Assessment of lightning jump algorithms using GOES-R GLM proxy data for severe weather detection

Previous studies indicate that increases in lightning production in thunderstorms often occur before a severe weather event. Schultz (2008) successfully quantified these lightning jumps so that significant lightning jumps could be used to signal for severe weather. This study utilizes Schultz's 2sigma algorithm using Geostationary Lightning Mapper (GLM) proxy data to try to produce positive results for severe weather detection using lightning jump algorithms. The 2sigma algorithm was modified and then optimized to create an algorithm that performs best with GLM proxy data. By changing the static flash rate threshold to 5 flashes min-1 and the dynamic standard deviation threshold to 1.70sigma, an algorithm that most accurately detects severe weather events based on statistical analysis of Probability of Detection, False Alarm Rate, Critical Success Index, Average Lead Time, and Heidke Skill Score was created. This algorithm has been termed the 1.70sigmaFR5 lightning jump algorithm.