Research on the optimization method of analytical four dimensional ensemble variational data assimilation

Jia Binhe; Li Wei; Liang Kangzhuang

doi:10.12284/hyxb2021129

Volume 43 Issue 10

Oct. 2021

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Jia Binhe，Li Wei，Liang Kangzhuang. Research on the optimization method of analytical four dimensional ensemble variational data assimilation[J]. Haiyang Xuebao，2021, 43(10)：61–69 doi: 10.12284/hyxb2021129

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Research on the optimization method of analytical four dimensional ensemble variational data assimilation

doi: 10.12284/hyxb2021129

Jia Binhe^1
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Li Wei^{1
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Liang Kangzhuang^{1
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School of Marine Science and Technology, Tianjin University, Tianjin 300072, China

Received Date: 2020-05-11
Rev Recd Date: 2020-11-23

Available Online: 2021-06-16

Publish Date: 2021-10-30

Abstract

Abstract

The traditional four-dimensional variational data assimilation method can optimize the parameters of the numerical model while assimilating the observation data. However, the traditional four-dimensional variational method needs to compile special adjoint models for different numerical models, so the portability of the traditional four-dimensional variational method is poor and a lot of resources are consumed in the calculation. In this paper, a new parameter optimization method based on the analytic four-dimensional ensemble variation is proposed, which expands the perturbation and constructs the ensemble based on the model parameters obtained by iterative search, and then explicitly calculates the covariance matrix, and obtains the analytic solution of the minimum value of the cost function, so as to avoid the use of adjoint model. Using Lorenz-63 model, single-parameter and multi-parameter numerical tests and optimization effect tests were carried out on the analytic four-dimensional ensemble variation method, and in the case of different assimilation time window length and observation sampling interval, the traditional four-dimensional variational method was used to compare with the new method, the results show that the new method has the same optimization performance as the traditional four-dimensional variational method, and it can converge to the truth value effectively, and the new method does not need to calculate adjoint mode, so it has good portability. This paper also test the assimilation effect of the new method with different ensemble members and true values of model parameters, and the results show that the new method is insensitive to the number of ensemble members and the true values of model parameters, and the data assimilation can be completed with fewer ensemble members.

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References

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