Study of sensitivity of the algorithm for assimilating small amount of data in the ocean dynamics model

• Published in: Physical Oceanography
• Main Authors: M.N. Kaurkin, R.A. Ibrayev
• Format: Article in Journal/Newspaper
• Language: English
• Published: Federal State Budget Scientific Institution «Marine Hydrophysical Institute of RAS» 2019
• Subjects: ocean dynamics modeling; observational data assimilation; ensemble optimal interpolation; eddy-resolving model; argo data; Oceanography; GC1-1581; North Atlantic
• Online Access: https://doi.org/10.22449/1573-160X-2019-2-96-103; https://doaj.org/article/9f917f78af4a4b5ba4cf322ec59811e3

Introduction. The analysis of the original parallel realization of the ensemble optimal interpolation (EnOI) method for data assimilation in the ocean dynamics model developed in the Institute of Numerical Mathematics and the Institute of Oceanology (INMIO model) with a resolution 0.1° for the North Atlantic region is given in the present paper. Data and methods. Based on the known (“true”) model state of the ocean, the temperature profiles (about 70 per day, up to 1500 m depth) were chosen and used as synthetic observational data. After the initial condition was perturbed, the numerical experiments were carried out to estimate speed and accuracy of approaching the entire model solution to the “true” state of the ocean as the temperature profiles were assimilated. Results. Both qualitative analysis of the results and the graphs of the root-mean-square and mean errors of the model solution are given. To study the method sensitivity to the amount of the observational data, the experiments with carried out. They showed that assimilation even of the isolated data could significantly increase the model forecast quality. Discussion and Conclusions. The experiments prove that application of the ensemble optimal interpolation method, even in case very few data, are assimilated in the model, can significantly improve quality both of the model forecast and the entire model solution for those regions where the observational data are very scarce or absent at all. Thus, due to assimilation of the data covering only 3–4 days, the root-mean-square error for the sea surface temperature model field decreases by 1.5oC, and the average deviation becomes equal almost to zero over the entire computational domain.