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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Latest update: 15 May 2021
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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.