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

Document identifier: oai:DiVA.org:ltu-75693
Access full text here:10.1007/s00376-019-9091-0
Keyword: Natural Sciences, WRF, Atmospheric science, Scandinavian Peninsula, Model evaluation, Hybrid statistical–dynamical downscaling, Cumulative Distribution Function-transform, Air temperature, Rymd- och flygteknik, Earth and Related Environmental Sciences, Maskinteknik, Teknik och teknologier, Aerospace Engineering, Mechanical Engineering, Engineering and Technology, Geovetenskap och miljövetenskap, Naturvetenskap, Atmosfärsvetenskap
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 11 Sustainable cities and communitiesSDG 13 Climate action
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

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).

Authors

Jianfeng Wang

Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
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Ricardo Fonseca

Luleå tekniska universitet; Rymdteknik
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Kendall Rutledge

Novia University of Applied Sciences, Vaasa, Finland
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Javier Martin-Torres

Luleå tekniska universitet; Rymdteknik; Instituto Andaluz de Ciencias de la Tierra, Granada, Spain The Pheasant Memorial Laboratory for Geochemistry and Cosmochemistry, Institute for Planetary Materials, Okayama University at Misasa, Tottori, Japan
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Jun Yu

Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
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