Exploring the Study of Tropical Cyclone Track Prediction based on Remote Sensing Technology

A typhoon is a meteorological disaster that causes significant harm to human society, and the frequent occurrence of typhoons in recent years has caused significant impacts and damage to the lives of residents in coastal areas. Against this background, this paper addresses the shortcomings of traditional typhoon monitoring methods and proposes three main observation tools: Multisource data fusion, Cyclone Global Navigation Satellite system (CYGNSS) monitoring of sea surface fragmentation, and the MFDLNet deep learning method that combines satellite microwave data. These methods are able to continuously and stably observe the surface climate and reduce the influence of weather on their observation by continuous monitoring and utilizing the advantages of remote sensing technology, thus making up for the shortcomings of traditional observation methods and improving the accuracy of ropical cyclone track prediction. This paper provides an outlook on the future development of typhoon path prediction technology, aiming to reduce the harm of typhoon disasters to human beings, and reduce casualties and economic losses.