中文摘要：

文中采用WRF非静力数值预报模式及其三维变分同化系统（WRF3D-Var）,对2006年1月13—14日发生在华北地区及山东半岛的一次大雾过程进行了包括GTS（Global Telecommunication System）资料、AMDAR（Aircraft Meteorological Data Relay）资料和9210资料的不同资料组合的三维变分同化试验,以及时间间隔分别为6、3和1h不同时间频率的循环同化试验,并以同化分析场为初始场进行了36h的模拟试验。对同化分析场和模拟结果进行了分析,分析结果表明,采用三维变分方法同化AMDAR等多种非常规观测资料后,分析场均有明显的改变,对雾区的模拟结果也有局部不同程度的修正。进一步分析起修正作用的原因得知同化资料后对低层的湿度和层结趋稳性有所改善。同化GTS资料对低层的增湿贡献明显,但对层结趋稳性贡献不大;而同化AMDAR资料主要使层结趋稳性明显,对增湿无贡献;9210资料对低层湿度和层结趋稳性均有贡献。不同时间间隔的循环同化试验表明,多时次的循环同化比单时次的同化分析增量要大,逐时循环同化与6和3h循环同化相比,可明显改善模拟效果。

英文摘要：

With the development of numerical weather simulation and forecasting, the spatial and temporal resolution of numerical model become much higher and the data assimilation updating cycle much shorter. Assimilation of more meteorological data has recently received an increasing interest to improve the numerical weather forecasts all over the word. But it is still facing many challenging issues, including how to process various data with appropriate data quality control, how to specify the spatial interpolation and discretization errors, and how to extract the meteorological information from various observations and unconventional meteorological data with the accuracy needed by numerical model. Three-dimensional variational data assimilation（3D-Var） allows these data to be assimilated into the model initial fields directly, which employs the non-linear model as a dynamic constraint to improve the analysis field. The WRF 3D-Var system, which is based on the nonhydrostatic mesoscale model WRF and developed from MM5 3D-Var, is a famous three-dimensional variational data assimilation system. In this paper, the nonhydrostatic mesoscale WRF and its 3D-Var system were used to study a dense fog event occurred in 13 - i4 January 2006. Three different observation data sets include the GTS（Global Telecommunication System） data, AMDAR（Aircraft Meteorological Data Relay）data and 9210 data were assimilated into the initial analysis fields in three experiments. Also the experiments of different time interval （1 h, 3 h, 6 h） assimilation were performed to get the six different analysis fields needed by the six simulation experiments. While the control simulation experiment was performed without assimilation, and its initial fields were taken only from the National Center for Environmental Prediction （NCEP） re-analysis data. The results indicate that three assimilation experiments using 3 different kinds of data sets have to different extent corrected the analysis fields, thus showing obvious positive effect on fo