资料同化方法研究进展  

Advancement in Data Assimilation Method Research

作　　者：张爱忠[1] 齐琳琳 纪飞 李俊[1] 

机构地区：[1]解放军理工大学气象学院,南京211101 [2]空军航空气象研究所,北京100085

出　　处：《气象科技》2005年第5期385-389,393,共6页Meteorological Science and Technology

摘　　要：数值天气预报模式的不断完善和大气观测探测资料(特别是卫星、雷达等非常规探测资料)的大量涌现,推动着资料同化方法的逐步发展。文章主要回顾了资料同化方法研究的发展过程、目前的应用现状以及对未来同化方法的展望。随着人们对资料同化含义的深入理解,对于资料同化的研究由初始的探索阶段逐步发展到以经验性为主的同化方法,主要包括SCM和nudging;统计方法的引入,成为资料同化方法研究发展道路上具有重要意义的一个里程碑,从而出现了多元统计插值同化,比如OI,3D-Var以及PSAS;针对背景误差协方差固定不变与实际情况的差异,考虑时间维的四维资料同化方法成为目前国际上较为主流的同化研究方法,其中以4D-Var和Kalman滤波为代表;随着计算机技术的进步,更加合理的四维资料同化方法将会成为未来业务预报中主要的资料同化方法。Along with the improving of numerical weather prediction models and the application of atmospheric observational data, especially non-conventional sounding data such as satellite and radar, data assimilation methods have made further advancement. Previous data assimilation approaches are reviewed, and the current studies and prospects in the field are described. The data assimilation researches experienced several stages from those early explorations to the subsequent techniques mainly based on experiences （including SCM and nudging）, then statistical and the multivariate statistical interpolation （OI,3D-Varand PSAS） methods. Considering the differ- ence between the fact that background error covariance keeps unchanged and the actual situation, the current as- similation techniques take into account the time dimension of sounding data, which are becoming popular inter- nationally and represented by 4D-Var and Kalman filtering techniques. With the development of computer technology, more perfect FDDA methods will play an important role in the future operational prediction.

关 键 词：资料同化 经验性同化 多元插值同化 四维同化 数值天气预报 四维资料同化 KALMAN滤波 天气预报模式 探测资料 误差协方差 计算机技术 大气观测 统计方法 多元统计 

分 类 号：P458.121.1[天文地球—大气科学及气象学] O211.64[理学—概率论与数理统计]