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In this presentation we will provide an overview and present preliminary results from a multi-institutional collaborative project, which seeks to detect gigantic jets over hemispheric scales using a combination orbital and ground-based sensors and machine learning. Gigantic jets are a type of transient luminous event (TLE, Pasko 2010, doi: 10.1029/2009JA014860) that escape the cloud top of a thunderstorm and propagate up to the lower ionosphere (80-100 km altitude), transferring tens to hundreds of Coulombs of charge. Our detection methodology primarily uses the Geostationary Lightning Mapper (GLM), which is a staring optical imager in geostationary orbit that detects the 777.4 nm (OI) triplet commonly emitted by lightning (Goodman et al. 2013, doi: 10.1016/j.atmosres.2013.01.006). Gigantic jets have been shown to have unique signatures in the GLM data from past studies (Boggs et al. 2019, doi: 10.1029/2019GL082278; Boggs et al. 2022, doi: 10.1126/sciadv.abl8731). Thus far, we have built a preliminary, supervised machine learning model that detects potential gigantic jets using GLM, and begun development on a series of vetting techniques to confirm the detections as real gigantic jets. The vetting techniques use a combination of low frequency (LF) and extremely low frequency (ELF) sferic data, in combination with stereo GLM measurements. When our detection methodology grows in maturity, we will deploy it to all past GLM data (2018-present), with the potential to detect thousands of events each year, allowing correlation with other meteorological and atmospheric measurements. We will share the database of gigantic jet detections publicly during and at project conclusion (2025), allowing other researchers to use this data for their own research.
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An Automated Pipeline for Detecting Gigantic Jets: Preliminary Results
Gigantic jets are a type of transient luminous event (TLE, Pasko 2010, doi: 10.1029/2009JA014860) that escape the cloud top of a thunderstorm and propagate up to the lower ionosphere (80-100 km altitude), transferring tens to hundreds of Coulombs of charge. Due to rarity of observations, it is still not understood how they affect the lower ionosphere and what storm systems produce them. In this presentation we will provide an overview and present preliminary results from a multi-institutional collaborative project, which seeks to detect gigantic jets over hemispheric scales using a combination of orbital and ground-based sensors and machine learning. Our pipeline has the potential to detect significantly more gigantic jets (thousands) than current methods, which relies on using ground-based cameras. We will build a large database of gigantic jet detections, and correlate the events with a Very Low Frequency (VLF) remote sensing network (Cohen et al. 2009, doi: 10.1109/TGRS.2009.2028334) to understand how they perturb the lower ionosphere – in addition to other meteorological datasets. Our detection methodology primarily uses the Geostationary Lightning Mapper (GLM), which is a staring optical imager in geostationary orbit that detects the 777.4 nm (OI) triplet commonly emitted by lightning (Goodman et al. 2013, doi: 10.1016/j.atmosres.2013.01.006). Gigantic jets have been shown to have unique signatures in the GLM data from past studies (Boggs et al. 2019, doi: 10.1029/2019GL082278; Boggs et al. 2022, doi: 10.1126/sciadv.abl8731). Thus far, we have built a preliminary, supervised machine learning model that detects potential gigantic jets using GLM, and begun development on a series of vetting techniques to confirm the detections as real gigantic jets. The vetting techniques use a combination of low frequency (LF) and extremely low frequency (ELF) sferic data, in combination with stereo GLM measurements that provide optical source altitude. In addition, we will soon be able to calculate optical stereo sources with GLM on GOES-16 and the newly launched Lightning Imager on EUMETSAT, significantly expanding the stereo region of detection. When our detection methodology grows in maturity, we will deploy it to all past GLM data (2018-present) and share the database publicly, allowing other researchers to use this data for their own research.
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- Award ID(s):
- 2230383
- PAR ID:
- 10534190
- Publisher / Repository:
- American Geophysical Union
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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