Articles | Volume 15, issue 18
Development and technical paper
16 Sep 2022
Development and technical paper |  | 16 Sep 2022

Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset

Sébastien Gardoll and Olivier Boucher


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-147', Anonymous Referee #1, 18 May 2022
    • AC1: 'Reply on RC1', Sébastien Gardoll, 22 Jul 2022
  • RC2: 'Comment on egusphere-2022-147', Anonymous Referee #2, 20 May 2022
    • AC2: 'Reply on RC2', Sébastien Gardoll, 22 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Sébastien Gardoll on behalf of the Authors (22 Jul 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (25 Jul 2022) by Po-Lun Ma
RR by Anonymous Referee #2 (09 Aug 2022)
RR by Anonymous Referee #1 (16 Aug 2022)
ED: Publish subject to technical corrections (18 Aug 2022) by Po-Lun Ma
AR by Sébastien Gardoll on behalf of the Authors (31 Aug 2022)  Author's response   Manuscript 
Short summary
Tropical cyclones (TCs) are one of the most devastating natural disasters, which justifies monitoring and prediction in the context of a changing climate. In this study, we have adapted and tested a convolutional neural network (CNN) for the classification of reanalysis outputs (ERA5 and MERRA-2 labeled by HURDAT2) according to the presence or absence of TCs. We tested the impact of interpolation and of "mixing and matching" the training and test sets on the performance of the CNN.