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Meteorology and Atmospheric Physics

, Volume 103, Issue 1–4, pp 253–265 | Cite as

Statistical downscaling of near-surface wind over complex terrain in southern France

  • T. SalamehEmail author
  • P. Drobinski
  • M. Vrac
  • P. Naveau
Article

Summary

Accurate and rapid determination of near-surface wind fields in a complex area (orography, inhomogeneous surface properties) is a challenge for applications like the evaluation of wind energy production, the prediction of pollution transport and hazardous conditions for aeronautics and ship navigation, or the estimation of damage to farm plantations, among others. This paper presents a statistical downscaling approach based on generalized additive models that provides accurate, rapid and relatively transparent simulations of local-scale near-surface wind field based on a method calibrated on both large-scale upper air and surface atmospheric fields. Our statistical method is used to downscale near-surface wind components to weather surface stations in southern France from ERA-40 reanalyses between 1991 and 2001. The region of interest is characterized by the presence of major mountain ranges which play a major role in redirecting large-scale circulations making difficult the prediction of local wind. This study compares the performance of our statistical approach with different sets of explanatory variables, to explain the near-surface wind field variability. The performances are interpreted by evaluating the contribution of the explanatory variables in the equations of motion. This approach generates accurate depictions of the local surface wind field, and allows to go one step further in statistical wind speed downscaling. Indeed, it is adapted to explain wind components and not only wind speed and energy in contrast to past studies and it is suited for complex terrain and robust to time averaging in this region.

Keywords

Generalize Additive Model Wind Component Complex Terrain Statistical Downscaling Relative Vorticity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Institut Pierre Simon Laplace/Laboratoire de Météorologie DynamiqueEcole Polytechnique/ENS/UPMC/CNRSPalaiseauFrance
  2. 2.Institut Pierre Simon Laplace/Laboratoire des Sciences du Climat et de l’EnvironnementCEA/UVSQ/CNRSGif-sur-YvetteFrance

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