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Autoregressive logistic regression applied to atmospheric circulation patterns.

Abstract: Autoregressive logistic regression models have been successfully applied in medical and pharmacology research fields, and in simple models to analyze weather types. The main purpose of this paper is to introduce a general framework to study atmospheric circulation patterns capable of dealing simultaneously with: seasonality, interannual variability, long-term trends, and autocorrelation of different orders. To show its effectiveness on modeling performance, daily atmospheric circulation patterns identified from observed sea level pressure fields over the Northeastern Atlantic, have been analyzed using this framework. Model predictions are compared with probabilities from the historical database, showing very good fitting diagnostics. In addition, the fitted model is used to simulate the evolution over time of atmospheric circulation patterns using Monte Carlo method. Simulation results are statistically consistent with respect to the historical sequence in terms of (1) probability of occurrence of the different weather types, (2) transition probabilities and (3) persistence. The proposed model constitutes an easy-to-use and powerful tool for a better understanding of the climate system.

 Fuente: Climate Dynamics, 2014, 42(1-2), 537-552

 Editorial: Springer

 Año de publicación: 2014

 Nº de páginas: 15

 Tipo de publicación: Artículo de Revista

 DOI: 10.1007/s00382-013-1690-3

 ISSN: 0930-7575,1432-0894

 Proyecto español: ??AMVAR?? CTM2010-15009

 Proyecto europeo: info:eu-repo/grantAgreement/EC/FP7/244104/EU/Innovative coastal technologies for safer European coasts in a changing climate/THESEUS/

 Url de la publicación: http://dx.doi.org/10.1007/s00382-013-1690-3