中文摘要：

将反演的云微物理量和垂直速度采用“牛顿连续松弛逼近”（Nudging）技术应用到GRAPES模式中,对一次梅雨锋暴雨过程进行了数值模拟。通过设计不同的试验方案,分别对水汽、液态水和垂直速度对Nudging效果以及预报结果的影响进行了考察。研究发现,采用Nudging初始化方法,可使背景场与观测反演资料相协调,实现了模式中对流的热启动,模式预报的开始时刻就产生降水,缩短Spin-up时间。水汽对降水至关重要,对降水的强度和持续时间都有重要影响;云水、雨水和垂直速度决定了初始时刻对流的强弱分布并产生降水;水平风场决定了系统的移动方向,对预报降水的落区有重要影响。模式比较成功地模拟了6 h的降水过程,中尺度天气系统的时空特征比较明显,对流中心上升速度最大值约2.0 m/s,云水含量400 hPa以上较大,最大值约1.5 g/kg,雨水含量500 hPa以下较大,最大约3.0 g/kg,底层辐合高层辐散。反演资料对降水的影响随预报时间的增加而减弱,预报1 h之内反演资料有明显影响,3 h之后的预报则主要依赖模式自身。鉴于仅使用一部雷达资料的反射率因子资料,雷达资料没有覆盖整个模式区域,天气系统被截断,反演和同化过程还采用了一些经验参数方法等原因,数值模拟结果与雷达观测之间还存在一定的差异,有待于更深入的研究。

英文摘要：

Microphysical variables and vertical velocity retrieved were incorporated using the nudging method into the initial data assimilation of GRAPES （Global/Regional Assimilation and Prediction System） model at each time step of the integration by adding an extra term to the prognostic equation. Simulation experiments of a torrential rain event were carried out using the Doppler radar observations of Hefei, Anhui province at 02：00 BST 5 July 2003 and the GRAPES model developed by CAMS. Different experiments were designed to investigate the effects of water vapor, liquid water and vertical velocity on Nudging and prediction results. Some conclusions were drawn as follows： Nudging technique was effective in forcing the model forecast gradually to the observation information, yielding the thermodynamically and dynamically balanced analysis field, and correspondingly, the spin-up phenomenon of the model has been to some extent removed. As viewed from the simulation results, water vapor is vital to precipitation, and a governing factor of the amount and duration of precipitation; the initial cloud water, rain water and vertical velocity determine the strength distribution of convection and precipitation at the beginning time of forecast; the horizontal wind field steers the motion of the mesoscale weather system embedded in and impacts the position of precipitation zone to a large extent. The model successfully forecasted the precipitation process within 6 hours, and the distinct characteristics of the mesoscale weather system, such as the updraft velocity in the center of convective structure is about 2.0 m/s, the maximum cloud water content of about 1.5 g/kg occurs above the level of 400 hPa and the maximum rain water content of about 3.0 g/kg below 400 hPa, and there is the notable convergence of air flow at the low level of the convective structure and the distinctive divergence in the upper level. The simulation experiments show that the influence of the initial retrieval data on prediction is weakening with