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

被引：1

作者：

Kang, Youn-Hee ^{[1
]}

Kwak, Do Young ^{[1
]}

Park, Kyungjeen ^{[2
]}

机构：

[1] Korea Adv Inst Sci & Technol, Dept Math Sci, Taejon 305701, South Korea

[2] Korea Meteorol Adm, Numer Model Dev Div, Seoul, South Korea

来源：

TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY | 2014年 / 66卷

关键词：

numerical weather prediction; variational data assimilation; minimization procedure; multigrid methods; cell centered finite difference;

D O I：

10.3402/tellusa.v66.20217

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

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.

引用

页数：9