the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impacts of cloud microphysics parameterizations on simulated aerosol–cloud interactions for deep convective clouds over Houston
Yuwei Zhang
Zhanqing Li
Daniel Rosenfeld
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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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Whether increased aerosol increases or decreases liquid cloud mass has been a longstanding question. Observed correlations suggest that aerosols thin liquid cloud, but we are able to show that observations were consistent with an increase in liquid cloud in response to aerosols by leveraging a model where causality could be traced.