Abstract
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.
Highlights
In this paper, (i) the applicability of the Generalized Kalman Filter (GKF) method is shown; (ii) the results of calculations obtained by using the GKF and Ensemble Optimal Interpolation (EnOI) methods are compared, and it is shown that the GKF method has some advantages over EnOI; (iii) analysis of model fields is carried out by using the GKF data assimilation method, and it is shown that this method is capable of reconstructing the synoptic structure of fields in the Atlantic
To assess the quality of data assimilation (DA), we introduce the following variables
Filter (GKF) DA method is shown; this method was compared with the Ensemble Optimal
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The fundamental difference between the GKF (Generalized Kalman Filter) DA method developed by the authors of this paper and the widely used stochastic dynamic method—Ensemble Kalman Filter (EnKF) is in the fact that the GKF method uses the difference between the model and observed values of characteristics at a given time instant, and the data observed trend, which is explicitly included in the assimilation algorithm for the correction Such an approach has several evident advantages: for example, a model can have a systematic error (a bias), which, will not be considered in Mathematics 2021, 9, 2371 the final formulae since it will be subtracted when calculating the difference of the model characteristics for two successive time instants. In this paper, (i) the applicability of the GKF method is shown; (ii) the results of calculations obtained by using the GKF and EnOI methods are compared, and it is shown that the GKF method has some advantages over EnOI; (iii) analysis of model fields is carried out by using the GKF data assimilation method, and it is shown that this method is capable of reconstructing the synoptic structure of fields in the Atlantic
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