Editorial Type:
Article
Article Type:
Research Article

Wind-Climate Estimation Based on Mesoscale and Microscale Modeling: Statistical–Dynamical Downscaling for Wind Energy Applications

Jake Badger Department for Wind Energy (DTU Wind Energy), Technical University of Denmark, Roskilde, Denmark

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Helmut Frank Deutscher Wetterdienst, Offenbach am Main, Germany

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Andrea N. Hahmann Department for Wind Energy (DTU Wind Energy), Technical University of Denmark, Roskilde, Denmark

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Gregor Giebel Department for Wind Energy (DTU Wind Energy), Technical University of Denmark, Roskilde, Denmark

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Print Publication:
01 Aug 2014
DOI:
https://doi.org/10.1175/JAMC-D-13-0147.1
Page(s):
1901–1919
Restricted access

Abstract

This paper demonstrates that a statistical–dynamical method can be used to accurately estimate the wind climate at a wind farm site. In particular, postprocessing of mesoscale model output allows an efficient calculation of the local wind climate required for wind resource estimation at a wind turbine site. The method is divided into two parts: 1) preprocessing, in which the configurations for the mesoscale model simulations are determined, and 2) postprocessing, in which the data from the mesoscale simulations are prepared for wind energy application. Results from idealized mesoscale modeling experiments for a challenging wind farm site in northern Spain are presented to support the preprocessing method. Comparisons of modeling results with measurements from the same wind farm site are presented to support the postprocessing method. The crucial element in postprocessing is the bridging of mesoscale modeling data to microscale modeling input data, via a so-called generalization method. With this method, very high-resolution wind resource mapping can be achieved.

Corresponding author address: Jake Badger, Dept. of Wind Energy (DTU Wind Energy), Technical University of Denmark, Risø Campus, Frederiksborgvej 399, P.O. Box 49, 4000 Roskilde, Denmark. E-mail: jaba@dtu.dk

Abstract

This paper demonstrates that a statistical–dynamical method can be used to accurately estimate the wind climate at a wind farm site. In particular, postprocessing of mesoscale model output allows an efficient calculation of the local wind climate required for wind resource estimation at a wind turbine site. The method is divided into two parts: 1) preprocessing, in which the configurations for the mesoscale model simulations are determined, and 2) postprocessing, in which the data from the mesoscale simulations are prepared for wind energy application. Results from idealized mesoscale modeling experiments for a challenging wind farm site in northern Spain are presented to support the preprocessing method. Comparisons of modeling results with measurements from the same wind farm site are presented to support the postprocessing method. The crucial element in postprocessing is the bridging of mesoscale modeling data to microscale modeling input data, via a so-called generalization method. With this method, very high-resolution wind resource mapping can be achieved.

Corresponding author address: Jake Badger, Dept. of Wind Energy (DTU Wind Energy), Technical University of Denmark, Risø Campus, Frederiksborgvej 399, P.O. Box 49, 4000 Roskilde, Denmark. E-mail: jaba@dtu.dk
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Journal of Applied Meteorology and Climatology

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