In the lightning rods categorized as ESE an intermittent voltage impulse is applied to the lightning rod to modulate the electric field at its tip in an attempt to speed up the initiation of a connecting leader from the lightning rod when it is under the influence of a down coming stepped leader. In this paper it is shown that due to the stepping nature of the down coming stepped leader, there is a natural modulation of the electric field at the tip of any lightning rod exposed to the lightning stepped leaders and this modulation is much more intense than any artificial modulation that is possible under practical conditions. Based on the results it is claimed that artificial modulation of the electric field at the tip of lightning rods by applying voltage pulses to it is an unnecessary endeavor because the nature itself has endowed the tip of the lightning rod with a modulating electric field. Thus, as far as the effectiveness of artificial modulation of the tip electric field is concerned, there could be no difference in the lightning attachment efficiency between ESE and Franklin lightning rods.

The horizontal storm structure surrounding lightning ignited wildfires is examined using Vaisala’s National Lightning Detection Network (NLDN), NCEP’s Stage IV gauge corrected radar precipitation mosaic, and the US Forest Service’s Fire Occurrence Database. Though lightning flash density peaks strongly around fire ignitions on the instantaneous 1 km scale, on the hourly 10 km scale both the lightning and precipitation peaks are typically offset from fire ignitions. Lightning density is higher and precipitation lower around ignition points compared to non-ignition points. Both regression and horizontal distributions are consistent with the claim that positive flashes have a stronger association with ignition than negative flashes, but the statistical significance remains ambiguous

Currently, the number of lightning casualties and casualty rates have significantly reduced in developed countries, but there has been no significant reduction in developing countries. On the one hand, this is due to the high frequency of lightning; on the other hand, the vulnerability of people in developing countries is also an influencing factor. Through case analysis and summary, this paper expounds on lightning injury’s mechanism and clinical manifestations. It points out that lightning injury is mainly related to heart problems and the impact on the nervous system, rather than burns, which usually cause fewer consequences. Medical treatment needs to pay attention to the order and principles of treatment. Implementing CPR and auxiliary AED is the most effective way to save lives. Attention should be paid to the practical and effective treatment and nursing of lightning stroke sequelae. Finally, combined with the situation of lightning casualties in china, the existing problems are put forward during the process of early treatment, post-hospital treatment, and nursing observation, which provides an objective basis for the cognition of the scientific nature of the lightning injury.

Atmospheric lightning is an outcome of extreme complex physical processes occurring in the atmosphere. Cloud-to-ground (CG) lightning is considered as a natural disaster. Understanding the importance of CG lightning and implication of the lightning phenomena, Global Climate Observing System (GCOS), world meteorological organization, in its report in the year 2016, introduced the lightning as an Essential Climate Variable (ECV). The present report uses the Lightning Detection Sensor Network (LDSN) established by the National Remote Sensing Centre, Indian Space Research Organization over India to generate the Lightning ECV. A use case of these ECVs are also showcased for an event in Bihar, India, when 42 deaths were reported at locations with large number of CG occurrences.

The atmospheric lightning is one of the natural disasters which need to be considered in the sustainable development. The National Remote Sensing Centre (NRSC) has installed 46 lightning detection sensors (LDS) across the country. These sensors work in radio frequencies having their detection range up to 1000 km with more than 98% detection efficiency within 300 km range. The network is put in a way that sensors have about 50% sensor-to-sensor overlap in their detection ranges to enable high geolocation accuracy and maintain redundancy. The NRSC-LDS network (LDSN) estimates the current associated with each cloud to ground (CG) lightning flash which can be utilized for the risk assessment. The current estimates from one network to the other need to be inter-calibrated and harmonized. Present study compares the LDSN current estimates with Lightning Location Network (LLN) estimates and results are discussed with their impact on the estimation of vulnerable zones. A case study of lightning-induced fire event is presented to showcase the importance of the currents associated with lightning for future planning.

Thunderstorms endager life and infrastructure. Accurate and precise prediction of thunderstorms are therefore helpful to enable protections measures and to reduce the risks. This manuscript presents the latest developments to improve thunderstorm forecasting in the first few hours. This includes the description and discussion of a new Julia-based method (JuliaTSnos) for the temporal extrapolation of thunderstorms and the blending of this method with the numerical weather prediction model (NWP) ICON. The combination of ICON and JuliaTSnow attempts to overcome the limitations associated with the pure extrapolation of observations wit Atmospheric Motion Vectors (AMVs) and thus increase the prediction horizon. For the blending the operational ICON-D2 is used, but also the experimental ICON-RUC, which is performed with a higher data assimilation update cycle. The blended products are evaluated against lightning data. The Critical Success Index (CSI) for the blended RUC product is higher for all forecast time steps. This is mainly due to higher resolution of the AMVs (prediction hours 0-2) and the rapid update cycle of ICON-RUC (prediction hours 2-6). The results demonstrate the potential of the rapid update cycle to improve the short term forecasts of thunderstorms. Also the transition between AMV driven nowcasting to NWP is much smoother in the blended RUC product, which points to the advantages of fast data assimilation for seamless predictions. The CSI is well above the critical value of 0.5 for the 0-2 hour forecasts. However, also with RUC the CSI drops below 0.5 as soon as the last forecast is more than 3 hours away from the last data assimilation, indicating the lack of the model physics to accurately predict thunderstorms. This lack is simply a result of chaos theory. Within this scope the role of NWP in comparison with Artificial Intelligence (AI) is discussed and it is concluded that AI could replace physical short term forecasts in the near future.

Thunderstorms are among the most common and most dangerous meteorological hazards in the world. They cause lightning and can lead to strong wind gusts, squall lines, hail and heavy precipitation combined with flooding, and therefore pose a threat to health and life, can cause enormous property damage and also endanger flight savety. Monitoring and forecast of thunderstorms are therefore important topics. In this work a novel method for the detection and forecast of thunderstorms and strong convection is presented. The detection is based on the global GLD360 lightning data in combination with satellite information from the satellite series Meteosat, HIMAWAARI and GOES, covering the complete geostationary ring. Three severity levels are defined depending on the occurrence of lightning and the brightness temperature difference of the water vapour channels and the infrared window channel (∼ 10.8 μm). The detection of thunderstorms and strong convection is the basis for the nowcasting up to 2 hours, which is performed with the optical flow method TV-L1. This method provides the needed atmospheric motion vectors for the extrapolation of the thunderstorm movement. Both, the validation results as well as the feedback of the customers show the great value of the new NowCastSat-Aviation (NCS-A) method. For example, the Critical Success Index (CSI) is with 0.64 still quite high for the 60 minute forecast of severe thunderstorms. The method is operated 24/7 by the German Weather Service (DWD) and used to provide thunderstorm information to aviation customers and the central weather forecast unit of DWD.

Lightning has received a lot of attention in scientific literature during the recent decade, not only because it is an impressive atmospheric phenomenon but also its associations with severe storms that cause unprecedented damages to agriculture, electric power networks, property, and life. This study assessed the Spatio-temporal characteristics of lightning occurrence with elevation in Uganda using lightning flash and elevation datasets for a period of fifteen years (1998-2013). Datasets used in this study included daily lightning flashes as captured by Lightning Imaging Sensor (LIS) aboard on Tropical Rainfall Measurement Mission (TRMM) satellite and elevation data in form of Digital Elevation Model (DEM) obtained from the Shuttle Radar Topography Mission (SRTM). Spatio-temporal results indicated that ~80% of areas with an elevation that ranges from 800-1200 m above mean sea level (masl) in Uganda had severe lightning occurrences and ~20% of areas with an elevation greater than 1200 m (masl) had severe lightning occurrences. The country received an enhanced number of lighting events with the highest number in 1999. Subsequently, a reduced trend was observed from 2002 to 2007 followed by an increment in the number of lightning events in (2010, 2011, 2012, and 2013). The intensity of the events decreased gradually though two peaks were observed, (1998-2001) and (2010-2013). Furthermore, results indicate escalations in the frequency and duration of lightning events from 60 times in 1998 to approximately 200 times in 2013 and from 1000 microseconds in 1998 to more than 2000 microseconds in 2013. Generally, the country experienced an enhanced increase in lighting occurrences over the study period which therefore calls for urgent actions to combat the root cause and also provide effective measures to reduce the impacts of lightning strikes.

The Lightning Network (LN), a second-layer protocol built on top of the Bitcoin blockchain, is an innovative digital payment solution that offers increased convenience, speed, and cost-effectiveness to consumers and businesses alike. However, there is limited literature available on the characteristics of this nascent technology, the depth and breadth of the various business LN-related applications as well as relevant adoption/implementation challenges. This study aims to contribute to the understanding of the LN’s characteristics, its potential in enhancing business operations and its applicability across different sectors, while taking into account adoption and implementation challenges. We apply a narrative review methodology using a semi-systematic approach to examine new and emerging business models empowered by the LN and its characteristics, topology, performance, privacy, and security. We analyze the data to identify key themes and trends in the literature, offering a critical analysis of the strengths and weaknesses of the existing literature. Based on the findings, we provide several clusters of fruitful areas for future research directions. The findings of this study will help businesses make more informed decisions about adopting and leveraging the LN to improve their operations and enhance customer experience.

Physical reason for the small scale tortuosity observed in sparks and lightning channels is unknown at present. In this paper it is suggested that the small scale tortuosity of the discharge channels is caused by the natural tendency for subsequent leader streamer bursts to avoid each other but at the same time to align as much as possible along the direction of the background electric field. This process will give rise to a discharge channel that re-orients in space during each streamer burst creating the small scale tortuosity.

A physical model based on the mechanism observed in experimental investigations is introduced to describe the formation of negative leader steps. Starting with a small length of a space leader located at the periphery of the negative streamer system of the stepped leader the model simulates the growth and the subsequent formation of the leader step. Based on the model, the average step length, the average step forming time and the average stepped leader propagation speed is estimated as a function of prospective return stroke peak current. The results show that the average step length and the average leader speed increases with increasing prospective return stroke current. The results also show that the speed of the stepped leader increases as it approaches the ground. For a 30 kA prospective return stroke current the average leader speed obtained is about 5 x 10⁵ m/s and the average step length was about 10 m. The results obtained are in reasonable agreement with the experimental observations.

This study analyses the detection efficiency of the Worldwide Lightning Location Network (WWLLN) of sensors over Spain, a comparison is made with the Meteorological Spanish Agency (AEMET) Lightning Detection Network. The WWLLN operates a planetary distributed network of Very Low Frequency (VLF) antennas which detect lightning VLF signals around the Earth. Very high currents from lightning strokes radiate strong VLF signals in the band 6-22 kHz, which are detected up to 10,000 km, therefore WWLLN receiving sensors can be sparsely spaced. However the detection capability of VLF signals depends on the environment, and clean areas with a good ground and no metal parts around must be selected for proper reception. For the area of Spain and Portugal we had two WWLLN sensors in 2012, and three sensors later on for the data of 2020, 2021 and 2022. The sensors in Iberian Peninsula are at a distance of around 760 km; that is very close in comparison with other WWLLN sensors in the Earth which are around 5.000-15.000 km. The WWLLN sensors send the timing of the detection to the central sensor where the location of the lightning is determined with an improved algorithm when a mini-mum of 5 sensors detect the VLF signal. We show that detection efficiency was excellent and consistent with previous studies. Lightning location data from 1st January 2012 to 30st April 2012 from AEMET was used to analyze time and space shared events with the WWLLN network with 0.5s time difference and 20 km distance. The results improved in 2020, 2021 and 2022. More than 40,000 lightning strikes were analyzed in the present work, and we found a detection efficiency > 38%.

Lightning is responsible for most annually burned area by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms. Here, we describe the development and verification of a probabilistic lightning-strike algorithm designed to run on a uniform 20-km grid over the Continental USA and Alaska. The algorithm consists of a large set of logistic equations parameterized via logistic regression using long-term data records of observed lightning strikes and meteorological reanalysis fields from NOAA. Principal Component Analysis was employed to extract 13 Principal Components (strong predictors) from a list of 611 potential predictors. Our statistical analysis revealed that the occurrence of cloud-to-ground lightning strikes primarily depends on three factors: horizontal temperature distribution by pressure levels, amount of low-level atmospheric moisture, and wind vectors. These physical variables impact the vertical separation of electric charges in the lower troposphere during storms causing the voltage potential between ground and the cloud deck to increase to a level that triggers electrical discharges. Results from a forecast verification using independent data showed excellent model skill, thus making this algorithm suitable for inclusion into software systems forecasting the chance of wildfire ignitions.

In this article we report the first investigation over time of the atmospheric conditions around TGFs occurrence, using GPS sensors in combination with geostationary satellite observations and ERA5 reanalyses data. The goal is to understand which characteristics are favourable to the development of these events and to investigate if any precursor signals can be expected. A total of 9 TGFs, occurred at a distance lower than 45 km from a GPS sensor, were analysed and two of them are shown here as an example analysis. Moreover, the lightning activity, collected by the World Wide Lightning Location Network (WWLLN) was used in order to identify any links and correlations with TGF occurrence and PWV trends. The combined use of GPS and the stroke rate trends identified, for all cases, a recurred pattern in which an increase of PWV is observed on a timescale of about two hours before the TGF occurrence that can be placed within the lightning peak. The temporal relation between the PWV trend and TGF occurrence is strictly related to the position of GPS sensors in relation to TGF coordinates. The life cycle of these storms observed by geostationary sensors, described TGFs producing clouds as intense with a wide range of extensions and, in all cases, the TGF is located at the edge of the convective cell. Furthermore, the satellite data give an added value in associating the GPS water vapor trend to the convective cell generating the TGF. The investigation with ERA5 reanalyses data showed that TGFs mainly occur in convective environment with not exceptional values with respect to the monthly average value of parameters measured in the same location. Moreover the analysis showed the strong potential of the use of GPS data for the troposphere characterization in areas with complex territorial morphology. This study provided indications on the dynamics of convective systems linked to TGFs and will certainly help refine our understanding on their production highlights a potential approach through the use of GPS data to explore the lightning activity trend and the TGFs occurrence.

We have developed additive logistic models for the occurrence of lightning, large (≥ 2 cm), and very large (≥ 5 cm) hail to investigate the evolution of these hazards in the past, in the future, and for forecasting applications. The models, trained with lightning observations, hail reports, and predictors from atmospheric reanalysis, assign an hourly probability to any location and time on a 0.25° × 0.25° × 1-hourly grid as a function of reanalysis-derived predictor parameters, selected following an ingredients- based approach. The resulting hail models outperform the Significant Hail Parameter and the simulated climatological spatial distributions and annual cycles of lightning and hail are consistent with observations from storm report databases, radar, and lightning detection data. As a corollary result, CAPE released above the -10°C isotherm was found to be a more universally skilful predictor for large hail than CAPE. In the period 1950–2021, the models applied to the ERA5 reanalysis indicate significant increases of lightning and hail across most of Europe, primarily due to rising low-level moisture. The strongest modelled hail increases occur in northern Italy with increasing rapidity after 2010. Here, very large hail has become 3 times more likely than it was in the 1950s. Across North America trends are comparatively small, apart from isolated significant increases in the direct lee of the Rocky Mountains and across the Canadian Plains. In the southern Plains, a period of enhanced storm activity occurred in the 1980s and 1990s.

The development of extremely powerful thunderstorm which took place on August 19, 2015 is discussed in this paper. High depth hail cloud originated on the Black Sea Coast and classified as a supercell as well as several weaker hailstorms passed more than 1000 km over Northern Caucasus of Russia, the Caspian Sea, and then invaded the territory of Kazakhstan. During more than 20 hours of existence this supercell produced heavy hail, rain, intense lightning discharges, gust and tornado which rarely occurs in the region. The study of the structure and characteristics of the thunderstorm during the formation of electrical discharges and their frequency were of particular interest. According to the forecast, development of convective clouds and separate thunderstorms were expected, though the powerful hail process was not expected due to small vertical temperature gradients and the absence of cold fronts. Supercell was tracked by 5 radars located in this area, which showed its right-hand development with clock-wise deviation from the leading stream on 40-50 degrees to the right and the resulting speed of propagation was about 60-85km/h. The maximum reflectivity factor exceeded value 75dBZ, top of the clouds reached 15-16km and the height of the hail core raised on 11.2km. The size of hailstones size on most of the hail path was 2–3cm, and at the peak of cloud development - 4–5cm. Maximum frequencies of cloud-to-ground flashes of negative and positive polarities reached 30-35min-1 and 60-70min-1 correspondingly, while frequency of cloud-to-cloud flashes was significantly higher and amounted up to 300-500min-1 at the peak of the supercell development. An important fact is that the maximum frequency of flashes of different types coincided in time, showing that the reason of all discharges is similar. Total current of the cloud-to-ground flashes of positive and negative polarities was almost identical in magnitude and differed by sign. It was 200-300 kiloampere at the peak of thunderstorm development. The minimum value of radiation temperature, measured by SEVIRI radiometer installed onboard of Meteosat-10 satellite in 10.8 μm channel, was near to -60ºC. The minimum temperature value on the top of the supercell was comparable to coupled radar and sounding data. The most intensive precipitation flux derived from radiometric measurements was about 22000m3/sec; at the same period radars assessments showed precipitation up to 550mm/h (mixed phase precipitation) and size of hail 4.5cm. The combined satellite-radar-lightning data analysis showed that radar derived characteristics of the supercell reached their maximums earlier than maximum in lightning activity. The highest correlation coefficient between radar and lightning characteristics of the supercell storm was found for pair maximum reflectivity and intensity of LF (0.55) and VHF (0.66) discharges. Estimations of relationship between hail size and lightning activity showed that with increasing hail size, thunderstorm activity increases for both cloud-to-ground and intracloud flashes (on the level 0.46 - 0.59). Analysis of doppler-polarimetric data showed strong inflow zone associated with tornado. Tornadic debris signature was manifested by radar reflectivity factor ZH > 60 dBZ, differential reflectivity ZDR > -1 dB, copolar cross-correlation coefficient ρHV < 0.6, and it was collocated with the tornado vortex signature. Doppler velocities in mesocyclone zone reached values -43 and +63 m/s. Prominent radar echo hook was identified in 1.5 km layer above the ground, while ZDR columns was relatively narrow (4–8 km wide) and not very deep (4.5 km).

In this study, in order to explore the failure mode of ZnO varistors under multiple lightning stroke, a 5-pulse 8/20 μs nominal lightning current with pulse intervals of 50 ms was applied to the ZnO varistors. Scanning electron microscopy (SEM) and X-ray diffractometry (XRD) were used to analyze the microstructure of the material. The failure processes of ZnO varistors caused by multiple lightning impulse current were described. The performance changes of ZnO varistors after multiple lightning impulses were analyzed from macro and micro perspectives. According to the results of this study’s experiments, the macroscopic failure mode of the ZnO varistors after multiple lightning impulse was that the electrical parameters deteriorate rapidly with the increase of the number of impulse groups, and finally destroyed by side-corner cracking. The microstructural examination indicated that after the multiple lightning strokes, the proportion of Bi in the several crystal phases had been converted, the grain size of ZnO varistors became smaller, and the white intergranular phase (Bi-rich grain boundary layer) increased significantly. The failure mechanism was thermal damage and grain boundary structure damage caused by temperature gradient thermal stress generated by multiple lightning current.

We use the third version of the Canadian Local Climate Model as a diagnostic tool to study the climatology of observed CG lightning activity at Maniwaki (latitude: 46,23°N; Longitude: 75,58°W). We examine the dependence between the hourly lightning activity and the related atmospheric variables during the warm season of sixteen years (between 1984 and 2004). The goal of this research is: a) to evaluate the atmospheric static state evolution and its moisture contents for conditions having generated lightning occurrence, b) to develop a CG lightning parameterization, and c) to verify this CG lightning parameterization on other Canadian areas. The freezing level altitude and the precipitable water content are used to estimate the static air instability and its moisture content respectively. These two parameters are served to develop the CG lightning parameterization. A comparison between the observations and simulations CG lightning occurrence and frequency at Maniwaki showed a mean absolute error rate of 27% and 55% respectively. We apply this parameterization at four Canadian regions, distributed from west to east. The simulated CG lightning results are comparable to observed CG lightning at Maniwaki and tested regions. The application of the CG lightning parameterization to the daily data enabled us to find the monthly results. This application represents a preliminary stage for validation this parameterization in regional numerical models in Canada during the historic period.

We evaluated the detection efficiency and location accuracy of lightning discharges in Japan using Blitzortung.org, a volunteer-based network for locating lightning discharges from sferics measured by VLF electromagnetic receivers that have been deployed worldwide in recent years. A comparison of the flash rate (the detected lightning rate per area and period) from Blitzortung.org with that from the satellite-based OTD/LIS and the ground-based World Wide Lightning Location Network (WWLLN) observations showed that Blitzortung.org clearly observed intense lightning activity in and around the Kanto area, including Tokyo, in summer, which is typical of Japanese lightning activity. However, it did not clearly observe lightning activity in and around the Nansei Islands, including Okinawa. Conversely, Blitzortung.org observed winter lightning activity in the Hokuriku area and off the Kanto. In addition, event studies have compared the detection efficiency and location accuracy of Blitzortung.org with those of the Japanese Lightning Location Network (JLDN) to infer their absolute values. The latest detection efficiency of Blitzortung.org in the Kanto area was estimated at roughly 90%. The mean location accuracy was estimated at up to 5.6 km.

: This study investigates the use of various thunderstorm indices in predicting severe thunderstorms events during the monsoon season in four different regions in India. The research evaluates the performance of the prediction model using a model skill score and utilizes the Weather Research and Forecasting (WRF) model with the double moment microphysics scheme to simulate model cases. It also compares fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for predicting severe thunderstorms events. The results of this study show that in-corporating thunderstorm indices with model skill scores improves severe thunderstorms fore-casting in the monsoon season in India. The result revealed that determining the optimal threshold for each index is crucial in achieving accurate predictions. The study also highlights the importance of considering multiple indices rather than relying on a single index to predict severe thunderstorms events. The advance indices such as Energy Helicity Index (EHI), Supercell Composite Parameter (SCP), mainly works well with extreme severe thunderstorms. The simplistic indices can predict the weak or severe thunderstorm easily. The use of multiple thunderstorm indices can also help mete-orologists to make more accurate predictions, which can further enhance public safety. In conclu-sion, this study demonstrates the potential of incorporating thunderstorm indices with model skill scores like HSS and TSS and combinations of different skill scores in severe thunderstorms fore-casting during the monsoon season in India. Future research can build upon the findings of this study to develop more accurate and reliable severe weather forecasting models.

This study investigates the failure of a roof with steel truss construction of a factory building in Tekirdag in North-western part of Turkey. The failure occurred under hefty weather conditions including thunderbolt, lightning strikes, heavy rain and fierce winds. In order to interpret the reason for the failure, the effects of different combinations of factors on the design and dimensioning of the roof are checked. Therefore, finite element analysis is performed several times under different assumptions and considering different factors aiming to determine the dominant ones that are responsible for the failure using the commercial software Abaqus (Dassault Systèmes, Vélizy-Villacoublay, France). Each loading condition gives out a characteristic form of failure. The scenario with the most similar form of failure to the real collapse is considered as the most likely scenario of failure. Also, the factors included in this scenario are expected to be the responsible factors for the partial collapse of the steel truss structure.

Whistler mode waves play a major role in regulating the lifetime of trapped electrons in the Earth's radiation belts. Specifically, whistler mode hiss waves are one of the mechanisms that maintains the slot region between the inner and outer radiation belts. The generation mechanism of hiss is a topic still under debate with at least three prominent theories present in the literature. Lightning generated whistlers in their ducted or non-ducted modes, are considered to be one of the possible sources of hiss. We present a study of new observations from the Radio Receiver Instrument (RRI) on the Enhanced Polar Outflow Probe (ePOP: currently known as SWARM-E). RRI consists of two orthogonal dipole antennas, which enables polarization measurements, when the satellite boresight is parallel to the geomagnetic field. Here we present 75 passes of ePOP - RRI from 2014 - 2018, in which lightning whistlers and hiss waves were observed. In more than 50% of those passes hiss is found to co-exist with the lightning whistlers. Moreover, the whistler observations are correlated with observations of wave power at the lower-hybrid resonance. The observations and a whistler mode ray-tracing study suggest that multiple-hop lightning induced whistlers can be a source of hiss and plasma instabilities in the magnetosphere.

When overhead lines are impacted by lightning, the traveling wave of the fault contains a wealth of fault information. The accurate extraction of feature quantities from transient components and their classification is fundamental to the identification of lightning faults. The extraction process may involve modal aliasing, optimal wavelet base issues, and inconsistencies between the lightning strike distance and the fault point, which have the potential to impact the effectiveness of recognition. This paper presents a method for identifying lightning strike faults by utilizing Kullback-Leibler (KL) divergence enhanced Variational Mode Decomposition (VMD) and Symmetric Geometry Mode Decomposition (SGMD) improved with Permutation Entropy (PE) to address the aforementioned issues. A model of a 220 kV overhead line is constructed using real faults to replicate scenarios of winding strike, counterstrike, and short-circuit. The three-phase voltage is chosen and then sub-jected to Karenbaren decoupling in order to transform it into zero mode, line mode 1, and line mode 2. The zero mode voltage is decomposed using KL-VMD and PE-SGMD methods, and lightning identification criteria are developed based on various transient energy ratios. The research findings demonstrate that the criteria effectively differentiate between winding strike, counterstrike, and short-circuit faults, thus confirming the accuracy and efficacy of the lightning fault identification criteria utilizing KL-VMD and PE-SGMD.

Keywords: Lightning; Land Cover and Land Use; Topographic Effects, Itacaiúnas River Hydrographic Basin

Lightning strike can cause a considerable damage in aircraft parts made from semiconducting materials such as Carbon Fiber Reinforced Plastics (CFRPs). Therefore, in recent years, the lightning strike phenomenon has attracted the interest of the academic community and the aircraft industry. Until now, the problem has been addressed mainly experimentally, while the reported numerical works are very limited. In the present work, a coupled electro-thermal FE model has been developed using the ANSYS commercial FE code to simulate the lightning strike damage in unidirectional CFRP laminates due to the Joule heat flux phenomenon. The model is based on the SOLID69 thermoelectric element and applies a non-linear, time-transient analysis. The main input to the model is the thermal-electrical properties of the composite material which vary with temperature. Using the model, a parametric study on the effect of mesh density and peak intensity on the thermal damage has been performed. Three electrical lightning strikes of low (10 kA), medium (30 kA) and high peak intensity (40 kA) have been applied according to the SAE ARP 5412 standard. The electro-thermal model has been validated against a numerical model from the literature. The numerical results reveal that the increase of peak intensity leads to the increase of the area and penetration depth of matrix thermal damage (pyrolysis) as well as to the increase of the area of fiber damage (deterioration and ablation). Through progressive damage modeling, the residual tensile strength of the CFRP plate after being subjected to lightning strike of different peak intensity has been predicted. Lightning strike initial damage has been simulated by translating the thermal field into degradation of elastic properties of the lamina. The results show an increase in the accumulated matrix damage and a decrease of tensile strength due to the initial lightning strike damage. For the maximum peak intensity of 40 kA, a decrease in tensile strength of 4.8% has been predicted

General characteristics of K changes together with their fine structure associated with ground flashes in Sri Lanka in the tropics are presented. It is found that on average there are about 2 K changes associated with each return stroke. Analysis of the fine structure of the K changes shows that the K change is a chaotic pulse burst. Some of these chaotic pulse bursts start and the others end as a regular pulse bursts. Sometimes the chaotic part occurs in between two regular pulse bursts. This is in agreement with the recent published results that claim that chaotic pulse bursts are a random superposition of regular pulse bursts. The results show that the small step fields identified in the literature as K changes are the static fields associated with these pulse bursts.

The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, we traverse the path to the most recent breakthroughs in deep learning research. We encapsulate these diverse methods, shedding light on their progression and summarizing their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance our understanding of these methodologies, allowing us to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, our aim is to bolster our preparedness against the growing unpredictability of our climate and ensure a proactive stance towards lightning prediction.

While thunderstorms can pose severe risks to property and life, forecasting remains challenging, even at short lead times, as these often arise in meta-stable atmospheric conditions. In this paper, we examine the question of how well we could perform short-term (up to 180min) forecasts using exclusively multi-spectral satellite images and past lighting events as data. We employ representation learning based on deep convolutional neural networks in an “end-to-end” fashion. Here, a crucial problem is handling the imbalance of the positive and negative classes appropriately in order to be able to obtain predictive results (which is not addressed by many previous machine-learning-based approaches). The resulting network outperforms previous methods based on physically-based features and optical flow methods (similar to operational prediction models) and generalizes across different years. A closer examination of the classifier performance over time and under masking of input data indicates that the learned model actually draws most information from structures in the visible spectrum, with infrared imaging sustaining some classification performance during the night.

The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on cancelling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky Gorey filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning processes. This method can issue a warning signal up to 22 minutes in advance for lightning processes.