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On the 4D Variational Data Assimilation with Constraint Conditions


doi: 10.1007/s00376-001-0028-y

  • An investigation is carried out on the problem involved in 4D variational data assimilation (VDA) with constraint conditions based on a finite-element shallow-water equation model. In the investigation, the adjoint technology, penalty method and augmented Lagrangian method are used in constraint optimization field to minimize the defined constraint objective functions. The results of the numerical experiments show that the optimal solutions are obtained if the functions reach the minima. VDA with constraint conditions controlling the growth of gravity oscillations is efficient to eliminate perturbation and produces optimal initial field. It seems that this method can also be applied to the problem in numerical weather prediction.
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    [6] HUANG Sixun, CAO Xiaoqun, DU Huadong, WANG Tingfang, XIANG Jie, 2006: Retrieval of Atmospheric and Oceanic Parameters and the Relevant Numerical Calculation, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 106-117.  doi: 10.1007/s00376-006-0011-8
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Manuscript History

Manuscript received: 10 November 2001
Manuscript revised: 10 November 2001
通讯作者: 陈斌, bchen63@163.com
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On the 4D Variational Data Assimilation with Constraint Conditions

  • 1. Chengdu University of Information Technology, Chengdu 610041

Abstract: An investigation is carried out on the problem involved in 4D variational data assimilation (VDA) with constraint conditions based on a finite-element shallow-water equation model. In the investigation, the adjoint technology, penalty method and augmented Lagrangian method are used in constraint optimization field to minimize the defined constraint objective functions. The results of the numerical experiments show that the optimal solutions are obtained if the functions reach the minima. VDA with constraint conditions controlling the growth of gravity oscillations is efficient to eliminate perturbation and produces optimal initial field. It seems that this method can also be applied to the problem in numerical weather prediction.

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