Handling error propagation in sequential data assimilation using an evolutionary strategy

BAI Yulong; LI Xin; and HUANG Chunlin

doi:10.1007/s00376-012-2115-7

Abstract:

An evolutionary strategy-based error parameterization method that searches for the most ideal error adjustment factors was developed to obtain better assimilation results. Numerical experiments were designed using some classical nonlinear models (i.e., the Lorenz－63 model and the Lorenz-96 model). Crossover and mutation error adjustment factors of evolutionary strategy were investigated in four aspects: the initial conditions of the Lorenz model, ensemble sizes, observation covariance, and the observation intervals. The search for error adjustment factors is usually performed using trial－and－error methods. To solve this difficult problem, a new data assimilation system coupled with genetic algorithms was developed. The method was tested in some simplified model frameworks, and the results are encouraging. The evolutionary strategy-based error handling methods performed robustly under both perfect and imperfect model scenarios in the Lorenz-96 model. However, the application of the methodology to more complex atmospheric or land surface models remains to be tested.

摘要: An evolutionary strategy-based error parameterization method that searches for the most ideal error adjustment factors was developed to obtain better assimilation results. Numerical experiments were designed using some classical nonlinear models (i.e., the Lorenz-63 model and the Lorenz-96 model). Crossover and mutation error adjustment factors of evolutionary strategy were investigated in four aspects: the initial conditions of the Lorenz model, ensemble sizes, observation covariance, and the observation intervals. The search for error adjustment factors is usually performed using trial-and-error methods. To solve this difficult problem, a new data assimilation system coupled with genetic algorithms was developed. The method was tested in some simplified model frameworks, and the results are encouraging. The evolutionary strategy-based error handling methods performed robustly under both perfect and imperfect model scenarios in the Lorenz-96 model. However, the application of the methodology to more complex atmospheric or land surface models remains to be tested.

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Handling error propagation in sequential data assimilation using an evolutionary strategy

Corresponding author: BAI Yulong;

1. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000;

2. College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou 730070

Manuscript received: 2012-05-28

Manuscript revised: 2012-09-04

Fund Project: We are very grateful to the two anonymous reviewers for their constructive suggestions and useful advice. This work is supported by the NSFC (National Science Foundation of China) project (Grant Nos. 41061038 and 40925004), project Land Surface Modeling and Data Assimilation Research (Grant No. 2009AA122104) from the National High Technology Research and One Hundred Person Project of the Chinese Academy of Sciences Multi-sensor Hydrological Data Assimilation for Key Hydrological Variables in Cold and Arid Regions (Grant No. 29Y127D01).

Abstract: An evolutionary strategy-based error parameterization method that searches for the most ideal error adjustment factors was developed to obtain better assimilation results. Numerical experiments were designed using some classical nonlinear models (i.e., the Lorenz－63 model and the Lorenz-96 model). Crossover and mutation error adjustment factors of evolutionary strategy were investigated in four aspects: the initial conditions of the Lorenz model, ensemble sizes, observation covariance, and the observation intervals. The search for error adjustment factors is usually performed using trial－and－error methods. To solve this difficult problem, a new data assimilation system coupled with genetic algorithms was developed. The method was tested in some simplified model frameworks, and the results are encouraging. The evolutionary strategy-based error handling methods performed robustly under both perfect and imperfect model scenarios in the Lorenz-96 model. However, the application of the methodology to more complex atmospheric or land surface models remains to be tested.

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Handling error propagation in sequential data assimilation using an evolutionary strategy

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