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ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Atmospheric Science
Volume 12 - 2024 |
doi: 10.3389/feart.2024.1376605
A Physics-Based Ensemble Machine-Learning Approach to Identifying a Relationship Between Lightning Indices and Binary Lightning Hazard
Provisionally accepted- Savannah River National Laboratory (DOE), Aiken, United States
To convert lightning indices generated by numerical weather prediction experiments into binary lightning hazard, a machine-learning tool was developed. This tool, consisting of parallel multilayer perceptron classifiers, was trained on an ensemble of planetary boundary layer schemes and microphysics parameterizations that generated four different lightning indices over one week. In a subsequent week, the multi-physics ensemble was applied and the machine-learning tool was used to evaluate the accuracy. Unintuitively, the machine-learning tool performed better on the testing dataset than the training dataset. Much of the error may be attributed to mischaracterizing the convection. The combination of the machine learning model and simulations could not differentiate between cloud-to-cloud lightning and cloud-to-ground lightning, despite being trained on cloud-toground lightning. It was found that the simulation most representative of the local operational model was the most accurate simulation tested.
Keywords: lightning, machine-learning, numerical-weather-prediction, Microphysics, planetary-boundary-layer
Received: 25 Jan 2024; Accepted: 05 Sep 2024.
Copyright: © 2024 Thomas and Noble. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Andrew M. Thomas, Savannah River National Laboratory (DOE), Aiken, United States
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