Volume 40 Issue 1
Feb.  2021
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Yumin Chen, Jie Xiang, Huadong Du, Sixun Huang, Qingtao Song. Study and application of an improved four-dimensional variational assimilation system based on the physical-space statistical analysis for the South China Sea[J]. Acta Oceanologica Sinica, 2021, 40(1): 135-146. doi: 10.1007/s13131-021-1701-x
Citation: Yumin Chen, Jie Xiang, Huadong Du, Sixun Huang, Qingtao Song. Study and application of an improved four-dimensional variational assimilation system based on the physical-space statistical analysis for the South China Sea[J]. Acta Oceanologica Sinica, 2021, 40(1): 135-146. doi: 10.1007/s13131-021-1701-x

Study and application of an improved four-dimensional variational assimilation system based on the physical-space statistical analysis for the South China Sea

doi: 10.1007/s13131-021-1701-x
Funds:  The National Key Research and Development Program of China under contract Nos 2017YFC1501803 and 2018YFC1506903; the National Natural Science Foundation of China under contract Nos 91730304, 41475021 and 41575026.
More Information
  • Corresponding author: E-mail: huadong.du@gmail.com
  • Received Date: 2020-02-11
  • Accepted Date: 2020-03-11
  • Available Online: 2021-04-21
  • Publish Date: 2021-01-25
  • The four-dimensional variational assimilation (4D-Var) has been widely used in meteorological and oceanographic data assimilation. This method is usually implemented in the model space, known as primal approach (P4D-Var). Alternatively, physical space analysis system (4D-PSAS) is proposed to reduce the computation cost, in which the 4D-Var problem is solved in physical space (i.e., observation space). In this study, the conjugate gradient (CG) algorithm, implemented in the 4D-PSAS system is evaluated and it is found that the non-monotonic change of the gradient norm of 4D-PSAS cost function causes artificial oscillations of cost function in the iteration process. The reason of non-monotonic variation of gradient norm in 4D-PSAS is then analyzed. In order to overcome the non-monotonic variation of gradient norm, a new algorithm, Minimum Residual (MINRES) algorithm, is implemented in the process of assimilation iteration in this study. Our experimental results show that the improved 4D-PSAS with the MINRES algorithm guarantees the monotonic reduction of gradient norm of cost function, greatly improves the convergence properties of 4D-PSAS as well, and significantly restrains the numerical noises associated with the traditional 4D-PSAS system.
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