Deep learning in extracting tropical cyclone intensity and wind radius information from satellite infrared images—A review

Abstract

Tropical cyclones (TCs) seriously endanger human life and the safety of property. Real-time monitoring of TCs has been one of the focal points in meteorological studies. With the development of space technology and sensor technology, satellite remote sensing has become the main means of monitoring TCs. Furthermore, with its superior data mining capability, deep learning has shown advantages over traditional physical or statistical-based algorithms in the geosciences. As a result, more deep-learning algorithms are being developed and applied to extract TC information. This paper systematically reviews the deep-learning frameworks used for TC information extraction and then gives two typical applications of deep-learning models for TC intensity and wind radius estimation. In addition, the authors present an outlook on the future perspectives of deep learning in TC information extraction.

摘要

热带气旋 (TC) 严重危害人类生命和财产安全, TC的实时监测一直是研究热点, 随着空间和传感器技术的发展, 卫星遥感已成为监测TC的主要手段. 此外, 深度学习具有卓越的数据挖掘能力, 在地球科学中的表现优于基于物理或统计的算法, 越来越多的深度学习算法被开发和应用于TC信息的提取, 本文系统地回顾了深度学习在TC信息提取中的应用, 并给出了深度学习模型在TC强度和风圈半径提取中的应用. 此外, 本文还展望了深度学习在TC信息提取中的应用前景.