中文摘要：

给出适应性观测理论和集合变换卡尔曼滤波方法及其研究现状的综述。重点介绍了集合变换卡尔曼滤波方法及其相关的一些问题。在数值预报领域，一种新的途径是利用数值预报系统信息在预报时效内确定出某些区域，如果在这些区域进行补充观测，可以最有效地改进预报技能。这种方法被称为适应性或目标观测，所确定的观测区域称为敏感区，敏感区内增加观测后分析质量将得到改善，对后续的预报技能可产生最大的预期影响。目前适应性观测研究已经成为世界气象组织（WMO）组织的THORPEX计划的一个子计划。集合变换卡尔曼滤波（The Ensemble Transform Kalman Filer，简称ETKF）是一种次优的卡尔曼滤波方案，最早是作为一种适应性观测算法提出的，现在还被用于集合预报初始扰动的生成。ETKF方法不仅可以同化观测资料，而且可以估计出观测对预报误差的影响。它与其它集合卡尔曼滤波方案不同之处在于：ETKF利用集合变换和无量纲化的思想求解与观测有关的误差协方差矩阵，可以快速估计出不同附加观测造成的预报误差协方差的减少量，预报误差减少最多的一组观测所对应的区域就是所寻找的敏感区。

英文摘要：

This paper presents the main idea of adaptive observation and ensemble transform Kalman filter and their development. It gives account of the Ensemble Transform Kalman Filter theory and some issues related to this method. Recently strategies were developed that use forecast-system information to identify locations where additional observations would provide maximal improvements in the expected skill of forecast. We refer to these as adaptive observation, or targeted observation, commonly called targeting. Targeting identifies localized areas, referred to as sensitive region, in which the quality of the analysis has the greatest expected influence on the subsequent skill of the forecast. Now the adaptive observation becomes a sub-program of THORPEX. The Ensemble Transform Kalman filter is initially proposed as an adaptive observation method, later it is used in the ensemble forecast. The ETKF is a sub-optimal Kalman filter scheme. Like other Kalman filter, it provides a framework for assimilating observations and also for estimating the effect of observations on the forecast error covariance. It differs from other ensemble Kalman filter in that it uses ensemble transformation and a normalization to rapidly obtain the prediction error covariance matrix associated with a particular deployment of observation resources. This rapidity enables it to quickly assess the ability of a large number of future feasible sequences of observational networks to reduce forecast error variance.