Analysis of Satellite and Ground Based Lightning Detection Network Performance in Photographed Supercell and Multicell Events

Modern sensors measure multiple facets of the physical characteristics of electric discharges in our atmosphere. This study, examines cloud-to-ground (CG) and intracloud (IC) flashes, analyzing the charge and flash structure across multiple sensors and networks. CG and IC lightning were recorded using an iPhone wide-angle camera, which is coincident with lightning observed by the GOES-16 and -18 Geostationary Lightning Mappers (GLM), the West Texas and Oklahoma Lightning Mapping Array (WTLMA, OKLMA, LMA), the National Lightning Detection Network (NLDN), and Earth Networks Total Lightning Network (ENTLN) lightning sensors. Examples of IC and CG lightning for both polarities were captured and analyzed. Stroke polarity and type were obtained from the low-frequency (LF) networks, complemented by high resolution channel locations indicated by the very high frequency (VHF) WTLMA source locations. Over 3,800 lightning images were recorded from four separate case studies, with two cases (over 3,400 images) analyzed (240 images in depth). These studies included supercells and multicells during the day and night. The goal of this analysis is to determine which sensing methods are most reliable and which need improvements based on validation via LMA and video stills. In this study, it was found that the most prevalent flashes were weak (1-5 kA) +ICs. Additionally, this study investigated detection efficiency (DE), which varied by 10-50% across networks throughout both cases, missed detections, and stroke misclassifications. It was found that flashes missed by one GLM were very rare and when a flash was missed, it was correlated to flash properties measured by the LMA. Long duration, higher altitude, large size flashes that occurred at night had the greatest DE. Misclassifications between cases remained low at 3.28% and 6.50% for the multicell and supercell respectively. It is important for all lightning measurements to be classified accurately, for it leads to more accurate lightning climatology records and aids in lightning prediction and forecasting.