This article is cited in 11 papers

Comparison of data assimilation methods into hydrodynamic models of ocean circulation

K. P. Belyaev^ab, A. A. Kuleshov^a, I. N. Smirnov^c, C. A. S. Tanajura<sup>d

a</sup> Keldysh Institute of Applied Mathematics of Russian Academy of Science
^b Shirshov Institute of Oceanology of Russian Academy of Science
^c Lomonosov Moscow State University, Faculty of Computational Mathematics and Cybernetics
^d Federal University of Bahia, Salvador, Brazil

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.

Keywords: ocean modelling, data assimilation, generalized Kalman filter, ensemble interpolation method, satellite altimetry data.

Received: 12.02.2018

 English version: Mathematical Models and Computer Simulations, 2019, 11:4, 564–574