大气与环境光学学报 ›› 2026, Vol. 21 ›› Issue (4): 566-579.doi: 10.3969/j.issn.1673-6141.2026.04.004

• 大气光学 • 上一篇    

基于2015―2019年CloudSat卫星探测的江淮地区梅雨期云系宏微观特征分析

于彩霞 1,2,3,4,5, 石春娥 3,4, 王羽飞 6, 吴文玉 3,4*   

  1. 1 中国科学院合肥物质科学研究院安徽光学精密机械研究所, 中国科学院大气光学重点实验室, 安徽 合肥 230031; 2 中国科学技术大学, 安徽 合肥 230026; 3 安徽省气象科学研究所, 大气科学与卫星遥感安徽省重点实验室, 安徽 合肥 230031; 4 寿县国家气候观象台, 中国气象局淮河流域典型农田生态气象野外科学试验基地, 安徽 寿县 232200; 5 中国气象局流域强降水重点开放实验室, 暴雨监测预警湖北省重点实验室, 中国气象局武汉暴雨研究所, 湖北 武汉 430205; 6 中国气象局吉林省人民政府人工影响天气联合开放实验室, 吉林 长春 130062
  • 收稿日期:2023-07-05 修回日期:2023-09-14 接受日期:2023-11-07 出版日期:2026-07-28 发布日期:2026-07-28
  • 通讯作者: E-mail: wuwy2000@126.com E-mail:wuwy2000@126.com
  • 作者简介:于彩霞 (1988- ), 女, 山东威海人, 硕士, 高级工程师, 主要从事云遥感及大气水循环方面的研究。E-mail: xiaoyu_abcd@126.com
  • 基金资助:
    国家自然科学基金 (U2342206), 安徽省自然科学基金 (2208085UQ01), 中国气象局流域强降水重点开放实验室开放基金项目 (2023BHR-Y23), 2024 年度中国气象局创新发展专项 (CXFZ2024J048, CXFZ2026J137), 中国气象局智能气象观测技术重点开放实验室 开放研究课题 (ZNGC2025MS14), 安徽省气象局自主创新发展专项 (AHQXZC202312), 安徽省气象科学研究所自立项目 (AHQKSZL2020-3)

Macro- and micro-physical characteristics analysis of cloud systems during Mei-yu period in the Yangtze-Huaihe region based on 2015–2019 CloudSat satellite detection

YU Caixia1,2,3,4,5, SHI Chune3,4, WANG Yufei6, WU Wenyu3,4*   

  1. 1 Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, HFIPS, Chinese Academy of Sciences, Hefei 230031, China; 2 University of Science and Technology of China, Hefei 230026, China; 3 Anhui Province Key Laboratory of Atmospheric Sciences and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei 230031, China; 4 Shouxian National Climate Observatory, Typical Farmland Eco-Meteorology Field Scientific Test Base of China Meteorological Administration in Huaihe River Basin, Shouxian 232200, China; 5 China Meteorological Administration Basin Heavy Rainfall Key Laboratory, Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China; 6 China Meteorological Administration -Jilin Provincial People's Government Joint Open Laboratory for Weather Modification, Changchun 130062, China
  • Received:2023-07-05 Revised:2023-09-14 Accepted:2023-11-07 Online:2026-07-28 Published:2026-07-28
  • Contact: wu wenyu E-mail:wuwy2000@126.com

摘要: 本研究基于CloudSat 卫星二级产品数据, 对2015―2019 年江淮地区梅雨期云系的宏微观特征进行了分析。 研究结果表明, 梅雨期间江淮地区以单层云为主, 占比为49.3%, 其次为两层云, 占比33.7%。在云类分布中, 高云占 主导地位, 其中卷云占比34.5%, 高积云次之; 主要降水云系中, 深对流云和雨层云分别占7.0%和4.4%。该地区梅雨 期云系的平均液态水含量为383.2 mg/m3, 在3 km高度以下和5~9 km高度范围内随高度增加而减小, 3~5 km高度区 间变化趋势不明显, 基本维持在400.0 mg/m3左右; 冰水含量平均为90.2 mg/m3, 在4~9 km高度范围内呈上升趋势, 高值区位于7~11 km高度范围内。液态水粒子的平均有效半径为15 μm, 其中大于12 μm 的样本占比达67.2%, 且在 3 km高度以下随高度增加而减小; 冰水粒子的平均有效半径为69 μm, 最大值为101 μm, 出现在5.3 km高度处。水云 的平均云滴数浓度为55 cm−3, 在3 km高度以下和5~9 km高度范围内随高度增加而减小, 3~5 km高度区间维持在 70 cm−3左右; 冰云的平均云滴数浓度则整体随高度增加而上升。在31°N附近, 由于深对流云占比高达15.2%, 云层发 展深厚 (平均云厚为5.2 km), 同时, 14~16 km高度区间的冰水含量和冰云云滴粒子数浓度显著高于其他区域。深对 流云中冰云粒子的垂直结构特征与所有云系的平均垂直结构相似, 但各高度上的数值均高于整体统计结果, 且垂直 变化趋势更为显著。深对流云、雨层云和积云的降水发生频率分别为93.0%、86.1%和68.1%, 其中, 深对流云和雨层 云降水以单层云为主, 积云降水则以单层云和两层云为主。

关键词: 卫星探测, CloudSat, 垂直结构, 云, 江淮地区

Abstract: Objective More than 50% of the Earth's surface is covered by clouds. Clouds play a crucial role in the Earth atmosphere system, serving as key regulators of the Earth's radiation balance and global water cycle. The characteristics of cloud vertical structure, including cloud water content, cloud water path, cloud top and base heights, and cloud thickness, reflect the internal dynamic and thermodynamic processes of clouds and the mechanisms of cloud precipitation microphysics, which have a significant influence on atmospheric radiation transfer. The uncertainty of cloud vertical structure remains one of the major obstacles to understanding the impact of clouds on climate. Therefore, an in depth understanding of cloud vertical structure is of great significance for further research on climate change. In this study, CloudSat satellite observation data were employed to analyze the vertical structure characteristics, as well as the macro- and micro-physical characteristics, of cloud systems over the Yangtze-Huaihe region during the Meiyu period, in order to deepen the understanding of cloud system features in this region. Methods Based on the Level 2 product data from the Cloud Profile Radar (CPR) onboard the CloudSat satellite, the satellite transit orbit data during the Mei-yu period (mid-June to mid-July) from 2015 to 2019 over the Yangtze-Huaihe region (28°N–34°N, 110°E–122°E) were selected. During the study period, a total of 53,040 satellite profiles passed over the region. For each profile, the 2B-GEOPROF product was first used to determine whether there were clouds present at the subsatellite pixel. When there were clouds, the 2B-CLDCLASS product and 2B-CWC-RO would be used to analyze the macroand micro-physical parameters of clouds, including cloud layer distribution, cloud type, cloud top and cloud base heights, and cloud microphysical parameters (cloud water content, effective radius, number concentration). In addition, the microphysical characteristics of deep convective clouds and the precipitation features of different cloud layers were also analyzed. Results and Discussion The macro- and micro-physical characteristics of clouds during the Mei-yu period in the Yangtze- Huaihe region were analyzed using CloudSat satellite product data from 2015 to 2019. The results indicate that single-layer clouds dominate in the Yangtze-Huaihe region during the Mei-yu period, accounting for 49.3%, followed by two-layer clouds, accounting for 33.7%. Cirrus clouds account for 34.5% of all cloud types, followed by high cumulus clouds, with high-level clouds being predominant overall. The primary precipitation-producing cloud systems are deep convective clouds and nimbostratus, accounting for 7.0% and 4.4%, respectively. The average liquid water content of the cloud system during the Mei-yu period is 383.2 mg/m3, decreasing with height below 3 km and in range of 5–9 km, while remaining relatively stable at approximately 400.0 mg/m3 in the 3–5 km height layer. The average ice water content is 90.2 mg/m3, exhibiting an increasing trend in the 4–9 km altitude range, with a high -value region occurring in the 7–11 km altitude range. The average effective radius of liquid water droplets is 15 μm, with values exceeding 12 μm accounting for 67.2% of the total samples, and decreasing with heights below 3 km. The average effective radius of ice crystals is 69 μm, with a maximum value of 101 μm observed at a height of 5.3 km. The average droplet concentration in water clouds is 55 cm−3, which decreases with height below 3 km and in the 5–9 km altitude range, and remains nearly constant at approximately 70 cm−3 in the 3–5 km height layer. The average droplet concentration in ice clouds generally increases with height. Around 31°N, the high proportion of deep convective clouds (15.2%) results in the development of deep clouds (with a mean cloud thickness of 5.2 km), and both the ice water content and ice cloud droplet concentration in the 14–16 km height layer are significantly higher than in other regions. The vertical structure of ice cloud particles in deep convective clouds is similar to that of all cloud systems, while exhibits higher values at each altitude and a more pronounced vertical variation trend. The precipitation frequencies for deep convective clouds, nimbostratus, and cumulus are 93.0%, 86.1%, and 68.1%, respectively. The precipitation in deep convective clouds and nimbostratus is primarily associated with single-layer clouds, whereas the precipitation in cumulus arises mainly from both single-layer and two-layer cloud systems. Conclusions Based on the CloudSat satellite observations from 2015 to 2019, this study systematically investigates the vertical structural characteristics, macro- and micro-physical properties of cloud systems over the Yangtze – Huaihe region during the Meiyu period. These results provide an observational benchmark for understanding cloud vertical heterogeneity in this subtropical monsoon region and offer valuable constraints for evaluating cloud parameterizations in numerical models and improving satellite‑based precipitation retrieval algorithms.

Key words: satellite detection, CloudSat, vertical structure, cloud, Yangtze-Huaihe region

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