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Coupling a two-way nested primitive equation model and a statistical SST predictor of the Ligurian Sea via data assimilation
Barth, A.; Alvera-Azcarate, A.; Beckers, J.-M.; Rixen, M. (2006). Coupling a two-way nested primitive equation model and a statistical SST predictor of the Ligurian Sea via data assimilation. Ocean Modelling 13(3-4): 255-270. dx.doi.org/10.1016/j.ocemod.2006.02.003
In: Ocean Modelling. Elsevier: Oxford. ISSN 1463-5003, more
Peer reviewed article  

Available in  Authors 
    VLIZ: Open Repository 231409 [ OMA ]

Author keywords
    data assimilation; two-way nested model; reduced-rank Kalman filter;

Authors  Top 
  • Barth, A.
  • Alvera-Azcarate, A.
  • Beckers, J.-M., more
  • Rixen, M.

Abstract
    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.

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