Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance

Abstract. In order to aid feature selection in thunderstorm nowcasting, we present an analysis of the utility of various sources of data for machine-learning-based nowcasting of hazards related to thunderstorms. We considered ground-based radar data, satellite-based imagery and lightning observations, forecast data from numerical weather prediction (NWP) and the topography from a digital elevation model (DEM), ending up with 106 different predictive variables. We evaluated machine-learning models to nowcast radar reflectivity (representing precipitation), lightning occurrence, and the 45 dBZ radar echo top height that can be used as an indicator of hail, producing predictions for lead times up to 60 min. The study was carried out in an area in the northeast United States, where observations from the Geostationary Operational Environmental Satellite 16 are available and can be used as a proxy for the upcoming Meteosat Third Generation capabilities in Europe. The benefits of the data sources were evaluated using two complementary approaches: using feature importance reported by the machine learning model based on gradient boosted trees, and by repeating the analysis using all possible combinations of the data sources. The two approaches sometimes yielded seemingly contradictory results, as the feature importance reported by the gradient boosting algorithm sometimes disregards certain features that are still useful in the absence of more powerful predictors, while at times it overstates the importance of other features. We found that the radar data is overall the most important predictor, the satellite imagery is beneficial for all of the studied predictands, and the lightning data is very useful for nowcasting lightning but of limited use for the other hazards. The benefits of the NWP data are more limited over the nowcast period, and we did not find evidence that the nowcast benefits from the DEM data.