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.
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.
the Creative Commons Attribution 4.0 License.
Technical note
| 
20 Dec 2024
Technical note |
| 20 Dec 2024
| 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
School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Zhuhai 519082, China
School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Zhuhai 519082, China
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites and Innovation Center for FengYun Meteorological Satellite (FYSIC), National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Han Lin
Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National and Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China
Yongen Liang
School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Zhuhai 519082, China
Binlong Chen
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites and Innovation Center for FengYun Meteorological Satellite (FYSIC), National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Zhigang Yao
Beijing Institute of Applied Meteorology, Beijing 100029, China
Na Xu
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites and Innovation Center for FengYun Meteorological Satellite (FYSIC), National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Miao Zhang
Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites and Innovation Center for FengYun Meteorological Satellite (FYSIC), National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
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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.
Although machine learning technology is advanced in the field of satellite remote sensing, the...
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