Quantitative Loss Assessment of Typhoons Based on the Chinese Typhoon Disaster Model"Eye of Wind"
CHEN Sining
ZHAO Yanxia
GONG Ting
ZHAO Dajun
ZHANG Yi
SUN Qing
YANG Caifeng
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.
Keywords:catastrophe modelloss assessmentcatastrophe insurancerisk reduction
Publication Date:2025-08-30
Online Publishing Date:2025-09-23(First online date of this platform, not the publication date of the document)
Pages:8( 468-475 )
