Gatlin (2006) laid the framework for an operational lightning jump algorithm (LJA) to aid in increasing severe weather warning lead times. Gatlin demonstrated that a total lightning trend based product could be devised to aid in severe weather decision making. One drawback to Gatlin's approach is that the algorithm was developed primarily for application to isolated severe storms (i.e. supercells), with reduced focus on other forms of convection (e.g. non severe thunderstorms, mesoscale convective systems, tropical storm remnants). In order to develop a more universally applicable LJA, understanding these other more frequently occurring forms of convection and the lightning behavior therein is vital to the further development of a working LJA.

This study has three main objectives. First, we wish to understand typical and/or atypical lightning behaviors in the broader spectrum of convection relative to testing and development of the LJA algorithm in non-supercellular situations. For this effort we take the approach of convective structure and flash rate statistics for many cases. The results from each case study are then placed into a database from which robust statistics can be derived. The ensemble of statistics will then aid in distinguishing between severe and non severe convection using information pulled from the total lightning data. Additionally, comparisons with traditional radar derived products are made to try and correlate increases in lightning to observed radar parameters, thus increasing the ability to nowcast storm severity. These three items will enable development and refinement of an efficient LJA algorithm that can be used in an operational setting to aid in the short term forecasting of severe weather.