During DC-3 TEST lightning stroke data were collected in 1-hour reports which contain cloud-to-ground lightning stroke data and cloud flash discharges. The data are available in ASCII NAPLN extended data format. The reports have been combined into daily tar files. Data are available for May 1-15 of 2011. All users must read and agree to the ERAU-USPLN Data Access Policy associated with this proprietary data set.

During PREDICT lightning stroke data were collected in 1-hour reports which contain cloud-to-ground lightning stroke data and cloud flash discharges. The data are available in ASCII NAPLN extended data format. The reports have been combined into daily tar files. Data are available for August 15 - September 30 of 2010. All users must read and agree to the ERAU-USPLN Data Access Policy associated with this proprietary data set.

The World Wide Lightning Location Network (WWLLN) has monitored global lightning since late 2004. Since 2013, the number of global WWLLN sensors has remained largely consistent. This WWLLN Monthly Thunder Hour dataset is calculated from lightning detections from 1 January 2013 onward and is an ongoing dataset. A thunder hour is an hour during which thunder can be heard at a given location. Thunder hours represent a historical measure of lightning occurrence and a metric of thunderstorm frequency that is comparatively less sensitive to geographic variations in the detection capabilities of a lightning location system. Thunder hours are the number of hours in a given month during which at least two WWLLN strokes were observed within 15 km of each grid point. Each file includes the monthly accumulated thunder hours for one year. The data are provided at 0.05° latitude and longitude resolution.

Comprehensive Bitcoin holdings, market data, and treasury information for Lightning Network Public Channels ()

Cloud-to-ground and intracloud lightning flash data from the Earth Networks Total Lightning Network (ENTLN) for the ACCLIP campaign period and region of interest.

I collected this data about the structure of the Lightning network from December 2019 to March 2020 for use in one research. Now I am happy to share this data so that you can do something interesting too.

Dataset structure

The dataset contains two folders: channels (which are edges in terms of the graph) and nodes (list of additional node's features like geo coordinates, alias of the node in the network and etc.)

The filename is the timestamp of the snapshot in the format %Y_%m_%d_%h_%m_%s. So you can match files from nodes and channels folders by the filename.

This data set contains every detection of cloud-to-ground and cloud-to-cloud lightning from the INPE (Instituto Nacional de Pesquisas Espaciais) BrasilDAT (Sistema Brasileiro de Detecção de Descargas atmosféricas) lightning network during the RELAMPAGO (Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations) field season.

The Global Hydrology Resource Center generates a cloud-to-ground lightning product from the data collected from the U.S. National Lightning Detection Network, a commercial lightning detection network operated by Global Atmospherics, Inc. (GAI), formerly Geomet Data Services. The daily products are produced by binning the number of flashes occurring in each pixel (pixel is approximately 8 km by 8 km) during a 24 hr period (00 UTC to 00 UTC). The data set begins on July 8, 1994 and continues through the present.

The Lightning Imaging Sensor (LIS) Science Computing Facility (SCF) generates a cloud-to-ground lightning product from the data collected from the U.S. National Lightning Detection Network, a commercial lightning detection network operated by Global Atmospherics, Inc. (GAI), formerly Geomet Data Services. The lightning products are made by binning the number of flashes that occur over a 15 min period to a pixel (pixel is approximately 8 km by 8 km). The data set begins on July 19, 1994 and continues through the present. This data is distributed by the Global Hydrology Resource Center (GHRC).

The Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) Lightning Mapping Array (LMA) was an 11-station, ground-based network located in north-central Argentina from November 2018 to April 2019 in support of the RELAMPAGO field campaign. The RELAMPAGO campaign aimed to characterize the atmospheric conditions and terrain effects that facilitate the initiation and growth of intense weather systems in this region of South America. The LMA maps Very High Frequency (VHF) emissions from lightning in three dimensions. These emissions have also been grouped, temporally and spatially, into individual flashes, and the flash characteristics analyzed to produce gridded products. The dataset was produced by NASA Marshall Space Flight Center (MSFC), via an agreement with the National Oceanic and Atmospheric Administration (NOAA), in order to serve as a validation dataset for the Geostationary Lightning Mapper (GLM). These LMA data are available from November 8, 2018 through April 20, 2019 in ASCII, HDF5, and netCDF-4 format.

The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series

This repository contains global lightning stroke density and stroke power calculated from georeferenced stroke count data from the World Wide Lightning Location Network WWLLN. The real-time raw stroke count data were reprocessed by WWLLN to remove artifacts and improve geolocation, which resulted in the "AE" georeferenced and timestamped stroke count data. These data were then gridded at 0.5 degree 5 arc-minute and hourly resolution, converted into density, and corrected for detection efficiency using the WWLLN global gridded detection efficiency maps. Mean, median, and standard deviation of stroke power are also provided at 30-minute resolution. The corrected hourly rasters were then aggregated into daily and monthly totals and into a multi-year monthly mean climatology. The data cover the period 2010-2023 and will be updated in the coming years.

For a complete description of the data see:

Kaplan, J. O., & Lau, K. H.-K. (2021). The WGLC global gridded lightning climatology and time series. Earth System Science Data, 13(7), 3219-3237. doi:10.5194/essd-13-3219-2021

Kaplan, J. O., & Lau, K. H.-K. (2022). World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update. Earth System Science Data, 14(12), 5665-5670. doi:10.5194/essd-14-5665-2022

The data are stored in a NetCDF (version 4) files and have the following attributes:

Spatial extent: Entire Earth

Spatial reference system (SRS): Unprojected (geographic, WGS84)

Spatial resolution: half-degree and 5 arc-minute

Temporal extent: 2010-2022

Temporal resolution: daily and monthly1,2

Variables included in this release

Lightning density (strokes km-2 day-1)

Lightning mean, median, and standard deviation of stroke power (MW, 30 arc-minute version only)

For further details, see https://github.com/ARVE-Research/WGLC

1*4748 elements in the time dimension for daily data; 156 for monthly data; 12 for the climatology.

2*Daily fields currently available at 30-minute resolution only.

The WWLLN Global Lightning Climatology and timeseries (WGLC) © 2024 by Jed O. Kaplan is licensed under CC BY-SA 4.0

The NAMMA Lightning ZEUS data is provided by World-ZEUS Long Range Lightning Monitoring Network Data obtained from radio atmospheric signals located at thirteen ground stations spread across the European and African continents and Brazil from August 1, 2006 to October 1, 2006. Lightning activity occurring over a large part of the globe is continuously monitored at varying spatial accuracy (e.g. 10-20 km within and >50 km outside the network periphery) and high temporal (1 msec) resolution. Time is determined by the Arrival Time Difference between the time series from the pairs of the receivers. These data files were generated during support of the NASA African Monsoon Multidisciplinary Analyses (NAMMA) campaign, a field research investigation sponsored by the Science Mission Directorate of the National Aeronautics and Space Administration (NASA). This mission was based in the Cape Verde Islands, 350 miles off the coast of Senegal in west Africa. Commencing in August 2006, NASA scientists employed surface observation networks and aircraft to characterize the evolution and structure of African Easterly Waves (AEWs) and Mesoscale Convective Systems over continental western Africa, and their associated impacts on regional water and energy budgets.

This data set contains 5 minute maps of lightning strikes over the north central United States from the USPLN (United States Precision Lightning Network) operated by WSI. The imagery were developed by NCAR/EOL.

RSS and JSON services that allow the query of data from the lightning detection network. The service supports a date range as a parameter and returns the number, type and position of the rays detected in that period. The parameters that can be passed to this JSON and RSS can be consulted in the associated documentation.

The Canadian Lightning Detection Network (CLDN) provides lightning monitoring across most of Canada. The data distributed here represents a spatio-temporal aggregation of the observations of this network available with an accuracy of a few hundred meters. More precisely, every 10 minutes, the reported observations are processed in the following way: The location of observed lightning (cloud-to-ground and intra-cloud) in the last 10 minutes is extracted. Using a regular horizontal grid of about 2.5km by 2.5km, the number of observed lightning flashes within each grid cell is calculated. These grid data are normalized by the exact area of each cell (in km2) and by the accumulation period (10min) to obtain an observed flash density expressed in km-2 and min-1. A mask is applied to remove data located more than 250km from Canadian land or sea borders.

The dataset used in this study is consist with observation and model results. Lightning data is obtained from regional Beijing Broadband Lightning Network (BLNet) comprised by 16 stations, which could detect the total lightning including intra-cloud lightning and cloud-to-ground lightning with lightning information of location, polarity, type, height and detection error. The average detection efficiency for total flashes is 93.2%, and the identification efficiency of BLNET for CG flashes is 73.9%. Radar data is obtained from the operational radar network of five operational S-band or C-band Doppler radars around Beijing area with the horizontal resolution of 0.01˚ (latitude) × 0.01˚ (longitude).The version of WPSv4.1.2, WRFDAv4.1.2 and WRFv4.1.2 are used in this study. Lightning data assimilation scheme (LDA) was proposed based on the relationship between lightning and updraft. The lightning data used for assimilation is provided in the dataset. The dataset volume is 85M.

Dataset has been generated from the Monte Carlo simulations of lightning flashovers on medium voltage (MV) distribution lines. It is suitable for training machine learning models for classifying lightning flashovers on distribution lines, as well as for line insulation coordination studies. The dataset is hierarchical in nature (see below for more information) and class imbalanced.

Following five different types of lightning interaction with the MV distribution line have been simulated: (1) direct strike to phase conductor (when there is no shield wire present on the line), (2) direct strike to phase conductor with shield wire(s) present on the line (i.e. shielding failure), (3) direct strike to shield wire with backflashover event, (4) indirect near-by lightning strike to ground where shield wire is not present, and (5) indirect near-by lightning strike to ground where shield wire is present on the line. Last two types of lightning interactions induce overvoltage on the phase conductors by radiating EM fields from the strike channel that are coupled to the line conductors. Shield wire(s) provide shielding effects to direct, as well as screening effects to indirect, lightning strikes.

Dataset consists of the following variables:

• 'dist': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,
• 'ampl': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),
• 'veloc': velocity of the lightning return stroke current (m/us), generated from the Uniform distribution [50, 500] m/us,
• 'shield': binary indicator that signals presence or absence of the shield wire(s) on the line (0/1), generated from the Bernoulli distribution with a 50% probability,
• 'Ri': average value of the impulse impedance of the tower's grounding (Ohm), generated from the Normal distribution (clipped at zero on the left side),
• 'EGM': electrogeometric model used for analyzing striking distances of the distribution line's tower; following options are available: 'Wagner', 'Young', 'AW', 'BW', 'Love', and 'Anderson', where 'AW' stands for Armstrong & Whitehead, while 'BW' means Brown & Whitehead model; statistical distribution of EGM models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.1, 0.2, 0.1, 0.1, 0.3, 0.2],
• 'height': height of the phase conductors of the distribution line (m); distribution line has flat configuration of phase conductors with following heights: 10, 12, 14 m; twin shield wires, if present, are 1.5 m above the phase conductors and 3 m apart; data set consists of 10000 simulations for each line height,
• 'flash': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome (binary class).

Note: It should be mentioned that the critical flashover voltage (CFO) level of the line is taken at 150 kV, and that the diameters of the phase conductors and shield wires are, respectively, 10 mm and 5 mm. Also, average grounding resistance of the shield wire is assumed at 10 Ohm. Dataset is class imbalanced and consists in total of 30000 simulations, with 10000 simulations for each of the three different MV distribution line heights (geometry).

Important: Use the latest version of the dataset!

Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references below.

References:

J. A. Martinez and F. Gonzalez-Molina, "Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks," in IEEE Transactions on Power Delivery, vol. 20, no. 3, pp. 2219-2226, July 2005, doi: 10.1109/TPWRD.2005.848734.

A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.

Financial overview and grant giving statistics of African Centres for Lightning and Electromagnetics Network Inc.

[Version 1.2] This version of the dataset fixes a bug found in the previous versions (see below for more information).

Dataset has been generated from the Monte Carlo simulations of lightning flashovers on medium voltage (MV) distribution lines. It is suitable for training machine learning models for classifying lightning flashovers on distribution lines, as well as for line insulation coordination studies. The dataset is hierarchical in nature (see below for more information) and class imbalanced.

Following five different types of lightning interaction with the MV distribution line have been simulated: (1) direct strike to phase conductor (when there is no shield wire present on the line), (2) direct strike to phase conductor with shield wire(s) present on the line (i.e. shielding failure), (3) direct strike to shield wire with backflashover event, (4) indirect near-by lightning strike to ground where shield wire is not present, and (5) indirect near-by lightning strike to ground where shield wire is present on the line. Last two types of lightning interactions induce overvoltage on the phase conductors by radiating EM fields from the strike channel that are coupled to the line conductors. Shield wire(s) provide shielding effects to direct, as well as screening effects to indirect, lightning strikes.

Dataset consists of the following variables:

• 'dist': perpendicular distance of the lightning strike location from the distribution line axis (m), generated from the Uniform distribution [0, 500] m,
• 'ampl': lightning current amplitude of the strike (kA), generated from the Log-Normal distribution (see IEC 60071 for additional information),
• 'veloc': velocity of the lightning return stroke current (m/us), generated from the Uniform distribution [50, 500] m/us,
• 'shield': binary indicator that signals presence or absence of the shield wire(s) on the line (0/1), generated from the Bernoulli distribution with a 50% probability,
• 'Ri': average value of the impulse impedance of the tower's grounding (Ohm), generated from the Normal distribution (clipped at zero on the left side) with median value of 50 Ohm and standard deviation of 12.5 Ohm; it should be mentioned that the impulse impedance is often much larger than the associated grounding resistance value, which is why a rather high value of 50 Ohm have been used here,
• 'EGM': electrogeometric model used for analyzing striking distances of the distribution line's tower; following options are available: 'Wagner', 'Young', 'AW', 'BW', 'Love', and 'Anderson', where 'AW' stands for Armstrong & Whitehead, while 'BW' means Brown & Whitehead model; statistical distribution of EGM models follows a user-defined discrete categorical distribution with respective probabilities: p = [0.1, 0.2, 0.1, 0.1, 0.3, 0.2],
• 'CFO': critical flashover voltage level of the distribution line's insulation (kV); following three levels have been used: 150, 150, and 160 kV, respectively, for three different distribution lines of height 10, 12, and 14 m,
• 'height': height of the phase conductors of the distribution line (m); distribution line has flat configuration of phase conductors with following heights: 10, 12, and 14 m; twin shield wires, if present, are 1.5 m above the phase conductors and 3 m apart; data set consists of 10000 simulations for each line height,
• 'flash': binary indicator that signals if the flashover has been recorded (1) or not (0). This variable is the outcome (binary class).

Note: It should be mentioned that the critical flashover voltage (CFO) level of the line is taken at 150 kV for the first two lines (10 m and 12 m) and 160 kV for the third line (14 m), and that the diameters of the phase conductors and shield wires for all treated lines are, respectively, 10 mm and 5 mm. Also, average grounding resistance of the shield wire is assumed at 10 Ohm for all treated cases (it has no discernible influence on the flashover rate). Dataset is class imbalanced and consists in total of 30000 simulations, with 10000 simulations for each of the three different MV distribution line heights (geometry) and CFO levels.

Important: Version 1.2 of the dataset fixes an important bug found in the previous data sets, where the column 'Ri' contained duplicate data from the column 'veloc'. This issue is now resolved.

Mathematical background used for the analysis of lightning interaction with the MV distribution line can be found in the references below.

References:

J. A. Martinez and F. Gonzalez-Molina, "Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks," in IEEE Transactions on Power Delivery, vol. 20, no. 3, pp. 2219-2226, July 2005, doi: 10.1109/TPWRD.2005.848734.

A. R. Hileman, "Insulation Coordination for Power Systems", CRC Press, Boca Raton, FL, 1999.

This dataset contains the daily number of lightning strikes in 20km grid boxes collected throughout Alaska as part of the AK NSF EPSCoR Fire and Ice program.The dataset is is part of a historical study to evaluate the predictability of lightning in Alaska. These data were derived from the historical lightning strikes recorded by Alaska Lightning Detection Database (ALDN) for 1986 - 2017. The data were gridded to 20km for ease of comparison with existing downscaled climate data by counting the number of lightning flashes that occurred in each grid box. The 2012-2017 are only available in the form of the individual flashes while the number of flashes in each strike were estimated for 1986-2011 based on the observed multiplicity parameter. Purpose The data were prepared to improve forecasts of lightning in Alaska. These forecasts have historically focused on short-term weather at the National Weather Service but the data are being explored for subseasonal to seasonal forecasting applications. Lineage Observed cloud-to-ground lightning strike data were obtained from the Alaska Lightning Detection Network (ALDN) for 1986–2015 (1987 and 1989 are missing data). The ALDN data consist of the location, date, and time of each lightning strike determined by a network of magnetic-direction-finding stations. The number of lightning strikes over land were counted within each 20-km grid box. The count of strikes was produced at a daily scale. The lightning data were homogenized (the sensor network has been changed over time) by exploiting the strike multiplicity information that was included in the pre-2012 data, which provides an estimate of the number of strokes that occurred within each flash of lightning. The multiplicity parameter (i.e., the number of strokes) was summed for the pre-2012 data instead of counting each flash that occurred in each 20-km grid box. This simple approach provides an estimated number of lightning strokes each year over the 1986–2011 period that is more in line with how lightning was observed during the 2012–15 period in the interior.