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Synchronicity in Predictive Modelling: a New View of Data Assimilation : Volume 13, Issue 6 (03/11/2006)

By Duane, G. S.

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Book Id: WPLBN0004019794
Format Type: PDF Article :
File Size: Pages 12
Reproduction Date: 2015

Title: Synchronicity in Predictive Modelling: a New View of Data Assimilation : Volume 13, Issue 6 (03/11/2006)  
Author: Duane, G. S.
Volume: Vol. 13, Issue 6
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2006
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Tribbia, J. J., Weiss, J. B., & Duane, G. S. (2006). Synchronicity in Predictive Modelling: a New View of Data Assimilation : Volume 13, Issue 6 (03/11/2006). Retrieved from http://www.netlibrary.net/


Description
Description: National Center for Atmospheric Research, PO Box 3000, Boulder, CO 80307, USA. The problem of data assimilation can be viewed as one of synchronizing two dynamical systems, one representing truth and the other representing model, with a unidirectional flow of information between the two. Synchronization of truth and model defines a general view of data assimilation, as machine perception, that is reminiscent of the Jung-Pauli notion of synchronicity between matter and mind. The dynamical systems paradigm of the synchronization of a pair of loosely coupled chaotic systems is expected to be useful because quasi-2D geophysical fluid models have been shown to synchronize when only medium-scale modes are coupled. The synchronization approach is equivalent to standard approaches based on least-squares optimization, including Kalman filtering, except in highly non-linear regions of state space where observational noise links regimes with qualitatively different dynamics. The synchronization approach is used to calculate covariance inflation factors from parameters describing the bimodality of a one-dimensional system. The factors agree in overall magnitude with those used in operational practice on an ad hoc basis. The calculation is robust against the introduction of stochastic model error arising from unresolved scales.

Summary
Synchronicity in predictive modelling: a new view of data assimilation

Excerpt
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