基于中国台风巨灾模型“风·眼”的台风定量损失评估

陈思宁; 赵艳霞; 宫婷; 赵大军; 张祎; 孙擎; 杨彩凤

doi:10.16032/j.issn.1004-4965.2025.040

基于中国台风巨灾模型“风·眼”的台风定量损失评估

doi: 10.16032/j.issn.1004-4965.2025.040

陈思宁¹,
赵艳霞^2, ,,
宫婷³,
赵大军¹,
张祎¹,
孙擎¹,
杨彩凤⁴

1.
中国气象科学研究院灾害天气科学与技术全国重点实验室，北京 100081
2.
复旦大学大气与海洋科学系/大气科学研究院，上海 200438
3.
中再巨灾风险管理股份有限公司，北京 100071
4.
北京万云科技开发有限公司，北京 100081

基金项目:

国家重点研发计划课题 2023YFC3008503

中国气象局“金融气象”重点创新团队项目 CMA2024ZD03

中国气象局创新发展专项 CXFZ2025J100

中国气象服务协会气象科技创新平台项目 CMSA2024MB014

详细信息

通讯作者:
赵艳霞，女，黑龙江省人，研究员，主要从事气象风险与保险、金融气象指数研究。E-mail：zhaoyanxia@cma.gov.cn

中图分类号: P49
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出版历程
- 收稿日期: 2024-03-07
- 修回日期: 2025-01-02
- 网络出版日期: 2025-09-09
- 刊出日期: 2025-08-20

Quantitative Loss Assessment of Typhoons Based on the Chinese Typhoon Disaster Model "Eye of Wind"

1.
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, Beijing 100081, China
2.
Department of Atmospheric and Oceanic Sciences/Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
3.
China Re Catastrophe Risk Management Company LTD., Beijing 100071, China
4.
Beijing WANYUN Science & Technology Development Company LTD., Beijing 100081, China

摘要

摘要: 针对我国台风巨灾保险发展缓慢、风险量化能力不足以及缺乏用于开展定量化评估巨灾损失的先进模型等突出问题，中再巨灾风险管理股份有限公司联合中国气象科学研究院等多家专业研究机构，成功研发了具有自主知识产权且面向保险行业的中国台风巨灾模型——“风·眼”。以2023年发生的两个典型台风“苏拉”和“卡努”为例，基于“风·眼”系统，并结合不同台风预报路径情景，对两次台风造成的潜在经济损失开展了定量化的预评估工作。据测算，台风“苏拉”对广东省、福建省、广西壮族自治区、江西省局部地区造成的房屋、居民家庭财产、工矿企业以及公益设施类损失约为15~67亿元。台风“卡努”对辽宁省、吉林省和黑龙江省局部地区造成的同类综合损失约为0.5~5.0亿元。通过与实际灾害损失情况对比分析，结果显示该模型表现良好。该模型将为台风巨灾风险的精准识别、量化评估、精算定价、业务组合优化、风险累积控制和再保方案设计等核心环节提供关键技术支撑，助力提高我国保险行业对台风巨灾风险的综合管理能力与水平。
- 巨灾模型 /
- 损失评估 /
- 巨灾保险 /
- 风险减量
Abstract: To address the slow development of typhoon catastrophe insurance in China, insufficient risk quantification abilities, and lack of sophisticated catastrophe models for the quantitative catastrophic loss assessment, China Re Catastrophe Risk Management Company, in collaboration with the Chinese Academy of Meteorological Sciences and other professional research institutions, has successfully developed a real-time typhoon loss assessment system called "Eye of Wind". This typhoon catastrophe model, engineered for the insurance industry, is based on independent intellectual property rights. Utilizing the "Eye of Wind" system across different typhoon forecast path scenarios, this study conducts a quantitative pre-assessment of potential economic losses from two representative typhoons in 2023, "Saola" and "Khanun". According to the model's estimation, Typhoon "Saola" would cause comprehensive property damage losses of approximately 1.5-6.7 billion CNY in some areas of Guangdong Province, Fujian Province, Guangxi Zhuang Autonomous Region, and Jiangxi Province, including residential, industrial and mining enterprises, and public infrastructure losses. For Typhoon "Khanun", the model predicts it would cause 50-500 million CNY in similar aspects of losses in affected regions of Liaoning Province, Jilin Province, and Heilongjiang Province. Compared with actual conditions, the results show that the "Eye of Wind" model performs well in simulating the disaster losses. This model will provide crucial technical support for the typhoon catastrophe identification, quantitative assessment, actuarial pricing, portfolio optimization, risk accumulation control, and reinsurance structuring, significantly enhancing China's insurance industry in typhoon catastrophe risk management.
- catastrophe model /
- loss assessment /
- catastrophe insurance /
- risk reduction

HTML全文

图 1 “风·眼”平台设计技术路线

下载: 全尺寸图片幻灯片

图 2 “风·眼”平台界面图

下载: 全尺寸图片幻灯片

图 3 情景一模拟的台风“苏拉”洪涝分布图（a）以及损失风险分布图（b）

下载: 全尺寸图片幻灯片

图 4 情景一模拟的台风“卡努”洪涝分布图（a）和损失风险图（b）

下载: 全尺寸图片幻灯片

表 1 实时台风损失预估的五种情景

分析情景	情景说明
情景一	采用中国气象局（CMA）台风预报路径模拟损失
情景二	采用日本气象厅（JMA）台风预报路径模拟损失
情景三	采用美国联合台风警报中心（JTWC）台风预报路径模拟损失
情景四	考虑预报误差，在CMA台风预报路径基础上，加扰动强度上限模拟损失
情景五	考虑预报误差，在CMA台风预报路径基础上，加扰动强度下限模拟损失

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参考文献(20)

[1]	唐一宁, 冶磊, 李煜斌, 等. 台风"山竹"降水非对称特征及其与环境流场的相互作用研究[J]. 热带气象学报, 2023, 39(5): 774-784.
[2]	张晓宇, 韦波, 杨昊宇, 等. 基于GIS的广东省台风灾害风险性评价[J]. 热带气象学报, 2018, 34 (6): 783-790.
[3]	雷小途, 陈佩燕, 杨玉华, 等. 中国台风灾情特征及其灾害客观评估方法[J]. 气象学报, 2009, 67(5): 875-883.
[4]	王秀荣, 张立生, 李维邦. 台风灾害综合等级评判模型改进及应用分析[J]. 气象, 2018, 44 (2): 304-312.
[5]	LU Y, YIN J, CHEN P, et al. A machine learning-based parameterized tropical cyclone precipitation model[J]. Int J Disaster Risk Sci, 2024, 15: 972-985.
[6]	WANG Y J, WEN S S, LI X C, et al. Spatiotemporal distributions of influential tropical cyclones and associated economic losses in China in 1984-2015[J]. Nat Hazard, 2016, 84(3): 2 009-2 030.
[7]	FISCHER T, SU B D, WEN S S. Spatio-temporal analysis of economic losses from tropical cyclones in affected provinces of China for the last 30 years (1984—2013)[J]. Nat Hazard Rev, 2015, 16(4): 04015010.
[8]	WANG H, XU M, ONYEJURUWA A, et al. Tropical cyclone damages in Mainland China over 2005—2016: losses analysis and implications[J]. Environ Dev Sustain, 2019, 21(6): 3 077-3 092.
[9]	CHEN W, LU Y, SUN S, et al. Hazard footprint-based normalization of economic losses from tropical cyclones in China during 1983—2015 [J]. Int J Disaster Risk Sci, 2018, 9(2): 195-206.
[10]	GROSSI P, KUNREUTHER H. Catastrophe modeling: a new approach to managing risk[M]. Springer US, 2005.
[11]	VICKERY P J, MASTERS F J, POWELL M D, et al. Hurricane hazard modeling: The past, present, and future[J]. J Wind Eng Ind Aerodyn, 2009, 97(7-8): 392-405.
[12]	YING M, ZHANG W, YU H, et al. An overview of the China Meteorological Administration tropical cyclone database[J]. J Atmos Oceanic Technol, 2014, 31(2): 287-301.
[13]	LU X Q, YU H, YING M, et al. Western North Pacific tropical cyclone database created by the China Meteorological Administration[J]. Adv Atmos Sci, 2021, 38(4): 690-699.
[14]	WU Z L, MA T F, JIANG H, et al. Multi-scale seismic hazard and risk in the China mainland with implication for the preparedness, mitigation, and management of earthquake disasters: An overview[J]. Int J Disast Risk Re, 2013, 4: 21-33.
[15]	WU Z L, MA T F. 2014. The 2008 Wenchuan, China, earthquake[M]//ISMAIL-ZADEH A, URRUTIA FUCUGAUCHI J, KIJKO A, TAKEUCHI K, ZALIAPIN I. In extreme natural hazards, disaster risks and societal implications. Cambridge: Cambridge University Press, 2014: 301-309.
[16]	VICKERY P J, LIN J, SKERLJ P F, et al. HAZUS-MH hurricane model methodology. Ⅰ: Hurricane hazard, terrain, and wind load modeling[J]. Nat Hazards Rev, 2006, 7(2): 82-93.
[17]	丁元昊, 马腾飞. 地震指数: 保险创新与实践[J]. 金融博览, 2016(17): 15-16.
[18]	海娇, 牟青洋, 叶涛, 等. 浙江省农房自然灾害保险费率厘定研究[J]. 灾害学, 2021, (4): 67-73.
[19]	AYRES R U, KNEESE A V. Production, consumption and externalities[J]. Am Econ Rev, 1969, 59(3): 282-297.
[20]	中国巨灾模型研发团队. "风眼"台风实时损失评估平台[R]. 中国巨灾风险管理股份有限公司, 2022.