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Optimization And Verification Of The Statistical Dynamics-full Track Synthesis Method For Typhoon Hazard Analysis

Posted on:2021-05-17Degree:MasterType:Thesis
Country:ChinaCandidate:T T WuFull Text:PDF
GTID:2370330611998063Subject:Civil engineering
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Located on the west coast of the western North Pacific,China is one of the countries most affected by tropical cyclones in the world.From 1963 to 2019,the loss caused by tropical cyclones in China accounted for more than 50% of the loss of natural disasters,which poses a major threat to the safety of people's lives and property.In order to make the policy of preventing wind and disaster scientifically,the government departments' disaster prevention and mitigation planning,insurance and reinsurance all need to carry on the quantitative analysis to the typhoon hazard and risk.Due to the very limited history of typhoons,a common method of estimating the risk of typhoon disaster is to simulate a large number of typhoon samples based on the statistical data of historical observations of tropical cyclones to expand typhoon historical data.The full track synthetic tropical cyclone model can synthesize a large number of samples of typhoon path and intensity from generation to extinction,and can analyze risk of typhoon disaster in a large area,which is the most potential typhoon random model to assess typhoon hazard.In this paper,the model is further optimized and verified on the basis of statistical dynamic full track typhoon synthesis model,and the typhoon activity in the western North Pacific is simulated.The main contents are as follows:Firstly,a tropical cyclone generation model based on logistic regression is established,which takes tropical cyclones in the western North Pacific as the research object.This model can directly predict the occurrence probability of tropical cyclone by considering the environmental factors that affect the marine environment of tropical cyclone.By comparing with historical records and other generation models considering physical mechanism,the simulation results of tropical cyclone generation model based on logistic regression are consistent with the historical distribution,which indicates that the tropical cyclone generation model established in this paper can better simulate the location and number of tropical cyclones.In this paper,a tropical cyclone intensity model is established based on the logistic model.Incorporate the influence of sea-atmosphere environmental factors into the intensity growth rate in the logistic model,and the intensity samples of tropical cyclones were divided into five categories according to the typhoon intensity grade to establish five-block intensity growth intensity models respectively.By comparing the mean value and coefficient of variation of the historical typhoon in the western North Pacific and the typhoons that landed on the each section of Chinese coast,it can be found that the average intensity simulated by the intensity model based on logistic model developed in this paper is consistent with the historical average intensity,but the simulated intensity variation coefficient is lower than that of history.It indicates that the intensity of the simulation is closer to the average,which may be related to the model not considering the error term.Finally,this paper compares the 100,000 typhoon event sets of the western North Pacific simulated by the full track synthesis typhoon model with the best path data of China Meteorological Administration(CMA,1949-2017)and the Joint Typhoon Warning Center(JTWC,1949-2017),and found that the two best path data sets are very different in recording typhoon intensity,especially the intensity attenuation of landing typhoon in China.Three improved schemes are proposed for the model,considering the influence of error term when simulating typhoon intensity,and the parameter of maximum wind speed radius after typhoon landing,and the regression model of maximum wind speed radius should be established on land and sea respectively.
Keywords/Search Tags:tropical cyclone, typhoon, typhoon hazard, logistic regression, logistic model
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