大气数据同化方法的研究与应用进展

简要介绍了大气数据同化的基本思想与方法，阐述了松弛逼近法、Kalman滤波和变分约束法三种大气数据同化方法研究状况和应用进展情况。

The Developments and Applications of Atmosphereic Data Assimilation

Atmospheric data assimilation techniques are motivated forward by the advance of numerical weather prediction models and the increasing rapidly observations, including the great amount of unconventional data obtained by remote sensing. There are mainly three general concepts that have been discussed repeatedly for data assimilation in meteorology. The variational (especially adjoint variational) method has been a popular and fully studied scheme, which, however, has a drawback that model errors (system noise) are not taken into account due to the imperfection of the numerical model. The second class of methods are those described as sequential data assimilation, which are represented by Kalman filters. The third class is nudging method, which is simple but efficient and used widely. The above three methods are discussed in this paper after a brief introduction of atmospheric data assimilation. The last section of the paper presents the applications of atmospheric data assimilation.