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自然冰雪晶粒子观测及形状分类研究进展

段婧 郭恒 胡金蓉 周旭 吴锡 陈宝君

段婧,郭恒,胡金蓉,周旭,吴锡,陈宝君. 2023. 自然冰雪晶粒子观测及形状分类研究进展. 气象学报,81(5):685-701 doi: 10.11676/qxxb2023.20220180
引用本文: 段婧,郭恒,胡金蓉,周旭,吴锡,陈宝君. 2023. 自然冰雪晶粒子观测及形状分类研究进展. 气象学报,81(5):685-701 doi: 10.11676/qxxb2023.20220180
Duan Jing, Guo Heng, Hu Jinrong, Zhou Xu, Wu Xi, Chen Baojun. 2023. Research progress in observation and shape classification of natural snow and ice crystals. Acta Meteorologica Sinica, 81(5):685-701 doi: 10.11676/qxxb2023.20220180
Citation: Duan Jing, Guo Heng, Hu Jinrong, Zhou Xu, Wu Xi, Chen Baojun. 2023. Research progress in observation and shape classification of natural snow and ice crystals. Acta Meteorologica Sinica, 81(5):685-701 doi: 10.11676/qxxb2023.20220180

自然冰雪晶粒子观测及形状分类研究进展

doi: 10.11676/qxxb2023.20220180
基金项目: 中国气象局云雾物理环境重点开放实验室项目(2020Z00722)、国家自然科学基金项目(42175109)、中国气象局重点创新团队(CMA2022ZD10)、四川省科技计划项目(2023YFQ0072)、中国气象局西北区域人影科学试验研究项目(RYSY201909)。
详细信息
    作者简介:

    段婧,主要从事云降水物理与人工影响天气、多源云降水探测资料融合应用研究。E-mail:duanjing@cma.gov.cn

  • 中图分类号: P401

Research progress in observation and shape classification of natural snow and ice crystals

  • 摘要: 冰雪晶是云中水成物的重要组成部分,鉴于不同形状的冰雪晶形成及增长的物理条件、过程经历不同,为此准确判断观测冰雪晶粒子的形状是揭示云微物理结构和降水机制的重要依据。文中概述了近半个多世纪以来中外观测冰雪晶粒子的方式和手段。梳理了对冰雪晶粒子的测量和形状分类技术的发展历程。分析总结了冰雪晶观测及其形状分类识别技术的新进展,并对未来发展进行了展望。旨在促进和推动以冰雪晶形状为基础的中国云微物理结构与降水机制的深入研究。

     

  • 图 1  冰雪晶粒子的国际分类法 (Schaefer,1954

    Figure 1.  International classification of ice and snow crystals (Schaefer,1954

    图 2  Magono等 (1966) 的天然雪晶气象分类

    Figure 2.  Meteorological classification of natural snow crystals by Magano et al (1966

    图 3  Kikuchi等 (2013) 全球冰雪晶分类方案中各种类示意

    Figure 3.  Schematic diagram of various classes of Kikuchi et al (2013) global classification scheme of snow and ice crystals

    图 4  Sandra在基律纳发现的34个冰雪晶新形状 (Vázquez-Martín,et al,2020) (右下角为1 mm参考尺度)

    Figure 4.  The 34 new shapes of snow and ice crydtals found in Kiruna by Sandra (Vázquez-Martín,et al,2020) (with a 1 mm scale bar shown as reference at lower right corner)

    图 5  卷积神经网络对冰雪晶自动分类技术 (Touloupas,et al,2020) 在瑞士阿尔卑斯山区某一时刻观测应用示例 (Lauber,et al,2021)( 注:这里的成熟雪晶为丛集或凇附雪晶)

    Figure 5.  An application example of using the convolutional neural network (Touloupas,et al,2020) to classify ice and snow crystals of one slice in the Swiss Alps (Lauber,et al,2021)( note aged ice crystals here defined as aggregates and rimed particles)

    图 6  卷积神经网络对冰雪晶自动分类技术针对北极地区一次混合云降雪过程时序图的应用示例 (Pasquier,et al,2022

    Figure 6.  A application example of using the convolutional neural network to classify ice and snow crystals of time series data in the North Arctic region (Pasquier,et al,2022

    表  1  测量冰雪晶图像的光学阵列探头的相关参数

    Table  1.   Parameters of ice-snow-crystal imaging probe

    仪器名称设备厂家测量范围(μm)分辨率(μm)线性阵列单元数阵列类型
    二维云粒子探头2D-C PMS 33—1056 33 32 线性
    二维降水粒子探头2D-P PMS 200—6400 200 32 线性
    二维灰度降水粒子探头2D-GA2 PMS 30—1920 30 64 线性
    云粒子图像探头CIP DMT 25—1550 25 64 线性
    降水粒子图像探头PIP DMT 100—6200 100 64 线性
    高体积降雨分光仪HVPS SPEC 150—19200 150 128 线性
    二维立体成像光阵列探头2D-S SPEC 10—1280 10 128 双线性
    云粒子成像探头CPI SPEC >3 2.3 平面
    三视图云粒子成像探头3V-CPI SPEC 10—1280 10 128 线性(触发)+平面
    云粒子成像仪ZBT-CPI ZBT 25—1550 25 64 线性
    降水粒子成像仪ZBT-PPI ZBT 10—6200 100 64 线性
    下载: 导出CSV

    表  2  冰雪晶观测技术在云室中的应用

    Table  2.   Observations of ice and snow crystals used in cloud chamber

    云室名称国家机构云室高
    (m)
    云室直径
    (m)
    云室体积
    (m3
    云室最低
    温度(℃)
    冰雪晶观测
    仪器/方法
    参考文献
    中型云室中国中国气象
    科学研究院
    14.8396−45激光全息滴谱仪和
    高速摄影机
    张纪淮(1986
    小型垂直过冷云风洞中国中国科学院1.60.7×0.91−30载玻片和显微镜照相龚乃虎等(1992
    MRI日本日本气象研究所1.811.4 −100CPITajiri等(2013
    MICC英国曼彻斯特大学1017.9−50两台CPIConnolly等(2012
    CLOUD欧洲欧洲核子研究组织3.7326.1−703V-CPINichman等(2015
    下载: 导出CSV
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  • 收稿日期:  2022-10-23
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