【摘要】 气象学领域各种观测 (特别是遥感遥测等非常规观测 )数据的大量增多和数值天气预报模式的不断进步 ,推动气象数据同化技术不断发展。回顾了 Kalman滤波在气象数据同化中的引入和几个发展阶段 ;介绍了 Kalman滤波 (尤其是简化 Kalman滤波和总体 Kalman滤波 )在气象数据同化中的重要地位和应用进展。更多还原

【Abstract】 Meteorological data assimilation techniques are motivated forward by the advance of numerical weather prediction models and the increasing rapidly observations, including the great part of unconventional data obtained by remote measurement methods. There are mainly two general concepts that have been discussed repeatedly for data assimilation in meteorology. The variational (especially adjoint variational) method has been the popular and most used scheme, which, however, has a drawback that model errors (system noise) are not taken into account. Another class of methods are those described as sequential data assimilation, which are represented by Kalman filters. The introduction of Kalman filters and their developmental stages in the meteorological data assimilation field are presented in this paper, as well as that the importance and applications of Kalman filters, particularly simplified Kalman filters and ensemble Kalman filters. Due to that they have the ability to consider model errors and let assimilation results not drift away from observations, Kalman filters are paid more and more attentions, though they need much of computational load. Compared with the current advance abroad, the developments and applications of Kalman filters in China are lagged. However, there will be a bright prospect for them with the improvements of computational conditions.更多还原