Tornadoes in North America

Magnitude, Fatalities, Injuries, and Crop Loss Data

By Homeland Infrastructure Foundation [source]

About this dataset

This dataset compiles historical data on tornadoes in the United States, Puerto Rico, and the U.S. Virgin Islands – providing a critical resource to researchers and policy-makers alike. Obtained from the National Weather Service's Storm Prediction Center (SPC), it contains an intricate wealth of information that sheds light onto patterns of tornado outbreaks across time & geographical space yielding insights into factors like magnitude, fatalities/injuries caused and losses incurred by these devastating weather disasters. With attributes such as Start Longitude/Latitude, End Longitude/Latitude, Day of Origin & Time Zone – this dataset will enable a comprehensive analysis of changes over time in regards to both intensity & frequency for those interested in studying climate change and its impact on extreme weather events such as tornadoes. For disaster management personnel dealing with natural hazards like floods or hurricanes - a familiarity with this dataset can help identify areas prone to frequent storms - thereby empowering proactive measures towards their mitigation.*

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How to use the dataset

This dataset contains historical tornado tracks in the United States, Puerto Rico, and the U.S. Virgin Islands. The data was obtained from the National Weather Service's Storm Prediction Center (SPC). It includes thirty-seven columns of statistics which you can use to analyze when, where, and how frequently tornadoes occur in North America over time.

Research Ideas

• Creating a tornado watch and warning system using Geographic Information Systems (GIS) technology to track and predict the path of dangerous storms.
• Developing an insurance system that gives detailed information on historical data related to natural disasters including tornadoes, hurricanes, floods, etc., in order to better assess risk levels for insuring homes and businesses in vulnerable areas.
• Developing an app that provides real-time notifications for potential tornadoes by utilizing the dataset's coordinates and forecasting data from the National Weather Service (NWS). The app could even provide shelter locations near users based on their current location ensuring that people are aware of potential active threats nearby them quickly increasing safety levels as much as possible when these hazardous events occur

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Historical_Tornado_Tracks.csv | Column name | Description | |:--------------|:-------------------------------------| | OM | Origin Mode (Point or Line) (String) | | YR | Year (Integer) | | MO | Month (Integer) | | DY | Day (Integer) | | DATE | Date (String) | | TIME | Time (String) | | TZ | Time Zone (String) | | ST | State (String) | | STF | FIPS State Code (String) | | STN | State Name (String) | | MAG | Magnitude (Integer) | | INJ | Injuries (Integer) | | FAT | Fatalities (Integer) | | LOSS | Loss (Integer) | | CLOSS | Crop Loss (Integer) | | SLAT | Starting Latitude (Float) | | SLON | Starting Longitude (Float) | | ELAT | Ending Latitude (Float) | | ELON | Ending Longitude (Float) | | LEN | Length of Track (Float) ...

The core breeding range of Swainson's warbler (Limnothlypis swainsonii) overlaps a zone of exceptionally high tornado frequency in southeastern North America. The importance of tornadoes in creating breeding habitat for this globally rare warbler and other disturbance-dependent species has been largely overlooked. This paper estimates tornado frequency (1950–2021) and forest disturbance in the 240 counties and parishes in which breeding was documented from 1988 to 2014. The frequency of destructive tornadoes (EF1-EF5) varied 6-fold across the breeding range with a peak in the Gulf Coast states. Counties from east Texas to Alabama experienced the lowest median return interval of 5.4 years per 1000 km2, resulting in approximately 2477 ha of forest damage per 1000 km2 per century, based on current forestland cover. Tornadoes were significantly less frequent north and east of the core breeding range, with return intervals increasing to 9.1 years per 1000 km2 for breeding counties on the Atlantic coastal plain, 10.2 years per 1000 km2 in the Ozark Mountains, and 32.3 years per 1000 km2 in the Appalachian Mountains. Breeding counties within 150 km of the coastline from east Texas to North Carolina are also subjected to the highest frequency of hurricanes in the Western Hemisphere. Hurricanes often cause massive forest damage but archived meteorological and forestry data are insufficient to estimate the aggregate extent of forest disturbance in breeding counties. Nevertheless, the combined impact of tornadoes and hurricanes in the pre-Anthropogenic era was likely sufficient to produce a dynamic mosaic of early-successional forest crucial for the breeding ecology of Swainson's warbler. To ensure the long-term survival of this rare warbler, it is advisable to develop habitat management plans that incorporate remote sensing data on early-successional forest generated by catastrophic storms as well as anthropogenic activities. This dataset comprises a catalog of 1717 song recordings of male Swainson's warblers (Limnothlypis swainsonii) on breeding territories in the southeastern United States. Songs were recorded from 1988 to 2014. The spreadsheet includes song recording field number (GRG), state, county or parish, date, latitude, and longitude. Breeding territories were located in 240 counties and parishes, which served as the geographic template for storm data analysis. Geographic coordinates were plotted in Fig 1 of "Catastrophic storms, forest disturbance, and the natural history of Swainson's warbler" (doi.org/10.1002/ece3.11099). Questions or inquiries regarding the dataset can be directed to the author.
Methods Geolocation of territorial Swainson's warblers. From 1988 to 2014, I surveyed breeding populations in 15 states as part of a comprehensive study of the warbler’s natural history. These surveys targeted Swainson’s warbler and were not incidental components of broader community censuses. Territorial males were documented in 240 counties and parishes documented by song recordings. Surveys were conducted during the breeding period, which began on 22 April in the Gulf Coast states and ended on 30 June in the Appalachian Mountains. I surveyed a wide spectrum of forestland and shrubland habitats, broadly classified as “forest land” by the USDA on public and private land and along waterways. Most breeding territories of this monogamous species were located using playback of songs, utilizing a protocol that was field-tested and fine-tuned in the late 1980s on the breeding and wintering ranges. Territorial males respond to playback by approaching the song source and delivering agitated “chip” notes, but usually refrain from singing until the playback source retreats or playback ends. Response to playback, mate-guarding, persistence during “playback-and-follow” trials, and counter-singing with other males were regarded as evidence of territoriality. Mist-netting or other handling was not required to document territoriality. The geographic coordinates of territories were recorded on site with Garmin™ GPS receivers (post-1998) or with Google Earth Pro from field notes and maps. All fieldwork was performed by the author.

National Risk Index Version: March 2023 (1.19.0)A Tornado is a narrow, violently rotating column of air that extends from the base of a thunderstorm to the ground and is visible only if it forms a condensation funnel made up of water droplets, dust and debris. Annualized frequency values for Tornadoes are in units of events per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

This dataset consists of Level III weather radar products collected from Next-Generation Radar (NEXRAD) stations located in the contiguous United States, Alaska, Hawaii, U.S. territories and at military base sites. NEXRAD is a network of 160 high-resolution Doppler weather radars operated by the NOAA National Weather Service (NWS), the Federal Aviation Administration (FAA), and the U.S. Air Force (USAF). Doppler radars detect atmospheric precipitation and winds, which allow scientists to track and anticipate weather events, such as rain, ice pellets, snow, hail, and tornadoes, as well as some non-weather objects like birds and insects. NEXRAD stations use the Weather Surveillance Radar - 1988, Doppler (WSR-88D) system. This is a 10 cm wavelength (S-Band) radar that operates at a frequency between 2,700 and 3,000 MHz. The radar system operates in two basic modes: a slow-scanning Clear Air Mode (Mode B) for analyzing air movements when there is little or no precipitation activity in the area, and a Precipitation Mode (Mode A) with a faster scan for tracking active weather. The two modes employ nine Volume Coverage Patterns (VCPs) to adequately sample the atmosphere based on weather conditions. A VCP is a series of 360 degree sweeps of the antenna at pre-determined elevation angles and pulse repetition frequencies completed in a specified period of time. The radar scan times 4.5, 5, 6 or 10 minutes depending on the selected VCP. During 2008, the WSR-88D radars were upgraded to produce increased spatial resolution data, called Super Resolution. The earlier Legacy Resolution data provides radar reflectivity at 1.0 degree azimuthal by 1 km range gate resolution to a range of 460 km, and Doppler velocity and spectrum width at 1.0 degree azimuthal by 250 m range gate resolution to a range of 230 km. The upgraded Super Resolution data provides radar reflectivity at 0.5 degree azimuthal by 250 m range gate resolution to a range of 460 km, and Doppler velocity and spectrum width at 0.5 degree azimuthal by 250 m range gate resolution to a range of 300 km. Super resolution makes a compromise of slightly decreased noise reduction for a large gain in resolution. In 2010, the deployment of the Dual Polarization (Dual Pol) capability to NEXRAD sites began with the first operational Dual Pol radar in May 2011. Dual Pol radar capability adds vertical polarization to the previous horizontal radar waves, in order to more accurately discern the return signal. This allows the radar to better distinguish between types of precipitation (e.g., rain, hail and snow), improves rainfall estimates, improves data retrieval in mountainous terrain, and aids in removal of non-weather artifacts. The NEXRAD products are divided in two data processing levels. The lower Level II data are base products at original resolution. Level II data are recorded at all NWS and most USAF and FAA WSR-88D sites. From the Level II quantities, computer processing generates numerous meteorological analysis Level III products. The Level III data consists of reduced resolution, low-bandwidth, base products as well as many derived, post-processed products. Level III products are recorded at most U.S. sites, though non-US sites do not have Level III products. There are over 40 Level III products available from the NCDC. General products for Level III include the base and composite reflectivity, storm relative velocity, vertical integrated liquid, echo tops and VAD wind profile. Precipitation products for Level III include estimated ground accumulated rainfall amounts for one and three hour periods, storm totals, and digital arrays. Estimates are based on reflectivity to rainfall rate (Z-R) relationships. Overlay products for Level III are alphanumeric data that give detailed information on certain parameters for an identified storm cell. These include storm structure, hail index, mesocyclone identification, tornadic vortex signature, and storm tracking information. Radar messages for Level III are sent by the radar site to users in order to know more about the radar status and special product data. NEXRAD data are provided to the NOAA National Climatic Data Center for archiving and dissemination to users. Data coverage varies by station and ranges from May 1992 to 1 day from present. Most stations began observing in the mid-1990s, and most period of records are continuous.