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Comparison of data-driven methods for downscaling...
Journal article

Comparison of data-driven methods for downscaling ensemble weather forecasts

Abstract

Abstract. This study investigates dynamically different data-driven methods, specifically a statistical downscaling model (SDSM), a time lagged feedforward neural network (TLFN), and an evolutionary polynomial regression (EPR) technique for downscaling numerical weather ensemble forecasts generated by a medium range forecast (MRF) model. Given the coarse resolution (about 200-km grid spacing) of the MRF model, an optimal use of the weather …

Authors

Liu X; Coulibaly P; Evora N

Journal

Hydrology and Earth System Sciences, Vol. 12, No. 2, pp. 615–624

Publisher

Copernicus Publications

DOI

10.5194/hess-12-615-2008

ISSN

1027-5606