Articles | Volume 17, issue 12
https://doi.org/10.5194/amt-17-3765-2024
© 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/amt-17-3765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging
Jingxuan Luo
College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Yubing Pan
Institute of Urban Meteorology, China Meteorological Administration (CMA), Beijing 100089, China
Debin Su
College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
Jinhua Zhong
College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
Lingxiao Wu
Key Laboratory for Cosmic Rays of the Ministry of Education, Tibet University, Lhasa 850000, China
Wei Zhao
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Xiaoru Hu
College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Zhengchao Qi
College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Daren Lu
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Yinan Wang
CORRESPONDING AUTHOR
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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Short summary
Accurate cloud quantification is critical for climate research. We developed a novel computer vision framework using deep neural networks and clustering algorithms for cloud classification and segmentation from ground-based all-sky images. After a full year of observational training, our model achieves over 95 % accuracy on four cloud types. The framework enhances quantitative analysis to support climate research by providing reliable cloud data.
Accurate cloud quantification is critical for climate research. We developed a novel computer...