Reference : Coupling a two-way nested primitive equation model and a statistical SST predictor of...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
http://hdl.handle.net/2268/4306
Coupling a two-way nested primitive equation model and a statistical SST predictor of the Ligurian Sea via data assimilation
English
Barth, Alexander mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > Océanographie physique >]
Alvera Azcarate, Aïda mailto [Université de Liège - ULg > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Beckers, Jean-Marie mailto [Université de Liège - ULg > Département d'astrophys.]
Rixen, Michel [> > > >]
2006
Ocean Modelling
Elsevier Sci Ltd
13
3-4
255-270
Yes (verified by ORBi)
International
1463-5003
Oxford
[en] data assimilation ; two-way nested model ; reduced-rank Kalman filter ; Ligurian Sea
[en] A primitive equation model and a statistical predictor are coupled by data assimilation in order to combine the strength of both approaches. In this work, the system of two-way nested models centred in the Ligurian Sea and the satellite-based ocean forecasting (SOFT) system predicting the sea surface temperature (SST) are used. The data assimilation scheme is a simplified reduced order Kalman filter based on a constant error space. The assimilation of predicted SST improves the forecast of the hydrodynamic model compared to the forecast obtained by assimilating past SST observations used by the statistical predictor. This study shows that the SST of the SOFT predictor can be used to correct atmospheric heat fluxes. Traditionally this is done by relaxing the model SST towards the climatological SST. Therefore, the assimilation of SOFT SST and climatological SST are also compared. (c) 2006 Elsevier Ltd. All rights reserved.
GHER/MARE
http://hdl.handle.net/2268/4306
10.1016/j.ocemod.2006.02.003

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