NDLI: Autoregressive logistic regression applied to atmospheric circulation patterns

Content Provider	Springer Nature Link
Author	Guanche, Y. Mínguez, R. Méndez, F. J.
Copyright Year	2013
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.
Starting Page	537
Ending Page	552
Page Count	16
File Format	PDF
ISSN	09307575
Journal	Climate Dynamics
Volume Number	42
Issue Number	1-2
e-ISSN	14320894
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-02-17
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Autoregressive logistic regression Circulation patterns Simulation Geophysics/Geodesy Climatology Oceanography
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science

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