Geodesic
Numerical experiments with the NEMO ocean circulation model and the assimilation of observational data from ARGO
Matematičeskoe modelirovanie, Tome 35 (2023) no. 3, pp. 93-105

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The paper studies the spatiotemporal variability of the characteristics of the ocean circulation model Nucleus for European Modeling of the Ocean (NEMO) with data assimilation in conjunction with the Generalized Kalman filtering (GKF) method, previously developed by the authors. In the present work, numerical experiments were carried out with the global version of the NEMO model on the grid ORCA1 and using a principally new approach for determining the key parameters of the GKF method. Simulation was carried out on a selected time interval of 1 month of the spatiotemporal variability of ocean characteristics created by the NEMO model, both using the proposed data assimilation method with the archive of observational data from Argo drifters at different horizons, and without assimilation. The results of numerical experiments are analyzed.
Keywords: ocean modeling, NEMO model, generalized Kalman filter, Argo drifter data.
Mots-clés : observational data assimilation 
@article{MM_2023_35_3_a5,
     author = {K. P. Belyaev and A. A. Kuleshov and Yu. D. Resnyanskii and I. N. Smirnov and R. Yu. Fadeev},
     title = {Numerical experiments with the {NEMO} ocean circulation model and the assimilation of observational data from {ARGO}},
     journal = {Matemati\v{c}eskoe modelirovanie},
     pages = {93--105},
     publisher = {mathdoc},
     volume = {35},
     number = {3},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MM_2023_35_3_a5/}
}
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K. P. Belyaev; A. A. Kuleshov; Yu. D. Resnyanskii; I. N. Smirnov; R. Yu. Fadeev. Numerical experiments with the NEMO ocean circulation model and the assimilation of observational data from ARGO. Matematičeskoe modelirovanie, Tome 35 (2023) no. 3, pp. 93-105. http://geodesic.mathdoc.fr/item/MM_2023_35_3_a5/