Advances in Earth Science ›› 2018, Vol. 33 ›› Issue (8): 874-883. doi: 10.11867/j.issn.1001-8166.2018.08.0874

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Localization Analysis of Data Assimilation Methods Coupled with Fuzzy Control Algorithms

Mingheng Chang( ), Yulong Bai *( ), Xiaoyan Ma, Ruoyu Meng, Lili Wang   

  1. College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou 730070, China
  • Received:2018-04-28 Revised:2018-06-04 Online:2018-08-10 Published:2018-09-14
  • Contact: Yulong Bai;
  • About author:

    First author:Chang Mingheng(1992-), male ,Qin'an County, Gansu Province, Master student. Research areas include data assimilation observation error. E-mail:

  • Supported by:
    Project supported by the National Natural Science Foundation of China “The application of intelligent algorithms with evolutionary computing in data assimilation about error processing”(No.41461078);The Northwest Normal University Scientific Research Improvement Team Foundation “Intelligent computing theory and application”(No.NWNU-LKQN-1706).

Mingheng Chang, Yulong Bai, Xiaoyan Ma, Ruoyu Meng, Lili Wang. Localization Analysis of Data Assimilation Methods Coupled with Fuzzy Control Algorithms[J]. Advances in Earth Science, 2018, 33(8): 874-883.

Due to the fake correlation between distance-observations and assimilation-states during data assimilation, more attention has been paid to the localization method. Meanwhile, in the case of assimilation with a small number of sets, the observation data is difficult to be used effectively, which makes the assimilation effect not good enough. Therefore, a new fuzzy control was proposed to analyze the local method. The fuzzy control algorithm was used to judge the distance between the observation point and the status update point and to construct the fuzzy weight of the observation position. The study aimed to make use of the nonlinear Lorenz-96 model to compare the Fuzzy control combine Local Analysis algorithm (FLETKF) and Fuzzy control combine Ensemble Transform Kalman Filter method (FETKF), local Ensemble Transform Kalman Fliter (LETKF) and Ensemble Transform Kalman Filter algorithm (ETKF) when the nonlinear forced parameter changed. In addition, the strengths and weaknesses of four algorithms were discussed by different intensities. The results show that the new method can obtain more effective observation weights, avoiding the false correlation between long-distance observations and state variables, reducing the errors caused by the observation data which is difficult to be used effectively. Under different assimilation strength, FLETKF can maintain good robustness. However, in terms of assimilation time, the construction of the equivalent weight of the observation position requires additional time because the localization assimilation method of fuzzy control determines the distance between the observation point and the status update point. Parallel computing performance needs further study.

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