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A Rapid Optimization Algorithm for GPS Data Assimilation


doi: 10.1007/BF02690801

  • Global Positioning System (GPS) meteorology data variational assimilation can be reduced to theproblem of a large-scale unconstrained optimization. Because the dimension of this problem is too large,most optimal algorithms cannot be performed. In order to make GPS/MET data assimilation able tosatisfy the demand of numerical weather prediction, finding an algorithm with a great convergence rateof iteration will be the most important thing. A new method is presented that dynamically combines thelimited memory BFGS (L-BFGS) method with the Hessian-free Newton(HFN) method, and it has a goodrate of convergence in iteration. The numerical tests indicate that the computational efficiency of themethod is better than the L-BFGS and HFN methods.
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Manuscript History

Manuscript received: 10 May 2003
Manuscript revised: 10 May 2003
通讯作者: 陈斌, bchen63@163.com
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A Rapid Optimization Algorithm for GPS Data Assimilation

  • 1. LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Harbin Institute of Technology, Harbin 150001,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;Harbin Institute of Technology, Harbin 150001

Abstract: Global Positioning System (GPS) meteorology data variational assimilation can be reduced to theproblem of a large-scale unconstrained optimization. Because the dimension of this problem is too large,most optimal algorithms cannot be performed. In order to make GPS/MET data assimilation able tosatisfy the demand of numerical weather prediction, finding an algorithm with a great convergence rateof iteration will be the most important thing. A new method is presented that dynamically combines thelimited memory BFGS (L-BFGS) method with the Hessian-free Newton(HFN) method, and it has a goodrate of convergence in iteration. The numerical tests indicate that the computational efficiency of themethod is better than the L-BFGS and HFN methods.

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