the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Vertical profiles of cloud condensation nuclei number concentration and its empirical estimate from aerosol optical properties over the North China Plain
Rui Zhang
Zhanqing Li
Zhibin Wang
Russell R. Dickerson
Xinrong Ren
Fei Wang
Ying Gao
Xi Chen
Jialu Xu
Yafang Cheng
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Several machine learning models are applied to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains ARM site during the summer months of 2012–2020. We find that the random forest model is the best predictor among common classifiers. We rank variables in terms of their effectiveness in nowcasting ENTLN lightning and identify geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors.
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Clouds over the Southern Ocean are crucial to Earth's energy balance, but understanding the factors that control them is complex. Our research examines how weather patterns affect tiny particles called cloud condensation nuclei (CCN), which influence cloud properties. Using data from Kennaook / Cape Grim, we found that winter air from Antarctica brings cleaner conditions with lower CCN, while summer patterns from Australia transport more particles. Precipitation also helps reduce CCN in winter.