Articles | Volume 13, issue 5
https://doi.org/10.5194/amt-13-2257-2020
https://doi.org/10.5194/amt-13-2257-2020
Research article
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11 May 2020
Research article | Highlight paper |  | 11 May 2020

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

Chenxi Wang, Steven Platnick, Kerry Meyer, Zhibo Zhang, and Yaping Zhou

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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Chenxi Wang on behalf of the Authors (21 Feb 2020)  Manuscript 
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
AR by Chenxi Wang on behalf of the Authors (13 Apr 2020)
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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.