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Environmental Impact Factor Analysis And Forecast Modeling Of Typhoon Intensity Abrupt Changes In The South China Sea

Posted on:2022-08-24Degree:MasterType:Thesis
Country:ChinaCandidate:Y ChenFull Text:PDF
GTID:2510306539950189Subject:Science of meteorology
Abstract/Summary:PDF Full Text Request
Based on the best track data of tropical cyclones(TC)released by Shanghai Typhoon Research Institute of China Meteorological Administration,FNL global analysis data released by National Center for environmental prediction of the United States,and sea surface temperature data set released by National Oceanic and Atmospheric Administration of the United States from 2000 to 2018,this paper synthetically analyzes the top 24 factors of TC rapid enhancement(RI)and rapid weakening(RW)in the South China Sea.The key environmental factors affecting the TC RI and RW in the South China Sea are found out respectively according to the time evolution characteristics within 10 hours,and the TC RI and RW events in the South China Sea are predicted by the model.(1)It is found that the RI TC in the South China Sea has the following characteristics:from the first 24 hours to the start-up time,the positive relative vorticity of 1000–300 hPa increases continuously;the divergence and convergence of the boundary layer of 200–150hPa increase continuously;the deep wind shear between the upper level and 850 hPa decreases;the ambient moist layer is deep,especially the air in the southeast of the middle and upper level center is moist;the sea surface temperature is slightly higher;the departure from the maximum potential intensity(POT)on the west and north side of the center exceeds40 ms-124 h before the start-up.Further analysis shows that the strong divergence in the upper troposphere and the strong convergence in the boundary layer,the weak vertical wind shear(VWS)in the deep environment,the higher sea surface temperature in the north of the center;the larger POT in the west and north of the center,the high relative humidity in the middle and upper troposphere and the fast migration speed provide favorable environmental conditions for the occurrence of TC RI in the South China Sea,which are the key factors affecting TCRI in the South China Sea,and their importance to RI decreases in turn.(2)It is found that the RW TC in the South China Sea has the following characteristics:the upper layer divergence and the lower layer convergence in the first 24 hours are too strong and show a rapid weakening trend with time;the deep shear between the upper layer and 850hPa has been greater than 11 ms-1;the atmosphere in the middle and upper layers is dry,and the POT values in the west and north sides of the center are negative;the meridional gradient of sea surface temperature is large,and the sea surface temperature in the north side is lower than 26°C.Further analysis shows that the negative POT in the north of the center in the first12 hours,the lower sea surface temperature in the north of the center in the first 12 hours,the stronger VWS between the middle and upper levels and the lower relative humidity in the middle and upper levels are the key environmental factors affecting the TC RW in the South China Sea,and their importance to the RW decreases in turn.(3)Based on the environmental factors calculated by significant difference regions,logistic regression model and linear discriminant analysis model were established for TC RI and RW events in the South China Sea.By analyzing the prediction results of RI,the error of independent test year of logistic regression model and linear discriminant analysis model is less than that of modeling year,and the prediction effect of logistic regression model is better than that of linear discriminant analysis model;by analyzing the prediction results of RW,although the logistic regression model performs well in modeling year,the independent test year performs poorly;while linear discriminant analysis model has high accuracy and high false alarm rate.
Keywords/Search Tags:typhoons in the South China Sea, intensity catastrophe, environmental impact factors, statistical forecast mode
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