A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model  

作　　者：Jianfeng WANG Ricardo M.FONSECA Kendall RUTLEDGE Javier MARTÍN-TORRES Jun YU 

机构地区：[1]Department of Mathematics and Mathematical Statistics,UmeåUniversity,SE 90187,UmeåSweden [2]Group of Atmospheric Science,Division of Space Technology,Department of Computer Science,Electrical and Space Engineering,LuleåUniversity of Technology,SE 97187 Luleå,Sweden [3]Novia University of Applied Sciences,PO BOX 6,FI-65201 Vaasa,Finland [4]Instituto Andaluz de Ciencias de la Tierra,18100 Granada,Spain [5]The Pheasant Memorial Laboratory for Geochemistry and Cosmochemistry,Institute for Planetary Materials,Okayama University at Misasa,Tottori 682-0193,Japan

出　　处：《Advances in Atmospheric Sciences》2020年第1期57-74,共18页大气科学进展（英文版）

基　　金：Botnia-Atlantica, an EU-programme financing cross border cooperation projects in Sweden, Finland and Norway, for their support of this work through the WindCoE project

摘　　要：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).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 

分 类 号：P456.7[天文地球—大气科学及气象学]