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

被引：0

作者：

Belyaev K.P. ^{[1
,2
]}

Kuleshov A.A. ^{[2
]}

Resnyanskii Y.D. ^{[3
]}

Smirnov I.N. ^{[4
]}

Fadeev R.Y. ^{[3
,5
]}

机构：

[1] Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow

[2] Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow

[3] Hydrometeorological Research Center of the Russian Federation, Moscow

[4] Lomonosov Moscow State University, Moscow

[5] Marchuk Institute of Computational Mathematics, Russian Academy of Sciences, Moscow

来源：

Mathematical Models and Computer Simulations | 2023年 / 15卷 / 5期

基金：

俄罗斯科学基金会;

关键词：

Argo drifter data; generalized Kalman filter; NEMO; observational data assimilation; ocean modeling;

D O I：

10.1134/S2070048223050022

中图分类号：

学科分类号：

摘要：

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. © 2023, Pleiades Publishing, Ltd.

引用

页码：842 / 849

页数：7

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