Articles | Volume 17, issue 10
https://doi.org/10.5194/amt-17-3323-2024
https://doi.org/10.5194/amt-17-3323-2024
Research article
 | 
31 May 2024
Research article |  | 31 May 2024

Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network

Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.

Viewed

Total article views: 5,171 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,968 1,964 239 5,171 590 276 367
  • HTML: 2,968
  • PDF: 1,964
  • XML: 239
  • Total: 5,171
  • Supplement: 590
  • BibTeX: 276
  • EndNote: 367
Views and downloads (calculated since 05 Dec 2023)
Cumulative views and downloads (calculated since 05 Dec 2023)

Viewed (geographical distribution)

Total article views: 5,171 (including HTML, PDF, and XML) Thereof 5,082 with geography defined and 89 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 28 Oct 2026
Download
Short summary
Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Share
Altmetrics
Final-revised paper
Preprint