Markov processes, dynamic entropies and the statistical prediction of mesoscale weather regimes

C. Nicolis; W. Ebeling; C. Baraldi

doi:10.3402/tellusa.v49i1.12215

Markov processes, dynamic entropies and the statistical prediction of mesoscale weather regimes

Tellus

Volume 49 (1997): Issue 1

By: C. Nicolis, W. Ebeling and C. Baraldi

Open Access

|Jan 1997

Abstract

The data pertaining to transitions between weather regimes grouped in 3 clusters are analyzed. Evidence that the dynamics of transitions between the regimes is not a first order Markov process is presented on the basis of the properties of residence time distributions. This conclusion is corroborated further by an entropy analysis revealing that the process is characterized by long range temporal correlations. Simple models of this behavior are developed and the repercussions on the problem of prediction are discussed.

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Articles in this issue

DOI: https://doi.org/10.3402/tellusa.v49i1.12215 | Journal eISSN: 3035-9554

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Language: English

Page range: 108 - 118

Submitted on: Jan 12, 1996

Accepted on: Apr 18, 1996

Published on: Jan 1, 1997

Published by: Stockholm University Press

In partnership with: Paradigm Publishing Services

© 1997 C. Nicolis, W. Ebeling, C. Baraldi, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 49 (1997): Issue 1