An Analysis of the Lightning Jump Algorithm Using the GOES-16 Geostationary Lightning Mapper

This project implements the two-sigma lightning jump algorithm (LJA), initially developed using Lightning Mapping Arrays (LMAs), with GOES-16 Geostationary Lightning Mapper (GLM) flashes, evaluates its performance, and identifies possible adjustments to the algorithm to optimize operational skill. The GLM is projected to have lower detection efficiency (DE) (70-90 percent) than operational LMAs (95-99 percent). The reduced GLM DE coupled with the coarser spatial resolution of the GLM could have impacts on flash rates and trends that could affect the LJA in various ways. Initial deep dive comparisons between the GLM and LMA show a number of significant differences. For example, LMAs saw up to three times as many flashes as the GLM at times and the timing and overall number of jumps were often not in agreement between the two. Also there were relatively strong Pearson correlations between LMA flashes and radar-derived Vertically Integrated Liquid (VIL) and Maximum Expected Size of Hail (MESH) at 0.47 and 0.42 respectively compared to relatively weak Pearson correlations for GLM at 0.20 and 0.14 respectively. These differences suggest that a larger sample size study must be conducted in order to properly evaluate and optimize the LJA with the GLM flashes. This study will analyze and do sensitivity testing on the GLM LJA on storms in the order of thousands and compare them to various verification methods from prior studies including storm reports and radar proxies for storm intensity and severity such as the maximum expected size of hail.