Research involving both polarimetric radar and lightning data has, in the past, been approached in a case study manner due to the large datasets involved. This case study approach, though valuable, lacks statistical significance, making it difficult to generalize findings. For this reason, a framework was developed to study large volumes of data in a statistical sense. In order to test its performance, the framework was compared against previously studied cases from the Severe Thunderstorm Electrification and Precipitation Study (STEPS) field campaign.

For the tests, gridded radar volumes with fuzzy logic hydrometeor identification were used in conjunction with Lightning Mapping Array (LMA) data taken during the STEPS campaign. A hybrid of the Storm Cell Identification and Tracking (SCIT) and the Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) algorithms was used to identify and track cells. LMA Very High Frequency (VHF) sources were automatically sorted into flashes. Charge layers were also identified. The processed radar and lightning datasets were fed into the statistical framework, producing one netCDF file for each radar volume containing all the radar and lightning information fed into the framework.

The data in the final files was used to emulate plots produced in past case studies of STEPS data. The goal of this process is to see if the results show the previous case study analyses are comparable to the results obtained from the statistical framework. Importantly, it is anticipated that the same trends and conclusions presented in the previous work can be drawn from the data processed by the statistical framework in a fraction of the time. These results will be reported on in more detail in the extended abstract.

As testing continues, the statistical framework will continue to develop and will be useful for new statistical work and/or case studies, comparisons between automated and hand analysis techniques, and examining the differences between applying different methods of radar and lightning analyses, such as comparing results between different cell tracking algorithms. We hope to report on applications to other datasets beyond the STEPS dataset at the forthcoming conference.