贵州高原北侧冬季降雪滴谱特征与积雪深度反演

Characteristics of Winter Snowfall Drop Spectrum and Inversion of Snow Depth on North of Guizhou Plateau

摘要:

利用2018—2023年贵州高原北侧冬季降雪滴谱数据，采用统计方法对比分析雨滴谱和雪滴谱的粒子数目、粒子直径和下落速度的分布特征，研究降雪量、积雪深度与粒子相态、粒子数目、粒子尺度的变化趋势一致性，检验粒子体积反演降雪量、积雪深度算法的适用性。结果表明：①雪滴谱直径谱宽分布在5～15 mm之间，平均直径谱宽＞8 mm，属于宽谱降水类型；雨滴谱直径谱宽分布在1～5 mm之间，平均直径谱宽小于3 mm，属于窄谱降水类型。②大多数雪粒子分布在速度谱宽＜5 m·s^-1以下的区域内，这是由雪粒子形状偏平、密度较小、尺度较大而导致。③降雪量反演结果与实测结果的一致性和相关性均优于积雪深度反演，这与环境温度作用下的降水相态转化、积雪融化速率有关。④把粒子数占比、粒子速度谱宽作为冬季降雪天气类型识别具有很好的指示作用，降雪粒子下落至地面的起止时间和粒子数目可以直观反映整个降雪天气过程的演变特征。

Abstract:

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.