基于GNSS观测的2023北京特大暴雨分析

doi:10.11947/j.AGCS.2025.20230548

测绘学报 ›› 2025, Vol. 54 ›› Issue (1): 14-25.doi: 10.11947/j.AGCS.2025.20230548

• 大地测量学与导航 • 上一篇

基于GNSS观测的2023北京特大暴雨分析

杨飞¹(), 汪莹莹¹, 李志才¹(), 余博尧², 武军郦³, 曹云昌⁴, 张澍²

^1.中国矿业大学（北京）地球科学与测绘工程学院，北京　100083
^2.北京迅腾智慧科技股份有限公司，北京　100029
^3.国家基础地理信息中心，北京　100830
^4.中国气象局气象探测中心，北京　100081

收稿日期:2023-11-25 修回日期:2024-12-25 发布日期:2025-02-17
通讯作者: 李志才 E-mail:yangfei@cumtb.edu.cn;zcli@cumtb.edu.cn
作者简介:杨飞（1991—），男，博士，副教授，研究方向为GNSS高精度数据处理及GNSS气象学。　E-mail：yangfei@cumtb.edu.cn
基金资助:
国家自然科学基金(42204022);中央高校基本科研业务费专项(2024ZKPYDC02)

Analysis of heavy rainstorm in Beijing in 2023 based on GNSS observations

Fei YANG¹(), Yingying WANG¹, Zhicai LI¹(), Boyao YU², Junli WU³, Yunchang CAO⁴, Shu ZHANG²

^1.College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
^2.Beijing CNTEN Smart Technology Company Limited, Beijing 100029, China
^3.National Geomatics Center of China, Beijing 100830, China
^4.Meteorological Observation Centre of CMA, Beijing 100081, China

Received:2023-11-25 Revised:2024-12-25 Published:2025-02-17
Contact: Zhicai LI E-mail:yangfei@cumtb.edu.cn;zcli@cumtb.edu.cn
About author:YANG Fei (1991—), male, PhD, associate professor, majors in GNSS high-precision data processing and GNSS meteorology.　E-mail: yangfei@cumtb.edu.cn
Supported by:
The National Natural Science Foundation of China(42204022);The Fundamental Research Funds for the Central Universities(2024ZKPYDC02)

摘要/Abstract

摘要：

2023年7月底，受台风“杜苏芮”“卡努”及地形等因素综合影响，北京及周边发生罕见的极端暴雨，造成严重的人员伤亡与经济损失；大气可降水量（PWV）作为影响降雨的主要因素之一，探究暴雨发生及发展过程与PWV的关系，对于进一步建立暴雨预警模型有重大意义。本文选取UTC 2023年7月25日至8月1日北京及周边地区34个GNSS站点、34个气象站、1个探空站点和ERA5数据，采用GAMIT 10.71反演高精度GNSS-PWV；提出了一种改进的插值算法并获取了本次极端暴雨期间北京及周边地区高时空分辨率的PWV空间数据；利用探空与ERA5数据，从多个角度对反演的PWV精度进行了详细评估；最后结合气象站降雨量数据，从时间和空间的角度分析了PWV变化与极端降雨之间的关系，并首次探讨了对流层延迟梯度和降雨走势的关系。结果表明，GNSS-PWV与RS-PWV的相关系数达到0.99，RMSE和bias约为0.52 mm和-0.52 mm；GNSS-PWV与ERA5-PWV的RMSE小于6 mm，bias范围为-4~1.5 mm；GNSS-PWV格网数据与ERA5反演的格网结果RMSE和bias均值分别为4 mm和1 mm。通过分析PWV与并址气象站降水观测的时间序列，发现PWV在此次降雨前极速上升，在降水过程中仍累积上升，降水结束后无法立刻消散的特性，这与此次降水接连受台风“杜苏芮”和“卡努”影响相关；其次，各站点处对流层延迟梯度展现出指向东北方向的一致性，这与PWV高值在空间上呈现由西南向东北的输送趋势一致，与实际降水路线相符。

关键词: GNSS气象学, 大气可降水量, 降雨量

Abstract:

By the end of July 2023, Beijing and its surrounding areas were severely impacted by an extreme rainstorm, which was the result of a combination of typhoon “Doksuri” “Khanun”, and geographic factors. The precipitable water vapor (PWV) is one of the key factors influencing rainfall, to explore its relationship with rainfall in different process of the rainstorm is of great significance for further establishment of a rainstorm warning model. In this study, 34 GNSS stations, 34 meteorological stations, 1 radiosonde station and ERA5 datasets in and around Beijing were utilized, the GNSS-PWV data with high accuracy from July 25th, 2023 to August 1st, 2023 were obtained using GAMIT 10.71. An improved interpolation algorithm has been proposed to retrieve gridded PWV data with a high spatiotemporal resolution. Then, the accuracy of the GNSS-PWV was evaluated from multiple perspectives using the radiosonde and ERA5 data as references. Finally, the relationship between the PWV variation and extreme rainfall and the relationship between tropospheric delay gradient and rainfall trend are analyzed from the perspective of time and space by the combination of the rainfall data from the meteorological stations. Results showed that the correlation coefficient between GNSS-PWV and RS-PWV was up to 0.99, the root mean square error (RMSE) and bias were about 0.52 mm and -0.52 mm, respectively. In the comparison with ERA5 data, the RMSE of GNSS-PWV is less than 6 mm and the bias range is -4~1.5 mm, the gridded PWV has a RMSE of about 4 mm and a bias about 1 mm. The spatiotemporal analysis shows that the PWV increases sharply before the occurrence of this rainstorm, keeps increasing during the rainstorm, and could not dissipate immediately after the end of the rainstorm. This phenomenon is related to the co-influence of “Doksuri” and “Khanun”. In addition, the tropospheric delay gradient at each station shows a consistent northeastward direction, which is consistent with the transport trend of PWV high value from southwest to northeast in space, and is consistent with the actual precipitation route.

Key words: GNSS meteorology, PWV, precipitation

中图分类号:

P228

杨飞, 汪莹莹, 李志才, 余博尧, 武军郦, 曹云昌, 张澍. 基于GNSS观测的2023北京特大暴雨分析[J]. 测绘学报, 2025, 54(1): 14-25.

Fei YANG, Yingying WANG, Zhicai LI, Boyao YU, Junli WU, Yunchang CAO, Shu ZHANG. Analysis of heavy rainstorm in Beijing in 2023 based on GNSS observations[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(1): 14-25.

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基于GNSS观测的2023北京特大暴雨分析

Analysis of heavy rainstorm in Beijing in 2023 based on GNSS observations

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