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Statistical Analysis of Physical Characteristics Calculated by NEMO Model After Data Assimilation

Author

Listed:
  • Konstantin Belyaev

    (Shirshov Institute of Oceanology, Russian Academy of Sciences, 117997 Moscow, Russia)

  • Andrey Kuleshov

    (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, 125047 Moscow, Russia)

  • Ilya Smirnov

    (Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 119991 Moscow, Russia)

Abstract

The main goal of this study is to develop a method for finding the joint probability distribution of the state of the characteristics of the NEMO (Nucleus for European Modeling of the Ocean) ocean dynamics model with data assimilation using the Generalized Kalman filter (GKF) method developed earlier by the authors. The method for finding the joint distribution is based on the Karhunen–Loeve decomposition of the covariance function of the joint characteristics of the ocean. Numerical calculations of the dynamics of ocean currents, surface and subsurface ocean temperatures, and water salinity were carried out, both with and without assimilation of observational data from the Argo project drifters. The joint probability distributions of temperature and salinity at individual points in the world ocean at different depths were obtained and analyzed. The Atlantic Meridional Overturning Circulation (AMOC) system was also simulated using the NEMO model with and without data assimilation, and these results were compared to each other and analyzed.

Suggested Citation

  • Konstantin Belyaev & Andrey Kuleshov & Ilya Smirnov, 2025. "Statistical Analysis of Physical Characteristics Calculated by NEMO Model After Data Assimilation," Mathematics, MDPI, vol. 13(6), pages 1-13, March.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:6:p:948-:d:1611326
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    References listed on IDEAS

    as
    1. Korolev, V.Yu. & Chertok, A.V. & Korchagin, A.Yu. & Zeifman, A.I., 2015. "Modeling high-frequency order flow imbalance by functional limit theorems for two-sided risk processes," Applied Mathematics and Computation, Elsevier, vol. 253(C), pages 224-241.
    2. Konstantin Belyaev & Andrey Kuleshov & Natalia Tuchkova & Clemente A.S. Tanajura, 2018. "An optimal data assimilation method and its application to the numerical simulation of the ocean dynamics," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 24(1), pages 12-25, January.
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