Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 5
Articles | Volume 13, issue 5
https://doi.org/10.5194/amt-13-2257-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-2257-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 7,201 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
5,804 1,267 130 7,201 148 172
  • HTML: 5,804
  • PDF: 1,267
  • XML: 130
  • Total: 7,201
  • BibTeX: 148
  • EndNote: 172
Views and downloads (calculated since 20 Nov 2019)
Cumulative views and downloads (calculated since 20 Nov 2019)

Viewed (geographical distribution)

Total article views: 7,201 (including HTML, PDF, and XML) Thereof 6,673 with geography defined and 528 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
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.
Read more
Share