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Assessment of Various Statistical Downscaling Methods for Downscaling Precipitation in Florida
World Environmental and Water Resources Congress 2013, 2013Several 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).
Aneesh Goly, Ramesh S. V. Teegavarapu
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Analog Models for Empirical-Statistical Downscaling
2021Global climate models (GCM) are fundamental tools for weather forecasting and climate predictions at different time scales, from intraseasonal prediction to climate change projections. Their design allows GCMs to simulate the global climate adequately, but they are not able to skillfully simulate local/regional climates.
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Automated regression-based statistical downscaling tool
Environmental Modelling & Software, 2008Many impact studies require climate change information at a finer resolution than that provided by Global Climate Models (GCMs). In the last 10 years, downscaling techniques, both dynamical (i.e. Regional Climate Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios.
Hessami, Massoud +3 more
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Statistical-dynamical downscaling of wind climatologies
Journal of Wind Engineering and Industrial Aerodynamics, 1997Abstract A statistical-dynamical downscaling procedure is applied for an investigation into the availability of wind power over a region of 80 × 87 km which covers flat and hilly terrain. The approach is based on the statistical coupling of a regionally representative wind climate with a numerical atmospheric mesoscale model.
Heinz-Theo Mengelkamp +2 more
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Uncertainty analysis of statistical downscaling methods
Journal of Hydrology, 2006Three downscaling models namely Statistical Down-Scaling Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG) model and Artificial Neural Network (ANN) model have been compared in terms various uncertainty assessments exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperatures. In case of daily
Mohammad Sajjad Khan +2 more
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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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