  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 and Information System (ALDIS), which has been
transformed to a binary target variable labelling the ERA5 cells as cells with
lightning activity and cells without lightning activity. 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. SHAP values highlight the
atmospheric processes learned by the network which identifies cloud ice and
snow content in the upper and mid-troposphere as very 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. Depending on the region, the neural network also exploits the
vertical wind or mass profiles to correctly classify cells with lightning
activity.
