Articles | Volume 15, issue 6
Atmos. Meas. Tech., 15, 1829–1848, 2022
https://doi.org/10.5194/amt-15-1829-2022
Atmos. Meas. Tech., 15, 1829–1848, 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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-405', Anonymous Referee #1, 31 Dec 2021
    • AC1: 'Reply on RC1', Biao Tong, 13 Jan 2022
  • RC2: 'Comment on amt-2021-405', Anonymous Referee #2, 02 Feb 2022
    • AC2: 'Reply on RC2', Biao Tong, 11 Feb 2022
  • RC3: 'Comment on amt-2021-405', Anonymous Referee #3, 11 Feb 2022
    • AC3: 'Reply on RC3', Biao Tong, 13 Feb 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Biao Tong on behalf of the Authors (15 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (16 Feb 2022) by Lars Hoffmann
AR by Biao Tong on behalf of the Authors (21 Feb 2022)  Author's response    Manuscript
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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.