Precipitation clouds are visible aggregates of hydrometeor in the air that floating in the atmosphere after condensation, which can be divided into stratiform cloud and convective cloud. Different precipitation clouds often accompany different precipitation processes. Accurate identification of precipitation clouds is significant for the prediction of severe precipitation processes. Traditional identification methods mostly depend on the differences of radar reflectivity distribution morphology between stratiform and convective precipitation clouds in three-dimensional space. However, all of them have a common shortcoming that the radial velocity data detected by Doppler Weather Radar has not been applied to the identification of precipitation clouds because it is insensitive to the convective movement in the vertical direction. This paper proposes a new method for precipitation clouds identification based on deep learning algorithm, which is according the distribution morphology of multiple radar data. It mainly includes three parts, which are Constant Altitude Plan Position Indicator data (CAPPI) interpolation for radar reflectivity, Radial projection of the ground horizontal wind field by using radial velocity data, and the precipitation clouds identification based on Faster-RCNN. The testing result shows that the method proposed in this paper performs better than the traditional methods in terms of precision. Moreover, this method boasts great advantages in running time and adaptive ability.

降水云是凝结后漂浮在大气中的空气中可见的水凝物聚集体，可分为层状云和对流云。不同的降水云经常伴随着不同的降水过程。准确识别降水云对预测严重的降水过程具有重要意义。传统的识别方法主要取决于三维空间中层状和对流降水云之间雷达反射率分布形态的差异。但是，所有这些方法都有一个共同的缺点，那就是多普勒天气雷达检测到的径向速度数据尚未用于降水云的识别，因为它对垂直方向的对流运动不敏感。根据多雷达数据的分布形态，提出了一种基于深度学习算法的降水云识别新方法。它主要包括三个部分：用于雷达反射率的恒定高度计划位置指示符数据（CAPPI）插值，使用径向速度数据的地面水平风场的径向投影以及基于Faster-RCNN的降水云识别。测试结果表明，本文提出的方法在精度上优于传统方法。而且，该方法在运行时间和自适应能力上都具有很大的优势。它主要包括三个部分：用于雷达反射率的恒定高度计划位置指示符数据（CAPPI）插值，使用径向速度数据的地面水平风场的径向投影以及基于Faster-RCNN的降水云识别。测试结果表明，本文提出的方法在精度上优于传统方法。而且，该方法在运行时间和自适应能力上都具有很大的优势。它主要包括三个部分：用于雷达反射率的恒定高度计划位置指示符数据（CAPPI）插值，使用径向速度数据的地面水平风场的径向投影以及基于Faster-RCNN的降水云识别。测试结果表明，本文提出的方法在精度上优于传统方法。而且，该方法在运行时间和自适应能力上都具有很大的优势。测试结果表明，本文提出的方法在精度上优于传统方法。而且，该方法在运行时间和自适应能力上都具有很大的优势。测试结果表明，本文提出的方法在精度上优于传统方法。而且，该方法在运行时间和自适应能力上都具有很大的优势。