Correction of the model dynamics for the Northern seas using observational altimetry data

The earlier derived data assimilation method called Generalized Kalman filter (GKF) is applied in conjunction with the Nucleus for European Modelling of the Ocean (NEMO) circulation model to the calculation of the dynamics in the North Seas of Russia. By assimilating the satellite altimetry data from archive AVISO (Archiving, validating and interpolating of satellite observations) this method corrects the direct model calculations and improves the ocean state. The model fields, in particular, sea level and sea surface temperature with and without assimilation are constructed and compared with each other. The brief analysis of the results is also performed.

Generalized Kalman Filter and Ensemble Optimal Interpolation, Their Comparison and Application to the Hybrid Coordinate Ocean Model

In this paper, we consider a recently developed data assimilation method, the Generalized Kalman Filter (GKF), which is a generalization of the widely-used Ensemble Optimal Interpolation (EnOI) method. Both methods are applied for modeling the Atlantic Ocean circulation using the known Hybrid Coordinate Ocean Model. The along-track altimetry data taken from the Archiving, Validating and Interpolating Satellite Oceanography Data (AVISO) were used for data assimilation and other data from independent archives of observations; particularly, the temperature and salinity data from the Pilot Research Array in the Tropical Atlantic were used for independent comparison. Several numerical experiments were performed with their results discussed and analyzed. It is shown that values of the ocean state variables obtained in the calculations using the GKF method are closer to the observations in terms of standard metrics in comparison with the calculations using the standard data assimilation method EnOI. Furthermore, the GKF method requires less computational effort compared to the EnOI method.

A generalized Kalman filter with its precision in recursive form when the stochastic model is misspecified

In this contribution, we introduce a generalized Kalman filter with precision in recursive form when the stochastic model is misspecified. The filter allows for a relaxed dynamic model in which not all state vector elements are connected in time. The filter is equipped with a recursion of the actual error-variance matrices so as to provide an easy-to-use tool for the efficient and rigorous precision analysis of the filter in case the underlying stochastic model is misspecified. Different mechanizations of the filter are presented, including a generalization of the concept of predicted residuals as needed for the recursive quality control of the filter.