  Thunderstorms pose a major hazard to society and economy, which calls for
reliable thunderstorm forecasts. In this work, we introduce a Signature-based
Approach of identifying Lightning Activity using MAchine learning (SALAMA), a
feedforward neural network model for identifying thunderstorm occurrence in
numerical weather prediction (NWP) data. The model is trained on
convection-resolving ensemble forecasts over Central Europe and lightning
observations. Given only a set of pixel-wise input parameters that are
extracted from NWP data and related to thunderstorm development, SALAMA infers
the probability of thunderstorm occurrence in a reliably calibrated manner. For
lead times up to eleven hours, we find a forecast skill superior to
classification based only on NWP reflectivity. Varying the spatiotemporal
criteria by which we associate lightning observations with NWP data, we show
that the time scale for skillful thunderstorm predictions increases linearly
with the spatial scale of the forecast.
