Prediction of intensity and scale variation trend of tropical cyclones combined the quantile regression with grey power model

Abstract

Global climate change has significantly altered the number and intensity of tropical cyclones in the Northwest Pacific Ocean, resulting in substantial losses for both residents and the economy in the southeast coastal regions of China, as well as affecting the operating efficiency and safety of offshore wind farms. Therefore, quantitatively analyzing the temporal characteristics of tropical cyclones and precisely predicting their trend is crucial for mitigating disasters in coastal countries. This paper analyzes the interannual variation characteristics of tropical cyclones making landfall in China from 1980 to 2022 based on the quantile regression method. Grey power model was also used to predict the quantile regression curves of key features of TCs making landfall in China in the next five normal, El Niño and La Niña years, and to speculate on the future changes of tropical cyclones in the northwestern Pacific Ocean with statistical data. The results show that the interannual pattern of maximum wind speeds of tropical TCs making landfall in China under the 0.1–0.9 quantile is significantly affected by the El Niño-Southern Oscillation (ENSO), and the maximum wind speeds of tropical cyclones in normal, El Niño, and La Niña years are all slightly increased and accompanied by an increase in the duration. The location of the landfalling TCs in normal and El Niño years is moving northeastward, and the latitude of the landfalling TCs in La Niña years is approaching 16 ° N. The predicted wind speeds of the TCs in normal and El Niño years are also slightly increased with the increase of the duration. From the quantile regression curves predicted using the gray power model, El Niño years will bring more extreme weather. Tropical cyclones making landfall in normal and El Niño years may decrease in the southern part of China and increase in the northern part, which may be related to the expansion of TCs due to global warming. The results of this research can provide useful references for climate change research, disaster prevention and mitigation, and related policy making in China.