A Multiscale Variational Data Assimilation Scheme: Formulation and Illustration

被引:80
作者
Li, Zhijin [1 ]
McWilliams, James C. [2 ]
Ide, Kayo [3 ,4 ,5 ,6 ]
Farrara, John D. [7 ]
机构
[1] CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA
[2] Univ Calif Los Angeles, Dept Atmospher & Ocean Sci, Los Angeles, CA USA
[3] Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA
[4] Univ Maryland, Ctr Sci Computat & Math Modeling, College Pk, MD 20742 USA
[5] Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA
[6] Univ Maryland, Inst Phys Sci & Technol, College Pk, MD 20742 USA
[7] Univ Calif Los Angeles, Joint Inst Reg Earth Syst Sci & Engn, Los Angeles, CA USA
关键词
RANGE FORECAST ERRORS; STATISTICAL STRUCTURE; RADIOSONDE DATA; ENSEMBLE DATA; COVARIANCES; PREDICTION; SYSTEM;
D O I
10.1175/MWR-D-14-00384.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
A multiscale data assimilation (MS-DA) scheme is formulated for fine-resolution models. A decomposition of the cost function is derived for a set of distinct spatial scales. The decomposed cost function allows for the background error covariance to be estimated separately for the distinct spatial scales, and multi-decorrelation scales to be explicitly incorporated in the background error covariance. MS-DA minimizes the partitioned cost functions sequentially from large to small scales. The multi-decorrelation length scale background error covariance enhances the spreading of sparse observations and prevents fine structures in high-resolution observations from being overly smoothed. The decomposition of the cost function also provides an avenue for mitigating the effects of scale aliasing and representativeness errors that inherently exist in a multiscale system, thus further improving the effectiveness of the assimilation of high-resolution observations. A set of one-dimensional experiments is performed to examine the properties of the MS-DA scheme. Emphasis is placed on the assimilation of patchy high-resolution observations representing radar and satellite measurements, alongside sparse observations representing those from conventional in situ platforms. The results illustrate how MS-DA improves the effectiveness of the assimilation of both these types of observations simultaneously.
引用
收藏
页码:3804 / 3822
页数:19
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