Sample records for ensemble downscaling mred

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  3. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

    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.

  5. A WRF-based ensemble data assimilation system for dynamic downscaling of satellite precipitation information (Invited)

    NASA Astrophysics Data System (ADS)

    Zhang, S. Q.; Hou, A. Y.; Zupanski, M.; Cheung, S.

    2010-12-01

    For many hydrological applications, dynamic downscaling from global analyses has been used to provide local scale information on spatial and temporal distribution of precipitation and other associated environmental parameters. In the near future the NASA Global Precipitation Measurement (GPM) Mission will provide new sources of precipitation observations with unprecedented spatial and temporal coverage for better understanding and prediction of climate, weather and hydro-meteorological processes. However, in terms of using precipitation observations in global analyses and forecasts, the capability of current operational systems is generally limited by the global model resolution, the requirement of linearization of parameterized cloud physics, and the static forecast error statistics often with no distinction for clear sky or storm. In order to maximize the utilization of satellite precipitation observations in dynamic downscaling for hydrological applications, an ensemble data assimilation system (Goddard-WRF-EDAS) has been developed jointly by NASA Goddard and Colorado State University (CSU). The system takes advantages of the cloud-resolving high-resolution of the Weather Research and Forecasting (WRF) model with NASA Goddard microphysics and the flow-dependent estimation of forecast error covariance from the Maximum Likelihood Ensemble Filter (MLEF). Satellite observed radiances in precipitation regions are assimilated using Goddard Satellite Data Simulator Unit (SDSU) as the observation operator. Experimental results using current available satellite precipitation data (AMSR-E and TRMM-TMI) are presented to investigate the ability of the assimilation system in ingesting information from in-situ and satellite observations to produce dynamically downscaled precipitation. The results from the assimilation of precipitation-affected microwave radiances in a storm case and in a heavy rainfall event demonstrate the data impact to down-scaled precipitation and associated environmental parameters.

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

  8. 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

  9. Regional Climate Models Downscaling in the Alpine Area with Multimodel SuperEnsemble

    NASA Astrophysics Data System (ADS)

    Cane, D.; Barbarino, S.; Renier, L.; Ronchi, C.

    2012-04-01

    The climatic scenarios show a strong signal of warming in the Alpine area already for the mid XXI century. The climate simulation, however, even when obtained with Regional Climate Models (RCMs), are affected by strong errors where compared with observations in the control period, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization. In this work we use a selection of RCMs runs from the ENSEMBLES project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project ENSEMBLES with an available resolution of 25 km. For the study area of Piemonte daily temperature and precipitation observations (1957-present) were carefully gridded on a 14-km grid over Piemonte Region with an Optimal Interpolation technique. We applied the Multimodel SuperEnsemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We propose also the first application to RCMs of a brand new probabilistic Multimodel SuperEnsemble Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces the monthly behaviour of observed precipitation in the control period far better than the direct model outputs.

  10. Regional climate models downscaling in the Alpine area with Multimodel SuperEnsemble

    NASA Astrophysics Data System (ADS)

    Cane, D.; Barbarino, S.; Renier, L. A.; Ronchi, C.

    2012-08-01

    The climatic scenarios show a strong signal of warming in the Alpine area already for the mid XXI century. The climate simulations, however, even when obtained with Regional Climate Models (RCMs), are affected by strong errors where compared with observations, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization. Therefore the aim of this work is reducing these model biases using a specific post processing statistic technique to obtain a more suitable projection of climate change scenarios in the Alpine area. For our purposes we use a selection of RCMs runs from the ENSEMBLES project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project ENSEMBLES with an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (1957-present) were carefully gridded on a 14-km grid over Piedmont Region with an Optimal Interpolation technique. Hence, we applied the Multimodel SuperEnsemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We propose also the first application to RCMS of a brand new probabilistic Multimodel SuperEnsemble Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces well the monthly behaviour of precipitation in the control period.

  11. Future projections of labor hours based on WBGT for Tokyo and Osaka, Japan, using multi-period ensemble dynamical downscale simulations

    NASA Astrophysics Data System (ADS)

    Suzuki-Parker, Asuka; Kusaka, Hiroyuki

    2015-05-01

    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 ensemble multi-period (the 2000s, 2030s, 2050s, 2070s, and 2090s) climate projections using dynamical downscaling 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.

  12. A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

    Microsoft Academic Search

    Michelle T. H. van Vliet; Stephen Blenkinsop; Aidan Burton; Colin Harpham; Hans Peter Broers; Hayley J. Fowler

    2012-01-01

    Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate\\u000a some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model\\u000a ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and\\u000a potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km2) in

  13. A Novel approach for monitoring cyanobacterial blooms using an ensemble based system from MODIS imagery downscaled to 250 metres spatial resolution

    NASA Astrophysics Data System (ADS)

    El Alem, A.; Chokmani, K.; Laurion, I.; El-Adlouni, S. E.

    2014-12-01

    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 ensemble systems are more general and stable. In the present study, an ensemble 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 downscaled 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.

  14. 161 MRED Students from 29 States and 73 Undergrad Institutions Founded in 2004, the two-year, full-time, 57-credit professional Master of Real Estate Development

    E-print Network

    Duchowski, Andrew T.

    -year, full-time, 57-credit professional Master of Real Estate Development (MRED) program is jointly offered, Architecture, City and Regional Planning, and Real Estate Development. The program is highly competitive of prior real estate experience. #12;2 WE WANT OUR STUDENTS TO BE GREAT PLACEMAKERS, NOT JUST BUILDERS

  15. Downscaling of NWP Data

    NSDL National Science Digital Library

    2014-09-14

    Forecasters utilize downscaled NWP products when producing forecasts of predictable features, such as terrain-related and coastal features, at finer resolution than provided by most NWP models directly. This module is designed to help the forecaster determine which downscaled products are most appropriate for a given forecast situation and the types of further corrections the forecaster will have to create. This module engages the learner through interactive case examples illustrating and comparing the major capabilities and limitations of some commonly-used downscaled products for 2-m temperatures and 10-m winds. Products covered include Gridded MOS, PRISM, NCEP downscaling for NAM and for NAEFS, downscaling in the AWIPS Graphical Forecast Editor, and the use of high-resolution models to perform downscaling.

  16. Operational predictability of monthly average maximum temperature over the Iberian Peninsula using DEMETER simulations and downscaling

    Microsoft Academic Search

    M. Dolores Frías; Jesús Fernández; Jon Sáenz; Concepción Rodríguez-Puebla

    2005-01-01

    The multi-model ensemble for seasonal to interannual prediction developed in the European Union project DEMETER has been used to quantify the predictability of monthly average maximum temperature that could be achieved operationally over the Iberian Peninsula. Statistical downscaling based on canonical correlation analysis is applied to increase the spatial resolution available from the global models. The downscaling is based on

  17. A new Multi-Scale Data Assimilation Algorithm to Downscale Satellite-Based Soil Moisture Data

    Microsoft Academic Search

    N. N. Das; B. P. Mohanty; Y. Efendiev

    2008-01-01

    The study focuses on downscaling of soil moisture from coarse remote sensing footprints to finer scales. Two approaches are proposed for soil moisture downscaling. The first approach provides the probability distribution functions at the finer scales with no information about the spatial organization of soil moisture fields. The second approach implements a multiscale ensemble Kalman filter (EnKF) that assimilates remotely

  18. Dynamic coupling of support vector machine and K-nearest neighbour for downscaling daily rainfall

    NASA Astrophysics Data System (ADS)

    Devak, Manjula; Dhanya, C. T.; Gosain, A. K.

    2015-06-01

    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 downscaling variables to finer resolution. This demands a careful selection of a suitable downscaling model, to alleviate the downscaling uncertainty. In this study, it is proposed to develop a dynamic framework for downscaling 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 ensemble of outputs by identifying various plausible model parameter combinations. The performance of this framework for downscaling 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 ensemble is found to be efficient than single best simulation with optimum parameter combinations. The efficacy of hybrid SVM-KNN ensemble downscaling model is established through detailed investigations. The future downscaled 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 downscaling uncertainty and also eliminates the inherent assumption of relationship stationarity considered in many downscaling models.

  19. Development of sampling downscaling

    NASA Astrophysics Data System (ADS)

    Kuno, R.; Inatsu, M.

    2012-12-01

    This study has developed a new dynamical downscaling (DDS) method named sampling downscaling (SmDS), in which the probability density function (PDF) of regional climatic variables can be evaluated with reducing computational cost by sampling typically anomalous seasons in terms of global variability and then performing the DDS integration only for the selected period. The purpose of this study is to validate the SmDS method in a case of the long-term DDS integration conducted for the nested domain around Hokkaido, a northern Japanese island. First, the singular value decomposition (SVD) analysis extracted a synchronized variation between regional precipitation and global climate anomaly. Second, a few of years with high and low SVD indices are selected under the hypothesis that extreme statistics of precipitation with a non-Gaussian distribution must be related to a global climate pattern. Finally, extreme statistics for a DDS experiment only with sampled years are compared with those for the DDS experiment with full integration years. The SVD analysis for daily precipitation over Hokkaido and 500-hPa geopotential height in Northern Hemisphere winter revealed that the snowfall in western Hokkaido was highly correlated with the Eurasian and/or Western Pacific pattern. The Kolmogorov-Smirnov test and the 99th percentile statistics showed that the SmDS approximately reproduced PDFs and extreme statistics of precipitation even with a few of sampled seasons. It was also shown that the sampling in the SmDS method was much more effective than the random sampling.

  20. Multisite rainfall downscaling and disaggregation in a tropical urban area

    NASA Astrophysics Data System (ADS)

    Lu, Y.; Qin, X. S.

    2014-02-01

    A systematic downscaling-disaggregation study was conducted over Singapore Island, with an aim to generate high spatial and temporal resolution rainfall data under future climate-change conditions. The study consisted of two major components. The first part was to perform an inter-comparison of various alternatives of downscaling and disaggregation methods based on observed data. This included (i) single-site generalized linear model (GLM) plus K-nearest neighbor (KNN) (S-G-K) vs. multisite GLM (M-G) for spatial downscaling, (ii) HYETOS vs. KNN for single-site disaggregation, and (iii) KNN vs. MuDRain (Multivariate Rainfall Disaggregation tool) for multisite disaggregation. The results revealed that, for multisite downscaling, M-G performs better than S-G-K in covering the observed data with a lower RMSE value; for single-site disaggregation, KNN could better keep the basic statistics (i.e. standard deviation, lag-1 autocorrelation and probability of wet hour) than HYETOS; for multisite disaggregation, MuDRain outperformed KNN in fitting interstation correlations. In the second part of the study, an integrated downscaling-disaggregation framework based on M-G, KNN, and MuDRain was used to generate hourly rainfall at multiple sites. The results indicated that the downscaled and disaggregated rainfall data based on multiple ensembles from HadCM3 for the period from 1980 to 2010 could well cover the observed mean rainfall amount and extreme data, and also reasonably keep the spatial correlations both at daily and hourly timescales. The framework was also used to project future rainfall conditions under HadCM3 SRES A2 and B2 scenarios. It was indicated that the annual rainfall amount could reduce up to 5% at the end of this century, but the rainfall of wet season and extreme hourly rainfall could notably increase.

  1. Downscaling of seasonal soil moisture forecasts using satellite data

    NASA Astrophysics Data System (ADS)

    Schneider, S.; Jann, A.; Schellander-Gorgas, T.

    2014-08-01

    A new approach to downscaling soil moisture forecasts from the seasonal ensemble prediction forecasting system of the ECMWF (European Centre for Medium-Range Weather Forecasts) is presented in this study. Soil moisture forecasts from this system are rarely used nowadays, although they could provide valuable information. Weaknesses of the model soil scheme in forecasting soil water content and the low spatial resolution of the seasonal forecasts are the main reason why soil water information has hardly been used so far. The basic idea to overcome some of these problems is the application of additional information provided by two satellite sensors (ASCAT and Envisat ASAR) to improve the forecast quality, mainly to reduce model bias and increase the spatial resolution. Seasonal forecasts from 2011 and 2012 have been compared to in situ measurement sites in Kenya to test this two-step approach. Results confirm that this downscaling is adding skill to the seasonal forecasts.

  2. Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

    NASA Astrophysics Data System (ADS)

    Roberts, J. B.; Robertson, F. R.; Bosilovich, M. G.; Lyon, B.

    2013-12-01

    The U.S. National Multi-Model Ensemble 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 downscaling 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 downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling 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.

  3. The Influence of Downscaling Models and Observations on Future Hydrochemistry Reponses of Forest Watersheds

    NASA Astrophysics Data System (ADS)

    Pourmokhtarian, A.; Driscoll, C. T.; Campbell, J. L.; Hayhoe, K.; Stoner, A. M. K.

    2014-12-01

    Most projections of climate change impacts on ecosystems rely on multiple climate model projections, but utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used in the complex mountainous terrain of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three different downscaling methods: the monthly delta method (or the "change factor method"); monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD); and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs, from four AOGCMs (CCSM3, HadCM3, PCM, and GFDL-CM2) driven by higher (A1fi) and lower (B1) future emission scenarios, on two sets of observations (1/8th degree resolution grid vs. individual weather station) to generate the high-resolution climate input for the hydrochemical model PnET-BGC (ensemble of 48 runs). The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model both had a major impact on modeled soil moisture and streamflow which in turn affected forest growth, net nitrification and stream chemistry. Specifically, the delta method, the simplest downscaling approach evaluated, was highly sensitive to the observations used, resulting in projections that were significantly different than those produced with the BCSD and ARRM methods. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce poor results in model applications run at higher temporal and/or spatial resolutions. These results underscore the importance of carefully considering the observations and downscaling method used to generate climate change projections for smaller scale modeling studies.

  4. A new Multi-Scale Data Assimilation Algorithm to Downscale Satellite-Based Soil Moisture Data

    NASA Astrophysics Data System (ADS)

    Das, N. N.; Mohanty, B. P.; Efendiev, Y.

    2008-12-01

    The study focuses on downscaling of soil moisture from coarse remote sensing footprints to finer scales. Two approaches are proposed for soil moisture downscaling. The first approach provides the probability distribution functions at the finer scales with no information about the spatial organization of soil moisture fields. The second approach implements a multiscale ensemble Kalman filter (EnKF) that assimilates remotely sensed coarse scale soil moisture footprint, attributes of fine scale geophysical parameters/variables (i.e., soil texture: %sand, vegetation: NDVI, topography: slope, and precipitation) and coarse/fine scale simulation into a spatial characterization of soil moisture evolution at the finer scales. To downscale the remotely sensed coarse scale soil moisture to another spatial scale, the multiscale EnKF uses a bridging model. The bridging model infers the pixel-specific scaling coefficient from the compatible geophysical parameters/variables that influence the soil moisture evolution process at that particular spatial scale. Data from diverse hydroclimatic regions from the semiarid Arizona, the agricultural landscape of Iowa, and the grassland/rangeland of Oklahoma are used in the study to implement the multiscale downscaling algorithm. The results demonstrate that the bridging model of multiscale EnKF helps to characterize the evolution of soil moisture within the remotely sensed footprint. Validation conducted at the finest scale also shows reasonable agreement between the measured field data and the simulated downscaled soil moisture evolution.

  5. Using satellite products to evaluate statistical downscaling with generalised linear models

    NASA Astrophysics Data System (ADS)

    Bergin, Emma; Buytaert, Wouter; Kwok-Pan, Chun; Turner, Andrew; Chawla, Ila; Mujumdar, Pradeep

    2015-04-01

    Generalised linear models (GLMs) have been around for some time and are routinely used for statistical downscaling of rainfall data. However, in many regions it is difficult to evaluate them because of a lack of in situ data. Downscaling 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 downscaling using satellite products. We fit a multi-site downscaling 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 downscaled NCEP reanalysis rainfall to form an ensemble of rainfall observations. We compare the uncertainty of the observations with 100 realisations from GLM simulations. We find that our ensemble of observations falls within the envelope of uncertainty from the GLM simulations during the monsoon season. Downscaling models are frequently evaluated only for their performance using average statistics. More detailed analyses of daily rainfall plots therefore give increased confidence that downscaling 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 downscaling models.

  6. Multivariate Downscaling of Decadal Climate Change Projections over the Sunbelt

    NASA Astrophysics Data System (ADS)

    DAS Bhowmik, R.; Arumugam, S.; Sinha, T.; Mahinthakumar, K.

    2014-12-01

    Bias Correction and Statistical downscaling (BCSD) of precipitation and temperature are commonly required to bring the large scale variables available from GCMs to a finer grid-scale for ingesting them into watershed models. Most of the currently employed procedures on BCSD primarily consider a univariate approach by developing a statistical relationship between large-scale precipitation/temperature with the local-scale precipitation/temperature ignoring the interdependency between the two variables. In this study, an asynchronous Canonical Correlation Analysis (CCA) approach is proposed for downscaling multiple climatic variables by preserving the temporal correlations among them. The method was first applied on historical runs of climate model inter-comparison project-5 (CMIP5) for the period 1951-1999 and compared with bias corrected dataset using univariate approach from Bureau of Reclamation. Further, the method was applied on decadal runs of CMIP5 models and compared with univariate asynchronous regression results. A metric, fraction bias was defined, and distribution of fraction bias from ensemble was considered for comparing with univariate approach. CCA relatively performs better in preserving the cross-correlation at grids where observed cross correlations are significant, while reducing fraction biases in mean and standard deviation.

  7. Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe

    NASA Astrophysics Data System (ADS)

    Sunyer, M. A.; Hundecha, Y.; Lawrence, D.; Madsen, H.; Willems, P.; Martinkova, M.; Vormoor, K.; Bürger, G.; Hanel, M.; Kriau?i?nien?, J.; Loukas, A.; Osuch, M.; Yücel, I.

    2015-04-01

    Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods (SDMs) often used in climate change impact studies. Four methods are based on change factors (CFs), three are bias correction (BC) methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from 15 regional climate models (RCMs) from the ENSEMBLES project for 11 catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the SDMs vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between CFs and BC methods. The performance of the BC methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and SDMs indicates that at least 30% and up to approximately half of the total variance is derived from the SDMs. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need for considering an ensemble of both SDMs and climate models. Recommendations are provided for the selection of the most suitable SDMs to include in the analysis.

  8. Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South Africa

    Microsoft Academic Search

    B. C. Hewitson; R. G. Crane

    2006-01-01

    This paper discusses issues that surround the development of empirical downscaling techniques as context for presenting a new approach based on self-organizing maps (SOMs). The technique is applied to the downscaling of daily precipitation over South Africa. SOMs are used to characterize the state of the atmosphere on a localized domain surrounding each target location on the basis of NCEP

  9. Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale

    NASA Astrophysics Data System (ADS)

    Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob

    2010-05-01

    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 downscaling from the European MACC ensemble 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 downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling 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" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.

  10. Statistical Downscaling of AGCM Precipitation Output with a Formatted Regression Frame

    NASA Astrophysics Data System (ADS)

    Kim, Sunmin; Tachikawa, Yasoto; Nakakita, Eiichi

    2015-04-01

    The downscaling issue has been taking an important role to bridge research in climate change and impact assessment. Especially, the SDS (statistical downscaling) issue has a long history of research and development in the field of hydrology and several types of SDS methods are already successful in other applications. The main advantage of SDS compared to DDS (dynamic downscaling) is that it does not take high computing resources, and can easily apply to any place with a minimum of observation data available. However, SDS also has limitations. Some statistical relationships between model variables are not strong enough to build a stable SDS model. Most critically, we cannot sure whether the statistical relationship developed with the present climate data can simulate the statistical relationship of the future climate. We have been developing a SDS method that can avoid the critical issue of the conventional SDS method, and take as many advantages of DDS as possible, based on analyzing two different spatial resolutions of AGCM outputs, 20-km and 60-km. By establishing a statistical relationship between the 60-km and 20-km output for both present and future separately, and by applying the relationship to the ensemble output of 60-km AGCM, it is able to produce ensemble output at 20-km spatial resolution with the independent statistical relationship for the present and future climates. In details, the downscaling target is 60-km resolution of daily precipitation for 20-km resolution data. We have considered a window having (3x60-km)x(3x60-km) of area, and the downscaling target is the 3x3 of 20-km resolution grids in the center of the downscaling window. For the evaluation of the proposed method, we have prepared 15 years (1979-1993) of observation data, and identify the parameters with the square root information filter scheme. We optimize the parameters on a monthly basis, and apply the regression model to 10 more years of testing period (1994-2004). The proposed regression model provides very effective and efficient results with a certain level of estimation error.

  11. Ensemble Methods

    NASA Astrophysics Data System (ADS)

    Re, Matteo; Valentini, Giorgio

    2012-03-01

    Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles 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 ensemble, 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 ensemble [158]. In the literature, a plethora of terms other than ensembles 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 ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, 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 ensembles to different application domains. For instance, Allwein, Schapire, and Singer interpreted the improved generalization capabilities of ensembles 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, ensembles improve on single learning machines, and moreover large experimental studies compared the effectiveness of different ensemble 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 ensemble methods using off-the-shelf computer platforms. However, as explained in Section 26.2 there are deeper reasons to use ensembles of learning machines, motivated by the intrinsic characteristics of the ensemble methods. The main aim of this chapter is to introduce ensemble 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 ensemble 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 ensemble methods proposed in the literature. In Section 26.4 and 26.5, we present an overview of the main supervised ensemble methods reported in the literature, adopting a simple taxonomy, originally proposed in Ref. [201]. Applications of ensemble methods are only marginally considered, but a specific section on some relevant applications of ensemble methods in astronomy and astrophysics has been added (Section 26.6). The conclusion (Section 26.7) ends this pap

  12. Performance Preserving Network Downscaling Fragkiskos Papadopoulos, Konstantinos Psounis, Ramesh Govindan

    E-print Network

    Performance Preserving Network Downscaling Fragkiskos Papadopoulos, Konstantinos Psounis, Ramesh] is the addition of a new prong--a class of performance-preserving network downscaling techniques that lets of the network, where the downscaling is designed to preserve one or more aspects of the original network

  13. Precipitation Downscaling Products for Hydrologic Applications (Invited)

    NASA Astrophysics Data System (ADS)

    Gutmann, E. D.; Pruitt, T.; Liu, C.; Clark, M. P.; Brekke, L. D.; Arnold, J.; Raff, D. A.; Rasmussen, R.

    2013-12-01

    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 downscaling techniques, with only limited use of dynamic downscaling 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 downscaling precipitation. We will discuss four major statistical downscaling 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 downscaling 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 downscaling 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.

  14. Preprint Downscaling Climate Change Salath 02/02/2005 Downscaling Simulations of future Global Climate with Application to Hydrologic

    E-print Network

    Salathé Jr., Eric P.

    Preprint Downscaling Climate Change ­ Salathé 02/02/2005 Downscaling Simulations of future Global approaches the problem of downscaling global climate model simulations with an emphasis on validating simulation while preserving much of the statistics of interannual variability in the climate model

  15. Selecting downscaled climate projections for water resource impacts and adaptation

    NASA Astrophysics Data System (ADS)

    Vidal, Jean-Philippe; Hingray, Benoît

    2015-04-01

    Increasingly large ensembles 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 ensemble 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 ensemble of 3000 daily transient gridded climate projections was made available for this study. It was built from different runs of 4 ENSEMBLES Stream2 GCMs, statistically downscaled 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 downscaling 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 ensemble 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

  16. Ensembl 2015.

    PubMed

    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

    2015-01-01

    Ensembl (http://www.ensembl.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.ensembl.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.ensembl.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 Ensembl. The Ensembl code base itself is more accessible: it is now hosted on our GitHub organization page (https://github.com/Ensembl) under an Apache 2.0 open source license. PMID:25352552

  17. Downscaling using Probabilistic Gaussian Copula Regression model

    NASA Astrophysics Data System (ADS)

    Ben Alaya, M. A.; Chebana, F.; Ouarda, T.

    2014-12-01

    Atmosphere-ocean general circulation models (AOGCMs) are useful to simulate large-scale climate evolutions. However, AOGCM data resolution is too coarse for regional and local climate studies. Downscaling techniques have been developed to refine AOGCM data and provide information at more relevant scales especially for hydrological applications. Among a wide range of available approaches, regression-based methods are commonly used for this purpose. When several variables are considered at one or multiple sites, regression models are employed to reproduce the observed climate characteristics at small scale, such as the variability and the relationship between sites and variables. The objective of the present talk is to introduce a probabilistic Gaussian copula regression (PGCR) model for simultaneously downscaling multiple variables at several sites. The proposed PGCR model relies on a probabilistic framework to specify the marginal distribution for each downscaled variable at a given day through AOGCM predictors, and handles multivariate dependence between sites and variables using a Gaussian copula. The proposed model is applied for the downscaling of AOGCM data to daily precipitation and minimum and maximum temperatures in the southern part of Quebec, Canada. In addition, to assess the potential of the proposed method, reanalysis products are used in this study. Results of the study indicate the superiority of the proposed model over classical regression-based methods.

  18. Statistical downscaling of CMIP5 outputs for projecting future changes in rainfall in the Onkaparinga catchment.

    PubMed

    Rashid, Md Mamunur; Beecham, Simon; Chowdhury, Rezaul K

    2015-10-15

    A generalized linear model was fitted to stochastically downscaled 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 ensembles (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 downscaling 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 downscaled 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 downscaled 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

  19. Data Assimilation Methods for Hydrologic Downscaling

    NASA Astrophysics Data System (ADS)

    Pan, M.; Wood, E. F.; Luo, L.

    2012-12-01

    Data assimilation techniques have been among the most useful tools in Earth sciences. As for their applications in hydrology, significant efforts have been devoted to improving the predictions of dynamic models, e.g., catchment hydrologic models, land surface models (LSM), and ultimately general circulation models (GCM), using various types of observational data, e.g. remotely sensed surface parameters. Here we focus on the applications to a fundamentally important but less explored category of problems - estimating hydrologic quantities of interest across different spatial and temporal scales, and the primarily problem is downscaling in space and time (since upscaling is in most cases trivial). Downscaling plays a vital role in bridging the scale gaps between various types of modeling and observation systems, for example, from the relatively coarse GCM to LSM, and to catchment scale models, and from coarse resolution remote sensors (long wavelength or gravitational) to fine resolution sensors (visible/infrared). Through downscaling, fine scale applications (e.g. catchment hydrologic models, local geo-chemical and geo-biological models) can make use of predictions from coarse scale models (e.g. weather/climate models) or coarse resolution remote sensing measurements. Our downscaling approach will rely on both (a) the physical models to parameterize the related cross-scale physical processes and to link hydrologic variables defined at one scale to another, and (b) the mathematical tools to properly handle the uncertainties during the estimation and as well as to help quantify those cross-scale relationships too difficult for the physical models. We showcase the downscaling of two hydrologic variables: (1) deriving spatial fields of land surface runoff from river streamflow measurements and (2) creating fine resolution soil moisture data from coarse resolution remote sensing retrievals or dynamic models. In the runoff case, all the measurements are collected in the form of river streamflow, which is an integrated response to the spatial field of runoff in time. A routing model captures this integration process in space and time, and the downscaling is essentially to invert such a routing process (i.e. to disaggregate streamflow in time and space) using data assimilation techniques and background estimates of the runoff field. In the soil moisture case, the redistribution of soil moisture at fine scales is controlled by factors like topography and soil/vegetation properties. Some of these processes are well captured by the topographic index-based TOPMODEL and other more difficult scaling relationships can be lumped into a multi-scale statistical model.

  20. Climate variability and projected change in the western United States: regional downscaling and drought statistics

    NASA Astrophysics Data System (ADS)

    Gutzler, David S.; Robbins, Tessia O.

    2011-09-01

    Climate change in the twenty-first century, projected by a large ensemble average of global coupled models forced by a mid-range (A1B) radiative forcing scenario, is downscaled to Climate Divisions across the western United States. A simple empirical downscaling 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 ensemble-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 downscaling 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.

  1. Empirical-Statistical Downscaling in Climate Modeling

    NASA Astrophysics Data System (ADS)

    Benestad, R. E.

    2004-10-01

    Research into possible impacts of a climate change requires descriptions of local and regional descriptions of climate. For instance, the local and regional aspect of a climate change is stressed in the U.S. Strategic Plan for the Climate Change Science Program (CCSP) (http://www.climatescience.gov/Library/stratplan2003/default.htm). Global climate models (GCMs) are important tools for studying climate change and making projections for the future. Although GCMs provide realistic representations of large-scale aspects of climate, they generally do not give good descriptions of the local and regional scales. It is nevertheless possible to relate large-scale climatic features to smaller spatial scales. There are two main approaches for deriving information on local or regional scales from the global climate scenarios generated by GCMs: (1) numerical downscaling (also known as ``dynamical downscaling'') involving a nested regional climate model (RCM) or (2) empirical-statistical downscaling employing statistical relationships between the large-scale climatic state and local variations derived from historical data records.

  2. Statistical-dynamical downscaling for wind energy potentials: Evaluation and applications to decadal hindcasts and climate change projections

    NASA Astrophysics Data System (ADS)

    Reyers, Mark; Pinto, Joaquim G.; Moemken, Julia

    2015-04-01

    A statistical-dynamical downscaling (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 downscaled 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 downscaling (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 downscale the full ensemble 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 ensembles.

  3. "Going the Extra Mile in Downscaling: Why Downscaling is not jut "Plug-and-Play"

    EPA Science Inventory

    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 downscaling the Comm...

  4. Stochastic Lagrangian Method for Downscaling Problems in Computational Fluid Dynamics

    E-print Network

    Paris-Sud XI, Université de

    mathematicians as long as deterministic tools are used. Among others, let us quote the Adaptative Mesh Refinement for the downscaling in Computational Fluid Dynamics (CFD). For numerous practical reasons (computational cost, we consider a new approach for the downscaling in CFD, although the authors are particu- larly

  5. Ensembl 2014

    PubMed Central

    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.

    2014-01-01

    Ensembl (http://www.ensembl.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

  6. The Personal Software Process: Downscaling the factory

    NASA Technical Reports Server (NTRS)

    Roy, Daniel M.

    1994-01-01

    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 down-scaled 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.

  7. A full sensitivity analysis of a the analogue downscaling method of precipitation for use in climate change impact studies

    NASA Astrophysics Data System (ADS)

    Wetterhall, F.; Pappenberger, F.; Cloke, H.; McGregor, G.; Freer, J.; He, Y.; Wilson, M.

    2009-04-01

    Global climate models (GCMs) are the best tools available to assess the change to atmospheric circulation that an increase in radiatively active gases might lead to. However, it is a well known problem that GCMs cannot fully resolve the local weather variables, especially precipitation, that are important for hydrological impact studies. Downscaling methods are therfore needed to bridge this gap. The Analogue downscaling (AM) method is a simple statistical downscaling technique using historical observations of weather variables to model future predictions of the same variable. The analogues are typically chosen by comparing features of the large-scale circulation field, such as mean sea level pressure (MSLP) or geopotential heights (GPH). There are many methods for analysing the large-scale circulation, and in this study Tewelus-Wobus Scores (TWS) were selected. TWS compares gradients in the large-scale circulation field and has been used in earlier analogue downscaling studies together with precipitation. Although the AM cannot model precipitation values outside its historical values, it can nevertheless be used in climate change studies. The method can model wet spells over a number of days that succeed observed values, and this might be more important than single day events in terms of flooding. Also, the technique offers possibilities to model ensembles of precipitation time series. The methodology in this study was to apply a total sensitivity analysis to the analogue method. The parameters varied were (1) choice of large-scale predictor both in terms of single predictors and combinations, (2) method of calculating the distance between grid points, (3) areal extent of the predictor, (4) temporal window of the predictor, (5) number of analogues in an ensemble and (6) weighting of the predictor in terms of directional flow. The predictors included in the study were MSLP, GPH, zonal and meriodonal winds, and specific humidity at different pressure levels. The methodology was evaluated over ensembles of predictand time series. The results indicate that MSLP together with specific humidity are the best predictors to assess future change in precipitation. The number of analogues in an ensemble prediction should not be less than 30. The conclusion of this study was that AM is promising in terms of applying it to scenario runs with GCM output.

  8. Statistical Testing of Dynamically Downscaled Rainfall Data for the East Coast of Australia

    NASA Astrophysics Data System (ADS)

    Parana Manage, Nadeeka; Lockart, Natalie; Willgoose, Garry; Kuczera, George

    2015-04-01

    This study performs a validation of statistical properties of downscaled climate data, concentrating on the rainfall which is required for hydrology predictions used in reservoir simulations. The data sets used in this study have been produced by the NARCliM (NSW/ACT Regional Climate Modelling) project which provides a dynamically downscaled climate dataset for South-East Australia at 10km resolution. NARCliM has used three configurations of the Weather Research Forecasting Regional Climate Model and four different GCMs (MIROC-medres 3.2, ECHAM5, CCCMA 3.1 and CSIRO mk3.0) from CMIP3 to perform twelve ensembles of simulations for current and future climates. Additionally to the GCM-driven simulations, three control run simulations driven by the NCEP/NCAR reanalysis for the entire period of 1950-2009 has also been performed by the project. The validation has been performed in the Upper Hunter region of Australia which is a semi-arid to arid region 200 kilometres North-West of Sydney. The analysis used the time series of downscaled rainfall data and ground based measurements for selected Bureau of Meteorology rainfall stations within the study area. The initial testing of the gridded rainfall was focused on the autoregressive characteristics of time series because the reservoir performance depends on long-term average runoffs. A correlation analysis was performed for fortnightly, monthly and annual averaged time resolutions showing a good statistical match between reanalysis and ground truth. The spatial variation of the statistics of gridded rainfall series were calculated and plotted at the catchment scale. The spatial correlation analysis shows a poor agreement between NARCliM data and ground truth at each time resolution. However, the spatial variability plots show a strong link between the statistics and orography at the catchment scale.

  9. Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations.

    NASA Astrophysics Data System (ADS)

    López López, Patricia; Wanders, Niko; Sutanudjaja, Edwin; Renzullo, Luigi; Sterk, Geert; Schellekens, Jaap; Bierkens, Marc

    2015-04-01

    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 downscaled global climatological data (from 0.5o downscaled 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. Downscaled satellite derived soil moisture (from 0.5o downscaled to 0.1o resolution) from AMSR-E and streamflow observations collected from 25 gauging stations are assimilated using an ensemble 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 downscaled 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 downscaled 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.

  10. Enhancing Local Climate Projections of Precipitation: Assets and Limitations of Quantile Mapping Techniques for Statistical Downscaling

    NASA Astrophysics Data System (ADS)

    Ivanov, Martin; Kotlarski, Sven; Schär, Christoph

    2015-04-01

    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 ENSEMBLES project is downscaled 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 downscaling 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 downscaling 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 downscaling 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.

  11. A downscaling framework for L band radiobrightness temperature imagery

    NASA Astrophysics Data System (ADS)

    Parada, Laura M.; Liang, Xu

    2003-11-01

    In this paper we introduce a general downscaling framework and apply it to L band microwave radiobrightness temperature fields retrieved from electronically scanned thinned array radiometer (ESTAR). The gist of the downscaling scheme presented in this paper is the statistical characterization of scale-invariant properties of the wavelet coefficients or fluctuations from long memory 1/f processes. We test the proposed downscaling framework with the radiobrightness temperature images collected during the Southern Great Plains hydrology experiment of 1997. We produce realizations of radiobrightness temperature at 800-m resolution given a mean-area value at approximately 30-km resolution (the near-future expected operational scale). The results obtained evince that the proposed downscaling methodology is capable of accurately preserving the variability and overall structure of spatial dependence of the observed radiobrightness temperature fields.

  12. Downscaling Global Weather Forecast Outputs Using ANN for Flood Prediction

    Microsoft Academic Search

    Nam Do Hoai; Keiko Udo; Akira Mano

    2011-01-01

    Downscaling global weather prediction model outputs to individual locations or\\u000alocal scales is a common practice for operational weather forecast in order to\\u000acorrect the model outputs at subgrid scales. This paper presents an\\u000aempirical-statistical downscaling method for precipitation prediction which uses\\u000aa feed-forward multilayer perceptron (MLP) neural network. The MLP architecture\\u000awas optimized by considering physical bases that determine

  13. High resolution probabilistic precipitation forecast over Spain combining the statistical downscaling tool PROMETEO and the AEMET short range EPS system (AEMET/SREPS)

    NASA Astrophysics Data System (ADS)

    Cofino, A. S.; Santos, C.; Garcia-Moya, J. A.; Gutierrez, J. M.; Orfila, B.

    2009-04-01

    The Short-Range Ensemble Prediction System (SREPS) is a multi-LAM (UM, HIRLAM, MM5, LM and HRM) multi analysis/boundary conditions (ECMWF, UKMetOffice, DWD and GFS) run twice a day by AEMET (72 hours lead time) over a European domain, with a total of 5 (LAMs) x 4 (GCMs) = 20 members. One of the main goals of this project is analyzing the impact of models and boundary conditions in the short-range high-resolution forecasted precipitation. A previous validation of this method has been done considering a set of climate networks in Spain, France and Germany, by interpolating the prediction to the gauge locations (SREPS, 2008). In this work we compare these results with those obtained by using a statistical downscaling method to post-process the global predictions, obtaining an "advanced interpolation" for the local precipitation using climate network precipitation observations. In particular, we apply the PROMETEO downscaling system based on analogs and compare the SREPS ensemble of 20 members with the PROMETEO statistical ensemble of 5 (analog ensemble) x 4 (GCMs) = 20 members. Moreover, we will also compare the performance of a combined approach post-processing the SREPS outputs using the PROMETEO system. References: SREPS 2008. 2008 EWGLAM-SRNWP Meeting (http://www.aemet.es/documentos/va/divulgacion/conferencias/prediccion/Ewglam/PRED_CSantos.pdf)

  14. Intermodel variability of future changes in the Baiu rainband estimated by the pseudo global warming downscaling method

    NASA Astrophysics Data System (ADS)

    Kawase, Hiroaki; Yoshikane, Takao; Hara, Masayuki; Kimura, Fujio; Yasunari, Tetsuzo; Ailikun, Borjiginte; Ueda, Hiroaki; Inoue, Tomoshige

    2009-12-01

    Changes in the Baiu rainband owing to global warming are assessed by the pseudo global warming downscaling method (PGW-DS). The PGW-DS is similar to the conventional dynamical downscaling method using a regional climate model (RCM), but the boundary conditions of the RCM are obtained by adding the difference between the future and present climates simulated by coupled general circulation models (CGCMs) into the 6-hourly reanalysis data in a control period. We conducted the multiple PGW-DS runs using the selected Coupled Model Intercomparison Project Phase 3 (CMIP3) multimodel data set, giving better performance around East Asia in June, and the PGW-DS run using the multiselected CGCM model ensemble mean (PGW-MME run). The PGW-MME and PGW-DS runs show an increase in precipitation over the Baiu rainband and the southward shift of the Baiu rainband. The PGW-MME run has good similarity to the average of all PGW-DS runs. This fact indicates that an average of the multiple PGW-DS runs can be replaced by a single PGW-DS run using the multiselected CGCM ensemble mean, reducing the significant computational expense. In comparison with the GCM projections, the PGW-DS runs reduce the intermodel variability in the Baiu rainband caused by the CGCMs themselves.

  15. Statistical Downscaling of Wintertime Temperatures over South Korea

    NASA Astrophysics Data System (ADS)

    Lee, S. Y.; Kim, K. Y.

    2014-12-01

    Reanalysis data have global coverage and faithfully render large-scale phenomena. On the other hand, regional and small-scale characteristics of atmospheric variability are poorly resolved. In an attempt to improve reanalysis data for regional use, statistical downscaling method is developed based on CSEOF analysis. Low-resolution data are downscaled into a high-resolution data. The developed algorithm is applied to National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP/NCAR) reanalysis data and European Center of Medium range Weather Forecast (ECMWF) ERA-interim reanalysis data to downscale them into a form of Korea Meteorological Administration (KMA) measurements at 60 stations over the Korean Peninsula. The developed downscaling algorithm is evaluated by predicting winter daily temperatures from Nov 17 - Mar 16 for the period of 34 years (1979-2013). For validation of the method, the Jackknife method is used, in which winter daily temperature is predicted over a one-year period not used for training. This procedure is repeated for the entire data period. The mean and variance of the resulting downscaled dataset match well with those of the KMA measurements. Validation results show that correlation increases and error variance decreases significantly at grid points near the KMA stations with and without the seasonal cycle. We will also address the utility of this technique for downscaling model predictions based on future scenarios.

  16. Assessing short to medium range ensemble streamflow forecast approaches in small to medium scale watersheds across CONUS

    NASA Astrophysics Data System (ADS)

    Wood, A. W.; Newman, A. J.; Brekke, L. D.; Arnold, J. R.; Clark, M. P.

    2014-12-01

    As part of the Hydrologic Ensemble Forecast Service, the US National Weather Service River Forecasting Centers have implemented short to medium range ensemble streamflow forecasts. Hydrologic models are forced with meteorological forecast ensembles derived using a downscaling and calibration technique, MEFP, that leverages correlations at multiple temporal scales between large scale GEFS forecast ensemble mean and local scale observed precipitation and temperature. Strengths of MEFP include its use of multi-decade hindcast for calibration of local scale forecasts and production of verification information, but possible weaknesses include the use of precipitation and temperature ensemble mean information only, which requires the statistical synthesis of ensemble members. We explore whether using a larger set of atmospheric predictors and full ensemble members from the GEFS can lead to greater meteorological and hydrological predictability. Using 30+ year streamflow hindcasts, we evaluate 1-15 day streamflow predictions using the Snow-17/Sacramento hydrologic modeling approach in small to medium-sized watersheds across CONUS. We compare the MEFP approach and performance with regressive and analog-based statistical downscaling and calibration methods that rely on a range of atmospheric predictors to produce watershed-scale ensemble forecasts. This presentation describes the strengths and weaknesses of the two approaches.

  17. Downscaling of slip distribution for strong earthquakes

    NASA Astrophysics Data System (ADS)

    Yoshida, T.; Oya, S.; Kuzuha, Y.

    2013-12-01

    We intend to develop a downscaling model to enhance the earthquake slip distribution resolution. Slip distributions have been obtained by other researchers using various inversion methods. As a downscaling model, we are discussing fractal models that include mono-fractal models (fractional Brownian motion, fBm; fractional Lévy motion, fLm) and multi-fractal models as candidates. Log - log-linearity of k (wave number) versus E (k) (power spectrum) is the necessary condition for fractality: the slip distribution is expected to satisfy log - log-linearity described above if we can apply fractal model to a slip distribution as a downscaling model. Therefore, we conducted spectrum analyses using slip distributions of 11 earthquakes as explained below. 1) Spectrum analyses using one-dimensional slip distributions (strike direction) were conducted. 2) Averaging of some results of power spectrum (dip direction) was conducted. Results show that, from the viewpoint of log - log-linearity, applying a fractal model to slip distributions can be inferred as valid. We adopt the filtering method after Lavallée (2008) to generate fBm/ fLm. In that method, generated white noises (random numbers) are filtered using a power law type filter (log - log-linearity of the spectrum). Lavallée (2008) described that Lévy white noise that generates fLm is more appropriate than the Gaussian white noise which generates fBm. In addition, if the 'alpha' parameter of the Lévy law, which governs the degree of attenuation of tails of the probability distribution, is 2.0, then the Lévy distribution is equivalent to the Gauss distribution. We analyzed slip distributions of 11 earthquakes: the Tohoku earthquake (Wei et al., 2011), Haiti earthquake (Sladen, 2010), Simeulue earthquake (Sladen, 2008), eastern Sichuan earthquake (Sladen, 2008), Peru earthquake (Konca, 2007), Tocopilla earthquake (Sladen, 2007), Kuril earthquake (Sladen, 2007), Benkulu earthquake (Konca, 2007), and southern Java earthquake (Konca, 2006)). We obtained the following results. 1) Log - log-linearity (slope of the linear relationship is ' - ?') of k versus E(k) holds for all earthquakes. 2) For example, ? = 3.70 and ? = 1.96 for the Tohoku earthquake (2011) and ? = 4.16 and ? = 2.00 for the Haiti earthquake (2010). For these cases, the Gauss' law is appropriate because alpha is almost 2.00. 3) However, ? = 5.25 and ? = 1.25 for the Peru earthquake (2007) and ? = 2.24 and ? = 1.57 for the Simeulue earthquake (2008). For these earthquakes, the Lévy law is more appropriate because ? is far from 2.0. 4) Although Lavallée (2003, 2008) concluded that the Lévy law is more appropriate than the Gauss' law for white noise, which is later filtered, our results show that the Gauss law is appropriate for some earthquakes. Lavallée and Archuleta, 2003, Stochastic modeling of slip spatial complexities for the 1979 Imperial Valley, California, earthquake, GEOPHYSICAL RESEARCH LETTERS, 30(5). Lavallée, 2008, On the random nature of earthquake source and ground motion: A unified theory, ADVANCES IN GEOPHYSICS, 50, Chap 16.

  18. Comparison between dynamical and stochastic downscaling methods in central Italy

    NASA Astrophysics Data System (ADS)

    Camici, Stefania; Palazzi, Elisa; Pieri, Alexandre; Brocca, Luca; Moramarco, Tommaso; Provenzale, Antonello

    2015-04-01

    Global climate models (GCMs) are the primary tool to assess future climate change. However, most GCMs currently do not provide reliable information on scales below about 100 km and, hence, cannot be used as a direct input of hydrological models for climate change impact assessments. Therefore, a wide range of statistical and dynamical downscaling methods have been developed to overcome the scale discrepancy between the GCM climatic scenarios and the resolution required for hydrological applications and impact studies. In this context, the selection of a suitable downscaling method is an important issue. The use of different spatial domains, predictor variables, predictands and assessment criteria makes the relative performance of different methods difficult to achieve and general rules to select a priori the best downscaling method do not exist. Additionally, many studies have shown that, depending on the hydrological variable, each downscaling method significantly contributes to the overall uncertainty of the final hydrological response. Therefore, it is strongly recommended to test/evaluate different downscaling methods by using ground-based data before applying them to climate model data. In this study, the daily rainfall data from the ERA-Interim re-analysis database (provided by the European Centre for Medium-Range Weather Forecasts, ECMWF) for the period 1979-2008 and with a resolution of about 80 km, are downscaled using both dynamical and statistical methods. In the first case, the Weather Research and Forecasting (WRF) model was nested into the ERA-Interim re-analysis system to achieve a spatial resolution of about 4 km; in the second one, the stochastic rainfall downscaling method called RainFARM was applied to the ERA-Interim data to obtain one stochastic realization of the rainfall field with a resolution of ~1 km. The downscaled rainfall data obtained with the two methods are then used to force a continuous rainfall-runoff model in order to obtain a hydrological response in terms of discharge output. Preliminary results show that both downscaling methods are able to reproduce the statistical properties and temporal pattern of rainfall observations while the results in terms of discharge will be shown at the conference session. This analysis will provide useful guidelines for the selection of the best performing downscaling approach applied to rainfall data in this particular study area.

  19. Downscaling biogeochemistry in the Benguela eastern boundary current

    NASA Astrophysics Data System (ADS)

    Machu, E.; Goubanova, K.; Le Vu, B.; Gutknecht, E.; Garçon, V.

    2015-06-01

    Dynamical downscaling 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 downscale 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 downscaling. 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 downscaling on ROMS solution. Together with a finer resolution of dynamical processes and of bathymetric features (continental shelf and Walvis Ridge), wind downscaling 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 downscaling 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 downscaled ocean model with higher resolution winds than those issued from an AOGCM, results in improved representation of physical and biogeochemical processes.

  20. Climate change at local level : let's look around downscaling

    NASA Astrophysics Data System (ADS)

    Ravenel, H.; Jan, J.; Moisselin, J. M.; Pagé, C.

    2009-09-01

    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 downscaling 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 downscaling exercise have been launched : For impacts on precipitation pattern, IPCC models do not really agree on this zone, so downscaling is not really pertinent. For temperature, the climate change signal is clearer, but downscaling won't give much more information. Of course on other meteorogical parameters or on other variable that are linked to meteorological parameters, downscaling could be of interest and will probably be necessary. With or without downscaling, the stake is to build, at a local level, mechanisms which are similar to IPCC and UNFCCC. In that context, downscaling could either be helpful or create a kind of black box effect which will hamper real dialogues between stakeholders.

  1. The ultimate downscaling limit of FETs.

    SciTech Connect

    Mamaluy, Denis; Gao, Xujiao; Tierney, Brian David

    2014-10-01

    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 down-scaling 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.

  2. Hydrologic extremes - an intercomparison of multiple gridded statistical downscaling methods

    NASA Astrophysics Data System (ADS)

    Werner, A. T.; Cannon, A. J.

    2015-06-01

    Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling 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, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling 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 downscaling 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 downscaling target data. The skill of the downscaling 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 downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled 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.

  3. Spatiostatistical downscaling of soil moisture in an assimilation framework

    NASA Astrophysics Data System (ADS)

    Kaheil, Y. H.; Gill, M.; McKee, M.; Bastidas, L.

    2006-12-01

    The scale reconciliation issue has gained in extra attention with remote sensing data coming in and the shift towards the distributed approach for hydrologic modeling. The purpose of the current research is to develop a method to disaggregate coarse resolution remote sensing data to fine resolutions more appropriate in hydrologic studies. Disaggregation is done with the help of point measurements on the ground. The downscaling of remote sensing data is achieved by three main steps namely: initialization, spatial pattern mimicking, and assimilation. The assimilation step also excerpts the information coming from the point measurements. These three steps provide means of capturing both spatial trend and physics of the process at multiple resolution levels while downscaling. The approach has been applied and validated by downscaling images for two cases. In the first case a synthetically generated random field based on the statistical properties of point measurements is reproduced at fine scale and coarse resolutions. The algorithm was able to account for spatial and vertical properties for this synthetic case. In the second case a soil moisture field from SGP 97 experiments is downscaled from a resolution of 800m x 800m to a resolution of 50m x 50m. It is also shown that how the assimilation step helped to improve the approximation of the downscaled fields.

  4. A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts

    E-print Network

    Washington at Seattle, University of

    A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts Enrica;A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts Enrica. Abstract Nonhomogeneous hidden Markov models (NHMMs) provide a relatively simple framework for simulating

  5. Credibility of statistical downscaling under nonstationary climate

    NASA Astrophysics Data System (ADS)

    Salvi, Kaustubh; Ghosh, Subimal; Ganguly, Auroop R.

    2015-06-01

    Statistical downscaling (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.

  6. Operational Downscaling of Soil Moisture Fields Using Ancillary Data

    NASA Astrophysics Data System (ADS)

    Kim, G.; Barros, A. P.

    2001-05-01

    The scaling analysis of large-scale soil moisture data from the Southern Great Plains Hydrology experiment (SGP'97) showed that the scaling behavior of soil moisture is multifractal varying with the scale of observations and hydroclimatological conditions which can be explained with scaling behavior of soil hydraulic properties. These results suggested that it should be possible to use the spatial patterns of ancillary data at high resolution such as the sand content of soils as spatial basis functions for downscaling. This hypothesis was investigated by applying a modified fractal interpolation method for downscaling soil moisture from the SGP'97 experiment using ancillary data. The methodology should be especially useful for downscaling large-scale remotely-sensed estimates of soil moisture (e.g. AMSR) to the scales of operational hydrologic models.

  7. A standardized framework for evaluating the skill of regional climate downscaling techniques

    Microsoft Academic Search

    Katharine Anne Hayhoe

    2010-01-01

    Regional climate impact assessments require high-resolution projections to resolve local factors that modify the impact of global-scale forcing. To generate these projections, global climate model simulations are commonly downscaled using a variety of statistical and dynamical techniques. Despite the essential role of downscaling in regional assessments, there is no standard approach to evaluating various downscaling methods. Hence, impact communities often

  8. Algorithmen Cluster Ensembles

    E-print Network

    Morik, Katharina

    Einleitung Algorithmen Analyse Cluster Ensembles Combining Multiple Partitions Daniel Spierling 11. Oktober 2007 Daniel Spierling Cluster Ensembles #12;Einleitung Algorithmen Analyse Inhaltsverzeichnis Einleitung Aufgabenstellung Verarbeitungsmöglichkeit Algorithmen Cluster-based Similarity Partitioning

  9. Exploring Ensemble Visualization

    PubMed Central

    Phadke, Madhura N.; Pinto, Lifford; Alabi, Femi; Harter, Jonathan; Taylor, Russell M.; Wu, Xunlei; Petersen, Hannah; Bass, Steffen A.; Healey, Christopher G.

    2012-01-01

    An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data. PMID:22347540

  10. Passive Microwave Soil Moisture Downscaling Using Vegetation and Surface Temperatures

    NASA Astrophysics Data System (ADS)

    Fang, B.; Lakshmi, V.

    2012-12-01

    Soil moisture satellite estimates are available from a variety of passive microwave satellite missions, but their resolution is frequently too large for use by land managers and action agencies. In this article, a soil moisture downscaling algorithm based on look-up curves between daily temperature change and daily average soil moisture is presented and developed to bridge the scale. The algorithm was derived from 1/8o spatial resolution North American Land Data Assimilation System (NLDAS-2) surface temperature and soil moisture data, and also used 5 km Advanced Very High Resolution Radiometer (AVHRR) and 1km Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) as look-up dataset for different vegetation and surface temperature conditions. The differences between 1km MODIS temperature downscaled soil moisture values and Advanced Microwave Scanning Radiometer - EOS (AMSR-E) soil moisture values were used to modify AMSR-E soil moistures. The 1km downscaled soil moisture maps display greater details on the spatial pattern of soil moisture distribution. Two sets of ground-based measurements, the Oklahoma Mesonet and the Little Washita Micronet were used to validate the algorithm. The Root Mean Square Error (RMSE) of the 1km downscaled soil moisture versus Oklahoma Mesonet observations for clear days ranges from 0.119~0.168, whereas the RMSE of 1km downscaled soil moisture versus the Little Washita Watershed observations ranges from 0.022~0.077. The results demonstrate that the 1 km downscaled soil moisture has better agreement with watershed in situ data compared to the other sources of soil moisture.

  11. Addressing deterministic and stochastic variance in statistical downscaling

    NASA Astrophysics Data System (ADS)

    Hewitson, Bruce; Jack, Christopher; Coop, Lisa

    2013-04-01

    Downscaling seeks to add appropriate temporal and spatial variance to low resolution GCM predictor fields. In doing so, there is a deterministic component that is conditioned by the GCM, and a residual component which may be considered as undetermined and/or stochastic variance. For application in many sectors, such as hydrology, extreme events, or multi-year drought, a downscaling method that ignores aspects of the sources of variance risks providing significantly misleading results. Different statistical downscaling approaches deal with this situation in different ways. Analogue pattern-perturbation approaches inherently accommodate the range of temporal and spatial variance, although are vulnerable to spatial stationarity issues. Transfer function based downscaling is very good at capturing the deterministic component but may lose the high-frequency stochastic variance. Weather generator approaches are excellent at the capturing the spectrum of variance, but may require special approaches to handle the low-frequency deterministic variance, and their weather generator parameters may be particularly vulnerable to stationarity. Thus in practice, most statistical downscaling methods (should) include some explicit treatment to accommodate the spectrum of variance on different time scales, and that includes both the deterministic and stochastic components. We present a method that uses the daily observed data as a sample set spanning the continuum of possibilities in an n-dimensional predictor-space. The nature of the distribution of predictand response values (the downscaling target variable, e.g. precipitation) within the local domain of a position within the predictor space describes the balance between deterministic and stochastic variability. A response distribution within a local domain of the predictor-space with high variance reflects a dominance of stochastic variability within that region of the predictor space. In contrast, a response distribution with low variance reflects a dominance of deterministic variability. By explicitly using both information aspects within the predictor space a downscaled response time series may be created that captures the continuum of variance on different time scales. Because the method explicitly determines the mean deterministic and the stochastic components within the predictor space, the method allows for disaggregating the balance of variance as a function of predictor state, geographic place, and time. Examples of each of these disaggregations are presented, leading to a mapping of the mean ratio between the deterministic and stochastic variance across the Africa CORDEX region. The results show that the ratio has notable spatial and temporal dependencies, and highlights regional issues of downscaling robustness.

  12. Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation

    NASA Astrophysics Data System (ADS)

    Zhang, Hua; Huang, Guo H.

    2013-02-01

    Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.

  13. Rainfall Downscaling Conditional on Upper-air Atmospheric Predictors: Improved Assessment of Rainfall Statistics in a Changing Climate

    NASA Astrophysics Data System (ADS)

    Langousis, Andreas; Mamalakis, Antonis; Deidda, Roberto; Marrocu, Marino

    2015-04-01

    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 downscaling, 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 downscaling 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 downscaling 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 ENSEMBLES 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 downscaling scheme. To our knowledge, this is the first time that climate model rainfall and statistically downscaled precipitation are compared to catchment-averaged MAP at a daily resolution. The obtained results are promising, since the proposed downscaling 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 downscaling 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.

  14. Downscaling approach to develop future sub-daily IDF relations for Canberra Airport Region, Australia

    NASA Astrophysics Data System (ADS)

    Herath, H. M. S. M.; Sarukkalige, P. R.; Nguyen, V. T. V.

    2015-06-01

    Downscaling of climate projections is the most adopted method to assess the impacts of climate change at regional and local scale. In the last decade, downscaling techniques which provide reasonable improvement to resolution of General Circulation Models' (GCMs) output are developed in notable manner. Most of these techniques are limited to spatial downscaling of GCMs' output and still there is a high demand to develop temporal downscaling approaches. As the main objective of this study, combined approach of spatial and temporal downscaling is developed to improve the resolution of rainfall predicted by GCMs. Canberra airport region is subjected to this study and the applicability of proposed downscaling approach is evaluated for Sydney, Melbourne, Brisbane, Adelaide, Perth and Darwin regions. Statistical Downscaling Model (SDSM) is used to spatial downscaling and numerical model based on scaling invariant concept is used to temporal downscaling of rainfalls. National Centre of Environmental Prediction (NCEP) data is used in SDSM model calibration and validation. Regression based bias correction function is used to improve the accuracy of downscaled annual maximum rainfalls using HadCM3-A2. By analysing the non-central moments of observed rainfalls, single time regime (from 30 min to 24 h) is identified which exist scaling behaviour and it is used to estimate the sub daily extreme rainfall depths from daily downscaled rainfalls. Finally, as the major output of this study, Intensity Duration Frequency (IDF) relations are developed for the future periods of 2020s, 2050s and 2080s in the context of climate change.

  15. Using a Coupled Lake Model with WRF for Dynamical Downscaling

    EPA Science Inventory

    The Weather Research and Forecasting (WRF) model is used to downscale 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...

  16. Downscaled inverses for M-channel lapped transforms

    Microsoft Academic Search

    R. L. de Queiroz; R. Eschbach

    1997-01-01

    Compressed images may be decompressed for devices using different resolutions. Full decompression and rescaling in the space domain is a very expensive method. We studied downscaled inverses where the image is decompressed partially and a reduced inverse transform is used to recover the image. We studied the design of fast inverses, for a given forward transform. General solutions are presented

  17. Downscaling Indonesian Precipitation Using Large-scale Meteorological Fields

    E-print Network

    Vimont, Daniel J.

    precipitation for Indonesia. Downscaling techniques are most skillful over the southern islands (Java and Bali October-February (central and eastern Java, and the eastern islands in Indonesia's archipelago) (Mc-scale precipitation over Indonesia from fields that describe the large-scale circulation and hydrological cycle

  18. Stochastic space-time downscaling of GCM precipitation

    NASA Astrophysics Data System (ADS)

    Kang, B.; Ramirez, J. A.

    2002-05-01

    A new downscaling algorithm is presented. The algorithm is a composite scheme consisting of 2 sub-models, a Stochastic Space-Time Disaggregation Model (SSTDM), which accounts for the hierarchical structure of the spatial and temporal statistical dependencies of precipitation and an Intermittent Random Cascade Model (IRCM) which accounts for the scale invariant feature and reproduces self-similarity structure and spatial intermittent cluster formation. The model is applied to downscale rainfall output of the Canadian Global Climate Model II, whose spatial resolution is approximately 3.7degree(about 400km), to the 2*2km-scale field. For SSTDM, Valencia and Schaake's general disaggregation model(1973), which was originally developed for multi-site, multi-season streamflow disaggregation, was modified in this work for use in the downscaling of grid precipitation. IRCM adopted the mass re-distribution concept of the multiplicative random cascade structure (Kang and Ramirez, 2002, Kang and Ramirez, 2001, Over and Gupta, 1996). This model was tested first on the aggregated(256*256km) and original(2*2km) daily NEXRAD precipitation field of July, 1997. The spatial correlation of the simulated precipitation fields reasonably reproduces the patterns of the observations. The lag-1(day) temporal correlation of the simulation, as a function of scale, reproduces the observed behavior adequately. However, the intermittency parameter, beta, appears to be underestimated by the model. This model is then applied for downscaling output of CGCMII produced by CCCma(Canadian Center for Climate modeling and analysis).

  19. On regional dynamical downscaling for the assessment and projection of temperature and precipitation extremes across Tasmania, Australia

    NASA Astrophysics Data System (ADS)

    White, Christopher J.; McInnes, Kathleen L.; Cechet, Robert P.; Corney, Stuart P.; Grose, Michael R.; Holz, Gregory K.; Katzfey, Jack J.; Bindoff, Nathaniel L.

    2013-12-01

    The ability of an ensemble of six GCMs, downscaled to a 0.1° lat/lon grid using the Conformal Cubic Atmospheric Model over Tasmania, Australia, to simulate observed extreme temperature and precipitation climatologies and statewide trends is assessed for 1961-2009 using a suite of extreme indices. The downscaled simulations have high skill in reproducing extreme temperatures, with the majority of models reproducing the statewide averaged sign and magnitude of recent observed trends of increasing warm days and warm nights and decreasing frost days. The warm spell duration index is however underestimated, while variance is generally overrepresented in the extreme temperature range across most regions. The simulations show a lower level of skill in modelling the amplitude of the extreme precipitation indices such as very wet days, but simulate the observed spatial patterns and variability. In general, simulations of dry extreme precipitation indices are underestimated in dryer areas and wet extremes indices are underestimated in wetter areas. Using two SRES emissions scenarios, the simulations indicate a significant increase in warm nights compared to a slightly more moderate increase in warm days, and an increase in maximum 1- and 5- day precipitation intensities interspersed with longer consecutive dry spells across Tasmania during the twenty-first century.

  20. A spatial hybrid approach for downscaling of extreme precipitation fields

    NASA Astrophysics Data System (ADS)

    Bechler, Aurélien; Vrac, Mathieu; Bel, Liliane

    2015-05-01

    For a few decades, climate models are used to provide future scenarios of precipitation with increasingly higher spatial resolution. However, this resolution is not yet sufficient to describe efficiently what happens at local scale. Dynamical and statistical methods of downscaling have been developed and allow us to make the link between two levels of resolution and enable us to get values at a local scale based on large-scale information from global or regional climate models. Nevertheless, both the extreme behavior and the spatial structures are not well described by these downscaling methods. We propose a two-step methodology, called spatial hybrid downscaling (SHD), to solve this problem. The first step consists in applying a univariate (i.e., one-dimensional) statistical downscaling to link the high- and low-resolution variables at some given locations. Once this 1d-link is performed, a conditional simulation algorithm of max-stable processes adapted to the extremal t process enables us to get conditional distributions of extreme precipitation at any point of the region. An application is performed on precipitation data in the south of France where extreme (Cevenol) events have major impacts (e.g., floods). Different versions of the SHD approach are tested. Most of them show particularly good results regarding univariate and multivariate criteria and overcome classical downscaling techniques tested in comparison. Furthermore, these conclusions are robust to the choice of the 1d-link functions tested and to the choice of the conditioning points to drive the conditional local-scale simulations performed by the SHD approach.

  1. Downscaling climate model output for water resources impacts assessment (Invited)

    NASA Astrophysics Data System (ADS)

    Maurer, E. P.; Pierce, D. W.; Cayan, D. R.

    2013-12-01

    Water agencies in the U.S. and around the globe are beginning to wrap climate change projections into their planning procedures, recognizing that ongoing human-induced changes to hydrology can affect water management in significant ways. Future hydrology changes are derived using global climate model (GCM) projections, though their output is at a spatial scale that is too coarse to meet the needs of those concerned with local and regional impacts. Those investigating local impacts have employed a range of techniques for downscaling, the process of translating GCM output to a more locally-relevant spatial scale. Recent projects have produced libraries of publicly-available downscaled climate projections, enabling managers, researchers and others to focus on impacts studies, drawing from a shared pool of fine-scale climate data. Besides the obvious advantage to data users, who no longer need to develop expertise in downscaling prior to examining impacts, the use of the downscaled data by hundreds of people has allowed a crowdsourcing approach to examining the data. The wide variety of applications employed by different users has revealed characteristics not discovered during the initial data set production. This has led to a deeper look at the downscaling methods, including the assumptions and effect of bias correction of GCM output. Here new findings are presented related to the assumption of stationarity in the relationships between large- and fine-scale climate, as well as the impact of quantile mapping bias correction on precipitation trends. The validity of these assumptions can influence the interpretations of impacts studies using data derived using these standard statistical methods and help point the way to improved methods.

  2. Comparing climate change impacts on crops in Belgium based on CMIP3 and EU-ENSEMBLES multi-model ensembles

    NASA Astrophysics Data System (ADS)

    Vanuytrecht, E.; Raes, D.; Willems, P.; Semenov, M.

    2012-04-01

    Global Circulation Models (GCMs) are sophisticated tools to study the future evolution of the climate. Yet, the coarse scale of GCMs of hundreds of kilometers raises questions about the suitability for agricultural impact assessments. These assessments are often made at field level and require consideration of interactions at sub-GCM grid scale (e.g., elevation-dependent climatic changes). Regional climate models (RCMs) were developed to provide climate projections at a spatial scale of 25-50 km for limited regions, e.g. Europe (Giorgi and Mearns, 1991). Climate projections from GCMs or RCMs are available as multi-model ensembles. These ensembles are based on large data sets of simulations produced by modelling groups worldwide, who performed a set of coordinated climate experiments in which climate models were run for a common set of experiments and various emissions scenarios (Knutti et al., 2010). The use of multi-model ensembles in climate change studies is an important step in quantifying uncertainty in impact predictions, which will underpin more informed decisions for adaptation and mitigation to changing climate (Semenov and Stratonovitch, 2010). The objective of our study was to evaluate the effect of the spatial scale of climate projections on climate change impacts for cereals in Belgium. Climate scenarios were based on two multi-model ensembles, one comprising 15 GCMs of the Coupled Model Intercomparison Project phase 3 (CMIP3; Meehl et al., 2007) with spatial resolution of 200-300 km, the other comprising 9 RCMs of the EU-ENSEMBLES project (van der Linden and Mitchell, 2009) with spatial resolution of 25 km. To be useful for agricultural impact assessments, the projections of GCMs and RCMs were downscaled to the field level. Long series (240 cropping seasons) of local-scale climate scenarios were generated by the LARS-WG weather generator (Semenov et al., 2010) via statistical inference. Crop growth and development were simulated with the AquaCrop model for maize and the Sirius model for winter wheat. Our study showed that for maize significantly different yield changes were predicted for future scenarios based on CMIP3 and EU-ENSEMBLES ensembles, respectively. Whereas under CMIP3 scenarios the overall impact on maize yield was mostly negative, there was a positive yield impact under ENSEMBLES scenarios. In contrast, changes in winter wheat yields were very similar for the two ensembles. Our results demonstrated that the use of the EU-ENSEMBLES ensemble allowed further exploration of uncertainties in agricultural impacts in Belgium, and we hypothesize that even more added value from the use of RCMs could be anticipated in European regions with complex topography where projections from GCMs and RCMs would be significantly different.

  3. Regional climate change projections over South America based on the CLARIS-LPB RCM ensemble

    NASA Astrophysics Data System (ADS)

    Samuelsson, Patrick; Solman, Silvina; Sanchez, Enrique; Rocha, Rosmeri; Li, Laurent; Marengo, José; Remedio, Armelle; Berbery, Hugo

    2013-04-01

    CLARIS-LPB was an EU FP7 financed Europe-South America Network for Climate Change Assessment and Impact Studies in La Plata Basin. CLARIS-LPB has created the first ensemble ever of RCM downscalings over South America. Here we present the climate change scenarios for a near future period (2011-2040) and for a far future period (2071-2100). The ensemble is based on seven RCMs driven by three CMIP3 GCMs for emission scenario SRES A1B. The RCM model domains cover all of South America, with a horizontal resolution of approximately 50 km, but project focus has been on results over the La Plata Basin. The ensemble mean for temperature change shows more warming over tropical South America than over the southern part of the continent. During summer (DJF) the Low-Parana and Uruguay regions show less warming than the surrounding regions. For the ensemble mean of precipitation changes the patterns are almost the same for near and far future but with larger values for far future. Thus overall trends do not change with time. The near future shows in general small changes over large areas (less than ±10%). For JJA a dry tendency is seen over eastern Brazil that becomes stronger and extends geographically with time. In near future most models show a drying trend over this area. In far future almost all models agree on the drying. For DJF a wet tendency is seen over the La Plata basin area which becomes stronger with time. In near future almost all downscalings agree on this wet tendency and in far future all downscalings agree on the sign. The RCM ensemble is unbalanced with respect to forcing GCMs. 6 out of 11(10) simulations use ECHAM5 for the near(far) future period while 4(3) use HadCM3 and only one IPSL. Thus, all ensemble mean values will be tilted towards ECHAM5. It is of course possible to compensate for this imbalance among GCMs by some weighting but no such weighting has been applied for the current analysis. The north-south gradient in warming is in general stronger in the ECHAM5 downscalings and is also more evident during JJA than during DJF. The HadCM3 and IPSL downscalings give larger warming in near future than ECHAM5 downscalings. This tendency is still present in far future but differences connected to GCMs are then much less evident. For precipitation the spread in trends and amounts of changes between different downscalings are much larger than for temperature. In contrast to temperature the precipitation patterns are in general more similar for the same RCM than for the same GCM. Thus, the results are sensitive for how precipitation processes are parameterized and/or for how local surface-atmosphere feedback mechanisms are simulated. Looking at a certain RCM and period the patterns for near and far futures are similar but stronger for the far future period.

  4. The Ensemble Canon

    NASA Technical Reports Server (NTRS)

    MIittman, David S

    2011-01-01

    Ensemble 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, Ensemble offers a low-risk, politically neutral path towards a tighter integration of operations tools. The Ensemble project is a highly successful, ongoing collaboration among NASA Centers. Since 2004, the Ensemble project has supported the development of mission operations software for NASA's Exploration Systems, Science, and Space Operations Directorates.

  5. Stochastic Cascade Dynamical Downscaling of Precipitation over Complex Terrain

    NASA Astrophysics Data System (ADS)

    Posadas, A.; Duffaut, L. E.; Jones, C.; Carvalho, L. V.; Carbajal, M.; Heidinger, H.; Quiroz, R.

    2013-12-01

    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 downscaling 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 downscaling 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 downscaling 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 downscaled to gridded 1 km layers with the Multifractal downscaling 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 Downscaling of Precipitation over Complex Terrain

  6. Representative meteorological ensembles of change climate change in the Araucanía Region, Chile.

    NASA Astrophysics Data System (ADS)

    Cepeda, Javier; Vargas, Ximena

    2015-04-01

    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 ensembles 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 downscaling is performed; first spatial downscaling is carried out, and then a temporal downscaling of the daily precipitation data for each month is made. Ensembles whit transfer functions without discontinuities or those with the least were preferred. From this analysis we selected four ensembles. 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 ensemble are the most representative historical conditions.

  7. Validation of WRF Downscaling Capabilities Over Western Australia to Detect Rainfall and Temperature Extremes

    NASA Astrophysics Data System (ADS)

    Andrys, J.; Lyons, T.; Kala, J.

    2013-12-01

    When evaluating the merits of regional climate simulations, one of the most compelling arguments for this high resolution, dynamical downscaling approach is its ability to simulate the extremes of temperature and precipitation with greater skill than lower resolution models. A historical (1970-2000), ensemble regional climate simulation using WRF was performed over Western Australia at a 50km, 10km and 5km resolution in order to evaluate the effectiveness of the model in simulating annually extreme climate events as defined by the core climate indices of the CCI/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). Five temperature and five precipitation indices were chosen and the capacity of the simulation to detect the temporal and spatial structure of these indices was assessed. Validation took place through comparisons to observational CSIRO Australia Water Availability Project (AWAP) daily gridded minimum and maximum temperature and precipitation data and RCM simulations driven by ERA-Interim lateral boundary conditions over the same area. The study is part one of a two part project to examine future changes in extreme temperature and precipitation in the region and the influence of land cover change and anthropogenic greenhouse gases on these changes.

  8. The fundamental downscaling limit of field effect transistors

    NASA Astrophysics Data System (ADS)

    Mamaluy, Denis; Gao, Xujiao

    2015-05-01

    We predict that within next 15 years a fundamental down-scaling 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 downscaling limit. Alternative possibilities are discussed to continue the increase of logic element densities for room temperature operation below the said limit.

  9. Scaling Limits of Dyson's ?-ENSEMBLE

    NASA Astrophysics Data System (ADS)

    Valkó, Benedek

    2010-03-01

    Dyson's ?-ensemble gives a one parameter generalization for the joint eigenvalue distribution of the Gaussian orthogonal and unitary ensemble. We review some recent results on the scaling limits of this ensemble and also discuss some large deviation results.

  10. Universality of General $?$-Ensembles

    E-print Network

    Paul Bourgade; Laszlo Erdos; Horng-Tzer Yau

    2012-02-05

    We prove the universality of the $\\beta$-ensembles with convex analytic potentials and for any $\\beta>0$, i.e. we show that the spacing distributions of log-gases at any inverse temperature $\\beta$ coincide with those of the Gaussian $\\beta$-ensembles.

  11. Extended Ensemble Monte Carlo

    Microsoft Academic Search

    Yukito Iba

    2001-01-01

    ``Extended Ensemble Monte Carlo'' is a generic term that indicates a set of algorithms, which are now popular in a variety of fields in physics and statistical information processing. Exchange Monte Carlo (Metropolis-Coupled Chain, Parallel Tempering), Simulated Tempering (Expanded Ensemble Monte Carlo) and Multicanonical Monte Carlo (Adaptive Umbrella Sampling) are typical members of this family. Here, we give a cross-disciplinary

  12. Advanced Review Cluster ensembles

    E-print Network

    Ghosh, Joydeep

    Advanced Review Cluster ensembles Joydeep Ghosh and Ayan Acharya Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as the consensus solution. Consensus clustering can be used to generate more robust and stable clustering results compared

  13. Downscaling the climate change for oceans around Australia

    NASA Astrophysics Data System (ADS)

    Chamberlain, M. A.; Sun, C.; Matear, R. J.; Feng, M.; Phipps, S. J.

    2012-09-01

    At present, global climate models used to project changes in climate poorly resolve mesoscale ocean features such as boundary currents and eddies. These missing features may be important to realistically project the marine impacts of climate change. Here we present a framework for dynamically downscaling coarse climate change projections utilising a near-global ocean model that resolves these features in the Australasian region, with coarser resolution elsewhere. A time-slice projection for a 2060s ocean was obtained by adding climate change anomalies to initial conditions and surface fluxes of a near-global eddy-resolving ocean model. Climate change anomalies are derived from the differences between present and projected climates from a coarse global climate model. These anomalies are added to observed fields, thereby reducing the effect of model bias from the climate model. The downscaling model used here is ocean-only and does not include the effects that changes in the ocean state will have on the atmosphere and air-sea fluxes. We use restoring of the sea surface temperature and salinity to approximate real-ocean feedback on heat flux and to keep the salinity stable. Extra experiments with different feedback parameterisations are run to test the sensitivity of the projection. Consistent spatial differences emerge in sea surface temperature, salinity, stratification and transport between the downscaled projections and those of the climate model. Also, the spatial differences become established rapidly (< 3 yr), indicating the importance of mesoscale resolution. However, the differences in the magnitude of the difference between experiments show that feedback of the ocean onto the air-sea fluxes is still important in determining the state of the ocean in these projections. Until such a time when it is feasible to regularly run a global climate model with eddy resolution, our framework for ocean climate change downscaling provides an attractive way to explore the response of mesoscale ocean features with climate change and their effect on the broader ocean.

  14. Soil moisture downscaling across climate regions and its emergent properties

    NASA Astrophysics Data System (ADS)

    Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto

    2011-11-01

    Land surface models of water and energy fluxes can benefit from the characterization of soil moisture variability provided by robust downscaling algorithms over a wide range of climatic settings. In this study, we present the application of a multifractal-based statistical downscaling scheme using 800 m aircraft-derived soil moisture data collected during three field campaigns in contrasting climatic regimes. The disaggregation scheme was tested in a previous work using data of the Southern Great Plains experiment in 1997 (SGP97) in a temperate region in Oklahoma. Here, we explore its capability on different climates by using data from two other campaigns: Soil Moisture Experiment in 2002 (SMEX02), in an agricultural region with subhumid climate in Iowa, and Soil Moisture Experiment in 2004 (SMEX04), conducted in two semiarid areas in Arizona and Sonora (Mexico). We first demonstrate the presence of multifractality in soil moisture fields over the scale range from 0.8 km (aircraft footprint) to 25.6 km (satellite footprint) over most wetness conditions. Next, we identify an empirical regional calibration relation linking model parameters with the spatial mean soil moisture and coarse-scale predictors that account for topography, soil texture, and land cover in each site. The downscaling model shows good performance in a broad range of conditions, except for a few cases where specific physiographic features introduce relevant spatial nonhomogeneity in the soil moisture field. The calibrated downscaling model is then applied to study the relation between spatial variability and mean soil moisture across the different climate settings. In such a way, we explain the diverse shapes presented in previous studies and suggest possible physical explanations for intraregional and interregional differences.

  15. Characterizing Uncertainties in Hydrologic Extremes: Statistical vs. Dynamical Downscaling

    NASA Astrophysics Data System (ADS)

    Mauger, G. S.; Salathe, E. P., Jr.

    2013-12-01

    Numerous agencies are now charged with considering the impacts of climate change in management decisions, both from the standpoint of adapting to changing conditions and minimizing emissions of greenhouse gases. These decisions require robust projections of change and defensible estimates of their uncertainty. We present work that is specifically focused on characterizing the uncertainty in projections of hydrologic extremes. Much recent work has been devoted to characterizing the uncertainty in hydrologic projections due to differences in downscaling methodology (e.g., Abatzoglou and Brown, 2012; Bürger et al., 2012; Rasmussen et al., 2011; Wetterhall et al., 2012) and among hydrologic models (e.g., Bennett et al., 2012; Clark et al., 2008; Fenicia et al., 2008; Smith and Marshall, 2010; Vano et al., 2012). These have established a basis for such analyses, but have generally focused on the implications for monthly and annual flows rather than flow extremes. In addition, few among these have been focused within the Pacific Northwest. In this work we assess the uncertainty in projected changes to hydrologic extremes associated with dynamical vs. statistical downscaling. The analysis is focused on 3 distinct watersheds within the Pacific Northwest - the Skagit, Green, and Willamette river basins. Results highlight the sensitivity of flood projections to downscaling approach and hydrologic model assumptions. Sensitivities are characterized as a function of geographic location, hydrologic regime, and climate - identifying circumstances under which projections are reliable and others in which answers differ markedly based on methodology. For example, one notable result is that dynamically downscaled projections appear to refute the assumed relationship between watershed type (snow-dominant vs. rain-dominant) and projected changes to flood risk - currently considered a key indicator of future flood risk. Results presented here provide key information for decision-making as well as for prioritizing future impacts research.

  16. Ensemble Forecasting Explained

    NSDL National Science Digital Library

    2014-09-14

    This module, the latest in our series on Numerical Weather Prediction, covers the theory and use of ensemble prediction systems (EPSs). The module will help forecasters develop an understanding of the basis for EPSs, the skills to interpret ensemble products, and strategies for their use in the forecast process. It contains six sections: an Introduction that briefly presents background theory; Generation, which describes how ensemble systems are constructed; Statistical Concepts, which provides a brief refresher on knowledge required for ensemble product interpretation; Summarizing Data, which describes common ensemble forecast products; Verification, which discusses how EPSs performance is assessed and documented; and Case Applications, which provides links to a number of forecast cases illustrating the use of EPSs in the forecast process. Questions and Exercises are offered throughout to help you test your learning and provide practical examples. The module also includes a pre-assessment and module final quiz.

  17. Evaluating the utility of dynamical downscaling in agricultural impacts projections

    PubMed Central

    Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J.

    2014-01-01

    Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled 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 downscaling 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

  18. Large-Scale Weather Generator for Downscaling Precipitation

    NASA Astrophysics Data System (ADS)

    Thober, Stephan; Samaniego, Luis; Bardossy, Andras

    2013-04-01

    Well parametrized distributed precipitation-runoff models are able to correctly quantify hydrological state variables (e.g. streamflow, soil moisture, among others) for the past decades. In order to estimate future risks associated with hydrometeorological extremes, it is necessary to incorporate information about the future weather and climate. A common approach is to downscale Regional Climate Model (RCM) projections. Therefore, various statistical downscaling schemes, utilizing diverse mathematical methods, have been developed. One kind of statistical downscaling technique is the so called Weather Generator (WG). These algorithms provide meteorological time series as the realization of a stochastic process. First, single- and multi-site models were developed. Recently, however WG at sub-daily scales and on gridded spatial resolution have captured the interest because of the new development in distributed hydrological modelling. A standard approach for a multi-site WG is to sample a multivariate normal process for all locations. Doing so, it is necessary to calculate the Cholesky factor of the cross-covariance matrix to guarantee a spatially consistent sampling. In general, gridded WGs are an extension of multi-site WGs to larger domains (i.e. >10000 grid cells). On these large grids, it is not possible to accurately determine the Cholesky factor and further enhancements are required. In this work, a framework for a WG is proposed, which provides meteorological time-series on a large scale grid, e.g. 4 km grid of Germany. It employs a sequential Gaussian simulation method, conditioning the value of a grid cell only on a neighborhood, not on the whole field. This methodology is incorporated into a multi-scale downscaling scheme, which is able to provide precipitation data sets at different spatial and temporal resolutions, ranging from 4 km to 32 km, and from days to months, respectively. This framework uses a copula approach for spatial downscaling, exploiting the strong dependence between different spatial scales, and a multiplicative cascade approach for the temporal disaggregation. This study incorporates a gridded, daily data set for the domain of Germany at a 4 km resolution. The data set was interpolated by external drift kriging of station data from the German Weather Service (DWD) and spans over the time period from 1961 to 2000. The data set was aggregated to the different spatio-temporal scales resolutions investigated in this study. The proposed methodology provides precipitation time series at the resolution and grid sizes required by large hydrological application (at national level). First results indicate that the framework is able to consistently preserve precipitation statistics including variability at multiple spatio-temporal resolutions. Nevertheless, it has to be investigated, whether rainfall extremes are correctly represented.

  19. Precipitation Prediction in North Africa Based on Statistical Downscaling

    NASA Astrophysics Data System (ADS)

    Molina, J. M.; Zaitchik, B.

    2013-12-01

    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 downscaling techniques. A number of such downscaling methods have been proposed in the literature, and although all of them have advantages and limitations depending on the specific downscaling problem, most of them have been developed and tested in developed countries. In this research, we explore the use of statistical downscaling 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 downscaling 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.

  20. Comparative Assessment of Statistical Downscaling Methods for Precipitation in Florida

    NASA Astrophysics Data System (ADS)

    Goly, A.; Teegavarapu, R. S.

    2012-12-01

    Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by General Circulation Models (GCMs). GCMs in general are capable in capturing the large-scale circulation patterns and correctly model smoothly varying fields such as surface pressure, but it is extremely unlikely that these models properly reproduce non-smooth fields such as precipitation. This paper presents and compares different statistical downscaling methods involving Multiple Linear Regression (MLR), Positive Coefficient Regression (PCR), Stepwise Regression (SWR) and Support Vector Machine (SVM) for estimation of rainfall in the state of Florida, USA, which is considered to be a climatically sensitive region. The explanatory variables/predictors used in the current study are mean sea-level pressure, air temperature, geo-potential height, specific humidity, U-wind and V-wind. Principal Component Analysis (PCA) and Fuzzy C-Means (FCM) clustering techniques are used to reduce the dimensionality of the dataset and identify the circulation patterns on precipitation in different clusters. Downscaled precipitation data obtained from widely used Bias-Correction Spatial Disaggregation (BCSD) downscaling technique is compared along with the other downscaling methods. The performance of the models is evaluated using various performance measures and it was found that the SVM model performed better than all the other models in reproducing most monthly rainfall statistics at 18 locations. Output from the third generation Canadian Global Climate Model (CGCM3) GCM for A1B scenario was used for future precipitation projection. For the projection period 2001-2010, MLR was used and evaluated as a substitute to the traditional spatial interpolation linking the variables at the GCM grid to NCEP grid scale. It has been found that the choice of linking variables from GCM to NCEP grid by MLR yielded superior statistics at most of the stations (12 out of 18) and show a better reproduction of the monthly precipitation.

  1. Rainfall Downscaling Conditional on Upper-air Variables: Assessing Rainfall Statistics in a Changing Climate

    NASA Astrophysics Data System (ADS)

    Langousis, Andreas; Deidda, Roberto; Marrocu, Marino; Kaleris, Vassilios

    2014-05-01

    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 downscaling 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 ENSEMBLES 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 downscaling 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.

  2. Developing High-Resolution Inundation Estimates through a Downscaling of Brightness Temperature Measurements

    NASA Astrophysics Data System (ADS)

    Fisher, C. K.; Wood, E. F.

    2014-12-01

    There is currently a large demand for high-resolution estimates of inundation extent and flooding for applications in water management, risk assessment and hydrologic modeling. In many regions of the world it is possible to examine the extent of past inundation from visible and infrared imagery provided by sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS); however, this is not possible in regions that are densely vegetated or are under persistent cloud cover. As a result of this, there is a need for alternative methodologies that make use of other remotely sensed data sources to inform high-resolution estimates of inundation. One such data source is the AMSR-E/Aqua 37 GHz vertically and horizontally polarized brightness temperature measurements, which have been used in previous studies to estimate the extent of inundated areas and which can make observations in vegetated or cloudy regions. The objective of this work was to develop a decision tree classifier based downscaling methodology by which inundation extent can be obtained at a high resolution, based on microwave brightness temperature measurements and high resolution topographic information. Using a random forest classifier that combined the AMSR-E 37GHz brightness temperatures (~12km mean spatial resolution) and a number of high-resolution topographic indices derived from the National Elevation Dataset for the United States (30m spatial resolution), a high-resolution estimate of inundation was created. A case study of this work is presented for the severe flooding that occurred in Iowa during the summer of 2008. Training and validation data for the random forest classifier were derived from an ensemble of previously existing estimates of inundation from sources such as MODIS imagery, as well as simulated inundation extents generated from a hydrologic routing model. Results of this work suggest that the decision tree based downscaling has skill in producing high-resolution estimates of inundation when informed by the brightness temperature measurements along with high quality training data and can be used to estimate the likelihood of inundation for the region of interest.

  3. Downscaling scheme to drive soil-vegetation-atmosphere transfer models

    NASA Astrophysics Data System (ADS)

    Schomburg, Annika; Venema, Victor; Lindau, Ralf; Ament, Felix; Simmer, Clemens

    2010-05-01

    The earth's surface is characterized by heterogeneity at a broad range of scales. Weather forecast models and climate models are not able to resolve this heterogeneity at the smaller scales. Many processes in the soil or at the surface, however, are highly nonlinear. This holds, for example, for evaporation processes, where stomata or aerodynamic resistances are nonlinear functions of the local micro-climate. Other examples are threshold dependent processes, e.g., the generation of runoff or the melting of snow. It has been shown that using averaged parameters in the computation of these processes leads to errors and especially biases, due to the involved nonlinearities. Thus it is necessary to account for the sub-grid scale surface heterogeneities in atmospheric modeling. One approach to take the variability of the earth's surface into account is the mosaic approach. Here the soil-vegetation-atmosphere transfer (SVAT) model is run on an explicit higher resolution than the atmospheric part of a coupled model, which is feasible due to generally lower computational costs of a SVAT model compared to the atmospheric part. The question arises how to deal with the scale differences at the interface between the two resolutions. Usually the assumption of a homogeneous forcing for all sub-pixels is made. However, over a heterogeneous surface, usually the boundary layer is also heterogeneous. Thus, by assuming a constant atmospheric forcing again biases in the turbulent heat fluxes may occur due to neglected atmospheric forcing variability. Therefore we have developed and tested a downscaling scheme to disaggregate the atmospheric variables of the lower atmosphere that are used as input to force a SVAT model. Our downscaling scheme consists of three steps: 1) a bi-quadratic spline interpolation of the coarse-resolution field; 2) a "deterministic" part, where relationships between surface and near-surface variables are exploited; and 3) a noise-generation step, in which the still missing, not explained, variance is added as noise. The scheme has been developed and tested based on high-resolution (400 m) model output of the weather forecast (and regional climate) COSMO model. Downscaling steps 1 and 2 reduce the error made by the homogeneous assumption considerably, whereas the third step leads to close agreement of the sub-grid scale variance with the reference. This is, however, achieved at the cost of higher root mean square errors. Thus, before applying the downscaling system to atmospheric data a decision should be made whether the lowest possible errors (apply only downscaling step 1 and 2) or a most realistic sub-grid scale variability (apply also step 3) is desired. This downscaling scheme is currently being implemented into the COSMO model, where it will be used in combination with the mosaic approach. However, this downscaling scheme can also be applied to drive stand-alone SVAT models or hydrological models, which usually also need high-resolution atmospheric forcing data.

  4. A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size

    E-print Network

    Hansens, Jim

    A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size Andrew. R.Lawrence@ecmwf.int #12;Abstract An ensemble-based data assimilation approach is used to transform old en- semble. The impact of the transformations are propagated for- ward in time over the ensemble's forecast period

  5. Ensemble Data Mining Methods

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.

    2004-01-01

    Ensemble 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 ensemble 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 ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

  6. Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

    NASA Astrophysics Data System (ADS)

    Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin

    2015-04-01

    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 Downscaling method for Hydrology) statistical downscaling 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 ensemble 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 downscaling. 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

  7. A nonparametric kernel regression model for downscaling multisite daily precipitation in the Mahanadi basin

    NASA Astrophysics Data System (ADS)

    Kannan, S.; Ghosh, Subimal

    2013-03-01

    Hydrologic impacts of global climate change are usually assessed by downscaling large-scale climate variables, simulated by general circulation models (GCMs), to local-scale hydrometeorological variables. Conventional multisite statistical downscaling techniques often fail to capture spatial dependence of rainfall amounts as well as hydrometeorological extremes. To overcome these limitations, a downscaling algorithm is proposed, which first simulates the rainfall state of an entire study area/river basin, from large-scale climate variables, with classification and regression trees, and then projects multisite rainfall amounts using a nonparametric kernel regression estimator, conditioned on the estimated rainfall state. The concept of a common rainfall state for the entire study area, using it as an input for projections of rainfall amount, is found to be advantageous in capturing the cross correlation between rainfalls at different downscaling locations. Temporal variability and extremities of rainfall are captured in downscaling with multivariate kernel regression. The proposed model is applied for downscaling daily monsoon precipitation at eight locations in the Mahanadi River basin of eastern India. The model performance is compared, with a recently developed conditional random field based as well as with established multisite downscaling models, and is found to be superior. Analysis of future rainfall scenarios, projected with the developed downscaling model, reveals considerable changes in rainfall intensity and dry and wet spell lengths, among other things, at different locations. An increasing trend of rainfall is projected for the lower (southern) Mahanadi River basin, and a decreasing trend is observed in the upper (northern) Mahanadi River basin.

  8. Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of

    E-print Network

    Comparison of three downscaling methods in simulating the impact of climate change on the hydrology a demand in assessments on the impact of climate change hy- drological systems. The purpose of the study, the uncertainty related to the downscaling and bias-correction of the climate simulation must be taken

  9. Looking for added value in Australian downscaling for climate change studies

    NASA Astrophysics Data System (ADS)

    Grose, Michael

    2015-04-01

    Downscaling 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 downscaling can raise rather than diminish uncertainties. Validation of downscaling 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 downscaling outputs used for all of Australia and in regional climate change studies over smaller domains. We show that downscaling 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, downscaling can produce projected changes that are not clearly related to finer resolution and are difficult to interpret. In some cases, each downscaling method gives a different range of results and different messages about projected rainfall change for a region. This shows that downscaling 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.

  10. Downscaling Middle East Rainfall using a Support Vector Machine and Hidden Markov Model

    E-print Network

    Robertson, Andrew W.

    and Environmental Engineering Keywords: hidden markov model, downscaling, precipitation, Middle East http Rainfall using a Support Vector Machine and Hidden Markov Model Rana Samuels1,2 , Andrew W. Robertson3 with a non-homogeneous hidden Markov model (NHMM) to downscale daily station rainfall sequences over Israel

  11. A strategy for downscaling SMOS-based soil moisture

    NASA Astrophysics Data System (ADS)

    Pan, M.; Sahoo, A. K.; Wood, E. F.

    2010-12-01

    The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission was launched in November 2009, and has been providing 1.4GHz (L-band) observations. A number of ongoing SMOS-related research efforts have been focusing on retrieving top surface soil moisture from the measurements and validation of such measurements and retrievals. For soil moisture detection, the SMOS sensor can only achieve a relatively low spatial resolution of about 50km. But the variability of soil moisture field is still quite high below 50km scale due to land surface heterogeneities like elevation, vegetation cover, soil texture, etc. For this reason, a lot of hydrologic applications, for example, regional land surface modeling and data assimilation studies, are performed at an increasingly finer resolution (down to 1km) and they would expect finer soil moisture fields. So in the long run, the relatively coarse soil moisture retrievals will limit their value in many applications, and spatially downscaled products are very much needed. We propose and test a strategy to downscale the SMOS-based soil moisture products to ~1km or finer. The basic idea is to relate soil moisture to other physical parameters available at higher resolution, for example, elevation, topography, vegetation cover, soil texture, land surface temperature and so on. At places with strong topography, the fine scale soil moisture is primarily controlled by gravity-driven horizontal movement of surface water. In such areas, we can relate soil moisture to topographic features through catchment hydrologic models like the TOPMODEL. In flat areas, soil texture and vegetation properties may pose a greater impact than topography. In this case, we will explore the use of high resolution vegetation information or land surface temperature for downscaling.

  12. Explore the use of two different deep learning architectures for downscaling: Convolutional Neural Network (CNN) and the Deep Belief

    E-print Network

    Sridharan, Mohan

    known as downscaling · Use machine learning algorithms based on deep architectures to identify and modelMethods Explore the use of two different deep learning architectures for downscaling: Convolutional deeplearning.net/tutorial/DBN.html. Deep Learning Applied to Climate Data Downscaling Aaron Hester1 and Mohan

  13. A new project on development and application of comprehensive downscaling methods over Hokkaido.

    NASA Astrophysics Data System (ADS)

    Inatsu, M.; Yamada, T. J.; Sato, T.; Nakamura, K.; Matsuoka, N.; Komatsu, A.; Pokhrel, Y. N.; Sugimoto, S.; Miyazaki, S.

    2012-04-01

    A new project on development and application of comprehensive downscaling methods over Hokkaido started as one of the branches of "Research Program on climate change adaptation" funded by Ministry of Education, Sports, Culture, Science, and Technology of Japan in 2010. Our group will develop two new downscaling algorithms in order to get more information on the uncertainty of high/low temperatures or heavy rainfall. Both of the algorithms called "sampling downscaling" and "hybrid downscaling" are based upon the mixed use of statistical and dynamical downscaling ideas. Another point of the project is to evaluate the effect of land-use changes in Hokkaido, where the major pioneering began only about a century ago. Scientific outcomes on climate changes in Hokkaido from the project will be provided to not only public sectors in Hokkaido but also people who live in Hokkaido through a graphical-user-interface system just like a weather forecast system in a forecast-center's webpage.

  14. Design of a Regional Climate Model Ensemble That Incorporates Model Performance and Independence

    NASA Astrophysics Data System (ADS)

    Evans, J. P.; Argüeso, D.; Di Luca, A.; Olson, R.

    2014-12-01

    Due to various commonalities in model design and construction current climate models do not provide independent predictions of climate. Studies indicate that the information contained within the CMIP3 ensemble (25 models) is only equivalent to a set of perhaps five to ten independent models. This suggests that through judicious selection of models one could retain much of the information content of the full ensemble within a smaller sub-ensemble. Given constraints of computational resources we seek to select the most independent Global Climate Models (GCMs) to downscale from, and Regional Climate Models (RCMs) to downscale with, when producing a regional climate projection ensemble. Thus retaining the maximum information content possible. We propose a method to perform this selection that satisfies the following criteria The chosen models perform adequately for the recent past compared to observations. The chosen models do not exhibit the same strengths and weaknesses in their representation of the climate (i.e. they are independent). And for the GCMs The chosen models span the plausible future change space. An application of this method has been performed for the NARCliM project. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project for the Australian area. It will provide a comprehensive dynamically downscaled climate dataset for the CORDEX-AustralAsia region at 50km, and South-East Australia at a resolution of 10km. NARCliM data will be used by the NSW and ACT governments to design their climate change adaptation plans.Using this process an ensemble of 12 simulations (4 GCMs, 3 RCMs) for each period is obtained (Evans et al. 2014). Additionally to the GCM-driven simulations, 3 control run simulations driven by the NCEP/NCAR reanalysis for the entire period of 1950-2009 are also performed in order to validate the RCMs performance in the area. In this talk, we will present the initial evaluation results of the GCM driven simulations and the projected future changes. Evans, J. P., Ji, F., Lee, C., Smith, P., Argüeso, D., and Fita, L.: A regional climate modelling projection ensemble experiment - NARCliM, Geosci. Model Dev., 7, 621-629, doi:10.5194/gmd-7-621-2013, 2014.

  15. Assessing Climate change impacts on river basins in New Zealand using model based downscaling, statistical downscaling and regional climate modelling

    NASA Astrophysics Data System (ADS)

    Zammit, C.; Diettrich, J.; Sood, A.

    2013-12-01

    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. Downscaling 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) Downscaling to the New Zealand Virtual Climate Station Network grid (Tait et al, 2006); ii) Semi-empirical (statistical) downscaling (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.

  16. Statistical downscaling of Global Model output to predict extreme point rainfall

    NASA Astrophysics Data System (ADS)

    Hewson, Tim

    2015-04-01

    Spatial patterns of precipitation accumulation, as derived for example from radar snapshots, commonly have a form that depends on extraneous factors such as atmospheric structure. For example in strong wind dynamic rainfall situations there is often little spatial variability, in strong wind convective situations totals tend to vary greatly in one (wind-normal) direction, and in light wind convective situations great variability can be seen in all directions. It is in the latter case that extreme point totals are most likely, and it is also in this instance that grid-scale forecasts from (for example) a global model will exhibit the largest errors, when verified against observations at points. It will be shown that the relationships between model-based parameters such as wind speed, and sub-grid variability in observed totals are extraordinarily strong, and that these relationships can be used to predict the probability of occurrence of extreme rainfall at a point. For example, one can imagine two scenarios in which a model predicts, respectively, 20 and 10mm of rain in 12h. Yet if other model parameters are favourable the chance of observing 100mm at a point within the gridbox can be considerably higher in the *second* scenario. The above thus provides a new way by which single model and ensemble precipitation total forecasts can be downscaled, to give useful probabilistic forecasts of extreme precipitation, which in turn has practical applications in flash flood prediction. The concepts will be described, along with the statistical relationships identified to date, and future directions for this work.

  17. Nonuniform circular ensembles.

    PubMed

    Kumar, Sandeep; Pandey, Akhilesh

    2008-08-01

    We consider circular ensembles with nonuniform weight functions. We investigate the universality of short-range and long-range level fluctuations, which are important in the study of quantum chaotic systems. We analyze a set of hierarchic relations among the correlation functions to obtain the level density for a wide class of potentials and to demonstrate universality of correlation functions in the case of weak periodic potentials (where the term potential refers to the logarithm of the weight function). Analytic study of circular unitary ensemble is done with the help of orthogonal polynomials on the unit circle. For circular orthogonal and symplectic ensembles, we introduce skew-orthogonal polynomials on the unit circle. We consider the asymptotic forms of the polynomials for the three types of ensembles with weak potentials to give a proof of the universality. The analytic results are verified by Monte Carlo simulations of the ensembles with different weight functions. We also discuss the implications of these results in the context of conductance fluctuations in mesoscopic systems and show that the universality breaks down for strong potentials. PMID:18850918

  18. Toward Robust and Efficient Climate Downscaling for Wind Energy

    NASA Astrophysics Data System (ADS)

    Vanvyve, E.; Rife, D.; Pinto, J. O.; Monaghan, A. J.; Davis, C. A.

    2011-12-01

    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 downscaling to quantify the wind resource at a prospective wind farm can be cut by two thirds from the current industry practice of downscaling 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.

  19. Impact of nesting strategies in dynamical downscaling of reanalysis data

    NASA Astrophysics Data System (ADS)

    Beck, A.; Ahrens, B.; Stadlbacher, K.

    2004-10-01

    Coarse-grid global numerical weather simulations or analysis data have to be downscaled, e.g., with nested limited-area models (LAMs), for regional interpretation. Here, the impact of different one-way nesting strategies on precipitation simulations over the European Alps with the LAM ALADIN is studied. The LAM is forced by initial and lateral boundary data derived from ERA40 reanalyses with 120 km horizontal gridspacing and 6 h update interval. The nesting strategies considered include relaxation-based techniques with direct nesting of the high-resolution LAM (horizontal gridspacing ?x = 12 km; domain size 2800 × 2500 km2) or double nesting with an intermediate-resolution nest (?x = 50 km). Additionally, the impact of a spectral initialization technique is investigated. Results indicate that the considered nesting strategies are comparably successful in terms of precipitation simulation, despite the large resolution jump (120 to 12 km) involved. Thus, the cheapest method in terms of computational resources, i.e., direct nesting, seems to be the most adequate for dynamical downscaling of reanalysis data over complex terrain.

  20. Passive microwave soil moisture downscaling using NLDAS and MODIS data

    NASA Astrophysics Data System (ADS)

    Fang, B.; Lakshmi, V.

    2011-12-01

    The soil moisture retrieved from Advanced Microwave Scanning Radiometer (AMSR-E) is an important variable that has been applied in agriculture, hydrology, climate and weather. However, low spatial resolution (1/4 degree) of AMSR-E derived soil moisture cannot fulfill the requirements of high spatial resolution. In this paper, we studied the relationship between three factors: daily surface temperature range, Normalized Difference Vegetation Index (NDVI) and daily averaged soil moisture, using the 30-year period North America Land Data Assimilation System (NLDAS) Land Surface Temperature (LST) and soil moisture products (1/8 degree); MODIS (Moderate-Resolution Imaging Spectroradiometer) and AVHRR (Advanced Very High Resolution Radiometer) NDVI products (1/20 degree) in Oklahoma. We derived relationships between temperature difference and soil moisture under different vegetation conditions then derived the downscaled soil moisture by comparing the difference between AMSR-E and MODIS retrieved soil moisture. This soil moisture downscaling method not only provides disaggregated soil moisture data for passive microwave sensors, but also for active microwave sensors, such as Soil Moisture Active Passive Mission (SMAP).

  1. Downscaling Of SMOS Data Using NDVI, Elevation, and Sand Fraction

    NASA Astrophysics Data System (ADS)

    Mejia, J. C.; Seo, D.; Lakhankar, T.

    2012-12-01

    Surface soil moisture information at high spatial resolution is necessary for better forecasting and understanding of various hydrological, meteorological and ecological models. Microwave remote sensing systems show great potential in retrieving soil moisture information on daily basis. However, major limitations using passive microwave system are due to lower spatial resolution. Accurate fine-scale soil moisture observations are needed at a consistent basis to be used for local and regional scale models. In the absence of consistent high resolution soil moisture datasets, downscaling procedures enable to convert coarse resolution surface soil moisture estimates to high and liable resolution soil moisture estimates. Surface soil moisture distributions and dynamics depend greatly on vegetation (NDVI), topographic (EL), and sand (SF) features. The downscaling algorithm is based on the understanding of each of these physical parameter (NDVI, EL, and SF) and coarse remote sensing data and how they impact soil moisture retrievals. Results suggest that not all physical parameter (NDVI, El, and SF) affect surface soil moisture equally, since every region has its own soil composition. Unhealthy vegetation can be due to high sand fraction or seasonal change, or vice versa.

  2. An integrated downscaling system for seasonal rainfall prediction

    NASA Astrophysics Data System (ADS)

    Wu, W.; Liu, Y.; Ge, M.; Rostkier-Edelstein, D.

    2012-12-01

    Advancements in understanding of the climate system and in climate modeling have promoted operational seasonal climate prediction. Such seasonal prediction provides reasonable global perspectives and outlooks of the climate in few months advance. However, its usefulness has been limited due to its coarse resolution (~200 km) and unsatisfactory forecasting on hydro-meteorological fields like precipitation. To bridge the gaps between the hydro-climatological needs at regional and local scales and the global scale seasonal forecasts, an integrated statistical and dynamical downscaling system is developed at the Research Applications Laboratory of NCAR. The system includes a cost-effective statistical algorithm: k-nearest neighbors, and a WRF-based dynamical downscaling. The system is validated with DOE/NCEP reanalysis for 28 years of hindcasts, and with NCEP Climate Forecast System operational forecasts. It demonstrated the system is an effective value-adding utility to the global scale prediction, and has been implemented for real-time operation and hydro-climatological applications by a regional water authority. In this talk, we will present the algorithms, forecasts, validations and limitations at 18 rain-gauge stations along a major river.

  3. Dynamical downscaling of CMIP5 global circulation models over CORDEX-Africa with COSMO-CLM: evaluation over the present climate and analysis of the added value

    NASA Astrophysics Data System (ADS)

    Dosio, Alessandro; Panitz, Hans-Jürgen; Schubert-Frisius, Martina; Lüthi, Daniel

    2015-05-01

    In this work we present the results of the application of the consortium for small-scale modeling (COSMO) regional climate model (COSMO-CLM, hereafter, CCLM) over Africa in the context of the coordinated regional climate downscaling experiment. An ensemble of climate change projections has been created by downscaling the simulations of four global climate models (GCM), namely: MPI-ESM-LR, HadGEM2-ES, CNRM-CM5, and EC-Earth. Here we compare the results of CCLM to those of the driving GCMs over the present climate, in order to investigate whether RCMs are effectively able to add value, at regional scale, to the performances of GCMs. It is found that, in general, the geographical distribution of mean sea level pressure, surface temperature and seasonal precipitation is strongly affected by the boundary conditions (i.e. driving GCMs), and seasonal statistics are not always improved by the downscaling. However, CCLM is generally able to better represent the annual cycle of precipitation, in particular over Southern Africa and the West Africa monsoon (WAM) area. By performing a singular spectrum analysis it is found that CCLM is able to reproduce satisfactorily the annual and sub-annual principal components of the precipitation time series over the Guinea Gulf, whereas the GCMs are in general not able to simulate the bimodal distribution due to the passage of the WAM and show a unimodal precipitation annual cycle. Furthermore, it is shown that CCLM is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet and dry days, and the frequency of heavy rain events.

  4. A holistic, multi-scale dynamic downscaling framework for climate impact assessments and challenges of addressing finer-scale watershed dynamics

    NASA Astrophysics Data System (ADS)

    Kim, Jongho; Ivanov, Valeriy Y.

    2015-03-01

    We present a state-of-the-art holistic, multi-scale dynamic downscaling approach suited to address climate change impacts on hydrologic metrics and hydraulic regime of surface flow at the "scale of human decisions" in ungauged basins. The framework rests on stochastic and physical downscaling techniques that permit one-way crossing 106-100 m scales, with a specific emphasis on 'nesting' hydraulic assessments within a coarser-scale hydrologic model. Future climate projections for the location of Manchester watershed (MI) are obtained from an ensemble of General Circulation Models of the 3rd phase of the Coupled Model Intercomparison Project database and downscaled to a "point" scale using a weather generator. To represent the natural variability of historic and future climates, we generated continuous time series of 300 years for the locations of 3 meteorological stations located in the vicinity of the ungauged basin. To make such a multi-scale approach computationally feasible, we identified the months of May and August as the periods of specific interest based on ecohydrologic considerations. Analyses of historic and future simulation results for the identified periods show that the same median rainfall obtained by accounting for climate natural variability triggers hydrologically-mediated non-uniqueness in flow variables resolved at the hydraulic scale. An emerging challenge is that uncertainty initiated at the hydrologic scale is not necessarily preserved at smaller-scale flow variables, because of non-linearity of underlying physical processes, which ultimately can mask climate uncertainty. We stress the necessity of augmenting climate-level uncertainties of emission scenario, multi-model, and natural variability with uncertainties arising due to non-linearities in smaller-scale processes.

  5. Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors

    NASA Astrophysics Data System (ADS)

    Khan, Mohammad Sajjad; Coulibaly, Paulin; Dibike, Yonas

    2006-09-01

    Three downscaling models, namely the Statistical Down-Scaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS-WG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperature. The uncertainty attributes are described by the model errors and the 95% confidence intervals in the estimates of means and variances of downscaled data. The significance of those errors has been examined by suitable statistical tests at the 95% confidence level. The 95% confidence intervals in the estimates of means and variances of downscaled data have been estimated using the bootstrapping method and compared with the observed data. The study has been carried out using 40 years of observed and downscaled daily precipitation data and daily maximum and minimum temperature data, starting from 1961 to 2000. In all the downscaling experiments, the simulated predictors of the Canadian Global Climate Model (CGCM1) have been used. The uncertainty assessment results indicate that, in daily precipitation downscaling, the LARS-WG model errors are significant at the 95% confidence level only in a very few months, the SDSM errors are significant in some months, and the ANN model errors are significant in almost all months of the year. In downscaling daily maximum and minimum temperature, the performance of all three models is similar in terms of model errors evaluation at the 95% confidence level. But, according to the evaluation of variability and uncertainty in the estimates of means and variances of downscaled precipitation and temperature, the performances of the LARS-WG model and the SDSM are almost similar, whereas the ANN model performance is found to be poor in that consideration. Further assessment of those models, in terms of skewness and average dry-spell length comparison between observed and downscaled daily precipitation, indicates that the downscaled daily precipitation skewness and average dry-spell lengths of the LARS-WG model and the SDSM are closer to the observed data, whereas the ANN model downscaled precipitation underestimated those statistics in all months.

  6. The Ensembl REST API: Ensembl Data for Any Language

    PubMed Central

    Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R. S.; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul

    2015-01-01

    Motivation: We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. Availability and implementation: The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest. Contact: ayates@ebi.ac.uk or flicek@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25236461

  7. Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

    USGS Publications Warehouse

    Wootten, Adrienne; Smith, Kara; Boyles, Ryan; Terando, Adam J.; Stefanova, Lydia; Misra, Vasru; Smith, Tom; Blodgett, David L.; Semazzi, Fredrick

    2014-01-01

    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 downscaled climate projections (or downscaled datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these downscaled datasets, known as downscaling, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for downscaling and the number of downscaled 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 downscaled datasets, how do these model outputs compare to each other? Which variables are available, and are certain downscaled 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 downscaled 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.

  8. Predicting Seasonal Precipitation Over Sri Lanka Using Statistical Downscaling

    NASA Astrophysics Data System (ADS)

    Fernando, D. N.; Robinson, D. A.

    2008-12-01

    October to November (ON) rains provide critical moisture for the growing period of the main rice cultivation season in Sri Lanka that lasts from October to March. Decisions on rice cultivation are made at a seasonal conference convened each year in September. Such decisions are presently based on climatological rainfall in the past 30 years, water levels in irrigation reservoirs and farmers' indigenous knowledge related to historical analogues of wind-direction in September. Past studies documented the skill in seasonal climate predictability in tropical regions in the boreal fall. In recent years there has been a proliferation of seasonal climate forecasts from Global Circulation Models (GCMs). Given the above facts, and the long record of precipitation observations at hundreds of rain gauges scattered across Sri Lanka, it is useful to examine whether statistical downscaling of precipitation could provide additional climate information that could be used for decision-making in agriculture and water resources management This paper analyzes the skill in ON precipitation totals over Sri Lanka by downscaling regional atmospheric variables, identified as affecting ON precipitation, from GCMs. A diagnostic analysis using historical precipitation observations at 145 rain gauges from 1961-2005 and reanalysis climate data reveals that ON precipitation is significantly correlated with September mean sea level pressure (MSLP) over the domain 40°E-270°E and 30°S-20°N and contemporaneous geopotential height anomalies at 200hPa and 850hPa over the domain 40°-270°E and 30°S-45°N. The Model Output Statistics (MOS) approach is utilized to develop seasonal predictions from hindcasts of September MSLP and October-November geopotential height anomalies at 200hPa and 850hPa from the ECHAM4.5 GCM (two versions: forced with constructed analogue SSTs; and persisted anomalies) and the fully-coupled NCEP-CFS GCM. ON precipitation forecasts are derived using cross validated Canonical Correlation Analysis (CCA). Precipitation skill assessments are made by computing Hit Skill Scores based on downscaled tercile - i.e. whether above-normal, near-normal or below-normal - precipitation.

  9. Statistical downscaling of summer precipitation over northwestern South America

    NASA Astrophysics Data System (ADS)

    Palomino Lemus, Reiner; Córdoba Machado, Samir; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda; Jesús Esteban Parra, María

    2015-04-01

    In this study a statistical downscaling (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 downscaled 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 downscaling, 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).

  10. Projections of the Ganges-Brahmaputra precipitation: downscaled from GCM predictors

    USGS Publications Warehouse

    Pervez, Md Shahriar; Henebry, Geoffrey M.

    2014-01-01

    Downscaling Global Climate Model (GCM) projections of future climate is critical for impact studies. Downscaling enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical Downscaling Model (SDSM) to downscale 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia—the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in downscaling the precipitation. Downscaling was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the downscaled precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 downscaled precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change. Precipitation during and after the monsoon is likely to increase in both basins under the A1B and A2 emission scenarios; whereas, the pre-monsoon precipitation is likely to decrease. Peak monsoon precipitation is likely to shift from July to August, and may impact the livelihoods of large rural populations linked to subsistence agriculture in the basins. Uncertainty analysis of the downscaled precipitation indicated that the uncertainty in the downscaled precipitation was less than the uncertainty in the original CGCM3.1 precipitation; hence, the CGCM3.1 downscaled precipitation was a better input for the regional hydrological impact studies. However, downscaled precipitation from multiple GCMs is suggested for comprehensive impact studies.

  11. Protective Garment Ensemble

    NASA Technical Reports Server (NTRS)

    Wakefield, M. E.

    1982-01-01

    Protective garment ensemble with internally-mounted environmental- control unit contains its own air supply. Alternatively, a remote-environmental control unit or an air line is attached at the umbilical quick disconnect. Unit uses liquid air that is vaporized to provide both breathing air and cooling. Totally enclosed garment protects against toxic substances.

  12. PRINCETON UNIVERSITY WIND ENSEMBLE

    E-print Network

    Rowley, Clarence W.

    members, both undergraduate and graduate students, making this year's Wind Ensemble the largest ever and challenging programs in recent memory, including "Armenian Dances" by Alfred Reed and "Mars" and "Jupiter and the Philly Pops, Manhattan Transfer DVD, PBS Concert with Johnny Mathis and a Grammy Award winning CD

  13. Online Ensemble Learning

    Microsoft Academic Search

    Nikunj C. Oza

    2000-01-01

    Ensemble learning methods train combinations of base models, which may be decision trees, neural networks, or others traditionally used in supervised learning. En- semble methods have gained popularity because many re- searchers have demonstrated their superior prediction per- formance relative to single models on a variety of prob- lems especially when the correlations of the errors made by the base

  14. Music Ensemble: Course Proposal.

    ERIC Educational Resources Information Center

    Kovach, Brian

    A proposal is presented for a Music Ensemble course to be offered at the Community College of Philadelphia for music students who have had previous vocal or instrumental training. A standardized course proposal cover form is followed by a statement of purpose for the course, a list of major course goals, a course outline, and a bibliography. Next,…

  15. Neural Network Ensembles

    Microsoft Academic Search

    Lars Kai Hansen; Peter Salamon

    1990-01-01

    Several means for improving the performance and training of neural networks for classification are proposed. Crossvalidation is used as a tool for optimizing network parameters and architecture. It is shown that the remaining residual generalization error can be reduced by invoking ensembles of similar networks

  16. Bayesian Cluster Ensembles Hongjun Wang

    E-print Network

    Banerjee, Arindam

    Bayesian Cluster Ensembles Hongjun Wang Hanhuai Shan Arindam Banerjee Abstract Cluster ensembles provide a framework for combining mul- tiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of the basic cluster ensemble problem, notably including

  17. Statistical downscaling for winter streamflow in Douro River

    NASA Astrophysics Data System (ADS)

    Jesús Esteban Parra, María; Hidalgo Muñoz, José Manuel; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda

    2015-04-01

    In this paper we have obtained climate change projections for winter flow of the Douro River in the period 2071-2100 by applying the technique of Partial Regression and various General Circulation Models of CMIP5. The streamflow data base used has been provided by the Center for Studies and Experimentation of Public Works, CEDEX. Series from gauing stations and reservoirs with less than 10% of missing data (filled by regression with well correlated neighboring stations) have been considered. The homogeneity of these series has been evaluated through the Pettit test and degree of human alteration by the Common Area Index. The application of these criteria led to the selection of 42 streamflow time series homogeneously distributed over the basin, covering the period 1951-2011. For these streamflow data, winter seasonal values were obtained by averaging the monthly values from January to March. Statistical downscaling models for the streamflow have been fitted using as predictors the main atmospheric modes of variability over the North Atlantic region. These modes have been obtained using winter sea level pressure data of the NCEP reanalysis, averaged for the months from December to February. Period 1951-1995 was used for calibration, while 1996-2011 period was used in validating the adjusted models. In general, these models are able to reproduce about 70% of the variability of the winter streamflow of the Douro River. Finally, the obtained statistical models have been applied to obtain projections for 2071-2100 period, using outputs from different CMIP5 models under the RPC8.5 scenario. The results for the end of the century show modest declines of winter streamflow in this river for most of the models. Keywords: Statistical downscaling, streamflow, Douro River, climate change. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

  18. Extreme events over the contiguous United States portrayed in a CESM-WRF dynamical downscaling framework

    E-print Network

    CAI, LEI

    2014-12-31

    A dynamical downscaling framework is adopted to explore historical (1950-1999) and projected (2050-2099) behavior of extreme precipitation (PR), maximum temperature (TMAX) and minimum temperature (TMIN) events within the contiguous United States...

  19. Convolu'onal Neural Networks for Climate Downscaling Ranjini Swaminathan*,+, Mohan Sridharan* and Katharine Hayhoe+

    E-print Network

    Gelfond, Michael

    of global change on local to regional scale climate, including precipitaConvolu'onal Neural Networks for Climate Downscaling Ranjini Swaminathan*,+, Mohan Sridharan* and Katharine Hayhoe+ +Climate Science Center, *Department

  20. Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe

    Microsoft Academic Search

    M. Vrac; P. Marbaix; D. Paillard; P. Naveau

    2007-01-01

    Local-scale climate information is increasingly needed for the study of past, present and future climate changes. In this study we develop a non-linear statistical downscaling method to generate local temperatures and precipitation values from large-scale variables of a Earth System Model of Intermediate Complexity (here CLIMBER). Our statistical downscaling scheme is based on the concept of Generalized Additive Models (GAMs),

  1. SDSM-DC: A smarter approach to downscaling for decision-making? (Invited)

    NASA Astrophysics Data System (ADS)

    Wilby, R. L.; Dawson, C. W.

    2013-12-01

    General Circulation Model (GCM) output has been used for downscaling and impact assessments for at least 25 years. Downscaling 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' downscaling typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use downscaling 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 DownScaling Model (SDSM) over the last decade. This sample offers insights to downscaling 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 downscaling 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 downscaling 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.

  2. Developing Climate-Informed Ensemble Streamflow Forecasts over the Colorado River Basin

    NASA Astrophysics Data System (ADS)

    Miller, W. P.; Lhotak, J.; Werner, K.; Stokes, M.

    2014-12-01

    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, ensemble 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 ensemble forecasts for use by resource managers in their operational decision-making process. These ensemble 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 downscaled 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. Ensemble streamflow forecasts reflecting the impacts of climate change are then developed. These forecasts are subsequently compared to non-informed ensemble streamflow forecasts to evaluate the changing range of streamflow forecasts and risk over the Colorado River Basin. Ensemble forecasts may be compared through the use of a reservoir operations planning model, providing resource managers with ensemble 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.

  3. The Ensembl Analysis Pipeline

    PubMed Central

    Potter, Simon C.; Clarke, Laura; Curwen, Val; Keenan, Stephen; Mongin, Emmanuel; Searle, Stephen M.J.; Stabenau, Arne; Storey, Roy; Clamp, Michele

    2004-01-01

    The Ensembl pipeline is an extension to the Ensembl system which allows automated annotation of genomic sequence. The software comprises two parts. First, there is a set of Perl modules (“Runnables” and “RunnableDBs”) which are `wrappers' for a variety of commonly used analysis tools. These retrieve sequence data from a relational database, run the analysis, and write the results back to the database. They inherit from a common interface, which simplifies the writing of new wrapper modules. On top of this sits a job submission system (the “RuleManager”) which allows efficient and reliable submission of large numbers of jobs to a compute farm. Here we describe the fundamental software components of the pipeline, and we also highlight some features of the Sanger installation which were necessary to enable the pipeline to scale to whole-genome analysis. PMID:15123589

  4. VALUE - A Framework to Validate Downscaling Approaches for Climate Change Studies

    NASA Astrophysics Data System (ADS)

    Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilke, Renate A. I.

    2015-04-01

    VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. Here, we present the key ingredients of this framework. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

  5. Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

    SciTech Connect

    Kumar, Jitendra [ORNL] [ORNL; Brooks, Bjørn-Gustaf J. [University of Illinois, Urbana-Champaign] [University of Illinois, Urbana-Champaign; Thornton, Peter E [ORNL] [ORNL; Dietze, Michael [University of Illinois, Urbana-Champaign] [University of Illinois, Urbana-Champaign

    2012-01-01

    A new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled 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 downscaled 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 downscaling 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 downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling 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.

  6. Future changes of wind energy potentials over Europe in a large CMIP5 multi-model ensemble

    NASA Astrophysics Data System (ADS)

    Reyers, Mark; Moemken, Julia; Pinto, Joaquim G.

    2015-04-01

    A statistical-dynamical downscaling 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 ensemble are considered. The downscaling 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 ensemble 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 ensemble 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 ensemble 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 ensembles are needed to provide a better quantification and understanding of the future changes.

  7. Improving dynamical downscaling of thunderstorms in New England

    NASA Astrophysics Data System (ADS)

    Frediani, M. E.; Anagnostou, E. N.; Hopson, T. M.; Hacker, J.

    2013-12-01

    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 downscale 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 downscaling 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.

  8. Estimating climate change for Southeast Europe: a dynamical downscaling approach

    NASA Astrophysics Data System (ADS)

    Sotiropoulou, Rafaella-Eleni P.; Tagaris, Efthimios; Sotiropoulos, Andreas; Spanos, Ioannis; Milonas, Panagiotis; Michaelakis, Antonios

    2015-04-01

    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 downscale 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 downscaling 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.

  9. Radar-guided radiometer downscaling for combined soil moisture retrieval

    NASA Astrophysics Data System (ADS)

    Stampoulis, D.; Haddad, Z. S.; Anagnostou, E. N.

    2013-12-01

    Combining the advantages of both active and passive microwave measurements in a soil moisture-retrieval can dramatically increase resolution and sensitivity. Simultaneous remote sensing observations of the normalized radar cross section (?0) and emissivity (?) will be jointly used to ultimately achieve an improved soil moisture-retrieval algorithm. The ?0 values are derived from the Precipitation Radar (PR) from TRMM (product 2A21 V7) while the ? values are derived from the brightness temperatures (BTs) measured by the passive microwave radiometric system TRMM Microwave Imager (TMI) (product 1B11 V7). Emissivity values are used instead of BTs because they are more directly related to water content. The coarse-resolution passive measurements (TMI) are first downscaled to match the finer resolution of the active ones (PR) via a Kalman filter, with which the error of the TMI instrument in terms of emissivity is parameterized so that different weights will be given to the PR and TMI measurements. The downscaling is performed over the state of Oklahoma, for 'no-rain' conditions (indicated by PR), for high PR incidence angles, in order to obtain simultaneous measurements of the two instruments (because of different scanning geometries, synchronized measurements of both instruments can only be achieved at high PR incidence angles), for each TMI channel separately (not including the two high-resolution ones and the 21.3 GHz), for the early morning hours only (active and passive sensors retrieve information on soil moisture at different depths and this discrepancy becomes even greater in the late afternoon hours of the day, therefore selecting only the early-morning overpasses will mitigate this effect), and for different regions within Oklahoma. The regions are selected based on land class. Regions with homogeneous vegetation cover are examined separately from regions characterized by heterogeneous vegetation cover. Oklahoma was selected as the area of study, because of its variety of land classes and the availability of ground-based validation soil moisture data. Is there a correlation between radar backscatter and emissivity values?

  10. Globally downscaled climate projections for assessing the conservation impacts of climate change.

    PubMed

    Tabor, Karyn; Williams, John W

    2010-03-01

    Assessing the potential impacts of 21st-century climate change on species distributions and ecological processes requires climate scenarios with sufficient spatial resolution to represent the varying effects of climate change across heterogeneous physical, biological, and cultural landscapes. Unfortunately, the native resolutions of global climate models (usually approximately 2 degrees x 2 degrees or coarser) are inadequate for modeling future changes in, e.g., biodiversity, species distributions, crop yields, and water resources. Also, 21st-century climate projections must be debiased prior to use, i.e., corrected for systematic offsets between modeled representations and observations of present climates. We have downscaled future temperature and precipitation projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model data set to 10-minute resolution and debiased these simulations using the change-factor approach and observational data from the Climatic Research Unit (CRU). These downscaled data sets are available online and include monthly mean temperatures and precipitation for 2041-2060 and 2081-2100, for 24 climate models and the A1B, A2, and B1 emission scenarios. This paper describes the downscaling method and compares the downscaled and native-resolution simulations. Sharp differences between the original and downscaled data sets are apparent at regional to continental scales, particularly for temperature in mountainous areas and in areas with substantial differences between observed and simulated 20th-century climatologies. Although these data sets in principle could be downscaled further, a key practical limitation is the density of observational networks, particularly for precipitation-related variables in tropical mountainous regions. These downscaled data sets can be used for a variety of climate-impact assessments, including assessments of 21st-century climate-change impacts on biodiversity and species distributions. PMID:20405806

  11. Mid-Century Warming in the Los Angeles Region and its Uncertainty using Dynamical and Statistical Downscaling

    NASA Astrophysics Data System (ADS)

    Sun, F.; Hall, A. D.; Walton, D.; Capps, S. B.; Qu, X.; Huang, H. J.; Berg, N.; Jousse, A.; Schwartz, M.; Nakamura, M.; Cerezo-Mota, R.

    2012-12-01

    Using a combination of dynamical and statistical downscaling techniques, we projected mid-21st century warming in the Los Angeles region at 2-km resolution. To account for uncertainty associated with the trajectory of future greenhouse gas emissions, we examined projections for both "business-as-usual" (RCP8.5) and "mitigation" (RCP2.6) emissions scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5). To account for the considerable uncertainty associated with choice of global climate model, we downscaled results for all available global climate models in CMIP5. For the business-as-usual scenario, we find that by the mid-21st century, the most likely warming is roughly 2.6°C averaged over the region's land areas, with a 95% confidence that the warming lies between 0.9 and 4.2°C. The high resolution of the projections reveals a pronounced spatial pattern in the warming: High elevations and inland areas separated from the coast by at least one mountain complex warm 20 to 50% more than the areas near the coast or within the Los Angeles basin. This warming pattern is especially apparent in summertime. The summertime warming contrast between the inland and coastal zones has a large effect on the most likely expected number of extremely hot days per year. Coastal locations and areas within the Los Angeles basin see roughly two to three times the number of extremely hot days, while high elevations and inland areas typically experience approximately three to five times the number of extremely hot days. Under the mitigation emissions scenario, the most likely warming and increase in heat extremes are somewhat smaller. However, the majority of the warming seen in the business-as-usual scenario still occurs at all locations in the most likely case under the mitigation scenario, and heat extremes still increase significantly. This warming study is the first part of a series studies of our project. More climate change impacts on the Santa Ana wind, rainfall, snowfall and snowmelt, cloud and surface hydrology are forthcoming and could be found in www.atmos.ucla.edu/csrl.he ensemble-mean, annual-mean surface air temperature change and its uncertainty from the available CMIP5 GCMs under the RCP8.5 (left) and RCP2.6 (right) emissions scenarios, unit: °C.

  12. Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

    NASA Technical Reports Server (NTRS)

    Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice, Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel

    2013-01-01

    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 downscaling 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.

  13. Downscaling NASA Climatological Data to Produce Detailed Climate Zone Maps

    NASA Technical Reports Server (NTRS)

    Chandler, William S.; Hoell, James M.; Westberg, David J.; Whitlock, Charles H.; Zhang, Taiping; Stackhouse, P. W.

    2011-01-01

    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 downscaling 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.

  14. Downscaling the environmental associations and spatial patterns of species richness.

    PubMed

    Keil, Petr; Jetz, Walter

    2014-06-01

    We introduce a method that enables the estimation of species richness environment association and prediction of geographic patterns of species richness at grains finer than the original grain of observation. The method is based on a hierarchical model that uses coarse-grain values of species richness and fine-grain environmental data as input. In the model, the (unobserved) fine-grain species richness is linked to the observed fine-grain environment and upscaled using a simple species-area relationship (SAR). The upscaled values are then stochastically linked to the observed coarse-grain species richness. We tested the method on Southern African Bird Atlas data by downscaling richness from 2 degrees to 0.25 degrees (-250 km to -30 km) resolution. When prior knowledge of the SAR slope (average species turnover within coarse-grain cells) was available, the method predicted the fine-grain relationship between richness and environment and provided fine-grain predictions of richness that closely resembled results from native fine-grain models. Without the SAR knowledge the method still accurately quantified the richness-environment relationship, but accurately predicted only relative (rank) values of richness. The approach can be easily extended and it is a powerful path for cross-scale statistical modeling of richness-environment relationships, and for the provision of high-resolution maps for basic science and conservation. PMID:24988779

  15. Downscaling Meteorological Data For Use In Hydrological Modelling

    NASA Astrophysics Data System (ADS)

    Born, K.; Gumpert, M.; Schulz, O.

    The IMPETUS Westafricaproject focusses on water availability, use and management as well as on impacts of climate variability on the water cycle in two climate risk regions: The river catchments of the Qued Drâa in Morocco and of the Ouémé in Benin. The catchment of the Drâa river in Morooco is located in the vicinity of the High Atlas Mountains. Thus, rainfall and snowmelt contribute to the water balance of this semi-arid catchment. For hydrological modelling of longer periods, distributed me- teorological data are necessary to compute surface fluxes, which actually connect soil/vegetation cover and the atmosphere. In opposite to very expensive methods of downscaling with prognostic meteorological models, a relatively simple, diagnostic model was constructed to calculate spatially distributed snow and rain fields as well as surface fluxes for larger time scales (days to months). The model physics are described in some detail. A first insight into the performance of the model is given by comparison with observational data of a small climatic station network in the region of interest.

  16. Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

    NASA Astrophysics Data System (ADS)

    Al-Hamdan, M. Z.; Crosson, W. L.; Estes, M. G., Jr.; Estes, S. M.; Quattrochi, D. A.; Johnson, D.

    2013-12-01

    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 downscaling 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.

  17. Using a coupled lake model with WRF for dynamical downscaling

    NASA Astrophysics Data System (ADS)

    Mallard, Megan S.; Nolte, Christopher G.; Bullock, O. Russell; Spero, Tanya L.; Gula, Jonathan

    2014-06-01

    The Weather Research and Forecasting (WRF) model is used to downscale a coarse reanalysis (National Centers for Environmental Prediction-Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine the consequences of using different methods for setting lake temperatures and ice on predicted 2 m temperature and precipitation in the Great Lakes region. A control simulation is performed where lake surface temperatures and ice coverage are interpolated from the GCM proxy. Because the R2 represents the five Great Lakes with only three grid points, ice formation is poorly represented, with large, deep lakes freezing abruptly. Unrealistic temperature gradients appear in areas where the coarse-scale fields have no inland water points nearby and lake temperatures on the finer grid are set using oceanic points from the GCM proxy. Using WRF coupled with the Freshwater Lake (FLake) model reduces errors in lake temperatures and significantly improves the timing and extent of ice coverage. Overall, WRF-FLake increases the accuracy of 2 m temperature compared to the control simulation where lake variables are interpolated from R2. However, the decreased error in FLake-simulated lake temperatures exacerbates an existing wet bias in monthly precipitation relative to the control run because the erroneously cool lake temperatures interpolated from R2 in the control run tend to suppress overactive precipitation.

  18. Dynamic Downscaling of Seasonal Simulations over South America

    Microsoft Academic Search

    Vasubandhu Misra; Paul A. Dirmeyer; Ben P. Kirtman

    2003-01-01

    In this paper multiple atmospheric global circulation model (AGCM) integrations at T42 spectral truncation and prescribed sea surface temperature were used to drive regional spectral model (RSM) simulations at 80-km resolution for the austral summer season (January-February-March). Relative to the AGCM, the RSM improves the ensemble mean simulation of precipitation and the lower- and upper-level tropospheric circulation over both tropical

  19. Density of states for Gaussian unitary ensemble, Gaussian orthogonal ensemble, and interpolating ensembles through supersymmetric approach

    NASA Astrophysics Data System (ADS)

    Shamis, Mira

    2013-11-01

    We use the supersymmetric formalism to derive an integral formula for the density of states of the Gaussian Orthogonal Ensemble, 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 Ensemble. We also apply our method to the interpolating ensembles of Mehta-Pandey.

  20. Density of states for Gaussian unitary ensemble, Gaussian orthogonal ensemble, and interpolating ensembles through supersymmetric approach

    SciTech Connect

    Shamis, Mira, E-mail: mshamis@princeton.edu [Department of Mathematics, Princeton University, Princeton New Jersey 08544 (United States) [Department of Mathematics, Princeton University, Princeton New Jersey 08544 (United States); Institute for Advanced Study, Einstein Drive, Princeton, New Jersey 08540 (United States)

    2013-11-15

    We use the supersymmetric formalism to derive an integral formula for the density of states of the Gaussian Orthogonal Ensemble, 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 Ensemble. We also apply our method to the interpolating ensembles of Mehta–Pandey.

  1. USING ENSEMBLE PREDICTIONS TO SIMULATE FIELD-SCALE SOIL WATER TIME SERIES WITH UPSCALED AND DOWNSCALED SOIL HYDRAULIC PROPERTIES

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  2. Multinomial logistic regression ensembles.

    PubMed

    Lee, Kyewon; Ahn, Hongshik; Moon, Hojin; Kodell, Ralph L; Chen, James J

    2013-05-01

    This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the proposed method can handle a huge database without a constraint needed for analyzing high-dimensional data, and the random partition can improve the prediction accuracy by reducing the correlation among base classifiers. The proposed method is implemented using R, and the performance including overall prediction accuracy, sensitivity, and specificity for each category is evaluated on two real data sets and simulation data sets. To investigate the quality of prediction in terms of sensitivity and specificity, the area under the receiver operating characteristic (ROC) curve (AUC) is also examined. The performance of the proposed model is compared to a single multinomial logit model and it shows a substantial improvement in overall prediction accuracy. The proposed method is also compared with other classification methods such as the random forest, support vector machines, and random multinomial logit model. PMID:23611203

  3. Assessing Fire Weather Index using statistical downscaling and spatial interpolation techniques in Greece

    NASA Astrophysics Data System (ADS)

    Karali, Anna; Giannakopoulos, Christos; Frias, Maria Dolores; Hatzaki, Maria; Roussos, Anargyros; Casanueva, Ana

    2013-04-01

    Forest fires have always been present in the Mediterranean ecosystems, thus they constitute a major ecological and socio-economic issue. The last few decades though, the number of forest fires has significantly increased, as well as their severity and impact on the environment. Local fire danger projections are often required when dealing with wild fire research. In the present study the application of statistical downscaling and spatial interpolation methods was performed to the Canadian Fire Weather Index (FWI), in order to assess forest fire risk in Greece. The FWI is used worldwide (including the Mediterranean basin) to estimate the fire danger in a generalized fuel type, based solely on weather observations. The meteorological inputs to the FWI System are noon values of dry-bulb temperature, air relative humidity, 10m wind speed and precipitation during the previous 24 hours. The statistical downscaling methods are based on a statistical model that takes into account empirical relationships between large scale variables (used as predictors) and local scale variables. In the framework of the current study the statistical downscaling portal developed by the Santander Meteorology Group (https://www.meteo.unican.es/downscaling) in the framework of the EU project CLIMRUN (www.climrun.eu) was used to downscale non standard parameters related to forest fire risk. In this study, two different approaches were adopted. Firstly, the analogue downscaling technique was directly performed to the FWI index values and secondly the same downscaling technique was performed indirectly through the meteorological inputs of the index. In both cases, the statistical downscaling portal was used considering the ERA-Interim reanalysis as predictands due to the lack of observations at noon. Additionally, a three-dimensional (3D) interpolation method of position and elevation, based on Thin Plate Splines (TPS) was used, to interpolate the ERA-Interim data used to calculate the index. Results from this method were compared with the statistical downscaling results obtained from the portal. Finally, FWI was computed using weather observations obtained from the Hellenic National Meteorological Service, mainly in the south continental part of Greece and a comparison with the previous results was performed.

  4. Spatial downscaling of global satellite soil moisture data using temperature vegetation dryness index

    NASA Astrophysics Data System (ADS)

    Peng, Jian; Loew, Alexander; Zhang, Shiqiang; Wang, Jie

    2015-04-01

    Microwave remote sensing has been largely applied to retrieve soil moisture (SM). An obvious advantage of microwave sensors is that SM can be obtained regardless of atmospheric conditions. However, existing global SM products, based on microwave remote sensing only provide observations at coarse spatial resolution, which hampers its application in regional hydrological studies. On the other hand, the Temperature Vegetation Dryness Index (TVDI) based on high spatial resolution visible and infrared satellite observations has been widely used to monitor the SM status. The aim of this study is to develop a simple and efficient downscaling approach for estimating accurate SM at high spatial resolution. The TVDI calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) is used as a unique scaling factor to downscale the coarse resolution SM product that has been developed under the framework of the European Space Agency's Climate Change Initiative (ESA CCI) projects. The original and downscaled SM estimates are further validated against the in-situ SM observations collected in Yunnan province (southwest China). It is found that the downscaled estimates can maintain the accuracy of ECV_SM, and have the same spatial resolution of the MODIS datasets. Local hydrological applications such as drought monitoring, and water planning and management will benefit a lot from the possibility of obtaining high resolution SM estimate with the proposed downscaling approach.

  5. Future changes in the West African Monsoon: A COSMO-CLM and RCA4 multimodel ensemble study

    NASA Astrophysics Data System (ADS)

    Anders, Ivonne; Gbobaniyi, Emiola

    2014-05-01

    In this multi-model multi-ensemble study, we intercompare results from two regional climate simulation ensembles to see how well they reproduce the known main features of the West African Monsoon (WAM). Each ensemble was created under the ongoing CORDEX-Africa activities by using the regional climate models (RCA4 and COSMO-CLM) to downscale four coupled atmosphere ocean general circulation models (AOGCMs), namely, CNRM-CM5, HadGEM2-ES, EC-EARTH, and MPI-ESM-LR. Spatial resolution of the driving AOGCMs varies from about 1° to 3° while all regional simulations are at the same 0.44° resolution. Future climate projections from the RCP8.5 scenario are analyzed and inter-compared for both ensembles in order to assess deviations and uncertainties. The main focus in our analysis is on the projected WAM rainy season statistics. We look at projected changes in onset and cessation, total precipitation and temperature toward the end of the century (2071-2100) for different time scales spanning seasonal climatologies, annual cycles and interannual variability, and a number of spatial scales covering the Sahel, the Gulf of Guinea and the entire West Africa. Differences in the ensemble projections are linked to the parameterizations employed in both regional models and the influence of this is discussed.

  6. Edge Universality of Beta Ensembles

    NASA Astrophysics Data System (ADS)

    Bourgade, Paul; Erdös, László; Yau, Horng-Tzer

    2014-11-01

    We prove the edge universality of the beta ensembles for any , provided that the limiting spectrum is supported on a single interval, and the external potential is and regular. We also prove that the edge universality holds for generalized Wigner matrices for all symmetry classes. Moreover, our results allow us to extend bulk universality for beta ensembles from analytic potentials to potentials in class.

  7. 4, 655717, 2007 Ensemble forecasts

    E-print Network

    Boyer, Edmond

    HESSD 4, 655­717, 2007 Ensemble forecasts J. Schaake et al. Title Page Abstract Introduction Discussion EGU Hydrol. Earth Syst. Sci. Discuss., 4, 655­717, 2007 www.hydrol-earth-syst-sci-discuss.net/4/655 Correspondence to: J. Schaake (john.schaake@noaa.gov) 655 #12;HESSD 4, 655­717, 2007 Ensemble forecasts J

  8. The Ensembl genome database project

    Microsoft Academic Search

    Tim J. P. Hubbard; Daniel Barker; Ewan Birney; Graham Cameron; Yuan Chen; Laura Clarke; Tony Cox; James A. Cuff; Val Curwen; Thomas Down; Richard Durbin; Eduardo Eyras; James Gilbert; Martin Hammond; Lukasz Huminiecki; Arek Kasprzyk; Heikki Lehväslaiho; Philip Lijnzaad; Craig Melsopp; Emmanuel Mongin; Roger Pettett; Matthew R. Pocock; Simon C. Potter; Alastair Rust; Esther Schmidt; Stephen M. J. Searle; Guy Slater; James Smith; William Spooner; Arne Stabenau; Jim Stalker; Elia Stupka; Abel Ureta-vidal; Imre Vastrik; Michele E. Clamp

    2002-01-01

    The Ensembl (http:\\/\\/www.ensembl.org\\/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also

  9. Effect of downscaling nano-copper interconnects on the microstructure revealed by high resolution TEM-orientation-mapping

    E-print Network

    Ferreira, Paulo J.

    Effect of downscaling nano-copper interconnects on the microstructure revealed by high resolution Nanotechnology 23 (2012) 135702 (7pp) doi:10.1088/0957-4484/23/13/135702 Effect of downscaling nano-copper@mail.utexas.edu Received 14 December 2011 Published 14 March 2012 Online at stacks.iop.org/Nano/23/135702 Abstract

  10. A DOWNSCALING METHOD FOR DISTRIBUTING SURFACE SOIL MOISTURE WITHIN A MICROWAVE PIXEL: APPLICATION TO MONSOON’90 1780

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A downscaling method for microwave surface soil moisture is applied to PBMR data collected during the Monsoon ‘90 experiment. The downscaling method requires: (1) the coarse resolution microwave observations, (2) the fine-scale distribution of soil temperature, and (3) the fine-scale distribution o...

  11. DOWN-SCALING OF SEBAL DERIVED EVAPOTRANSPIRATION MAPS1 FROM MODIS (250m) TO LANDSAT (30m) SCALE2

    E-print Network

    Borchers, Brian

    1 DOWN-SCALING OF SEBAL DERIVED EVAPOTRANSPIRATION MAPS1 FROM MODIS (250m) TO LANDSAT (30m) SCALE2 resolution from MODIS are14 available daily and one image covers a relatively large area (swath width 2,330km). This paper15 considers the feasibility of applying various down-scaling methods to combine MODIS and16

  12. A Simple Downscaling Algorithm for Remotely Sensed Land Surface Temperature

    NASA Astrophysics Data System (ADS)

    Sandholt, I.; Nielsen, C.; Stisen, S.

    2009-05-01

    The method is illustrated using a combination of MODIS NDVI data with a spatial resolution of 250m and 3 Km Meteosat Second Generation SEVIRI LST data. Geostationary Earth Observation data carry a large potential for assessment of surface state variables. Not the least the European Meteosat Second Generation platform with its SEVIRI sensor is well suited for studies of the dynamics of land surfaces due to its high temporal frequency (15 minutes) and its red, Near Infrared (NIR) channels that provides vegetation indices, and its two split window channels in the thermal infrared for assessment of Land Surface Temperature (LST). For some applications the spatial resolution in geostationary data is too coarse. Due to the low statial resolution of 4.8 km at nadir for the SEVIRI sensor, a means of providing sub pixel information is sought for. By combining and properly scaling two types of satellite images, namely data from the MODIS sensor onboard the polar orbiting platforms TERRA and AQUA and the coarse resolution MSG-SEVIRI, we exploit the best from two worlds. The vegetation index/surface temperature space has been used in a vast number of studies for assessment of air temperature, soil moisture, dryness indices, evapotranspiration and for studies of land use change. In this paper, we present an improved method to derive a finer resolution Land Surface Temperature (LST). A new, deterministic scaling method has been applied, and is compared to existing deterministic downscaling methods based on LST and NDVI. We also compare our results from in situ measurements of LST from the Dahra test site in West Africa.

  13. Prediction of design flood discharge by statistical downscaling and General Circulation Models

    NASA Astrophysics Data System (ADS)

    Tofiq, F. A.; Guven, A.

    2014-09-01

    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. Downscaling, 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 downscaling 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 downscaling the peak monthly discharges directly from daily surface weather variables (NCEP and CGCM3) without involving any rainfall-runoff models.

  14. Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational ?1-Norm Regularization in the Derivative Domain

    NASA Astrophysics Data System (ADS)

    Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.

    2014-05-01

    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 downscaling 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 downscaling 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 downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) 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 downscaling methodology to indirectly learn the observation operator from a data base of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

  15. Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

    NASA Technical Reports Server (NTRS)

    Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.

    2013-01-01

    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 downscaling 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 downscaling 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 downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) 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 downscaling 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 downscaling of a hurricane precipitation field.

  16. Extremes of European temperature in ENSEMBLES regional climate models

    NASA Astrophysics Data System (ADS)

    Frias, M. Dolores; Minguez, Roberto; Gutierrez, Jose Manuel; Mendez, Fernando J.

    2010-05-01

    In recent years, there has been an increasing interest in studying the impacts of climate extremes in different sectors (agriculture, energy, insurance, etc.). In particular, extreme temperatures and heat waves have had a big impact in European socioeconomic activities during the last years (e.g. the 2003 heat wave in France); moreover, climate change has the potential to alter the prevalence and severity of extremes thus given rise to more severe impacts with unpredictable consequences. Regional climate models offer the opportunity to analyze and project in different future scenarios the variability of extremes at regional time scales. In the present work, we estimate changes of maximum temperatures in Europe using two state-of-the-art regional circulation models from the EU ENSEMBLES project. Regional climate models are used as dynamical downscaling tools to provide simulations on smaller scales than those represented for global climate models. Extremes are expressed in terms of return values derived from a time-dependent generalized extreme value (GEV) model for monthly maxima. The study focuses on the end of the 20th century (1961-2000), used as a calibration/validation period, and analyzes the changes projected for the period 2020-2050 considering the A1B emission scenario.

  17. Bayesian ensemble forecast of river stages and ensemble size requirements

    NASA Astrophysics Data System (ADS)

    Herr, Henry D.; Krzysztofowicz, Roman

    2010-06-01

    SummaryThe problem is to provide a short-term, probabilistic forecast of a river stage time series {H1,…,HN} based on a probabilistic quantitative precipitation forecast. The Bayesian forecasting system (BFS) for this problem is implemented as a Monte-Carlo algorithm that generates an ensemble of realizations of the river stage time series. This article (i) shows how the analytic-numerical BFS can be used as a generator of the Bayesian ensemble forecast (BEF), (ii) demonstrates the properties of the BEF, and (iii) investigates the sample size requirements for ensemble forecasts (produced by the BFS or by any other system). The investigation of the ensemble size requirements exploits the unique advantage of the BFS, which outputs the exact, analytic, predictive distribution function of the stochastic process {H1,…,HN}, as well as can generate an ensemble of realizations of this process from which a sample estimate of the predictive distribution function can be constructed. By comparing the analytic distribution with its sample estimates from ensembles of different sizes, the smallest ensemble size M? required to ensure a specified expected accuracy can be inferred. Numerical experiments in four river basins demonstrate that M? depends upon the kind of probabilistic forecast that is constructed from the ensemble. Three kinds of forecasts are constructed: (i) a probabilistic river stage forecast (PRSF), which for each time n (n=1,…,N) specifies a predictive distribution function of Hn; (ii) a probabilistic stage transition forecast (PSTF), which for each time n specifies a family (for all h) of predictive one-step transition distribution functions from H=h to Hn; and (iii) a probabilistic flood forecast (PFF), which for each time n specifies a predictive distribution function of max{H1,…,Hn}. Overall, the experimental results demonstrate that the smallest ensemble size M? required for accurate estimation (or numerical representation) of these predictive distribution functions is (i) insensitive to experimental factors and on the order of several hundreds for the PRSF and the PFF and (ii) sensitive to experimental factors and on the order of several thousands for the PSTF. The general conclusions for system developers are that the ensemble size is an important design variable, and that the optimal ensemble size M? depends upon the purpose of the forecast: for dynamic control problems (which require a PSTF), M? is likely to be larger by a factor of 3-20 than it is for static decision problems (which require a PRSF or a PFF).

  18. Ensemble numerical forecasts of the sporadic Kuroshio water intrusion (kyucho) into shelf and coastal waters

    NASA Astrophysics Data System (ADS)

    Isobe, Atsuhiko; Kako, Shin'ichiro; Guo, Xinyu; Takeoka, Hidetaka

    2012-04-01

    The finite volume coastal ocean model downscaling ocean reanalysis and forecast data provided by the Japan Coastal Ocean Predictability Experiment (JCOPE2) are used to forecast sudden Kuroshio water intrusion events (kyucho) induced by frontal waves amplified south of the Bungo Channel in 2010. Two-month hindcast computations give initial conditions of the following 3-month forecasts computations which consist of ten ensemble members. The temperature time series computed by these ten members are averaged to compare with that actually observed in the Bungo Channel, where sudden temperature rises related to kyucho events are remarkable in February, August, and September. Overall, the intense kyucho events actually observed in these months are predicted successfully. However, intense kyucho events are forecasted frequently during the period of May through June even though intense kyucho events are absent during this period in the actual ocean. It is suggested that the present downscaling forecast model requires reliable lateral boundary conditions provided by JCOPE2 data to which numerous Argo data are assimilated to enhance the accuracy. In addition, it seems likely that the model accuracy is reduced by small eddies moving along the shelf break.

  19. A downscaling method for the assessment of local climate change

    NASA Astrophysics Data System (ADS)

    Bruno, E.; Portoghese, I.; Vurro, M.

    2009-04-01

    The use of complimentary models is necessary to study the impact of climate change scenarios on the hydrological response at different space-time scales. However, the structure of GCMs is such that their space resolution (hundreds of kilometres) is too coarse and not adequate to describe the variability of extreme events at basin scale (Burlando and Rosso, 2002). To bridge the space-time gap between the climate scenarios and the usual scale of the inputs for hydrological prediction models is a fundamental requisite for the evaluation of climate change impacts on water resources. Since models operate a simplification of a complex reality, their results cannot be expected to fit with climate observations. Identifying local climate scenarios for impact analysis implies the definition of more detailed local scenario by downscaling GCMs or RCMs results. Among the output correction methods we consider the statistical approach by Déqué (2007) reported as a ‘Variable correction method' in which the correction of model outputs is obtained by a function build with the observation dataset and operating a quantile-quantile transformation (Q-Q transform). However, in the case of daily precipitation fields the Q-Q transform is not able to correct the temporal property of the model output concerning the dry-wet lacunarity process. An alternative correction method is proposed based on a stochastic description of the arrival-duration-intensity processes in coherence with the Poissonian Rectangular Pulse scheme (PRP) (Eagleson, 1972). In this proposed approach, the Q-Q transform is applied to the PRP variables derived from the daily rainfall datasets. Consequently the corrected PRP parameters are used for the synthetic generation of statistically homogeneous rainfall time series that mimic the persistency of daily observations for the reference period. Then the PRP parameters are forced through the GCM scenarios to generate local scale rainfall records for the 21st century. The statistical parameters characterizing daily storm occurrence, storm intensity and duration needed to apply the PRP scheme are considered among STARDEX collection of extreme indices.

  20. A Spatio-Temporal Downscaler for Output From Numerical Models.

    PubMed

    Berrocal, Veronica J; Gelfand, Alan E; Holland, David M

    2010-06-01

    Often, in environmental data collection, data arise from two sources: numerical models and monitoring networks. The first source provides predictions at the level of grid cells, while the second source gives measurements at points. The first is characterized by full spatial coverage of the region of interest, high temporal resolution, no missing data but consequential calibration concerns. The second tends to be sparsely collected in space with coarser temporal resolution, often with missing data but, where recorded, provides, essentially, the true value. Accommodating the spatial misalignment between the two types of data is of fundamental importance for both improved predictions of exposure as well as for evaluation and calibration of the numerical model. In this article we propose a simple, fully model-based strategy to downscale the output from numerical models to point level. The static spatial model, specified within a Bayesian framework, regresses the observed data on the numerical model output using spatially-varying coefficients which are specified through a correlated spatial Gaussian process.As an example, we apply our method to ozone concentration data for the eastern U.S. and compare it to Bayesian melding (Fuentes and Raftery 2005) and ordinary kriging (Cressie 1993; Chilès and Delfiner 1999). Our results show that our method outperforms Bayesian melding in terms of computing speed and it is superior to both Bayesian melding and ordinary kriging in terms of predictive performance; predictions obtained with our method are better calibrated and predictive intervals have empirical coverage closer to the nominal values. Moreover, our model can be easily extended to accommodate for the temporal dimension. In this regard, we consider several spatio-temporal versions of the static model. We compare them using out-of-sample predictions of ozone concentration for the eastern U.S. for the period May 1-October 15, 2001. For the best choice, we present a summary of the analysis. Supplemental material, including color versions of Figures 4, 5, 6, 7, and 8, and MCMC diagnostic plots, are available online. PMID:21113385

  1. Ensemble manifold regularization.

    PubMed

    Geng, Bo; Tao, Dacheng; Xu, Chao; Yang, Linjun; Hua, Xian-Sheng

    2012-06-01

    We propose an automatic approximation of the intrinsic manifold for general semi-supervised learning (SSL) problems. Unfortunately, it is not trivial to define an optimization function to obtain optimal hyperparameters. Usually, cross validation is applied, but it does not necessarily scale up. Other problems derive from the suboptimality incurred by discrete grid search and the overfitting. Therefore, we develop an ensemble manifold regularization (EMR) framework to approximate the intrinsic manifold by combining several initial guesses. Algorithmically, we designed EMR carefully so it 1) learns both the composite manifold and the semi-supervised learner jointly, 2) is fully automatic for learning the intrinsic manifold hyperparameters implicitly, 3) is conditionally optimal for intrinsic manifold approximation under a mild and reasonable assumption, and 4) is scalable for a large number of candidate manifold hyperparameters, from both time and space perspectives. Furthermore, we prove the convergence property of EMR to the deterministic matrix at rate root-n. Extensive experiments over both synthetic and real data sets demonstrate the effectiveness of the proposed framework. PMID:22371429

  2. Climate change scenarios of temperature and precipitation over five Italian regions for the period 2021-2050 obtained by statistical downscaling models

    NASA Astrophysics Data System (ADS)

    Tomozeiu, R.; Tomei, F.; Villani, G.; Pasqui, M.

    2010-09-01

    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 downscaling technique applied to the ENSEMBLES 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 downscaling model has been applied to the predictors derived from the ENSEMBLES 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.

  3. Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century

    E-print Network

    Rothman, Daniel

    climate models. Tropical cyclones downscaled from the climate of the period 1950­2005 are compared frequency of events is consistent with increases in a genesis potential index based on monthly mean global. It has been known for at least 60 y that tropical cyclones are driven by surface enthalpy fluxes (1, 2

  4. Technical Challenges and Solutions in Representing Lakes when using WRF in Downscaling Applications

    EPA Science Inventory

    The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional ...

  5. Fast downscaled inverses for images compressed with M-channel lapped transforms

    Microsoft Academic Search

    Ricardo L. De Queiroz; Reiner Eschbach

    1997-01-01

    Compressed images may be decompressed and displayed or printed using different devices at different resolutions. Full decompression and rescaling in space domain is a very expensive method. We studied downscaled inverses where the image is decompressed partially, and a reduced inverse transform is used to recover the image. In this fashion, fewer transform coefficients are used and the synthesis process

  6. Downscaling local extreme temperature changes in south-eastern Australia from the CSIRO Mark2 GCM

    Microsoft Academic Search

    Sascha Schubert

    1998-01-01

    Climate impact studies crucially rely on climate change information at high spatial and temporal resolutions. Since the most developed tools for estimating future climate change - the general circulation models (GCMs) - still operate on rather coarse spatial scales, their output has to be downscaled in order to provide the needed high resolution input for climate impact models.In this study,

  7. Climate downscaling for estimating glacier mass balances in northwestern North America: Validation with a USGS

    E-print Network

    Bhatt, Uma

    Climate downscaling 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

  8. dsclim: A software package to downscale climate scenarios at regional scale using a weather-typing

    E-print Network

    , weather-typing, france, SAFRAN, SCRATCH08, tool, software #12;Introduction Nowadays, global climatedsclim: A software package to downscale climate scenarios at regional scale using a weather-typing based statistical methodology Christian Pagé Laurent Terray Julien Boé Climate Modelling and Global

  9. Using downscaling to reproduce the Iberian Upwelling dynamics and to simulate future scenarios

    Microsoft Academic Search

    Ana Cordeiro Pires; Rita Nolasco; Jesus Dubert; Alfredo Rocha; Alvaro Peliz

    2010-01-01

    This work aims at a better understanding of the variability of the Iberian Upwelling Ecosystem, not only in the present regime, but also in a future climate change scenario. The purpose of the present study is to assess whether downscaling is an appropriate methodology for the region and its physical processes. Numerical simulations with the Regional Ocean Modelling System (ROMS)

  10. Downscaling Satellite Soil Moisture Estimates in the Southern Great Plains through a

    E-print Network

    Vivoni, Enrique R.

    Downscaling Satellite Soil Moisture Estimates in the Southern Great Plains through a Calibrated 2009 #12;Motivation · Soil moisture () is a key variable controlling energy and water fluxes among soil-grid variability starting from remotely- sensed estimates (Crow and Wood, 2002; Kim and Barros, 2002; Das

  11. Demonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations

    E-print Network

    Evans, Jason

    , filling gaps in spatial data, evaluating downscaled variables with available remote sensing images including skin surface temperature (TSK), soil moisture (SMOIS), and latent heat flux (LH). The performance model simulations but also in feature sharpening in remote sensing applications through image fusion

  12. The utility of MODIS products for downscaling AMSR-E soil moisture estimates

    NASA Astrophysics Data System (ADS)

    Kim, J.; Hogue, T.

    2009-05-01

    Recently, the use of microwave observations has been highlighted as a complementary tool for evaluating land surface properties. Microwave observations are less affected by clouds, water vapor and aerosol and also contain valuable soil moisture information. However, a critical limitation in these observations is the coarse spatial resolution attributed to the complex retrieval process. As a result, the best hope for providing high- resolution microwave-related products is to integrate visible, NIR/Infrared and microwave observations. In this context, we explore a downscaling approach for combining surface temperature and vegetation indices from higher spatial resolution MODIS (1km) and soil moisture from lower spatial resolution AMSR-E (25km) to obtain soil moisture at the MODIS scale (1km). The linkage is based on l integrating high resolution surface temperature and vegetation indices, through the triangle/trapezoid model, to provide an indication of relative variations in surface wetness conditions. These relative variations provide weighting parameters for downscaling of the large footprint (25km) of soil moisture to the MODIS scale (1km). Initial evaluation of the downscaled soil moisture product is undertaken at a series of Soil Moisture EXperiment (SMEX) sites conducted annually from 2002 to 2004 (Iowa, Georgia and Arizona). This concentrated validation yields insight into how well the proposed downscaling method integrates multi-scale data and the usefulness of MODIS observations in compensating for the coarse resolution of microwave observations.

  13. Generalization of a statistical downscaling model to provide local climate change projections for Australia

    Microsoft Academic Search

    B. Timbal; E. Fernandez; Z. Li

    2009-01-01

    Climate change information required for impact studies is of a much finer spatial scale than climate models can directly provide. Statistical downscaling models (SDMs) are commonly used to fill this scale gap. SDMs are based on the view that the regional climate is conditioned by two factors: (1) the large- scale climatic state and (2) local physiographic features. An SDM

  14. Passive microwave soil moisture downscaling using vegetation index and skin surface temperature

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture downscaling algorithm based on a regression relationship bet...

  15. Statistical Downscaling of WRF-Chem Model: An Air Quality Analysis over Bogota, Colombia

    NASA Astrophysics Data System (ADS)

    Kumar, Anikender; Rojas, Nestor

    2015-04-01

    Statistical downscaling 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 downscale the model output at each monitoring stations. The results confirm that the statistically downscaled outputs reduce simulated errors by up to 25%. This study provides a general overview of statistical downscaling of chemical transport models and can constitute a reference for future air quality modeling exercises over Bogota and other Colombian cities.

  16. A coupled stochastic spacetime intermittent random cascade model for rainfall downscaling

    E-print Network

    Ramírez, Jorge A.

    A coupled stochastic spacetime intermittent random cascade model for rainfall downscaling Boosik; published 21 October 2010. [1] Analysis of Next Generation Weather Radar rainfall data indicates that for the central United States, rainfall exhibits a composite behavior with respect to its spatial and temporal

  17. Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

    NASA Technical Reports Server (NTRS)

    Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard

    2013-01-01

    Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (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 Downscaling and Bias Correction (SDBC) approach. Based on these downscaled 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 downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.

  18. Addressing impacts of different statistical downscaling methods on large scale hydrologic simulations

    NASA Astrophysics Data System (ADS)

    Mizukami, N.; Clark, M. P.; Gutmann, E. D.; Mendoza, P. A.; Brekke, L. D.; Arnold, J.; Raff, D. A.

    2013-12-01

    Many hydrologic assessments, such as evaluations of climate change impacts on water resources, require downscaled climate model outputs to force hydrologic simulations at a spatial resolution finer than the climate models' native scale. Statistical downscaling is an attractive alternative to dynamical downscaling methods for continental scale hydrologic applications because of its lower computational cost. The goal of this study is to illustrate and compare how the errors in precipitation and temperature produced by different statistical downscaling methods propagate into hydrologic simulations. Multi-decadal hydrologic simulations were performed with three process-based hydrologic models (CLM, VIC, and PRMS) forced by multiple climate datasets over the contiguous United States. The forcing datasets include climate data derived from gauge observations (M02) as well as climate data downscaled from the NCEP-NCAR reanalysis using 4 statistical downscaling methods for a domain with 12-km grid spacing: two forms of Bias Corrected Spatially Disaggregated methods (BCSD-monthly and BCSD-daily), Bias Corrected Constructed Analogue (BCCA), and Asynchronous Regression (AR). Our results show that both BCCA and BCSD-daily underestimate extreme precipitation events while AR produces these correctly at the scale at which the simulations were run but does not scale them up appropriately to larger basin scales like HUC-4 and HUC-2. These artifacts lead to a poor representation of flooding events when hydrologic models are forced by these methods over a range of spatial scales. We also illustrate that errors in precipitation depths dominate impacts on runoff depth estimations, and that errors in wet day frequency have a larger effect on shortwave radiation estimations than do the errors in temperatures; this error subsequently affects the partitioning of precipitation into evaporation and runoff as we show over mountainous areas of the upper Colorado River. Finally we show the inter-model differences across our simulations are generally lower than than inter-forcing data differences. We conclude with preliminary guidance on sound methodological choices for future climate impact studies using these methods. Comparison of annual precipitation between statistically downscaled data and observation (M02) and illustration of how these differences propagate into hydrologic simulations with two models. Figure shows the simulations over the western United States.

  19. Downscaling Soil Moisture Product from SMOS for Monitoring Agricultural Droughts in South America

    NASA Astrophysics Data System (ADS)

    Nagarajan, K.; Fu, C.; Judge, J.; Fraisse, C.

    2012-12-01

    Availability of reliable near-surface soil moisture (SM) estimates at fine spatial resolutions of 1 km and at temporal resolutions of a few days is critical for accurate quantification of drought impacts on crop yields and recommending meaningful management and adaptation strategies. The recently launched European Space Agency-Soil Moisture and Ocean Salinity (ESA-SMOS) and the near-future NASA-Soil Moisture Active Passive (SMAP) missions provide unprecedented, global SM product every 2-3 days at spatial resolutions of ~50 km. In addition, the SMAP will provide a SM product at 10 km . Downscaling the above SM products to 1km is essential for any meaningful drought-related application in agricultural terrains. Optimal downscaling should retain information from higher-order moments and leverage information from auxiliary remote sensing products that are available at fine resolutions. In this study, a novel downscaling methodology based upon information theory was implemented to obtain distributed SM at 1 km every 3 days, using the SM product from SMOS. Observations of land surface temperature (LST), leaf area index (LAI) and land cover (LC) at 1 km from MODIS, and precipitation at 25 km from TRMM, were used as auxiliary information to facilitate the downscaling process. The use of information-theory in downscaling provides a hierarchical decomposition of image data that is optimal in terms of the transfer of information across scales and is therefore a better alternative to methods that use second-order statistics only. The downscaling methodology was implemented over the agricultural regions in the lower La Plata Basin (L-LPB) in South America. The L-LPB region is of great economic value in South America, where agricultural cover makes up about 25% of the continent's land area and is vulnerable to high losses in crop yields due to agricultural drought . Both remote sensing and in situ observations (precipitation, temperature, and soil moisture) obtained during the drought period of 2007-2008 were used to train the downscaling methodology. Observations obtained during the growing season of 2010, during which ESA-SMOS observations were available, was used to demonstrate the feasibility of the methodology for monitoring agricultural droughts.

  20. Simulating maximum and minimum temperature over Greece: a comparison of three downscaling techniques

    NASA Astrophysics Data System (ADS)

    Kostopoulou, E.; Giannakopoulos, C.; Anagnostopoulou, C.; Tolika, K.; Maheras, P.; Vafiadis, M.; Founda, D.

    2007-09-01

    Statistical downscaling techniques have been developed for the generation of maximum and minimum temperatures in Greece. This research focuses on the four conventional seasons, and three downscaling approaches, Multiple Linear Regression using a circulation type approach (MLRct), Canonical Correlation Analysis (CCA) and Artificial Neural Networks (ANNs), are employed and compared to assess their performance skills. Models were developed individually for each variable (Tmax, Tmin), station and season. The accuracy of downscaled values has been quantified in terms of a number of performance criteria, such as differences of the mean and standard deviation ratios between observed and modelled data, the correlation coefficients of the two sets, and also the RMSEs of the downscaled values relative to the observed. All methods revealed that during the cool season Tmax tends to be better reproduced, whereas Tmin is overestimated, particularly over western Greece, which is characterised by higher orography. With respect to the warm season, the simulation of Tmax reveals greater divergence, whereas Tmin is better generated. The distinction between the three techniques is somewhat blurred. None of the methods were found to be superior to the others and each has been shown to be a good estimator in some cases. This study concludes that all proposed methods comprise useful tools for simulating daily temperatures, as the high correlation coefficients, between observed and downscaled values, have demonstrated. However, the importance of local factors, which affect the natural variability of temperature, has been emphasised indicating that the geography of a region constitutes an important and rather complex factor, which should be included in models to improve their performance.

  1. Downscaling of precipitation for climate change scenarios: A support vector machine approach

    NASA Astrophysics Data System (ADS)

    Tripathi, Shivam; Srinivas, V. V.; Nanjundiah, Ravi S.

    2006-11-01

    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 downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling 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 downscaling 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 downscaling 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 downscaling, and are suitable for conducting climate impact studies.

  2. Global Ensemble Predictions of 2009's Tropical Cyclones Initialized with an Ensemble Kalman Filter

    E-print Network

    Hamill, Tom

    Laboratory, Global Systems Division, Boulder, Colorado 1st revision, submitted to Monthly Weather summer tropical cyclones (TCs) from two experimental global numerical weather prediction ensemble1 Global Ensemble Predictions of 2009's Tropical Cyclones Initialized with an Ensemble Kalman

  3. Evolution of the Canadian regional ensemble prediction system

    NASA Astrophysics Data System (ADS)

    Frenette, R.; Charron, M.; Li, X.; Gagnon, N.; Lavaysse, C.; Belair, S.; Carrera, M.; Yau, P.; Candille, G.

    2010-09-01

    A regional ensemble prediction system (REPS) over North America is expected to become operational at the Canadian Meteorological Centre (CMC) in late 2010 or early 2011. Different configurations of the REPS have already been tested and verified at different locations and time periods. The system was used during the Beijing 2008 summer Olympics and for the North American domain with a focus over southern British Columbia, Canada, during the 2010 Vancouver Olympics. It will also provide forecasts for tropical storms and hurricanes for the Haïti area during the summer and autumn of 2010. The Canadian Global Environmental Multiscale (GEM) model has been designed with the possibility to be run as a limited area model (GEM-LAM). The Canadian REPS is composed of 20 members running the GEM-LAM at a near 33 km grid spacing and with the same physical parameterizations as those present in the operational global deterministic prediction system at CMC. Two initial perturbation strategies (moist targeted singular vectors [SV] and the ensemble Kalman filter [EnKF]), as well as two stochastic methods for perturbations of parameterizations were verified against surface and upper air (rawinsondes) observations during summer and winter periods to determine which system has the best forecast abilities. For the SV-based REPS, 20 initial conditions (IC) are generated using a targeted SV perturbation method. These ICs are then used to run 20 global GEMs that will provide the lateral boundary conditions (LBCs) for each GEM-LAM. For the EnKF-based REPS, the 20 LBCs are built by downscaling the 20 members of the Canadian global ensemble prediction system (GEPS) to the resolution of the REPS. Verifications indicate that the EnKF approach gives better skill for summer and winter periods. The skill difference between the two systems comes mainly from the reliability attribute (smaller bias and reduced under-dispersion). Stochastic perturbations on model physical tendencies and on physical parameters were both tested. These two perturbation methods show a significant improvement in the reliability skill but tend to slightly degrade the resolution. Nevertheless, both systems show an overall improvement in the skill. The physical tendencies perturbation method showed the best scores and was chosen. Research to improve the system using surface parameter perturbations is presently ongoing. Initial results show improved skill for surface during the summer season when perturbations are done on fields related to the land surface scheme such as the albedo, soil temperature and moisture.

  4. Optimal control of inhomogeneous ensembles

    NASA Astrophysics Data System (ADS)

    Ruths, Justin Arthur Ernest

    This dissertation is concerned with formulating the problem and developing methods for the synthesis of optimal, open-loop inputs for large numbers of identically structured dynamical systems that exhibit variation in the values of characteristic parameters across the collection, or ensemble. Our goal is to steer the family of systems from an initial state (or pattern) to a desired state (or pattern) with the same common control while compensating for the inherent dispersion caused by the inhomogeneous parameter values. We compose an optimal ensemble control problem and develop a computational method based on pseudospectral approximations to solve these complex problems. This class of ensemble systems is strongly motivated by natural complications in the control of quantum phenomena, especially in magnetic resonance; however, similar structures are prevalent in a variety of other applications. From another perspective, the same methodology can be used to analyze systems that have uncertainty in the values of characteristic parameters, which are ubiquitous throughout science and engineering.

  5. Employing multi-objective Genetic Programming to the downscaling of near-surface atmospheric fields

    NASA Astrophysics Data System (ADS)

    Zerenner, Tanja; Venema, Victor; Friederichs, Petra; Simmer, Clemens

    2015-04-01

    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 downscaling 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 downscaling 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 downscaling rules which recover realistic fine-scale structures from the coarsened fields at 2.8 km resolution. Hence we are currently downscaling 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 downscaling 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 downscaling 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.

  6. A downscaling method for distributing surface soil moisture within a microwave pixel: Application to the Monsoon '90 data

    Microsoft Academic Search

    O. Merlin; A. Chehbouni; Y. H. Kerr; D. C. Goodrich

    2006-01-01

    A downscaling method for the near-surface soil moisture retrieved from passive microwave sensors is applied to the PBMR data collected during the Monsoon '90 experiment. The downscaling method requires (1) the coarse resolution microwave observations, (2) the fine-scale distribution of soil temperature and (3) the fine-scale distribution of surface conditions composed of atmospheric forcing and the parameters involved in the

  7. Quantum metrology with molecular ensembles

    SciTech Connect

    Schaffry, Marcus; Gauger, Erik M. [Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH (United Kingdom); Morton, John J. L. [CAESR, Clarendon Laboratory, Department of Physics, University of Oxford, OX1 3PU (United Kingdom); Fitzsimons, Joseph [Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH (United Kingdom); Institute for Quantum Computing, University of Waterloo, Waterloo, Ontario (Canada); Benjamin, Simon C. [Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH (United Kingdom); Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543 (Singapore); Lovett, Brendon W. [Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH (United Kingdom); School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS (United Kingdom)

    2010-10-15

    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 ensemble of spin-active molecules. We identify a third essential resource: the change in ensemble 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.

  8. Quantum Gibbs ensemble Monte Carlo

    SciTech Connect

    Fantoni, Riccardo, E-mail: rfantoni@ts.infn.it [Dipartimento di Scienze Molecolari e Nanosistemi, Università Ca’ Foscari Venezia, Calle Larga S. Marta DD2137, I-30123 Venezia (Italy); Moroni, Saverio, E-mail: moroni@democritos.it [DEMOCRITOS National Simulation Center, Istituto Officina dei Materiali del CNR and SISSA Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, I-34136 Trieste (Italy)

    2014-09-21

    We present a path integral Monte Carlo method which is the full quantum analogue of the Gibbs ensemble Monte Carlo method of Panagiotopoulos to study the gas-liquid coexistence line of a classical fluid. Unlike previous extensions of Gibbs ensemble 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.

  9. Changes in water and wind resources across the central and northeastern U.S. 2060-2010 in 24 km WRF downscale climate simulations

    NASA Astrophysics Data System (ADS)

    Birkel, S. D.; Maasch, K. A.; Oglesby, R. J.; Fulginiti, L.; Trindade, F.; Hays, C.

    2012-12-01

    GCM ensembles for the IPCC AR4 indicate that by 2060 water and wind resources will change appreciably over the central and northeastern U.S. In order to investigate these possible changes on a scale relevant for agriculture and offshore wind-power planners, we produced 24 km downscale simulations using the Weather Research and Forecasting (WRF) model. Our simulations span the years 2006-2010 and 2056-2060 with boundary conditions supplied by CCSM4 (IPCC emissions scenario RCP 8.5). By calculating the difference between the simulated time periods we find: 1) ~10% decrease in total annual precipitation across the southern half of the Ogallala aquifer in the central U.S., and ~10% increase across the northeastern states; and 2) Minimal change in annual-average 10-meter wind strength across the study areas, but with significant changes seasonal values. Interrogation of the simulation results is ongoing, and a complete synthesis will be presented at the annual meeting.

  10. Ensemble Averages when ?is a Square Integer

    E-print Network

    Christopher D. Sinclair

    2010-08-25

    We give a hyperpfaffian formulation of partition functions and ensemble averages for Hermitian and circular ensembles when L is an arbitrary integer and \\beta=L^2 and when L is an odd integer and \\beta=L^2 +1.

  11. Algorithms on ensemble quantum computers

    Microsoft Academic Search

    P. Oscar Boykin; Tal Mor; Vwani P. Roychowdhury; Farrokh Vatan

    2010-01-01

    In ensemble (or bulk) quantum computation, measurements of qubits in an individual com- puter cannot be performed. Instead, only ex- pectation values can be measured. As a re- sult of this limitation on the model of com- putation, various important algorithms cannot be processed directly on such computers, and must be modified. We provide modifications of various existing protocols, including

  12. Critic-driven ensemble classification

    Microsoft Academic Search

    David J. Miller; Lian Yan

    1999-01-01

    We develop new rules for combining the estimates obtained from each classifier in an ensemble, in order to address problems involving multiple (>2) classes. A variety of techniques have been previously suggested, including averaging probability estimates from each classifier, as well as hard (0-1) voting schemes. In this work, we introduce the notion of a critic associated with each classifier,

  13. Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output

    Microsoft Academic Search

    S. Kannan; Subimal Ghosh

    2011-01-01

    Conventional statistical downscaling techniques for prediction of multi-site rainfall in a river basin fail to capture the\\u000a correlation between multiple sites and thus are inadequate to model the variability of rainfall. The present study addresses\\u000a this problem through representation of the pattern of multi-site rainfall using rainfall state in a river basin. A model based\\u000a on K-means clustering technique coupled

  14. Downscaling of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.

    2010-12-01

    High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image downscaling has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform downscaling of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for downscaling problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the downscaled 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.

  15. An intercomparison of statistical downscaling methods used for water resource assessments in the United States

    NASA Astrophysics Data System (ADS)

    Gutmann, Ethan; Pruitt, Tom; Clark, Martyn P.; Brekke, Levi; Arnold, Jeffrey R.; Raff, David A.; Rasmussen, Roy M.

    2014-09-01

    Information relevant for most hydrologic applications cannot be obtained directly from the native-scale outputs of climate models. As a result the climate model output must be downscaled, often using statistical methods. The plethora of statistical downscaling methods requires end-users to make a selection. This work is intended to provide end-users with aid in making an informed selection. We assess four commonly used statistical downscaling methods: daily and monthly disaggregated-to-daily Bias Corrected Spatial Disaggregation (BCSDd, BCSDm), Asynchronous Regression (AR), and Bias Corrected Constructed Analog (BCCA) as applied to a continental-scale domain and a regional domain (BCCAr). These methods are applied to the NCEP/NCAR Reanalysis, as a surrogate for a climate model, to downscale precipitation to a 12 km gridded observation data set. Skill is evaluated by comparing precipitation at daily, monthly, and annual temporal resolutions at individual grid cells and at aggregated scales. BCSDd and the BCCA methods overestimate wet day fraction, and underestimate extreme events. The AR method reproduces extreme events and wet day fraction well at the grid-cell scale, but over (under) estimates extreme events (wet day fraction) at aggregated scales. BCSDm reproduces extreme events and wet day fractions well at all space and time scales, but is limited to rescaling current weather patterns. In addition, we analyze the choice of calibration data set by looking at both a 12 km and a 6 km observational data set; the 6 km observed data set has more wet days and smaller extreme events than the 12 km product, the opposite of expected scaling.

  16. Extended-Range High-Resolution Dynamical Downscaling over a Continental-Scale Domain

    NASA Astrophysics Data System (ADS)

    Husain, S. Z.; Separovic, L.; Yu, W.; Fernig, D.

    2014-12-01

    High-resolution mesoscale simulations, when applied for downscaling 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 downscale 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 downscaling strategy have significantly improved spatiotemporal variance compared to those from the operational forecasts, and any time series generated from the downscaled fields do not suffer from discontinuities due to switching between the consecutive forecasts.

  17. Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment

    NASA Astrophysics Data System (ADS)

    Vrac, M.; Drobinski, P.; Merlo, A.; Herrmann, M.; Lavaysse, C.; Li, L.; Somot, S.

    2012-09-01

    ERA-40 reanalyses, and simulations from three regional climate models (RCMs) (ALADIN, LMDZ, and WRF) and from one statistical downscaling model (CDF-t) are used to evaluate the uncertainty in downscaling of wind, temperature, and rainfall cumulative distribution functions (CDFs) for eight stations in the French Mediterranean basin over 1991-2000. The uncertainty is quantified using the Cramer-von Mises score (CvM) to measure the "distance" between the simulated and observed CDFs. The ability of the three RCMs and CDF-t to simulate the "climate" variability is quantified with the explained variance, variance ratio and extreme occurrence. The study shows that despite their differences, the three RCMs display very similar performance. In terms of global distributions (i.e. CvM), all models perform better than ERA-40 for both seasons and variables. However, looking at variance criteria, RCMs are not always much better than ERA-40 reanalyses, whereas CDF-t produces accurate results when applied to ERA-40. In a second step, a combined statistical/dynamical downscaling approach has been used, consisting in applying CDF-t to the RCM outputs. It shows that CDF-t applied to the RCM outputs does not necessarily produce better results than those from CDF-t directly applied to ERA-40. It also shows that CDF-t applied to RCMs generally improves the downscaled CDFs and that the "additional" added value of CDF-t applied to the RCMs is independent of the performance of the RCMs in terms of CvM, explained variance, variance ratio and extreme occurrence.

  18. Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency

    Microsoft Academic Search

    Olivier Merlin; Jeffrey P. Walker; Abdelghani Chehbouni; Yann Kerr

    2008-01-01

    A deterministic approach for downscaling ?40 km resolution Soil Moisture and Ocean Salinity (SMOS) observations is developed from 1 km resolution MODerate resolution Imaging Spectroradiometer (MODIS) data. To account for the lower soil moisture sensitivity of MODIS surface temperature compared to that of L-band brightness temperature, the disaggregation scale is fixed to 10 times the spatial resolution of MODIS thermal data (10 km).

  19. Online Ensemble Learning Nikunj Chandrakant Oza

    E-print Network

    Oza, Nikunj C.

    Online Ensemble Learning by Nikunj Chandrakant Oza B.S. (Massachusetts Institute of Technology Date Date Date University of California at Berkeley 2001 #12;Online Ensemble Learning Copyright 2001 by Nikunj Chandrakant Oza #12;1 Abstract Online Ensemble Learning by Nikunj Chandrakant Oza Doctor

  20. Online Learning with Ensembles R. Urbanczik

    E-print Network

    Senn, Walter

    Online Learning with Ensembles R. Urbanczik Neural Computing Research Group Aston University Aston Triangle, Birmingham B4 7ET, U.K. February 8, 2000 Abstract Supervised online learning with an ensemble as an ensemble of f-students. Online learning, where each training example is presented just once to the student

  1. A spatial downscaling procedure of MODIS derived actual evapotranspiration using Landsat images at central Greece

    NASA Astrophysics Data System (ADS)

    Spiliotopoulos, M.; Adaktylou, N.; Loukas, A.; Michalopoulou, H.; Mylopoulos, N.; Toulios, L.

    2013-08-01

    In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to derive daily actual evapotranspiration (ETa) distributions from Landsat and MODIS images separately. The study area is the Lake Karla basin in Thessaly, Central Greece. Meteorological data from the archive of Center for Research and Technology, Thessaly (CERETETH) have also been used. The methodology was developed using satellite and ground data for the period of summer 2007. Landsat and MODIS imagery were combined in order to have data with high temporal and spatial resolution (downscaling). The downscaling technique applied is the output downscaling with regression between images. This technique disaggregates imagery by applying linear regression between two MODIS products to the previous or subsequent Landsat product. After the calculation of a first order linear regression between two MODIS-derived ETa maps the next step is the regression to the ETa map derived from the prior Landsat image to predict the disaggregated subsequent Landsat ETa map. The results are satisfactory, giving the general trend of ETa derived from the original SEBAL procedure.

  2. About the Relevance of Downscaling for Nonlinear Problems in Porous Media

    NASA Astrophysics Data System (ADS)

    Leroy, V.; Bernard, D.

    2014-12-01

    In multiscale systems, scale separation is a major challenge in many industrial applications. The resulting complexity was, in the beginning, dealt with using phenomenological models and macroscopic approaches. Improvements in upscaling methods then allowed deriving macroscopic models from micro-scale transport models, greatly improving the understanding of experimentally observed phenomena. However, the investigation of many problems involving highly nonlinear phenomena (e.g. high-temperature heat transfer, chemistry, high-concentration mass transfer, etc.) remains out of the reach of current upscaling methods, even though the associated physics can be described with reasonable accuracy at the microscopic scale, mainly because the effects of nonlinearity can often not be fully passed from the microscopic scale to the macroscopic one without knowing the state of the medium at the microscopic scale. From this observation comes the idea of using a multiscale approach to investigate problems requiring exchange of information between scales. While in upscaling, information goes from the micro-scale to the macro-scale, downscaling does the opposite and allows the reconstruction of information in a limited region of the micro-scale, based on macro-scale information. Used together, upscaling and downscaling allow the exchange of information between both scales. This multiscale approach facilitates the investigation of highly nonlinear problems or that of cases with evolving micro-geometry. This presentation first aims at showing the relevance of a multiscale approach for transport in porous media and shows promising results yielded by the downscaling methodology for nonlinear heat transfer.

  3. Applying downscaled global climate model data to a hydrodynamic surface-water and groundwater model

    USGS Publications Warehouse

    Swain, Eric; Stefanova, Lydia; Smith, Thomas

    2014-01-01

    Precipitation data from Global Climate Models have been downscaled to smaller regions. Adapting this downscaled 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 downscaled 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.

  4. Evaluation of Future Precipitation Scenario Using Statistical Downscaling MODEL over Three Climatic Region of Nepal Himalaya

    NASA Astrophysics Data System (ADS)

    Sigdel, M.

    2014-12-01

    Statistical downscaling model (SDSM) was applied in downscaling 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 downscaled 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.

  5. A new dynamical downscaling approach with GCM bias corrections and spectral nudging

    NASA Astrophysics Data System (ADS)

    Xu, Zhongfeng; Yang, Zong-Liang

    2015-04-01

    To improve confidence in regional projections of future climate, a new dynamical downscaling (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 downscaled mean climate and climate variability relative to other GCM-driven RCM downscaling 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.

  6. Potential for downscaling soil moisture maps derived from spaceborne imaging radar data

    NASA Astrophysics Data System (ADS)

    Crow, Wade T.; Wood, Eric F.; Dubayah, Ralph

    2000-01-01

    The presence of nonlinear relationships between surface soil moisture and various hydrologic processes suggests that grid-scale water and energy fluxes cannot be accurately modeled without subgrid-scale soil moisture information. For land surface and energy balance models run over continental- to global-scale domains, accurate fine-scale soil moisture observations are nearly impossible to obtain on a consistent basis and will likely remain so through the next generation of soil moisture remote sensors. In the absence of such data sets, an alternative approach is to generalize the statistical behavior of soil moisture fields across the relevant range of spatial scales. Downscaling procedures offer the possibility that the fine-scale statistical properties of soil moisture fields can be inferred from coarse-scale data. Such an approach was used for a 29×200 km transect of 25 m active radar data acquired over Oklahoma by NASA's spaceborne imaging radar imaging (SIR-C) mission on April 12, 1994. Using a soil dielectric inversion model, the radar data were processed to provide estimates of surface soil dielectric values, which can be equated to volumetric soil moisture content. The soil moisture field along each strip was analyzed for evidence of spatial scaling for scales ranging from 100 to 6400 m. Results suggest that a spatial scaling assumption may not always be an appropriate basis for a downscaling approach. Prospects for the development of a more robust downscaling procedure for soil moisture are discussed.

  7. Coupled ensemble flow line advection and analysis.

    PubMed

    Guo, Hanqi; Yuan, Xiaoru; Huang, Jian; Zhu, Xiaomin

    2013-12-01

    Ensemble run simulations are becoming increasingly widespread. In this work, we couple particle advection with pathline analysis to visualize and reveal the differences among the flow fields of ensemble runs. Our method first constructs a variation field using a Lagrangian-based distance metric. The variation field characterizes the variation between vector fields of the ensemble runs, by extracting and visualizing the variation of pathlines within ensemble. Parallelism in a MapReduce style is leveraged to handle data processing and computing at scale. Using our prototype system, we demonstrate how scientists can effectively explore and investigate differences within ensemble simulations. PMID:24051840

  8. The Future of Land-Use in the United States: Downscaling SRES Emission Scenarios

    NASA Astrophysics Data System (ADS)

    Sleeter, B. M.; Sohl, T. L.

    2010-12-01

    Scenarios have emerged as useful tools to explore uncertain futures in ecological systems. We describe research being initiated by the U.S. Geological Survey (USGS) to develop a comprehensive portfolio of land-use and land-cover (LULC) scenarios for the United States. The USGS has identified LULC scenarios as a major area of future research and the USGS LandCarbon research project has adopted a scenario-based approach for forecasting changes in LULC that may result in changes to ecosystem carbon flux and greenhouse gas (GHG) emissions. Scenarios generally require knowledge of history and current conditions, and specific understanding about how drivers of change have acted to influence the historical and current condition. We describe a methodology to downscale Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) by leveraging three primary sources of information: 1) comprehensive land-use histories developed through remote sensing and survey data, 2) analytical model outputs from global integrated assessment models (IAMs), and 3) expert knowledge. SRES scenarios are organized along two primary dimensions described by economic versus environmental preferences and global versus regional influences. The SRES process identified the primary drivers of GHG emissions and provided narrative storylines describing their unique characteristics within each scenario family. We describe an iterative process to downscale SRES storylines to the national and regional scale for the United States. Based on downscaled narratives and guided by expert opinion, IAM outputs are used to extend land-use histories into the future. A wide range of qualitative and quantitative products will be developed throughout the downscaling process. Qualitative narratives will be developed at national and regional scales and case studies to downscale to local scales will be explored. Quantitative data includes scenarios of LULC (rates of change, composition, and conversions) at national and regional scales. Quantitative LULC scenarios are useful inputs for LULC models, which can be used to spatially allocate LULC demand on the landscape, resulting in high-resolution annual maps of LULC consistent with IPCC SRES global scenarios.

  9. Modeling climate change impacts on hydrological variability using an efficient multi-site GCM downscaling method

    NASA Astrophysics Data System (ADS)

    LI, Z.; Lü, Z.

    2014-12-01

    The coarse resolution of GCM outputs cannot match the high resolution input requirement of hydrological models and thus are inappropriate for impact assessment of climate change. Though numerous downscaling techniques have been used to gap the mismatch, the methods based on single site cannot be used by the distributed hydrological models for hydrological extreme simulation since the flood in one subbasin can be offset by the adjacent ones due to the ignorance of multi-site spatiotemporal correlation of meteorological variables. This study developed a multi-site downscaling method based on a two-stage weather generator (TSWG) through three steps: (i) spatially downscaling GCMs with a transfer function method; (ii) temporally downscaling GCMs with a single-site weather generator; (iii) reconstructing the spatiotemporal correlations with a post-processing and nonparametric shuffle procedure. Five GCMs (CanESM2, CSIRO_3.6.0, GFDL_CM3, HadGEM2-AO and MPI-ESM-LR) under four RCPs (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) were used to generate climate scenarios for the period of 2011-2040. The hydrological simulation was carried out by SWAT in the Jing River catchment on the Loess Plateau. Future annual mean precipitation would change by -7.7% to 9.2%, annual mean maximum and minimum temperature would increase by 1.4-1.8 ? and 1.1-1.4 ?, respectively. Overall, future climate tended to be warmer and drier under most GCMs and RCPs, and this trend would be more significant for flood season; however, the variations of monthly precipitation would be greater than present. The annual mean streamflow would change by -18% to 38% and be more variable. The monthly streamflow would be more variable for most months due to the increase of monthly maximum streamflow and decrease of monthly minimum streamflow. Therefore, the stremflow in the Jing River should be paid more attention for its possible disasters. The multi-site downscaling method proposed in this study is efficient and performs well for its spatiotemporal correlation reconstruction and hydrological variability simulation, which provides a powerful tool for impact assessment of climate changes.

  10. Uncertainty Analysis of Downscaled CMIP5 Precipitation Data for Louisiana, USA

    NASA Astrophysics Data System (ADS)

    Sumi, S. J.; Tamanna, M.; Chivoiu, B.; Habib, E. H.

    2014-12-01

    The downscaled CMIP3 and CMIP5 Climate and Hydrology Projections dataset contains fine spatial resolution translations of climate projections over the contiguous United States developed using two downscaling techniques (monthly Bias Correction Spatial Disaggregation (BCSD) and daily Bias Correction Constructed Analogs (BCCA)). The objective of this study is to assess the uncertainty of the CMIP5 downscaled general circulation models (GCM). We performed an analysis of the daily, monthly, seasonal and annual variability of precipitation downloaded from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections website for the state of Louisiana, USA at 0.125° x 0.125° resolution. A data set of daily gridded observations of precipitation of a rectangular boundary covering Louisiana is used to assess the validity of 21 downscaled GCMs for the 1950-1999 period. The following statistics are computed using the CMIP5 observed dataset with respect to the 21 models: the correlation coefficient, the bias, the normalized bias, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). A measure of variability simulated by each model is computed as the ratio of its standard deviation, in both space and time, to the corresponding standard deviation of the observation. The correlation and MAPE statistics are also computed for each of the nine climate divisions of Louisiana. Some of the patterns that we observed are: 1) Average annual precipitation rate shows similar spatial distribution for all the models within a range of 3.27 to 4.75 mm/day from Northwest to Southeast. 2) Standard deviation of summer (JJA) precipitation (mm/day) for the models maintains lower value than the observation whereas they have similar spatial patterns and range of values in winter (NDJ). 3) Correlation coefficients of annual precipitation of models against observation have a range of -0.48 to 0.36 with variable spatial distribution by model. 4) Most of the models show negative correlation coefficients in summer and positive in winter. 5) MAE shows similar spatial distribution for all the models within a range of 5.20 to 7.43 mm/day from Northwest to Southeast of Louisiana. 6) Highest values of correlation coefficients are found at seasonal scale within a range of 0.36 to 0.46.

  11. [Evaluating the performance of the UCLA method for spatially downscaling soil moisture products using three Ts/VI indices].

    PubMed

    Ling, Zi-Wei; He, Long-Bin; Zeng, Hui

    2014-02-01

    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 downscale the coarse-resolution soil moisture products for use in regional- or watershed-scale studies. The UCLA method is one of the methods for spatially downscaling 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 downscaling. In this paper, we compared the performance of the UCLA method for spatially downscaling 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 downscaled results using the three Ts/VI indices were all reasonable. However, the downscaled 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 downscaling 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

  12. Combination of remote sensing data products to derive spatial climatologies of "degree days" and downscale meteorological reanalyses: application to the Upper Indus Basin

    NASA Astrophysics Data System (ADS)

    Forsythe, N. D.; Rutter, N.; Brock, B. W.; Fowler, H. J.; Blenkinsop, S.

    2014-12-01

    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 downscale an ensemble of modern global meteorological reanalyses including ERA-Interim, NCEP CFSR, NASA MERRA and JRA-55 which overlap MODIS instrument record. This downscaling feasibility assessment is a prerequisite to applying the method to regional climate projections.

  13. Future changes in African temperature and precipitation in an ensemble of Africa-CORDEX regional climate model simulations

    NASA Astrophysics Data System (ADS)

    Kjellström, Erik; Nikulin, Grigory; Gbobaniyi, Emiola; Jones, Colin

    2013-04-01

    In this study we investigate possible changes in temperature and precipitation on a regional scale over Africa from 1961 to 2100. We use data from two ensembles of climate simulations, one global and one regional, over the Africa-CORDEX domain. The global ensemble includes eight coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CNRM-CM5, HadGEM2-ES, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. In the regional ensemble all 8 AOGCMs are downscaled at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Two forcing scenarios are considered, RCP 4.5 and 8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, we investigate the benefit of the higher horizontal resolution in RCA4 by comparing the results to the coarser driving AOGCM data. The significance of the results is investigated by comparing to i) the model simulated natural variability, and, ii) the biases in the control period. Results dealing with changes in the seasonal cycle of temperature and precipitation are presented. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.

  14. Precursory signals in analysis ensemble spread

    NASA Astrophysics Data System (ADS)

    Enomoto, Takeshi; Hattori, Miki; Miyoshi, Takemasa; Yamane, Shozo

    2010-04-01

    Time evolution of the ensemble spread of an experimental ensemble atmospheric reanalysis ALERA is examined in relation to various meteorological phenomena. The analysis ensemble spread increases about two days prior to westerly bursts in the eastern Indian Ocean. Precursory signals are also found in the monsoon onset. The analysis ensemble spread is large at the leading edge of the Somali jet and it grows as the jet extends eastward. Over Vietnam analysis ensemble spread is maximized several weeks before the maximum of the monsoon westerlies. In the stratosphere the analysis ensemble spread takes the maximum value a few days prior to a sudden warming. Our findings indicate that the ensemble analysis contains additional information on atmospheric uncertainty of scientific interest, which may also have practical value.

  15. Triticeae Resources in Ensembl Plants

    PubMed Central

    Bolser, Dan M.; Kerhornou, Arnaud; Walts, Brandon; Kersey, Paul

    2015-01-01

    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. Ensembl Plants (http://plants.ensembl.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 Ensembl 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 Ensembl 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

  16. Dimensionality Reduction Through Classifier Ensembles

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)

    1999-01-01

    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 ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

  17. Utility of coarse and downscaled soil moisture products at L-band for hydrologic modeling at the catchment scale

    NASA Astrophysics Data System (ADS)

    Mascaro, Giuseppe; Vivoni, Enrique R.

    2012-05-01

    Demonstrating the utility of satellite-based soil moisture (?) products for hydrologic modeling at high resolution is a critical component of mission design. In this study, we utilize aircraft and ground ? data collected during the SMEX04 experiment in Sonora (Mexico) to compare two downscaling frameworks using C-band and L-band sensors. We show that the L-band framework, which mimics the disaggregation of SMAP products, has considerably better performance than the C-band framework simulating the downscaling of AMSR-E. Disaggregated L-band ? fields are able to characterize with reasonable accuracy the ? variability at multiple extent scales, including the SMAP footprint and the catchment scale, and along an elevation transect. We then test the utility of coarse and downscaled C- and L-band ? estimates for hydrologic simulations through data assimilation experiments using a distributed hydrologic model. Results reveal that the model prognostic capability is significantly enhanced when using L-band ? fields at the SMAP scale and, to a greater extent, when downscaled L-band ? fields are assimilated. L-band data assimilation leads to higher model fidelity relative to ground data as well as more realistic soil moisture patterns at the catchment scale. This study indicates the potential value of satellite-based L-band sensors for hydrologic modeling when coupled with a statistical downscaling algorithm.

  18. A Modified Ensemble Framework for Drought Estimation

    NASA Astrophysics Data System (ADS)

    Alobaidi, M. H.; Marpu, P. R.; Ouarda, T.

    2014-12-01

    Drought estimation at ungauged sites is a difficult task due to various challenges such as scale and limited availability and information about hydrologic neighborhoods. Ensemble regression has been recently utilized in modeling various hydrologic systems and showed advantage over classical regression approaches to such studies. A challenging task in ensemble modeling is the proper training of the ensemble's individual learners and the ensemble combiners. In this work, an ensemble framework is proposed to enhance the generalization ability of the sub-ensemble models and its combiner. Information mixtures between the subsamples are introduced. Such measure is dedicated to the ensemble members and ensemble 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 ensemble members in addition to different ensemble 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.

  19. A statistical description of neural ensemble dynamics.

    PubMed

    Long, John D; Carmena, Jose M

    2011-01-01

    The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an ensemble. Inferring these connections is often intractable because the set of possible interactions grows exponentially with ensemble size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing changes in the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility. PMID:22319486

  20. ICEMAP250: Sea Ice Mapping At 250m Resolution Using Downscaled Modis Data

    NASA Astrophysics Data System (ADS)

    Gignac, Charles; Bernier, Monique; Chokmani, Karem; Poulin, Jimmy

    2015-04-01

    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 downscaling approaches were used. To downscale 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 downscale the IST to 250m, uses a KNN regression between cloud masked NDSI and IST at 1KM to, after the initial CCRS downscaling, injects 250m NDSI values into the KNN regression parameters therefore building a new, 250m downscaled 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 downscaling 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.

  1. Downscaling summer rainfall in the UK from North Atlantic ocean temperatures Hydrology and Earth System Sciences, 5(2), 245257 (2001) EGS

    E-print Network

    Paris-Sud XI, Université de

    2001-01-01

    Downscaling summer rainfall in the UK from North Atlantic ocean temperatures 245 Hydrology Atlantic ocean temperatures R.L.Wilby Department of Geography, King's College London, Strand, London, WC2R occurrence process. Key words: North Atlantic, ocean temperatures, downscaling, rainfall, forecasting, UK

  2. Evaluation of a weather generator-based method for statistically downscaling non-stationary climate scenarios for impact assessment at a point scale

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The non-stationarity is a major concern for statistically downscaling climate change scenarios for impact assessment. This study is to evaluate whether a statistical downscaling method is fully applicable to generate daily precipitation under non-stationary conditions in a wide range of climatic zo...

  3. Downscaling of Extreme Precipitation: Proposing a New Statistical Approach and Investigating a Taken-for-Granted Assumption

    NASA Astrophysics Data System (ADS)

    Elshorbagy, Amin; Alam, Shahabul

    2015-04-01

    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, downscaling of variables (e.g., precipitation) from coarse spatial and temporal scales to finer ones is inevitable. Downscaling 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 downscaling. 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 downscaling for IDF-related applications. In statistical downscaling 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 downscaling 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 downscaling 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 downscaling 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 downscaling methods; that is the validity of the downscaling 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 downscaling relationship that is generic and GCM-invariant might lead to more reliable downscaling of future projections, even though the higher reliability comes at the cost of accuracy.

  4. Dynamical Downscaling of GCM Simulations: Toward the Improvement of Forecast Bias over California

    SciTech Connect

    Chin, H S

    2008-09-24

    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 downscaling (SD) methods have become popular methods for filling the gap between global and local-to-regional climate applications. Recent inter-comparison studies of these downscaling techniques show that both downscaling 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 downscaling 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.

  5. Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation

    NASA Astrophysics Data System (ADS)

    Hwang, S.; Graham, W. D.

    2013-11-01

    There are a number of statistical techniques that downscale coarse climate information from general circulation models (GCMs). However, many of them do not reproduce the small-scale spatial variability of precipitation exhibited by the observed meteorological data, which is an important factor for predicting hydrologic response to climatic forcing. In this study a new downscaling technique (Bias-Correction and Stochastic Analog method; BCSA) was developed to produce stochastic realizations of bias-corrected daily GCM precipitation fields that preserve both the spatial autocorrelation structure of observed daily precipitation sequences and the observed temporal frequency distribution of daily rainfall over space. We used the BCSA method to downscale 4 different daily GCM precipitation predictions from 1961 to 1999 over the state of Florida, and compared the skill of the method to results obtained with the commonly used bias-correction and spatial disaggregation (BCSD) approach, a modified version of BCSD which reverses the order of spatial disaggregation and bias-correction (SDBC), and the bias-correction and constructed analog (BCCA) method. Spatial and temporal statistics, transition probabilities, wet/dry spell lengths, spatial correlation indices, and variograms for wet (June through September) and dry (October through May) seasons were calculated for each method. Results showed that (1) BCCA underestimated mean daily precipitation for both wet and dry seasons while the BCSD, SDBC and BCSA methods accurately reproduced these characteristics, (2) the BCSD and BCCA methods underestimated temporal variability of daily precipitation and thus did not reproduce daily precipitation standard deviations, transition probabilities or wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) the BCSD, BCCA and SDBC methods underestimated spatial variability in daily precipitation resulting in underprediction of spatial variance and overprediction of spatial correlation, whereas the new stochastic technique (BCSA) replicated observed spatial statistics for both the wet and dry seasons. This study underscores the need to carefully select a downscaling method that reproduces all precipitation characteristics important for the hydrologic system under consideration if local hydrologic impacts of climate variability and change are going to be reasonably predicted. For low-relief, rainfall-dominated watersheds, where reproducing small-scale spatiotemporal precipitation variability is important, the BCSA method is recommended for use over the BCSD, BCCA, or SDBC methods.

  6. Climatological Downscaling and Evaluation of AGRMET Precipitation Analyses Over the Continental U.S.

    NASA Astrophysics Data System (ADS)

    Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Tian, Y.; Zeng, J.

    2007-05-01

    The spatially distributed application of a land surface model (LSM) over a region of interest requires the application of similarly distributed precipitation fields that can be derived from various sources, including surface gauge networks, surface-based radar, and orbital platforms. The spatial variability of precipitation influences the spatial organization of soil temperature and moisture states and, consequently, the spatial variability of land- atmosphere fluxes. The accuracy of spatially-distributed precipitation fields can contribute significantly to the uncertainty of model-based hydrological states and fluxes at the land surface. Collaborations between the Air Force Weather Agency (AFWA), NASA, and Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and downscaling of precipitation fields. Climatological information included in the LIS-based downscaling procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and near-real-time simulations in regions of interest. This work focuses on value added to the AGRMET precipitation product by the inclusion of high-quality climatological information on a monthly time scale. The AGRMET method uses microwave-based satellite precipitation estimates from various polar-orbiting platforms (NOAA POES and DMSP), infrared-based estimates from geostationary platforms (GOES, METEOSAT, etc.), related cloud analysis products, and surface gauge observations in a complex and hierarchical blending process. Results from processing of the legacy AGRMET precipitation products over the U.S. using LIS-based methods for downscaling, both with and without climatological factors, are evaluated against high-resolution monthly analyses using the PRISM knowledge- based method (Daly et al. 2002). It is demonstrated that the incorporation of climatological information in a downscaling procedure can significantly enhance the accuracy, and potential utility, of AFWA precipitation products for military and civilian customer applications.

  7. A Gaussian mixture ensemble transform filter

    E-print Network

    Reich, Sebastian

    2011-01-01

    We generalize the popular ensemble Kalman filter to an ensemble transform filter where the prior distribution can take the form of a Gaussian mixture. The design of the filter is based on a continuous formulation of the Bayesian filter analysis step. We call the new filter algorithm the ensemble Gaussian mixture filter (EGMF). The EGMF is implemented for two simple test problems (Brownian dynamics in one dimension and a modified Lorenz-96 model).

  8. Ensemble modeling of CME propagation

    NASA Astrophysics Data System (ADS)

    Lee, C. O.; Arge, C. N.; Henney, C. J.; Odstrcil, D.; Millward, G. H.; Pizzo, V. J.

    2014-12-01

    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 ensemble 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 ensemble 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 ensemble, 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.

  9. Forecasting European Droughts using the North American Multi-Model Ensemble (NMME)

    NASA Astrophysics Data System (ADS)

    Thober, Stephan; Kumar, Rohini; Samaniego, Luis; Sheffield, Justin; Schäfer, David; Mai, Juliane

    2015-04-01

    Soil moisture droughts have the potential to diminish crop yields causing economic damage or even threatening the livelihood of societies. State-of-the-art drought forecasting systems incorporate seasonal meteorological forecasts to estimate future drought conditions. Meteorological forecasting skill (in particular that of precipitation), however, is limited to a few weeks because of the chaotic behaviour of the atmosphere. One of the most important challenges in drought forecasting is to understand how the uncertainty in the atmospheric forcings (e.g., precipitation and temperature) is further propagated into hydrologic variables such as soil moisture. The North American Multi-Model Ensemble (NMME) provides the latest collection of a multi-institutional seasonal forecasting ensemble for precipitation and temperature. In this study, we analyse the skill of NMME forecasts for predicting European drought events. The monthly NMME forecasts are downscaled to daily values to force the mesoscale hydrological model (mHM). The mHM soil moisture forecasts obtained with the forcings of the dynamical models are then compared against those obtained with the Ensemble Streamflow Prediction (ESP) approach. ESP recombines historical meteorological forcings to create a new ensemble forecast. Both forecasts are compared against reference soil moisture conditions obtained using observation based meteorological forcings. The study is conducted for the period from 1982 to 2009 and covers a large part of the Pan-European domain (10°W to 40°E and 35°N to 55°N). Results indicate that NMME forecasts are better at predicting the reference soil moisture variability as compared to ESP. For example, NMME explains 50% of the variability in contrast to only 31% by ESP at a six-month lead time. The Equitable Threat Skill Score (ETS), which combines the hit and false alarm rates, is analysed for drought events using a 0.2 threshold of a soil moisture percentile index. On average, the NMME based ensemble forecasts have consistently higher skill than the ESP based ones (ETS of 13% as compared to 5% at a six-month lead time). Additionally, the ETS ensemble spread of NMME forecasts is considerably narrower than that of ESP; the lower boundary of the NMME ensemble spread coincides most of the time with the ensemble median of ESP. Among the NMME models, NCEP-CFSv2 outperforms the other models in terms of ETS most of the time. Removing the three worst performing models does not deteriorate the ensemble performance (neither in skill nor in spread), but would substantially reduce the computational resources required in an operational forecasting system. For major European drought events (e.g., 1990, 1992, 2003, and 2007), NMME forecasts tend to underestimate area under drought and drought magnitude during times of drought development. During drought recovery, this underestimation is weaker for area under drought or even reversed into an overestimation for drought magnitude. This indicates that the NMME models are too wet during drought development and too dry during drought recovery. In summary, soil moisture drought forecasts by NMME are more skillful than those of an ESP based approach. However, they still show systematic biases in reproducing the observed drought dynamics during drought development and recovery.

  10. PSO (FU 2101) Ensemble-forecasts for wind power

    E-print Network

    PSO (FU 2101) Ensemble-forecasts for wind power Wind Power Ensemble Forecasting Using Wind Speed the problems of (i) transforming the meteorological ensembles to wind power ensembles and, (ii) correcting) data. However, quite often the actual wind power production is outside the range of ensemble forecast

  11. Joys of Community Ensemble Playing: The Case of the Happy Roll Elastic Ensemble in Taiwan

    ERIC Educational Resources Information Center

    Hsieh, Yuan-Mei; Kao, Kai-Chi

    2012-01-01

    The Happy Roll Elastic Ensemble (HREE) is a community music ensemble supported by Tainan Culture Centre in Taiwan. With enjoyment and friendship as its primary goals, it aims to facilitate the joys of ensemble playing and the spirit of social networking. This article highlights the key aspects of HREE's development in its first two years…

  12. The beta-Wishart ensemble

    NASA Astrophysics Data System (ADS)

    Dubbs, Alexander; Edelman, Alan; Koev, Plamen; Venkataramana, Praveen

    2013-08-01

    We introduce a "broken-arrow" matrix model for the ?-Wishart ensemble, 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 ensembles 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.

  13. Probabilistic Description of Stellar Ensembles

    NASA Astrophysics Data System (ADS)

    Cerviño, Miguel

    I describe the modeling of stellar ensembles in terms of probability distributions. This modeling is primary characterized by the number of stars included in the considered resolution element, whatever its physical (stellar cluster) or artificial (pixel/IFU) nature. It provides a solution of the direct problem of characterizing probabilistically the observables of stellar ensembles as a function of their physical properties. In addition, this characterization implies that intensive properties (like color indices) are intrinsically biased observables, although the bias decreases when the number of stars in the resolution element increases. In the case of a low number of stars in the resolution element (N<105), the distributions of intensive and extensive observables follow nontrivial probability distributions. Such a situation ??? can be computed by means of Monte Carlo simulations where data mining techniques would be applied. Regarding the inverse problem of obtaining physical parameters from observational data, I show how some of the scatter in the data provides valuable physical information since it is related to the system size (and the number of stars in the resolution element). However, making use of such ??? information requires following iterative procedures in the data analysis.

  14. Assimilation of precipitation-affected microwave radiances in a cloud-resolving WRF ensemble data assimilation system

    NASA Astrophysics Data System (ADS)

    Zhang, S. Q.; Zupanski, M.; Hou, A. Y.; Lin, X.; Cheung, S.

    2010-12-01

    In the last decade the progress in satellite precipitation estimation and the advance in precipitation assimilation techniques proved to have positive impact on the quality of atmospheric analyses and forecasts. Direct assimilation of rain-affected radiances presents new challenge to optimal utilization of satellite precipitation observations in numeric weather and climate predictions. Current operational and research methodologies are generally limited to relatively coarse resolution models and prescribed static error statistics, and commonly require tangent linear model and adjoint model for the highly non-linear cloud and precipitation physics. To address some of these challenges, a WRF ensemble data assimilation system (Goddard-WRF-EDAS) at cloud-resolving scales has been developed jointly by NASA/GSFC and Colorado State University (CSU). The system employs the Weather Research and Forecasting (WRF) model with NASA Goddard microphysics schemes, and the Maximum Likelihood Ensemble Filter (MLEF). Precipitation affected radiances are assimilated with Goddard Satellite Data Simulator Unit (SDSU) as the observation operator. In addition to the boundary forcing constructed from operational global analysis, NCEP operational data stream is also assimilated to ensure realistic representation of dynamic circulation in the regional domains. Using the ensemble assimilation approach, the forecast error-statistics is updated by ensemble forecasts, and information is extracted from precipitation observations along with other types of data to produce dynamically consistent precipitation analyses and forecasts. We present experimental results of assimilating precipitation-affected microwave radiances over land in middle latitudes. The results demonstrate the data impact to the downscaled precipitation short term forecasts and information propagation from precipitation data to dynamic fields. The error statistics of microphysical control variables and their relationship to the observable innovations in radiance space are examined. The evaluation of background error covariance, in particular the cross-covariance between microphysical and dynamical variables will also be discussed.

  15. Ice sheet dynamics within an earth system model: downscaling, coupling and first results

    NASA Astrophysics Data System (ADS)

    Barbi, D.; Lohmann, G.; Grosfeld, K.; Thoma, M.

    2014-09-01

    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 downscaling is crucial for ice sheet stability, especially for southern Greenland where surface temperatures are close to the melting point. The downscaling 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.

  16. Development of sampling downscaling: a case for wintertime precipitation in Hokkaido

    NASA Astrophysics Data System (ADS)

    Kuno, Ryusuke; Inatsu, Masaru

    2014-07-01

    This study has developed sampling downscaling (SmDS), in which dynamical downscaling (DDS) is executed for a few of period selected from a long-term integration by general circulation model based on an observed statistical relationship between large-scale climate and regional-scale precipitation. SmDS expectedly produces climatology and frequency distribution of precipitation over a nested region with reducing computational cost, if a global-scale climate pattern mostly controls regional-scale weather statistics. Here SmDS was attempted for wintertime precipitation over Hokkaido, Japan, because a linkage between snowfall and sea-level pressure patterns has been known by Japanese synopticians and it can be detected by singular value decomposition (SVD) analysis on wintertime inter-annual variability during the period from 1980/1981 to 2009/2010 for precipitation over Hokkaido and moisture flux convergence around there. DDS for the full period over the same domain was also performed for comparison with SmDS. SmDS selected two winters from the top and two winters from the bottom of the projection onto the first SVD mode. It was found that, comparing with the full DDS, SmDS indeed provided unbiased statistics for average but exaggerated extreme statistics such as heavy rainfall frequency. It was also shown that the sampling in the SmDS method was much more effective than the random sampling.

  17. AMPA receptor downscaling at the onset of Alzheimer’s disease pathology in double knockin mice

    PubMed Central

    Chang, Eric H.; Savage, Mary J.; Flood, Dorothy G.; Thomas, Justin M.; Levy, Robert B.; Mahadomrongkul, Veeravan; Shirao, Tomoaki; Aoki, Chiye; Huerta, Patricio T.

    2006-01-01

    It is widely thought that Alzheimer’s disease (AD) begins as a malfunction of synapses, eventually leading to cognitive impairment and dementia. Homeostatic synaptic scaling is a mechanism that could be crucial at the onset of AD but has not been examined experimentally. In this process, the synaptic strength of a neuron is modified so that the overall excitability of the cell is maintained. Here, we investigate whether synaptic scaling mediated by l-?-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) contributes to pathology in double knockin (2×KI) mice carrying human mutations in the genes for amyloid precursor protein and presenilin-1. By using whole-cell recordings, we show that 2×KI mice exhibit age-related downscaling of AMPAR-mediated evoked currents and spontaneous, miniature currents. Electron microscopic analysis further corroborates the synaptic AMPAR decrease. Additionally, 2×KI mice show age-related deficits in bidirectional plasticity (long-term potentiation and long-term depression) and memory flexibility. These results suggest that AMPARs are important synaptic targets for AD and provide evidence that cognitive impairment may involve downscaling of postsynaptic AMPAR function. PMID:16492745

  18. Climate change projection in the Northwest Pacific marginal seas through dynamic downscaling

    NASA Astrophysics Data System (ADS)

    Seo, Gwang-Ho; Cho, Yang-Ki; Choi, Byoung-Ju; Kim, Kwang-Yul; Kim, Bong-guk; Tak, Yong-jin

    2014-06-01

    This study presents future climate change projections in the Northwest Pacific (NWP) marginal seas using dynamic downscaling from global climate models (GCMs). A regional climate model (RCM) for the Northwest Pacific Ocean was setup and integrated over the period from 2001 to 2100. The model used forcing fields from three different GCM simulations to downscale the effect of global climate change. MIROC, ECHAM, and HADCM were selected to provide climate change signals for the RCM. These signals were calculated from the GCMs using Cyclostationary Empirical Orthogonal Function analysis and added to the present lateral open boundary and the surface forcing. The RCM was validated by comparing hindcast result with the observation. It was able to project detailed regional climate change processes that GCMs were not able to resolve. A relatively large increases of water temperature were found in the marginal seas. However, only a marginal change was found along the Kuroshio path. Heat supply to the atmosphere decreases in most study areas due to a slower warming of the sea surface compared to the atmosphere. The RCM projection suggests that the temperature of the Yellow Sea Bottom Cold Water will gradually increase by 2100. Volume transports through major straits except the Taiwan Strait in the marginal seas are projected to increase slightly in future. Increased northeasterly wind stress in the East China Sea may also result in the transport change.

  19. Two Topics in Seasonal Streamflow Forecasting: Soil Moisture Initialization Error and Precipitation Downscaling

    NASA Technical Reports Server (NTRS)

    Koster, Randal; Walker, Greg; Mahanama, Sarith; Reichle, Rolf

    2012-01-01

    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 downscaling 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 downscaling 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.

  20. Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

    NASA Astrophysics Data System (ADS)

    Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

    2015-03-01

    A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

  1. Spatial and temporal characteristics of rainfall in Africa: Summary statistics for temporal downscaling

    NASA Astrophysics Data System (ADS)

    Kaptué, Armel T.; Hanan, Niall P.; Prihodko, Lara; Ramirez, Jorge A.

    2015-04-01

    An understanding of rainfall characteristics at multiple spatiotemporal scales is of great importance for hydrological, biogeochemical, and land surface modeling studies. In the present study, patterns of rainfall are analyzed over the African continent based on 3 hourly 0.25° Tropical Rainfall Measuring Mission (TRMM) estimates between 1998 and 2012 to produce monthly statistical summaries. The selected rain event properties are multiyear means of precipitation total amount (mm), event frequency (number), rate (mm/h), and duration (h) calculated independently for each calendar month. Analysis of 3 hourly and daily events in the 1998-2012 period suggests that rainfall amount can be summarized using gamma probability density functions. Assuming stationarity, gamma probability density functions of the total depth of 3 hourly and daily events are estimated and then used for temporal downscaling of monthly rainfall estimates (past or future). As a result, we generate 3 hourly and daily rainfall estimates that are pixelwise statistically indistinguishable from the observations while preserving monthly totals. Example scripts are provided that can be used to access monthly statistics and implement downscaling using archival (or projected) monthly rainfall estimates. These statistics could also be utilized for the assessment of rainfall from atmospheric models.

  2. Evaluating Bartlett-Lewis models for stochastic downscaling of regional climate model precipitation

    NASA Astrophysics Data System (ADS)

    Vernieuwe, Hilde; Verhoest, Niko; Onof, Christian; Willems, Patrick; De Baets, Bernard

    2015-04-01

    Regional climate models (RCMs) provide daily precipitation data. However, for many hydrological applications, this time scale is too coarse, as data at hourly or sub-hourly scale are required. Although several statistical downscaling techniques exist, we investigate whether Bartlett-Lewis rectangular pulses models could be used for generating time series of precipitation at 10-minute or hourly resolution based on precipitation statistics calculated from (RCM-modelled) daily precipitation. To assess this hypothesis, the 105-year 10-minute time series of precipitation observed at Uccle (Belgium), is used as test case. First, it is shown that the Bartlett-Lewis models maintain the temporal scaling behaviour of different moments (mean, variance, auto-covariance) and zero depth probabilities. Then, Bartlett-Lewis models are calibrated using statistics at aggregation levels of one, two and three days, in order to model precipitation time series at a 10-minute resolution. Statistics including moments and extreme values, calculated at subdaily levels (10 min., 1 hour, …) , are then compared to those of the original time series. It is found that the Bartlett-Lewis models permit to model precipitation time series at (sub-)hourly levels given daily statistics and therefore allow for a stochastic downscaling regional climate model precipitation predictions.

  3. Estimating Precipitation from Space: new directions in variational downscaling and data fusion with emphasis on extremes

    NASA Astrophysics Data System (ADS)

    Foufoula, E.; Ebtehaj, M.

    2013-05-01

    Downscaling, data fusion, and data assimilation of non-Gaussian fields are problems of fundamental importance in the atmospheric, hydrometeorologic, and oceanic sciences. The increasing availability of satellite data, e.g. precipitation from TRMM and the forthcoming GPM mission as well as soil moisture from SMAP, at multiple resolutions and accuracies has fueled renewed interest in these problems towards the development of estimation frameworks that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying fields. In this paper, we present a new and unifying formalism for statistical estimation (downscaling and data fusion) of multi-sensor, multi-scale precipitation measurements. The formalism is constructed to explicitly allow the preservation of some key geometrical and statistical properties of precipitation, such as extreme gradients (indicative of the presence of rainbands and multi-cellular spatial patterns) and non-Gaussian statistics. While we restrict our presentation and examples in the spatial domain, extension to time, and/or space-time can be obtained. The proposed framework draws upon: (1) recent observations that precipitation fields exhibit "sparsity" in a gradient or wavelet domain and a probability distribution well approximated by a Generalized Gaussian, and (2) new theoretical developments in the signal processing and optimization communities for non-linear, non-smooth data recovery from noisy, blurred and downsampled signals via regularized estimation.

  4. A Hybrid Ensemble Kalman Filter for Nonlinear Dynamics 

    E-print Network

    Watanabe, Shingo

    2011-02-22

    In this thesis, we propose two novel approaches for hybrid Ensemble Kalman Filter (EnKF) to overcome limitations of the traditional EnKF. The first approach is to swap the ensemble mean for the ensemble mode estimation to ...

  5. Towards stochastically downscaled precipitation in the Tropics based on a robust 1DD combined satellite product and a high resolution IR-based rain mask

    NASA Astrophysics Data System (ADS)

    Guilloteau, Clement; Roca, Rémy; Gosset, Marielle

    2015-04-01

    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 downscaling ? 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 ensemble consistent with the original TAPEER estimation. This binary mask generation is the first step for the generation of quantitative rain fields ensemble at GEO-IR resolution.

  6. Ensemble Machine Methods for DNA Binding

    Microsoft Academic Search

    Yue Fan; Mark A. Kon; Charles Delisi

    2008-01-01

    We introduce three ensemble machine learning methods for analysis of biological DNA binding by transcription fac- tors (TFs). The goal is to identify both TF target genes and their binding motifs. Subspace-valued weak learners (formed from an ensemble of different motif finding algo- rithms) combine candidate motifs as probability weight ma- trices (PWM), which are then translated into subspaces of

  7. Conductor gestures influence evaluations of ensemble performance

    PubMed Central

    Morrison, Steven J.; Price, Harry E.; Smedley, Eric M.; Meals, Cory D.

    2014-01-01

    Previous research has found that listener evaluations of ensemble 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 ensemble 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 ensemble 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 ensemble’s articulation, dynamics, technique, and tempo along with overall expressivity. Results showed significantly higher evaluations for performances featuring high rather than low conducting expressivity regardless of the ensemble’s performance quality. Evaluations for both articulation and dynamics were strongly and positively correlated with evaluations of overall ensemble expressivity. PMID:25104944

  8. Ensemble: cooperative proximity-based authentication

    Microsoft Academic Search

    Andre Kalamandeen; Adin Scannell; Eyal de Lara; Anmol Sheth; Anthony LaMarca

    2010-01-01

    Ensemble is a system that uses a collection of trusted personal devices to provide proximity-based authentication in pervasive environments. Users are able to securely pair their personal devices with previously unknown devices by simply placing them close to each other (e.g., users can pair their phones by just bringing them into proximity). Ensemble leverages a user's growing collection of trusted

  9. Spectral verification of a mesoscale ensemble

    Microsoft Academic Search

    Claire Vincent; Caroline Draxl; Gregor Giebel; Pierre Pinson; C. Möhrlen; J. Jørgensen

    2009-01-01

    In this work, an adaptive spectral method is used to verify members of the Multi-Scheme Ensemble Prediction System (MSEPS), setup for the Horns Reef offshore wind farm near the Danish North Sea coast. All 75 ensemble members are run in the same model grid with a resolution of 5km. The members differ in their numerical formulation, mainly in the fast

  10. Fine-Tuning Your Ensemble's Jazz Style.

    ERIC Educational Resources Information Center

    Garcia, Antonio J.

    1991-01-01

    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 ensemble style. Addresses jazz group problems such as beat, tempo, staying in tune, wind power, and solo/ensemble lines. Discusses percussionists, bassists,…

  11. Memory for Multiple Visual Ensembles in Infancy

    ERIC Educational Resources Information Center

    Zosh, Jennifer M.; Halberda, Justin; Feigenson, Lisa

    2011-01-01

    The number of individual items that can be maintained in working memory is limited. One solution to this problem is to store representations of ensembles that contain summary information about large numbers of items (e.g., the approximate number or cumulative area of a group of many items). Here we explored the developmental origins of ensemble

  12. Visual stimuli recruit intrinsically generated cortical ensembles

    PubMed Central

    Miller, Jae-eun Kang; Ayzenshtat, Inbal; Carrillo-Reid, Luis; Yuste, Rafael

    2014-01-01

    The cortical microcircuit is built with recurrent excitatory connections, and it has long been suggested that the purpose of this design is to enable intrinsically driven reverberating activity. To understand the dynamics of neocortical intrinsic activity better, we performed two-photon calcium imaging of populations of neurons from the primary visual cortex of awake mice during visual stimulation and spontaneous activity. In both conditions, cortical activity is dominated by coactive groups of neurons, forming ensembles whose activation cannot be explained by the independent firing properties of their contributing neurons, considered in isolation. Moreover, individual neurons flexibly join multiple ensembles, vastly expanding the encoding potential of the circuit. Intriguingly, the same coactive ensembles can repeat spontaneously and in response to visual stimuli, indicating that stimulus-evoked responses arise from activating these intrinsic building blocks. Although the spatial properties of stimulus-driven and spontaneous ensembles are similar, spontaneous ensembles are active at random intervals, whereas visually evoked ensembles are time-locked to stimuli. We conclude that neuronal ensembles, built by the coactivation of flexible groups of neurons, are emergent functional units of cortical activity and propose that visual stimuli recruit intrinsically generated ensembles to represent visual attributes. PMID:25201983

  13. DIVACE: Diverse and Accurate Ensemble Learning Algorithm

    Microsoft Academic Search

    Arjun Chandra; Xin Yao

    2004-01-01

    In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise the ensemble. There exists a tradeo as to what should be the optimal measures of diversity and accuracy. The aim of this paper is to address this issue. We propose

  14. Advancing Data Clustering via Projective Clustering Ensembles

    E-print Network

    Domeniconi, Carlotta

    Advancing Data Clustering via Projective Clustering Ensembles Francesco Gullo DEIS Dept. University of Calabria 87036 Rende (CS), Italy tagarelli@deis.unical.it ABSTRACT Projective Clustering Ensembles (PCE) are a very recent advance in data clustering research which combines the two powerful tools of clustering

  15. Selective SVM Ensembles Based on Modified BPSO

    Microsoft Academic Search

    Hong-da Zhang; Xiao-dan Wang; Chong-ming Wu; Bo Ji; Hai-long Xu

    2008-01-01

    Selective ensemble is effective for improve the classification performance through taking full advantage of the diversity and supplement between base classifiers. A BPSO (binary particle swarm optimization) based selective SVM ensemble approach is proposed to ensure the diversity and supplement among base classifiers in the training phase and high performance in the selection phase. Firstly, bootstrap method introduced by Bagging

  16. 794 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 6, NO. 6, JUNE 1997 Fast Downscaled Inverses for Images Compressed

    E-print Network

    de Queiroz, Ricardo L.

    794 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 6, NO. 6, JUNE 1997 Fast Downscaled Inverses. INTRODUCTION IMAGE processing has become very popular since images were brought into the desktop computer resolution image. Another reason is the fact that it may be a very slow process, as more pixels than

  17. Impact of downscaling and poly-gate depletion on the RF noise parameters of advanced nMOS transistors

    Microsoft Academic Search

    Sebastien Nuttinck; Andries J. Scholten; Luuk F. Tiemeijer; Florence Cubaynes; Charles Dachs; Celine Detcheverry; Erwin A. Hijzen

    2006-01-01

    For the first time, the effects of poly depletion on the RF noise performance of advanced CMOS transistors are reported and analyzed. Based on measurements and physical device simulations we quantify the increasing danger of poly gate depletion with downscaling on the RF noise parameters of CMOS devices. While poly depletion does not affect the minimum noise figure, it results

  18. A non-linear statistical downscaling model: El Nin~o//Southern Oscillation impact on precipitation over New Caledonia

    E-print Network

    is positively corre- lated with the Southern Oscillation Index (SOI) as demon- strated by Nicet and DelcroixA non-linear statistical downscaling model: El Nin~o//Southern Oscillation impact on precipitation for simulating New Caledonian rainfall anomalies induced by El Nin~o/Southern Oscillation (ENSO). The use

  19. Manju Hemakumara, Jetse Kalma, Jeffrey Walker, and Garry Willgoose (2004), Downscaling of low resolution passive microwave soil moisture

    E-print Network

    Walker, Jeff

    2004-01-01

    attributes. Second, the paper reports on downscaling of the low resolution AMSR-E near-surface soil moisture resolution passive microwave soil moisture observations, in Proceedings of the 2nd international CAHMDA soil moisture observations Manju Hemakumara1, Jetse Kalma1, Jeffrey Walker2, and Garry Willgoose3 1

  20. Characterization and SpaceTime Downscaling of the Inundation Extent over the Inner Niger Delta Using GIEMS and MODIS Data

    E-print Network

    Characterization and Space­Time Downscaling of the Inundation Extent over the Inner Niger Delta the Moderate Resolution Imaging Spectroradiometer (MODIS). The study concentrates on the Inner Niger Delta this analysis for the Inner Niger Delta. The methods are very general and may be applied to many basins

  1. A Portable Regional Weather and Climate Downscaling System Using GEOS5, LIS6, WRF, and the NASA Workflow Tool

    Microsoft Academic Search

    E. M. Kemp; W. M. Putman; J. Gurganus; R. W. Burns; M. R. Damon; G. R. McConaughy; M. S. Seablom; G. S. Wojcik

    2009-01-01

    We present a regional downscaling system (RDS) suitable for high-resolution weather and climate simulations in multiple supercomputing environments. The RDS is built on the NASA Workflow Tool, a software framework for configuring, running, and managing computer models on multiple platforms with a graphical user interface. The Workflow Tool is used to run the NASA Goddard Earth Observing System Model Version

  2. The Greenland ice sheet: modelling the surface mass balance from GCM output with a new statistical downscaling technique

    NASA Astrophysics Data System (ADS)

    Geyer, M.; Salas Y Melia, D.; Brun, E.; Dumont, M.

    2013-06-01

    The aim of this study is to derive a realistic estimation of the Surface Mass Balance (SMB) of the Greenland ice sheet (GrIS) through statistical downscaling of Global Coupled Model (GCM) outputs. To this end, climate simulations performed with the CNRM-CM5.1 Atmosphere-Ocean GCM within the CMIP5 (Coupled Model Intercomparison Project phase 5) framework are used for the period 1850-2300. From the year 2006, two different emission scenarios are considered (RCP4.5 and RCP8.5). Simulations of SMB performed with the detailed snowpack model Crocus driven by CNRM-CM5.1 surface atmospheric forcings serve as a reference. On the basis of these simulations, statistical relationships between total precipitation, snow-ratio, snowmelt, sublimation and near-surface air temperature are established. This leads to the formulation of SMB variation as a function of temperature variation. Based on this function, a downscaling technique is proposed in order to refine 150 km horizontal resolution SMB output from CNRM-CM5.1 to a 15 km resolution grid. This leads to a much better estimation of SMB along the GrIS margins, where steep topography gradients are not correctly represented at low-resolution. For the recent past (1989-2008), the integrated SMB over the GrIS is respectively 309 and 243 Gt yr-1 for raw and downscaled CNRM-CM5.1. In comparison, the Crocus snowpack model forced with ERA-Interim yields a value of 245 Gt yr-1. The major part of the remaining discrepancy between Crocus and downscaled CNRM-CM5.1 SMB is due to the different snow albedo representation. The difference between the raw and the downscaled SMB tends to increase with near-surface air temperature via an increase in snowmelt.

  3. Impact of the lateral boundary conditions resolution on dynamical downscaling of precipitation in mediterranean spain

    NASA Astrophysics Data System (ADS)

    Kim, Do Wan; Liu, Wing Kam; Yoon, Young-Cheol; Belytschko, Ted; Lee, Sang-Ho

    2007-10-01

    Conclusions on the General Circulation Models (GCMs) horizontal and temporal optimum resolution for dynamical downscaling of rainfall in Mediterranean Spain are derived based on the statistical analysis of mesoscale simulations of past events. These events correspond to the 165 heavy rainfall days during 1984 1993, which are simulated with the HIRLAM mesoscale model. The model is nested within the European Centre for Medium-Range Weather Forecasts atmospheric grid analyses. We represent the spectrum of GCMs resolutions currently applied in climate change research by using varying horizontal and temporal resolutions of these analyses. Three sets of simulations are designed using input data with 1°, 2° and 3° horizontal resolutions (available at 6 h intervals), and three additional sets are designed using 1° horizontal resolution with less frequent boundary conditions updated every 12, 24 and 48 h. The quality of the daily rainfall forecasts is verified against rain-gauge observations using correlation and root mean square error analysis as well as Relative Operating Characteristic curves. Spatial distribution of average precipitation fields are also computed and verified against observations. For the whole Mediterranean Spain, model skill is not appreciably improved when using enhanced spatial input data, suggesting that there is no clear benefit in using high resolution data from General Circulation Model for the regional downscaling of precipitation under the conditions tested. However, significant differences are found in verification scores when boundary conditions are interpolated less frequently than 12 h apart. The analysis is particularized for six major rain bearing flow regimes that affect the region, and differences in model performance are found among the flow types, with slightly better forecasts for Atlantic and cold front passage flows. A remarkable spatial variability in forecast quality is found in the domain, with an overall tendency for higher Relative Operating Characteristic scores in the west and north of the region and over highlands, where the two previous flow regimes are quite influential. The findings of this study could be of help for dynamical downscaling design applied to future precipitation scenarios in the region, as well as to better establish confidence intervals on its results.

  4. Hybrid Data Assimilation without Ensemble Filtering

    NASA Technical Reports Server (NTRS)

    Todling, Ricardo; Akkraoui, Amal El

    2014-01-01

    The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the ensemble is generated using a square-root ensemble 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 ensemble solution close to the variational solution; we also found it necessary to re-center the members of the ensemble 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 ensemble. This led us to consider a hybrid strategy in which the members of the ensemble are generated by simply converting the variational analysis to the resolution of the ensemble 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.

  5. Diffusion for an ensemble of Hamiltonians

    E-print Network

    Or Alus; Shmuel Fishman

    2014-09-17

    Two ensembles of standard maps are studied analytically and numerically. In particular the diffusion coefficient is calculated. For one type of ensemble the chaotic parameter is chosen at random from a Gaussian distribution and is then kept fixed, while for the other type it varies from step to step. The effect of averaging out the details is evaluated and in particular it is found to be much more effective in the process of the second type. The work may shed light on the possible properties of different ensembles of mixed systems.

  6. Downscaling near-surface atmospheric fields with multi-objective Genetic Programming

    E-print Network

    Zerenner, Tanja; Friederichs, Petra; Simmer, Clemens

    2014-01-01

    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 often more expensive atmospheric models, for instance, are typically employed at a coarser resolution than land-surface and subsurface models. Thus up- and downscaling 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 equations or short programs that reconstruct the fine-scale fields (e.g., 400 m resolution) of the near-surface atmospheric state variables from the coarse atmospheric model output (e.g., 2.8 km resolution). Like artificial neural networks, GP can flexibly incorp...

  7. Development of a Dynamic Downscaling strategy for Ganga Basin and Investigation of the Hydrological Pattern

    NASA Astrophysics Data System (ADS)

    Chaudhuri, C.; Srivastava, R.; Tripathi, S. N.

    2012-04-01

    The interaction between climate and hydrology is highly complex and non-linear. In India, the synoptic scale atmospheric flow, diversity of local topography, vegetation, climatic conditions, and high population density, etc., interact with one another to give a unique weather distribution. The interaction between the large scale climate and local scale hydrologic cycle is very important in regional scale hydrological modelling. The Weather Research and Forecasting (WRF) model is a numerical weather prediction and atmospheric simulation system designed to resolve this interaction at regional scale. WRF has been used earlier to investigate the downscaling methodology over the United States (Lo et al., 2008). We study the impact of climatic condition on Ganga basin hydrologic cycle using WRF. A single domain with a resolution of 25 km was used to cover the whole of India and the region of interest and validation is the entire Ganga basin. We performed the downscaling for the year 2010 with five configurations: (1) one continuous time integration with single initialization, (2) time integration with monthly reinitialization, (3) single initialization but with 3-D nudging without relaxation of PBL (4) same as 3 but with relaxation of PBL and (5) same as 4 but with spectral nudging relaxation. The results are compared against the synoptic observations taken over the Ganga basin. The 5th method has the best skill, followed by 4th, 3rd , 2nd and 1st . The results show that the nudging generates realistic regional climatic pattern which cannot be achieved simply by updating the boundary conditions. To find out the Hydrological interaction, trend and pattern over the Ganga Basin, the Hydrological fields of the best model (Spectral Nudging) are analysed. The rainfall patterns are compared with TRMM 3B42 daily data. The precipitation, surface temperature, and the regional wind pattern is reasonably simulated. The study reveals the power of WRF in resolving the climatic and hydrological interactions and also shows that the WRF can be used in making an accurate forecast. The rainfall distribution shows some degree of correlation with the TRMM at the middle Indo-Gangetic plane, along the foothills of Himalaya, and over some portion of Tibetian Plateau. The seasonality index of Hydrologic variables like Rainfall, Surface runoff and Soil moisture show a level of seasonal pattern over the Indo-Gangetic plane but the degree of seasonality pattern is weak at the foothills of Himalaya. The hydrological fields like surface run off, base flow, soil moisture distribution and soil temperature show the expected regional variations and seasonal patterns. The dynamical downscaling outperforms the interpolation of climatic variables over space and time. This implies the suitability of WRF to study the hydrological cycle over a data sparse region and, probably, to study the effect of potential climate change on it. Reference: Jeff Chun-Fung Lo, Zong-Liang Yang, and Roger A. Pielke Sr., 2008, Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model, Journal of Geophysical Research, Vol 113, D09112

  8. Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation

    E-print Network

    Ebtehaj, Ardeshir Mohammad

    2012-01-01

    Improved estimation of hydrometeorological states from down-sampled observations and background model forecasts in a noisy environment, has been a subject of growing research in the past decades. Here, we introduce a unified framework that ties together the problems of downscaling, data fusion and data assimilation as ill-posed inverse problems. This framework seeks solutions beyond the classic least squares estimation paradigms by imposing proper regularization, which are constraints consistent with the degree of smoothness and probabilistic structure of the underlying state. We review relevant regularization methods in derivative space and extend classic formulations of the aforementioned problems with particular emphasis on hydrologic and atmospheric applications. Informed by the statistical characteristics of the state variable of interest, the central results of the paper suggest that proper regularization can lead to a more accurate and stable recovery of the true state and hence more skillful forecasts...

  9. Statistical downscaling and attribution of air temperature change patterns in the Valencia region (1948-2011)

    NASA Astrophysics Data System (ADS)

    Miró Pérez, Juan Javier; Estrela Navarro, María José; Olcina Cantos, Jorge

    2015-04-01

    This study is based on the statistical downscaling and spatial interpolation of high-resolution temperatures (90 m) over the 1948-2011 period performed for the Valencia Region (east Iberian Peninsula) after considering local topographical factors in the fine-scale distribution of temperatures. The objective was to detect the areas that were potentially more vulnerable to air temperature change. This allowed the detection of local climate change patterns, which were analyzed and found to be consistent in spatial and temporal terms. These patterns indicate a more marked warming tendency in higher parts of reliefs and their slopes. However, this tendency is less pronounced in bottoms of valleys and on coastal plains, particularly for minimum temperatures, while the tendency for increasing maximum temperatures becomes more generalised. These patterns seem to connect well with regional changes in pressure fields, wind frequency, precipitation patterns and sea surface temperature.

  10. Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling

    PubMed Central

    Stoy, Paul C.; Quaife, Tristan

    2015-01-01

    Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling 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

  11. A Dynamical Downscaling study over the Great Lakes Region Using WRF-Lake: Historical Simulation

    NASA Astrophysics Data System (ADS)

    Xiao, C.; Lofgren, B. M.

    2014-12-01

    As the largest group of fresh water bodies on Earth, the Laurentian Great Lakes have significant influence on local and regional weather and climate through their unique physical features compared with the surrounding land. Due to the limited spatial resolution and computational efficiency of general circulation models (GCMs), the Great Lakes are geometrically ignored or idealized into several grid cells in GCMs. Thus, the nested regional climate modeling (RCM) technique, known as dynamical downscaling, serves as a feasible solution to fill the gap. The latest Weather Research and Forecasting model (WRF) is employed to dynamically downscale the historical simulation produced by the Geophysical Fluid Dynamics Laboratory-Coupled Model (GFDL-CM3) from 1970-2005. An updated lake scheme originated from the Community Land Model is implemented in the latest WRF version 3.6. It is a one-dimensional mass and energy balance scheme with 20-25 model layers, including up to 5 snow layers on the lake ice, 10 water layers, and 10 soil layers on the lake bottom. The lake scheme is used with actual lake points and lake depth. The preliminary results show that WRF-Lake model, with a fine horizontal resolution and realistic lake representation, provides significantly improved hydroclimates, in terms of lake surface temperature, annual cycle of precipitation, ice content, and lake-effect snowfall. Those improvements suggest that better resolution of the lakes and the mesoscale process of lake-atmosphere interaction are crucial to understanding the climate and climate change in the Great Lakes region.

  12. Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling

    PubMed Central

    Chang, Howard H.; Hu, Xuefei; Liu, Yang

    2014-01-01

    There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 ?m in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial–temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003–2005. Via cross-validation experiments, our model had an out-of-sample prediction R2 of 0.78 and a root mean-squared error (RMSE) of 3.61 ?g/m3 between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial–temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined. PMID:24368510

  13. Validation of the Regional Climate Model ALARO with different dynamical downscaling approaches and different horizontal resolutions

    NASA Astrophysics Data System (ADS)

    Berckmans, Julie; Hamdi, Rafiq; De Troch, Rozemien; Giot, Olivier

    2015-04-01

    At the Royal Meteorological Institute of Belgium (RMI), climate simulations are performed with the regional climate model (RCM) ALARO, a version of the ALADIN model with improved physical parameterizations. In order to obtain high-resolution information of the regional climate, lateral bounary conditions (LBC) are prescribed from the global climate model (GCM) ARPEGE. Dynamical downscaling is commonly done in a continuous long-term simulation, with the initialisation of the model at the start and driven by the regularly updated LBCs of the GCM. Recently, more interest exists in the dynamical downscaling approach of frequent reinitializations of the climate simulations. For these experiments, the model is initialised daily and driven for 24 hours by the GCM. However, the surface is either initialised daily together with the atmosphere or free to evolve continuously. The surface scheme implemented in ALARO is SURFEX, which can be either run in coupled mode or in stand-alone mode. The regional climate is simulated on different domains, on a 20km horizontal resolution over Western-Europe and a 4km horizontal resolution over Belgium. Besides, SURFEX allows to perform a stand-alone or offline simulation on 1km horizontal resolution over Belgium. This research is in the framework of the project MASC: "Modelling and Assessing Surface Change Impacts on Belgian and Western European Climate", a 4-year project funded by the Belgian Federal Government. The overall aim of the project is to study the feedbacks between climate changes and land surface changes in order to improve regional climate model projections at the decennial scale over Belgium and Western Europe and thus to provide better climate projections and climate change evaluation tools to policy makers, stakeholders and the scientific community.

  14. A downscaling framework for brightness temperature and near surface soil moisture images derived from ESTAR

    NASA Astrophysics Data System (ADS)

    Parada, L. M.; Liang, X.

    2002-12-01

    Brightness temperature images derived from electronically scanned thin array radiometer (ESTAR) may be used for validation of or assimilation into radiation transfer models, and for derivation of near-surface soil moisture images. Near-surface soil moisture may in turn be assimilated into land-surface models to improve their predictive capabilities. Thus, the availability of such images is crucial for a better understanding and characterization of atmosphere-surface dynamics and for improving weather forecasts. It is expected that brightness temperature images taken from space may eventually be available at a resolution of 10-km by 10-km. Various researchers have reported that the derived near surface soil moisture images posses scaling properties over scales ranging from 200-m to 90-km. These findings suggest that it may be possible to statistically characterize the effects of sub-grid variability of soil moisture. This study presents a new downscaling framework for brightness temperature and soil moisture images derived from ESTAR. The simple mathematical model used for this purpose is clearly defined. Validation is performed with the brightness temperature images taken during the Southern Great Plains Hydrology Experiment of 1997 (SGP97). The results obtained show that the proposed downscaling scheme is capable of accurately capturing the first and second order statistics of the observed brightness temperature images. The work presented here constitutes a first attempt to understand the spatial structure of brightness temperature and soil moisture images when viewed at different resolutions so that we may eventually be able to evaluate the effects of sub-grid variations of these variables in our land-surface representations and weather forecasts.

  15. Downscaling and assimilating remotely sensed soil moisture data for the TOPLATS model

    NASA Astrophysics Data System (ADS)

    Pan, F.; Peters-Lidard, C.

    2001-05-01

    The sub-grid-scale or sub-field-scale (i.e., <100 meter) soil moisture information can be important for numerical simulation of grid-scale energy and water fluxes and proper representation of runoff generation processes. Recent research suggests that a promising approach to estimate sub-grid scale soil moisture is assimilation of remotely sensed data into fine resolution hydrologic models. Airborne L-band passive microwave sensors flown during recent field campaigns, e.g., Washita'92, Southern Great Plains Hydrology Experiment (SGP)'97, and SGP'99, provide soil moisture products at spatial resolutions of order 100s of meters, which is coarser than the typical fine resolution of hydrologic models such as TOPLATS (e.g., 30m). Future satellite-based soil moisture products will have spatial resolutions of order 10 km; therefore, how to downscale or interpolate coarser resolution data to a finer grid for hydrologic models will become very important. In this talk, we will present three different approaches to deal with this problem: (1) Match the hydrologic model resolution to that of the remotely sensed soil moisture products; (2) Spatially interpolate the remotely sensed soil moisture products to the finer grid of the models; and (3) Match the mean of the modeled soil moisture to the mean of the remotely sensed soil moisture product, preserving the high moments of the modeled soil moisture, without spatial interpolation. For the second method, besides the spatial correlation of soil moisture, we incorporate the correlations between soil moisture and other variables (e.g., precipitation, radiation, land cover, soil, and topography) into our spatial interpolation of remotely sensed soil moisture. Previous studies of soil moisture scaling characteristics by the authors and others are incorporated into the downscaling methodologies. The performance of each approach is evaluated over Washita '92 and SGP97 regions.

  16. Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling.

    PubMed

    Stoy, Paul C; Quaife, Tristan

    2015-01-01

    Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling 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

  17. Ensuring Strong Dominance of the Leading Eigenvalues for Cluster Ensembles

    E-print Network

    Kung, H. T.

    ensembles with regular structures, such as alpha-beta cluster ensembles defined in Section III, we can. ALPHA-BETA CLUSTER ENSEMBLE We focus on a special class of cluster ensembles, called alpha-beta cluster matrix to a diagonally dominant one as described in . The alpha-beta clusters represent a natural model

  18. Statistical Mechanics without Ensembles Akira Shimizu

    E-print Network

    Shimizu, Akira

    Statistical Mechanics without Ensembles Akira Shimizu Department of Basic Science, University. Introduction: Principles of statistical mechanics revisited. 2. Thermal Pure Quantum states (TPQs) 3. Formulation of statistical mechanics with TPQs (a) Construction of a new class of TPQs (b) Genuine

  19. On Ensemble Techniques for AIXI Approximation

    E-print Network

    Hutter, Marcus

    On Ensemble Techniques for AIXI Approximation Joel Veness1 , Peter Sunehag2 , and Marcus Hutter2 1 compression performance across many well-known benchmarks. Within reinforcement learning (Sutton and Barto

  20. Experimental observation of a generalized Gibbs ensemble.

    PubMed

    Langen, Tim; Erne, Sebastian; Geiger, Remi; Rauer, Bernhard; Schweigler, Thomas; Kuhnert, Maximilian; Rohringer, Wolfgang; Mazets, Igor E; Gasenzer, Thomas; Schmiedmayer, Jörg

    2015-04-10

    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 ensembles fail to describe the large class of systems that exhibit nontrivial conserved quantities, and generalized ensembles 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 ensemble. This is verified through a detailed study of correlation functions up to 10th order. The applicability of the generalized ensemble description for isolated quantum many-body systems points to a natural emergence of classical statistical properties from the microscopic unitary quantum evolution. PMID:25859041

  1. Dynamical stability of entanglement between spin ensembles

    E-print Network

    H. T. Ng; S. Bose

    2009-03-03

    We study the dynamical stability of the entanglement between the two spin ensembles in the presence of an environment. For a comparative study, we consider the two cases: a single spin ensemble, and two ensembles linearly coupled to a bath, respectively. In both circumstances, we assume the validity of the Markovian approximation for the bath. We examine the robustness of the state by means of the growth of the linear entropy which gives a measure of the purity of the system. We find out macroscopic entangled states of two spin ensembles can stably exist in a common bath. This result may be very useful to generate and detect macroscopic entanglement in a common noisy environment and even a stable macroscopic memory.

  2. Hybrid Ensembles for Improved Force Matching

    E-print Network

    Wang, Lee-Ping

    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 ensemble ...

  3. Probing the denatured state ensemble with fluorescence 

    E-print Network

    Alston, Roy Willis

    2004-09-30

    To understand protein stability and the mechanism of protein folding, it is essential that we gain a better understanding of the ensemble of conformations that make up the denatured state of a protein. The primary goal of the research described...

  4. Statistical mechanics in the extended Gaussian ensemble

    NASA Astrophysics Data System (ADS)

    Johal, Ramandeep S.; Planes, Antoni; Vives, Eduard

    2003-11-01

    The extended Gaussian ensemble (EGE) is introduced as a generalization of the canonical ensemble. This ensemble is a further extension of the Gaussian ensemble 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.

  5. Regional climate downscaling with prior statistical correction of the global climate1 A. Colette (1), R. Vautard (2), M. Vrac (2)3

    E-print Network

    Paris-Sud XI, Université de

    . Colette (1), R. Vautard (2), M. Vrac (2)3 1. Institut National de l'Environnement Industriel et des Corresponding author address : augustin.colette@ineris.fr8 Abstract9 A novel climate downscaling methodology

  6. Quantum networking with atomic ensembles

    NASA Astrophysics Data System (ADS)

    Matsukevich, Dzmitry

    Quantum communication networks enable secure transmission of information between remote sites. However, at present, photon losses in the optical fiber limit communication distances to less than 150 kilometers. The quantum repeater idea allows extension of these distances. In practice, it involves the ability to store quantum information for a long time in atomic systems and coherently transfer quantum states between matter and light. Previously known schemes involved atomic Raman transitions in the UV or near-infrared and suffered from severe loss in optical fiber that precluded long-distance quantum communication. In this thesis a practical quantum telecommunication scheme based on cascade atomic transitions is proposed, with particular reference to cold alkali metal ensembles. Within this proposal, essential building blocks for a quantum network architecture are demonstrated experimentally, including storage and retrieval of single photons transmitted between remote quantum memories, collapses and revivals of quantum memories, deterministic generation of single photons via conditional quantum evolution, quantum state transfer between atomic and photonic qubits, entanglement of atomic and photonic qubits, entanglement of remote atomic qubits, and entanglement of a pair of 1530 nm and 780 nm photons. These results pave the way for construction of a realistic quantum repeater for long distance quantum communication.

  7. Quantum Networking with Atomic Ensembles

    NASA Astrophysics Data System (ADS)

    Matsukevich, Dzmitry

    2007-06-01

    Quantum communication networks enable secure transmission of information between remote sites. However, at present, photon losses in the optical fiber limit communication distances to less than 150 kilometers. The quantum repeater idea allows extension of these distances. In practice, it involves the ability to store quantum information for a long time in atomic systems and coherently transfer quantum states between matter and light. Previously known schemes involved atomic Raman transitions in the UV or near-infrared and suffered from severe loss in optical fiber that precluded long-distance quantum communication. In this work a practical quantum telecommunication scheme based on cascade atomic transitions is proposed, with particular reference to cold alkali metal ensembles. Within this proposal, essential building blocks for a quantum network architecture are demonstrated experimentally, including storage and retrieval of single photons transmitted between remote quantum memories, collapses and revivals of quantum memories, deterministic generation of single photons via conditional quantum evolution, quantum state transfer between atomic and photonic qubits, entanglement of atomic and photonic qubits, entanglement of remote atomic qubits, and entanglement of a pair of 1530 nm and 780 nm photons. These results pave the way for construction of a realistic quantum repeater for long distance quantum communication.

  8. Atomic clock ensemble in space

    NASA Astrophysics Data System (ADS)

    Cacciapuoti, L.; Salomon, C.

    2011-12-01

    Atomic Clock Ensemble in Space (ACES) is a mission using high-performance clocks and links to test fundamental laws of physics in space. Operated in the microgravity environment of the International Space Station, the ACES clocks, PHARAO and SHM, will generate a frequency reference reaching instability and inaccuracy at the 1 · 10-16 level. A link in the microwave domain (MWL) and an optical link (ELT) will make the ACES clock signal available to ground laboratories equipped with atomic clocks. Space-to-ground and ground-to-ground comparisons of atomic frequency standards will be used to test Einstein's theory of general relativity including a precision measurement of the gravitational red-shift, a search for time variations of fundamental constants, and Lorentz Invariance tests. Applications in geodesy, optical time transfer, and ranging will also be supported. ACES has now reached an advanced technology maturity, with engineering models completed and successfully tested and flight hardware under development. This paper presents the ACES mission concept and the status of its main instruments.

  9. Multiobjective information theoretic ensemble selection

    NASA Astrophysics Data System (ADS)

    Card, Stuart W.; Mohan, Chilukuri K.

    2009-05-01

    In evolutionary learning, the sine qua non is evolvability, which requires heritability of fitness and a balance between exploitation and exploration. Unfortunately, commonly used fitness measures, such as root mean squared error (RMSE), often fail to reward individuals whose presence in the population is needed to explain important data variance; and indicators of diversity generally are not only incommensurate with those of fitness but also essentially arbitrary. Thus, due to poor scaling, deception, etc., apparently relatively high fitness individuals in early generations may not contain the building blocks needed to evolve optimal solutions in later generations. To reward individuals for their potential incremental contributions to the solution of the overall problem, heritable information theoretic functionals are developed that incorporate diversity considerations into fitness, explicitly identifying building blocks suitable for recombination (e.g. for non-random mating). Algorithms for estimating these functionals from either discrete or continuous data are illustrated by application to input selection in a high dimensional industrial process control data set. Multiobjective information theoretic ensemble selection is shown to avoid some known feature selection pitfalls.

  10. Examine the potential of spatial downscaling of TRMM precipitation with environmental variables: An evaluation for the Ohio River Basin

    NASA Astrophysics Data System (ADS)

    Yoon, Y.; Beighley, E., II

    2014-12-01

    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 downscaling with environmental variables derived from finer scale remote sensing data? The goal is to downscale 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 downscaling 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 downscaling methods is a significant challenge. Here, we use a data assimilation technique to interpolate the residual component and generate the downscaled precipitation. Results are presented for the Ohio River Basin. The final downscaled TRMM is evaluated by comparing with the National Center for Environmental Predication (NCEP) Stage VI precipitation and rainfall gauge data.

  11. Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations

    NASA Astrophysics Data System (ADS)

    Vaittinada Ayar, Pradeebane; Vrac, Mathieu; Bastin, Sophie; Carreau, Julie; Déqué, Michel; Gallardo, Clemente

    2015-05-01

    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 downscaling. Downscaling 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 downscaling 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 Downscaling Models (SDMs) and five Regional Climate Models (RCMs) are compared in terms of precipitation outputs. The downscaled 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 downscaling model to be used according to the statistical properties of the local-scale climate data to drive properly specific impact models.

  12. Exploration trees and conformal loop ensembles

    E-print Network

    Scott Sheffield

    2006-11-23

    We construct and study the conformal loop ensembles CLE(kappa), defined for all kappa between 8/3 and 8, using branching variants of SLE(kappa) called exploration trees. The conformal loop ensembles are random collections of countably many loops in a planar domain that are characterized by certain conformal invariance and Markov properties. We conjecture that they are the scaling limits of various random loop models from statistical physics, including the O(n) loop models.

  13. Temperature fluctuations in the canonical ensemble

    NASA Technical Reports Server (NTRS)

    Chui, T. C. P.; Swanson, D. R.; Adriaans, M. J.; Nissen, J. A.; Lipa, J. A.

    1992-01-01

    We report the first quantitative measurements of spontaneous temperature fluctuations in a physical system well modeled by a canonical ensemble. Using superconducting magnetometers and a carefully controlled thermal environment, we have measured the noise spectra of paramagnetic salt thermometers that were coupled to thermal reservoirs at 2 K. The noise spectra were found to be in very good agreement with the predictions of the fluctuation-dissipation theorem. Our observations are at variance with some interpretations of fluctuations in the canonical ensemble.

  14. Online Ensemble Learning: An Empirical Study

    Microsoft Academic Search

    Alan Fern; Robert Givan

    2003-01-01

    We study resource-limited online learning, motivated by the problem of conditional-branch outcome prediction in computer architecture. In particular, we consider (parallel) time and space-efficient ensemble learners for online settings, empirically demonstrating benefits similar to those shown previously for offline ensembles. Our learning algorithms are inspired by the previously published “boosting by filtering” framework as well as the offline Arc-x4 boosting-style

  15. Memory for multiple visual ensembles in infancy

    PubMed Central

    Zosh, Jennifer M.; Halberda, Justin; Feigenson, Lisa

    2011-01-01

    The number of individual items that can be maintained in working memory is limited. One solution to this problem is to store representations of ensembles that contain summary information about large numbers of items (e.g., the approximate number or cumulative area of a group of many items). Here we explored the developmental origins of ensemble representations by asking whether infants represent ensembles, and if so, how many at one time. We habituated 9-month old infants to arrays containing 2, 3, or 4 spatially intermixed colored subsets of dots, then asked whether they detected a numerical change to one of the subsets or to the superset of all dots. Experiment Series 1 showed that infants detected a numerical change to one of the subsets when the array contained 2 subsets, but not 3 or 4 subsets. Experiment Series 2 showed that infants detected a change to the superset of all dots no matter how many subsets were presented. Experiment 3 showed that infants represented both the approximate number and the cumulative surface area of these ensembles. Our results suggest that infants, like adults (Halberda, Sires & Feigenson, 2006), can store quantitative information about 2 subsets plus the superset: a total of 3 ensembles. This converges with the known limit on the number of individual objects infants and adults can store, and suggests that, throughout development, an ensemble functions much like an individual object for working memory. PMID:21355663

  16. The Ensemble Verification System (EVS): A software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations

    Microsoft Academic Search

    James D. Brown; Julie Demargne; Dong-Jun Seo; Yuqiong Liu

    2010-01-01

    Ensemble forecasting is widely used in meteorology and, increasingly, in hydrology to quantify and propagate uncertainty. In practice, ensemble forecasts cannot account for every source of uncertainty, and many uncertainties are difficult to quantify accurately. Thus, ensemble forecasts are subject to errors, which may be correlated in space and time and may be systematic. Ensemble verification is necessary to quantify

  17. The future climate characteristics of the Carpathian Basin based on a regional climate model mini-ensemble

    NASA Astrophysics Data System (ADS)

    Krüzselyi, I.; Bartholy, J.; Horányi, A.; Pieczka, I.; Pongrácz, R.; Szabó, P.; Szépszó, G.; Torma, Cs.

    2011-04-01

    Four regional climate models (RCMs) were adapted in Hungary for the dynamical downscaling of the global climate projections over the Carpathian Basin: (i) the ALADIN-Climate model developed by Météo France on the basis of the ALADIN short-range modelling system; (ii) the PRECIS model available from the UK Met Office Hadley Centre; (iii) the RegCM model originally developed at the US National Center for Atmospheric Research, is maintained at the International Centre for Theoretical Physics in Trieste; and (iv) the REMO model developed by the Max Planck Institute for Meteorology in Hamburg. The RCMs are different in terms of dynamical model formulation, physical parameterisations; moreover, in the completed simulations they use different spatial resolutions, integration domains and lateral boundary conditions for the scenario experiments. Therefore, the results of the four RCMs can be considered as a small ensemble providing information about various kinds of uncertainties in the future projections over the target area, i.e., Hungary. After the validation of the temperature and precipitation patterns against measurements, mean changes and some extreme characteristics of these patterns (including their statistical significance) have been assessed focusing on the periods of 2021-2050 and 2071-2100 relative to the 1961-1990 model reference period. The ensemble evaluation indicates that the temperature-related changes of the different RCMs are in good agreement over the Carpathian Basin and these tendencies manifest in the general warming conditions. The precipitation changes cannot be identified so clearly: seasonally large differences can be recognised among the projections and between the two periods. An overview is given about the results of the mini-ensemble and special emphasis is put on estimating the uncertainties in the simulations for Hungary.

  18. Future changes in European temperature and precipitation in an ensemble of Europe-CORDEX regional climate model simulations

    NASA Astrophysics Data System (ADS)

    Kjellström, Erik; Nikulin, Grigory; Jones, Colin

    2013-04-01

    In this study we investigate possible changes in temperature and precipitation on a regional scale over Europe from 1961 to 2100. We use data from two ensembles of climate simulations, one global and one regional, over the Europe-CORDEX domain. The global ensemble includes nine coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CNRM-CM5, HadGEM2-ES, IPSL-CM5A-MR, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. In the regional ensemble all 9 AOGCMs are downscaled at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Two forcing scenarios are considered, RCP 4.5 and 8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, we investigate the benefit of the higher horizontal resolution, in RCA4 by comparing the results to the coarser driving AOGCM data. The significance of the results is investigated by comparing to i) the model simulated natural variability, and, ii) the biases in the control period. Results dealing with changes in the seasonal cycle of temperature and precipitation and their relation to changes in the large-scale atmospheric circulation are presented. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.

  19. Downscaling Satellite-based Passive Microwave Observations Using the Principle of Relevant Information and Auxiliary High Resolution Remote Sensing Products

    NASA Astrophysics Data System (ADS)

    Nagarajan, K.; Judge, J.; Principe, J.

    2011-12-01

    Hydrometeorological models simulate the atmospheric and hydrological processes at scales of 1- 10 km that are significantly influenced by the local and regional availability of soil moisture. Microwave observations at frequencies < 10 GHz are highly sensitive to changes in near-surface moisture and have been widely used to retrieve soil moisture information. While satellite-based active microwave observations are available at spatial resolutions of hundreds of meters, with temporal resolutions of several weeks, passive observations are obtained only at tens of kilometers with temporal resolutions of sub daily to 2-3 days. The European Space Agency-Soil Moisture and Ocean Salinity (ESA-SMOS) and the near-future NASA-Soil Moisture Active Passive (SMAP) missions will provide unprecedented passive microwave observations of brightness temperatures (TB) at the L-band frequency of 1.4 GHz. These products will be available at spatial resolutions of about 40-50 km and need to be downscaled to 1 km to merge them with models for data assimilation and to study the effects of land surface heterogeneity such as dynamic vegetation conditions. Very few studies have directly downscaled coarse-resolution TB observations to match model scales. Since downscaling is an ill-posed problem, additional information is required at the fine scales and some studies have leveraged auxiliary high-resolution remote sensing (RS) products in downscaling TB. Most of the above studies involve a) physical models that are computationally intensive when extended to global scales, or b) multi-scale algorithms that impose hierarchical models on TB assuming spatial homogeneity, or c) statistical algorithms that are based on second-order statistics such as variances and correlations. These approaches are therefore sub-optimal when applied to the real data or extended to regional/global scales. Optimal downscaling requires computationally-efficient algorithms that retain information from higher-order moments, especially under heterogeneous land surface conditions. Novel transformation functions leveraging physical relationships and recent advances in signal processing techniques can be used to transform information from high-resolution RS products into TB. In this study, a downscaling methodology was developed using the Principle of Relevant Information (PRI) to downscale observations of TB from 50 km to 200 m using observations of land surface temperature, leaf area index, and land cover at 200 m. The PRI provides a hierarchical decomposition of image data that is optimal in terms of the transfer of information across scales and is therefore a better alternative to methods that use second-order statistics only. Non-parametric probability density functions and Bayes' rule was used to transform information from the RS products into TB. An Observing System Simulation Experiment was developed under heterogeneous and dynamic vegetation conditions to generate synthetic observations at 200m to evaluate the downscaling methodology and the transformation functions.

  20. Down-scale analysis for water scarcity in response to soil–water conservation on Loess Plateau of China

    Microsoft Academic Search

    He Xiubin; Li Zhanbin; Hao Mingde; Tang Keli; Zheng Fengli

    2003-01-01

    Water scarcity is one of the most prominent issues of discussion worldwide concerned with sustainable development, especially in the arid and semi-arid areas. On the Loess Plateau of China, population growth and fast-growing cities and industries have caused ever-increasing competition for water. The present paper shows a down-scale analysis on how the region wide mass action of soil–water conservation ecologically

  1. Downscaling soil moisture in the southern Great Plains through a calibrated multifractal model for land surface modeling applications

    Microsoft Academic Search

    Giuseppe Mascaro; Enrique R. Vivoni; Roberto Deidda

    2010-01-01

    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 downscaling model based on multifractal theory using aircraft-based (800 m) $\\\\theta$ estimates collected during the southern Great Plains experiment in 1997 (SGP97). We first

  2. Downscaling Satellite Soil Moisture Estimates in the Southern Great Plains through a Calibrated Multifractal Model for Land Surface Applications

    NASA Astrophysics Data System (ADS)

    Mascaro, G.; Vivoni, E. R.; Deidda, R.

    2009-12-01

    Satellite passive microwave sensors are able to provide soil moisture estimates at a scale on the order of 25-50 km. However, several studies have indicated that land-surface simulations can be significantly improved if finer resolution soil moisture estimates are available. As a result, operational algorithms are required to downscale satellite measurements. In this work, we present the results from the calibration and validation of a downscaling model of soil moisture based on multifractal theory. Our application is based on aggregations of aircraft (800 m) soil moisture estimates collected during the Southern Great Plains experiment in 1997 (SGP97). For this purpose, we selected nine 25.6 x 25.6 km2 domains (approximately a satellite pixel) over the SGP97 region spanning different conditions of terrain, land cover, and soil type. After showing the presence of scale invariance and multifractality in the soil moisture fields in each domain, we estimated the downscaling model parameters and tested the performance of a set of calibration approaches based on different coarse-scale predictors. Results demonstrate that small-scale soil moisture variability is adequately reproduced across the entire region when a non-stationary component (the spatial mean soil moisture) and a stationary component accounting for static features (i.e. topography, soil texture, vegetation) are utilized. Performance degrades in the northern domains characterized by the presence of harvested agricultural areas that introduce heterogeneity that the model is not able to fully reproduce. Our study furnishes a regional approach through which satellite soil moisture estimates can be downscaled in the study area for use in land-surface models and data assimilation systems.

  3. Interactive Ensembles Without Loss of Spread Information

    NASA Astrophysics Data System (ADS)

    Duane, Gregory; Shen, Mao-Lin; Wiegerinck, Wim

    2014-05-01

    If the members of an ensemble of different models are allowed to interact with one another in run time, predictive skill can be improved as compared to that of any individual model or any average of indvidual model outputs. Inter-model connections in such an interactive ensemble can be trained, using historical data, so that the resulting ``supermodel" synchronizes with reality when used in weather-prediction mode, where the individual models perform data assimilation from each other (with trainable inter-model "observation error") as well as from real observations. In climate-projection mode, parameters of the individual models are changed, as might occur from an increase in GHG levels, and one obtains relevant statistical properties of the new supermodel attractor. In simple cases, it has been shown that training of the inter-model connections with the old parameter values gives a supermodel that is still predictive when the parameter values are changed. It might seem that by allowing ensemble members to interact and synchronize, we lose the advantage of using the ensemble to estimate uncertainty in prediction/projection from ensemble spread. Here we investigate the possibility of extending the machine learning scheme to estimate uncertainty in the trained connections, so as to effectively form an ensemble of supermodels. A larger training set is generally required to learn the uncertainty in the values found, but the task can be reduced by restricting the possible connection values to a discrete set. An alternative strategy is simply to import the spread information from an ordinary, non-interactive ensemble. We examine and compare the two strategies, using a variety of models, and reason about their applicability to the case of climate models that differ in their parameterizations of a sub-gridscale process.

  4. Identifying and Comparing CMIP5 Ensembles and the Resultant Hydrologic Conditions for the Colorado River Basin

    NASA Astrophysics Data System (ADS)

    Santos, N. I.; Miller, W. P.; Piechota, T. C.; Nowak, K. C.; Bunk, D.

    2014-12-01

    Climate projections for the Colorado River Basin (CRB) provide multiple scenarios that illustrate an uncertain and highly variable future. It is hypothesized that climate models which capture the historical sea surface temperature (SST) and climate relationships for the CRB might garner more weight or consideration when analyzing future conditions. This study analyzes the historical and downscaled Coupled Model Intercomparison Project 5 (CMIP5) climatology and hydrology data, along with SST data, using singular variable decomposition (SVD) to identify regions of significant correlation between the Pacific Ocean and the CRB. SVD is a statistical tool capable of isolating important modes of variability when identifying coupled relationships between datasets involving gridded arrays and time series. SVD analysis was performed between historical SST and basin temperature, precipitation, and runoff conditions. The historical period analysis was performed prior to performing SVD analysis on all future climate projections to establish a baseline for the coupled SST and climate/hydrology relationship. An inter-model comparison reveals variable regions of significance and correlation magnitudes when all CMIP5 models are analyzed throughout the historical period. The models that captured certain historical features/relationships were then analyzed as a subset against the full ensemble to determine if any refinement or consensus in future projected conditions exists. This study has identified a subset of CMIP5 models hypothesized to replicate the coupled SST and climate/hydrology conditions in the CRB. With this knowledge, a comparison between subset and ensemble model projections provides insight into the magnitude of the climate and hydrology projection differences for the CRB. This study was performed to develop a subset of climate projections and to determine the differences between ensemble and subset climate model projections. The results of the study can potentially assist CRB resource managers in determining the applicability of climate and hydrology projections and reduce the uncertainty of future conditions. The outcome of this study will further assist with characterizing the range of possible climate, hydrology, and drought conditions in the Colorado River Basin.

  5. HEPS4Power - Extended-range Hydrometeorological Ensemble Predictions for Improved Hydropower Operations and Revenues

    NASA Astrophysics Data System (ADS)

    Bogner, Konrad; Monhart, Samuel; Liniger, Mark; Spririg, Christoph; Jordan, Fred; Zappa, Massimiliano

    2015-04-01

    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 downscaling and post-processing of ensemble 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 ensemble 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 ensemble predictions.

  6. Downscaling soil moisture in the southern Great Plains through a calibrated multifractal model for land surface modeling applications

    NASA Astrophysics Data System (ADS)

    Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto

    2010-08-01

    Accounting for small-scale spatial heterogeneity of soil moisture (?) 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 downscaling model based on multifractal theory using aircraft-based (800 m) ? estimates collected during the southern Great Plains experiment in 1997 (SGP97). We first demonstrate the presence of scale invariance and multifractality in ? fields of nine square domains of size 25.6 × 25.6 km2, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale ? distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) 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 ? 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 downscaling 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 ? distribution required by land surface models and data assimilation systems.

  7. Dynamically downscaling wind storms over complex terrain with WRF: establishing the model performance and associated uncertainties

    NASA Astrophysics Data System (ADS)

    José Gómez-Navarro, Juan; Raible, Christoph C.

    2015-04-01

    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 downscaled 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 downscaled 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.

  8. Downscaling Ocean Conditions: Initial Results using a Quasigeostrophic and Realistic Ocean Model

    NASA Astrophysics Data System (ADS)

    Katavouta, Anna; Thompson, Keith

    2014-05-01

    Previous theoretical work (Henshaw et al, 2003) has shown that the small-scale modes of variability of solutions of the unforced, incompressible Navier-Stokes equation, and Burgers' equation, can be reconstructed with surprisingly high accuracy from the time history of a few of the large-scale modes. Motivated by this theoretical work we first describe a straightforward method for assimilating information on the large scales in order to recover the small scale oceanic variability. The method is based on nudging in specific wavebands and frequencies and is similar to the so-called spectral nudging method that has been used successfully for atmospheric downscaling with limited area models (e.g. von Storch et al., 2000). The validity of the method is tested using a quasigestrophic model configured to simulate a double ocean gyre separated by an unstable mid-ocean jet. It is shown that important features of the ocean circulation including the position of the meandering mid-ocean jet and associated pinch-off eddies can indeed be recovered from the time history of a small number of large-scales modes. The benefit of assimilating additional time series of observations from a limited number of locations, that alone are too sparse to significantly improve the recovery of the small scales using traditional assimilation techniques, is also demonstrated using several twin experiments. The final part of the study outlines the application of the approach using a realistic high resolution (1/36 degree) model, based on the NEMO (Nucleus for European Modelling of the Ocean) modeling framework, configured for the Scotian Shelf of the east coast of Canada. The large scale conditions used in this application are obtained from the HYCOM (HYbrid Coordinate Ocean Model) + NCODA (Navy Coupled Ocean Data Assimilation) global 1/12 degree analysis product. Henshaw, W., Kreiss, H.-O., Ystrom, J., 2003. Numerical experiments on the interaction between the larger- and the small-scale motion of the Navier-Stokes equations. Multiscale Modeling and Simulation 1, 119-149. von Storch, H., Langenberg, H., Feser, F., 2000. A spectral nudging technique for dynamical downscaling purposes. Monthly Weather Review 128, 3664-3673.

  9. Is the PMIP ensemble large enough?

    NASA Astrophysics Data System (ADS)

    Hargreaves, Julia C.; Annan, James D.

    2015-04-01

    Climate sensitivity, defined as the change in globally averaged surface air temperature for a doubling of atmospheric carbon dioxide, differs between models, and is uncertain for the real climate system. The Last Glacial Maximum has been a major focus for attempts to estimate climate sensitivity because it is the most recent period in the past when atmospheric CO2 (and surface temperature) was very different to the modern climate. Using the previous (PMIP2) generation of models, Hargreaves et al. (2012) found a statistically significant correlation between tropical temperature change at the LGM and equilibrium sensitivity, and used this relationship to generate an estimate of the climate sensitivity of 0.5-4.0 °C. However, one major caveat was the small size of the ensemble on which this calculation was based, and therefore it was proposed that the forthcoming PMIP3/CMIP5 ensemble would be an interesting test of this correlation and might provide further information due to the increased ensemble size which was anticipated to be as many as 15 models. The PMIP3 ensemble presently contains only 7 models with climate sensitivity estimates, but repeating the analysis from Hargreaves et al. (2012), we obtain a quite different result for the new ensemble, finding no correlation and therefore no constraint on climate sensitivity. Combining the PMIP2 and PMIP3 ensembles by taking the mean of the outputs where more than one integration was performed by closely related models. This gives a total of 11 simulations and a weak correlation between tropical temperature at the LGM and equilibrium climate sensitivity, which is barely significant at the 90% level. This provides an estimate of climate sensitivity in the range of 1.4-4.4 °C, but the tenuous nature of the correlation cannot be ignored when assessing the credibility of this result. Reasons for the differing results between the two ensembles require further investigation, but one conclusion to draw is that the small ensemble size is a major issue hindering confidence in attempts to constrain the multi-model ensembles using data.

  10. A Bayesian Ensemble Approach for Epidemiological Projections

    PubMed Central

    Lindström, Tom; Tildesley, Michael; Webb, Colleen

    2015-01-01

    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, ensemble 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 ensembles 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 ensemble 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 ensembles 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 ensemble modeling of disease outbreaks. PMID:25927892

  11. Coupling spin ensembles via superconducting flux qubits

    E-print Network

    Yueyin Qiu; Wei Xiong; Lin Tian; J. Q. You

    2014-09-10

    We study a hybrid quantum system consisting of spin ensembles and superconducting flux qubits, where each spin ensemble is realized using the nitrogen-vacancy centers in a diamond crystal and the nearest-neighbor spin ensembles are effectively coupled via a flux qubit.We show that the coupling strengths between flux qubits and spin ensembles can reach the strong and even ultrastrong coupling regimes by either engineering the hybrid structure in advance or tuning the excitation frequencies of spin ensembles via external magnetic fields. When extending the hybrid structure to an array with equal coupling strengths, we find that in the strong-coupling regime, the hybrid array is reduced to a tight-binding model of a one-dimensional bosonic lattice. In the ultrastrong-coupling regime, it exhibits quasiparticle excitations separated from the ground state by an energy gap. Moreover, these quasiparticle excitations and the ground state are stable under a certain condition that is tunable via the external magnetic field. This may provide an experimentally accessible method to probe the instability of the system.

  12. Ensemble habitat mapping of invasive plant species

    USGS Publications Warehouse

    Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.

    2010-01-01

    Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble 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 ensemble 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, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.

  13. Using Dynamically Downscaled Rainfall Data to Investigate Soil Moisture Spatial Patterns Across Eastern Australia and the Impact of Projected Climate Change Trends

    NASA Astrophysics Data System (ADS)

    Lockart, Natalie; Graddon, Andrew; Willgoose, Garry; Kuczera, George; Coutts-Smith, Aaron

    2015-04-01

    The wetness of the soil in a region plays a major role in hydrological processes and land-surface-atmosphere interactions. For example, the wetness of the soil prior to a rainfall event plays an important role in determining how much rainfall is converted to runoff. This can impact the size and timing of any resulting flood. This study used dynamically downscaled GCM rainfall projections from the New South Wales (NSW) / ACT Regional Climate Modelling (NARCliM) project to simulate soil moisture at 10km resolution across NSW, Australia (approximately 1000km x 1000km). The NARCliM project has produced an ensemble of regional climate projections for south-east Australia, in particular 10 by 10 km resolution hourly rainfall data. 12 different projections of rainfall have been produced for three 20 year time periods; 1990-2009, 2020-2039 and 2060-2079. An hourly time series of soil moisture was simulated from each of the rainfall data sets using the Australian Water Balance Model (AWBM), which in turn had been calibrated to daily data from a HYDRUS model calibrated to field soil moisture from the SASMAS field site. Using the simulated soil moisture timeseries, contour maps of the soil moisture statistics (such as median and 5% soil moisture values) were developed for NSW. We also examined the joint probability of extreme rainfall and antecedent soil moisture prior to extreme rainfall events. Rainfall events of varying durations were considered. For short duration rainfall events, an initial analysis showed that a clear relationship exists between the antecedent soil moisture and extreme rainfall; as the rainfall depth increases so too does the antecedent soil moisture. This will inform procedures for estimating the antecedent soil moisture used in engineering hydrology flood studies. Our results suggest that a normalised antecedent soil moisture relationship we have derived may be geographically regionalisable and robust against changes in governing climate, and so may be applicable outside our study area and suitable for climate change adaptation studies. Results from the 12 rainfall climate change projections and three future time periods will be presented. The impact of climate change on the soil moisture statistics as well as the soil moisture-extreme rainfall relationship will also be presented.

  14. Towards an Efficient and Global Downscaling Methodology Based on Multifractal Models for Satellite-Based Soil Moisture Estimates

    NASA Astrophysics Data System (ADS)

    Mascaro, G.; Vivoni, E. R.; Deidda, R.

    2010-12-01

    The characterization of soil moisture spatial variability at high resolution is important to improve the simulation of water and energy exchanges among soil, vegetation and the atmosphere. As a result, algorithms are required to disaggregate or downscale the available but coarse (25-50 km) satellite products provided by passive microwave sensors. Important properties sought for the downscaling algorithms include: (i) efficiency, in order to be operationally applied with minimal parameterization and low computational demand, and (ii) robustness, in order to be usable in a wide range of environmental conditions and climatic settings. In a recent study, we applied a parameter-parsimonious downscaling model based on the multifractal theory by using aircraft-based soil moisture data collected during the SGP97 experiment. A single, regional calibration relation for the downscaling model was found to be a function of the mean soil moisture value in coarse domains representing a satellite footprint, and ancillary predictors accounting for soil texture, land cover and topography. Through this relation, the model was able to efficiently and adequately reproduce the small-scale (800 m) probability distribution of soil moisture in coarse satellite pixels. A limitation of this and other prior efforts for soil moisture downscaling is their application in a single region over short time periods. In this work, we illustrate the applicability of the multifractal approach to other datasets covering additional environmental settings and climatic conditions. These include: (i) SGP99 collected during drier conditions in Oklahoma, (ii) SMEX02 collected in a flat, agricultural area with moderate to heavy water content in Iowa, and (iii) SMEX04 conducted in two semiarid regions with complex topography in Arizona and Sonora (Mexico). We calibrate the model at each site as a function of local static (topography, soil and vegetation) and dynamic (mean soil moisture content) ancillary factors. Results show that: (i) the calibration relations are similar to the ones obtained with SGP97 data, suggesting the robustness of the procedure, and (ii) model performance in reproducing the small-scale soil moisture distribution are adequate in most of the cases, except for fields exhibiting statistical spatial inhomogeneity. This work contributes towards the development of an efficient, operational methodology able to characterize different kinds of sub-grid soil moisture distributions in satellite footprints of several climatic regions and environmental conditions.

  15. Disease and Phenotype Data at Ensembl

    PubMed Central

    Spudich, Giulietta M.; Fernández-Suárez, Xosè M.

    2011-01-01

    Biological databases are an important resource for the life sciences community. Accessing the hundreds of databases supporting molecular biology and related fields is a daunting and time-consuming task. Integrating this information into one access point is a necessity for the life sciences community, which includes researchers focusing on human disease. Here we discuss the Ensembl genome browser, which acts as a single entry point with Graphical User Interface to data from multiple projects, including OMIM, dbSNP, and the NHGRI GWAS catalog. Ensembl provides a comprehensive source of annotation for the human genome, along with other species of biomedical interest. In this unit, we explore how to use the Ensembl genome browser in example queries related to human genetic diseases. Support protocols demonstrate quick sequence export using the BioMart tool. PMID:21400687

  16. Building Ensemble-Based Data Assimilation Systems

    NASA Astrophysics Data System (ADS)

    Nerger, Lars; Kirchgessner, Paul

    2015-04-01

    Different strategies for implementing ensemble-based data assimilation systems are discussed. Ensemble filters like ensemble Kalman filters and particle filters can be implemented so that they are nearly independent from the model into which they assimilate observations. In particular, an online coupling strategy is computational efficient. In this coupling strategy, subroutine calls for the data assimilation are directly inserted into the source code of an existing numerical model and augment the numerical model to become a data assimilative model. The online coupling shows an excellent computational scalability on supercomputers and is hence well suited for high-dimensional numerical models, including coupled earth system models. Further a clear separation of the model and data assimilation components allows to continue the development of both components separately. Using the example of the parallel data assimilation framework (PDAF, http://pdaf.awi.de) and the ocean model NEMO, it is demonstrated how the online coupling can be achieved with minimal changes to the numerical model.

  17. Ensemble approach for differentiation of malignant melanoma

    NASA Astrophysics Data System (ADS)

    Rastgoo, Mojdeh; Morel, Olivier; Marzani, Franck; Garcia, Rafael

    2015-04-01

    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 ensemble 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 ensembles such as random forest perform better than single learner. Using random forest ensemble and combination of color and texture features, our framework achieved the highest sensitivity of 94% and specificity of 92%.

  18. Ensemble Forecast of Major Solar Flares

    NASA Astrophysics Data System (ADS)

    Guerra, J. A.; Pulkkinen, A. A.; Uritsky, V. M.

    2014-12-01

    We present the results from the first ensemble prediction model for solar flares of M and X classes. Using the forecasts from 4 flare-predictor methods hosted at the Community Coordinated Modeling Center (NASA-GSFC), this model provides an ensemble forecast by combining flaring probabilities from individual methods. Performance-based combination weights are calculated using a procedure that applies a threshold to the probability time series and then minimizes the chi squared parameter between the model and observed flare time series. We used a sample of 13 recent active regions that produced several M- and X-class flares. We will discuss the results of the new ensemble approach and show how the method can be used in a real-time environment for flare predictions.

  19. Future Temperatures and Precipitations in the Arid Northern-Central Chile: A Multi-Model Downscaling Approach

    NASA Astrophysics Data System (ADS)

    Souvignet, M.; Heinrich, J.

    2010-03-01

    Downscaling 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 downscaling 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 Downscaling 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 downscaling 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 downscaled 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 downscaled scenarios. Whereas maximum and minimum temperatures were accurately simulated by both downscaling methods, precipitation simulations returned weaker results. SDSM proved to have a poor ability to simulate extreme rainfall events and few conclusions could be draw

  20. Hydrologic Ensemble Prediction: Challenges and Opportunities

    NASA Astrophysics Data System (ADS)

    Schaake, J.; Bradley, A.

    2005-12-01

    Ensemble forecast techniques are beginning to be used for hydrological prediction by operational hydrological services throughout the world. These techniques are attractive because they allow effects of a wide range of sources of uncertainty on hydrological forecasts to be accounted for. Not only does ensemble prediction in hydrology offer a general approach to probabilistic prediction, it offers a significant new approach to improve hydrological forecast accuracy as well. But, there are many scientific challenges that must be overcome to provide users with high quality hydrologic ensemble forecasts. A new international project the Hydrologic Ensemble Prediction Experiment (HEPEX) was started last year to organize the scientific community to meet these challenges. Its main objective is to bring the international hydrological community together with the meteorological community to demonstrate how to produce reliable hydrological ensemble for decisions for the benefit of public health and safety, the economy and the environment. Topics that will be addressed by the HEPEX scientific community include techniques for using weather and climate information in hydrologic prediction systems, new methods in hydrologic prediction, data assimilation issues in hydrology and hydrometeorology, verification and correction of ensemble weather and hydrologic forecasts, and better quantification of uncertainty in hydrological prediction. As pathway for addressing these topics, HEPEX will set up demonstration test bed projects and compile data sets for the intercomparison of coupled systems for atmospheric and hydrologic forecasting, and their assessment for meeting end users' needs for decision-making. Test bed projects have been proposed in North and South America, Europe, and Asia, and have a focus ranging from short-range flood forecasting to seasonal predictions for water supply. For example, within the United States, ongoing activities in seasonal prediction as part of the GEWEX Americas Prediction Project (GAPP) will contribute to the HEPEX effort. This presentation reports on the outcomes from the second international HEPEX workshop at NCAR in July, as well as planned future activities.

  1. Downscaling ferroelectric field effect transistors by using ferroelectric Si-doped HfO2

    NASA Astrophysics Data System (ADS)

    Martin, Dominik; Yurchuk, Ekaterina; Müller, Stefan; Müller, Johannes; Paul, Jan; Sundquist, Jonas; Slesazeck, Stefan; Schlösser, Till; van Bentum, Ralf; Trentzsch, Martin; Schröder, Uwe; Mikolajick, Thomas

    2013-10-01

    Throughout the 22 nm technology node HfO2 is established as a reliable gate dielectric in contemporary complementary metal oxide semiconductor (CMOS) technology. The working principle of ferroelectric field effect transistors FeFET has also been demonstrated for some time for dielectric materials like Pb[ZrxTi1-x]O3 and SrBi2Ta2O9. However, integrating these into contemporary downscaled CMOS technology nodes is not trivial due to the necessity of an extremely thick gate stack. Recent developments have shown HfO2 to have ferroelectric properties, given the proper doping. Moreover, these doped HfO2 thin films only require layer thicknesses similar to the ones already in use in CMOS technology. This work will show how the incorporation of Si induces ferroelectricity in HfO2 based capacitor structures and finally demonstrate non-volatile storage in nFeFETs down to a gate length of 100 nm. A memory window of 0.41 V can be retained after 20,000 switching cycles. Retention can be extrapolated to 10 years.

  2. From Rainfall Downscaling to Rainfall Retrieval: Inverse Problems of Similar Nature

    NASA Astrophysics Data System (ADS)

    Foufoula, Efi; Ebtehaj, Mohammad

    2014-05-01

    Satellite-based rainfall estimation offers the possibility of tracking global patterns of rainfall over ocean and land for large-scale hydrologic modeling, and for improved analysis of local and regional rainfall where ground observations are not available. In the past decade, high-resolution retrieval of rainfall from their spaceborne microwave spectral radiative fluxes has been an active area of research in the hydro-meteorological community. However, most current retrieval algorithms cannot properly reproduce low and extreme rainfall intensities and the small-scale precipitation variability, especially over land and coastal areas - important for hydrologic predictions and hazard mitigation. In this research, we introduce a new approach to the spaceborne passive microwave rainfall retrieval problem. The proposed methodology is inspired by the state-of-the-art supervised manifold learning and Bayesian shrinkage estimation paradigms and takes advantage of precipitation sparsity, recently documented and explored by the authors in precipitation downscaling, estimation, and data assimilation. The retrieval methodology relies on a sparsity-promoting search between two dictionaries that encode rainfall intensities and their spectral signatures. The proposed framework is examined using observations of the active precipitation radar (PR) and the passive microwave imager (TMI) on board of the Tropical Rainfall Measuring Mission (TRMM) satellite. The essence of the algorithm is explained and its advantages are highlighted in comparison with the outputs of the currently operational algorithms.

  3. Downscaling limits and confinement effects in the miniaturization of porous polymer monoliths in narrow bore capillaries

    PubMed Central

    Nischang, Ivo; Svec, Frantisek; Fréchet, Jean M.J.

    2009-01-01

    Monolithic poly(butyl methacrylate-co-ethylene dimethacrylate) columns have been prepared in capillaries ranging in inner diameter from 5 to 75 ?m using thermally initiated free-radical polymerization of a mixture of butyl methacrylate, ethylene dimethacrylate and porogens at different temperatures. Scanning electron microscopy and the measurement of hydrodynamic properties reveal that the downward scalability of the monolithic columns is greatly affected by the confinement effect of the capillary wall resulting from the decreased volume-to-surface ratio as the capillary diameter is decreased. The downscaling process is affected most by the polymerization temperature, the diffusion of the propagating radicals, and the density of coverage of polymerizable groups on the inner walls of the capillary. Optimization of all these factors enables the preparation of monolithic structures in capillaries with inner diameters as low as 5 ?m while retaining the desirable properties of monoliths prepared in much larger capillaries. Under these conditions, the formation of undesired dense polymer layers attached to the capillary wall was minimized. The chromatographic performance of 10, 25 and 50 ?m capillaries evaluated in the reversed phase gradient separation of three proteins showed no change in elution times at identical flow velocities and gradient times while peak elution width was the smallest with the narrowest capillary. PMID:19642657

  4. Edge irregularities in extremely down-scaled graphene nanoribbon devices: role of channel width

    NASA Astrophysics Data System (ADS)

    Manoharan, M.; Mizuta, Hiroshi

    2014-12-01

    When it comes to extremely downscaled graphene device research, it is imperative to develop a comprehensive understanding of what kinds of edge irregularities are likely to occur in the realistic graphene nanoribbons (GNRs) as well as their impact on the electronic and transport properties of GNRs. Here we present the first-principle calculations of the formation energy of the edge vacancies and protrusions in the armchair GNRs (AGNRs) with widths ranging from 9 to 12 carbon atoms and zigzag GNRs (ZGNRs). We also examine their influence on the electronic states and transport characteristics of the GNRs. The formation energy calculations show that double vacancy (DV) edge defects and zigzag protrusions are the most likely edge irregularities in the AGNRs. The DV edge defects increase the bandgap in 11-AGNRs and decrease the bandgap in 9, 10, 12-AGNRs. Zigzag protrusions widen the bandgap in 9, 12-AGNRs and reduce the bandgap in 10, 11-AGNRs. Edge defects induced wave function localization leads to the anti-resonant transmission characteristics. Edges of the ZGNRs show a high tendency to be modified by the exothermic effect. However, their current carrying capacity is not compromised by the edge irregularities.

  5. Downscaling limits and confinement effects in the miniaturization of porous polymer monoliths in narrow bore capillaries.

    PubMed

    Nischang, Ivo; Svec, Frantisek; Fréchet, Jean M J

    2009-09-01

    Monolithic poly(butyl methacrylate-co-ethylene dimethacrylate) columns have been prepared in capillaries ranging in inner diameter from 5 to 75 microm using thermally initiated free-radical polymerization of a mixture of butyl methacrylate, ethylene dimethacrylate, and porogens at different temperatures. Scanning electron microscopy and the measurement of hydrodynamic properties reveal that the downward scalability of the monolithic columns is greatly affected by the confinement effect of the capillary wall resulting from the decreased volume-to-surface ratio as the capillary diameter is decreased. The downscaling process is affected most by the polymerization temperature, the diffusion of the propagating radicals, and the density of coverage of polymerizable groups on the inner walls of the capillary. Optimization of all these factors enables the preparation of monolithic structures in capillaries with inner diameters as low as 5 microm while retaining the desirable properties of monoliths prepared in much larger capillaries. Under these conditions, formation of undesired dense polymer layers attached to the capillary wall was minimized. The chromatographic performance of 10, 25, and 50 microm capillaries evaluated in the reversed phase gradient separation of three proteins showed no change in elution times at identical flow velocities and gradient times, while peak elution width was the smallest with the narrowest capillary. PMID:19642657

  6. A statistical downscaling method for daily air temperature in data-sparse, glaciated mountain environments

    NASA Astrophysics Data System (ADS)

    Hofer, M.; Marzeion, B.; Mölg, T.

    2015-03-01

    This study presents a statistical downscaling (SD) method for high-altitude, glaciated mountain ranges. The SD method uses an a priori selection strategy of the predictor (i.e., predictor selection without data analysis). In the SD model validation, emphasis is put on appropriately considering the pitfalls of short observational data records that are typical of high mountains. An application example is shown, with daily mean air temperature from several sites (all in the Cordillera Blanca, Peru) as target variables, and reanalysis data as predictors. Results reveal strong seasonal variations of the predictors' performance, with the maximum skill evident for the wet (and transitional) season months January to May (and September), and the lowest skill for the dry season months June and July. The minimum number of observations (here, daily means) required per calendar month to obtain statistically significant skill ranges from 40 to 140. With increasing data availability, the SD model skill tends to increase. Applied to a choice of different atmospheric reanalysis predictor variables, the presented skill assessment identifies only air temperature and geopotential height as significant predictors for local-scale air temperature. Accounting for natural periodicity in the data is vital in the SD procedure to avoid spuriously high performances of certain predictors, as demonstrated here for near-surface air temperature. The presented SD procedure can be applied to high-resolution, Gaussian target variables in various climatic and geo-environmental settings, without the requirement of subjective optimization.

  7. Predicting and downscaling ENSO impacts on intraseasonal precipitation statistics in California: The 1997/98 event

    USGS Publications Warehouse

    Gershunov, A.; Barnett, T.P.; Cayan, D.R.; Tubbs, T.; Goddard, L.

    2000-01-01

    Three long-range forecasting methods have been evaluated for prediction and downscaling of seasonal and intraseasonal precipitation statistics in California. Full-statistical, hybrid-dynamical - statistical and full-dynamical approaches have been used to forecast El Nin??o - Southern Oscillation (ENSO) - related total precipitation, daily precipitation frequency, and average intensity anomalies during the January - March season. For El Nin??o winters, the hybrid approach emerges as the best performer, while La Nin??a forecasting skill is poor. The full-statistical forecasting method features reasonable forecasting skill for both La Nin??a and El Nin??o winters. The performance of the full-dynamical approach could not be evaluated as rigorously as that of the other two forecasting schemes. Although the full-dynamical forecasting approach is expected to outperform simpler forecasting schemes in the long run, evidence is presented to conclude that, at present, the full-dynamical forecasting approach is the least viable of the three, at least in California. The authors suggest that operational forecasting of any intraseasonal temperature, precipitation, or streamflow statistic derivable from the available records is possible now for ENSO-extreme years.

  8. Analysis of climate projections for the Carpathian Region using dynamical downscaling

    NASA Astrophysics Data System (ADS)

    Bartholy, Judit; Pongracz, Rita; Pieczka, Ildiko; Andre, Karolina

    2015-04-01

    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 downscaling 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.

  9. The evolution of down-scale virus filtration equipment for virus clearance studies.

    PubMed

    Wieser, Andreas; Berting, Andreas; Medek, Christian; Poelsler, Gerhard; Kreil, Thomas R

    2015-03-01

    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 down-scaled 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

  10. Downscaling surfing conditions in nearshore areas: seasonal, interannual and long term variability

    NASA Astrophysics Data System (ADS)

    Losada, I.; Espejo, A.; Mendez, F.

    2012-12-01

    During the last years several artificial surf reefs have been constructed. Most of them have had slight effect on improving surf quality and in some cases the results have not been enough satisfactory. One cause of this lack of success can be blamed to an incipient design and construction technique but also because the location of these structures responds to socio-economic principles rather than in oceanographic ones. This work describes a hybrid downscaling method used to get detailed tracing spectral wave and wind data in order to determine historical surf quality time series in a regional (10-200 km) or a local scale. Our assessment is conducted by means of an objective and standardized index on the basis of expert judgment that takes into account the multivariate character of the surf resource. The availability of long hourly time series (more than 60 years) of the surf quality allows the statistical analysis at any particular spot. Thus, we offer a reliable tool for identifying optimum reef location, and determining spatial patterns of surf consistency in terms of seasonal, interannual and long term variability. This method have been applied in the north-eastern coast of Spain identifying world class spots like Mundaka and other places where in spite of occurring good wave-wind conditions there is no bathymetric anomalies able to produce peeling waves. This method provides the decision making process when planning an artificial surf reef construction and at the same time offers useful information for coastal management and surf related stakeholders.

  11. Accounting for ensemble variance inaccuracy with Hybrid Ensemble 4D-VAR

    NASA Astrophysics Data System (ADS)

    Bishop, C. H.; Kuhl, D. D.; Satterfield, E.; Tom, R.

    2011-12-01

    Inevitably, neither ensemble covariances nor static covariance models are equal to the true error covariance matrix given past observations. To better understand the distribution of true error covariances given a single imperfect ensemble covariance, we begin by considering an idealized univariate model in which Bayes' theorem can be used to derive the distribution of true error variances given an imperfect ensemble variance. The equation for the mean of this distribution shows that a Hybrid error variance formulation is more accurate than either formulations based solely on ensemble variances or formulations based solely on static climatological variances. We show how this Hybrid best estimate of error variance may be derived from a large number of realizations of (innovation, ensemble-variance) pairs. The approach assumes that the climatological distribution of true error variances is an inverse-gamma distribution and that the distribution of ensemble variances given a single true error variance is a gamma distribution. To help explain and justify this approach we use a "replicate Earth" paradigm and an Ensemble Kalman filter applied to Lorenz's (2005) simple model 1. We then apply these theoretically derived weights to the newly built Navy-Hybrid-4DVAR scheme. The forecast performance using the theoretical weights was found to be as good as that from weights obtained from a much more computationally expensive brute force tuning method. Thus, the new theory provided a justification for the Hybrid plus tools to facilitate its implementation.

  12. Quantum measurement of a mesoscopic spin ensemble

    SciTech Connect

    Giedke, G. [Institut fuer Quantenelektronik, ETH Zuerich, Wolfgang-Pauli-Strasse 16, 8093 Zurich (Switzerland); Max-Planck-Institut fuer Quantenoptik, H.-Kopfermann-Str., 85748 Garching (Germany); Taylor, J. M.; Lukin, M. D. [Department of Physics, Harvard University, Cambridge, Massachusetts 02138 (United States); D'Alessandro, D. [Department of Mathematics, Iowa State University, Ames, Iowa 50011 (United States); Imamoglu, A. [Institut fuer Quantenelektronik, ETH Zuerich, Wolfgang-Pauli-Strasse 16, 8093 Zurich (Switzerland)

    2006-09-15

    We describe a method for precise estimation of the polarization of a mesoscopic spin ensemble 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 ensemble 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.

  13. Ensemble computing for the petroleum industry

    SciTech Connect

    Annaratone, M.; Dossa, D. [Digital Equipment Corp., Maynard, MA (United States)

    1995-02-01

    Computer downsizing is one of the most often used buzzwords in today`s competitive business, and the petroleum industry is at the forefront of this revolution. Ensemble computing provides the key for computer downsizing with its first incarnation, i.e., workstation farms. This paper concerns the importance of increasing the productivity cycle and not just the execution time of a job. The authors introduce the concept of ensemble computing and workstation farms. The they discuss how different computing paradigms can be addressed by workstation farms.

  14. Downscaling of South America present climate driven by 4-member HadCM3 runs

    Microsoft Academic Search

    Sin Chan Chou; José A. Marengo; André A. Lyra; Gustavo Sueiro; José F. Pesquero; Lincoln M. Alves; Gillian Kay; Richard Betts; Diego J. Chagas; Jorge L. Gomes; Josiane F. Bustamante; Priscila Tavares

    2010-01-01

    The objective of this work is to evaluate climate simulations over South America using the regional Eta Model driven by four\\u000a members of an ensemble of the UK Met Office Hadley Centre HadCM3 global model. The Eta Model has been modified with the purpose\\u000a of performing long-term decadal integrations and has shown to reproduce “present climate”—the period 1961–1990—reasonably\\u000a well when

  15. Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts

    E-print Network

    Raftery, Adrian

    Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts VERONICA ensembles 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

  16. Subspace Clustering, Ensemble Clustering, Alternative Clustering, Multiview Clustering

    E-print Network

    Kriegel, Hans-Peter

    Subspace Clustering, Ensemble Clustering, Alternative Clustering, Multiview Clustering: What Can We Though subspace clustering, ensemble clustering, alterna- tive clustering, and multiview clustering clustering. Based on this survey, we try to identify problems where the different research areas could

  17. Estimating Spatial Disturbution of Surface Soil Mositure Conditions Using a Downscale Technique with Thermal Inertia Retrieved from AMSR2 Soil Moisture Products

    NASA Astrophysics Data System (ADS)

    Matsushima, D.; Kimura, R.

    2014-12-01

    We examined that a method for estimating spatial distribution of surface soil moisture conditions with a fine grid scale. The AMSR2 soil moisture products which have been produced by Japan Aerospace Exploration Agency (JAXA) with a coarse grid scale (50 km) were downscaled by a spatial distribution of thermal inertia (3 km), which was retrived from a two-source linear surface heat budget model using an optimization method in which the input variables such as MODIS surface temperature, insolation retrieved from a geostationary satellite, and surface meterological data were incorporated. The downscale technique employed a characteristic that thermal inertia is almost proportional to volumetric soil moisture content, which was found in results of some field experiments. This downscale technique was applied to the AMSR2 products of summer season of central Mongolia where typical and dry steppe were dominated. A preliminary result of the downscaling technique showed fairly good result that the values of downscaled soil moisture were varied in response to rainfall events at some grids where meteorological stations existed, while the values of AMSR2 were not significantly changed.

  18. Black Hole Statistical Mechanics and The Angular Velocity Ensemble

    E-print Network

    Mitchell Thomson; Charles C. Dyer

    2012-03-29

    An new ensemble - the angular velocity ensemble - is derived using Jaynes' method of maximising entropy subject to prior information constraints. The relevance of the ensemble to black holes is motivated by a discussion of external parameters in statistical mechanics and their absence from the Hamiltonian of general relativity. It is shown how this leads to difficulty in deriving entropy as a function of state and recovering the first law of thermodynamics from the microcanonical and canonical ensembles applied to black holes.

  19. Ensemble Generation for Models of Multimodal Systems

    Microsoft Academic Search

    Robert N. Miller; Laura L. Ehret

    2002-01-01

    In this work the performance of ensembles generated by commonly used methods in a nonlinear system with multiple attractors is examined. The model used here is a spectral truncation of a barotropic quasigeostrophic channel model. The system studied here has 44 state variables, great enough to exhibit the problems associated with high state dimension, but small enough so that experiments

  20. Mining battlefield information using ensemble classifiers

    Microsoft Academic Search

    Xiansheng Xu; Tao Wang; Zhenzheng Ouyang

    2010-01-01

    To help handle battlefield information superiority to decision superiority (i.e. to rapidly arrive at better decisions than adversaries can respond to), many scientific, technical and technological challenges must be addressed. The most critical of those are information fusion and management at different levels, communication. This paper decribes battlefield information as data streams and mining it using ensemble classifiers, and focusing

  1. The Hydrologic Ensemble Prediction Experiment (HEPEX)

    NASA Astrophysics Data System (ADS)

    Wood, Andy; Wetterhall, Fredrik; Ramos, Maria-Helena

    2015-04-01

    The Hydrologic Ensemble 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 ensemble forecasts for decision support. HEPEX pursues this goal through research efforts and practical implementations involving six core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble 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.

  2. Ensemble predictions of runoff in ungauged catchments

    Microsoft Academic Search

    Neil McIntyre; Hyosang Lee; Howard Wheater; Andy Young; Thorsten Wagener

    2005-01-01

    A new approach to regionalization of conceptual rainfall-runoff models is presented on the basis of ensemble modeling and model averaging. It is argued that in principle, this approach represents an improvement on the established procedure of regressing parameter values against numeric catchment descriptors. Using daily data from 127 catchments in the United Kingdom, alternative schemes for defining prior and posterior

  3. 7, 97179767, 2007 Ensemble-based

    E-print Network

    Paris-Sud XI, Université de

    Atmospheric Chemistry Division, Earth and Sun Systems Laboratory, National Center for Atmospheric Research, PO Chemistry and Physics Discussions Evaluating model performance of an ensemble-based chemical data, Colorado, 80307-3000, USA 3 Chemistry and Dynamics Branch, NASA Langley Research Center, Hampton, Virginia

  4. Stochastic Weather Generator Based Ensemble Streamflow Forecasting

    E-print Network

    Stochastic Weather Generator Based Ensemble Streamflow Forecasting by Nina Marie Caraway B Forecasting written by Nina Marie Caraway has been approved for the Department of Civil Engineering Balaji mentioned discipline. #12;iii Caraway, Nina Marie (M.S., Civil Engineering) Stochastic Weather Generator

  5. A Mixture Model for Clustering Ensembles

    Microsoft Academic Search

    Alexander P. Topchy; Anil K. Jain; William F. Punch

    2004-01-01

    Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. We offer a probabilistic model of consensus using a finite mixture of multinomial distributions in a space of

  6. Ensembles of Partitions via Data Resampling

    Microsoft Academic Search

    Behrouz Minaei-bidgoli; Alexander P. Topchy; William F. Punch

    2004-01-01

    The combination of multiple clusterings is a difficult problem in the practice of distributed data mining. Both the cluster generation mechanism and the partition integration process influence the quality of the combinations. In this paper we propose a data resampling approach for building cluster ensembles that are both robust and stable. In particular, we investigate the effectiveness of a bootstrapping

  7. Bias/Variance Tradeoff and Ensemble Methods

    E-print Network

    Gogate, Vibhav

    + s2 Expected prediction error = Variance + Bias2 + Noise2 #12;Bias, Variance, and Noise · Variance: EBias/Variance Tradeoff and Ensemble Methods Vibhav Gogate The University of Texas at Dallas Machine learning CS 6375 Slide courtesy of Tom Dietterich and Vincent Ng #12;Outline · Bias-Variance Decomposition

  8. Understanding the Ensemble Pianist: A Theoretical Framework

    ERIC Educational Resources Information Center

    Kokotsaki, Dimitra

    2007-01-01

    The aim of this study was to develop a theoretical model of the attainment of high quality in musical ensemble performance as perceived by the pianist and to identify the factors affecting this process. The research has followed an inductive interpretative approach, applying qualitative methods. The analytic material was collected through the…

  9. Ensemble of classifiers for detecting network intrusion

    Microsoft Academic Search

    Mrutyunjaya Panda; Manas Ranjan Patra

    2009-01-01

    Intrusion detection technology is an effective approach to deal with problems of malicious attacks on computer networks. In this paper, we present an intrusion detection model based on Ensemble of classifiers such as AdaBoost, MultiBoosting and Bagging to gain more opportunity of training misclassified samples and reduce the error rate by the majority voting of involved classifiers. Our main goal

  10. Online Ensemble Learning: An Empirical Study

    Microsoft Academic Search

    Alan Fern; Robert Givan

    2000-01-01

    We study resource-limited online learning, motivated by the problem of conditional-branch outcome predic- tion in computer architecture. In particular, we consider (parallel) time and space-efficient ensemble learners for online settings, empirically demonstrating benefits similar to those shown previously for offline ensem- bles. Our learning algorithms are inspired by the previously published \\

  11. Classifier Ensemble Recommendation Pyry Matikainen1

    E-print Network

    Goldstein, Seth Copen

    Classifier Ensemble Recommendation Pyry Matikainen1 Rahul Sukthankar2,1 Martial Hebert1 pmatikai Research Abstract. The problem of training classifiers from limited data is one that particularly affects classifier from a large library even with highly impoverished training data. We consider alternatives for ex

  12. Face recognition using ensembles of networks

    Microsoft Academic Search

    S. Gutta; J. Huang; B. Takacs; H. Wechslerc

    1996-01-01

    We describe a novel approach for fully automated face recognition and show its feasibility on a large database of facial images (FERET). Our approach, based on a hybrid architecture consisting of an ensemble of radial basis function (RBF) neural networks and inductive decision trees, combines the merits of “abstractive” features with those of “holistic” template matching. The benefits of our

  13. Online Ensemble Learning Nikunj C. Oza

    E-print Network

    Oza, Nikunj C.

    Online Ensemble Learning Nikunj C. Oza Computer Science Division University of California Berkeley, or in data mining tasks where the datasets are large enough that multiple passes would require a pro and/or input features. We have developed input decimation (Tumer & Oza 1999), a technique that uses

  14. Equivalence of Julesz Ensembles and FRAME Models

    Microsoft Academic Search

    Ying Nian Wu; Song Chun Zhu; Xiuwen Liu

    2000-01-01

    In the past thirty years, research on textures has been pursued along two different lines. The first line of research, pioneered by Julesz (1962, IRE Transactions of Information Theory, IT-8:84–92), seeks essential ingredients in terms of features and statistics in human texture perception. This leads us to a mathematical definition of textures in terms of Julesz ensembles (Zhu et al.,

  15. Extreme nesting in the conformal loop ensemble

    E-print Network

    Jason Miller; Samuel S. Watson; David B. Wilson

    2014-12-21

    The conformal loop ensemble CLE$_\\kappa$ with parameter $8/3 compute the almost-sure Hausdorff dimension of the set of points $z$ for which the number of CLE loops surrounding the disk of radius $\\varepsilon$ centered at $z$ has asymptotic growth $\

  16. Forecasting financial time series with ensemble learning

    Microsoft Academic Search

    Yaohui Bai; Jiancheng Sun; Jianguo Luo; Xiaobin Zhang

    2010-01-01

    The forecasting of financial time series is a challenging problem that has been addressed by many researchers due to the possible profit. We provide an analysis of using classical time series method to create an ensemble of exponential smoothing and ARIMA to solve forecasting tasks of financial time series. The algorithm is tested on several financial time series of different

  17. Ensembles of k-nearest neighbors and dimensionality reduction

    Microsoft Academic Search

    Oleg Okun; Helen Priisalu

    2008-01-01

    In this paper, ensembles of k-nearest neighbors classifiers are explored for gene expression cancer classification, where each classifier is linked to a randomly selected subset of genes. It is experimentally demonstrated using five datasets that such ensembles can yield both good accuracy and dimensionality reduction. If a characteristic called dataset complexity guides which random subset to include into an ensemble,

  18. Lung cancer cell identification based on artificial neural network ensembles

    Microsoft Academic Search

    Zhi-hua Zhou; Yuan Jiang; Yu-bin Yang; Shi-Fu Chen

    2002-01-01

    An artificial neural network ensemble is a learning paradigm where several artificial neural networks are jointly used to solve a problem. In this paper, an automatic pathological diagnosis procedure named Neural Ensemble based Detection (NED) is proposed, which utilizes an artificial neural network ensemble to identify lung cancer cells in the images of the specimens of needle biopsies obtained from

  19. Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams

    Microsoft Academic Search

    Peng Zhang; Xingquan Zhu; Jianlong Tan; Li Guo

    2010-01-01

    Ensemble learning is a commonly used tool for building prediction models from data streams, due to its intrinsic merits of handling large volumes stream data. Despite of its extraordinary successes in stream data mining, existing ensemble models, in stream data environments, mainly fall into the ensemble classifiers category, without realizing that building classifiers requires labor intensive labeling process, and it

  20. Enabling fast prediction for ensemble models on data streams

    Microsoft Academic Search

    Peng Zhang; Jun Li; Peng Wang; Byron J. Gao; Xingquan Zhu; Li Guo

    2011-01-01

    Ensemble learning has become a common tool for data stream classification, being able to handle large volumes of stream data and concept drifting. Previous studies focus on building accurate prediction models from stream data. However, a linear scan of a large number of base classifiers in the ensemble during prediction incurs significant costs in response time, preventing ensemble learning from

  1. DDD: A New Ensemble Approach For Dealing With Concept Drift

    E-print Network

    Yao, Xin

    1 DDD: A New Ensemble Approach For Dealing With Concept Drift Leandro L. Minku, Student Member in the presence of concept drifts. A recent study revealed that different diversity levels in an ensemble conditions, with very few exceptions. Index Terms--Concept drift, online learning, ensembles of learning

  2. Pooled Flood Frequency Analysis Using Neural Network Ensembles

    Microsoft Academic Search

    C. Shu; D. H. Burn

    2004-01-01

    Neural network ensembles are applied as regression models for pooled flood frequency analysis by relating catchment descriptors to the target flood quantiles. A neural network ensemble is a group of artificial neural networks (ANNs) trained for the same task with their predictions combined to generate a unique output. Recent studies have shown that the ensemble approach can significantly improve the

  3. Time and ensemble averaging in time series analysis

    Microsoft Academic Search

    Miroslaw Latka; Massimiliano Ignaccolo; Wojciech Jernajczyk; Bruce J. West

    2010-01-01

    In many applications expectation values are calculated by partitioning a single experimental time series into an ensemble of data segments of equal length. Such single trajectory ensemble (STE) is a counterpart to a multiple trajectory ensemble (MTE) used whenever independent measurements or realizations of a stochastic process are available. The equivalence of STE and MTE for stationary systems was postulated

  4. Physiological Responses to Wearing a Prototype Firefighter Ensemble Compared with a Standard Ensemble

    Microsoft Academic Search

    W. Jon Williams; Aitor Coca; Raymond Roberge; Angie Shepherd; Jeffrey Powell; Ronald E. Shaffer

    2011-01-01

    This study investigated the physiological responses to wearing a standard firefighter ensemble (SE) and a prototype ensemble (PE) modified from the SE that contained additional features, such as magnetic ring enclosures at the glove-sleeve interface, integrated boot-pant interface, integrated hood-SCBA facepiece interface, and a novel hose arrangement that rerouted self-contained breathing apparatus (SCBA) exhaust gases back into the upper portion

  5. High resolution downscaling with WRF: reproducing observed climate in high topography islands

    NASA Astrophysics Data System (ADS)

    Miranda, P. M.; Tome, R.; Azevedo, E. B.; Teixeira, M.

    2013-12-01

    Isolated islands are specially vulnerable to climate change. However, their climate is generally not explicitly reproduced in GCMs, or even in most Regional Climate Models, due to their size and complex topography. On the other hand, the isolated nature of their location may allow the use of high resolution in domains of limited size, with oceanic boundary conditions all around directly given by a GCM. It is important to know, though, how far do we need to go in horizontal resolution in order to reproduce the main features of observed climate and if the proposed method has significant advantages in relation to simpler procedures. This paper uses the WRF model to downscale global fields given by ERA-Interim and by three runs of the EC-Earth Climate Model (Hazeleger et al 2010): a control run representing the 1961-1990 climate, and two scenario runs corresponding to scenarios RCP4.5 and RCP8.5 up to the end of the 21st century. The WRF simulations builds on experience reproducing the climate in Iberia, at 9km horizontal resolution (Soares et al 2012, Cardoso et al 2013), which resulted in a good match with observations not only in what concerns the mean values of temperature and precipitation, but also the statistical distribution of high rank quantiles of daily precipitation (up to percentile 99.9). Here the WRF model is used on a nested grid configuration, with a larger domain simulated at 27km resolution and an inner domain at 6km. The cases of Madeira and Azores, 11 islands of different sizes in the subtropical North Atlantic, are simulated. Broadly speaking, results indicate significant improvements in the representation of observed precipitation in all islands in the ERA-Interim period, at the highest resolution. In the case of Madeira, the largest and bulkiest of the set, the improvement is the most remarkable, whereas in smaller islands there is a suggestion that the used resolution is still too coarse. The excellent results obtained by WRF in the Madeira ERA-Interim case indicate the ability of this model to perform as a regional climate model at high resolution, a requirement for the explicit simulation of climate in many isolated islands. Results obtained in the control and scenario runs are also analyzed, indicating significant changes in the precipitation climate in Madeira Island, and more subtle changes in the Azores. Finally, the WRF results are compared against two alternative downscaling techniques using an air mass transformation model and the precipitation model of Smith and Barstad (2005) References Barstad I, Smith RD (2005) Journal of Hydrometeorology 6, 85-99. Cardoso et al (2012) International Journal of Climatology, DOI: 10.1002/joc.361 Hazeleger et al (2010), Bulletim of the American Meteorological Society, 91, 1357-1363 . doi: 10.1175/2010BAMS2877.1 Soares et al (2012) Climate Dynamics, DOI: 10.1007/s00382-012-1315-2.

  6. Regional-to-Urban Enviro-HIRLAM Downscaling for Meteorological and Chemical Patterns over Chinese Megacities

    NASA Astrophysics Data System (ADS)

    Mahura, Alexander; Nuterman, Roman; Gonzalez-Aparicio, Iratxe; Amstrup, Bjarne; Baklanov, Alexander; Yang, Xiaohua; Nielsen, Kristian

    2015-04-01

    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 downscaling 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.

  7. Downscaling Solar Power Output to 4-Seconds for Use in Integration Studies (Presentation)

    SciTech Connect

    Hummon, M.; Weekley, A.; Searight, K.; Clark, K.

    2013-10-01

    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 downscaling 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.

  8. Downscaling of Bulgarian chemical weather forecast from Bulgaria region to Sofia city

    NASA Astrophysics Data System (ADS)

    Syrakov, D.; Etropolska, I.; Prodanova, M.; Slavov, K.; Ganev, K.; Miloshev, N.; Ljubenov, T.

    2013-10-01

    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 downscaling 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 (http://www.niggg.bas.bg/cw3/).

  9. High-resolution stochastic downscaling of climate models: simulating wind advection, cloud cover and precipitation

    NASA Astrophysics Data System (ADS)

    Peleg, Nadav; Fatichi, Simone; Burlando, Paolo

    2015-04-01

    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 downscaling 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.

  10. Statistical downscaling of temperature extremes in the Mediterranean area under future climate change

    NASA Astrophysics Data System (ADS)

    Beck, Alexander; Hertig, Elke; Jacobeit, Jucundus

    2014-05-01

    Statistical approaches are developed to estimate parameters of climate change in the Mediterranean area with a focus on non-stationarities arising in the relationship between regional climate variables and their large-scale predictors. Hereby particular attention is paid to the analysis of temperature extremes which affect many components of the geosystem and therefore are of particular interest in the scope of future climate change. The E-OBS dataset (Haylock et al., 2008) delivers gridded data of the maximum temperature on a daily basis for the period from January 1950 till December 2012 with a spatial resolution of 0.25° x 0.25°. In order to analyze the data of different regions, a principal component analysis is performed and the representative grid box, i.e. the grid box with the highest loading, is separated for every principal component. The daily 95%-percentile for every month and season is computed. Additionally, time series with 5 consecutive days exceeding the 95%-percentiles were generated. Furthermore, extreme value distributions like the generalized pareto distribution (GPD) are fitted to the time series. Non-stationarities in the predictors-temperature relationships are analyzed in the percentile-based time series as well as in the parameters of the extreme value distribution. In addition to the analysis of the extreme part of the temperature distribution, analyses will concentrate on the whole distribution in order to get a more complete idea regarding temperature changes in the Mediterranean area. This is achieved by fitting mixture models to the temperature data. Subsequently, a perfect prog downscaling approach is used to to assess future temperature change under enhanced greenhouse gas conditions. Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones, and M. New (2008), A European daily high-resolution gridded data set of surface temperature and precipitation for 1950 - 2006, J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.

  11. Downscaling Solar Power Output to 4-Seconds for Use in Integration Studies: Preprint

    SciTech Connect

    Hummon, M.; Weekley, A.; Searight, K.; Clark, K.

    2013-10-01

    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 downscaling 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.

  12. A Physiographic Approach to Downscaling Remotely Sensed Fractional Snow Cover Data

    NASA Astrophysics Data System (ADS)

    Walters, R. D.; Watson, K. A.; Flores, A. N.

    2012-12-01

    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 downscale 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.

  13. Dynamical downscaling forecasts of Western North Pacific tropical cyclone genesis and landfall

    NASA Astrophysics Data System (ADS)

    Huang, Wan-Ru; Chan, Johnny C. L.

    2014-04-01

    This study evaluates the potential use of the regional climate model version 3 (RegCM3) driven by (1) the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) data during 1982-2001 and (2) the NCEP Climate Forecast System Version 2 (CFS2) hindcast data during 2000-2010 in forecasting Western North Pacific (WNP) tropical cyclone (TC) activity. The first experiment is conducted to investigate the ability of the model in generating a good climatology of TC activity in spatial and temporal scales, so the model could be used in the second experiment to test its ability in forecasting TC genesis and landfall. Both experiments extend through the May to October WNP-TC season. Results show that the use of RegCM3 driven by the CFSR achieves a better simulation on the temporal and spatial variation of WNP-TC genesis during 1982-2001, as compared to previous studies using the same model but driven by the ERA40 reanalysis. In addition, diagnoses on the use of RegCM3 driven by the CFS2 point out that the 2000-2010 WNP-TC genesis locations and numbers from the model are very similar to those from the observations. The skill of RegCM3 in the forecasts of landfalling TCs is higher over the Southeast Asian region than over the other sub-regions of East Asia. Potential causes for such regional differences are discussed. Most importantly, statistical analyses show that the use of RegCM3 driven by the CFS2 gives a better forecast skill than the use of CFS2 alone for the prediction of WNP-TCs making landfall in East Asia. This indicates that the use of a dynamical downscaling method for the global forecast data would likely lead to a higher forecast skill of regional TC landfalls in most of the East Asian region.

  14. Influence of similarity measures on the performance of the analog method for downscaling daily precipitation

    NASA Astrophysics Data System (ADS)

    Matulla, C.; Zhang, X.; Wang, X. L.; Wang, J.; Zorita, E.; Wagner, S.; von Storch, H.

    2008-02-01

    This study examines the performance of the analog method for downscaling daily precipitation. The evaluation is performed for (1) a number of similarity measures for searching analogs, (2) various ways to include the past atmospheric evolution, and (3) different truncations in EOF space. It is carried out for two regions with complex topographic structures, and with distinct climatic characteristics, namely, California’s Central Valley (together with the Sierra Nevada) and the European Alps. NCEP/NCAR reanalysis data are used to represent the large scale state of the atmosphere over the regions. The assessment is based on simulating daily precipitation for 103 stations for the month of January, for the years 1950 2004 in the California region, and for 70 stations in the European Alps (January 1948 2004). Generally, simulated precipitation is in better agreement with observations in the California region than in the European Alps. Similarity measures such as the Euclidean norm, the sum of absolute differences and the angle between two atmospheric states perform better than measures which introduce additional weightings to principal components (e.g., the Mahalanobis distance). The best choice seems dependent upon the target variable. Lengths of wet spells, for instance, are best simulated by using the angular similarity measure. Overall, the Euclidean norm performs satisfactorily in most cases and hence is a reasonable first choice, whereas the use of Mahalanobis distance is less advisable. The performance of the analog method improves by including large-scale information for bygone days, particularly, for the simulation of wet and dry spells. Optimal performance is obtained when about 85 90% of the total predictor variability is retained.

  15. UMASS LOWELL'sUMASS LOWELL's New EnglandNew England SeniorSenior Wind EnsembleWind Ensemble

    E-print Network

    Massachusetts at Lowell, University of

    UMASS LOWELL'sUMASS LOWELL's New EnglandNew England SeniorSenior Wind EnsembleWind Ensemble (978 to participate, and that there is no objection to his or her participation, in the New England Youth Wind and responsibilities surrounding my / my child's participation in UMass Lowell's New England Youth Wind Ensemble and

  16. Incorporation of seasonal climate forecasts in the ensemble streamflow prediction system

    NASA Astrophysics Data System (ADS)

    Gan, T. Y.; Gobena, A.

    2012-04-01

    A technique for incorporating 0-3 months lead temperature and precipitation forecasts from two Canadian numerical weather prediction (NWP) models into the ensemble streamflow prediction (ESP) system is presented. The technique involves downscaling monthly NWP forecast outputs to station locations using the model output statistics (MOS) approach and then temporally disaggregating the monthly forecasts into daily input weather data suitable for driving a hydrologic model. The daily weather sequence for a desired month is generated by a nearest neighbor re-sampling of one of the years in the historical record, and then modifying the daily weather data for the same month of the re-sampled year so as to reproduce the MOS-based monthly forecast value. Streamflow forecasts from the MOS-based scheme are compared to pre-ESP and post-ESP re-sampling schemes without seasonal climate forecast guidance. In the pre-ESP scheme, daily weather inputs for the hydrologic model were conditionally re-sampled from historical records. In the post-ESP scheme, streamflow traces produced by the climatic ESP system were conditionally re-sampled. The three schemes were applied to the Bow and Castle rivers, both located in the headwaters of the South Saskatchewan River basin in the province of Alberta, Canada. Correlations between the MOS-based median forecast and observed flow for the Castle River were consistently higher than those based on the pre-ESP and post-ESP schemes. Other skill measures showed mixed results, with the MOS-based forecasts being more skillful in some cases and less skillful in others. All three schemes exhibited better skill for above-normal flow categories than for below-normal categories. It is also shown that considerable improvement in the ESP forecast skill could be achieved through more accurate simulation of streamflow, particularly for forecast issue dates late in the water year.

  17. Ensemble Modeling of CME Propagation and Geoeffectiveness

    NASA Astrophysics Data System (ADS)

    Mays, M. Leila; Taktakishvili, Aleksandre; Pulkkinen, Antti; MacNeice, Peter; Rastätter, Lutz; Odstrcil, Dusan; Jian, Lan; Richardson, Ian

    2015-04-01

    Ensemble 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 ensemble 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 ensemble 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 ensemble 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 ensemble 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 ensemble 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 ensembles 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 ensembles 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 ensemble mean CME arrival time and impact forecast from a variety of models/methods.

  18. Building an ensemble of climate scenarios for decision-making in hydrology: benefits, pitfalls and uncertainties

    NASA Astrophysics Data System (ADS)

    Braun, Marco; Chaumont, Diane

    2013-04-01

    Using climate model output to explore climate change impacts on hydrology requires several considerations, choices and methods in the post treatment of the datasets. In the effort of producing a comprehensive data base of climate change scenarios for over 300 watersheds in the Canadian province of Québec, a selection of state of the art procedures were applied to an ensemble comprising 87 climate simulations. The climate data ensemble is based on global climate simulations from the Coupled Model Intercomparison Project - Phase 3 (CMIP3) and regional climate simulations from the North American Regional Climate Change Assessment Program (NARCCAP) and operational simulations produced at Ouranos. Information on the response of hydrological systems to changing climate conditions can be derived by linking climate simulations with hydrological models. However, the direct use of raw climate model output variables as drivers for hydrological models is limited by issues such as spatial resolution and the calibration of hydro models with observations. Methods for downscaling and bias correcting the data are required to achieve seamless integration of climate simulations with hydro models. The effects on the results of four different approaches to data post processing were explored and compared. We present the lessons learned from building the largest data base yet for multiple stakeholders in the hydro power and water management sector in Québec putting an emphasis on the benefits and pitfalls in choosing simulations, extracting the data, performing bias corrections and documenting the results. A discussion of the sources and significance of uncertainties in the data will also be included. The climatological data base was subsequently used by the state owned hydro power company Hydro-Québec and the Centre d'expertise hydrique du Québec (CEHQ), the provincial water authority, to simulate future stream flows and analyse the impacts on hydrological indicators. While this submission focuses on the production of climatic scenarios for application in hydrology, the submission « The (cQ)2 project: assessing watershed scale hydrological changes for the province of Québec at the 2050 horizon, a collaborative framework » by Catherine Guay describes how Hydro-Québec and CEHQ put the data into use.

  19. Ensemble-based multi-scale assimilation

    NASA Astrophysics Data System (ADS)

    Ravela, S.; Hansen, J.; Hill, C.; Hill, H.; Marshall, J.

    2003-04-01

    We develop ensemble methods for constraining numerical models due to errors induced both by uncertain initial states and model structure. In the present paper, circulation phenomena are physically simulated in a laboratory and sensors are used to extract observations (velocity, temperature, etc.). Ensembles of the MITGCM constructed across variations in state and model-parameterizations are assimilated with observations over sliding multi-scale assimilation windows to regulate the trajectory of the model attractors vis a vis the system attractor. The novel contribution of this work is in bringing together the use of multi-scale assimilations, physical processes of moderate complexity, techniques for extracting flow and providing physically meaningful ways to alter analyses for minimizing model/data misfit.

  20. ABCD of beta ensembles and topological strings

    NASA Astrophysics Data System (ADS)

    Krefl, Daniel; Walcher, Johannes

    2012-11-01

    We study ?-ensembles with B N , C N , and D N eigenvalue measure and their relation with refined topological strings. Our results generalize the familiar connections between local topological strings and matrix models leading to A N measure, and illustrate that all those classical eigenvalue ensembles, and their topological string counterparts, are elated one to another via various deformations and specializations, quantum shifts and discrete quotients. We review the solution of the Gaussian models via Macdonald identities, and interpret them as conifold theories. The interpolation between the various models is plainly apparent in this case. For general polynomial potential, we calculate the partition function in the multi-cut phase in a perturbative fashion, beyond tree-level in the large- N limit. The relation to refined topological string orientifolds on the corresponding local geometry is discussed along the way.

  1. Statistical ensembles for money and debt

    NASA Astrophysics Data System (ADS)

    Viaggiu, Stefano; Lionetto, Andrea; Bargigli, Leonardo; Longo, Michele

    2012-10-01

    We build a statistical ensemble 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 ensemble for the Pareto distribution.

  2. An Introduction to Ensemble Streamflow Prediction

    NSDL National Science Digital Library

    COMET

    2007-01-30

    The “Introduction to Ensemble Streamflow Prediction” module provides basic information on probabilistic streamflow forecasting. In this webcast, Dr. Richard Koehler, the National Hydrologic Sciences Training Coordinator for NOAA's NWS, presents information about the types of organizations that might use probabilistic streamflow forecasts as well as foundation concepts and background for ESP methods. The module begins with a brief review of hydrologic models including deterministic, stochastic, and scenario-based approaches. It then provides an overview of time-series approaches including a summary of traditional techniques such as flood frequency, flood analysis, statistical analysis, and trend analysis. Finally, the module presents the basics of ESP techniques including an explanation of its strengths, weaknesses, and appropriate application. The module also provides guidance on how to interpret ensemble forecast products.

  3. Using the CMIP ensemble for climate prediction

    NASA Astrophysics Data System (ADS)

    Annan, James; Hargreaves, Julia

    2015-04-01

    The collection of GCMs which contribute to CMIP are often described as an ensemble 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) ensemble 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.

  4. Ensemble Modeling of Kinematic Open Channel Flow

    NASA Astrophysics Data System (ADS)

    Ercan, A.; Kavvas, M. L.

    2012-12-01

    In this study, stochastic modeling of kinematic open channel flow is performed. Nonlocal Lagrangian-Eulerian Fokker-Planck Equation of the kinematic open channel flow process under uncertain channel properties and lateral flow is developed utilizing the stochastic method of characteristics. Monte Carlo simulations applied to two numerical test problems showed that the developed Fokker-Planck equation is capable of modeling ensemble behavior of the kinematic open channel flow process.

  5. On orthogonal and symplectic matrix ensembles

    Microsoft Academic Search

    Craig A. Tracy; Harold Widom

    1996-01-01

    The focus of this paper is on the probability,E?(O;J), that a setJ consisting of a finite union of intervals contains no eigenvalues for the finiteN Gaussian Orthogonal (?=1) and Gaussian Symplectic (?=4) Ensembles and their respective scaling limits both in the bulk and at the edge of the spectrum. We show how these probabilities can be expressed in terms of

  6. The Mark III Hypercube-Ensemble Computers

    NASA Technical Reports Server (NTRS)

    Peterson, John C.; Tuazon, Jesus O.; Lieberman, Don; Pniel, Moshe

    1988-01-01

    Mark III Hypercube concept applied in development of series of increasingly powerful computers. Processor of each node of Mark III Hypercube ensemble is specialized computer containing three subprocessors and shared main memory. Solves problem quickly by simultaneously processing part of problem at each such node and passing combined results to host computer. Disciplines benefitting from speed and memory capacity include astrophysics, geophysics, chemistry, weather, high-energy physics, applied mechanics, image processing, oil exploration, aircraft design, and microcircuit design.

  7. Loop equation analysis of the circular ? ensembles

    NASA Astrophysics Data System (ADS)

    Witte, N. S.; Forrester, P. J.

    2015-02-01

    We construct a hierarchy of loop equations for invariant circular ensembles. 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 ensemble. 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 ensemble, 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.

  8. Flexible ligand docking using conformational ensembles.

    PubMed Central

    Lorber, D. M.; Shoichet, B. K.

    1998-01-01

    Molecular docking algorithms suggest possible structures for molecular complexes. They are used to model biological function and to discover potential ligands. A present challenge for docking algorithms is the treatment of molecular flexibility. Here, the rigid body program, DOCK, is modified to allow it to rapidly fit multiple conformations of ligands. Conformations of a given molecule are pre-calculated in the same frame of reference, so that each conformer shares a common rigid fragment with all other conformations. The ligand conformers are then docked together, as an ensemble, into a receptor binding site. This takes advantage of the redundancy present in differing conformers of the same molecule. The algorithm was tested using three organic ligand protein systems and two protein-protein systems. Both the bound and unbound conformations of the receptors were used. The ligand ensemble method found conformations that resembled those determined in X-ray crystal structures (RMS values typically less than 1.5 A). To test the method's usefulness for inhibitor discovery, multi-compound and multi-conformer databases were screened for compounds known to bind to dihydrofolate reductase and compounds known to bind to thymidylate synthase. In both cases, known inhibitors and substrates were identified in conformations resembling those observed experimentally. The ligand ensemble method was 100-fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 molecules in one to four CPU days on a workstation. PMID:9568900

  9. Efficient Agent-Based Cluster Ensembles

    NASA Technical Reports Server (NTRS)

    Agogino, Adrian; Tumer, Kagan

    2006-01-01

    Numerous domains ranging from distributed data acquisition to knowledge reuse need to solve the cluster ensemble problem of combining multiple clusterings into a single unified clustering. Unfortunately current non-agent-based cluster combining methods do not work in a distributed environment, are not robust to corrupted clusterings and require centralized access to all original clusterings. Overcoming these issues will allow cluster ensembles to be used in fundamentally distributed and failure-prone domains such as data acquisition from satellite constellations, in addition to domains demanding confidentiality such as combining clusterings of user profiles. This paper proposes an efficient, distributed, agent-based clustering ensemble method that addresses these issues. In this approach each agent is assigned a small subset of the data and votes on which final cluster its data points should belong to. The final clustering is then evaluated by a global utility, computed in a distributed way. This clustering is also evaluated using an agent-specific utility that is shown to be easier for the agents to maximize. Results show that agents using the agent-specific utility can achieve better performance than traditional non-agent based methods and are effective even when up to 50% of the agents fail.

  10. Effect of downscaling nano-copper interconnects on the microstructure revealed by high resolution TEM-orientation-mapping

    NASA Astrophysics Data System (ADS)

    Ganesh, K. J.; Darbal, A. D.; Rajasekhara, S.; Rohrer, G. S.; Barmak, K.; Ferreira, P. J.

    2012-04-01

    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 downscaling. Lines of width 1.8 µm exhibit a strong <111> normal texture and comprise large micron-size grains. Upon downscaling 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.

  11. Effect of downscaling nano-copper interconnects on the microstructure revealed by high resolution TEM-orientation-mapping.

    PubMed

    Ganesh, K J; Darbal, A D; Rajasekhara, S; Rohrer, G S; Barmak, K; Ferreira, P J

    2012-04-01

    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 downscaling. Lines of width 1.8 µm exhibit a strong <111> normal texture and comprise large micron-size grains. Upon downscaling 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

  12. Performance of downscaled regional climate simulations using a variable-resolution regional climate model: Tasmania as a test case

    NASA Astrophysics Data System (ADS)

    Corney, Stuart; Grose, Michael; Bennett, James C.; White, Christopher; Katzfey, Jack; McGregor, John; Holz, Greg; Bindoff, Nathaniel L.

    2013-11-01

    this study we develop methods for dynamically downscaling output from six general circulation models (GCMs) for two emissions scenarios using a variable-resolution atmospheric climate model. The use of multiple GCMs and emissions scenarios gives an estimate of model range in projected changes to the mean climate across the region. By modeling the atmosphere at a very fine scale, the simulations capture processes that are important to regional weather and climate at length scales that are subgrid scale for the host GCM. We find that with a multistaged process of increased resolution and the application of bias adjustment methods, the ability of the simulation to reproduce observed conditions improves, with greater than 95% of the spatial variance explained for temperature and about 90% for rainfall. Furthermore, downscaling leads to a significant improvement for the temporal distribution of variables commonly used in applied analyses, reproducing seasonal variability in line with observations. This seasonal signal is not evident in the GCMs. This multistaged approach allows progressive improvement in the skill of the simulations in order to resolve key processes over the region with quantifiable improvements in the correlations with observations.

  13. The Use of Statistical Downscaling to Project Regional Climate Changes as they Relate to Future Energy Production

    NASA Astrophysics Data System (ADS)

    Werth, D. W.; O'Steen, L.; Chen, K.; Altinakar, M. S.; Garrett, A.; Aleman, S.; Ramalingam, V.

    2010-12-01

    Global climate change has the potential for profound impacts on society, and poses significant challenges to government and industry in the areas of energy security and sustainability. Given that the ability to exploit energy resources often depends on the climate, the possibility of climate change means we cannot simply assume that the untapped potential of today will still exist in the future. Predictions of future climate are generally based on global climate models (GCMs) which, due to computational limitations, are run at spatial resolutions of hundreds of kilometers. While the results from these models can predict climatic trends averaged over large spatial and temporal scales, their ability to describe the effects of atmospheric phenomena that affect weather on regional to local scales is inadequate. We propose the use of several optimized statistical downscaling techniques that can infer climate change at the local scale from coarse resolution GCM predictions, and apply the results to assess future sustainability for two sources of energy production dependent on adequate water resources: nuclear power (through the dissipation of waste heat from cooling towers, ponds, etc.) and hydroelectric power. All methods will be trained with 20th century data, and applied to data from the years 2040-2049 to get the local-scale changes. Models of cooling tower operation and hydropower potential will then use the downscaled data to predict the possible changes in energy production, and the implications of climate change on plant siting, design, and contribution to the future energy grid can then be examined.

  14. WRF Dynamical Downscaling of the Twentieth Century Reanalysis for China 1.Climatic Means during 1981-2010

    NASA Astrophysics Data System (ADS)

    Kong, Xianghui; Bi, Xunqiang

    2015-04-01

    This study presents a dynamically downscaled climatology over East Asia by using the non-hydrostatic Weather Research and Forecasting (WRF) model, forced by the Twentieth Century Reanalysis (20CR-v2). The whole experiment is a 111 year (1900-2010) continuous run at 50 km horizontal resolution. Climatic means among observations, the driving fields and WRF results during the last three decades (1981-2010) are examined in continental China, and our focus is on surface air (2-m) temperature and precipitation in both summer and winter. WRF dynamically downscaling is able to reproduce the main features of surface air temperature in two seasons in China, and outperforms the driving fields in regional details due to topographic forcing. Surface air temperature biases are reduced as much as 1~2°.For precipitation, the simulated results can reproduce the decreasing pattern from southeast to northwest China in winter. For summer rainfall, the WRF simulated results reproduce the right magnitude of heavy rainfall center around the southeastern coastal area, better than the driving field. One of the significant improvements is that an unrealistic center of summer precipitation in Southeast China in 20CR-v2 is eliminated. However, the simulated results underestimate winter surface air temperature in northern China and winter rainfall in some regions in southeast China.

  15. Effects on Storm-Water Management for Three Major US Cities Using Location Specific Extreme Precipitation Dynamical Downscaling

    NASA Astrophysics Data System (ADS)

    Pelle, A.; Allen, M.; Fu, J. S.

    2013-12-01

    With rising population and increasing urban density, it is of pivotal importance for urban planners to plan for increasing extreme precipitation events. Climate models indicate that an increase in global mean temperature will lead to increased frequency and intensity of storms of a variety of types. Analysis of results from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) has demonstrated that global climate models severely underestimate precipitation, however. Preliminary results from dynamical downscaling indicate that Philadelphia, Pennsylvania is expected to experience the greatest increase of precipitation due to an increase in annual extreme events in the US. New York City, New York and Chicago, Illinois are anticipated to have similarly large increases in annual extreme precipitation events. In order to produce more accurate results, we downscale Philadelphia, Chicago, and New York City using the Weather Research and Forecasting model (WRF). We analyze historical precipitation data and WRF output utilizing a Log Pearson Type III (LP3) distribution for frequency of extreme precipitation events. This study aims to determine the likelihood of extreme precipitation in future years and its effect on the of cost of stormwater management for these three cities.

  16. Downscaled climate change impacts on agricultural water resources in Puerto Rico

    SciTech Connect

    Harmsen, E.W.; Miller, N.L.; Schlegel, N.J.; Gonzalez, J.E.

    2009-04-01

    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 downscaled 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.

  17. Downscaling parameters from groundwater model scale to properties of the constituting litho classes

    NASA Astrophysics Data System (ADS)

    Lourens, Aris; van Geer, Frans

    2015-04-01

    Like other numerical models, groundwater models are created using the best knowledge available. Still, these models usually suffer from data uncertainty and model misconceptions. Calibration of such a model may yield parameter values with which the model produces output more closely to the observed values of the dependent variables than the uncalibrated model does. In groundwater models, the model parameters are often an aggregation of two or more observed properties. For example, the transmissivity is defined as the product of the layer thickness and the conductivity of the deposits, and the vertical resistance as the quotient of the layer thickness and the conductivity. Moreover, the parameters used in groundwater models are often constructed by vertical upscaling and horizontally interpolation of small geological units (litho-layers). When calibrating the groundwater model parameters, a better fit to the groundwater head data is achieved, but it is not clear to what extent the thickness or the conductivity of the individual litho-layers should be modified. This may yield parameter values at the litho-layer scale which are not very likely from geological point of view. The question is how can we downscale the calibrated model parameters to arrive at the most likely set of conductivities and thicknesses of the individual litho-layers, respecting the prior uncertainty from geological point of view. Here, we present a method to find the most likely values of parameters of constituting litho-layers of an aquitard, based on the parameter values of a calibrated groundwater model. The objective of this method is twofold. On one hand, finding the most likely parameter values for the thicknesses and the hydraulic conductivities of each individual litho layer. On the other hand, the most likely parameter values of the litho-layers may be very unlikely from geological perspective and, herewith, indicate connectional model errors. The properties of each litho-class at the borehole scale are upscaled and interpolated to the grid cell scale of the groundwater model, using the complete probability density function (PDF) of the parameter values. Herewith, the joint PDF of all litho-classes at every grid cell is available. Assuming the calibrated parameter value being the truth, the maximum likelihood values of the conductivity and layer thickness of each litho-class at each grid cell can be determined. All random variables are described by piecewise linear PDFs, which makes the use of a wide variety of PDFs possible and the calculations feasible. The method is illustrated with an example derived from a real world groundwater model.

  18. Statistical downscaling of regional climate scenarios for the French Alps : Impacts on snow cover

    NASA Astrophysics Data System (ADS)

    Rousselot, M.; Durand, Y.; Giraud, G.; Mérindol, L.; Déqué, M.; Sanchez, E.; Pagé, C.; Hasan, A.

    2010-12-01

    Mountain areas are particularly vulnerable to climate change. Owing to the complexity of mountain terrain, climate research at scales relevant for impacts studies and decisive for stakeholders is challenging. A possible way to bridge the gap between these fine scales and those of the general circulation models (GCMs) consists of combining high-resolution simulations of Regional Climate Models (RCMs) to statistical downscaling methods. The present work is based on such an approach. It aims at investigating the impacts of climate change on snow cover in the French Alps for the periods 2021-2050 and 2071-2100 under several IPCC hypotheses. An analogue method based on high resolution atmospheric fields from various RCMs and climate reanalyses is used to simulate local climate scenarios. These scenarios, which provide meteorological parameters relevant for snowpack evolution, subsequently feed the CROCUS snow model. In these simulations, various sources of uncertainties are thus considered (several greenhouse gases emission scenarios and RCMs). Results are obtained for different regions of the French Alps at various altitudes. For all scenarios, temperature increase is relatively uniform over the Alps. This regional warming is larger than that generally modeled at the global scale (IPCC, 2007), and particularly strong in summer. Annual precipitation amounts seem to decrease, mainly as a result of decreasing precipitation trends in summer and fall. As a result of these climatic evolutions, there is a general decrease of the mean winter snow depth and seasonal snow duration for all massifs. Winter snow depths are particularly reduced in the Northern Alps. However, the impact on seasonal snow duration is more significant in the Southern and Extreme Southern Alps, since these regions are already characterized by small winter snow depths at low elevations. Reference : IPCC (2007a). Climate change 2007 : The physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. In : Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H.L. Miller (eds.). Cambridge University Press, Cambridge, UK and New York, NY, USA. This work is performed in the framework of the SCAMPEI ANR (French research project).

  19. Representing model error in ensemble data assimilation

    NASA Astrophysics Data System (ADS)

    Cardinali, C.; Žagar, N.; Radnoti, G.; Buizza, R.

    2014-09-01

    The paper investigates a method to represent model error in the ensemble data assimilation (EDA) system. The ECMWF operational EDA simulates the effect of both observations and model uncertainties. Observation errors are represented by perturbations with statistics characterized by the observation error covariance matrix whilst the model uncertainties are represented by stochastic perturbations added to the physical tendencies to simulate the effect of random errors in the physical parameterizations (ST-method). In this work an alternative method (XB-method) is proposed to simulate model uncertainties by adding perturbations to the model background field. In this way the error represented is not just restricted to model error in the usual sense but potentially extends to any form of background error. The perturbations have the same correlation as the background error covariance matrix and their magnitude is computed from comparing the high-resolution operational innovation variances with the ensemble variances when the ensemble is obtained by perturbing only the observations (OBS-method). The XB-method has been designed to represent the short range model error relevant for the data assimilation window. Spread diagnostic shows that the XB-method generates a larger spread than the ST-method that is operationally used at ECMWF, in particular in the extratropics. Three-dimensional normal-mode diagnostics indicate that XB-EDA spread projects more than the spread from the other EDAs onto the easterly inertia-gravity modes associated with equatorial Kelvin waves, tropical dynamics and, in general, model error sources. The background error statistics from the above described EDAs have been employed in the assimilation system. The assimilation system performance showed that the XB-method background error statistics increase the observation influence in the analysis process. The other EDA background error statistics, when inflated by a global factor, generate analyses with 30-50% smaller degree of freedom of signal. XB-EDA background error variances have not been inflated. The presented EDAs have been used to generate the initial perturbations of the ECMWF ensemble prediction system (EPS) of which the XB-EDA induces the largest EPS spread, also in the medium range, leading to a more reliable ensemble. Compared to ST-EDA, XB-EDA leads to a small improvement of the EPS ignorance skill score at day 3 and 7.

  20. De praeceptis ferendis: good practice in multi-model ensembles

    NASA Astrophysics Data System (ADS)

    Kioutsioukis, I.; Galmarini, S.

    2014-06-01

    Ensembles of air quality models have been formally and empirically shown to outperform single models in many cases. Evidence suggests that ensemble error is reduced when the members form a diverse and accurate ensemble. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to provide better predictions. There exists a trade-off between diversity and accuracy for which one cannot be gained without expenses of the other. Theoretical aspects like the bias-variance-covariance decomposition and the accuracy-diversity decomposition are linked together and support the importance of creating ensemble that incorporates both the elements. Hence, the common practice of unconditional averaging of models without prior manipulation limits the advantages of ensemble averaging. We demonstrate the importance of ensemble accuracy and diversity through an inter-comparison of ensemble products for which a sound mathematical framework exists, and provide specific recommendations for model selection and weighting for multi model ensembles. To this end we have devised statistical tools that can be used for diagnostic evaluation of ensemble modelling products, complementing existing operational methods.

  1. Ensemble Data Assimilation Without Ensembles: Methodology and Application to Ocean Data Assimilation

    NASA Technical Reports Server (NTRS)

    Keppenne, Christian L.; Rienecker, Michele M.; Kovach, Robin M.; Vernieres, Guillaume

    2013-01-01

    Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble 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 ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble 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.

  2. Review of scenario selection and downscaling methods for the assessment of climate change impacts on hydrology in the United States pacific northwest

    Microsoft Academic Search

    Eric P. Salathé Jr; Philip W. Mote; Matthew W. Wiley

    2007-01-01

    This paper reviews methods that have been used to evaluate global climate simulations and to downscale global climate scenarios for the assessment of climate impacts on hydrologic systems in the Pacific Northwest, USA. The approach described has been developed to facilitate integrated assessment research in support of regional resource management. Global climate model scenarios are evaluated and selected based on

  3. Regional climate models downscaling in the Alpine area with multimodel superensemble

    NASA Astrophysics Data System (ADS)

    Cane, D.; Barbarino, S.; Renier, L. A.; Ronchi, C.

    2013-05-01

    The climatic scenarios show a strong signal of warming in the Alpine area already for the mid-XXI century. The climate simulations, however, even when obtained with regional climate models (RCMs), are affected by strong errors when compared with observations, due both to their difficulties in representing the complex orography of the Alps and to limitations in their physical parametrization. Therefore, the aim of this work is to reduce these model biases by using a specific post processing statistic technique, in order to obtain a more suitable projection of climate change scenarios in the Alpine area. For our purposes we used a selection of regional climate models (RCMs) runs which were developed in the framework of the ENSEMBLES project. They were carefully chosen with the aim to maximise the variety of leading global climate models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observations for the greater Alpine area were extracted from the European dataset E-OBS (produced by the ENSEMBLES project), which have an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (covering the period from 1957 to the present) were carefully gridded on a 14 km grid over Piedmont region through the use of an optimal interpolation technique. Hence, we applied the multimodel superensemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We also proposed the application of a brand new probabilistic multimodel superensemble dressing technique, already applied to weather forecast models successfully, to RCMS: the aim was to estimate precipitation fields, with careful description of precipitation probability density functions conditioned to the model outputs. This technique allowed for reducing the strong precipitation overestimation, arising from the use of RCMs, over the Alpine chain and to reproduce well the monthly behaviour of precipitation in the control period.

  4. Climate Change Impacts on Precipitation and Groundwater Recharge in Denmark: A Distributed Hydrological Modeling Study using Multiple Downscaling Methods on the Climate Inputs

    NASA Astrophysics Data System (ADS)

    Seaby, L. P.; Refsgaard, J.; Sonnenborg, T.; Jensen, K. H.

    2011-12-01

    Future changes in climate are expected to result in more extreme hydrological conditions globally. For Denmark, most climate models predict increases in annual precipitation, with higher intensity rainfall events occurring in winter and reduced precipitation and higher evapotranspiration in summer. Changes in the quantity, timing, and delivery of precipitation is expected to result in higher rates of groundwater recharge in the winter months, as well as flooding and water logging in low lying areas, and decreased water tables, dry root zones, and reduced low flows in the summer months. There is, however, variability between climate models on the direction and strength of the climate change signal. Additionally, regional climate models (RCMs) are subject to systematic errors making their outputs, especially precipitation, require further downscaling and bias correction prior to use in hydrological simulations. Consequently, hydrological outputs simulated under climate change compound the uncertainties within individual climate model predictions, between various climate models, and in the choice of downscaling and bias correction method. This study compares 11 transient climate change scenarios from the EU project ENSMEBLES, which makes available a matrix of GCM-RCM pairings for all of Europe at a 25 km2 grid scale to the year 2100. Temperature, precipitation, and potential evapotranspiration (calculated from climate model outputs) are downscaled using two methods: a monthly delta change approach that transfers absolute (state variables) or relative (flux variables) climate change from the RCM scenarios to the observed data, and a seasonal histogram equalization method that fits gamma distributions based on the instensity of daily observed and scenario data (flux variables) and scales scenario data based on the difference in gamma functions. Downscaling is spatially distributed within Denmark according to the seven sub-model regions in the National Water Resources Model (DK-model), delineated based on natural hydrological boundaries. The MIKE-SHE based DK-model is composed of a relatively simple root zone component for estimating net precipitation, a comprehensive three-dimensional groundwater component for estimating recharge and hydraulic head in different geological layers, and a river component for stream flow routing and calculating stream-aquifer interaction. The downscaled climate change scenarios are used to force DK-model simulations of hydrogeological outputs for all of Denmark. Precipitation changes and differences in rainfall intensity under the two downscaling methods are analyzed for all 11 scenarios. The impact of climate change on groundwater recharge and the relationship with rainfall intensity and potential evapotranspiration is analyzed across the seven sub-regions in Denmark and between all climate scenarios using both downscaling methods.

  5. Good Concatenated Code Ensembles for the Binary Erasure Channel

    E-print Network

    Amat, Alexandre Graell i

    2009-01-01

    In this work, we give good concatenated code ensembles for the binary erasure channel (BEC). In particular, we consider repeat multiple-accumulate (RMA) code ensembles formed by the serial concatenation of a repetition code with multiple accumulators, and the hybrid concatenated code (HCC) ensembles recently introduced by Koller et al. (5th Int. Symp. on Turbo Codes & Rel. Topics, Lausanne, Switzerland) consisting of an outer multiple parallel concatenated code serially concatenated with an inner accumulator. We introduce stopping sets for iterative constituent code oriented decoding using maximum a posteriori erasure correction in the constituent codes. We then analyze the asymptotic stopping set distribution for RMA and HCC ensembles and show that their stopping distance hmin, defined as the size of the smallest nonempty stopping set, asymptotically grows linearly with the block length. Thus, these code ensembles are good for the BEC. It is shown that for RMA code ensembles, contrary to the asymptotic m...

  6. Argumentation Based Joint Learning: A Novel Ensemble Learning Approach

    PubMed Central

    Xu, Junyi; Yao, Li; Li, Le

    2015-01-01

    Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble 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 ensemble classifier and improve the performance of classification. PMID:25966359

  7. Impact of altimetry data on ENSO ensemble initializations and predictions

    NASA Astrophysics Data System (ADS)

    Zheng, Fei; Zhu, Jiang; Zhang, Rong-Hua

    2007-07-01

    The El Niño/Southern Oscillation (ENSO) predictions strongly depend on the accuracy and dynamical consistency of the coupled initial conditions. Based on the proposed ensemble Kalman filter (EnKF), a new initialization scheme for the ENSO ensemble prediction system (EPS) was designed and tested in an intermediate coupled model (ICM). The inclusion of this scheme in the ICM leads to substantial improvements in ENSO prediction skill via the successful assimilation of both observed sea surface temperature (SST) and TOPEX/Poseidon/Jason-1 (T/P/J) altimeter data into the initial ensemble conditions. Comparisons with the original ensemble hindcast experiment show that the ensemble prediction skills were significantly improved out to a 12-month lead time by improving sea level (SL) initial conditions for better parameterization of subsurface thermal effects. It is clearly demonstrated that improvement in forecast skill can result from the multivariate and multi-observational ensemble data assimilation.

  8. A Framework for Non-Equilibrium Statistical Ensemble Theory

    NASA Astrophysics Data System (ADS)

    Bi, Qiao; He, Zu-Tan; Liu, Jie

    2011-07-01

    Since Gibbs synthesized a general equilibrium statistical ensemble theory, many theorists have attempted to generalized the theory to non-equilibrium domain, however the status of the theory of non-equilibrium phenomena can not be said so well established as the Gibbsian ensemble theory. In this work, we present a formalism for the non-equilibrium statistical ensemble based on a subdynamic kinetic equation (SKE) rooted from the Brussels-Austin school and followed by some up-to-date works. The constructed key is to use a similarity transformation between Gibbsian ensembles formalism based on Liouville equation and the subdynamic ensemble formalism based on the SKE. Using this formalism, we study the spin-Boson system, as cases of weak coupling or strongly coupling, and obtain the reduced density operators for the Canonical ensembles easily.

  9. Evaluating characteristics of dry spell changes in Lake Urmia Basin using an ensemble CMIP5 GCM models

    NASA Astrophysics Data System (ADS)

    Fazel, Nasim; Berndtsson, Ronny; Bertacchi Uvo, Cintia; Klove, Bjorn; Madani, Kaveh

    2015-04-01

    Drought is a natural phenomenon that can cause significant environmental, ecological, and socio-economic losses in water scarce regions. Studies of drought under climate change are essential for water resources planning and management. Dry spells and number of consecutive days with precipitation below a certain threshold can be used to identify the severity of hydrological drought. In this study, we analyzed the projected changes of number of dry days in two future periods, 2011-2040 and 2071-2100, for both seasonal and annual time scales in the Lake Urmia Basin. The lake and its wetlands, located in northwestern Iran, have invaluable environmental, social, and economic importance for the region. The lake level has been shrinking dramatically since 1995 and now the water volume is less than 30% of its original. Moreover, frequent dry spells have struck the region and effected the region's water resources and lake ecosystem as in other parts of Iran too. Analyzing future drought and dry spells characteristics in the region is crucial for sustainable water management and lake restoration plans. We used daily projected precipitation from 20 climate models used in the CMIP5 (Coupled Model Inter-comparison Project Phase 5) driven by three representative paths, RCP2.6, RCP4.5, and, RCP8.5. The model outputs were statistically downscaled and validated based on the historical observation period 1980-2010. We defined days with precipitation less than 1 mm as dry days for both observation periods and model projections. The model validation showed that all models underestimated the number of dry days. An ensemble based on the validation results consisting of five models which were in best agreement with observations was used to assess the changes in number of future dry days in Lake Urmia Basin. The entire ensemble showed increase in number of dry days for all seasons. The projected changes in winter and spring were larger than for summer and autumn. All models projected dryer winter and spring periods in the near and far future periods. The ensemble mean for future annual dry days increased by 6.5 % to 7.3% for the different climate change related emission and concentration pathway RCP2.6, RCP4.5, and, RCP8.5.

  10. Accuracy Updated Ensemble for Data Streams with Concept Drift

    Microsoft Academic Search

    Dariusz Brzezi?ski; Jerzy Stefanowski

    \\u000a In this paper we study the problem of constructing accurate block-based ensemble classifiers from time evolving data streams.\\u000a AWE is the best-known representative of these ensembles. We propose a new algorithm called Accuracy Updated Ensemble (AUE),\\u000a which extends AWE by using online component classifiers and updating them according to the current distribution. Additional\\u000a modifications of weighting functions solve problems with

  11. Data assimilation using an ensemble Kalman filter tech

    Microsoft Academic Search

    H. L. Mitchell

    1998-01-01

    ABSTRACT The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique,referred to as ensemble,Kalman,filtering) is examined,in an idealized environment. Using a three-level, quasigeostrophic, T21 model and simulated observations, experiments are performed,in a perfect-model context. By using forward,interpolation operators from the model,state to the observations, the ensemble Kalman filter is able to

  12. Some analytical results on critic-driven ensemble classification

    Microsoft Academic Search

    David J. Miller; Lian Yan

    1999-01-01

    We (1999) proposed a framework for ensemble classification wherein auxiliary networks, dubbed critics, are used to provide reliability information on the ensemble's individual classifiers\\/experts. We showed experimentally that critic-driven combining schemes extend the applicability of ensemble methods by overcoming the usual requirement that the individual classifier error rate p must be less than 0.5. Here, we support our previous work

  13. Extreme Precipitation in the San Francisco Bay Area: Comparing Downscaling Methodologies' Skill in Representing Extreme Precipitation in Hindcasts and Differences in Their Projections

    NASA Astrophysics Data System (ADS)

    Chiang, F.; Milesi, C.; Costa-Cabral, M. C.; Rath, J.; Wang, W.; Podolske, J. R.

    2014-12-01

    Despite the growing availability of high-resolution datasets of spatially downscaled CMIP5 projections, few studies have explored the differences in extreme precipitation events that stem from the choice of downscaling method, or from the specific climatological datasets that are used for the bias correction and spatial disaggregation. Here we take three different statistically downscaled 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 downscaling 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 downscaling 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.

  14. Dynamical downscaling of IPSL-CM5 CMIP5 historical simulations over the Mediterranean: benefits on the representation of regional surface winds and cyclogenesis

    NASA Astrophysics Data System (ADS)

    Flaounas, Emmanouil; Drobinski, Philippe; Bastin, Sophie

    2013-05-01

    The Mediterranean region is identified as one of the two main hot-spots of climate change and also known to have the highest concentration of cyclones in the world. These atmospheric features contribute significantly to the regional climate but they are not reproduced by the Atmosphere-Ocean General Circulation Models (AOGCM), due to their coarse horizontal resolution, which have recently been run in the frame of the 5th Climate Model Intercomparison Project. This article investigates the benefit of dynamically downscaling the Institut Pierre Simon Laplace (IPSL) AOGCM (IPSL-CM5) historical simulation by the weather and research forecasting (WRF) for the representation of the Mediterranean surface winds and cyclonic activity. Indeed, when considering IPSL-CM5 atmospheric fields, the dramatic underestimation of the cyclonic activity in the most cyclogenetic region of the world jeopardizes our ability to investigate in-depth the Mediterranean regional climate and trend in the context of global change. The WRF model shows remarkable skill to reproduce regional cyclogenesis. Indeed, cyclones occurrence is quasi-absent in IPSL-CM5 data but when applying dynamical downscaling their spatial-temporal variability is very close to the re-analysis. This is a clear benefit of dynamical downscaling in regions of strong topographic forcing. This "steady" source of forcing allows the production of lee cyclogenesis and the development of strong cyclones, whatever the quality of the large-scale circulation provided at the WRF's boundaries by IPSL-CM5. However, dynamical downscaling still presents disadvantages as for instance the fact that large-scale inaccurate features of the IPSL-CM5 regional circulation are replicated by WRF due to the boundary controlled (small domain) simulation. The advantages and disadvantages of dynamical downscaling are thoroughly discussed in this paper revealing its importance for climate research, especially in the context of future scenarios and wind impacts.

  15. A proxy for high-resolution regional reanalysis for the Southeast United States: assessment of precipitation variability in dynamically downscaled reanalyses

    USGS Publications Warehouse

    Stefanova, Lydia; Misra, Vasubandhu; Chan, Steven; Griffin, Melissa; O'Brien, James J.; Smith, Thomas J., III

    2012-01-01

    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 downscaled 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 downscaled 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 downscaled reanalyses remains, as in the "parent" reanalyses, overestimated. Given the degree of success that dynamical downscaling 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 downscaling is a viable proxy for high-resolution conventional reanalysis.

  16. Seasonal hydrological ensemble forecasts over Europe

    NASA Astrophysics Data System (ADS)

    Arnal, Louise; Wetterhall, Fredrik; Pappenberger, Florian

    2015-04-01

    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 Ensemble 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 ensemble members, extended to 51 members every three months. The ESP forecasts comprise 20 ensembles 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.

  17. Gibbs entropy of network ensembles by cavity methods

    NASA Astrophysics Data System (ADS)

    Anand, Kartik; Bianconi, Ginestra

    2010-07-01

    The Gibbs entropy of a microcanonical network ensemble 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 ensembles. We derive exact expression for this correspondence using the cavity methods for the configuration model, for the ensembles with constraint degree sequence and community structure and for the ensemble with constraint degree sequence and number of links at a given distance.

  18. A probabilistic approach to forecast the uncertainty with ensemble spread

    NASA Astrophysics Data System (ADS)

    Van Schaeybroeck, Bert; Vannitsem, Stéphane

    2015-04-01

    For most purposes the information gathered from an ensemble forecast is the ensemble mean and its uncertainty. The ensemble spread is commonly used as a measure of the uncertainty. We propose a method to assess whether the ensemble spread is a good measure of uncertainty and to bring forward an underlying spread-skill relationship. Forecasting the uncertainty should be probabilistic of nature. This implies that, if only the ensemble spread is available, a probability density function (PDF) for the uncertainty forecast must be reconstructed based on one parameter. Different models are introduced for the composition of such PDFs and evaluated for different spread-error metrics. The uncertainty forecast can then be verified based on probabilistic skill scores. For a perfectly reliable forecast the spread-error relationship is strongly heteroscedastic since the error can take a wide range of values, proportional to the ensemble spread. This makes a proper statistical assessment of the spread-skill relation intricate. However, it is shown that a logarithmic transformation of both spread and error allows for alleviating the heteroscedasticity. A linear regression analysis can then be performed to check whether the flow-dependent spread is a realistic indicator of the uncertainty and to what extent ensemble underdispersion or overdispersion depends on the ensemble spread. The methods are tested on the ensemble forecast of wind and geopotential height of the European Centre of Medium-range forecasts (ECMWF) over Europe and Africa. A comparison is also made with spread-skill analysis based on binning methods.

  19. Probing an ensemble of superconducting devices

    NASA Astrophysics Data System (ADS)

    Sears, Adam; Hover, David; Gudmundsen, Theodore; Yoder, Jonilyn L.; Kamal, Archana; Yan, Fei; Gustavsson, Simon; Kerman, Andrew; Oliver, William

    2015-03-01

    We present experimental results on a system in which we use a flux qubit to probe an ensemble of weakly coupled superconducting devices. We employ standard qubit metrology techniques to reveal global device properties. In addition, we discuss the connection with engineered environmental decoherence. This work is sponsored by the Assistant Secretary of Defense for Research & Engineering under Air Force Contract FA8721-05-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government.

  20. Robin Cox Ensemble A quintet of strings, clarinet, and percussion, Robin Cox Ensemble

    E-print Network

    Baltisberger, Jay H.

    performance. McCurdy/Wright Consort The Consort's multimedia performance links the words of Langston Hughes' poetry to jazz and to visual images of people, places, and events of the Harlem Renaissance. Dirk Powell Norman. The ensemble's extraordinary performances range from traditional to renaissance to baroque music

  1. Downscaling of snow depth and river discharge in Japan by the Pseudo-Global-Warming Method

    NASA Astrophysics Data System (ADS)

    Kimura, F.; Ma, X.; Hara, M.; Advanced Atmosphere-Ocean-Land Modeling Program

    2010-12-01

    Although a heavy snowfall often brings disaster, snow cover is one of the major water resources in Japan. Even during the winter, the monthly mean of the surface air temperature often exceeds 0 deg. in large parts of the heavy snow areas along the Sea of Japan. Thus, snow cover may be seriously reduced in these areas as a result of global warming, which is caused by an increase in greenhouse gases. This study estimates the impact of global warming on the snow depth in Japan during early winter. Some dynamical downscaling experiments are conducted by the Pseudo-Global-Warming method for the future projection of snow cover. By the hindcast runs, precipitation, snow depth, and surface air temperature show good agreement with the AMeDAS station data observed in a High-Snow-Cover (HSC) year and a Low-Snow-Cover (LSC) yea. Pseudo-Global-Warming runs for these years indicate that the decreasing ratios of the snow water are more significant in the areas whose altitude is less than 1500 m. The increase of the air temperature is one of the major factors for the decrease in snow water, since the present mean air temperature in most of these areas is near 0 deg. even in winter. On the other hand, the change in the aerial-mean precipitation due to global warming is less than 15% in both years. To evaluate the impact of the reduction of snow cover to water resource, a hydrological simulation is also made for the Agano River basin, which locates in Niigata and Fukushima Prefectures. The Agano River drains into the Sea of Japan and is the second largest river in Japan with annual discharge of about 12.9 billion m3. A hind cast experiment is carried out for the two decades from 1980 to 1999. The average correlation coefficient of 0.79 for the monthly mean discharge in the winter season indicates that the interannual variation of the river discharge could be reproduced and that the method is useful for climate change study. Then the hydrological response to the future global warming in the 2070s is investigated. Assuming the reference present climate period of 1990s, the monthly mean discharge for the 2070s is projected to increase by approximately 43% in January and 55% in February, but to decrease by approximately 38% in April and 32% in May. The flood peak in the hydrograph will shift to approximately one month earlier, i.e., from April in the 1990s to March in the 2070s. Furthermore, the 10-year average of snowfall amount is projected to be approximately 49.5% lower in the 2070s than that in the 1990s. Acknowledgment: This work was supported by the Global Environment Research Fund (S-5-3) of the Ministry of the Environment, Japan. References 1. Ma, X., T. Yoshikane, M. Hara, Y. Wakazuki, H. G Takahashi, and F. Kimura, 2010: Hydrological response to future climate change in the Agano River basin, Japan, Hydrological Research Letters, 4, 25-29 2. Hara,M., T.Yoshikane, H.Kawase and F.Kimura 2008:Impact of the Estimation of Global Warming on Snow Depth in Japan by the Pseudo-Global-Warming Method. Hydrological Research Letters 2 61-64.

  2. Spatial similarity and transferability of analog dates for precipitation downscaling over France

    NASA Astrophysics Data System (ADS)

    Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine; Autin, Philémon; Gailhard, Joël; Zin, Isabella; Obled, Charles

    2014-05-01

    High-resolution weather scenarios generated for climate change impact studies from the output of climate models have to be spatially coherent. Analog Models (AMs) have a high potential to generate such scenarios. For each prediction day, they use as scenario the weather observed for days in a historical archive that are analog according to different predictors. When a same 'analog date' is chosen for a prediction at several sites, the spatial coherency is automatically fulfilled. The optimal predictors and next the optimal analog dates are however expected to depend on the location for which the prediction has to be made. In this study, a set of 8,981 locally AMs - specifically optimized for the probabilistic prediction of 8,981 local precipitation 'stations' over France - is used to explore the two following questions: How does the domain-optimized AM perform for precipitation prediction at another location if the analogy domain used to identify the analog dates (in terms of spatial shape of 1000 and 500 hPa geopotential fields) is optimized to predict precipitation at a given location (question of transferability)? To what extent are the analog dates derived from a first AM domain-optimized for a given location similar to those of a second AM domain-optimized for a second location (question of similarity)? The mean similarity level of analog dates obtained from two different AMs is assessed with the percentage of issued predictions for which the number of identical analog dates is larger to a given percentage threshold. The spatial transferability is assessed with the loss of prediction performance - expressed by the Continuous Ranked Probability Skill Score (CRPSS) - when the transposed AM is used instead of the locally domain-optimized one. In our case, the mean similarity level is very low excepted when the two locations are very close. The spatial transferability of the optimal analog dates obtained for a given location is conversely very wide: when they are used for the prediction at all other locations, the loss of prediction performance is very low over large area (up to 500 km) and a quasi-optimal prediction can be obtained. The spatial transferability is sensitive to the presence of high mountainous massifs. It also depends on the parameters of the AM. For instance, it decreases when the length of the archive from which the analog dates are identified grows or when humidity is used as a second level analogy predictor. In these cases, the lower spatial transferability of the analog prediction model is associated to a refinement of the model. For locations that are up to 400 km far from the location used for the optimization, the performance improvement due to the introduction of humidity predictor is larger than the higher performance loss resulting from the poorer transferability of the model. Chardon, J., Hingray, B., Favre, A.C., Autin, P., Gailhard, J., Zin, I., Obled, C. Spatial similarity and transferability of analog dates for precipitation downscaling over France. Journal of Climate. (accepted with minor revision)

  3. Micro climate Simulation in new Town `Hashtgerd' using downscaled climate data

    NASA Astrophysics Data System (ADS)

    Sodoudi, S.

    2010-12-01

    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, downscaled 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).

  4. Extension of the SIM Hydrometeorological Reanalysis Over the Entire 20th Century by Combination of Observations and Statistical Downscaling

    NASA Astrophysics Data System (ADS)

    Minvielle, M.; Céron, J.; Page, C.

    2013-12-01

    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 downscaling 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 downscaling 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 downscaled 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 downscaling 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.

  5. Statistical Downscaling of Last Glacial Maximum and mid-Holocene climate simululations over the Continental United States

    NASA Astrophysics Data System (ADS)

    Mondal, Y.; Chiang, J. C. H.; Koo, M.

    2014-12-01

    We document the creation of new high-resolution temperature and precipitation fields over the continental United States during the Last Glacial Maximum (LGM) and mid-Holocene intended for hind-casting species distributions and other biotic scenarios. Global climate simulations do not have the resolution to capture local climate variability that is needed to model ecological and biological variability. To this end, we use a recently developed statistical downscaling method, Equidistant CDF Matching (EDCDFm), developed by Li et al. (2010) [1] to create synthetic high-resolution estimates of the LGM and mid-Holocene climate over the continental United States. We find that this method works well for temperature but performs poorly for precipitation. This required processing over 1.5 billion time series. To do this, we wrote cluster-computing routines in MATLAB and implemented them on Amazon Elastic Compute Cloud.

  6. Quantum canonical ensemble: a projection operator approach

    E-print Network

    Wim Magnus; Fons Brosens

    2015-05-19

    Fixing the number of particles $N$, the quantum canonical ensemble 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 ensemble, 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.

  7. Characterizing the conformational ensemble of monomeric polyglutamine.

    PubMed

    Wang, Xiaoling; Vitalis, Andreas; Wyczalkowski, Matthew A; Pappu, Rohit V

    2006-05-01

    Studies of synthetic polyglutamine peptides in vitro have established that polyglutamine peptides aggregate via a classic nucleation and growth mechanism. Chen and colleagues [Proc Natl Acad Sci U S A 2002;99:11884-11889] have found that monomeric polyglutamine, which is a disordered statistical coil in solution, is the critical nucleus for aggregation. Therefore, nucleation of beta-sheet-rich aggregates requires an initial disorder to order transition, which is a highly unfavorable thermodynamic reaction. The questions of interest to us are as follows: What are the statistical fluctuations that drive beta-sheet formation in monomeric polyglutamine? How do these fluctuations vary with chain length? And why is this process thermodynamically unfavorable, that is, why is monomeric polyglutamine disordered? To answer these questions we use multiple molecular dynamics simulations to provide quantitative characterization of conformational ensembles for two short polyglutamine peptides. We find that the ensemble for polyglutamine is indeed disordered. However, the disorder is inherently different from that of denatured proteins and the average compactness and magnitude of conformational fluctuations increase with chain length. Most importantly, the effective concentration of sidechain primary amides around backbone units is inherently high and peptide units are solvated either by hydrogen bonds to sidechains or surrounding water molecules. Due to the multiplicity of backbone solvation modes the probability associated with any specific backbone conformation is small, resulting in a conformational entropy bottleneck which makes beta-sheet formation in monomeric polyglutamine thermodynamically unfavorable. PMID:16299774

  8. Group theory for embedded random matrix ensembles

    NASA Astrophysics Data System (ADS)

    Kota, V. K. B.

    2015-04-01

    Embedded random matrix ensembles 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 ensembles with further symmetries are discussed.

  9. Paleo-Permafrost Distribution Downscaled in South America: Examination of the GCM-based maps with the observations

    NASA Astrophysics Data System (ADS)

    Saito, K.; Trombotto, D.; Bigelow, N. H.; Marchenko, S. S.; Romanovsky, V. E.; Walsh, J. E.; Hendricks, A.; Yoshikawa, K.

    2013-12-01

    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), downscaled 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 downscaled 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.

  10. Trend in Surface-Water Balance over the Western United States from Downscaled CMIP5 Climate Projections

    NASA Astrophysics Data System (ADS)

    Xiong, J.; Wang, W.; Melton, F. S.; Milesi, C.; Nemani, R. R.

    2013-12-01

    Trend in Surface-Water Balance over the Western United States from Downscaled CMIP5 Climate Projections Jun Xiong, Weile Wang, Forrest Melton, Cristina Milesi, and Ramakrishna R Nemani The projected changes in Earth's climate will have important consequences on the hydrological cycles over the western United States. Previous studies have suggested that increases in surface temperature can promote the melting of snowpack on the Pacific Coast Ranges, which leads to a shift in water outflow towards earlier spring but subsequently reduces water availability in summer and autumn. However, the uncertainties of the projected hydrological changes remain high in coarse resolution climate projections. In this study, we use NEX-DCP30, a new archive of downscaled CMIP5 climate projections, to evaluate the impact of climate changes on the surface water balance over the western United States between 1950 to 2100 at a 30-arc-second (~800m) spatial resolution. Using the Terrestrial Observational and Prediction System (TOPS), we examine seasonal changes in precipitation, snow water equivalent (SWE), and outflow in thirteen different water resource regions. Declining trends are identified in modeled SWE across the region in response to the warming temperature. Most SWE reductions are associated with increases in winter monthly averaged minimum temperature, in particular when the minimum temperatures increases to -5°C or above. The trends of declining SWE/P ratios are most pronounced in March, corresponding to widespread warming at this time. Correspondingly, significant increases in outflow from the mountainous watersheds are found over the whole western United States. These significant changes in the spatial extent and the timing of the surface hydrological cycles are expected to have important impacts on regional water balance and the associated carbon cycles.

  11. A physically-based approach to downscale coarse snow model output for hydrologic modeling at hillslope scales

    NASA Astrophysics Data System (ADS)

    Walters, R. D.; McNamara, J. P.; Marshall, H.; Flores, A. N.

    2011-12-01

    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 spatiotemporal interactions between and among the snow, soil, and vegetation. These watersheds are also critically important from a water supply perspective and are among the most sensitive to climate change. We propose a novel, computationally inexpensive downscaling procedure to approximate snowmelt spatial variability within the 1-km grid cells of the operational SNOw Data Assimilation System (SNODAS). This approach combines available data from several sources into a simple, comprehensive snow melt factor equation: a function of snow surface albedo, forest canopy fraction and relative hillslope insolation. Albedo parameters are obtained via NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A1 500-m product. The Multi-Resolution Land Characteristics Consortium (MRLC) produces forest canopy grids in its National Land Cover Dataset (NLCD) from which the canopy fraction information is extracted. A sub 10-m digital elevation model drives a solar radiation distribution algorithm to produce insolation slope factor ratio grids. Under the assumption that higher slope factor (more insolation-prone) grid cells will melt first in an ephemeral snowpack, cumulative density distributions of these slope factor grids are generated and proportionally truncated according to MODIS fractional snow covered area (fSCA) observations; thereby, melt is effectively terminated in cells where an insolation-based argument suggests snow is no longer present. Point lysimeter melt data are compared with values obtained from the procedure to justify the use of downscaled SNODAS melt estimates as input to a complex, distributed hydrology model.

  12. Downscaling a Global Climate Model to Simulate Climate Change Impacts on U.S. Regional and Urban Air Quality

    NASA Technical Reports Server (NTRS)

    Trail, M.; Tsimpidi, A. P.; Liu, P.; Tsigaridis, K.; Hu, Y.; Nenes, A.; Russell, A. G.

    2013-01-01

    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 downscale 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 downscaling 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.

  13. Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms

    E-print Network

    McLaughlin, Dennis

    This paper considers the incorporation of constraints to enforce physically based conservation laws in the ensemble Kalman filter. In particular, constraints are used to ensure that the ensemble members and the ensemble ...

  14. BA, FERRONATO, GAMBOLATI AND TEATINI: ENSEMBLE SMOOTHING OF LAND SUBSIDENCE MEASUREMENTS FOR RESERVOIR GEOMECHANICAL CHARACTERIZATION

    E-print Network

    Bau, Domenico A.

    BAÙ, FERRONATO, GAMBOLATI AND TEATINI: ENSEMBLE SMOOTHING OF LAND SUBSIDENCE MEASUREMENTS FOR RESERVOIR GEOMECHANICAL CHARACTERIZATION 1 of 43 Ensemble Smoothing of Land Subsidence Measurements: ENSEMBLE SMOOTHING OF LAND SUBSIDENCE MEASUREMENTS FOR RESERVOIR GEOMECHANICAL CHARACTERIZATION 2 of 43

  15. Heralded amplification for precision measurements with spin ensembles

    SciTech Connect

    Brunner, Nicolas [H. H. Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL (United Kingdom); Polzik, Eugene S. [Niels Bohr Institute, Danish Quantum Optics Center QUANTOP, Copenhagen University, Blegdamsvej 17, DK-2100 Copenhagen O (Denmark); Simon, Christoph [Institute for Quantum Information Science and Department of Physics and Astronomy, University of Calgary, Calgary T2N 1N4, Alberta (Canada)

    2011-10-15

    We propose a simple heralded amplification scheme for small rotations of the collective spin of an ensemble of particles. Our protocol makes use of two basic primitives for quantum memories, namely, partial mapping of light onto an ensemble, 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.

  16. Phase averaging of image ensembles by using cepstral gradients

    Microsoft Academic Search

    Herbert W. Swan

    1983-01-01

    The direct Fourier phase averaging of an ensemble of randomly blurred images has long been thought to be too difficult a problem to undertake realistically owing to the necessity of proper phase unwrapping. It is shown that it is nevertheless possible to average the Fourier phase information in an image ensemble without calculating phases by using the technique of cepstral

  17. Preferences of and Attitudes toward Treble Choral Ensembles

    ERIC Educational Resources Information Center

    Wilson, Jill M.

    2012-01-01

    In choral ensembles, 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 ensemble in a program. The purpose of this research was to…

  18. A Coverage Based Ensemble Algorithm (CBEA) for Streaming Data

    Microsoft Academic Search

    John A. Rushing; Sara J. Graves; Evans Criswell; Amy Lin

    2004-01-01

    Ensemble classifier methods have been developed to learn from streaming data, and adapt to concept drift. One strategy employed to adapt to concept drift is to rank the classifiers in the ensemble based on their performance on the most recent samples. However, this strategy is problematic when the samples are coming from different portions of the sample space during different

  19. Tracking recurrent concept drift in streaming data using ensemble classifiers

    Microsoft Academic Search

    Sasthakumar Ramamurthy; Raj Bhatnagar

    2007-01-01

    Streaming data may consist of multiple drifting concepts each having its own underlying data distribution. We present an ensemble learning based approach to handle the data streams having multiple underlying modes. We build a global set of classifiers from sequential data chunks; ensembles are then selected from this global set of classifiers, and new classifiers created if needed, to represent

  20. Adaptive Classifiers-Ensemble System for Tracking Concept Drift

    Microsoft Academic Search

    K. Nishida; K. Yamauchi

    2007-01-01

    Adapting to various types of concept drift is important for dealing with real-world online learning problems. To achieve this, we previously reported an online learning system that uses an ensemble of classifiers, the adaptive classifiers-ensemble (ACE) system. ACE consists of one online classifier, many batch classifiers, and a drift detection mechanism. To improve the performance of ACE, we have improved

  1. Probability distributions in statistical ensembles with conserved charges

    E-print Network

    J. Cleymans; K. Redlich; L. Turko

    2005-03-10

    The probability distributions for charged particle numbers and their densities are derived in statistical ensembles with conservation laws. It is shown that if this limit is properly taken then the canonical and grand canonical ensembles are equivalent. This equivalence is proven on the most general, probability distribution level.

  2. Neural Network Ensembles, Cross Validation, and Active Learning

    Microsoft Academic Search

    Anders Krogh

    1995-01-01

    Learning of continuous valued functions using neural network ensembles(committees) can give improved accuracy, reliable estimationof the generalization error, and active learning. The ambiguityis defined as the variation of the output of ensemble members averagedover unlabeled data, so it quantifies the disagreement amongthe networks. It is discussed how to use the ambiguity in combinationwith cross-validation to give a reliable estimate of

  3. A molecular dynamics method for simulations in the canonical ensemble

    Microsoft Academic Search

    Shuichi Nosé

    1984-01-01

    A molecular dynamics simulation method which can generate configurations belonging to the canonical (T, V, N) ensemble or the constant temperature constant pressure (T, P, N) ensemble, is proposed. The physical system of interest consists of N particles (f degrees of freedom), to which an external, macroscopic variable and its conjugate momentum are added. This device allows the total energy

  4. Energy fluctuations and the ensemble equivalence in Tsallis statistics

    Microsoft Academic Search

    Liyan Liu; Jiulin Du

    2008-01-01

    We investigate the general property of the energy fluctuation in the canonical ensemble and the ensemble equivalence in Tsallis statistics. By taking the generalized ideal gas and the generalized harmonic oscillators as examples, we show that, when the particle number N is large enough, the relative fluctuation of the energy is proportional to 1\\/N in the new statistics, instead of

  5. Global Ensemble Predic1ons of 2009's Tropical Cyclones

    E-print Network

    Hamill, Tom

    and Joint Typhoon Warning Center 5 #12;Ensemble systems evaluated · Run ourselves on NSF U TexasFng). 7 #12;Rules for including a parFcular storm in "homogeneous" comparisons of models A vs. B · Storm must be tracked and at least tropical depression strength at iniFal Fme of forecast · Ensemble

  6. Ensemble Learning of Colorectal Cancer Survival Chris Roadknight

    E-print Network

    Aickelin, Uwe

    Ensemble Learning of Colorectal Cancer Survival Rates Chris Roadknight School of Computing Science--ensemble 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

  7. Ris-R-1527(EN) Wind Power Prediction using Ensembles

    E-print Network

    Risø-R-1527(EN) Wind Power Prediction using Ensembles Gregor Giebel (ed.), Jake Badger, Lars, Lars Voulund Title: Wind Power Prediction using Ensembles Risø-R-1527(EN) September 2005 ISSN 0106 from the operational use - Elsam 35 5.2.1 Control room functions 35 5.2.2 Use of wind power predictions

  8. PSO (FU 2101) Ensemble-forecasts for wind power

    E-print Network

    PSO (FU 2101) Ensemble-forecasts for wind power Analysis of the Results of an On-line Wind Power Ensemble- forecasts for wind power (FU2101) a demo-application producing quantile forecasts of wind power correct) quantile forecasts of the wind power production are generated by the application. However

  9. Binary classification using ensemble neural networks and interval neutrosophic sets

    Microsoft Academic Search

    Pawalai Kraipeerapun; Chun Che Fung

    2009-01-01

    This paper presents an ensemble neural network and interval neutrosophic sets approach to the problem of binary classification. A bagging technique is applied to an ensemble of pairs of neural networks created to predict degree of truth membership, indeterminacy membership, and false membership values in the interval neutrosophic sets. In our approach, the error and vagueness are quantified in the

  10. Ensemble Neural Networks Using Interval Neutrosophic Sets and Bagging

    Microsoft Academic Search

    Pawalai Kraipeerapun; Chun Che Fung; Kok Wai Wong

    2009-01-01

    This paper presents an approach totheproblem of binary classification using ensemble neural networks based on in- terval neutrosophic sets and bagging technique. Each com- ponent in the ensemble consists of a pair of neural networks trained to predict the degree of truth and false membership values. Uncertainties in the prediction are also estimated and represented using the indeterminacy membership val-

  11. Ensemble Neural Networks Using Interval Neutrosophic Sets and Bagging

    Microsoft Academic Search

    Pawalai Kraipeerapun; Chun Che Fung; Kok Wai Wong

    2007-01-01

    This paper presents an approach to the problem of binary classification using ensemble neural networks based on interval neutrosophic sets and bagging technique. Each component in the ensemble consists of a pair of neural networks trained to predict the degree of truth and false membership values. Uncertainties in the prediction are also estimated and represented using the indeterminacy membership values.

  12. XXXX, 123 De Gruyter YYYY Ensemble Modeling of Biological Systems

    E-print Network

    Swigon, David

    XXXX, 1­23 © De Gruyter YYYY Ensemble Modeling of Biological Systems David Swigon Abstract. Mathematical modeling of biological systems must cope with difficulties that are rarely present in traditional modeling and its applications to biological systems. Keywords. ensemble modeling, Bayesian inference

  13. Clustering Ensembles: Models of Consensus and Weak Partitions

    Microsoft Academic Search

    Alexander P. Topchy; Anil K. Jain; William F. Punch

    2005-01-01

    Clustering ensembles have emerged as a powerful method for improving both the robustness as well as the stability of unsupervised classifica tion solutions. However, finding a consensus clustering from multiple partitions is a difficult probl em that can be approached from graph-based, combinatorial or statistical perspectives. This study extends previous research on clustering ensembles in several respects. First, we introduce

  14. Gender and Attraction: Predicting Middle School Performance Ensemble Participation

    ERIC Educational Resources Information Center

    Warnock, Emery C.

    2009-01-01

    This study was designed to predict middle school sixth graders' group membership in band (n = 81), chorus (n = 45), and as non-participants in music performance ensembles (n = 127), as determined by gender and factors on the Attraction Toward School Performance Ensemble (ATSPE) scale (alpha = 0.88). Students completed the ATSPE as elementary fifth…

  15. Using A Neural Network to Approximate An Ensemble of Classifiers

    E-print Network

    Martinez, Tony R.

    Using A Neural Network to Approximate An Ensemble of Classifiers Xinchuan Zeng and Tony R. Martinez.g., Bagging, Boosting) of constructing and combining an ensemble of classifiers have recently been shown capable of improving accuracy of a class of commonly used classifiers (e.g., decision trees, neural

  16. Evolutionary Ensemble for In Silico Prediction of Ames Test Mutagenicity

    E-print Network

    Yao, Xin

    of chemicals without animal testing. This paper de- scribes a novel machine learning ensemble approach of in silico models as alternative approaches to mutagenicity assessment of chemicals without animal testingEvolutionary Ensemble for In Silico Prediction of Ames Test Mutagenicity Huanhuan Chen and Xin Yao

  17. Sparse Regression Ensembles in In nite and Finite Hypothesis Spaces

    E-print Network

    Varela, Carlos

    hypothesis problems. One uses column generation simplex-type algorithm and the other adopts an exponential hypothesis sets. For classi#12;cation, the ensemble generates the label, which is the weighted majoritySparse Regression Ensembles in In#12;nite and Finite Hypothesis Spaces Gunnar Ratsch #3; Ayhan

  18. Ensemble Learning with Evolutionary Computation: Application to Feature Ranking

    E-print Network

    Marchiori, Elena

    Ensemble Learning with Evolutionary Computation: Application to Feature Ranking Kees Jong1 , Elena of hypotheses independently learned from the dataset. Each hypothesis induces a ranking on the features, and EFR achieves the aggregation of these feature rankings. Based on the same principles as ensemble learning

  19. Neural Network Ensembles Based Approach for Mineral Prospectivity Prediction

    Microsoft Academic Search

    Vanaja Iyer; Chun Che Fung; Warick Brown; Kok Wai Wong

    2009-01-01

    In mining industry, accurate identification of new geographic locations that are favourable for mineral exploration is very important. However, definitive prediction of such locations is not an easy task. In recent years, the use of neural networks ensemble approach to the classification problem has gained much attention. This paper discusses the results obtained from using different neural network (NN) ensemble

  20. When Networks Disagree: Ensemble Methods for Hybrid Neural Networks

    Microsoft Academic Search

    1993-01-01

    This paper presents a general theoretical framework for ensemble methods of constructingsignificantly improved regression estimates. Given a population of regression estimators, weconstruct a hybrid estimator which is as good or better in the MSE sense than any estimatorin the population. We argue that the ensemble method presented has several properties: 1) Itefficiently uses all the networks of a population -

  1. Study of ensemble learning-based fusion prognostics

    Microsoft Academic Search

    Sun Jianzhong; Zuo Hongfu; Yang Haibin; Michael Pecht

    2010-01-01

    In this paper we explore the effectiveness of ensemble learning in the failure prognosis field by MLP neural network. An effective ensemble should consist of a set of learners that are both accurate and diverse. In the training stage, we use the Adaboost. R2 technique to train several weak learners (multi layer perceptron network-MLP) to increase the diversity of the

  2. Ensemble Forecasting at NCEP and the Breeding Method

    Microsoft Academic Search

    Zoltan Toth; Eugenia Kalnay

    1997-01-01

    The breeding method has been used to generate perturbations for ensemble forecasting at the National Centers for Environmental Prediction (formerly known as the National Meteorological Center) since December 1992. At that time a single breeding cycle with a pair of bred forecasts was implemented. In March 1994, the ensemble was expanded to seven independent breeding cycles on the Cray C90

  3. Practice Makes Perfect?: Effective Practice Instruction in Large Ensembles

    ERIC Educational Resources Information Center

    Prichard, Stephanie

    2012-01-01

    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-ensemble rehearsals. Using a step-by-step approach, large-ensemble conductors can teach students to identify and isolate…

  4. Estimating change in extreme European precipitation using a multimodel ensemble

    Microsoft Academic Search

    H. J. Fowler; M. Ekström; S. Blenkinsop; A. P. Smith

    2007-01-01

    (1) Using the results from multimodel ensembles enables the assessment of model uncertainty in present and future estimates of extremes and the production of probabilities for regional or local-scale change. Six regional climate model (RCM) integrations from the PRUDENCE ensemble are used together with extreme value analysis to assess changes to precipitation extremes over Europe by 2070-2100 under the SRES

  5. Wind-Wave Probabilistic Forecasting based on Ensemble

    E-print Network

    calibration methods are tested on ECMWF ensemble predictions over the offshore platform FINO1 located are tested on the ECMWF ensemble 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

  6. Excitations and benchmark ensemble density functional theory for two electrons

    SciTech Connect

    Pribram-Jones, Aurora; Burke, Kieron [Department of Chemistry, University of California-Irvine, Irvine, California 92697 (United States)] [Department of Chemistry, University of California-Irvine, Irvine, California 92697 (United States); Yang, Zeng-hui; Ullrich, Carsten A. [Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211 (United States)] [Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211 (United States); Trail, John R.; Needs, Richard J. [Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE (United Kingdom)] [Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE (United Kingdom)

    2014-05-14

    A new method for extracting ensemble Kohn-Sham potentials from accurate excited state densities is applied to a variety of two-electron systems, exploring the behavior of exact ensemble 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 ensemble, singlet-triplet-singlet ensemble, and triplet bi-ensemble.

  7. Sampling-based learning control of inhomogeneous quantum ensembles

    NASA Astrophysics Data System (ADS)

    Chen, Chunlin; Dong, Daoyi; Long, Ruixing; Petersen, Ian R.; Rabitz, Herschel A.

    2014-02-01

    Compensation for parameter dispersion is a significant challenge for control of inhomogeneous quantum ensembles. In this paper, we present the systematic methodology of sampling-based learning control (SLC) for simultaneously steering the members of inhomogeneous quantum ensembles to the same desired state. The SLC method is employed for optimal control of the state-to-state transition probability for inhomogeneous quantum ensembles of spins as well as ?-type atomic systems. The procedure involves the steps of (i) training and (ii) testing. In the training step, a generalized system is constructed by sampling members according to the distribution of inhomogeneous parameters drawn from the ensemble. A gradient flow based learning and optimization algorithm is adopted to find an optimal control for the generalized system. In the process of testing, a number of additional ensemble members are randomly selected to evaluate the control performance. Numerical results are presented, showing the effectiveness of the SLC method.

  8. Ensemble Data Assimilation for Streamflow Forecasting: Experiments with Ensemble Kalman Filter and Particle Filter

    NASA Astrophysics Data System (ADS)

    Hirpa, F. A.; Gebremichael, M.; Hopson, T. M.; Wojick, R.

    2011-12-01

    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 Ensemble 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 ensemble 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.

  9. Optical polarization of nuclear ensembles in diamond

    E-print Network

    Ran Fischer; Andrey Jarmola; Pauli Kehayias; Dmitry Budker

    2013-01-21

    We report polarization of a dense nuclear-spin ensemble 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.

  10. Uncertainty in dispersion forecasts using meteorological ensembles

    SciTech Connect

    Chin, H N; Leach, M J

    1999-07-12

    The usefulness of dispersion forecasts depends on proper interpretation of results. Understanding the uncertainty in model predictions and the range of possible outcomes is critical for determining the optimal course of action in response to terrorist attacks. One of the objectives for the Modeling and Prediction initiative is creating tools for emergency planning for special events such as the upcoming the Olympics. Meteorological forecasts hours to days in advance are used to estimate the dispersion at the time of the event. Howev