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Identification and validation of atmospheric extremes are essential to monitoring climate change, to addressing engineering and safety concerns, and to promoting technological advancement. An international World Meteorological Organization evaluation committee has critically adjudicated and recommended acceptance of two lightning megaflash events (horizontal mesoscale lightning discharges of >100 km in length) as new global extremes using analysis of Geostationary Lightning Mapper data. The world's greatest extent for an individual lightning flash is a single flash that covered a horizontal distance of 709 ± 8 km (441 ± 5 mi) across parts of southern Brazil on 31 October 2018. The greatest duration for a single lightning flash is 16.730 ± 0.002 s from a flash that developed continuously over northern Argentina on 4 March 2019. Plain Language Summary: Analysis of new satellite data has identified lightning extremes for horizontal distance (709 km) and greatest duration (16.730 s). Key Points: Analysis of new satellite data identifies far‐larger lightning flashes (termed "megaflashes"; flashes > 100 km) than previously detectedTwo megaflash events are identified from space that exceed global lightning extremes (horizontal length, duration) by a factor of twoThe new megaflash extremes: horizontal distance is 709 km on 31 October 2018 (Brazil); duration is 16.730 s on 4 March 2019 (Argentina) [ABSTRACT FROM AUTHOR]

A validation study of the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite 16 (GOES‐16) was done using a ground‐based lightning mapping array (LMA) deployed as part of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign in Argentina. GLM detected lightning with 74.6% efficiency over 61 thunderstorm days in December 2018 through April 2019. However, GLM detection efficiency (DE) was negatively correlated (r = −0.49) with LMA flash rate. GLM DE also was negatively correlated with LMA flash altitude (r = −0.24), reflecting the influence of multiple competing trends. GLM DE was positively correlated (r = 0.27) with number of LMA sources in a flash, indicating improved DE for larger flashes. During periods with anomalously electrified storms, GLM DE was reduced to 50.9%. Statistics were found to be sensitive to analysis criteria, but most of the above trends remained consistent regardless of specific criteria. Because the methodology allowed a GLM flash to match more than one LMA flash, actual GLM flash rate was a factor of 2.9 lower than the LMA flash rate, and this ratio grew larger as LMA flash rate increased. A sensitivity study examined the impact of improved DE for smaller flashes; that is, an improved sensor (or algorithm) that was better able to detect and distinguish between separate small lightning flashes. The results showed improved correlation with LMA flash rates, as well as improved ability to identify lightning jumps associated with intensifying convection. Plain Language Summary: Based on a comparison with a ground‐based, three‐dimensional lightning detection system in Argentina, the Geostationary Lightning Mapper (GLM) on board the Geostationary Operational Environmental Satellite 16 (GOES‐16) detects lightning with nearly 75% efficiency, which meets its requirements. However, that detection efficiency decreases a lot when thunderstorms produce a lot of lightning at once, or small lightning flashes, or when lightning occurs deeper in the cloud where it is more difficult for the optical pulse to make its way to cloud top. This makes GLM somewhat less useful during the most intense part of a storm's life. However, if GLM or a similar sensor could be made more sensitive, either with improved hardware design or better data processing, then it would become more useful in intense storms. Key Points: The Geostationary Lightning Mapper detected lightning with overall 75% efficiency relative to a ground network in ArgentinaDetection efficiency depended significantly on day/night, and on flash rate, size, altitude, and the presence of anomalous lightningAn improved sensor that could better detect and distinguish between small flashes would provide more information about storm evolution [ABSTRACT FROM AUTHOR]

Atmospheric electrical activity is one of the most damaging meteorological phenomena. Studies suggest that storm electrical discharges are correlated with severe weather events such as hail, strong surface winds, etc. To study these correlations, in addition to real data, numerical simulations can be used. In this work, we investigated the electrical activity of simulated thunderstorms using the WRF‐ELEC model over a case study of the RELAMPAGO field campaign (11 December 2018) associated with the occurrence of generalized deep moist convection in central Argentina including some supercell storms. The WRF‐ELEC model successfully reproduced the convective event. A tracking algorithm was used to individualize different convective cells and to study the relationship between their electric activity and different microphysical and kinematic variables. The model reproduced the expected correlations and time lags between these variables. In particular, the model showed that the best correlation occurred between the graupel and hail mass and the electric activity of the storms; this is expected given the key role of these microphysical species in the charge separation processes. Also, 5 m·s−1 updraft volume and cloud top temperature (CTT) temporal evolutions presented good correlations with the electrical activity. Maximum updraft show a less but still good correlation with the electrical activity. We also investigated the correlation between the electrical activity and the hail and graupel concentration near the surface (as an indicator of hail precipitation) and the near‐surface winds and we found that correlation is strong. In particular, for near‐surface ice, we determined that peaks in electrical activity precede peaks in hail and graupel precipitation and that electrical activity could be used as a proxy of this high‐impact weather event. [ABSTRACT FROM AUTHOR]

The electrical charge carried by raindrops provides significant information about thunderstorm electrification mechanisms, since the charge acquired by hydrometeors is closely related to the microphysical processes that they undergo within clouds. Investigation of charges on raindrops was conducted during the Remote sensing of Electrification, Lightning, And Meso‐scale/micro‐scale Processes with Adaptive Ground Observations field campaign. A newly designed instrument was used to determine simultaneously the fall velocity and charge for precipitating particles. Hydrometeor size and charge were measured in Córdoba city, Argentina, during electrified storms. Temporal series of size‐charge of single raindrops were recorded for two storms, which were also monitored with a Parsivel disdrometer and Lightning Mapping Array. The results show that the magnitude of the electric charges range between 1 and 50 pC and more than 90% of the charges are mainly carried by raindrops >1 mm, even though most of the raindrops are smaller than 1 mm. Furthermore, the measurement series show charged hydrometeors of both signs all the time. A correlation between the sizes and the charges carried by the raindrops was found in both storms. Key Points: The charge carried by precipitation particles is closely related to the microphysical processes in thunderstormsThe bulk of the measured charged raindrops are the larger than 1 mmA correlation between the sizes and the charges carried by the raindrops was found, in good agreement with the non‐inductive mechanism [ABSTRACT FROM AUTHOR]

High-energetic charged particles, such as solar protons, and phenomena such as Forbush decreases are eligible candidates to affect the global electric circuit. These effects have been studied by analyzing disturbances of the potential gradient in ground-based measurements in fair weather regions. In this paper, we investigate deviations in the potential gradient diurnal curve, during solar proton events, and Forbush decreases, from the mean values obtained in fair weather conditions. In each situation, we select only events which are not accompanied or followed by the other. Using the superposed epoch analysis, in order to enhance the visualization of small effects, we study the potential gradient data recorded between January 2010 and December 2019 at two stations located at low and middle-latitudes, and at two different altitudes: the Complejo Astronómico El Leoncito (CASLEO), Argentina: 31.78°S, 2550 m a.s.l., and the Geophysical Observatory in Świder (SWIDER), Poland: 52.12°N, 100 m a.s.l., respectively. For intense solar proton events (>100 MeV) we found a statistically significant increase of the potential gradient after solar proton events recorded at CASLEO and no such deviation in the potential gradient recorded at SWIDER. For Forbush decrease events (greater than 4%), no significant deviation of the potential gradient after the start of the event was found in both stations, however for very intense Forbush decreases (>7%) we found an increase of the potential gradient recorded at CASLEO. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Despite the fact that positive lightning is more energetic and more likely to result in severe damage than negative lightning, it remains much less understood than its negative counterpart. We report on a totally unexpected behavior of positive lightning produced by a supercell thunderstorm in Argentina. Specifically, the observed positive lightning flashes were composed of multiple strokes retracing the same channel to ground, which differs from the expectation that positive lightning rarely delivers charge to the same place on the ground in a discrete manner. Our observations show that the behavior of subsequent leaders in positive lightning can be very similar to that of subsequent leaders in negative lightning. Our findings call for a change to our current understanding of positive lightning. Key Points: 54 of 84 multiple‐stroke +CG flashes produced by a supercell in Argentina had subsequent strokes following pre‐existing channels to groundSpeeds of descending positive leaders that traverse a channel created by a previous positive stroke are reported for the first timeSubsequent leaders in positive lightning initiated in the same way as counterparts in negative lightning [ABSTRACT FROM AUTHOR]

A particular strength of lightning remote sensing is the variety of lightning types observed, each with a unique occurrence context and characteristically different emission. Distinct energetic intracloud (EIC) lightning discharges—compact intracloud lightning discharges (CIDs) and energetic intracloud pulses (EIPs)—produce intense RF radiation, suggesting large currents inside the cloud, and they also have different production mechanisms and occurrence contexts. A Low‐Frequency (LF) lightning remote sensing instrument array was deployed during the RELAMPAGO field campaign in west central Argentina, designed to investigate convective storms that produce high‐impact weather. LF data from the campaign can provide a valuable data set for researching the lightning context of EICs in a variety of subtropical convective storms. This paper describes the production of an LF‐CID data set in RELAMPAGO and includes a preliminary analysis of CID prevalence. Geolocated lightning events and their corresponding observed waveforms from the RELAMPAGO LF data set are used in the classification of EICs. Height estimates based on skywave reflections are computed, where prefit residual data editing is used to improve robustness against outliers. Even if EIPs occurred within the network, given the low number of very high‐peak current events and receiver saturation, automatic classification of EIPs may not be feasible using this data. The classification of CIDs, on the other hand, is straightforward and their properties, for both positive and negative polarity, are investigated. A few RELAMPAGO case studies are also presented, where high variability of CID prevalence in ordinary storms and high‐altitude positive CIDs, possibly in overshooting tops, are observed. Key Points: Classification and height estimation of energetic intracloud lightning are investigated using RELAMPAGO Low‐Frequency (LF) lightning waveformsA small number of high‐peak current events and saturation of LF receivers hinder the observation of Energetic In‐Cloud Pulses in RELAMPAGOA catalog of RELAMPAGO Compact Intracloud Discharges is produced to be used in future study of their occurrence in different storm types [ABSTRACT FROM AUTHOR]

The lightning data products generated by the low‐frequency (LF) radio lightning locating system (LLS) deployed during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observation (RELAMPAGO) field campaign in Argentina provide a valuable data set to research the lightning evolution and characteristics of convective storms that produce high‐impact weather. LF LLS data sets offer a practical range for mesoscale studies, allowing for the observation of lightning characteristics of storms such as mesoscale convective systems or large convective lines that travel longer distances which are not necessarily staying in range of regional VHF‐based lightning detection systems throughout their lifetime. LF LLSs also provide different information than optical space‐borne lightning detectors. Lightning measurements exclusive to LF systems include discharge peak current, lightning polarity, and lightning type classification based on the lightning‐emitted radio waveform. Furthermore, these measurements can provide additional information on flash rates (e.g., positive cloud‐to‐ground flash rate) or narrow bipolar events which may often be associated with dynamically intense convection. In this article, the geolocation and data processing of the LF data set collected during RELAMPAGO is fully described and its performance characterized, with location accuracy better than 10 km. The detection efficiency (DE) of the data set is compared to that of the Geostationary Lightning Mapper, and spatiotemporal DE losses in the LF data set are discussed. Storm case studies on November 10, 2018, highlight the strengths of the data set, which include robust flash clustering and insightful flash rate and peak current measures, while illustrating how its limitations, including DE losses, can be managed. Key Points: Geolocation and processing of the low‐frequency (LF) data set collected during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observation campaign is fully described and characterizedUnique LF observations include discharge peak current, polarity, and possibly type classification based on the emitted radio waveformLF spatiotemporal detection efficiency and storm case studies are discussed and compared to Geostationary Lightning Mapper data [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Electromagnetic signals emitted by lightning can be detected by sensors that are hundreds of kilometers away. Those signals are used to distinguish if the lightning discharge reaches the ground or remains inside the cloud. Higher accuracy of lightning type classification can lead to a better assessment of lightning risk and is also beneficial to lightning climatology studies. We present a machine‐learning–based algorithm to classify lightning pulses. The accuracy of the algorithm is 97%, which is better than those for classification algorithms used by lightning detection networks. Key Points: The classification accuracy of the machine‐learning–based algorithm using a representative lightning pulse data set is 97%Unlike multiparameter classification algorithms, the extraction of pulse parameters is not needed for this machine‐learning algorithmThis algorithm provides a probability for each prediction, which is a valuable metric for prediction uncertainty [ABSTRACT FROM AUTHOR]