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| Titel |
Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model |
| VerfasserIn |
G. A. Ruggiero, Y. Ourmières, E. Cosme, J. Blum, D. Auroux, J. Verron |
| 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 ; 22, no. 2 ; Nr. 22, no. 2 (2015-04-29), S.233-248 |
| Datensatznummer |
250120977
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| Publikation (Nr.) |
 copernicus.org/npg-22-233-2015.pdf |
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| Zusammenfassung |
| The diffusive back-and-forth nudging (DBFN) is an easy-to-implement iterative
data assimilation method based on the well-known nudging method. It consists
of a sequence of forward and backward model integrations, within a given time
window, both of them using a feedback term to the observations. Therefore, in
the DBFN, the nudging asymptotic behaviour is translated into an infinite number of iterations within a
bounded time domain. In this method, the backward integration is carried out
thanks to what is called backward model, which is basically the forward model
with reversed time step sign. To maintain numeral stability, the diffusion
terms also have their sign reversed, giving a diffusive character to the
algorithm. In this article the DBFN performance to control a primitive
equation ocean model is investigated. In this kind of model non-resolved
scales are modelled by diffusion operators which dissipate energy that cascade from large to small scales.
Thus, in this article, the DBFN approximations and their consequences for the
data assimilation system set-up are analysed. Our main result is that the DBFN may provide results which are
comparable to those produced by a 4Dvar implementation with a much simpler
implementation and a shorter CPU time for convergence. The conducted
sensitivity tests show that the 4Dvar profits of long assimilation windows to
propagate surface information downwards, and that for the DBFN, it is worth
using short assimilation windows to reduce the impact of diffusion-induced
errors. Moreover, the DBFN is less sensitive to the first guess than the
4Dvar. |
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