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| Titel |
Data assimilation experiments using the 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 |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 1, no. 2 ; Nr. 1, no. 2 (2014-07-16), S.1073-1131 |
| Datensatznummer |
250115114
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| Publikation (Nr.) |
 copernicus.org/npgd-1-1073-2014.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 in 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 behavior 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 modeled by diffusion operators which
dissipate energy that cascade from large to small scales. Thus, in
this article the DBFN approximations and their consequences on the
data assimilation system set-up are analyzed. 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. |
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