Articles | Volume 13, issue 5
Atmos. Meas. Tech., 13, 2257–2277, 2020
https://doi.org/10.5194/amt-13-2257-2020
Atmos. Meas. Tech., 13, 2257–2277, 2020
https://doi.org/10.5194/amt-13-2257-2020

Research article 11 May 2020

Research article | 11 May 2020

A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations

Chenxi Wang et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (05 Mar 2020)  Author's response
ED: Referee Nomination & Report Request started (08 Apr 2020) by Sebastian Schmidt
RR by Anonymous Referee #1 (09 Apr 2020)
RR by Anonymous Referee #2 (09 Apr 2020)
ED: Publish as is (12 Apr 2020) by Sebastian Schmidt
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Short summary
A machine-learning (ML)-based approach that can be used for cloud mask and phase detection is developed. An all-day model that uses infrared (IR) observations and a daytime model that uses shortwave and IR observations from a passive instrument are trained separately for different surface types. The training datasets are selected by using reference pixel types from collocated space lidar. The ML approach is validated carefully and the overall performance is better than traditional methods.