The reflectivity threshold of the convective cloud center should be determined according to the reflectivity distribution of the region near the convective cloud center, and the influence radius should be calculated by the reflectivity of the convective cloud center. pointed out the irrationality of using reflectivity threshold and influence radius to determine convective cloud region. Moreover, precipitation clouds within a fixed radius of the convective cloud center are all convective clouds by default, otherwise they are stratiform clouds. determine the convective cloud center through the radar reflectivity threshold. The limitation of this method is that it can only determine the center of convective cloud, and it is easy to misjudge the precipitation area of convective cloud with weak precipitation intensity nearby. When the precipitation intensity exceeds a certain fixed threshold, the precipitation cloud is considered as convective cloud otherwise, it is stratiform cloud. proposed a method to distinguish the precipitation cloud types by using rainfall gauge measurement data. The limitation of these methods is that the zero-layer bright band is visible only when the stratiform cloud precipitation develops to maturity. For the region with the phenomenon of the zero-layer bright band, the precipitation clouds type defaults to stratiform cloud otherwise it is convective cloud. In earlier research, the zero-layer bright band is mostly used as the basis for the identification of precipitation cloud types. Doppler weather radar, referred as a greatest tool for detecting weather processes, can not only detect the distribution of precipitation clouds in the atmosphere, but also detect the movement trend of precipitation clouds.

The capability of microwaves to penetrate cloud and rain has placed the weather radar in an unchallenged position for remotely surveying the atmosphere. In recent decades, the precipitation clouds identification based on ground-based weather radar detection data, has been widely used in radar quantitative precipitation estimation, weather modification, and aviation meteorology. Moreover, this method boasts great advantages in running time and adaptive ability. The testing result shows that the method proposed in this paper performs better than the traditional methods in terms of precision. 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. 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.

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. Traditional identification methods mostly depend on the differences of radar reflectivity distribution morphology between stratiform and convective precipitation clouds in three-dimensional space. Accurate identification of precipitation clouds is significant for the prediction of severe precipitation processes.

Different precipitation clouds often accompany different precipitation processes. 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.

Dc weather radar in motion