Identification of thunderstorm occurrence in NWP forecasts using neural networks

Thunderstorm forecasts with lead times of more than one hour usually rely on the post-processing of numerical weather prediction (NWP) data. Thanks to machine learning methods, this post-processing step has seen encouraging improvement in recent years. In this work, we introduce SALAMA, a feedforward neural network model for identifying thunderstorm occurrence in numerical weather prediction data. The model is trained on convection-resolving ensemble forecasts over Central Europe while lightning observations serve as ground truth. We believe that our work represents the first application of a neural network for thunderstorm forecasting to ensemble data with a fine resolution of only 2km. We solve a binary classification task: 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. Particular emphasis is put on making the model reliable. We quantify classification skill through established scores from the meteorological and machine learning community and carefully estimate model uncertainty. For lead times up to eleven hours, we find a classification skill superior to classification based only on convective available potential energy. Varying the spatiotemporal criteria by which we associate lightning observations with NWP data, we estimate the advection speed of thunderstorms in the atmosphere and show that the time scale for skillful thunderstorm predictions increases linearly with the spatial scale of the forecast. All predictors entering our model are available in real time, which makes SALAMA readily available for operational use.