http://www.cnr.it/ontology/cnr/individuo/prodotto/ID45105
Data assimilation as a nonlinear dynamical systems problem: Stability and convergence of the prediction-assimilation system (Articolo in rivista)
- Type
- Label
- Data assimilation as a nonlinear dynamical systems problem: Stability and convergence of the prediction-assimilation system (Articolo in rivista) (literal)
- Anno
- 2008-01-01T00:00:00+01:00 (literal)
- Alternative label
Carrassi A., Ghil M., Trevisan A., Uboldi F. (2008)
Data assimilation as a nonlinear dynamical systems problem: Stability and convergence of the prediction-assimilation system
in Chaos (Woodbury N.Y.)
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Carrassi A., Ghil M., Trevisan A., Uboldi F. (literal)
- Pagina inizio
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- Rivista
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
- DOI: 10.1063/1.2909862
(literal)
- Note
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- ISAC-CNR
Institut Royal Météorologique de Belgique, Bruxelles
École Normale Supérieure, Paris
University of California, Los Angeles
(literal)
- Titolo
- Data assimilation as a nonlinear dynamical systems problem: Stability and convergence of the prediction-assimilation system (literal)
- Abstract
- 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 systems 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. (literal)
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- Autore CNR
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