the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Relationships between the planetary boundary layer height and surface pollutants derived from lidar observations over China: regional pattern and influencing factors
Tianning Su
Ralph Kahn
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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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