Abstract
Two different data assimilation methods are compared: the author’s method of the generalized Kalman filter (GKF) proposed earlier and the standard ensemble objective interpolation (EnOI) method, which is a particular case of the ensemble Kalman filter (EnKF) scheme. The methods are compared with respect to different criteria, in particular, the criterion of the forecasting error minimum and a posteriori error minimum over a given time interval. The Archiving, Validating and Interpolating Satellite Oceanography Data (AVISO), i.e., the altimetry data, was used as the observation data; the Hybrid Circulation Ocean Model (HYCOM) model was used as a basic numerical model of ocean circulation. It has been shown that the GKF method has a number of advantages over the EnOI method in particular, it provides the better temporal forecast error. In addition, the results of numerical experiments with different data assimilation methods are analyzed and their results are compared with the control experiment, i.e., the HYCOM model without data assimilation. The computation results are also compared with independent observations. The conclusion is made that the studied assimilation methods can be applied to forecast the state of the ocean.
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ACKNOWLEDGMENTS
The calculations were performed with the use of the resources of the supercomputer complex of Lomonosov Moscow State University (Russia) and the remote IBM computer Iemanja of the Federal University of Bahia (Salvador, Brazil).
Funding
This work was supported by the Russian Science Foundation, project no. 14-11-00434.
C.A.S. Tanajura is grateful to Brazilian National Agency of Petroleum, Natural Gas and Biofuels.
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Translated by E. Smirnova
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Belyaev, K.P., Kuleshov, A.A., Smirnov, I.N. et al. Comparison of Data Assimilation Methods in Hydrodynamics Ocean Circulation Models. Math Models Comput Simul 11, 564–574 (2019). https://doi.org/10.1134/S2070048219040045
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DOI: https://doi.org/10.1134/S2070048219040045