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An atmospheric-to-marine synoptic classification for statistical downscaling marine climate

Abstract: A regression-guided classification is implemented in statistical downscaling models based on weather types for downscaling multivariate wave climate and modelling extreme events. The semi-supervised method classifies the atmospheric circulation conditions (predictor) and the estimations from a regression model linking the circulation with local marine climate (filtered predictand). A weighted factor controls the influence of the predictor and predictand in the weather patterns to improve the performance of the classification to reflect local marine climate characteristics. In addition to the analysis of the variance explained by the predictor and the predictand, the selection of the optimal value of the weighted factor is based on the predictand response in order to avoid subjectivity in the solution. The statistical models using the guided classification are applied in the North Atlantic. The new technique reduces the dispersion of the multivariate predictand within weather types and improves the model skill to downscale waves and to reproduce extremes.

 Fuente: Ocean Dynamics December 2016, Volume 66, Issue 12, pp 1589–1601

Editorial: Springer

 Fecha de publicación: 01/12/2016

Nº de páginas: 13

Tipo de publicación: Artículo de Revista

 DOI: 10.1007/s10236-016-1004-5

ISSN: 1616-7341,1616-7228

 Proyecto español: BIA2015-70644-R

Url de la publicación: http://link.springer.com/article/10.1007/s10236-016-1004-5