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Abstract. Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Machine learning (ML) excels in exploring and revealing complex relationships between meteorological variables such as those measured at the South Great Plains (SGP) Atmospheric Radiation Measurement (ARM) site; a site that provides an unprecedented level of detail on atmospheric conditions and clouds. Several commonly used ML models have been applied to analyse the relationship between ARM data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the SGP site during the summers (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the Random Forest model is the best predictor among common classifiers. It predicted lightning occurrence with an accuracy of 76.9 % and an area under curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in predicting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined on hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables such as equivalent potential temperature and mid-altitude variables such as minimum equivalent potential temperature have a large contrast between no-lightning and frequent-lightning hours. Finally, a notable positive relationship between intra-cloud (IC) flash fraction and the square root of CAPE was found suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction. 

Abstract. Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE (CAPE) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction. 

Abstract This study assesses the reliability and limitations of the Geostationary Lightning Mapper (GLM) in detecting continuing currents by comparing observations from ground‐based high‐speed cameras with GLM‐16 data. Our findings show that the GLM's one‐group detection efficiency (DE_1) is 53%, while the more stringent five‐consecutive‐group detection efficiency (DE_5) is 10%. Optical signals detected by the GLM predominantly occur during the early stages of continuing currents. Additionally, there is a notable disparity in detection efficiencies between positive and negative continuing currents, with positive continuing currents being detected more frequently. The application of the logistic regression model developed by Fairman and Bitzer (2022) further illustrates the limitations in continuing current identification. The study underscores the challenges of relying solely on satellite data to monitor and analyze continuing currents, emphasizing the need for advancements in detection technologies and methodologies to reliably detect continuing current at a large spatial scale. 

Abstract We report on the mountain top observation of three terrestrial gamma‐ray flashes (TGFs) that occurred during the summer storm season of 2021. To our knowledge, these are the first TGFs observed in a mountaintop environment and the first published European TGFs observed from the ground. A gamma‐ray sensitive detector was located at the base of the Säntis Tower in Switzerland and observed three unique TGF events with coincident radio sferic data characteristic of TGFs seen from space. We will show an example of a “slow pulse” radio signature (Cummer et al., 2011,https://doi.org/10.1029/2011GL048099; Lu et al., 2011,https://doi.org/10.1029/2010JA016141; Pu et al., 2019,https://doi.org/10.1029/2019GL082743; Pu et al., 2020,https://doi.org/10.1029/2020GL089427), a −EIP (Lyu et al., 2016,https://doi.org/10.1002/2016GL070154; Lyu et al., 2021,https://doi.org/10.1029/2021GL093627; Wada et al., 2020,https://doi.org/10.1029/2019JD031730), and a double peak TGF associated with an extraordinarily powerful and complicated positive‐polarity sferic, where each TGF peak is possibly preceded by a short burst of stepped leader emission. 

Abstract We observed two Terrestrial Gamma‐ray Flashes (TGFs) in Uchinada, Japan associated with negative cloud‐to‐ground lightning strokes exactly 1 year apart on 18 December 2020 and 2021. The events were remarkable for their lateral distance from the associated strokes—each about 5 km away from the detector site. Not only was that lateral distance remarkable on its own for a ground based detection, but the low‐altitude profile of winter thunderstorms in Japan would suggest the detections occurred at unprecedented nadir angles—73.3° off axis for the 2020 event with the standard assumption of a vertically oriented TGF. Unsurprisingly, Monte Carlo simulations of the straightforward interpretation of these events yield fluences 2 orders of magnitude lower than observed data. We investigate a variety of ways to attempt to resolve the contradiction between expected and observed behavior. 

Abstract Previous studies have shown that subsequent leaders in positive cloud‐to‐ground lightning (+CG) flashes rarely traverse pre‐existing channels to ground. In this paper, we present evidence that this actually can be common, at least for some thunderstorms. Observations of +CG flashes in a supercell storm in Argentina by Córdoba Argentina Marx Meter Array (CAMMA) are presented, in which 54 (64%) of 84 multiple‐stroke +CG flashes had subsequent leaders following a pre‐existing channel to ground. These subsequent positive leaders are found to behave similarly to their negative counterparts, including propagation speeds along pre‐existing channels with a median of 8 × 10⁶ m/s, which is comparable to that of negative dart leaders. Two representative multiple‐stroke +CG flashes are presented and discussed in detail. The observations reported herein call for an update to the traditional explanation of the disparity between positive and negative lightning. 

Noetzli, J., Christiansen, H.H, Guglielmin, M., Hrbáček, F., Hu, G., Isaksen, K., Magnin, F., Pogliotti, P., Smith, S. L., Zhao, L. and Streletskiy, D. A. 2024. Permafrost temperature and active layer thickness. In: State of the Climate in 2023. Bulletin of the American Meteorological Society, 105 (8), S43–S44, https://doi.org/10.1175/BAMS-D-24-0116.1