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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 

This work-in-progress poster reports on the development process of a virtual environment to support embodied cognition about the scale of scientific entities from subatomic particles to galaxies. Research shows that learners struggle to comprehend the sizes of entities beyond human scale. In order to determine specific entities to use in the virtual environment, a document analysis of US K-undergraduate science education standards was undertaken. Entities, categories of entities, and ranges of sizes were identified. 

The pressing nature of climate change and its associated impacts requires a climate literate citizenry. Climate change education in K-12 settings may provide a unique opportunity to make inroads towards climate literacy. However, many K-12 teachers avoid teaching climate change because they are uncomfortable with the subject or do not see its relevance to their curriculum. Removing barriers to climate change professional development (CCPD) for teachers may help increase confidence in teaching about climate change. To understand the perceived barriers to participating in CCPD, a survey was conducted with 54 middle school science teachers who did not respond to a previous invitation to participate in a CCPD program. The most significant barrier was time to participate. The participants were also asked to rate their confidence about whether climate change is happening. The results were compared between teachers who were confident climate change was happening and those who were not to examine whether these beliefs influenced teachers’ perceptions of barriers. Those who were confident climate change was happening were less likely to perceive administrative support, interest in the workshop, and knowledge of climate change content as barriers. However, both groups of teachers reported that time was the primary barrier rather than the topic. This suggests that, rather than developing unique strategies, existing best practices in teacher professional development can be used to support CCPD opportunities. Additional recommendations include thinking creatively about how to create time for teachers to attend and making the professional development directly relevant to teacher’s local contexts. 

Abstract Melt inclusions with large, positive Sr anomalies have been described in multiple tectonic settings, and the origins of this unusual geochemical feature are debated. Three origins have been proposed, all involving plagioclase as the source of the elevated Sr: (i) direct assimilation of plagioclase‐rich lithologies, (ii) recycled lower oceanic gabbro in the mantle source, and (iii) shallow‐level diffusive interaction between present day lower oceanic crust (i.e., plagioclase‐bearing lithologies) and the percolating melt. A “ghost plagioclase” signature (i.e., a large, positive Sr anomaly without associated high Al₂O₃) is present in melt inclusions from Mauna Loa. We present new⁸⁷Sr/⁸⁶Sr measurements of individual olivine‐hosted melt inclusions from three Hawaiian volcanoes, Mauna Loa, Loihi, and Koolau. The data set includes a Mauna Loa melt inclusion with the highest reported Sr anomaly (or highest (Sr/Ce)_N, which is 7.2) for Hawai'i. All melt inclusions have⁸⁷Sr/⁸⁶Sr values within the range reported previously for the lavas from each volcano. Critically, the⁸⁷Sr/⁸⁶Sr of the high (Sr/Ce)_Nmelt inclusion lies within the narrow range of⁸⁷Sr/⁸⁶Sr for Mauna Loa melts that lack high (Sr/Ce)_Nsignatures. Therefore, to explain the high (Sr/Ce)_Nratio of the ghost plagioclase signature using an ancient recycled gabbro, the gabbro‐infused mantle source would have had to evolve, by chance, to have the same⁸⁷Sr/⁸⁶Sr as the source of the Mauna Loa melts that lack a recycled gabbro (ghost plagioclase) signature. Alternatively, shallow‐level diffusive interactions between Mauna Loa plagioclase‐rich cumulates and a percolating mantle‐derived melt provides a simpler explanation for the presence of the high (Sr/Ce)_NMauna Loa melts.