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The study addresses the elevated occurrence of lightning activity and associated incidents over Kerala, India, where the topography is complex. It aims to systematically investigate the spatiotemporal variations in lightning activity while elucidating the intricate relationships of lightning occurrences with dynamic and thermodynamic variables. Lightning distribution over India indicates relatively prominent lightning activity in Kerala. Analysis of climatological data reveals that the peak of lightning activity in Kerala is observed in April, in the pre-monsoon season, with an average of 0.2 ∓ 0.05 flashes km -2 day -1 . Notably, the Kottayam district and its nearby areas exhibited high lightning frequencies of ≥ 0.3 flashes km -2 day -1 during this period. A secondary peak in lightning activity was recorded in October from the post-monsoon season, though comparatively less intense than during the pre-monsoon season (0.05 ∓ 0.008 flashes km -2 day -1 ). However, the regions west of the Palakkad Gap (PG) experience less lightning incidence. Further, the spatial analysis of dynamic and thermodynamic parameters (Convective Available Potential Energy (CAPE), K-Index, and pressure vertical velocity at 500 hPa) proved a clear and causative association with lightning occurrences in Kerala. The study also analyses the moisture transport to explore its migration during periods of heightened lightning activity. The trends observed in CAPE exhibit a significant correlation with lightning activity, especially during the pre-monsoon season.

Based on the detection data obtained by the LMI (Lightning Mapping Imager)—China’s first satellite-based lightning observation payload—from 2018–2022, combined with the ERA5 (ECMWF Reanalysis v5) reanalysis data of the same period, the temporal and spatial characteristics of lightning activities over the Tibetan Plateau and its response to the atmospheric circulation background are studied in detail in this paper. Based on the LMI data, we obtained consistent and continuous long-time-series lightning observation data for the whole region of the plateau for the first time, and the results show that the lightning density in the Tibetan Plateau is much smaller than that in the central and eastern land regions of China (CELR) at the same latitude. Lightning activity was unevenly distributed over the plateau and had obvious seasonal variation characteristics. The monthly amount of lightning and its ratio in the total amount of lightning for the whole year show the characteristics of “increasing first and then decreasing”. Most lightning occurs in June and July, which is about a month earlier than that in the CELR. The amount of lightning fluctuated in May and decreased rapidly after August, which is consistent with the local convective thunderstorm season. The hourly lightning frequency at different altitudes over the Tibetan Plateau is consistent with local convections and unique topography, and it is closely related to the features of the local night rain. The results also reveal comparative features between lightning and the atmospheric circulation background on the plateau, such as the wind field, CAPE (convective available potential energy), temperature, and humidity at 500 hPa. In the context of global warming, the average temperature in the central and western regions of the plateau increased in the past five years. This shows that the Tibetan Plateau, as a summer heat source, has a gradual warming trend, and the corresponding convections and lightning activities are also increasing gradually. Lightning activities can be used as an indicator of DCSs (deep convective systems). This paper gives a more comprehensive understanding of the characteristics of lightning activities all over the Tibetan Plateau, especially in the western part of the plateau, which lacked ground-based lightning observation data before. In addition, it reveals the comparative features between the lightning activities and the circulation background over the plateau in the past five years, which is helpful for further understanding the contribution of lightning activities to the plateau’s climate change. It can provide some reference for monitoring and researching the severe convective weather over the Tibetan Plateau.

The results of numerical experiments with the three-dimensional nonstationary bin model of convective clouds with a detailed consideration of hydrothermodynamic, microphysical, and electrical processes are presented. The structure of the cloud is investigated at different time moments for comparison with radar and lightning location observations in the North Caucasus. The possibility is analyzed of improving the quality of processing and interpretation of simulation and observation data on deep convective (with thunderstorm and hail) clouds using the developed three-dimensional visualization software.

Lightning is an indicator and a driver of climate change. Here, using satellite observations of lightning flash rate and ERA5 reanalysis, we find that the spatial pattern of summer lightning over northern circumpolar regions exhibits a strong positive relationship with the product of convective available potential energy (CAPE) and precipitation. Applying this relationship to Climate Model Intercomparison Project Phase 5 climate projections for a high-emissions scenario (RCP8.5) shows an increase in CAPE (86 ± 22%) and precipitation (17 ± 2%) in areas underlain by permafrost, causing summer lightning to increase by 112 ± 38% by the end of the century (2081–2100). Future flash rates at the northern treeline are comparable to current levels 480 km to the south in boreal forests. We hypothesize that lightning increases may induce a fire–vegetation feedback whereby more burning in Arctic tundra expedites the northward migration of boreal trees, with the potential to accelerate the positive feedback associated with permafrost soil carbon release.Changes in lightning activity are uncertain under climate change. The authors project that summer lightning in the Arctic is likely to more than double by the end of the century, with implications for lightning-strike tundra wildfires and associated carbon release from permafrost.

On 15 January 2022, Hunga Volcano in Tonga produced the most violent eruption in the modern satellite era, sending a water‐rich plume at least 58 km high. Using a combination of satellite‐ and ground‐based sensors, we investigate the astonishing rate of volcanic lightning (>2,600 flashes min−1) and what it reveals about the dynamics of the submarine eruption. In map view, lightning locations form radially expanding rings. We show that the initial lightning ring is co‐located with an internal gravity wave traveling >80 m s−1 in the stratospheric umbrella cloud. Buoyant oscillations of the plume's overshooting top generated the gravity waves, which enhanced turbulent particle interactions and triggered high‐current electrical discharges at unusually high altitudes. Our analysis attributes the intense lightning activity to an exceptional mass eruption rate (>5 × 109 kg s−1), rapidly expanding umbrella cloud, and entrainment of abundant seawater vaporized from magma‐water interaction at the submarine vent. Plain Language Summary The eruption of Tonga's underwater Hunga Volcano culminated on 15 January 2022 with a giant volcanic plume that rose out of the ocean and into the mesosphere. This plume created record‐breaking amounts of volcanic lightning observed both from space and by radio antennas on the ground thousands of kilometers away. We show that the eruption created more lightning than any storm yet documented on Earth, including supercells and tropical cyclones. The volcanic plume rose to its maximum height and expanded outward as an umbrella cloud, creating fast‐moving concentric ripples known as gravity waves, analogous to a rock dropped in a pond. Point locations of lightning flashes also expanded outward in a pattern of donut‐shaped rings, following the movement of these ripples. Optically bright lightning was detected at unusually high altitudes, in regions of the volcanic cloud 20–30 km above sea level. Our findings show that a sufficiently powerful volcanic plume can create its own weather system, sustaining the conditions for electrical activity at heights and rates not previously observed. Overall, remote detection of lightning contributed to a detailed timeline of this historic eruption and, more broadly, provides a valuable tool for monitoring and nowcasting hazards of explosive volcanism worldwide. Key Points This eruption produced the most intense lightning rates ever documented in Earth's atmosphere Lightning rings expand with enormous gravity waves in the umbrella cloud, caused by buoyant oscillation of the overshooting plume top Volcanic lightning and satellite analysis reveal at least four phases of eruptive activity from 02:57–15:12 UTC on 15 January 2022

The effect of aerosols on lightning has been examined in many studies, but its mechanisms are complex and far from understood. This study investigated the influence of aerosols on cloud-to-ground (CG) lightning during both afternoon (12:00–18:00 Beijing Time) and night (23:00–05:00 Beijing Time) in the Sichuan Basin by analysing 9-year datasets of CG lightning, aerosol loading, dynamic-thermodynamic, and cloud-related data from ground-based measurements, satellite, and model reanalysis to understand the difference in the influences of aerosols under conditions with and without solar radiation. The relationship between lightning and aerosol optical depth (AOD) is nonlinear in the afternoon and at night with a turning point at AOD ≅ 0.3. When AOD is less than 0.3, increasing AOD will lead to an increase in lightning flashes both in the afternoon and at night. When the AOD exceeds 0.3, the increase of AOD will reduce the lightning flashes in the afternoon but have no obvious effect on the lightning flashes at night. The different relationship between aerosol loading and lightning flashes in the afternoon and at night after AOD exceeds 0.3 is related to the changes in solar radiation in these two periods. In the afternoon, excessive aerosols reduce the solar radiation reaching the ground through its direct and indirect radiative effects, resulting in the decrease of the surface temperature, increasing atmospheric stability, inhibiting convection, and thus reducing lightning. At night, due to the absence of solar radiation, the influence of aerosols on surface temperature is weakened; thus, the inhibition of aerosols on lightning activity is weakened.

A new lightning-flash and convective initiation climatology is developed over the Alpine area, one of the hotspots for lightning activity in Europe. The climatology uses cloud-to-ground (CG) data from the European Cooperation for Lightning Detection (EUCLID) network, occurring from 2005 to 2019. The CG lightning data are gridded at a resolution of approximately 2 km and 10 min. A new and simple method of identifying convective initiation (CI) events applies a spatiotemporal mask to the CG data to determine CI timing and location. Although the method depends on a few empirical thresholds, sensitivity tests show the results to be robust. The maximum activity for both CG flashes and CI events is observed from mid-May to mid-September, with a peak at the end of July; the peak in the diurnal cycle occurs in the afternoon. CI is mainly concentrated over and around the Alps, particularly in northern and northeastern Italy. Since many thunderstorms follow the prevailing midlatitude westerly flow, a peak of CG flashes extends from the mountains into the plains and coastal areas of northeastern Italy and Slovenia. CG flashes and CI events over the sea/coast occur less frequently than in plains and mountains, have a weaker diurnal cycle, and have a seasonal maximum in autumn instead of summer.

Cloud-to-ground (CG) lightning data from the European Cooperation for Lightning Detection (EUCLID) network over the period 2006–2014 are explored. Mean CG flash densities vary over the European continent, with the highest density of about 6 km−2 yr−1 found at the intersection of the borders between Austria, Italy and Slovenia. The majority of lightning activity takes place between May and September, accounting for 85 % of the total observed CG activity. Furthermore, the thunderstorm season reaches its highest activity in July, while the diurnal cycle peaks around 15:00 UTC. A difference between CG flashes over land and sea becomes apparent when looking at the peak current estimates. It is found that flashes with higher peak currents occur in greater proportion over sea than over land.

Lightning activity constitute the major destructive component of thunderstorms over India. Hence, an understanding of the long-term variability in lightning occurrence and intensity and their interrelation with various causative factors is required. Long-term (1998–2014) Tropical Rainfall Measuring Mission (TRMM) satellite-based lightning observations depict the most frequent lightning occurrences along the Himalayan foothills, the Indo-Gangetic plains and coastal regions, while the intensity of these lightning strikes is found to be strongest along the coastal regions and in the Bay of Bengal. In addition, both of the abovementioned lightning properties show a very strong intensification (∼ 1 %–2.5 % annually) across all Indian regions during the 1998–2014 period with the maximum trends along the coasts. Accordingly, a detailed statistical dominance analysis is performed which reveals total column water vapor (TCWV) to be the dominant factor behind the intensification in lightning events, while instability, measured by the convective available potential energy (CAPE), and aerosol optical depth (AOD) jointly control the lightning frequency trends. An increase in surface temperatures has led to enhanced instability and, hence, stronger moisture transport to the upper-troposphere and lower-stratosphere regions, especially along the coasts. This transported moisture helps deplete the ozone concentration, leading to reduced temperatures and elevated equilibrium levels, which finally results in stronger and more frequent lightning events, as also evidenced by the trend analysis. Consequently, the relationships between lightning and its causative factors have been expressed in the form of multilinear regression equations, which are then employed in multiple global circulation models (GCMs) to understand the long-term impact of urbanization on lightning over the period from 1950 to 2100. The analysis reveals a uniform increase in lightning occurrence and intensity using both urbanization scenarios; however, accelerated growth is observed in the RCP8.5 projections after the year 2050, as also observed from the surface warming trends. As a result, lightning frequency and intensity values across the Indian region are expected to increase ∼ 10 %–25 % and 15 %–50 %, respectively, by the end of the century with the highest risk along the coasts; hence, this requires immediate attention from policymakers.

Precise and timely lightning nowcasting is still a great challenge for meteorologists. In this study, a new semantic segmentation deep learning network for cloud-to-ground (CG) lightning nowcasting, named LightningNet, has been developed. This network is based on multisource observation data, including data from a geostationary meteorological satellite, Doppler weather radar network, and CG lightning location system. LightningNet, with an encoder–decoder architecture, consists of 20 three-dimensional convolutional layers, pooling and upsampling layers, normalization layers, and a softmax classifier. The central–eastern and southern China was selected as the study area, with considerations given to the topography and spatial coverage of the weather radar and lightning observation networks. Brightness temperatures ( T B ) of six infrared bands from the Himawari-8 satellite, composite reflectivity mosaic, and CG lightning densities were used as the predictors because of their close relationships with lightning activity. The multisource data were first interpolated into a uniform spatial/temporal resolution of 0.05° × 0.05°/10 min, and then training and test datasets were constructed, respectively. LightningNet was trained to extract the features of lightning initiation, development, and dissipation. The evaluation results demonstrated that LightningNet was able to achieve good performance of 0–1-h lightning nowcasts using the multisource data. The probability of detection, the false alarm ratio, the area under relative operating characteristic curve, and the threat score (TS) of LightningNet with all three types of data reached 0.633, 0.386, 0.931, and 0.453, respectively. Because geostationary meteorological satellite and radar both possess the capability of capturing lightning initiation (LI) features, LightningNet also showed good performance in LI nowcasting. When all three types of data were used, more than 50% LI was predicted accurately and the TS exceeded 0.36. LightningNet’s nowcast performance using triple-source data was clearly superior to that using only single-source or dual-source data, and these findings indicate that LightningNet has good capability of combining multisource data effectively to produce more reliable lightning nowcasts.