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

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

doi:https://doi.org/10.5194/acp-23-14547-2023

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

Data sets

Cloud Condensation Nuclei Particle Counter (AOSCCN1COL) J. Uin, E. Andrews, C. Salwen, O. Enekwizu, and C. Hayes https://doi.org/10.5439/1984587

Cloud Condensation Nuclei Particle Counter (AOSCCN2COLAAVG) A. Koontz, J. Uin, E. Andrews, O. Enekwizu, C. Hayes, and C. Salwen https://doi.org/10.5439/1323894

Active Remote Sensing of CLouds (ARSCL) product using Ka-band ARM Zenith Radars (ARSCLKAZR1KOLLIAS) K. Johnson, M. Jensen, and S. Giangrande https://doi.org/10.5439/1228768

Active Remote Sensing of CLouds (ARSCL) product using Ka-band ARM Zenith Radars (ARSCLKAZR1KOLLIAS) K. Johnson, S. Giangrande, and T. Toto https://doi.org/10.5439/1393437

Cloud Type Classification (CLDTYPE) D. Zhang, Y. Shi, and L. Riihimaki https://doi.org/10.5439/1349884

Interpolated Sonde (INTERPOLATEDSONDE) M. Jensen, S. Giangrande, T. Fairless, and A. Zhou https://doi.org/10.5439/1095316

Planetary Boundary Layer Height (PBLHTMPL1SAWYERLI) C. Sivaraman and D. Zhang https://doi.org/10.5439/1637942

Video Disdrometer (VDIS) D. Wang and M. J. Bartholomew https://doi.org/10.5439/1992988

ERA5 hourly data on pressure levels from 1940 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J-N. Thépaut https://doi.org/10.24381/cds.bd0915c6

Download Daily Data US EPA - U.S. Environmental Protection Agency https://www.epa.gov/outdoor-air-quality-data/download-daily-data

MERRA-2 tavg1_2d_aer_Nx: 2d, 1-Hourly, Time-averaged, Single-Level, Assimilation, Aerosol Diagnostics V5.12.4 Global Modeling and Assimilation Office (GMAO) https://doi.org/10.5067/KLICLTZ8EM9D

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.