umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia using the WRF model
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
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.
Show others and affiliations
2019 (English)In: Advances in Atmospheric Sciences, ISSN 0256-1530, E-ISSN 1861-9533Article in journal (Refereed) Epub ahead of print
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).

Place, publisher, year, edition, pages
Springer, 2019.
Keywords [en]
WRF, air temperature, CDF-t, hybrid statistical-dynamical downscaling, model evaluation, Scandinavian Peninsula.
National Category
Probability Theory and Statistics Meteorology and Atmospheric Sciences
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-162956DOI: 10.1007/s00376-019-9091-0OAI: oai:DiVA.org:umu-162956DiVA, id: diva2:1348167
Projects
WindCoEAvailable from: 2019-09-03 Created: 2019-09-03 Last updated: 2019-09-03

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Wang, JianfengYu, Jun
By organisation
Department of Mathematics and Mathematical Statistics
In the same journal
Advances in Atmospheric Sciences
Probability Theory and StatisticsMeteorology and Atmospheric Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 22 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf