Articles | Volume 15, issue 6
https://doi.org/10.5194/amt-15-1829-2022
https://doi.org/10.5194/amt-15-1829-2022
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, Xiangfei Sun, Jiyang Fu, Yuncheng He, and Pakwai Chan

Related authors

Super Typhoons Mangkhut (2018) and Saola (2023) during landfall: comparison and insights for wind engineering practice
Yujie Liu, Yuncheng He, Pakwai Chan, Aiming Liu, and Qijun Gao
EGUsphere, https://doi.org/10.5194/egusphere-2024-3223,https://doi.org/10.5194/egusphere-2024-3223, 2024
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Adjustment of 1 min rain gauge time series using co-located drop size distribution and wind speed measurements
Arianna Cauteruccio, Mattia Stagnaro, Luca G. Lanza, and Pak-Wai Chan
Atmos. Meas. Tech., 16, 4155–4163, https://doi.org/10.5194/amt-16-4155-2023,https://doi.org/10.5194/amt-16-4155-2023, 2023
Short summary
Introduction of a Trans-scale Numerical Simulation Framework Focusing on Urban Boundary Layer: WOCSS V1.0
Wei Li, Shuo Leng, Sunwei Li, Zhenzhong Hu, and Pakwai Chan
EGUsphere, https://doi.org/10.5194/egusphere-2023-482,https://doi.org/10.5194/egusphere-2023-482, 2023
Preprint archived
Short summary
Impact of the COVID-19 pandemic on the observed vertical distributions of PM2.5, NOx, and O3 from a tower in the Pearl River Delta
Lei Li, Chao Lu, Pak-Wai Chan, Zi-Juan Lan, Wen-Hai Zhang, Hong-Long Yang, and Hai-Chao Wang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-579,https://doi.org/10.5194/acp-2021-579, 2021
Revised manuscript not accepted
Short summary
Performance of post-processing algorithms for rainfall intensity using measurements from tipping-bucket rain gauges
Mattia Stagnaro, Matteo Colli, Luca Giovanni Lanza, and Pak Wai Chan
Atmos. Meas. Tech., 9, 5699–5706, https://doi.org/10.5194/amt-9-5699-2016,https://doi.org/10.5194/amt-9-5699-2016, 2016
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Global Navigation Satellite System (GNSS) radio occultation climatologies mapped by machine learning and Bayesian interpolation
Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Alain Geiger, Laura Crocetti, Gregor Moeller, Benedikt Soja, and Markus Rothacher
Atmos. Meas. Tech., 18, 57–72, https://doi.org/10.5194/amt-18-57-2025,https://doi.org/10.5194/amt-18-57-2025, 2025
Short summary
Determination of low-level temperature profiles from microwave radiometer observations during rain
Andreas Foth, Moritz Lochmann, Pablo Saavedra Garfias, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 7169–7181, https://doi.org/10.5194/amt-17-7169-2024,https://doi.org/10.5194/amt-17-7169-2024, 2024
Short summary
Aeolus lidar surface return (LSR) at 355 nm as a new Aeolus Level-2A product
Lev D. Labzovskii, Gerd-Jan van Zadelhoff, David P. Donovan, Jos de Kloe, L. Gijsbert Tilstra, Ad Stoffelen, Damien Josset, and Piet Stammes
Atmos. Meas. Tech., 17, 7183–7208, https://doi.org/10.5194/amt-17-7183-2024,https://doi.org/10.5194/amt-17-7183-2024, 2024
Short summary
Sampling the diurnal and annual cycles of the Earth's energy imbalance with constellations of satellite-borne radiometers
Thomas Hocking, Thorsten Mauritsen, and Linda Megner
Atmos. Meas. Tech., 17, 7077–7095, https://doi.org/10.5194/amt-17-7077-2024,https://doi.org/10.5194/amt-17-7077-2024, 2024
Short summary
Retrieval of top-of-atmosphere fluxes from combined EarthCARE lidar, imager, and broadband radiometer observations: the BMA-FLX product
Almudena Velázquez Blázquez, Carlos Domenech, Edward Baudrez, Nicolas Clerbaux, Carla Salas Molar, and Nils Madenach
Atmos. Meas. Tech., 17, 7007–7026, https://doi.org/10.5194/amt-17-7007-2024,https://doi.org/10.5194/amt-17-7007-2024, 2024
Short summary

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, https://doi.org/10.1109/WACV.2018.00097, 2018. 
Chen, R., Zhang, W., and Wang, X.: Machine learning in tropical cyclone forecast modeling: A review, Atmosphere-Basel, 11, 676, https://doi.org/10.3390/atmos11070676, 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, https://doi.org/10.1109/TGRS.2002.802455, 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, https://doi.org/10.11834/jig.20140618, 2014. 
Download
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.