Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model
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. The...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-04-01
|
| Series: | Nonlinear Processes in Geophysics |
| Online Access: | http://www.nonlin-processes-geophys.net/22/233/2015/npg-22-233-2015.pdf |
| _version_ | 1818195058397020160 |
|---|---|
| author | G. A. Ruggiero Y. Ourmières E. Cosme J. Blum D. Auroux J. Verron |
| author_facet | G. A. Ruggiero Y. Ourmières E. Cosme J. Blum D. Auroux J. Verron |
| author_sort | G. A. Ruggiero |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T01:12:09Z |
| format | Article |
| id | doaj.art-0ff25df63c4d4659bbdc80cdad178494 |
| institution | Directory Open Access Journal |
| issn | 1023-5809 1607-7946 |
| language | English |
| last_indexed | 2024-12-12T01:12:09Z |
| publishDate | 2015-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Nonlinear Processes in Geophysics |
| spelling | doaj.art-0ff25df63c4d4659bbdc80cdad1784942022-12-22T00:43:27ZengCopernicus PublicationsNonlinear Processes in Geophysics1023-58091607-79462015-04-0122223324810.5194/npg-22-233-2015Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean modelG. A. Ruggiero0Y. Ourmières1E. Cosme2J. Blum3D. Auroux4J. Verron5Université de Nice Sophia-Antipolis/LJAD, Nice, FranceUniversité du Sud Toulon-Var, Aix-Marseille Université, CNRS/INSU, IRD, Mediterranean Institute of Oceanography (MIO), FranceUniversité Joseph Fourier/LGGE, Grenoble, FranceUniversité de Nice Sophia-Antipolis/LJAD, Nice, FranceUniversité de Nice Sophia-Antipolis/LJAD, Nice, FranceCNRS/LGGE, Grenoble, FranceThe 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.http://www.nonlin-processes-geophys.net/22/233/2015/npg-22-233-2015.pdf |
| spellingShingle | G. A. Ruggiero Y. Ourmières E. Cosme J. Blum D. Auroux J. Verron Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model Nonlinear Processes in Geophysics |
| title | Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model |
| title_full | Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model |
| title_fullStr | Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model |
| title_full_unstemmed | Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model |
| title_short | Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model |
| title_sort | data assimilation experiments using diffusive back and forth nudging for the nemo ocean model |
| url | http://www.nonlin-processes-geophys.net/22/233/2015/npg-22-233-2015.pdf |
| work_keys_str_mv | AT garuggiero dataassimilationexperimentsusingdiffusivebackandforthnudgingforthenemooceanmodel AT yourmieres dataassimilationexperimentsusingdiffusivebackandforthnudgingforthenemooceanmodel AT ecosme dataassimilationexperimentsusingdiffusivebackandforthnudgingforthenemooceanmodel AT jblum dataassimilationexperimentsusingdiffusivebackandforthnudgingforthenemooceanmodel AT dauroux dataassimilationexperimentsusingdiffusivebackandforthnudgingforthenemooceanmodel AT jverron dataassimilationexperimentsusingdiffusivebackandforthnudgingforthenemooceanmodel |