Articles | Volume 23, issue 22
https://doi.org/10.5194/acp-23-14547-2023
https://doi.org/10.5194/acp-23-14547-2023
Research article
 | 
24 Nov 2023
Research article |  | 24 Nov 2023

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Siyu Shan, Dale Allen, Zhanqing Li, Kenneth Pickering, and Jeff Lapierre

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Cited articles

Altaratz, O., Koren, I., Yair, Y., and Price, C.: Lightning response to smoke from Amazonian fires, Geophys. Res. Lett., 37, L07801, https://doi.org/10.1029/2010GL042679, 2010. 
Bang, S. D. and Zipser, E. J.: Seeking reasons for the differences in size spectra of electrified storms over land and ocean, J. Geophys. Res.-Atmos., 121, 9048–9068, https://doi.org/10.1002/2016JD025150, 2016. 
Bartholomew, M. J.: Impact Disdrometers Instrument Handbook (No. DOE/SCARM-TR-111). DOE Office of Science Atmospheric Radiation Measurement (ARM) Program (United States), https://doi.org/10.2172/1251384, 2016. 
Bell, T. L., Rosenfeld, D., Kim, K. M., Yoo, J. M., Lee, M. I., and Hahnenberger, M.: Midweek increase in US summer rain and storm heights suggests air pollution invigorates rainstorms, J. Geophys. Res.-Atmos., 113, D02209, https://doi.org/10.1029/2007JD008623, 2008. 
Bell, T. L., Rosenfeld, D., and Kim, K. M.: Weekly cycle of lightning: Evidence of storm invigoration by pollution, Geophys. Res. Lett., 36, L23805, https://doi.org/10.1029/2009GL040915, 2009. 
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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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