EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Identifying Lightning Processes in ERA5 Soundings with Deep Learning

Tobias Hell1, Gregor Ehrensperger1, Georg J. Mayr2, and Thorsten Simon1,3
Tobias Hell et al.
  • 1Department of Mathematics, Universität Innsbruck, Innsbruck, Austria
  • 2Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
  • 3Department of Statistics, Universität Innsbruck, Innsbruck, Austria

Atmospheric environments favorable for lightning and convection are commonly represented by proxies or parameterizations based on expert knowledge such as CAPE, wind shears, charge separation, or combinations thereof. Recent developments in the field of machine learning, high resolution reanalyses, and accurate lightning observations open possibilities for identifying tailored proxies without prior expert knowledge. To identify vertical profiles favorable for lightning, a deep neural network links ERA5 vertical profiles of cloud physics, mass field variables and wind to lightning location data from the Austrian Lightning Detection & Information System (ALDIS), which has been transformed to a binary target variable labelling the ERA5 cells as lightning and no lightning cells. The ERA5 parameters are taken on model levels beyond the tropopause forming an input layer of approx. 670 features. The data of 2010 - 2018 serve as training/validation. On independent test data, 2019, the deep network outperforms a reference with features based on meteorological expertise. Shapley values highlight the atmospheric processes learned by the network which identifies cloud ice and snow content in the upper and mid-troposphere as relevant features. As these patterns correspond to the separation of charge in thunderstorm cloud, the deep learning model can serve as physically meaningful description of lightning. 

How to cite: Hell, T., Ehrensperger, G., Mayr, G. J., and Simon, T.: Identifying Lightning Processes in ERA5 Soundings with Deep Learning, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16098,, 2023.