Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 23, issue 22
Articles | Volume 23, issue 22
https://doi.org/10.5194/acp-23-14547-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-14547-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 22
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

Viewed

Total article views: 1,199 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
930 218 51 1,199 32 34
  • HTML: 930
  • PDF: 218
  • XML: 51
  • Total: 1,199
  • BibTeX: 32
  • EndNote: 34
Views and downloads (calculated since 12 Jun 2023)
Cumulative views and downloads (calculated since 12 Jun 2023)

Viewed (geographical distribution)

Total article views: 1,199 (including HTML, PDF, and XML) Thereof 1,164 with geography defined and 35 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary

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.

Read more
Altmetrics
Final-revised paper
Preprint