Preprints
https://doi.org/10.5194/os-2019-56
https://doi.org/10.5194/os-2019-56
20 Jun 2019
 | 20 Jun 2019
Status: this preprint was under review for the journal OS but the revision was not accepted.

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 Belyaev, Andrey Kuleshov, Ilya Smirnov, and Clemente A. S. Tanajura

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.

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Konstantin Belyaev, Andrey Kuleshov, Ilya Smirnov, and Clemente A. S. Tanajura
 
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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Konstantin Belyaev, Andrey Kuleshov, Ilya Smirnov, and Clemente A. S. Tanajura
Konstantin Belyaev, Andrey Kuleshov, Ilya Smirnov, and Clemente A. S. Tanajura

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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.