Identification of tropical cyclone centre based on satellite images via deep learning techniques

A tropical cyclone (TC) is a highly destructive natural disaster. Accurate identification of key parameters of TCs is prerequisite for most TC-related research and practices. The centre position is one of TC's basic parameters. However, comparison of TC best track data released by different meteorological institutes usually indicates a noticeable discrepancy for this parameter among varied data sources. In this study, efforts are made towards identifying the centre location of TCs via deep learning techniques, based on TC satellite cloud images (SCIs). Six deep learning models are analysed and compared. YOLOv4 model achieved a confidence of 99.84%, which is better than other models. In addition, we further explore the factors affecting the positioning accuracy of the YOLOv4 model and its application to the location identification of multiple TCs and the tracking of individual TCs. Results demonstrate that the YOLOv4 model has a probability exceeding 99% for identifying multiple TC locations and also performs well for single TC tracking.

Automated summary

This study utilizes deep learning techniques to identify the center location of tropical cyclones (TCs) based on TC satellite cloud images. Comparing six deep learning models, the YOLOv4 model achieved the highest confidence score and demonstrated excellent performance in identifying multiple TC locations and tracking individual TCs.