集合四维变分资料同化研究进展
Research Progress in Ensemble Four-Dimensions Variational Data Assimilation
中文摘要：
背景误差协方差矩阵的精确定义是构建高水平资料同化系统的先决条件。传统四维变分资料同化（4D-Var）方法将观测资料处理转化成以动力模式为约束的泛函极小化问题，通过调整控制变量，使指定时间窗口内由控制变量得到的模式预报结果与实际观测资料之间的偏差达到最小。该方法在同化窗口内可以利用模式的切线性和伴随隐式地改变背景误差协方差，能够在某种程度上满足快速发展的天气过程。但是大部分业务中心的四维变分资料同化系统仍采用静态化的背景误差协方差矩阵模型来缓解背景误差协方差矩阵的维度问题，即矩阵维数远大于可用信息量。随着计算机科学的迅猛发展，维度问题可以进一步通过集合的方法缓解。集合四维变分资料同化就是基于这一目标通过构造多个能反映出背景误差协方差分布特征的样本集合来弥补可用信息量的不足。该方法目前已在ECMWF、Mete-France等业务中心实现业务化，为确定性四维变分资料同化系统提供流依赖背景误差协方差估计。简要介绍了集合四维变分资料同化方法的基本原理；其次以ECMWF为例，概述了四维变分资料同化系统的业务现状，重点阐述了系统在开发过程中需要解决的扰动、滤波、校正等一些关键技术；最后探讨集合四维变分资料同化系统目前存在的问题和未来可能的研究方向。
中文关键词：
背景误差协方差矩阵，集合四维变分资料同化，扰动，流依赖
KeyWords：
background error covariance, ensemble, four-dimensional data assimilation, disturb, flow-dependent
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 defined 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, flow-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 flow-depended background error covariance for the high-resolution determined 4D-Var system. In this thesis, the basic theory of the En4DVar method is demonstrated briefly, 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 fields in the En4DVar.