Advances in Atmospheric Sciences

, Volume 30, Issue 4, pp 1096–1105 | Cite as

Handling error propagation in sequential data assimilation using an evolutionary strategy

  • Yulong Bai (摆玉龙)
  • Xin Li (李 新)
  • Chunlin Huang (黄春林)


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.

Key words

data assimilation error propagation evolutionary strategies 


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Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yulong Bai (摆玉龙)
    • 1
    • 2
  • Xin Li (李 新)
    • 1
  • Chunlin Huang (黄春林)
    • 1
  1. 1.Cold and Arid Regions Environmental and Engineering Research InstituteChinese Academy of SciencesLanzhouChina
  2. 2.College of Physics and Electrical EngineeringNorthwest Normal UniversityLanzhouChina

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