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A hybrid statistical-dynamical downscaling of Air Temperature over Scandinavia Using the WRF model

Advances in Atmospheric Sciences 2020年第1期37卷 57-74页

作者： Jianfeng WANG Ricardo M.FONSECA Kendall RUTLEDGE Javier MARTÍN-TORRES Jun YU Department of Mathematics and Mathematical Statistics UmeåUniversitySE 90187UmeåSweden Group of Atmospheric Science Division of Space TechnologyDepartment of Computer ScienceElectrical and Space EngineeringLuleåUniversity of TechnologySE 97187 LuleåSweden Novia University of Applied Sciences PO BOX 6FI-65201 VaasaFinland Instituto Andaluz de Ciencias de la Tierra 18100 GranadaSpain The Pheasant Memorial Laboratory for Geochemistry and Cosmochemistry Institute for Planetary MaterialsOkayama University at MisasaTottori 682-0193Japan

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).

关键词： WRF air temperature Cumulative Distribution Function-transform hybrid statistical–dynamical downscaling,model evaluation Scandinavian Peninsula

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