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.

Highlights

  • As one of the most destructive natural disasters, tropical cyclone (TC) can cause severe casualties and economic losses in Tropical Cyclones (TCs)-prone areas

  • This paper presents a study on the identification of TC fingerprint via Deep Convolutional Neural Network (DCNN) 65 techniques

  • Results from previous attempts show 355 that the fingerprint features highlighted in the heat maps of the DCNN model (Figure 3) could not be focused on TC clouds, the prediction accuracy of the model was pretty high

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Summary

Introduction

As one of the most destructive natural disasters, tropical cyclone (TC) can cause severe casualties and economic losses in TC-prone areas. Statistics show that an average of 30 TCs developing over the Northwest Pacific Ocean ever year, about one-third of which can make landfall in China, 25 resulting in an annual economic loss of $5.6 billion. With rapid development of urbanization in the coastal region of China, TC-induced disasters are expected to get even more severe. To mitigate TC-induced disasters, real-time monitoring and forecasting of TCs activity are essential. To this end, various kinds of devices and techniques have been developed and utilized, such as radiosonding balloons, weather radar, wind profilers, airborne GPS-dropsonde, aircraft-based remote sensing equipment, and ever updating numerical models for 30 weather prediction. Since the 20th century, satellite started to be used in meteorology.

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