Lightning Jump Integration into the Severe Weather Warning Process

Correlations between rapid increases in total lightning (i.e. “lightning jumps”) and the manifestation of severe weather at the surface has been well known for over two decades. Recent work has focused on understanding the physical linkages to storm intensity and the development of automated techniques for identifying, verifying, and relating these rapid increases in total lightning to other intensity metrics of thunderstorms to be used within an operational warning framework. Schultz et al. (2009, 2011) tested several configurations of the lightning jump algorithm (LJA) and determined the 2sigma (jump ratio of 2) approach to be the best using hundreds of thunderstorms covering various storm types. The findings of that study elicited further investigation into specific relationships between the lightning jump and storm properties in supercells and quasi-linear convective systems (QLCSs). Findings indicate that lightning jumps often occur prior to the initial development of a mesocyclone, providing early warning of when a thunderstorm is transitioning to a supercellular structure. Furthermore, lightning and lightning jumps likely help identify the most severe portion of large QLCSs where the updrafts are most intense.

Verification of the lightning jump technique must be performed in order to understand strengths and weaknesses, thus evaluation of the LJA relies on accurate severe storm reports. The challenges with using NOAA's National Climatic Data Center's (NCDC) Storm Data have been noted in the literature. Inaccurate reporting can negatively affect the evaluation of the LJA. Therefore, we are examining enhanced methods of verification. For hailstorms in particular, we are utilizing the Severe Hazards Analysis & Verification Experiment (SHAVE) dataset and comparing the lightning data to radar parameters such as maximum expected size of hail (MESH) and vertically integrated liquid (VIL).

To begin the transition of the lightning jump from a research algorithm to an operational product this study utilized regional lightning mapping array networks across the United States to develop automated thunderstorm tracking using radar and lightning data, as well as placed the algorithm in front of forecasters for feedback and testing in real time during the NOAA Hazardous Weather Testbed during the Spring Experiment. As we move towards receiving hemispheric lightning data with the Geostationary Lightning Mapper (GLM) on the GOES-R satellite, the LJA is also being tested using a proxy GLM data set to ready the algorithm for use with a space-based optical lightning data set. This presentation will highlight all of these efforts to provide a summary of the LJA to date and look towards future lightning jump applications.