Search Results (175)

Coastal areas are prone to thunderstorms. Lightning strikes can damage power facilities and communication systems, thereby leading to serious consequences. The lightning location network achieves lightning location through data fusion from multiple lightning locator nodes and can detect the location and intensity of lightning in real time. It is an important facility for thunderstorm warning and protection in coastal areas. However, when a sensor node in a lightning location network experiences a soft fault, it causes distortion in the lightning location. To achieve fault diagnosis of lightning locator nodes in a multi-node data fusion mode, this study proposes a new lightning location mode: the observer pattern. This paper first analyzes the main factors contributing to the error of the lightning location algorithm under this mode, proposes an observer pattern estimation algorithm (OPE) for lightning location, and defines the proportion of improvement in lightning positioning accuracy (PI) caused by the OPE algorithm. By analyzing the changes in PI in the process of lightning location, this study further proposes a diagnostic algorithm (OPSFD) for soft-fault nodes in a lightning location network. The simulation experiments in the paper demonstrate that the OPE algorithm can effectively improve the positioning accuracy of existing lightning location networks. Therefore, the OPE algorithm is also a low-cost and efficient method for improving the accuracy of existing lightning location networks, and it is suitable for the actual deployment and upgrading of current lightning locators. Meanwhile, the experimental results show that when a soft fault causes the observation error of the node to exceed the normal range, the OPSFD algorithm proposed in this study can effectively diagnose the faulty node. Full article

We present lightning-induced ionospheric perturbations in narrowband very-low-frequency (VLF) signals from the transmitters NWC (21.82° S, 114.17° E, 19.8 kHz) and VTX (8.4° N, 77.8° E, 18.6 kHz) recorded at the low-latitude station Dehradun (DDN; 30.3° N, 78.0° E) over a 12-month period from September 2020 to October 2021. Early/slow VLF events, VLF LOREs, and step-like VLF LOREs associated with lightning were analyzed for their onset and recovery times. This study utilized data from the World Wide Lightning Location Network (WWLLN), which provides lightning locations and energy estimates. The results show that early/slow VLF events occur most frequently, accounting for approximately 68% of cases, followed by VLF LOREs at 12%, and step-like VLF LOREs at 10%. Furthermore, we observed that 100% of the VLF perturbing events occurred during the nighttime, which is not entirely consistent with previous studies. Moreover, more than 60% of VLF LOREs were associated with lightning energies of approximately 1 kJ, and about 40% were associated with lightning energies of ~10 kJ. Step-like VLF LOREs were linked to WWLLN energies between 1 and 5 kJ. The observed WWLLN energy range is somewhat lower than the energies reported in previous studies. Scattering characteristics revealed that 87.3% of events were associated with wide-angle scattering, while approximately 12.6% were linked to narrow-angle scattering. LWPC version 2.1 was used to simulate these perturbing events and to estimate the reflection height (H′, in km) and the exponential sharpness factor (β, in km⁻¹) corresponding to changes in D-region electron density. The reflection height (H′, in km) and the exponential sharpness factor (β, in km⁻¹) of the D-region varied from 83 to 87 km and from 0.42 to 0.79 km⁻¹ for early/slow VLF events, from 83 to 85 km and from 0.5 to 0.75 km⁻¹ for step-like VLF LOREs, and from 81 to 83 km and from 0.75 to 0.81 km⁻¹ for VLF LOREs, respectively. Full article

Lightning strikes are a major hazard in tropical regions, especially in northern Brazil, where open-area industries such as mining are highly exposed. This study proposes an octant-based multi-objective optimization approach for spatial lightning alert systems, focusing on minimizing both false alarm rate (FAR) and failure-to-warn (FTW). The method uses NSGA-III to optimize a configuration vector consisting of directional radii and alert thresholds, based solely on historical lightning location data. Experiments were conducted using four years of cloud-to-ground lightning data from a mining area in Pará, Brazil. Fifteen independent runs were executed, each with 96 individuals and up to 150 generations. The results showed a clear trade-off between FAR and FTW, with optimal solutions achieving up to 16% reduction in FAR and 50% reduction in FTW when compared to a quadrant-based baseline. The use of the hypervolume metric confirmed consistent convergence across runs. Sensitivity analysis revealed spatial patterns in optimal configurations, supporting the use of directional tuning. The proposed approach provides a flexible and interpretable model for risk-based alert strategies, compliant with safety regulations such as NBR 5419/2015 and NR-22. It offers a viable solution for automated alert generation in high-risk environments, especially where detailed meteorological data is unavailable. Full article

Lightning-induced voltages (LIVs) computation is crucial for lightning protection of power systems and equipment, yet the effect of complex terrain on LIVs remains not fully evaluated. This study establishes a three-dimensional finite-difference time-domain model to investigate the LIVs over Gaussian-shaped mountainous terrain, considering different lightning strike locations. Simulation results show that the influence of Gaussian-shaped mountains on LIVs is directly related to the lightning strike location. Compared with the flat ground scenario, the LIVs’ amplitude can increase by approximately 56% when lightning strikes the mountain top. However, for lightning strikes to the ground adjacent to the mountain, the LIVs’ amplitude is attenuated to varying degrees due to the shielding effect of the mountain. Additionally, the influences of line configuration, as well as mountain height and width on the LIVs, are evaluated. Full article

Lightning is one of the most severe natural disasters, characterized by its sudden onset, short duration, and significant damage. Existing quality control (QC) schemes for millisecond-level lightning observation data from a single source are primarily limited by the instrument and equipment, leading to inadequate monitoring, forecasting, and early warning accuracy in severe convective weather. This study proposes a comprehensive QC scheme for lightning location data from the China Meteorological Administration ground-based National Lightning Detection Network (CMA-LDN). The scheme integrates radar composite reflectivity (CREF) and FY-4A cloud-top brightness temperature (TBB), exploring the coupled relationship between lightning activity and severe weather processes. Through experimental analysis of convective processes over different time periods, QC thresholds are established based on the CREF, TBB, and area ratio. In this research, CREF ≥ 10 dBZ, TBB ≤ 270 K, and an 80% area ratio are tuned to filter false signals. Based on the regional threshold and area ratio results, gross error elimination and spatiotemporal clustering are combined to achieve an overall QC rate of 28.7%. The most effective quality control (QC) method is spatial-temporal clustering, achieving a QC efficiency of 20.9%. The processed lightning data are further merged with CREF and generated a 1 km and 6 min resolution lightning location dataset, which significantly improves the accuracy of ground-based lightning detection and supports operational forecasting of severe convective weather. Full article

The single-site lightning detection system can provide timely and effective information on lightning activity in areas where a multi-site lightning network cannot be built. Using deep learning, the single-site lightning detection achieves better performance than traditional methods, but it is highly dependent on the quality of the training dataset. To address this, this paper proposes a method called Tri-Pre to improve dataset quality and thereby enhance the performance of single-site cloud-to-ground lightning detection based on deep learning. After using the Tri-Pre method, the location model’s distance estimation error decreases by 36.08%. For lightning with propagation distances greater than 1000 km, the average relative error of the results from the built model based on the Tri-Pre method is 3.78%. When verified using additional measured data, the model also shows satisfactory accuracy, particularly for lightning with propagation distances beyond 1000 km. Specifically, for lightning with propagation distances between 1500 and 1600 km, the average relative location error is approximately 5.46%. Full article

This paper aims to enhance lightning positioning technology and data processing algorithms using very-high-frequency (VHF) lightning radar. It focuses on achieving three-dimensional imaging of plasma channels formed during lightning. By extracting key features from lightning echo signals received by VHF radar, we utilize a unique active and passive integrated positioning technology to locate the lightning radiation source. This algorithm effectively overcomes the limitations of traditional positioning methods. Experimental results show that the integrated positioning algorithm maintains accuracy while significantly increasing the number of positioning points, which supports subsequent imaging of lightning plasma channels. To illustrate the dendritic structure of the lightning channel, we employed a density-based clustering algorithm to eliminate noise points unrelated to the lightning source, enhancing imaging clarity. The methods presented in this study successfully meet the experiment’s goals and are significant for locating lightning radiation sources and understanding the dendritic structure changes in plasma channels during lightning propagation. Full article

Thunderstorms are weather phenomena that comprise thunder and lightning. They typically result in heavy precipitation, including rain, snow, and hail. Thunderstorms have adverse effects on flight at both the ground and the upper levels of the troposphere. The characteristics of the thunderstorm of Istanbul International Airport (International Civil Aviation Organization (ICAO) code: LTFM) have been investigated because it is currently one of the busiest airports in Europe and the seventh-busiest airport in the world. Geopotential height (m), temperature (°C), dewpoint temperature (°C), relative humidity (%), mixing ratio (g kg⁻¹), wind direction (°), and wind speed (knots) data for the ground level and upper levels of the İstanbul radiosonde station were obtained from the Turkish State Meteorological Service (TSMS) for 29 October 2018 and 1 January 2023. Surface data were regularly collected by the automatic weather stations near the runway and the upper-level data were collected by the radiosonde system located in the Kartal district of İstanbul. Thunderstorm statistics, stability indices, and meteorological variables at the upper levels were evaluated for this period. Thunderstorms were observed to be more frequent during the summer, with a total of 51 events. June had the highest number of thunderstorm events with a total of 32. This averages eight events per year. A total of 72.22% occurred during trough and cold front transitions. The K index and total totals index represented the thunderstorm events better than other stability indices. In total, 75% of the thunderstorm days were represented by these two stability indices. The results are similar to the covering of this area: the convective available potential energy (CAPE) values which are commonly used for atmospheric instability are low during thunderstorm events, and the K and total totals indices are better represented for thunderstorm events. This study investigates thunderstorm events at the LTFM, providing critical insights into aviation safety and operational efficiency. The research aims to improve flight planning, reduce weather-related disruptions, and increase safety and also serves as a reference for airports with similar climatic conditions. Full article

To overcome the limitations of spatiotemporal feature extraction that are inherent in conventional lightning warning algorithms relying solely on temporal analysis, we propose a novel prediction framework integrating a Graph Convolutional Network (GCN), Long Short-Term Memory (LSTM) architecture, and a multi-head attention mechanism. The methodology innovatively constructs station adjacency matrices based on geographical distances between meteorological monitoring stations in Qingdao, Shandong Province, China, where GCN layers capture inter-station spatial dependencies while LSTM units extract localized temporal dynamics. A dedicated multi-head attention module was developed to enable adaptive fusion of global spatiotemporal patterns, significantly enhancing lightning warning level prediction accuracy at target locations. The GCN-LSTM model achieved 93% accuracy, 59% precision, 64% recall, and a 59% F1 score. Experimental evaluation on operational meteorological data demonstrated the model’s superior performance: it achieved statistically significant accuracy improvements of 6% (p = 0.019), 3% (p = 0.026), and 2% (p = 0.03) over conventional LSTM, TGCN, and CNN-RNN baselines, respectively. Comprehensive assessments through precision–recall analysis, confusion matrix decomposition, and spatial generalizability tests confirmed the framework’s robustness. The key theoretical advancement introduced by this study lies in the synergistic coupling of graph-based spatial modeling with deep temporal sequence learning, augmented by attention-driven feature fusion—an architectural innovation addressing critical gaps in existing single-modality approaches. This methodology establishes a new paradigm for extreme weather prediction with direct applications in lightning hazard mitigation. Full article

Lightning is associated with severe thunderstorm events and causes hundreds of deaths annually in Brazil. Additionally, it is responsible for losses amounting to millions in Brazil’s electricity and telecommunication sectors. Between November 2011 and March 2012, the CHUVA-Vale do Paraíba (CHUVA-Vale) campaign was conducted in the Vale do Paraíba region and the Metropolitan Area of São Paulo (MASP), located in southeastern São Paulo state, Brazil, to enhance the understanding of cloud processes, including lightning. During the campaign, several instruments were available: a meteorological radar, lightning location systems, rain gauges, a vertical-pointing radar, a surface tower, and others. In this context, the main goal of this study was to evaluate the temporal evolution of lightning properties, such as frequency, type (cloud-to-ground (CG) and intracloud (IC) lightning), peak current, length, and duration, in the MASP between November 2011 and March 2012. To achieve this objective, lightning data from the Brazilian Lightning Detection Network (BrasilDAT) and the São Paulo Lightning Mapping Array (SPLMA) were utilized. The maximum amount of lightning for the BrasilDAT (322,598 events/month) occurred in January, while for the SPLMA (150,566 events/month), it occurred in February, suggesting that thunderstorms displayed typical summer behavior in the studied region. Most of lightning registered by the BrasilDAT were concentrated between 2:00 and 5:00 pm local time, with a maximum of 5.0 × 10⁴, 6.2 × 10³, and 95 events/month.hour for IC, −CG, and +CG lightning, respectively. These results are associated with the favorable conditions of diurnal atmospheric instability caused by surface heating. Regarding the lightning properties from the SPLMA, longer-duration lightning (up to 0.4 s) and larger spatial extension (up to 14 km) occurred during the nighttime period (0–6:00 am local time), while the highest lightning frequency (up to 9 × 10⁴ events month⁻¹ h⁻¹) was observed in the afternoon (3–4:00 pm local time). Full article

Transient analysis in power systems is essential for identifying deficiencies in the system, as well as for the protection and design of equipment. Transients can arise from natural events or network operations; in either case, they have the potential to cause significant damage to transmission lines, protection devices, generators, or transformers. This study examines a 20 kA, 1.2/50 µs lightning strike on a distributed-parameter transmission line connected to a power transformer. The voltage distributions across the winding sections on the neutral grounded high-voltage side of a disc-structured power transformer were obtained using the state-space method. An equivalent circuit for the state-space model was also developed in the Alternative Transients Program–Electromagnetic Transients Program (ATP-EMTP), and the results from both methods were compared. Both approaches revealed that the voltage waveforms in the transformer’s winding sections were consistent, with the voltage distribution decreasing linearly. Additionally, the voltage–current waves reached the transformer with a specific delay, depending on the characteristics of the transmission line and the location of the lightning strike. The impact of an increase in the grounding resistance value on the high-voltage side of the transformer on voltage distribution and peak voltage levels was examined. The proposed method effectively captures the voltage–current behavior of the transmission line and transformer windings during transient conditions. It is concluded that the state-space method serves as a viable alternative for transient analysis in power systems and can enhance the design of protection equipment and winding insulation studies. Full article

An improved method for identifying nighttime tweek signals in WHU VLF measurements was developed by redesigning the extraction process and validated through comparison with World-Wide Lightning Location Network (WWLLN) data. Using the enhanced method, 1,728,032 tweek signals were identified from four years (2018–2021) of VLF data, forming the most comprehensive tweek dataset for the mid–low latitude region in China. Statistical analysis reveals distinct nighttime variations in tweek occurrence rates, which increase from 18:00 LT to 20:00 LT, remain high until 04:00 LT, and gradually decrease towards sunrise. Seasonal differences in propagation distance are evident, ranging from ~2000 km in summer to ~4000 km in winter, corresponding to the seasonal shift of lightning activity. The cutoff frequency showed apparent daily and seasonal fluctuations, and the trends of daily variation are opposite between winter and summer. The annual variation in cutoff frequency presents a pattern different from previous cognition, with a minimum of 1.62 kHz in summer and a maximum of 1.68 kHz in winter, influenced by the magnetic cyclotron frequency at ionospheric reflection points. These findings improve the understanding of nighttime tweek characteristics and ionospheric dynamics in East Asia, offering valuable insights for ionospheric research and VLF communication systems. Full article

Between 23 and 25 January 2020, the Metropolitan Region of Belo Horizonte (MRBH) in Brazil experienced 32 natural disasters, which affected 90,000 people, resulted in 13 fatalities, and caused economic damages of approximately USD 250 million. This study aims to describe the synoptic and mesoscale conditions that triggered these natural disasters in the MRBH and the physical properties of the associated clouds and precipitation. To achieve this, we analyzed data from various sources, including natural disaster records from the National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), GOES-16 satellite imagery, soil moisture data from the Soil Moisture Active Passive (SMAP) satellite mission, ERA5 reanalysis, reflectivity from weather radar, and lightning data from the Lightning Location System. The South Atlantic Convergence Zone, coupled with a low-pressure system off the southeast coast of Brazil, was the predominant synoptic pattern responsible for creating favorable conditions for precipitation during the studied period. Clouds and precipitating cells, with cloud-top temperatures below −65 °C, over several days contributed to the high precipitation volumes and lightning activity. Prolonged rainfall, with a maximum of 240 mm day⁻¹ and 48 mm h⁻¹, combined with the region’s soil characteristics, enhanced water infiltration and was critical in triggering and intensifying natural disasters. These findings highlight the importance of monitoring atmospheric conditions in conjunction with soil moisture over an extended period to provide additional information for mitigating the impacts of natural disasters. Full article

The simulations presented here are based on the observational data of lightning electric currents associated with the eruption of the Hunga Tonga volcano in January 2022. The response of the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system to unprecedented lightning currents is theoretically investigated at low frequencies, including ultra low frequency (ULF), extremely low frequency (ELF), and very low frequency (VLF) ranges. The electric current source due to lightning near the location of the Hunga Tonga volcano eruption has a wide-band frequency spectrum determined in this paper based on a data-driven approach. The spectrum is monotonous in the VLF range but has many significant details at the lower frequencies (ULF, ELF). The decreasing amplitude tendency is maintained at frequencies exceeding 0.1 Hz. The density of effective lightning current in the ULF range reaches the value of the order of 10⁻⁷ A/m². A combined dynamic/quasi-stationary method has been developed to simulate ULF penetration through the lithosphere (Earth)–atmosphere–ionosphere–magnetosphere system. This method is suitable for the ULF range down to 10⁻⁴ Hz. The electromagnetic field is determined from the dynamics in the ionosphere and from a quasi-stationary approach in the atmosphere, considering not only the electric component but also the magnetic one. An analytical/numerical method has been developed to investigate the excitation of the global Schumann resonator and the eigenmodes of the coupled Schumann and ionospheric Alfvén resonators in the ELF range and the eigenmodes of the Earth–ionosphere waveguide in the VLF range. A complex dispersion equation for the corresponding disturbances is derived. It is shown that oscillations at the first resonance frequency in the Schumann resonator can simultaneously cause noticeable excitation of the local ionospheric Alfvén resonator, whose parameters depend on the angle between the geomagnetic field and the vertical direction. VLF propagation is possible over distances of 3000–10,000 km in the waveguide Earth–ionosphere. The results of simulations are compared with the published experimental data. Full article

Lightning is an electrical discharge phenomenon in the atmosphere caused by charge separation in clouds, which is divided into cloud-to-ground (CG) and cloud-to-cloud (CC) lightning. In recent years, research on the characteristics of multiple-stroke ground lightning and the amplitude of lightning currents has attracted significant attention. The amplitude of lightning currents serves as fundamental data for lightning protection in power systems. Its accurate measurement is crucial for designing and safeguarding power systems. This paper obtains data from a lightning location system and analyzes the probability density distribution of lightning current amplitudes. It is found that the median of lightning currents gradually decreases with an increasing number of multiple strokes, and there is a trend in the change of lightning current steepness. As the number of strokes increases, the median value of amplitude distribution gradually decreases, while the steepness coefficient shows an increasing trend. These research findings contribute to a deeper understanding of the characteristics of lightning and provide important references for lightning prevention and disaster reduction. Full article