Articles | Volume 15, issue 9
Atmos. Meas. Tech., 15, 3031–3051, 2022
https://doi.org/10.5194/amt-15-3031-2022
Atmos. Meas. Tech., 15, 3031–3051, 2022
https://doi.org/10.5194/amt-15-3031-2022
Research article
17 May 2022
Research article | 17 May 2022

Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data

Daniel Robbins 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-2021-422', Antti Lipponen, 10 Feb 2022
  • RC2: 'Comment on amt-2021-422', Anonymous Referee #2, 17 Feb 2022
  • RC3: 'Comment on amt-2021-422', Anonymous Referee #3, 21 Feb 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Daniel Robbins on behalf of the Authors (04 Apr 2022)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (05 Apr 2022) by Thomas Eck
RR by Antti Lipponen (20 Apr 2022)
RR by Anonymous Referee #2 (20 Apr 2022)
ED: Publish subject to technical corrections (20 Apr 2022) by Thomas Eck
AR by Daniel Robbins on behalf of the Authors (27 Apr 2022)  Author's response    Manuscript
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Short summary
A neural network (NN)-based cloud mask for a geostationary satellite instrument, AHI, is developed using collocated data and is better at not classifying thick aerosols as clouds versus the Japanese Meteorological Association and the Bureau of Meteorology masks, identifying 1.13 and 1.29 times as many non-cloud pixels than each mask, respectively. The improvement during the day likely comes from including the shortest wavelength bands from AHI in the NN mask, which the other masks do not use.