Lightning Detection Data Research Articles

Ice Crystal Alignment: A Concept for High-Temporal-Resolution Monitoring Using Polarimetric Phased Array Radar

Abstract This article describes a concept whereby future operational polarimetric phased array radars (PPAR) routinely monitor ice crystal alignment regions caused by thundercloud electric fields with volume scan updates (∼12 min−1) sufficient to resolve the temporal variation due to lightning and subsequent rapid electric field regeneration in nonsevere thunderstorms. Routine observations of crystal alignment regions may enhance thunderstorm nowcasting through comparison of their temporal and spatial structure with other polarimetric signatures, integration with lightning detection data, and assimilation into convection resolving numerical weather prediction models. If crystal alignment observations indicate strong electrification well in advance of the first lightning strike and likewise reliably indicate the decay of strong electric fields at the end of a storm, this capability may improve warning for lightning-sensitive activities such as airport ramp operations and space launch. Experimental observations of crystal alignment volumes in central Oklahoma severe storms and their relation to those storms’ structures are presented and used to motivate discussion of possible PPAR architectures. In one case—a tornadic supercell—these observations illustrate an important limitation. Even the hypothesized 12 min−1 volume scan update rate would not resolve the temporal variation of the crystal alignment regions in such storms, suggesting that special, adaptive scanning methods may be appropriate for such storms. We describe how future operational phased array radars could support a crystal alignment measurement mode via parallel, time-multiplexed processing and discuss potential impacts on the radar’s primary weather observation mission. We conclude by discussing research needed to better understand technical challenges and operational benefits.

Tracking Lightning Through 3D Thunder Source Location With Distributed Acoustic Sensing

AbstractInvestigating lightning is of key significance in understanding the lightning mechanism and mitigating lightning hazards. We reported an experiment of investigating lightning through three‐dimensional (3D) thunder locating using a Distributed Acoustic Sensing (DAS) array in Hefei, China. In this experiment, we recast a 7.7 km long urban telecom optical fiber cable as 3,850 sensors using the DAS technique. From dense DAS recording, we manually identified 101 thunder events during six positive cloud‐to‐ground (CG) lightning flashes within 20 min. The DAS recorded thunder signals are dominated by direct acoustic waves rather than air‐ground coupled surface waves. The thunder events were then located using the arrival times of thunder signals. The locations and amplitudes of thunder events are generally consistent with those from the conventional lightning detection data set and broadband magnetic field. There is likely a correlation between the maximum strength of thunder events and the highest peak current for individual CG flashes. Moreover, the comparison with weather radar observations indicates that lightning usually originated from areas of high reflectivity (e.g., ≥50dBz) with diffusely distributed (from ground surface to ∼5 km altitude) thunder events and extended in a narrow altitude range of 3–5 km to areas with low radar reflectivity.

Modeled Multidecadal Trends of Lightning and (Very) Large Hail in Europe and North America (1950–2021)

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

Seamless Lightning Nowcasting with Recurrent-Convolutional Deep Learning

Abstract A deep learning model is presented to nowcast the occurrence of lightning at a 5-min time resolution 60 min into the future. The model is based on a recurrent-convolutional architecture that allows it to recognize and predict the spatiotemporal development of convection, including the motion, growth and decay of thunderstorm cells. The predictions are performed on a stationary grid, without the use of storm object detection and tracking. The input data, collected from an area in and surrounding Switzerland, comprise ground-based radar data, visible/infrared satellite data and derived cloud products, lightning detection, numerical weather prediction, and digital elevation model data. We analyze different alternative loss functions, class weighting strategies and model features, providing guidelines for future studies to select loss functions optimally and to properly calibrate the probabilistic predictions of their model. On the basis of these analyses, we use focal loss in this study but conclude that it only provides a small benefit over cross entropy, which is a viable option if recalibration of the model is not practical. The model achieves a pixelwise critical success index (CSI) of 0.45 to predict lightning occurrence within 8 km over the 60-min nowcast period, ranging from a CSI of 0.75 at a 5-min lead time to a CSI of 0.32 at a 60-min lead time. Significance Statement We have developed a method based on artificial intelligence to forecast the occurrence of lightning at 5-min intervals within the next hour from the forecast time. The method utilizes a neural network that learns to predict lightning from a set of training images containing lightning detection data, weather radar observations, satellite imagery, weather forecasts, and elevation data. We find that the network is able to predict the motion, growth, and decay of lightning-producing thunderstorms and that, when properly tuned, it can accurately determine the probability of lightning occurring. This is expected to permit more informed decisions to be made about short-term lightning risks in fields such as civil protection, electricity-grid management, and aviation.

Production of Terrestrial Gamma‐Ray Flashes During the Early Stages of Lightning Flashes

AbstractTerrestrial Gamma‐ray Flashes (TGFs) are short emissions of high energy photons associated with thunderstorms. It has been known since the discovery of TGFs that they are associated with lightning, and several case studies have shown that the TGFs are produced at the initial phase of the lightning flash. However, it has not been tested whether this is true in general. By using the largest TGF sample up to date, combined with ground‐based radio lightning detection data, we perform a statistical study to test this. One of the TGF missions is the Atmosphere‐Space Interactions Monitor (ASIM) consisting of the innovative combination of X‐ and gamma‐ray detectors, optical photometers and cameras. This allows us to investigate the temporal relation between gamma‐rays produced by TGFs and the optical signal produced by lightning discharges. Based on stacking analysis of the TGF sample and ground‐based measurements of associated lightning activity, together with the high temporal resolution of the optical signal from the ASIM photometers, it is shown that TGFs are produced in the beginning of the lightning flashes. In addition, for a significant fraction of the TGFs, the lightning activity detected in radio is enhanced in an interval between 150 and 750 ms following the TGFs, and is co‐located with the lightning associated with the TGFs. The enhanced lightning activity is not evident in a randomly selected sample of flashes. This indicates that the activity between 150 and 750 ms is a characteristic property of a significant fraction of flashes that start with a TGF.

A Frontal Thunderstorm With Several Multi‐Cell Lines Found to Produce Energetic Preliminary Breakdown

AbstractWe combine electromagnetic measurements with meteorological and lightning detection data to explain an observation of unusually strong preliminary breakdown (PB) produced by a thunderstorm system that developed along the Mediterranean Coast of Southern France in the early hours of 19 June 2013. This multi‐cellular storm was composed of several parallel convective lines in the NW‐SE direction. Our analysis focuses on 10 sequences of energetic electromagnetic PB pulses recorded by two receivers located at different distances from this thunderstorm. The peak currents, which generated these strong PB pulses, reached −36 kA. The initial polarity of all observed energetic PB pulses confirmed the movement of the negative charge downward, as in case of PB pulses preceding negative cloud‐to‐ground discharges. The locations of PB pulses appeared in areas with none or very weak lightning activity. Most PB pulses were initiated in small, short‐living, rapidly moving convective storm cells characterized by low reflectivity values (generally <40 dBZ), weak vertical development, and low flash density. Our findings indicate that the observed thunderstorm might possess temporary strong negatively charged pockets located above a strong positive charge region at low‐level. Such charge arrangement, likely explains our observation of unusually strong PB pulses and the absence of RS pulses in electromagnetic recordings.

A verification of the lightning detection data from FY-4A LMI as compared with ADTD-2

In this paper the match percentage (MP) of lightning frequency in Jiangsu, China from June to August in 2019 is analyzed, by comparing the lightning mapping imager (LMI) data from FengYun (FY)-4A geostationary meteorological satellite with the Advanced Direction and Time-of-arrival Detecting (ADTD)-2 total lightning location data and radar composite reflectivity. The results show that the MP increases with the spatial matching window, yet when the matching window is larger than 0.8° (~80 km), the MP tends to be stable. Furthermore, the spatial distribution of lightning and the characteristics of the match lightning (ML) current are analyzed. The horizontal detection error of LMI for isolated thunderstorm clusters is about 0.15 ± 0.003° (~15 km). The amplitude of the lightning current is an important but not the only factor affecting the satellite's detection efficiency. Through the 10 min analysis of a large-scale and long-term thunderstorm process in Jiangsu province on July 6, 2019, it is found that the lightning detected by LMI and ADTD-2 occurred mostly in the region with strong echoes above 40 dBZ or in its vicinity. With the development of the thunderstorm, the lightning detected by ADTD-2 gradually increased and reached a maximum at 14:40. Subsequently, with the weakening of the thunderstorm system the lightning detected by ADTD-2 gradually decreased. However, the lightning detected by LMI was less all the time before 18:30 and then increased gradually. In the fourth stage (19:40 on July 6–00:20 on July 7), LMI has the highest relative detection efficiency of 12.78%–14.66% using ADTD-2 as a reference, indicating that the satellite detection is relatively less efficient during the daytime.

Application of machine learning to large hail prediction - The importance of radar reflectivity, lightning occurrence and convective parameters derived from ERA5

This study presents a concept for coupling remote sensing data and environmental variables with machine learning techniques for the prediction of large hail events. In particular, we want to address the following question: How would one improve the performance of large hail warnings / forecasts if thermodynamic and kinematic parameters derived from a numerical weather prediction model are combined with real-time remote sensing data? For this purpose, POLRAD radar reflectivity, EUCLID lightning detection data, and convective indices calculated from the ERA5 reanalysis are combined and then compared with large hail reports from Poland (2008–2017). The data fusion of multiple sources, coupled with the machine learning approach, makes it possible to greatly improve the robustness of large hail prediction compared to any single product commonly used in operational forecasting. This is especially noticeable with the reduced number of false alarms. Although the created machine learning models are mainly driven by radar reflectivity, composite thermodynamic and kinematic indices such as Hail Size Index (HSI), Significant Hail Parameter (SHIP), Large Hail Parameter (LGHAIL), and WMAXSHEAR provide an added value to a model's performance. The accuracy achieved by a random forest model brings with it encouraging prospects for future research with respect to operational forecasters (who may fill in the gaps within NWP-derived data with remotely sensed measurement) and climatological studies that aim to investigate past and future changes in severe weather occurrences.

Study on lightning risk assessment and early warning for UHV DC transmission channel

Operation data show lightning faults account for >70% for the main ultra-high voltage (UHV) DC transmission channels, very different from the design view. In order to accurately master the lightning characteristics and the lightning protection performance of the line so as to propose solutions to weak points, this study firstly obtains and analyses the density and strength distributions of lightning risk source. Then the study proposes a set of risk assessment process where the key model electrogeometric model is improved according to the polarity effect of DC line. Then the work realises the calculation of the lightning shielding failure risk of single tower and whole line. The example shows the assessment result is consistent to the line's actual operation. Next, to further evaluate and predict the lightning risk in real time, the study adopts the backpropagation neural network algorithm to integrate the lightning detection data, atmospheric electric fields, and radar echoes to develop the early warning model of lightning risk source, and proposed a method to realise the early warning of lightning damage risk for UHV DC channels. The results show that the effective warning ratio is 73% and the failure-to-warn ratio is 27% which indicates very good application effects.

Development of Lightning Nowcasting and Warning Technique and Its Application

The Chinese Academy of Meteorological Sciences Lightning Nowcasting and Warning System (CAMS_LNWS) was designed to predict lightning within the upcoming 0-1 h and provide lightning activity potential and warning products. Multiple remote sensing data and numerical simulation of an electrification and discharge model were integrated in the system. Two core algorithms were implemented: (1) an area identification, tracking, and extrapolating algorithm and (2) a decision tree algorithm. The system was designed using a framework and modular structure, and integrated warning methods were applied in the warning program. Two new algorithms related to the early warning of the first lightning and thunderstorm dissipation were also introduced into the system during the upgrade process. Thunderstorms occurring in Beijing, Tianjin, and Hebei during 2016-2017 were used to evaluate the CAMS_LNWS by the low-frequency cloud to ground lightning detection data, and the results showed that the system has good forecasting and warning ability for local lightning activities. The TS score in 0-1 h ranged from 0.11 to 0.32, with a mean of 0.20. Operational experiments and promotional work for the CAMS_LNWS are now in progress.

The NOAA/CIMSS ProbSevere Model: Incorporation of Total Lightning and Validation

Abstract The empirical Probability of Severe (ProbSevere) model, developed by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS), automatically extracts information related to thunderstorm development from several data sources to produce timely, short-term, statistical forecasts of thunderstorm intensity. More specifically, ProbSevere utilizes short-term numerical weather prediction guidance (NWP), geostationary satellite, ground-based radar, and ground-based lightning data to determine the probability that convective storm cells will produce severe weather up to 90 min in the future. ProbSevere guidance, which updates approximately every 2 min, is available to National Weather Service (NWS) Weather Forecast Offices with very short latency. This paper focuses on the integration of ground-based lightning detection data into ProbSevere. In addition, a thorough validation analysis is presented. The validation analysis demonstrates that ProbSevere has slightly less skill compared to NWS severe weather warnings, but can offer greater lead time to initial hazards. Feedback from NWS users has been highly favorable, with most forecasters responding that ProbSevere increases confidence and lead time in numerous warning situations.

卫星与地基闪电探测资料在闪电活动研究中的综合应用

卫星与地基闪电探测资料在闪电活动研究中的综合应用

Cloud-to-ground lightning activity in Colombia and the influence of topography

Lightning activity on the Colombian mountains, where the altitude varies from 0 to more than 5000MSL, is studied based on VLF/LF lightning detection data and using a 2012–2013 dataset. The influence of altitude is observed by evaluating cloud-to-ground lightning incidence at different altitude intervals. The relationship between ground flash density and altitude gradient vectors is studied. Results show a clear dependence of the flash density on elevation.

An Evaluation of Thunderstorm Observations in Southern Ontario Using Automated Lightning Detection Data

AbstractHigh-impact weather events, such as thunderstorms and their associated hazards, are aspects of a changing climate that are likely to have an adverse effect on society. Southern Ontario is Canada’s most populated region as well as the region of Canada that receives the most thunderstorms. Before completing climatological studies of thunderstorms in southern Ontario, it is important to determine whether historical thunderstorm data are reliable. Archived thunderstorm data are available from eight 24-h-staffed weather stations across southern Ontario. The data may be subject to observer bias. This study compared the manual observations of thunderstorms with automated data from the Canadian Lightning Detection Network. It was found that the data that are based on the manual observations are reliable enough that any significant trends in thunderstorm occurrence over time should be apparent. Because of the small-scale nature of thunderstorms, however, the data may only be valid for small distances (up to 10 km) around each weather station. A diurnal bias was also discovered, with manual thunderstorm observations being slightly better at night.

Online Monitoring and Transmission System for Lightning Detection Network

Lightning detection data is a kind of distributed information with high real-timeliness. After the lightning events are detected by the lightning detection network, the original detection data need to be collected and concentrated rapidly and timely for unified management and processing. In this paper, an online monitoring and transmission system for lightning detection information is presented. The system consists of two major components: the remote monitoring module and the response & transmission module. File system watcher (FSW) tool and the trigger mechanism are applied to the remote monitoring module to watch the generation of lightning detection data both in file directory or database. And then the newest origin detection data can be transmitted to the detection center of the lightning detection network by the response & transmit module based on the communication network. The proposed system is developed and exemplified in Wuhan lightning detection network, China. The application results indicates that the system is reliable and reasonable in the online monitoring and transmission for lightning detection data, which can provide a strong support to the lightning detection network

Relationship between human observations of thunderstorms and the PERUN lightning detection network in Poland

Research presents an overview on thunderstorm occurrence in Poland and focuses mainly on the relationship between human observations of thunderstorms (SYNOP daily summaries) and instrumental lightning detection data (PERUN network) in the timeframe between 2002 and 2013. The total of 4,952,203 cloud-to-ground flashes (2082days with thunderstorms) derived from the PERUN lightning database, and 12,419 daily thunderstorm SYNOP reports from 44 meteorological stations (1417days with thunderstorms) are compared. Within the use of two different computational methods, we define the threshold value of the human average observational thunderstorm detection range within a meteorological station. Results indicate that the average of this value ranges from 16.9km (Delta computational method) to 18.3km (threat score computational method). Given the limitations of both methods, we believe that the average of these two (17.5km) may be the most reliable estimate that expresses how lightning is perceived by humans. Large differences in observational range values between some of the stations (e.g. from 12km in Bielsko-Biała to 24km in Łeba) indicate that thunderstorm measurements performed by humans are not homogeneous and are prone to errors. We estimate that an average increase/decrease of observational range by approximately 1km results in 1 additional/redundant day in the average annual number of thunderstorm days in the climatological sense. Results indicate that already existing thunderstorm climatology papers that are based on SYNOP thunderstorm reports may present presumably not entirely reliable results and overestimate or underestimate values from the real distribution.

A Research on the Characteristics of Lightning in Hailstorms of Beijing Area by the Method of Analysis the Lightning Detection Data and Numerical Simulation

In this paper, by using lightning detection material of Beijing area, we analyzing the characteristics of lightning in four hailstorms. The results show that the mean percentage of cloud-to-ground (CG) lightning accounting for the total lightning is 8.28%, which is lower than the normal thunderstorms. On the contrary, the CG lightning has a higher proportion of positive CG than the normal thunderstorms. All cases variation trend of lightning frequency show a conspicuous intensive increase before the hail reaches the ground. The peak lightning frequency usually occurs 5 to 80min earlier than hailstone falling. These characteristics may have contribution to the forecasting of hailstorms of Beijing area. Another work was done by using a two-dimensional numerical model which includes dynamic, microphysics, electrification and discharge to simulate these four instances. The results present similar characteristics with the actual detection data. This will lay a foundation for the continuing studies of the relationship between hailstorm and lightning and the inherent reason of the relationship by using the method of numerical simulation.

A Satellite-Based Convective Cloud Object Tracking and Multipurpose Data Fusion Tool with Application to Developing Convection

Abstract Studying deep convective clouds requires the use of available observation platforms with high temporal and spatial resolution, as well as other non–remote sensing meteorological data (i.e., numerical weather prediction model output, conventional observations, etc.). Such data are often at different temporal and spatial resolutions, and consequently, there exists the need to fuse these different meteorological datasets into a single framework. This paper introduces a methodology to identify and track convective cloud objects from convective cloud infancy [as few as three Geostationary Operational Environmental Satellite (GOES) infrared (IR) pixels] into the mature phase (hundreds of GOES IR pixels) using only geostationary imager IR window observations for the purpose of monitoring the initial growth of convective clouds. The object tracking system described within builds upon the Warning Decision Support System-Integrated Information (WDSS-II) object tracking capabilities. The system uses an IR-window-based field as input to WDSS-II for cloud object identification and tracking and a Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin (UW-CIMSS)-developed postprocessing algorithm to combine WDSS-II cloud object output. The final output of the system is used to fuse multiple meteorological datasets into a single cloud object framework. The object tracking system performance analysis shows improved object tracking performance with both increased temporal resolution of the geostationary data and increased cloud object size. The system output is demonstrated as an effective means for fusing a variety of meteorological data including raw satellite observations, satellite algorithm output, radar observations, and derived output, numerical weather prediction model output, and lightning detection data for studying the initial growth of deep convective clouds and temporal trends of such data.

Characteristics of lightning radiation source distribution and charge structure of squall line

Lightning information indicates the intensity and the development of severe convection. The characteristics of lightning activity and charge structure of a squall line over Beijing on 13 June 2010 are analyzed by using the SAFIR3000 lightning detection data, Doppler weather radar and precipitation data. Results show that the lightning radiation sources are the major part in the convective leading region with a high reflectivity in front of the squall line, and the number of lightning radiation sources gradually increases in the back of stratiform region only at the dissipating stage. The correlation coefficient between the total lightning and convective precipitation is found to be 0.82, and that of intra-cloud lightning and convective precipitation is 0.76. It is inferred that the lightning is closely related to dynamical and microphysical processes of the squall line. According to the lightning radiation distribution, the charge structure of squall line is also discussed. At the mature stage of the squall line, the lightning radiation sources have two layers with the upper level centered at 11 km and the lower level at 6 km. Based on the bidirectional leader of lightning propagation and by assuming VHF source density maxima being most likely associated with the positive charge region, it is concluded that the squall line is characterized by a tripole charge structure with a middle charge region between the levels of 8 km and 10 km, and the two positive charge regions at the levels of 10 to 12 km and 4 to 7 km above the ground, respectively.

A comparison of lightning activity and convective indices over some monsoon-prone areas of China

Using 10-year lightning localization data observed by the TRMM (Tropical Rainfall Measuring Mission) Lightning Imaging Sensor (LIS), the relationship between lightning activity and a series of convective indices was investigated over nine monsoon-prone areas of China in which high-impact weather (HIW) events are frequently observed. Two methods were used to verify and reconstruct LIS lightning data. First, LIS lightning flash data were verified by both surface thunderstorm reports and ground-based lightning detection data. Seasonal, monthly, and 5-day distributions of LIS observed lightning activity agree well with the surface reports and ground-based lightning observations. Second, due to LIS's low sampling frequency, a data reconstruction and compensation scheme for LIS lightning observations was designed using both LIS lightning seasonal diurnal cycles and surface thunderstorm reports. After data reconstruction, five lightning products were derived: daily mean and maximum LIS flash rate, daily mean and maximum LIS lightning cell rate, and number of lightning days per five day period. Then, a series of convective indices describing convection conditions were derived from radiosonde data according to atmospheric instability and convective potential analysis. Correlation analysis for each study region was done between 10-year lightning derived products and corresponding convective indices by 5-day periods. The correlation analysis results show that higher lightning flash rate and lightning probability are associated with more unstable air and smaller vertical wind shear in a nearly saturated lower layer in most of the study regions. But the correlation varies from region to region. The best correlation between lightning activity and convective indices was found in eastern and southern China, whereas the correlation is lowest in some inland or basin topography regions in which topographic effects are more significant. Moreover, ambient moisture plays a much more important role in the convective development of thunderstorms in southern China than other regions. Thunderstorm development mechanism differences among regions were also discussed. Based on the close relationship between lightning activity and convective indices, some regression equations for forecasting 5-day mean or maximum LIS lightning flash rate and lightning area (a thunderstorm cell) rate, and 5-day lightning days for the study regions were developed using convective indices as predictors. The verifications show that the convective index-based lightning forecast methods can provide a reasonable lightning outlook including probability and lightning flash rate forecasts for a 5-day period.