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A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia using the WRF model
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik.
Group of Atmospheric Science, Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology.
Novia University of Applied Sciences, Vaasa, Finland.
Group of Atmospheric Science, Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology.
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2020 (Engelska)Ingår i: Advances in Atmospheric Sciences, ISSN 0256-1530, E-ISSN 1861-9533, Vol. 37, s. 57-74Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

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 local scale estimates produced by downscaling. These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute (FMI) 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 (CFSR) data over the region at 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 (Q-Q) plots, and quantitatively, through the Cramer-von Mises (CvM), mean absolute error (MAE), and root-mean-square Error (RMSE) 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 proved to be less computationally expensive, and also to give more skillful temperature forecasts (at least for the Finnish near-coastal region).

Ort, förlag, år, upplaga, sidor
Springer, 2020. Vol. 37, s. 57-74
Nyckelord [en]
WRF, air temperature, CDF-t, hybrid statistical-dynamical downscaling, model evaluation, Scandinavian Peninsula.
Nationell ämneskategori
Sannolikhetsteori och statistik Meteorologi och atmosfärforskning
Forskningsämne
matematisk statistik
Identifikatorer
URN: urn:nbn:se:umu:diva-162956DOI: 10.1007/s00376-019-9091-0ISI: 000518185100005OAI: oai:DiVA.org:umu-162956DiVA, id: diva2:1348167
Projekt
WindCoETillgänglig från: 2019-09-03 Skapad: 2019-09-03 Senast uppdaterad: 2020-03-23Bibliografiskt granskad

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Wang, JianfengYu, Jun

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Wang, JianfengYu, Jun
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Institutionen för matematik och matematisk statistik
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Advances in Atmospheric Sciences
Sannolikhetsteori och statistikMeteorologi och atmosfärforskning

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