Articles | Volume 14, issue 10
Atmos. Meas. Tech., 14, 6695–6710, 2021
https://doi.org/10.5194/amt-14-6695-2021
Atmos. Meas. Tech., 14, 6695–6710, 2021
https://doi.org/10.5194/amt-14-6695-2021

Research article 18 Oct 2021

Research article | 18 Oct 2021

Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning

Bu-Yo Kim 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-2021-179', Anonymous Referee #1, 15 Aug 2021
    • AC1: 'Response to Reviewer #1', Bu-Yo Kim, 02 Sep 2021
  • RC2: 'Comment on amt-2021-179', Anonymous Referee #2, 17 Aug 2021
    • AC2: 'Response to Reviewer #2', Bu-Yo Kim, 02 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Bu-Yo Kim on behalf of the Authors (02 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (03 Sep 2021) by Dmitry Efremenko
RR by Anonymous Referee #1 (04 Sep 2021)
RR by Anonymous Referee #2 (20 Sep 2021)
ED: Publish as is (20 Sep 2021) by Dmitry Efremenko
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Short summary
This study investigates a method for 24 h cloud cover calculation using a camera-based imager and supervised machine learning methods. The cloud cover is calculated by learning the statistical characteristics of the ratio, difference, and luminance using RGB channels of the image with a machine learning model. The proposed approach is suitable for nowcasting because it has higher learning and prediction speed than the method in which the many pixels of a 2D image are learned.