2023Mathematical Models and Computer SimulationsRequires access

Numerical Experiments with the Nemo Ocean Circulation Model and the Assimilation of Observational Data from Argo Drifters

Konstantin Belyaev, А. А. Кулешов, Yu. D. Resnyanskii, Ilya Smirnov, R. Yu. Fadeev

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Abstract

Abstract In this paper the spatiotemporal variability of the characteristics of the ocean circulation model, the Nucleus for European Modeling of the Ocean (NEMO), is studied with data assimilation in conjunction with the generalized Kalman filtering (GKF) method, previously developed by the authors. In this study, the numerical experiments were carried out with the global version of the NEMO model on the ORCA1 grid 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 the 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 the numerical experiments are analyzed.

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What this paper is about

Abstract In this paper the spatiotemporal variability of the characteristics of the ocean circulation model, the Nucleus for European Modeling of the Ocean (NEMO), is studied with data assimilation in conjunction with the generalized Kalman filtering (GKF) method, previously developed by the authors. In this study, the numerical experiments were carried out with the global version of the NEMO model on the ORCA1 grid 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 the 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 the numerical experiments are analyzed.

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Available abstract

Abstract In this paper the spatiotemporal variability of the characteristics of the ocean circulation model, the Nucleus for European Modeling of the Ocean (NEMO), is studied with data assimilation in conjunction with the generalized Kalman filtering (GKF) method, previously developed by the authors. In this study, the numerical experiments were carried out with the global version of the NEMO model on the ORCA1 grid 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 the 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 the numerical experiments are analyzed.

Key concepts: Argo, Data assimilation, Predictability, Meteorology, Ocean current, Climatology, Kalman filter, Computer science

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Numerical Experiments with the Nemo Ocean Circulation Model and the Assimilation of Observational Data from Argo Drifters — Research Paper | ScholarLens