Articles | Volume 13, issue 5
Research article
 | Highlight paper
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


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
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.