the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Potential impact of aerosols on convective clouds revealed by Himawari-8 observations over different terrain types in eastern China
Tianmeng Chen
Ralph A. Kahn
Daniel Rosenfeld
Jianping Guo
Wenchao Han
Dandan Chen
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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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Uncertainty with respect to cloud phases over the Southern Ocean and Arctic marine regions leads to large uncertainties in the radiation budget of weather and climate models. This study investigates the phases of low-base and mid-base clouds using satellite-based remote sensing data. A comprehensive analysis of the correlation of cloud phase with various parameters, such as temperature, aerosols, sea ice, vertical and horizontal cloud extent, and cloud radiative effect, is presented.