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Discussion papers
https://doi.org/10.5194/os-2019-56
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-2019-56
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 20 Jun 2019

Submitted as: research article | 20 Jun 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Ocean Science (OS).

A hybrid data assimilation method and its comparison with an Ensemble Optimal Interpolation scheme in conjunction with the numerical ocean model using altimetry data

Konstantin Belyaev1,2, Andrey Kuleshov2, Ilya Smirnov3, and Clemente A. S. Tanajura4 Konstantin Belyaev et al.
  • 1Shirshov Institute of Oceanology of Russian Academy of Sciences, Moscow, 117997, Russia
  • 2Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, Moscow, 125047, Russia
  • 3Lomonosov Moscow State University, Faculty of Computational Mathematics and Cybernetics, Moscow, 119991, Russia
  • 4Federal University of Bahia, Physics Institute and Center for Research in Geophysics and Geology, Salvador BA, 40170-280, Brazil

Abstract. An original hybrid data assimilation scheme recently developed is presented and tested. The scheme is based on the application of the theory of diffusion random processes. It is applied here in conjunction with the Hybrid-Coordinate Ocean Model (HYCOM) to assimilate altimetry data from the Archiving, Validating and Interpolating Satellite Oceanography Data (AVISO) in the Atlantic. Several numerical experiments were conducted and their results were analyzed. It is shown that the method is able to assimilate data and to produce analyses closer to observations. It also conserves the model balance. This method allows calculating the confidence range of the analyses by estimating their errors The presented method is compared with the Ensemble Optimal Interpolation scheme (EnOI) and it is shown that it has several advantages, in particular, it provides a better forecast and requires less computational cost.

Konstantin Belyaev et al.
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Konstantin Belyaev et al.
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Short summary
The authors data assimilation method recently developed is presented and tested together with the ocean circulation model. It is shown that the method is able to assimilate data. It produces analyses closer to observations and also has several advantages in comparison with the traditional data assimilation schemes, for instance, with the Ensemble Optimal Interpolation scheme (EnOI). It provides a better forecast and requires less computational consumptions.
The authors data assimilation method recently developed is presented and tested together with...
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