Research on Typhoon Multi-Stage Cloud Characteristics Based on Deep Learning

Abstract

Analyzing the development and evolution characteristics of typhoons are conducive to improving typhoon monitoring and optimizing early warning models. Based on the deep learning model YOLOv5 and Himawari-8 data products, this study analyzes the movement path and cloud evolution of typhoon “Infa”. The specific conclusions of this study are as follows. (1) Based on the YOLOv5 model and brightness temperature perturbation algorithm, the central positioning of the typhoon is realized, where the Himawari-8 bright temperature image is used as the input of the model and the output of the model is the typhoon range boundary. The results show that this method was 90% accurate for monitoring ocular typhoons and 83% accurate for blind typhoons. The typhoon center location determined by the brightness temperature perturbation algorithm closely matched the CMA best-path dataset (CMA) (goodness of fit ≈0.99). (2) This study observed that as typhoons developed, cloud parameters evolved with the cloud cluster becoming denser. However, as the typhoon neared land, the cloud structure collapsed and cloud parameters decreased rapidly. (3) Changes in the typhoon cloud system were linked to topography and surface temperature. Changes in cloud optical thickness (COT) were influenced by the digital elevation model (correlation −0.18), while changes in cloud top temperature (CTT) and cloud top height (CTH) were primarily affected by surface temperature changes (correlation values: CTT −0.69, CTH −0.37). This suggests that the ocean environment supports the vertical development of typhoon clouds and precipitation. In summary, this study optimized the positioning simulation of typhoon movement paths and cloud change trends, and this is helpful for improving the early warning and response-ability of typhoons in coastal cities and for reducing the threat of typhoons to the daily life of residents in coastal areas.