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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 4
Nonlin. Processes Geophys., 19, 439–447, 2012
https://doi.org/10.5194/npg-19-439-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 19, 439–447, 2012
https://doi.org/10.5194/npg-19-439-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 14 Aug 2012

Research article | 14 Aug 2012

Evolutionary modeling-based approach for model errors correction

S. Q. Wan1,2, W. P. He3, L. Wang4, W. Jiang5, and W. Zhang6 S. Q. Wan et al.
  • 1Yangzhou Meteorological Office, Yangzhou, China
  • 2Department of Physics, Yangzhou University, Yangzhou, China
  • 3National Climate Center, China Meteorological Administration, Beijing, China
  • 4School of Computer Science, Beijing Institute of Technology, Beijing, China
  • 5Jiangsu Meteorological Bauru, Nanjing, China
  • 6Department of Atmospheric Sciences, Lanzhou University, Lanzhou, China

Abstract. The inverse problem of using the information of historical data to estimate model errors is one of the science frontier research topics. In this study, we investigate such a problem using the classic Lorenz (1963) equation as a prediction model and the Lorenz equation with a periodic evolutionary function as an accurate representation of reality to generate "observational data."

On the basis of the intelligent features of evolutionary modeling (EM), including self-organization, self-adaptive and self-learning, the dynamic information contained in the historical data can be identified and extracted by computer automatically. Thereby, a new approach is proposed to estimate model errors based on EM in the present paper. Numerical tests demonstrate the ability of the new approach to correct model structural errors. In fact, it can actualize the combination of the statistics and dynamics to certain extent.

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