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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; in revised form: 14 February 2011 / Accepted: 20 February 2011 / Published: 7 March 2011
Abstract: 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.
Keywords: predictability; information theory; statistical physics
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MDPI and ACS Style
Kleeman, R. Information Theory and Dynamical System Predictability. Entropy 2011, 13, 612-649.
Kleeman R. Information Theory and Dynamical System Predictability. Entropy. 2011; 13(3):612-649.
Kleeman, Richard. 2011. "Information Theory and Dynamical System Predictability." Entropy 13, no. 3: 612-649.