Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 24, issue 24
Articles | Volume 24, issue 24
https://doi.org/10.5194/acp-24-14239-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-14239-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 24
Technical note
 | 
20 Dec 2024
Technical note |  | 20 Dec 2024

Technical note: Applicability of physics-based and machine-learning-based algorithms of a geostationary satellite in retrieving the diurnal cycle of cloud base height

Mengyuan Wang, Min Min, Jun Li, Han Lin, Yongen Liang, Binlong Chen, Zhigang Yao, Na Xu, and Miao Zhang

Viewed

Total article views: 1,564 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,276 241 47 1,564 70 42 58
  • HTML: 1,276
  • PDF: 241
  • XML: 47
  • Total: 1,564
  • Supplement: 70
  • BibTeX: 42
  • EndNote: 58
Views and downloads (calculated since 17 Jun 2024)
Cumulative views and downloads (calculated since 17 Jun 2024)

Viewed (geographical distribution)

Total article views: 1,564 (including HTML, PDF, and XML) Thereof 1,564 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Oct 2025
Download
Short summary
Although machine learning technology is advanced in the field of satellite remote sensing, the physical inversion algorithm based on cloud base height can better capture the daily variation in the characteristics of the cloud base.
Read more
Share
Altmetrics
Final-revised paper
Preprint