Analysis of the Model Characteristics in the North Atlantic Simulated by the NEMO Model with Data Assimilation
2023
Konstantin Belyaev | Andrey Kuleshov | Ilya Smirnov
The main aim of this work is to study the spatial&ndash:temporal variability of the model&rsquo:s physical and spectral characteristics in the process of assimilation of observed ocean surface height data from the AVISO (Archiving, Validating and Interpolation Satellite Observation) archive in combination with the NEMO (Nucleus for European Modeling of the Ocean) ocean circulation model for a period of two months. For data assimilation, the GKF (Generalized Kalman filter) method, previously developed by the authors, is used. The purpose of this work is to study the spatial&ndash:temporal structure of the simulated characteristics using decomposition into eigenvalues and eigenvectors (Karhunen&ndash:Loeve decomposition method). The feature of the GKF method is the fact that the constructed Kalman weight matrix multiplied by the vector of observational data can be represented as a weighted sum of eigenvectors and eigenvalues (spectral characteristics of the matrix), which describe the spatial and temporal structure of corrections to the model. The main investigations are focused on the North Atlantic. Their variability in time and space is estimated in this study. Calculations of the main ocean characteristics, such as the surface height, temperature, salinity, and the current velocities on the surface and in the depths, both with and without assimilation of observational data, over a time interval of 60 days, were performed by using a high-performance computing system. The calculation results have shown that the main spatial variability of characteristics after data assimilation is consistent with the localization of the currents in the North Atlantic.
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