Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 10
Articles | Volume 17, issue 10
https://doi.org/10.5194/amt-17-3323-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-3323-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 10
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: 899 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
694 161 44 899 64 35 32
  • HTML: 694
  • PDF: 161
  • XML: 44
  • Total: 899
  • Supplement: 64
  • BibTeX: 35
  • EndNote: 32
Views and downloads (calculated since 05 Dec 2023)
Cumulative views and downloads (calculated since 05 Dec 2023)

Viewed (geographical distribution)

Total article views: 899 (including HTML, PDF, and XML) Thereof 887 with geography defined and 12 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
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.
Read more