An application of a data assimilation method based on the diffusion stochastic process theory using altimetry data in Atlantic

Konstantin P. Belyaev 1 , Andrey A. Kuleshov 2 , and Clemente A. S. Tanajura 3
  • 1 Shirshov Institute of Oceanology, Moscow, Russia. Oceanographic Modelling and Observational Network (REMO), Federal University of Bahia (UFBA), Salvador, Brazil
  • 2 Keldysh Institute of Applied Mathematics of the RAS, Moscow, Russia
  • 3 Oceanographic Modelling and Observational Network (REMO), Federal University of Bahia (UFBA), Salvador, Brazil. Department of Earth and Environmental Physics, Physics Institute, Federal University of Bahia (UFBA), Salvador, Brazil. Department of Ocean Sciences, University of California, Santa Cruz (UCSC), USA
Konstantin P. Belyaev, Andrey A. Kuleshov and Clemente A. S. Tanajura

Abstract

A data assimilation (DA) method based on the application of the diffusion stochastic process theory, particularly, of the Fokker-Planck equation, is considered. The method was introduced in the previous works; however, it is substantially modified and extended to the multivariate case in the current study. For the first time, the method is here applied to the assimilation of sea surface height anomalies (SSHA) into the Hybrid Coordinate Ocean Model (HYCOM) over the Atlantic Ocean. The impact of assimilation of SSHA is investigated and compared with the assimilation by an Ensemble Optimal Interpolation method (EnOI). The time series of the analyses produced by both assimilation methods are evaluated against the results from a free model run without assimilation. This study shows that the proposed assimilation technique has some advantages in comparison with EnOI analysis. Particularly, it is shown that it provides slightly smaller error and is computationally efficient. The method may be applied to assimilate other data such as observed sea surface temperature and vertical profiles of temperature and salinity.

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