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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 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 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). 

Abstract The Córdoba Argentina Marx Meter Array (CAMMA), consisting of 10 second‐generation Huntsville Alabama Marx Meter Array (HAMMA 2) sensors, operated at Córdoba, Argentina, during the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign in late 2018. Initial results obtained from the campaign demonstrate that the new sensor is able to provide a significantly more detailed depiction of various lightning processes than its first generation. The lightning flashes mapped by the CAMMA and a colocated Lightning Mapping Array (LMA) were compared. The overall flash structures mapped by the CAMMA and the LMA look similar for most of the flashes. However, comparisons at smaller time scale show that the majority of CAMMA and LMA sources are not concurrent, indicating that unmatched sources were possibly due to different physical processes in leader propagation dominating different frequencies and differences in data processing and location techniques.