Lightning Flash Detection System (LFDS) [Slides]
Lightning Flash Detection System (LFDS) is a viable solution for detecting cloud-to-ground lightning.
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Lightning Flash Detection System (LFDS) is a viable solution for detecting cloud-to-ground lightning.
The Electromagnetics Group wanted another system for detecting lightning flashes near/around the Pantex Plant. The current system in use detects lightning by detecting RF signals at a particular frequency. LFDS detects lightning by detecting flashes of light in the visual and near IR spectrums. This work is primarily focused on detecting cloud-to-ground lightning.
A quantum dot (QD) lightning detection and warning (LDW) system and method. This LDW system and method find broader applicability to spark and other transient optical event detection as well. The QDs are operable for receiving ultraviolet (UV), infrared (IR), visible, x-ray, and/or gamma ray radiation emanating from lightning or the like and generating visible radiation that may be detected and utilized to generate topological event information, such that property, human life, and the like may be safeguarded.
While multiple lightning detection systems provide geographical locations of lightning events across the globe, robust lightning altitude measurements on a global scale have proven elusive. Space-based platforms have an advantageous viewing geometry for making these measurements, but prior studies with the Fast On-orbit Recording of Transient Events (FORTE) satellite were limited to a few thousand events. In this study, we apply the same technique for calculating source altitude from the previous efforts to a large catalog of hundreds of thousands of global FORTE in-cloud lightning events that were coincident with flashes geolocated by its lightning imager between 1997 and 2003. We use this new data set to document global variations in lightning altitude. As in previous studies, we find that FORTE primarily resolves sources from the upper (positive) charge layer at ~11 km altitude in normal thunderstorms. However, sources are also recorded from other charge layers in the storm and from leaders developing between layers. In particular, we note a pronounced increase in source altitude in the first 20 ms of FORTE flashes from the negative leader developing upward into the upper positive charge layer. Regions known for wintertime and/or stratiform lightning have increased contributions from low-altitude sources, while tropical regions particularly around Panama and the Maritime Continent have the greatest concentrations of high-altitude sources.
Abstract The electrification of volcanic plumes has been described intermittently since at least the time of Pliny the Younger and the 79 AD eruption of Vesuvius. Although sometimes disregarded in the past as secondary effects, recent work suggests that the electrical properties of volcanic plumes reveal intrinsic and otherwise inaccessible parameters of explosive eruptions. An increasing number of volcanic lightning studies across the last decade have shown that electrification is ubiquitous in volcanic plumes. Technological advances in engineering and numerical modelling, paired with close observation of recent eruptions and dedicated laboratory studies (shock-tube and current impulse experiments), show that charge generation and electrical activity are related to the physical, chemical, and dynamic processes underpinning the eruption itself. Refining our understanding of volcanic plume electrification will continue advancing the fundamental understanding of eruptive processes to improve volcano monitoring. Realizing this goal, however, requires an interdisciplinary approach at the intersection of volcanology, atmospheric science, atmospheric electricity, and engineering. Our paper summarizes the rapid and steady progress achieved in recent volcanic lightning research and provides a vision for future developments in this growing field.
We use a cluster feature data set for the Fast On-orbit Recording of Transient Events (FORTE) satellite that combines detections from its pixelated Lightning Locating System (LLS), photodiode detector (PDD) and Radio-Frequency (RF) instrumentation to generate statistics describing the frequency and timing of lightning events detected by each instrument during lightning flashes. Coincident observations from the same vantage point allow us to directly compare flash details that can be resolved by the wide Field of View (FOV) instruments relative to the pixelated LLS—whose design is based on NASA's Lightning Imaging Sensor. We find that both the PDD and RF system typically generate more detections than the lightning imager (mean: 1.5 PDD events per LLS group, 2 RF events per LLS group) from pulses that are either not sufficiently bright in the optical band (in the case of RF) or that lack the optical energy density (in the case of the PDD) required to trigger one of the pixels on the LLS imaging array. This includes additional activity before the first LLS group or after the final LLS group. These FORTE results demonstrate that certain lightning processes would be better resolved by wide-FOV optical and RF instruments than lightning imagers. Current/future space-based missions that use/plan to use similar instruments will improve our understanding of flash evolution by resolving details missed by lightning imagers.
Understanding the lightning science behind the lightning detected by remote sensing systems is crucial to Sandia’s remote sensing program. Improved understanding of lightning properties can lead to improvements of onboard and/or ground-based background signal discrimination.
Lightning processes generate a diverse collection of optical pulses from current traversing the lightning channels. These signals are then broadened spatially and temporally via scattering in the clouds. The resulting waveforms measured from space with instruments like the photodiode detector (PDD) on the Fast On-orbit Recording of Transient Events (FORTE) satellite have a variety of shapes. In this study, we use coincident optical and Radio-Frequency measurements to document the properties of optical PDD waveforms associated with different types of lightning, estimate delays from scattering in the clouds, and comment on how pulse shape impacts optical lightning detection. We find that the attributes of optical pulses recorded by the PDD are consistent with prior studies, but vary globally and with event amplitude. The most powerful lightning tends to be single-peaked with faster rise times (median: ~100 µs) and shorter effective widths (median: ~400 µs) than normal lightning. Particularly dim events, meanwhile, include cases of broad optical waveforms with sustained optical emission throughout the PDD record, which the pixelated FORTE Lightning Location System (LLS) instrument has difficulty detecting. We propose that this is due to the optical signal being divided between individual LLS pixels that are each, individually, not bright enough to trigger. We use PDD waveforms and Monte Carlo radiative transfer modeling to demonstrate that increasing the temporal and spatial resolution of a pixelated lightning imager will make it more difficult to detect these broad/dim pulses as their energy becomes divided between additional pixels/integration frames.
Raikoke, a small, unmonitored volcano in the Kuril Islands, erupted in June 2019. We integrate data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System (IMS) infrasound network, and global lightning detection network (GLD360) with information from local authorities and social media to retrospectively characterize the eruptive sequence and improve understanding of the pre-, syn- and post- eruptive behavior. In this work, we observe six infrasound pulses beginning on 21 June at 17:49:55 UTC as well as the main Plinian phase on 21 June at 22:29 UTC. Each pulse is tracked in space and time using lightning and satellite imagery as the plumes drift eastward. Post-eruption visible satellite imagery shows expansion of the island's surface area, an increase in crater size, and a possibly-linked algal bloom south of the island. We use thermal satellite imagery and plume modeling to estimate plume height at 10–12 km asl and 1.5–2 × 10 6 kg/s mass eruption rate. Remote infrasound data provide insight into syn-eruptive changes in eruption intensity. Our analysis illustrates the value of interdisciplinary analyses of remote data to illuminate eruptive processes. However, our inability to identify deformation, pre-eruptive outgassing, and thermal signals, which may reflect the relatively short duration (~12 h) of the eruption and minimal land area around the volcano and/or the character of closed-system eruptions, highlights current limitations in the application of remote sensing for eruption detection and characterization.
During the 2022 New Mexico monsoon season, we deployed two X-ray scintillation detectors, coupled with a 180 MHz data acquisition system to detect X-rays from natural lightning at the Langmuir Lab mountain-top facility, located at 3.3 km above mean sea level. Data acquisition was triggered by an electric field antenna calibrated to pick up lightning within a few km of the X-ray detectors. We report the energies of over 240 individual photons, ranging between 13 keV and 3.8 MeV, as registered by the LaBr3(Ce) scintillation detector. These detections were associated with four lightning flashes. Particularly, four-stepped leaders and seven dart leaders produced energetic radiation. Importantly, the reported photon energies allowed us to confirm that the X-ray energy distribution of natural stepped and dart leaders follows a power-law distribution with an exponent ranging between 1.09 and 1.96, with stepped leaders having a harder spectrum. Characterization of the associated leaders and return strokes was done with four different electric field sensing antennas, which can measure a wide range of time scales, from the static storm field to the fast change associated with dart leaders.
Retrospective eruption characterization is valuable for advancing our understanding of volcanic systems and evaluating our observational capabilities, especially with remote technologies (defined here as a space-borne system or non-local, ground-based instrumentation which include regional and remote infrasound sensors). In June 2019, the open-system Ulawun volcano, Papua New Guinea, produced a VEI 4 eruption. We combined data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System infrasound network, and GLD360 globally detected lightning with information from the local authorities and social media to characterize the pre-, syn- and post-eruptive behaviour. The Rabaul Volcano Observatory recorded ~24 h of seismicity and detected SO 2 emissions ~16 h before the visually-documented start of the Plinian phase on 26 June at 04:20 UTC. Infrasound and SO2 detections suggest the eruption started during the night on 24 June 2019 at 10:39 UTC ~38 h before ash detections with a gas-dominated jetting phase. Local reports and infrasound detections show that the second phase of the eruption started on 25 June 19:28 UTC with ~6 h of jetting. The first detected lightning occurred on 26 June 00:14 UTC, and ash emissions were first detected by Himawari-8 at 01:00 UTC. Post-eruptive satellite imagery indicates new flow deposits to the south and north of the edifice and ash fall to the west and southwest. In particular, regional infrasound data provided novel insight into eruption onset and syn-eruptive changes in intensity. We conclude that, while remote observations are sufficient for detection and tracking of syn-eruptive changes, key challenges in data latency, acquisition, and synthesis must be addressed to improve future near-real-time characterization of eruptions at minimally-monitored or unmonitored volcanoes.
At the Savannah River Site (SRS), employees receive automated broadcast notification about lightning only after three strikes have already occurred near the site boundary. To increase employee safety, it is preferential to give employees lead time before lightning strikes occur. We compared measurements from an on-site electric field mill to lightning detection data from the National Lightning Detection Network and the Geostationary Lightning Mapper. Using a difference threshold, we determined that the electric field mill provided a lead time greater than 6 minutes for 95% of lightning events from 2009-2020, with an average lead time of 56 minutes. Detection of events were limited to a 7-mile radius around the field mill. We also identified that the field mill threshold generated many false detections not clearly identified. False detections and detections from only precipitation can be reduced by using a second threshold without creating too many missed detections (Type II errors). The two thresholds used together provide the best information about rapidly changing electric fields and aid in advanced detection necessary to improve the lightning warning system used at SRS.
The Integrated Nuclear Detonation Detection (iNDD) project seeks to develop two statistical constructs. The first will fuse operational data from Nuclear Detonation Detection (NDD) systems in order to determine whether a nuclear event has likely occurred; this is referred to as the “fused detector.” The assorted detection domains of NDD systems span independent regimes with uncorrelated backgrounds; for example, lightning is a background for satellite-based systems, while earthquakes are a background for seismic systems.
At the Savannah River Site, employees receive warnings about lightning only after three strikes have already occurred near the site. In order to increase employee safety, it is preferential to warn employees of a lightning threat much sooner. We used data from an on-site Electric Field Mill to measure values of the atmospheric electric charge. We also used lightning detection data from the National Lightning Detection Network and the Geostationary Lightning Mapper. By comparing the data of each, we can determine how long before or after a lightning strike did the Electric Field Mill first measure within our lightning detection threshold. If the detection threshold occurs before the strike does, this provides a lead time for knowing about lightning risks before they happen. Our findings conclude that 95% of lightning strikes have a lead time in the electric field where we can predict the lightning before it occurs and warn employees of the potential lightning threat. However, our lightning detection threshold can occur even without lightning or precipitation. Finding how these false lightning alarms affect our data is necessary to improving the lightning warning system.
We report ground-based observations of a terrestrial gamma-ray flash (TGF) associated with a strong negative cloud-to-ground lightning strike (-CG) which occurred near Los Alamos National Laboratory (LANL) in New Mexico, USA. Gamma photons were detected by the Terrestrial High-energy Observations of Radiation (THOR) instrument, developed at the University of California, Santa Cruz, which has been hosted at LANL since 2022. Simultaneous measurements were also made with the 3-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) system, which includes 3D lightning mapping, two fast antennas, and an additional plastic scintillator. Additional field change waveforms were obtained from the Earth Networks Total Lightning Network (ENTLN). The lightning initiated about 1 km above the ground, leading to a powerful -CG with an ENTLN peak current of −237 kA and bipolar field change matching the recently described “energetic compact stroke” (ECS) shape. The TGF was then observed ∼35 µs after the start of the return stroke. We also observe an electric field pulse likely produced by the TGF either in isolation or by coupling to the lightning channel. Based on modeling of the radiated electric field and photon propagation through the atmosphere we infer that the TGF source was on the order of 10 17 photons. This TGF and associated lightning are extremely similar to some recently reported ECS TGFs in coastal Japan in winter, but we report the first observation of this phenomenon outside of Japan in a different climate, terrain, and season.
Islanding occurs when a load is energized solely by local generators and can result in frequency and voltage instability, changes in current, and poor power quality. Poor power quality can interrupt industrial operations, damage sensitive electrical equipment, and induce outages upon the resynchronization of the island with the grid. This study proposes an islanding detection method employing a Duffing oscillator to analyze voltage fluctuations at the point of common coupling (PCC) under a high-noise environment. Unlike existing methods, which overlook the noise effect, this paper mitigates noise impact on islanding detection. Power system noise in PCC measurements arises from switching transients, harmonics, grounding issues, voltage sags and swells, electromagnetic interference, and power quality issues that affect islanding detection. Transient events like lightning-induced traveling waves to the PCC can also introduce noise levels exceeding the voltage amplitude by more than seven times, thus disturbing conventional detection techniques. The noise interferes with measurements and increases the nondetection zone (NDZ), causing failed or delayed islanding detection. The Duffing oscillator nonlinear dynamics enable detection capabilities at a high noise level. The proposed method is designed to detect the PCC voltage fluctuations based on the IEEE standard 1547 through the Duffing oscillator. For the voltages beyond the threshold, the Duffing oscillator phase trajectory changes from periodic to chaotic mode and sends an islanded operation command to the inverter. The proposed islanding detection method distinguishes switching transients and faults from an islanded operation. Experimental validation of the method is conducted using a 3.6 kW PV setup.
Optical emissions associated with Terrestrial Gamma ray Flashes (TGFs) have recently become important subjects in space-based and ground-based observations as they can help us understand how TGFs are produced during thunderstorms. In this paper, we present the first time-resolved leader spectra of the optical component associated with a downward TGF. The TGF was observed by the Telescope Array Surface Detector (TASD) simultaneously with other lightning detectors, including a Lightning Mapping Array (LMA), an INTerFerometer (INTF), a Fast Antenna (FA), and a spectroscopic system. The spectroscopic system recorded leader spectra at 29,900 frames per second (33.44 μs time resolution), covering a spectral range from 400 to 900 nm, with 2.1 nm per pixel. The recordings of the leader spectra began 11.7 ms before the -18 kA return stroke and at a height of 2.37 km above the ground. These spectra reveal that optical emissions of singly ionized nitrogen and oxygen occur between 167 μs before and 267 μs after the TGF detection, while optical emissions of neutrals (H I, 656 nm; N I, 744 nm, and O I, 777 nm) occur right at the moment of the detection. The time-dependent spectra reveal differences in the optical emissions of lightning leaders with and without downward TGFs.
Power distribution systems are geographically dispersed by nature. It may be affected by various factors, such as vegetation, weather, animal and human behaviors. Present response procedures to an outage event massively rely on expert experience and thus tend to be time-consuming. Automatic outage event detection and classification will help to reduce the responding and restoration time. However, this issue is less addressed with existing research done in this area. In this applied research, a set of waveform pre-processing techniques are first proposed to prepare the waveform data for being used as inputs to the classification algorithm. Further, a machine learning-based algorithm is proposed to classify the outage events according to their root causes, e.g. tree contact, animal contact, lightning, etc. Available data include three phase current & voltage waveforms and contextual information during the distribution system outages. The proposed machine learning algorithm takes the current and voltage waveforms as direct inputs in search of features that humans are unable to capture. Real data provided by a distribution company in the East Tennessee region is used to test the proposed pre-processing techniques and the classification algorithm.