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 hybrid data assimilation method and its comparison with an Ensemble Optimal Interpolation scheme in conjunction with the numerical ocean model using altimetry data

An original hybrid data assimilation scheme recently developed is presented and tested. The scheme is based on the application of the theory of diffusion random processes. It is applied here in conjunction with the Hybrid-Coordinate Ocean Model (HYCOM) to assimilate altimetry data from the Archiving, Validating and Interpolating Satellite Oceanography Data (AVISO) in the Atlantic. Several numerical experiments were conducted and their results were analyzed. It is shown that the method is able to assimilate data and to produce analyses closer to observations. It also conserves the model balance. This method allows calculating the confidence range of the analyses by estimating their errors The presented method is compared with the Ensemble Optimal Interpolation scheme (EnOI) and it is shown that it has several advantages, in particular, it provides a better forecast and requires less computational cost.