气象科技进展2016，Vol.6Issue(5)：14-23,10.DOI:10.3969/j.issn.2095-1973.2016.05.002
集合四维变分资料同化研究进展
Research Progress in Ensemble Four-Dimensions Variational Data Assimilation
摘要
Abstract
Accurate background error covariance is the foundation for all advanced data assimilation systems. For four dimensions data assimilation (4D-Var), assimilating the observation data is converted to a question of cost function minimization which is constricted by atmosphere dynamic model. By adjusting the control vectors, the distance between model trajectory and real time observations reached its minimal value over whole assimilation time window. As background error covariance evolves according to the adjoint and tangent linear model, it can adapt to rapid development weather. However, most of operational 4D-Var systems still adopt simi-climatic background error covariance model compromised by huge dimensionality, which can’t be exactly deifned with all available information. As the rapid development of computer science, the problem of dimensionality can be released by ensemble method. Ensemble four dimensionality data assimilation (En4DVar) employed several independent perturbed analysis forecast cycles to remedy the limited information synchronously. In this scheme, lfow-dependent background error covariance can be estimated from the differences between ensemble members. Several famous numeric prediction centers, such as ECMWF, Mete-France, adopted it to provide lfow-depended background error covariance for the high-resolution determined 4D-Var system. In this thesis, the basic theory of the En4DVar method is demonstrated brielfy, followed by a description of currently application at ECMWF, and focusing on the disturbing, filtering, calibration as well as other key techniques for helping to improve the precision of estimates. The last part presents an investigation of some issues in current operation and possibly future research ifelds in the En4DVar.关键词
背景误差协方差矩阵/集合四维变分资料同化/扰动/流依赖Key words
background error covariance/ensemble/four-dimensional data assimilation/disturb/lfow-dependent引用本文复制引用
刘柏年,皇群博,张卫民,曹小群,赵军,赵延来..集合四维变分资料同化研究进展[J].气象科技进展,2016,6(5):14-23,10.基金项目
国家自然科学基金项目 （）
