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Clustering methods for statistical downscaling in short-range weather forecast
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dc.contributor.authorGutiérrez Llorente, José Manueles_ES
dc.contributor.authorCofiño, Antonio S.es_ES
dc.contributor.authorCano Trueba, Rafaeles_ES
dc.contributor.authorRodríguez, Miguel Ángeles_ES
dc.identifier.citationMonthly Weather Review. 2004, 132(9), p. 2169-2183es_ES
dc.description.abstractIn 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.es_ES
dc.description.sponsorshipThe 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.-
dc.publisherAmerican Meteorological Societyes_ES
dc.subjectClustering methodses_ES
dc.subjectStatistical downscalinges_ES
dc.subjectShort-range weather forecastes_ES
dc.titleClustering methods for statistical downscaling in short-range weather forecastes_ES
Appears in Collections:Artículos científicos 2000-2004

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