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Sample records for ensemble downscaling mred

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. Downscaling a perturbed physics ensemble over the CORDEX Africa domain

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  7. Probabilistic Interpretation of Regression-Based Downscaled Seasonal Ensemble Predictions with the Estimation of Uncertainty

    NASA Astrophysics Data System (ADS)

    Min, Y.; Kryjov, V.; Oh, J.

    2010-12-01

    A regression-based statistical downscaling method from global multi-model ensemble (MME) forecasts has been developed. The novelty of the method is in the estimation of uncertainties originating from both regression and ensemble spread of model forecasts, as a part of the regression analysis. The method has been tested on the prediction of wintertime temperature and precipitation for 60 Korean stations by downscaling from the MME forecasts of 850hPa temperature, seal-level pressure and 500hPa geopotential height. Total estimated uncertainty, expressed in terms of the forecast variance, is comparable with the variance of the target station variable for both temperature and precipitation. Different sources of uncertainty have been evaluated and their contributions compared. It is shown that although the uncertainty associated with the deviation from the linear model is usually the largest, a comparable contribution to the uncertainty can come from the combination of the sampling error in the regression line slope and the ensemble spread. Assessment of the skill of the regression-based downscaling probabilistic MME (PMME) method with estimated total uncertainty (hereafter, Dsc PMME) has been performed on the basis of cross-validated retrospective forecasts for 23 winter seasons (1981/82 - 2003/04). For comparison, raw model-predicted probabilistic forecasts (hearafter, Raw PMME) derived from APCC operational PMME system [Min et al. 2009] have been interpolated to the station locations and its skill has been also estimated. In the present study, data from hindcast experiments from six operational prediction models with lead time of one month and target season DJF are used for an assessment of downscaled forecasts. These model datasets match the requirements of the Seasonal Prediction Model Intercomparison Project-2/Historical Forecast Project (SMIP2/HFP). It should be noted that all these data are used in the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) in its operational predictions. The downscaled PMME forecasts estimated with the described method are shown to clearly outperform the raw PMME forecasts of regional temperature and precipitation. The aggregated ROC score of temperature (precipitation) in the above-normal category is 0.65 (0.66) and in the below normal category is 0.72 (0.67) which is significantly higher than the corresponding skill of the raw PMME. Thus, the suggested method is demonstrated to be a useful approach for quantifying forecast uncertainty and for regional downscaling from model-based global seasonal forecasts.

  8. 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.

  9. 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

  10. 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://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/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://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://www.personal.leeds.ac.uk/~stapdb/research/rain_paper.pdf','EPRINT'); return false;" href="http://www.personal.leeds.ac.uk/~stapdb/research/rain_paper.pdf"><span id="translatedtitle">The <span class="hlt">Downscaling</span> of Rain Gauge Time-Series By Multiplicative Beta Cascade</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Baxter, Paul D.</p> <p></p> <p>1 The <span class="hlt">Downscaling</span> of Rain Gauge Time-Series By Multiplicative Beta Cascade Kevin S. Paulson: <span class="hlt">Downscaling</span> Rain Gauge Time-Series. Key Words: rain, rain gauge, <span class="hlt">downscaling</span>, multifractal, multiplicative cascade #12;2 Abstract This paper develops a <span class="hlt">downscaling</span> algorithm capable of producing <span class="hlt">ensembles</span> of rain</p> </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://civil.colorado.edu/~balajir/my-papers/knn-downscale-wrr.pdf','EPRINT'); return false;" href="http://civil.colorado.edu/~balajir/my-papers/knn-downscale-wrr.pdf"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> using K-nearest neighbors Subhrendu Gangopadhyay1</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Balaji, Rajagopalan</p> <p></p> <p>for Environmental Prediction 1998 medium-range forecast model output. The K-nn algorithm queries days similar function and randomly sampled to generate <span class="hlt">ensembles</span>. A set of 15 medium-range forecast runs was used in atmospheric forecasts at local scales. [3] In short, statistical <span class="hlt">downscaling</span> develops relation- ships between</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://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://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> </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://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://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://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241249&keyword=Fusion&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=42774556&CFTOKEN=24859434','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=241249&keyword=Fusion&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=42774556&CFTOKEN=24859434"><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://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/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://adsabs.harvard.edu/abs/2013AGUFM.H21A1005Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H21A1005Z"><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, T.; Venema, V.; Simmer, C.</p> <p>2013-12-01</p> <p>The coupling of models for the different components of the soil-vegetation-atmosphere system is required to understand component interactions and feedback processes. The Transregional Collaborative Research Center 32 (TR 32) has developed a coupled modeling platform, TerrSysMP, consisting of the atmospheric model COSMO, the land-surface model CLM, and the hydrological model ParFlow. These component models are usually operated at different resolutions in space and time owing to the dominant processes. These different scales should also be considered in the coupling mode, because it is for instance unfeasible to run the computationally quite expensive atmospheric models at the usually much higher spatial resolution required by hydrological models. Thus up- and <span class="hlt">downscaling</span> procedures are required at the interface between atmospheric model and land-surface/subsurface models. Here we present an advanced atmospheric <span class="hlt">downscaling</span> scheme, that creates realistic fine-scale fields (e.g. 400 m resolution) of the atmospheric state variables from the coarse atmospheric model output (e.g. 2.8 km resolution). The mixed physical/statistical scheme is developed from a training data set of high-resolution atmospheric model runs covering a range different weather conditions using Genetic Programming (GP). GP originates from machine learning: From a set of functions (arithmetic expressions, IF-statements, etc.) and terminals (constants or variables) GP generates potential solutions to a given problem while minimizing a fitness or cost function. We use a multi-objective approach that aims at fitting spatial structures, spatially distributed variance and spatio-temporal correlation of the fields. We account for the spatio-temporal nature of the data in two ways. On the one hand we offer GP potential predictors, which are based on our physical understanding of the atmospheric processes involved (spatial and temporal gradients, etc.). On the other hand we include functions operating on spatial fields, which are partly adapted from image classification. Our preliminary results show that realistic fine-scale structures can be retrieved from the coarse scale input, which constitutes a major advancement compared to the usually applied interpolations methods. Example for <span class="hlt">downscaling</span> of near-surface temperature during an almost clear-sky night. Colorbar values are given in Kelvin.</p> </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://www.ciesin.org/documents/Downscaling_CLEARED_000.pdf','EPRINT'); return false;" href="http://www.ciesin.org/documents/Downscaling_CLEARED_000.pdf"><span id="translatedtitle">A Review of <span class="hlt">Downscaling</span> Methods for Climate Change Projections 1 A REVIEW OF <span class="hlt">DOWNSCALING</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Columbia University</p> <p></p> <p>A Review of <span class="hlt">Downscaling</span> Methods for Climate Change Projections 1 A REVIEW OF <span class="hlt">DOWNSCALING</span> METHODS FOR CLIMATE CHANGE PROJECTIONS SEPTEMBER 2014 This report is made possible by the support of the American to Climate Change (ARCC) Burlington, Vermont Tel.: 802.658.3890 Patricia.Caffrey@tetratech.com Anna Farmer</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 “<span class="hlt">ensemble</span>” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. <span class="hlt">Ensembles</span> are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote <span class="hlt">ensemble</span>, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall <span class="hlt">ensemble</span> [158]. In the literature, a plethora of terms other than <span class="hlt">ensembles</span> has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term <span class="hlt">ensemble</span> in its widest meaning, in order to include the whole range of combination methods. Nowadays, <span class="hlt">ensemble</span> methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on <span class="hlt">ensemble</span> methods is witnessed by conferences and workshops specifically devoted to <span class="hlt">ensembles</span>, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been proposed to explain the characteristics and the successful application of <span class="hlt">ensembles</span> to different application domains. For instance, Allwein, Schapire, and Singer interpreted the improved generalization capabilities of <span class="hlt">ensembles</span> of learning machines in the framework of large margin classifiers [4,177], Kleinberg in the context of stochastic discrimination theory [112], and Breiman and Friedman in the light of the bias-variance analysis borrowed from classical statistics [21,70]. Empirical studies showed that both in classification and regression problems, <span class="hlt">ensembles</span> improve on single learning machines, and moreover large experimental studies compared the effectiveness of different <span class="hlt">ensemble</span> methods on benchmark data sets [10,11,49,188]. The interest in this research area is motivated also by the availability of very fast computers and networks of workstations at a relatively low cost that allow the implementation and the experimentation of complex <span class="hlt">ensemble</span> methods using off-the-shelf computer platforms. However, as explained in Section 26.2 there are deeper reasons to use <span class="hlt">ensembles</span> of learning machines, motivated by the intrinsic characteristics of the <span class="hlt">ensemble</span> methods. The main aim of this chapter is to introduce <span class="hlt">ensemble</span> methods and to provide an overview and a bibliography of the main areas of research, without pretending to be exhaustive or to explain the detailed characteristics of each <span class="hlt">ensemble</span> method. The paper is organized as follows. In the next section, the main theoretical and practical reasons for combining multiple learners are introduced. Section 26.3 depicts the main taxonomies on <span class="hlt">ensemble</span> methods proposed in the literature. In Section 26.4 and 26.5, we present an overview of the main supervised <span class="hlt">ensemble</span> methods reported in the literature, adopting a simple taxonomy, originally proposed in Ref. [201]. Applications of <span class="hlt">ensemble</span> methods are only marginally considered, but a specific section on some relevant applications of <span class="hlt">ensemble</span> methods in astronomy and astrophysics has been added (Section 26.6). The conclusion (Section 26.7) ends this pap</p> </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/2012AtmRe.118..346W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AtmRe.118..346W"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of climate forecast system seasonal predictions for the Southeastern Mediterranean</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wu, Wanli; Liu, Yubao; Ge, Ming; Rostkier-Edelstein, Dorita; Descombes, Gael; Kunin, Pavel; Warner, Thomas; Swerdlin, Scott; Givati, Amir; Hopson, Thomas; Yates, David</p> <p>2012-11-01</p> <p>Most of the annual rainfall in the Southeastern Mediterranean falls in the wet season from November to March. It is associated with Mediterranean cyclones, and is sensitive to climate variability. Predicting the wet season precipitation with a few months advance is highly valuable for water resource planning and climate-associated risk management in this semi-arid region. The regional water resource managements and climate-sensitive economic activities have relied on seasonal forecasts from global climate prediction centers. However due to their coarse resolutions, global seasonal forecasts lack regional and local scale information required by regional and local water resource managements. In this study, an analog statistical-<span class="hlt">downscaling</span> algorithm, k-nearest neighbors (KNN), was introduced to bridge the gap between the coarse forecasts from global models and the needed fine-scale information for the Southeastern Mediterranean. The algorithm, driven by the NCEP Climate Forecast System (CFS) operational forecast and the NCEP/DOE reanalysis, provides monthly precipitations at 2-4 months of lead-time at 18 stations within the major regional hydrological basins. Large-scale predictors for KNN were objectively determined by the correlations between the station historic daily precipitation and variables in reanalysis and CFS reforecast. Besides a single deterministic forecast, this study constructed sixty <span class="hlt">ensemble</span> members for probabilistic estimates. The KNN algorithm demonstrated its robustness when validated with NCEP/DOE reanalysis from 1981 to 2009 as hindcasts before applied to <span class="hlt">downscale</span> CFS forecasts. The <span class="hlt">downscaled</span> predictions show fine-scale information, such as station-to-station variability. The verification against observations shows improved skills of this <span class="hlt">downscaling</span> utility relative to the CFS model. The KNN-based <span class="hlt">downscaling</span> system has been in operation for the Israel Water Authority predicting precipitation and driving hydrologic models estimating river flow and aquifer charge for water supply.</p> </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=48055530&CFTOKEN=58609306','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=48055530&CFTOKEN=58609306"><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/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/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://www.atmos.washington.edu/~salathe/papers/Salathe_Downscale_IJOC2004.pdf','EPRINT'); return false;" href="http://www.atmos.washington.edu/~salathe/papers/Salathe_Downscale_IJOC2004.pdf"><span id="translatedtitle">Preprint <span class="hlt">Downscaling</span> Climate Change Salath 02/02/2005 <span class="hlt">Downscaling</span> Simulations of future Global Climate with Application to Hydrologic</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Salathé Jr., Eric P.</p> <p></p> <p>Preprint <span class="hlt">Downscaling</span> Climate Change ­ Salathé 02/02/2005 <span class="hlt">Downscaling</span> Simulations of future Global approaches the problem of <span class="hlt">downscaling</span> global climate model simulations with an emphasis on validating simulation while preserving much of the statistics of interannual variability in the climate model</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> </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/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://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://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://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://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://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.ncbi.nlm.nih.gov/pubmed/26026419','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26026419"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Rashid, Md Mamunur; Beecham, Simon; Chowdhury, Rezaul K</p> <p>2015-10-15</p> <p>A generalized linear model was fitted to stochastically <span class="hlt">downscaled</span> multi-site daily rainfall projections from CMIP5 General Circulation Models (GCMs) for the Onkaparinga catchment in South Australia to assess future changes to hydrologically relevant metrics. For this purpose three GCMs, two multi-model <span class="hlt">ensembles</span> (one by averaging the predictors of GCMs and the other by regressing the predictors of GCMs against reanalysis datasets) and two scenarios (RCP4.5 and RCP8.5) were considered. The <span class="hlt">downscaling</span> model was able to reasonably reproduce the observed historical rainfall statistics when the model was driven by NCEP reanalysis datasets. Significant bias was observed in the rainfall when <span class="hlt">downscaled</span> from historical outputs of GCMs. Bias was corrected using the Frequency Adapted Quantile Mapping technique. Future changes in rainfall were computed from the bias corrected <span class="hlt">downscaled</span> rainfall forced by GCM outputs for the period 2041-2060 and these were then compared to the base period 1961-2000. The results show that annual and seasonal rainfalls are likely to significantly decrease for all models and scenarios in the future. The number of dry days and maximum consecutive dry days will increase whereas the number of wet days and maximum consecutive wet days will decrease. Future changes of daily rainfall occurrence sequences combined with a reduction in rainfall amounts will lead to a drier catchment, thereby reducing the runoff potential. Because this is a catchment that is a significant source of Adelaide's water supply, irrigation water and water for maintaining environmental flows, an effective climate change adaptation strategy is needed in order to face future potential water shortages. PMID:26026419</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/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://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://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/2014EGUGA..16.6323P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.6323P"><span id="translatedtitle">Statistical-dynamical <span class="hlt">downscaling</span> for wind energy potentials: Evaluation and applications to decadal hindcasts and climate change projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pinto, Joaquim G.; Reyers, Mark; Mömken, Julia</p> <p>2014-05-01</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/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://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1761443','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1761443"><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=pmc">PubMed Central</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> () 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://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://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280783&keyword=weather&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=43056645&CFTOKEN=26387491','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=280783&keyword=weather&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=43056645&CFTOKEN=26387491"><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/2060/19950024819','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.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://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/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> </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/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/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/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/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://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 <span class="hlt">ensemble</span>…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.7445D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.7445D"><span id="translatedtitle"><span class="hlt">Downscaling</span> of RCM outputs for representative catchments in the Mediterranean region, for the 1951-2100 time-frame</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Deidda, Roberto; Marrocu, Marino; Pusceddu, Gabriella; Langousis, Andreas; Mascaro, Giuseppe; Caroletti, Giulio</p> <p>2013-04-01</p> <p>Within the activities of the EU FP7 CLIMB project (www.climb-fp7.eu), we developed <span class="hlt">downscaling</span> procedures to reliably assess climate forcing at hydrologically relevant scales, and applied them to six representative hydrological basins located in the Mediterranean region: Riu Mannu and Noce in Italy, Chiba in Tunisia, Kocaeli in Turkey, Thau in France, and Gaza in Palestine. As a first step towards this aim, we used daily precipitation and temperature data from the gridded E-OBS project (www.ecad.eu/dailydata), as reference fields, to rank 14 Regional Climate Model (RCM) outputs from the <span class="hlt">ENSEMBLES</span> project (http://<span class="hlt">ensembles</span>-eu.metoffice.com). The four best performing model outputs were selected, with the additional constraint of maintaining 2 outputs obtained from running different RCMs driven by the same GCM, and 2 runs from the same RCM driven by different GCMs. For these four RCM-GCM model combinations, a set of <span class="hlt">downscaling</span> techniques were developed and applied, for the period 1951-2100, to variables used in hydrological modeling (i.e. precipitation; mean, maximum and minimum daily temperatures; direct solar radiation, relative humidity, magnitude and direction of surface winds). The quality of the final products is discussed, together with the results obtained after applying a bias reduction procedure to daily temperature and precipitation fields.</p> </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> <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/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://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/2012JGRD..11717116H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012JGRD..11717116H"><span id="translatedtitle">Predictor selection for <span class="hlt">downscaling</span> GCM data with LASSO</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hammami, Dorra; Lee, Tae Sam; Ouarda, Taha B. M. J.; Lee, Jonghyun</p> <p>2012-09-01</p> <p>Over the last 10 years, <span class="hlt">downscaling</span> techniques, including both dynamical (i.e., the regional climate model) and statistical methods, have been widely developed to provide climate change information at a finer resolution than that provided by global climate models (GCMs). Because one of the major aims of <span class="hlt">downscaling</span> techniques is to provide the most accurate information possible, data analysts have tried a number of approaches to improve predictor selection, which is one of the most important steps in <span class="hlt">downscaling</span> techniques. Classical methods such as regression techniques, particularly stepwise regression (SWR), have been employed for <span class="hlt">downscaling</span>. However, SWR presents some limits, such as deficiencies in dealing with collinearity problems, while also providing overly complex models. Thus, the least absolute shrinkage and selection operator (LASSO) technique, which is a penalized regression method, is presented as another alternative for predictor selection in <span class="hlt">downscaling</span> GCM data. It may allow for more accurate and clear models that can properly deal with collinearity problems. Therefore, the objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Québec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters for comparison.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.atmo.arizona.edu/~castro/Reviewedpubs/R-4.pdf','EPRINT'); return false;" href="http://www.atmo.arizona.edu/~castro/Reviewedpubs/R-4.pdf"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span>: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS)</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Castro, Christopher L.</p> <p></p> <p>Dynamical <span class="hlt">downscaling</span>: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS) Christopher L. Castro, Roger A. Pielke Sr., and Giovanni Leoncini Department by dynamical <span class="hlt">downscaling</span> is quantitatively evaluated by considering the spectral behavior of the Regional</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/2013JGRD..118.5147T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JGRD..118.5147T"><span id="translatedtitle">Improving the seasonal forecast for summertime South China rainfall using 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>Tung, Ying Lut; Tam, Chi-Yung; Sohn, Soo-Jin; Chu, Jung-Lien</p> <p>2013-06-01</p> <p>The performance of various seasonal forecast systems in predicting the station-scale summer rainfall in South China (SC) was assessed and was compared with that based on a statistical <span class="hlt">downscaling</span> scheme. Hindcast experiments from 11 dynamical models covering the period of 1983-2003 were taken from the Asia-Pacific Economic Cooperation Climate Center multimodel <span class="hlt">ensemble</span>. Based on observations, singular value decomposition analysis (SVDA) showed that SC precipitation is strongly related to the broad-scale sea level pressure (SLP) variation over Southeast Asia, western north Pacific, and part of the Indian Ocean. Analogous covariability was also found between model hindcasts and the observed station precipitation. Based on these results from SVDA, a statistical <span class="hlt">downscaling</span> scheme for predicting SC station rainfall with model SLP as predictor was constructed. In general, the statistical scheme is superior to the original model prediction in two geographical regions, namely, western SC (near Guangxi) and eastern coastal SC (eastern Guangdong to part of Fujian). Further analysis indicated that dynamical models are able to reproduce the large-scale circulation patterns associated with the recurrent modes of SC rainfall, but not the local circulation features. This probably leads to erroneous rainfall predictions in some locations. On the other hand, the statistical scheme was able to map the broad-scale SLP patterns onto the station-scale rainfall anomalies, thereby correcting some of the model biases. Overall, our results demonstrate how SC summer rainfall predictions can be improved by tapping the source of predictability related to large-scale circulation signals from dynamical models.</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://www4.stat.ncsu.edu/~reich/papers/Multires.pdf','EPRINT'); return false;" href="http://www4.stat.ncsu.edu/~reich/papers/Multires.pdf"><span id="translatedtitle">Biometrics , 124 A spectral method for spatial <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Reich, Brian J.</p> <p></p> <p>Biometrics , 1­24 2014 A spectral method for spatial <span class="hlt">downscaling</span> Brian J Reich North Carolina State. This paper has been submitted for consideration for publication in Biometrics #12;A spectral method at the point-level. This framework, known as statistical down- #12;2 Biometrics, 2014 scaling, allows us</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://web.science.unsw.edu.au/~jasone/publications/evans2011.pdf','EPRINT'); return false;" href="http://web.science.unsw.edu.au/~jasone/publications/evans2011.pdf"><span id="translatedtitle">CORDEX An international climate <span class="hlt">downscaling</span> J.P. Evans1</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Evans, Jason</p> <p></p> <p>, regional climate modelling, CMIP5, AustralAsia 19th International Congress on Modelling and Simulation (GCMs) are the main tools used to project the extent of this future climate change. The Coupled ModelCORDEX ­ An international climate <span class="hlt">downscaling</span> initiative J.P. Evans1 1 Climate Change Research</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=282623&keyword=weather&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=43056645&CFTOKEN=26387491','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=282623&keyword=weather&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=43056645&CFTOKEN=26387491"><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> </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/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> <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://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/2015ThApC.122..159S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.122..159S"><span id="translatedtitle">Potential improvements to statistical <span class="hlt">downscaling</span> of general circulation model outputs to catchment streamflows with <span class="hlt">downscaled</span> precipitation and evaporation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sachindra, D. A.; Huang, F.; Barton, A.; Perera, B. J. C.</p> <p>2015-10-01</p> <p>An existing streamflow <span class="hlt">downscaling</span> model (SDM(original)), was modified with the outputs of a precipitation <span class="hlt">downscaling</span> model (PDM) and an evaporation <span class="hlt">downscaling</span> model (EDM) as additional inputs, for improving streamflow projections. For this purpose, lag 0, lag 1 and lag 2 outputs of PDM were individually introduced to SDM(original) as additional inputs, and then it was calibrated and validated. Performances of the resulting modified models were assessed using Nash-Sutcliffe efficiency (NSE) during calibration and validation. It was found that the use of lag 0 precipitation as an additional input to SDM(original) improves NSE in calibration and validation. This modified streamflow <span class="hlt">downscaling</span> model is called SDM(lag0_preci). Then lag 0, lag 1 and lag 2 evaporation of EDM were individually introduced to SDM(lag0_preci) as additional inputs and it was calibrated and validated. The resulting models showed signs of over-fitting in calibration and under-fitting in validation. Hence, SDM(lag0_preci) was selected as the best model. When SDM(lag0_preci) was run with observed lag 0 precipitation, a large improvement in NSE was seen. This proved that if precipitation produced by the PDM can accurately reproduce the observations, improved precipitation predictions will produce better streamflow predictions.</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/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/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://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.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://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Climate&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DClimate','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20150023406&hterms=Climate&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%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/2013JGRD..118.2136H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JGRD..118.2136H"><span id="translatedtitle">Constrained dynamical <span class="hlt">downscaling</span> for assessment of climate impacts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Harkey, M.; Holloway, T.</p> <p>2013-03-01</p> <p><title type="main">AbstractTo assess climate change impacts on hydrology, conservation biology, and air quality, impact studies typically require future climate data with spatial resolution high enough to resolve urban-rural gradients, complex topography, and sub-synoptic atmospheric phenomena. We present here an approach to dynamical <span class="hlt">downscaling</span> using analysis nudging, where the entire domain is constrained to coarser-resolution parent data. Here meteorology from the North American Regional Reanalysis and the North American Regional Climate Change Assessment Program data archive are used as parent data and <span class="hlt">downscaled</span> with the Advanced Research version of the Weather Research and Forecasting model to a 12 km × 12 km horizontal resolution over the Eastern U.S. Our results show when analysis nudging is applied to all variables at all levels, mean fractional errors relative to parent data are less than 2% for maximum 2 m temperatures, less than 15% for minimum 2 m temperatures, and less than 18% for10 m wind speeds. However, the skill of representing fields that are not nudged, such as boundary layer height and precipitation, is less clear. Our results indicate that though nudging can be a useful tool for consistent, comparable studies of <span class="hlt">downscaling</span> climate for regional and local impacts, which variables are nudged and at what levels should be carefully considered based on the climate impact(s) of study.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24872455','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24872455"><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=pubmed">PubMed</a></p> <p>Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J</p> <p>2014-06-17</p> <p>Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical <span class="hlt">downscaling</span>--nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output--to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn <span class="hlt">downscaled</span> by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically <span class="hlt">downscaling</span> raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections. PMID:24872455</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://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 <span class="hlt">downscaling</span>—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn <span class="hlt">downscaled</span> by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically <span class="hlt">downscaling</span> raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections. PMID:24872455</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009WRR....4511411M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009WRR....4511411M"><span id="translatedtitle">Using probabilistic climate change information from a multimodel <span class="hlt">ensemble</span> for water resources assessment</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Manning, L. J.; Hall, J. W.; Fowler, H. J.; Kilsby, C. G.; Tebaldi, C.</p> <p>2009-11-01</p> <p>Increasing availability of <span class="hlt">ensemble</span> outputs from general circulation models (GCMs) and regional climate models (RCMs) permits fuller examination of the implications of climate uncertainties in hydrological systems. A Bayesian statistical framework is used to combine projections by weighting and to generate probability distributions of local climate change from an <span class="hlt">ensemble</span> of RCM outputs. A stochastic weather generator produces corresponding daily series of rainfall and potential evapotranspiration, which are input into a catchment rainfall-runoff model to estimate future water abstraction availability. The method is applied to the Thames catchment in the United Kingdom, where comparison with previous studies shows that different <span class="hlt">downscaling</span> methods produce significantly different flow predictions and that this is partly attributable to potential evapotranspiration predictions. An extended sensitivity test exploring the effect of the weights and assumptions associated with combining climate model projections illustrates that under all plausible assumptions the <span class="hlt">ensemble</span> implies a significant reduction in catchment water resource availability.</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/2015JGRD..120.1023D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..120.1023D"><span id="translatedtitle">Transferability in the future climate of a statistical <span class="hlt">downscaling</span> method for precipitation in France</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dayon, G.; Boé, J.; Martin, E.</p> <p>2015-02-01</p> <p>A statistical <span class="hlt">downscaling</span> approach for precipitation in France based on the analog method and its evaluation for different combinations of predictors is described, with focus on the transferability of the method to the future climate. First, the realism of <span class="hlt">downscaled</span> present-day precipitation climatology and interannual variability for different combinations of predictors from four reanalyses is assessed. Satisfactory results are obtained, but elaborated predictors do not lead to major and consistent across-reanalyses improvements. The <span class="hlt">downscaling</span> method is then evaluated on its capacity to capture precipitation trends in the last decades. As uncertainties in <span class="hlt">downscaled</span> trends due to the choice of the reanalysis are large and observed trends are weak, this analysis does not lead to strong conclusions on the applicability of the method to a changing climate. The temporal transferability is then assessed thanks to a perfect model framework. The statistical <span class="hlt">downscaling</span> relationship is built using present-day predictors and precipitation simulated by 12 regional climate models. The entire projections are then <span class="hlt">downscaled</span>, and future <span class="hlt">downscaled</span> and simulated precipitation changes are compared. A good temporal transferability is obtained only with a specific combination of predictors. Finally, the regional climate models are <span class="hlt">downscaled</span>, thanks to the relationship built with reanalyses and observations, for the best combination of predictors. Results are similar to the changes simulated by the models, which reinforces our confidence in the realism of the models and of the <span class="hlt">downscaling</span> method. Uncertainties in precipitation change due to reanalyses are found to be limited compared to those due to regional simulations.</p> </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://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://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> </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://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/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/2009AGUFM.H33E0922B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.H33E0922B"><span id="translatedtitle">Expansion of the On-line Archive "Statistically <span class="hlt">Downscaled</span> WCRP CMIP3 Climate Projections"</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brekke, L. D.; Pruitt, T.; Maurer, E. P.; Das, T.; Duffy, P.; White, K.</p> <p>2009-12-01</p> <p>Presentation highlights status and plans for a public-access archive of <span class="hlt">downscaled</span> CMIP3 climate projections. Incorporating climate projection information into long-term evaluations of water and energy resources requires analysts to have access to projections at "basin-relevant" resolution. Such projections would ideally be bias-corrected to account for climate model tendencies to systematically simulate historical conditions different than observed. In 2007, the U.S. Bureau of Reclamation, Santa Clara University and Lawrence Livermore National Laboratory (LLNL) collaborated to develop an archive of 112 bias-corrected and spatially disaggregated (BCSD) CMIP3 temperature and precipitation projections. These projections were generated using 16 CMIP3 models to simulate three emissions pathways (A2, A1b, and B1) from one or more initializations (runs). Projections are specified on a monthly time step from 1950-2099 and at 0.125 degree spatial resolution within the North American Land Data Assimilation System domain (i.e. contiguous U.S., southern Canada and northern Mexico). Archive data are freely accessible at LLNL Green Data Oasis (url). Since being launched, the archive has served over 3500 data requests by nearly 500 users in support of a range of planning, research and educational activities. Archive developers continue to look for ways to improve the archive and respond to user needs. One request has been to serve the intermediate datasets generated during the BCSD procedure, helping users to interpret the relative influences of the bias-correction and spatial disaggregation on the transformed CMIP3 output. This request has been addressed with intermediate datasets now posted at the archive web-site. Another request relates closely to studying hydrologic and ecological impacts under climate change, where users are asking for projected diurnal temperature information (e.g., projected daily minimum and maximum temperature) and daily time step resolution. In response, archive developers are adding content in 2010, teaming with Scripps Institution of Oceanography (through their NOAA-RISA California-Nevada Applications Program and the California Climate Change Center) to apply a new daily <span class="hlt">downscaling</span> technique to a sub-<span class="hlt">ensemble</span> of the archive’s CMIP3 projections. The new technique, Bias-Corrected Constructed Analogs, combines the BC part of BCSD with a recently developed technique that preserves the daily sequencing structure of CMIP3 projections (Constructed Analogs, or CA). Such data will more easily serve hydrologic and ecological impacts assessments, and offer an opportunity to evaluate projection uncertainty associated with <span class="hlt">downscaling</span> technique. Looking ahead to the arrival CMIP5 projections, archive collaborators have plans apply both BCSD and BCCA over the contiguous U.S. consistent with CMIP3 applications above, and also apply BCSD globally at a 0.5 degree spatial resolution. The latter effort involves collaboration with U.S. Army Corps of Engineers (USACE) and Climate Central.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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://www.image.ucar.edu/~ssain/publications/narccap_gfdl.pdf','EPRINT'); return false;" href="http://www.image.ucar.edu/~ssain/publications/narccap_gfdl.pdf"><span id="translatedtitle">Functional ANOVA and Regional Climate Experiments: A Statistical Analysis of Dynamic <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Sain, Steve</p> <p></p> <p>Functional ANOVA and Regional Climate Experiments: A Statistical Analysis of Dynamic <span class="hlt">Downscaling</span> for dynamic <span class="hlt">downscaling</span> of global models. In this paper, we discuss an initial analysis of a subset changes. Moreover, the scientific consensus that human activities has led to warming of the atmosphere has</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://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/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://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://adsabs.harvard.edu/abs/2003PhDT........88W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003PhDT........88W"><span id="translatedtitle">Using climate model <span class="hlt">ensemble</span> forecasts for seasonal hydrologic prediction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wood, Andrew Whitaker</p> <p></p> <p>Seasonal hydrologic forecasting has long played an invaluable role in the development and use of water resources. Despite notable advances in the science and practice of climate prediction, current approaches of hydrologists and water managers largely fail to incorporate seasonal climate forecast information that has become operationally available during the last decade. This study is motivated by the view that a combination of hydrologic and climate prediction methods affords a new opportunity to improve hydrologic forecast skill. A relatively direct statistical approach for achieving this combination (i.e., <span class="hlt">downscaling</span>) was formulated that used <span class="hlt">ensemble</span> climate model forecasts with a six month lead time produced by the NCEP/CPC Global Spectral Model (GSM) as input to the macroscale Variable Infiltration Capacity hydrologic model to produce <span class="hlt">ensemble</span> runoff and streamflow forecasts. The approach involved the bias correction of climate model precipitation and temperature fields, and spatial and temporal disaggregation from monthly climate model scale (about 2 degrees latitude by longitude) fields to daily hydrology model scale (1/8 degrees) inputs. A qualitative evaluation of the approach in the eastern U.S. suggested that it was successful in translating climate forecast signals to local hydrologic variables and streamflow, but that the dominant influence on forecast results tended to be persistence in initial hydrologic conditions. The suitability of the statistical <span class="hlt">downscaling</span> approach for supporting hydrologic simulation was then assessed (using a continuous retrospective 20-year climate simulation from the DOE Parallel Climate Model) relative to dynamical <span class="hlt">downscaling</span> via a regional, meso-scale climate model. The statistical approach generally outperformed the dynamical approach, in that the dynamical approach alone required additional bias-correction to reproduce the retrospective hydrology as well as the statistical approach. Finally, using 21 years of retrospective forecasts for the western U.S., the skill of the GSM-based hydrologic forecasts was assessed relative to NWS Extended Streamflow Prediction (ESP) method forecasts. Because of unexceptional GSM climate forecasts, the GSM-based and ESP hydrologic forecasts generally showed similar skill. During strong ENSO anomalies, however, GSM-based forecasts yielded higher forecast skill in the Sacramento-San Joachin and Columbia River basins, but lower skill in the Colorado and upper Rio Grande River basins.</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://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/2015NJPh...17h1001B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015NJPh...17h1001B"><span id="translatedtitle"><span class="hlt">Ensemble</span> strong coupling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barnes, W. L.</p> <p>2015-08-01</p> <p>Strong coupling between light and an <span class="hlt">ensemble</span> of molecules leads to the formation of new hybrid states and offers the exciting prospect of a new route to control material properties. Now a theoretical model has been introduced to complement the recent observation of strong coupling between the vibrational modes of molecules and an electromagnetic (cavity) mode. This new work by del Pino et al (2015 New J. Phys. 17 053040) makes an important contribution by offering fresh insight into the underlying physics, especially into the role of dephasing processes in determining the dynamics of <span class="hlt">ensemble</span> strong coupling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ensemble.cms.vt.edu/training_resources/Ensemble_Nav.pdf','EPRINT'); return false;" href="http://www.ensemble.cms.vt.edu/training_resources/Ensemble_Nav.pdf"><span id="translatedtitle">Navigation within the <span class="hlt">Ensemble</span> CMS Navigation within the <span class="hlt">Ensemble</span> CMS</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p>Navigation within the <span class="hlt">Ensemble</span> CMS 1 Navigation within the <span class="hlt">Ensemble</span> CMS Presented by Jacques Walker Assisted by Alex McCarthy Objectives · Importance of navigation · How navigation works in <span class="hlt">Ensemble</span> · Exercises · Homepage · Site and Image Headers · Navigation Elements · Important Notes 2 Importance</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://dspace.mit.edu/handle/1721.1/90185','EPRINT'); return false;" href="http://dspace.mit.edu/handle/1721.1/90185"><span id="translatedtitle">Beta-<span class="hlt">ensembles</span> with covariance</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Dubbs, Alexander</p> <p>2014-01-01</p> <p>This thesis presents analytic samplers for the [beta]-Wishart and [beta]-MANOVA <span class="hlt">ensembles</span> with diagonal covariance. These generalize the [beta]-<span class="hlt">ensembles</span> of Dumitriu-Edelman, Lippert, Killip-Nenciu, Forrester-Rains, and ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=teaching+AND+Management+AND+music&pg=5&id=EJ663675','ERIC'); return false;" href="http://eric.ed.gov/?q=teaching+AND+Management+AND+music&pg=5&id=EJ663675"><span id="translatedtitle">Classroom Management for <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>Bauer, William I.</p> <p>2001-01-01</p> <p>Discusses topics essential to good classroom management for <span class="hlt">ensemble</span> music teachers. Explores the importance of planning and preparation, good teaching practice within the classroom, and using an effective discipline plan to deal with any behavior problems in the classroom. Includes a bibliography of further resources. (CMK)</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://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://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> </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://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308919&keyword=agriculture&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=56083361&CFTOKEN=46500818','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=308919&keyword=agriculture&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=56083361&CFTOKEN=46500818"><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://ccr.aos.wisc.edu/resources/publications/pdfs/CCR_1208.pdf','EPRINT'); return false;" href="http://ccr.aos.wisc.edu/resources/publications/pdfs/CCR_1208.pdf"><span id="translatedtitle">Journal of Climate Dynamically <span class="hlt">Downscaled</span> Projections of Lake-Effect Snow in the Great Lakes Basin</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Wisconsin at Madison, University of</p> <p></p> <p>the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model Version Four (Reg to the representative concentration pathway 8.5 (RCP8.5).15 The <span class="hlt">downscaling</span> is performed using the Abdus Salam</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/nlin/0603075v1','EPRINT'); return false;" href="http://arxiv.org/pdf/nlin/0603075v1"><span id="translatedtitle">Fast <span class="hlt">Ensemble</span> Smoothing</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>S. Ravela; D. McLaughlin</p> <p>2006-03-31</p> <p>Smoothing is essential to many oceanographic, meteorological and hydrological applications. The interval smoothing problem updates all desired states within a time interval using all available observations. The fixed-lag smoothing problem updates only a fixed number of states prior to the observation at current time. The fixed-lag smoothing problem is, in general, thought to be computationally faster than a fixed-interval smoother, and can be an appropriate approximation for long interval-smoothing problems. In this paper, we use an <span class="hlt">ensemble</span>-based approach to fixed-interval and fixed-lag smoothing, and synthesize two algorithms. The first algorithm produces a linear time solution to the interval smoothing problem with a fixed factor, and the second one produces a fixed-lag solution that is independent of the lag length. Identical-twin experiments conducted with the Lorenz-95 model show that for lag lengths approximately equal to the error doubling time, or for long intervals the proposed methods can provide significant computational savings. These results suggest that <span class="hlt">ensemble</span> methods yield both fixed-interval and fixed-lag smoothing solutions that cost little additional effort over filtering and model propagation, in the sense that in practical <span class="hlt">ensemble</span> application the additional increment is a small fraction of either filtering or model propagation costs. We also show that fixed-interval smoothing can perform as fast as fixed-lag smoothing and may be advantageous when memory is not an issue.</p> </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/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/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/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://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/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/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/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/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://arxiv.org/pdf/1007.2491v1','EPRINT'); return false;" href="http://arxiv.org/pdf/1007.2491v1"><span id="translatedtitle"><span class="hlt">Ensemble</span> based quantum metrology</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Marcus Schaffry; Erik M. Gauger; John J. L. Morton; Joseph Fitzsimons; Simon C. Benjamin; Brendon W. Lovett</p> <p>2010-07-15</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 favourably with the resources employed, such as system size and the time required. Here we consider measurement of magnetic field strength using an <span class="hlt">ensemble</span> of spins, and we identify a third essential resource: the initial system polarisation, i.e. the low entropy of the original state. We find that performance depends crucially on the form of decoherence present; for a plausible dephasing model, we describe a quantum strategy which can indeed beat the standard quantum limit.</p> </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://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://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://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://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/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://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> </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/2013JMP....54k3505S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JMP....54k3505S"><span id="translatedtitle">Density of states for Gaussian unitary <span class="hlt">ensemble</span>, Gaussian orthogonal <span class="hlt">ensemble</span>, and interpolating <span class="hlt">ensembles</span> through supersymmetric approach</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shamis, Mira</p> <p>2013-11-01</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/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://climate.snu.ac.kr/2005_new/pub/papers/p108.pdf','EPRINT'); return false;" href="http://climate.snu.ac.kr/2005_new/pub/papers/p108.pdf"><span id="translatedtitle">Optimal initial perturbations for El Nino <span class="hlt">ensemble</span> prediction with <span class="hlt">ensemble</span> Kalman filter</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Kang, In-Sik</p> <p></p> <p>Optimal initial perturbations for El Nino <span class="hlt">ensemble</span> prediction with <span class="hlt">ensemble</span> Kalman filter Yoo of an <span class="hlt">ensemble</span> Kalman filter (EnKF). Among the initial conditions gene- rated by EnKF, <span class="hlt">ensemble</span> members with fast. Keywords <span class="hlt">Ensemble</span> Kalman filter Á Seasonal prediction Á Optimal initial perturbation Á <span class="hlt">Ensemble</span> prediction</p> </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://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/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://ntrs.nasa.gov/search.jsp?R=19940006497&hterms=petit&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dpetit','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=19940006497&hterms=petit&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dpetit"><span id="translatedtitle">An <span class="hlt">ensemble</span> pulsar time</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Petit, Gerard; Thomas, Claudine; Tavella, Patrizia</p> <p>1993-01-01</p> <p>Millisecond pulsars are galactic objects that exhibit a very stable spinning period. Several tens of these celestial clocks have now been discovered, which opens the possibility that an average time scale may be deduced through a long-term stability algorithm. Such an <span class="hlt">ensemble</span> average makes it possible to reduce the level of the instabilities originating from the pulsars or from other sources of noise, which are unknown but independent. The basis for such an algorithm is presented and applied to real pulsar data. It is shown that pulsar time could shortly become more stable than the present atomic time, for averaging times of a few years. Pulsar time can also be used as a flywheel to maintain the accuracy of atomic time in case of temporary failure of the primary standards, or to transfer the improved accuracy of future standards back to the present.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1312.2600v2','EPRINT'); return false;" href="http://arxiv.org/pdf/1312.2600v2"><span id="translatedtitle">KPZ line <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Ivan Corwin; Alan Hammond</p> <p>2015-07-13</p> <p>For each t>1 we construct an N-indexed <span class="hlt">ensemble</span> of random continuous curves with three properties: (1) The lowest indexed curve is distributed as the time t Hopf-Cole solution to the Kardar-Parisi-Zhang (KPZ) stochastic PDE with narrow wedge initial data; (2) The entire <span class="hlt">ensemble</span> satisfies a resampling invariance which we call the H-Brownian Gibbs property (with H(x)=e^{x}); (3) Increments of the lowest indexed curve, when centered by -t/24 and scaled down vertically by t^{1/3} and horizontally by t^{2/3}, remain uniformly absolutely continuous (i.e. have tight Radon-Nikodym derivatives) with respect to Brownian bridges as time t goes to infinity. This construction uses as inputs the diffusion that O'Connell discovered in relation to the O'Connell-Yor semi-discrete Brownian polymer, the convergence result of Moreno Flores-Quastel-Remenik of the lowest indexed curve of that diffusion to the solution of the KPZ equation with narrow wedge initial data, and the one-point distribution formula proved by Amir-Corwin-Quastel for the solution of the KPZ equation with narrow wedge initial data. We provide four main applications of this construction: (1) Uniform (as t goes to infinity) Brownian absolute continuity of the time t solution to the KPZ equation with narrow wedge initial data, even when scaled vertically by t^{1/3} and horizontally by t^{2/3}; (2) Universality of the t^{1/3} one-point (vertical) fluctuation scale for the solution of the KPZ equation with general initial data; (3) Concentration in the t^{2/3} scale for the endpoint of the continuum directed random polymer; (4) Exponential upper and lower tail bounds for the solution at fixed time of the KPZ equation with general initial data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JHyd..517.1145T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JHyd..517.1145T"><span id="translatedtitle">Prediction of design flood discharge by statistical <span class="hlt">downscaling</span> and General Circulation Models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tofiq, F. A.; Guven, A.</p> <p>2014-09-01</p> <p>The global warming and the climate change have caused an observed change in the hydrological data; therefore, forecasters need re-calculated scenarios in many situations. <span class="hlt">Downscaling</span>, which is reduction of time and space dimensions in climate models, will most probably be the future of climate change research. However, it may not be possible to redesign an existing dam but at least precaution parameters can be taken for the worse scenarios of flood in the downstream of the dam location. The purpose of this study is to develop a new approach for predicting the peak monthly discharges from statistical <span class="hlt">downscaling</span> using linear genetic programming (LGP). Attempts were made to evaluate the impacts of the global warming and climate change on determining of the flood discharge by considering different scenarios of General Circulation Models. Reasonable results were achieved in <span class="hlt">downscaling</span> the peak monthly discharges directly from daily surface weather variables (NCEP and CGCM3) without involving any rainfall-runoff models.</p> </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://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://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://oaktrust.library.tamu.edu//handle/1969.1/155462','EPRINT'); return false;" href="http://oaktrust.library.tamu.edu//handle/1969.1/155462"><span id="translatedtitle">Assessment of Statistically <span class="hlt">Downscaled</span> CMIP5 Simulations of the North American Monsoon System </span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Hernandez, Manuel</p> <p>2015-07-10</p> <p>) extended monsoon domain from 1979 to 1999 for NARR (green line), original GCM output (red line), and <span class="hlt">downscaled</span> GCM output (blue line). CNRM-CM5, HadGEM2-CC, and HadGEM2-ES are shown from left to right.. 70 Figure 3.10 Interannual temperature... variability from 1979 to 1999 for the core monsoon domain in NARR (green), <span class="hlt">downscaled</span> output (blue), and coarsely resolved output (red) for the monsoon season. …………………………………………………………... 72 Figure 3.11 Interannual precipitation variability from 1979 to 1999...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20150019486&hterms=climate+water&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dclimate%2Bwater','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20150019486&hterms=climate+water&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dclimate%2Bwater"><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/2015JGRD..120.7316S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JGRD..120.7316S"><span id="translatedtitle">Toward a seasonal precipitation prediction system for West Africa: Performance of CFSv2 and high-resolution dynamical <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Siegmund, Jonatan; Bliefernicht, Jan; Laux, Patrick; Kunstmann, Harald</p> <p>2015-08-01</p> <p>Seasonal precipitation forecasts are important sources of information for early drought and famine warnings in West Africa. This study presents an assessment of the monthly precipitation forecast of the Climate Forecast System version 2 (CFSv2) for three agroecological zones (Sudan-Sahel, Sudan, and Guinean zone) of the Volta Basin. The CFSv2 performance is evaluated for the Sahel drought 1983 and for all August months of the reforecast period (1982-2009) with lead times up to 8 months using a quantile-quantile transformation for bias correction. In addition, an operational experiment is performed for the rainy season 2013 to analyze the performance of a dynamical <span class="hlt">downscaling</span> approach for this region. Twenty-two CFSv2 <span class="hlt">ensemble</span> members initialized in February 2013 are transferred to a resolution of 10 km × 10 km using the Weather and Research Forecasting (WRF) model. Since the uncorrected CFSv2 precipitation forecasts are characterized by a high uncertainty (up to 175% of the observed variability), the quantile-quantile transformation can clearly reduce this overestimation with the potential to provide skillful and valuable early warnings of precipitation deficits and excess up to 6 months in ahead, particularly for the Sudan-Sahel zone. The operational experiment illustrates that CFSv2-WRF can reduce the CFSv2 uncertainty (up to 69%) for monthly precipitation and the onset of the rainy season but has still strong deficits regarding the northward progression of the rain belt. Further studies are necessary for a more robust assessment of the techniques applied in this study to confirm these promising outcomes.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://advising.uoregon.edu/AA_Pages/documents/ensembles_list_2015-162.pdf','EPRINT'); return false;" href="http://advising.uoregon.edu/AA_Pages/documents/ensembles_list_2015-162.pdf"><span id="translatedtitle">Oregon Wind <span class="hlt">Ensemble</span> MUS 395 2 credits*</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p>and Gospel <span class="hlt">Ensemble</span> with a strong background in the tradition of Black gospel music. Gospel <span class="hlt">Ensemble</span> MUS 397 are music majors, students studying in other areas often qualify for this <span class="hlt">ensemble</span>. Campus Band MUS 395 ­ 1 for the non-music major. Campus band is a popular <span class="hlt">ensemble</span> choice among many music majors who are interested</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 t</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/0909.3418v1','EPRINT'); return false;" href="http://arxiv.org/pdf/0909.3418v1"><span id="translatedtitle">Conformal Universality in Normal Matrix <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Alexei M. Veneziani; Tiago Pereira; Domingos H. U. Marchetti</p> <p>2009-09-18</p> <p>A remarkable property of Hermitian <span class="hlt">ensembles</span> is their universal behavior, that is, once properly rescaled the eigenvalue statistics does not depend on particularities of the <span class="hlt">ensemble</span>. Recently, normal matrix <span class="hlt">ensembles</span> have attracted increasing attention, however, questions on universality for these <span class="hlt">ensembles</span> still remain under debate. We analyze the universality properties of random normal <span class="hlt">ensembles</span>. We show that the concept of universality used for Hermitian <span class="hlt">ensembles</span> cannot be directly extrapolated to normal <span class="hlt">ensembles</span>. Moreover, we show that the eigenvalue statistics of random normal matrices with radially symmetric potential can be made universal under a conformal transformation.</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> </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://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://iri.columbia.edu/~awr/papers/jcli1624_revised.pdf','EPRINT'); return false;" href="http://iri.columbia.edu/~awr/papers/jcli1624_revised.pdf"><span id="translatedtitle">Weather types and rainfall in Senegal. Part II: <span class="hlt">Downscaling</span> of GCM Simulations Vincent Moron1</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Robertson, Andrew W.</p> <p></p> <p>1 Weather types and rainfall in Senegal. Part II: <span class="hlt">Downscaling</span> of GCM Simulations Vincent Moron1 rainfall sequences from general circulation model (GCM) simulations are inter-compared over Senegal, using influence the interannual variability of rainfall in Senegal. In contrast, the local scaling exaggerates</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://www.gi.alaska.edu/~bhatt/publications/zhang_etal_2007b.pdf','EPRINT'); return false;" href="http://www.gi.alaska.edu/~bhatt/publications/zhang_etal_2007b.pdf"><span id="translatedtitle">Climate <span class="hlt">downscaling</span> for estimating glacier mass balances in northwestern North America: Validation with a USGS</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Bhatt, Uma</p> <p></p> <p>Climate <span class="hlt">downscaling</span> for estimating glacier mass balances in northwestern North America: Validation] An atmosphere/glacier modeling system is described for estimating the mass balances of glaciers in both current to force a precipitation- temperature-area-altitude (PTAA) glacier mass balance model with daily maximum</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ece.uvic.ca/~wslu/Publications/Lu-Conference/C06-7.pdf','EPRINT'); return false;" href="http://www.ece.uvic.ca/~wslu/Publications/Lu-Conference/C06-7.pdf"><span id="translatedtitle">Adaptive <span class="hlt">Down-Scaling</span> Techniques for JPEG-Based Low Bit-Rate Image Coding</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Lu, Wu-Sheng</p> <p></p> <p>Adaptive <span class="hlt">Down-Scaling</span> Techniques for JPEG-Based Low Bit-Rate Image Coding Ana-Maria Sevcenco and Wu sevcenco@engr.uvic.ca, wslu@ece.uvic.ca Abstract ­ The DCT-based JPEG standard remains to be the most popular compression utility for digital images despite the emerging wavelet-based JPEG-2000 standard</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> <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://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=307740&keyword=%28Alternative+AND+energy%29&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=48051561&CFTOKEN=98273789','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=307740&keyword=%28Alternative+AND+energy%29&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=48051561&CFTOKEN=98273789"><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=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/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/2060/20140010385','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://dspace.mit.edu/handle/1721.1/47844','EPRINT'); return false;" href="http://dspace.mit.edu/handle/1721.1/47844"><span id="translatedtitle"><span class="hlt">Ensemble</span> regression : using <span class="hlt">ensemble</span> model output for atmospheric dynamics and prediction</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Gombos, Daniel (Daniel Lawrence)</p> <p>2009-01-01</p> <p><span class="hlt">Ensemble</span> regression (ER) is a linear inversion technique that uses <span class="hlt">ensemble</span> statistics from atmospheric model output to make dynamical inferences and forecasts. ER defines a multivariate regression operator using <span class="hlt">ensemble</span> ...</p> </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/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://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://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://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://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://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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.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://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&keyword=wind&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=48159948&CFTOKEN=44420004','EPA-EIMS'); return false;" href="http://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=269553&keyword=wind&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=48159948&CFTOKEN=44420004"><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.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/2011AdWR...34..990P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AdWR...34..990P"><span id="translatedtitle">A stochastic framework for <span class="hlt">downscaling</span> processes of spatial averages based on the property of spectral multiscaling and its statistical diagnosis on spatio-temporal rainfall fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pavlopoulos, Harry</p> <p>2011-08-01</p> <p>Spectral multi-scaling postulates a power-law type of scaling of spectral distribution functions of stationary processes of spatial averages, over nested and geometrically similar sub-regions of the spatial parameter space of a given spatio-temporal random field. Presently a new framework is formulated for <span class="hlt">down-scaling</span> processes of spatial averages, following naturally from the postulate of spectral multi-scaling, and key ingredients required for its implementation are described. Moreover, results from an extensive diagnostic study are presented, seeking statistical evidence supportive of spectral multi-scaling. Such evidence emerges from two sources of data. One is a 13 year long historical record of radar observations of rainfall in southeastern UK (Chenies radar), with high spatial (2 km) and temporal (5 min) resolution. The other is an <span class="hlt">ensemble</span> of rain rate fields simulated by a spatio-temporal random pulse model fitted to the historical data. The results are consistent between historical and simulated rainfall data, indicating frequency-dependent scaling relationships interpreted as evidence of spectral multi-scaling across a range of spatial scales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/0807.1250v3','EPRINT'); return false;" href="http://arxiv.org/pdf/0807.1250v3"><span id="translatedtitle">Multimode Memories in Atomic <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>J. Nunn; K. Reim; K. C. Lee; V. O. Lorenz; B. Sussman; I. A. Walmsley; D. Jaksch</p> <p>2009-01-12</p> <p>The ability to store multiple optical modes in a quantum memory allows for increased efficiency of quantum communication and computation. Here we compute the multimode capacity of a variety of quantum memory protocols based on light storage in <span class="hlt">ensembles</span> of atoms. We find that adding a controlled inhomogeneous broadening improves this capacity significantly.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3349308','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3349308"><span id="translatedtitle">Temporal <span class="hlt">Downscaling</span> of Crop Coefficient and Crop Water Requirement from Growing Stage to Substage Scales</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</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/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/2001AGUFMNG51C..08B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001AGUFMNG51C..08B"><span id="translatedtitle">Thunderstorm-Scale <span class="hlt">Ensemble</span> Forecasting</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brooks, H. E.; Elmore, K. L.</p> <p>2001-12-01</p> <p>Thunderstorms present an important weather forecast problem. Both the initiation and the general behavior of convection in the atmosphere are challenging to predict. In order to resolve the gross features of individual convective elements in numerical models, grid spacing of a kilometer or less is required, much finer than the current generation of operational numerical weather forecasting models. Sensitivities to small changes in the initial conditions and the physical parameterizations in the models have been shown to have important effects on some occasions at that scale. In order to address the predictability of thunderstorms with numerical models and to experiment with techniques to provide operationally useful information for weather forecasters, an <span class="hlt">ensemble</span> of thunderstorm simulations initalized with conditions from larger scale models has been run experimentally in near-real time since June 2001 at the National Severe Storms Laboratory for use by forecasters from the National Weather Service's Storm Prediction Center. In addition, previous cases run in a non-operational, research mode have been expanded to look at sensitivity to inclusion of ice-phase microphysics. In general, it appears that forecasters find the information from the <span class="hlt">ensemble</span> frequently agrees with their subjective assessment of the thunderstorm potential. This suggests that if computational resources were sufficient, the <span class="hlt">ensemble</span> could play an important role in the thunderstorm forecasting process. The ice-phase microphysics sensitivity tests have shown that, in some cases, the inclusion of ice in the <span class="hlt">ensemble</span> has a profound impact on the estimate of the probable lifetime of storms. The sensitivity due to the changes in model formulation may be as large or larger than the sensitivity due to initial condition uncertainty. Other parameterizations have not been tested in this experiment to date, but it suggests that consideration of both initial condition uncertainty and model formulation may be necessary to develop an <span class="hlt">ensemble</span> that provides reliable guidance about thunderstorms for weather forecasters. >http://www.spc.noaa.gov/exper/Spring_2001/elmore</a></p> </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://t2.physik.tu-dortmund.de/de/mitglieder/anders/arbeiten/Thesis_Jovchev.pdf','EPRINT'); return false;" href="http://t2.physik.tu-dortmund.de/de/mitglieder/anders/arbeiten/Thesis_Jovchev.pdf"><span id="translatedtitle">Spindephasierung und kohrente Kontrolle eines <span class="hlt">Ensembles</span> von</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Anders, Frithjof</p> <p></p> <p>Spindephasierung und kohärente Kontrolle eines <span class="hlt">Ensembles</span> von Quantenpunkten Spindephasing and coherent control of an <span class="hlt">ensemble</span> of quantum dots Master-Thesis von Andre Jovchev April 2012 Institut für Festkörperphysik AG Grewe #12;Spindephasierung und kohärente Kontrolle eines <span class="hlt">Ensembles</span> von Quantenpunkten</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ti.arc.nasa.gov/m/profile/oza/files/oza01.pdf','EPRINT'); return false;" href="http://ti.arc.nasa.gov/m/profile/oza/files/oza01.pdf"><span id="translatedtitle">Online <span class="hlt">Ensemble</span> Learning Nikunj Chandrakant Oza</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Oza, Nikunj C.</p> <p></p> <p>Date Date Date University of California at Berkeley 2001 #12;Online <span class="hlt">Ensemble</span> Learning Copyright 2001Online <span class="hlt">Ensemble</span> Learning by Nikunj Chandrakant Oza B.S. (Massachusetts Institute of Technology by Nikunj Chandrakant Oza #12;1 Abstract Online <span class="hlt">Ensemble</span> Learning by Nikunj Chandrakant Oza Doctor</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://users.math.uni-potsdam.de/~sreich/10_5.pdf','EPRINT'); return false;" href="http://users.math.uni-potsdam.de/~sreich/10_5.pdf"><span id="translatedtitle"><span class="hlt">Ensemble</span> Kalman and H filters Sebastian Reich</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Reich, Sebastian</p> <p></p> <p><span class="hlt">Ensemble</span> Kalman and H filters Sebastian Reich Universität Potsdam, Institut für Mathematik, Am Neuen Palais 10, D-14469 Potsdam Abstract. The <span class="hlt">ensemble</span> Kalman filter has become a popular method for nonlinear data assimilation. Standard <span class="hlt">ensemble</span> Kalman filter implementations need to be modified to avoid</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.B31D0049B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.B31D0049B"><span 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://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://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> <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/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/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/2013AGUFM.A33E0270C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A33E0270C"><span id="translatedtitle">Statistical <span class="hlt">Downscaling</span> of General Circulation Model Output for the Northern Great Plains: A Comparative Analysis of <span class="hlt">Downscaling</span> Methods for Temperature and Precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Coburn, J.</p> <p>2013-12-01</p> <p>General Circulation Models have come to be the foremost dynamical tools to understanding and predicting the complex changes associated with climate change, yet the grid spacing of these models are far too course for use in local and regional impact studies. Recent research has highlighted the possibility to <span class="hlt">downscale</span> these course resolution data through regional climate models (RCMs) or through statistical means. Given the current changes in climate due to natural and human made forcings and the importance of the Northern Great Plains (NGP) to the global agricultural food supply, it is important to gain insight into the small scale effects these changes will have on this important region. Here is presented an analysis of three statistical <span class="hlt">downscaling</span> methods for translating the course scale information to a more usable scale planners and the public can use for more effective decision making. Regression and weather typing methods are applied to ten GCM outputs and compared for their effectiveness and ability to accurately generate fine scale daily and monthly temperature and precipitation data for the NGP. The implications are explored by utilizing the most robust method to extrapolate the outcomes of the 2.6, 4.5 and 8.5 RCP experiments for future climate.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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/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://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/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/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/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/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://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/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://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/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://arxiv.org/pdf/1501.00219.pdf','EPRINT'); return false;" href="http://arxiv.org/pdf/1501.00219.pdf"><span id="translatedtitle">Spectral diagonal <span class="hlt">ensemble</span> Kalman filters</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Kasanický, Ivan; Vejmelka, Martin</p> <p>2015-01-01</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 aproximation of the covariance when the covariance itself is diagonal in the spectral basis, as is the case, e.g., for a second-order stationary random field and the Fourier basis. The method is extended by wavelets to the case when the state variables are random fields, which are not spatially homogeneous. Efficient implementations by the fast Fourier transform (FFT) and discrete wavelet transform (DWT) are presented for several types of observations, including high-dimensional data given on a part of the domain, such as radar and satellite images. Computational experiments confirm that the method performs well on the Lorenz 96 problem and the shallow water equations with very small <span class="hlt">ensembles</span> and over multiple analysis cycles.</p> </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://www.ncbi.nlm.nih.gov/pubmed/24830256','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24830256"><span id="translatedtitle">[Evaluating the performance of the UCLA method for spatially <span class="hlt">downscaling</span> soil moisture products using three Ts/VI indices].</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ling, Zi-Wei; He, Long-Bin; Zeng, Hui</p> <p>2014-02-01</p> <p>Soil moisture products derived from microwave remote sensing data are commonly used in the studies of large-scale water resources or climate change. However, the spatial resolutions of these products are usually too coarse to be used in regional- or watershed-scale studies. Therefore, it is necessary to spatially <span class="hlt">downscale</span> the coarse-resolution soil moisture products for use in regional- or watershed-scale studies. The UCLA method is one of the methods for spatially <span class="hlt">downscaling</span> soil moisture products. In this method, the spatial indices (Ts/VI indices) calculated from land surface temperature and vegetation index are used as auxiliary variables for spatial <span class="hlt">downscaling</span>. In this paper, we compared the performance of the UCLA method for spatially <span class="hlt">downscaling</span> the coarse-resolution AMSR-E soil moisture products, using three Ts/VI indices as auxiliary variables, i. e., the soil wetness index (SW), temperature vegetation dryness index (TVDI), and vegetation temperature condition index (VTCI). These auxiliary variables were calculated from the products of MODIS land surface temperature (MYD11A1) and MODIS vegetation index (MYD13A2). The <span class="hlt">downscaled</span> results using the three Ts/VI indices were all reasonable. However, the <span class="hlt">downscaled</span> results using TVDI and VTCI were better than using SW. Therefore, we concluded that TVDI and VTCI are more suitable than SW to be used as the auxiliary variable when applying the UCLA method for <span class="hlt">downscaling</span> soil moisture products. Finally, we discussed the error sources of applying the UCLA method, such as measurement errors of coarse resolution soil products, calculation errors from spatial indices, and errors from the UCLA method itself, and we also discussed the potential improvements of future research. PMID:24830256</p> </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://arxiv.org/pdf/1509.03220v1','EPRINT'); return false;" href="http://arxiv.org/pdf/1509.03220v1"><span id="translatedtitle">State <span class="hlt">Ensembles</span> and Quantum Entropy</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Subhash Kak</p> <p>2015-08-31</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.ncbi.nlm.nih.gov/pubmed/25432969','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25432969"><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=pubmed">PubMed</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> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/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://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/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://adsabs.harvard.edu/abs/2015EGUGA..17.1490G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.1490G"><span id="translatedtitle">ICEMAP250: Sea Ice Mapping At 250m Resolution Using <span class="hlt">Downscaled</span> Modis Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gignac, Charles; Bernier, Monique; Chokmani, Karem; Poulin, Jimmy</p> <p>2015-04-01</p> <p>IceMap250 is an automated and autonomous algorithm, focused on producing sea ice presence maps for any area covered by the MODIS Terra sensor at a 250m spatial resolution and on a daily basis. The IceMap250 algorithm, like its parent lower resolution version, uses data from reflective bands 2,4 & 6 and emissive bands 31 & 32 of the MODIS Terra sensor to build ancillary conditions dataset used to detect sea ice presence. The first condition of ice presence is the detection of snow at the surface. This is done using a threshold of >0.4 on the Normalized Difference Snow Index (NDSI). The second condition, determined empirically during the development of the original IceMap algorithm, is a reflectance greater than 11% in MODIS Terra Band 2. The final condition, based on thermal information, is to detect an ice surface temperature (IST) lower than 271.4 K, which corresponds to the freezing point of sea salt water. If these three conditions are respected, ice is detected; otherwise, water is expected to be present. To achieve a 250m spatial resolution in NDSI, Band 2 and IST, two <span class="hlt">downscaling</span> approaches were used. To <span class="hlt">downscale</span> bands 3-7 to a 250m spatial resolution, the Canadian Centre for Remote Sensing algorithm, based on focal regression, is used. An innovative method to <span class="hlt">downscale</span> the IST to 250m, uses a KNN regression between cloud masked NDSI and IST at 1KM to, after the initial CCRS <span class="hlt">downscaling</span>, injects 250m NDSI values into the KNN regression parameters therefore building a new, 250m <span class="hlt">downscaled</span> IST. Validation tests have been run on 5 days periods for each 'season' of the ice regime; the freeze-up, the stable cover and the meltdown. The first results of the IceMap250 algorithm make it clear that adaptations have to be taken to correct the diverse seasonal effects due to cloud cover and the smoothing effect caused by the regression approaches. During the freeze-up season, the dense cloud cover makes it difficult to precisely distinguish ice and water from clouds with high accuracy. An important quantity of clouds isn't masked with the MODIS cloud-mask therefore causing the problem of cloud contamination in the classification result. During the stable cover season, the main issue comes from the fact that IST at 250m is a result of a <span class="hlt">downscaling</span> approach that smooth the temperature pattern, therefore making it difficult to identify narrow ice zones that are the majority of the open water zones found during the stable cover period. As for the meltdown period, this is where the algorithm displays its best performance (~90% accuracy) since the cloud cover is rare and the water area is wide, making it clear for the algorithm to identify. Methods to improve the cloud masking and the IST smoothing issues are actually investigated.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/0904.4898v1','EPRINT'); return false;" href="http://arxiv.org/pdf/0904.4898v1"><span id="translatedtitle">Random matrix <span class="hlt">ensembles</span> associated with Lax matrices</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>E. Bogomolny; O. Giraud; C. Schmit</p> <p>2009-04-30</p> <p>A method to generate new classes of random matrix <span class="hlt">ensembles</span> is proposed. Random matrices from these <span class="hlt">ensembles</span> are Lax matrices of classically integrable systems with a certain distribution of momenta and coordinates. The existence of an integrable structure permits to calculate the joint distribution of eigenvalues for these matrices analytically. Spectral statistics of these <span class="hlt">ensembles</span> are quite unusual and in many cases give rigorously new examples of intermediate statistics.</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://adsabs.harvard.edu/abs/2014AGUFMSM24A..03L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMSM24A..03L"><span id="translatedtitle"><span class="hlt">Ensemble</span> modeling of CME propagation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, C. O.; Arge, C. N.; Henney, C. J.; Odstrcil, D.; Millward, G. H.; Pizzo, V. J.</p> <p>2014-12-01</p> <p>The Wang-Sheeley-Arge(WSA)-Enlil-cone modeling system is used for making routine arrival time forecasts of the Earth-directed "halo" coronal mass ejections (CMEs), since they typically produce the most geoeffective events. A major objective of this work is to better understand the sensitivity of the WSA-Enlil modeling results to input model parameters and how these parameters contribute to the overall model uncertainty and performance. We present <span class="hlt">ensemble</span> modeling results for a simple halo CME event that occurred on 15 February 2011 and a succession of three halo CME events that occurred on 2-4 August 2011. During this period the Solar TErrestrial RElations Observatory (STEREO) A and B spacecraft viewed the CMEs over the solar limb, thereby providing more reliable constraints on the initial CME geometries during the manual cone fitting process. To investigate the sensitivity of the modeled CME arrival times to small variations in the input cone properties, for each CME event we create an <span class="hlt">ensemble</span> of numerical simulations based on multiple sets of cone parameters. We find that the accuracy of the modeled arrival times not only depends on the initial input CME geometry, but also on the reliable specification of the background solar wind, which is driven by the input maps of the photospheric magnetic field. As part of the modeling <span class="hlt">ensemble</span>, we simulate the CME events using the traditional daily updated maps as well as those that are produced by the Air Force data Assimilative Photospheric flux Transport (ADAPT) model, which provide a more instantaneous snapshot of the photospheric field distribution. For the August 2011 events, in particular, we find that the accuracy in the arrival time predictions also depends on whether the cone parameters for all three CMEs are specified in a single WSA-Enlil simulation. The inclusion/exclusion of one or two of the preceding CMEs affects the solar wind conditions through which the succeeding CME propagates.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/93918','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/93918"><span id="translatedtitle">Comparison of two general circulation models to <span class="hlt">downscale</span> temperature and precipitation under climate change</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Matyasovszky, I.; Bogardi, I.</p> <p>1994-12-01</p> <p>A semiempirical approach for <span class="hlt">downscaling</span> general circulation model (GCM) based daily atmospheric circulation patterns (CP) and predicting local climatological variables under climate change is developed. Specifically, the daily 500-hPa surface outputs of the Canadian Climate Center (CCC) and Max Planck Institute (MPI) temperature and precipitation in Nebraska. Three series of data are analyzed: historical data, 1 x CO{sub 2} GCM results and 2 x CO{sub 2} GCM results. Between these three data sets, no significant difference can be detected in either CP typology (constructed by principal component analysis and k means method) or stochastic properties of daily time series (Markov matrix). On the other hand, the average geopotential height of the 500-hPa pressure field exhibits significant change, labeled the {Delta}CO{sub 2} effect, between the 1 x CO{sub 2} and 2 x CO{sub 2} cases. Accordingly, climate change is assumed to be represented by the historical average geopotential height augmented by the {Delta}CO{sub 2} increment. It is found that both the CCC and MPI GCMs lead to predicting a winter temperature increase of 3{degrees}-6{degrees}C, a smaller but significant increase in spring and fall temperatures, and no increase in summer. The probability of precipitation occurrence is found to remain almost unchanged, as well as the dry period duration. The estimates of local response to climate change depend upon the location and the GCM used for <span class="hlt">downscaling</span> the CP. The MPI GCM, which includes an ocean-atmosphere coupling, appears to yield smaller <span class="hlt">downscaled</span> changes than the purely atmosphere-based CCC GCM.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JPRS...97...78W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JPRS...97...78W"><span id="translatedtitle">Modeling diurnal land temperature cycles over Los Angeles using <span class="hlt">downscaled</span> GOES imagery</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weng, Qihao; Fu, Peng</p> <p>2014-11-01</p> <p>Land surface temperature is a key parameter for monitoring urban heat islands, assessing heat related risks, and estimating building energy consumption. These environmental issues are characterized by high temporal variability. A possible solution from the remote sensing perspective is to utilize geostationary satellites images, for instance, images from Geostationary Operational Environmental System (GOES) and Meteosat Second Generation (MSG). These satellite systems, however, with coarse spatial but high temporal resolution (sub-hourly imagery at 3-10 km resolution), often limit their usage to meteorological forecasting and global climate modeling. Therefore, how to develop efficient and effective methods to disaggregate these coarse resolution images to a proper scale suitable for regional and local studies need be explored. In this study, we propose a least square support vector machine (LSSVM) method to achieve the goal of <span class="hlt">downscaling</span> of GOES image data to half-hourly 1-km LSTs by fusing it with MODIS data products and Shuttle Radar Topography Mission (SRTM) digital elevation data. The result of <span class="hlt">downscaling</span> suggests that the proposed method successfully disaggregated GOES images to half-hourly 1-km LSTs with accuracy of approximately 2.5 K when validated against with MODIS LSTs at the same over-passing time. The synthetic LST datasets were further explored for monitoring of surface urban heat island (UHI) in the Los Angeles region by extracting key diurnal temperature cycle (DTC) parameters. It is found that the datasets and DTC derived parameters were more suitable for monitoring of daytime- other than nighttime-UHI. With the <span class="hlt">downscaled</span> GOES 1-km LSTs, the diurnal temperature variations can well be characterized. An accuracy of about 2.5 K was achieved in terms of the fitted results at both 1 km and 5 km resolutions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.5228S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5228S"><span id="translatedtitle">A Time-scale Decomposed Threshold Regression <span class="hlt">Downscaling</span> Approach to Forecasting South China Early Summer Rainfall</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Song, Linye; Duan, Wansuo; Li, Yun; Mao, Jiangyu</p> <p>2015-04-01</p> <p>A time-scale decomposed threshold regression (TSDTR) <span class="hlt">downscaling</span> approach to forecasting South China early summer rainfall (SCESR) is described by using long-term observed station rainfall data and the National Oceanic and Atmospheric Administration Extended Reconstructed sea surface temperature (SST) data. It makes use of two distinct regression <span class="hlt">downscaling</span> models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models were developed based on the partial least square (PLS) regression technique linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specially, using the datasets in the calibration period 1915-1984, the variability of SCESR and SST were decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model was fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation (PDO). On the interdecadal timescale, a standard PLS regression model was fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction was obtained by the sum of the outputs from both interannual and interdecadal models. Results show that the TSDTR <span class="hlt">downscaling</span> approach achieved a reasonable skill to predict the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns may provide a new perspective to improve the climate predictions.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JGRD..118..520H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JGRD..118..520H"><span id="translatedtitle">A novel approach to statistical <span class="hlt">downscaling</span> considering nonstationarities: application to daily precipitation in the Mediterranean area</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hertig, E.; Jacobeit, J.</p> <p>2013-01-01</p> <p>In the present study, nonstationarities in predictor-predictand relationships within the framework of statistical <span class="hlt">downscaling</span> are investigated. In this context, a novel validation approach is introduced in which nonstationarities are explicitly taken into account. The method is based on results from running calibration periods. The (non)overlaps of the bootstrap confidence interval of the mean model performance (derived by averaging the performances of all calibration/verification periods) and the bootstrap confidence intervals of the individual model errors are used to identify (non)stationary model performance. The specified procedure is demonstrated for mean daily precipitation in the Mediterranean area using the bias to assess model skill. A combined circulation-based and transfer function-based approach is employed as a <span class="hlt">downscaling</span> technique. In this context, large-scale seasonal atmospheric regimes, synoptic-scale daily circulation patterns, and their within-type characteristics, are related to daily station-based precipitation. Results show that nonstationarities are due to varying predictors-precipitation relationships of specific circulation configurations. In this regard, frequency changes of circulation patterns can damp or increase the effects of nonstationary relationships. Within the scope of assessing future precipitation changes under increased greenhouse warming conditions, the identification and analysis of nonstationarities in the predictors-precipitation relationships leads to a substantiated selection of specific statistical <span class="hlt">downscaling</span> models for the future assessments. Using RCP4.5 scenario assumptions, strong increases of daily precipitation become apparent over large parts of the western and northern Mediterranean regions in winter. In spring, summer, and autumn, decreases of precipitation until the end of the 21st century clearly dominate over the entire Mediterranean area.</p> </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://arxiv.org/pdf/quant-ph/0201128v2','EPRINT'); return false;" href="http://arxiv.org/pdf/quant-ph/0201128v2"><span id="translatedtitle">Entangling many atomic <span class="hlt">ensembles</span> through laser manipulation</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>L. -M. Duan</p> <p>2002-04-24</p> <p>We propose an experimentally feasible scheme to generate Greenberger-Horne-Zeilinger (GHZ) type of maximal entanglement between many atomic <span class="hlt">ensembles</span> based on laser manipulation and single-photon detection. The scheme, with inherent fault tolerance to the dominant noise and efficient scaling of the efficiency with the number of <span class="hlt">ensembles</span>, allows to maximally entangle many atomic <span class="hlt">ensemble</span> within the reach of current technology. Such a maximum entanglement of many <span class="hlt">ensembles</span> has wide applications in demonstration of quantum nonlocality, high-precision spectroscopy, and quantum information processing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.7872E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.7872E"><span id="translatedtitle"><span class="hlt">Downscaling</span> of Extreme Precipitation: Proposing a New Statistical Approach and Investigating a Taken-for-Granted Assumption</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Elshorbagy, Amin; Alam, Shahabul</p> <p>2015-04-01</p> <p>In spite of the ability of General Circulation Models (GCMs) to predict and generate atmospheric variables under pre-identified climate change scenarios, their coarse horizontal scale is an obstacle for impact studies. Therefore, <span class="hlt">downscaling</span> of variables (e.g., precipitation) from coarse spatial and temporal scales to finer ones is inevitable. <span class="hlt">Downscaling</span> methods are classified into various types ranging from applications related to short term numerical weather prediction to multidecadal global climate prediction. For engineering applications of impact assessment of climate change on infrastructure, the multidecadal global climate projection, is the most widely used type. One of the important engineering applications of climate change impact assessment is the development and reconstruction of intensity-duration-frequency (IDF) curves under possible climate change. IDF curves are widely used for design and management of urban hydrosystems. Their construction requires accurate information about intense short duration rainfall, including sub-hourly, extremes. Previous attempts were made to construct IDF curves in various places under climate change using dynamical and statistical <span class="hlt">downscaling</span>. The deficiency of GCMs, and even RCMs, in representing local surface conditions, especially extreme weather and convective precipitation in many areas, necessitates the use of statistical <span class="hlt">downscaling</span> for IDF-related applications. In statistical <span class="hlt">downscaling</span> methods, and in particular regression-based methods, the search is always for the optimum set of inputs at a coarser scale that act as predictors for the desired surface weather variable (predictand) at the local finer scale. The grid box nearest to the local site may not provide the optimum predictor-predictand relationship. In fact, even the set of predictors varies from one region to another. In this study, a novel approach using genetic programming (GP) for specific application of <span class="hlt">downscaling</span> annual maximum precipitation (AMPs) is presented. For constructing IDF-curves, only AMPs of different durations are needed. Strong correlation between the AMPs at the coarse-grid scale as output from GCMs and AMPs at the local finer scale is observed in many locations worldwide even though such a correlation may not exist between the corresponding time series of continuous precipitation records. The use of the GP technique, in particular its genetic symbolic regression variant, for <span class="hlt">downscaling</span> the annual maximum precipitation is further expanded in two ways. First, the exploration and feature extraction capabilities of GP are utilized to develop both GCM-variant and GCM-invariant <span class="hlt">downscaling</span> models/mathematical expressions. Second, the developed models as well as clustering methods and statistical tests are used to investigate a fundamental assumption of all statistical <span class="hlt">downscaling</span> methods; that is the validity of the <span class="hlt">downscaling</span> relationship developed based on a historical time period (e.g., 1960-1990) for the same task during future periods (e.g., up to year 2100). The proposed approach is applied to the case of constructing IDF curves for the City of Saskatoon, Canada. This study reveals that developing a <span class="hlt">downscaling</span> relationship that is generic and GCM-invariant might lead to more reliable <span class="hlt">downscaling</span> of future projections, even though the higher reliability comes at the cost of accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010EGUGA..12.2389Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010EGUGA..12.2389Z"><span id="translatedtitle">Comparing empirical <span class="hlt">downscaling</span> methods within different kinds of terrain applied on the edge to climate impact research</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zuvela-Aloise, Maja; Matulla, Christoph; Auer, Inge; Böhm, Reinhard; Lexer, Manfred J.; Scheifinger, Helfried</p> <p>2010-05-01</p> <p>We use some statistical <span class="hlt">downscaling</span> techniques to derive local scale scenarios of future daily and monthly temperature and precipitation for the Alpine region. We utilize large scale NCEP/NCAR reanalysis data to establish empirical models and evaluate their performance against long term climate records from Austrian monitoring stations (forest sites, riverside fish population distributions, glaciers or phenological gardens across Europe etc.) for the second half of the 20th century. The performance of different <span class="hlt">downscaling</span> methods (multiple linear regression, canonical correlation analysis, the analog method) is analyzed. These methods are applied to derive transient climate change scenarios from ECHAM4/5 runs. <span class="hlt">Downscaled</span> data have been used in climate risk assessment studies to evaluate the sensitivity of the Austrian forests, fish stocks, phenological occurrence dates etc. to scenarios of climatic change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/945745','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/945745"><span id="translatedtitle">Dynamical <span class="hlt">Downscaling</span> of GCM Simulations: Toward the Improvement of Forecast Bias over California</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Chin, H S</p> <p>2008-09-24</p> <p>The effects of climate change will mostly be felt on local to regional scales. However, global climate models (GCMs) are unable to produce reliable climate information on the scale needed to assess regional climate-change impacts and variability as a result of coarse grid resolution and inadequate model physics though their capability is improving. Therefore, dynamical and statistical <span class="hlt">downscaling</span> (SD) methods have become popular methods for filling the gap between global and local-to-regional climate applications. Recent inter-comparison studies of these <span class="hlt">downscaling</span> techniques show that both <span class="hlt">downscaling</span> methods have similar skill in simulating the mean and variability of present climate conditions while they show significant differences for future climate conditions (Leung et al., 2003). One difficulty with the SD method is that it relies on predictor-predict and relationships, which may not hold in future climate conditions. In addition, it is now commonly accepted that the dynamical <span class="hlt">downscaling</span> with the regional climate model (RCM) is more skillful at the resolving orographic climate effect than the driving coarser-grid GCM simulations. To assess the possible societal impacts of climate changes, many RCMs have been developed and used to provide a better projection of future regional-scale climates for guiding policies in economy, ecosystem, water supply, agriculture, human health, and air quality (Giorgi et al., 1994; Leung and Ghan, 1999; Leung et al., 2003; Liang et al., 2004; Kim, 2004; Duffy et al., 2006). Although many regional climate features, such as seasonal mean and extreme precipitation have been successfully captured in these RCMs, obvious biases of simulated precipitation remain, particularly the winter wet bias commonly seen in mountain regions of the Western United States. The importance of regional climate research over California is not only because California has the largest population in the nation, but California has one of the most sophisticated water collection and distribution systems in the world. Therefore, adapting California's water management system to climate change presents significant challenges. Besides, the strong scale interaction between atmospheric circulation and topography in this region provides a challenging testbed for RCMs. Thus, the success of California winter precipitation forecast over mountains would greatly help develop a reliable water management system to adapt to climate change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26283237','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26283237"><span id="translatedtitle">Uniform Reduction of Scalar Coupling by Real-Time Homonuclear J-<span class="hlt">Downscaled</span> NMR.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Glanzer, Simon; Zangger, Klaus</p> <p>2015-10-01</p> <p>Scalar coupling in proton NMR spectra provides important information for the structural analysis. However, the low resolution due to the resulting signal splitting, together with the rather narrow spectral range of hydrogen, often prevents the extraction of J-coupling information. Here we present a method to achieve real-time homonuclear J-<span class="hlt">downscaling</span>. Thereby, all J-values are uniformly reduced by an arbitrary scaling factor. In the resulting one-dimensional spectra, signal overlap is reduced, while scalar coupling information is still available. PMID:26283237</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=276487','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=276487"><span id="translatedtitle">Evaluation of a weather generator-based method for statistically <span class="hlt">downscaling</span> non-stationary climate scenarios for impact assessment at a point scale</span></a></p> <p><a target="_blank" href="http://www.ars.usda.gov/services/TekTran.htm">Technology Transfer Automated Retrieval System (TEKTRAN)</a></p> <p></p> <p></p> <p>The non-stationarity is a major concern for statistically <span class="hlt">downscaling</span> climate change scenarios for impact assessment. This study is to evaluate whether a statistical <span class="hlt">downscaling</span> method is fully applicable to generate daily precipitation under non-stationary conditions in a wide range of climatic zo...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26032515','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26032515"><span id="translatedtitle">Residue-level global and local <span class="hlt">ensemble-ensemble</span> comparisons of protein domains.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Clark, Sarah A; Tronrud, Dale E; Andrew Karplus, P</p> <p>2015-09-01</p> <p>Many methods of protein structure generation such as NMR-based solution structure determination and template-based modeling do not produce a single model, but an <span class="hlt">ensemble</span> of models consistent with the available information. Current strategies for comparing <span class="hlt">ensembles</span> lose information because they use only a single representative structure. Here, we describe the <span class="hlt">ENSEMBLATOR</span> and its novel strategy to directly compare two <span class="hlt">ensembles</span> containing the same atoms to identify significant global and local backbone differences between them on per-atom and per-residue levels, respectively. The <span class="hlt">ENSEMBLATOR</span> has four components: eePREP (ee for <span class="hlt">ensemble-ensemble</span>), which selects atoms common to all models; eeCORE, which identifies atoms belonging to a cutoff-distance dependent common core; eeGLOBAL, which globally superimposes all models using the defined core atoms and calculates for each atom the two intraensemble variations, the interensemble variation, and the closest approach of members of the two <span class="hlt">ensembles</span>; and eeLOCAL, which performs a local overlay of each dipeptide and, using a novel measure of local backbone similarity, reports the same four variations as eeGLOBAL. The combination of eeGLOBAL and eeLOCAL analyses identifies the most significant differences between <span class="hlt">ensembles</span>. We illustrate the <span class="hlt">ENSEMBLATOR</span>'s capabilities by showing how using it to analyze NMR <span class="hlt">ensembles</span> and to compare NMR <span class="hlt">ensembles</span> with crystal structures provides novel insights compared to published studies. One of these studies leads us to suggest that a "consistency check" of NMR-derived <span class="hlt">ensembles</span> may be a useful analysis step for NMR-based structure determinations in general. The <span class="hlt">ENSEMBLATOR</span> 1.0 is available as a first generation tool to carry out <span class="hlt">ensemble-ensemble</span> comparisons. PMID:26032515</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy..tmp..404R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy..tmp..404R"><span id="translatedtitle">Spatial, temporal and frequency based climate change assessment in Columbia River Basin using multi <span class="hlt">downscaled</span>-scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rana, Arun; Moradkhani, Hamid</p> <p>2015-10-01</p> <p>Uncertainties in climate modelling are well documented in literature. Global Climate Models (GCMs) are often used to <span class="hlt">downscale</span> the climatic parameters on a regional scale. In the present work, we have analyzed the changes in precipitation and temperature for future scenario period of 2070-2099 with respect to historical period of 1970-2000 from statistically <span class="hlt">downscaled</span> GCM projections in Columbia River Basin (CRB). Analysis is performed using two different statistically <span class="hlt">downscaled</span> climate projections (with ten GCMs <span class="hlt">downscaled</span> products each, for RCP 4.5 and RCP 8.5, from CMIP5 dataset) namely, those from the Bias Correction and Spatial <span class="hlt">Downscaling</span> (BCSD) technique generated at Portland State University and from the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. The two datasets for BCSD and MACA are <span class="hlt">downscaled</span> from observed data for both scenarios projections i.e. RCP4.5 and RCP8.5. Analysis is performed using spatial change (yearly scale), temporal change (monthly scale), percentile change (seasonal scale), quantile change (yearly scale), and wavelet analysis (yearly scale) in the future period from the historical period, respectively, at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice versa for temperature. Most of the models have indicated considerate positive change in quantiles and percentiles for both precipitation and temperature. Wavelet analysis provided insights into possible explanation to changes in precipitation.</p> </li> </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://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://arxiv.org/pdf/1509.00652.pdf','EPRINT'); return false;" href="http://arxiv.org/pdf/1509.00652.pdf"><span id="translatedtitle">Hierarchical Bayes <span class="hlt">Ensemble</span> Kalman Filtering</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Tsyrulnikov, Michael</p> <p>2015-01-01</p> <p><span class="hlt">Ensemble</span> Kalman filtering (EnKF), when applied to high-dimensional systems, suffers from an inevitably small affordable <span class="hlt">ensemble</span> size, which results in poor estimates of the background error covariance matrix ${\\bf B}$. The common remedy is a kind of regularization, usually an ad-hoc spatial covariance localization (tapering) combined with artificial covariance inflation. Instead of using an ad-hoc regularization, we adopt the idea by Myrseth and Omre (2010) and explicitly admit that the ${\\bf B}$ matrix is unknown and random and estimate it along with the state (${\\bf x}$) in an optimal hierarchical Bayes analysis scheme. We separate forecast errors into predictability errors (i.e. forecast errors due to uncertainties in the initial data) and model errors (forecast errors due to imperfections in the forecast model) and include the two respective components ${\\bf P}$ and ${\\bf Q}$ of the ${\\bf B}$ matrix into the extended control vector $({\\bf x},{\\bf P},{\\bf Q})$. Similarly, we break the traditional backgrou...</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.ncbi.nlm.nih.gov/pubmed/23836646','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/23836646"><span id="translatedtitle"><span class="hlt">Downscaling</span> CMIP5 climate models shows increased tropical cyclone activity over the 21st century.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Emanuel, Kerry A</p> <p>2013-07-23</p> <p>A recently developed technique for simulating large [O(10(4))] numbers of tropical cyclones in climate states described by global gridded data is applied to simulations of historical and future climate states simulated by six Coupled Model Intercomparison Project 5 (CMIP5) global climate models. Tropical cyclones <span class="hlt">downscaled</span> from the climate of the period 1950-2005 are compared with those of the 21st century in simulations that stipulate that the radiative forcing from greenhouse gases increases by over preindustrial values. In contrast to storms that appear explicitly in most global models, the frequency of <span class="hlt">downscaled</span> tropical cyclones increases during the 21st century in most locations. The intensity of such storms, as measured by their maximum wind speeds, also increases, in agreement with previous results. Increases in tropical cyclone activity are most prominent in the western North Pacific, but are evident in other regions except for the southwestern Pacific. The increased frequency of events is consistent with increases in a genesis potential index based on monthly mean global model output. These results are compared and contrasted with other inferences concerning the effect of global warming on tropical cyclones. PMID:23836646</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC12C..04V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC12C..04V"><span id="translatedtitle">A Computationally Efficient Platform To Examine the Efficacy of Regional <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>Vigh, J. L.; Ammann, C. M.; Rood, R. B.; Barsugli, J. J.; Guentchev, G.</p> <p>2013-12-01</p> <p>A primary goal of the National Climate Predictions and Projections (NCPP) platform is to develop an extensive set of standardized and interoperable evaluation tools to examine the efficacy of various regional climate <span class="hlt">downscaling</span> techniques. To this end, a highly efficient NCL-based evaluation and comparison engine has been developed to compute period statistics on the monthly, seasonal, and annual timescales from many gridded daily datasets. The initial implementation of the engine computes metrics such as the mean, median, interannual standard deviation, monthly lag-1autocorrelation, and the various percentiles. These are computed for the daily maximum and minimum temperature, the daily mean temperature, the diurnal temperature range, and daily accumulated precipitation. Additionally, the engine computes the mean, median, max, and min of a number of application-oriented indices and climate extreme indices on the monthly and annual timescales. Output includes not only plots of the above metrics and indices, but the underlying NetCDF dataset used to create each plot. Metadata and internal attributes provide full data provenance of the source datasets and a description of the computations. Through this standardized approach to statistical analysis of massive datasets, we aim to provide end-users with a comprehensive suite of evaluations and comparisons between <span class="hlt">downscaling</span> methods. This will enable applications-oriented users greater access to these data by lowering the barriers to data usage and understanding.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015APJAS..51...77J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015APJAS..51...77J"><span id="translatedtitle">Projected change in East Asian summer monsoon by dynamic <span class="hlt">downscaling</span>: Moisture budget analysis</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jung, Chun-Yong; Shin, Ho-Jeong; Jang, Chan Joo; Kim, Hyung-Jin</p> <p>2015-02-01</p> <p>The summer monsoon considerably affects water resource and natural hazards including flood and drought in East Asia, one of the world's most densely populated area. In this study, we investigate future changes in summer precipitation over East Asia induced by global warming through dynamical <span class="hlt">downscaling</span> with the Weather Research and Forecast model. We have selected a global model from the Coupled Model Intercomparison Project Phase 5 based on an objective evaluation for East Asian summer monsoon and applied its climate change under Representative Concentration Pathway 4.5 scenario to a pseudo global warming method. Unlike the previous studies that focused on a qualitative description of projected precipitation changes over East Asia, this study tried to identify the physical causes of the precipitation changes by analyzing a local moisture budget. Projected changes in precipitation over the eastern foothills area of Tibetan Plateau including Sichuan Basin and Yangtze River displayed a contrasting pattern: a decrease in its northern area and an increase in its southern area. A local moisture budget analysis indicated the precipitation increase over the southern area can be mainly attributed to an increase in horizontal wind convergence and surface evaporation. On the other hand, the precipitation decrease over the northern area can be largely explained by horizontal advection of dry air from the northern continent and by divergent wind flow. Regional changes in future precipitation in East Asia are likely to be attributed to different mechanisms which can be better resolved by regional dynamical <span class="hlt">downscaling</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013OcMod..72..153M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013OcMod..72..153M"><span id="translatedtitle">An uncoupled dynamical <span class="hlt">downscaling</span> for the North Sea: Method 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>Mathis, M.; Mayer, B.; Pohlmann, T.</p> <p>2013-12-01</p> <p>A reliable regional modeling system for uncoupled dynamical <span class="hlt">downscaling</span> simulations of potential global climate change scenarios in the North Sea is presented. The HAMSOM regional shelf ocean model is forced with results from the MPIOM global ocean model at the open lateral boundaries of the model domain, and with results from the REMO regional atmosphere model at the air-sea interface. The evaluation of the model chain is based on the North Sea regionalization for the period 1951-2000 of the global historic control run 20C3M for the IPCC SRES scenario runs under the CMIP3 model generation. To reproduce reasonable long-term statistics of hydrodynamic conditions in the North Sea, a bias correction method relative to ERA40 reanalysis data and WOA-2001 climatology is applied to the forcing variables. Comparisons of the HAMSOM model results with observational water temperature and salinity climatologies are presented as well as with previously published research of volume transports, residence and flushing times, NAO correlations, surface heat and fresh water fluxes, and thermocline parameters. In general, the model results agree reasonably with the given references, thereby qualifying the presented concept as an appropriate tool for dynamical <span class="hlt">downscaling</span> of global scenario runs for the North Sea.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014GMD.....7.2003B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014GMD.....7.2003B"><span id="translatedtitle">Ice sheet dynamics within an earth system model: <span class="hlt">downscaling</span>, coupling and first results</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Barbi, D.; Lohmann, G.; Grosfeld, K.; Thoma, M.</p> <p>2014-09-01</p> <p>We present first results from a coupled model setup, consisting of the state-of-the-art ice sheet model RIMBAY (Revised Ice Model Based on frAnk pattYn), and the community earth system model COSMOS. We show that special care has to be provided in order to ensure physical distributions of the forcings as well as numeric stability of the involved models. We demonstrate that a suitable statistical <span class="hlt">downscaling</span> is crucial for ice sheet stability, especially for southern Greenland where surface temperatures are close to the melting point. The <span class="hlt">downscaling</span> of net snow accumulation is based on an empirical relationship between surface slope and rainfall. The simulated ice sheet does not show dramatic loss of ice volume for pre-industrial conditions and is comparable with present-day ice orography. A sensitivity study with high CO2 level is used to demonstrate the effects of dynamic ice sheets onto climate compared to the standard setup with prescribed ice sheets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20120016072','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20120016072"><span id="translatedtitle">Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation <span class="hlt">Downscaling</span></span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Koster, Randal; Walker, Greg; Mahanama, Sarith; Reichle, Rolf</p> <p>2012-01-01</p> <p>Continental-scale offline simulations with a land surface model are used to address two important issues in the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which the <span class="hlt">downscaling</span> of seasonal precipitation forecasts, if it could be done accurately, would improve streamflow forecasts. The reduction in streamflow forecast skill (with forecasted streamflow measured against observations) associated with adding noise to a soil moisture field is found to be, to first order, proportional to the average reduction in the accuracy of the soil moisture field itself. This result has implications for streamflow forecast improvement under satellite-based soil moisture measurement programs. In the second and more idealized ("perfect model") analysis, precipitation <span class="hlt">downscaling</span> is found to have an impact on large-scale streamflow forecasts only if two conditions are met: (i) evaporation variance is significant relative to the precipitation variance, and (ii) the subgrid spatial variance of precipitation is adequately large. In the large-scale continental region studied (the conterminous United States), these two conditions are met in only a somewhat limited area.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMNG33A3812N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMNG33A3812N"><span id="translatedtitle">Assessing Hydrological and Energy Budgets in Amazonia through Regional <span class="hlt">Downscaling</span>, and Comparisons with Global Reanalysis Products</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nunes, A.; Ivanov, V. Y.</p> <p>2014-12-01</p> <p>Although current global reanalyses provide reasonably accurate large-scale features of the atmosphere, systematic errors are still found in the hydrological and energy budgets of such products. In the tropics, precipitation is particularly challenging to model, which is also adversely affected by the scarcity of hydrometeorological datasets in the region. With the goal of producing <span class="hlt">downscaled</span> analyses that are appropriate for a climate assessment at regional scales, a regional spectral model has used a combination of precipitation assimilation with scale-selective bias correction. The latter is similar to the spectral nudging technique, which prevents the departure of the regional model's internal states from the large-scale forcing. The target area in this study is the Amazon region, where large errors are detected in reanalysis precipitation. To generate the <span class="hlt">downscaled</span> analysis, the regional climate model used NCEP/DOE R2 global reanalysis as the initial and lateral boundary conditions, and assimilated NOAA's Climate Prediction Center (CPC) MORPHed precipitation (CMORPH), available at 0.25-degree resolution, every 3 hours. The regional model's precipitation was successfully brought closer to the observations, in comparison to the NCEP global reanalysis products, as a result of the impact of a precipitation assimilation scheme on cumulus-convection parameterization, and improved boundary forcing achieved through a new version of scale-selective bias correction. Water and energy budget terms were also evaluated against global reanalyses and other datasets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.121..605D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.121..605D"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of temperature using three techniques in the Tons River basin in Central India</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Duhan, Darshana; Pandey, Ashish</p> <p>2015-08-01</p> <p>In this study, <span class="hlt">downscaling</span> models were developed for the projections of monthly maximum and minimum air temperature for three stations, namely, Allahabad, Satna, and Rewa in Tons River basin, which is a sub-basin of the Ganges River in Central India. The three <span class="hlt">downscaling</span> techniques, namely, multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LS-SVM), were used for the development of models, and best identified model was used for simulations of future predictand (temperature) using third-generation Canadian Coupled Global Climate Model (CGCM3) simulation of A2 emission scenario for the period 2001-2100. The performance of the models was evaluated based on four statistical performance indicators. To reduce the bias in monthly projected temperature series, bias correction technique was employed. The results show that all the models are able to simulate temperature; however, LS-SVM models perform slightly better than ANN and MLR. The best identified LS-SVM models are then employed to project future temperature. The results of future projections show the increasing trends in maximum and minimum temperature for A2 scenario. Further, it is observed that minimum temperature will increase at greater rate than maximum temperature.</p> </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=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.bangor.ac.uk/~mas00a/papers/jrlkcatpami06.pdf','EPRINT'); return false;" href="http://www.bangor.ac.uk/~mas00a/papers/jrlkcatpami06.pdf"><span id="translatedtitle">Rotation Forest: A New Classifier <span class="hlt">Ensemble</span> Method</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Kuncheva, Ludmila I.</p> <p></p> <p>Rotation Forest: A New Classifier <span class="hlt">Ensemble</span> Method Juan J. Rodri´guez, Member, IEEE Computer Society "forest." Accuracy is sought by keeping all principal components and also using the whole data set to train each base classifier. Using WEKA, we examined the Rotation Forest <span class="hlt">ensemble</span> on a random selection</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/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://seor.gmu.edu/~klaskey/papers/Wang_etal_NBCE.pdf','EPRINT'); return false;" href="http://seor.gmu.edu/~klaskey/papers/Wang_etal_NBCE.pdf"><span id="translatedtitle">Nonparametric Bayesian Clustering <span class="hlt">Ensembles</span> , Carlotta Domeniconi1</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Laskey, Kathryn Blackmond</p> <p></p> <p>oriented approaches. Topchy et al. [28] proposed a mixture-membership model for clustering <span class="hlt">ensembles</span>. Wang the Dirichlet Process Mixture (DPM) model [22]. The following sections show how the DPM model can be adaptedNonparametric Bayesian Clustering <span class="hlt">Ensembles</span> Pu Wang1 , Carlotta Domeniconi1 , and Kathryn Blackmond</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=numerical+AND+solution&pg=6&id=EJ934197','ERIC'); return false;" href="http://eric.ed.gov/?q=numerical+AND+solution&pg=6&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://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/2015EGUGA..17.5735G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.5735G"><span id="translatedtitle">Towards stochastically <span class="hlt">downscaled</span> precipitation in the Tropics based on a robust 1DD combined satellite product and a high resolution IR-based rain mask</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guilloteau, Clement; Roca, Rémy; Gosset, Marielle</p> <p>2015-04-01</p> <p>In the Tropics where the ground-based rain gauges network is very sparse, satellite rainfall estimates are becoming a compulsory source of information for various applications: hydrological modeling, water resources management or vegetation-monitoring. The tropical Tropical Amount of Precipitation with Estimate of Error (TAPEER) algorithm, developed within the framework of Megha-Tropiques satellite mission is a robust estimate of surface rainfall accumulations at the daily, one degree resolution. TAPEER validation in West Africa has proven its accuracy. Nevertheless applications that involve non-linear processes (such as surface runoff) require finer space / time resolution than one degree one day, or at least the statistical characterization of the sub-grid rainfall variability. TAPEER is based on a Universally Adjusted Global Precipitation Index (UAGPI) technique. The one degree, one day estimation relies on the combination of observations from microwave radiometers embarked on the 7 platforms forming the GPM constellation of low earth orbit satellites together with geostationary infra-red (GEO-IR) imagery. TAPEER provides as an intermediate product a high-resolution rain-mask based on the GEO-IR information (2.8 km, 15 min in Africa). The main question of this work is, how to use this high-resolution mask information as a constraint for <span class="hlt">downscaling</span> ? This work first presents the multi-scale evaluation of TAPEER's rain detection mask against ground X-band polarimetric radar data and TRMM precipitation radar data in West Africa, through wavelet transform. Other algorithms (climate prediction center morphing technique CMORPH, global satellite mapping of precipitation GSMaP, multi-sensor precipitation estimate MPE) detection capabilities are also evaluated. Spatio-temporal wavelet filtering of the detection mask is then used to compute precipitation probability at the GEO-IR resolution. The wavelet tool is finally used to stochastically generate rain / no rain field <span class="hlt">ensemble</span> consistent with the original TAPEER estimation. This binary mask generation is the first step for the generation of quantitative rain fields <span class="hlt">ensemble</span> at GEO-IR resolution.</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://arxiv.org/pdf/0803.3274v3','EPRINT'); return false;" href="http://arxiv.org/pdf/0803.3274v3"><span id="translatedtitle">Topological quantization of <span class="hlt">ensemble</span> averages</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Emil Prodan</p> <p>2008-10-04</p> <p>We define the current of a quantum observable and, under well defined conditions, we connect its <span class="hlt">ensemble</span> average to the index of a Fredholm operator. The present work builds on a formalism developed by Kellendonk and Schulz-Baldes \\cite{Kellendonk:2004p597} to study the quantization of edge currents for continuous magnetic Schroedinger operators. The generalization given here may be a useful tool to scientists looking for novel manifestations of the topological quantization. As a new application, we show that the differential conductance of atomic wires is given by the index of a certain operator. We also comment on how the formalism can be used to probe the existence of edge states.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1501.01936v1','EPRINT'); return false;" href="http://arxiv.org/pdf/1501.01936v1"><span id="translatedtitle">Quasi-power law <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Grzegorz Wilk; Zbigniew W?odarczyk</p> <p>2014-12-12</p> <p>Quasi-power law <span class="hlt">ensembles</span> are discussed from the perspective of nonextensive Tsallis distributions characterized by a nonextensive parameter $q$. A number of possible sources of such distributions are presented in more detail. It is further demonstrated that data suggest that nonextensive parameters deduced from Tsallis distributions functions $f\\left(p_T\\right)$, $q_1$, and from multiplicity distributions (connected with Tsallis entropy), $q_2$, are not identical and that they are connected via $q_1 + q_2 = 2$. It is also shown that Tsallis distributions can be obtained directly from Shannon information entropy, provided some special constraints are imposed. They are connected with the type of dynamical processes under consideration (additive or multiplicative). Finally, it is shown how a Tsallis distribution can accommodate the log-oscillating behavior apparently seen in some multiparticle data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1501.01936.pdf','EPRINT'); return false;" href="http://arxiv.org/pdf/1501.01936.pdf"><span id="translatedtitle">Quasi-power law <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Wilk, Grzegorz</p> <p>2015-01-01</p> <p>Quasi-power law <span class="hlt">ensembles</span> are discussed from the perspective of nonextensive Tsallis distributions characterized by a nonextensive parameter $q$. A number of possible sources of such distributions are presented in more detail. It is further demonstrated that data suggest that nonextensive parameters deduced from Tsallis distributions functions $f\\left(p_T\\right)$, $q_1$, and from multiplicity distributions (connected with Tsallis entropy), $q_2$, are not identical and that they are connected via $q_1 + q_2 = 2$. It is also shown that Tsallis distributions can be obtained directly from Shannon information entropy, provided some special constraints are imposed. They are connected with the type of dynamical processes under consideration (additive or multiplicative). Finally, it is shown how a Tsallis distribution can accommodate the log-oscillating behavior apparently seen in some multiparticle data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JHyd..488..136J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JHyd..488..136J"><span id="translatedtitle">Databased comparison of Sparse Bayesian Learning and Multiple Linear Regression for statistical <span class="hlt">downscaling</span> of low flow indices</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Joshi, Deepti; St-Hilaire, André; Daigle, Anik; Ouarda, Taha B. M. J.</p> <p>2013-04-01</p> <p>SummaryThis study attempts to compare the performance of two statistical <span class="hlt">downscaling</span> frameworks in <span class="hlt">downscaling</span> hydrological indices (descriptive statistics) characterizing the low flow regimes of three rivers in Eastern Canada - Moisie, Romaine and Ouelle. The statistical models selected are Relevance Vector Machine (RVM), an implementation of Sparse Bayesian Learning, and the Automated Statistical <span class="hlt">Downscaling</span> tool (ASD), an implementation of Multiple Linear Regression. Inputs to both frameworks involve climate variables significantly (? = 0.05) correlated with the indices. These variables were processed using Canonical Correlation Analysis and the resulting canonical variates scores were used as input to RVM to estimate the selected low flow indices. In ASD, the significantly correlated climate variables were subjected to backward stepwise predictor selection and the selected predictors were subsequently used to estimate the selected low flow indices using Multiple Linear Regression. With respect to the correlation between climate variables and the selected low flow indices, it was observed that all indices are influenced, primarily, by wind components (Vertical, Zonal and Meridonal) and humidity variables (Specific and Relative Humidity). The <span class="hlt">downscaling</span> performance of the framework involving RVM was found to be better than ASD in terms of Relative Root Mean Square Error, Relative Mean Absolute Bias and Coefficient of Determination. In all cases, the former resulted in less variability of the performance indices between calibration and validation sets, implying better generalization ability than for the latter.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.unh.edu/erg/sites/www.unh.edu.erg/files/ray_et_al_rse_2010_2.pdf','EPRINT'); return false;" href="http://www.unh.edu/erg/sites/www.unh.edu.erg/files/ray_et_al_rse_2010_2.pdf"><span id="translatedtitle">Landslide susceptibility mapping using <span class="hlt">downscaled</span> AMSR-E soil moisture: A case study from Cleveland Corral, California, US</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p>Landslide susceptibility mapping using <span class="hlt">downscaled</span> AMSR-E soil moisture: A case study from Cleveland in revised form 28 April 2010 Accepted 31 May 2010 Keywords: AMSR-E Remote sensing VIC-3L Landslide Soil moisture data can provide routine updates of slope conditions necessary for landslide predictions</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=192252','TEKTRAN'); return false;" href="http://www.ars.usda.gov/research/publications/Publications.htm?seq_no_115=192252"><span id="translatedtitle">A COMPARISON OF EXPLICIT AND IMPLICIT SPATIAL <span class="hlt">DOWNSCALING</span> OF GCM OUTPUT FOR SOIL EROSION AND CROP PRODUCTION ASSESSMENTS</span></a></p> <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>Spatial <span class="hlt">downscaling</span> of climate change scenarios can be a significant source of uncertainty in simulating climatic impact on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to t...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.A33E0289O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.A33E0289O"><span id="translatedtitle">Regional <span class="hlt">downscaling</span> of temporal resolution in near-surface wind from statistically <span class="hlt">downscaled</span> Global Climate Models (GCMs) for use in San Francisco Bay coastal flood modeling</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>O'Neill, A.; Erikson, L. H.; Barnard, P.</p> <p>2013-12-01</p> <p>While Global Climate Models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically <span class="hlt">downscaled</span> GCM projections from Multivariate Adaptive Constructed Analogues (MACA) provide daily near-surface winds at an appropriate spatial resolution for wave modeling within San Francisco Bay. Using 30 years (1975-2004) of climatological data from four representative stations around San Francisco Bay, a library of example daily wind conditions for four corresponding over-water sub-regions is constructed. Empirical cumulative distribution functions (ECDFs) of station conditions are compared to MACA GFDL hindcasts to create correction factors, which are then applied to 21st century MACA wind projections. For each projection day, a best match example is identified via least squares error among all stations from the library. The best match's daily variation in velocity components (u/v) is used as an analogue of representative wind variation and is applied at 3-hour increments about the corresponding sub-region's projected u/v values. High temporal resolution reconstructions using this methodology on hindcast MACA fields from 1975-2004 accurately recreate extreme wind values within the San Francisco Bay, and because these extremes in wind forcing are of key importance in wave and subsequent coastal flood modeling, this represents a valuable method of generating near-surface wind vectors for use in coastal flood modeling.</p> </li> <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://pubs.er.usgs.gov/publication/70022771','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70022771"><span id="translatedtitle">A comparison of delta change and <span class="hlt">downscaled</span> GCM scenarios for three mountainous basins in the United States</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Hay, L.E.; Wilby, R.L.; Leavesley, G.H.</p> <p>2000-01-01</p> <p>Simulated daily precipitation, temperature, and runoff time series were compared in three mountainous basins in the United States: (1) the Animas River basin in Colorado, (2) the East Fork of the Carson River basin in Nevada and California, and (3) the Cle Elum River basin in Washington State. Two methods of climate scenario generation were compared: delta change and statistical <span class="hlt">downscaling</span>. The delta change method uses differences between simulated current and future climate conditions from the Hadley Centre for Climate Prediction and Research (HadCM2) General Circulation Model (GCM) added to observed time series of climate variables. A statistical <span class="hlt">downscaling</span> (SDS) model was developed for each basin using station data and output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis regridded to the scale of HadCM2. The SDS model was then used to simulate local climate variables using HadCM2 output for current and future conditions. Surface climate variables from each scenario were used in a precipitation-runoff model. Results from this study show that, in the basins tested, a precipitation-runoff model can simulate realistic runoff series for current conditions using statistically <span class="hlt">downscaled</span> NCEP output. But, use of <span class="hlt">downscaled</span> HadCM2 output for current or future climate assessments are questionable because the GCM does not produce accurate estimates of the surface variables needed for runoff in these regions. Given the uncertainties in the GCMs ability to simulate current conditions based on either the delta change or <span class="hlt">downscaling</span> approaches, future climate assessments based on either of these approaches must be treated with caution.</p> </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://arxiv.org/pdf/1110.3296v2','EPRINT'); return false;" href="http://arxiv.org/pdf/1110.3296v2"><span id="translatedtitle">Time as a parameter of statistical <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Sergei Viznyuk</p> <p>2011-11-26</p> <p>The notion of time is derived as a parameter of statistical <span class="hlt">ensemble</span> representing the underlying system. Varying population numbers of microstates in statistical <span class="hlt">ensemble</span> result in different expectation values corresponding to different times. We show a single parameter which equates to the notion of time is logarithm of the total number of microstates in statistical <span class="hlt">ensemble</span>. We discuss the implications of proposed model for some topics of modern physics: Poincar\\'e recurrence theorem vs. Second Law of Thermodynamics, matter vs. anti-matter asymmetry of the universe, expansion of the universe, Big Bang.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ntrs.nasa.gov/search.jsp?R=20020061294&hterms=climate+models+compared&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dclimate%2Bmodels%2Bcompared','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20020061294&hterms=climate+models+compared&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D90%26Ntt%3Dclimate%2Bmodels%2Bcompared"><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/2013SoPh..285..349L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013SoPh..285..349L"><span id="translatedtitle"><span class="hlt">Ensemble</span> Modeling of CME Propagation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, C. O.; Arge, C. N.; Odstr?il, D.; Millward, G.; Pizzo, V.; Quinn, J. M.; Henney, C. J.</p> <p>2013-07-01</p> <p>The current progression toward solar maximum provides a unique opportunity to use multi-perspective spacecraft observations together with numerical models to better understand the evolution and propagation of coronal mass ejections (CMEs). Of interest to both the scientific and forecasting communities are the Earth-directed "halo" CMEs, since they typically produce the most geoeffective events. However, determining the actual initial geometries of halo CMEs is a challenge due to the plane-of-sky projection effects. Thus the recent 15 February 2011 halo CME event has been selected for this study. During this event the Solar TErrestrial RElations Observatory (STEREO) A and B spacecraft were fortuitously located ˜ 90° away from the Sun-Earth line such that the CME was viewed as a limb event from these two spacecraft, thereby providing a more reliable constraint on the initial CME geometry. These multi-perspective observations were utilized to provide a simple geometrical description that assumes a cone shape for a CME to calculate its angular width and central position. The event was simulated using the coupled Wang-Sheeley-Arge (WSA)-Enlil 3D numerical solar corona-solar wind model. Daily updated global photospheric magnetic field maps were used to drive the background solar wind. To improve our modeling techniques, the sensitivity of the modeled CME arrival times to the initial input CME geometry was assessed by creating an <span class="hlt">ensemble</span> of numerical simulations based on multiple sets of cone parameters for this event. It was found that the accuracy of the modeled arrival times not only depends on the initial input CME geometry, but also on the accuracy of the modeled solar wind background, which is driven by the input maps of the photospheric field. To improve the modeling of the background solar wind, the recently developed data-assimilated magnetic field synoptic maps produced by the Air Force Data Assimilative Photospheric flux Transport (ADAPT) model were used. The ADAPT maps provide a more instantaneous snapshot of the global photospheric field distribution than that provided by traditional daily updated synoptic maps. Using ADAPT to drive the background solar wind, an <span class="hlt">ensemble</span> set of eight different CME arrival times was generated, where the spread in the predictions was ˜ 13 hours and was nearly centered on the observed CME shock arrival time.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..1510686L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..1510686L"><span id="translatedtitle">A comparison of dynamical and statistical <span class="hlt">downscaling</span> methods for regional wave climate projections along French coastlines.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Mendez, Fernando</p> <p>2013-04-01</p> <p>Wave climate forecasting is a major issue for numerous marine and coastal related activities, such as offshore industries, flooding risks assessment and wave energy resource evaluation, among others. Generally, there are two main ways to predict the impacts of the climate change on the wave climate at regional scale: the dynamical and the statistical <span class="hlt">downscaling</span> of GCM (Global Climate Model). In this study, both methods have been applied on the French coast (Atlantic , English Channel and North Sea shoreline) under three climate change scenarios (A1B, A2, B1) simulated with the GCM ARPEGE-CLIMAT, from Météo-France (AR4, IPCC). The aim of the work is to characterise the wave climatology of the 21st century and compare the statistical and dynamical methods pointing out advantages and disadvantages of each approach. The statistical <span class="hlt">downscaling</span> method proposed by the Environmental Hydraulics Institute of Cantabria (Spain) has been applied (Menendez et al., 2011). At a particular location, the sea-state climate (Predictand Y) is defined as a function, Y=f(X), of several atmospheric circulation patterns (Predictor X). Assuming these climate associations between predictor and predictand are stationary, the statistical approach has been used to project the future wave conditions with reference to the GCM. The statistical relations between predictor and predictand have been established over 31 years, from 1979 to 2009. The predictor is built as the 3-days-averaged squared sea level pressure gradient from the hourly CFSR database (Climate Forecast System Reanalysis, http://cfs.ncep.noaa.gov/cfsr/). The predictand has been extracted from the 31-years hindcast sea-state database ANEMOC-2 performed with the 3G spectral wave model TOMAWAC (Benoit et al., 1996), developed at EDF R&D LNHE and Saint-Venant Laboratory for Hydraulics and forced by the CFSR 10m wind field. Significant wave height, peak period and mean wave direction have been extracted with an hourly-resolution at 110 coastal locations along the French coast. The model, based on the BAJ parameterization of the source terms (Bidlot et al, 2007) was calibrated against ten years of GlobWave altimeter observations (2000-2009) and validated through deep and shallow water buoy observations. The dynamical <span class="hlt">downscaling</span> method has been performed with the same numerical wave model TOMAWAC used for building ANEMOC-2. Forecast simulations are forced by the 10m wind fields of ARPEGE-CLIMAT (A1B, A2, B1) from 2010 to 2100. The model covers the Atlantic Ocean and uses a spatial resolution along the French and European coast of 10 and 20 km respectively. The results of the model are stored with a time resolution of one hour. References: Benoit M., Marcos F., and F. Becq, (1996). Development of a third generation shallow-water wave model with unstructured spatial meshing. Proc. 25th Int. Conf. on Coastal Eng., (ICCE'1996), Orlando (Florida, USA), pp 465-478. Bidlot J-R, Janssen P. and Adballa S., (2007). A revised formulation of ocean wave dissipation and its model impact, technical memorandum ECMWF n°509. Menendez, M., Mendez, F.J., Izaguirre,C., Camus, P., Espejo, A., Canovas, V., Minguez, R., Losada, I.J., Medina, R. (2011). Statistical <span class="hlt">Downscaling</span> of Multivariate Wave Climate Using a Weather Type Approach, 12th International Workshop on Wave Hindcasting and Forecasting and 3rd Coastal Hazard Symposium, Kona (Hawaii).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMOS33C1081L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMOS33C1081L"><span id="translatedtitle">Dynamical <span class="hlt">downscaling</span> of future sea-level change in the western North Pacific using ROMS</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Z.</p> <p>2014-12-01</p> <p>The western North Pacific to the east of Japan is one of the regions where sea-level rise is predicted to the larger than the global mean in CMIP5 models as shown in IPCC AR5. One of the causes of the spatial variations of the sea-level is change in ocean circulation, and this effect can be important in western boundary regions, where changes of strong western boundary current can cause large sea-level changes. However, the current climate models cannot properly represent western boundary currents due to coarse model resolution. Therefore, it is desirable to perform <span class="hlt">downscaling</span> of future sea-level changes using a regional ocean model with a high model resolution for western boundary current regions using forcings and boundary conditions taken from climate model outputs. This study investigates future regional sea-level rise by performing dynamical <span class="hlt">downscaling</span> in the western North Pacific, using the regional ocean model system (ROMS) with eddy-permitting 0.25-degree resolution over the North Pacific. In order to evaluate possible extremely large regional sea-level rise, the Model for Interdisciplinary Research on Climate Earth System Model (MIROC-ESM) under RCP8.5 scenario is chosen because this model exhibits large sea-level rise among CMIP5 models in this region. ROMS are run for two epochs; one is 1950-2000 and the other is 2051-2100, and the last 20-years are analyzed. The model integration is now ongoing, and the major differences between the two runs will be reported at the meeting.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC52A..06H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC52A..06H"><span id="translatedtitle">Intersections of <span class="hlt">downscaling</span>, the ethics of climate services, and regional research grand challenges.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hewitson, B.; Jack, C. D.; Gutowski, W. J., Jr.</p> <p>2014-12-01</p> <p>Possibly the leading complication for users of climate information for policy and adaptation is the confusing mix of contrasting data sets that offer widely differing (and often times fundamentally contradictory) indications of the magnitude and direction of past and future regional climate change. In this light, the most pressing scientific-societal challenge is the need to find new ways to understand the sources of conflicting messages from multi-model, multi-method and multi-scale disparities, to develop and implement new analytical methodologies to address this difficulty and so to advance the interpretation and communication of robust climate information to decision makers. Compounding this challenge is the growth of climate services which, in view of the confusing mix of climate change messages, raises serious concerns as to the ethics of communication and dissemination of regional climate change data.The Working Group on Regional Climate (WGRC) of the World Climate Research Program (WCRP) oversees the CORDEX <span class="hlt">downscaling</span> program which offers a systematic approach to compare the CMIP5 GCMs alongside RCMs and Empirical-statistical (ESD) <span class="hlt">downscaling</span> within a common experimental design, and which facilitates the evaluation and assessment of the relative information content and sources of error. Using results from the CORDEX RCM and ESD evaluation experiment, and set against the regional messages from the CMIP5 GCMs, we examine the differing messages that arise from each data source. These are then considered in terms of the implications of consequence if each data source were to be independently adopted in a real world use-case scenario. This is then cast in the context of the emerging developments on the distillation dilemma - where the pressing need is for multi-method integration - and how this relates to the WCRP regional research grand challenges.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26067835','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26067835"><span id="translatedtitle">Probabilistic <span class="hlt">Downscaling</span> of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Stoy, Paul C; Quaife, Tristan</p> <p>2015-01-01</p> <p>Upscaling ecological information to larger scales in space and <span class="hlt">downscaling</span> remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. <span class="hlt">Downscaling</span> often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (?) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a ? value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes. PMID:26067835</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4467079','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4467079"><span id="translatedtitle">Probabilistic <span class="hlt">Downscaling</span> of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Stoy, Paul C.; Quaife, Tristan</p> <p>2015-01-01</p> <p>Upscaling ecological information to larger scales in space and <span class="hlt">downscaling</span> remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. <span class="hlt">Downscaling</span> often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (?) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a ? value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes. PMID:26067835</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1108.4918v1','EPRINT'); return false;" href="http://arxiv.org/pdf/1108.4918v1"><span id="translatedtitle"><span class="hlt">Ensemble</span> Dynamics and Bred Vectors</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Nusret Balci; Anna L. Mazzucato; Juan M. Restrepo; George R. Sell</p> <p>2011-08-24</p> <p>We introduce the new concept of an EBV to assess the sensitivity of model outputs to changes in initial conditions for weather forecasting. The new algorithm, which we call the "<span class="hlt">Ensemble</span> Bred Vector" or EBV, is based on collective dynamics in essential ways. By construction, the EBV algorithm produces one or more dominant vectors. We investigate the performance of EBV, comparing it to the BV algorithm as well as the finite-time Lyapunov Vectors. We give a theoretical justification to the observed fact that the vectors produced by BV, EBV, and the finite-time Lyapunov vectors are similar for small amplitudes. Numerical comparisons of BV and EBV for the 3-equation Lorenz model and for a forced, dissipative partial differential equation of Cahn-Hilliard type that arises in modeling the thermohaline circulation, demonstrate that the EBV yields a size-ordered description of the perturbation field, and is more robust than the BV in the higher nonlinear regime. The EBV yields insight into the fractal structure of the Lorenz attractor, and of the inertial manifold for the Cahn-Hilliard-type partial differential equation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1108.4918.pdf','EPRINT'); return false;" href="http://arxiv.org/pdf/1108.4918.pdf"><span id="translatedtitle"><span class="hlt">Ensemble</span> Dynamics and Bred Vectors</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Balci, Nusret; Restrepo, Juan M; Sell, George R</p> <p>2011-01-01</p> <p>We introduce the new concept of an EBV to assess the sensitivity of model outputs to changes in initial conditions for weather forecasting. The new algorithm, which we call the "<span class="hlt">Ensemble</span> Bred Vector" or EBV, is based on collective dynamics in essential ways. By construction, the EBV algorithm produces one or more dominant vectors. We investigate the performance of EBV, comparing it to the BV algorithm as well as the finite-time Lyapunov Vectors. We give a theoretical justification to the observed fact that the vectors produced by BV, EBV, and the finite-time Lyapunov vectors are similar for small amplitudes. Numerical comparisons of BV and EBV for the 3-equation Lorenz model and for a forced, dissipative partial differential equation of Cahn-Hilliard type that arises in modeling the thermohaline circulation, demonstrate that the EBV yields a size-ordered description of the perturbation field, and is more robust than the BV in the higher nonlinear regime. The EBV yields insight into the fractal structure of th...</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/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://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://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://kuscholarworks.ku.edu/handle/1808/9849','EPRINT'); return false;" href="http://kuscholarworks.ku.edu/handle/1808/9849"><span id="translatedtitle">Symphony No. 1 for Wind <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Woodhouse, Ryan</p> <p>2012-05-31</p> <p>Symphony No. 1 for Wind <span class="hlt">Ensemble</span> is a three-movement work lasting twenty to twenty-two minutes. While symphonies by Paul Hindemith, Vincent Persichetti, Frank Ticheli, John Corigliano, James Barnes, and David Maslanka are important contributions...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=wind+AND+band&pg=2&id=EJ176993','ERIC'); return false;" href="http://eric.ed.gov/?q=wind+AND+band&pg=2&id=EJ176993"><span id="translatedtitle">Music Literature for Band and Wind <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>De Young, Derald</p> <p>1977-01-01</p> <p>Provides some music literature sources for band and wind <span class="hlt">ensembles</span>. Since music literature is crucial to both musical groups and the music curriculum evolves from music, these references are most important. (RK)</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://dspace.mit.edu/handle/1721.1/69604','EPRINT'); return false;" href="http://dspace.mit.edu/handle/1721.1/69604"><span id="translatedtitle">Hybrid <span class="hlt">Ensembles</span> for Improved Force Matching</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Wang, Lee-Ping</p> <p></p> <p>Force matching is a method for parameterizing empirical potentials in which the empirical parameters are fitted to a reference potential energy surface (PES). Typically, training data are sampled from a canonical <span class="hlt">ensemble</span> ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://quantum.phys.cmu.edu/QCQI/qitd422.pdf','EPRINT'); return false;" href="http://quantum.phys.cmu.edu/QCQI/qitd422.pdf"><span id="translatedtitle">Density Operators and <span class="hlt">Ensembles</span> Robert B. Griffiths</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Griffiths, Robert B.</p> <p></p> <p>qitd422 Density Operators and <span class="hlt">Ensembles</span> Robert B. Griffiths Version of 30 January 2014 Contents 1 Density Operators 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 Interpretation and use of density operators</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003JApMe..42..308D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003JApMe..42..308D"><span id="translatedtitle">Evaluation of an <span class="hlt">Ensemble</span> Dispersion Calculation.</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Draxler, Roland R.</p> <p>2003-02-01</p> <p>A Lagrangian transport and dispersion model was modified to generate multiple simulations from a single meteorological dataset. Each member of the simulation was computed by assuming a ±1-gridpoint shift in the horizontal direction and a ±250-m shift in the vertical direction of the particle position, with respect to the meteorological data. The configuration resulted in 27 <span class="hlt">ensemble</span> members. Each member was assumed to have an equal probability. The model was tested by creating an <span class="hlt">ensemble</span> of daily average air concentrations for 3 months at 75 measurement locations over the eastern half of the United States during the Across North America Tracer Experiment (ANATEX). Two generic graphical displays were developed to summarize the <span class="hlt">ensemble</span> prediction and the resulting concentration probabilities for a specific event: a probability-exceed plot and a concentration-probability plot. Although a cumulative distribution of the <span class="hlt">ensemble</span> probabilities compared favorably with the measurement data, the resulting distribution was not uniform. This result was attributed to release height sensitivity. The trajectory <span class="hlt">ensemble</span> approach accounts for about 41%-47% of the variance in the measurement data. This residual uncertainty is caused by other model and data errors that are not included in the <span class="hlt">ensemble</span> design.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1611466A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1611466A"><span id="translatedtitle">Inundation <span class="hlt">downscaling</span> for the development of a long-term and global inundation database compatible to SWOT mission</span></a></p> <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. Even for climate applications, the GIEMS resolution might be limited given recent results on the key importance of the smallest ponds in the emission of CH4, as compared to the largest ones. If the inundation extent is combined to altimetry measurements to obtain water volume changes, and finally river discharge to the ocean (Frappart et al. 2011), then a better resolved inundation extent will also improve the accuracy of these estimates. In the context of the SWOT mission, the <span class="hlt">downscaling</span> of GIEMS has multiple applications uses but a major one will be to use the SWOT retrievals to develop a <span class="hlt">downscaling</span> of GIEMS. This SWOT-compatible <span class="hlt">downscaling</span> could then be used to built a SWOT-compatible high-resolution database back in time from 1993 to the SWOT launch date. This extension of SWOT record is necessary to perform climate studies related to climate change. This paper present three approaches to do <span class="hlt">downscale</span> GIEMS. Two basins will be considered for illustrative purpose, Amazon, Niger and Mekhong. - Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and <span class="hlt">downscaling</span> of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeorology, in press, 2014. - Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, <span class="hlt">downscaled</span> from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. - Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett., 39(L08403), 2012. - Frappart, F.; F. Papa, A. Guntner, S. Werth, J. Santos da Silva, J. Tomasella, F. Seyler, C. Prigent, W.B. Rossow, S. Calmant, and M.-P. Bonnet. Satellite-based estimates of groundwater storage variations in large drainage basins with extensive floodplains. Remote Sens. Environ., 115 :1588-1594, 2011.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFM.H51U..07Y','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFM.H51U..07Y"><span id="translatedtitle">Examine the potential of spatial <span class="hlt">downscaling</span> of TRMM precipitation with environmental variables: An evaluation for the Ohio 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>Yoon, Y.; Beighley, E., II</p> <p>2014-12-01</p> <p>Accurately quantifying precipitation in both space and time is a central challenge in hydrologic modelling. Data products from the Tropical Rainfall Measuring Mission (TRMM) are commonly used as precipitation forcings in many models. TRMM provides 3-hr precipitation estimates at a near-global scale (-50? S to 50?N) with a 0.25 degree spatial resolution. However, when applied in regional scale hydrologic models, the spatial resolution of the TRMM is often too coarse limiting our ability to simulate relevant hydrologic processes.This study focuses on addressing the science question: can we improve the spatial resolution of the TRMM using statistical <span class="hlt">downscaling</span> with environmental variables derived from finer scale remote sensing data? The goal is to <span class="hlt">downscale</span> the TRMM resolution from 0.25 degrees (25 km) to 0.05 degrees (about 5 km). In our approach, we first identify environmental variables (i.e., vegetation cover, topography, and temperature) that are related to the formation of or result from precipitation by exploring their statistical relationships with TRMM precipitation at varying temporal scales (i.e., daily, monthly, and yearly) using an analysis of variance in multiple regression. The MODIS vegetation index, MODIS leaf area index, and SPOT vegetation are examined as a proxy for vegetation. To represent the topography, the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is used. MODIS land surface temperature are used for temperature. Second, we characterize a residual component, which cannot be explained by the statistical relationship between precipitation and environmental variables, to improve the accuracy of the <span class="hlt">downscaling</span> results. For example, recent studies have shown that approximately 30-40% of the variability in annual precipitation cannot be explained by vegetation and elevation characteristics. According for this unexplained variability in statistical <span class="hlt">downscaling</span> methods is a significant challenge. Here, we use a data assimilation technique to interpolate the residual component and generate the <span class="hlt">downscaled</span> precipitation. Results are presented for the Ohio River Basin. The final <span class="hlt">downscaled</span> TRMM is evaluated by comparing with the National Center for Environmental Predication (NCEP) Stage VI precipitation and rainfall gauge data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ClDy..tmp..185V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ClDy..tmp..185V"><span id="translatedtitle">Intercomparison of statistical and dynamical <span class="hlt">downscaling</span> models under the EURO- and MED-CORDEX initiative framework: present climate evaluations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vaittinada Ayar, Pradeebane; Vrac, Mathieu; Bastin, Sophie; Carreau, Julie; Déqué, Michel; Gallardo, Clemente</p> <p>2015-05-01</p> <p>Given the coarse spatial resolution of General Circulation Models, finer scale projections of variables affected by local-scale processes such as precipitation are often needed to drive impacts models, for example in hydrology or ecology among other fields. This need for high-resolution data leads to apply projection techniques called <span class="hlt">downscaling</span>. <span class="hlt">Downscaling</span> can be performed according to two approaches: dynamical and statistical models. The latter approach is constituted by various statistical families conceptually different. If several studies have made some intercomparisons of existing <span class="hlt">downscaling</span> models, none of them included all those families and approaches in a manner that all the models are equally considered. To this end, the present study conducts an intercomparison exercise under the EURO- and MED-CORDEX initiative hindcast framework. Six Statistical <span class="hlt">Downscaling</span> Models (SDMs) and five Regional Climate Models (RCMs) are compared in terms of precipitation outputs. The <span class="hlt">downscaled</span> simulations are driven by the ERAinterim reanalyses over the 1989-2008 period over a common area at 0.44° of resolution. The 11 models are evaluated according to four aspects of the precipitation: occurrence, intensity, as well as spatial and temporal properties. For each aspect, one or several indicators are computed to discriminate the models. The results indicate that marginal properties of rain occurrence and intensity are better modelled by stochastic and resampling-based SDMs, while spatial and temporal variability are better modelled by RCMs and resampling-based SDM. These general conclusions have to be considered with caution because they rely on the chosen indicators and could change when considering other specific criteria. The indicators suit specific purpose and therefore the model evaluation results depend on the end-users point of view and how they intend to use with model outputs. Nevertheless, building on previous intercomparison exercises, this study provides a consistent intercomparison framework, including both SDMs and RCMs, which is designed to be flexible, i.e., other models and indicators can easily be added. More generally, this framework provides a tool to select the <span class="hlt">downscaling</span> model to be used according to the statistical properties of the local-scale climate data to drive properly specific impact models.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4024107','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4024107"><span id="translatedtitle">ON THE CONVERGENCE OF THE <span class="hlt">ENSEMBLE</span> KALMAN FILTER</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Mandel, Jan; Cobb, Loren; Beezley, Jonathan D.</p> <p>2013-01-01</p> <p>Convergence of the <span class="hlt">ensemble</span> Kalman filter in the limit for large <span class="hlt">ensembles</span> to the Kalman filter is proved. In each step of the filter, convergence of the <span class="hlt">ensemble</span> sample covariance follows from a weak law of large numbers for exchangeable random variables, the continuous mapping theorem gives convergence in probability of the <span class="hlt">ensemble</span> members, and Lp bounds on the <span class="hlt">ensemble</span> then give Lp convergence. PMID:24843228</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.H41J..03B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.H41J..03B"><span id="translatedtitle"><span class="hlt">Ensemble</span> postprocessing for probabilistic quantitative precipitation forecasts</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bentzien, S.; Friederichs, P.</p> <p>2012-12-01</p> <p>Precipitation is one of the most difficult weather variables to predict in hydrometeorological applications. In order to assess the uncertainty inherent in deterministic numerical weather prediction (NWP), meteorological services around the globe develop <span class="hlt">ensemble</span> prediction systems (EPS) based on high-resolution NWP systems. With non-hydrostatic model dynamics and without parameterization of deep moist convection, high-resolution NWP models are able to describe convective processes in more detail and provide more realistic mesoscale structures. However, precipitation forecasts are still affected by displacement errors, systematic biases and fast error growth on small scales. Probabilistic guidance can be achieved from an <span class="hlt">ensemble</span> setup which accounts for model error and uncertainty of initial and boundary conditions. The German Meteorological Service (Deutscher Wetterdienst, DWD) provides such an <span class="hlt">ensemble</span> system based on the German-focused limited-area model COSMO-DE. With a horizontal grid-spacing of 2.8 km, COSMO-DE is the convection-permitting high-resolution part of the operational model chain at DWD. The COSMO-DE-EPS consists of 20 realizations of COSMO-DE, driven by initial and boundary conditions derived from 4 global models and 5 perturbations of model physics. <span class="hlt">Ensemble</span> systems like COSMO-DE-EPS are often limited with respect to <span class="hlt">ensemble</span> size due to the immense computational costs. As a consequence, they can be biased and exhibit insufficient <span class="hlt">ensemble</span> spread, and probabilistic forecasts may be not well calibrated. In this study, probabilistic quantitative precipitation forecasts are derived from COSMO-DE-EPS and evaluated at more than 1000 rain gauges located all over Germany. COSMO-DE-EPS is a frequently updated <span class="hlt">ensemble</span> system, initialized 8 times a day. We use the time-lagged approach to inexpensively increase <span class="hlt">ensemble</span> spread, which results in more reliable forecasts especially for extreme precipitation events. Moreover, we will show that statistical postprocessing can compensate deficiencies in calibration of biased and underdispersive <span class="hlt">ensemble</span> forecasts and should be considered as an integral part of an <span class="hlt">ensemble</span> prediction system. The relative gain in predictive skill is evaluated for logistic regression which provides well calibrated forecast for the probability of precipitation and threshold exceedance. Quantile regression is used to obtain skillfull probabilistic forecasts of extreme precipitation events. The selection of predictive covariates is done by penalized regression based on the least absolute shrinkage and selection operator (LASSO).</p> </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://www.sci.utah.edu/~kpotter/publications/potter-2009-EVSV.pdf','EPRINT'); return false;" href="http://www.sci.utah.edu/~kpotter/publications/potter-2009-EVSV.pdf"><span id="translatedtitle"><span class="hlt">Ensemble</span>-Vis: A Framework for the Statistical Visualization of <span class="hlt">Ensemble</span> Data</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Potter, Kristin</p> <p></p> <p>and multivalued over both space and time. Due to their complexity and size, <span class="hlt">ensembles</span> provide challenges in data to support the visual analysis of <span class="hlt">ensemble</span> data with a focus on the discovery and evaluation of simulation- tion runs. These outcomes have both quantitative aspects, such as the probability of freezing rain</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26326091','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26326091"><span id="translatedtitle">Illumination discrimination depends on scene surface <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>Krieger, Avery; Dubin, Hilary; Pearce, Bradley; Aston, Stacey; Hurlbert, Anya; Brainard, David; Radonjic, Ana</p> <p>2015-09-01</p> <p>The ability to discriminate between scenes under different illuminations may provide insight into how the visual system represents and perhaps discounts changes in illumination (Pearce et al. 2014). Here we examine whether there is an interaction between sensitivity to illumination changes in different chromatic directions and the chromaticity of the surfaces in the scene. Simulated stimulus scenes were rendered hyperspectrally using RenderToolbox3 and displayed stereoscopically. Each scene was specified as a room covered with rectangular, uniform matte surfaces of widely different colors. Across the three surface <span class="hlt">ensembles</span> used, the shape and position of surfaces was fixed but their assigned surface reflectance varied. Under simulated illuminant D67, one surface <span class="hlt">ensemble</span> was roughly 'neutral' in average chromaticity (mean xy: [0.32, 0.35]), while the other two were 'reddish-blue' ([0.36, 0.34]) and 'yellowish-green' ([0.39, 0.42]) relative to it. For each <span class="hlt">ensemble</span> we measured illumination discrimination thresholds along four different chromatic directions ('blue', 'yellow', 'red' and 'green') using a staircase procedure. The subjects viewed the target scene (simulated illuminant D67) and two comparison scenes - one identical to the target and another rendered under the test illuminant - and judged which of the comparison scenes matched the target. Varying average scene chromaticity had an effect on illumination discrimination thresholds, and that effect was different for different illuminant-change directions. Notably, thresholds for the 'blue' illumination-change direction were higher for the 'yellowish-green' surface <span class="hlt">ensemble</span> than for the 'neutral' (+5.6?E) and 'reddish-blue' (+6.1?E) <span class="hlt">ensembles</span>, while thresholds for the 'red' illumination change-direction were lowest for the 'reddish-blue' <span class="hlt">ensemble</span> (-3.7?E relative to the 'neutral'; -4.1?E relative to the 'yellowish-green' <span class="hlt">ensemble</span>). Our results show that characterization of illumination discrimination must take the scene surface <span class="hlt">ensemble</span> into account, and that the relative discriminability of illumination changes in different chromatic directions is influenced by the average chromaticity of the surface <span class="hlt">ensemble</span>. Meeting abstract presented at VSS 2015. PMID:26326091</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25927892','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25927892"><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=pubmed">PubMed</a></p> <p>Lindström, Tom; Tildesley, Michael; Webb, Colleen</p> <p>2015-04-01</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.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://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://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> </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://arxiv.org/pdf/1201.5213v1','EPRINT'); return false;" href="http://arxiv.org/pdf/1201.5213v1"><span id="translatedtitle">Nonequilibrium representative <span class="hlt">ensembles</span> for isolated quantum systems</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>V. I. Yukalov</p> <p>2012-01-25</p> <p>An isolated quantum system is considered, prepared in a nonequilibrium initial state. In order to uniquely define the system dynamics, one has to construct a representative statistical <span class="hlt">ensemble</span>. From the principle of least action it follows that the role of the evolution generator is played by a grand Hamiltonian, but not merely by its energy part. A theorem is proved expressing the commutators of field operators with operator products through variational derivatives of these products. A consequence of this theorem is the equivalence of the variational equations for field operators with the Heisenberg equations for the latter. A finite quantum system cannot equilibrate in the strict sense. But it can tend to a quasi-stationary state characterized by ergodic averages and the appropriate representative <span class="hlt">ensemble</span> depending on initial conditions. Microcanonical <span class="hlt">ensemble</span>, arising in the eigenstate thermalization, is just a particular case of representative <span class="hlt">ensembles</span>. Quasi-stationary representative <span class="hlt">ensembles</span> are defined by the principle of minimal information. The latter also implies the minimization of an effective thermodynamic potential.</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://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://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/2011AGUFM.H41C1049F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H41C1049F"><span id="translatedtitle">Developing a regional retrospective <span class="hlt">ensemble</span> precipitation dataset for watershed hydrology modeling, Idaho, USA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Flores, A. N.; Smith, K.; LaPorte, P.</p> <p>2011-12-01</p> <p>Applications like flood forecasting, military trafficability assessment, and slope stability analysis necessitate the use of models capable of resolving hydrologic states and fluxes at spatial scales of hillslopes (e.g., 10s to 100s m). These models typically require precipitation forcings at spatial scales of kilometers or better and time intervals of hours. Yet in especially rugged terrain that typifies much of the Western US and throughout much of the developing world, precipitation data at these spatiotemporal resolutions is difficult to come by. Ground-based weather radars have significant problems in high-relief settings and are sparsely located, leaving significant gaps in coverage and high uncertainties. Precipitation gages provide accurate data at points but are very sparsely located and their placement is often not representative, yielding significant coverage gaps in a spatial and physiographic sense. Numerical weather prediction efforts have made precipitation data, including critically important information on precipitation phase, available globally and in near real-time. However, these datasets present watershed modelers with two problems: (1) spatial scales of many of these datasets are tens of kilometers or coarser, (2) numerical weather models used to generate these datasets include a land surface parameterization that in some circumstances can significantly affect precipitation predictions. We report on the development of a regional precipitation dataset for Idaho that leverages: (1) a dataset derived from a numerical weather prediction model, (2) gages within Idaho that report hourly precipitation data, and (3) a long-term precipitation climatology dataset. Hourly precipitation estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA) are stochastically <span class="hlt">downscaled</span> using a hybrid orographic and statistical model from their native resolution (1/2 x 2/3 degrees) to a resolution of approximately 1 km. <span class="hlt">Downscaled</span> precipitation realizations are conditioned on hourly observations from reporting gages and then conditioned again on the Parameter-elevation Regressions on Independent Slopes Model (PRISM) at the monthly timescale to reflect orographic precipitation trends common to watersheds of the Western US. While this methodology potentially introduces cross-pollination of errors due to the re-use of precipitation gage data, it nevertheless achieves an <span class="hlt">ensemble</span>-based precipitation estimate and appropriate measures of uncertainty at a spatiotemporal resolution appropriate for watershed modeling.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003EAEJA.....4845S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003EAEJA.....4845S"><span id="translatedtitle">Discharge simulation using <span class="hlt">downscaled</span> spatial rainfall field by introducing correlation effect in random cascade method</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Shrestha, R. K.; Tachikawa, Y.; Takara, K.</p> <p>2003-04-01</p> <p>The simulation of spatial rainfall field based on non-homogenous random cascade method disaggregates a regionally averaged rainfall such as the GCM output. The cascade-generators are used to disaggregate and produce spatial patterns across the region (Over and Gupta, 1996; Chatchai et al. 2000; Tachikawa et al. 2003). However, the disaggregated data is rarely used to produce discharge by using distributed hydrological model. The hesitation to use disaggregated GCM data in discharge simulation is mainly due to lower reliability to reproduce spatial pattern and higher chance of magnitude fluctuation in a few trials of disaggregation. Long term disaggregation results, which are expected to produce true spatial pattern, may not be convenient for practical discharge simulation. A modified method is tested by keeping the volume balanced and forcing the location of cascade generators on the basis of spatial correlation of rainfall field with respect to surround regions. In this method, a reference matrix is prepared, which is calculated for every target grid by summing the multiplication of rainfall magnitude and spatial correlation coefficient of the respective reference grids. The reference matrix is used to adjust the location of random generator in two ways -- hierarchically and statistically. So, this method is designated as Hierarchical and Statistical Adjustment (HSA) method. The HSA method preserves the magnitude of random cascade generators but modifies the location. Unlike the previous non-homogenous random cascade method, this method produced similar spatial patterns as that of ground truth in every realization, which is a clear indication of improved reliability of the disaggregation method from coarse GCM output to a finer resolution as demanded by the hydrological model. The forced volume balance may be justified from the engineering aspect to maintain the same input quantity of rainfall in a watershed for hydrologic simulation purpose. The <span class="hlt">downscaled</span> data is used to calculate the discharge by using a macro scale distributed hydrological model and the effect of <span class="hlt">downscaling</span> is observed. The experiments are conducted on Huaihe River basin in China taking three sub-basins namely Bengbu (132,300 km^2), Wangjiaba (29,000 km^2) and Suiping (2000 km^2). The obtained results show a promising ability of using this technique in prediction of ungauged basins.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.4743J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.4743J"><span id="translatedtitle">Dynamically <span class="hlt">downscaling</span> wind storms over complex terrain with WRF: establishing the model performance and associated uncertainties</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>José Gómez-Navarro, Juan; Raible, Christoph C.</p> <p>2015-04-01</p> <p>This study aims at identifying a setup of the Weather Research and Forecasting (WRF) model that minimises systematic errors in hindcast simulations focused on the simulation of surface wind over complex topography. The existence of many options to configure this kind of simulation, e.g. the choice of PBL scheme, the nesting techniques or the number of vertical levels, leads to an important level of uncertainty that needs to be addressed prior the use of the <span class="hlt">downscaled</span> product. The sensitivity of the model performance to these factors is assessed in this study. To accomplish this evaluation, a number of sensitivity simulations reaching a spatial resolution of 2 km are carried out and compared to an observational dataset. Given the importance of wind storms, the analysis is based on case studies selected from 24 historical wind storms that caused great economic damage in Switzerland. These situations are <span class="hlt">downscaled</span> using a total of 9 different model setups, but sharing the same driving data set: Era Interim. The PBL schemes evaluated are selected with the aim of spanning a great part of the uncertainty space. The results show that the unresolved topography leads to a general overestimation of wind speed in WRF. However, this error can be substantially ameliorated by a suitable choice of the PBL scheme, which also yields an improvement of the spatial structure of wind speed. Wind direction, although generally well reproduced by the simulation, is not very sensitive to this choice and presents systematic errors that can not be reduced with a suitable model configuration. Further sensitivity tests are carried out aiming at identifying the role of three types of nesting: not nudging at all, re-forecast runs, analysis nudging and spectral nudging. Results indicate that restricting the freedom of the model to develop large-scale disturbances generally increases the temporal agreement with respect to the observations, although none of such techniques outperforms the others. Thus we conclude that nudging techniques are generally advisable when the simulation aims at reproducing real situations, where the temporal agreement is important. Finally, the necessary number of vertical levels is addressed. The analysis demonstrates that 40 vertical levels is a sensible choice, since experiments doubling the number of levels do not yield more reliable results, whereas it increases the computational cost.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/0912.2491v1','EPRINT'); return false;" href="http://arxiv.org/pdf/0912.2491v1"><span id="translatedtitle">Force balance in canonical <span class="hlt">ensembles</span> of static granular packings</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Brian P. Tighe; Thijs J. H. Vlugt</p> <p>2009-12-13</p> <p>We investigate the role of local force balance in the transition from a microcanonical <span class="hlt">ensemble</span> of static granular packings, characterized by an invariant stress, to a canonical <span class="hlt">ensemble</span>. Packings in two dimensions admit a reciprocal tiling, and a collective effect of force balance is that the area of this tiling is also invariant in a microcanonical <span class="hlt">ensemble</span>. We present analytical relations between stress, tiling area and tiling area fluctuations, and show that a canonical <span class="hlt">ensemble</span> can be characterized by an intensive thermodynamic parameter conjugate to one or the other. We test the equivalence of different <span class="hlt">ensembles</span> through the first canonical simulations of the force network <span class="hlt">ensemble</span>, a model system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22365877','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22365877"><span id="translatedtitle">On large deviations for <span class="hlt">ensembles</span> of distributions</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Khrychev, D A</p> <p>2013-11-30</p> <p>The paper is concerned with the large deviations problem in the Freidlin-Wentzell formulation without the assumption of the uniqueness of the solution to the equation involving white noise. In other words, it is assumed that for each ?>0 the nonempty set P{sub ?} of weak solutions is not necessarily a singleton. Analogues of a number of concepts in the theory of large deviations are introduced for the set (P{sub ?}, ?>0), hereafter referred to as an <span class="hlt">ensemble</span> of distributions. The <span class="hlt">ensembles</span> of weak solutions of an n-dimensional stochastic Navier-Stokes system and stochastic wave equation with power-law nonlinearity are shown to be uniformly exponentially tight. An idempotent Wiener process in a Hilbert space and idempotent partial differential equations are defined. The accumulation points in the sense of large deviations of the <span class="hlt">ensembles</span> in question are shown to be weak solutions of the corresponding idempotent equations. Bibliography: 14 titles.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1111.6306.pdf','EPRINT'); return false;" href="http://arxiv.org/pdf/1111.6306.pdf"><span id="translatedtitle">Control and Synchronization of Neuron <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Li, Jr-Shin; Ruths, Justin</p> <p>2011-01-01</p> <p>Synchronization of oscillations is a phenomenon prevalent in natural, social, and engineering systems. Controlling synchronization of oscillating systems is motivated by a wide range of applications from neurological treatment of Parkinson's disease to the design of neurocomputers. In this article, we study the control of an <span class="hlt">ensemble</span> of uncoupled neuron oscillators described by phase models. We examine controllability of such a neuron <span class="hlt">ensemble</span> for various phase models and, furthermore, study the related optimal control problems. In particular, by employing Pontryagin's maximum principle, we analytically derive optimal controls for spiking single- and two-neuron systems, and analyze the applicability of the latter to an <span class="hlt">ensemble</span> system. Finally, we present a robust computational method for optimal control of spiking neurons based on pseudospectral approximations. The methodology developed here is universal to the control of general nonlinear phase oscillators.</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/2015ThApC.tmp..108A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.tmp..108A"><span id="translatedtitle">Best convective parameterization scheme within RegCM4 to <span class="hlt">downscale</span> CMIP5 multi-model data for the CORDEX-MENA/Arab domain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Almazroui, Mansour; Islam, Md. Nazrul; Al-Khalaf, A. K.; Saeed, Fahad</p> <p>2015-04-01</p> <p>A suitable convective parameterization scheme within Regional Climate Model version 4.3.4 (RegCM4) developed by the Abdus Salam International Centre for Theoretical Physics, Trieste, Italy, is investigated through 12 sensitivity runs for the period 2000-2010. RegCM4 is driven with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim 6-hourly boundary condition fields for the CORDEX-MENA/Arab domain. Besides ERA-Interim lateral boundary conditions data, the Climatic Research Unit (CRU) data is also used to assess the performance of RegCM4. Different statistical measures are taken into consideration in assessing model performance for 11 sub-domains throughout the analysis domain, out of which 7 (4) sub-domains give drier (wetter) conditions for the area of interest. There is no common best option for the simulation of both rainfall and temperature (with lowest bias); however, one option each for temperature and rainfall has been found to be superior among the 12 options investigated in this study. These best options for the two variables vary from region to region as well. Overall, RegCM4 simulates large pressure and water vapor values along with lower wind speeds compared to the driving fields, which are the key sources of bias in simulating rainfall and temperature. Based on the climatic characteristics of most of the Arab countries located within the study domain, the drier sub-domains are given priority in the selection of a suitable convective scheme, albeit with a compromise for both rainfall and temperature simulations. The most suitable option Grell over Land and Emanuel over Ocean in wet (GLEO wet) delivers a rainfall wet bias of 2.96 % and a temperature cold bias of 0.26 °C, compared to CRU data. An <span class="hlt">ensemble</span> derived from all 12 runs provides unsatisfactory results for rainfall (28.92 %) and temperature (-0.54 °C) bias in the drier region because some options highly overestimate rainfall (reaching up to 200 %) and underestimate temperature (reaching up to -1.16 °C). Overall, a suitable option (GLEO wet) is recommended for <span class="hlt">downscaling</span> the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model database using RegCM4 for the CORDEX-MENA/Arab domain for its use in future climate change impact studies.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.A23C0182H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.A23C0182H"><span id="translatedtitle">Future regional climate assessment for Southwest U.S. summer monsoon region using dynamically <span class="hlt">downscaled</span> IPCC scenarios</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hsin-I, C.; Castro, C. L.; Carrillo, C.; Dominguez, F.; Rivera, E. R.</p> <p>2011-12-01</p> <p>Future climate projection in Southwest U.S., specifically precipitation, is becoming an important topic for policy makers. Current large scale projections from the Intergovernmental Panel on Climate Change (IPCC) global climate models (GCMs) are in good agreement with respect to temperature increase, however, they have generally a poor climatological representation of precipitation in the Southwest U.S. The problem is particularly acute in the warm season, when terrain-forced monsoon thunderstorms are the dominant source of precipitation. To enhance the ability of simulating the warm season precipitation over Southwest, U.S., a regional climate model (RCM) is used to dynamically <span class="hlt">downscale</span> select IPCC climate scenario forcing data. In addition to changes in the mean climate, we also have great interest in the frequency and intensity of future extreme precipitation events. Severe thunderstorms during the warm season in Southwest U.S. are highly associated with the North American monsoon (NAM). IPCC GCMs generally project warmer and drier future conditions in arid to semi-arid regions. Also, observational evidence shows that there have already been significant increases in precipitation intensity worldwide and in U.S. We hypothesize that the summer climate of the Southwest in the future will have more intense droughts and heavy rainfall events. . Two "well performing" IPCC AR4 models (MPI-ECHAM5 and HADcm3) were selected for dynamical <span class="hlt">downscaling</span>, based on their performances over Southwest U.S., for dynamical <span class="hlt">downscaling</span> over the contiguous U.S. and Mexico. The Weather Research and Forecast (WRF) model is used as the regional climate model to generate continuous historical and climate change projection information for the approximate period from mid-20th century to end of 21st century. Preliminary analyses show improvements in the climatological representation of the model simulated summer monsoon rainfall in the Southwest for the 20th century. There is also, a reasonable characterization of the interannual variability in model simulated rainfall in relation to Pacific sea surface temperature forcing (i.e. El Niño and Pacific Decadal Variability). Although both IPCC-<span class="hlt">downscaled</span> WRF scenarios improve the climatological representation of the monsoon, the departure of 21st century Southwest monsoon precipitation is not consistent (WRF-MPI: drying future, WRF-HADcm3: wetter future). Our objective is to answer the following questions: (1) what is the driving force for future climate under both dynamically <span class="hlt">downscaled</span> IPCC scenarios? (2) do the driving mechanisms of natural climate variability change in the future?(3) what are the characteristics of extreme precipitation events associated with the wet/dry 21st century regional model climate projections?</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMGC11C1008P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMGC11C1008P"><span id="translatedtitle">A Method for <span class="hlt">Downscaling</span> Regional Climate Model Projections of Temperature and Precipitation Using Local Topographic Lapse Rates</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Praskievicz, S. J.; Bartlein, P. J.</p> <p>2012-12-01</p> <p>One of the major challenges of climate change impact analysis is the development of appropriate approaches for <span class="hlt">downscaling</span> climate change scenarios. Output from regional climate models (RCMs) using general circulation model (GCM) output as boundary conditions provides dynamically <span class="hlt">downscaled</span> scenarios, but the resolution of RCMs, while significantly less coarse than that of GCMs, is still often in the range of tens of kilometers. In mountainous regions, this resolution results in a smoothed topography and, consequently, inaccurate simulation of orographic effects on temperature and precipitation. In such regions, the simulated present and future climate is not necessarily well-represented by RCMs, which can bias impact analysis results. One approach for dealing with this bias is to adjust the model output to the real-world topography of a region, which requires the estimation of local topographic lapse rates for temperature and precipitation. Here we use the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset, which consists of monthly temperature and precipitation for the contiguous United States on a 30-arc second (approximately 4-km) grid, based on regressions of observed climate station data against elevation, slope, aspect, and other topographic variables. We used the PRISM data to calculate local monthly topographic lapse rates for maximum and minimum temperature and precipitation for the northwestern United States through singular value decomposition regression of elevation against climatic variables within a neighborhood of grid cells. The resulting lapse rates show climatologically reasonable spatial patterns such as winter temperature inversions in valleys, positive summer temperature lapse rates in the coastal zone, and high positive precipitation lapse rates on the windward side of mountain ranges. We applied these lapse rates as a topographic correction of RCM output from the North American Regional Reanalysis (NARR) to produce high-resolution grids of temperature and precipitation for the NARR period and compared the resulting <span class="hlt">downscaled</span> timeseries to station data. Skill scores indicate that this lapse rate-based approach performs well relative to simple interpolations of the NARR data to station locations or to average climatology or persistence. We then applied the lapse rate-based <span class="hlt">downscaling</span> to RCM projections of future climate change from the North American Regional Climate Change Assessment Program (NARCCAP). The local topographic lapse rate <span class="hlt">downscaling</span> approach can produce high-resolution, topographically corrected climate change scenarios for use in hydrological modeling or other applications. The method is especially useful in mountainous regions, in which RCMs have difficulty resolving steep local gradients of temperature and precipitation resulting from topography.</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://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://adsabs.harvard.edu/abs/2012NJPh...14c3034S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012NJPh...14c3034S"><span id="translatedtitle">Beyond pure state entanglement for atomic <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>Stasi?ska, Julia; Paganelli, Simone; Sanpera, Anna</p> <p>2012-03-01</p> <p>We analyze multipartite entanglement between atomic <span class="hlt">ensembles</span> within quantum matter-light interfaces. In our proposal, a polarized light beam crosses sequentially several polarized atomic <span class="hlt">ensembles</span> impinging on each of them at a given angle ?i. These angles are crucial parameters for shaping the entanglement since they are directly connected to the appropriate combinations of the collective atomic spins that are squeezed. We exploit such a scheme to go beyond the pure state paradigm proposing realistic experimental settings to address multipartite mixed state entanglement in continuous variables.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/182818','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/182818"><span id="translatedtitle">Effect of increasing greenhouse gases on Indian monsoon rainfall as <span class="hlt">downscaled</span> from the ECHAM coupled model</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Singh, S.V.; Storch, H.V.</p> <p>1994-12-31</p> <p>It is more or less accepted that the increasing anthropogenic gases will result in global warming through the greenhouse effect. The major influence of this will be felt in the form of ice melts and rising sea levels. The influence on regional climates like monsoons is not very clear. Since the monsoons arise due to surface heating, one would expect that global warming will lead to more vigorous monsoons. The expected change in a climate parameter can be studied by analyzing the historical data and then extrapolating in time. Alternatively, one can use the state-of-the-art coupled GCMs which are able to simulate the earth`s climate with reasonable accuracy. Both methods have some limitations. The first method cannot adequately consider the nonlinearity, and the second method may not be efficient for regional scales. So that the projections can be trusted, the regional features should be well simulated. None of the current models are able to simulate the Indian monsoon satisfactorily. Therefore it is desirable to infer the expected change in monsoons from other large and near global scale features which are better simulated. This approach, which depends on the concurrent association between a large-scale modeled feature and a regional scale, is known as <span class="hlt">downscaling</span>, after Storch et al., and is adopted here to project the Indian monsoon rainfall for the next 100 years from the ECHAM T21 coupled model.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014WRR....50..540B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014WRR....50..540B"><span id="translatedtitle">Stochastic <span class="hlt">downscaling</span> of precipitation to high-resolution scenarios in orographically complex regions: 1. Model evaluation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bordoy, R.; Burlando, P.</p> <p>2014-01-01</p> <p>The simulation of space-time precipitation has been studied since the late 1980s. However, there are still many open issues concerning the most appropriate approach to simulate it, specially in highly heterogeneous areas, such as in mountain environments. For this reason, we present here a comprehensive investigation of the Space-Time Neyman-Scott Rectangular Pulses model, with the purpose of analyzing its performance in a challenging Alpine environment of Switzerland and identifying weaknesses that can drive future improvements. The results point at the suitability of the model in reproducing not only the basic statistics at different temporal aggregations, but also the more challenging distributional and scaling properties. The intrinsic stationarity of the model in space, induced by the parameter estimation procedure, poses occasional limitations with regard to the accurate simulation of the variability of the observed climate characteristics, which are strongly influenced by local microclimates. However, the model is able, even in the complex Alpine environment, to preserve the spatial patterns observed in the actual precipitation process. The study allowed (i) to conclude about the robustness of the model and its suitability for multisite <span class="hlt">downscaling</span> of precipitation estimated from climate model simulations, as reported in the companion paper, and (ii) to put in evidence some limitations that require further consideration to improve space-time rainfall generation.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014FrES....8..457L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014FrES....8..457L"><span id="translatedtitle">Regional climate model <span class="hlt">downscaling</span> may improve the prediction of alien plant species distributions</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, Shuyan; Liang, Xin-Zhong; Gao, Wei; Stohlgren, Thomas J.</p> <p>2014-12-01</p> <p>Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a <span class="hlt">downscaled</span> Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990-1999) to the future (2046-2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa ( Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/25220795','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/25220795"><span id="translatedtitle">The evolution of <span class="hlt">down-scale</span> virus filtration equipment for virus clearance studies.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wieser, Andreas; Berting, Andreas; Medek, Christian; Poelsler, Gerhard; Kreil, Thomas R</p> <p>2015-03-01</p> <p>The role of virus filtration in assuring the safety of biopharmaceutical products has gained importance in recent years. This is due to the fundamental advantages of virus filtration, which conceptually can remove all pathogens as long as their size is larger than the biomolecule of commercial interest, while at the same time being neutral to the biological activity of biopharmaceutical compound(s). Major progress has been made in the development of adequate filtration membranes that can remove even smaller viruses, or possibly even all. Establishing <span class="hlt">down-scaled</span> models for virus clearance studies that are fully equivalent with respect to operating parameters at manufacturing scale is a continuing challenge. This is especially true for virus filtration procedures where virus clearance studies at small-scale determine the operating parameters, which can be used at manufacturing scale. This has limited volume-to-filter-area-ratios, with significant impact on process economics. An advanced small-scale model of virus filtration, which allows the investigation of the full complexity of these processes, is described here. It includes the automated monitoring and control of all process parameters, as well as an electronic data acquisition system, which is fully compliant with current regulatory requirements for electronic records in a pharmaceutical environment. PMID:25220795</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24817359','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24817359"><span id="translatedtitle">Effect of <span class="hlt">downscaling</span> on the linearity range of a calibration curve in spectrofluorimetry.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kwapiszewski, Radoslaw; Szczudlowska, Justyna; Kwapiszewska, Karina; Dybko, Artur; Brzozka, Zbigniew</p> <p>2014-07-01</p> <p>Interest in the microfluidic environment, owing to its unique physical properties, is increasing in much innovative chemical, biological, or medicinal research. The possibility of exploiting and using new phenomena makes the microscale a powerful tool to improve currently used macroscopic methods and approaches. Previously, we reported that an increase in the surface area to volume ratio of a measuring cell could provide a wider linear range for fluorescein (Kwapiszewski et al., Anal. Bioanal. Chem. 403:151-155, 2012). Here, we present a broader study in this field to confirm the assumptions we presented before. We studied fluorophores with a large and a small Stokes shift using a standard cuvette and fabricated microfluidic detection cells having different surface area to volume ratios. We analyzed the effect of different configurations of the detection cell on the measured fluorescence signal. We also took into consideration the effect of concentration on the emission spectrum, and the effect of the surface area to volume ratio on the limit of linearity of the response of the selected fluorophores. We observed that <span class="hlt">downscaling</span>, leading to an increase in the probability of collisions between molecules and cell walls with no energy transfer, results in an increase in the limit of linearity of the calibration curve of fluorophores. The results obtained suggest that microfluidic systems can be an alternative to the currently used approaches for widening the linearity of a calibration curve. Therefore, microsystems can be useful for studies of optically dense samples and samples that should not be diluted. PMID:24817359</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JGRD..11913651S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JGRD..11913651S"><span id="translatedtitle">High-resolution surface analysis for extended-range <span class="hlt">downscaling</span> with limited-area atmospheric models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Separovic, Leo; Husain, Syed Zahid; Yu, Wei; Fernig, David</p> <p>2014-12-01</p> <p>High-resolution limited-area model (LAM) simulations are frequently employed to <span class="hlt">downscale</span> coarse-resolution objective analyses over a specified area of the globe using high-resolution computational grids. When LAMs are integrated over extended time frames, from months to years, they are prone to deviations in land surface variables that can be harmful to the quality of the simulated near-surface fields. Nudging of the prognostic surface fields toward a reference-gridded data set is therefore devised in order to prevent the atmospheric model from diverging from the expected values. This paper presents a method to generate high-resolution analyses of land-surface variables, such as surface canopy temperature, soil moisture, and snow conditions, to be used for the relaxation of lower boundary conditions in extended-range LAM simulations. The proposed method is based on performing offline simulations with an external surface model, forced with the near-surface meteorological fields derived from short-range forecast, operational analyses, and observed temperatures and humidity. Results show that the outputs of the surface model obtained in the present study have potential to improve the near-surface atmospheric fields in extended-range LAM integrations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.B31B0389H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.B31B0389H"><span id="translatedtitle">Upscaling and <span class="hlt">downscaling</span> of greenhouse gas fluxes in the Alaskan Arctic</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hahn, M. S.; Billesbach, D. P.; Chafe, O.; Siegrist, J.; Torn, M. S.</p> <p>2013-12-01</p> <p>The effects of climate change in the Arctic are of global concern due to the large stores of carbon in the permafrost soils. Forecasts for these ecosystems contain large uncertainties due in part to the highly heterogeneous landscape, which even impedes current understanding of greenhouse gas fluxes and their controls. Before climate change effects can be predicted accurately, we must have a better understanding of current conditions. Extreme conditions and sparse infrastructure make studying Arctic processes difficult. As a result, much of what we know about Arctic trace gas exchange comes only from a sparse set of chamber measurements and an even sparser set of eddy flux towers. Large scale arctic greenhouse gas fluxes are estimated primarily through upscaling these measurements. In this study, we explored scaling bias and uncertainty through upscaling chamber, and <span class="hlt">downscaling</span> eddy covariance carbon dioxide and methane flux measurements. ` We conducted fieldwork at the Barrow Environmental Observatory (BEO), Barrow, Alaska, as part of the U.S. DOE Next Generation Ecosystem Experiments (NGEE-Arctic). Several different types of microtopographical features are present on site. These landforms influence many environmental processes including those affecting carbon exchange. At several times over the growing season, we collected chamber flux measurements for all the landscape features near our eddy covariance flux tower. We evaluated scaling uncertainty and bias based on the landscape composition in the flux footprint. This work may allow us to identify under what conditions scaling uncertainty is highest and may also help inform future sampling strategies aimed at reducing these uncertainties.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..17.2067B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.2067B"><span id="translatedtitle">Analysis of climate projections for the Carpathian Region using dynamical <span class="hlt">downscaling</span></span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bartholy, Judit; Pongracz, Rita; Pieczka, Ildiko; Andre, Karolina</p> <p>2015-04-01</p> <p>Hungarian national climate and adaptation strategies have been recently revised, and a National Adaptation Geo-information System (NAGIS) is currently under development. This platform will serve as a central data collection for various end-users, impact researchers, and decision makers on national level in Hungary. In order to satisfy the demands for climate projection inputs within this framework, RegCM4.3 is one of the regional climate models used to provide results for detailed regional scale analysis and specific impact studies. RegCM is a 3-dimensional, sigma-coordinate, primitive equation model, originally developed by Giorgi et al. Currently, it is available from the ICTP (Abdus Salam International Centre for Theoretical Physics). We have already completed experiments with 50 km horizontal resolution covering both the second half of the past century (1951-2005), and the future (i.e., the 21st century, 2006-2100) using HadGEM2 global model outputs as initial and lateral boundary conditions. The outputs of the 50 km runs drive the further <span class="hlt">downscaling</span> experiments using 10 km as a horizontal resolution for a smaller domain covering Central Europe with special focus on the Carpathian Region. For the future, RCP4.5 scenario run is analysed in this poster, and moreover, preliminary results of the RCP8.5 scenario run are also presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010avh..confE..24S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010avh..confE..24S"><span id="translatedtitle">Future Temperatures and Precipitations in the Arid Northern-Central Chile: A Multi-Model <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>Souvignet, M.; Heinrich, J.</p> <p>2010-03-01</p> <p><span class="hlt">Downscaling</span> of global climate outputs is necessary to transfer projections of potential climate change scenarios to local levels. This is of special interest to dry mountainous areas, which are particularly vulnerable to climate change due to risks of reduced freshwater availability. These areas play a key role for hydrology since they usually receive the highest local precipitation rates stored in form of snow and glaciers. In the central-northern Chile (Norte Chico, 26-33ºS), where agriculture still serves as a backbone of the economy as well as ensures the well being of people, the knowledge of water resources availability is essential. The region is characterised by a semiarid climate with a mean annual precipitation inferior to 100mm. Moreover, the local climate is also highly influenced by the ENSO phenomenon, which accounts for the strong inter-annual variability in precipitation patterns. Although historical and spatially extensive precipitation data in the headwaters of the basins in this region are not readily available, records at coastal stations show worrisome trends. For instance, the average precipitation in La Serena, the most important city located in the Coquimbo Region, has decreased dramatically in the past 100 years. The 30-year monthly average has decreased from 170 mm in the early 20th century to values less than 80 mm nowadays. Climate Change is expected to strengthen this pattern in the region, and therefore strongly influence local hydrological patterns. The objectives of this study are i) to develop climate change scenarios (2046-2099) for the Norte Chico using multi-model predictions in terms of temperatures and precipitations, and ii) to compare the efficiency of two <span class="hlt">downscaling</span> techniques in arid mountainous regions. In addition, this study aims at iii) providing decision makers with sound analysis of potential impact of Climate Change on streamflow in the region. For the present study, future local climate scenarios were developed for maximum, minimum temperature and precipitation in the research area based on four different General Circulation Models (GCMs). On the first hand, the Statistical <span class="hlt">Downscaling</span> Model (SDSM) was used. This model is based on a multiple linear regression method and is best described as a hybrid of the stochastic weather generator and transfer function methods. One common advantage of statistical <span class="hlt">downscaling</span> is that it ensures the maintenance of local spatial and temporal variability in generating realistic data time series. On the other hand and for comparison purposes, the Change Factor method was used. This methodology is relatively straightforward and ideal for rapid climate change assessment. The outputs of the HadCM3, CGCM3.1, GDFL-CM2 and MRI-CGCM2.3.2 A1 and B2 scenarios were <span class="hlt">downscaled</span> with both methodologies and thereafter compared by means of several hydro-meteorological indices for a 55-years period (2045-2099). Preliminary results indicate that local temperatures are expected to rise in the region, whereas precipitations may decrease. However, minimum and maximum temperatures might increase at a faster rate at higher altitude areas. In addition, the Cordillera mountain range may encounter and longer winters with a dramatic decrease of icing days (Tmax<0°C). As for precipitation, both SRES scenarios for all models return a diminishing tendency, though the A2 scenario results show a faster decrease rate. Results indicate potential strong inter-seasonal and inter-annual perturbations in Rainfall in the region. Consequently, the Norte Chico will possibly see its streamflow strongly impacted with a resulting high variability at the seasonal and inter-annual level. A probabilistic analysis of the projections of the four GCMs provided a better representation of uncertainties linked with <span class="hlt">downscaled</span> scenarios. Whereas maximum and minimum temperatures were accurately simulated by both <span class="hlt">downscaling</span> methods, precipitation simulations returned weaker results. SDSM proved to have a poor ability to simulate extreme rainfall events and few conclusions could be draw</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.cs.bham.ac.uk/~wbl/biblio/cache/bin/cache.php?Hengpraprohm:thesis,http___home.npru.ac.th_supoj_research_FullReport_4771832221-MyThesis.pdf,http://home.npru.ac.th/supoj/research/FullReport/4771832221-MyThesis.pdf','EPRINT'); return false;" href="http://www.cs.bham.ac.uk/~wbl/biblio/cache/bin/cache.php?Hengpraprohm:thesis,http___home.npru.ac.th_supoj_research_FullReport_4771832221-MyThesis.pdf,http://home.npru.ac.th/supoj/research/FullReport/4771832221-MyThesis.pdf"><span id="translatedtitle"><span class="hlt">ENSEMBLE</span> GENETIC PROGRAMMING CLASSIFIER FOR MICROARRAY DATA Mr. Supoj Hengpraprohm</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Fernandez, Thomas</p> <p></p> <p>COMPUTER ENGINEERING KEYWORDS : CLASSIFICATION / GENETIC PROGRAMMING / <span class="hlt">ENSEMBLE</span> METHOD / MICROARRAY DATA 2551 #12;<span class="hlt">ENSEMBLE</span> GENETIC PROGRAMMING CLASSIFIER FOR MICROARRAY DATA Mr. Supoj of Philosophy Program in Computer Engineering Department of Computer Engineering Faculty of Engineering</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.cs.kent.ac.uk/pubs/2006/2905/content.pdf','EPRINT'); return false;" href="http://www.cs.kent.ac.uk/pubs/2006/2905/content.pdf"><span id="translatedtitle">Time-Space <span class="hlt">Ensemble</span> Strategies for Automatic Music Genre Classification</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Kent, University of</p> <p></p> <p>Time-Space <span class="hlt">Ensemble</span> Strategies for Automatic Music Genre Classification Carlos N. Silla Jr., Celso {silla,kaestner,alekoe}@ppgia.pucpr.br Abstract. In this paper we propose a novel time­space <span class="hlt">ensemble</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.stat.washington.edu/raftery/Research/PDF/Berrocal2007.pdf','EPRINT'); return false;" href="http://www.stat.washington.edu/raftery/Research/PDF/Berrocal2007.pdf"><span id="translatedtitle">Combining Spatial Statistical and <span class="hlt">Ensemble</span> Information in Probabilistic Weather Forecasts</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Raftery, Adrian</p> <p></p> <p>Combining Spatial Statistical and <span class="hlt">Ensemble</span> Information in Probabilistic Weather Forecasts VERONICA <span class="hlt">ensembles</span> that generates calibrated probabilistic forecast products for weather quantities at indi- vidual perturbation (GOP) method, and extends BMA to generate calibrated probabilistic forecasts of whole weather</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.bcri.ucc.ie/FILES/PUBS/BCRI_65.pdf','EPRINT'); return false;" href="http://www.bcri.ucc.ie/FILES/PUBS/BCRI_65.pdf"><span id="translatedtitle">Modelling macroeconomic flows related to large <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Schellekens, Michel P.</p> <p></p> <p>Modelling macroeconomic flows related to large <span class="hlt">ensembles</span> of elementary exchange operations A) . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3 More general macro-economic systems (Question Q3 is this paper about? We begin with a very informal discussion. An important goal of mathemat- ical (macro)economics</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://theorycenter.cs.uchicago.edu/REU/2014/presentations/chen.pdf','EPRINT'); return false;" href="http://theorycenter.cs.uchicago.edu/REU/2014/presentations/chen.pdf"><span id="translatedtitle">Learning <span class="hlt">Ensembles</span> of Convolutional Neural Networks</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>May, J. Peter</p> <p></p> <p>Learning <span class="hlt">Ensembles</span> of Convolutional Neural Networks Liran Chen! Faculty mentor: Greg Shakhnarovich systems. ! The database contains 60,000 training images and 10,000 testing images. #12;Convolutional Neural Network ·Inspired by biological processes! ! ·A type of feed-forward artificial neural network</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://cadswes.colorado.edu/sites/default/files/PDF/Theses-PhD/Caraway_Thesis-2012.pdf','EPRINT'); return false;" href="http://cadswes.colorado.edu/sites/default/files/PDF/Theses-PhD/Caraway_Thesis-2012.pdf"><span id="translatedtitle">Stochastic Weather Generator Based <span class="hlt">Ensemble</span> Streamflow Forecasting</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p>model's current initial conditions, to produce <span class="hlt">ensemble</span> streamflow. There are two major drawbacks on the previous day's precipitation state and weather vector and current day's precipitation state. A K weather sequences at 66 locations in the San Juan River Basin. The proposed method generates a rich</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://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://www.bioinf.uni-freiburg.de/Publications/Schmiedl:etal:_exparnap:RECOMB2012.pdf','EPRINT'); return false;" href="http://www.bioinf.uni-freiburg.de/Publications/Schmiedl:etal:_exparnap:RECOMB2012.pdf"><span id="translatedtitle">Exact Pattern Matching for RNA Structure <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Will, Sebastian</p> <p></p> <p>, Institute of Computer Science, Albert-Ludwigs-Universit¨at, Freiburg, Germany, {will, USA 5 Center for Biological Signaling Studies (BIOSS), Albert-Ludwigs-Universit¨at, Freiburg, Germany such sets of exact matchings in entire Boltzmann-distributed struc- ture <span class="hlt">ensembles</span> of two RNAs. Due</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.cs.bham.ac.uk/~xin/papers/ChandraYao_IDEAL04.pdf','EPRINT'); return false;" href="http://www.cs.bham.ac.uk/~xin/papers/ChandraYao_IDEAL04.pdf"><span id="translatedtitle">DIVACE: Diverse and Accurate <span class="hlt">Ensemble</span> Learning Algorithm</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Yao, Xin</p> <p></p> <p>systems in recent years. Liu and Yao [8] proposed the Negative Correlation Learning (NCL) algorithm approach which is inspired by the MPANN and NCL algorithms but has a few differences. The idea and that of accuracy in the <span class="hlt">ensemble</span> context. Then, certain aspects of MPANN and NCL algorithms, which are the main</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://www.ima.umn.edu/preprints/pp2006/2103.pdf','EPRINT'); return false;" href="http://www.ima.umn.edu/preprints/pp2006/2103.pdf"><span id="translatedtitle">STATISTICAL CHARACTERIZATION OF PROTEIN <span class="hlt">ENSEMBLES</span> Diego Rother</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p>framework and apply it to artificial data and protein <span class="hlt">ensembles</span> obtained from molecular dynamics simulations in its determination by the (imperfect) measurement of some property (i.e., diffraction, magnetic resonance, etc.), also produces variability, since those methods generally optimize a model to fit</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> </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://matrix.seas.ucla.edu/books/7.11-Ghoniem.pdf','EPRINT'); return false;" href="http://matrix.seas.ucla.edu/books/7.11-Ghoniem.pdf"><span id="translatedtitle">MODELING THE DYNAMICS OF DISLOCATION <span class="hlt">ENSEMBLES</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Ghoniem, Nasr M.</p> <p></p> <p>7.11 MODELING THE DYNAMICS OF DISLOCATION <span class="hlt">ENSEMBLES</span> Nasr M. Ghoniem Department of Mechanical with definitive wavelengths. It is common to observe persistent slip bands (PSBs), shear bands, dislocation pile ups, dislocation cells and sub grains. However, a satisfactory description of realistic dislocation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=developing+AND+listening+AND+skills&pg=4&id=EJ744386','ERIC'); return false;" href="http://eric.ed.gov/?q=developing+AND+listening+AND+skills&pg=4&id=EJ744386"><span id="translatedtitle">Developing Musical Listening in Performance <span class="hlt">Ensemble</span> 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>Zerull, David S.</p> <p>2006-01-01</p> <p>In this article, the author contends that in order to fulfill music education's purpose of developing a child's capacity to make and experience music, attention must be paid to developing listening in performance <span class="hlt">ensemble</span> classes. Music educators cannot assume that being involved in the refinement and performance of music contributes to becoming a…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.jucs.org/jucs_19_14/ensemble_an_elearning_framework/jucs_19_14_2127_2149_queiros.ps','EPRINT'); return false;" href="http://www.jucs.org/jucs_19_14/ensemble_an_elearning_framework/jucs_19_14_2127_2149_queiros.ps"><span id="translatedtitle"><span class="hlt">Ensemble</span> an ELearning Framework Ricardo Queiros</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p><span class="hlt">Ensemble</span> ­ an E­Learning Framework Ricardo Queirâ??os (CRACS & INESC­Porto LA & DI­ESEIG/IPP Porto, University of Porto Porto, Portugal zp@dcc.fc.up.pt) Abstract: E­Learning frameworks are conceptual tools to organize networks of e­ learning services. Most frameworks cover areas that go beyond the scope of e­learning</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.inference.eng.cam.ac.uk/jwm1003/thesis.ps.gz','EPRINT'); return false;" href="http://www.inference.eng.cam.ac.uk/jwm1003/thesis.ps.gz"><span id="translatedtitle"><span class="hlt">Ensemble</span> Learning for Independent Component Analysis</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>MacKay, David J.C.</p> <p></p> <p><span class="hlt">Ensemble</span> Learning for Independent Component Analysis James W. Miskin Selwyn College Cambridge: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . James W. Miskin Selwyn College Cambridge December 20th, 2000 i #12; Abstract This thesis is concerned A dissertation submitted in candidature for the degree of Doctor of Philosophy, University of Cambridge</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://people.fas.harvard.edu/~lucas/Pollen%20Grains.pdf','EPRINT'); return false;" href="http://people.fas.harvard.edu/~lucas/Pollen%20Grains.pdf"><span id="translatedtitle">statistical physics canonical <span class="hlt">ensemble</span> Pollen Grains</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p>statistical physics canonical <span class="hlt">ensemble</span> Pollen Grains If a pollen grain is placed on the surface of squares of side length b. The pollen grain is a square of side length B b. Ignore the kientic energy wall of the pollen grain. Assume that the surface of the liquid is a square of length L, with L B, b</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/2014EGUGA..16.1103H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.1103H"><span id="translatedtitle">Partitioning internal variability and model uncertainty components in a multireplicate multimodel <span class="hlt">ensemble</span> of hydrometeorological future projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hingray, Benoit; Saïd, Mériem; Lafaysse, Matthieu; Gailhlard, Joël; Mezghani, Abdelkader</p> <p>2014-05-01</p> <p>A simple and robust framework was proposed by Hingray and Mériem (2013) for the partitioning of the different components of internal variability and model uncertainty in a multireplicate multimodel <span class="hlt">ensemble</span> (MRMME) of climate projections obtained for a suite of statistical <span class="hlt">downscaling</span> models (SDMs) and global climate models (GCMs). It is based on the quasi-ergodic assumption for transient climate simulations. Model uncertainty components are estimated from the noise-free signals of each modeling chain using a two-way ANOVA framework. The residuals from the noise-free signal are used to estimate the large and small scale internal variability (IV) components associated with each considered GCM/SDM configuration. This framework makes it possible to take into account all runs and replicates available from any climate <span class="hlt">ensemble</span> of opportunity. This quasi-ergodic ANOVA framework was applied to the MRMME of hydrometeorological simulations produced for the Upper Durance River basin (French Alps) over the 1860-2100 period within the RIWER2030 research project (http://www.lthe.fr/RIWER2030/). The different uncertainty sources were quantified as a function of lead time for projected changes in temperature, precipitation, evaporation losses, snow cover and discharges (Lafaysse et al., 2013). For temperature, GCM uncertainty prevails and, as opposed to IV, SDM uncertainty is non-negligible. Significant warming and in turn significant changes are predicted for evaporation, snow cover and seasonality of discharges. For precipitation, GCM and SDM uncertainty components are of the same order. Despite high model uncertainty, the non-zero climate change response of simulation chains is significant and annual precipitation is expected to decrease. However, high values are obtained for the large and small scale components of IV, inherited respectively from the GCMs and the different replicates of a given SDM. The same applies for annual discharge. The uncertainty in values that could be experienced for any given future period is therefore very high. For both discharge and precipitation, even the sign of future realizations is uncertain at a 90% confidence level. These findings have important implications. As for GCM uncertainty, SDM uncertainty cannot be neglected. The same applies for both components of internal variability. Climate change impact studies based on single SDM realizations are likely to be no more relevant than those based on single GCM runs (or small <span class="hlt">ensembles</span>). When they are intended to provide information for climate change adaptation, they may lead to poor decisions. In the present case, it would be better to adapt to IV of precipitation than to the precipitation decrease obtained from the mean climate change response of simulation chains. Hingray, B., Hendrickx, F., Bourqui M., Creutin, J.D., François, B., Gailhard, J., Lafaysse, M., Lemoine, N., Mathevet, T., Mezghani, A., Monteil, C., RIWER2030:Climats Régionaux et Incertitudes, Ressource en Eau et Gestion de 1860 à 2100.Projet ANR VMCS 2009-2012. Rapport Final. LTHE,EDF,Grenoble. Hingray, B., Saïd, M. (in revision). Partitioning internal variability and model uncertainty components in a multimodel multireplicate <span class="hlt">ensemble</span> of climate projections. J.Climate Lafaysse, M., Hingray, B., Gailhard, J., Mezghani, A., Terray, L. (in revision). Internal variability and model uncertainty components in a multireplicate multimodel <span class="hlt">ensemble</span> of hydrometeorological projections. Wat. Resour. Res.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.math.fsu.edu/~aluffi/archive/paper240.pdf','EPRINT'); return false;" href="http://www.math.fsu.edu/~aluffi/archive/paper240.pdf"><span id="translatedtitle"><span class="hlt">Ensemble</span> Particle Filter with Posterior Gaussian By X. Xiong1</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p><span class="hlt">Ensemble</span> Particle Filter with Posterior Gaussian Resampling By X. Xiong1 and I. M. Navon1 1School March 2005 ABSTRACT An <span class="hlt">ensemble</span> particle filter(EnPF) was recently developed as a fully nonlinear fil- ter of Bayesian conditional probability estimation, along with the well known <span class="hlt">ensemble</span> Kalman filter</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.atmos.umd.edu/~carton/pdfs/penny13.pdf','EPRINT'); return false;" href="http://www.atmos.umd.edu/~carton/pdfs/penny13.pdf"><span id="translatedtitle">Monthly Weather Review The Hybrid Local <span class="hlt">Ensemble</span> Transform Kalman Filter</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Carton, James</p> <p></p> <p>Monthly Weather Review The Hybrid Local <span class="hlt">Ensemble</span> Transform Kalman Filter --Manuscript Draft-- Manuscript Number: Full Title: The Hybrid Local <span class="hlt">Ensemble</span> Transform Kalman Filter Article Type: Expedited elements of <span class="hlt">ensemble</span> Kalman filters (EnKF) and variational methods. While most approaches have focused</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.atmos.umd.edu/theses_archive/2005/jjliu/scholarpaper.pdf','EPRINT'); return false;" href="http://www.atmos.umd.edu/theses_archive/2005/jjliu/scholarpaper.pdf"><span id="translatedtitle">Adaptive Observation Strategies with the Local <span class="hlt">Ensemble</span> Transform Kalman Filter</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Maryland at College Park, University of</p> <p></p> <p>Adaptive Observation Strategies with the Local <span class="hlt">Ensemble</span> Transform Kalman Filter A scholarly paper of multiple breeding with perturbed observations). With a 1024 <span class="hlt">ensemble</span> members <span class="hlt">Ensemble</span> Kalman Filter (En Kalman Filter (LETKF, Hunt, 2005). LETKF is a square root EnKF that does data assimilation over small</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MNRAS.455..438A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MNRAS.455..438A"><span id="translatedtitle">The MIP <span class="hlt">ensemble</span> simulation: local <span class="hlt">ensemble</span> statistics in the Cosmic Web</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Aragon-Calvo, M. A.</p> <p>2016-01-01</p> <p>We present a new technique that allows us to compute <span class="hlt">ensemble</span> statistics on a local basis, directly relating halo properties to their local environment. This is achieved by the use of a correlated <span class="hlt">ensemble</span> in which the large-scale structure (LSS) is common to all realizations while having each an independent halo population. The correlated <span class="hlt">ensemble</span> can be stacked, effectively increasing the halo number density by an arbitrary factor, thus breaking the fundamental limit in the halo number density given by the halo mass function. This technique allows us to compute local <span class="hlt">ensemble</span> statistics of the matter/halo distribution at any position in the simulation box, while removing the intrinsic stochasticity in the halo formation process and directly relating halo properties to their environment. We introduce the Multum In Parvo correlated <span class="hlt">ensemble</span> simulation consisting of 220 realizations on a 32 h-1 Mpc box with 2563 particles each. This is equivalent in terms of effective volume and number of particles to a box of ˜193 h-1 Mpc of side with ˜15403 particles containing ˜5 × 106 haloes with a minimum mass of 3.25 × 109 h-1 M?. The potential of the technique presented here is illustrated by computing the local <span class="hlt">ensemble</span> statistics of the halo ellipticity and halo shape-LSS alignment. We show that, while there are general trends in the ellipticity and alignment of haloes with their LSS, there are also significant spatial variations which has important implications for observational studies of galaxy shape and alignment.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('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://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/2015ThApC.119...83N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.119...83N"><span id="translatedtitle">Statistical <span class="hlt">downscaling</span> of regional climate models in Bulgarian mountains and some projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nojarov, Peter</p> <p>2015-01-01</p> <p>Air temperature and precipitation data from three high mountainous Bulgarian stations were used as well as outputs from nine regional climate models (RCMs) for air temperatures and eight RCMs for precipitation. Data from 40-year experiments driven by the ERA-40 reanalysis (temporal coverage from 1961 to 2000) of the ECMWF were employed for calibration of statistical <span class="hlt">downscaling</span> models. Statistical methods were used in this research—Spearman and Pearson correlation, Mann-Whitney test, multiple linear regression, generalized linear models, etc. Projections, based on SRES A1B scenario and RCMs driven by four different GCMs, were made for the following future 30-years periods: 2005-2034, 2035-2064, and 2065-2094. RCMs ETHZ-CLM, DMI-ARPEGE-HIRHAM, HadRM3Q0, and HadRM3Q16 show the best correlation with observed air temperatures in mountain stations. RCMs ETHZ-CLM, HadRM3Q16, and RACMO have the best relationship with precipitation. Constructed monthly multiple linear regression models describe well enough air temperatures throughout the entire year. Monthly GLMs describe better precipitation in January, March, August, and September, as well as peak Musala and Cherni vrah precipitation. Projections for future 30-year periods indicate that air temperatures are expected to rise by 2065-2094 at all of the three investigated stations with 2.8 to 3.2 °C. This increase is mainly due to the summer months. Annual precipitation amounts are expected to decrease by the period 2065-2094 at all the three stations with about 7 to 17 %. Some increase of annual precipitation amounts in the beginning of twenty-first century against the general negative trend could happen at Musala station, which is probably due to the increase in frequency of liquid precipitation.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1710348P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1710348P"><span id="translatedtitle">High-resolution stochastic <span class="hlt">downscaling</span> of climate models: simulating wind advection, cloud cover and precipitation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peleg, Nadav; Fatichi, Simone; Burlando, Paolo</p> <p>2015-04-01</p> <p>A new stochastic approach to generate wind advection, cloud cover and precipitation fields is presented with the aim of formulating a space-time weather generator characterized by fields with high spatial and temporal resolution (e.g., 1 km x 1 km and 5 min). Its use is suitable for stochastic <span class="hlt">downscaling</span> of climate scenarios in the context of hydrological, ecological and geomorphological applications. The approach is based on concepts from the Advanced WEather GENerator (AWE-GEN) presented by Fatichi et al. (2011, Adv. Water Resour.), the Space-Time Realizations of Areal Precipitation model (STREAP) introduced by Paschalis et al. (2013, Water Resour. Res.), and the High-Resolution Synoptically conditioned Weather Generator (HiReS-WG) presented by Peleg and Morin (2014, Water Resour. Res.). Advection fields are generated on the basis of the 500 hPa u and v wind direction variables derived from global or regional climate models. The advection velocity and direction are parameterized using Kappa and von Mises distributions respectively. A random Gaussian fields is generated using a fast Fourier transform to preserve the spatial correlation of advection. The cloud cover area, total precipitation area and mean advection of the field are coupled using a multi-autoregressive model. The approach is relatively parsimonious in terms of computational demand and, in the context of climate change, allows generating many stochastic realizations of current and projected climate in a fast and efficient way. A preliminary test of the approach is presented with reference to a case study in a complex orography terrain in the Swiss Alps.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AIPC.1561..120S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AIPC.1561..120S"><span id="translatedtitle"><span class="hlt">Downscaling</span> of Bulgarian chemical weather forecast from Bulgaria region to Sofia city</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Syrakov, D.; Etropolska, I.; Prodanova, M.; Slavov, K.; Ganev, K.; Miloshev, N.; Ljubenov, T.</p> <p>2013-10-01</p> <p>In the paper, Bulgarian Chemical Weather Forecast System (BgCWFS), version 3, will be described end the respective end-user products will be demonstrated. Chemical Weather is understood as concentration distribution of some key pollutants in a particular area and its changes during some forecast period. In Bulgaria, a prototype of such a system was built in the frame of a project with the National Science fund. It covers a relatively small domain including Bulgaria that requires the use of chemical boundary conditions (CBC) from similar foreign systems. The last version of the System is built in the frame of EU FP7 project PASODOBLE. Following its requirements, concentration data (CBC) for the region of Bulgaria are provided by SILAM System of Finish Meteorological Institute. It operates over the whole European region but is able to provide data for any European sub-domain by its THREDDS service. The customer makes an Internet request containing all necessary parameters - sub-region dimensions, pollutants, period of forecast etc. In a few minutes, the request is proceeded and all required data is downloaded as a single NetCDF file. This file is post-processed as to obtain the necessary boundary conditions. The new version of the system is built on the base of the nesting approach - two other domains with increasing resolution are nested in the Bulgaria one <span class="hlt">downscaling</span> to 1 km space resolution over Sofia city. The System is fully atomized. Computations start at 00 UTC every day and the forecast period is 72 hours. It is based on the well known models WRF (Mesometeorological Model) and US EPA dispersion model CMAQ (Chemical Transport Model). As emission input the 2010 inventory data prepared by Bulgarian environmental authorities is exploited. The results are presented in the System's web-site (<monospace>http://www.niggg.bas.bg/cw3/</monospace>).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1097304','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1097304"><span id="translatedtitle"><span class="hlt">Downscaling</span> Solar Power Output to 4-Seconds for Use in Integration Studies (Presentation)</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Hummon, M.; Weekley, A.; Searight, K.; Clark, K.</p> <p>2013-10-01</p> <p>High penetration renewable integration studies require solar power data with high spatial and temporal accuracy to quantify the impact of high frequency solar power ramps on the operation of the system. Our previous work concentrated on <span class="hlt">downscaling</span> solar power from one hour to one minute by simulation. This method used clearness classifications to categorize temporal and spatial variability, and iterative methods to simulate intra-hour clearness variability. We determined that solar power ramp correlations between sites decrease with distance and the duration of the ramp, starting at around 0.6 for 30-minute ramps between sites that are less than 20 km apart. The sub-hour irradiance algorithm we developed has a noise floor that causes the correlations to approach ~0.005. Below one minute, the majority of the correlations of solar power ramps between sites less than 20 km apart are zero, and thus a new method to simulate intra-minute variability is needed. These intra-minute solar power ramps can be simulated using several methods, three of which we evaluate: a cubic spline fit to the one-minute solar power data; projection of the power spectral density toward the higher frequency domain; and average high frequency power spectral density from measured data. Each of these methods either under- or over-estimates the variability of intra-minute solar power ramps. We show that an optimized weighted linear sum of methods, dependent on the classification of temporal variability of the segment of one-minute solar power data, yields time series and ramp distributions similar to measured high-resolution solar irradiance data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1107457','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1107457"><span id="translatedtitle"><span class="hlt">Downscaling</span> Solar Power Output to 4-Seconds for Use in Integration Studies: Preprint</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Hummon, M.; Weekley, A.; Searight, K.; Clark, K.</p> <p>2013-10-01</p> <p>High penetration renewable integration studies require solar power data with high spatial and temporal accuracy to quantify the impact of high frequency solar power ramps on the operation of the system. Our previous work concentrated on <span class="hlt">downscaling</span> solar power from one hour to one minute by simulation. This method used clearness classifications to categorize temporal and spatial variability, and iterative methods to simulate intra-hour clearness variability. We determined that solar power ramp correlations between sites decrease with distance and the duration of the ramp, starting at around 0.6 for 30-minute ramps between sites that are less than 20 km apart. The sub-hour irradiance algorithm we developed has a noise floor that causes the correlations to approach ~0.005. Below one minute, the majority of the correlations of solar power ramps between sites less than 20 km apart are zero, and thus a new method to simulate intra-minute variability is needed. These intra-minute solar power ramps can be simulated using several methods, three of which we evaluate: a cubic spline fit to the one-minute solar power data; projection of the power spectral density toward the higher frequency domain; and average high frequency power spectral density from measured data. Each of these methods either under- or over-estimates the variability of intra-minute solar power ramps. We show that an optimized weighted linear sum of methods, dependent on the classification of temporal variability of the segment of one-minute solar power data, yields time series and ramp distributions similar to measured high-resolution solar irradiance data.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.C21C0633W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.C21C0633W"><span id="translatedtitle">A Physiographic Approach to <span class="hlt">Downscaling</span> Remotely Sensed Fractional Snow Cover Data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Walters, R. D.; Watson, K. A.; Flores, A. N.</p> <p>2012-12-01</p> <p>Improved characterization of hydrologic states like soil moisture and snow water equivalent at scales of individual hillslopes (i.e., 10s to 100s of meters) would substantially benefit applications ranging from flood-forecasting to military trafficability assessment. In seasonally snow-covered mountain watersheds, complex topography influences the evolution of areal snow cover. Various satellite remote sensing data are able to capture the extent of snow covered area with spatial or temporal limitations depending on the particular product. For instance, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, aboard Terra and Aqua satellites, produces fractional snow covered area (fSCA) grids daily but with 500-m spatial resolution. Conversely, the Landsat system can estimate binary snow cover at 30-m spacing but only on a 16-day return interval. Since variable snow ablation occurs within these spatiotemporal boundaries, it is desirable to estimate the snow cover at higher resolution. Here we propose a simple method to <span class="hlt">downscale</span> daily MODIS fSCA data to 30-m resolution binary snow cover estimates. The algorithm computes a terrain score as a linear weighted average of two physiographic variables: elevation and relative insolation slope factor. Shuttle Radar Topography Mission (SRTM) data (30-m, co-registered with Landsat) are used to extract the elevation and to compute the radiation data. Under the assumption that low-elevation and high-insolation pixels will have melted first in an ephemeral snowpack, cells within each MODIS window are assigned a binary snow cover classification such that the fSCA observation is satisfied. Terrain score weights are optimized according to historical Landsat scenes within regions of southwestern Idaho. Blind test results in the same regions show good model performance (< 10%) when MODIS and Landsat are in agreement regarding snow cover fraction. The model is thus at the mercy of fSCA accuracy and may not perform as well when used in higher alpine catchments where variables such as wind redistribution and sloughing dominate.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMED11A0717K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMED11A0717K"><span id="translatedtitle"><span class="hlt">Downscaling</span> GRACE satellite data for sub-region groundwater storage estimates in California's Central Valley</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kuss, A. M.; Newcomer, M. E.; Hsu, W.; Bourai, A.; Puranam, A.; Landerer, F. W.; Schmidt, C.</p> <p>2012-12-01</p> <p>The Central Valley aquifer (CVA) is a vital economic and environmental resource for California and the United States, and supplies water for one of the most agriculturally productive regions in the world. Recent estimates of groundwater (GW) availability in California have indicated declines in GW levels that may pose a threat to sustainable groundwater use in this region. The Gravity Recovery and Climate Experiment (GRACE) can be used to estimate variations in total water storage (TWS) and are therefore used to estimate GW storage changes within the CVA. However, using GRACE data in the CVA is challenging due to the coarse spatial resolution and increased error. To compensate for this, we used a statistical <span class="hlt">downscaling</span> approach applied to GRACE data at the sub-region level using GW storage estimates from the California Department of Water Resources' (DWR) C2VSim hydrological model. This method produced a spatially and temporally variable GW anomaly dataset for sub-region GW management and for analysis of GW changes influenced by spatial and temporal variability. An additional challenge for this region is the influence of natural climate variability, altering GW recharge and influencing pumping practices. Understanding the effects of climate variability on GW storage changes, may improve GRACE TWS and GW estimates during periods of increased rain or droughts. Thus, the GRACE TWS and GW storage estimates were compared to the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) using singular spectral analysis (SSA). Results from SSA indicate that variations in GRACE TWS are moderately correlated to PDO (10-25 year cycle), although low correlations were observed when compared to ENSO (2-7 year cycle). The incorporation of these new methods for estimating variations in groundwater storage in highly productive aquifers may improve water management techniques in California.</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/2015EGUGA..17.1549M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..17.1549M"><span id="translatedtitle">Regional-to-Urban Enviro-HIRLAM <span class="hlt">Downscaling</span> for Meteorological and Chemical Patterns over Chinese Megacities</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mahura, Alexander; Nuterman, Roman; Gonzalez-Aparicio, Iratxe; Amstrup, Bjarne; Baklanov, Alexander; Yang, Xiaohua; Nielsen, Kristian</p> <p>2015-04-01</p> <p>Due to strong economic growth in the past decades, air pollution became a serious problem in megacities and major industrial agglomerations of China. So, information on air quality in these urbanized areas is important for population. In particular, the metropolitan areas of Shanghai, Beijing, and Pearl River Delta are well known as main regions with serious air pollution issues. One of the aims of the EU FP7 MarcoPolo project is to improve existing regional-meso-urban/city scale air quality forecasts using improved emission inventories and to validate modelling results using satellite and ground-based measurements. The Enviro-HIRLAM (Environment - HIgh Resolution Limited Area Model) adapted for the Shanghai region of China is applied for forecasting. The model is urbanized using the Building Effects Parameterization module, which describes different types of urban districts such as industrial commercial, city center, high density and residential with its own characteristics. For sensitivity studies, the model was run in <span class="hlt">downscaling</span> chain from regional-to-urban scales at subsequent horizontal resolutions of 15-5-2.5 km for selected dates with elevated pollution levels and unfavorable meteorological conditions. For these dates, the effects of urbanization are analyzed for atmospheric transport, dispersion, deposition, and chemical transformations. The evaluation of formation and development of meteorological and chemical/aerosol patterns due to influence of the urban areas is performed. The impact of selected (in a model domain) megacities of China is estimated on regional-to-urban scales, as well as relationship between air pollution and meteorology are studied.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC33B..04C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC33B..04C"><span id="translatedtitle">Uncertainty quantification in <span class="hlt">downscaling</span> procedures for effective decisions in energy systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Constantinescu, E. M.</p> <p>2010-12-01</p> <p>Weather is a major driver both of energy supply and demand, and with the massive adoption of renewable energy sources and changing economic and producer-consumer paradigms, the management of the next-generation energy systems is becoming ever more challenging. The operational and planning decisions in energy systems are guided by efficiency and reliability, and therefore a central role in these decisions will be played by the ability to obtain weather condition forecasts with accurate uncertainty estimates. The appropriate temporal and spatial resolutions needed for effective decision-making, be it operational or planning, is not clear. It is arguably certain however, that such temporal scales as hourly variations of temperature or wind conditions and ramp events are essential in this process. Planning activities involve decade or decades-long projections of weather. One sensible way to achieve this is to embed regional weather models in a global climate system. This strategy acts as a <span class="hlt">downscaling</span> procedure. Uncertainty modeling techniques must be developed in order to quantify and minimize forecast errors as well as target variables that impact the decision-making process the most. We discuss the challenges of obtaining a realistic uncertainty quantification estimate using mathematical algorithms based on scalable matrix-free computations and physics-based statistical models. The process of making decisions for energy management systems based on future weather scenarios is a very complex problem. We shall focus on the challenges in generating wind power predictions based on regional weather predictions, and discuss the implications of making the common assumptions about the uncertainty models.</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://ntrs.nasa.gov/search.jsp?R=20110015976&hterms=soil+conditions&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dsoil%2Bconditions','NASA-TRS'); return false;" href="http://ntrs.nasa.gov/search.jsp?R=20110015976&hterms=soil+conditions&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D80%26Ntt%3Dsoil%2Bconditions"><span id="translatedtitle"><span class="hlt">Downscaling</span> Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto</p> <p>2010-01-01</p> <p>Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a <span class="hlt">downscaling</span> model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the <span class="hlt">downscaling</span> model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture <theta>) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional <span class="hlt">downscaling</span> relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.</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> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/15006550','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/15006550"><span id="translatedtitle">Mid-Century <span class="hlt">Ensemble</span> Regional Climate Change Scenarios for the Western United States</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Leung, Lai R.; Qian, Yun; Bian, Xindi; Washington, Warren M.; Han, Jongil; Roads, John O.</p> <p>2004-01-01</p> <p>To study the impacts of climate change on water resources in the western U.S., global climate simulations were produced using the National Center for Atmospheric Research/Department of Energy (NCAR/DOE) Parallel Climate Model (PCM). The Penn State/NCAR Mesoscale Model (MM5) was used to <span class="hlt">downscale</span> the PCM control (1995-2015) and three future (2040-2060) climate simulations to yield <span class="hlt">ensemble</span> regional climate simulations at 40 km spatial resolution for the western U.S. This paper focuses on analyses of regional simulations in the Columbia River and Sacramento-San Joaquin River Basins. Results based on the regional simulations show that by mid-century, the average regional warming of 1-2.5oC strongly affects snowpack in the western U.S. Along coastal mountains, reduction in annual snowpack is about 70%. Besides changes in mean temperature, precipitation, and snowpack, cold season extreme daily precipitation is found to increase by 5 to 15 mm/day (15-20%) along the Cascades and the Sierra. The warming results in increased rainfall over snowfall and reduced snow accumulation (or earlier snowmelt) during the cold season. In the Columbia River Basin, these changes are accompanied by more frequent rain-on-snow events. Overall, they induce higher likelihood of wintertime flooding and reduced runoff and soil moisture in the summer. Such changes could have serious impacts on water resources and agriculture in the western U.S. Changes in surface water and energy budgets in the Columbia River and Sacramento-San Joaquin basins are driven mainly by changes in surface temperature, which are statistically significant at the 0.95 confidence level. Changes in precipitation, however, are spatially incoherent and not statistically significant except for the drying trend during summer.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.1740C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.1740C"><span id="translatedtitle">On the application of the Principal Component Analysis for an efficient climate <span class="hlt">downscaling</span> of surface wind fields</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chavez, Roberto; Lozano, Sergio; Correia, Pedro; Sanz-Rodrigo, Javier; Probst, Oliver</p> <p>2013-04-01</p> <p>With the purpose of efficiently and reliably generating long-term wind resource maps for the wind energy industry, the application and verification of a statistical methodology for the climate <span class="hlt">downscaling</span> of wind fields at surface level is presented in this work. This procedure is based on the combination of the Monte Carlo and the Principal Component Analysis (PCA) statistical methods. Firstly the Monte Carlo method is used to create a huge number of daily-based annual time series, so called climate representative years, by the stratified sampling of a 33-year-long time series corresponding to the available period of the NCAR/NCEP global reanalysis data set (R-2). Secondly the representative years are evaluated such that the best set is chosen according to its capability to recreate the Sea Level Pressure (SLP) temporal and spatial fields from the R-2 data set. The measure of this correspondence is based on the Euclidean distance between the Empirical Orthogonal Functions (EOF) spaces generated by the PCA (Principal Component Analysis) decomposition of the SLP fields from both the long-term and the representative year data sets. The methodology was verified by comparing the selected 365-days period against a 9-year period of wind fields generated by dynamical <span class="hlt">downscaling</span> the Global Forecast System data with the mesoscale model SKIRON for the Iberian Peninsula. These results showed that, compared to the traditional method of dynamical <span class="hlt">downscaling</span> any random 365-days period, the error in the average wind velocity by the PCA's representative year was reduced by almost 30%. Moreover the Mean Absolute Errors (MAE) in the monthly and daily wind profiles were also reduced by almost 25% along all SKIRON grid points. These results showed also that the methodology presented maximum error values in the wind speed mean of 0.8 m/s and maximum MAE in the monthly curves of 0.7 m/s. Besides the bulk numbers, this work shows the spatial distribution of the errors across the Iberian domain and additional wind statistics such as the velocity and directional frequency. Additional repetitions were performed to prove the reliability and robustness of this kind-of statistical-dynamical <span class="hlt">downscaling</span> method.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012PhyA..391.4839V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012PhyA..391.4839V"><span id="translatedtitle">Statistical <span class="hlt">ensembles</span> for money and debt</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Viaggiu, Stefano; Lionetto, Andrea; Bargigli, Leonardo; Longo, Michele</p> <p>2012-10-01</p> <p>We build a statistical <span class="hlt">ensemble</span> representation of two economic models describing respectively, in simplified terms, a payment system and a credit market. To this purpose we adopt the Boltzmann-Gibbs distribution where the role of the Hamiltonian is taken by the total money supply (i.e. including money created from debt) of a set of interacting economic agents. As a result, we can read the main thermodynamic quantities in terms of monetary ones. In particular, we define for the credit market model a work term which is related to the impact of monetary policy on credit creation. Furthermore, with our formalism we recover and extend some results concerning the temperature of an economic system, previously presented in the literature by considering only the monetary base as a conserved quantity. Finally, we study the statistical <span class="hlt">ensemble</span> for the Pareto distribution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1713282A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713282A"><span id="translatedtitle">Using the CMIP <span class="hlt">ensemble</span> for climate prediction</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Annan, James; Hargreaves, Julia</p> <p>2015-04-01</p> <p>The collection of GCMs which contribute to CMIP are often described as an <span class="hlt">ensemble</span> of opportunity, with no specific overall design or sampling strategy. Thus, it is challenging to generate probabilistic predictions from these simulations. A particular issue that has raised much discussion is regarding the independence (or otherwise) of evidence arising both from observational analyses, and different model simulations. Climate models broadly agree on such features as overall CO2-forced global warming, with amplification of this warming at high latitudes and over land, and an intensified hydrological cycle. Does this large (and growing) <span class="hlt">ensemble</span> of consistent models justify increased confidence in their results, or are they all merely replicating the same errors? And how should we combine observational evidence arising from the observed period of warming, together with paleoclimate analyses and model simulations? We will show a way forward based on rigorous mathematical definitions and understanding which has been generally lacking in the literature to date.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PhRvL.113p0504R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhRvL.113p0504R"><span id="translatedtitle">Quantum Data Compression of a Qubit <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>Rozema, Lee A.; Mahler, Dylan H.; Hayat, Alex; Turner, Peter S.; Steinberg, Aephraim M.</p> <p>2014-10-01</p> <p>Data compression is a ubiquitous aspect of modern information technology, and the advent of quantum information raises the question of what types of compression are feasible for quantum data, where it is especially relevant given the extreme difficulty involved in creating reliable quantum memories. We present a protocol in which an <span class="hlt">ensemble</span> of quantum bits (qubits) can in principle be perfectly compressed into exponentially fewer qubits. We then experimentally implement our algorithm, compressing three photonic qubits into two. This protocol sheds light on the subtle differences between quantum and classical information. Furthermore, since data compression stores all of the available information about the quantum state in fewer physical qubits, it could allow for a vast reduction in the amount of quantum memory required to store a quantum <span class="hlt">ensemble</span>, making even today's limited quantum memories far more powerful than previously recognized.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhRvL.115r8701F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvL.115r8701F"><span id="translatedtitle">Sampling Motif-Constrained <span class="hlt">Ensembles</span> of Networks</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fischer, Rico; Leitão, Jorge C.; Peixoto, Tiago P.; Altmann, Eduardo G.</p> <p>2015-10-01</p> <p>The statistical significance of network properties is conditioned on null models which satisfy specified properties but that are otherwise random. Exponential random graph models are a principled theoretical framework to generate such constrained <span class="hlt">ensembles</span>, but which often fail in practice, either due to model inconsistency or due to the impossibility to sample networks from them. These problems affect the important case of networks with prescribed clustering coefficient or number of small connected subgraphs (motifs). In this Letter we use the Wang-Landau method to obtain a multicanonical sampling that overcomes both these problems. We sample, in polynomial time, networks with arbitrary degree sequences from <span class="hlt">ensembles</span> with imposed motifs counts. Applying this method to social networks, we investigate the relation between transitivity and homophily, and we quantify the correlation between different types of motifs, finding that single motifs can explain up to 60% of the variation of motif profiles.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1409.5005v3','EPRINT'); return false;" href="http://arxiv.org/pdf/1409.5005v3"><span id="translatedtitle">Diffusion For <span class="hlt">Ensembles</span> of Standard Maps</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Or Alus; Shmuel Fishman</p> <p>2015-08-18</p> <p>Two types of random evolution processes are studied for <span class="hlt">ensembles</span> of the standard map with driving parameter $K$ that determines its degree of stochasticity. For one type of processes the parameter $K$ is chosen at random from a Gaussian distribution and is then kept fixed, while for the other type it varies from step to step. In addition, noise that can be arbitrarily weak is added. The <span class="hlt">ensemble</span> average and the average over noise of the diffusion coefficient is calculated for both types of processes. These two types of processes are relevant for two types of experimental situations as explained in the paper. Both types of processes destroy fine details of the dynamics, and the second process is found to be more effective in destroying the fine details. We hope that this work is a step in the efforts for developing a statistical theory for systems with mixed phase space (regular in some parts and chaotic in other parts).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1110.0264.pdf','EPRINT'); return false;" href="http://arxiv.org/pdf/1110.0264.pdf"><span id="translatedtitle">Face Recognition using Optimal Representation <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Li, Hanxi; Gao, Yongsheng</p> <p>2011-01-01</p> <p>Recently, the face recognizers based on linear representations have been shown to deliver state-of-the-art performance. In real-world applications, however, face images usually suffer from expressions, disguises and random occlusions. The problematic facial parts undermine the validity of the linear-subspace assumption and thus the recognition performance deteriorates significantly. In this work, we address the problem in a learning-inference-mixed fashion. By observing that the linear-subspace assumption is more reliable on certain face patches rather than on the holistic face, some Bayesian Patch Representations (BPRs) are randomly generated and interpreted according to the Bayes' theory. We then train an <span class="hlt">ensemble</span> model over the patch-representations by minimizing the empirical risk w.r.t the "leave-one-out margins". The obtained model is termed Optimal Representation <span class="hlt">Ensemble</span> (ORE), since it guarantees the optimality from the perspective of Empirical Risk Minimization. To handle the unknown patterns in tes...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhRvE..92d2904A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvE..92d2904A"><span id="translatedtitle">Diffusion for <span class="hlt">ensembles</span> of standard maps</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alus, Or; Fishman, Shmuel</p> <p>2015-10-01</p> <p>Two types of random evolution processes are studied for <span class="hlt">ensembles</span> of the standard map with driving parameter K that determines its degree of stochasticity. For one type of process the parameter K is chosen at random from a Gaussian distribution and is then kept fixed, while for the other type it varies from step to step. In addition, noise that can be arbitrarily weak is added. The <span class="hlt">ensemble</span> average and the average over noise of the diffusion coefficient are calculated for both types of processes. These two types of processes are relevant for two types of experimental situations as explained in the paper. Both types of processes destroy fine details of the dynamics, and the second process is found to be more effective in destroying the fine details. We hope that this work is a step in the efforts for developing a statistical theory for systems with mixed phase space (regular in some parts and chaotic in other parts).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhRvA..92b2314V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvA..92b2314V"><span id="translatedtitle">Dynamical rephasing of <span class="hlt">ensembles</span> of qudits</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vitanov, Nikolay V.</p> <p>2015-08-01</p> <p>Dynamical decoupling is an established tool for protecting the quantum state of a qubit from decoherence. This paper extends the simplest dynamical decoupling technique for qubits—the Carr-Purcell-Meiboom-Gill's two-pulse rephasing sequence—to qudits with an arbitrary number of states. The pulse sequences introduced here are particularly well suited for dynamical rephasing of the collective coherence of inhomogeneously broadened <span class="hlt">ensembles</span> of atomic qudits.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1401.0218v2','EPRINT'); return false;" href="http://arxiv.org/pdf/1401.0218v2"><span id="translatedtitle">The conformal loop <span class="hlt">ensemble</span> nesting field</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Jason Miller; Samuel S. Watson; David B. Wilson</p> <p>2014-12-21</p> <p>The conformal loop <span class="hlt">ensemble</span> CLE$_\\kappa$ with parameter $8/3 nesting field. We generalize this result by assigning i.i.d. weights to the loops, and we treat an alternate notion of convergence to the nesting field in the case where the weight distribution has mean zero. We also establish estimates for moments of the number of CLE loops surrounding two given points.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/1134523','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/1134523"><span id="translatedtitle"><span class="hlt">Downscaling</span> Global Land Cover Projections from an Integrated Assessment Model for Use in Regional Analyses: Results and Evaluation for the US from 2005 to 2095</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>West, Tristram O.; Le Page, Yannick LB; Huang, Maoyi; Wolf, Julie; Thomson, Allison M.</p> <p>2014-06-05</p> <p>Projections of land cover change generated from Integrated Assessment Models (IAM) and other economic-based models can be applied for analyses of environmental impacts at subregional and landscape scales. For those IAM and economic models that project land use at the sub-continental or regional scale, these projections must be <span class="hlt">downscaled</span> and spatially distributed prior to use in climate or ecosystem models. <span class="hlt">Downscaling</span> efforts to date have been conducted at the national extent with relatively high spatial resolution (30m) and at the global extent with relatively coarse spatial resolution (0.5 degree).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHEP...02..173W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHEP...02..173W"><span id="translatedtitle">Loop equation analysis of the circular ? <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>Witte, N. S.; Forrester, P. J.</p> <p>2015-02-01</p> <p>We construct a hierarchy of loop equations for invariant circular <span class="hlt">ensembles</span>. These are valid for general classes of potentials and for arbitrary inverse temperatures Re ? > 0 and number of eigenvalues N. Using matching arguments for the resolvent functions of linear statistics f( ?) = ( ? + z)/( ? - z) in a particular asymptotic regime, the global regime, we systematically develop the corresponding large N expansion and apply this solution scheme to the Dyson circular <span class="hlt">ensemble</span>. Currently we can compute the second resolvent function to ten orders in this expansion and also its general Fourier coefficient or moment mk to an equivalent length. The leading large N, large k, k/ N fixed form of the moments can be related to the small wave-number expansion of the structure function in the bulk, scaled Dyson circular <span class="hlt">ensemble</span>, known from earlier work. From the moment expansion we conjecture some exact partial fraction forms for the low k moments. For all of the forgoing results we have made a comparison with the exactly soluble cases of ? = 1, 2, 4, general N and even, positive ?, N = 2, 3.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015APS..MART20004C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015APS..MART20004C"><span id="translatedtitle">Cavity Cooling for <span class="hlt">Ensemble</span> Spin Systems</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cory, David</p> <p>2015-03-01</p> <p>Recently there has been a surge of interest in exploring thermodynamics in quantum systems where dissipative effects can be exploited to perform useful work. One such example is quantum state engineering where a quantum state of high purity may be prepared by dissipative coupling through a cold thermal bath. This has been used to great effect in many quantum systems where cavity cooling has been used to cool mechanical modes to their quantum ground state through coupling to the resolved sidebands of a high-Q resonator. In this talk we explore how these techniques may be applied to an <span class="hlt">ensemble</span> spin system. This is an attractive process as it potentially allows for parallel remove of entropy from a large number of quantum systems, enabling an <span class="hlt">ensemble</span> to achieve a polarization greater than thermal equilibrium, and potentially on a time scale much shorter than thermal relaxation processes. This is achieved by the coupled angular momentum subspaces of the <span class="hlt">ensemble</span> behaving as larger effective spins, overcoming the weak individual coupling of individual spins to a microwave resonator. Cavity cooling is shown to cool each of these subspaces to their respective ground state, however an additional algorithmic step or dissipative process is required to couple between these subspaces and enable cooling to the full ground state of the joint system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4393075','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4393075"><span id="translatedtitle">Hierarchical <span class="hlt">Ensemble</span> Methods for Protein Function Prediction</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p></p> <p>2014-01-01</p> <p>Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on <span class="hlt">ensembles</span> of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” <span class="hlt">ensemble</span> decision, taking into account the hierarchical relationships between classes. The main hierarchical <span class="hlt">ensemble</span> methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954</p> </li> </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/2014AGUFMGC33A0492C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC33A0492C"><span id="translatedtitle"><span class="hlt">Downscaling</span> of South America present climate forced by three global models</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chou, S. C.; Lyra, A. A.; Sueiro, G.; Mourao, C. F.; Silva, A.; Chagas, G.; Gomes, J. L.; Rodrigues, D. C.; Pilotto, I.; Tavares, P. S.; Campos, D. A.; Dereczynski, C. P.; Bustamante, J. F.; Chagas, D.</p> <p>2014-12-01</p> <p>The objective of this work is to evaluate the <span class="hlt">downscaling</span> of three global coupled ocean-atmosphere models by setting up the Regional Climate Model at 20-km resolution over the domain that encompasses South America, Central America and parts of the adjacent oceans. The RCM is the Eta model used at CPTEC since 1997 for weather forecasts, since 2000 for seasonal forecasts and since 2012 for climate change studies. The model has suffered some upgrades and has turn into a finite volume model. Some examples of upgrades are the vertical coordinate refinement, the vertical advection scheme, some physics parameters, etc. To run for the time range of several decades and to synchronize with the global models, the calendar of this version of this model was modified. The global models are: the Hadley Centre model, HadGEM2-ES, the Japanese MIROC5 model, and the Brazilian BESM2.3.1 model. Their resolutions range from about 250 km to about 150 km. The present period simulations started from about 1960 until 2005. This step is the preparation for the future climate change scenarios runs driven by the same global models. Evaluations were based on CRU data. The mean spatial distribution of precipitation and temperature showed agreement against the observations. The simulated precipitation and temperature fields from the Eta showed correct seasonality along the year. The regional model simulations driven by BESM showed the least amount of precipitation over the tropical Pacific and Atlantic oceans, whereas the simulations driven by MIROC5 showed the largest amount in those oceans. The mean seasonal cycle of precipitation for three major regions in Brazil showed underestimate in the Amazon region, but overestimate in Central-South Brazilian region. The mean seasonal cycle of temperature were underestimated along all the year. The frequency distribution of precipitation showed that the regional model reach more intense precipitation rates than the global models, and similarly for temperature extremes. Trends of extreme indicators such as consecutive dry days, total annual precipitation were also calculated and compared against some previous works.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.usgs.gov/of/2011/1238/','USGSPUBS'); return false;" href="http://pubs.usgs.gov/of/2011/1238/"><span id="translatedtitle">Dynamically <span class="hlt">downscaled</span> climate simulations over North America: Methods, evaluation, and supporting documentation for users</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Hostetler, S.W.; Alder, J.R.; Allan, A.M.</p> <p>2011-01-01</p> <p>We have completed an array of high-resolution simulations of present and future climate over Western North America (WNA) and Eastern North America (ENA) by dynamically <span class="hlt">downscaling</span> global climate simulations using a regional climate model, RegCM3. The simulations are intended to provide long time series of internally consistent surface and atmospheric variables for use in climate-related research. In addition to providing high-resolution weather and climate data for the past, present, and future, we have developed an integrated data flow and methodology for processing, summarizing, viewing, and delivering the climate datasets to a wide range of potential users. Our simulations were run over 50- and 15-kilometer model grids in an attempt to capture more of the climatic detail associated with processes such as topographic forcing than can be captured by general circulation models (GCMs). The simulations were run using output from four GCMs. All simulations span the present (for example, 1968-1999), common periods of the future (2040-2069), and two simulations continuously cover 2010-2099. The trace gas concentrations in our simulations were the same as those of the GCMs: the IPCC 20th century time series for 1968-1999 and the A2 time series for simulations of the future. We demonstrate that RegCM3 is capable of producing present day annual and seasonal climatologies of air temperature and precipitation that are in good agreement with observations. Important features of the high-resolution climatology of temperature, precipitation, snow water equivalent (SWE), and soil moisture are consistently reproduced in all model runs over WNA and ENA. The simulations provide a potential range of future climate change for selected decades and display common patterns of the direction and magnitude of changes. As expected, there are some model to model differences that limit interpretability and give rise to uncertainties. Here, we provide background information about the GCMs and the RegCM3, a basic evaluation of the model output and examples of simulated future climate. We also provide information needed to access the web applications for visualizing and downloading the data, and give complete metadata that describe the variables in the datasets.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/966125','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/966125"><span id="translatedtitle"><span class="hlt">Downscaled</span> climate change impacts on agricultural water resources in Puerto Rico</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Harmsen, E.W.; Miller, N.L.; Schlegel, N.J.; Gonzalez, J.E.</p> <p>2009-04-01</p> <p>The purpose of this study is to estimate reference evapotranspiration (ET{sub o}), rainfall deficit (rainfall - ET{sub o}) and relative crop yield reduction for a generic crop under climate change conditions for three locations in Puerto Rico: Adjuntas, Mayaguez, and Lajas. Reference evapotranspiration is estimated by the Penman-Monteith method. Rainfall and temperature data were statistically <span class="hlt">downscaled</span> and evaluated using the DOE/NCAR PCM global circulation model projections for the B1 (low), A2 (mid-high) and A1fi (high) emission scenarios of the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios. Relative crop yield reductions were estimated from a function dependent water stress factor, which is a function of soil moisture content. Average soil moisture content for the three locations was determined by means of a simple water balance approach. Results from the analysis indicate that the rainy season will become wetter and the dry season will become drier. The 20-year mean 1990-2010 September rainfall excess (i.e., rainfall - ET{sub o} > 0) increased for all scenarios and locations from 149.8 to 356.4 mm for 2080-2100. Similarly, the 20-year average February rainfall deficit (i.e., rainfall - ET{sub o} < 0) decreased from a -26.1 mm for 1990-2010 to -72.1 mm for the year 2080-2100. The results suggest that additional water could be saved during the wet months to offset increased irrigation requirements during the dry months. Relative crop yield reduction did not change significantly under the B1 projected emissions scenario, but increased by approximately 20% during the summer months under the A1fi emissions scenario. Components of the annual water balance for the three climate change scenarios are rainfall, evapotranspiration (adjusted for soil moisture), surface runoff, aquifer recharge and change in soil moisture storage. Under the A1fi scenario, for all locations, annual evapotranspiration decreased owing to lower soil moisture, surface runoff decreased, and aquifer recharge increased. Aquifer recharge increased at all three locations because the majority of recharge occurs during the wet season and the wet season became wetter. This is good news from a groundwater production standpoint. Increasing aquifer recharge also suggests that groundwater levels may increase and this may help to minimize saltwater intrusion near the coasts as sea levels increase, provided that groundwater use is not over-subscribed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70045588','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70045588"><span id="translatedtitle"><span class="hlt">Downscaling</span> future climate projections to the watershed scale: a north San Francisco Bay estuary case study</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Micheli, Elisabeth; Flint, Lorraine; Flint, Alan; Weiss, Stuart; Kennedy, Morgan</p> <p>2012-01-01</p> <p>We modeled the hydrology of basins draining into the northern portion of the San Francisco Bay Estuary (North San Pablo Bay) using a regional water balance model (Basin Characterization Model; BCM) to estimate potential effects of climate change at the watershed scale. The BCM calculates water balance components, including runoff, recharge, evapotranspiration, soil moisture, and stream flow, based on climate, topography, soils and underlying geology, and the solar-driven energy balance. We <span class="hlt">downscaled</span> historical and projected precipitation and air temperature values derived from weather stations and global General Circulation Models (GCMs) to a spatial scale of 270 m. We then used the BCM to estimate hydrologic response to climate change for four scenarios spanning this century (2000–2100). Historical climate patterns show that Marin’s coastal regions are typically on the order of 2 °C cooler and receive five percent more precipitation compared to the inland valleys of Sonoma and Napa because of marine influences and local topography. By the last 30 years of this century, North Bay scenarios project average minimum temperatures to increase by 1.0 °C to 3.1 °C and average maximum temperatures to increase by 2.1 °C to 3.4 °C (in comparison to conditions experienced over the last 30 years, 1981–2010). Precipitation projections for the 21st century vary between GCMs (ranging from 2 to 15% wetter than the 20th-century average). Temperature forcing increases the variability of modeled runoff, recharge, and stream discharge, and shifts hydrologic cycle timing. For both high- and low-rainfall scenarios, by the close of this century warming is projected to amplify late-season climatic water deficit (a measure of drought stress on soils) by 8% to 21%. Hydrologic variability within a single river basin demonstrated at the scale of subwatersheds may prove an important consideration for water managers in the face of climate change. Our results suggest that in arid environments characterized by high topo-climatic variability, land and water managers need indicators of local watershed hydrology response to complement regional temperature and precipitation estimates. Our results also suggest that temperature forcing may generate greater drought stress affecting soils and stream flows than can be estimated by variability in precipitation alone.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EGUGA..15.3487K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EGUGA..15.3487K"><span id="translatedtitle">Regional Climate <span class="hlt">Downscaling</span> Using a High-resolution Global Atmospheric Model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kunhu Bangalath, Hamza; Stenchikov, Georgiy; Osipov, Sergey</p> <p>2013-04-01</p> <p>In this study, we used HIRAM, a high-resolution atmospheric model [Zhao et al., 2009] for climate <span class="hlt">downscaling</span> with the horizontal grid spacing of 25 km. Our simulations followed the CORDEX protocol [Giorgi et al., 2009] and were conducted for historic (1975-2006) and future (2005-2050) periods using both RCP 4.5 and RCP 8.5 scenarios. Compared with the Geophysical Fluid Dynamics Laboratory (GFDL) AM2.0 and AM2.1 [Delworth et al., 2006], HIRAM uses enhanced vertical discretization on 32 vertical layers instead of 24 and replaces the relaxed Arakawa-Schubert convective closure with the one developed at the University of Washington. The model retains the surface flux, boundary layer, large-scale cloud microphysics, and radiative transfer modules from the AM2 family [Delworth et al., 2006]. HIRAM also employs a cubed-sphere implementation (here at 25-km resolution) of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. In our simulations, the Sea Surface Temperatures (SSTs) from the GFDL Earth System Model runs, ESM2M and ESM2G, performed for the International Panel for Climate Change AR5 project with a latitude-longitude grid of 2°x2.5° were adopted as the bottom boundary conditions over the sea. We used prescribed time-varying greenhouse gas and stratospheric/tropospheric aerosol distribution datasets to reproduce the observed radiative forcing in the model as described by Delworth et al. [2006]. Here, we present results for the CORDEX Middle East and North Africa domain and compared them with the coarse-resolution ESM2M/ESM2G simulations as well as with the nested regional model projections. Delworth, T. et al. (2006), GFDL's CM2 Global Coupled Models. Part I: Formulation and Simulation Characteristics, J. Climate, 19, 643-674. Giorgi, F., C. Jones, and G. Asrar (2009), Addressing climate information needs at the regional level: The CORDEX framework. WMO Bull., 58, 175-183 Zhao, M., I. M. Held, S-J. Lin, and G.A. Vecchi (2009), Simulations of Global Hurricane Climatology, Interannual Variability, and Response to Global Warming Using a 50km Resolution GCM, J. Climate, 33, 6653-6678.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015HESSD..12.2561S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015HESSD..12.2561S"><span id="translatedtitle"><span class="hlt">Downscaling</span> future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman-Scott 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>Sørup, H. J. D.; Christensen, O. B.; Arnbjerg-Nielsen, K.; Mikkelsen, P. S.</p> <p>2015-02-01</p> <p>Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a Spatio-Temporal Neyman-Scott Rectangular Pulses weather generator (WG). Precipitation time series for fitting the model are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 by 60 km model domain. The model simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for <span class="hlt">downscaling</span> climate change signals from Regional Climate Models (RCMs) with spatial resolutions of 25 and 8 km respectively. Six different RCM simulations are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for <span class="hlt">downscaling</span> RCM precipitation output for use in urban hydrology.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/22418052','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/22418052"><span id="translatedtitle">Effect of <span class="hlt">downscaling</span> nano-copper interconnects on the microstructure revealed by high resolution TEM-orientation-mapping.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Ganesh, K J; Darbal, A D; Rajasekhara, S; Rohrer, G S; Barmak, K; Ferreira, P J</p> <p>2012-04-01</p> <p>In this work, a recently developed electron diffraction technique called diffraction scanning transmission electron microscopy (D-STEM) is coupled with precession electron microscopy to obtain quantitative local texture information in damascene copper interconnects (1.8 µm-70 nm in width) with a spatial resolution of less than 5 nm. Misorientation and trace analysis is performed to investigate the grain boundary distribution in these lines. The results reveal strong variations in texture and grain boundary distribution of the copper lines upon <span class="hlt">downscaling</span>. Lines of width 1.8 µm exhibit a strong <111> normal texture and comprise large micron-size grains. Upon <span class="hlt">downscaling</span> to 180 nm, a {111}<110> bi-axial texture has been observed. In contrast, narrower lines of widths 120 and 70 nm reveal sidewall growth of {111} grains and a dominant <110> normal texture. The microstructure in these lines comprises clusters of small grains separated by high angle boundaries in the vicinity of large grains. The fraction of coherent twin boundaries also reduces with decreasing line width. PMID:22418052</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMNG31A3791A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMNG31A3791A"><span id="translatedtitle">A Modified Artifitial Neural Network <span class="hlt">Ensemble</span> Framework for Drought Estimation</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Alobaidi, M. H.; Marpu, P. R.; Ouarda, T.</p> <p>2014-12-01</p> <p>Drought estimation at ungauged sites is a difficult task due to various challenges such as scale and limited availability and information about hydrologic neighborhoods. <span class="hlt">Ensemble</span> regression has been recently utilized in modeling various hydrologic systems and showed advantage over classical regression approaches to such studies. A challenging task in <span class="hlt">ensemble</span> modeling is the proper training of the <span class="hlt">ensemble</span>'s individual learners and the <span class="hlt">ensemble</span> combiners. In this work, an <span class="hlt">ensemble</span> framework is proposed to enhance the generalization ability of the sub-<span class="hlt">ensemble</span> models and its combiner. Information mixtures between the subsamples are introduced. Such measure is dedicated to the <span class="hlt">ensemble</span> members and <span class="hlt">ensemble</span> combiners. Controlled homogeneity magnitudes are then stimulated and induced in the proposed model via a two-stage resampling algorithm. Artificial neural networks (ANNs) were used as <span class="hlt">ensemble</span> members in addition to different <span class="hlt">ensemble</span> integration plans. The model provided superior results when compared to previous models applied to the case study in this work. The root mean squared error (RMSE) in the testing phase for the drought quantiles improved by 67% - 76%. The bias error (BIAS) also showed 61% - 95% improvement.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011280','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011280"><span id="translatedtitle"><span class="hlt">Ensemble</span> Data Assimilation Without <span class="hlt">Ensembles</span>: Methodology and Application to Ocean Data Assimilation</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Keppenne, Christian L.; Rienecker, Michele M.; Kovach, Robin M.; Vernieres, Guillaume</p> <p>2013-01-01</p> <p>Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the <span class="hlt">ensemble</span> Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an <span class="hlt">ensemble</span> in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an <span class="hlt">ensemble</span> sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4428879','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4428879"><span id="translatedtitle">Argumentation Based Joint Learning: A Novel <span class="hlt">Ensemble</span> Learning Approach</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Xu, Junyi; Yao, Li; Li, Le</p> <p>2015-01-01</p> <p>Recently, <span class="hlt">ensemble</span> learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel <span class="hlt">ensemble</span> learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, <span class="hlt">ensemble</span> learning, and association rule mining. In AMAJL, argumentation technology is introduced as an <span class="hlt">ensemble</span> strategy to integrate multiple base classifiers and generate a high performance <span class="hlt">ensemble</span> classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for <span class="hlt">ensemble</span> classifier and improve the performance of classification. PMID:25966359</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1509.04447.pdf','EPRINT'); return false;" href="http://arxiv.org/pdf/1509.04447.pdf"><span id="translatedtitle">Bayesian <span class="hlt">ensemble</span> refinement by replica simulations and reweighting</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Hummer, Gerhard</p> <p>2015-01-01</p> <p>We describe different Bayesian <span class="hlt">ensemble</span> refinement methods, examine their interrelation, and discuss their practical application. With <span class="hlt">ensemble</span> refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of <span class="hlt">ensemble</span>-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian <span class="hlt">ensemble</span> distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "<span class="hlt">ensemble</span> refinement of SAXS" (EROS) formulation. Bayesian replica <span class="hlt">ensemble</span> refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We find that the strength of the restraint scales with the number of replicas and we show that this sca...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.aoml.noaa.gov/phod/Liu_et_al_2015_JMS.pdf','EPRINT'); return false;" href="http://www.aoml.noaa.gov/phod/Liu_et_al_2015_JMS.pdf"><span id="translatedtitle">Potential impact of climate change on the Intra-Americas Sea: Part-1. A dynamic <span class="hlt">downscaling</span> of the CMIP5 model projections</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p>-Americas Sea Gulf of Mexico Caribbean Sea This study examines the potential impact of anthropogenic greenhouse warming on the Intra-Americas Sea (IAS, Caribbean Sea and Gulf of Mexico) by <span class="hlt">downscaling</span> the Coupled Model to the Atlantic Multidecadal Oscillation and a meridional dipole pattern between the GoM and Caribbean Sea</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ieor.berkeley.edu/~kaminsky/Reprints/MJ_PK_ABN_13.pdf','EPRINT'); return false;" href="http://ieor.berkeley.edu/~kaminsky/Reprints/MJ_PK_ABN_13.pdf"><span id="translatedtitle">European Transport \\ Trasporti Europei (2013) Issue 54, Paper n 4, ISSN 1825-3997 <span class="hlt">Downscaling</span> the consolidation of goods state of the art</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Kaminsky, Philip M.</p> <p>2013-01-01</p> <p>the consolidation of goods ­ state of the art and transferability of micro-consolidation initiatives Milena Janjevic experiences have focused on <span class="hlt">downscaling</span> the consolidation effort by bundling the goods much closer the physical micro- consolidation of goods and to provide guidelines on selecting the most appropriate</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EL....11030001P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EL....11030001P"><span id="translatedtitle">Universal spectral correlations in <span class="hlt">ensembles</span> of random normal matrices</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prakash, Ravi; Pandey, Akhilesh</p> <p>2015-05-01</p> <p>We consider non-Gaussian <span class="hlt">ensembles</span> of random normal matrices with the constraint that the <span class="hlt">ensembles</span> are invariant under unitary transformations. We show that the level density of eigenvalues exhibits disk or ring structure in the complex plane. We also show that the n-eigenvalue correlation and the spacing distribution are universal and identical to that of complex (Gaussian) Ginibre <span class="hlt">ensemble</span>. Our results are confirmed by Monte Carlo calculations. We verify the universality for dissipative quantum kicked rotor systems.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://kuscholarworks.ku.edu/handle/1808/17718','EPRINT'); return false;" href="http://kuscholarworks.ku.edu/handle/1808/17718"><span id="translatedtitle">Concerto Grosso #2 (Concierto Sefardico) for chamber <span class="hlt">ensemble</span> and organ</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Levi, Sabin</p> <p>2004-01-01</p> <p>CONCERTO GROSSO #2 (CONCIERTO SEFARDICO) FOR CHAMBER <span class="hlt">ENSEMBLE</span> AND ORGAN by Sabin Levi B.Mus. - organ performance, Jerusalem Rubin Academy of Music and Dance, 1995 B. Mus. - composition, Jerusalem Rubin Academy of Music and Dance, 1995 M... of the requirements for the degree of Doctor of Musical Arts (Composition) Date Defended: October, 2004 Sabin Levi Concerto Grosso #2 (ConciertoSefardico) for chamber <span class="hlt">ensemble</span> and organ 2004 Abstract The Concerto Grosso #2 is written for a chamber <span class="hlt">ensemble</span>...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1412.6642v1','EPRINT'); return false;" href="http://arxiv.org/pdf/1412.6642v1"><span id="translatedtitle">Universal spectral correlations in <span class="hlt">ensembles</span> of random normal matrices</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Ravi Prakash; Akhilesh Pandey</p> <p>2014-12-20</p> <p>We consider non-gaussian <span class="hlt">ensembles</span> of random normal matrices with the constraint that the <span class="hlt">ensembles</span> are invariant under unitary transformations. We show that the level density of eigenvalues exhibits disk to ring transition in the complex plane. We also show that the n-eigenvalue correlation and the spacing distribution are universal and identical to that of complex (Gaussian) Ginibre <span class="hlt">ensemble</span>. Our results are confirmed by Monte Carlo calculations. We verify the universality for dissipative quantum kicked rotor system.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014PhRvE..90d2144V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014PhRvE..90d2144V"><span id="translatedtitle">Spectral density of the noncentral correlated Wishart <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>Vinayak</p> <p>2014-10-01</p> <p>Wishart <span class="hlt">ensembles</span> of random matrix theory have been useful in modeling positive definite matrices encountered in classical and quantum chaotic systems. We consider nonzero means for the entries of the constituting matrix A which defines the correlated Wishart matrix as W =AA† , and refer to the <span class="hlt">ensemble</span> of such Wishart matrices as the noncentral correlated Wishart <span class="hlt">ensemble</span> (nc-CWE). We derive the Pastur self-consistent equation which describes the spectral density of nc-CWE at large matrix dimension.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1713322A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1713322A"><span id="translatedtitle">Seasonal hydrological <span class="hlt">ensemble</span> forecasts over Europe</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Arnal, Louise; Wetterhall, Fredrik; Pappenberger, Florian</p> <p>2015-04-01</p> <p>Seasonal forecasts have an important socio-economic value in hydro-meteorological forecasting. The applications are for example hydropower management, spring flood prediction and water resources management. The latter includes prediction of low flows, primordial for navigation, water quality assessment, droughts and agricultural water needs. Traditionally, seasonal hydrological forecasts are done using the observed discharge from previous years, so called <span class="hlt">Ensemble</span> Streamflow Prediction (ESP). With the recent increasing development of seasonal meteorological forecasts, the incentive for developing and improving seasonal hydrological forecasts is great. In this study, a seasonal hydrological forecast, driven by the ECMWF's System 4 (SEA), was compared with an ESP of modelled discharge using observations. The hydrological model used for both forecasts was the LISFLOOD model, run over a European domain with a spatial resolution of 5 km. The forecasts were produced from 1990 until the present time, with a daily time step. They were issued once a month with a lead time of seven months. The SEA forecasts are constituted of 15 <span class="hlt">ensemble</span> members, extended to 51 members every three months. The ESP forecasts comprise 20 <span class="hlt">ensembles</span> and served as a benchmark for this comparative study. The forecast systems were compared using a diverse set of verification metrics, such as continuous ranked probability scores, ROC curves, anomaly correlation coefficients and Nash-Sutcliffe efficiency coefficients. These metrics were computed over several time-scales, ranging from a weekly to a six-months basis, for each season. The evaluation enabled the investigation of several aspects of seasonal forecasting, such as limits of predictability, timing of high and low flows, as well as exceedance of percentiles. The analysis aimed at exploring the spatial distribution and timely evolution of the limits of predictability.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1512.05017.pdf','EPRINT'); return false;" href="http://arxiv.org/pdf/1512.05017.pdf"><span id="translatedtitle">Cavity-controlled chemistry in molecular <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Herrera, Felipe</p> <p>2015-01-01</p> <p>The demonstration of strong and ultrastrong coupling regimes of cavity QED with polyatomic molecules has opened new routes to control chemical dynamics at the nanoscale. We show that strong resonant coupling of a cavity field with an electronic transition can effectively decouple collective electronic and nuclear degrees of freedom in a disordered molecular <span class="hlt">ensemble</span>, even for molecules with high-frequency quantum vibrational modes having strong electron-vibration interactions. This type of polaron decoupling can be used to control chemical reactions. We show that the rate of electron transfer reactions in a cavity can be orders of magnitude larger than in free space, for a wide class of organic molecular species.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013PhRvL.111y3001H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013PhRvL.111y3001H"><span id="translatedtitle">Accurate Atom Counting in Mesoscopic <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>Hume, D. B.; Stroescu, I.; Joos, M.; Muessel, W.; Strobel, H.; Oberthaler, M. K.</p> <p>2013-12-01</p> <p>Many cold atom experiments rely on precise atom number detection, especially in the context of quantum-enhanced metrology where effects at the single particle level are important. Here, we investigate the limits of atom number counting via resonant fluorescence detection for mesoscopic samples of trapped atoms. We characterize the precision of these fluorescence measurements beginning from the single-atom level up to more than one thousand. By investigating the primary noise sources, we obtain single-atom resolution for atom numbers as high as 1200. This capability is an essential prerequisite for future experiments with highly entangled states of mesoscopic atomic <span class="hlt">ensembles</span>.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_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://www.ncbi.nlm.nih.gov/pubmed/24483741','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24483741"><span id="translatedtitle">Accurate atom counting in mesoscopic <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>Hume, D B; Stroescu, I; Joos, M; Muessel, W; Strobel, H; Oberthaler, M K</p> <p>2013-12-20</p> <p>Many cold atom experiments rely on precise atom number detection, especially in the context of quantum-enhanced metrology where effects at the single particle level are important. Here, we investigate the limits of atom number counting via resonant fluorescence detection for mesoscopic samples of trapped atoms. We characterize the precision of these fluorescence measurements beginning from the single-atom level up to more than one thousand. By investigating the primary noise sources, we obtain single-atom resolution for atom numbers as high as 1200. This capability is an essential prerequisite for future experiments with highly entangled states of mesoscopic atomic <span class="hlt">ensembles</span>. PMID:24483741</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/math-ph/0601024v1','EPRINT'); return false;" href="http://arxiv.org/pdf/math-ph/0601024v1"><span id="translatedtitle">Quantum conductance problems and the Jacobi <span class="hlt">ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>P. J. Forrester</p> <p>2006-01-12</p> <p>In one dimensional transport problems the scattering matrix $S$ is decomposed into a block structure corresponding to reflection and transmission matrices at the two ends. For $S$ a random unitary matrix, the singular value probability distribution function of these blocks is calculated. The same is done when $S$ is constrained to be symmetric, or to be self dual quaternion real, or when $S$ has real elements, or has real quaternion elements. Three methods are used: metric forms; a variant of the Ingham-Seigel matrix integral; and a theorem specifying the Jacobi random matrix <span class="hlt">ensemble</span> in terms of Wishart distributed matrices.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009CMaPh.288...43S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009CMaPh.288...43S"><span id="translatedtitle">Conformal Radii for Conformal Loop <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>Schramm, Oded; Sheffield, Scott; Wilson, David B.</p> <p>2009-05-01</p> <p>The conformal loop <span class="hlt">ensembles</span> CLE ? , defined for 8/3 ? ? ? 8, are random collections of loops in a planar domain which are conjectured scaling limits of the O( n) loop models. We calculate the distribution of the conformal radii of the nested loops surrounding a deterministic point. Our results agree with predictions made by Cardy and Ziff and by Kenyon and Wilson for the O( n) model. We also compute the expectation dimension of the CLE ? gasket, which consists of points not surrounded by any loop, to be 2 - {(8 - kappa)(3kappa - 8)}/{32kappa} , which agrees with the fractal dimension given by Duplantier for the O( n) model gasket.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/54414','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/54414"><span id="translatedtitle">Collective rhythmicity in biological oscillator <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Rogers, J.L.; Wille, L.T.</p> <p>1993-12-31</p> <p>Many biological phenomena, including peristalsis, circadian rhythms, and cardiac synchronization, may be modeled in terms of the collective behavior of non-linear oscillator <span class="hlt">ensembles</span>. The possible types of synergetics include modelocking, amplitude death, quasiperiodicity, and chaos. This paper presents large-scale simulations on a SIMD-computer with a mesh architecture (the MasPar-1 or DECmpp 12000 system) of one- and two-dimensional arrays of disparate limit-cycle oscillators. The simulations have been optimized for this type of architecture and permit the determination of the collective behavior over time scales and for system sizes that are much larger than has hitherto been feasible.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/0712.1616v1','EPRINT'); return false;" href="http://arxiv.org/pdf/0712.1616v1"><span id="translatedtitle">Unambiguous comparison of <span class="hlt">ensembles</span> of quantum states</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Michal Sedlak; Mario Ziman; Vladimir Buzek; Mark Hillery</p> <p>2007-12-11</p> <p>We present a solution of the problem of the optimal unambiguous comparison of two <span class="hlt">ensembles</span> of unknown quantum states (psi_1)^k and (psi_2)^l. We consider two cases: 1) The two unknown states psi_1 and psi_2 are arbitrary states of qudits. 2) Alternatively, they are coherent states of a harmonic oscillator. For the case of coherent states we propose a simple experimental realization of the optimal "comparison" machine composed of a finite number of beam-splitters and a single photodetector.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/21811801','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/21811801"><span id="translatedtitle">Design <span class="hlt">ensemble</span> machine learning model for breast cancer diagnosis.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hsieh, Sheau-Ling; Hsieh, Sung-Huai; Cheng, Po-Hsun; Chen, Chi-Huang; Hsu, Kai-Ping; Lee, I-Shun; Wang, Zhenyu; Lai, Feipei</p> <p>2012-10-01</p> <p>In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, <span class="hlt">ensemble</span> ones have been developed for classifications. In addition, a combined <span class="hlt">ensemble</span> model with these three schemes has been constructed for further validations. The experimental results indicate that the <span class="hlt">ensemble</span> learning performs better than individual single ones. Moreover, the combined <span class="hlt">ensemble</span> model illustrates the highest accuracy of classifications for the breast cancer among all models. PMID:21811801</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/20866574','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/20866574"><span id="translatedtitle">Gibbs entropy of network <span class="hlt">ensembles</span> by cavity methods.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Anand, Kartik; Bianconi, Ginestra</p> <p>2010-07-01</p> <p>The Gibbs entropy of a microcanonical network <span class="hlt">ensemble</span> is the logarithm of the number of network configurations compatible with a set of hard constraints. This quantity characterizes the level of order and randomness encoded in features of a given real network. Here, we show how to relate this entropy to large deviations of conjugated canonical <span class="hlt">ensembles</span>. We derive exact expression for this correspondence using the cavity methods for the configuration model, for the <span class="hlt">ensembles</span> with constraint degree sequence and community structure and for the <span class="hlt">ensemble</span> with constraint degree sequence and number of links at a given distance. PMID:20866574</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009ems..confE.140C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009ems..confE.140C"><span id="translatedtitle">New technique for <span class="hlt">ensemble</span> dressing combining Multimodel Super<span class="hlt">Ensemble</span> and precipitation PDF</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cane, D.; Milelli, M.</p> <p>2009-09-01</p> <p>The Multimodel Super<span class="hlt">Ensemble</span> technique (Krishnamurti et al., Science 285, 1548-1550, 1999) is a postprocessing method for the estimation of weather forecast parameters reducing direct model output errors. It differs from other <span class="hlt">ensemble</span> analysis techniques by the use of an adequate weighting of the input forecast models to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the difference between the model and the observed field during a so-called training period. Although it can be applied successfully on the continuous parameters like temperature, humidity, wind speed and mean sea level pressure (Cane and Milelli, Meteorologische Zeitschrift, 15, 2, 2006), the Multimodel Super<span class="hlt">Ensemble</span> gives good results also when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present our methodology for the Multimodel precipitation forecasts applied on a wide spectrum of results over Piemonte very dense non-GTS weather station network. We will focus particularly on an accurate statistical method for bias correction and on the <span class="hlt">ensemble</span> dressing in agreement with the observed precipitation forecast-conditioned PDF. Acknowledgement: this work is supported by the Italian Civil Defence Department.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ESSDD...5..475P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ESSDD...5..475P"><span id="translatedtitle">Future Flows Climate: an <span class="hlt">ensemble</span> of 1-km climate change projections for hydrological application in Great Britain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prudhomme, C.; Dadson, S.; Morris, D.; Williamson, J.; Goodsell, G.; Crooks, S.; Boelee, L.; Davies, H.; Buys, G.; Lafon, T.; Watts, G.</p> <p>2012-06-01</p> <p>1. The dataset Future Flows Climate was developed as part of the project "Future Flows and Groundwater Levels" to provide a consistent set of climate change projections for the whole of Great Britain at both space and time resolutions appropriate for hydrological applications, and to enable for climate change uncertainty and climate variability to be accounted for in the assessment of their possible impacts on the environment. 2. Future Flows Climate is derived from the Hadley Centre's <span class="hlt">ensemble</span> Projection HadRM3-PPE that is part of the basis of UKCP09 and includes projections in available precipitation (water available to hydrological processes after snow and ice storages have been accounted for) and potential evapotranspiration. It corresponds to an 11-member <span class="hlt">ensemble</span> of transient projections from January 1950 to December 2098, each a single realisation from a different variant of HadRM3. Data are provided on a 1-km grid over the HadRM3 land areas at a daily (available precipitation) and monthly (PE) time step as NetCDF files. 3. Because systematic biases in temperature and precipitation were found between HadRM3-PPE and gridded temperature and precipitation observations for the 1962-1991 period, a monthly bias correction procedure was undertaken, based on a linear correction for temperature and a quantile-mapping correction (using the gamma distribution) for precipitation followed by a spatial <span class="hlt">downscaling</span>. Available precipitation was derived from the bias-corrected precipitation and temperature time series using a simple elevation-dependant snow-melt model. Potential evapotranspiration time series were calculated for each month using the FAO-56 Penman Montieth equations and bias-corrected temperature, cloud cover, relative humidity and wind speed from HadRM3-PPE along with latitude of the grid and the day of the year. 4. Future Flows Climate is freely available for non commercial use under certain licensing conditions. It is the dataset used to generate Future Flows Hydrology, an <span class="hlt">ensemble</span> of transient projections of daily river flow and monthly groundwater time series for representative river basins and boreholes in Great Britain. 5. <a href="http://dx.doi.org/10.5285/bad1514f-119e-44a4-8e1e-442735bb9797"target="_blank">doi:10.5285/bad1514f-119e-44a4-8e1e-442735bb9797</a></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ESSD....4..143P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ESSD....4..143P"><span id="translatedtitle">Future Flows Climate: an <span class="hlt">ensemble</span> of 1-km climate change projections for hydrological application in Great Britain</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Prudhomme, C.; Dadson, S.; Morris, D.; Williamson, J.; Goodsell, G.; Crooks, S.; Boelee, L.; Davies, H.; Buys, G.; Lafon, T.; Watts, G.</p> <p>2012-11-01</p> <p>The dataset Future Flows Climate was developed as part of the project ''Future Flows and Groundwater Levels'' to provide a consistent set of climate change projections for the whole of Great Britain at both space and time resolutions appropriate for hydrological applications, and to enable climate change uncertainty and climate variability to be accounted for in the assessment of their possible impacts on the environment. Future Flows Climate is derived from the Hadley Centre's <span class="hlt">ensemble</span> projection HadRM3-PPE that is part of the basis of UKCP09 and includes projections in available precipitation (water available to hydrological processes after snow and ice storages have been accounted for) and potential evapotranspiration. It corresponds to an 11-member <span class="hlt">ensemble</span> of transient projections from January 1950 to December 2098, each a single realisation from a different variant of HadRM3. Data are provided on a 1-km grid over the HadRM3 land areas at a daily (available precipitation) and monthly (PE) time step as netCDF files. Because systematic biases in temperature and precipitation were found between HadRM3-PPE and gridded temperature and precipitation observations for the 1962-1991 period, a monthly bias correction procedure was undertaken, based on a linear correction for temperature and a quantile-mapping correction (using the gamma distribution) for precipitation followed by a spatial <span class="hlt">downscaling</span>. Available precipitation was derived from the bias-corrected precipitation and temperature time series using a simple elevation-dependant snow-melt model. Potential evapotranspiration time series were calculated for each month using the FAO-56 Penman-Monteith equations and bias-corrected temperature, cloud cover, relative humidity and wind speed from HadRM3-PPE along with latitude of the grid and the day of the year. Future Flows Climate is freely available for non-commercial use under certain licensing conditions. It is the dataset used to generate Future Flows Hydrology, an <span class="hlt">ensemble</span> of transient projections of daily river flow and monthly groundwater time series for representative river basins and boreholes in Great Britain. <a href="http://dx.doi.org/10.5285/bad1514f-119e-44a4-8e1e-442735bb9797"target="_blank">doi:10.5285/bad1514f-119e-44a4-8e1e-442735bb9797</a>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009AGUFM.B33A0373V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009AGUFM.B33A0373V"><span id="translatedtitle"><span class="hlt">Downscaling</span> to the Climate Near the Ground: Measurements and Modeling Along the Macro-, Meso-, Topo-, and Microclimate Hierarchy</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>van de Ven, C.; Weiss, S. B.</p> <p>2009-12-01</p> <p>Most climate models are expressed at regional scales, with resolutions on the scales of kilometers. When used for ecological modeling, these climate models help explain only broad-scale trends, such as latitudinal and upslope migration of plants. However, more refined ecological models require <span class="hlt">down-scaled</span> climate data at ecologically relevant spatial scales, and the goal of this presentation is to demonstrate robust <span class="hlt">downscaling</span> methods. For example, in the White Mountains, eastern California, tree species, including bristlecone pine (Pinus longaeva) are seen moving not just upslope, but also sideways across aspects, and downslope into areas characterized by cold air drainage. Macroclimate in the White Mountains is semi-arid, residing in the rain shadow of the Sierra Nevada. Macroclimate is modified by mesoscale effects of mountain ranges, where climate becomes wetter and colder with elevation, the temperature decreasing according to the regionally and temporally-specific lapse rate. Local topography further modifies climate, where slope angle, aspect, and topographic position further impact the temperature at a given site. Finally, plants experience extremely localized microclimate, where surrounding vegetation provide differing degrees of shade. We measured and modeled topoclimate across the White Mountains using iButton Thermochron temperature data loggers during late summer in 2006 and 2008, and have documented effects of microclimatic temperature differences between sites in the open and shaded by shrubs. Starting with PRISM 800m data, we derived mesoscale lapse rates. Then, we calculated temperature differentials between each Thermochron and a long-term weather station in the middle of the range at Crooked Creek Valley. We modeled month-specific minimum temperature differentials by regressing the Thermochron-weather station minimum temperature differentials with various topographic parameters. Topographic position, the absolute value of topographic position, and slope combined to provide a very close fit (r2>0.9) to measured inversions of >8°C. Although topoclimatic maximum temperature models have been more elusive, regressions with degree hours greater than zero (DH>0) have been modeled with September insolation and slope (r2=0.7). In paired experiments, Thermochrons also recorded the temperature differences between the environment under sagebrush (Artemisia tridentata) and in the open, with an average minimum temperature difference of 2.1°C, and maximum temperature difference of 4.5°C. When we incorporate hourly weather station data, the strength of the inversion is weakened by wind, higher relative humidity, and cloudiness. This hierarchical modeling provides a template for <span class="hlt">downscaling</span> climate and weather to ecologically relevant scales.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://dx.doi.org/10.1007/s00382-011-1230-y','USGSPUBS'); return false;" href="http://dx.doi.org/10.1007/s00382-011-1230-y"><span id="translatedtitle">A proxy for high-resolution regional reanalysis for the Southeast United States: assessment of precipitation variability in dynamically <span class="hlt">downscaled</span> reanalyses</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Stefanova, Lydia; Misra, Vasubandhu; Chan, Steven; Griffin, Melissa; O'Brien, James J.; Smith, Thomas J., III</p> <p>2012-01-01</p> <p>We present an analysis of the seasonal, subseasonal, and diurnal variability of rainfall from COAPS Land- Atmosphere Regional Reanalysis for the Southeast at 10-km resolution (CLARReS10). Most of our assessment focuses on the representation of summertime subseasonal and diurnal variability.Summer precipitation in the Southeast United States is a particularly challenging modeling problem because of the variety of regional-scale phenomena, such as sea breeze, thunderstorms and squall lines, which are not adequately resolved in coarse atmospheric reanalyses but contribute significantly to the hydrological budget over the region. We find that the dynamically <span class="hlt">downscaled</span> reanalyses are in good agreement with station and gridded observations in terms of both the relative seasonal distribution and the diurnal structure of precipitation, although total precipitation amounts tend to be systematically overestimated. The diurnal cycle of summer precipitation in the <span class="hlt">downscaled</span> reanalyses is in very good agreement with station observations and a clear improvement both over their "parent" reanalyses and over newer-generation reanalyses. The seasonal cycle of precipitation is particularly well simulated in the Florida; this we attribute to the ability of the regional model to provide a more accurate representation of the spatial and temporal structure of finer-scale phenomena such as fronts and sea breezes. Over the northern portion of the domain summer precipitation in the <span class="hlt">downscaled</span> reanalyses remains, as in the "parent" reanalyses, overestimated. Given the degree of success that dynamical <span class="hlt">downscaling</span> of reanalyses demonstrates in the simulation of the characteristics of regional precipitation, its favorable comparison to conventional newer-generation reanalyses and its cost-effectiveness, we conclude that for the Southeast United states such <span class="hlt">downscaling</span> is a viable proxy for high-resolution conventional reanalysis.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMGC51A0390C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMGC51A0390C"><span id="translatedtitle">Extreme Precipitation in the San Francisco Bay Area: Comparing <span class="hlt">Downscaling</span> Methodologies' Skill in Representing Extreme Precipitation in Hindcasts and Differences in Their Projections</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chiang, F.; Milesi, C.; Costa-Cabral, M. C.; Rath, J.; Wang, W.; Podolske, J. R.</p> <p>2014-12-01</p> <p>Despite the growing availability of high-resolution datasets of spatially <span class="hlt">downscaled</span> CMIP5 projections, few studies have explored the differences in extreme precipitation events that stem from the choice of <span class="hlt">downscaling</span> method, or from the specific climatological datasets that are used for the bias correction and spatial disaggregation. Here we take three different statistically <span class="hlt">downscaled</span> methods applied to CMIP5 global climate models and analyze their extreme precipitation events, hindcasted and projected, for the location of NASA Ames Research Center, in South San Francisco Bay. The <span class="hlt">downscaling</span> methods analyzed are: i) Bias Correction Spatial Disaggregation (BCSD), ii) Bias Correction Constructed Analogs (BCCA), and iii) Extreme-value model based on synoptic climate predictors. We fit a generalized extreme value distribution (GEV) to datasets i and ii and use statistical tests to determine the significance of differences in the fitted GEV parameters. We explore the implications of the GEV parameter differences by comparing the daily precipitation values corresponding to 100-year, 500-year and 1,000-year return periods in the three datasets. The implications of how extreme daily values are assumed to change with spatial scale, from the gage location (a point location), to a small grid cell (1 km) or a larger grid cell (12 km), are explored. From our preliminary results, BCCA and BCSD projections predict that extreme precipitation events will be on the rise, and may have the potential to cause flooding at NASA Ames, and in the surrounding Bay Area. These <span class="hlt">downscaling</span> methods can be studied in further detail in different regions of the contiguous US, and be used by local water resource management agencies in planning infrastructural adaptations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMGC51I0828S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMGC51I0828S"><span id="translatedtitle">Micro climate Simulation in new Town `Hashtgerd' using <span class="hlt">downscaled</span> climate data</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sodoudi, S.</p> <p>2010-12-01</p> <p>One of the objectives of climatological part of project Young Cities ‘Developing Energy-Efficient Urban Fabric in the Tehran-Karaj Region’ is to simulate the micro climate (with 1m resolution) in 35ha of new town Hashtgerd, which is located 65 km far from mega city Tehran. The Project aims are developing, implementing and evaluating building and planning schemes and technologies which allow to plan and build sustainable, energy-efficient and climate sensible form mass housing settlements in arid and semi-arid regions (energy-efficient fabric). Climate sensitive form also means designing and planning for climate change and its related effects for Hashtgerd New Town. By configuration of buildings and open spaces according to solar radiation, wind and vegetation, climate sensitive urban form can create outdoor thermal comfort. To simulate the climate on small spatial scales, the micro climate model Envi-met has been used to simulate the micro climate in 35 ha. The Eulerian model ENVI-met is a micro-scale climate model which gives information about the influence of architecture and buildings as well as vegetation and green area on the micro climate up to 1 m resolution. Envi-met has been run with information from topography, <span class="hlt">downscaled</span> climate data with neuro-fuzzy method, meteorological measurements, building height and different vegetation variants (low and high number of trees) The first results were compared with each other and show In semi-arid climates the protection from solar radiation is of major importance. This can be achieved by implementation of vegetation and geometry of buildings. Due to the geographical location and related sun’s orbit the degree of shading in this area is rather low. Technical construction such awnings have to be implemented. A second important factor is wind. The design follows the idea to block the prevailing winds from west and northwest as well as the hot and dusty winds in summer time from the southeast but at the same time to allow the cooler north-south winds from Alborz Mountains to channel through the site. The quarter’s low skyline follows the topography and therefore the buildings have a maximum of three floors (carpet style). This style of buildings allows free movement of air, which is of high importance for fresh air supply. The simulation results show calm wind in inner courtyards in 2m height too. A third factor of importance is the vegetation with its positive effects on the microclimate. The increase of inbuilt areas in 35 ha reduces the heat island effect through cooling caused by vegetation and increase of air humidity which caused by trees evaporation. The simulation results show that high number of trees leads to lower soil moisture about 3 g/ kg and low wind speed near the surface. Vegetation on the road sides leads to a surface temperature decrease of 9 °K. Increase of planting distance caused turbulence near the surface and the close planting increased the Turbulent Kinetic Energy (TKE).</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A53F0198Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A53F0198Z"><span id="translatedtitle">A <span class="hlt">downscale</span> experiment on the extreme heavy rainfall case over southern China in June 2005</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zhu, X.; Chen, G.; Sha, W.; Iwasaki, T.</p> <p>2012-12-01</p> <p>1. Introduction Extensive flood over southern China, brings a severe toll to the livelihood of local residents. Mesoscale heavy rainstorms, interacted with other multi-scale systems and structures, have played an important role in triggering and maintaining precipitation in southern China. By understanding the dynamic and physical mechanism, it is of great help to improve the predictability and accuracy for the heavy rainstorms. Thus, the motivation of the present work is to investigate the multi-scale interaction that leads to an intense precipitation case on June 2005 by carrying out the <span class="hlt">downscale</span> experiments using a non-hydrostatic model (JMA-NHM) model. 2. Model settings In this test run, we employ three domains that focus on southern China (D1), Pearl River Delta (D2) and Guangzhou city (D3), respectively. All domains have 80*80 grids horizontal and 50 layers vertical. Their spatial resolutions are 15km, 4km and 1km. D1 run begins at 00LT on June 18 and drives D2 run after 3 hours; the later then drives D3 run after another 3 hours. 3. Results and discussion The results show that there area two east-west oriented rain belts over the eastern part of southern China during this long-lasting, wide-range heavy frontal rainfall. One is generated over the inland mountain region, the other is located along the windward coastline. Precipitation reaches the peak at early afternoon for the inland belt while the maximum appears in the morning for the coast belt. The extreme rainfall is mainly caused by the activities of mesoscale convective systems (MCSs) within the rainband. The MCSs formed at the upstream of the frontal rainband during midnight and then move eastward in the daytime. During this heavy rainfall process, the plentiful moisture content over southern China and monsoon surge lead to the establishment of the moist convective instability. Then the low level jet, shear line and low-level convergence offer beneficial dynamic conditions for the lifting in the inland frontal zone and the coastal line. Upper level divergence is caused by streak and subtropical high. Moreover, the weak cold air intrusion helps to sustain the front. These stably sustained environment provide favorable conditions for the repeatedly-ocuuring MCSs that result in the extensive heavy rainfalls. 4. Ongoing works Based on these high-resolution experiments, data assimilation are to be performed to capture both the synoptic and mesoscale processes and improve the accuracy of forecast on the formation, development of the precipitation system. Multi-scale interactions among these processes are to be examined as well. On the other hand, the influences of local complex surface such as mountain range and coastal line also need to be estimated in order to have a better understanding on the timing and location of extreme heavy rainfalls.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1010.0107v2','EPRINT'); return false;" href="http://arxiv.org/pdf/1010.0107v2"><span id="translatedtitle">Entanglement in a Solid State Spin <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Stephanie Simmons; Richard M. Brown; Helge Riemann; Nikolai V. Abrosimov; Peter Becker; Hans-Joachim Pohl; Mike L. W. Thewalt; Kohei M. Itoh; John J. L. Morton</p> <p>2010-11-24</p> <p>Entanglement is the quintessential quantum phenomenon and a necessary ingredient in most emerging quantum technologies, including quantum repeaters, quantum information processing (QIP) and the strongest forms of quantum cryptography. Spin <span class="hlt">ensembles</span>, such as those in liquid state nuclear magnetic resonance, have been powerful in the development of quantum control methods, however, these demonstrations contained no entanglement and ultimately constitute classical simulations of quantum algorithms. Here we report the on-demand generation of entanglement between an <span class="hlt">ensemble</span> of electron and nuclear spins in isotopically engineered phosphorus-doped silicon. We combined high field/low temperature electron spin resonance (3.4 T, 2.9 K) with hyperpolarisation of the 31P nuclear spin to obtain an initial state of sufficient purity to create a non-classical, inseparable state. The state was verified using density matrix tomography based on geometric phase gates, and had a fidelity of 98% compared with the ideal state at this field and temperature. The entanglement operation was performed simultaneously, with high fidelity, to 10^10 spin pairs, and represents an essential requirement of a silicon-based quantum information processor.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/22689637','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/22689637"><span id="translatedtitle">CARNA--alignment of RNA structure <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>Sorescu, Dragos Alexandru; Möhl, Mathias; Mann, Martin; Backofen, Rolf; Will, Sebastian</p> <p>2012-07-01</p> <p>Due to recent algorithmic progress, tools for the gold standard of comparative RNA analysis, namely Sankoff-style simultaneous alignment and folding, are now readily applicable. Such approaches, however, compare RNAs with respect to a simultaneously predicted, single, nested consensus structure. To make multiple alignment of RNAs available in cases, where this limitation of the standard approach is critical, we introduce a web server that provides a complete and convenient interface to the RNA structure alignment tool 'CARNA'. This tool uniquely supports RNAs with multiple conserved structures per RNA and aligns pseudoknots intrinsically; these features are highly desirable for aligning riboswitches, RNAs with conserved folding pathways, or pseudoknots. We represent structural input and output information as base pair probability dot plots; this provides large flexibility in the input, ranging from fixed structures to structure <span class="hlt">ensembles</span>, and enables immediate visual analysis of the results. In contrast to conventional Sankoff-style approaches, 'CARNA' optimizes all structural similarities in the input simultaneously, for example across an entire RNA structure <span class="hlt">ensemble</span>. Even compared with already costly Sankoff-style alignment, 'CARNA' solves an intrinsically much harder problem by applying advanced, constraint-based, algorithmic techniques. Although 'CARNA' is specialized to the alignment of RNAs with several conserved structures, its performance on RNAs in general is on par with state-of-the-art general-purpose RNA alignment tools, as we show in a Bralibase 2.1 benchmark. The web server is freely available at http://rna.informatik.uni-freiburg.de/CARNA. PMID:22689637</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3394245','PMC'); return false;" href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3394245"><span id="translatedtitle">CARNA—alignment of RNA structure <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>Sorescu, Drago? Alexandru; Möhl, Mathias; Mann, Martin; Backofen, Rolf; Will, Sebastian</p> <p>2012-01-01</p> <p>Due to recent algorithmic progress, tools for the gold standard of comparative RNA analysis, namely Sankoff-style simultaneous alignment and folding, are now readily applicable. Such approaches, however, compare RNAs with respect to a simultaneously predicted, single, nested consensus structure. To make multiple alignment of RNAs available in cases, where this limitation of the standard approach is critical, we introduce a web server that provides a complete and convenient interface to the RNA structure alignment tool ‘CARNA’. This tool uniquely supports RNAs with multiple conserved structures per RNA and aligns pseudoknots intrinsically; these features are highly desirable for aligning riboswitches, RNAs with conserved folding pathways, or pseudoknots. We represent structural input and output information as base pair probability dot plots; this provides large flexibility in the input, ranging from fixed structures to structure <span class="hlt">ensembles</span>, and enables immediate visual analysis of the results. In contrast to conventional Sankoff-style approaches, ‘CARNA’ optimizes all structural similarities in the input simultaneously, for example across an entire RNA structure <span class="hlt">ensemble</span>. Even compared with already costly Sankoff-style alignment, ‘CARNA’ solves an intrinsically much harder problem by applying advanced, constraint-based, algorithmic techniques. Although ‘CARNA’ is specialized to the alignment of RNAs with several conserved structures, its performance on RNAs in general is on par with state-of-the-art general-purpose RNA alignment tools, as we show in a Bralibase 2.1 benchmark. The web server is freely available at http://rna.informatik.uni-freiburg.de/CARNA. PMID:22689637</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1505.04923v1','EPRINT'); return false;" href="http://arxiv.org/pdf/1505.04923v1"><span id="translatedtitle">Quantum canonical <span class="hlt">ensemble</span>: a projection operator approach</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Wim Magnus; Fons Brosens</p> <p>2015-05-19</p> <p>Fixing the number of particles $N$, the quantum canonical <span class="hlt">ensemble</span> imposes a constraint on the occupation numbers of single-particle states. The constraint particularly hampers the systematic calculation of the partition function and any relevant thermodynamic expectation value for arbitrary $N$ since, unlike the case of the grand-canonical <span class="hlt">ensemble</span>, traces in the $N$-particle Hilbert space fail to factorize into simple traces over single-particle states. In this paper we introduce a projection operator that enables a constraint-free computation of the partition function and its derived quantities, at the price of an angular or contour integration. Being applicable to both bosonic and fermionic non-interacting systems in arbitrary dimensions, the projection operator approach provides closed-form expressions for the partition function $Z_N$ and the Helmholtz free energy $F_{\\! N}$ as well as for two- and four-point correlation functions. While appearing only as a secondary quantity in the present context, the chemical potential potential emerges as a by-product from the relation $\\mu_N = F_{\\! N+1} - F_{\\! N}$, as illustrated for a two-dimensional fermion gas with $N$ ranging between 2 and 500.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JPhCS.597a2050K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JPhCS.597a2050K"><span id="translatedtitle">Group theory for embedded random matrix <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>Kota, V. K. B.</p> <p>2015-04-01</p> <p>Embedded random matrix <span class="hlt">ensembles</span> are generic models for describing statistical properties of finite isolated quantum many-particle systems. For the simplest spinless fermion (or boson) systems with say m fermions (or bosons) in N single particle states and interacting with say k-body interactions, we have EGUE(k) [embedded GUE of k-body interactions) with GUE embedding and the embedding algebra is U(N). In this paper, using EGUE(k) representation for a Hamiltonian that is fc-body and an independent EGUE(t) representation for a transition operator that is t-body and employing the embedding U(N) algebra, finite-N formulas for moments up to order four are derived, for the first time, for the transition strength densities (transition strengths multiplied by the density of states at the initial and final energies). In the asymptotic limit, these formulas reduce to those derived for the EGOE version and establish that in general bivariate transition strength densities take bivariate Gaussian form for isolated finite quantum systems. Extension of these results for other types of transition operators and EGUE <span class="hlt">ensembles</span> with further symmetries are discussed.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_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/2013ASPC..467..349K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ASPC..467..349K"><span id="translatedtitle">Feasibility of a <span class="hlt">down-scaled</span> HEMP-Thruster as possible N-propulsion system for LISA</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Keller, A.; Köhler, P.; Gärtner, W.; Hey, F. G.; Berger, M.; Braxmaier, C.; Feili, D.; Weise, D.; Johann, U.</p> <p>2013-01-01</p> <p>An experimental feasibility study on <span class="hlt">down-scaling</span> HEMP thrusters to textmu N thrust levels as required e.g. for NGO is presented. Prototypes are used to probe the operation space as well as measuring the divergence angle of the plume and ion acceleration voltage by means of Faraday Cups and a Retarding Potential Analyser in order to gain a deeper understanding of the influence of design parameters. From the measured values thrust and specific impulse are calculated using simple models. Stable operation with a calculated thrust down to 70 N has been demonstrated and divergence efficiencies of about 0.5 are observed. Due to the importance of the thrust value and uncertainties of the models it is clearly desirable to measure the thrust and thrust noise directly with a thrust balance. Such a device is under construction, with a picometer noise level heterodyne interferometer as optical readout.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70093982','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70093982"><span id="translatedtitle">Streamflow changes in the Sierra Nevada, California, simulated using a statistically <span class="hlt">downscaled</span> general circulation model scenario of climate change</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Wilby, Robert L.; Dettinger, Michael D.</p> <p>2000-01-01</p> <p>Taken collectively, the <span class="hlt">downscaling</span> results suggest significant changes to both the timing and magnitude of streamflows in the Sierra Nevada by the end of the 21st Century. In the higher elevation basins, the HadCM2 scenario implies more annual streamflow and more streamflow during the spring and summer months that are critical for water-resources management in California. Depending on the relative significance of rainfall runoff and snowmelt, each basin responds in its own way to regional climate forcing. Generally, then, climate scenarios need to be specified — by whatever means — with sufficient temporal and spatial resolution to capture subtle orographic influences if projections of climate-change responses are to be useful and reproducible.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGC43C1067M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGC43C1067M"><span id="translatedtitle">Extension of the SIM Hydrometeorological Reanalysis Over the Entire 20th Century by Combination of Observations 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>Minvielle, M.; Céron, J.; Page, C.</p> <p>2013-12-01</p> <p>The SAFRAN-ISBA-MODCOU (SIM) system is a combination of three different components: an atmospheric analysis system (SAFRAN) providing the atmospheric forcing for a land surface model (ISBA) that computes surface water and energy budgets and a hydrological model (MODCOU) that provides river flows and level of several aquifers. The variables generated by the SIM chain constitute the SIM reanalysis and the current version only covers the 1958-2012 period. However, long climate datasets are required for evaluation and verification of climate hindcasts/forecasts and to isolate the contribution of natural decadal variability from that of anthropogenic forcing to climate variations. The aim of this work is to extend of the fine-mesh SIM reanalysis to the entire 20th century, especially focusing on temperature and rainfall over France, but also soil wetness and river flows. This extension will first allow a detailed investigation of the influence of decadal variability on France at very fine spatial scales and will provide crucial information for climate model evaluation. Before 1958, the density of available observations from Météo-France necessary to force SAFRAN (rainfall, snow, wind, temperature, humidity, cloudiness) is much lower than today, and not sufficient to produce a correct SIM reanalysis. That's why is has been decided to use the available atmospheric observations over the past decades combined to a statistical <span class="hlt">downscaling</span> algorithm to overcome the lack of observations. The DSCLIM software package implemented by the CERFACS and using a weather typing based statistical methodology will be used as statistical <span class="hlt">downscaling</span> method to reconstruct the atmospheric variables necessary to force the ISBA-MODCOU hydrological component. The first stage of this work was to estimate and compare the bias and strengths of the two approaches in their ability to reconstruct the past decades. In this sense, SIM hydro-meteorological experiments were performed for some recent years, with a number of observations artificially reduced to a number similar to years 1910, 1930 and 1950. Concurrently, the same recent years have been <span class="hlt">downscaled</span> by DSCLIM and used to force ISBA-MODCOU. Afterwards, some additional experiments with some modified parameters in the DSCLIM algorithm have been performed in order to adapt the methodology to the study case, and thus trying to improve its performances. Several configurations of the DSCLIM algorithm were applied to the entire century, using the NOAA20CR reanalysis as large-scale predictor. The reconstructed atmospheric variables are compared to the available observations over the entire century to estimate the ability of the statistical <span class="hlt">downscaling</span> method to reproduce a correct interannual to multidecadal variability. Finally, a novel method is tested: available observations over past decades are introduced in the DSCLIM algorithm, in order to obtain a reconstructed dataset as realistic as possible.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..1610254R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..1610254R"><span id="translatedtitle">Evaluation of Dynamical <span class="hlt">Downscaling</span> Resolution Effect on Wind Energy Forecast Value for a Wind Farm in Central Sweden</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rosgaard, Martin; Hahmann, Andrea; Skov Nielsen, Torben; Giebel, Gregor; Ejnar Sørensen, Poul; Madsen, Henrik</p> <p>2014-05-01</p> <p>For any energy system relying on wind power, accurate forecasts of wind fluctuations are essential for efficient integration into the power grid. Increased forecast precision allows end-users to plan day-ahead operation with reduced risk of penalties which in turn supports the feasibility of wind energy. This study aims to quantify value added to wind energy forecasts in the 12-48 hour leadtime by <span class="hlt">downscaling</span> global numerical weather prediction (NWP) data using a limited-area NWP model. The accuracy of statistical wind power forecasting tools depends strongly on this NWP input. Typical performance metrics are mean absolute error or root mean square error for predicted- against observed wind power production, and these metrics are closely related to wind speed forecast bias and correlation with observations. Wind speed bias can be handled in the statistical wind power forecasting model, though it is entirely up to it's NWP input to describe the wind speed correlation correctly. The basis of comparison for forecasts is data from the Stor-Rotliden wind farm in central Sweden. The surrounding forest adds to the forecasting challenge, thus motivating the <span class="hlt">downscaling</span> experiment as the potential for wind power forecast improvement is higher in complex terrain. The 40 Vestas V90 turbines were erected in 2009 and correspond to 78MWe installed electrical capacity. Forecasts from global and limited-area NWP models, together covering five different horizontal computational grid spacings of ~50km down to ~1km, are studied for a yearlong, continuous time period. The preliminary results shown quantify forecast strengths and weaknesses for each NWP model resolution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20140011350','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20140011350"><span id="translatedtitle"><span class="hlt">Downscaling</span> a Global Climate Model to Simulate Climate Change Impacts on U.S. Regional and Urban Air Quality</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Trail, M.; Tsimpidi, A. P.; Liu, P.; Tsigaridis, K.; Hu, Y.; Nenes, A.; Russell, A. G.</p> <p>2013-01-01</p> <p>Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with WRF to <span class="hlt">downscale</span> NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the continental United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF RCM to produce hourly meteorological fields. The <span class="hlt">downscaling</span> technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12km by 12km resolution, as well as the effect of evolving climate conditions on the air quality at major U.S. cities. The high resolution simulations produce somewhat different results than the coarse resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the U.S. during fall (Western U.S., Texas, Northeastern, and Southeastern U.S), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (Northeast). We also find that daily peak temperatures tend to increase in most major cities in the U.S. which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMPP31C1878S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMPP31C1878S"><span id="translatedtitle">Paleo-Permafrost Distribution <span class="hlt">Downscaled</span> in South America: Examination of the GCM-based maps with the observations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Saito, K.; Trombotto, D.; Bigelow, N. H.; Marchenko, S. S.; Romanovsky, V. E.; Walsh, J. E.; Hendricks, A.; Yoshikawa, K.</p> <p>2013-12-01</p> <p>In this paper, we show our attempt to compare the potential regional frozen ground distribution in South America for the present-day, mid-Holocene and the Last Glacial Maximum (LGM), <span class="hlt">downscaled</span> from the outputs of the sets of global climate model (GCM)s, participating in recent Paleoclimate Model Intercomparison Project (PMIP2 and PMIP3). Due to relatively small portion of the terrestrial areas compared to that of the Northern Hemisphere, the frozen ground distribution in the Southern Hemisphere has not been intensively surveyed and/or mapped, except for the Andes. This scale and recognition gap is one of the reasons why the GCM results have not been widely used in investigations and applications in geography or geomorphology, although field surveys in these disciplines have intensively been conducted in the middle latitude in South America, from the Andes through Patagonia to Tierra del Fuego, to evidence the periglacial processes and to determine the distribution, and their change, in the Quaternary. The PMIP2 <span class="hlt">downscaled</span> regional maps successfully showed the likely presence of frozen ground, such as permafrost in the Andes for 0ka, whereas the original coarse-resolution global maps failed. However, it still showed insufficient and/or incorrect classifications, e.g., lowland in Patagonia and Tierra del Fuego that are not underlain by permafrost today but were in 21ka, failed to produce the LGM permafrost. The mid-latitude mountains with the Pleistocene permafrost evidence, such as Extra-Andean Mountains and Ventania, also failed to be reproduced. This discrepancy in the PMIP2 products is likely due to the regional warm bias in South America, in contrast to the cool bias on hemispheric scales, which has been improved in PMIP3 products.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/26146163','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/26146163"><span id="translatedtitle">Impacts of climate change on precipitation and discharge extremes through the use of statistical <span class="hlt">downscaling</span> approaches in a Mediterranean basin.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Piras, Monica; Mascaro, Giuseppe; Deidda, Roberto; Vivoni, Enrique R</p> <p>2016-02-01</p> <p>Mediterranean region is characterized by high precipitation variability often enhanced by orography, with strong seasonality and large inter-annual fluctuations, and by high heterogeneity of terrain and land surface properties. As a consequence, catchments in this area are often prone to the occurrence of hydrometeorological extremes, including storms, floods and flash-floods. A number of climate studies focused in the Mediterranean region predict that extreme events will occur with higher intensity and frequency, thus requiring further analyses to assess their effect at the land surface, particularly in small- and medium-sized watersheds. In this study, climate and hydrologic simulations produced within the Climate Induced Changes on the Hydrology of Mediterranean Basins (CLIMB) EU FP7 research project were used to analyze how precipitation extremes propagate into discharge extremes in the Rio Mannu basin (472.5km(2)), located in Sardinia, Italy. The basin hydrologic response to climate forcings in a reference (1971-2000) and a future (2041-2070) period was simulated through the combined use of a set of global and regional climate models, statistical <span class="hlt">downscaling</span> techniques, and a process based distributed hydrologic model. We analyzed and compared the distribution of annual maxima extracted from hourly and daily precipitation and peak discharge time series, simulated by the hydrologic model under climate forcing. For this aim, yearly maxima were fit by the Generalized Extreme Value (GEV) distribution using a regional approach. Next, we discussed commonality and contrasting behaviors of precipitation and discharge maxima distributions to better understand how hydrological transformations impact propagation of extremes. Finally, we show how rainfall statistical <span class="hlt">downscaling</span> algorithms produce more reliable forcings for hydrological models than coarse climate model outputs. PMID:26146163</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013GMDD....6.2517T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013GMDD....6.2517T"><span id="translatedtitle"><span class="hlt">Downscaling</span> a global climate model to simulate climate change impacts on US regional and urban air quality</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Trail, M.; Tsimpidi, A. P.; Liu, P.; Tsigaridis, K.; Hu, Y.; Nenes, A.; Russell, A. G.</p> <p>2013-04-01</p> <p>Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with WRF to <span class="hlt">downscale</span> NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the continental United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF RCM to produce hourly meteorological fields. The <span class="hlt">downscaling</span> technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12 km by 12 km resolution, as well as the effect of evolving climate conditions on the air quality at major US cities. The high resolution simulations produce somewhat different results than the coarse resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the US during fall (Western US, Texas, Northeastern, and Southeastern US), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (northeast). We also find that daily peak temperatures tend to increase in most major cities in the US which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMGC52B..03J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMGC52B..03J"><span id="translatedtitle">Physical-Statistical <span class="hlt">Downscaling</span> of Model Wind Speed and Solar Radiation: Forecasting Wind and Solar Energy in Nevada</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiang, J.; Koracin, D.; King, K. C.</p> <p>2011-12-01</p> <p>High temporal variability in wind speed and downward shortwave flux at ground surface has been evidenced by observations. The values also change spatially due to topography, cloud cover and other characteristics of the planetary boundary layer. Numerical weather prediction provides grid-scale resolved values; however, the sub-grid-scale part generally contributes more to variances of model wind speed and/or solar radiation. This part is parameterized, and not explicitly resolved. Electricity integration costs for wind and/or solar energy may be decreased if the variances and range of uncertainty are well explained to transmission system operators/electricity traders. In this study, month-long simulations in the summer and winter were conducted using the Weather Research and Forecasting (WRF) model. Observed wind and solar radiation data from four 50-m meteorological towers and one 80-m tower were used for evaluation of the model results and statistical analysis regarding the representativeness. Statistical characteristics of the observed and simulated data are analyzed. Physical <span class="hlt">downscaling</span> of model wind and downward shortwave flux at the ground surface was obtained, with consideration of the influence of topography, cloud cover, turbulence kinetic energy and other characteristics of the PBL. The results show that the temporal variance of shortwave flux is greater than that of the wind power density, but the spatial variance of the wind power density is much greater than that of the shortwave flux. Furthermore, the WRF results are compared with the Operational Multiscale Environment model with Grid Adaptivity (OMEGA) model results. Physical <span class="hlt">downscaling</span> methods with different parameters are introduced and implemented. The representativeness of model results and observed data are discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFM.A41I0086L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFM.A41I0086L"><span id="translatedtitle"><span class="hlt">Ensemble</span> simulations to study the impact of land use change of Atlanta to regional climate</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.; Hu, Y.; Stone, B.; Vargo, J.; Nenes, A.; Russell, A.; Trail, M.; Tsimpidi, A.</p> <p>2012-12-01</p> <p>Studies show that urban areas may be the "first responders" to climate change (Rosenzweig et al., 2010). Of particular interest is the potential increased temperatures in urban areas, due to use of structures and surfaces that increase local heating, and how that may impact health, air quality and other environmental factors. In response, interest has grown as to how the modification of land use in urban areas, in order to mitigate the adverse effects of urbanization can serve to reduce local temperatures, and how climate is impacted more regionally. Studies have been conducted to investigate the impact of land use change on local or regional climate by dynamic <span class="hlt">downscaling</span> using regional climate models (RCMs), the boundary conditions (BCs) and initial conditions (ICs) of which result from coarser-resolution reanalysis data or general circulation models (GCMs). However, few studies have focused on demonstrating whether the land use change in local areas significantly impacts the climate of the larger region of the domain, and the spatial scale of the impact from urban-scale changes. This work investigated the significance of the impact of land use change in the Atlanta city area on different scales, using a range of modeling resolutions, including the contiguous US (with 36km resolution), the southeastern US (with 12km resolution) and the state of Georgia (with 4km resolution). We used WRF version 3.1.1 with and ran continuous from June to August of a simulated year 2050, driven by GISS ModelE with inputs corresponding to RCP4.5. During the simulation, spectral nudging is used in the 36km resolution domain to maintain the climate patterns with scales larger than 2000km. Two-way nesting is also used in order to take into account the feedback of nesting domains across model domains. Two land use cases over the Atlanta city are chosen. For the base case, most of the urban area of Atlanta is covered with forest; while for the second, "impervious" case, all the urban area within 30 miles of the center of Atlanta is replaced with asphalt. This choice is made to maximize the potential effects and scales of impact. To make the two cases different as much as possible, a constant green vegetation fraction of 1.0 is assigned to the forest over the Atlanta; while 0.0 is assigned to the asphalt. To test the significance of the impact of land use change, 5 <span class="hlt">ensemble</span> members were generated for each land use case using different initial conditions. The results of student's t test found that the impact of land use change in Atlanta city has a very local impact. This finding indicates that using WRF, applied at continental and regional scales, with BCs from the GCM and with spectral nudging, is appropriate. Although our results showed the impact is very local, results may change when meteorological conditions change or the area where land use changes is increased. Therefore, when investigating the land use change relevant issues, similar testing is suggested in order to demonstrate that the domain is large enough so that <span class="hlt">downscaling</span> by RCMs is an appropriate approach. References: Rosenzweig, C., W. Solecki, S.A. Hammer, and S. Mehrotra, 2010: Cities lead the way in climate-change action. Nature, 467, 909-911, doi:10.1038/467909a</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.cs.uoi.gr/~kblekas/papers/AngryBER_2015.pdf','EPRINT'); return false;" href="http://www.cs.uoi.gr/~kblekas/papers/AngryBER_2015.pdf"><span id="translatedtitle">A Bayesian <span class="hlt">Ensemble</span> Regression Framework on the Angry Birds Game</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Blekas, Konstantinos</p> <p></p> <p>1 A Bayesian <span class="hlt">Ensemble</span> Regression Framework on the Angry Birds Game Nikolaos Tziortziotis, GeorgiosBER, an intelligent agent architecture on the Angry Birds domain that employs a Bayesian <span class="hlt">ensemble</span> inference mechanism of objects' material and bird type has its own regression model. We address the problem of action selection</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/21583318','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/21583318"><span id="translatedtitle">Crossover <span class="hlt">ensembles</span> of random matrices and skew-orthogonal polynomials</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Kumar, Santosh; Pandey, Akhilesh</p> <p>2011-08-15</p> <p>Highlights: > We study crossover <span class="hlt">ensembles</span> of Jacobi family of random matrices. > We consider correlations for orthogonal-unitary and symplectic-unitary crossovers. > We use the method of skew-orthogonal polynomials and quaternion determinants. > We prove universality of spectral correlations in crossover <span class="hlt">ensembles</span>. > We discuss applications to quantum conductance and communication theory problems. - Abstract: In a recent paper (S. Kumar, A. Pandey, Phys. Rev. E, 79, 2009, p. 026211) we considered Jacobi family (including Laguerre and Gaussian cases) of random matrix <span class="hlt">ensembles</span> and reported exact solutions of crossover problems involving time-reversal symmetry breaking. In the present paper we give details of the work. We start with Dyson's Brownian motion description of random matrix <span class="hlt">ensembles</span> and obtain universal hierarchic relations among the unfolded correlation functions. For arbitrary dimensions we derive the joint probability density (jpd) of eigenvalues for all transitions leading to unitary <span class="hlt">ensembles</span> as equilibrium <span class="hlt">ensembles</span>. We focus on the orthogonal-unitary and symplectic-unitary crossovers and give generic expressions for jpd of eigenvalues, two-point kernels and n-level correlation functions. This involves generalization of the theory of skew-orthogonal polynomials to crossover <span class="hlt">ensembles</span>. We also consider crossovers in the circular <span class="hlt">ensembles</span> to show the generality of our method. In the large dimensionality limit, correlations in spectra with arbitrary initial density are shown to be universal when expressed in terms of a rescaled symmetry breaking parameter. Applications of our crossover results to communication theory and quantum conductance problems are also briefly discussed.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70031043','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70031043"><span id="translatedtitle">Improving land resource evaluation using fuzzy neural network <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>XUE, Y.-J.; HU, Y.-M.; Liu, S.-G.; YANG, J.-F.; CHEN, Q.-C.; BAO, S.-T.</p> <p>2007-01-01</p> <p>Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts' experiences. In order to overcome the shortcoming, we presented a fuzzy neural network <span class="hlt">ensemble</span> method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network (BPNN), a fuzzy radial basis function neural network (RBFNN), a fuzzy BPNN <span class="hlt">ensemble</span>, and a fuzzy RBFNN <span class="hlt">ensemble</span> were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN <span class="hlt">ensemble</span> and the fuzzy RBFNN <span class="hlt">ensemble</span> were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN <span class="hlt">ensemble</span> was lower than that of the single fuzzy BPNN or fuzzy BPNN <span class="hlt">ensemble</span>, respectively. By using the fuzzy neural network <span class="hlt">ensembles</span>, the validity of land resource evaluation was improved and reliance on land evaluators' experiences was considerably reduced. ?? 2007 Soil Science Society of China.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=music+AND+music&pg=5&id=EJ919102','ERIC'); return false;" href="http://eric.ed.gov/?q=music+AND+music&pg=5&id=EJ919102"><span id="translatedtitle">Idea Bank: Chamber Music within the Large <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>Neidlinger, Erica</p> <p>2011-01-01</p> <p>Many music educators incorporate chamber music in their <span class="hlt">ensemble</span> programs--an excellent way to promote musical independence. However, they rarely think of the large <span class="hlt">ensemble</span> as myriad chamber interactions. Rehearsals become more productive when greater responsibility for music-making is placed on the individual student. This article presents some…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=lead&pg=5&id=EJ1009350','ERIC'); return false;" href="http://eric.ed.gov/?q=lead&pg=5&id=EJ1009350"><span id="translatedtitle">Programming in the Zone: Repertoire Selection for the Large <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>Hopkins, Michael</p> <p>2013-01-01</p> <p>One of the great challenges <span class="hlt">ensemble</span> directors face is selecting high-quality repertoire that matches the musical and technical levels of their <span class="hlt">ensembles</span>. Thoughtful repertoire selection can lead to increased student motivation as well as greater enthusiasm for the music program from parents, administrators, teachers, and community members. Common…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.cs.bham.ac.uk/~xin/papers/ChenYaoCEC07.pdf','EPRINT'); return false;" href="http://www.cs.bham.ac.uk/~xin/papers/ChenYaoCEC07.pdf"><span id="translatedtitle">Evolutionary Random Neural <span class="hlt">Ensembles</span> Based on Negative Correlation Learning</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Yao, Xin</p> <p></p> <p>. Evolving the <span class="hlt">ensemble</span> with negative correlation learning emphasizes not only the accuracy of individual NNs without any extra data points, serves another benefit of this algorithm. The proposed algorithm][5] and empir- ical studies [6] that the generalization ability of <span class="hlt">ensemble</span> depends greatly on both accuracy</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.cs.bham.ac.uk/~xin/papers/ChenYaoICIC07.pdf','EPRINT'); return false;" href="http://www.cs.bham.ac.uk/~xin/papers/ChenYaoICIC07.pdf"><span id="translatedtitle">Evolutionary <span class="hlt">Ensemble</span> for In Silico Prediction of Ames Test Mutagenicity</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Yao, Xin</p> <p></p> <p>of chemicals without animal testing. This paper de- scribes a novel machine learning <span class="hlt">ensemble</span> approach of in silico models as alternative approaches to mutagenicity assessment of chemicals without animal testingEvolutionary <span class="hlt">Ensemble</span> for In Silico Prediction of Ames Test Mutagenicity Huanhuan Chen and Xin Yao</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ccl.cse.nd.edu/research/papers/wq-python-pyhpc2011.pdf','EPRINT'); return false;" href="http://ccl.cse.nd.edu/research/papers/wq-python-pyhpc2011.pdf"><span id="translatedtitle">Work Queue + Python: A Framework For Scalable Scientific <span class="hlt">Ensemble</span> Applications</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Thain, Douglas</p> <p></p> <p>Work Queue + Python: A Framework For Scalable Scientific <span class="hlt">Ensemble</span> Applications Peter Bui, Dinesh Work Queue, a flexible master/- worker framework for building large scale scientific <span class="hlt">ensemble</span> ap- plications that span many machines including clusters, grids, and clouds. In this paper, we describe Work</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=network+AND+training&pg=2&id=ED518411','ERIC'); return false;" href="http://eric.ed.gov/?q=network+AND+training&pg=2&id=ED518411"><span id="translatedtitle">Competitive Learning Neural Network <span class="hlt">Ensemble</span> Weighted by Predicted Performance</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>Ye, Qiang</p> <p>2010-01-01</p> <p><span class="hlt">Ensemble</span> approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes <span class="hlt">ensemble</span> performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.weizmann.ac.il/home/bnmisha/publications/papers/gili.pdf','EPRINT'); return false;" href="http://www.weizmann.ac.il/home/bnmisha/publications/papers/gili.pdf"><span id="translatedtitle">Dynamics of population rate codes in <span class="hlt">ensembles</span> of neocortical neurons</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Tsodyks, Misha</p> <p></p> <p>Dynamics of population rate codes in <span class="hlt">ensembles</span> of neocortical neurons Silberberg, G.1 , Bethge, M.2- dicating that neuronal <span class="hlt">ensembles</span> faithfully transmit rapidly changing signals to each other. Apart from signal-to-noise issues, population codes are funda- mentally constrained by the neuronal dynamics</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.mbfys.ru.nl/staff/v.gomez/pres_ijcnn06.pdf','EPRINT'); return false;" href="http://www.mbfys.ru.nl/staff/v.gomez/pres_ijcnn06.pdf"><span id="translatedtitle">Event modelling of message interchange in stochastic neural <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Gómez, Vicenç</p> <p></p> <p>Event modelling of message interchange in stochastic neural <span class="hlt">ensembles</span> Vicenç Gomez Andreas;Outline Introduction Mesoscopic event-driven modeling A case study: discrete and stochastic <span class="hlt">ensembles</span> not determine the microstates #12;Existing event-driven approaches The membrane potential drives the simulation</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=music+AND+thompson&pg=4&id=EJ397133','ERIC'); return false;" href="http://eric.ed.gov/?q=music+AND+thompson&pg=4&id=EJ397133"><span id="translatedtitle">A Different Drum: Percussion <span class="hlt">Ensembles</span> in General Music.</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>Morris, J. David; Thompson, Keith P.</p> <p>1989-01-01</p> <p>Encourages the use of percussion <span class="hlt">ensembles</span> in upper elementary and secondary general music classes because of their high motivational value, the ease of achieving performance, and the minimal need for music reading skills. Offers guidelines for introducing percussion <span class="hlt">ensemble</span> music, including an annotated discography listing selections to…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/EJ996057.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/EJ996057.pdf"><span id="translatedtitle">Preferences of and Attitudes toward Treble Choral <span class="hlt">Ensembles</span></span></a></p> <p><a target="_blank" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Wilson, Jill M.</p> <p>2012-01-01</p> <p>In choral <span class="hlt">ensembles</span>, a pursuit where females far outnumber males, concern exists that females are being devalued. Attitudes of female choral singers may be negatively affected by the gender imbalance that exists in mixed choirs and by the placement of the mixed choir as the most select <span class="hlt">ensemble</span> in a program. The purpose of this research was to…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.imm.dtu.dk/pubdb/views/edoc_download.php/6490/pdf/imm6490.pdf','EPRINT'); return false;" href="http://www.imm.dtu.dk/pubdb/views/edoc_download.php/6490/pdf/imm6490.pdf"><span id="translatedtitle">Wind-Wave Probabilistic Forecasting based on <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p>calibration methods are tested on ECMWF <span class="hlt">ensemble</span> predictions over the offshore platform FINO1 located are tested on the ECMWF <span class="hlt">ensemble</span> forecasts over the offshore measurement platforms FINO1 located in the North have to be jointly taken into account in some decision-making problems, e.g. offshore wind farm</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ma.utexas.edu/mp_arc/c/07/07-201.pdf','EPRINT'); return false;" href="http://www.ma.utexas.edu/mp_arc/c/07/07-201.pdf"><span id="translatedtitle">Logistic <span class="hlt">Ensembles</span> for Principal Direction and Random Spherical Linear Oracles</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p>Logistic <span class="hlt">Ensembles</span> for Principal Direction and Random Spherical Linear Oracles Leif E. Peterson, Matthew A. Coleman Abstract--Principal direction linear oracle (PDLO) and ran- dom spherical linear oracle (RSLO) <span class="hlt">ensemble</span> classifiers for DNA microarray gene expression data are proposed. The oracle assigns</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://ima.ac.uk/wp-content/uploads/2014/09/ieee_civemsa_colon_cancer_chris_web.pdf','EPRINT'); return false;" href="http://ima.ac.uk/wp-content/uploads/2014/09/ieee_civemsa_colon_cancer_chris_web.pdf"><span id="translatedtitle"><span class="hlt">Ensemble</span> Learning of Colorectal Cancer Survival Chris Roadknight</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Aickelin, Uwe</p> <p></p> <p><span class="hlt">Ensemble</span> Learning of Colorectal Cancer Survival Rates Chris Roadknight School of Computing Science--<span class="hlt">ensemble</span> learning; anti-learning; colorectal cancer. I. INTRODUCTION Colorectal cancer is the third most commonly diagnosed cancer in the world. Colorectal cancers start in the lining of the bowel and grow into the muscle</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://eric.ed.gov/?q=conductors&pg=3&id=EJ987234','ERIC'); return false;" href="http://eric.ed.gov/?q=conductors&pg=3&id=EJ987234"><span id="translatedtitle">Practice Makes Perfect?: Effective Practice Instruction in Large <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>Prichard, Stephanie</p> <p>2012-01-01</p> <p>Helping young musicians learn how to practice effectively is a challenge faced by all music educators. This article presents a system of individual music practice instruction that can be seamlessly integrated within large-<span class="hlt">ensemble</span> rehearsals. Using a step-by-step approach, large-<span class="hlt">ensemble</span> conductors can teach students to identify and isolate…</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://users.math.uni-potsdam.de/~sreich/11_4.pdf','EPRINT'); return false;" href="http://users.math.uni-potsdam.de/~sreich/11_4.pdf"><span id="translatedtitle">An <span class="hlt">ensemble</span> Kalman-Bucy filter for continuous data assimilation</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Reich, Sebastian</p> <p></p> <p>An <span class="hlt">ensemble</span> Kalman-Bucy filter for continuous data assimilation Kay Bergemann and Sebastian Reich@uni-potsdam.de #12;Abstract1 The <span class="hlt">ensemble</span> Kalman filter has emerged as a promising filter algorithm for nonlinear2 differential equations subject to intermittent observations. In this paper, we extend the well-3 known Kalman</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/20696111','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/20696111"><span id="translatedtitle">Probability distributions in statistical <span class="hlt">ensembles</span> with conserved charges</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Cleymans, J.; Redlich, K.; Turko, L.</p> <p>2005-04-01</p> <p>The probability distributions for charged particle numbers and their densities are derived in statistical <span class="hlt">ensembles</span> with conservation laws. It is shown that if this limit is properly taken, then the canonical and grand canonical <span class="hlt">ensembles</span> are equivalent. This equivalence is proven on the most general probability distribution level.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://globalchange.mit.edu/files/document/MITJPSPGC_Reprint07-13.pdf','EPRINT'); return false;" href="http://globalchange.mit.edu/files/document/MITJPSPGC_Reprint07-13.pdf"><span id="translatedtitle"><span class="hlt">Ensemble</span> climate predictions using climate models and observational constraints</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p></p> <p></p> <p>REVIEW <span class="hlt">Ensemble</span> climate predictions using climate models and observational constraints BY PETER A. STOTT 1,* AND CHRIS E. FOREST 2 1 Hadley Centre for Climate Change (Reading Unit), Meteorology Building for constraining climate predictions based on observations of past climate change. The first uses large <span class="hlt">ensembles</span></p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://users.math.uni-potsdam.de/~sreich/10_2.pdf','EPRINT'); return false;" href="http://users.math.uni-potsdam.de/~sreich/10_2.pdf"><span id="translatedtitle">A mollified <span class="hlt">ensemble</span> Kalman filter Kay Bergemann Sebastian Reich</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Reich, Sebastian</p> <p></p> <p>A mollified <span class="hlt">ensemble</span> Kalman filter Kay Bergemann Sebastian Reich May 19, 2010 Abstract It is well Kalman filters, might lead to spurious high frequency adjustment processes in the model dynamics. Various arises naturally from a recently proposed continuous formulation of the <span class="hlt">ensemble</span> Kalman analysis step</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.weatherchaos.umd.edu/papers/Kuhl_et_al_2005.pdf','EPRINT'); return false;" href="http://www.weatherchaos.umd.edu/papers/Kuhl_et_al_2005.pdf"><span id="translatedtitle">Assessing Predictability with a Local <span class="hlt">Ensemble</span> Kalman David D. Kuhl</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Maryland at College Park, University of</p> <p></p> <p>Assessing Predictability with a Local <span class="hlt">Ensemble</span> Kalman Filter David D. Kuhl Department observations of the "true" states with the Local <span class="hlt">Ensemble</span> Kalman Filter (LEKF) data assimilation scheme-based Kalman filter data assimilation schemes. 1 #12;1 Introduction In dynamical systems theory, predictability</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22080313','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22080313"><span id="translatedtitle">Heralded amplification for precision measurements with spin <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Brunner, Nicolas; Polzik, Eugene S.; Simon, Christoph</p> <p>2011-10-15</p> <p>We propose a simple heralded amplification scheme for small rotations of the collective spin of an <span class="hlt">ensemble</span> of particles. Our protocol makes use of two basic primitives for quantum memories, namely, partial mapping of light onto an <span class="hlt">ensemble</span>, and conversion of a collective spin excitation into light. The proposed scheme should be realizable with current technology, with potential applications to atomic clocks and magnetometry.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.era.lib.ed.ac.uk/handle/1842/3672','EPRINT'); return false;" href="http://www.era.lib.ed.ac.uk/handle/1842/3672"><span id="translatedtitle">Efficient Online classification using an <span class="hlt">Ensemble</span> of Bayesian Linear Logistic Regressors </span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Edakunni, Narayanan U.; Vijayakumar, Sethu</p> <p>2009-01-01</p> <p>We present a novel <span class="hlt">ensemble</span> of logistic linear regressors that combines the robustness of online Bayesian learning with the flexibility of <span class="hlt">ensembles</span>. The <span class="hlt">ensemble</span> of classifiers are built on top of a Randomly Varying ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://dspace.mit.edu/handle/1721.1/90312','EPRINT'); return false;" href="http://dspace.mit.edu/handle/1721.1/90312"><span id="translatedtitle">Conservation of Mass and Preservation of Positivity with <span class="hlt">Ensemble</span>-Type Kalman Filter Algorithms</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>McLaughlin, Dennis</p> <p></p> <p>This paper considers the incorporation of constraints to enforce physically based conservation laws in the <span class="hlt">ensemble</span> Kalman filter. In particular, constraints are used to ensure that the <span class="hlt">ensemble</span> members and the <span class="hlt">ensemble</span> ...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/22252945','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/22252945"><span id="translatedtitle">Excitations and benchmark <span class="hlt">ensemble</span> density functional theory for two electrons</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Pribram-Jones, Aurora; Burke, Kieron; Yang, Zeng-hui; Ullrich, Carsten A.; Trail, John R.; Needs, Richard J.</p> <p>2014-05-14</p> <p>A new method for extracting <span class="hlt">ensemble</span> Kohn-Sham potentials from accurate excited state densities is applied to a variety of two-electron systems, exploring the behavior of exact <span class="hlt">ensemble</span> density functional theory. The issue of separating the Hartree energy and the choice of degenerate eigenstates is explored. A new approximation, spin eigenstate Hartree-exchange, is derived. Exact conditions that are proven include the signs of the correlation energy components and the asymptotic behavior of the potential for small weights of the excited states. Many energy components are given as a function of the weights for two electrons in a one-dimensional flat box, in a box with a large barrier to create charge transfer excitations, in a three-dimensional harmonic well (Hooke's atom), and for the He atom singlet-triplet <span class="hlt">ensemble</span>, singlet-triplet-singlet <span class="hlt">ensemble</span>, and triplet bi-<span class="hlt">ensemble</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JHyd..524..789H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JHyd..524..789H"><span id="translatedtitle"><span class="hlt">Ensemble</span> Bayesian forecasting system Part I: Theory and algorithms</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Herr, Henry D.; Krzysztofowicz, Roman</p> <p>2015-05-01</p> <p>The <span class="hlt">ensemble</span> Bayesian forecasting system (EBFS), whose theory was published in 2001, is developed for the purpose of quantifying the total uncertainty about a discrete-time, continuous-state, non-stationary stochastic process such as a time series of stages, discharges, or volumes at a river gauge. The EBFS is built of three components: an input <span class="hlt">ensemble</span> forecaster (IEF), which simulates the uncertainty associated with random inputs; a deterministic hydrologic model (of any complexity), which simulates physical processes within a river basin; and a hydrologic uncertainty processor (HUP), which simulates the hydrologic uncertainty (an aggregate of all uncertainties except input). It works as a Monte Carlo simulator: an <span class="hlt">ensemble</span> of time series of inputs (e.g., precipitation amounts) generated by the IEF is transformed deterministically through a hydrologic model into an <span class="hlt">ensemble</span> of time series of outputs, which is next transformed stochastically by the HUP into an <span class="hlt">ensemble</span> of time series of predictands (e.g., river stages). Previous research indicated that in order to attain an acceptable sampling error, the <span class="hlt">ensemble</span> size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts). The computing time needed to run the hydrologic model this many times renders the straightforward simulations operationally infeasible. This motivates the development of the <span class="hlt">ensemble</span> Bayesian forecasting system with randomization (EBFSR), which takes full advantage of the analytic meta-Gaussian HUP and generates multiple <span class="hlt">ensemble</span> members after each run of the hydrologic model; this auxiliary randomization reduces the required size of the meteorological input <span class="hlt">ensemble</span> and makes it operationally feasible to generate a Bayesian <span class="hlt">ensemble</span> forecast of large size. Such a forecast quantifies the total uncertainty, is well calibrated against the prior (climatic) distribution of predictand, possesses a Bayesian coherence property, constitutes a random sample of the predictand, and has an acceptable sampling error-which makes it suitable for rational decision making under uncertainty.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1207.3339v1','EPRINT'); return false;" href="http://arxiv.org/pdf/1207.3339v1"><span id="translatedtitle">Magnetic field imaging with NV <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>L. M. Pham; D. Le Sage; P. L. Stanwix; T. K. Yeung; D. Glenn; A. Trifonov; P. Cappellaro; P. R. Hemmer; M. D. Lukin; H. Park; A. Yacoby; R. L. Walsworth</p> <p>2012-07-13</p> <p>We demonstrate a method of imaging spatially varying magnetic fields using a thin layer of nitrogen-vacancy (NV) centers at the surface of a diamond chip. Fluorescence emitted by the two-dimensional NV <span class="hlt">ensemble</span> is detected by a CCD array, from which a vector magnetic field pattern is reconstructed. As a demonstration, AC current is passed through wires placed on the diamond chip surface, and the resulting AC magnetic field patterns are imaged using an echo-based technique with sub-micron resolution over a 140 \\mu m x 140 \\mu m field of view, giving single-pixel sensitivity ~100 nT/\\sqrt{Hz}. We discuss ongoing efforts to further improve sensitivity and potential bioimaging applications such as real-time imaging of activity in functional, cultured networks of neurons.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1202.1072v4','EPRINT'); return false;" href="http://arxiv.org/pdf/1202.1072v4"><span id="translatedtitle">Optical polarization of nuclear <span class="hlt">ensembles</span> in diamond</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Ran Fischer; Andrey Jarmola; Pauli Kehayias; Dmitry Budker</p> <p>2013-01-21</p> <p>We report polarization of a dense nuclear-spin <span class="hlt">ensemble</span> in diamond and its dependence on magnetic field and temperature. The polarization method is based on the transfer of electron spin polarization of negatively charged nitrogen vacancy color centers to the nuclear spins via the excited-state level anti-crossing of the center. We polarize 90% of the 14N nuclear spins within the NV centers, and 70% of the proximal 13C nuclear spins with hyperfine interaction strength of 13-14 MHz. Magnetic-field dependence of the polarization reveals sharp decrease in polarization at specific field values corresponding to cross-relaxation with substitutional nitrogen centers, while temperature dependence of the polarization reveals that high polarization persists down to 50 K. This work enables polarization of the 13C in bulk diamond, which is of interest in applications of nuclear magnetic resonance, in quantum memories of hybrid quantum devices, and in sensing.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/22575690','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/22575690"><span id="translatedtitle">Recognizing Emotions From an <span class="hlt">Ensemble</span> of Features.</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tariq, U; Kai-Hsiang Lin; Zhen Li; Xi Zhou; Zhaowen Wang; Vuong Le; Huang, T S; Xutao Lv; Han, T X</p> <p>2012-08-01</p> <p>This paper details the authors' efforts to push the baseline of emotion recognition performance on the Geneva Multimodal Emotion Portrayals (GEMEP) Facial Expression Recognition and Analysis database. Both subject-dependent and subject-independent emotion recognition scenarios are addressed in this paper. The approach toward solving this problem involves face detection, followed by key-point identification, then feature generation, and then, finally, classification. An <span class="hlt">ensemble</span> of features consisting of hierarchical Gaussianization, scale-invariant feature transform, and some coarse motion features have been used. In the classification stage, we used support vector machines. The classification task has been divided into person-specific and person-independent emotion recognitions using face recognition with either manual labels or automatic algorithms. We achieve 100% performance for the person-specific one, 66% performance for the person-independent one, and 80% performance for overall results, in terms of classification rate, for emotion recognition with manual identification of subjects. PMID:22575690</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_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_23 --> <div id="page_24" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="461"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1509.08551.pdf','EPRINT'); return false;" href="http://arxiv.org/pdf/1509.08551.pdf"><span id="translatedtitle">Predicting protein dynamics from structural <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Copperman, J</p> <p>2015-01-01</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 e...</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/servlets/purl/1024213','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/servlets/purl/1024213"><span id="translatedtitle">ARM Cloud Retrieval <span class="hlt">Ensemble</span> Data Set (ACRED)</span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Zhao, C; Xie, S; Klein, SA; McCoy, R; Comstock, JM; Delanoë, J; Deng, M; Dunn, M; Hogan, RJ; Jensen, MP; Mace, GG; McFarlane, SA; O’Connor, EJ; Protat, A; Shupe, MD; Turner, D; Wang, Z</p> <p>2011-09-12</p> <p>This document describes a new Atmospheric Radiation Measurement (ARM) data set, the ARM Cloud Retrieval <span class="hlt">Ensemble</span> Data Set (ACRED), which is created by assembling nine existing ground-based cloud retrievals of ARM measurements from different cloud retrieval algorithms. The current version of ACRED includes an hourly average of nine ground-based retrievals with vertical resolution of 45 m for 512 layers. The techniques used for the nine cloud retrievals are briefly described in this document. This document also outlines the ACRED data availability, variables, and the nine retrieval products. Technical details about the generation of ACRED, such as the methods used for time average and vertical re-grid, are also provided.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/0810.2349v1','EPRINT'); return false;" href="http://arxiv.org/pdf/0810.2349v1"><span id="translatedtitle">Finite Density Simulations with Canonical <span class="hlt">Ensemble</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Anyi Li; Xiangfei Meng; Andrei Alexandru; Keh-Fei Liu</p> <p>2008-10-14</p> <p>QCD at non-zero baryon density is expected to have a critical point where the zero-density cross-over turns into a first order phase transition. To identify this point we scan the density-temperature space using a canonical <span class="hlt">ensemble</span> method. For a given temperature, we plot the chemical potential as a function of density looking for an "S-shape" as a signal for a first order transition. We carried out simulations using Wilson fermions with $m_\\pi \\approx 1{GeV}$ on $6^3\\times 4$ lattices. As a benchmark, we ran four flavors simulations where we observe a clear signal. In the two flavors case we do not see any signal for temperatures as low as $0.83 T_c$. Preliminary results for the three flavor case are also presented.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFM.H41I..07H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFM.H41I..07H"><span id="translatedtitle"><span class="hlt">Ensemble</span> Data Assimilation for Streamflow Forecasting: Experiments with <span class="hlt">Ensemble</span> Kalman Filter and Particle Filter</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hirpa, F. A.; Gebremichael, M.; Hopson, T. M.; Wojick, R.</p> <p>2011-12-01</p> <p>We present results of data assimilation of ground discharge observation and remotely sensed soil moisture observations into Sacramento Soil Moisture Accounting (SACSMA) model in a small watershed (1593 km2) in Minnesota, the Unites States. Specifically, we perform assimilation experiments with <span class="hlt">Ensemble</span> Kalman Filter (EnKF) and Particle Filter (PF) in order to improve streamflow forecast accuracy at six hourly time step. The EnKF updates the soil moisture states in the SACSMA from the relative errors of the model and observations, while the PF adjust the weights of the state <span class="hlt">ensemble</span> members based on the likelihood of the forecast. Results of the improvements of each filter over the reference model (without data assimilation) will be presented. Finally, the EnKF and PF are coupled together to further improve the streamflow forecast accuracy.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.knmi.nl/publications/fulltexts/sk2010_copy1.pdf','EPRINT'); return false;" href="http://www.knmi.nl/publications/fulltexts/sk2010_copy1.pdf"><span id="translatedtitle">A Comparison between Raw <span class="hlt">Ensemble</span> Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF <span class="hlt">Ensemble</span> Precipitation Reforecasts</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Schmeits, Maurice</p> <p></p> <p>Logistic Regression Using ECMWF <span class="hlt">Ensemble</span> Precipitation Reforecasts MAURICE J. SCHMEITS AND KEES J. KOK between the raw <span class="hlt">ensemble</span> output, Bayesian model averaging (BMA), and extended logistic regression (LR was made between a large number of statistical methods, such as logistic regression (LR; Brelsford</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015ThApC.tmp..198E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015ThApC.tmp..198E"><span id="translatedtitle">Assessment of climate change <span class="hlt">downscaling</span> and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Etemadi, Halimeh; Samadi, S. Zahra; Sharifikia, Mohammad; Smoak, Joseph M.</p> <p>2015-07-01</p> <p>Mangrove wetlands exist in the transition zone between terrestrial and marine environments and have remarkable ecological and socio-economic value. This study uses climate change <span class="hlt">downscaling</span> to address the question of non-stationarity influences on mangrove variations (expansion and contraction) within an arid coastal region. Our two-step approach includes <span class="hlt">downscaling</span> models and uncertainty assessment, followed by a non-stationary and trend procedure using the Extreme Value Analysis (extRemes code). The Long Ashton Research Station Weather Generator (LARS-WG) model along with two different general circulation model (GCMs) (MIRH and HadCM3) were used to <span class="hlt">downscale</span> climatic variables during current (1968-2011) and future (2011-2030, 2045-2065, and 2080-2099) periods. Parametric and non-parametric bootstrapping uncertainty tests demonstrated that the LARS-WGS model skillfully <span class="hlt">downscaled</span> climatic variables at the 95 % significance level. <span class="hlt">Downscaling</span> results using MIHR model show that minimum and maximum temperatures will increase in the future (2011-2030, 2045-2065, and 2080-2099) during winter and summer in a range of +4.21 and +4.7 °C, and +3.62 and +3.55 °C, respectively. HadCM3 analysis also revealed an increase in minimum (˜+3.03 °C) and maximum (˜+3.3 °C) temperatures during wet and dry seasons. In addition, we examined how much mangrove area has changed during the past decades and, thus, if climate change non-stationarity impacts mangrove ecosystems. Our results using remote sensing techniques and the non-parametric Mann-Whitney two-sample test indicated a sharp decline in mangrove area during 1972,1987, and 1997 periods (p value = 0.002). Non-stationary assessment using the generalized extreme value (GEV) distributions by including mangrove area as a covariate further indicated that the null hypothesis of the stationary climate (no trend) should be rejected due to the very low p values for precipitation (p value = 0.0027), minimum (p value = 0.000000029) and maximum (p value = 0.00016) temperatures. Based on non-stationary analysis and an upward trend in <span class="hlt">downscaled</span> temperature extremes, climate change may control mangrove development in the future.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://pubs.er.usgs.gov/publication/70035825','USGSPUBS'); return false;" href="http://pubs.er.usgs.gov/publication/70035825"><span id="translatedtitle">Assessing the impact of land use change on hydrology by <span class="hlt">ensemble</span> modelling (LUCHEM) II: <span class="hlt">Ensemble</span> combinations and predictions</span></a></p> <p><a target="_blank" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.</p> <p>2009-01-01</p> <p>This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various <span class="hlt">ensemble</span> predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model <span class="hlt">ensemble</span>. The 10 resulting single-model <span class="hlt">ensembles</span> are combined in various ways to produce multi-model <span class="hlt">ensemble</span> predictions. Both the single-model <span class="hlt">ensembles</span> and the multi-model <span class="hlt">ensembles</span> are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the <span class="hlt">ensembles</span> they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean <span class="hlt">ensemble</span> (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional <span class="hlt">ensembles</span>, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean <span class="hlt">ensemble</span>. However a conditional <span class="hlt">ensemble</span> that discriminates between rising and receding flows shows moderate improvement. An analysis of <span class="hlt">ensemble</span> predictions shows that the best <span class="hlt">ensembles</span> are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in multi-model <span class="hlt">ensembles</span>. The reasons behind these observations may relate to the effects of the weighting schemes, non-stationarity of the climate series and possible cross-correlations between models. Crown Copyright ?? 2008.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JCoPh.292...30R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JCoPh.292...30R"><span id="translatedtitle"><span class="hlt">Ensemble</span>-type numerical uncertainty information from single model integrations</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rauser, Florian; Marotzke, Jochem; Korn, Peter</p> <p>2015-07-01</p> <p>We suggest an algorithm that quantifies the discretization error of time-dependent physical quantities of interest (goals) for numerical models of geophysical fluid dynamics. The goal discretization error is estimated using a sum of weighted local discretization errors. The key feature of our algorithm is that these local discretization errors are interpreted as realizations of a random process. The random process is determined by the model and the flow state. From a class of local error random processes we select a suitable specific random process by integrating the model over a short time interval at different resolutions. The weights of the influences of the local discretization errors on the goal are modeled as goal sensitivities, which are calculated via automatic differentiation. The integration of the weighted realizations of local error random processes yields a posterior <span class="hlt">ensemble</span> of goal approximations from a single run of the numerical model. From the posterior <span class="hlt">ensemble</span> we derive the uncertainty information of the goal discretization error. This algorithm bypasses the requirement of detailed knowledge about the models discretization to generate numerical error estimates. The algorithm is evaluated for the spherical shallow-water equations. For two standard test cases we successfully estimate the error of regional potential energy, track its evolution, and compare it to standard <span class="hlt">ensemble</span> techniques. The posterior <span class="hlt">ensemble</span> shares linear-error-growth properties with <span class="hlt">ensembles</span> of multiple model integrations when comparably perturbed. The posterior <span class="hlt">ensemble</span> numerical error estimates are of comparable size as those of a stochastic physics <span class="hlt">ensemble</span>.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/12005990','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/12005990"><span id="translatedtitle">Skew-orthogonal polynomials and random-matrix <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>Ghosh, Saugata; Pandey, Akhilesh</p> <p>2002-04-01</p> <p>There is considerable interest in understanding the relation between random-matrix <span class="hlt">ensembles</span> and quantum chaotic systems in the context of the universality of energy-level correlations. In this connection, while Gaussian <span class="hlt">ensembles</span> of random matrices have been studied extensively, not much is known about <span class="hlt">ensembles</span> with non-Gaussian weight functions. Dyson has shown that the n-level correlation functions can be expressed in terms of a kernel function involving orthogonal and skew-orthogonal polynomials--orthogonal for matrix <span class="hlt">ensembles</span> with unitary invariance and skew orthogonal for <span class="hlt">ensembles</span> with orthogonal and symplectic invariances. We have obtained the following results. (1) Skew-orthogonal polynomials of both types are derived for the Jacobi class of weight functions including the limiting cases of associated Laguerre and Hermite (or Gaussian). (2) Matrix-integral representations are given for the general weight functions. (3) Asymptotic forms of the polynomials are obtained rigorously for the Jacobi class and in the form of an ansatz for the general case. (4) For the three types of <span class="hlt">ensembles</span>, the (asymptotic) n-level correlation functions with appropriate scaling are shown to be universal, being independent of the weight function and location in the spectrum, and identical with the well-known Gaussian results. This provides a rigorous justification for the universality of the Gaussian <span class="hlt">ensemble</span> results observed in quantum chaotic systems. As expected, the level density is not universal. PMID:12005990</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16..675G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16..675G"><span id="translatedtitle">Can the combined use of an <span class="hlt">ensemble</span> based modelling approach and the analysis of measured meteorological trends lead to increased confidence in climate change impact assessments?</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gädeke, Anne; Koch, Hagen; Pohle, Ina; Grünewald, Uwe</p> <p>2014-05-01</p> <p>In anthropogenically heavily impacted river catchments, such as the Lusatian river catchments of Spree and Schwarze Elster (Germany), the robust assessment of possible impacts of climate change on the regional water resources is of high relevance for the development and implementation of suitable climate change adaptation strategies. Large uncertainties inherent in future climate projections may, however, reduce the willingness of regional stakeholder to develop and implement suitable adaptation strategies to climate change. This study provides an overview of different possibilities to consider uncertainties in climate change impact assessments by means of (1) an <span class="hlt">ensemble</span> based modelling approach and (2) the incorporation of measured and simulated meteorological trends. The <span class="hlt">ensemble</span> based modelling approach consists of the meteorological output of four climate <span class="hlt">downscaling</span> approaches (DAs) (two dynamical and two statistical DAs (113 realisations in total)), which drive different model configurations of two conceptually different hydrological models (HBV-light and WaSiM-ETH). As study area serve three near natural subcatchments of the Spree and Schwarze Elster river catchments. The objective of incorporating measured meteorological trends into the analysis was twofold: measured trends can (i) serve as a mean to validate the results of the DAs and (ii) be regarded as harbinger for the future direction of change. Moreover, regional stakeholders seem to have more trust in measurements than in modelling results. In order to evaluate the nature of the trends, both gradual (Mann-Kendall test) and step changes (Pettitt test) are considered as well as both temporal and spatial correlations in the data. The results of the <span class="hlt">ensemble</span> based modelling chain show that depending on the type (dynamical or statistical) of DA used, opposing trends in precipitation, actual evapotranspiration and discharge are simulated in the scenario period (2031-2060). While the statistical DAs simulate a strong decrease in future long term annual precipitation, the dynamical DAs simulate a tendency towards increasing precipitation. The trend analysis suggests that precipitation has not changed significantly during the period 1961-2006. Therefore, the decrease simulated by the statistical DAs should be interpreted as a rather dry future projection. Concerning air temperature, measured and simulated trends agree on a positive trend. Also the uncertainty related to the hydrological model within the climate change modelling chain is comparably low when long-term averages are considered but increases significantly during extreme events. This proposed framework of combining an <span class="hlt">ensemble</span> based modelling approach with measured trend analysis is a promising approach for regional stakeholders to gain more confidence into the final results of climate change impact assessments. However, climate change impact assessments will remain highly uncertain. Thus, flexible adaptation strategies need to be developed which should not only consider climate but also other aspects of global change.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/23705420','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/23705420"><span id="translatedtitle">[The relationship between the variation rate of MODIS land surface temperature and AMSR-E soil moisture and its application to <span class="hlt">downscaling</span>].</span></a></p> <p><a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wang, An-Qi; Xie, Chao; Shi, Jian-Cheng; Gong, Hui-Li</p> <p>2013-03-01</p> <p>Using AMSR-E soil moisture, MODIS land surface temperature (Ts) and vegetation index product, the authors discuss the relationship between the variation rate of land surface temperature and surface soil moisture. Selecting the plains region of central United States as the study area, the authors propose the distribution triangle of the variation rate of land surface temperature and soil moisture. In the present paper, temperature variation and vegetation index (TVVI), a new index containing the information of temperature variation and vegetation, is introduced. The authors prove that TVVI and soil moisture show a steady relationship of exponential function; and build a quantitative model of soil moisture(SM) and instantaneous surface temperature variation (VTs). The authors later achieve <span class="hlt">downscaling</span> of AMSR-E soil moisture data, through the above stated functional relationships and high-resolution MODIS data. Comparison with measured data on ground surface indicates that this method of <span class="hlt">downscaling</span> is of high precision PMID:23705420</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ESD.....3...33H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ESD.....3...33H"><span id="translatedtitle"><span class="hlt">Downscaling</span> climate change scenarios for apple pest and disease modeling in Switzerland</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hirschi, M.; Stoeckli, S.; Dubrovsky, M.; Spirig, C.; Calanca, P.; Rotach, M. W.; Fischer, A. M.; Duffy, B.; Samietz, J.</p> <p>2012-02-01</p> <p>As a consequence of current and projected climate change in temperate regions of Europe, agricultural pests and diseases are expected to occur more frequently and possibly to extend to previously non-affected regions. Given their economic and ecological relevance, detailed forecasting tools for various pests and diseases have been developed, which model their phenology, depending on actual weather conditions, and suggest management decisions on that basis. Assessing the future risk of pest-related damages requires future weather data at high temporal and spatial resolution. Here, we use a combined stochastic weather generator and re-sampling procedure for producing site-specific hourly weather series representing present and future (1980-2009 and 2045-2074 time periods) climate conditions in Switzerland. The climate change scenarios originate from the <span class="hlt">ENSEMBLES</span> multi-model projections and provide probabilistic information on future regional changes in temperature and precipitation. Hourly weather series are produced by first generating daily weather data for these climate scenarios and then using a nearest neighbor re-sampling approach for creating realistic diurnal cycles. These hourly weather series are then used for modeling the impact of climate change on important life phases of the codling moth and on the number of predicted infection days of fire blight. Codling moth (Cydia pomonella) and fire blight (Erwinia amylovora) are two major pest and disease threats to apple, one of the most important commercial and rural crops across Europe. Results for the codling moth indicate a shift in the occurrence and duration of life phases relevant for pest control. In southern Switzerland, a 3rd generation per season occurs only very rarely under today's climate conditions but is projected to become normal in the 2045-2074 time period. While the potential risk for a 3rd generation is also significantly increasing in northern Switzerland (for most stations from roughly 1% on average today to over 60% in the future for the median climate change signal of the multi-model projections), the actual risk will critically depend on the pace of the adaptation of the codling moth with respect to the critical photoperiod. To control this additional generation, an intensification and prolongation of control measures (e.g. insecticides) will be required, implying an increasing risk of pesticide resistances. For fire blight, the projected changes in infection days are less certain due to uncertainties in the leaf wetness approximation and the simulation of the blooming period. Two compensating effects are projected, warmer temperatures favoring infections are balanced by a temperature-induced advancement of the blooming period, leading to no significant change in the number of infection days under future climate conditions for most stations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011ESDD....2..493H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011ESDD....2..493H"><span id="translatedtitle"><span class="hlt">Downscaling</span> climate change scenarios for apple pest and disease modeling in Switzerland</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Hirschi, M.; Stoeckli, S.; Dubrovsky, M.; Spirig, C.; Calanca, P.; Rotach, M. W.; Fischer, A. M.; Duffy, B.; Samietz, J.</p> <p>2011-08-01</p> <p>As a consequence of current and projected climate change in temperate regions of Europe, agricultural pests and diseases are expected to occur more frequently and possibly to extend to previously not affected regions. Given their economic and ecological relevance, detailed forecasting tools for various pests and diseases have been developed, which model their phenology depending on actual weather conditions and suggest management decisions on that basis. Assessing the future risk of pest-related damages requires future weather data at high temporal and spatial resolution. Here, we use a combined stochastic weather generator and re-sampling procedure for producing site-specific hourly weather series representing present and future (1980-2009 and 2045-2074 time periods) climate conditions in Switzerland. The climate change scenarios originate from the <span class="hlt">ENSEMBLES</span> multi-model projections and provide probabilistic information on future regional changes in temperature and precipitation. Hourly weather series are produced by first generating daily weather data for these climate scenarios and then using a nearest neighbor re-sampling approach for creating realistic diurnal cycles. These hourly weather series are then used for modeling the impact of climate change on important life phases of the codling moth and on the number of predicted infection days of fire blight. Codling moth (Cydia pomonella) and fire blight (Erwinia amylovora) are two major pest and disease threats to apple, one of the most important commercial and rural crops across Europe. Results for the codling moth indicate a shift in the occurrence and duration of life phases relevant for pest control. In southern Switzerland, a 3rd generation per season occurs only very rarely under today's climate conditions but is projected to become normal in the 2045-2074 time period. While the potential risk for a 3rd generation is also significantly increasing in northern Switzerland (for most stations from roughly 1 % on average today to over 60 % in the future for the median climate change signal of the multi-model projections), the actual risk will critically depend on the pace of the adaptation of the codling moth with respect to the critical photoperiod. To control this additional generation, an intensification and prolongation of control measures (e.g., insecticides) will be required, implying an increasing risk of pesticide resistances. For fire blight, the projected changes in infection days are less certain due to uncertainties in the leaf wetness approximation and the simulation of the blooming period. Two compensating effects are projected, warmer temperatures favoring infections are balanced by a temperature-induced advancement of the blooming period, leading to no significant change in the number of infection days under future climate conditions for most stations.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://hdl.handle.net/2060/20150003243','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20150003243"><span id="translatedtitle">Device and Method for Gathering <span class="hlt">Ensemble</span> Data Sets</span></a></p> <p><a target="_blank" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Racette, Paul E. (Inventor)</p> <p>2014-01-01</p> <p>An <span class="hlt">ensemble</span> detector uses calibrated noise references to produce <span class="hlt">ensemble</span> sets of data from which properties of non-stationary processes may be extracted. The <span class="hlt">ensemble</span> detector comprising: a receiver; a switching device coupled to the receiver, the switching device configured to selectively connect each of a plurality of reference noise signals to the receiver; and a gain modulation circuit coupled to the receiver and configured to vary a gain of the receiver based on a forcing signal; whereby the switching device selectively connects each of the plurality of reference noise signals to the receiver to produce an output signal derived from the plurality of reference noise signals and the forcing signal.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1501.05165.pdf','EPRINT'); return false;" href="http://arxiv.org/pdf/1501.05165.pdf"><span id="translatedtitle">Filtering single atoms from Rydberg blockaded mesoscopic <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Petrosyan, David; Mølmer, Klaus</p> <p>2015-01-01</p> <p>We propose an efficient method to filter out single atoms from trapped <span class="hlt">ensembles</span> with unknown number of atoms. The method employs stimulated adiabatic passage to reversibly transfer a single atom to the Rydberg state which blocks subsequent Rydberg excitation of all the other atoms within the <span class="hlt">ensemble</span>. This triggers the excitation of Rydberg blockaded atoms to short lived intermediate states and their subsequent decay to untrapped states. Using an auxiliary microwave field to carefully engineer the dissipation, we obtain a nearly deterministic single-atom source. Our method is applicable to small atomic <span class="hlt">ensembles</span> in individual microtraps and in lattice arrays.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1501.05165v1','EPRINT'); return false;" href="http://arxiv.org/pdf/1501.05165v1"><span id="translatedtitle">Filtering single atoms from Rydberg blockaded mesoscopic <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>David Petrosyan; D. D. Bhaktavatsala Rao; Klaus Mølmer</p> <p>2015-01-21</p> <p>We propose an efficient method to filter out single atoms from trapped <span class="hlt">ensembles</span> with unknown number of atoms. The method employs stimulated adiabatic passage to reversibly transfer a single atom to the Rydberg state which blocks subsequent Rydberg excitation of all the other atoms within the <span class="hlt">ensemble</span>. This triggers the excitation of Rydberg blockaded atoms to short lived intermediate states and their subsequent decay to untrapped states. Using an auxiliary microwave field to carefully engineer the dissipation, we obtain a nearly deterministic single-atom source. Our method is applicable to small atomic <span class="hlt">ensembles</span> in individual microtraps and in lattice arrays.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/quant-ph/0307055v1','EPRINT'); return false;" href="http://arxiv.org/pdf/quant-ph/0307055v1"><span id="translatedtitle">Parallel Quantum Computing in a Single <span class="hlt">Ensemble</span> Quantum Computer</span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Gui Lu Long; Li Xiao</p> <p>2003-07-08</p> <p>We propose a parallel quantum computing mode for <span class="hlt">ensemble</span> quantum computer. In this mode, some qubits can be in pure states while other qubits in mixed states. It enables a single <span class="hlt">ensemble</span> quantum computer to perform $"$single-instruction-multi-data" type of parallel computation. In Grover's algorithm and Shor's algorithm, parallel quantum computing can provide additional speedup. In addition, it also makes a fuller use of qubit resources in an <span class="hlt">ensemble</span> quantum computer. As a result, some qubits discarded in the preparation of an effective pure state in the Schulman-Varizani, and the Cleve-DiVincenzo algorithms can be re-utilized.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhRvA..91d3402P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhRvA..91d3402P"><span id="translatedtitle">Filtering single atoms from Rydberg-blockaded mesoscopic <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>Petrosyan, David; Rao, D. D. Bhaktavatsala; Mølmer, Klaus</p> <p>2015-04-01</p> <p>We propose an efficient method to filter out single atoms from trapped <span class="hlt">ensembles</span> with unknown numbers of atoms. The method employs stimulated adiabatic passage to reversibly transfer a single atom to the Rydberg state which blocks subsequent Rydberg excitation of all the other atoms within the <span class="hlt">ensemble</span>. This triggers the excitation of Rydberg-blockaded atoms to short-lived intermediate states and their subsequent decay to untrapped states. Using an auxiliary microwave field to carefully engineer the dissipation, we obtain a nearly deterministic single-atom source. Our method is applicable to small atomic <span class="hlt">ensembles</span> in individual microtraps and in lattice arrays.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.osti.gov/scitech/biblio/21408788','SCIGOV-STC'); return false;" href="http://www.osti.gov/scitech/biblio/21408788"><span id="translatedtitle">Quantum repeaters based on Rydberg-blockade-coupled atomic <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/scitech">SciTech Connect</a></p> <p>Han Yang; He Bing; Heshami, Khabat; Simon, Christoph; Li Chengzu</p> <p>2010-05-15</p> <p>We propose a scheme for realizing quantum repeaters with Rydberg-blockade-coupled atomic <span class="hlt">ensembles</span>, based on a recently proposed collective encoding strategy. Rydberg-blockade-mediated two-qubit gates and efficient cooperative photon emission are employed to create <span class="hlt">ensemble</span>-photon entanglement. Thanks to deterministic entanglement swapping operations via Rydberg-based two-qubit gates, and to the suppression of multiexcitation errors by the blockade effect, the entanglement distribution rate of the present scheme is higher by orders of magnitude than the rates achieved by other <span class="hlt">ensemble</span>-based repeaters. We also show how to realize temporal multiplexing with this system, which offers an additional speedup in entanglement distribution.</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://www.ncbi.nlm.nih.gov/pubmed/24033017','PUBMED'); return false;" href="http://www.ncbi.nlm.nih.gov/pubmed/24033017"><span id="translatedtitle">Efficient atomic clocks operated with several atomic <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>Borregaard, J; Sørensen, A S</p> <p>2013-08-30</p> <p>Atomic clocks are typically operated by locking a local oscillator (LO) to a single atomic <span class="hlt">ensemble</span>. In this Letter, we propose a scheme where the LO is locked to several atomic <span class="hlt">ensembles</span> instead of one. This results in an exponential improvement compared to the conventional method and provides a stability of the clock scaling as (?N)(-m/2) with N being the number of atoms in each of the m <span class="hlt">ensembles</span> and ? a constant depending on the protocol being used to lock the LO. PMID:24033017</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_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_24 --> <div id="page_25" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></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="481"> <li> <p><a target="_blank" onclick="trackOutboundLink('http://arxiv.org/pdf/1304.5944v3','EPRINT'); return false;" href="http://arxiv.org/pdf/1304.5944v3"><span id="translatedtitle">Efficient atomic clocks operated with several atomic <span class="hlt">ensembles</span></span></a></p> <p><a target="_blank" href="http://www.osti.gov/eprints/">E-print Network</a></p> <p>Johannes Borregaard; Anders S. Sørensen</p> <p>2014-10-09</p> <p>Atomic clocks are typically operated by locking a local oscillator (LO) to a single atomic <span class="hlt">ensemble</span>. In this article we propose a scheme where the LO is locked to several atomic <span class="hlt">ensembles</span> instead of one. This results in an exponential improvement compared to the conventional method and provides a stability of the clock scaling as $(\\alpha N)^{-m/2}$ with $N$ being the number of atoms in each of the $m$ <span class="hlt">ensembles</span> and $\\alpha$ is a constant depending on the protocol being used to lock the LO</p> </li> <li> <p><a target="_blank" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EGUGA..1712807C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EGUGA..1712807C"><span id="translatedtitle">Soil texture reclassification by an <span class="hlt">ensemble</span> model</span></a></p> <p><a target="_blank" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cisty, Milan; Hlavcova, Kamila</p> <p>2015-04-01</p> <p>Many environmental problems in which soil data serves as an inputs to simulation models are not restricted to national boundaries and therefore require international cooperation if solutions are to be found. The classification of soils according to their texture is one of the basic methods used for soil description. The term "soil texture" indicates the distribution of soil particles in the soil according to their size (diameter). The most preferred representation of texture classification is a grading curve. Because not all countries use the same classification system, databases from these countries cannot provide us with uniform data, which can serve as the inputs for various computations or models. This study deals with a description of a texture system reclassification to USDA classification system by the proposed model on a data set from Slovakia originally labeled by Slovakian national classification system. However, the authors of the paper suppose that the methodology proposed could be used more generally and that the information provided is also applicable when dealing with other existing soil texture classification systems. Some researchers have already proposed to fit the measured PSDs by various continuous parametric grading curves. When gaining such a relationship, it is possible to obtain a granular fraction's percentage ratio in the sample under consideration