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Multigrid state vector for data assimilation in a two-way nested model of the Ligurian Sea
Barth, A.; Alvera-Azcárate, A.; Beckers, J.-M.; Rixen, M.; Vandenbulcke, L. (2007). Multigrid state vector for data assimilation in a two-way nested model of the Ligurian Sea. J. Mar. Syst. 65(1-4): 41-59. https://dx.doi.org/10.1016/j.jmarsys.2005.07.006
In: Journal of Marine Systems. Elsevier: Tokyo; Oxford; New York; Amsterdam. ISSN 0924-7963; e-ISSN 1879-1573, meer
Ook verschenen in:
Desaubies, Y.; Rixen, M.; Beckers, J.-M. (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(1-4). Elsevier: Amsterdam. 588 pp., meer
Peer reviewed article  
Beschikbaar in  Auteurs 
Trefwoord
    Marien/Kust
Author keywords
    data assimilation; two-way nested model; reduced-rank Kalman filter;
Auteurs  Top 
  • Barth, A.
  • Alvera-Azcárate, A.
  • Beckers, J.-M.
  • Rixen, M.
  • Vandenbulcke, L.
Abstract
    A system of two nested models composed by a coarse resolution model of the Mediterranean Sea, an intermediate resolution model of the Provencal Basin and a high resolution model of the Ligurian Sea is coupled with a Kalman-filter based assimilation method. The state vector for the data assimilation is composed by the temperature, salinity and elevation of the three models. The forecast error is estimated by an ensemble run of 200 members by perturbing initial condition and atmospheric forcings. The 50 dominant empirical orthogonal functions (EOF) are taken as the error covariance of the model forecast. This error covariance is assumed to be constant in time. Sea surface temperature (SST) and sea surface height (SSH) are assimilated in this system.
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