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| Titel |
A nudging-based data assimilation method: the Back and Forth Nudging (BFN) algorithm |
| VerfasserIn |
D. Auroux, J. Blum |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 15, no. 2 ; Nr. 15, no. 2 (2008-03-27), S.305-319 |
| Datensatznummer |
250012618
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| Publikation (Nr.) |
 copernicus.org/npg-15-305-2008.pdf |
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| Zusammenfassung |
| This paper deals with a new data assimilation algorithm, called Back and
Forth Nudging. The standard nudging technique consists in adding to the
equations of the model a relaxation term that is supposed to force the
observations to the model. The BFN algorithm consists in repeatedly performing forward and
backward integrations of the model with relaxation (or nudging) terms, using
opposite signs in the direct and inverse integrations, so as to make the
backward evolution numerically stable. This algorithm has first been tested on
the standard Lorenz model with discrete observations (perfect or noisy) and
compared with the variational assimilation method. The same type of study has
then been performed on the viscous Burgers equation, comparing again with the
variational method and focusing on the time evolution of the reconstruction error,
i.e.
the difference
between the reference trajectory and the identified one over a time period composed of
an assimilation period followed by a prediction period. The possible use of
the BFN algorithm as an initialization for the variational method has also been investigated.
Finally the algorithm has been tested on a layered
quasi-geostrophic model with sea-surface height observations. The
behaviours of the two algorithms have been compared in the presence of perfect or
noisy observations, and also for imperfect models. This has allowed us to reach a conclusion
concerning the relative performances of the two algorithms. |
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