WRF-EnKF系统对中国南方一次暴雨过程确定性预报的试验

文章利用美国宾州州立大学的WRF EnKF(Ensemble Kalman Filter)实时预报系统(Real time Penn State WRF EnKF System)，针对2013年5月15—16日发生在中国南方的暴雨过程进行了数值预报试验，以初步检验该系统对我国南方降水确定性预报的效果。数值试验采用2013年5月14日08时（北京时）起报的6 h间隔的1°×1° NCEP GFS (globle forecast system) 60 h预报数据（预报到5月16日20时）作为初始条件和边界条件。其中，控制试验不同化任何观测资料，同化试验通过集合卡尔曼滤波方法同化常规探空资料，分别进行确定性预报。结果表明：利用WRF EnKF系统同化常规探空资料，显著改善了数值预报的初始场，减小了各物理量的预报偏差和预报均方根误差，进而提高了此次暴雨过程的降水落区和强度的预报准确率。

Deterministic Prediction Experiment of One Torrential Rainfall Event in Southern China Using a WRF EnKF System

Two convection permitting numerical experiments were conducted with the WRF model to examine the impact of assimilating sounding data by using an EnKF method for deterministic prediction of a torrential rainfall event over southern China during 15-16 May 2013. The initial and lateral boundary conditions were based on the NCEP GFS 1°×1° 60 h gridded forecast data which were available every 6 h from 08:00 BT 14 May to 20:00 BT 16 May 2013. The two experiments, NODA and DA, differed only in the initial conditions: while NODA was initialized from the NCEP GFS data at 08:00 BT 14 May, DA was from an ensemble mean of 30 analysis members at 08:00 BT 15 May, which was generated using the WRF EnKF system with conventional sounding data at 20:00 BT 14 May, 02:00 BT and 08:00 BT 15 May assimilated. The results show that, compared to NODA, not only the initial conditions of DA are much closer to the observed fields, but also the DA predicted physical parameters are improved in terms of both biases and root mean square errors, leading to a more accurate prediction of the location and magnitude of precipitation from DA.