搜索结果：Lightning

The increasing integration of solar power plants into transmission grids has raised concerns about their vulnerability to disturbances, particularly lightning strokes. Solar energy, while offering significant environmental and economic benefits, faces challenges when connected to transmission lines that are prone to lightning discharges. This paper investigates the impact of lightning events on solar power plants, focusing on overvoltage effects. Lightning stroke simulations were conducted at various distances from the solar power plant along the transmission line, considering scenarios with and without surge arrester. Key lightning parameters such as peak current, front time, and tail time were varied to simulate different lightning strokes. The study also includes a Fourier transform analysis of the resulting overvoltages with and without a surge arrester, along with the Hilbert marginal spectrum of these overvoltages. The results provide insights into the effectiveness of surge arresters in mitigating lightning overvoltages and highlight the importance of proper protective measures for enhancing the reliability and safety of solar power plants connected to transmission networks.

Lightning plays a crucial role in the Earth's climate system, yet existing parameterizations for use in forecasting and earth system models show room for improvement in capturing spatial and temporal variations in its frequency. This study develops deep learning-based parameterizations of lightning stroke density using meteorological variables from the ERA and IMERG datasets. Convolutional neural networks (CNNs) with U-Net architectures are trained using World Wide Lightning Location Network (WWLLN) data from 2010 to 2021 and evaluated on WWLLN lightning observations from 2022 and 2023. The CNNs reduce the average domain mean bias by an order of magnitude and produce significantly higher Fractions Skill Score (FSS) values across all lightning regimes compared to the multiplicative product of CAPE and precipitation. The CNNs show skill relative to previously published parameterizations over the oceans especially, with r2 values as high as 0.93 achieved between the best performing modeled and observed lightning stroke density climatologies. The CNNs are also able to accurately capture the 12-hourly evolution of lightning spatial patterns on an event-scale with high skill. These resul

Lightning strikes are one of the leading causes of death among natural disasters in tropical regions. The Congo rainforests host the highest rates of lightning flashes in the world and the lightning properties in this region have a strong seasonality owing to the cross-equatorial movements of the Inter Tropical Convergence Zone (ITCZ) throughout the year. The Lightning Flash Rates (LFR) are found to peak during the Equinoctial months while lightning radiance assumes the strongest values during the Boreal Summer months. Across the Tropical Rainfall Measuring Mission (TRMM) duration, (1998-2015), annual LFR in the Congo rainforest increases steadily, however, the average and the peak lightning radiances decrease by ~1%/year and ~1.5%/year respectively which is counter-intuitive to the expected intensification in weather extremes in a warming climate. An in-depth analysis highlighted the influence of moisture convergence over the Congo Basin from the Atlantic as the primary contributor to lightning compared to the aerosol nucleation and thermodynamic instability effects during the boreal summers. Further, it has been proposed that in a gradually warming climate, the strength of the pr

The Bitcoin Lightning Network, launched in 2018, serves as a layer 2 scaling solution for Bitcoin. The Lightning Network allows users to establish channels between each other and subsequently exchange off-chain payments. Together, these channels form a network that facilitates payments between parties even if they do not have a channel in common. The Lightning Network has gained popularity over the past five years as it offers an attractive alternative to on-chain transactions by substantially reducing transaction costs and processing times. Nevertheless, due to the privacy-centric design of the Lightning Network, little is understood about its inner workings. In this work, we conduct a measurement study of the Lightning Network to shed light on the lifecycle of channels. By combining Lightning gossip messages with on-chain Bitcoin data, we investigate the lifecycle of a channel from its opening through its lifetime to its closing. In particular, our analysis offers unique insights into the utilization patterns of the Lightning Network. Even more so, through decoding the channel closing transactions, we obtain the first dataset of Lightning Network payments, observe the imbalance o

Lightning, a common feature of severe meteorological conditions, poses significant risks, from direct human injuries to substantial economic losses. These risks are further exacerbated by climate change. Early and accurate prediction of lightning would enable preventive measures to safeguard people, protect property, and minimize economic losses. In this paper, we present DeepLight, a novel deep learning architecture for predicting lightning occurrences. Existing prediction models face several critical limitations: i) they often struggle to capture the dynamic spatial context and the inherent randomness of lightning events, including whether lightning occurs and its variability in location and timing even under similar meteorological conditions; ii) they underutilize key observational data, such as radar reflectivity and cloud properties; and iii) they rely heavily on Numerical Weather Prediction (NWP) systems, which are both computationally expensive and highly sensitive to parameter settings. To overcome these challenges, DeepLight leverages multi-source meteorological data, including radar reflectivity, cloud properties, and historical lightning occurrences through a dual-encode

Accurate forecasting of high-impact weather, specifically extreme precipitation and lightning, remains a significant challenge in numerical weather prediction (NWP) due to the complexity of atmospheric microphysics. While deep-learning models have shown promise in large-scale forecasting, they often suffer from systematic under-prediction of rare, high-intensity events and localized convective showers when optimized with conventional loss functions like Mean Squared Error (MSE). This study proposes an Event-Aware multi-task deep-learning post-processing framework designed to improve the representation of convective processes by leveraging lightning observations. The model jointly predicts precipitation amount, rainfall probability, and lightning occurrence using a shared-backbone Patch-cGAN (Conditional Generative Adversarial Network) architecture. To address the rare event problem, we introduce a lightning-informed loss-weighting strategy that element-wisely multiplies the MSE component by a spatial weight map derived from observed lightning strikes, forcing the model to prioritize accuracy in convective regions during training. Evaluations conducted over the Korean Peninsula duri

Upward lightning is much rarer than downward lightning and requires tall ($100+$~m) structures to initiate. It may be either triggered by other lightning discharges or completely self-initiated. While conventional lightning location systems reliably detect downward lightning, they miss a specific flash type of upward lightning that consists only of a continuous current. Globally, only few specially instrumented towers can detect this flash type. The proliferation of wind turbines in combination with large damage from upward lightning necessitates an improved understanding under which conditions the self-initiated and the undetected subtype of upward lightning occur. To find larger-scale meteorological conditions favorable for self-initiated and undetectable upward lightning, this study uses a random forest machine learning model. It combines direct measurements at the specially instrumented tower at Gaisberg mountain in Austria with explanatory variables from larger-scale atmospheric reanalysis data (ERA5). Atmospheric variables reliably explain whether upward lightning is self-initiated by the tower or triggered by other lightning discharges. The most important variable is the hei

Payment channel networks are an approach to improve the scalability of blockchain-based cryptocurrencies. The Lightning Network is a payment channel network built for Bitcoin that is already used in practice. Because the Lightning Network is used for transfer of financial value, its security in the presence of adversarial participants should be verified. The Lightning protocol's complexity makes it hard to assess whether the protocol is secure. To enable computer-aided security verification of Lightning, we formalize the protocol in TLA+ and formally specify the security property that honest users are guaranteed to retrieve their correct balance. While model checking provides a fully automated verification of the security property, the state space of the protocol's specification is so large that model checking becomes unfeasible. We make model checking the Lightning Network possible using two refinement steps that we verify using proofs. In a first step, we prove that the model of time used in the protocol can be abstracted using ideas from the research of timed automata. In a second step, we prove that it suffices to model check the protocol for single payment channels and the pro

Based on the electron-ion model, parameters of ball and bead lightning are calculated. The model allows us to estimate maximum size of ball lightning, its energy content, electric charge and magnetic field, to determine equilibrium conditions between positively charged ions located inside and outer shell containing rapidly moving electrons. An explanation is given to the fact that shells are observed inside ball lightning that differ from each other in color of glow. The model describes structure of not only ball lightning, but also bead lightning. The long-term stability of bead lightning is associated with the balance of neighboring beads under action of magnetic force of their attraction and electric force of repulsion, which exceed in magnitude the force of wind pressure.

Lightning has been suggested to play a role in triggering the occurrence of bio-ready chemical species. Future missions (PLATO, ARIEL, HWO, LIFE) and ground-based ELTs will investigate the atmospheres of potentially habitable exoplanets. We aim to study the effect of lightning on the atmospheric chemistry, how it affects false-positive and false-negative biosignatures, and if its effect would be observable on an exo-Earth and on TRAPPIST-1 planets. We use a combination of laboratory experiments, photochemical and radiative transfer modelling. With spark discharge experiments in N2-CO2-H2 gas mixtures, representing a range of possible rocky-planet atmospheres, we investigate the production of potential lightning signatures (CO, NO), possible biosignature gases (N2O, NH3, CH4), and important prebiotic precursors (HCN, Urea). Photochemical simulations are conducted for oxygen-rich and anoxic atmospheres for rocky planets in the habitable zones of the Sun and TRAPPIST-1 for a range of lightning flash rates. Synthetic spectra are calculated using SMART to study the atmosphere's reflectance, emission, and transmission spectra. Lightning enhances the spectral features of NO, NO2, and, in

While magnetic monopoles have extensive theoretical justification for their existence, but have proved elusive to observe, ball lightning is both relatively frequently observed and largely unexplained theoretically. It was first proposed in 1990 that ball lightning might result from the catalysis of nuclear fission by a magnetic monopole. The observed frequency of ball lightning does not conflict with current upper theoretical or observational bounds for magnetic monopole flux. Some possible mechanisms to account for the association of magnetic-monopole-caused ball lightning with thunderstorms are described, and proposals for further observational and theoretical research are made.

A model of lightning that captures the propagation of lightning channels on an irregular lattice is developed. The irregular lattice allows us to capture large two-dimensional systems (2 km x 2 km), while allowing grid refinement (on the order of cm) near areas of interest. Furthermore, the direction of lightning propagation is not biased in the orthogonal or diagonal directions of regular lattices. The probability of lightning strikes on people in standing positions, people in crouched positions, people near a tree and cattle are estimated.

Lightning is an important electrical phenomenon, known to exist in several Solar System planets. It carries information on convection and cloud formation, and may be important for pre-biotic chemistry. Exoplanets and brown dwarfs have been shown to host environments appropriate for the initiation of lightning discharges. In this PhD project, I aim to determine if lightning on exoplanets and brown dwarfs can be more energetic than it is known from Solar System planets, what are the most promising signatures to look for, and if these "exo-lightning" signatures can be detected from Earth. This thesis focuses on three major topics. First I discuss a lightning climatology study of Earth, Jupiter, Saturn, and Venus. I apply the obtained lightning statistics to extrasolar planets in order to give a first estimate on lightning occurrence on exoplanets and brown dwarfs. Next, I introduce a short study of potential lightning activity on the exoplanet HAT-P-11b, based on previous radio observations. Related to this, I discuss a first estimate of observability of lightning from close brown dwarfs, with the optical Danish Telescope. The final part of my project focuses on a lightning radio mode

The utilization of Very Low Frequency (VLF) electromagnetic signals in navigation systems is widespread. However, the non-stationary behavior of lightning signals can affect VLF electromagnetic signal transmission. Accurately classifying lightning signals is important for reducing interference and noise in VLF, thereby improving the reliability and overall performance of navigation systems. In recent years, the evolution of deep learning, specifically Convolutional Neural Network (CNNs), has sparked a transformation in lightning classification, surpassing traditional statistical methodologies. Existing CNN models have limitations as they overlook the diverse attributes of lightning signals across different scales and neglect the significance of temporal sequencing in sequential signals. This study introduces an innovative multi-scale residual transform (MRTransformer) that not only has the ability to discern intricate fine-grained patterns while also weighing the significance of different aspects within the input lightning signal sequence. This model performs the attributes of the lightning signal across different scales and the level of accuracy reached 90% in the classification.

After more than 180 years of research, ball lightning is still an unsolved problem in atmospheric physics. Since no progress can be expected without a controlled production of such objects in a laboratory, this report analyses a carefully selected subset of the observations, focusing on cases where the creation of ball lightning has been witnessed in order to identify the circumstances of their creation. Surprisingly, it was possible to establish that in many cases negative corona was involved as a precursor. Free electrons produced in a negative corona appear to be required for a number of processes, especially the creation of the visible plasma of the ball lightning and in forming a receiving antenna for the electromagnet pulse of the return stroke of the initiating cloud-ground lightning. In a different line of arguments, localized electromagnetic structures, being special solutions of Maxwells equations, were identified as the most likely model for the physical nature of ball lightning. The antenna required to produce such a structure can also be due to the free electrons. The free electron hypothesis allows outlining further actions in terms of data collection, computer simula

The article is devoted to electromagnetic phenomena in the atmosphere. The set of experimental data on the thunderstorm activity is analyzed. It helps to identify a possible physical mechanism of lightning flashes. This mechanism can involve the formation of metallic bonds in thunderclouds. The analysis of the problem is performed at a microphysical level within the framework of quantum mechanics. The mechanism of appearance of metallic conductivity includes the resonant tunneling of electrons along resonance-percolation trajectories. Such bonds allow the charges from the vast cloud charged subsystems concentrate quickly in lightning channel. The formation of metal bonds in the thunderstorm cloudiness is described as the second-order phase transition. A successive mechanism for the process of formation and development of the lightning channel is suggested. This mechanism is associated with the change in the orientation of crystals in growing electric field. Possible consequences of the quantum-mechanical mechanism under discussion are compared with the results of observations.

Despite years of research, real-time diverse grasp synthesis for dexterous hands remains an unsolved core challenge in robotics and computer graphics. We present Lightning Grasp, a novel high-performance procedural grasp synthesis algorithm that achieves orders-of-magnitude speedups over state-of-the-art approaches, while enabling unsupervised grasp generation for irregular, tool-like objects. The method avoids many limitations of prior approaches, such as the need for carefully tuned energy functions and sensitive initialization. This breakthrough is driven by a key insight: decoupling complex geometric computation from the search process via a simple, efficient data structure - the Contact Field. This abstraction collapses the problem complexity, enabling a procedural search at unprecedented speeds. We open-source our system to propel further innovation in robotic manipulation.

We introduce PennyLane's Lightning suite, a collection of high-performance state-vector simulators targeting CPU, GPU, and HPC-native architectures and workloads. Quantum applications such as QAOA, VQE, and synthetic workloads are implemented to demonstrate the supported classical computing architectures and showcase the scale of problems that can be simulated using our tooling. We benchmark the performance of Lightning with backends supporting CPUs, as well as NVidia and AMD GPUs, and compare the results to other commonly used high-performance simulator packages, demonstrating where Lightning's implementations give performance leads. We show improved CPU performance by employing explicit SIMD intrinsics and multi-threading, batched task-based execution across multiple GPUs, and distributed forward and gradient-based quantum circuit executions across multiple nodes. Our data shows we can comfortably simulate a variety of circuits, giving examples with up to 30 qubits on a single device or node, and up to 41 qubits using multiple nodes.

The Pierre Auger Observatory has detected downward terrestrial gamma-ray flashes (TGFs) with its Surface Detector. A key to understanding this high-energy radiation in thunderstorms is to combine such measurements with measurements of lightning processes in their earliest stages. With eleven modified Auger Engineering Radio Array (AERA) stations we can build an interferometric lightning detection array working in the bandwidth between 30 - 80 MHz inside the Surface Detector array to precisely measure lightning stepped leaders in 3D. These measurements allow us to decipher the cause of TGFs and clarify the reason for the observed high-energy particles in thunderstorms. We will present the current status of the detection plans including the configuration of the interferometric lightning detection array and the steps to take as well as the reconstruction characteristics obtained with AERA.