrid dynamical-statistical model was developed, using products from nine one-tier climate models as inputs. It was found that, with model sea-level pressure (SLP) and 500 hPa geopotential height (Z500) as predictors, statistically <span class="hlt">downscaled</span> MME (DMME) precipitation and temperature predictions were substantially improved compared to those based on raw MME outputs. Limitations and possible causes of error of such a dynamical-statistical model, in the current framework of dynamical seasonal climate predictions, were also discussed. Finally, the method was used to construct a dynamical-statistical system for 6 month-lead drought predictions for 60 stations in South Korea. DMME was found to give reasonably skillful long-lead forecasts of SPEI for winter to spring. Moreover, DMME-based products clearly outperform the raw MME predictions, especially during extreme wet years. Our results could lead to more reliable climatic extreme predictions for policymakers and stakeholders in the water management sector, and for better mitigation and climate adaptations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1614240H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1614240H"><span id="translatedtitle">Multivariate <span class="hlt">Ensemble</span> Sensitivity with Localization</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hacker, Joshua; Lei, Lili</p> <p>2014-05-01</p> <p>So far in the literature, covariance localization (tapering) has not been applied when performing <span class="hlt">ensemble</span> sensitivity analysis. Sampling error in computing the sensitivities via lagged covariances leads to an over-estimation of the impact of a perturbation. Most commonly when computing sensitivities, the analysis covariance is approximated with the corresponding diagonal matrix. Two consequences follow: (1) the multi-variate sensitivity is approximated by a univariate sensitivity, and (2) sampling error in off-diagonal elements are obviated. It is unknown, however, how much information is lost by ignoring the off-diagonal elements in the full covariance. When forecasts depend on many details of the previous analysis, it is reasonable to expect that the diagonal approximation is too severe. The purpose of this presentation is to clarify the effects of the diagonal approximation, and investigate the need for localization when off-diagonal elements are considered. Motivated by examples arising from sensitivities estimated within a cycling mesoscale <span class="hlt">ensemble</span> data assimilation system, for easier interpretation we turn to the two-scale model first presented by Lorenz in 2005. We show that for most problems, an efficient matrix inversion is possible by finding a minimum-norm solution, and employing appropriate matrix factorization. Comparing the full inversion with off-diagonal elements, the fine-scale sensitivity estimates can be substantially different from those arising when the diagonal approximation is used. Localization on the sensitivity can be handled by an off-line empirical or Bayesian estimation technique. Because the sensitivity estimated from the full inversion is subject to sampling error, it is sensitive to the localization. The results show that compared to typical practices, more complete <span class="hlt">ensemble</span> sensitivity formulations may be needed to draw robust inferences in general.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/19820026226','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19820026226"><span id="translatedtitle"><span class="hlt">Ensemble</span> averaging of acoustic data</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Stefanski, P. K.</p> <p>1982-01-01</p> <p>A computer program called <span class="hlt">Ensemble</span> Averaging of Acoustic Data is documented. The program samples analog data, analyzes the data, and displays them in the time and frequency domains. Hard copies of the displays are the program's output. The documentation includes a description of the program and detailed user instructions for the program. This software was developed for use on the Ames 40- by 80-Foot Wind Tunnel's Dynamic Analysis System consisting of a PDP-11/45 computer, two RK05 disk drives, a tektronix 611 keyboard/display terminal, and FPE-4 Fourier Processing Element, and an analog-to-digital converter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20130001779&hterms=Kalman&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DKalman','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20130001779&hterms=Kalman&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3DKalman"><span id="translatedtitle">A Localized <span class="hlt">Ensemble</span> Kalman Smoother</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Butala, Mark D.</p> <p>2012-01-01</p> <p>Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized <span class="hlt">ensemble</span> Kalman smoother.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009LNCS.5856..481V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009LNCS.5856..481V"><span id="translatedtitle">Clustering <span class="hlt">Ensemble</span> Method for Heterogeneous Partitions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vega-Pons, Sandro; Ruiz-Shulcloper, José</p> <p></p> <p>Cluster <span class="hlt">ensemble</span> is a promising technique for improving the clustering results. An alternative to generate the cluster <span class="hlt">ensemble</span> is to use different representations of the data and different similarity measures between objects. This way, it is produced a cluster <span class="hlt">ensemble</span> conformed by heterogeneous partitions obtained with different point of views of the faced problem. This diversity enhances the cluster <span class="hlt">ensemble</span> but, it restricts the combination process since it makes difficult the use of the original data. In this paper, in order to solve these limitations, we propose a unified representation of the objects taking into account the whole information in the cluster <span class="hlt">ensemble</span>. This representation allows working with the original data of the problem regardless of the used generation mechanism. Also, this new representation is embedded in the WKF [1] algorithm making a more robust cluster <span class="hlt">ensemble</span> method. Experimental results with numerical, categorical and mixed datasets show the accuracy of the proposed method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1614315G','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guyennon, Nicolas; Romano, Emanuele; Mariani, Davide; Bruna Petrangeli, Anna; Portoghese, Ivan</p> <p>2014-05-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..12.8505P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..12.8505P"><span id="translatedtitle">Evaluation of soil moisture <span class="hlt">downscaling</span> using a simple thermal based proxy - the REMEDHUS network (Spain) example</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, J.; Niesel, J.; Loew, A.</p> <p>2015-08-01</p> <p>Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution in the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought predication. Therefore, various <span class="hlt">downscaling</span> methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple Vegetation Temperature Condition Index (VTCI) <span class="hlt">downscaling</span> scheme over a dense soil moisture observational network (REMEDHUS) in Spain. Firstly, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography and land cover heterogeneity, using data from MODIS and MSG SEVIRI. Then the <span class="hlt">downscaling</span> scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the <span class="hlt">downscaling</span> method can significantly improve the spatial details of CCI soil moisture while maintain the accuracy of CCI soil moisture. The accuracy level is comparable to other <span class="hlt">downscaling</span> methods that were also validated against REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying geostationary satellite for <span class="hlt">downscaling</span> soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI <span class="hlt">downscaling</span> method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1053X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1053X"><span id="translatedtitle">A Dynamical <span class="hlt">Downscaling</span> Approach with GCM Bias Corrections and Spectral Nudging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xu, Z.; Yang, Z.</p> <p>2013-12-01</p> <p>To reduce the biases in the regional climate <span class="hlt">downscaling</span> simulations, a dynamical <span class="hlt">downscaling</span> approach with GCM bias corrections and spectral nudging is developed and assessed over North America. Regional climate simulations are performed with the Weather Research and Forecasting (WRF) model embedded in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). To reduce the GCM biases, the GCM climatological means and the variances of interannual variations are adjusted based on the National Centers for Environmental Prediction-NCAR global reanalysis products (NNRP) before using them to drive WRF which is the same as our previous method. In this study, we further introduce spectral nudging to reduce the RCM-based biases. Two sets of WRF experiments are performed with and without spectral nudging. All WRF experiments are identical except that the initial and lateral boundary conditions are derived from the NNRP, the original GCM output, and the bias corrected GCM output, respectively. The GCM-driven RCM simulations with bias corrections and spectral nudging (IDDng) are compared with those without spectral nudging (IDD) and North American Regional Reanalysis (NARR) data to assess the additional reduction in RCM biases relative to the IDD approach. The results show that the spectral nudging introduces the effect of GCM bias correction into the RCM domain, thereby minimizing the climate drift resulting from the RCM biases. The GCM bias corrections and spectral nudging significantly improve the <span class="hlt">downscaled</span> mean climate and extreme temperature simulations. Our results suggest that both GCM bias corrections or spectral nudging are necessary to reduce the error of <span class="hlt">downscaled</span> climate. Only one of them does not guarantee better <span class="hlt">downscaling</span> simulation. The new dynamical <span class="hlt">downscaling</span> method can be applied to regional projection of future climate or <span class="hlt">downscaling</span> of GCM sensitivity simulations. Annual mean RMSEs. The RMSEs are computed over the verification region by monthly mean data over 1981-2010. Experimental design</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007PhyA..376..293C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007PhyA..376..293C"><span id="translatedtitle">Thermal statistical <span class="hlt">ensembles</span> of black holes</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chevalier, C.; Bustamante, M.; Debbasch, F.</p> <p>2007-03-01</p> <p>We consider statistical <span class="hlt">ensembles</span> of Schwarzschild black holes and prove that these <span class="hlt">ensembles</span> describe black holes of nonvanishing temperatures. The mean space times associated to these <span class="hlt">ensembles</span> are explored through exact computations of their energy distributions, total masses and calorific capacities. We discuss our results, with special emphasis on their connections with current and near future observations of astrophysical black holes, string theory and cosmology.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JMP....54h3507D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JMP....54h3507D"><span id="translatedtitle">The beta-Wishart <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dubbs, Alexander; Edelman, Alan; Koev, Plamen; Venkataramana, Praveen</p> <p>2013-08-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/5206885','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/5206885"><span id="translatedtitle">Forecast of iceberg <span class="hlt">ensemble</span> drift</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>El-Tahan, M.S.; El-Tahan, H.W.; Venkatesh, S.</p> <p>1983-05-01</p> <p>The objectives of the study are to gain a better understanding of the characteristics of iceberg motion and the factors controlling iceberg drift, and to develop an iceberg <span class="hlt">ensemble</span> drift forecast system to be operated by the Canadian Atmospheric Environment Service. An extensive review of field and theoretical studies on iceberg behaviour, and the factors controlling iceberg motion has been carried out. Long term and short term behaviour of icebergs are critically examined. A quantitative assessment of the effects of the factors controlling iceberg motion is presented. The study indicated that wind and currents are the primary driving forces. Coriolis Force and ocean surface slope also have significant effects. As for waves, only the higher waves have a significant effect. Iceberg drift is also affected by iceberg size characteristics. Based on the findings of the study a comprehensive computerized forecast system to predict the drift of iceberg <span class="hlt">ensembles</span> off Canada's east coast has been designed. The expected accuracy of the forecast system is discussed and recommendations are made for future improvements to the system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMGC11B1004B','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barsugli, J. J.; Guentchev, G.</p> <p>2012-12-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1410892K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1410892K"><span id="translatedtitle">Precipitation <span class="hlt">downscaling</span> and spatial trends in the Pamirs</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Knoche, M.; Ebert, C.; Rödiger, T.; Siebert, C.; Geyer, S.; Weise, S.; Gloaguen, R.; Merz, R.</p> <p>2012-04-01</p> <p>In the Tajik Pamirs in an area of 100,000 km2 only 20 meteorological stations exist which provide precipitation time series for more than 20 years. The access to the recorded data appears to be difficult, especially for recent data. The Pamirs span over altitudes from 2000 m to 7500m a.s.l. with an average altitude between 3500 and 4000m. Only the meteorological stations at the Fedchenko Glacier (4100m) and on the Akbaital Pass (4400m) are located on more or less exposed places. All other stations are located in valleys, between 2000 and 3800m. For precipitation interpolation and analyses, measurements between 3800m and 7000m are missing. In addition, it is difficult to outline orographic effects, because meteorological stations in the western Pamirs (where precipitation is highest) are located down in the valleys, while stations in the eastern Pamirs (where precipitation is lowest) are located on the plateau. As a result, a trend with decreasing rainfall from west to east misleadingly shows up as a reverse altitude effect. We use satellite-based snow cover data as qualitative indicator for precipitation regions which indicate, that a reverse altitude effect does not exist. Remote sensing precipitation datasets are available in spatial resolutions ranging from 8 by 8 km to 0.25 by 0.25 degree, such as the interpolation product APHRODITE. These spatial resolutions are not capable to capture rainfall heterogeneity on a catchment scale in the Pamirs. The altitude drop within a few kilometers is too high, that orographic rainfall could be displayed by the coarse resolutions. Therefore we <span class="hlt">downscale</span> TRMM and APHRODITE data with the MODIS Cloud Cover product (1km2) to analyze precipitation trends in a much higher resolution on the catchment scale. We validate our results based on area-normalized discharge time series of paired catchments, located west and east, respectively north and south of the mountain ranges in the southern central Pamirs.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.122..667H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.122..667H"><span id="translatedtitle">Considering observed and future nonstationarities in statistical <span class="hlt">downscaling</span> of Mediterranean precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hertig, Elke; Jacobeit, Jucundus</p> <p>2015-11-01</p> <p>Winter precipitation in the Mediterranean area for the twenty-first century was statistically <span class="hlt">downscaled</span> under the explicit consideration of nonstationarities. Nonstationarities arise from substantial modifications of the atmospheric circulation, which lead to significant changes of regional precipitation characteristics. For the detection of nonstationarities in the relationships of the large-scale circulation and regional precipitation in the observational period, statistical model performance under potentially nonstationary conditions was compared to model performance under stationarity. To account for nonstationarity in the future projections, circulation characteristics in general circulation model (GCM) output used to <span class="hlt">downscale</span> precipitation were also analysed. The correspondence of GCM and observed circulation characteristics was used to specifically select appropriate <span class="hlt">downscaling</span> models. Statistical model performance was affected by nonstationarities, which was most pronounced not only in the north-eastern Mediterranean regions but also in western Mediterranean North Africa. Furthermore, it was found that variability in the GCM data used for the projections is at least as large as seen in the observational period. This finding underlines the need to explicitly take nonstationarities in statistical <span class="hlt">downscaling</span> into account. As <span class="hlt">downscaling</span> result we obtain mainly a reduction of the probability of rain and a rather indifferent pattern regarding the change of the 75 % up to the 95 % quantiles for most regions of the Mediterranean area until the end of the twenty-first century were mainly obtained. However, due to the nonstationarities, results depend strongly on the specific time periods under consideration.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H41E0878C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H41E0878C"><span id="translatedtitle">Spatial <span class="hlt">Downscaling</span> of Remotely Sensed Soil Moisture Using Support Vector Machine in Northeast Asia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Choi, M.; Moon, H.; Kim, D.</p> <p>2014-12-01</p> <p>Recent advances in remote sensing of soil moisture have broadened the understanding of spatiotemporal behavior of soil moisture and contributed to major improvements in the associated research fields. However, large spatial coverage and short timescale notwithstanding, low spatial resolution of passive microwave soil moisture data has been frequently treated as major research problem in many studies, which suggested statistical or deterministic <span class="hlt">downscaling</span> method as a solution to obtain targeted spatial resolutions. This study suggests a methodology to <span class="hlt">downscale</span> 10 km and 25 km daily L3 volumetric soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) in 2013 in Northeast Asia using Support Vector Machine (SVM). In the presented methodology, hydrometeorological variables observed from satellite remote sensing which have physically significant relationship with soil moisture are chosen as predictor variables to estimate soil moisture in finer resolution. Separate <span class="hlt">downscaling</span> algorithms optimized for seasonal conditions are applied to achieve more accurate results of <span class="hlt">downscaled</span> soil moisture. A comparative analysis between in-situ and <span class="hlt">downscaled</span> soil moisture is also conducted for quantitatively assessing its accuracy. Further application can be carried out in hydrological modeling or prediction of extreme weather phenomena in fine spatial resolution based on the results of this study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H51I1328H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H51I1328H"><span id="translatedtitle"><span class="hlt">Downscaling</span> near-surface wind over complex terrain using a physically-based statistical modeling approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huang, H.; Capps, S. B.; Huang, S.; Hall, A. D.</p> <p>2013-12-01</p> <p>We develop and test a physically-based statistical modeling approach to <span class="hlt">downscale</span> coarse resolution reanalysis near-surface winds over a region of complex terrain. Our approach is guided by physical variables and meteorological relationships that are important for determining near-surface wind flow. First, 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 using 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 ten for our southern California study region. 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 less than 1.5 m s-1 and 30 degrees, respectively. This approach can greatly accelerate the production of near-surface wind fields at higher accuracy than reanalysis data, while reducing the need for 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 output with varying coarse-to-fine grid resolution ratios and domains of interest.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy...44..529H','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Huang, Hsin-Yuan; Capps, Scott B.; Huang, Shao-Ching; Hall, Alex</p> <p>2015-01-01</p> <p>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> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JESS..tmp...90A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JESS..tmp...90A"><span id="translatedtitle">Multilayer perceptron neural network for <span class="hlt">downscaling</span> rainfall in arid region: A case study of Baluchistan, Pakistan</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ahmed, Kamal; Shahid, Shamsuddin; Haroon, Sobri Bin; Xiao-jun, Wang</p> <p>2015-08-01</p> <p><span class="hlt">Downscaling</span> rainfall in an arid region is much challenging compared to wet region due to erratic and infrequent behaviour of rainfall in the arid region. The complexity is further aggregated due to scarcity of data in such regions. A multilayer perceptron (MLP) neural network has been proposed in the present study for the <span class="hlt">downscaling</span> of rainfall in the data scarce arid region of Baluchistan province of Pakistan, which is considered as one of the most vulnerable areas of Pakistan to climate change. The National Center for Environmental Prediction (NCEP) reanalysis datasets from 20 grid points surrounding the study area were used to select the predictors using principal component analysis. Monthly rainfall data for the time periods 1961-1990 and 1991-2001 were used for the calibration and validation of the MLP model, respectively. The performance of the model was assessed using various statistics including mean, variance, quartiles, root mean square error (RMSE), mean bias error (MBE), coefficient of determination (R 2) and Nash-Sutcliffe efficiency (NSE). Comparisons of mean monthly time series of observed and <span class="hlt">downscaled</span> rainfall showed good agreement during both calibration and validation periods, while the <span class="hlt">downscaling</span> model was found to underpredict rainfall variance in both periods. Other statistical parameters also revealed good agreement between observed and <span class="hlt">downscaled</span> rainfall during both calibration and validation periods in most of the stations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26938544','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26938544"><span id="translatedtitle">Evaluating the Appropriateness of <span class="hlt">Downscaled</span> Climate Information for Projecting Risks of Salmonella.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Guentchev, Galina S; Rood, Richard B; Ammann, Caspar M; Barsugli, Joseph J; Ebi, Kristie; Berrocal, Veronica; O'Neill, Marie S; Gronlund, Carina J; Vigh, Jonathan L; Koziol, Ben; Cinquini, Luca</p> <p>2016-01-01</p> <p>Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be <span class="hlt">downscaled</span> to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically <span class="hlt">downscaled</span> GCM simulations for 1971-2000-a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) <span class="hlt">downscaling</span> methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically <span class="hlt">downscaled</span> data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of <span class="hlt">downscaled</span> climate data and the potential for misinterpretation of future estimates of Salmonella infections. PMID:26938544</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Elastic&pg=3&id=EJ971454','ERIC'); return false;" href="http://eric.ed.gov/?q=Elastic&pg=3&id=EJ971454"><span id="translatedtitle">Joys of Community <span class="hlt">Ensemble</span> Playing: The Case of the Happy Roll Elastic <span class="hlt">Ensemble</span> in Taiwan</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Hsieh, Yuan-Mei; Kao, Kai-Chi</p> <p>2012-01-01</p> <p>The Happy Roll Elastic <span class="hlt">Ensemble</span> (HREE) is a community music <span class="hlt">ensemble</span> supported by Tainan Culture Centre in Taiwan. With enjoyment and friendship as its primary goals, it aims to facilitate the joys of <span class="hlt">ensemble</span> playing and the spirit of social networking. This article highlights the key aspects of HREE's development in its first two years…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=surface+AND+area&pg=5&id=EJ934197','ERIC'); return false;" href="http://eric.ed.gov/?q=surface+AND+area&pg=5&id=EJ934197"><span id="translatedtitle">Memory for Multiple Visual <span class="hlt">Ensembles</span> in Infancy</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Zosh, Jennifer M.; Halberda, Justin; Feigenson, Lisa</p> <p>2011-01-01</p> <p>The number of individual items that can be maintained in working memory is limited. One solution to this problem is to store representations of <span class="hlt">ensembles</span> that contain summary information about large numbers of items (e.g., the approximate number or cumulative area of a group of many items). Here we explored the developmental origins of <span class="hlt">ensemble</span>…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=Soprano&pg=2&id=EJ623982','ERIC'); return false;" href="http://eric.ed.gov/?q=Soprano&pg=2&id=EJ623982"><span id="translatedtitle">Introducing Recorder <span class="hlt">Ensembles</span> in General Music Classes.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Kersten, Fred</p> <p>2000-01-01</p> <p>Focuses on the use of recorder <span class="hlt">ensembles</span> in general music classes, discussing topics such as strategies for procuring soprano, alto, and bass recorders and <span class="hlt">ensemble</span> activities for grades 3-8. Provides a bibliography of resources for recorder playing and information on transposition and arranging music. (CMK)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/21192276','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/21192276"><span id="translatedtitle">Statistical <span class="hlt">ensembles</span> with fluctuating extensive quantities</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Gorenstein, M. I.; Hauer, M.</p> <p>2008-10-15</p> <p>We suggest an extension of the standard concept of statistical <span class="hlt">ensembles</span>. Namely, we introduce a class of <span class="hlt">ensembles</span> with extensive quantities fluctuating according to an externally given distribution. As an example, the influence of energy fluctuations on multiplicity fluctuations in limited segments of momentum space for a classical ultra-relativistic gas is considered. The system volume fluctuations are also discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4109431','PMC'); return false;" href="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> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Morrison, Steven J.; Price, Harry E.; Smedley, Eric M.; Meals, Cory D.</p> <p>2014-01-01</p> <p>Previous research has found that listener evaluations of <span class="hlt">ensemble</span> performances vary depending on the expressivity of the conductor’s gestures, even when performances are otherwise identical. It was the purpose of the present study to test whether this effect of visual information was evident in the evaluation of specific aspects of <span class="hlt">ensemble</span> performance: articulation and dynamics. We constructed a set of 32 music performances that combined auditory and visual information and were designed to feature a high degree of contrast along one of two target characteristics: articulation and dynamics. We paired each of four music excerpts recorded by a chamber <span class="hlt">ensemble</span> in both a high- and low-contrast condition with video of four conductors demonstrating high- and low-contrast gesture specifically appropriate to either articulation or dynamics. Using one of two equivalent test forms, college music majors and non-majors (N = 285) viewed sixteen 30 s performances and evaluated the quality of the <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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=pop+AND+music&pg=4&id=EJ622203','ERIC'); return false;" href="http://eric.ed.gov/?q=pop+AND+music&pg=4&id=EJ622203"><span id="translatedtitle">Popular Music and the Instrumental <span class="hlt">Ensemble</span>.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Boespflug, George</p> <p>1999-01-01</p> <p>Discusses popular music, the role of the musical performer as a creator, and the styles of jazz and popular music. Describes the pop <span class="hlt">ensemble</span> at the college level, focusing on improvisation, rehearsals, recording, and performance. Argues that pop <span class="hlt">ensembles</span> be used in junior and senior high school. (CMK)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=wind+AND+power&pg=7&id=EJ430552','ERIC'); return false;" href="http://eric.ed.gov/?q=wind+AND+power&pg=7&id=EJ430552"><span id="translatedtitle">Fine-Tuning Your <span class="hlt">Ensemble</span>'s Jazz Style.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Garcia, Antonio J.</p> <p>1991-01-01</p> <p>Proposes instructional strategies for directors of jazz groups, including guidelines for developing of skills necessary for good performance. Includes effective methods for positive changes in <span class="hlt">ensemble</span> style. Addresses jazz group problems such as beat, tempo, staying in tune, wind power, and solo/<span class="hlt">ensemble</span> lines. Discusses percussionists, bassists,…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25751882','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25751882"><span id="translatedtitle">Layered <span class="hlt">Ensemble</span> Architecture for Time Series Forecasting.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin</p> <p>2016-01-01</p> <p>Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered <span class="hlt">ensemble</span> architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an <span class="hlt">ensemble</span> of multilayer perceptron (MLP) networks. While the first <span class="hlt">ensemble</span> layer tries to find an appropriate lag, the second <span class="hlt">ensemble</span> layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an <span class="hlt">ensemble</span>. LEA trains different networks in the <span class="hlt">ensemble</span> by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the <span class="hlt">ensemble</span>. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other <span class="hlt">ensemble</span> and nonensemble methods. PMID:25751882</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1093136','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/1093136"><span id="translatedtitle">Image Change Detection via <span class="hlt">Ensemble</span> Learning</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Martin, Benjamin W; Vatsavai, Raju</p> <p>2013-01-01</p> <p>The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work, we explore the use of <span class="hlt">ensemble</span> learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. <span class="hlt">Ensemble</span> learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the <span class="hlt">ensemble</span>. The strength of the <span class="hlt">ensemble</span> lies in the fact that the individual classifiers in the <span class="hlt">ensemble</span> create a mixture of experts in which the final classification made by the <span class="hlt">ensemble</span> classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of <span class="hlt">ensemble</span> learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the <span class="hlt">ensemble</span> method has approximately 11.5% higher change detection accuracy than an individual classifier.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26613506','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26613506"><span id="translatedtitle"><span class="hlt">Ensemble</span> Docking from Homology Models.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Novoa, Eva Maria; Ribas de Pouplana, Lluis; Barril, Xavier; Orozco, Modesto</p> <p>2010-08-10</p> <p>We present here a systematic exploration of the quality of protein structures derived from homology modeling when used as templates for high-throughput docking. It is found that structures derived from homology modeling are often similar in quality for docking purposes than real crystal structures, even in cases where the template used to create the structural model shows only a moderate sequence identity with the protein of interest. We designed an "<span class="hlt">ensemble</span> docking" approach based on the use of multiple homology models. The method provides results which are usually of better quality than those expected from single experimental X-ray structures. The use of this approach allows us to increase around five times the universe of use of high-throughput docking approaches for human proteins, by covering over 75% of known human therapeutic targets. PMID:26613506</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GMD.....8.1085M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GMD.....8.1085M"><span id="translatedtitle">Technical challenges and solutions in representing lakes when using WRF in <span class="hlt">downscaling</span> applications</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mallard, M. S.; Nolte, C. G.; Spero, T. L.; Bullock, O. R.; Alapaty, K.; Herwehe, J. A.; Gula, J.; Bowden, J. H.</p> <p>2015-04-01</p> <p>The Weather Research and Forecasting (WRF) model is commonly used to make high-resolution future projections of regional climate by <span class="hlt">downscaling</span> global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional <span class="hlt">downscaled</span> fields, lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for <span class="hlt">downscaling</span> simulations are presented and contrasted.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1611713F','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fita, Lluís; Argüeso, Daniel; Evans, Jason P.; King, Andrew D.</p> <p>2014-05-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AtmRe.164...27M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AtmRe.164...27M"><span id="translatedtitle">Bayesian Inference aided analog <span class="hlt">downscaling</span> for near-surface winds in complex terrain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Manor, Alon; Berkovic, Sigalit</p> <p>2015-10-01</p> <p>Assessing atmospheric boundary layer flows in complex terrain for short-range real-time applications demands fast and reliable <span class="hlt">downscaling</span> from coarser-resolution meteorological data to the relevant scale. An ideal statistical <span class="hlt">downscaling</span> numerical experiment was performed for surface winds above complex terrain in Israel's northern Negev desert region. Dynamical <span class="hlt">downscaling</span> have been performed by the WRF model to create a historical database by the following two sets. The first set used 5 nested domains from 40.5 km to 0.5 km. The second set used 3 nested domains ranging from 40.5 km to 4.5 km. The 4.5 km data (stage 2) was defined as predictors while data on 0.5 km (stage 1) served as predictands for statistical <span class="hlt">downscaling</span>. Two statistical <span class="hlt">downscaling</span> algorithms: minimal distance analog and a Bayesian inference aided analog (hereafter Bayesian algorithm) were tested by the above data. Unlike most analog algorithms, the Bayesian algorithm refers to the probability to get the best analog instead of the minimal differences between predictands. The comparison of the two algorithms shows that the Bayesian approach yields improved results. The Bayesian algorithm reproduces the 0.5 km resolution dynamically <span class="hlt">downscaled</span> surface winds with an average absolute direction difference of 43 and 20 for calm winds and moderate/strong winds respectively. Its average wind speed error is ~ 1.1 ms- 1. ~ 40 days are sufficient to create a representative database. Given the database, the procedure is extremely fast (a few seconds) and easy to operate, which makes it suitable for real-time non-expert fast-response applications.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150010221','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150010221"><span id="translatedtitle"><span class="hlt">Downscaling</span> Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.</p> <p>2013-01-01</p> <p>The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for <span class="hlt">downscaling</span> and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for <span class="hlt">downscaling</span> satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the <span class="hlt">downscaling</span> problem as a discrete inverse problem and solve it via a regularized variational approach (variational <span class="hlt">downscaling</span>) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse <span class="hlt">downscaling</span> methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the <span class="hlt">downscaling</span> of a hurricane precipitation field.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC51A0740P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC51A0740P"><span id="translatedtitle">Weather Typing Statistical <span class="hlt">downscaling</span> with dsclim: diagnostics, and uncertainties in data provision for the impact community</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Page, C.; Sanchez, E.; Terray, L.</p> <p>2010-12-01</p> <p>Recently, an innovative statistical methodology has been developed to <span class="hlt">downscale</span> climate simulations in France using a weather-typing approach (Boé et al., 2006), and further developed (Pagé et al., 2009). It has been used to <span class="hlt">downscale</span> 15 CMIP3 models as well as 7 Météo-France ARPEGE climate numerical model (Salas et al., 2005) simulations. In the framework of the ANR-SCAMPEI project, dsclim has been carefully configured to be able to <span class="hlt">downscale</span> climate simulations over France mountainous areas. Some new diagnostics have been developed to analyze the performance of the methodology and its configuration. In parallel, several projects to make these <span class="hlt">downscaled</span> climate scenarios available to the impact community are going on, notably GICC-DRIAS and EU-IS-ENES. In the context of IS-ENES, several national Use Cases have been developed to formalize the steps needed to provide climate scenarios suitable for the impact community starting from the global climate scenarios data, and also taking into account the uncertainties. References Pagé, C., L. Terray et J. Boé, 2009: dsclim: A software package to <span class="hlt">downscale</span> climate scenarios at regional scale using a weather-typing based statistical methodology. Technical Report TR/CMGC/09/21, CERFACS, Toulouse, France. Boé, J., L. Terray, F. Habets, et E. Martin, 2006: A simple statistical-dynamical <span class="hlt">downscaling</span> scheme based on weather types and conditional resampling. J. Geophys. Res., 111, D21106. Salas y Mélia, D., F. Chauvin, M. Déqué, H. Douville, J.-F. Guérémy, P. Marquet, S. Planton, J.-F. Royer, and S. Tyteca, 2005: Description and validation of CNRM-CM3 global coupled climate model. Technical report, Centre national de recherches météorologiques, Groupe de Météorologie de Grande Echelle et Climat, Météo-France.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=277355','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=277355"><span id="translatedtitle">Assessment of the scale effect on statistical <span class="hlt">downscaling</span> quality at a station scale using a weather generator-based model</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>The resolution of General Circulation Models (GCMs) is too coarse to assess the fine scale or site-specific impacts of climate change. <span class="hlt">Downscaling</span> approaches including dynamical and statistical <span class="hlt">downscaling</span> have been developed to meet this requirement. As the resolution of climate model increases, it...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4624683','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4624683"><span id="translatedtitle">ENCORE: Software for Quantitative <span class="hlt">Ensemble</span> Comparison</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Tiberti, Matteo; Papaleo, Elena; Bengtsen, Tone; Boomsma, Wouter; Lindorff-Larsen, Kresten</p> <p>2015-01-01</p> <p>There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or <span class="hlt">ensemble</span> refinement approaches have provided conformational <span class="hlt">ensembles</span> that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural <span class="hlt">ensembles</span> in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational <span class="hlt">ensembles</span> generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for <span class="hlt">ensemble</span> comparison, that each can be used to quantify the similarity between conformational <span class="hlt">ensembles</span> by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing <span class="hlt">ensembles</span> generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural <span class="hlt">ensembles</span> refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the <span class="hlt">ensembles</span>. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads <span class="hlt">ensemble</span> data in many common formats, and can work with large trajectory files. PMID:26505632</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.213M','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mengelkamp, H.-T.; Huneke, S.; Geyer, J.</p> <p>2010-09-01</p> <p>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> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A41H0062U','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A41H0062U"><span id="translatedtitle">Diamond-NICAM-SPRINTARS: <span class="hlt">downscaling</span> and simulation results</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Uchida, J.</p> <p>2012-12-01</p> <p>As a part of initiative "Research Program on Climate Change Adaptation" (RECCA) which investigates how predicted large-scale climate change may affect a local weather, and further examines possible atmospheric hazards that cities may encounter due to such a climate change, thus to guide policy makers on implementing new environmental measures, a "Development of Seamless Chemical AssimiLation System and its Application for Atmospheric Environmental Materials" (SALSA) project is funded by the Japanese Ministry of Education, Culture, Sports, Science and Technology and is focused on creating a regional (local) scale assimilation system that can accurately recreate and predict a transport of carbon dioxide and other air pollutants. In this study, a regional model of the next generation global cloud-resolving model NICAM (Non-hydrostatic ICosahedral Atmospheric Model) (Tomita and Satoh, 2004) is used and ran together with a transport model SPRINTARS (Spectral Radiation Transport Model for Aerosol Species) (Takemura et al, 2000) and a chemical transport model CHASER (Sudo et al, 2002) to simulate aerosols across urban cities (over a Kanto region including metropolitan Tokyo). The presentation will mainly be on a "Diamond-NICAM" (Figure 1), a regional climate model version of the global climate model NICAM, and its dynamical <span class="hlt">downscaling</span> methodologies. Originally, a global NICAM can be described as twenty identical equilateral triangular-shaped panels covering the entire globe where grid points are at the corners of those panels, and to increase a resolution (called a "global-level" in NICAM), additional points are added at the middle of existing two adjacent points, so a number of panels increases by fourfold with an increment of one global-level. On the other hand, a Diamond-NICAM only uses two of those initial triangular-shaped panels, thus only covers part of the globe. In addition, NICAM uses an adaptive mesh scheme and its grid size can gradually decrease, as the grid points get closer to the point of focus. This "stretching" strength of grid points is called a "stretching ratio", and allows a Diamond-NICAM to adjust its domain size. Also, this eliminates a need for the multiple nesting as a grid size gradually and smoothly changes in the global NICAM. Area of Diamond-NICAM-SPRINTARS when g-level is set at 7 with a stretching ratio of 100.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/21113385','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/21113385"><span id="translatedtitle">A Spatio-Temporal <span class="hlt">Downscaler</span> for Output From Numerical Models.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Berrocal, Veronica J; Gelfand, Alan E; Holland, David M</p> <p>2010-06-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://dx.doi.org/10.1186/2192-1709-1-2','USGSPUBS'); return false;" href="http://dx.doi.org/10.1186/2192-1709-1-2"><span id="translatedtitle"><span class="hlt">Downscaling</span> future climate scenarios to fine scales for hydrologic and ecological modeling and analysis</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Flint, Lorraine E.; Flint, Alan L.</p> <p>2012-01-01</p> <p>The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the <span class="hlt">downscaling</span> while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale <span class="hlt">downscaling</span> to analyses of ecological processes influenced by topographic complexity.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20150019486&hterms=wildlife&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dwildlife','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20150019486&hterms=wildlife&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dwildlife"><span id="translatedtitle">The Practitioner's Dilemma: How to Assess the Credibility of <span class="hlt">Downscaled</span> Climate Projections</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Barsugli, Joseph J.; Guentchev, Galina; Horton, Radley M.; Wood, Andrew; Mearns, Lindo O.; Liang, Xin-Zhong; Winkler, Julia A.; Dixon, Keith; Hayhoe, Katharine; Rood, Richard B.; Goddard, Lisa; Ray, Andrea; Buja, Lawrence; Ammann, Caspar</p> <p>2013-01-01</p> <p>Suppose you are a city planner, regional water manager, or wildlife conservation specialist who is asked to include the potential impacts of climate variability and change in your risk management and planning efforts. What climate information would you use? The choice is often regional or local climate projections <span class="hlt">downscaled</span> from global climate models (GCMs; also known as general circulation models) to include detail at spatial and temporal scales that align with those of the decision problem. A few years ago this information was hard to come by. Now there is Web-based access to a proliferation of high-resolution climate projections derived with differing <span class="hlt">downscaling</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EOSTr..94..424B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EOSTr..94..424B"><span id="translatedtitle">The Practitioner's Dilemma: How to Assess the Credibility of <span class="hlt">Downscaled</span> Climate Projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barsugli, Joseph J.; Guentchev, Galina; Horton, Radley M.; Wood, Andrew; Mearns, Linda O.; Liang, Xin-Zhong; Winkler, Julie A.; Dixon, Keith; Hayhoe, Katharine; Rood, Richard B.; Goddard, Lisa; Ray, Andrea; Buja, Lawrence; Ammann, Caspar</p> <p>2013-11-01</p> <p>Suppose you are a city planner, regional water manager, or wildlife conservation specialist who is asked to include the potential impacts of climate variability and change in your risk management and planning efforts. What climate information would you use? The choice is often regional or local climate projections <span class="hlt">downscaled</span> from global climate models (GCMs; also known as general circulation models) to include detail at spatial and temporal scales that align with those of the decision problem. A few years ago this information was hard to come by. Now there is Web-based access to a proliferation of high-resolution climate projections derived with differing <span class="hlt">downscaling</span> methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.4703K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.4703K"><span id="translatedtitle">Future changes in African temperature and precipitation in an <span class="hlt">ensemble</span> of Africa-CORDEX regional climate model simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kjellström, Erik; Nikulin, Grigory; Gbobaniyi, Emiola; Jones, Colin</p> <p>2013-04-01</p> <p>In this study we investigate possible changes in temperature and precipitation on a regional scale over Africa from 1961 to 2100. We use data from two <span class="hlt">ensembles</span> of climate simulations, one global and one regional, over the Africa-CORDEX domain. The global <span class="hlt">ensemble</span> includes eight coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CNRM-CM5, HadGEM2-ES, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. In the regional <span class="hlt">ensemble</span> all 8 AOGCMs are <span class="hlt">downscaled</span> at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Two forcing scenarios are considered, RCP 4.5 and 8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, we investigate the benefit of the higher horizontal resolution in RCA4 by comparing the results to the coarser driving AOGCM data. The significance of the results is investigated by comparing to i) the model simulated natural variability, and, ii) the biases in the control period. Results dealing with changes in the seasonal cycle of temperature and precipitation are presented. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.401T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.401T"><span id="translatedtitle">Climate change scenarios of temperature and precipitation over five Italian regions for the period 2021-2050 obtained by statistical <span class="hlt">downscaling</span> models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tomozeiu, R.; Tomei, F.; Villani, G.; Pasqui, M.</p> <p>2010-09-01</p> <p>Climate change scenarios of seasonal maximum, minimum temperature and precipitation in five Italian regions, over the period 2021-2050 against 1961-1990 are assessed. The regions selected by the AGROSCENARI project are important from the local agricultural practises and are situated as follows: in the Northern Italy - Po valley and hilly area of Faenza; in Central part of Italy- Marche, Beneventano and Destra Sele, and in Sardinia Island - Oristano. A statistical <span class="hlt">downscaling</span> technique applied to the <span class="hlt">ENSEMBLES</span> global climate simulations, A1B scenario, is used to reach this objective. The method consists of a multivariate regression, based on Canonical Correlation Analysis, using as possible predictors mean sea level pressure, geopotential height at 500hPa and temperature at 850 hPa. The observational data set (predictands) for the selected regions is composed by a reconstruction of minimum, maximum temperature and precipitation daily data on a regular grid with a spatial resolution of 35 km, for 1951-2009 period (managed by the Meteorological and Climatological research unit for agriculture - Agricultural Research Council, CRA - CMA). First, a set-up of statistical model has been made using predictors from ERA40 reanalysis and the seasonal indices of temperature and precipitation from local scale, 1958-2002 period. Then, the statistical <span class="hlt">downscaling</span> model has been applied to the predictors derived from the <span class="hlt">ENSEMBLES</span> global climate models, A1B scenario, in order to obtain climate change scenario of temperature and precipitation at local scale, 2021-2050 period. The projections show that increases could be expected to occur under scenario conditions in all seasons, in both minimum and maximum temperature. The magnitude of changes is more intense during summer when the changes could reach values around 2°C for minimum and maximum temperature. In the case of precipitation, the pattern of changes is more complex, different from season to season and over the regions, a reduction of precipitation could be expected to occur during summer. The temperature and precipitation projections from hilly area of Faenza are then used as input in a weather generator, in order to produce a synthetic series of daily data. These series feed a water balance and crop growth model (CRITERIA) to evaluate the impact of climate change scenario in irrigation crop water needs, for 2021-2050 period. As reference crop the kiwifruit, which is characterised by high water need and widespread in this area, has been selected.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011180','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011180"><span id="translatedtitle">Hybrid Data Assimilation without <span class="hlt">Ensemble</span> Filtering</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Todling, Ricardo; Akkraoui, Amal El</p> <p>2014-01-01</p> <p>The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the <span class="hlt">ensemble</span> is generated using a square-root <span class="hlt">ensemble</span> Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member <span class="hlt">ensemble</span> solution close to the variational solution; we also found it necessary to re-center the members of the <span class="hlt">ensemble</span> about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the <span class="hlt">ensemble</span>. This led us to consider a hybrid strategy in which the members of the <span class="hlt">ensemble</span> are generated by simply converting the variational analysis to the resolution of the <span class="hlt">ensemble</span> and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/doepatents/biblio/1175481','DOE-PATENT-XML'); return false;" href="http://www.osti.gov/doepatents/biblio/1175481"><span id="translatedtitle">Creating <span class="hlt">ensembles</span> of decision trees through sampling</span></a></p> <p><a target="_blank" href="http://www.osti.gov/doepatents">DOEpatents</a></p> <p>Kamath, Chandrika; Cantu-Paz, Erick</p> <p>2005-08-30</p> <p>A system for decision tree <span class="hlt">ensembles</span> that includes a module to read the data, a module to sort the data, a module to evaluate a potential split of the data according to some criterion using a random sample of the data, a module to split the data, and a module to combine multiple decision trees in <span class="hlt">ensembles</span>. The decision tree method is based on statistical sampling techniques and includes the steps of reading the data; sorting the data; evaluating a potential split according to some criterion using a random sample of the data, splitting the data, and combining multiple decision trees in <span class="hlt">ensembles</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4910T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4910T"><span id="translatedtitle">Forecasting European Droughts using the North American Multi-Model <span class="hlt">Ensemble</span> (NMME)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Thober, Stephan; Kumar, Rohini; Samaniego, Luis; Sheffield, Justin; Schäfer, David; Mai, Juliane</p> <p>2015-04-01</p> <p>Soil moisture droughts have the potential to diminish crop yields causing economic damage or even threatening the livelihood of societies. State-of-the-art drought forecasting systems incorporate seasonal meteorological forecasts to estimate future drought conditions. Meteorological forecasting skill (in particular that of precipitation), however, is limited to a few weeks because of the chaotic behaviour of the atmosphere. One of the most important challenges in drought forecasting is to understand how the uncertainty in the atmospheric forcings (e.g., precipitation and temperature) is further propagated into hydrologic variables such as soil moisture. The North American Multi-Model <span class="hlt">Ensemble</span> (NMME) provides the latest collection of a multi-institutional seasonal forecasting <span class="hlt">ensemble</span> for precipitation and temperature. In this study, we analyse the skill of NMME forecasts for predicting European drought events. The monthly NMME forecasts are <span class="hlt">downscaled</span> to daily values to force the mesoscale hydrological model (mHM). The mHM soil moisture forecasts obtained with the forcings of the dynamical models are then compared against those obtained with the <span class="hlt">Ensemble</span> Streamflow Prediction (ESP) approach. ESP recombines historical meteorological forcings to create a new <span class="hlt">ensemble</span> forecast. Both forecasts are compared against reference soil moisture conditions obtained using observation based meteorological forcings. The study is conducted for the period from 1982 to 2009 and covers a large part of the Pan-European domain (10°W to 40°E and 35°N to 55°N). Results indicate that NMME forecasts are better at predicting the reference soil moisture variability as compared to ESP. For example, NMME explains 50% of the variability in contrast to only 31% by ESP at a six-month lead time. The Equitable Threat Skill Score (ETS), which combines the hit and false alarm rates, is analysed for drought events using a 0.2 threshold of a soil moisture percentile index. On average, the NMME based <span class="hlt">ensemble</span> forecasts have consistently higher skill than the ESP based ones (ETS of 13% as compared to 5% at a six-month lead time). Additionally, the ETS <span class="hlt">ensemble</span> spread of NMME forecasts is considerably narrower than that of ESP; the lower boundary of the NMME <span class="hlt">ensemble</span> spread coincides most of the time with the <span class="hlt">ensemble</span> median of ESP. Among the NMME models, NCEP-CFSv2 outperforms the other models in terms of ETS most of the time. Removing the three worst performing models does not deteriorate the <span class="hlt">ensemble</span> performance (neither in skill nor in spread), but would substantially reduce the computational resources required in an operational forecasting system. For major European drought events (e.g., 1990, 1992, 2003, and 2007), NMME forecasts tend to underestimate area under drought and drought magnitude during times of drought development. During drought recovery, this underestimation is weaker for area under drought or even reversed into an overestimation for drought magnitude. This indicates that the NMME models are too wet during drought development and too dry during drought recovery. In summary, soil moisture drought forecasts by NMME are more skillful than those of an ESP based approach. However, they still show systematic biases in reproducing the observed drought dynamics during drought development and recovery.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JSP...tmp...64B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JSP...tmp...64B"><span id="translatedtitle">Statistical <span class="hlt">Ensembles</span> for Economic Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bargigli, Leonardo</p> <p>2014-03-01</p> <p>Economic networks share with other social networks the fundamental property of sparsity. It is well known that the maximum entropy techniques usually employed to estimate or simulate weighted networks produce unrealistic dense topologies. At the same time, strengths should not be neglected, since they are related to core economic variables like supply and demand. To overcome this limitation, the exponential Bosonic model has been previously extended in order to obtain <span class="hlt">ensembles</span> where the average degree and strength sequences are simultaneously fixed (conditional geometric model). In this paper a new exponential model, which is the network equivalent of Boltzmann ideal systems, is introduced and then extended to the case of joint degree-strength constraints (conditional Poisson model). Finally, the fitness of these alternative models is tested against a number of networks. While the conditional geometric model generally provides a better goodness-of-fit in terms of log-likelihoods, the conditional Poisson model could nevertheless be preferred whenever it provides a higher similarity with original data. If we are interested instead only in topological properties, the simple Bernoulli model appears to be preferable to the correlated topologies of the two more complex models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=312466','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=312466"><span id="translatedtitle"><span class="hlt">Downscaling</span> Landsat 7 canopy reflectance employing a multi soil sensor platform</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Crop growth and yield can be efficiently monitored using canopy reflectance. The spatial resolution of freely available remote sensing data is however too coarse to fully understand spatial dynamics of crop status. In this manuscript Landsat 7 (L7) reflectance is <span class="hlt">downscaled</span> from the native resolutio...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=307740&keyword=LAKE+AND+ICE&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55598270&CFTOKEN=95214407','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=307740&keyword=LAKE+AND+ICE&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55598270&CFTOKEN=95214407"><span id="translatedtitle">Technical Challenges and Solutions in Representing Lakes when using WRF in <span class="hlt">Downscaling</span> Applications</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by <span class="hlt">downscaling</span> global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=296239','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=296239"><span id="translatedtitle">Passive microwave soil moisture <span class="hlt">downscaling</span> using vegetation index and skin surface temperature</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture <span class="hlt">downscaling</span> algorithm based on a regression relationship bet...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=308425','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=308425"><span id="translatedtitle">A method to <span class="hlt">downscale</span> soil moisture to fine-resolutions using topographic, vegetation, and soil data</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>Soil moisture can be estimated over large regions with spatial resolutions greater than 500 m, but many applications require finer resolutions (10 – 100 m grid cells). Several methods use topographic data to <span class="hlt">downscale</span>, but vegetation and soil patterns can also be important. In this paper, a downsc...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=213342','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=213342"><span id="translatedtitle">Reductions in seasonal climate forecast dependability as a result of <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>This research determines whether NOAA/CPC seasonal climate forecasts are skillful enough to retain utility after they have been <span class="hlt">downscaled</span> for use in crop models. Utility is assessed using net dependability, the product of the large-scale 3-month forecast dependability and a factor accounting for l...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JGRD..11617110N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JGRD..11617110N"><span id="translatedtitle">Projecting changes in future heavy rainfall events for Oahu, Hawaii: A statistical <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Norton, Chase W.; Chu, Pao-Shin; Schroeder, Thomas A.</p> <p>2011-09-01</p> <p>A statistical model based on nonlinear artificial neural networks is used to <span class="hlt">downscale</span> daily extreme precipitation events in Oahu, Hawaii, from general circulation model (GCM) outputs and projected into the future. From a suite of GCMs and their emission scenarios, two tests recommended by the International Panel on Climate Change are conducted and the ECHAM5 A2 is selected as the most appropriate one for <span class="hlt">downscaling</span> precipitation extremes for Oahu. The skill of the neural network model is highest in drier, leeward regions where orographic uplifting has less influence on daily extreme precipitation. The trained model is used with the ECHAM5 forced by emissions from the A2 scenario to simulate future daily precipitation on Oahu. A BCa bootstrap resampling method is used to provide 95% confidence intervals of the storm frequency and intensity for all three data sets (actual observations, <span class="hlt">downscaled</span> GCM output from the present-day climate, and <span class="hlt">downscaled</span> GCM output for future climate). Results suggest a tendency for increased frequency of heavy rainfall events but a decrease in rainfall intensity during the next 30 years (2011-2040) for the southern shoreline of Oahu.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1713722K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713722K"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of WRF-Chem Model: An Air Quality Analysis over Bogota, Colombia</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kumar, Anikender; Rojas, Nestor</p> <p>2015-04-01</p> <p>Statistical <span class="hlt">downscaling</span> is a technique that is used to extract high-resolution information from regional scale variables produced by coarse resolution models such as Chemical Transport Models (CTMs). The fully coupled WRF-Chem (Weather Research and Forecasting with Chemistry) model is used to simulate air quality over Bogota. Bogota is a tropical Andean megacity located over a high-altitude plateau in the middle of very complex terrain. The WRF-Chem model was adopted for simulating the hourly ozone concentrations. The computational domains were chosen of 120x120x32, 121x121x32 and 121x121x32 grid points with horizontal resolutions of 27, 9 and 3 km respectively. The model was initialized with real boundary conditions using NCAR-NCEP's Final Analysis (FNL) and a 1ox1o (~111 km x 111 km) resolution. Boundary conditions were updated every 6 hours using reanalysis data. The emission rates were obtained from global inventories, namely the REanalysis of the TROpospheric (RETRO) chemical composition and the Emission Database for Global Atmospheric Research (EDGAR). Multiple linear regression and artificial neural network techniques are used to <span class="hlt">downscale</span> the model output at each monitoring stations. The results confirm that the statistically <span class="hlt">downscaled</span> outputs reduce simulated errors by up to 25%. This study provides a general overview of statistical <span class="hlt">downscaling</span> of chemical transport models and can constitute a reference for future air quality modeling exercises over Bogota and other Colombian cities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015WRR....51.6244J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015WRR....51.6244J"><span id="translatedtitle">A space and time scale-dependent nonlinear geostatistical approach for <span class="hlt">downscaling</span> daily precipitation and temperature</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason; McCabe, Matthew F.; Sharma, Ashish</p> <p>2015-08-01</p> <p>A geostatistical framework is proposed to <span class="hlt">downscale</span> daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 and 10 km resolution for a 20 year period ranging from 1985 to 2004. The data are used to predict <span class="hlt">downscaled</span> climate variables for the year 2005. The result, for each <span class="hlt">downscaled</span> pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference data set indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple-point geostatistics approach is an efficient tool to be used for statistical <span class="hlt">downscaling</span> to obtain local-scale estimates of precipitation and temperature from General Circulation Models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012GeoRL..3913707C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012GeoRL..3913707C"><span id="translatedtitle">Regional climate <span class="hlt">downscaling</span> with prior statistical correction of the global climate forcing</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Colette, A.; Vautard, R.; Vrac, M.</p> <p>2012-07-01</p> <p>A novel climate <span class="hlt">downscaling</span> methodology that attempts to correct climate simulation biases is proposed. By combining an advanced statistical bias correction method with a dynamical <span class="hlt">downscaling</span> it constitutes a hybrid technique that yields nearly unbiased, high-resolution, physically consistent, three-dimensional fields that can be used for climate impact studies. The method is based on a prior statistical distribution correction of large-scale global climate model (GCM) 3-dimensional output fields to be taken as boundary forcing of a dynamical regional climate model (RCM). GCM fields are corrected using meteorological reanalyses. We evaluate this methodology over a decadal experiment. The improvement in terms of spatial and temporal variability is discussed against observations for a past period. The biases of the <span class="hlt">downscaled</span> fields are much lower using this hybrid technique, up to a factor 4 for the mean temperature bias compared to the dynamical <span class="hlt">downscaling</span> alone without prior bias correction. Precipitation biases are subsequently improved hence offering optimistic perspectives for climate impact studies.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=154374','TEKTRAN'); return false;" href="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> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015IJBm..tmp..138M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015IJBm..tmp..138M"><span id="translatedtitle">Dynamically <span class="hlt">downscaling</span> predictions for deciduous tree leaf emergence in California under current and future climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medvigy, David; Kim, Seung Hee; Kim, Jinwon; Kafatos, Menas C.</p> <p>2015-10-01</p> <p>Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical <span class="hlt">downscaling</span> of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically <span class="hlt">downscaled</span> to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of <span class="hlt">downscaling</span> from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of <span class="hlt">downscaling</span> are unlikely to be stationary.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014APJAS..50...83H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014APJAS..50...83H"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span>: Fundamental issues from an NWP point of view and recommendations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hong, Song-You; Kanamitsu, Masao</p> <p>2014-01-01</p> <p>Dynamical <span class="hlt">downscaling</span> has been recognized as a useful tool not only for the climate community, but also for associated application communities such as the environmental and hydrological societies. Although climate projection data are available in lower-resolution general circulation models (GCMs), higher-resolution climate projections using regional climate models (RCMs) have been obtained over various regions of the globe. Various model outputs from RCMs with a high resolution of even as high as a few km have become available with heavy weight on applications. However, from a scientific point of view in numerical atmospheric modeling, it is not clear how to objectively judge the degree of added value in the RCM output against the corresponding GCM results. A key factor responsible for skepticism is based on the fundamental limitations in the nesting approach between GCMs and RCMs. In this article, we review the current status of the dynamical <span class="hlt">downscaling</span> for climate prediction, focusing on basic assumptions that are scrutinized from a numerical weather prediction (NWP) point of view. Uncertainties in <span class="hlt">downscaling</span> due to the inconsistencies in the physics packages between GCMs and RCMs were revealed. Recommendations on how to tackle the ultimate goal of dynamical <span class="hlt">downscaling</span> were also described.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014OcDyn..64..927S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014OcDyn..64..927S"><span id="translatedtitle"><span class="hlt">Downscaling</span> IPCC control run and future scenario with focus on the Barents Sea</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sandø, Anne Britt; Melsom, Arne; Budgell, William Paul</p> <p>2014-07-01</p> <p>Global atmosphere-ocean general circulation models are the tool by which projections for climate changes due to radiative forcing scenarios have been produced. Further, regional atmospheric <span class="hlt">downscaling</span> of the global models may be applied in order to evaluate the details in, e.g., temperature and precipitation patterns. Similarly, detailed regional information is needed in order to assess the implications of future climate change for the marine ecosystems. However, regional results for climate change in the ocean are sparse. We present the results for the circulation and hydrography of the Barents Sea from the ocean component of two global models and from a corresponding pair of regional model configurations. The global models used are the GISS AOM and the NCAR CCSM3. The ROMS ocean model is used for the regional <span class="hlt">downscaling</span> of these results (ROMS-G and ROMS-N configurations, respectively). This investigation was undertaken in order to shed light on two questions that are essential in the context of regional ocean projections: (1) How should a regional model be set up in order to take advantage of the results from global projections; (2) What limits to quality in the results of regional models are imposed by the quality of global models? We approached the first question by initializing the ocean model in the control simulation by a realistic ocean analysis and specifying air-sea fluxes according to the results from the global models. For the projection simulation, the global models' oceanic anomalies from their control simulation results were added upon initialization. Regarding the second question, the present set of simulations includes regional <span class="hlt">downscalings</span> of the present-day climate as well as projected climate change. Thus, we study separately how <span class="hlt">downscaling</span> changes the results in the control climate case, and how scenario results are changed. For the present-day climate, we find that <span class="hlt">downscaling</span> reduces the differences in the Barents Sea between the original 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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JHyd..408....1F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JHyd..408....1F"><span id="translatedtitle">A comparison of multi-site daily rainfall <span class="hlt">downscaling</span> techniques under Australian conditions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Frost, Andrew J.; Charles, Stephen P.; Timbal, Bertrand; Chiew, Francis H. S.; Mehrotra, R.; Nguyen, Kim C.; Chandler, Richard E.; McGregor, John L.; Fu, Guobin; Kirono, Dewi G. C.; Fernandez, Elodie; Kent, David M.</p> <p>2011-09-01</p> <p>SummarySix methods of <span class="hlt">downscaling</span> GCM simulations to multi-site daily precipitation were applied to a set of 30 rain gauges located within South-Eastern Australia. The methods were tested at reproducing a range of statistics important within hydrological studies including inter-annual variability and spatial coherency using both NCEP/NCAR reanalysis and GCM predictors, thus testing the validity of GCM <span class="hlt">downscaled</span> predictions. The methods evaluated, all having found application in Australia previously, are: (1) the dynamical <span class="hlt">downscaling</span> Conformal-Cubic Atmospheric Model (CCAM) of McGregor (2005); the historical data based (2) Scaling method applied by Chiew et al. (2009) and (3) Analogue method of Timbal (2004); and three stochastic methods, (4) the GLIMCLIM (Generalised Linear Model for daily Climate time series) software package ( Chandler, 2002), (5) the Non-homogeneous Hidden Markov Model (NHMM) of Charles et al. (1999), and (6) the modified Markov model-kernel probability density estimation (MMM-KDE) <span class="hlt">downscaling</span> technique of Mehrotra and Sharma (2007). The results showed that the simple Scaling approach provided relatively robust results for a range of statistics when GCM forcing data was used, and was therefore recommended for regional water availability and planning studies (subject to certain limitations as mentioned in conclusion section). The stochastic methods better capture changes to a fuller range of rainfall statistics and are recommended for detailed catchment modelling studies. In particular, the stochastic methods show better results for daily extreme rainfall (e.g. flooding/low flow) as the simulations are not based purely on temporal/spatial rainfall patterns observed in the past, and a hybrid GLIMCLIM occurrence-KDE amounts model is recommended based on performance for individual statistics. For GCM <span class="hlt">downscaled</span> simulations, biases in annual mean and standard deviation of ±5% and -30% were observed typically, and no single model performed well over all timescales/statistics, suggesting that the user beware of model limitations when applying <span class="hlt">downscaling</span> methods for various purposes. A brief demonstration of predictor biases is presented, highlighting that biases observed in GCM predictors can cause poorer performance during GCM validation, and that investigation of these biases should inform choice of GCMs, GCM predictors, and the <span class="hlt">downscaling</span> methods that use them.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003PhRvE..68e6113J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003PhRvE..68e6113J"><span id="translatedtitle">Statistical mechanics in the extended Gaussian <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Johal, Ramandeep S.; Planes, Antoni; Vives, Eduard</p> <p>2003-11-01</p> <p>The extended Gaussian <span class="hlt">ensemble</span> (EGE) is introduced as a generalization of the canonical <span class="hlt">ensemble</span>. This <span class="hlt">ensemble</span> is a further extension of the Gaussian <span class="hlt">ensemble</span> introduced by Hetherington [J. Low Temp. Phys. 66, 145 (1987)]. The statistical mechanical formalism is derived both from the analysis of the system attached to a finite reservoir and from the maximum statistical entropy principle. The probability of each microstate depends on two parameters ? and ? which allow one to fix, independently, the mean energy of the system and the energy fluctuations, respectively. We establish the Legendre transform structure for the generalized thermodynamic potential and propose a stability criterion. We also compare the EGE probability distribution with the q-exponential distribution. As an example, an application to a system with few independent spins is presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20070023651&hterms=WEIGHT&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DWEIGHT','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20070023651&hterms=WEIGHT&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3DWEIGHT"><span id="translatedtitle"><span class="hlt">Ensemble</span> Weight Enumerators for Protograph LDPC Codes</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Divsalar, Dariush</p> <p>2006-01-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015Sci...348..207L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015Sci...348..207L"><span id="translatedtitle">Experimental observation of a generalized Gibbs <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Langen, Tim; Erne, Sebastian; Geiger, Remi; Rauer, Bernhard; Schweigler, Thomas; Kuhnert, Maximilian; Rohringer, Wolfgang; Mazets, Igor E.; Gasenzer, Thomas; Schmiedmayer, Jörg</p> <p>2015-04-01</p> <p>The description of the non-equilibrium dynamics of isolated quantum many-body systems within the framework of statistical mechanics is a fundamental open question. Conventional thermodynamical <span class="hlt">ensembles</span> fail to describe the large class of systems that exhibit nontrivial conserved quantities, and generalized <span class="hlt">ensembles</span> have been predicted to maximize entropy in these systems. We show experimentally that a degenerate one-dimensional Bose gas relaxes to a state that can be described by such a generalized <span class="hlt">ensemble</span>. This is verified through a detailed study of correlation functions up to 10th order. The applicability of the generalized <span class="hlt">ensemble</span> description for isolated quantum many-body systems points to a natural emergence of classical statistical properties from the microscopic unitary quantum evolution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25859041','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25859041"><span id="translatedtitle">Experimental observation of a generalized Gibbs <span class="hlt">ensemble</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Langen, Tim; Erne, Sebastian; Geiger, Remi; Rauer, Bernhard; Schweigler, Thomas; Kuhnert, Maximilian; Rohringer, Wolfgang; Mazets, Igor E; Gasenzer, Thomas; Schmiedmayer, Jörg</p> <p>2015-04-10</p> <p>The description of the non-equilibrium dynamics of isolated quantum many-body systems within the framework of statistical mechanics is a fundamental open question. Conventional thermodynamical <span class="hlt">ensembles</span> fail to describe the large class of systems that exhibit nontrivial conserved quantities, and generalized <span class="hlt">ensembles</span> have been predicted to maximize entropy in these systems. We show experimentally that a degenerate one-dimensional Bose gas relaxes to a state that can be described by such a generalized <span class="hlt">ensemble</span>. This is verified through a detailed study of correlation functions up to 10th order. The applicability of the generalized <span class="hlt">ensemble</span> description for isolated quantum many-body systems points to a natural emergence of classical statistical properties from the microscopic unitary quantum evolution. PMID:25859041</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140010385','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140010385"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard</p> <p>2013-01-01</p> <p>Statistical <span class="hlt">downscaling</span> can be used to efficiently <span class="hlt">downscale</span> a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically <span class="hlt">downscales</span> (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical <span class="hlt">Downscaling</span> and Bias Correction (SDBC) approach. Based on these <span class="hlt">downscaled</span> data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical <span class="hlt">downscaling</span> as an intermediate step does not lead to considerable differences in the results of statistical <span class="hlt">downscaling</span> for the study domain.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70048367','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70048367"><span id="translatedtitle">Climate <span class="hlt">downscaling</span> effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.</p> <p>2013-01-01</p> <p>High-resolution (<span class="hlt">downscaled</span>) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different <span class="hlt">downscaling</span> approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between <span class="hlt">downscaling</span> approaches and that the variation attributable to <span class="hlt">downscaling</span> technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between <span class="hlt">downscaling</span> techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of <span class="hlt">downscaling</span> applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative <span class="hlt">downscaling</span> methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006JHyd..330..621T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006JHyd..330..621T"><span id="translatedtitle"><span class="hlt">Downscaling</span> of precipitation for climate change scenarios: A support vector machine approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tripathi, Shivam; Srinivas, V. V.; Nanjundiah, Ravi S.</p> <p>2006-11-01</p> <p>SummaryThe Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be <span class="hlt">downscaled</span> to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical <span class="hlt">downscaling</span> of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based <span class="hlt">downscaling</span> model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional <span class="hlt">downscaling</span> using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical <span class="hlt">downscaling</span>, and are suitable for conducting climate impact studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010APJAS..46..425H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010APJAS..46..425H"><span id="translatedtitle">Future climate change scenarios over Korea using a multi-nested <span class="hlt">downscaling</span> system: A pilot study</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hong, Song-You; Moon, Nan-Kyoung; Lim, Kyo-Sun Sunny; Kim, Jong-Won</p> <p>2010-11-01</p> <p>This study examines a scenario of future summer climate change for the Korean peninsula using a multi-nested regional climate system. The global-scale scenario from the ECHAM5, which has a 200 km grid, was <span class="hlt">downscaled</span> to a 50 km grid over Asia using the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM). This allowed us to obtain large-scale forcing information for a one-way, double-nested Weather and Research Forecasting (WRF) model that consists of a 12 km grid over Korea and a 3 km grid near Seoul. As a pilot study prior to the multi-year simulation work the years 1995 and 2055 were selected for the present and future summers. This RSM-WRF multi-nested <span class="hlt">downscaling</span> system was evaluated by examining a <span class="hlt">downscaled</span> climatology in 1995 with the largescale forcing from the NCEP/Department of Energy (DOE) reanalysis. The changes in monsoonal flows over East Asia and the associated precipitation change scenario over Korea are highlighted. It is found that the RSM-WRF system is capable of reproducing large-scale features associated with the East-Asian summer monsoon (EASM) and its associated hydro-climate when it is nested by the NCEP/DOE reanalysis. The ECHAM5-based <span class="hlt">downscaled</span> climate for the present (1995) summer is found to suffer from a weakening of the low-level jet and sub-tropical high when compared the reanalysis-based climate. Predicted changes in summer monsoon circulations between 1995 and 2055 include a strengthened subtropical high and an intensified mid-level trough. The resulting projected summer precipitation is doubled over much of South Korea, accompanied by a pronounced surface warming with a maximum of about 2 K. It is suggested that <span class="hlt">downscaling</span> strategy of this study, with its cloud-resolving scale, makes it suitable for providing high-resolution meteorological data with which to derive hydrology or air pollution models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002AGUFM.H12G..02G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002AGUFM.H12G..02G"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span>: A Comparison of Multiple Linear Regression and k-Nearest Neighbor Approaches</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gangopadhyay, S.; Clark, M. P.; Rajagopalan, B.</p> <p>2002-12-01</p> <p>The success of short term (days to fortnight) streamflow forecasting largely depends on the skill of surface climate (e.g., precipitation and temperature) forecasts at local scales in the individual river basins. The surface climate forecasts are used to drive the hydrologic models for streamflow forecasting. Typically, Medium Range Forecast (MRF) models provide forecasts of large scale circulation variables (e.g. pressures, wind speed, relative humidity etc.) at different levels in the atmosphere on a regular grid - which are then used to "<span class="hlt">downscale</span>" to the surface climate at locations within the model grid box. Several statistical and dynamical methods are available for <span class="hlt">downscaling</span>. This paper compares the utility of two statistical <span class="hlt">downscaling</span> methodologies: (1) multiple linear regression (MLR) and (2) a nonparametric approach based on k-nearest neighbor (k-NN) bootstrap method, in providing local-scale information of precipitation and temperature at a network of stations in the Upper Colorado River Basin. <span class="hlt">Downscaling</span> to the stations is based on output of large scale circulation variables (i.e. predictors) from the NCEP Medium Range Forecast (MRF) database. Fourteen-day six hourly forecasts are developed using these two approaches, and their forecast skill evaluated. A stepwise regression is performed at each location to select the predictors for the MLR. The k-NN bootstrap technique resamples historical data based on their "nearness" to the current pattern in the predictor space. Prior to resampling a Principal Component Analysis (PCA) is performed on the predictor set to identify a small subset of predictors. Preliminary results using the MLR technique indicate a significant value in the <span class="hlt">downscaled</span> MRF output in predicting runoff in the Upper Colorado Basin. It is expected that the k-NN approach will match the skill of the MLR approach at individual stations, and will have the added advantage of preserving the spatial co-variability between stations, capturing nonlinearities in the relationship and non-gaussian error structure, and the consistency between forecasted precipitation and temperature.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JPhA...49c5101A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JPhA...49c5101A"><span id="translatedtitle">Native ultrametricity of sparse random <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Avetisov, V.; Krapivsky, P. L.; Nechaev, S.</p> <p>2016-01-01</p> <p>We investigate the eigenvalue density in <span class="hlt">ensembles</span> of large sparse Bernoulli random matrices. Analyzing in detail the spectral density of <span class="hlt">ensembles</span> of linear subgraphs, we discuss its ultrametric nature and show that near the spectrum boundary, the tails of the spectral density exhibit a Lifshitz singularity typical for Anderson localization. We pay attention to an intriguing connection of the spectral density to the Dedekind ?-function. We conjecture that ultrametricity emerges in rare-event statistics and is inherit to generic complex sparse systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26903095','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26903095"><span id="translatedtitle">Meaning of temperature in different thermostatistical <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hänggi, Peter; Hilbert, Stefan; Dunkel, Jörn</p> <p>2016-03-28</p> <p>Depending on the exact experimental conditions, the thermodynamic properties of physical systems can be related to one or more thermostatistical <span class="hlt">ensembles</span>. Here, we survey the notion of thermodynamic temperature in different statistical <span class="hlt">ensembles</span>, focusing in particular on subtleties that arise when <span class="hlt">ensembles</span> become non-equivalent. The 'mother' of all <span class="hlt">ensembles</span>, the microcanonical <span class="hlt">ensemble</span>, uses entropy and internal energy (the most fundamental, dynamically conserved quantity) to derive temperature as a secondary thermodynamic variable. Over the past century, some confusion has been caused by the fact that several competing microcanonical entropy definitions are used in the literature, most commonly the volume and surface entropies introduced by Gibbs. It can be proved, however, that only the volume entropy satisfies exactly the traditional form of the laws of thermodynamics for a broad class of physical systems, including all standard classical Hamiltonian systems, regardless of their size. This mathematically rigorous fact implies that negative 'absolute' temperatures and Carnot efficiencies more than 1 are not achievable within a standard thermodynamical framework. As an important offspring of microcanonical thermostatistics, we shall briefly consider the canonical <span class="hlt">ensemble</span> and comment on the validity of the Boltzmann weight factor. We conclude by addressing open mathematical problems that arise for systems with discrete energy spectra. PMID:26903095</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25104944','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25104944"><span id="translatedtitle">Conductor gestures influence evaluations of <span class="hlt">ensemble</span> performance.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Morrison, Steven J; Price, Harry E; Smedley, Eric M; Meals, Cory D</p> <p>2014-01-01</p> <p>Previous research has found that listener evaluations of <span class="hlt">ensemble</span> performances vary depending on the expressivity of the conductor's gestures, even when performances are otherwise identical. It was the purpose of the present study to test whether this effect of visual information was evident in the evaluation of specific aspects of <span class="hlt">ensemble</span> performance: articulation and dynamics. We constructed a set of 32 music performances that combined auditory and visual information and were designed to feature a high degree of contrast along one of two target characteristics: articulation and dynamics. We paired each of four music excerpts recorded by a chamber <span class="hlt">ensemble</span> in both a high- and low-contrast condition with video of four conductors demonstrating high- and low-contrast gesture specifically appropriate to either articulation or dynamics. Using one of two equivalent test forms, college music majors and non-majors (N = 285) viewed sixteen 30 s performances and evaluated the quality of the <span class="hlt">ensemble</span>'s articulation, dynamics, technique, and tempo along with overall expressivity. Results showed significantly higher evaluations for performances featuring high rather than low conducting expressivity regardless of the <span class="hlt">ensemble</span>'s performance quality. Evaluations for both articulation and dynamics were strongly and positively correlated with evaluations of overall <span class="hlt">ensemble</span> expressivity. PMID:25104944</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70131483','USGSPUBS'); return false;" href="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> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Singh, Ramesh K.; Senay, Gabriel B.; Velpuri, Naga Manohar; Bohms, Stefanie; Verdin, James P.</p> <p>2014-01-01</p> <p> <span class="hlt">Downscaling</span> is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for <span class="hlt">downscaling</span> MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and <span class="hlt">downscaling</span>. The regression with intercept method does have limitations in <span class="hlt">downscaling</span> if the slope and intercept are computed over a large area. A good agreement was obtained between <span class="hlt">downscaled</span> monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from ?16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and <span class="hlt">downscaled</span> monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between <span class="hlt">downscaled</span> monthly AET using LinZI method and the gridded FLUXNET dataset. The <span class="hlt">downscaled</span> monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed <span class="hlt">downscaling</span> method will be very useful in advancing the application of remotely sensed images in water resources planning and management.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010ems..confE.409G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010ems..confE.409G"><span id="translatedtitle">Ideas for a pattern-oriented approach towards a VERA analysis <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gorgas, T.; Dorninger, M.</p> <p>2010-09-01</p> <p>Ideas for a pattern-oriented approach towards a VERA analysis <span class="hlt">ensemble</span> For many applications in meteorology and especially for verification purposes it is important to have some information about the uncertainties of observation and analysis data. A high quality of these "reference data" is an absolute necessity as the uncertainties are reflected in verification measures. The VERA (Vienna Enhanced Resolution Analysis) scheme includes a sophisticated quality control tool which accounts for the correction of observational data and provides an estimation of the observation uncertainty. It is crucial for meteorologically and physically reliable analysis fields. VERA is based on a variational principle and does not need any first guess fields. It is therefore NWP model independent and can also be used as an unbiased reference for real time model verification. For <span class="hlt">downscaling</span> purposes VERA uses an a priori knowledge on small-scale physical processes over complex terrain, the so called "fingerprint technique", which transfers information from rich to data sparse regions. The enhanced Joint D-PHASE and COPS data set forms the data base for the analysis <span class="hlt">ensemble</span> study. For the WWRP projects D-PHASE and COPS a joint activity has been started to collect GTS and non-GTS data from the national and regional meteorological services in Central Europe for 2007. Data from more than 11.000 stations are available for high resolution analyses. The usage of random numbers as perturbations for <span class="hlt">ensemble</span> experiments is a common approach in meteorology. In most implementations, like for NWP-model <span class="hlt">ensemble</span> systems, the focus lies on error growth and propagation on the spatial and temporal scale. When defining errors in analysis fields we have to consider the fact that analyses are not time dependent and that no perturbation method aimed at temporal evolution is possible. Further, the method applied should respect two major sources of analysis errors: Observation errors AND analysis or interpolation errors. With the concept of an analysis <span class="hlt">ensemble</span> we hope to get a more detailed sight on both sources of analysis errors. For the computation of the VERA <span class="hlt">ensemble</span> members a sample of Gaussian random perturbations is produced for each station and parameter. The deviation of perturbations is based on the correction proposals by the VERA QC scheme to provide some "natural" limits for the <span class="hlt">ensemble</span>. In order to put more emphasis on the weather situation we aim to integrate the main synoptic field structures as weighting factors for the perturbations. Two widely approved approaches are used for the definition of these main field structures: The Principal Component Analysis and a 2D-Discrete Wavelet Transform. The results of tests concerning the implementation of this pattern-supported analysis <span class="hlt">ensemble</span> system and a comparison of the different approaches are given in the presentation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFM.H21E0877R','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Reaney, S. M.; Fowler, H. J.</p> <p>2008-12-01</p> <p>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> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A41H0065B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A41H0065B"><span id="translatedtitle">Changes in water and wind resources across the central and northeastern U.S. 2060-2010 in 24 km WRF <span class="hlt">downscale</span> climate simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Birkel, S. D.; Maasch, K. A.; Oglesby, R. J.; Fulginiti, L.; Trindade, F.; Hays, C.</p> <p>2012-12-01</p> <p>GCM <span class="hlt">ensembles</span> for the IPCC AR4 indicate that by 2060 water and wind resources will change appreciably over the central and northeastern U.S. In order to investigate these possible changes on a scale relevant for agriculture and offshore wind-power planners, we produced 24 km <span class="hlt">downscale</span> simulations using the Weather Research and Forecasting (WRF) model. Our simulations span the years 2006-2010 and 2056-2060 with boundary conditions supplied by CCSM4 (IPCC emissions scenario RCP 8.5). By calculating the difference between the simulated time periods we find: 1) ~10% decrease in total annual precipitation across the southern half of the Ogallala aquifer in the central U.S., and ~10% increase across the northeastern states; and 2) Minimal change in annual-average 10-meter wind strength across the study areas, but with significant changes seasonal values. Interrogation of the simulation results is ongoing, and a complete synthesis will be presented at the annual meeting.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25844624','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25844624"><span id="translatedtitle">Individual differences in <span class="hlt">ensemble</span> perception reveal multiple, independent levels of <span class="hlt">ensemble</span> representation.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Haberman, Jason; Brady, Timothy F; Alvarez, George A</p> <p>2015-04-01</p> <p><span class="hlt">Ensemble</span> perception, including the ability to "see the average" from a group of items, operates in numerous feature domains (size, orientation, speed, facial expression, etc.). Although the ubiquity of <span class="hlt">ensemble</span> representations is well established, the large-scale cognitive architecture of this process remains poorly defined. We address this using an individual differences approach. In a series of experiments, observers saw groups of objects and reported either a single item from the group or the average of the entire group. High-level <span class="hlt">ensemble</span> representations (e.g., average facial expression) showed complete independence from low-level <span class="hlt">ensemble</span> representations (e.g., average orientation). In contrast, low-level <span class="hlt">ensemble</span> representations (e.g., orientation and color) were correlated with each other, but not with high-level <span class="hlt">ensemble</span> representations (e.g., facial expression and person identity). These results suggest that there is not a single domain-general <span class="hlt">ensemble</span> mechanism, and that the relationship among various <span class="hlt">ensemble</span> representations depends on how proximal they are in representational space. PMID:25844624</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.A33A0222M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.A33A0222M"><span id="translatedtitle">High-resolution climate simulations for Central Europe: An assessment of dynamical and statistical <span class="hlt">downscaling</span> techniques</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Miksovsky, J.; Huth, R.; Halenka, T.; Belda, M.; Farda, A.; Skalak, P.; Stepanek, P.</p> <p>2009-12-01</p> <p>To bridge the resolution gap between the outputs of global climate models (GCMs) and finer-scale data needed for studies of the climate change impacts, two approaches are widely used: dynamical <span class="hlt">downscaling</span>, based on application of regional climate models (RCMs) embedded into the domain of the GCM simulation, and statistical <span class="hlt">downscaling</span> (SDS), using empirical transfer functions between the large-scale data generated by the GCM and local measurements. In our contribution, we compare the performance of different variants of both techniques for the region of Central Europe. The dynamical <span class="hlt">downscaling</span> is represented by the outputs of two regional models run in the 10 km horizontal grid, ALADIN-CLIMATE/CZ (co-developed by the Czech Hydrometeorological Institute and Meteo-France) and RegCM3 (developed by the Abdus Salam Centre for Theoretical Physics). The applied statistical methods were based on multiple linear regression, as well as on several of its nonlinear alternatives, including techniques employing artificial neural networks. Validation of the <span class="hlt">downscaling</span> outputs was carried out using measured data, gathered from weather stations in the Czech Republic, Slovakia, Austria and Hungary for the end of the 20th century; series of daily values of maximum and minimum temperature, precipitation and relative humidity were analyzed. None of the regional models or statistical <span class="hlt">downscaling</span> techniques could be identified as the universally best one. For instance, while most statistical methods misrepresented the shape of the statistical distribution of the target variables (especially in the more challenging cases such as estimation of daily precipitation), RCM-generated data often suffered from severe biases. It is also shown that further enhancement of the simulated fields of climate variables can be achieved through a combination of dynamical <span class="hlt">downscaling</span> and statistical postprocessing. This can not only be used to reduce biases and other systematic flaws in the generated time series, but also to further localize the RCM outputs beyond the resolution of their original grid. The resulting data then provide a suitable input for subsequent studies of the local climate and its change in the target region.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhDT.......147K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhDT.......147K"><span id="translatedtitle">A probabilistic perspective for statistical <span class="hlt">downscaling</span> of climate variables: Model development, application, and evaluation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kirchmeier-Young, Megan C.</p> <p></p> <p>While large-scale climate models provide valuable information globally, regional or local studies of climate impacts often benefit from much higher-resolution information. Statistical <span class="hlt">downscaling</span> is the process of extracting this local-scale information from the large scale via an empirical relationship. In contrast to deterministic methods, which predict a single value for each time step, in this study, a probabilistic method is developed that predicts a full probability density function (PDF) for each time step. This PDF represents the range of possible values of the local-scale variable, based on the given large-scale conditions for that day. As such, the probabilistic method holds several advantages over deterministic approaches, especially in its ability to realistically represent extreme events. The development of a probabilistic model to <span class="hlt">downscale</span> daily wind speed for a mid-western region is presented. A vector generalized linear model (VGLM) based on a gamma distribution is used to predict the mean and standard deviation of the local-scale wind speed as a function of the large-scale wind. This <span class="hlt">downscaling</span> model is then applied to output from a suite of CMIP5 GCMs and the changes in the distribution are assessed for a future scenario. Variability is large, particularly across models, though a strong correlation is identified between changes in the extremes and changes in the mean. Furthermore, the added value of producing a high-resolution grid from the <span class="hlt">downscaled</span> point data is investigated. The evaluation of the probabilistic <span class="hlt">downscaling</span> model takes a broader approach, focusing on more commonly used variables: daily precipitation and daily minimum and maximum temperatures. As verifying probabilistic output against a single observation presents unique challenges, a wide range of diverse metrics is presented to test both the daily distributions and characteristics of the realizations. A key focus is placed on non-standard variables (e.g., duration of heat waves) that are of importance to users of the <span class="hlt">downscaled</span> data in various impacts sectors. We emphasize the need to use an encompassing set of metrics that tailor to the needs of the dataset's users.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1710850Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1710850Z"><span id="translatedtitle">Employing multi-objective Genetic Programming to the <span class="hlt">downscaling</span> of near-surface atmospheric fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zerenner, Tanja; Venema, Victor; Friederichs, Petra; Simmer, Clemens</p> <p>2015-04-01</p> <p>The coupling of models for the different components of the Soil-Vegetation-Atmosphere-System is required to investigate component interactions and feedback processes. However, the component models for atmosphere, land-surface and subsurface are usually operated at different resolutions in space and time owing to the dominant processes. The computationally expensive atmospheric models are typically employed at a coarser resolution than land-surface and subsurface models. Thus up- and <span class="hlt">downscaling</span> procedures are required at the interface between the atmospheric model and the land-surface/subsurface models. We apply multi-objective Genetic Programming (GP) to a training data set of high-resolution atmospheric model runs to learn <span class="hlt">downscaling</span> rules, i. e., equations or short programs that reconstruct the fine-scale fields of the near-surface atmospheric state variables from the coarse atmospheric model output. Like artificial neural networks, GP can flexibly incorporate multivariate and nonlinear relations, but offers the advantage that the solutions are human readable and thus can be checked for physical consistency. Further, the Strength Pareto Approach for multi-objective fitness assignment allows to consider multiple characteristics of the fine-scale fields during the learning procedure. We have applied the described machine learning methodology to a training data set of 400 m resolution COSMO model runs to learn <span class="hlt">downscaling</span> rules which recover realistic fine-scale structures from the coarsened fields at 2.8 km resolution. Hence we are currently <span class="hlt">downscaling</span> by a factor of 7. The COSMO model is the weather forecast model developed and maintained by the German Weather Service and is contained in the Terrestrial Systems Modeling Platform (TerrSysMP), which couples the atmospheric COSMO model to land-surface model CLM and subsurface hydrological model ParFlow. Finally we aim at implementing the learned <span class="hlt">downscaling</span> rules in the TerrSysMP to achieve scale-consistent coupling between atmosphere and land-surface/subsurface. The presentation will cover the multi-objective GP methodology as well as examples illustrating its performance for <span class="hlt">downscaling</span> of near-surface temperature. The multi-objective GP methodology constitutes an advancement compared to linear regression conditioned on indicators especially for nights with strong radiative cooling. Although GP produces potentially nonlinear solutions, overfitting tendencies are only evident for few exceptions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&keyword=wind+AND+energy&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55633586&CFTOKEN=64366313','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&keyword=wind+AND+energy&actType=&TIMSType=+&TIMSSubTypeID=&DEID=&epaNumber=&ntisID=&archiveStatus=Both&ombCat=Any&dateBeginCreated=&dateEndCreated=&dateBeginPublishedPresented=&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&dateBeginCompleted=&dateEndCompleted=&personID=&role=Any&journalID=&publisherID=&sortBy=revisionDate&count=50&CFID=55633586&CFTOKEN=64366313"><span id="translatedtitle">An Observation-base investigation of nudging in WRF for <span class="hlt">downscaling</span> surface climate information to 12-km Grid Spacing</span></a></p> <p><a target="_blank" href="http://oaspub.epa.gov/eims/query.page">EPA Science Inventory</a></p> <p></p> <p></p> <p>Previous research has demonstrated the ability to use the Weather Research and Forecast (WRF) model and contemporary dynamical <span class="hlt">downscaling</span> methods to refine global climate modeling results to a horizontal resolution of 36 km. Environmental managers and urban planners have expre...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4415763','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4415763"><span id="translatedtitle">A Bayesian <span class="hlt">Ensemble</span> Approach for Epidemiological Projections</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Lindström, Tom; Tildesley, Michael; Webb, Colleen</p> <p>2015-01-01</p> <p>Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, <span class="hlt">ensemble</span> modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model <span class="hlt">ensembles</span> based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the <span class="hlt">ensemble</span> prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for <span class="hlt">ensembles</span> with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for <span class="hlt">ensemble</span> modeling of disease outbreaks. PMID:25927892</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/20136746','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/20136746"><span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Stohlgren, Thomas J; Ma, Peter; Kumar, Sunil; Rocca, Monique; Morisette, Jeffrey T; Jarnevich, Catherine S; Benson, Nate</p> <p>2010-02-01</p> <p><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis. PMID:20136746</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26565367','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26565367"><span id="translatedtitle">Simulations in generalized <span class="hlt">ensembles</span> through noninstantaneous switches.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Giovannelli, Edoardo; Cardini, Gianni; Chelli, Riccardo</p> <p>2015-10-01</p> <p>Generalized-<span class="hlt">ensemble</span> simulations, such as replica exchange and serial generalized-<span class="hlt">ensemble</span> methods, are powerful simulation tools to enhance sampling of free energy landscapes in systems with high energy barriers. In these methods, sampling is enhanced through instantaneous transitions of replicas, i.e., copies of the system, between different <span class="hlt">ensembles</span> characterized by some control parameter associated with thermodynamical variables (e.g., temperature or pressure) or collective mechanical variables (e.g., interatomic distances or torsional angles). An interesting evolution of these methodologies has been proposed by replacing the conventional instantaneous (trial) switches of replicas with noninstantaneous switches, realized by varying the control parameter in a finite time and accepting the final replica configuration with a Metropolis-like criterion based on the Crooks nonequilibrium work (CNW) theorem. Here we revise these techniques focusing on their correlation with the CNW theorem in the framework of Markovian processes. An outcome of this report is the derivation of the acceptance probability for noninstantaneous switches in serial generalized-<span class="hlt">ensemble</span> simulations, where we show that explicit knowledge of the time dependence of the weight factors entering such simulations is not necessary. A generalized relationship of the CNW theorem is also provided in terms of the underlying equilibrium probability distribution at a fixed control parameter. Illustrative calculations on a toy model are performed with serial generalized-<span class="hlt">ensemble</span> simulations, especially focusing on the different behavior of instantaneous and noninstantaneous replica transition schemes. PMID:26565367</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70035550','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70035550"><span id="translatedtitle"><span class="hlt">Ensemble</span> habitat mapping of invasive plant species</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.</p> <p>2010-01-01</p> <p><span class="hlt">Ensemble</span> species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. <span class="hlt">Ensemble</span> models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and <span class="hlt">ensemble</span> modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, <span class="hlt">ensemble</span> models were the only models that ranked in the top three models for both field validation and test data. <span class="hlt">Ensemble</span> models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006NPGeo..13..167H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006NPGeo..13..167H"><span id="translatedtitle">Growth of finite errors in <span class="hlt">ensemble</span> prediction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Harle, M.; Kwasniok, F.; Feudel, U.</p> <p>2006-06-01</p> <p>We study the predictability of chaotic conservative and dissipative maps in the context of <span class="hlt">ensemble</span> prediction. Finite-size perturbations around a reference trajectory are evolved under the full nonlinear system dynamics; this evolution is characterized by error growth factors and investigated as a function of prediction time and initial perturbation size. The distribution of perturbation growth is studied. We then focus on the worst-case predictability, i.e., the maximum error growth over all initial conditions. The estimate of the worst-case predictability obtained from the <span class="hlt">ensemble</span> approach is compared to the estimate given by the largest singular value of the linearized system dynamics. For small prediction times, the worst-case error growth obtained from the nonlinear <span class="hlt">ensemble</span> approach is exponential with prediction time; for large prediction times, a power-law dependence is observed the scaling exponent of which depends systematically on the initial error size. The question is addressed of how large an <span class="hlt">ensemble</span> is necessary to reliably estimate the maximum error growth factor. A power-law dependence of the error in the estimate of the growth factor on the <span class="hlt">ensemble</span> size is established empirically. Our results are valid for several markedly different chaotic conservative and dissipative systems, perhaps pointing to quite general features.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhRvE..92d3310G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvE..92d3310G"><span id="translatedtitle">Simulations in generalized <span class="hlt">ensembles</span> through noninstantaneous switches</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Giovannelli, Edoardo; Cardini, Gianni; Chelli, Riccardo</p> <p>2015-10-01</p> <p>Generalized-<span class="hlt">ensemble</span> simulations, such as replica exchange and serial generalized-<span class="hlt">ensemble</span> methods, are powerful simulation tools to enhance sampling of free energy landscapes in systems with high energy barriers. In these methods, sampling is enhanced through instantaneous transitions of replicas, i.e., copies of the system, between different <span class="hlt">ensembles</span> characterized by some control parameter associated with thermodynamical variables (e.g., temperature or pressure) or collective mechanical variables (e.g., interatomic distances or torsional angles). An interesting evolution of these methodologies has been proposed by replacing the conventional instantaneous (trial) switches of replicas with noninstantaneous switches, realized by varying the control parameter in a finite time and accepting the final replica configuration with a Metropolis-like criterion based on the Crooks nonequilibrium work (CNW) theorem. Here we revise these techniques focusing on their correlation with the CNW theorem in the framework of Markovian processes. An outcome of this report is the derivation of the acceptance probability for noninstantaneous switches in serial generalized-<span class="hlt">ensemble</span> simulations, where we show that explicit knowledge of the time dependence of the weight factors entering such simulations is not necessary. A generalized relationship of the CNW theorem is also provided in terms of the underlying equilibrium probability distribution at a fixed control parameter. Illustrative calculations on a toy model are performed with serial generalized-<span class="hlt">ensemble</span> simulations, especially focusing on the different behavior of instantaneous and noninstantaneous replica transition schemes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H43A1313W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H43A1313W"><span id="translatedtitle">The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment (HEPEX)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, A. W.; Thielen, J.; Pappenberger, F.; Schaake, J. C.; Hartman, R. K.</p> <p>2012-12-01</p> <p>The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF). With support from the US National Weather Service (NWS) and the European Commission (EC), the HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological <span class="hlt">ensemble</span> forecasts for decision support in emergency management and water resources sectors. The strategy to meet this goal includes meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. HEPEX has organized about a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Today, the HEPEX mission is to demonstrate the added value of hydrological <span class="hlt">ensemble</span> prediction systems (HEPS) for emergency management and water resources sectors to make decisions that have important consequences for economy, public health, safety, and the environment. HEPEX is now organised around six major themes that represent core elements of a hydrologic <span class="hlt">ensemble</span> prediction enterprise: input and pre-processing, <span class="hlt">ensemble</span> techniques, data assimilation, post-processing, verification, and communication and use in decision making. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AdSpR..54..655M','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mukherjee, Sandip; Joshi, P. K.; Garg, R. D.</p> <p>2014-08-01</p> <p>Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (∼1000 m) thermal images with high revisiting capability (∼1 day) are freely available. To bridge this gap, present study attempts to <span class="hlt">downscale</span> coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression <span class="hlt">downscaling</span> (LMSDS) and (v) Pace regression <span class="hlt">downscaling</span> (PRDS) are applied to <span class="hlt">downscale</span> LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and <span class="hlt">downscaled</span> to 480 m and 240 m resolution. The <span class="hlt">downscale</span> accuracy is achieved using LMSDS and PRDS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data <span class="hlt">downscaled</span> from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMSDS and PRDS models, respectively. The LMSDS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. <span class="hlt">Downscaling</span> model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1999JCli...12..258B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1999JCli...12..258B"><span id="translatedtitle">Verification of GCM-Generated Regional Seasonal Precipitation for Current Climate and of Statistical <span class="hlt">Downscaling</span> Estimates under Changing Climate Conditions.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Busuioc, Aristita; von Storch, Hans; Schnur, Reiner</p> <p>1999-01-01</p> <p>Empirical <span class="hlt">downscaling</span> procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a <span class="hlt">downscaling</span> technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical <span class="hlt">downscaling</span> procedures in climate change applications.The case considered is regional seasonal precipitation in Romania. The <span class="hlt">downscaling</span> model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). The climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in `time-slice' mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050.The <span class="hlt">downscaling</span> model is skillful for all seasons except spring. The general features of the large-scale SLP variability are reproduced fairly well by both GCMs in all seasons. The climate models reproduce the empirically determined precipitation-SLP link in winter, whereas the observed link is only partially captured for the other seasons. Thus, these models may be considered skillful with respect to regional precipitation during winter, and partially during the other seasons. Generally, applications of statistical <span class="hlt">downscaling</span> to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 × CO2 GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical <span class="hlt">downscaling</span>. Since the skill of the GCMs in regional terms is already established, it is concluded that the <span class="hlt">downscaling</span> technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/22619572','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/22619572"><span id="translatedtitle">Temporal <span class="hlt">downscaling</span> of crop coefficient and crop water requirement from growing stage to substage scales.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Shang, Songhao</p> <p>2012-01-01</p> <p>Crop water requirement is essential for agricultural water management, which is usually available for crop growing stages. However, crop water requirement values of monthly or weekly scales are more useful for water management. A method was proposed to <span class="hlt">downscale</span> crop coefficient and water requirement from growing stage to substage scales, which is based on the interpolation of accumulated crop and reference evapotranspiration calculated from their values in growing stages. The proposed method was compared with two straightforward methods, that is, direct interpolation of crop evapotranspiration and crop coefficient by assuming that stage average values occurred in the middle of the stage. These methods were tested with a simulated daily crop evapotranspiration series. Results indicate that the proposed method is more reliable, showing that the <span class="hlt">downscaled</span> crop evapotranspiration series is very close to the simulated ones. PMID:22619572</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.tmp..351G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.tmp..351G"><span id="translatedtitle">Validating the dynamic <span class="hlt">downscaling</span> ability of WRF for East Asian summer climate</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gao, Jiangbo; Hou, Wenjuan; Xue, Yongkang; Wu, Shaohong</p> <p>2015-12-01</p> <p>To better understand the regional climate model (RCM) performance for East Asian summer climate and the influencing factors, this study evaluated the dynamic <span class="hlt">downscaling</span> ability of the Weather Research Forecast (WRF) RCM. According to the comprehensive comparison studies on different physical processes and experimental settings, the optimal combination of WRF model setups can be obtained for East Asian precipitation and temperature simulations. Furthermore, based on the optimal combination, when compared with climate observations, WRF shows high ability to <span class="hlt">downscale</span> NCEP DOE Reanalysis-2, which provided initial and lateral boundary conditions for the WRF, especially for the precipitation simulation due to the better simulated low-level water vapor flux. However, the strengthened Western North Pacific Subtropical High (WPSH) from WRF simulation results in the positive anomaly for summer rainfall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC12C..05G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC12C..05G"><span id="translatedtitle">Lessons learned from the National Climate Predictions and Projections (NCPP) platform Workshop on Quantitative Evaluation of <span class="hlt">Downscaling</span> 2013</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guentchev, G.</p> <p>2013-12-01</p> <p>The mission of NCPP is to accelerate the provision of climate information on regional and local scale for use in adaptation planning and decision making through collaborative participation of a community of scientists and practitioners. A major focus is the development of a capability for objective and quantitative evaluation of <span class="hlt">downscaled</span> climate information in support of applications. NCPP recognizes the importance of focusing this evaluation effort on real-world applications and the necessity to work closely with the user community to deliver usable evaluations and guidance. This summer NCPP organized our first workshop on quantitative evaluation of <span class="hlt">downscaled</span> climate datasets (http://earthsystemcog.org/projects/<span class="hlt">downscaling</span>-2013/). Workshop participants included representatives from <span class="hlt">downscaling</span> efforts, applications partners from the health, ecological, agriculture and water resources impacts communities, and people working on data infrastructure, metadata, and standards development. The workshop exemplifies NCPP's approach of collaborative and participatory problem-solving where scientists are working together with practitioners to develop applications related evaluation. The set of observed and <span class="hlt">downscaled</span> datasets included for evaluation in the workshop were assessed using a variety of metrics to elucidate the statistical characteristics of temperature and precipitation time series. In addition, the <span class="hlt">downscaled</span> datasets were evaluated in terms of their representation of indices relevant to the participating applications working groups, more specifically related to human health and ecological impacts. The presentation will focus on sharing the lessons we learned from our workshop.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015SPIE.9534E..15R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015SPIE.9534E..15R"><span id="translatedtitle"><span class="hlt">Ensemble</span> approach for differentiation of malignant melanoma</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rastgoo, Mojdeh; Morel, Olivier; Marzani, Franck; Garcia, Rafael</p> <p>2015-04-01</p> <p>Melanoma is the deadliest type of skin cancer, yet it is the most treatable kind depending on its early diagnosis. The early prognosis of melanoma is a challenging task for both clinicians and dermatologists. Due to the importance of early diagnosis and in order to assist the dermatologists, we propose an automated framework based on <span class="hlt">ensemble</span> learning methods and dermoscopy images to differentiate melanoma from dysplastic and benign lesions. The evaluation of our framework on the recent and public dermoscopy benchmark (PH2 dataset) indicates the potential of proposed method. Our evaluation, using only global features, revealed that <span class="hlt">ensembles</span> such as random forest perform better than single learner. Using random forest <span class="hlt">ensemble</span> and combination of color and texture features, our framework achieved the highest sensitivity of 94% and specificity of 92%.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.7130N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.7130N"><span id="translatedtitle"><span class="hlt">Downscaling</span> RCM output to km resolution: effect on Greenland surface mass balance</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Noel, Brice; van de Berg, Willem Jan; van Meijgaard, Erik; Fettweis, Xavier; Machguth, Horst; van den Broeke, Michiel</p> <p>2015-04-01</p> <p>The relatively narrow ablation zone of the Greenland ice sheet (GrIS), typically ~10-150 km wide, is not often accurately resolved even in regional climate models (RCMs). This may lead to underestimation of melt, runoff and other SMB components. Sub-km resolution SMB components would be necessary to capture the spatial variability of SMB associated to local variations in topography. However, such high-resolution simulations would require a huge computational effort and are therefore only restricted to small regions and short periods. In this study, we statistically <span class="hlt">downscale</span> individual SMB components of the regional climate model RACMO2.3 for the period 1958-2013, using their height dependency. We apply a bi-linear interpolation from the original RACMO2 resolution of 11 km to 1 km, and correct for elevation differences between the native and interpolated grid. This method allows a reconstruction of the GrIS SMB as a function of individually <span class="hlt">downscaled</span> SMB components, i.e. precipitation, sublimation and runoff, instead of directly <span class="hlt">downscaling</span> SMB which would provide less physical insight in the final product. Interestingly, the spatially integrated amount of melt and runoff does not change significantly between the two fields. This is discussed and explained. Next we compare the modelled RACMO2.3 SMB values at the native 11 km grid and the <span class="hlt">downscaled</span> field to in-situ measurements from 108 stake sites situated in the ablation zone of the ice sheet, a subset of a newly compiled ablation dataset. Finally, we compare results at 1km with another regional climate model, MAR.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.B31D0049B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.B31D0049B"><span id="translatedtitle">Site Level Climate <span class="hlt">Downscaling</span> for Forecasting Water Balance Stress and Reslience of Acadian Boreal Trees</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brooks, B. G.; Serbin, S.</p> <p>2014-12-01</p> <p>A <span class="hlt">downscaling</span> framework is presented and applied to physiological and climatic data for projecting future climate resilience of one key boreal tree species, black spruce, in Cape Breton Highlands, Nova Scotia. The technique is based on a combination of probabilistic <span class="hlt">downscaling</span> methods and control system theory, which together are used to transform large-scale future climate input (air temperature, humidity) to local scale climate parameters important to plant biophysical processes (vapor pressure deficit). Large-scale forecast data from the Community Earth System Model were <span class="hlt">downscaled</span> spatially then temporally based on the cumulative distributions and sub-daily patterns from corresponding observational data at North Mountain (Cape Breton). Validation over historical decades shows that this technique provides hourly temperature and vapor pressure deficit data accurate to within 0.7%. Further we applied these environmental factors to a species specific empirical model of stomatal conductance for black spruce to compare differences in predicted water regulation response when large-scale (ESM) data are used as drivers versus localized data transformed using this new site-level <span class="hlt">downscaling</span> technique. We observe through this synthetic study that over historical to contemporary periods (1850-2006) differences between large-scale and localized forecasts of stomatal conductance were small but that future climate extremes (2006-2100) have a strong effect on derived water balance in black spruce. These results also suggest that black spruce in the Cape Breton Highlands may have biophysical responses to climate change that are not predicted by spatially coarse (1°) data, which does not include site level extremes that in this study are shown to strongly curb future growth rates in black spruce as present day climate extremes become common place.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..519.3163S','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shashikanth, K.; Madhusoodhanan, C. G.; Ghosh, Subimal; Eldho, T. I.; Rajendran, K.; Murtugudde, Raghu</p> <p>2014-11-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4792414','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4792414"><span id="translatedtitle">Using Random Forest to Improve the <span class="hlt">Downscaling</span> of Global Livestock Census Data</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Nicolas, Gaëlle; Robinson, Timothy P.; Wint, G. R. William; Conchedda, Giulia; Cinardi, Giuseppina; Gilbert, Marius</p> <p>2016-01-01</p> <p>Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a <span class="hlt">downscaling</span> methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and <span class="hlt">downscaling</span> at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to <span class="hlt">downscale</span> census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better <span class="hlt">downscaled</span> estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations. PMID:26977807</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012HESSD...910719D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012HESSD...910719D"><span id="translatedtitle">Evaluation of areal precipitation estimates based on <span class="hlt">downscaled</span> reanalysis and station data by hydrological modelling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Duethmann, D.; Zimmer, J.; Gafurov, A.; Güntner, A.; Merz, B.; Vorogushyn, S.</p> <p>2012-09-01</p> <p>In data sparse regions, as in many mountainous catchments, it is a challenge to generate suitable precipitation input fields for hydrological modelling, as station data do not provide enough information to derive areal precipitation estimates. This study presents a method using the spatial variation of precipitation from <span class="hlt">downscaled</span> reanalysis data for the interpolation of gauge observations. The second aim of this study is the evaluation of different precipitation estimates by hydrological modelling. Study area is the Karadarya catchment in Central Asia (11 700 km2). ERA-40 reanalysis data are <span class="hlt">downscaled</span> with the regional climate model Weather Research and Forecasting Model (WRF). Precipitation data from gauge observations are interpolated (i) using monthly accumulated WRF precipitation data, (ii) using monthly fields from multiple linear regression against topographical variables and (iii) with the inverse distance approach. These precipitation data sets are also compared to (iv) the direct use of the precipitation output from the WRF <span class="hlt">downscaled</span> ERA-40 data and (v) precipitation from the APHRODITE data set. Our study suggests that using monthly fields from <span class="hlt">downscaled</span> reanalysis data can be a good approach for the interpolation of station data in data sparse mountainous regions. Compared to mean annual precipitation from continental and global scale gridded data sets our precipitation estimates for the study area are considerably higher. The introduction of a calibrated precipitation bias factor for the comparison of different precipitation estimates by hydrological modelling allows for a more informed differentiation with regard to the temporal dynamics, on the one hand, and the overall bias, on the other hand. Uncertainty and sensitivity analyses suggest that our results are robust against uncertainties in the calibration parameters, other model parameters and inputs, and the selected calibration period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016NHESS..16..167M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016NHESS..16..167M"><span id="translatedtitle">Run-up parameterization and beach vulnerability assessment on a barrier island: a <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medellín, G.; Brinkkemper, J. A.; Torres-Freyermuth, A.; Appendini, C. M.; Mendoza, E. T.; Salles, P.</p> <p>2016-01-01</p> <p>We present a <span class="hlt">downscaling</span> approach for the study of wave-induced extreme water levels at a location on a barrier island in Yucatán (Mexico). Wave information from a 30-year wave hindcast is validated with in situ measurements at 8 m water depth. The maximum dissimilarity algorithm is employed for the selection of 600 representative cases, encompassing different combinations of wave characteristics and tidal level. The selected cases are propagated from 8 m water depth to the shore using the coupling of a third-generation wave model and a phase-resolving non-hydrostatic nonlinear shallow-water equation model. Extreme wave run-up, R2%, is estimated for the simulated cases and can be further employed to reconstruct the 30-year time series using an interpolation algorithm. <span class="hlt">Downscaling</span> results show run-up saturation during more energetic wave conditions and modulation owing to tides. The latter suggests that the R2% can be parameterized using a hyperbolic-like formulation with dependency on both wave height and tidal level. The new parametric formulation is in agreement with the <span class="hlt">downscaling</span> results (r2 = 0.78), allowing a fast calculation of wave-induced extreme water levels at this location. Finally, an assessment of beach vulnerability to wave-induced extreme water levels is conducted at the study area by employing the two approaches (reconstruction/parameterization) and a storm impact scale. The 30-year extreme water level hindcast allows the calculation of beach vulnerability as a function of return periods. It is shown that the <span class="hlt">downscaling</span>-derived parameterization provides reasonable results as compared with the numerical approach. This methodology can be extended to other locations and can be further improved by incorporating the storm surge contributions to the extreme water level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.A43F3330H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.A43F3330H"><span id="translatedtitle">Extended-Range High-Resolution Dynamical <span class="hlt">Downscaling</span> over a Continental-Scale Domain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Husain, S. Z.; Separovic, L.; Yu, W.; Fernig, D.</p> <p>2014-12-01</p> <p>High-resolution mesoscale simulations, when applied for <span class="hlt">downscaling</span> meteorological fields over large spatial domains and for extended time periods, can provide valuable information for many practical application scenarios including the weather-dependent renewable energy industry. In the present study, a strategy has been proposed to dynamically <span class="hlt">downscale</span> coarse-resolution meteorological fields from Environment Canada's regional analyses for a period of multiple years over the entire Canadian territory. The study demonstrates that a continuous mesoscale simulation over the entire domain is the most suitable approach in this regard. Large-scale deviations in the different meteorological fields pose the biggest challenge for extended-range simulations over continental scale domains, and the enforcement of the lateral boundary conditions is not sufficient to restrict such deviations. A scheme has therefore been developed to spectrally nudge the simulated high-resolution meteorological fields at the different model vertical levels towards those embedded in the coarse-resolution driving fields derived from the regional analyses. A series of experiments were carried out to determine the optimal nudging strategy including the appropriate nudging length scales, nudging vertical profile and temporal relaxation. A forcing strategy based on grid nudging of the different surface fields, including surface temperature, soil-moisture, and snow conditions, towards their expected values obtained from a high-resolution offline surface scheme was also devised to limit any considerable deviation in the evolving surface fields due to extended-range temporal integrations. The study shows that ensuring large-scale atmospheric similarities helps to deliver near-surface statistical scores for temperature, dew point temperature and horizontal wind speed that are better or comparable to the operational regional forecasts issued by Environment Canada. Furthermore, the meteorological fields resulting from the proposed <span class="hlt">downscaling</span> strategy have significantly improved spatiotemporal variance compared to those from the operational forecasts, and any time series generated from the <span class="hlt">downscaled</span> fields do not suffer from discontinuities due to switching between the consecutive forecasts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.120..341K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.120..341K"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> and future scenario generation of temperatures for Pakistan Region</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kazmi, Dildar Hussain; Li, Jianping; Rasul, Ghulam; Tong, Jiang; Ali, Gohar; Cheema, Sohail Babar; Liu, Luliu; Gemmer, Marco; Fischer, Thomas</p> <p>2015-04-01</p> <p>Finer climate change information on spatial scale is required for impact studies than that presently provided by global or regional climate models. It is especially true for regions like South Asia with complex topography, coastal or island locations, and the areas of highly heterogeneous land-cover. To deal with the situation, an inexpensive method (statistical <span class="hlt">downscaling</span>) has been adopted. Statistical <span class="hlt">DownScaling</span> Model (SDSM) employed for <span class="hlt">downscaling</span> of daily minimum and maximum temperature data of 44 national stations for base time (1961-1990) and then the future scenarios generated up to 2099. Observed as well as Predictors (product of National Oceanic and Atmospheric Administration) data were calibrated and tested on individual/multiple basis through linear regression. Future scenario was generated based on HadCM3 daily data for A2 and B2 story lines. The <span class="hlt">downscaled</span> data has been tested, and it has shown a relatively strong relationship with the observed in comparison to ECHAM5 data. Generally, the southern half of the country is considered vulnerable in terms of increasing temperatures, but the results of this study projects that in future, the northern belt in particular would have a possible threat of increasing tendency in air temperature. Especially, the northern areas (hosting the third largest ice reserves after the Polar Regions), an important feeding source for Indus River, are projected to be vulnerable in terms of increasing temperatures. Consequently, not only the hydro-agricultural sector but also the environmental conditions in the area may be at risk, in future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.9429S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.9429S"><span id="translatedtitle">Sensitivity of Hydrological Model Simulations to Underling Assumptions in a Stochastic <span class="hlt">Downscaling</span> method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sapriza, Gonzalo; Jodar, Jorge; Carrera, Jesús; Gupta, Hoshin V.</p> <p>2013-04-01</p> <p>Climate Change Impacts Studies (CCIS) for Water Resources Management (WRM) are of crucial importance for the human community and especially for water scarce Mediterranean- like regions, where the available water is expected to decrease due to climate change. General Circulation Models (GCM) are one of the most valuable tools available to perform CCIS. However, they cannot be directly applied to water resources evaluations due to their coarse spatial resolution and bias in their simulation of certain outputs, especially precipitation. <span class="hlt">Downscaling</span> methods have been developed to address this problem, by defining statistical relationships between the variables simulated by GCMs and local observations. Once these relationships are defined and tested via post evaluation during a control period, the relationship is used to generate synthetic time series for the future, based on the different future climate scenarios simulated by the GCMs. For CCIS in WRM, synthetic time series of precipitation and temperature are applied as input variables to run hydrological models and obtain future projections of hydrological response. The main drawbacks of this procedure are: (1) inevitably we have to assume time stationary in the <span class="hlt">downscaling</span> parameters (which in principle can vary with climate change), and (2) The <span class="hlt">downscaling</span> parameterizations are another source of model uncertainties that must be quantified and communicated. Here, we evaluate the sensitivity of hydrological model simulations to assumptions underlying a <span class="hlt">downscaling</span> method based on a Stochastic Rainfall Generating process (SRGP). The method is used to demonstrate that exact daily rainfall sequences are not necessary for climate impacts assessment, and that the "stochastically equivalent" rainfall sequence simulations provided by the model are both sufficient, and provide important added value in terms of realistic assessments of uncertainty. The method also establishes which parameters of the rainfall generating process are primary controllers of the impacts caused by climate variability/change, and which must therefore be given special consideration during long-term climate simulations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015NHESD...3.3077M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015NHESD...3.3077M"><span id="translatedtitle">Runup parameterization and beach vulnerability assessment on a barrier island: a <span class="hlt">downscaling</span> approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Medellín, G.; Brinkkemper, J. A.; Torres-Freyermuth, A.; Appendini, C. M.; Mendoza, E. T.; Salles, P.</p> <p>2015-05-01</p> <p>We present a <span class="hlt">downscaling</span> approach for the study of wave-induced extreme water levels at a location on a barrier island in Yucatan (Mexico). Wave information from a 30 year wave hindcast is validated with in situ measurements at 8 m water depth. The Maximum Dissimilarity Algorithm is employed for the selection of 600 representative cases, encompassing different wave characteristics and tidal level combinations. The selected cases are propagated from 8 m water depth till the shore using the coupling of a third-generation wave model and a phase-resolving non-hydrostatic Nonlinear Shallow Water Equations model. Extreme wave runup, R2%, is estimated for the simulated cases and can be further employed to reconstruct the 30 year period using an interpolation algorithm. <span class="hlt">Downscaling</span> results show runup saturation during more energetic wave conditions and modulation owing to tides. The latter suggests that the R2% can be parameterized using a hyperbolic-like formulation with dependency on both wave height and tidal level. The new parametric formulation is in agreement with the <span class="hlt">downscaling</span> results (r2 = 0.78), allowing a fast calculation of wave-induced extreme water levels at this location. Finally, an assessment of beach vulnerability to wave-induced extreme water level is conducted at the study area by employing the two approaches (reconstruction/parametrization) and a storm impact scale. The 30 year extreme water level hindcast allows the calculation of beach vulnerability as a function of return periods. It is shown that the <span class="hlt">downscaling</span>-derived parameterization provides reasonable results as compared with the numerical approach. This methodology can be extended to other locations and can be further improved by incorporating the storm surge contributions to the extreme water level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/15010453','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/15010453"><span id="translatedtitle">Hydrologic Implications of Dynamical and Statistical Approaches to <span class="hlt">Downscaling</span> Climate Model Outputs</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Wood, Andrew W; Leung, Lai R; Sridhar, V; Lettenmaier, D P</p> <p>2004-01-01</p> <p>Six approaches for <span class="hlt">downscaling</span> climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical <span class="hlt">downscaling</span> methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical <span class="hlt">downscaling</span> via a Regional Climate Model (RCM – at ½-degree spatial resolution), for <span class="hlt">downscaling</span> the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.tmp..135R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.tmp..135R"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of rainfall: a non-stationary and multi-resolution approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rashid, Md. Mamunur; Beecham, Simon; Chowdhury, Rezaul Kabir</p> <p>2015-04-01</p> <p>A novel <span class="hlt">downscaling</span> technique is proposed in this study whereby the original rainfall and reanalysis variables are first decomposed by wavelet transforms and rainfall is modelled using the semi-parametric additive model formulation of Generalized Additive Model in Location, Scale and Shape (GAMLSS). The flexibility of the GAMLSS model makes it feasible as a framework for non-stationary modelling. Decomposition of a rainfall series into different components is useful to separate the scale-dependent properties of the rainfall as this varies both temporally and spatially. The study was conducted at the Onkaparinga river catchment in South Australia. The model was calibrated over the period 1960 to 1990 and validated over the period 1991 to 2010. The model reproduced the monthly variability and statistics of the observed rainfall well with Nash-Sutcliffe efficiency (NSE) values of 0.66 and 0.65 for the calibration and validation periods, respectively. It also reproduced well the seasonal rainfall over the calibration (NSE = 0.37) and validation (NSE = 0.69) periods for all seasons. The proposed model was better than the tradition modelling approach (application of GAMLSS to the original rainfall series without decomposition) at reproducing the time-frequency properties of the observed rainfall, and yet it still preserved the statistics produced by the traditional modelling approach. When <span class="hlt">downscaling</span> models were developed with general circulation model (GCM) historical output datasets, the proposed wavelet-based <span class="hlt">downscaling</span> model outperformed the traditional <span class="hlt">downscaling</span> model in terms of reproducing monthly rainfall for both the calibration and validation periods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009PhRvA..79c2336O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009PhRvA..79c2336O"><span id="translatedtitle">Distinguishability measures between <span class="hlt">ensembles</span> of quantum states</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Oreshkov, Ognyan; Calsamiglia, John</p> <p>2009-03-01</p> <p>A quantum <span class="hlt">ensemble</span> {(px,?x)} is a set of quantum states each occurring randomly with a given probability. Quantum <span class="hlt">ensembles</span> are necessary to describe situations with incomplete a priori information, such as the output of a stochastic quantum channel (generalized measurement), and play a central role in quantum communication. In this paper, we propose measures of distance and fidelity between two quantum <span class="hlt">ensembles</span>. We consider two approaches: the first one is based on the ability to mimic one <span class="hlt">ensemble</span> given the other one as a resource and is closely related to the Monge-Kantorovich optimal transportation problem, while the second one uses the idea of extended-Hilbert-space (EHS) representations which introduce auxiliary pointer (or flag) states. Both types of measures enjoy a number of desirable properties. The Kantorovich measures, albeit monotonic under deterministic quantum operations, are not monotonic under generalized measurements. In contrast, the EHS measures are. This property can be regarded as a generalization of the monotonicity under deterministic maps of the trace distance and the fidelity between states. The EHS measures are equivalent to convex optimization problems and are bounded by the Kantorovich measures which are equivalent to linear programs. We present operational interpretations for both types of measures. We also show that the EHS fidelity between <span class="hlt">ensembles</span> provides an interpretation of the fidelity between mixed states as the fidelity between all pure-state <span class="hlt">ensembles</span> whose averages are equal to the mixed states being compared. We finally use the measures to define distance and fidelity for stochastic quantum channels and positive operator-valued measures. These quantities may be useful in the context of tomography of stochastic quantum channels and quantum detectors.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC13C..05A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC13C..05A"><span id="translatedtitle">Weather extremes in very large, high-resolution <span class="hlt">ensembles</span>: the weatherathome experiment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Allen, M. R.; Rosier, S.; Massey, N.; Rye, C.; Bowery, A.; Miller, J.; Otto, F.; Jones, R.; Wilson, S.; Mote, P.; Stone, D. A.; Yamazaki, Y. H.; Carrington, D.</p> <p>2011-12-01</p> <p>Resolution and <span class="hlt">ensemble</span> size are often seen as alternatives in climate modelling. Models with sufficient resolution to simulate many classes of extreme weather cannot normally be run often enough to assess the statistics of rare events, still less how these statistics may be changing. As a result, assessments of the impact of external forcing on regional climate extremes must be based either on statistical <span class="hlt">downscaling</span> from relatively coarse-resolution models, or statistical extrapolation from 10-year to 100-year events. Under the weatherathome experiment, part of the climateprediction.net initiative, we have compiled the Met Office Regional Climate Model HadRM3P to run on personal computer volunteered by the general public at 25 and 50km resolution, embedded within the HadAM3P global atmosphere model. With a global network of about 50,000 volunteers, this allows us to run time-slice <span class="hlt">ensembles</span> of essentially unlimited size, exploring the statistics of extreme weather under a range of scenarios for surface forcing and atmospheric composition, allowing for uncertainty in both boundary conditions and model parameters. Current experiments, developed with the support of Microsoft Research, focus on three regions, the Western USA, Europe and Southern Africa. We initially simulate the period 1959-2010 to establish which variables are realistically simulated by the model and on what scales. Our next experiments are focussing on the Event Attribution problem, exploring how the probability of various types of extreme weather would have been different over the recent past in a world unaffected by human influence, following the design of Pall et al (2011), but extended to a longer period and higher spatial resolution. We will present the first results of the unique, global, participatory experiment and discuss the implications for the attribution of recent weather events to anthropogenic influence on climate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120003771','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120003771"><span id="translatedtitle">Electrostatic Evaluation of the Propellant Handlers <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hogue, Michael D.; Calle, Carlos I.; Buhler, Charles</p> <p>2006-01-01</p> <p>The Self-Contained Atmospheric Protective <span class="hlt">Ensemble</span> (SCAPE) used in propellant handling at NASA's Kennedy Space Center (KSC) has recently completed a series of tests to determine its electrostatic properties of the coverall fabric used in the Propellant Handlers <span class="hlt">Ensemble</span> (PHE). Understanding these electrostatic properties are fundamental to ensuring safe operations when working with flammable rocket propellants such as hydrazine, methyl hydrazine, and unsymmetrical dimethyl hydrazine. These tests include surface resistivity, charge decay, triboelectric charging, and flame incendivity. In this presentation, we will discuss the results of these tests on the current PHE as well as new fabrics and materials being evaluated for the next generation of PHE.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/20857669','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/20857669"><span id="translatedtitle">Quantum measurement of a mesoscopic spin <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Giedke, G.; Taylor, J. M.; Lukin, M. D.; D'Alessandro, D.; Imamoglu, A.</p> <p>2006-09-15</p> <p>We describe a method for precise estimation of the polarization of a mesoscopic spin <span class="hlt">ensemble</span> by using its coupling to a single two-level system. Our approach requires a minimal number of measurements on the two-level system for a given measurement precision. We consider the application of this method to the case of nuclear-spin <span class="hlt">ensemble</span> defined by a single electron-charged quantum dot: we show that decreasing the electron spin dephasing due to nuclei and increasing the fidelity of nuclear-spin-based quantum memory could be within the reach of present day experiments.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/20861559','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/20861559"><span id="translatedtitle">Minimal redefinition of the OSV <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Parvizi, Shahrokh; Tavanfar, Alireza</p> <p>2006-12-15</p> <p>In the interesting conjecture, Z{sub BH}=Z{sub top}{sup 2}, proposed by Ooguri, Strominger, and Vafa (OSV), the black hole <span class="hlt">ensemble</span> is a mixed <span class="hlt">ensemble</span>. So if working in the complex polarization, the black hole degeneracy of states as obtained from the <span class="hlt">ensemble</span> inverse-Laplace integration, generically receives prefactors that do not respect the electric-magnetic duality. One way to handle this, as claimed recently, is working instead of the complex polarization in the real polarization. The other idea would be imposing nontrivial measures for the <span class="hlt">ensemble</span> sum in the complex polarization. We address this problem in the complex polarization, which is canonical, and upon a redefinition of the OSV <span class="hlt">ensemble</span> with variables as numerous as the electric potentials, show that for restoring the symmetry no non-Euclidean measure is needed. In detail, applying the electric-magnetic duality as a constraint governing the proper definition of the <span class="hlt">ensemble</span> variables, we rewrite the OSV free energy as a function of new variables that are combinations of the electric potentials and the black hole charges. Subsequently the Legendre transformation, which bridges between the entropy and the black hole free energy in terms of these variables, points to a generalized <span class="hlt">ensemble</span> that is well behaved in the complex polarization. In this context, we will consider all the cases of relevance: small and large black holes, with or without D6-brane charge. For the case of vanishing D6-brane, the new <span class="hlt">ensemble</span> is purely canonical and the electric-magnetic duality is restored exactly, leading to proper results for the black hole degeneracy of states to all orders in an asymptotic expansion. For more general cases as well, the construction does the job as far as the violation of the duality by the corresponding OSV result is restricted to a prefactor. In the case of black holes with nonvanishing D6-brane charge, in a concrete example, we shall show that there are cases where the duality violation goes beyond this restriction and imposing nontrivial measures is incapable of restoring the duality.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70095788','USGSPUBS'); return false;" href="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> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Swain, Eric; Stefanova, Lydia; Smith, Thomas</p> <p>2014-01-01</p> <p>Precipitation data from Global Climate Models have been <span class="hlt">downscaled</span> to smaller regions. Adapting this <span class="hlt">downscaled</span> precipitation data to a coupled hydrodynamic surface-water/groundwater model of southern Florida allows an examination of future conditions and their effect on groundwater levels, inundation patterns, surface-water stage and flows, and salinity. The <span class="hlt">downscaled</span> rainfall data include the 1996-2001 time series from the European Center for Medium-Range Weather Forecasting ERA-40 simulation and both the 1996-1999 and 2038-2057 time series from two global climate models: the Community Climate System Model (CCSM) and the Geophysical Fluid Dynamic Laboratory (GFDL). Synthesized surface-water inflow datasets were developed for the 2038-2057 simulations. The resulting hydrologic simulations, with and without a 30-cm sea-level rise, were compared with each other and field data to analyze a range of projected conditions. Simulations predicted generally higher future stage and groundwater levels and surface-water flows, with sea-level rise inducing higher coastal salinities. A coincident rise in sea level, precipitation and surface-water flows resulted in a narrower inland saline/fresh transition zone. The inland areas were affected more by the rainfall difference than the sea-level rise, and the rainfall differences make little difference in coastal inundation, but a larger difference in coastal salinities.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24824947','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24824947"><span id="translatedtitle">Design of a <span class="hlt">downscaling</span> method to estimate continuous data from discrete pollen monitoring in Tunisia.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Orlandi, Fabio; Oteros, Jose; Aguilera, Fátima; Ben Dhiab, Ali; Msallem, Monji; Fornaciari, Marco</p> <p>2014-07-01</p> <p>The study of microorganisms and biological particulate matter that transport passively through air is very important for an understanding of the real quality of air. Such monitoring is essential in several specific areas, such as public health, allergy studies, agronomy, indoor and outdoor conservation, and climate-change impact studies. Choosing the suitable monitoring method is an important step in aerobiological studies, so as to obtain reliable airborne data. In this study, we compare olive pollen data from two of the main air traps used in aerobiology, the Hirst and Cour air samplers, at three Tunisian sampling points, for 2009 to 2011. Moreover, a <span class="hlt">downscaling</span> method to perform daily Cour air sampler data estimates is designed. While Hirst air samplers can offer daily, and even bi-hourly data, Cour air samplers provide data for longer discrete sampling periods, which limits their usefulness for daily monitoring. Higher quantities of olive pollen capture were generally detected for the Hirst air sampler, and a <span class="hlt">downscaling</span> method that is developed in this study is used to model these differences. The effectiveness of this <span class="hlt">downscaling</span> method is demonstrated, which allows the potential use of Cour air sampler data series. These results improve the information that new Cour data and, importantly, historical Cour databases can provide for the understanding of phenological dates, airborne pollination curves, and allergenicity levels of air. PMID:24824947</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC43C0728S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC43C0728S"><span id="translatedtitle">Evaluation of Future Precipitation Scenario Using Statistical <span class="hlt">Downscaling</span> MODEL over Three Climatic Region of Nepal Himalaya</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sigdel, M.</p> <p>2014-12-01</p> <p>Statistical <span class="hlt">downscaling</span> model (SDSM) was applied in <span class="hlt">downscaling</span> precipitation in the three climatic regions such as humid, sub-humid and arid region of Nepal Himalaya. The study includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP reanalysis data, the validation of the model and the outputs of <span class="hlt">downscaled</span> scenarios A2 (high green house gases emission) and B2 (low green house gases emission) of the HadCM3 model for the future. Under both scenarios H3A2 and H3B2, during the prediction period of 2010-2099, the change of annual mean precipitation in the three climatic regions would present a tendency of surplus of precipitation as compared to the mean values of the base period. On the average for all three climatic regions of Nepal the annual mean precipitation would increase by about 13.75% under scenario H3A2 and increase near about 11.68% under scenario H3B2 in the 2050s. For the 2080s there would be increase of 8.28% and 13.30% under H3A2 and H3B2 respectively compared to the base period.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ClDy...42.2899E','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Etemadi, H.; Samadi, S.; Sharifikia, M.</p> <p>2014-06-01</p> <p>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> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AtmEn..67...46L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AtmEn..67...46L"><span id="translatedtitle">Impact of nesting resolution jump on dynamical <span class="hlt">downscaling</span> ozone concentrations over Belgium</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lauwaet, D.; Viaene, P.; Brisson, E.; van Noije, T.; Strunk, A.; Van Looy, S.; Maiheu, B.; Veldeman, N.; Blyth, L.; De Ridder, K.; Janssen, S.</p> <p>2013-03-01</p> <p>Global air quality datasets with a coarse resolution have to be <span class="hlt">downscaled</span> to become useful for regional interpretation, for instance by applying dynamical <span class="hlt">downscaling</span>. Here, the <span class="hlt">downscaling</span> ability of the regional air quality model AURORA (Air quality modelling in Urban Regions using an Optimal Resolution Approach) for surface ozone concentrations is evaluated over a model domain covering a large part of Belgium. The impact of two different one-way nesting resolution jumps is studied. Additionally, the effect of horizontal grid spacing on the simulation results is investigated. Model evaluation against measurements from a number of urban/suburban and rural background stations and a gridded interpolation map shows that the model is capable of reproducing the observed temporal and spatial patterns. The results indicate that the two applied nesting resolution jumps are comparably successful in simulating the surface ozone concentrations, despite the large resolution jump (25-3 km) in one of the approaches. The impact of horizontal resolution on the modelled time series at different types of locations is relatively small, with the average difference around 1% of the mean concentrations. However, the benefits of the higher resolution express themselves in the spatial correlations and temporal variances of the simulation results.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012EGUGA..1410829G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012EGUGA..1410829G"><span id="translatedtitle">Application of statistical <span class="hlt">downscaling</span> technique for the production of wine grapes (Vitis vinifera L.) in Spain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gaitán Fernández, E.; García Moreno, R.; Pino Otín, M. R.; Ribalaygua Batalla, J.</p> <p>2012-04-01</p> <p>Climate and soil are two of the most important limiting factors for agricultural production. Nowadays climate change has been documented in many geographical locations affecting different cropping systems. The General Circulation Models (GCM) has become important tools to simulate the more relevant aspects of the climate expected for the XXI century in the frame of climatic change. These models are able to reproduce the general features of the atmospheric dynamic but their low resolution (about 200 Km) avoids a proper simulation of lower scale meteorological effects. <span class="hlt">Downscaling</span> techniques allow overcoming this problem by adapting the model outcomes to local scale. In this context, FIC (Fundación para la Investigación del Clima) has developed a statistical <span class="hlt">downscaling</span> technique based on a two step analogue methods. This methodology has been broadly tested on national and international environments leading to excellent results on future climate models. In a collaboration project, this statistical <span class="hlt">downscaling</span> technique was applied to predict future scenarios for the grape growing systems in Spain. The application of such model is very important to predict expected climate for the different growing crops, mainly for grape, where the success of different varieties are highly related to climate and soil. The model allowed the implementation of agricultural conservation practices in the crop production, detecting highly sensible areas to negative impacts produced by any modification of climate in the different regions, mainly those protected with protected designation of origin, and the definition of new production areas with optimal edaphoclimatic conditions for the different varieties.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H43M1139L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H43M1139L"><span id="translatedtitle">About the Relevance of <span class="hlt">Downscaling</span> for Nonlinear Problems in Porous Media</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Leroy, V.; Bernard, D.</p> <p>2014-12-01</p> <p>In multiscale systems, scale separation is a major challenge in many industrial applications. The resulting complexity was, in the beginning, dealt with using phenomenological models and macroscopic approaches. Improvements in upscaling methods then allowed deriving macroscopic models from micro-scale transport models, greatly improving the understanding of experimentally observed phenomena. However, the investigation of many problems involving highly nonlinear phenomena (e.g. high-temperature heat transfer, chemistry, high-concentration mass transfer, etc.) remains out of the reach of current upscaling methods, even though the associated physics can be described with reasonable accuracy at the microscopic scale, mainly because the effects of nonlinearity can often not be fully passed from the microscopic scale to the macroscopic one without knowing the state of the medium at the microscopic scale. From this observation comes the idea of using a multiscale approach to investigate problems requiring exchange of information between scales. While in upscaling, information goes from the micro-scale to the macro-scale, <span class="hlt">downscaling</span> does the opposite and allows the reconstruction of information in a limited region of the micro-scale, based on macro-scale information. Used together, upscaling and <span class="hlt">downscaling</span> allow the exchange of information between both scales. This multiscale approach facilitates the investigation of highly nonlinear problems or that of cases with evolving micro-geometry. This presentation first aims at showing the relevance of a multiscale approach for transport in porous media and shows promising results yielded by the <span class="hlt">downscaling</span> methodology for nonlinear heat transfer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JGRD..120.3063X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..120.3063X"><span id="translatedtitle">A new dynamical <span class="hlt">downscaling</span> approach with GCM bias corrections and spectral nudging</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xu, Zhongfeng; Yang, Zong-Liang</p> <p>2015-04-01</p> <p>To improve confidence in regional projections of future climate, a new dynamical <span class="hlt">downscaling</span> (NDD) approach with both general circulation model (GCM) bias corrections and spectral nudging is developed and assessed over North America. GCM biases are corrected by adjusting GCM climatological means and variances based on reanalysis data before the GCM output is used to drive a regional climate model (RCM). Spectral nudging is also applied to constrain RCM-based biases. Three sets of RCM experiments are integrated over a 31 year period. In the first set of experiments, the model configurations are identical except that the initial and lateral boundary conditions are derived from either the original GCM output, the bias-corrected GCM output, or the reanalysis data. The second set of experiments is the same as the first set except spectral nudging is applied. The third set of experiments includes two sensitivity runs with both GCM bias corrections and nudging where the nudging strength is progressively reduced. All RCM simulations are assessed against North American Regional Reanalysis. The results show that NDD significantly improves the <span class="hlt">downscaled</span> mean climate and climate variability relative to other GCM-driven RCM <span class="hlt">downscaling</span> approach in terms of climatological mean air temperature, geopotential height, wind vectors, and surface air temperature variability. In the NDD approach, spectral nudging introduces the effects of GCM bias corrections throughout the RCM domain rather than just limiting them to the initial and lateral boundary conditions, thereby minimizing climate drifts resulting from both the GCM and RCM biases.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1052P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1052P"><span id="translatedtitle">Weather Typing Statistical <span class="hlt">Downscaling</span> with dsclim: Diagnostic methodology and configuration sensitivity</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Page, C.; Albertus, G.</p> <p>2013-12-01</p> <p>The 8-km output of the statistical <span class="hlt">downscaling</span> methodology dsclim has been used since a few years to perform impacts and adaptation studies in France. The dsclim method is resampling the Météo-France SAFRAN observation mesoscale analysis. Since then, the SAFRAN observation period has been extended from 1981-2005 to 1958-2012. At the same time, there are strong needs of cross-national impact studies, hence the required use of an European observation dataset in the methodology. In this context, a diagnostic package has been developed to properly evaluate the <span class="hlt">downscaling</span> methodology and its performance: it enables to evaluate the sensitivity and the impacts of the changes in its configuration, taking also properly into account stochastic aspects. In this study we evaluated the impacts on the results with respect to the extension of the learning period from 1981-2005 to 1958-2012, as well as the comparison on the use of the EOBS dataset instead of SAFRAN, having the objective of running dsclim over a larger region within the EU FP7 SPECS project and the EU COST Action VALUE <span class="hlt">downscaling</span> methods intercomparison. This study was funded by the EU project SPECS funded by the European Commission's Seventh Framework Research Programme under the grant agreement 243964.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014NPGD....1..615D','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Das, D.; Dy, J.; Ross, J.; Obradovic, Z.; Ganguly, A. R.</p> <p>2014-04-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014NPGeo..21.1145D','NASAADS'); return false;" href="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> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Das, D.; Dy, J.; Ross, J.; Obradovic, Z.; Ganguly, A. R.</p> <p>2014-12-01</p> <p>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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012JSemi..33g5008L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JSemi..33g5008L"><span id="translatedtitle">A high-speed mixed-signal <span class="hlt">down-scaling</span> circuit for DAB tuners</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lu, Tang; Zhigong, Wang; Jiahui, Xuan; Yang, Yang; Jian, Xu; Yong, Xu</p> <p>2012-07-01</p> <p>A high-speed mixed-signal <span class="hlt">down-scaling</span> circuit with low power consumption and low phase noise for use in digital audio broadcasting tuners has been realized and characterized. Some new circuit techniques are adopted to improve its performance. A dual-modulus prescaler (DMP) with low phase noise is realized with a kind of improved source-coupled logic (SCL) D-flip-flop (DFF) in the synchronous divider and a kind of improved complementary metal oxide semiconductor master-slave (CMOS MS)-DFF in the asynchronous divider. A new more accurate wire-load model is used to realize the pulse-swallow counter (PS counter). Fabricated in a 0.18-?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> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016OcSci..12...39G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016OcSci..12...39G"><span id="translatedtitle">On the feasibility of the use of wind SAR to <span class="hlt">downscale</span> waves on shallow water</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gutiérrez, O. Q.; Filipponi, F.; Taramelli, A.; Valentini, E.; Camus, P.; Méndez, F. J.</p> <p>2016-01-01</p> <p>In recent years, wave reanalyses have become popular as a powerful source of information for wave climate research and engineering applications. These wave reanalyses provide continuous time series of offshore wave parameters; nevertheless, in coastal areas or shallow water, waves are poorly described because spatial resolution is not detailed. By means of wave <span class="hlt">downscaling</span>, it is possible to increase spatial resolution in high temporal coverage simulations, using forcing from wind and offshore wave databases. Meanwhile, the reanalysis wave databases are enough to describe the wave climate at the limit of simulations; wind reanalyses at an adequate spatial resolution to describe the wind structure near the coast are not frequently available. Remote sensing synthetic aperture radar (SAR) has the ability to detect sea surface signatures and estimate wind fields at high resolution (up to 300 m) and high frequency. In this work a wave <span class="hlt">downscaling</span> is done on the northern Adriatic Sea, using a hybrid methodology and global wave and wind reanalysis as forcing. The wave fields produced were compared to wave fields produced with SAR winds that represent the two dominant wind regimes in the area: the bora (ENE direction) and sirocco (SE direction). Results show a good correlation between the waves forced with reanalysis wind and SAR wind. In addition, a validation of reanalysis is shown. This research demonstrates how Earth observation products, such as SAR wind fields, can be successfully up-taken into oceanographic modeling, producing similar <span class="hlt">downscaled</span> wave fields when compared to waves forced with reanalysis wind.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008AGUFMGC43C0762D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008AGUFMGC43C0762D"><span id="translatedtitle">Assessing Impacts of Climate Change in a Semi-Arid Watershed Using <span class="hlt">Downscaled</span> IPCC Climate Output</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dominguez, F.; Rajagopal, S.; Gupta, H. V.; Troch, P. A.; Durcik, M.</p> <p>2008-12-01</p> <p>This presentation discusses our research aimed at helping water managers at Salt River Project (SRP), Phoenix, assess long term climate change impacts for the Salt and Verde River basins, and make informed policy decisions. Our goal is to assess the future 100 year water balance by development, application and testing of a physically based distributed hydrologic model forced by <span class="hlt">downscaled</span> IPCC climate information. The variable infiltration capacity (VIC) model is set up to simulate historical observed streamflow at the outlet of Salt and Verde River basins using gridded observed precipitation and temperature data. The model is calibrated using the Shuffled Complex Evolution (SCE-UA) method incorporating observed climate elasticities of the Salt and Verde River basins. The most appropriate models and emission scenarios from the Global Climate Model's (GCM's) participating in the IPCC fourth assessment were then chosen and statistically <span class="hlt">downscaled</span> to incorporate ENSO variability. The forcing dataset created using the <span class="hlt">downscaled</span> data was used to analyze the basin scale responses to climate change. In this poster, the scenarios based on future climate forcing data will be presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.H33E0938R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H33E0938R"><span id="translatedtitle">Assessing impacts of climate change in a semi arid watershed using <span class="hlt">downscaled</span> IPCC climate output</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rajagopal, S.; Dominguez, F.; Gupta, H. V.; Troch, P. A.; Castro, C. L.</p> <p>2009-12-01</p> <p>This presentation discusses our research aimed at helping water managers at Salt River Project (SRP), Phoenix, assess long term climate change impacts for the Salt and Verde River basins, and make informed policy decisions. Our goal is to assess the change in future 100 year water balance variables in comparison to past observations by development, application and testing of a physically based distributed hydrologic model forced by <span class="hlt">downscaled</span> IPCC climate information. The variable infiltration capacity (VIC) model is set up to simulate historical observed streamflow at the outlet of Salt and Verde River basins using gridded observed precipitation and temperature data. The model is calibrated using the Shuffled Complex Evolution (SCE-UA) method incorporating observed climate elasticities of the Salt and Verde River basins. The MPI-ECHAM5, UK-HADCM3 model output for three emission scenarios used in the IPCC fourth assessment were chosen and statistically <span class="hlt">downscaled</span> to be incorporated with the VIC model. This forcing dataset was used to analyze the basin scale responses to climate change. In this presentation, the scenarios based on future climate forcing data will be presented. In addition results from a synthetic study using <span class="hlt">downscaled</span> future temperature and past precipitation and vice versa will be presented to check the robustness of the model to non-stationary input.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20130013812','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20130013812"><span id="translatedtitle"><span class="hlt">Ensemble</span> Eclipse: A Process for Prefab Development Environment for the <span class="hlt">Ensemble</span> Project</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wallick, Michael N.; Mittman, David S.; Shams, Khawaja, S.; Bachmann, Andrew G.; Ludowise, Melissa</p> <p>2013-01-01</p> <p>This software simplifies the process of having to set up an Eclipse IDE programming environment for the members of the cross-NASA center project, <span class="hlt">Ensemble</span>. It achieves this by assembling all the necessary add-ons and custom tools/preferences. This software is unique in that it allows developers in the <span class="hlt">Ensemble</span> Project (approximately 20 to 40 at any time) across multiple NASA centers to set up a development environment almost instantly and work on <span class="hlt">Ensemble</span> software. The software automatically has the source code repositories and other vital information and settings included. The Eclipse IDE is an open-source development framework. The NASA (<span class="hlt">Ensemble</span>-specific) version of the software includes <span class="hlt">Ensemble</span>-specific plug-ins as well as settings for the <span class="hlt">Ensemble</span> project. This software saves developers the time and hassle of setting up a programming environment, making sure that everything is set up in the correct manner for <span class="hlt">Ensemble</span> development. Existing software (i.e., standard Eclipse) requires an intensive setup process that is both time-consuming and error prone. This software is built once by a single user and tested, allowing other developers to simply download and use the software</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20020061294&hterms=ECC&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DECC','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20020061294&hterms=ECC&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3DECC"><span id="translatedtitle"><span class="hlt">Ensemble</span> Canonical Correlation Prediction of Seasonal Precipitation Over the United States: Raising the Bar for Dynamical Model Forecasts</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lau, William K. M.; Kim, Kyu-Myong; Shen, S. P.</p> <p>2001-01-01</p> <p>This paper presents preliminary results of an <span class="hlt">ensemble</span> canonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate <span class="hlt">downscaling</span> studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into non-overlapping sectors. The canonical SST modes for each sector are used as the predictor for the <span class="hlt">ensemble</span> hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all the regions of the US in every season compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible to the enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduces the spring predictability barrier over all the regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and additional local observations. The enhanced ECC forecast skill provides a new benchmark for evaluating dynamical model forecasts.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..522..110S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..522..110S"><span id="translatedtitle">Hydrological modelling using <span class="hlt">ensemble</span> satellite rainfall estimates in a sparsely gauged river basin: The need for whole-<span class="hlt">ensemble</span> calibration</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Skinner, Christopher J.; Bellerby, Timothy J.; Greatrex, Helen; Grimes, David I. F.</p> <p>2015-03-01</p> <p>The potential for satellite rainfall estimates to drive hydrological models has been long understood, but at the high spatial and temporal resolutions often required by these models the uncertainties in satellite rainfall inputs are both significant in magnitude and spatiotemporally autocorrelated. Conditional stochastic modelling of <span class="hlt">ensemble</span> observed fields provides one possible approach to representing this uncertainty in a form suitable for hydrological modelling. Previous studies have concentrated on the uncertainty within the satellite rainfall estimates themselves, sometimes applying <span class="hlt">ensemble</span> inputs to a pre-calibrated hydrological model. This approach does not account for the interaction between input uncertainty and model uncertainty and in particular the impact of input uncertainty on model calibration. Moreover, it may not be appropriate to use deterministic inputs to calibrate a model that is intended to be driven by using an <span class="hlt">ensemble</span>. A novel whole-<span class="hlt">ensemble</span> calibration approach has been developed to overcome some of these issues. This study used <span class="hlt">ensemble</span> rainfall inputs produced by a conditional satellite-driven stochastic rainfall generator (TAMSIM) to drive a version of the Pitman rainfall-runoff model, calibrated using the whole-<span class="hlt">ensemble</span> approach. Simulated <span class="hlt">ensemble</span> discharge outputs were assessed using metrics adapted from <span class="hlt">ensemble</span> forecast verification, showing that the <span class="hlt">ensemble</span> outputs produced using the whole-<span class="hlt">ensemble</span> calibrated Pitman model outperformed equivalent <span class="hlt">ensemble</span> outputs created using a Pitman model calibrated against either the <span class="hlt">ensemble</span> mean or a theoretical infinite-<span class="hlt">ensemble</span> expected value. Overall, for the verification period the whole-<span class="hlt">ensemble</span> calibration provided a mean RMSE of 61.7% of the mean wet season discharge, compared to 83.6% using a calibration based on the daily mean of the <span class="hlt">ensemble</span> estimates. Using a Brier's Skill Score to assess the performance of the <span class="hlt">ensemble</span> against a climatic estimate, the whole-<span class="hlt">ensemble</span> calibration provided a positive score for the main range of discharge events. The equivalent score for calibration against the <span class="hlt">ensemble</span> mean was negative, indicating it showed no skill versus the climatic estimate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AtmRe.167..156K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AtmRe.167..156K"><span id="translatedtitle">High resolution WRF <span class="hlt">ensemble</span> forecasting for irrigation: Multi-variable evaluation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kioutsioukis, Ioannis; de Meij, Alexander; Jakobs, Hermann; Katragkou, Eleni; Vinuesa, Jean-Francois; Kazantzidis, Andreas</p> <p>2016-01-01</p> <p>An <span class="hlt">ensemble</span> of meteorological simulations with the WRF model at convection-allowing resolution (2 km) is analysed in a multi-variable evaluation framework over Europe. Besides temperature and precipitation, utilized variables are relative humidity, boundary layer height, shortwave radiation, wind speed, convective and large-scale precipitation in view of explaining some of the biases. Furthermore, the forecast skill of evapotranspiration and irrigation water need is ultimately assessed. It is found that the modelled temperature exhibits a small but significant negative bias during the cold period in the snow-covered northeast regions. Total precipitation exhibits positive bias during all seasons but autumn, peaking in the spring months. The varying physics configurations resulted in significant differences for the simulated minimum temperature, summer rainfall, relative humidity, solar radiation and planetary boundary layer height. The interaction of the temperature and moisture profiles with the different microphysics schemes, results in excess convective precipitation using MYJ/WSM6 compared to YSU/Thompson. With respect to evapotranspiration and irrigation need, the errors using the MYJ configuration were in opposite directions and eventually cancel out, producing overall smaller biases. WRF was able to dynamically <span class="hlt">downscale</span> global forecast data into finer resolutions in space and time for hydro-meteorological applications such as the irrigation management. Its skill was sensitive to the geographical location and physical configuration, driven by the variable relative importance of evapotranspiration and rainfall.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.C41A0334F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.C41A0334F"><span id="translatedtitle">Combination of remote sensing data products to derive spatial climatologies of "degree days" and <span class="hlt">downscale</span> meteorological reanalyses: application to the Upper Indus Basin</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Forsythe, N. D.; Rutter, N.; Brock, B. W.; Fowler, H. J.; Blenkinsop, S.</p> <p>2014-12-01</p> <p>Lack of observations for the full range of required variables is a critical reason why many cryosphere-dominated hydrological modelling studies adopt a temperature index (degree day) approach to meltwater simulation rather than resolving the full surface energy balance. Thus spatial observations of "degree days" would be extremely useful in constraining model parameterisations. Even for models implementing a full energy balance, "degree day" observations provide a characterisation of the spatial distribution of climate inputs to the cryosphere-hydrological system. This study derives "degree days" for the Upper Indus Basin by merging remote sensing data products: snow cover duration (SCD), from MOD10A1 and land surface temperature (LST), from MOD11A1 and MYD11A1. Pixel-wise "degree days" are calculated, at imagery-dependent spatial resolution, by multiplying SCD by (above-freezing) daily LST. This is coherent with the snowpack-energy-to-runoff conversion used in temperature index algorithms. This allows assessment of the spatial variability of mass inputs (accumulated snowpack) because in nival regime areas - where complete ablation is regularly achieved - mass is the limiting constraint. The GLIMS Randolph Glacier Inventory is used to compare annual totals and seasonal timings of "degree days" over glaciated and nival zones. Terrain-classified statistics (by elevation and aspect) for the MODIS "degree-day" hybrid product are calculated to characterise of spatial precipitation distribution. While MODIS data products provide detailed spatial resolution relative to tributary catchment areas, the limited instrument record length is inadequate for assessing climatic trends and greatly limits use for hydrological model calibration and validation. While multi-decadal MODIS equivalent data products may be developed in the coming years, at present alternative methods are required for "degree day" trend analysis. This study thus investigates the use of the hybrid MODIS "degree day" product to <span class="hlt">downscale</span> an <span class="hlt">ensemble</span> of modern global meteorological reanalyses including ERA-Interim, NCEP CFSR, NASA MERRA and JRA-55 which overlap MODIS instrument record. This <span class="hlt">downscaling</span> feasibility assessment is a prerequisite to applying the method to regional climate projections.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H23G1307R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H23G1307R"><span id="translatedtitle">Assessing impacts of climate change in a semi arid watershed using <span class="hlt">downscaled</span> IPCC climate output</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rajagopal, S.; Dominguez, F.; Gupta, H. V.; Troch, P. A.; Castro, C. L.</p> <p>2010-12-01</p> <p>This presentation discusses our research aimed at helping water managers at Salt River Project (SRP), Phoenix, assess long term climate change impacts for the Salt and Verde River basins, and make informed policy decisions. Our goal was to assess the future 100 year water balance by development, application and testing of a physically based distributed hydrologic model forced by <span class="hlt">downscaled</span> IPCC climate information. The variable infiltration capacity (VIC) model was set up to simulate historical observed streamflow at the outlet of Salt and Verde River basins using gridded observed precipitation and temperature data. The model was calibrated using the Shuffled Complex Evolution (SCE-UA) optimization algorithm. The models found to best simulate the climatology of the region, (UK-HADCM3, MPI-ECHAM5, NCAR-CCSM3) and emission scenarios (A1B, A2, B1) from the Global Climate Model’s (GCM’s) participating in the IPCC fourth assessment were obtained from the bias-corrected and spatially <span class="hlt">downscaled</span> climate projections derived from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 multi-model dataset. The data was then temporally <span class="hlt">downscaled</span> to serve as forcing for the VIC model. This <span class="hlt">downscaled</span> forcing dataset was used to analyze the basin scale responses to climate change. Based on stakeholder feedback two additional GCM's one that represents a wet scenario and one that represents a dry scenario, were also <span class="hlt">downscaled</span> as mentioned above and run through the hydrologic model. All the models show a statistically significant increase in temperature over the 21st century. Due to increased winter temperatures the multi-model mean shows a significant decrease in storage capacity in the basin, viz. snow water equivalent. This decrease is already evident in observed SNOTEL records of the basin. Since these watersheds are snow dominated, the cold season multi-model mean streamflow shows a decreasing trend by the end of the century though the warm season streamflow tends to increase in response to increased summer precipitation. Increased summer streamflow does not compensate for the decrease in winter streamflow. In addition to the above analysis a synthetic study was performed to quantify the uncertainty in coupled GCM-Hydrologic model predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC41E..04D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC41E..04D"><span id="translatedtitle">Cluster analysis of explicitly and <span class="hlt">downscaled</span> simulated North Atlantic tropical cyclone tracks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Daloz, A.; Camargo, S. J.; Kossin, J. P.; Emanuel, K.</p> <p>2013-12-01</p> <p>The response of tropical cyclone (TC) activity to climate change is a question of major interest. In order to address this crucial issue, several types of models have been developed in the past, such as Global Climate Models (GCMs). However, the horizontal resolution of those models usually leads to some difficulties in resolving the inner core of TCs and then to properly simulate TC activity. In order to avoid this problem, an alternative tool has been developed by Emanuel (2005). This <span class="hlt">downscaling</span> technique uses tracks that are initiated by randomly seeding large areas of the tropics with weak vortices. Then the survival of the tracks is based on large-scale environmental conditions produced by GCMs in our case. Here we compare the statistics of TC tracks simulated explicitly in four GCMs to the results of the <span class="hlt">downscaling</span> technique driven by the four same GCMs in the present and future climates over the North Atlantic basin. Simulated tracks are objectively separated into four groups using a cluster technique (Kossin et al. 2010). The four clusters form zonal and meridional separations of tracks as shown in Figure 1. The meridional separation largely captures the separation between hybrid or baroclinic storms (clusters 1 and 2) and deep tropical systems (clusters 3 and 4), while the zonal separation segregates Gulf of Mexico and Cape Verde storms. Except for the seasonality, the <span class="hlt">downscaled</span> simulations better capture the general characteristics of the clusters (mean duration of the tracks, intensity...) compared with the explicit simulations, which present strong biases. In the second part of this study, we use three different scenarios to examine the possible future changes of the clusters from the <span class="hlt">downscaled</span> simulations. We explored the role of a warming of the SST, an increase in carbon dioxide and a combination of both ones. The results show that the response to each scenario is highly varying depending on the simulation examined. References - Kossin, J. P., S. J. Camargo, and M. Sitkowski, 2010: Climate modulation of North Atlantic hurricane tracks. Journal of Climate, 23, 3057-3076, DOI: 10.1175/2010JCLI3497.1. - Emanuel, K., 2005: Climate and Tropical Cyclone activity: A new <span class="hlt">downscaling</span> approach. Journal of Climate, 19, 4797-4802.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H41A0789L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H41A0789L"><span id="translatedtitle">Modeling climate change impacts on hydrological variability using an efficient multi-site GCM <span class="hlt">downscaling</span> method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>LI, Z.; Lü, Z.</p> <p>2014-12-01</p> <p>The coarse resolution of GCM outputs cannot match the high resolution input requirement of hydrological models and thus are inappropriate for impact assessment of climate change. Though numerous <span class="hlt">downscaling</span> techniques have been used to gap the mismatch, the methods based on single site cannot be used by the distributed hydrological models for hydrological extreme simulation since the flood in one subbasin can be offset by the adjacent ones due to the ignorance of multi-site spatiotemporal correlation of meteorological variables. This study developed a multi-site <span class="hlt">downscaling</span> method based on a two-stage weather generator (TSWG) through three steps: (i) spatially <span class="hlt">downscaling</span> GCMs with a transfer function method; (ii) temporally <span class="hlt">downscaling</span> GCMs with a single-site weather generator; (iii) reconstructing the spatiotemporal correlations with a post-processing and nonparametric shuffle procedure. Five GCMs (CanESM2, CSIRO_3.6.0, GFDL_CM3, HadGEM2-AO and MPI-ESM-LR) under four RCPs (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) were used to generate climate scenarios for the period of 2011-2040. The hydrological simulation was carried out by SWAT in the Jing River catchment on the Loess Plateau. Future annual mean precipitation would change by -7.7% to 9.2%, annual mean maximum and minimum temperature would increase by 1.4-1.8 ? and 1.1-1.4 ?, respectively. Overall, future climate tended to be warmer and drier under most GCMs and RCPs, and this trend would be more significant for flood season; however, the variations of monthly precipitation would be greater than present. The annual mean streamflow would change by -18% to 38% and be more variable. The monthly streamflow would be more variable for most months due to the increase of monthly maximum streamflow and decrease of monthly minimum streamflow. Therefore, the stremflow in the Jing River should be paid more attention for its possible disasters. The multi-site <span class="hlt">downscaling</span> method proposed in this study is efficient and performs well for its spatiotemporal correlation reconstruction and hydrological variability simulation, which provides a powerful tool for impact assessment of climate changes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC41F0662S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC41F0662S"><span id="translatedtitle">Uncertainty Analysis of <span class="hlt">Downscaled</span> CMIP5 Precipitation Data for Louisiana, USA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sumi, S. J.; Tamanna, M.; Chivoiu, B.; Habib, E. H.</p> <p>2014-12-01</p> <p>The <span class="hlt">downscaled</span> CMIP3 and CMIP5 Climate and Hydrology Projections dataset contains fine spatial resolution translations of climate projections over the contiguous United States developed using two <span class="hlt">downscaling</span> techniques (monthly Bias Correction Spatial Disaggregation (BCSD) and daily Bias Correction Constructed Analogs (BCCA)). The objective of this study is to assess the uncertainty of the CMIP5 <span class="hlt">downscaled</span> general circulation models (GCM). We performed an analysis of the daily, monthly, seasonal and annual variability of precipitation downloaded from the <span class="hlt">Downscaled</span> CMIP3 and CMIP5 Climate and Hydrology Projections website for the state of Louisiana, USA at 0.125° x 0.125° resolution. A data set of daily gridded observations of precipitation of a rectangular boundary covering Louisiana is used to assess the validity of 21 <span class="hlt">downscaled</span> GCMs for the 1950-1999 period. The following statistics are computed using the CMIP5 observed dataset with respect to the 21 models: the correlation coefficient, the bias, the normalized bias, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). A measure of variability simulated by each model is computed as the ratio of its standard deviation, in both space and time, to the corresponding standard deviation of the observation. The correlation and MAPE statistics are also computed for each of the nine climate divisions of Louisiana. Some of the patterns that we observed are: 1) Average annual precipitation rate shows similar spatial distribution for all the models within a range of 3.27 to 4.75 mm/day from Northwest to Southeast. 2) Standard deviation of summer (JJA) precipitation (mm/day) for the models maintains lower value than the observation whereas they have similar spatial patterns and range of values in winter (NDJ). 3) Correlation coefficients of annual precipitation of models against observation have a range of -0.48 to 0.36 with variable spatial distribution by model. 4) Most of the models show negative correlation coefficients in summer and positive in winter. 5) MAE shows similar spatial distribution for all the models within a range of 5.20 to 7.43 mm/day from Northwest to Southeast of Louisiana. 6) Highest values of correlation coefficients are found at seasonal scale within a range of 0.36 to 0.46.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFM.H22A..06M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFM.H22A..06M"><span id="translatedtitle">A nonparametric weather-state approach for <span class="hlt">downscaling</span> of multi-site precipitation occurrences</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mehrotra, R.; Sharma, A.</p> <p>2004-12-01</p> <p>The physical linkages between climate on the large scale and weather on the local scale form the basis of <span class="hlt">downscaling</span> approaches for assessing the impact of climate variability at point locations. The common approach frequently used for <span class="hlt">downscaling</span> of precipitation, considers discrete weather classes of the atmospheric patterns and simulates precipitation conditioned on these weather-states. This paper presents the development of a nonparametric weather-state <span class="hlt">downscaling</span> approach (KNN-W) and its comparison with traditional KNN resampling approach (KNN) and a parametric Non-homogenous Hidden Markov Model (NHMM). The KNN-W defines local scale weather as a function of a weather state that is continuous and auto-regressive in nature and depends on predictor variables representing synoptic atmospheric patterns. Such a formulation offers a simpler alternative to the weather-state based parametric approaches like NHMM. The KNN resampling approach considers a direct probabilistic relationship between the larger scale climatic variables and the local scale weather. On the other hand, the weather-state KNN <span class="hlt">downscaling</span> approach being structured on continuous weather-state formulation is more opt at representing temporal persistence. A weather-state of KNN-W is defined based on spatial rainfall distribution over the study region. The paper also considers the relative influence of atmospheric circulation variables on the conditional density formulation in the form of influence weights. In the comparison presented here, we applied these <span class="hlt">downscaling</span> approaches conditional on four atmospheric circulation variables, to estimate precipitation occurrences at a network of 30 raingauge locations around Sydney, Australia. Our results suggest that all the models perform well at representing spatial variations while they lack at representing temporal dependence at scales longer than a few days as exhibited through wet spell length characteristics. The weather-state based KNN approach is more successful in capturing the longer duration as well as extreme rainfall characteristics in comparison to direct KNN approach and NHMM. Local scale features that are difficult to represent through the large scale climate predictors are expectedly not reproduced by any approach.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhDT.......158C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhDT.......158C"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> for the Northern Great Plains: A comparison of bias correction and redundancy analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Coburn, Jacob Jimmie</p> <p></p> <p>The climate of the Earth is changing, and is primarily a result of our rampant industrialization over the past two centuries. These changes have manifested themselves in many ways over the whole of the Earth's surface and sub-systems, leading to the need to understand the changes and predict future outcomes. Coupled climate and general circulation - Earth system models (GCMs) allow for the analysis of dynamically active simulations over the whole of the planet, yet are limited by computational power. The model grids are coarse by design to perform within these computational constraints, which enables them to function and provide information at continental and larger scales, but which limit their ability to offer information for regional and local environments. Dynamical models created with higher resolutions allow for regional climate modeling yet are also limited by computational constraints and require detailed information to run. Statistical <span class="hlt">downscaling</span> seeks to bridge the gap between coarse GCM grids by utilizing observational data and statistical models to remove the biases from the data at the local level. There have been several types of statistical methods applied to this task over many different regions with some success. The goal of this study is to utilize two methods in particular, bias-corrected spatial disaggregation (BCSD) and redundancy analysis (RDA), to <span class="hlt">downscale</span> maximum and minimum temperature, as well as precipitation, for the Northern Great Plains (NGP) region. These methods are calibrated over the period 1950 -- 1970 using a 1/8 degree gridded dataset for 17 GCMs, then applied to a verification period (1970 -- 1999) and compared to observations over that period to assess the <span class="hlt">downscaled</span> models skill in capturing local NGP variability. These methods are also applied to future model runs forced via the representative concentration pathways (RCPs) low end (2.6), median (4.5) and high end (8.5) 21st Century forcings, which provides possible outlooks for local stakeholders over the coming decades. It is found that BCSD does well in <span class="hlt">downscaling</span> temperature and precipitation, as well as their various metrics. RDA provides more mixed success, with good skill demonstrated for temperatures but a strong wet bias in precipitation. It is noted, however, that RDA yielded better correlations to the observations. Future scenarios show broad ranges of projected outcomes that, as expected, increase with increasing forcing, though temperature shows stronger changes than precipitation, and BCSD exhibits higher sensitivity than RDA. Future research may help further constrain the results of these <span class="hlt">downscaling</span> methods, particularly RDA, by adopting further bias correction to the results.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4325279','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4325279"><span id="translatedtitle">NMR Studies of Dynamic Biomolecular Conformational <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Torchia, Dennis A.</p> <p>2015-01-01</p> <p>Multidimensional heteronuclear NMR approaches can provide nearly complete sequential signal assignments of isotopically enriched biomolecules. The availability of assignments together with measurements of spin relaxation rates, residual spin interactions, J-couplings and chemical shifts provides information at atomic resolution about internal dynamics on timescales ranging from ps to ms, both in solution and in the solid state. However, due to the complexity of biomolecules, it is not possible to extract a unique atomic-resolution description of biomolecular motions even from extensive NMR data when many conformations are sampled on multiple timescales. For this reason, powerful computational approaches are increasingly applied to large NMR data sets to elucidate conformational <span class="hlt">ensembles</span> sampled by biomolecules. In the past decade, considerable attention has been directed at an important class of biomolecules that function by binding to a wide variety of target molecules. Questions of current interest are: “Does the free biomolecule sample a conformational <span class="hlt">ensemble</span> that encompasses the conformations found when it binds to various targets; and if so, on what time scale is the <span class="hlt">ensemble</span> sampled?” This article reviews recent efforts to answer these questions, with a focus on comparing <span class="hlt">ensembles</span> obtained for the same biomolecules by different investigators. A detailed comparison of results obtained is provided for three biomolecules: ubiquitin, calmodulin and the HIV-1 trans-activation response RNA. PMID:25669739</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=large+AND+array&pg=3&id=EJ934197','ERIC'); return false;" href="http://eric.ed.gov/?q=large+AND+array&pg=3&id=EJ934197"><span id="translatedtitle">Memory for Multiple Visual <span class="hlt">Ensembles</span> in Infancy</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Zosh, Jennifer M.; Halberda, Justin; Feigenson, Lisa</p> <p>2011-01-01</p> <p>The number of individual items that can be maintained in working memory is limited. One solution to this problem is to store representations of <span class="hlt">ensembles</span> that contain summary information about large numbers of items (e.g., the approximate number or cumulative area of a group of many items). Here we explored the developmental origins of ensemble…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26578574','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26578574"><span id="translatedtitle"><span class="hlt">Ensembl</span> Genomes 2016: more genomes, more complexity.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kersey, Paul Julian; Allen, James E; Armean, Irina; Boddu, Sanjay; Bolt, Bruce J; Carvalho-Silva, Denise; Christensen, Mikkel; Davis, Paul; Falin, Lee J; Grabmueller, Christoph; Humphrey, Jay; Kerhornou, Arnaud; Khobova, Julia; Aranganathan, Naveen K; Langridge, Nicholas; Lowy, Ernesto; McDowall, Mark D; Maheswari, Uma; Nuhn, Michael; Ong, Chuang Kee; Overduin, Bert; Paulini, Michael; Pedro, Helder; Perry, Emily; Spudich, Giulietta; Tapanari, Electra; Walts, Brandon; Williams, Gareth; Tello-Ruiz, Marcela; Stein, Joshua; Wei, Sharon; Ware, Doreen; Bolser, Daniel M; Howe, Kevin L; Kulesha, Eugene; Lawson, Daniel; Maslen, Gareth; Staines, Daniel M</p> <p>2016-01-01</p> <p><span class="hlt">Ensembl</span> Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org). Together, the two resources provide a consistent set of programmatic and interactive interfaces to a rich range of data including reference sequence, gene models, transcriptional data, genetic variation and comparative analysis. This paper provides an update to the previous publications about the resource, with a focus on recent developments. These include the development of new analyses and views to represent polyploid genomes (of which bread wheat is the primary exemplar); and the continued up-scaling of the resource, which now includes over 23 000 bacterial genomes, 400 fungal genomes and 100 protist genomes, in addition to 55 genomes from invertebrate metazoa and 39 genomes from plants. This dramatic increase in the number of included genomes is one part of a broader effort to automate the integration of archival data (genome sequence, but also associated RNA sequence data and variant calls) within the context of reference genomes and make it available through the <span class="hlt">Ensembl</span> user interfaces. PMID:26578574</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JChPh.143x3131C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JChPh.143x3131C"><span id="translatedtitle">Predicting protein dynamics from structural <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Copperman, J.; Guenza, M. G.</p> <p>2015-12-01</p> <p>The biological properties of proteins are uniquely determined by their structure and dynamics. A protein in solution populates a structural <span class="hlt">ensemble</span> of metastable configurations around the global fold. From overall rotation to local fluctuations, the dynamics of proteins can cover several orders of magnitude in time scales. We propose a simulation-free coarse-grained approach which utilizes knowledge of the important metastable folded states of the protein to predict the protein dynamics. This approach is based upon the Langevin Equation for Protein Dynamics (LE4PD), a Langevin formalism in the coordinates of the protein backbone. The linear modes of this Langevin formalism organize the fluctuations of the protein, so that more extended dynamical cooperativity relates to increasing energy barriers to mode diffusion. The accuracy of the LE4PD is verified by analyzing the predicted dynamics across a set of seven different proteins for which both relaxation data and NMR solution structures are available. Using experimental NMR conformers as the input structural <span class="hlt">ensembles</span>, LE4PD predicts quantitatively accurate results, with correlation coefficient ? = 0.93 to NMR backbone relaxation measurements for the seven proteins. The NMR solution structure derived <span class="hlt">ensemble</span> and predicted dynamical relaxation is compared with molecular dynamics simulation-derived structural <span class="hlt">ensembles</span> and LE4PD predictions and is consistent in the time scale of the simulations. The use of the experimental NMR conformers frees the approach from computationally demanding simulations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED294775.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED294775.pdf"><span id="translatedtitle">The Honolulu Symphony In-School <span class="hlt">Ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Higa, Harold</p> <p></p> <p>The Honolulu (Hawaii) Symphony Orchestra's commitment to education includes young people's concerts and in-school <span class="hlt">ensembles</span>. The purpose of this booklet is to enhance the educational potential of in-school concerts through the presentation of information about the orchestra and music related concepts. Part 1 describes the orchestra's personnel,…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702859','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4702859"><span id="translatedtitle"><span class="hlt">Ensembl</span> Genomes 2016: more genomes, more complexity</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kersey, Paul Julian; Allen, James E.; Armean, Irina; Boddu, Sanjay; Bolt, Bruce J.; Carvalho-Silva, Denise; Christensen, Mikkel; Davis, Paul; Falin, Lee J.; Grabmueller, Christoph; Humphrey, Jay; Kerhornou, Arnaud; Khobova, Julia; Aranganathan, Naveen K.; Langridge, Nicholas; Lowy, Ernesto; McDowall, Mark D.; Maheswari, Uma; Nuhn, Michael; Ong, Chuang Kee; Overduin, Bert; Paulini, Michael; Pedro, Helder; Perry, Emily; Spudich, Giulietta; Tapanari, Electra; Walts, Brandon; Williams, Gareth; Tello–Ruiz, Marcela; Stein, Joshua; Wei, Sharon; Ware, Doreen; Bolser, Daniel M.; Howe, Kevin L.; Kulesha, Eugene; Lawson, Daniel; Maslen, Gareth; Staines, Daniel M.</p> <p>2016-01-01</p> <p><span class="hlt">Ensembl</span> Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the <span class="hlt">Ensembl</span> project (http://www.<span class="hlt">ensembl</span>.org). Together, the two resources provide a consistent set of programmatic and interactive interfaces to a rich range of data including reference sequence, gene models, transcriptional data, genetic variation and comparative analysis. This paper provides an update to the previous publications about the resource, with a focus on recent developments. These include the development of new analyses and views to represent polyploid genomes (of which bread wheat is the primary exemplar); and the continued up-scaling of the resource, which now includes over 23 000 bacterial genomes, 400 fungal genomes and 100 protist genomes, in addition to 55 genomes from invertebrate metazoa and 39 genomes from plants. This dramatic increase in the number of included genomes is one part of a broader effort to automate the integration of archival data (genome sequence, but also associated RNA sequence data and variant calls) within the context of reference genomes and make it available through the <span class="hlt">Ensembl</span> user interfaces. PMID:26578574</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011TellA..63..858N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011TellA..63..858N"><span id="translatedtitle">Calibrating probabilistic forecasts from an NWP <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nipen, Thomas; Stull, Roland</p> <p>2011-10-01</p> <p>A post-processing method for calibrating probabilistic forecasts of continuous weather variables is presented. The method takes an existing probability distribution and adjusts it such that it becomes calibrated in the long run. The original probability distributions can be ones such as are generated from a numerical weather prediction (NWP) <span class="hlt">ensemble</span> combined with a description of how uncertainty is represented by this <span class="hlt">ensemble</span>. The method uses a calibration function to relabel raw cumulative probabilities into calibrated cumulative probabilities based on where past observations verified on past raw probability forecasts. Applying the calibration method to existing probabilistic forecasts can be beneficial in cases where the underlying assumptions used to construct the probabilistic forecast are not in line with nature's generating process of the <span class="hlt">ensemble</span> and corresponding observation. The method was tested on a forecast data set with five different forecast variables and was verified against the corresponding analyses. The calibration method reduced the calibration deficiency of the forecasts down to the level expected for perfectly calibrated forecasts. When the raw forecasts exhibited calibration deficiencies, the calibration method improved the ignorance score significantly. It was also found that the <span class="hlt">ensemble</span>-uncertainty model used to create the original probability distribution affected the ignorance score.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26723616','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26723616"><span id="translatedtitle">Predicting protein dynamics from structural <span class="hlt">ensembles</span>.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Copperman, J; Guenza, M G</p> <p>2015-12-28</p> <p>The biological properties of proteins are uniquely determined by their structure and dynamics. A protein in solution populates a structural <span class="hlt">ensemble</span> of metastable configurations around the global fold. From overall rotation to local fluctuations, the dynamics of proteins can cover several orders of magnitude in time scales. We propose a simulation-free coarse-grained approach which utilizes knowledge of the important metastable folded states of the protein to predict the protein dynamics. This approach is based upon the Langevin Equation for Protein Dynamics (LE4PD), a Langevin formalism in the coordinates of the protein backbone. The linear modes of this Langevin formalism organize the fluctuations of the protein, so that more extended dynamical cooperativity relates to increasing energy barriers to mode diffusion. The accuracy of the LE4PD is verified by analyzing the predicted dynamics across a set of seven different proteins for which both relaxation data and NMR solution structures are available. Using experimental NMR conformers as the input structural <span class="hlt">ensembles</span>, LE4PD predicts quantitatively accurate results, with correlation coefficient ? = 0.93 to NMR backbone relaxation measurements for the seven proteins. The NMR solution structure derived <span class="hlt">ensemble</span> and predicted dynamical relaxation is compared with molecular dynamics simulation-derived structural <span class="hlt">ensembles</span> and LE4PD predictions and is consistent in the time scale of the simulations. The use of the experimental NMR conformers frees the approach from computationally demanding simulations. PMID:26723616</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1714639W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1714639W"><span id="translatedtitle">The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment (HEPEX)</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, Andy; Wetterhall, Fredrik; Ramos, Maria-Helena</p> <p>2015-04-01</p> <p>The Hydrologic <span class="hlt">Ensemble</span> Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF), and co-sponsored by the US National Weather Service (NWS) and the European Commission (EC). The HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological <span class="hlt">ensemble</span> forecasts for decision support. HEPEX pursues this goal through research efforts and practical implementations involving six core elements of a hydrologic <span class="hlt">ensemble</span> prediction enterprise: input and pre-processing, <span class="hlt">ensemble</span> techniques, data assimilation, post-processing, verification, and communication and use in decision making. HEPEX has grown through meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. In the last decade, HEPEX has organized over a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Through these interactions and an active online blog (www.hepex.org), HEPEX has built a strong and active community of nearly 400 researchers & practitioners around the world. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESS...19.4765P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESS...19.4765P"><span id="translatedtitle">Evaluation of soil moisture <span class="hlt">downscaling</span> using a simple thermal-based proxy - the REMEDHUS network (Spain) example</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, J.; Niesel, J.; Loew, A.</p> <p>2015-12-01</p> <p>Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution on the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought prediction. Therefore, various <span class="hlt">downscaling</span> methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple vegetation temperature condition index (VTCI) <span class="hlt">downscaling</span> scheme over a dense soil moisture observational network (REMEDHUS) in Spain. First, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography, and land cover heterogeneity, using data from Moderate Resolution Imaging Spectroradiometer~(MODIS) and MSG SEVIRI (METEOSAT Second Generation - Spinning Enhanced Visible and Infrared Imager). Then the <span class="hlt">downscaling</span> scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the <span class="hlt">downscaling</span> method can significantly improve the spatial details of CCI soil moisture while maintaining the accuracy of CCI soil moisture. The accuracy level is comparable to other <span class="hlt">downscaling</span> methods that were also validated against the REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying a geostationary satellite for <span class="hlt">downscaling</span> soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI <span class="hlt">downscaling</span> method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/323739','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/323739"><span id="translatedtitle">Verification of GCM-generated regional seasonal precipitation for current climate and of statistical <span class="hlt">downscaling</span> estimates under changing climate conditions</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Busuioc, A.; Storch, H. von; Schnur, R.</p> <p>1999-01-01</p> <p>Empirical <span class="hlt">downscaling</span> procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a <span class="hlt">downscaling</span> technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical <span class="hlt">downscaling</span> procedures in climate change applications. The case considered is regional seasonal precipitation in Romania. The <span class="hlt">downscaling</span> model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). The climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in time-slice mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050. Generally, applications of statistical <span class="hlt">downscaling</span> to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 x CO{sub 2} GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical <span class="hlt">downscaling</span>. Since the skill of the GCMs in regional terms is already established, it is concluded that the <span class="hlt">downscaling</span> technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015GeoRL..42.6710Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015GeoRL..42.6710Y"><span id="translatedtitle">Optimal <span class="hlt">ensemble</span> size of <span class="hlt">ensemble</span> Kalman filter in sequential soil moisture data assimilation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Yin, Jifu; Zhan, Xiwu; Zheng, Youfei; Hain, Christopher R.; Liu, Jicheng; Fang, Li</p> <p>2015-08-01</p> <p>The <span class="hlt">ensemble</span> Kalman filter (EnKF) has been extensively applied in sequential soil moisture data assimilation to improve the land surface model performance and in turn weather forecast capability. Usually, the <span class="hlt">ensemble</span> size of EnKF is determined with limited sensitivity experiments. Thus, the optimal <span class="hlt">ensemble</span> size may have never been reached. In this work, based on a series of mathematical derivations, we demonstrate that the maximum efficiency of the EnKF for assimilating observations into the models could be reached when the <span class="hlt">ensemble</span> size is set to 12. Simulation experiments are designed in this study under <span class="hlt">ensemble</span> size cases 2, 5, 12, 30, 50, 100, and 300 to support the mathematical derivations. All the simulations are conducted from 1 June to 30 September 2012 over southeast USA (from -90°W, 30°N to -80°W, 40°N) at 25 km resolution. We found that the simulations are perfectly consistent with the mathematical derivation. This optical <span class="hlt">ensemble</span> size may have theoretical implications on the implementation of EnKF in other sequential data assimilation problems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3021968','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3021968"><span id="translatedtitle">Identifying <span class="hlt">Ensembles</span> of Signal Transduction Models using Pareto Optimal <span class="hlt">Ensemble</span> Techniques (POETs)</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Song, Sang Ok; Chakrabarti, Anirikh; Varner, Jeffrey D.</p> <p>2010-01-01</p> <p>Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model <span class="hlt">ensembles</span> using multiobjective optimization. In this study, we used Pareto Optimal <span class="hlt">Ensemble</span> Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass action kinetics within an ordinary differential equation (ODE) framework (64-ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an <span class="hlt">ensemble</span> of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the <span class="hlt">ensemble</span> following the addition of extracellular ligand. Also, the <span class="hlt">ensemble</span> recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model <span class="hlt">ensembles</span> could capture qualitatively important network features without exact parameter information. PMID:20665647</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.4714K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.4714K"><span id="translatedtitle">Future changes in European temperature and precipitation in an <span class="hlt">ensemble</span> of Europe-CORDEX regional climate model simulations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kjellström, Erik; Nikulin, Grigory; Jones, Colin</p> <p>2013-04-01</p> <p>In this study we investigate possible changes in temperature and precipitation on a regional scale over Europe from 1961 to 2100. We use data from two <span class="hlt">ensembles</span> of climate simulations, one global and one regional, over the Europe-CORDEX domain. The global <span class="hlt">ensemble</span> includes nine coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CNRM-CM5, HadGEM2-ES, IPSL-CM5A-MR, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. In the regional <span class="hlt">ensemble</span> all 9 AOGCMs are <span class="hlt">downscaled</span> at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Two forcing scenarios are considered, RCP 4.5 and 8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, we investigate the benefit of the higher horizontal resolution, in RCA4 by comparing the results to the coarser driving AOGCM data. The significance of the results is investigated by comparing to i) the model simulated natural variability, and, ii) the biases in the control period. Results dealing with changes in the seasonal cycle of temperature and precipitation and their relation to changes in the large-scale atmospheric circulation are presented. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.7699M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.7699M"><span id="translatedtitle"><span class="hlt">Ensemble</span> Modeling of CME Propagation and Geoeffectiveness</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mays, M. Leila; Taktakishvili, Aleksandre; Pulkkinen, Antti; MacNeice, Peter; Rastätter, Lutz; Odstrcil, Dusan; Jian, Lan; Richardson, Ian</p> <p>2015-04-01</p> <p><span class="hlt">Ensemble</span> modeling of coronal mass ejections (CMEs) provides a probabilistic forecast of CME arrival time which includes an estimation of arrival time uncertainty from the spread and distribution of predictions and forecast confidence in the likelihood of CME arrival. The real-time <span class="hlt">ensemble</span> modeling of CME propagation uses the Wang-Sheeley-Arge (WSA)-ENLIL+Cone model installed at the {Community Coordinated Modeling Center} (CCMC) and executed in real-time at the CCMC/{Space Weather Research Center}. The current implementation of this <span class="hlt">ensemble</span> modeling method evaluates the sensitivity of WSA-ENLIL+Cone model simulations of CME propagation to initial CME parameters. We discuss the results of real-time <span class="hlt">ensemble</span> simulations for a total of 35 CME events which occurred between January 2013 - July 2014. For the 17 events where the CME was predicted to arrive at Earth, the mean absolute arrival time prediction error was 12.3 hours, which is comparable to the errors reported in other studies. For predictions of CME arrival at Earth the correct rejection rate is 62%, the false-alarm rate is 38%, the correct alarm ratio is 77%, and false alarm ratio is 23%. The arrival time was within the range of the <span class="hlt">ensemble</span> arrival predictions for 8 out of 17 events. The Brier Score for CME arrival predictions is 0.15 (where a score of 0 on a range of 0 to 1 is a perfect forecast), which indicates that on average, the predicted probability, or likelihood, of CME arrival is fairly accurate. The reliability of <span class="hlt">ensemble</span> CME arrival predictions is heavily dependent on the initial distribution of CME input parameters (e.g. speed, direction, and width), particularly the median and spread. Preliminary analysis of the probabilistic forecasts suggests undervariability, indicating that these <span class="hlt">ensembles</span> do not sample a wide enough spread in CME input parameters. Prediction errors can also arise from ambient model parameters, the accuracy of the solar wind background derived from coronal maps, or other model limitations. Finally, predictions of the KP geomagnetic index differ from observed values by less than one for 11 out of 17 of the <span class="hlt">ensembles</span> and KP prediction errors computed from the mean predicted KP show a mean absolute error of 1.3. The CCMC, located at NASA Goddard Space Flight Center, is an interagency partnership to facilitate community research and accelerate implementation of progress in research into space weather operations. The CCMC also serves the {Space Weather Scoreboard} website (http://kauai.ccmc.gsfc.nasa.gov/SWScoreBoard) to the research community who may submit CME arrival time predictions in real-time for a variety of forecasting methods. The website facilitates model validation under real-time conditions and enables collaboration. For every CME event table on the site, the average of all submitted forecasts is automatically computed, thus itself providing a community-wide <span class="hlt">ensemble</span> mean CME arrival time and impact forecast from a variety of models/methods.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1715091B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1715091B"><span id="translatedtitle">HEPS4Power - Extended-range Hydrometeorological <span class="hlt">Ensemble</span> Predictions for Improved Hydropower Operations and Revenues</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bogner, Konrad; Monhart, Samuel; Liniger, Mark; Spririg, Christoph; Jordan, Fred; Zappa, Massimiliano</p> <p>2015-04-01</p> <p>In recent years large progresses have been achieved in the operational prediction of floods and hydrological drought with up to ten days lead time. Both the public and the private sectors are currently using probabilistic runoff forecast in order to monitoring water resources and take actions when critical conditions are to be expected. The use of extended-range predictions with lead times exceeding 10 days is not yet established. The hydropower sector in particular might have large benefits from using hydro meteorological forecasts for the next 15 to 60 days in order to optimize the operations and the revenues from their watersheds, dams, captions, turbines and pumps. The new Swiss Competence Centers in Energy Research (SCCER) targets at boosting research related to energy issues in Switzerland. The objective of HEPS4POWER is to demonstrate that operational extended-range hydro meteorological forecasts have the potential to become very valuable tools for fine tuning the production of energy from hydropower systems. The project team covers a specific system-oriented value chain starting from the collection and forecast of meteorological data (MeteoSwiss), leading to the operational application of state-of-the-art hydrological models (WSL) and terminating with the experience in data presentation and power production forecasts for end-users (e-dric.ch). The first task of the HEPS4POWER will be the <span class="hlt">downscaling</span> and post-processing of <span class="hlt">ensemble</span> extended-range meteorological forecasts (EPS). The goal is to provide well-tailored forecasts of probabilistic nature that should be reliable in statistical and localized at catchment or even station level. The hydrology related task will consist in feeding the post-processed meteorological forecasts into a HEPS using a multi-model approach by implementing models with different complexity. Also in the case of the hydrological <span class="hlt">ensemble</span> predictions, post-processing techniques need to be tested in order to improve the quality of the forecasts against observed discharge. Analysis should be specifically oriented to the maximisation of hydroelectricity production. Thus, verification metrics should include economic measures like cost loss approaches. The final step will include the transfer of the HEPS system to several hydropower systems, the connection with the energy market prices and the development of probabilistic multi-reservoir production and management optimizations guidelines. The baseline model chain yielding three-days forecasts established for a hydropower system in southern-Switzerland will be presented alongside with the work-plan to achieve seasonal <span class="hlt">ensemble</span> predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFM.H23A1165W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFM.H23A1165W"><span id="translatedtitle">Generation of medium-range precipitation <span class="hlt">ensemble</span> forecasts from the GFS <span class="hlt">ensemble</span> mean at the basin scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, L.; Schaake, J. C.; Brown, J. D.; Demargne, J.; Hartman, R. K.</p> <p>2010-12-01</p> <p>The Office of Hydrologic Development at the National Weather Service and its partners have been developing an experimental system for Hydrologic <span class="hlt">Ensemble</span> Forecast Service (HEFS), with the goal of operationally producing reliable and skillful streamflow <span class="hlt">ensemble</span> forecasts out to about a year in the future using forcings from short, medium and long range numerical weather prediction models. A key component of the system is a preprocessor that extracts information from single-valued, as well as <span class="hlt">ensemble</span>, precipitation and temperature forecasts produced by a number of U.S. weather and climate forecast centers. The extracted forecast information is then turned into forcing <span class="hlt">ensembles</span> for the HEFS system at the basin scale. In this presentation, we describe the methodology employed in the preprocessor and demonstrate its performance in generating forcing precipitation <span class="hlt">ensembles</span> using the source <span class="hlt">ensembles</span> from the 1998 frozen version of the Global Forecast System (GFS), a medium range system developed by the National Center for Environmental Prediction. It is widely recognized that the raw <span class="hlt">ensemble</span> forecasts produced by numerical weather prediction models tend to be biased in the mean, spread and higher moments. Several recent studies show that the predictive skill of the raw <span class="hlt">ensemble</span> forecasts can often be captured by the <span class="hlt">ensemble</span> mean. Therefore one may use the <span class="hlt">ensemble</span> mean of the real-time forecasts to derive reliable <span class="hlt">ensembles</span> from the historical relationship of the observed and the <span class="hlt">ensemble</span> mean, provided that the relationship is representative of the future. We use the GFS <span class="hlt">ensemble</span> reforecasts, which are available for over 20 years, in calibrating the preprocessor. The output <span class="hlt">ensemble</span> traces are arranged according to the historically observed <span class="hlt">ensemble</span> traces to maintain the space-time statistical properties of precipitation and temperature <span class="hlt">ensemble</span> forecasts for multiple lead times and multiple locations. We will present dependent validation results for selected river basins in California.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H41I..05C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H41I..05C"><span id="translatedtitle">Stochastic Weather Generator Based <span class="hlt">Ensemble</span> Streamflow Forecasting</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Caraway, N.; Werner, K.; Rajagopalan, B.; Wood, A. W.</p> <p>2011-12-01</p> <p>Efficient water resources management owes considerably to skillful basin wide streamflow forecasts at both short (1-2 weeks) and long (seasonal and longer) time scales. The skillful projection of the streamflow probability density function (PDF) is especially of interest. Presently, the <span class="hlt">Ensemble</span> Streamflow Prediction (ESP) approach is used by River Forecasting Centers such as the Colorado Basin River Forecasting Center (CBRFC) with their hydrologic model to produce <span class="hlt">ensembles</span> and thus the PDF. The main drawback of this is that the number of <span class="hlt">ensembles</span> is limited to the number of years of the historical data, which is often quite small. CBRFC currently maintains a 30 year calibration period. Furthermore, if seasonal forecast information is included through a use of a subset of these years, the <span class="hlt">ensemble</span> size decreases substantially, further degrading the resolution of the estimated PDF. To improve on this, we propose a stochastic weather generator based approach coupled to the hydrologic modeling system. The weather generator uses a Markov Chain to simulate the precipitation state of a day (wet or dry) and a K-nearest neighbor (K-NN) resampling approach to simulate the daily weather vector. This stochastic weather generator can also produce daily weather sequences conditioned on seasonal categorical climate forecasts such as those issued by NOAA/CPC, as well as sequences at multiple locations across the basin. Daily weather sequences for a desired time horizon (1-2 weeks or seasonal) are produced using the K-NN weather generator; these are then driven through the hydrologic model to produce an <span class="hlt">ensemble</span> forecast of streamflow. The weather generator's ability to produce a rich variety of daily weather sequences enables increased resolution and more accurate estimation of the streamflow PDF. We demonstrate this approach to San Juan River Basin and present preliminary findings. First, results from the stochastic weather generator are presented showing that the generated sequences capture the historic variability across multiple locations in the basin quite well. We also show that the weather sequences the PDF of the weather attributes appropriately based on seasonal climate forecast. CBRFC's new Community Hydrologic Prediction System (CHPS) was used in conjunction with the generated weather sequences to produce <span class="hlt">ensembles</span> of streamflow. The skills from these simulations are compared with the existing ESP forecasting approach.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012GeoRL..3910403M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012GeoRL..3910403M"><span id="translatedtitle">Utility of coarse and <span class="hlt">downscaled</span> soil moisture products at L-band for hydrologic modeling at the catchment scale</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu