Spatial–Temporal Variability of the Calculated Characteristics of the Ocean in the Arctic Zone of Russia by Using the NEMO Model with Altimetry Data Assimilation

• Published in: Journal of Marine Science and Engineering
• Main Authors: Konstantin Belyaev, Andrey Kuleshov, Ilya Smirnov
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2020
• Subjects: model of ocean circulation NEMO; GKF data assimilation method; Arctic zone of Russia; Naval architecture. Shipbuilding. Marine engineering; VM1-989; Oceanography; GC1-1581; Arctic; North Atlantic
• Online Access: https://doi.org/10.3390/jmse8100753; https://doaj.org/article/fc179b3678b9447888a89c1050d86b69

The spatial–temporal variability of the calculated characteristics of the ocean in the Arctic zone of Russia is studied. In this study, the known hydrodynamic model of the ocean Nucleus for European Modelling of the Ocean (NEMO) is used with assimilation of observation data on the sea surface height taken from the Archiving, Validating and Interpolation Satellite Observation (AVISO) archive. We use the Generalized Kalman filter (GKF) method, developed earlier by the authors of this study, in conjunction with the method of decomposition of symmetric matrices into empirical orthogonal functions (EOF, Karhunen–Loeve decomposition). The investigations are focused mostly on the northern seas of Russia. The main characteristics of the ocean, such as the current velocity, sea surface height, and sea surface temperature are calculated with data assimilation (DA) and without DA (the control calculation). The calculation results are analyzed and their spatial–temporal variability over a time period of 14 days is studied. It is shown that the main spatial variability of characteristics after DA is in good agreement with the localization of currents in the North Atlantic and in the Arctic zone of Russia. The contribution of each of the eigenvectors and eigenvalues of the covariation matrix to the spatial–temporal variability of the calculated characteristics is shown by using the EOF analysis.