Articles | Volume 15, issue 6
Atmos. Meas. Tech., 15, 1829–1848, 2022
Atmos. Meas. Tech., 15, 1829–1848, 2022
Research article
25 Mar 2022
Research article | 25 Mar 2022

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

Biao Tong et al.

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Cited articles

Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V. N.: Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks, in: 2018 IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, USA, 12–15 March 2018, IEEE WACV, 839–847,, 2018. 
Chen, R., Zhang, W., and Wang, X.: Machine learning in tropical cyclone forecast modeling: A review, Atmosphere-Basel, 11, 676,, 2020. 
Di Vittorio, A. V. and Emery, W. J.: An automated, dynamic threshold cloud-masking algorithm for daytime AVHRR images over land, IEEE T. Geosci. Remote., 40, 1682–1694,, 2002. 
Dvorak, V. F.: Tropical cyclone intensity analysis using satellite data, in: NOAA Technical Report NESDIS, 11, US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, Washington, DC, USA, pp. 1–47, 1984. 
Geng, X. Q., Li, Z. W., and Yang, X. F.: Tropical cyclone auto-identification from stationary satellite imagery, Journal of Image and Graphics, 19, 964–970,, 2014. 
Short summary
In recent years, there has been numerous research on tropical cyclone (TC) observation based on satellite cloud images (SCIs), but most methods are limited by low efficiency and subjectivity. To overcome subjectivity and improve efficiency of traditional methods, this paper uses deep learning technology to do further research on fingerprint identification of TCs. Results provide an automatic and objective method to distinguish TCs from SCIs and are convenient for subsequent research.