Dataset Description: NOAA Lightning Strikes Dataset The NOAA (National Oceanic and Atmospheric Administration) Lightning Strikes dataset provides insights into lightning activity over a given region or time period. This dataset is a product of NOAA's weather monitoring and storm tracking systems, offering valuable information for meteorologists, researchers, and disaster management authorities.

Key Features - Provides geospatial and temporal insights into lightning activity. - Useful for environmental monitoring, climate studies, and storm impact analysis. - Combines numerical, temporal, and spatial data for versatile analysis.

This dataset contains cloud-to-ground lightning strike information collected by Vaisala's National Lightning Detection Network and aggregated into 0.1 x 0.1 degree tiles by the experts at the National Centers for Environmental Information (NCEI) as part of their Severe Weather Data Inventory. This data provides historical cloud-to-ground data aggregated into tiles that around roughly 11 KMs for redistribution. This provides users with the number of lightning strikes each day, as well as the center point for each tile. The sample queries below will help you get started using BigQuery's GIS capabilities to analyze the data. For more on BigQuery GIS, see the documentation available here. The data begins in 1987 and runs through current day, with a delay of a few days for processing. For near real-time lightning information, see the Cloud Public Data's metadata listing of GOES-16 data for cloud-to-cloud and cloud-to-ground strikes over the eastern half of the western hemisphere. GOES-17 data covering the western half of the western hemisphere will be available soon. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Map Information This nowCOAST time-enabled map service provides maps of experimental lightning strike density data from the NOAA/National Weather Service/NCEP's Ocean Prediction Center (OPC) which emulate (simulate) data from the future NOAA GOES-R Global Lightning Mapper (GLM). The purpose of this experimental product is to provide mariners and others with enhanced "awareness of developing and transitory thunderstorm activity, to give users the ability to determine whether a cloud system is producing lightning and if that activity is increasing or decreasing..." Lightning Strike Density, as opposed to display of individual strikes, highlights the location of lightning cores and trends of increasing and decreasing activity. The maps depict the density of lightning strikes during a 15 minute time period at an 8 km x 8 km spatial resolution. The lightning strike density maps cover the geographic area from 25 degrees South to 80 degrees North latitude and from 110 degrees East to 0 degrees West longitude. The map units are number of strikes per square km per minute multiplied by a scaling factor of 10^3. The strike density is color coded using a color scheme which allows the data to be easily seen when overlaid on GOES imagery and to distinguish values at low density values. The maps are updated on nowCOAST approximately every 15 minutes. The latest data depicted on the maps are approximately 12 minutes old (or older). The OPC lightning strike density product is still experimental and may not always be available. Given the spatial resolution and latency of the data, the data should NOT be used to activite your lightning safety plans. Always follow the safety rule: when you first hear thunder or see lightning in your area, activate your emergency plan. If outdoors, immediately seek shelter in a substantial building or a fully enclosed metal vehicle such as a car, truck or a van. Do not resume activities until 30 minutes after the last observed lightning or thunder. For more detailed information about the update schedule for the lightning strike density data maps on nowCOAST, please see: http://new.nowcoast.noaa.gov/help/#section=updateschedule Background Information The source for the data is OPC's gridded lightning strike density data on an 8 x 8 km grid. The gridded data emulate the spatial resolution of the future Global Lightning Mapper (GLM) instrument to be flown on the NOAA GOES-R series of geostationary satellites, with the first satellite scheduled for launch in early 2016. The gridded data is based on data from Vaisala's ground based Vaisala's U.S. National Lightning Detection Network (NLDN) and its global lightning detection network referred to as the Global Lightning Dataset (GLD360). These networks are capable of detecting cloud-to-ground strokes, cloud-to-ground flash information and survey level cloud lightning information. According to the National Lightning Safety Institute, NLDN uses radio frequency detectors in the spectrum 1.0 kHz through 400 kHz to measure energy discharges from lightning as well as approximate distance and direction. According to Vaisala, the GLD360 network is capable of a detection efficiency greater than 70% over most of the Northern Hemisphere with a median location accuracy of 5 km or better. OPC's experimental gridded data are coarser than the original source data from Vaisala's networks. The 15-minute gridded source data are updated at OPC every 15 minutes at 10 minutes past the valid time. The lightning strike density product from NWS/NCEP/OPC is considered a derived product or Level 5 product ("NOAA-generated products using lightning data as input but not displaying the contractor transmitted/provided lightning data") and is appropriate for public distribution. Time Information

This map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:

Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.

Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:

validtime: Valid timestamp.

starttime: Display start time.

endtime: Display end time.

reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time).

projmins: Number of minutes from reference time to valid time.

desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.

desigprojmins: Number of minutes from designated reference time to valid time.

Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST help documentation at: http://new.nowcoast.noaa.gov/help/#section=layerinfo

References Kithil, 2015: Overview of Lightning Detection Equipment, National Lightning Safety Institute, Louisville, CO. (Available from http://www.lightningsafety.com/nsli_ihm/detectors.html).NASA and NOAA, 2014: Geostationary Lightning Mapper (GLM). (Available at http://www.goes-r.gov/spacesegment/glm.html).NWS, 2013: Experimental Lightning Strike Density Product Description Document. NOAA/NWS/NCEP/Ocean Prediction Center, College Park, MD (Available at http://www.opc.ncep.noaa.gov/lightning/lightning_pdd.php and http://products.weather.gov/PDD/Experimental%20Lightning%20Strike%20Density%20Product%2020130913.pdf). ,li>NOAA Knows Lightning. NWS, Silver Spring, MD (Available at http://www.lightningsafety.noaa.gov/resources/lightning3_050714.pdf).) Siebers, A., 2013: Soliciting Comments until June 3, 2014 on an Experimental Lightning Strike Density product (Offshore Waters). Public Information Notice, NOAA/NWS Headquarters, Washington, DC (Available at http://www.nws.noaa.gov/om/notification/pns13lightning_strike_density.htm).

The data was gathered from Lightning Detection System which covers an area within central Java in Indonesia. The discrimination column informs about the polarity of the lightning strike, CG- means Cloud to Ground with negative polarity. Signal kA means the lightning current in Kilo Amperes. Sensors nb. shows the sum of the number of sensors used to detect lightning.

The National Lightning Detection Network, NLDN, consists of over 100 remote, ground-based sensing stations located across the United States that instantaneously detect the electromagnetic signals given off when lightning strikes the earth's surface. These remote sensors send the raw data via a satellite-based communications network to the Network Control Center operated by Vaisala Inc. in Tucson, Arizona. Within seconds of a lightning strike, the NCC's central analyzers process information on the location, time, polarity, and communicated to users across the country.

More information:
http://thunderstorm.vaisala.com

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.

Instantly provide second-by-second lightning and thunder positioning data* The download URL will be updated starting from September 15, 112*, please switch before December 31, 112 to avoid the old version link becoming invalid. For those who need to download a large amount of data, please apply for membership on the Meteorological Data Open Platform https://opendata.cwa.gov.tw/index

This dataset contains lightning strike data from the High Altitude Ice Crystals - High Ice Water Content (HAIC-HIWC) project that took place in Darwin, Australia. The data is from the Australian Bureau of Meteorology (BoM) and originates from the Global Position and Tracking Systems (GPATS). The files are grouped into .zip files by month.

Real-Time Global Lightning Strike Detection Dataset

This dataset contains real-time, high-frequency lightning strike events collected globally with hourly updates. It captures comprehensive information about atmospheric electrical discharge events across the Earth's surface.

Dataset Overview: • Global Coverage: Tracks lightning strikes worldwide • Update Frequency: Hourly updates for real-time data • Data Format: CSV with 14.41 MB per version • Columns: 7 key atmospheric and spatial parameters

Key Features: 1. Temporal Data: Precise timestamps of strike events 2. Spatial Information: Latitude and longitude coordinates 3. Regional Classification: Region identifiers for geographic analysis 4. Atmospheric Metrics: MDS (Minimum Detectable Signal), MC (Modal Count), GS (Ground Strike) values 5. Event Status: Strike validation and quality indicators

Use Cases: • Weather pattern analysis and thunderstorm tracking • Climate research and atmospheric science studies • Risk assessment for infrastructure and aviation • Machine learning model training for weather prediction • Environmental monitoring and natural disaster analysis

Data Quality: • High-frequency monitoring for accurate detection • Quality indicators for data reliability • Validated strike events

This dataset is ideal for researchers, data scientists, and meteorologists interested in atmospheric phenomena, weather patterns, and climate-related analysis.

This data set contains Pacific Ocean region lightning map and count imagery. The products include hourly maps of lightning strike locations overlaying GMS-6 satellite data, daily images of hourly lightning counts on that day, and a single image of hourly lightning counts over the T-PARC period. These are preliminary maps created in real-time during the field phase of T-PARC.

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.

Surface weather stations and lightning data for diurnal and monthly composities. Objectives: Study the influence of river breezes in the development of deep convection using lightning strokes as a proxy for deep convection. Experimental Design: - Two surface weather stations: 1 (one) at Ponta Pelada airport (North of Negro, Solimões and Amazonas rivers conjunction) and 1 (one) at GoAmazon T2 Site (west of Solimões River) - Lightning strikes from Vaisala Inc. Global Lightning Detection network (GLD360) Methods: - Lightning strikes by hour of the day to show diurnal cycle - Use GoAmazon IOP1 (Feb-Mar 2014) and IOP2 (Sep-Oct 2014) as "monthly" variations (i.e., IOP1 == Wet season and IOP2 == Dry season).

Link to Tableau Online: https://public.tableau.com/profile/publish/Vento/Painel1#!/publish-confirm

This file contains 1999 daily lightning activity data for the state of New Mexico. These data were collected by a network of lightning detection stations scattered throughout the western United States. More information regarding the LLP Lightning Locating System can be found in Maier et al. (1983).

This dataset contains 5 minute resolution lightning strike data for Central America.

The global lightning strike recorder market is experiencing robust growth, driven by increasing demand for advanced weather monitoring and safety systems across various sectors. The market's expansion is fueled by the rising need for accurate lightning data in power grid management, preventing costly outages and ensuring grid stability. Furthermore, the communication industry relies heavily on lightning strike recorders to protect infrastructure and maintain service reliability. The burgeoning renewable energy sector, particularly wind farms, is another significant driver, as precise lightning strike data is crucial for preventative maintenance and minimizing downtime. Growth is also being propelled by advancements in recorder technology, leading to more compact, portable, and cost-effective solutions, expanding their accessibility to a broader range of applications. While the market faces some restraints, such as the high initial investment costs for sophisticated systems and the need for specialized technical expertise, these are offset by the significant long-term benefits in terms of reduced operational losses and enhanced safety. Market segmentation reveals strong demand for fixed lightning strike recorders in infrastructure-heavy applications, while portable recorders are finding increasing usage in research and mobile monitoring. Geographic analysis points towards North America and Europe as leading markets, driven by advanced infrastructure and stringent safety regulations. However, growth in the Asia-Pacific region is anticipated to be significant in the coming years due to increasing investments in infrastructure development and renewable energy projects. The competitive landscape is characterized by several established players and emerging companies actively contributing to technological innovation and market penetration. Key players are focusing on strategic partnerships, product diversification, and geographical expansion to gain a competitive edge. The focus on developing advanced data analytics capabilities integrated with lightning strike recorders further enhances their value proposition, offering insights beyond basic strike detection. Future growth prospects look promising, with the continued integration of these recorders into smart grids, IoT applications, and improved weather forecasting systems. Government initiatives aimed at mitigating the risks associated with lightning strikes, particularly in vulnerable regions, are also expected to drive market demand. Considering the CAGR and market trends, a conservative estimate places the market size around $2 billion in 2025, with consistent annual growth predicted for the foreseeable future.

The global lightning strike recorder market is experiencing robust growth, driven by increasing demand for advanced weather monitoring and safety systems across diverse sectors. The market, estimated at $150 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $250 million by 2033. Key drivers include rising investments in renewable energy infrastructure (particularly wind farms), stringent safety regulations in industries like petrochemicals and power generation, and the growing adoption of smart city initiatives incorporating advanced weather forecasting. The demand for accurate lightning data is crucial for mitigating risks associated with lightning strikes, protecting critical infrastructure, and ensuring human safety. Market segmentation reveals significant growth potential in portable lightning strike recorders, driven by their ease of deployment and cost-effectiveness compared to fixed systems. Geographically, North America and Europe currently hold significant market share, but the Asia-Pacific region, especially China and India, is poised for rapid expansion due to ongoing industrialization and infrastructure development. The competitive landscape is moderately consolidated, with companies like LPI, Citel, Paratonex, and others vying for market share through technological innovation and strategic partnerships. However, the market also exhibits opportunities for new entrants, particularly those offering innovative solutions with improved data analytics capabilities and integration with IoT platforms. Challenges include the relatively high initial investment costs associated with some systems, coupled with ongoing maintenance and data management requirements. Future growth will likely be shaped by advancements in sensor technology, the development of more sophisticated data analysis tools that provide actionable insights, and the increasing adoption of cloud-based data storage and management solutions that facilitate remote monitoring and analysis of lightning strike data. This overall positive growth outlook is supported by a consistent demand for enhanced safety and operational efficiency across various industries vulnerable to lightning strikes.

Lightning Detection and Ranging (LDAR) Raw data consists of level 1 lightning data collected from February 25, 1997 through June 11, 2008. The LDR system is located at the Kennedy Space Center. The center latitude and longitude of the LDAR network is 28.5387 and -80.6428. All x, y, and z values represent distance (in meters) from this location. LDAR is a volumetric lightning mapping system providing near real-time location of lightning in support of Space Shuttle operations. These data are in ASCII format.

These data come from the lightning sensor on the barge. The sensor reports every lightning strike within a 300 mile (480km) radius and distinguishes CG-IC strikes and strike polarity. This dataset has been quality controlled.

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.

This dataset contains includes measurements of trees and lianas stem diameters and status (e.g. alive, dead), and lightning strike data for forest areas within the Ngel Nyaki Forest, collected between June 2018 and July 2021. We investigated tree mortality driven by lightning strikes in a 40-ha area at the Ngel Nyaki Forest Dynamic Plot, located in south-eastern Nigeria. Ngel Nyaki is a submontane forest with an elevation range of 1,588–1,690m and is part of the Smithsonian's Forest Global Earth Observatory (ForestGEO) network. In every census, we measured and tagged all trees and lianas that have a stem diameter at 1.3 m (or above buttresses) of ≥25 cm and notes were taken about the tree's living status (e.g., broken, hollow) or the trees death mode (e.g., uprooted, standing).