利用2018-2023年贵州DSG1型降水现象仪观测时间序列资料，对比分析雨雪雹三种降水类型的粒子数分布和尺度谱特征，建立了基于粒子数、粒子谱宽、粒子众数的降水现象类型识别综合判定算法并评估了算法的适用性。具体结论为：（1）雨、雪、雹滴谱的直径谱宽集中分布在1~8 mm、1~12 mm、5~12 mm，速度谱宽集中分布在 3~15 m∙s-1、3~5 m∙s-1，12~15 m∙s-1，粒子众数速度分别为 4. 4 m∙s-1、1. 1 m∙s-1、4. 4 m∙s-1，通过粒子下落速度可有效识别雨、雪降水类型。（2）雨滴谱、雹滴谱的雨粒子数占比分别为 50. 1%、64. 3%，雪滴谱的雪粒子数占比为 70. 2%，均在总粒子数半数以上；雹滴谱的冰雹粒子数占比为 0. 19%，高于雨滴谱的冰雹粒子数占比 0. 005%。（3）粒子直径≥3 mm 和粒子速度<5 m∙s-1的粒子，主要存在于降雪天气过程中，粒子直径≥5 mm和粒子速度≥10 m∙s-1的粒子，主要存在于冰雹和短时强降水天气过程中，提高对速度的限定可以改善冰雹粒子识别的准确性。（4）通过对降水现象类型识别综合判定算法评估，单一降水类型识别准确率达到 95% 以上，冰雹误报率仅为 1. 7%，可有效减少在短时强降水中误识别为冰雹的情况。

Based on the observation time series data of Guizhou DSG1 precipitation phenomenon instrument from 2018 to 2023，the particle number distribution and scale spectrum characteristics of rain，snow and hail three types precipitation were compared and analyzed，and an integrated determination algorithm for precipitation phenomenon type identification was established based on the particle number，particle spectral width，and particle plurality，and the applicability of the algorithm was evaluated. The specific conclusions are：（1）The diameter spectrum widths of rain，snow，and hail droplets are concentrated in the ranges of 1~8 mm，1~12 mm， and 5~12 mm，respectively. The velocity spectra are concentrated in the ranges of 3~15 m∙s-1，3~5 m∙s-1，12~ 15 m∙s-1，and the particle plurality velocities are 4. 4 m∙s-1，1. 1 m∙s-1 and 4. 4 m∙s-1. respectively. The rain and snow precipitation types can be effectively recognized by the particle falling velocities.（2）The percentages of rain particles in the raindrop and hail drop spectrum accounted for 50. 1% and 64. 3%，and the number of snow particles in the snowdrop spectrum accounted for 70. 2%，which exceeded half of the total number of particles. The percentage of hail particles in the hail droplet spectrum is 0. 19%，which is significantly higher than the short-term heavy precipitation（0. 005%）.（3）Particles with particle diameters greater than 3 mm and particle velocities of less than 5 m∙s-1 mainly exist in the process of snowfall. Particles with particle diameters greater than 5 mm and particle velocities greater than 10 m∙s-1 mainly exist in the process of hailstorms and short-term heavy precipitation. Increasing the velocity limit can improve the accuracy of hail particle recognition.（4）By evaluating the integrated determination algorithm for precipitation phenomenon type recognition，the accuracy of single precipitation type recognition reaches more than 95%，and the false alarm rate of hail is only 1. 7%，which can effectively reduce the cases of misrecognition as hail in short-term heavy precipitation.