| 沈菲菲,束艾青,许冬梅,王琛.多普勒雷达资料同化对北京“7.21”大暴雨过程模拟的影响[J].沙漠与绿洲气象,2020,14(2):50~60 |
| 多普勒雷达资料同化对北京“7.21”大暴雨过程模拟的影响 |
| Influence of Doppler Radar Data Assimilation on Simulation of |
| 投稿时间：2019-02-28 修订日期：2019-05-05 |
| DOI： |
| 中文关键词: WRF模式 多普勒雷达资料同化 三维变分 北京“721”暴雨 |
| 英文关键词:WRF mode Doppler radar data assimilation three-dimensional variation Beijing |
| 基金项目:国家自然科学基金项目（面上项目，重点项目，重大项目） |
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| 中文摘要: |
| 本文利用新一代中尺度预报模式WRFV3.6及其三维变分同化系统（WRF-3DVAR），对2012年7月21日北京地区的一次暴雨过程进行多普勒天气雷达径向风和反射率的同化试验研究，检验和探讨高时空分辨率多普勒天气雷达资料在改进模式初始场及提高对暴雨过程预报的准确率等方面的应用效果及意义。结果发现雷达资料同化能在初始场中加入反映产生降水的低层风场辐合的动力和锋前暖区充足的水汽条件的物理信息。可以在模式积分开始后改善初始场中水汽和风的分布，较快地模拟出局地对流系统的发生、发展，减小了由于中尺度观测资料不足而造成的模式初始场中中尺度信息缺乏的问题。径向速度的同化增加了中尺度信息，对初始流场的调整较为显著，侧重于改进风场。而雷达反射率资料的同化对初始温、湿场和强回波位置的调整更明显，侧重于改进湿度场。累计降水的预报结果显示，同化径向风资料对雨带的位置、范围有较好的改进，同化雷达反射率资料对暴雨强度的预报有明显的改善。雷达资料对于提高定量降水预报的精确度有着重要作用。 |
| 英文摘要: |
| Based on the new generation mesoscale forecasting model WRFV3.6 and its three-dimensional variational assimilation system (WRF-3DVAR), the simulation of a heavy rain case in Beijing on July 21, 2012 is conducted by assimilating the radial wind and reflectivity of Doppler weather radar. The study examines and discusses the application effect of assimilating the Doppler weather radar data with high spatial and temporal resolutions in improving the initial field and the accuracy of forecasting of the storm. It is found that radar data assimilation is able to add physical information which reflects the power of the low-level wind field convergence that produces precipitation and the sufficient water vapor conditions in warm zone in front of the front in initial field. Also, it is able to improve the distribution of water vapor and wind after the start of the mode integration. The occurrence and development of the local convective system are captured by improving the mesoscale information in the initial field from sufficient mesoscale observation data. The assimilation of radar radial velocity enriches the mesoscale information, which contributes to an obvious initial flow field adjustment thus improving the quantity of wind field. Besides, the assimilation of radar reflectivity data is more obvious for the adjustment of initial temperature, humidity fields, and strong echo position, leading to significant improvement in the humidity field. The forecast results of accumulated precipitation show that the assimilation of radial wind data improves the position, range and intensity of the rain band. Radar data plays an important role in improving the accuracy of quantitative precipitation forecasts. |
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