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The analysis of the winter snowdrop spectrum on the north of the Guizhou Plateau from 2018 to 2023 shows that the distribution of snow droplet patterns is closely related to the number, size, shape, and density of particles. The snowfall amount and snow depth are related to the accumulation of snow particles. The starting and ending time and the number of snow particles directly reflect the evolution characteristics of the whole snowfall process. The specific conclusions are as follows: (1) From the perspective of particle number and size spectrum distribution, the diameter spectrum width of the raindrops and snowdrops is mainly distributed between 1-5 mm and 5-15 mm. In winter, the northern side of the Guizhou Plateau often experiences weak rainfall events, with the number of drizzle particles being about 5% higher than that of rain particles. The average diameter of the raindrop spectrum is less than 3 mm, which belongs to the narrow-spectrum precipitation type. During winter snowfall, the proportion of snow particles exceeds 75%, and the snow particle size distribution is higher than that of raindrops. The diameter of the snowfall spectrum is greater than 8 mm, which belongs to the wide-spectrum precipitation type. This provides a good indication for identifying snow weather. (2) From the particle number velocity spectrum distribution, the velocity spectrum width of raindrops and snowflakes mainly ranges from 5-10 m·s^-1 to 3-5 m·s^-1. The mode of particle speed is 2.2 m·s^-1 and 1.1 mm·s^-1. In comparison, the raindrop spectrum and snowdrop velocity spectrum are distributed in two intervals above and below 5 m·s^-1, and the values of the particle grading velocity differ by a factor of 2. The shape of snow particles is flat, the density is small, and the size is large, which leads to a lower falling speed. Using the particle falling speed as a method to identify rain and snow precipitation types is highly representative. (3) In a single snowfall event, the number of all particles can be used as the quantity for calculating snow depth accumulation. There is a significant positive correlation between the measured snowfall and the inverted snowfall, and they show good consistency in the changing trend. However, the correlation and trend consistency between the measured snow depth and the inversion result are weaker than those of the snowfall inversion, which is related to ambient temperature conditions, precipitation phase transformation, and snow melting rate. (4) The measurement and inversion of snow depth are only meaningful when snow particles persist, so the identification of precipitation phase types is particularly critical, as it serves as an important basis for judging the accumulation time of snow particles. The snowfall amount and snow depth inverted using the particle number and particle diameter (particle volume) show good estimation results. The evolution characteristics of the snow weather process or the period of ground snow accumulation can be reproduced using characteristic quantities such as particle phase, particle number, snowfall, snow depth, and snow accumulation rate. This has a good guiding effect on the monitoring and evaluation of snowfall and snow accumulation.