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Gigantic jets (GJs) are a type of transient luminous event (TLE) which also includes sprites, elves, halos, and blue jets [Pasko2010, doi: 10.1029/2009JA014860]. However, GJs are unique in that they directly couple electric charge reservoirs in the troposphere (i.e. thunderclouds) with the lower ionosphere, allowing significant amounts of charge (100s of C) to flow between these regions. We do not understand how this affects the ionosphere and global electric circuit. Past observations are very limited, resulting from ground-based cameras getting lucky enough to capture an event while looking over a distant thunderstorm [Liu et al. 2015, doi: 10.1038/ncomms6995]. Additionally, GJ-producing storms are normally accompanied by substantial areas of stratiformclouds obscuring the view, and they tend to occur more often over the ocean. To solve this problem of limited detection capability, we have developed a pipeline that utilizes machine learning and sensor fusion of multiple sensing modalities (optical, VLF, ELF). Our pipeline can detect GJs over nearly a hemisphere and operate 24/7, potentially revolutionizing how GJs are detected and paving the way for other TLE and unique lightning event detection. Our pipeline begins by performing detection with data from the Geostationary Lightning Mapper (GLM), which is a staring optical imager in geostationary orbit that detects the 777.4 nm (OI) triplet from lightning leaders [Goodman et al. 2013, doi: 10.1016/j.atmosres.2013.01.006]. Gigantic jets have unique signatures in the GLM data from past studies [Boggs et al. 2019, doi: 10.1029/2019GL082278]. We have developed a supervised, ensemble machine learning classifier that detects potential gigantic jets in the GLM data. Considering we have an imbalanced dataset, we use data imbalance techniques such as Synthetic Minority Oversampling Technique (SMOTE) when training the classifier. Next, we combine data from multiple sensing modalities to vet the candidate GJs from the classifier in multiple stages. The first stage filters the candidate GJs to the stereo GLM region [Mach and Virts, 2021, doi: 10.1175/JTECH-D-21-0078.1], and calculates the stereo altitudes for all the events. GJs have stereo altitude sources consistently between 15-25 km altitude from the leader escaping the cloud top [Boggs et al. 2022, doi: 10.1126/sciadv.abl8731]. Next, we match the events spatiotemporally to GLD360 data to remove cloud-to-ground events. Subsequently, we use a statistical GOES ABI model (developed at GTRI) to filter out events that have differing parent storms from our truth database. Finally, we use a multi-parameter extremely low frequency (ELF) vetting model (developed by Duke) to filter out the remaining non-GJ events. After a few complete detection and vetting cycles, we have found tens of new events with a high degree of confidence. With further development of our pipeline and deployment to the entire GLM field-of-view (not limited to stereo region), we anticipate hundreds of new detections per year, significantly more than ground-based cameras, allowing for comprehensive studies relating gigantic jets to the other atmospheric phenomena 

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. 

In this study, we analyze 44 terrestrial gamma-ray flashes (TGFs) detected by the Fermi Gamma-ray Burst Monitor (GBM) occurring in 2014–2016 in conjunction with data from the U.S. National Lightning Detection Network (NLDN). We examine the characteristics of magnetic field waveforms measured by NLDN sensors for 61 pulses that occurred within 5 ms of the start-time of the TGF photon flux. For 21 (out of 44) TGFs, the associated NLDN pulse occurred almost simultaneously with (that is, within 200 μs of) the TGF. One TGF had two NLDN pulses within 200 μs. The median absolute time interval between the beginning of these near-simultaneous pulses and the TGF flux start-time is 50 μs. We speculate that these RF pulses are signatures of either TGF-associated relativistic electron avalanches or currents traveling in conducting paths “preconditioned” by TGF-associated electron beams. Compared to pulses that were not simultaneous with TGFs (but within 5 ms of one), simultaneous pulses had higher median absolute peak current (26 kA versus 11 kA), longer median threshold-to-peak rise time (14 μs versus 2.8 μs), and longer median peak-to-zero time (15 μs versus 5.5 μs). A majority (77%) of our simultaneous RF pulses had NLDN-estimated peak currents less than 50 kA indicating that TGF emissions can be associated with moderate-peak-amplitude processes. The lightning flash associated with one of the TGFs in our data set was observed by a Lightning Mapping Array, which reported a relatively high-power source at an altitude of 25 km occurring 101 μs after the GBM-reported TGF discovery-bin start-time.