"Increases in flash rates detected in ground-based lightning data can be a precursor to severe weather hazards. Lightning data from the Geostationary Lightning Mapper (GLM) aboard the GOES-16 satellite is not currently a systematic part of an operational model used by forecasters and is underutilized in severe storm research. We harness the spatial and temporal advantages of geostationary satellite data to create a machine learning model that augments the current meteorological practices and capabilities of forecasters during extreme weather. Our results suggest that false alarms for warned thunderstorms could be decreased by half, and that tornadoes and severe hail could be correctly identified 8 out of 10 times using GLM data.