Articles | Volume 17, issue 7
https://doi.org/10.5194/essd-17-3243-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-3243-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology based on deep-learning methods
Yuanyuan Wu
Nanjing-Helsinki Institute in Atmospheric and Earth System Sciences, Nanjing University, Nanjing, 210023, China
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Joint International Research Laboratory of Atmospheric and Earth System Sciences & Institute for Climate and Global Change Research, Nanjing, 210023, China
Nanjing-Helsinki Institute in Atmospheric and Earth System Sciences, Nanjing University, Nanjing, 210023, China
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Joint International Research Laboratory of Atmospheric and Earth System Sciences & Institute for Climate and Global Change Research, Nanjing, 210023, China
Yu Zhang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Joint International Research Laboratory of Atmospheric and Earth System Sciences & Institute for Climate and Global Change Research, Nanjing, 210023, China
Kang-En Huang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Boyang Zheng
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Yichuan Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Yanyun Li
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Quan Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Chen Zhou
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Yuan Liang
TianJi Weather Science and Technology Company, Beijing, 100000, China
Jianning Sun
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Minghuai Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Joint International Research Laboratory of Atmospheric and Earth System Sciences & Institute for Climate and Global Change Research, Nanjing, 210023, China
Daniel Rosenfeld
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
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Short summary
Based on a deep-learning method, we established a global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology. This aims to promote a comprehensive understanding of cloud dynamics and cloud–climate feedback. Closed mesoscale cellular convection (MCC) clouds occur more frequently at night, while suppressed cumulus clouds exhibit remarkable decreases. Solid stratus and MCC cloud types show clear seasonal variations.
Based on a deep-learning method, we established a global classification dataset of daytime and...
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