Lightning Signals Research Articles

A Lightning Classification Method Based on Convolutional Encoding Features

At present, for business lightning positioning systems, the classification of lightning discharge types is mostly based on lightning pulse signal features, and there is still a lot of room for improvement. We propose a lightning discharge classification method based on convolutional encoding features. This method utilizes convolutional neural networks to extract encoding features, and uses random forests to classify the extracted encoding features, achieving high accuracy discrimination for various lightning discharge events. Compared with traditional multi-parameter-based methods, the new method proposed in this paper has the ability to identify multiple lightning discharge events and does not require precise detailed feature engineering to extract individual pulse parameters. The accuracy of this method for identifying lightning discharge types in intra-cloud flash (IC), cloud-to-ground flash (CG), and narrow bipolar events (NBEs) is 97%, which is higher than that of multi-parameter methods. Moreover, our method can complete the classification task of lightning signals at a faster speed. Under the same conditions, the new method only requires 28.2 µs to identify one pulse, while deep learning-based methods require 300 µs. This method has faster recognition speed and higher accuracy in identifying multiple discharge types, which can better meet the needs of real-time business positioning.

A REMOTE SENSING SATELLITE LIGHTING DETECTION SYSTEM WITH HIGH DETECTION RATE AND LOW COMPLEXITY

Abstract. A payload was designed and implemented to do real-time and continuous lightning detection which can be used on remote sensing satellite. The payload consists of sensor unit and electronics unit. Based on the analysis of lighting signal spectral characteristics, the sensor unit adopts ultra-narrowband filtering technology to realize lightning signal spectral filtering, and uses high frame rate area array CMOS device as photosensitive element to obtain image information. In order to reduce the complexity and hardware resources of the electronics unit, a continuous mean elimination method is used to detect lightning signals according to the instantaneous characteristics of lightning. The self-adaptive double threshold recognition method is utilized to detect the recognized lightning signal, which significantly reduces the detection time with a detection rate up to 95%. The electronics unit has the function of quick adjustment of detection threshold through instruction, which provides a more flexible application space for lightning characteristics analysis on the ground. In addition, the system can be used to detect other point or surface target by the change of optical design according to the different characteristics of the detection target, such as the large-scale fire disaster or military target.

New Lightning‐Derived Vertical Total Electron Content Data Provide Unique Global Ionospheric Measurements

AbstractA newly released, novel ionospheric data set of global gridded vertical total electron content (VTEC) is introduced in this paper. This VTEC data set, provided by Los Alamos National Laboratory (LANL), is derived from very high frequency (VHF; defined as 30–300 MHz) broadband radio‐frequency (RF) measurements of lightning made by U.S. Department of Defense sensing systems on board Global Positioning System (GPS) satellites. This paper presents the new data set (LANL VTEC), discusses the errors inherent in VHF TEC estimation due to ionospheric dispersion, and compares the LANL VTEC to two community standard VTEC gridded products: Jet Propulsion Laboratory's Global Ionospheric Model (JPL GIM) and the CEDAR community's Open Madrigal VTEC gridded measurements of L‐band GNSS (global navigation satellite systems) TEC. We find that the LANL VTEC data have an offset of 3 TECU from CEDAR Madrigal GNSS VTEC and a full‐width‐half‐maximum (FWHM) of 6 TECU. In comparison, the offset between LANL VTEC and the JPL GIM model is −3 TECU, but with a FWHM of 5 TECU. We also compare to Jason‐3 VTEC measurements over the ocean, finding an offset of less than 0.5 TECU and an FWHM of <5 TECU. Because this technique uses a completely different methodology to determine TEC, the sources of errors are distinct from the typical ground‐based GNSS L‐band (GHz) TEC measurements. Also, because it is derived from RF lightning signals, this data set provides measurements in regions that are not well covered by ground‐based GPS measurements, such as over oceans and over central Africa.

Application of a Modified Empirical Wavelet Transform Method in VLF/LF Lightning Electric Field Signals

In this paper, to realize a better adaptive method for the lightning electric field signal denoising, we firstly compared the decomposition results of three methods called the EMD (empirical mode decomposition), the CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), and the EWT (empirical wavelet transform) by artificial signals, respectively, and found that the EWT was better than the other two methods. Then, a MEWT (modified empirical wavelet transform) method based on the EWT was presented for processing the natural lightning signals data. By using our MEWT method, we processed three types of electric field signal data with different frequency bands radiated by the lightning step leader, the cloud pulse and the return stroke, respectively, and the VLF (very low frequency) lightning signals propagating different distances from 500 km to 3500 km, by using the data of the fast electric field change sensors from Nanjing Lightning Location Network (NLLN) in 2018 and the data of the fast electric field change sensors and the VLF electric antennas from the NUIST Wide-range Lightning Location System (NWLLS) in 2021. The results showed that our presented MEWT method could adaptively process different lightning signal data with different frequencies from the step leader, the cloud pulse, and the return stroke; for the lightning VLF signal data from 500 km to 3500 km, the MEWT also achieved a better noise reduction effect. After denoising the signal by using our MEWT, the detection ability of the fast electric field change sensor was improved, and more weak lightning signals could be identified.

Performance Evaluation of Raspberry Pi as an IoT Edge Signal Processing Device for a Real-time Flash Flood Forecasting System

The Raspberry Pi has evolved in recent years into a popular, low-cost, tiny computer for a wide range of IoT applications. Raspberry Pi is not only successful for data collection but also for data processing, including data storage and analysis. Thus, this study investigates the capability of Raspberry Pi as an edge processing device for capturing lightning strike signals in predicting flash flood locations. An electric and magnetic sensor (EMS) is connected to a Raspberry Pi in the experiment setup. The Raspberry Pi is then used to process digitised lightning signals. From the experiment, Raspberry Pi’s performance is measured using the performance metrics: central processing unit (CPU) usage and temperature. The results revealed that the Raspberry Pi could handle the real-time collection and processing of lightning signals from the EMSs without affecting the hardware capability.

Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping

Several processing methods have been proposed for estimating the real pattern of the temporal location and spatial map of the lightning strikes. However, due to the complexity of lightning signals, providing accurate lightning maps estimation remains a challenging task. This paper presents a cross-correlation wavelet-domain-based particle swarm optimization (CCWD-PSO) technique for an accurate and robust representation of lightning mapping. The CCWD method provides an initial estimate of the lightning map, while the PSO attempts to optimize the trajectory of the lightning map by finding the optimal sliding window of the cross-correlation. The technique was further enhanced through the introduction of a novel lightning event extraction method that enables faster processing of the lightning mapping. The CCWD-PSO method was validated and verified using three narrow bipolar events (NBEs) flashes. The observed results demonstrate that this technique offers high accuracy in representing the real lightning mapping with low estimation errors.

Development and Verification of Indirect Lightning-Induced Transient Protection Circuit for Avionics System

In this paper, an indirect lightning-induced transient protection circuit for avionics system is proposed, and its effectiveness is verified. The proposed circuit consists of a metal oxide varistor (MOV), a transient voltage suppression (TVS) diode, and a resistor. Compared with the conventional circuits (MOV or TVS diode), the improved noise suppression of the proposed circuit against indirect lightning strikes are experimentally verified in accordance with radio technical commission for aeronautics (RTCA) DO-160G Sec. 22. The highest attenuation levels of indirect lightning strike WF5A reference voltage and current signals are approximately 91.0% and approximately 98.4% for the input lightning signals, respectively.

Using the World Wide Lightning Location Network (WWLLN) to Study Very Low Frequency Transmission in the Earth‐Ionosphere Waveguide: 1. Comparison With a Full‐Wave Model

AbstractWe investigate a novel way to quantify Very Low Frequency transmission in the Earth‐Ionosphere Waveguide, using data from the World Wide Lightning Location Network (WWLLN). The lightning signals from intense and long‐duration storm clusters are recorded at several stations. Any individual stroke amplitude is in principle unknown, so that the recorded electric field from that stroke varies semi‐randomly from the recorded field due to other strokes from that storm cluster. Thus, it is not possible to straightforwardly infer the channel characteristics from a stroke recorded at a single station. However, if two stations record the signal from the same stroke, then the inter‐station ratio of the recorded amplitude on the two fixed propagation paths is (in the absence of noise) independent of source power. We develop a procedure to provide information on time‐variations in the waveguide transmission, using an approach based on ratios of amplitudes from pair of stations which record the same strokes. These amplitude‐ratio data are then compared to an existing model of full‐wave Very Low Frequency reflection from the underside of the ionosphere.

Effects of Day/Night Factor on the Detection Performance of FY4A Lightning Mapping Imager in Hainan, China

In this study, the effect of day/night factor on the detection performance of the FY4A lightning mapping imager (LMI) is evaluated using the Bayesian theorem, and by comparing it to the measurements made by a ground-based low-frequency magnetic field lightning location system. Both the datasets were collected in the summers of 2019–2020 in Hainan, China. The results show that for the observed summer thunderstorms in Hainan, the daytime detection efficiencies of LMI (DELMI) were 20.41~35.53% lower than the nighttime DELMI. Compared to other space-based lightning sensors (lightning imaging sensors/optical transient detectors (LIS/OTD) and geostationary lightning mapper (GLM)), the detection performance of LMI is more significantly influenced by the day/night factor. The DELMI rapidly dropped within about four hours after sunrise while it increased before sunset. For the storms that formed at night and lasted for an entire day, the DELMI remained relatively low during the daytime, even as the thunderstorms intensified. The poor detection performance of LMI during daytime is probably because of the sunlight reflection by clouds and atmosphere, which results in larger background radiative energy density (RED) than that at night. During night, LMI captured the lightning signals well with low RED (8.38~10.63 μJ sr−1 m−2 nm−1). However, during daytime, signals with RED less than 77.12 μJ sr−1 m−2 nm−1 were filtered, thus lightning groups could rarely be identified by LMI, except those with extremely high RED. Due to the limitations of the Bayesian theorem, the obtained DE in this study was “relative” DE rather than “absolute” DE. To obtain the absolute DE of LMI, the total lightning density is necessary but can hardly be measured. Nonetheless, the results shown here clearly indicate the strong impact of day/night factor on the detection performance of LMI, and can be used to improve the design and post-processing method of LMI.

Non‐Detection of Lightning During the Second Parker Solar Probe Venus Gravity Assist

AbstractThe Parker Solar Probe (PSP) spacecraft completed its second Venus gravity assist maneuver (VGA2) on December 26, 2019. For a 20 min interval surrounding closest approach, the PSP/FIELDS Radio Frequency Spectrometer (RFS) was set to “burst mode,” recording radio spectra from 1.3 to 19.2 MHz at sub‐second cadence. We analyze this burst mode data, searching for signatures of radio frequency “sferic” emission from lightning discharges. During the burst mode interval, only four spectra were observed with strong impulsive signals, and all four could be attributed to saturation of the RFS high gain stage by in situ electrostatic plasma waves. These RFS measurements during VGA2 are consistent with previous non‐detection of radio frequency lightning signals from Venus reported by Gurnett et al. (2001, https://doi.org/10.1038/35053009).

Modeling the Transmission of Optical Lightning Signals Through Complex 3‐D Cloud Scenes

AbstractSpace‐based lightning imagers have shown that complex cloud scenes consisting of multiple tall convective features, anvil clouds, and warm boundary cloud layers are illuminated by lightning in many different ways depending on where the lightning occurs and how energetic it is. Modifications to the optical lightning signals from radiative transfer in the cloud medium can lead to reductions in detection efficiency and location accuracy for these instruments and can also cause some of the optical signals that are detected to have unexpected spatial energy distributions. In this study, we perform Monte Carlo radiative transfer simulations of optical lightning emissions in clouds with complex 3‐D geometries to shed some light on the origins of certain irregular radiance patterns that have been recorded from orbit. We show that diffuse reflections off nearby cloud faces can explain lightning signals in nonelectrified clouds, tall clouds can result in poor optical transmission and suppressed radiances that could lead to missed events and that particularly favorable viewing conditions can cause otherwise normal lightning to produce a superbolt that is orders of magnitude brighter than the same flash seen from a different angle.

Preliminary Observations from the China Fengyun-4A Lightning Mapping Imager and Its Optical Radiation Characteristics

The Fengyun-4A (FY-4A) Lightning Mapping Imager (LMI) is the first satellite-borne lightning imager developed in China, which can detect lightning over China and its neighboring regions based on a geostationary satellite platform. In this study, the spatial distribution and temporal variation characteristics of lightning activity over China and its neighboring regions were analyzed in detail based on 2018 LMI observations. The observation characteristics of the LMI were revealed through a comparison with the Tropical Rainfall Measuring Mission (TRMM)-Lightning Imaging Sensor (LIS) and World Wide Lightning Location Network (WWLLN) observations. Moreover, the optical radiation characteristics of lightning signals detected by the LMI were examined. Factors that may affect LMI detection were discussed by analyzing the differences in optical radiation characteristics between LMI and LIS flashes. The results are as follows. Spatially, the flash density distribution pattern detected by the LMI was similar to those detected by the LIS and WWLLN. High-flash density regions were mainly concentrated over Southeastern China and Northeastern India. Temporally, LMI flashes exhibited notable seasonal and diurnal variation characteristics. The LMI detected a concentrated lightning outbreak over Northeastern India in the premonsoon season and over Southeastern China in the monsoon season, which was consistent with LIS and WWLLN observations. LMI-observed diurnal peak flash rates occurred in the afternoon over most of the regions. There was a “stepwise” decrease in the LMI-observed optical radiance, footprint size, duration, and number of groups per flash, from the ocean to the coastal regions to the inland regions. LMI flashes exhibited higher optical radiance but lasted for shorter durations than LIS flashes. LMI observations are not only related to instrument performance but are also closely linked to onboard and ground data processing. In future, targeted improvements can be made to the data processing algorithm for the LMI to further enhance its detection capability.

A new approach to map lightning channels based on low-frequency interferometry

A purely interferometric algorithm, named imaging algorithm, has been developed for mapping lightning in the low-frequency band. The imaging algorithm locates a lightning source by computing the three-dimensional correlation of the lightning signals arriving at a pair of lightning sensors, and then summing the correlations of all antenna pairs. This algorithm does not locate the source by solving the non-linear time-of-arrival (TOA) equations, instead the source is located at the global maxima of the total correlation. The lightning map produced by the imaging algorithm for one intracloud flash was compared with that of the hybrid interferometric-TOA algorithm. The imaging algorithm can locate more sources than the hybrid algorithm and produce more complete lightning maps. We also demonstrated that the imaging algorithm can locate multiple concurrent lightning sources even though the recorded signals from the pulses overlap in time at some stations, which is not possible with a traditional TOA-based technique.

Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping

Lightning mapping systems based on perpendicular crossed baseline interferometer (ITF) technology have been developed rapidly in recent years. Several processing methods have been proposed to estimate the temporal location and spatial map of lightning strikes. In this paper, a single very high frequency (VHF) ITF is used to simulate and augment the lightning maps. We perform a comparative study of using different processing techniques and procedures to enhance the localization and mapping of lightning VHF radiation. The benchmark environment involves the use of different noise reduction and cross-correlation methods. Moreover, interpolation techniques are introduced to smoothen the correlation peaks for more accurate lightning localization. A positive narrow bipolar event (NBE) lightning discharge is analyzed and the mapping procedure is confirmed using both simulated and measured lightning signals. The results indicate that a good estimation of lightning radiation sources is achieved when using wavelet denoising and cross-correlations in wavelet-domain (CCWD) with a minimal error of 3.46°. The investigations carried out in this study confirm that the ITF mapping system could effectively map the lightning VHF radiation source.

Characteristics of lightning signals over the Tibetan Plateau and the capability of FY-4A LMI lightning detection in the Plateau

ABSTRACTBecause of its topographic features, lightning flashes are unique over the Tibetan Plateau. The Lightning Mapper Imager (LMI) on board the launched China’s Fengyun (FY)-4A geostationary satellite is a useful tool for studying these lightning flashes. In this study, firstly, based on data acquired by the Lightning Imaging Sensor (LIS) on board the Tropical Rainfall Measuring Mission (TRMM) satellite in the period 1998–2013, the optical radiation characteristics of lightning flashes over the Tibetan Plateau were compared with those over the central and eastern land region (CELR) of China at the same latitude. The results showed that lightning flashes over the Tibetan Plateau had a mean optical radiance of 422.35 μJ sr–1 m–2 nm–1, a mean duration of 210.79 ms and a mean radiation area of 217.33 km2, whereas the lightning flashes over the CELR had a mean optical radiance of 692.83 μJ sr–1 m–2 nm–1, a mean duration of 295.29 ms and a mean radiation area of 290.85 km2. Evidently, lightning flashes over the Tibetan Plateau had significantly lower radiance, shorter life cycles, and smaller radiation areas than those over the CELR. Secondly, the data for lightning events over the Tibetan Plateau acquired from the FY-4A LMI and International Space Station (ISS) LIS were compared from 21 March 2017 to 25 September 2017, and from 20 March 2018 to 18 September 2018. The correlation between the frequency distribution of the optical radiance of lightning events observed by the two lightning imagers was 99.33%, but due to the footprint area of a LMI pixel is larger than that of a LIS pixel, the mean radiance of lightning events over the Tibetan Plateau measured by the FY-4A LMI was only 1/5 of that measured by the ISS LIS. Based on the statistical results, a time/space coincidence window of 2.1 s/33 km was selected. A resulting mean coincidence ratio (CR) of 3.66% was found for the FY-4A LMI and ISS LIS lightning events for the Tibetan Plateau. The spatial distribution of the CR values was correlated with the optical radiance of lightning events and the duration of lightning flashes as well as the number of events in flashes, with the Pearson’s correlation coefficients (r) of 0.80, 0.82 and 0.78, respectively. These results suggest that the optical radiation characteristics of lightning flashes over the Tibetan Plateau have a great impact on their detection by the FY-4A LMI. For observation of lightning flashes over the Tibetan Plateau using the FY-4A LMI, it is necessary to enable the Real-Time Event Processor (RTEP) algorithm to more finely distinguish various regions based on lightning flash characteristics and to set a more flexible and smaller threshold for extracting lightning events.

Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods.

Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning.

Lightning Mapping: Techniques, Challenges, and Opportunities

Despite the significant progress in the understanding of the phenomenon of lightning and the physics behind it, locating and mapping its occurrence remain a challenge. Such localization and mapping of very high frequency (VHF) lightning radiation sources provide a foundation for the subsequent research on predicting lightning, saving lives, and protecting valuable assets. A major technical challenge in attempting to map the sources of lightning is mapping accuracy. The three common electromagnetic radio frequency-based lightning locating techniques are magnetic direction finder, time of arrival, and interferometer (ITF). Understanding these approaches requires critically reviewing previous attempts. The performance and reliability of each method are evaluated on the basis of the mapping accuracy obtained from lightning data from different sources. In this work, we review various methods for lightning mapping. We study the approaches, describe their techniques, analyze their merits and demerits, classify them, and derive few opportunities for further research. We find that the ITF system is the most effective method and that its performance may be improved further. One approach is to improve how lightning signals are preprocessed and how noise is filtered. Signal processing can also be utilized to improve mapping accuracy by introducing methods such as wavelet transform in place of conventional cross-correlation approaches.

Three‐Dimensional Mapping of Lightning‐Produced Ionospheric Reflections

AbstractThe powerful high‐frequency/very high frequency radio emissions that occur during lightning flashes can be used as a signal of opportunity to study the bottom side ionosphere. The lightning emission is bright, broad spectrum, and short in duration, providing an ideal signal of opportunity for making ionograms. This study continues previous work in Obenberger et al. (2018), where the direct line of sight signal from lightning can be cross correlated with megahertz frequency radio telescope observations to reveal ionogram traces created from the reflected lightning signals. This process was further developed to automate production of ionograms made from individual lightning flashes over the course of several hours, as well as create new techniques to detect the lightning signal using the all‐sky‐imaging mode. By using the Long Wavelength Array Sevilleta radio telescope as an interferometer, the point of reflection of the lightning signal for each frequency of the ionogram can be located in the ionosphere, instantaneously revealing density gradients within the ionosphere on minute time scales. We also explore the minimum size stations required for the application of this technique, which we found to be at least 32 antennas.

A comparative study on the lightning sferics associated with terrestrial gamma-ray flashes observed in Americas and Asia

We examine the broadband (<1 Hz to 400 kHz) electromagnetic lightning signals associated with Terrestrial Gamma-ray Flashes (TGFs) detected by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) and the Gamma-ray Burst Monitor (GBM) on the Fermi Gamma-ray Space Telescope during 2010/2011. The TGF-associated lightning sferics are mainly recorded at two stations located near National Cheng-Kung University in Taiwan and near Duke University in the United States, respectively. The general features exhibited by the TGF-associated lightning sferics are consistent with previous findings that gamma-rays in TGFs are typically produced during a slow process that creates a considerable (but not necessarily) charge moment change within several milliseconds. In some cases, this slow process can be attributed to the upward negative leader during the initial stage of normal intra-cloud (IC) lightning, and it is usually punctuated by one or several fast discharges, the major one of which (i.e. TGF-related discharge) is closely involved in the gamma-ray production. The equivalent peak current of TGF-related discharges could be as high as >+500 kA, and the associated charge transfer is also considerable (typically >+20 C km). The observed complexity of TGF-associated lightning emissions can also be interpreted in the context of the initial development of normal IC lightning flashes, where the upward negative leader drives the millisecond-scale current and may also provide the seed electrons for avalanche multiplication in the upper part of active thunderstorms. Our analyses show that the thunderstorms in the land area of South China produce TGFs that can be readily observed by Fermi/GBM, and the future ground-based coordinated observations in this area would be rather promising to gain more insights into the physical mechanism of TGFs.

An algorithm for estimating the detection efficiency of a lightning location system

This article puts forward an algorithm for estimating the detection efficiency (DE) of a lightning location system (LLS). The algorithm can be applied to lightning flash/stroke density correction and LLS performance evaluation. Fundamentally, the generalized extreme value (GEV) distribution was found to best fit the probability distribution of the signal strengths of the lightning observed by the ADvanced TOA and Direction system (ADTD) detectors in Beijing, China. With respect to this probability distribution, we estimated the single-station acceptance damped by the uneven underlying land surface conductivity, which is determined by overlapping the soil-type polygons with the propagating paths of electromagnetic lightning signals. Accounting for the multi-detector location modes supported by single-station acceptance, the iterative algorithm was applied for deducing the network DE. In this case study, the DE estimates were lower in the mountainous areas than in the plains. This can be due to the low underlying conductivity of the mountainous areas, which creates a high attenuation effect on the lightning electromagnetic signals, and the greater distances from the lightning detectors. The CG lightning flash/stroke density corrected for the DEs revealed that the CG lightning flash/stroke densities in the mountains are lower than that in the highly urbanized plains.