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Multigrid methods for improving the variational data assimilation in numerical weather prediction

Tellus: Series A, Dynamic Meteorology and Oceanography. 2014;66(0):1-9 DOI 10.3402/tellusa.v66.20217

 

Journal Homepage

Journal Title: Tellus: Series A, Dynamic Meteorology and Oceanography

ISSN: 1600-0870 (Online)

Publisher: Taylor & Francis Group

Society/Institution: International Meteorological Institute

LCC Subject Category: Geography. Anthropology. Recreation: Oceanography | Science: Physics: Meteorology. Climatology

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS


Youn-Hee Kang ( Department of Mathematical Science, KAIST, Daejeon, Republic of Korea)

Do Young Kwak ( Department of Mathematical Science, KAIST, Daejeon, Republic of Korea)

Kyungjeen Park ( Numerical Model Development Division, Korea Meteorological Administration, Seoul, Republic of Korea)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 20 weeks

 

Abstract | Full Text

Two conditions are needed to solve numerical weather prediction models: initial condition and boundary condition. The initial condition has an especially important bearing on the model performance. To get a good initial condition, many data assimilation techniques have been developed for the meteorological and the oceanographical fields. Currently, the most commonly used technique for operational applications is the 3 dimensional (3-D) or 4 dimensional variational data assimilation method. The numerical method used for the cost function minimising process is usually an iterative method such as the conjugate gradient. In this paper, we use the multigrid method based on the cell-centred finite difference on the variational data assimilation to improve the performance of the minimisation procedure for 3D-Var data assimilation.