Based on a short-time heavy rainfall in Anhui and the weather research and forecasting (WRF) model, the water vapor in the initial field of the model is retrieved using the statistical relationships of the reflectivity factor from the Doppler weather radar with the relative humidity and hydrometeor. Three-dimensional variational (3DVAR) assimilation method is used to assimilate the radar reflectivity factor and radial velocity, and then the impact of assimilating retrieved water vapor on the analysis and forecast of the torrential rain is assessed. The results show that, after assimilating the retrieved water vapor, the water vapor field in the model is significantly improved. The water vapor content in the middle layer of the model in the analyzed field is increased, corresponding well with the convective region. Meanwhile, the precipitation distribution during this weather process is successfully simulated. The mesoscale characteristics are better presented by the imageries of radar reflectivity factor, and false echoes are partially reduced. Besides, the prediction of short-time heavy rainfall regions is closer to the actual observations. After assimilating the retrieved water vapor, the simulated one-hour accumulated rainfall is closer to the actual observation, and the fraction skill score (FSS) is higher.

中文翻译：

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雷达资料同化在基于水汽反演的对流降水预报中的应用

以安徽某短时强降雨为基础，结合天气研究预报（WRF）模型，利用多普勒天气雷达反射率因子与相对湿度的统计关系反演模型初始场的水汽和水凝。采用三维变分(3DVAR)同化方法对雷达反射系数和径向速度进行同化，评估同化反演水汽对暴雨分析预报的影响。结果表明，对反演的水汽进行同化后，模型中的水汽场得到显着改善。分析场中模型中间层的水汽含量增加，与对流区相对应。同时，成功模拟了该天气过程中的降水分布。雷达反射率因子成像能更好地呈现中尺度特征，部分减少假回波。此外，短时强降雨区域的预测更接近于实际观测。同化取回的水汽后，模拟的一小时累计降雨量更接近实际观测值，分数技能分数（FSS）更高。