Many studies have shown that the characteristics of lightning such as size and peak current differ between ocean and continental thunderstorms. In general, the lightning in oceanic thunderstorms are larger and have higher peak current than in continental thunderstorms. The reason for these differences have been stipulated to be related to differences in thermal properties and aerosol concentration, however, there is still disagreement over which is the dominant mechanism. In this study, we focus on how thunderstorm trends, as opposed to lightning trends (i.e., flash density), are affected by these mechanisms. We develop a lightning clustering algorithm that takes individual lightning strokes and creates thunderstorms based on their spatiotemporal proximity. We use lightning data from the Earth Networks Total Lightning Network and compare storms from the Eastern U.S.A. to storms off that coast, where the detection efficiency of the network is still good. Once these thunderstorms are obtained, we can split them into ocean and land thunderstorms and compare various characteristics (size, duration, flash rate, polarity and IC/CG ratio, etc.) to determine if any differences stand out. In this presentation, we will discuss the clustering algorithm used, analyze the results of the study, and discuss implications.

The Earth Networks Total Lightning Network (ENTLN) is a global lighting detection network that has been operational since 2009. The ENTLN sensors are broadband electric field sensors that detect both intra-cloud (IC) and cloud-to-ground (CG) flashes and provide timing, location, classification, and peak current measurements. ENTLN consists of roughly 1600 wideband sensors deployed globally. Since its initial deployment, several improvements were made over the years to enhance its performance and usability. Notable ones are the addition of many new sensors each year to improve detection efficiency and extend global coverage. Firmware improvements have also been made to further increase sensitivity. A multi-parameter algorithm was incorporated to enhance IC and CG classification. To validate these improvements, Earth Networks has sponsored several studies to provide valuable feedback on performance improvements. This presentation will highlight two such studies. The first was performed at the Lightning Observatory in Gainesville (LOG), Florida using a combination of high-speed cameras and electric field sensors. For the 608 flashes in this study, a flash detection efficiency (DE) of 99% was found. Also, 97% of the flashes classified as CG by ENTLN algorithms were confirmed as CG via the measurements at LOG. The second study was performed at Langmuir Laboratory in New Mexico. In this study, 546 flashes were analyzed from three separate storms and ENTLN data was compared to simultaneously acquired interferometer (INTF) and electric field change array data (LEFA). Results show a total DE of 97.5%. Ninety one percent of flashes categorized at CG by EN were suggested to be CG by correlation of the LEFA+INTF data. Where EN determined the flash to be IC, LEFA+INTF agreed in 84% of cases.