Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 5
Articles | Volume 14, issue 5
https://doi.org/10.5194/amt-14-3371-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-3371-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 5
Research article
 | 
07 May 2021
Research article |  | 07 May 2021

Evaluation of Visible Infrared Imaging Radiometer Suite (VIIRS) neural network cloud detection against current operational cloud masks

Charles H. White, Andrew K. Heidinger, and Steven A. Ackerman

Viewed

Total article views: 2,017 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,351 621 45 2,017 43 50
  • HTML: 1,351
  • PDF: 621
  • XML: 45
  • Total: 2,017
  • BibTeX: 43
  • EndNote: 50
Views and downloads (calculated since 10 Nov 2020)
Cumulative views and downloads (calculated since 10 Nov 2020)

Viewed (geographical distribution)

Total article views: 2,017 (including HTML, PDF, and XML) Thereof 1,939 with geography defined and 78 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 03 May 2024
Download
Short summary
Automated detection of clouds in satellite imagery is an important practice that is useful for predicting and understanding both weather and climate. Cloud detection is often difficult at night and over cold surfaces. In this paper, we discuss how a complex statistical model (a neural network) can more accurately detect clouds compared to currently used approaches. Overall, our results suggest that our approach could result in more reliable assessments of global cloud cover.
Read more