Andrew Prata

and 8 more

Volcanic activity occurring in tropical moist atmospheres can promote deep convection and trigger volcanic thunderstorms. Intense heating at ground surface and entrainment of moist air generates positive buoyancy, rapidly transporting volcanic gases and ash particles up to the tropopause and beyond. Volcanically-induced deep convection, however, is rarely observed to last continuously for more than a day and so insights into the dynamics, microphysics and electrification processes are limited. Here we present a multidisciplinary study on an extreme case, where this phenomenon lasted for six days. We show that this unprecedented event was triggered and sustained by phreatomagmatic activity at Anak Krakatau volcano, Indonesia from 22-28 December 2018. During this period, a deep convective plume formed over the volcano and acted as a ‘volcanic freezer’ producing ~3 × 10⁹ kg of ice on average with maxima reaching ~10¹⁰ kg. Our satellite analyses reveal that the convective anvil cloud, reaching 16-18 km above sea level, was ice-rich and ash-poor. Cloud-top temperatures hovered around -80 °C and ice particles produced in the anvil were notably small (effective radius from 20-30 μm). Our modelling suggests that ice particles began to form above 5 km and experienced vigorous updrafts (>30 m/s). These findings explain the impressive number of lightning strikes (~100,000) recorded near the volcano during this time. Our results, together with the unique dataset we have compiled, provide new insights into volcanic and meteorological thunderstorms alike.

Richard Sonnenfeld

and 4 more

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.