Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 16, issue 21
Articles | Volume 16, issue 21
https://doi.org/10.5194/amt-16-5403-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-5403-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 21
Research article
 | 
13 Nov 2023
Research article |  | 13 Nov 2023

Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network

Bu-Yo Kim, Joo Wan Cha, and Yong Hee Lee

Viewed

Total article views: 927 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
668 200 59 927 48 49
  • HTML: 668
  • PDF: 200
  • XML: 59
  • Total: 927
  • BibTeX: 48
  • EndNote: 49
Views and downloads (calculated since 24 Jul 2023)
Cumulative views and downloads (calculated since 24 Jul 2023)

Viewed (geographical distribution)

Total article views: 927 (including HTML, PDF, and XML) Thereof 889 with geography defined and 38 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
A camera-based imager and convolutional neural network (CNN) were used to estimate ground cloud cover. Image data from 2019 were used for training and validation, and those from 2020 were used for testing. The CNN model exhibited high performance, with an accuracy of 0.92, RMSE of 1.40 tenths, and 93% agreement with observed cloud cover within ±2 tenths' difference. It also outperformed satellites and ceilometers and proved to be the most suitable for ground-based cloud cover estimation.
Read more