HR: 17:50h
AN: A24B-07    [Abstracts]
TI: Simultaneous Assimilation of Physical and Biological Observations into a 3D Coupled Marine Ecosystem Model Using Joint and Dual Kalman Filtering
AU: * Hoteit, I
EM: ihoteit@ucsd.edu
AF: Scripps Institution of Oceanography, 9500 Gilman Drive, La Jolla, CA 92093-0230, United States
AU: Korres, G
EM: gt@ath.hcmr.gr
AF: Hellenic Centre for Marine Research, P.O. BOX 712, Anavissos, 19013, Greece
AU: Triantafyllou, G
EM: korres@ath.hcmr.gr
AF: Hellenic Centre for Marine Research, P.O. BOX 712, Anavissos, 19013, Greece
AB: Two assimilation systems based on a suboptimal extended Kalman filter have been developed to simultaneously assimilate physical and biochemical data into a complex ecosystem model of the Eastern Mediterranean. The three-dimensional ecosystem model is composed of two on-line coupled sub-models: the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM). The filter is a variant of the extended Kalman filter which operates with low-rank error covariance matrices to reduce computational burden. Two different approaches have been considered: the "joint approach" and the "dual approach". In the first approach, one filter is used in which the state vector of the filter is composed of all prognostic variables of POM and ERSEM models. Basically, the numerical models are integrated forward in time to produce the (physical and biochemical) forecasts. The observations are then assimilated jointly to correct all forecast variables using the cross-correlations between all physical and biochemical forecast errors, which simultaneously provides the analyses for the physics and for the ecology. The dual approach consists of two filters, operating separately on the physics and on the ecology. We describe the two assimilation systems and discuss results of assimilation experiments.
DE: 3315 Data assimilation
SC: Atmospheric Sciences [A]
MN: 2007 Joint Assembly