Articles | Volume 24, issue 24
https://doi.org/10.5194/acp-24-14239-2024
https://doi.org/10.5194/acp-24-14239-2024
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

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Applicability of physics-based and machine-learning-based algorithms of 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
EGUsphere, https://doi.org/10.5194/egusphere-2023-2843,https://doi.org/10.5194/egusphere-2023-2843, 2023
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Cited articles

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Baum, B., Menzel, W. P., Frey, R., Tobin, D., Holz, R., and Ackerman, S.: MODIS cloud top property refinements for Collection 6, J. Appl. Meteorol. Clim., 51, 1145–1163, https://doi.org/10.1175/JAMC-D-11-0203.1, 2012. 
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai, Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y., Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama, H., Uesawa, D., Yokota, H., and Yoshida, R.: An introduction to Himawari-8/9—Japan's new-generation geostationary meteorological satellites, J. Meteorol. Soc. Jpn., Ser. II, 94, 151–183, https://doi.org/10.2151/jmsj.2016-009, 2016. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. 
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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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