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

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

段婧^1,,
郭恒²,
胡金蓉²,
周旭¹,
吴锡²,
陈宝君¹

1.
中国气象局云降水物理与人工影响天气重点开放实验室，北京，100081
2.
成都信息工程大学计算机学院，成都，610225

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
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出版历程
- 收稿日期: 2022-10-23
- 录用日期: 2023-08-23
- 修回日期: 2023-05-10
- 网络出版日期: 2023-05-11

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

1.
CMA Cloud-Precipitation Physics and Weather Modification Key Laboratory，Beijing 100081，China
2.
School of Computer Science，Chengdu University of Information Technology，Chengdu 610225，China

摘要

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。
- 冰雪晶粒子 /
- 飞机观测 /
- 粒子测量系统 /
- 形状分类 /
- 人工智能
Abstract: Snow and ice crystals are important components of hydrometeors in clouds. Different shapes of snow and ice crystals are determined by their different physical formation and growth processes. Accurate observation of the shapes of snow and ice crystals is an important prerequisite for revealing the microphysical structure and precipitation mechanism of clouds. This paper summarizes studies and methods of observing crystals of ice and snow in the past half-century. The development of crystal measurement and shape classification technology for snow and ice is reviewed. The new progress of snow and ice crystal observation and shape classification and recognition technology is analyzed and summarized. The aim is to provide a reference of images for further studies of cloud microphysical structure and precipitation mechanism in China.
- Ice and snow crystals /
- Aircraft observation /
- Particle measurement system /
- Shape classification /
- Artificial Intelligence

HTML全文

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）

下载: 全尺寸图片幻灯片

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）

下载: 全尺寸图片幻灯片

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

下载: 全尺寸图片幻灯片

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）

下载: 全尺寸图片幻灯片

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）

下载: 全尺寸图片幻灯片

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）

下载: 全尺寸图片幻灯片

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	14.8	3	96	−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	1.6	0.7×0.9	1	−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	1.8	1	1.4	−100	CPI	Tajiri等（2013）
MICC	英国	曼彻斯特大学	10	1	7.9	−50	两台CPI	Connolly等（2012）
CLOUD	欧洲	欧洲核子研究组织	3.7	3	26.1	−70	3V-CPI	Nichman等（2015）

下载: 导出CSV

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