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

Supplement

https://doi.org/10.5194/acp-24-14239-2024-supplement

Data sets

MODIS Atmosphere L2 Cloud Product (06_L2) S. Platnick et al. https://doi.org/10.5067/MODIS/MOD06_L2.061

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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.
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