A machine-learning approach to thunderstorm forecasting through post-processing of simulation data
This work addresses the need for reliable thunderstorm forecasts to mitigate societal and economic hazards, representing an incremental improvement in weather prediction.
The paper tackles thunderstorm forecasting by developing SALAMA, a feedforward neural network that processes numerical weather prediction data to predict thunderstorm occurrence, achieving superior forecast skill compared to reflectivity-based classification for lead times up to eleven hours.
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.