Abstract: Based on the radar cardinality data of 109 hailcloud cases observed from 2016 to 2018, PUP software processing and analysis were carried out. Combined with relevant business functions of the original Dalian artificial hail prevention decision command system, assisted analysis and value of radar characteristic parameters were conducted. The radar characteristic parameters of hail cloud occurrence and development, cloud top height, strong center height and vertical integral liquid water content were obtained. According to hail cloud theory and catalytic principle, hail cloud recognition and type discrimination based on SA new generation Doppler radar, as well as artificial hail prevention decision discrimination index and model were studied and established. In addition, 28 hail cloud cases in 2019 were verified and analyzed by using this index, and the accuracy rate reached 85.2%~86.8%. In particular, the radar data inversion analysis, such as hail cloud identification and hail prevention decision making, was carried out on the radar hail prevention decision command system during a severe hail weather process in autumn, as well as the comparative analysis of the old and new discriminant indexes, showed obvious results. It showed that there were some differences in characteristic parameters and human figure decision discriminant indexes of different types of radar, and the new ideas and methods of effective artificial hail prevention of strong hail cloud were summarized and discussed through the analysis and practical investigation of the artificial hail prevention technology of strong hail cloud.

Key words: radar characteristic parameters, artificial hail prevention, decision discrimination index and application