Reference : Application of a SEEK filter to a 1D biogeochemical model of the Ligurian Sea: Twin expe...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Multidisciplinary, general & others
Life sciences : Aquatic sciences & oceanology
http://hdl.handle.net/2268/4533
Application of a SEEK filter to a 1D biogeochemical model of the Ligurian Sea: Twin experiments and real in-situ data assimilation
English
Raick, Caroline [Université de Liège - ULg > Département des sciences et gestion de l'environnement > Océanologie >]
Alvera Azcarate, Aïda mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Barth, Alexander mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > Océanographie physique >]
Brankart, J. M. [> > > >]
Soetaert, K. [> > > >]
Grégoire, Marilaure mailto [Université de Liège - ULg > Département des sciences et gestion de l'environnement > Océanologie >]
Mar-2007
Journal of Marine Systems
Elsevier Science Bv
65
1-4
561-583
Yes (verified by ORBi)
International
0924-7963
Amsterdam
[en] data assimilation ; coupled physical-ecosystem model ; Kalman filter ; error analysis ; Ligurian Sea
[en] 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. (c) 2006 Elsevier B.V. All rights reserved.
Centre Interfacultaire de Recherches en Océanologie - MARE
http://hdl.handle.net/2268/4533
10.1016/j.jmarsys.2005.06.006

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