Analysis of the Model Characteristics in the North Atlantic Simulated by the NEMO Model with Data Assimilation
The main aim of this work is to study the spatial–temporal variability of the model’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...
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ftmdpi:oai:mdpi.com:/2077-1312/11/5/1078/ 2023-08-20T04:08:13+02:00 Analysis of the Model Characteristics in the North Atlantic Simulated by the NEMO Model with Data Assimilation Konstantin Belyaev Andrey Kuleshov Ilya Smirnov agris 2023-05-19 application/pdf https://doi.org/10.3390/jmse11051078 EN eng Multidisciplinary Digital Publishing Institute Physical Oceanography https://dx.doi.org/10.3390/jmse11051078 https://creativecommons.org/licenses/by/4.0/ Journal of Marine Science and Engineering; Volume 11; Issue 5; Pages: 1078 NEMO ocean circulation model GKF data assimilation method North Atlantic Karhunen–Loeve decomposition high-performance computer modeling Text 2023 ftmdpi https://doi.org/10.3390/jmse11051078 2023-08-01T10:08:49Z The main aim of this work is to study the spatial–temporal variability of the model’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–temporal structure of the simulated characteristics using decomposition into eigenvalues and eigenvectors (Karhunen–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. Text North Atlantic MDPI Open Access Publishing Journal of Marine Science and Engineering 11 5 1078 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
NEMO ocean circulation model GKF data assimilation method North Atlantic Karhunen–Loeve decomposition high-performance computer modeling |
spellingShingle |
NEMO ocean circulation model GKF data assimilation method North Atlantic Karhunen–Loeve decomposition high-performance computer modeling Konstantin Belyaev Andrey Kuleshov Ilya Smirnov Analysis of the Model Characteristics in the North Atlantic Simulated by the NEMO Model with Data Assimilation |
topic_facet |
NEMO ocean circulation model GKF data assimilation method North Atlantic Karhunen–Loeve decomposition high-performance computer modeling |
description |
The main aim of this work is to study the spatial–temporal variability of the model’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–temporal structure of the simulated characteristics using decomposition into eigenvalues and eigenvectors (Karhunen–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. |
format |
Text |
author |
Konstantin Belyaev Andrey Kuleshov Ilya Smirnov |
author_facet |
Konstantin Belyaev Andrey Kuleshov Ilya Smirnov |
author_sort |
Konstantin Belyaev |
title |
Analysis of the Model Characteristics in the North Atlantic Simulated by the NEMO Model with Data Assimilation |
title_short |
Analysis of the Model Characteristics in the North Atlantic Simulated by the NEMO Model with Data Assimilation |
title_full |
Analysis of the Model Characteristics in the North Atlantic Simulated by the NEMO Model with Data Assimilation |
title_fullStr |
Analysis of the Model Characteristics in the North Atlantic Simulated by the NEMO Model with Data Assimilation |
title_full_unstemmed |
Analysis of the Model Characteristics in the North Atlantic Simulated by the NEMO Model with Data Assimilation |
title_sort |
analysis of the model characteristics in the north atlantic simulated by the nemo model with data assimilation |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/jmse11051078 |
op_coverage |
agris |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Journal of Marine Science and Engineering; Volume 11; Issue 5; Pages: 1078 |
op_relation |
Physical Oceanography https://dx.doi.org/10.3390/jmse11051078 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/jmse11051078 |
container_title |
Journal of Marine Science and Engineering |
container_volume |
11 |
container_issue |
5 |
container_start_page |
1078 |
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1774720362274095104 |