Articles | Volume 15, issue 17
https://doi.org/10.5194/amt-15-5141-2022
https://doi.org/10.5194/amt-15-5141-2022
Research article
 | 
13 Sep 2022
Research article |  | 13 Sep 2022

Ice crystal images from optical array probes: classification with convolutional neural networks

Louis Jaffeux, Alfons Schwarzenböck, Pierre Coutris, and Christophe Duroure

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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-2022-72', Anonymous Referee #1, 03 Jun 2022
    • AC1: 'Reply on RC1', Louis Jaffeux, 29 Jun 2022
  • RC2: 'Comment on amt-2022-72', Anonymous Referee #2, 16 Jun 2022
    • AC2: 'Reply on RC2', Louis Jaffeux, 29 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Louis Jaffeux on behalf of the Authors (08 Jul 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (08 Jul 2022) by Maximilian Maahn
RR by Anonymous Referee #1 (09 Jul 2022)
RR by Anonymous Referee #2 (23 Jul 2022)
ED: Publish subject to technical corrections (23 Jul 2022) by Maximilian Maahn
AR by Louis Jaffeux on behalf of the Authors (27 Jul 2022)  Author's response    Manuscript
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Short summary
Optical array probes are instruments used aboard research aircraft to capture 2D images of ice or water particles in clouds. This study presents a new tool using innovative machine learning, called convolutional neural networks, designed to identify the shape of imaged ice particles for two of these imagers, namely 2DS and PIP. Such a tool will be a very strong asset for understanding cloud microphysics. Beyond traditional evaluation metrics, human inspections were performed of unknown data.