
Identification of mixed precipitation particles and analysis of scale spectrum characteristics of rain,snow and hail
ZOU Shuping, KE Liping, XIONG Kai, LI Dezhang, Huang Yu, Chen Bailian
Identification of mixed precipitation particles and analysis of scale spectrum characteristics of rain,snow and hail
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.
Raindrop spectrum /
precipitation type /
particle identification /
particle diameter /
falling speed
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