用四维变分法同化自动站降水资料  

Four-Dimensional Variational Assimilation of AWS Precipitation Data

作　　者：邵明轩[1] 陈敏[2] 陶祖钰[1] 陈露[3] 

机构地区：[1]北京大学物理学院大气科学系,北京100871 [2]中国气象局北京城市气象研究所,北京100089 [3]北京市气象局,北京100089

出　　处：《北京大学学报（自然科学版）》2005年第5期701-709,共9页Acta Scientiarum Naturalium Universitatis Pekinensis

基　　金：北京市自然科学基金(8032009);国家自然科学基金(40233036);北京市科委奥运基金(H020620190091)资助项目

摘　　要：用自动站降水资料作了四维变分同化试验。试验表明,由于它的加入,增加了初始场中的中尺度信息,改进了中尺度数值模式MM5的预报,增强了模拟开始阶段的降水量,改进了降水量的落区预报,减弱了模式开始阶段的“spinup”现象。试验还表明,自动站降水资料的时间变化信息,在同化时也起重要作用。Four-dimensional variation data assimilation （4D-VAR） is a logical and rigorous mathematical method to obtain the “best” estimate of the model initial conditions from observations and a priori knowledge of the atmospheric state. It is one of the most advanced data assimilation methods today. Automation weather station （AWS） precipitation data is assimilated by 4D-VAR in experiments. Experiment results show that, due to addition of information of AWS precipitation data, the initial field of test is enhanced in meso-scale information, and it matches the model better in thermo-dynamical mechanism. After assimilation, the simulation is improved. The precipitation during the start period in simulation is increased, and the situation of simulating precipitation matches real situation better. The “spin-up” problem of the model is weakened. Experiment results also show that temporal information of AWS precipitation data is very important for assimilation.

关 键 词：四维变分 自动站降水量同化 中尺度数值模式 暴雨模拟 

分 类 号：P456.7[天文地球—大气科学及气象学]