摘要
集合变分数据同化方法的同化效果对集合样本容量具有很强的依赖性,研究发现此问题的出现是因为其计算过程中分析增量被表示为集合扰动向量或其展开正交基向量的线性组合.这样的处理方法虽然避免了计算梯度而引入伴随模式,但是因为物理控制变量个数远大于集合样本容量,就会导致物理量的同化分析值对集合样本容量很敏感.根据此原因,提出了区域逐步分析方法,减小了同化分析区域内物理变量个数与集合样本容量数之间的比值,使问题得到解决.利用浅水方程模式进行资料同化数值试验表明,基于区域逐步分析的集合变分资料同化方法可以得到较好的结果,能明显提高同化的精度.
The ensemble variational data assimilation method may be subject to significant uncertainties due to the size of forecast ensemble. We found that this problem occurs because the analysis increment of this method is expressed as a linear combination of ensemble perturbation vectors or expansion of the orthogonal basis vectors. Though this method avoids introducing adjoint model while calculating the gradient of object function, the number of physical control variables is much larger than the sample size of forecast ensemble, which causes the assimilation results to be sensitive to the number of ensemble members. For this reason, the regional successive analysis scheme of ensemble variational method is proposed. By this scheme, the ratio between the number of physical control variables in analysis region and the sample size is decreased, so that it is expected that the problem can be solved. The results of numerical experiments using shallow water model show that the regional successive analysis scheme can give better assimilation results than traditional method, and the analysis precision is improved appreciably.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2014年第7期449-456,共8页
Acta Physica Sinica
基金
江苏省自然科学基金(批准号:BK20131065)
中国博士后科学基金(批准号:20110490185)
国家自然科学基金(批准号:41175090,41375106,41105065,41205073)
气象海洋学院基础理论研究基金资助的课题~~
关键词
区域逐步分析
集合变分
资料同化
regional successive analysis, ensemble variational method, data assimilation
