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Entropy 2011, 13(3), 612-649; doi:10.3390/e13030612

Information Theory and Dynamical System Predictability

Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA
Received: 25 January 2011 / Revised: 14 February 2011 / Accepted: 20 February 2011 / Published: 7 March 2011
(This article belongs to the Special Issue Advances in Information Theory)
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Predicting the future state of a turbulent dynamical system such as the atmosphere has been recognized for several decades to be an essentially statistical undertaking. Uncertainties from a variety of sources are magnified by dynamical mechanisms and given sufficient time, compromise any prediction. In the last decade or so this process of uncertainty evolution has been studied using a variety of tools from information theory. These provide both a conceptually general view of the problem as well as a way of probing its non-linearity. Here we review these advances from both a theoretical and practical perspective. Connections with other theoretical areas such as statistical mechanics are emphasized. The importance of obtaining practical results for prediction also guides the development presented. View Full-Text
Keywords: predictability; information theory; statistical physics predictability; information theory; statistical physics

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Kleeman, R. Information Theory and Dynamical System Predictability. Entropy 2011, 13, 612-649.

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