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Probabilistic downscaling approaches: Application to wind cumulative distribution functions
Author(s) -
Michelangeli P.A.,
Vrac M.,
Loukos H.
Publication year - 2009
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2009gl038401
Subject(s) - downscaling , cumulative distribution function , environmental science , quantile , probabilistic logic , meteorology , scale (ratio) , climatology , wind speed , mathematics , statistics , geology , geography , probability density function , precipitation , cartography
A statistical method is developed to generate local cumulative distribution functions (CDFs) of surface climate variables from large‐scale fields. Contrary to most downscaling methods producing continuous time series, our “probabilistic downscaling methods” (PDMs), named “CDF‐transform”, is designed to deal with and provide local‐scale CDFs through a transformation applied to large‐scale CDFs. First, our PDM is compared to a reference method (Quantile‐matching), and validated on a historical time period by downscaling CDFs of wind intensity anomalies over France, for reanalyses and simulations from a general circulation model (GCM). Then, CDF‐transform is applied to GCM output fields to project changes in wind intensity anomalies for the 21st century under A2 scenario. Results show a decrease in wind anomalies for most weather stations, ranging from less than 1% (in the South) to nearly 9% (in the North), with a maximum in the Brittany region.