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
 | 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: 5,706 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,630 1,001 75 5,706 83 85
  • HTML: 4,630
  • PDF: 1,001
  • XML: 75
  • Total: 5,706
  • BibTeX: 83
  • EndNote: 85
Views and downloads (calculated since 20 Nov 2019)
Cumulative views and downloads (calculated since 20 Nov 2019)

Viewed (geographical distribution)

Total article views: 5,706 (including HTML, PDF, and XML) Thereof 5,211 with geography defined and 495 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 15 Jun 2024
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.