4DEnVar: link with 4D state formulation of variational assimilation and different possible implementations

被引:60
作者
Desroziers, Gerald [1 ,2 ]
Camino, Jean-Thomas
Berre, Loik
机构
[1] Meteo France, CNRM GAME, F-31057 Toulouse, France
[2] CNRS, F-31057 Toulouse, France
关键词
variational assimilation; ensemble assimilation; minimization algorithms; preconditioning; TRANSFORM KALMAN FILTER; BACKGROUND-ERROR CORRELATIONS; ATMOSPHERIC DATA ASSIMILATION; ENSEMBLE DATA ASSIMILATION; PART I; OPERATIONAL IMPLEMENTATION; THEORETICAL ASPECTS; DUAL FORMULATION; ANALYSIS SCHEMES; SYSTEM;
D O I
10.1002/qj.2325
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
The four-dimensional ensemble variational (4DEnVar) formulation is receiving increasing interest, especially in numerical weather prediction centres, which until now have mostly relied on the four-dimensional variational (4D-Var) formalism. It may indeed combine some of the best features of variational and ensemble methods. In this article, it is shown that the 4DEnVar formulation is linked with the 4D state formulation of variational assimilation, and that the 4DEnVar is relatively easy to precondition, in addition of being parallelizable. Practical implementations of the 4DEnVar are also investigated and two new preconditioned algorithms are proposed. The hybrid formulation of 4DEnVar, combining static and ensemble background-error covariances, is discussed for the different possible algorithms. An application of the proposed implementations of 4DEnVar is shown with the Burgers model and compared to the use of 4D-Var.
引用
收藏
页码:2097 / 2110
页数:14
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