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Abstract Positive lightning discharges to ground (+CGs) are relatively rare and considerably less studied than negative ones (-CGs). We present observations of unusual transient phenomena occurring in +CGs and discuss their mechanisms. One of them is a brief electric coupling to a concurrent -CG initiated from a 257-m tall tower located 11 km from the +CG channel. A transient process (stroke) in the -CG flash appears to cause a transient luminosity enhancement (M-component) in the +CG channel. In the course of these essentially simultaneous transients, positive charge is in effect taken from the ground at the position of the tower and injected into the ground at the position of the +CG channel. Recoil leaders reactivating decayed +CG branches near the cloud base are each observed to cause a transient luminosity decrease (dip), as opposed to the expected luminosity increase, in the +CG main channel. 

Leaders of subsequent strokes in negative cloud-to-ground lightning are known to produce X-ray/gamma-ray emissions detectable at distances of a few kilometers or less from the lightning channel. These leaders usually develop in decayed but still warm channels of preceding strokes. We computed electric field waveforms at different points along the path of subsequent leader as those points are traversed by the leader tip. For a typical subsequent leader, the electric field peak is a few MV/m, which is sufficient for production of energetic radiation in a warm (reduced air density) channel. We examined the dependence of electric field peak on the leader model input parameters, including the prospective return-stroke peak current (a proxy for the leader tip potential) and leader propagation speed, and compared model predictions with measurements. 

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 To know if a lightning discharge reaches the ground or remains within the thundercloud is critical for lightning safety as cloud‐to‐ground lightning poses the greatest threat to life and property. The current classification methods for most lightning detection networks, which are based on the classification of electromagnetic pulses produced by lightning, still have plenty of room to improve, including some known issues to be addressed. We present a machine‐learning approach to classify lightning discharges. The classification model used in this study is based on Support Vector Machines (SVMs). Compared with traditional multiparameter methods, our algorithm does not require extraction of individual pulse parameters and additionally provides a probability for each prediction. Using a representative lightning pulse data collected by the Cordoba Marx Meter Array in Argentina, we found the classification accuracy of our machine‐learning algorithm to be 97%, which is higher than that for the existing lightning detection networks. 

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. 

Abstract The location accuracy of the U.S. National Lightning Detection Network (NLDN) has been evaluated using as ground‐truth rocket‐triggered lightning data or video records but only at a few specific locations. In this study, by using the NLDN data for the events attributable to lightning strikes to towers, the location error of the NLDN across the entire contiguous United States was evaluated for the first time. We found that, on average, the NLDN median location error reduced from 198 to 84 m after the 2013 NLDN upgrade. The location error at the periphery of the network is significantly larger than that in its interior. In the coastal regions, there is directional location bias toward the water. Simulation results suggest that the bias is related to the lengthening of field waveform front due to electromagnetic wave propagation over lossy ground coupled with the asymmetrical sensor configuration relative to the strike point (lack of offshore sensors).