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Methods of Assimilation of Sea Surface Temperature Satellite Data and Their Influence on the Reconstruction of Hydrophysical Fields of the Black, Azov, and Marmara Seas Using the Institute of Numerical Mathematics Ocean Model (INMOM)

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Abstract

The results are analyzed of the simulation of hydrophysical fields of the Black, Azov, and Marmara seas with the Institute of Numerical Mathematics Ocean Model (INMOM) implemented with a spatial resolution of 4 km, with various assimilation technique for sea surface temperature (SST) data from the SEVIRI sensor installed on MSG satellites. The relaxation (so called nudging) and the ensemble optimal interpolation (EnOI) with various assimilation time frequencies (3-, 6-, 12-, and 24-hour assimilation windows) were used as assimilation methods. It was shown that the assimilation of SST data via the EnOI made it possible to reproduce hydrophysical fields more accurately than via nudging or without assimilation at all. Even with the assimilation of SST data irregularly distributed over space and time, a decrease in the calculation error was observed over the entire sea area, and the structures of the zones of temperature rise or drop were more correctly simulated. The best results were achieved with via the EnOI assimilation with an increasing assimilation frequency over time. When SST data were assimilated using the EnOI, the mean error decreased from 0.16°C (24-hour assimilation window) to 0.08°C (3-hour assimilation window); accordingly, the absolute mean error decreased from 1.03 to 0.33°C, and the standard deviation decreased from 1.33 to 0.42°C. In addition, assimilation using the EnOI 3-hour window improved the reproduction of SST during the period of convection cooling. The assimilation of SST data also led to changes in the structure of the surface sea circulation. In some areas, the direction of currents varied within 5°–10°, and the velocity modulus changed by 3–5%. The assimilation of SST data only slightly reduced errors in the structure of the model vertical temperature profile, which can reach 2°C at depths of 30–40 m. At depths greater than 100 m, deviations did not exceed 0.05°C.

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Authors and Affiliations

  1. Zubov State Oceanographic Institute, 119034, Moscow, Russia

    V. V. Fomin & N. A. Diansky

  2. Lomonosov Moscow State University, GSP-1, 199991, Moscow, Russia

    N. A. Diansky

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  1. V. V. Fomin
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Translated from Meteorologiya i Gidrologiya, 2023, No. 2, pp. 15-30. https://doi.org/10.52002/0130-2906-2023-2-15-30.

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Fomin, V.V., Diansky, N.A. Methods of Assimilation of Sea Surface Temperature Satellite Data and Their Influence on the Reconstruction of Hydrophysical Fields of the Black, Azov, and Marmara Seas Using the Institute of Numerical Mathematics Ocean Model (INMOM). Russ. Meteorol. Hydrol. 48, 97–108 (2023). https://doi.org/10.3103/S1068373923020024

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