Abstract:
Two different assimilation methods are compared, namely, early proposed author’s method of generalized Kalman filtration (GKF) and standard objective ensemble interpolation method (EnOI) that is a partial case of extended Kalman filter scheme (EnKF). The methods are compared with respect to various criteria, in particular, with respect to minimum of the forecast error and with respect of a posterior error over a given timeinterval. As observed data we used the Archiving Validating and Interpolating Satellite Observation (AVISO) i.e. altimetry data, and as a base numerical model of the ocean circulation we chose the Hybrid Circulation Ocean Model (HYCOM). It is shown that the method GKF has a number of advantages comparing with the method EnOI. The computations of numerical experiments with different assimilation method are analyzed and their results are compared with the control experiments i.e. the HYCOM run without assimilation. The computation results are also verified with independent observations. The conclusion is made that the studied assimilation methods can be applied for the forecasting of the environment.
Citation:
K. P. Belyaev, A. A. Kuleshov, I. N. Smirnov, C. A. S. Tanajura, “Comparison of data assimilation methods into hydrodynamic models of ocean circulation”, Mat. Model., 30:12 (2018), 39–54; Math. Models Comput. Simul., 11:4 (2019), 564–574
This publication is cited in the following 10 articles:
K. P. Belyaev, A. A. Kuleshov, Yu. D. Resnyanskii, I. N. Smirnov, R. Yu. Fadeev, “Numerical experiments with the NEMO ocean circulation model and the assimilation of observational data from ARGO”, Math. Models Comput. Simul., 15:5 (2023), 842–849
Mahmoud Pirooznia, Mehdi Raoofian Naeeni, Mohammad J. Tourian, “Modeling total surface current in the Persian Gulf and the Oman Sea by combination of geodetic and hydrographic observations and assimilation with in situ current meter data”, Acta Geophys., 71:6 (2023), 2839
K. Belyaev, A. Kuleshov, I. Smirnov, C. A. S. Tanajura, “Generalized Kalman Filter and ensemble optimal interpolation, their comparison and application to the hybrid coordinate ocean model”, Mathematics, 9:19 (2021), 2371
I. D. Deinego, I. Ansorge, K. P. Belyaev, “Altimetry data assimilation into a numerical model of ocean dynamics in the South Atlantic”, Oceanology, 61:5 (2021), 613–624
A. Sanchez-Arcilla, J. Staneva, L. Cavaleri, M. Badger, J. Bidlot, J. T. Sorensen, L. B. Hansen, A. Martin, A. Saulter, M. Espino, M. M. Miglietta, M. Mestres, D. Bonaldo, P. Pezzutto, J. Schulz-Stellenfleth, A. Wiese, X. Larsen, S. Carniel, R. Bolanos, S. Abdalla, A. Tiesi, “CMEMS-based coastal analyses: conditioning, coupling and limits for applications”, Front. Mar. Sci., 8 (2021), 604741
K Belyaev, B Chetverushkin, A Kuleshov, I Smirnov, “Correction of the model dynamics for the Northern seas using observational altimetry data”, J. Phys.: Conf. Ser., 2131:2 (2021), 022113
K. Belyaev, A. Kuleshov, I. Smirnov, “Spatial-temporal variability of the calculated characteristics of the ocean in the arctic zone of russia by using the nemo model with altimetry data assimilation”, J. Mar. Sci. Eng., 8:10 (2020), 753
Belyaev K., Kuleshov A., Smirnov I., “Spatial Decomposition of Covariance Functions in Generalized Kalman Filter Method For Data Assimilation”, 2020 Ivannikov Ispras Open Conference (Ispras 2020), ed. Avetisyan A., IEEE, 2020, 125–129
Konstantin Belyaev, Andrey Kuleshov, Ilya Smirnov, Clemente A.S. Tanajura, 2019 3rd European Conference on Electrical Engineering and Computer Science (EECS), 2019, 90
Konstantin Belyaev, Andrey Kuleshov, Ilya Smirnov, 2019 Ivannikov Ispras Open Conference (ISPRAS), 2019, 87
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