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Application of a SEEK filter to a 1D biogeochemical model of the Ligurian Sea: Twin experiments and real in-situ data assimilation
Raick, C.; Alvera-Azcárate, A.; Barth, A.; Brankart, J.M.; Soetaert, K.; Grégoire, M. (2007). Application of a SEEK filter to a 1D biogeochemical model of the Ligurian Sea: Twin experiments and real in-situ data assimilation, in: Desaubies, Y. et al. Marine environmental monitoring and prediction. Selected papers from the 36th International Liège Colloquium on Ocean Dynamics, May 3-7, 2004. Journal of Marine Systems, 65(Special Issue 1-4): pp. 561-583. https://dx.doi.org/10.1016/j.jmarsys.2005.06.006
In: Desaubies, Y. et al. (2007). Marine environmental monitoring and prediction. Selected papers from the 36th International Liège Colloquium on Ocean Dynamics, May 3-7, 2004. Journal of Marine Systems, 65(Special Issue 1-4). Elsevier: Amsterdam. 1-588 pp.
In: Journal of Marine Systems. Elsevier: Tokyo; Oxford; New York; Amsterdam. ISSN 0924-7963; e-ISSN 1879-1573, meer
Peer reviewed article  

Beschikbaar in  Auteurs 
Documenttype: Congresbijdrage

Trefwoord
    Marien
Author keywords
    data assimilation; coupled physical-ecosystem model; Kalman filter;

Auteurs  Top 
  • Raick, C.
  • Alvera-Azcárate, A.
  • Barth, A.
  • Brankart, J.M.
  • Soetaert, K.
  • Grégoire, M.

Abstract
    The Singular Evolutive Extended Kalman (SEEK) filter has been implemented to assimilate in-situ data in a 1D coupled physical-ecosystem model of the Ligurian Sea. The biogeochemical model describes the partly decoupled nitrogen and carbon cycles of the pelagic food web. The GHER hydrodynamic model (1D version) is used to represent the physical forcings. The data assimilation scheme (SEEK filter) parameterizes the error statistics by means of a set of empirical orthogonal functions (EOFs). Twin experiments are first performed with the aim to choose the suitable experimental protocol (observation and estimation vectors, number of EOFs, frequency of the assimilation,...) and to assess the SEEK filter performances. This protocol is then applied to perform real data assimilation experiments using the DYFAMED data base. By assimilating phytoplankton observations, the method has allowed to improve not only the representation of the phytoplankton community, but also of other variables such as zooplankton and bacteria that evolve with model dynamics and that are not corrected by the data assimilation scheme. The validation of the assimilation method and the improvement of model results are studied by means of suitable error measurements.

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