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Russian Journal of Numerical Analysis and Mathematical Modelling

Editor-in-Chief: Dymnikov, Valentin P. / Kuznetsov, Yuri

Managing Editor: Vassilevski, Yuri V.

Editorial Board: Agoshkov, Valeri I. / Amosov, Andrey A. / Kaporin, Igor E. / Kobelkov, Georgy M. / Mikhailov, Gennady A. / Repin, Sergey I. / Shaidurov, Vladimir V. / Shokin, Yuri I. / Tyrtyshnikov, Eugene E.


IMPACT FACTOR 2018: 0.779

CiteScore 2018: 0.92

SCImago Journal Rank (SJR) 2018: 0.512
Source Normalized Impact per Paper (SNIP) 2018: 1.275

Mathematical Citation Quotient (MCQ) 2017: 0.13

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1569-3988
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Volume 31, Issue 3

Issues

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

Konstantin P. Belyaev
  • Shirshov Institute of Oceanology, Moscow, Russia. Oceanographic Modelling and Observational Network (REMO), Federal University of Bahia (UFBA), Salvador, Brazil
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Andrey A. Kuleshov / Clemente A. S. Tanajura
  • Corresponding author
  • 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
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-05-28 | DOI: https://doi.org/10.1515/rnam-2016-0014

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.

Keywords: Ocean data assimilation; Fokker-Planck equation; ensemble optimal interpolation; HYCOM; sea level anomaly data; Atlantic Ocean

MSC 2010: 65C20

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About the article

Received: 2015-09-28

Accepted: 2016-03-10

Published Online: 2016-05-28

Published in Print: 2016-06-01


Citation Information: Russian Journal of Numerical Analysis and Mathematical Modelling, Volume 31, Issue 3, Pages 137–147, ISSN (Online) 1569-3988, ISSN (Print) 0927-6467, DOI: https://doi.org/10.1515/rnam-2016-0014.

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