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A new approach for processing the remote sensing chlorophyll-α (Chl-α) before assimilating into an ecosystem model is applied in two contrasting, regarding productivity and nutrients availability, Mediterranean sites: the DYFAMED and POSEIDON E1-M3A fixed point open ocean observatories. The new approach derives optically weighted depth-distributed Chl-α profiles from satellite data based on the model simulated Chl-α vertical distribution and light attenuation coefficient. We use the 1D version of the operational ecological 3D POSEIDON model, based on the European Regional Seas Ecosystem Model (ERSEM). The required hydrodynamic properties are obtained (off-line) from the POSEIDON operational 3D hydrodynamic Mediterranean basin scale model. The data assimilation scheme is the singular evolutive interpolated Kalman (SEIK) filter, the ensemble variant of the singular evolutive extended Kalman (SEEK) filter. The performance of the proposed assimilation approach was evaluated against the Chl-α satellite data and the seasonal averages of available in situ data for nitrate, phosphate and Chl-α. An improvement of the model simulated near-surface and subsurface maximum Chl-α concentrations is obtained, especially at the DYFAMED site. Model nitrate is improved with assimilation, particularly with the new approach assimilating depth-distributed Chl-α, while model phosphate is slightly worse after assimilation. Additional sensitivity experiments were performed, showing a better performance of the new approach under different scenarios of model Chl-α deviation from pseudo-observations of surface Chl-α.

An application of an ensemble-based robust filter for data assimilation into an ecosystem model of the Cretan Sea is presented and discussed. The ecosystem model comprises two on-line coupled sub-models: the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM). The filtering scheme is based on the Singular Evolutive Interpolated Kalman (SEIK) filter which is implemented with a time-local H∞ filtering strategy to enhance robustness and performances during periods of strong ecosystem variability. Assimilation experiments in the Cretan Sea indicate that robustness can be achieved in the SEIK filter by introducing an adaptive inflation scheme of the modes of the filter error covariance matrix. Twin-experiments are performed to evaluate the performance of the assimilation system and to study the benefits of using robust filtering in an ensemble filtering framework. Pseudo-observations of surface chlorophyll, extracted from a model reference run, were assimilated every two days. Simulation results suggest that the adaptive inflation scheme significantly improves the behavior of the SEIK filter during periods of strong ecosystem variability.

Within the framework of the European INSEA project, an advanced assimilation system has been implemented for the Pagasitikos Gulf ecosystem. The system is based on a multivariate sequential data assimilation scheme that combines satellite ocean sea color (chlorophyll-a) data with the predictions of a three-dimensional coupled physical–biochemical model of the Pagasitikos Gulf ecosystem presented in a companion paper. The hydrodynamics are solved with a very high resolution (1/100°) implementation of the Princeton Ocean Model (POM). This model is nested within a coarser resolution model of the Aegean Sea which is part of the Greek POSEIDON forecasting system. The forecast of the Aegean Sea model, itself nested and initialized from a Mediterranean implementation of POM, is also used to periodically re-initalize the Pagatisikos hydrodynamics model using variational initialization techniques. The ecosystem dynamics of Pagasitikos are tackled with a stand-alone implementation of the European Seas Ecosystem Model (ERSEM). The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter, in which the error statistics are parameterized by means of a suitable set of Empirical Orthogonal Functions (EOFs). The assimilation experiments were performed for year 2003 and additionally for a 9-month period over 2006 during which the physical model was forced with the POSEIDON-ETA 6-hour atmospheric fields. The assimilation system is validated by assessing the relevance of the system in fitting the data, the impact of the assimilation on non-observed biochemical processes and the overall quality of the forecasts. Assimilation of either GlobColour in 2003 or SeaWiFS in 2006 chlorophyll-a data enhances the identification of the ecological state of the Pagasitikos Gulf. Results, however, suggest that subsurface ecological observations are needed to improve the controllability of the ecosystem in the deep layers.

We present the development and validation of an eddy-resolving sequential data assimilation system for the Aegean Sea hydrodynamics that has been developed as part of the Poseidon operational system. The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter which is an error subspace Extended Kalman filter that operates with low-rank error covariance matrices as a way to reduce the computational burden. The filter was used to correct the forecast state of a 1/20° Princeton Ocean Model (POM) of the Aegean Sea every time new observations were available. The model is forced with hourly fluxes from a 1/10° ETA regional atmospheric model and is one-way nested to a 1/10° POM model of the Eastern Mediterranean Sea. The assimilated data set is multivariate including weekly AVISO sea level anomalies, weekly AVHRR sea surface temperature, and daily Ferrybox sea surface salinity (SSS) along the ferry boat route from Piraeus to Heraklion. Data assimilation experiments are performed to validate the system over a 6-month period (January–June, 2004). In one of the assimilation experiments, where the Ferrybox SSS data were not assimilated and used as independent observations, the multivariate assimilation system was able to reduce the error on the SSS prediction. Additional assimilation of the Ferrybox data on a daily basis shows their positive impact on other predicted variables of the system and their significant local effect on the SSS prediction within the southern Aegean Sea.

This study investigates the effectiveness of the Singular Evolutive Extended Kalman filter (SEEK) and its variants (SEIK and SFEK filters) for data assimilation into a Princeton Ocean Model (POM) of the Mediterranean Sea. The SEEK filters are sub-optimal Kalman filters based on the approximation of the filter's error covariance matrices by singular low-rank matrices, reducing in this way extensive computational burden. At the initialization, the filters error covariance matrix is parameterized by a set of multivariate empirical orthogonal functions (EOFs) which describe the dominant modes of the system's variability. The Mediterranean model is implemented on a 1/4° × 1/4° horizontal grid with 25 sigma levels and is forced with 6-hour ECMWF re-analysis atmospheric data. Several twin experiments, in which pseudo-observations of altimetric data and/or data profiles were assimilated, were first performed to evaluate the filters performances and to study their sensitivities to different parameters and setups. The results of these experiments were very encouraging and helped in setting up an effective configuration for the assimilation of real data in near-real time situation. In the hindcast experiments, Topex/Poseidon and ERS weekly sea level anomaly data were first assimilated during 1993 and the filters solution was evaluated against independent Reynolds sea surface temperature (SST) analysis. The assimilation system was able to significantly enhance the consistency between the model and the assimilated data, although the improvement with respect to independent SST data was significantly less pronounced. The model SST was only improved after including SST data in the assimilation system.

The reduced-order extended Kalman (ROEK) filter has been introduced by Cane et al. (J. Geophys. Res. 101(1996) 599) as a means to reduce the cost of the extended Kalman filter. It essentially consists of projecting the dynamics of the model onto a low dimensional subspace obtained via an empirical orthogonal functions (EOF) analysis. However, the choice of the dimension of the reduced-state space (or the number of EOFs to be retained) remains a delicate question. Indeed, Cane et al. found that increasing the number of EOFs does not improve, and even sometimes worsens, the performance of the ROEK filter. We speculate that this is probably due to the optimal character of the EOF analysis that is optimal in a time-mean sense only. In this respect, we develop a simple efficient adaptive scheme to tune, according to the model mode, the dimension of the reduced-state space, which would be therefore variable in time. In a first application, twin experiments are conducted in a realistic setting of the Ocean Parallelise (OPA) model in the tropical Pacific. The observations are assumed to be synthetic altimeter data sampled according to the Topex/Poseidon mission features. The adaptive scheme is shown to improve the performance of the ROEK filter especially during model unstable periods.

A singular evolutive interpolated Kalman (SEIK) filter is used to assimilate pseudo-observations via twin simulation experiments in a complex three-dimensional coupled physical–biogeochemical model of the Cretan Sea. The simulation system comprises two on-line coupled sub-models: the three-dimensional Princeton Model and the European Regional Seas Ecosystem Model (ERSEM). In the SEIK filter, the estimation error is represented by an ensemble of state vectors, which are drawn randomly at every filtering step. In the twin experiments performed the predictions of the coupled model were corrected every 2 days using synthetic measurements extracted from a model reference run according to a network of 23 stations in the Cretan Sea. The filter is shown to be very efficient, with the assimilation results exhibiting a continuous decrease of the estimation error during the experimental period. D 2003 Elsevier Science B.V. All rights reserved.

Several studies have demonstrated the effectiveness of the singular evolutive extended Kalman (SEEK) filter and its interpolated variant called singular evolutive interpolated Kalman (SEIK) filter in their capacity to assimilate altimetric data into ocean models. However, these filters remain expensive for real operational assimilation. The purpose of this paper is to develop degraded forms of the SEIK filter which are less costly and yet perform reasonably well. Our approach essentially consists in simplifying the evolution of the correction basis of the SEIK filter, which is the most expensive part of this filter. To deal with model instabilities, we also introduce two adaptive tuning schemes to control the correction basis evolution and adjust the variable forgetting factor. Our filters have been implemented in a realistic setting of the OPA model over the tropical pacific zone and their performance studied through twin experiments in which the observations are taken to be synthetic altimeter data sampled on the sea surface. The SEIK filter is used as a reference for comparison. Our new filters perform nearly as well as the SEIK, but can be 2–30 times faster.