Comparison of Data Assimilation Methods in Hydrodynamics Ocean Circulation Models
- Authors: Belyaev K.P.1,2, Kuleshov A.A.2, Smirnov I.N.3, Tanajura C.A.4
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Affiliations:
- Shirshov Institute of Oceanology, Russian Academy of Sciences
- Keldysh Institute of Applied Mathematics, Russian Academy of Sciences
- Faculty of Computational Mathematics and Cybernetics, Moscow State University
- Federal University of Bahia
- Issue: Vol 11, No 4 (2019)
- Pages: 564-574
- Section: Article
- URL: https://journals.rcsi.science/2070-0482/article/view/203393
- DOI: https://doi.org/10.1134/S2070048219040045
- ID: 203393
Cite item
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.
About the authors
K. P. Belyaev
Shirshov Institute of Oceanology, Russian Academy of Sciences; Keldysh Institute of Applied Mathematics, Russian Academy of Sciences
Author for correspondence.
Email: kosbel55@gmail.com
Russian Federation, Moscow; Moscow
A. A. Kuleshov
Keldysh Institute of Applied Mathematics, Russian Academy of Sciences
Author for correspondence.
Email: andrew_kuleshov@mail.ru
Russian Federation, Moscow
I. N. Smirnov
Faculty of Computational Mathematics and Cybernetics, Moscow State University
Email: andrew_kuleshov@mail.ru
Russian Federation, Moscow
C. A. S. Tanajura
Federal University of Bahia
Email: andrew_kuleshov@mail.ru
Brazil, Salvador
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