中文摘要：

一个观察本地化计划被介绍进一基于整体三维变化(3DVar ) 吸收方法基于单个价值分解技术(SVD-En3DVar ) 到改善吸收技巧。一种逐点详述的分析技术在每观察的重量与增加在分析点和观察点之间的距离在哪个减少被采用。一套数字实验，模仿的 Doppler 雷达数据在被吸收进天气研究并且预报当模特儿，被设计测试计划。结果与在 SVD-En3DVar，其任何一个都不包括这类观察本地化用全球的原版和本地补丁计划获得的那些相比。观察本地化计划不仅在失踪的数据的区域消除假分析增长，而且避免从本地补丁计划产生的不连续的分析领域。新计划提供更好的分析领域和更多合理短期降雨预报比原来的计划。从 10 架雷达吸收真实数据的另外的预报实验显示短期的降水预报技巧能被吸收雷达数据改进，观察本地化计划比另外的二个计划提供一张更好的预报。

英文摘要：

An observation localization scheme is introduced into an ensemble-based three-dimensional variational （3DVar） assimilation method based on the singular value decomposition technique （SVD-En3DVar） to im- prove assimilation skill. A point-by-point analysis technique is adopted in which the weight of each obser- vation decreases with increasing distance between the analysis point and the observation point. A set of numerical experiments, in which simulated Doppler radar data are assimilated into the Weather Research and Forecasting （WRF） model, is designed to test the scheme. The results are compared with those ob- tained using the original global and local patch schemes in SVD-En3DVar, neither of which includes this type of observation localization. The observation localization scheme not only eliminates spurious analysis increments in areas of missing data, but also avoids the discontinuous analysis fields that arise from the local patch scheme. The new scheme provides better analysis fields and a more reasonable short-range rainfall forecast than the original schemes. Additional forecast experiments that assimilate real data from i0 radars indicate that the short-term precipitation forecast skill can be improved by assimilating radar data and the observation localization scheme provides a better forecast than the other two schemes.