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Atmospheric environments favorable for lightning and convection are commonlyrepresented by proxies or parameterizations based on expert knowledge such asCAPE, wind shears, charge separation, or combinations thereof. Recentdevelopments in the field of machine learning, high resolution reanalyses, andaccurate lightning observations open possibilities for identifying tailoredproxies without prior expert knowledge. To identify vertical profiles favorablefor lightning, a deep neural network links ERA5 vertical profiles of cloudphysics, mass field variables and wind to lightning location data from theAustrian Lightning Detection & Information System (ALDIS), which has beentransformed to a binary target variable labelling the ERA5 cells as lightningand no lightning cells. The ERA5 parameters are taken on model levels beyondthe tropopause forming an input layer of approx. 670 features. The data of2010-2018 serve as training/validation. On independent test data, 2019, thedeep network outperforms a reference with features based on meteorologicalexpertise. Shapley values highlight the atmospheric processes learned by thenetwork which identifies cloud ice and snow content in the upper andmid-troposphere as relevant features. As these patterns correspond to theseparation of charge in thunderstorm cloud, the deep learning model can serveas physically meaningful description of lightning.

Identifying Lightning Processes in ERA5 Soundings with Deep Learning