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JOURNALS // Informatika i Ee Primeneniya [Informatics and its Applications] // Archive

Inform. Primen., 2015 Volume 9, Issue 4, Pages 3–13 (Mi ia387)

This article is cited in 7 papers

Statistical modeling of air–sea turbulent heat fluxes by the method of moving separation of finite normal mixtures

V. Yu. Korolevab, A. K. Gorsheninbc, S. K. Gulevdef, K. P. Belyaevfg

a Faculty of Computational Mathematics and Cybernetics, M.V. Lomonosov Moscow State University, 1-52 Leninskiye Gory, GSP-1, Moscow 119991, Russian Federation
b Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
c Moscow State University of Information Technologies, Radioengineering, and Electronics, 78 Vernadskogo Ave., Moscow 119454, Russian Federation
d University of Kiel, Christian-Albrechts-Universität zu Kiel, 4 Christian-Albrechts-Platz, Kiel 24098, Germany
e Faculty of Geography, M.V. Lomonosov Moscow State University, 1 Leninskiye Gory, GSP-1, Moscow 119991, Russian Federation
f P.P. Shirshov Institute of Oceanology, 36 Nakhimovski Prosp., Moscow 117997, Russian Federation
g Federal University of Bahia, Rua Adhemar de Barros, no 500, Ondina, 40.710-110, Salvador, Bahia, Brazil

Abstract: The method of moving separation of mixtures is applied to the problem of statistical modeling of regularities in explicit and latent turbulent heat fluxes. The six-hour observations in the Atlantic region (NCEP-NCAR, 1948–2008) are used as initial data. The basic approximate mathematical model is a finite normal mixture with parameters depending on time. The methodology of moving separation of mixtures allows one to analyze the regularities in the variation of parameters and to capture the variability which can be associated with the trend as well as the irregular variation. An approach is proposed to the determination of the proportion of extreme observations in the original sample.

Keywords: finite normal mixtures; moving separation of mixtures; probabilistic models; data mining.

Received: 04.09.2015

DOI: 10.14357/19922264150401



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