Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images

Abstract

Tropical cyclones (TCs) are one of the most destructive natural disasters. For the prevention and mitigation of TC-induced disasters, real-time monitoring and prediction of TCs is essential. At present, satellite cloud images (SCIs) are utilized widely as a basic data source for such studies. Although great achievements have been made in this field, there is a lack of concern about on the identification of TC fingerprints from SCIs, which is usually involved as a prerequisite step for follow-up analyses. This paper presents a methodology which identifies TC fingerprints via deep convolutional neural network (DCNN) techniques based on SCIs of more than 200 TCs over the northwestern Pacific basin. In total, two DCNN models have been proposed and validated, which are able to identify the TCs from not only single TC-featured SCIs but also multiple TC-featured SCIs. Results show that both models can reach 96% of identification accuracy. As the TC intensity strengthens, the accuracy becomes better. To explore how these models work, heat maps are further extracted and analyzed. Results show that all the fingerprint features are focused on clouds during the testing process. For the majority of the TC images, the cloud features in TC's main parts, i.e., eye, eyewall, and primary rainbands, are most emphasized, reflecting a consistent pattern with the subjective method.