混合降水粒子识别与雨雪雹尺度谱特征分析

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  • 1. 贵州省山地气象科学研究所,贵州 贵阳 5500081
    2. 贵州省威宁彝族苗族回族自治县气象局,贵州 威宁 553100
    3. 贵州省务川仡佬族苗族自治县气象局,贵州 务川 564300
    4. 贵州省毕节市气象局,贵州 毕节 551700

网络出版日期: 2025-04-11

基金资助

贵州省气象高层次人才创新团队项目(黔气科合TD202403号);毕节市科学技术项目(毕科合[20238号);国家自然科学基金项目(42165001);贵州省科技支撑计划项目(黔科合支撑[2023]一般 194);贵州省科学技术基金项目(黔科合基础 ZK2023]一般200

Identification of mixed precipitation particles and analysis of scale spectrum characteristics of rainsnow and hail 

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  • 1. Guizhou Mountainous Meteorological Science research InstituteGuiyang 5500081GuizhouChina
    2. Bijie Meteorological BureauBijie 551700GuizhouChina
    3. Wuchuan County Meteorological Bureau of Guizhou ProvinceWuchuan 564300GuizhouChina

Online published: 2025-04-11

摘要

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 mm1~12 mm5~12 mm,速度谱宽集中分布在 3~15 m∙s-13~5 m∙s-112~15 m∙s-1,粒子众数速度分别为 4. 4 m∙s-11. 1 m∙s-14. 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。

本文引用格式

邹书平, 柯莉萍, 熊 凯, 李德章, 黄 钰, 陈百炼 . 混合降水粒子识别与雨雪雹尺度谱特征分析[J]. 高原气象, 0 : 1 . DOI: 10.7522/j.issn.1000-0534.2025.00025

Abstract

Based on the observation time series data of Guizhou DSG1 precipitation phenomenon instrument from 2018 to 2023the particle number distribution and scale spectrum characteristics of rainsnow and hail three types precipitation were compared and analyzedand an integrated determination algorithm for precipitation phenomenon type identification was established based on the particle numberparticle spectral widthand particle pluralityand the applicability of the algorithm was evaluated. The specific conclusions are:(1The diameter spectrum widths of rainsnowand hail droplets are concentrated in the ranges of 1~8 mm1~12 mmand 5~12 mmrespectively. The velocity spectra are concentrated in the ranges of 3~15 m∙s-13~5 m∙s-112~ 15 m∙s-1and the particle plurality velocities are 4. 4 m∙s-11. 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.2The 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 precipitation0. 005%.3Particles 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.4By evaluating the integrated determination algorithm for precipitation phenomenon type recognitionthe 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.

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