Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11765/1347
Clustering methods for statistical downscaling in short-range weather forecast
Title: Clustering methods for statistical downscaling in short-range weather forecast
Authors: Gutiérrez Llorente, José ManuelCofiño, Antonio S.Cano Trueba, RafaelRodríguez, Miguel Ángel
Keywords: Clustering methods; Statistical downscaling; Short-range weather forecast
Issue Date: 2004
Publisher: American Meteorological Society
Citation: Monthly Weather Review. 2004, 132(9), p. 2169-2183
Publisher version: http://dx.doi.org/10.1175/1520-0493(2004)132<2169:CMFSDI>2.0.CO;2
Abstract: In this paper we present an application of clustering algorithms for statistical downscaling in short-range weather forecast. The advantages of this technique compared with standard nearest neighbors analog methods are described both in terms of computational efficiency and forecast skill. We report some validation results of daily precipitation and maximum wind speed operative downscaling (lead time 1 to 5 days) on a network of 100 stations in the Iberian Peninsula the period 1998-1999. These results indicate that the weighting clustering method introduced in this paper clearly outperforms standard analog techniques for nfrequent, or extreme, events (precipitation > 20mm, wind > 80km/h). Outputs of an operative circulation model on different local-area or large-scale grids are considered to characterize the atmospheric circulation patterns, and the skill of both alternatives is compared.
Sponsorship : The authors are also grateful to the University of Cantabria, CSIC, and the Comisión Interministerial de Ciencia y Tecnología (CICYT; Grant REN2000-1572) for partial support of this work.
URI: http://hdl.handle.net/20.500.11765/1347
ISSN: 0027-0644
1520-0493
Appears in Collections:Artículos científicos 2000-2004


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