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

Konstantin Belyaev, Andrey Kuleshov () and Ilya Smirnov
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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

Mathematics, 2025, vol. 13, issue 6, 1-13

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.

Keywords: data analysis; Generalized Kalman filter (GKF); stochastic differential equations; data assimilation method; Karhunen–Loeve decomposition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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