Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 22
Articles | Volume 17, issue 22
https://doi.org/10.5194/amt-17-6697-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-6697-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 22
Research article
 | 
25 Nov 2024
Research article |  | 25 Nov 2024

Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations

Jinyi Xia and Li Guan

Viewed

Total article views: 1,009 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
731 194 84 1,009 31 36 38
  • HTML: 731
  • PDF: 194
  • XML: 84
  • Total: 1,009
  • Supplement: 31
  • BibTeX: 36
  • EndNote: 38
Views and downloads (calculated since 10 Jun 2024)
Cumulative views and downloads (calculated since 10 Jun 2024)

Viewed (geographical distribution)

Total article views: 1,009 (including HTML, PDF, and XML) Thereof 1,009 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 22 Jun 2025
Download
Short summary
This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP)  algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Read more
Share