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Downscaling statistical information: a statistical approach
If the shape of mathematical curves describing local weather statistics are systematically influenced by large-scale conditions and geographical factors, then it may be possible to downscale this kind of information directly. Such curves may include probability density functions (pdfs) for daily temperature/precipitation or intensity-duration-frequencyRasmus Benestad +5 more
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Statistical Downscaling for Climate Science
2019Global climate models are our main tool to generate quantitative climate projections, but these models do not resolve the effects of complex topography, regional scale atmospheric processes and small-scale extreme events. To understand potential regional climatic changes, and to provide information for regional-scale impact modeling and adaptation ...
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Statistical downscaling with artificial neural network
2023This dust-caused air pollution is becoming an dominant health concern for Southwest Asian. However, there is very limited amount of air quality data over this region to support environmental health research. General Circulation Model(GCM) can provide estimations for unobserved area in low spatial resolution.
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Statistical Downscaling versus Dynamic Downscaling: An Assessment Based upon a Sample Study
World Environmental and Water Resources Congress 2014, 2014In this study the precipitation variability between a statistical downscaling method (BCSD) and a dynamical downscaling method (MM5) that is based on the CCSM3 GCM control run for a historical period and on the CCSM3 GCM A1B emission scenario simulations for a projection period, is investigated by means of the normalized standard deviation and the ...
S. Jang, M. L. Kavvas
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Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods
International Journal of Climatology, 2006A precipitation downscaling method is presented using precipitation from a general circulation model (GCM) as predictor. The method extends a previous method from monthly to daily temporal resolution. The simplest form of the method corrects for biases in wet-day frequency and intensity. A more sophisticated variant also takes account of flow-dependent
Jürg Schmidli +2 more
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Empirical statistical downscaling with EPISODES in Austria
2021<p>EPISODES is an empirical statistical downscaling (ESD) method, which has been initiated and developed by the German Weather Service (DWD). Having resulted in good evaluation scores for Germany, the methodology it is also set-up and adapted for Austria at ZAMG and, hence, for an alpine territory with complex topography.</p>
Theresa Schellander-Gorgas +4 more
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International Journal of Remote Sensing, 2019
Spatially and temporally dense land surface temperature (LST) data are necessary to capture the high variability of the urban thermal environment. Sensors on board satellites with high revisit time cannot provide adequately detailed spatial information ...
Ilias Agathangelidis, C. Cartalis
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Spatially and temporally dense land surface temperature (LST) data are necessary to capture the high variability of the urban thermal environment. Sensors on board satellites with high revisit time cannot provide adequately detailed spatial information ...
Ilias Agathangelidis, C. Cartalis
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Theoretical and Applied Climatology, 2017
Statistical downscaling of Global Climate Models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have been applied to downscaling GCMs but recent advances in machine learning ...
T. Vandal, E. Kodra, A. Ganguly
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Statistical downscaling of Global Climate Models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have been applied to downscaling GCMs but recent advances in machine learning ...
T. Vandal, E. Kodra, A. Ganguly
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A Multi-stage Stochastic Approach for Statistical Downscaling of Rainfall
Water resources management, 2023J. George, A. P.
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Statistical downscaling of remotely-sensed soil moisture
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017Global soil moisture estimates at fine spatial resolutions is necessary for many applications. However, current spaceborne instruments have coarse resolution. In this study, we develop a new Artificial Neural Network (ANN) based disaggregation algorithm to downscale soil moisture observations from Soil Moisture Active Passive (SMAP) mission to a fine ...
S.H. Alemohammad +4 more
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