Accessibility navigation


Data assimilation as a nonlinear dynamical systems problem: stability and convergence of the prediction-assimilation system

Carrassi, A., Ghil, M., Trevisan, A. and Uboldi, F. (2008) Data assimilation as a nonlinear dynamical systems problem: stability and convergence of the prediction-assimilation system. Chaos: An Interdisciplinary Journal of Nonlinear Science, 18 (2). 023112. ISSN 1089-7682

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1063/1.2909862

Abstract/Summary

We study prediction-assimilation systems, which have become routine in meteorology and oceanography and are rapidly spreading to other areas of the geosciences and of continuum physics. The long-term, nonlinear stability of such a system leads to the uniqueness of its sequentially estimated solutions and is required for the convergence of these solutions to the system’s true, chaotic evolution. The key ideas of our approach are illustrated for a linearized Lorenz system. Stability of two nonlinear prediction-assimilation systems from dynamic meteorology is studied next via the complete spectrum of their Lyapunov exponents; these two systems are governed by a large set of ordinary and of partial differential equations, respectively. The degree of data-induced stabilization is crucial for the performance of such a system. This degree, in turn, depends on two key ingredients: (i) the observational network, either fixed or data-adaptive, and (ii) the assimilation method.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:89727
Publisher:American Institute of Physics

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation