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Comparison of data assimilation methods into hydrodynamic models of ocean circulation
 
PIIS023408790001935-2-1
DOI10.31857/S023408790001935-2
Publication type Article
Status Published
Authors
K. Belyaev 
Affiliation: Shirshov Institute of Oceanology of Russian Academy of Science
Address: Russian Federation, Moscow
A. Kuleshov
Affiliation: Keldysh Institute of Applied Mathematics of Russian Academy of Science
Address: Russian Federation, Moscow
I. Smirnov
Affiliation: Lomonosov Moscow State University, Faculty of Computational Mathematics and Cybernetics
Address: Russian Federation, Moscow
K. Tanajura
Affiliation: Federal University of Bahia
Address: Salvador, Brazil
Journal nameMatematicheskoe modelirovanie
EditionVolume 30 Number 12
Pages39-54
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.

Keywordsocean modelling, data assimilation, generalized Kalman filter, ensemble interpolation method, satellite altimetry data
AcknowledgmentThe study was carried out with the support of the Russian Science Foundation, project No. 14-11-00434.
Received10.11.2018
Publication date30.11.2018
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