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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. 

Abstract The Hunga‐Tonga Hunga‐Ha'apai volcano underwent a series of large‐magnitude eruptions that generated broad spectra of mechanical waves in the atmosphere. We investigate the spatial and temporal evolutions of fluctuations driven by atmospheric acoustic‐gravity waves (AGWs) and, in particular, the Lamb wave modes in high spatial resolution data sets measured over the Continental United States (CONUS), complemented with data over the Americas and the Pacific. Along with >800 barometer sites, tropospheric observations, and Total Electron Content data from >3,000 receivers, we report detections of volcano‐induced AGWs in mesopause and ionosphere‐thermosphere airglow imagery and Fabry‐Perot interferometry. We also report unique AGW signatures in the ionospheric D‐region, measured using Long‐Range Navigation pulsed low‐frequency transmitter signals. Although we observed fluctuations over a wide range of periods and speeds, we identify Lamb wave modes exhibiting 295–345 m s⁻¹phase front velocities with correlated spatial variability of their amplitudes from the Earth's surface to the ionosphere. Results suggest that the Lamb wave modes, tracked by our ray‐tracing modeling results, were accompanied by deep fluctuation fields coupled throughout the atmosphere, and were all largely consistent in arrival times with the sequence of eruptions over 8 hr. The ray results also highlight the importance of winds in reducing wave amplitudes at CONUS midlatitudes. The ability to identify and interpret Lamb wave modes and accompanying fluctuations on the basis of arrival times and speeds, despite complexity in their spectra and modulations by the inhomogeneous atmosphere, suggests opportunities for analysis and modeling to understand their signals to constrain features of hazardous events. 

Abstract Terrestrial Gamma‐ray Flashes (TGFs) are ten‐to‐hundreds of microsecond bursts of gamma‐rays produced when electrons in strong electric fields in thunderclouds are accelerated to relativistic energies. Space instruments have observed TGFs with source photon brightness down to ∼10¹⁷–10¹⁶. Based on space and aircraft observations, TGFs have been considered rare phenomena produced in association with very few lightning discharges. Space observations associated with lightning ground observations in the radio band have indicated that there exists a population of dimmer TGFs. Here we show observations of TGFs from aircraft altitude that were not detected by a space instrument viewing the same area. The TGFs were found through Monte Carlo modeling to be associated with 10¹⁵–10¹²photons at source, which is several orders of magnitude below what can be seen from space. Our results suggest that there exists a significant population of TGFs that are too weak to be observed from space. 

Abstract The LOw Frequency ARray (LOFAR) radio telescope possesses the unique capability to measure ultra‐high energy cosmic rays as well as image lightning discharges. This study presents a comparison between the inferred thunderstorm charge structures derived from cosmic‐ray measurements and from lightning flashes. Our results show a basic triple‐layered distribution: a positive upper layer, a main negative layer, and a positive lower layer. However, our cosmic‐ray measurement shows a bottom‐heavy structure, where the charge in the upper positively charged layer is smaller than that in the lower one. This is consistent with practically all lightning observations with LOFAR, showing well‐developed negative leader structures at altitudes below those where positive leaders are seen. This is very different from the vast majority of thundercloud charge structures seen around the world.