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

Content Provider	Springer Nature Link
Author	Camus, Paula Rueda, Ana Méndez, Fernando J. Losada, Iñigo J.
Copyright Year	2016
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.
Starting Page	1589
Ending Page	1601
Page Count	13
File Format	PDF
ISSN	16167341
Journal	Deutsche Hydrografische Zeitschrift
Volume Number	66
Issue Number	12
e-ISSN	16167228
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-10-27
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multivariate wave climate North Atlantic Ocean Statistical downscaling Regression-guided classification Weather types Oceanography Geophysics/Geodesy Atmospheric Sciences Fluid- and Aerodynamics Monitoring/Environmental Analysis
Content Type	Text
Resource Type	Article
Subject	Oceanography

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