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A novel approach to statistical-dynamical downscaling for long-term wind resource predictions

Chavez-Arroyo, R., Fernades-Correia, P., Lozano-Galiana, S., Sanz-Rodrigo, J., Amezcua, J. and Probst, O. (2018) A novel approach to statistical-dynamical downscaling for long-term wind resource predictions. Meteorological Applications, 25 (2). pp. 171-183. ISSN 1469-8080

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To link to this item DOI: 10.1002/met.1678

Abstract/Summary

A novel method to determine representative periods (typically a year) for the estimation of the longterm mesoscale wind resource has been proposed and compared to other recently published techniques. It provides a computationally lean while accurate solution of the problem of constructing long-term mesoscale wind maps through downscaling without having to go through a brute force procedure. Applications include a wider dissemination of mesoscale wind maps because of faster and cheaper execution, as well as greater flexibility for sensitivity analyses.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:70280
Publisher:Royal Meteorological Society

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