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

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 su-perior 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 esti-mation. In addition, the authors present an outlook on the future perspectives of deep learning in TC information extraction.