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

Abstract

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, lack of concerns on the identification of TC fingerprint from SCIs have become a potential issue, since it is a prerequisite step for follow-up analyses. This paper presents a methodology which identifies TC fingerprint via Deep Convolutional Neural Network (DCNN) techniques based on SCIs of more than 200 TCs over the Northwest Pacific basin. Two DCNN models have been proposed and validated, which are able to identify the TCs from not only single-TC featured SCIs but also multi-TCs 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 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.

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

“…During the past decades, various instruments have been developed and exploited for TC studies. Among them, meteorological satellites have been receiving increasingly more concerns due to its ability of providing round‐the‐clock remote sensing detection of TCs over an incomparably vast region (Tong et al ., 2022a). Currently, satellite cloud images (SCIs) perhaps serve as the most basic data for TC positioning (Kishtawal, 2016).…”