Abstract
In this study, two assimilation systems based on a suboptimal extended Kalman filter have been developed to simultaneously assimilate physical and biochemical data into an ecosystem model of the Eastern Mediterranean. The ecosystem model is composed of two on-line coupled sub-models: the three-dimensional Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM). The filter is a variant of the extended Kalman filter which makes use of 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, a unique filter is used in which the state vector of the filter is composed of the prognostic variables of both POM and ERSEM models. Basically, the numerical models are integrated forward in time to produce the (physical and biochemical) forecasts. The observations axe then assimilated simultaneously to correct all forecast variables using the cross-correlations between all physical and biochemical forecast errors, providing 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. The two assimilation systems were implemented and numerical experiments were performed to evaluate their performances.