A machine‐learning approach to thunderstorm forecasting through post‐processing of simulation data

Thunderstorms pose a major hazard to society and the economy, which calls for reliable thunderstorm forecasts. In this work, we introduce 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 11 h, 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. We present SALAMA, a neural network model for predicting the probability of thunderstorm occurrence by post‐processing convection‐resolving ensemble forecasts. Shown is a case visualization for June 23, 2023, at 2000 UTC, with pixels in which thunderstorm probability exceeds a decision threshold (red), and pixels in which lightning occurs (black contours). We carefully evaluate model reliability and investigate how model skill depends on the spatial scale of the forecast.