Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model
International audience 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 t...
Published in: | Nonlinear Processes in Geophysics |
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Main Authors: | , , , , , |
Other Authors: | , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2015
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Online Access: | https://amu.hal.science/hal-01232425 https://amu.hal.science/hal-01232425/document https://amu.hal.science/hal-01232425/file/npg-22-233-2015.pdf https://doi.org/10.5194/npg-22-233-2015 |
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HAL Université Côte d'Azur |
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English |
topic |
SHALLOW-WATER MODEL BOUNDARY-CONDITIONS ALTIMETER DATA CIRCULATION MODEL NUMERICAL-MODELS NORTH-ATLANTIC KALMAN FILTER INITIALIZATION ALGORITHM COEFFICIENTS [SDE.MCG]Environmental Sciences/Global Changes [SDE.ES]Environmental Sciences/Environment and Society [SDE.BE]Environmental Sciences/Biodiversity and Ecology |
spellingShingle |
SHALLOW-WATER MODEL BOUNDARY-CONDITIONS ALTIMETER DATA CIRCULATION MODEL NUMERICAL-MODELS NORTH-ATLANTIC KALMAN FILTER INITIALIZATION ALGORITHM COEFFICIENTS [SDE.MCG]Environmental Sciences/Global Changes [SDE.ES]Environmental Sciences/Environment and Society [SDE.BE]Environmental Sciences/Biodiversity and Ecology Ruggiero, G. A. Ourmieres, Yann Cosme, E Blum, J Auroux, D Verron, J Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model |
topic_facet |
SHALLOW-WATER MODEL BOUNDARY-CONDITIONS ALTIMETER DATA CIRCULATION MODEL NUMERICAL-MODELS NORTH-ATLANTIC KALMAN FILTER INITIALIZATION ALGORITHM COEFFICIENTS [SDE.MCG]Environmental Sciences/Global Changes [SDE.ES]Environmental Sciences/Environment and Society [SDE.BE]Environmental Sciences/Biodiversity and Ecology |
description |
International audience 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 dif-fusive 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 setup 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. |
author2 |
Laboratoire Jean Alexandre Dieudonné (LJAD) Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA) Institut méditerranéen d'océanologie (MIO) Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) Laboratoire de glaciologie et géophysique de l'environnement (LGGE) Observatoire des Sciences de l'Univers de Grenoble (OSUG) Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS) |
format |
Article in Journal/Newspaper |
author |
Ruggiero, G. A. Ourmieres, Yann Cosme, E Blum, J Auroux, D Verron, J |
author_facet |
Ruggiero, G. A. Ourmieres, Yann Cosme, E Blum, J Auroux, D Verron, J |
author_sort |
Ruggiero, G. A. |
title |
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_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_sort |
data assimilation experiments using diffusive back-and-forth nudging for the nemo ocean model |
publisher |
HAL CCSD |
publishDate |
2015 |
url |
https://amu.hal.science/hal-01232425 https://amu.hal.science/hal-01232425/document https://amu.hal.science/hal-01232425/file/npg-22-233-2015.pdf https://doi.org/10.5194/npg-22-233-2015 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
ISSN: 1023-5809 EISSN: 1607-7946 Nonlinear Processes in Geophysics https://amu.hal.science/hal-01232425 Nonlinear Processes in Geophysics, 2015, 22 (2), pp.233-248. ⟨10.5194/npg-22-233-2015⟩ http://www.nonlin-processes-geophys.net/22/233/2015/ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/npg-22-233-2015 hal-01232425 https://amu.hal.science/hal-01232425 https://amu.hal.science/hal-01232425/document https://amu.hal.science/hal-01232425/file/npg-22-233-2015.pdf doi:10.5194/npg-22-233-2015 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/npg-22-233-2015 |
container_title |
Nonlinear Processes in Geophysics |
container_volume |
22 |
container_issue |
2 |
container_start_page |
233 |
op_container_end_page |
248 |
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1799485417801646080 |
spelling |
ftunivcotedazur:oai:HAL:hal-01232425v1 2024-05-19T07:45:23+00:00 Data assimilation experiments using diffusive back-and-forth nudging for the NEMO ocean model Ruggiero, G. A. Ourmieres, Yann Cosme, E Blum, J Auroux, D Verron, J Laboratoire Jean Alexandre Dieudonné (LJAD) Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA) Institut méditerranéen d'océanologie (MIO) Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS) Laboratoire de glaciologie et géophysique de l'environnement (LGGE) Observatoire des Sciences de l'Univers de Grenoble (OSUG) Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS) 2015-04 https://amu.hal.science/hal-01232425 https://amu.hal.science/hal-01232425/document https://amu.hal.science/hal-01232425/file/npg-22-233-2015.pdf https://doi.org/10.5194/npg-22-233-2015 en eng HAL CCSD European Geosciences Union (EGU) info:eu-repo/semantics/altIdentifier/doi/10.5194/npg-22-233-2015 hal-01232425 https://amu.hal.science/hal-01232425 https://amu.hal.science/hal-01232425/document https://amu.hal.science/hal-01232425/file/npg-22-233-2015.pdf doi:10.5194/npg-22-233-2015 info:eu-repo/semantics/OpenAccess ISSN: 1023-5809 EISSN: 1607-7946 Nonlinear Processes in Geophysics https://amu.hal.science/hal-01232425 Nonlinear Processes in Geophysics, 2015, 22 (2), pp.233-248. ⟨10.5194/npg-22-233-2015⟩ http://www.nonlin-processes-geophys.net/22/233/2015/ SHALLOW-WATER MODEL BOUNDARY-CONDITIONS ALTIMETER DATA CIRCULATION MODEL NUMERICAL-MODELS NORTH-ATLANTIC KALMAN FILTER INITIALIZATION ALGORITHM COEFFICIENTS [SDE.MCG]Environmental Sciences/Global Changes [SDE.ES]Environmental Sciences/Environment and Society [SDE.BE]Environmental Sciences/Biodiversity and Ecology info:eu-repo/semantics/article Journal articles 2015 ftunivcotedazur https://doi.org/10.5194/npg-22-233-2015 2024-05-02T00:48:54Z International audience 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 dif-fusive 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 setup 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. Article in Journal/Newspaper North Atlantic HAL Université Côte d'Azur Nonlinear Processes in Geophysics 22 2 233 248 |