Tornado TracksThis feature layer, utilizing data from the National Oceanic and Atmospheric Administration (NOAA), displays tornadoes in the United States, Puerto Rico and U.S. Virgin Islands between 1950 and 2024. A tornado track shows the route of a tornado. Per NOAA, "A tornado is a narrow, violently rotating column of air that extends from a thunderstorm to the ground. Because wind is invisible, it is hard to see a tornado unless it forms a condensation funnel made up of water droplets, dust and debris. Tornadoes can be among the most violent phenomena of all atmospheric storms we experience. The most destructive tornadoes occur from supercells, which are rotating thunderstorms with a well-defined radar circulation called a mesocyclone. (Supercells can also produce damaging hail, severe non-tornadic winds, frequent lightning, and flash floods.)"EF-5 Tornado Track (May 3, 1999) near Oklahoma City, OklahomaData currency: December 30, 2024Data source: Storm Prediction CenterData modifications: Added field "Date_Calc"For more information: Severe Weather 101 - Tornadoes; NSSL Research: TornadoesSupport documentation: SPC Tornado, Hail, and Wind Database Format SpecificationFor feedback, please contact: ArcGIScomNationalMaps@esri.comNational Oceanic and Atmospheric AdministrationPer NOAA, its mission is "To understand and predict changes in climate, weather, ocean, and coasts, to share that knowledge and information with others, and to conserve and manage coastal and marine ecosystems and resources."

Tornado TracksThis feature layer, utilizing data from the National Oceanic and Atmospheric Administration (NOAA), displays tornadoes in the United States, Puerto Rico and U.S. Virgin Islands between 1950 and 2024. A tornado track shows the route of a tornado. Per NOAA, "A tornado is a narrow, violently rotating column of air that extends from a thunderstorm to the ground. Because wind is invisible, it is hard to see a tornado unless it forms a condensation funnel made up of water droplets, dust and debris. Tornadoes can be among the most violent phenomena of all atmospheric storms we experience. The most destructive tornadoes occur from supercells, which are rotating thunderstorms with a well-defined radar circulation called a mesocyclone. (Supercells can also produce damaging hail, severe non-tornadic winds, frequent lightning, and flash floods.)"EF-5 Tornado Track (May 3, 1999) near Oklahoma City, OklahomaData currency: December 30, 2024Data source: Storm Prediction CenterData modifications: Added field "Date_Calc"For more information: Severe Weather 101 - Tornadoes; NSSL Research: TornadoesSupport documentation: SPC Tornado, Hail, and Wind Database Format SpecificationFor feedback, please contact: ArcGIScomNationalMaps@esri.comNational Oceanic and Atmospheric AdministrationPer NOAA, its mission is "To understand and predict changes in climate, weather, ocean, and coasts, to share that knowledge and information with others, and to conserve and manage coastal and marine ecosystems and resources."

Tornadoes, sometimes called twisters, are high-speed columns of rotating air connecting a thunderstorm to the ground. These storms vary greatly in size and strength, and are difficult for scientists to predict. The average tornado damage path is about one and a half to three kilometers (one to two miles) with a width of 45 meters (50 yards); however, some paths can stretch more than 160 kilometers (100 miles) and have widths greater than three kilometers (two miles).

Tornado paths are so small and unpredictable, local National Weather Service (NWS) forecast offices usually only have about 14 minutes to alert residents with a tornado warning before the storm reaches them. Because of this, the NWS issues tornado watches over a large area to warn residents a tornado could form in their vicinity hours before one can touch the ground.

Tornadoes only form when a thunderstorm has a certain combination of winds. As winds at varying speeds and directions cause rising air to start spinning, warmer air continues to rise and cooler air begins to sink to the ground. Once there are enough rising and sinking gusts of wind, the air near the ground begins to rotate. The rotating air throughout the tornado eventually speeds up to spin around one axis and begins to move horizontally across the land. Most tornadoes originate from supercell thunderstorms in which there are drastic differences in air temperatures and wind speeds, but not all supercell thunderstorms produce tornadoes.

Tornadoes occur in many parts of the world, including Australia, Europe, Africa, South America, and Asia; however, about 75 percent of the world’s known tornadoes have formed in the United States. About 1,200 tornadoes hit the U.S. every year. Although tornado season refers to the time of year when the United States sees the most tornadoes, peak tornado season varies across regions of the U.S. The southern Plains experience peak tornado season from May to early June, the Gulf coast from March to April, and the northern Plains and upper Midwest see the most tornadoes in either June or July. Even though there are times of the year when tornadoes are most prominent, they can occur at any time given the right weather conditions.

To assess the wind speeds of a tornado, the NWS implemented the Enhanced Fujita Scale (EF Scale), a set of wind estimates based on the intensity of damage from structures in the path of the storm. Because buildings have varying structural integrity, the EF Scale incorporates 28 damage indicators, such as building type (for example, barn, school, motel, or shopping mall), structures (for example, gas station canopy, mobile home, or transmission line tower) and trees (for example, hardwood or softwood). These damage indicators are then given a damage rating between 1 and 8, in which 1 = no damage and 8 = completely destroyed. From the values given for each damage indicator, the NWS derives an EF number between 0 and 5 that estimates the overall intensity of the tornado.

EF-0: Gale winds with speeds between 105 and 137 kmph (65-85 mph) EF-1: Moderate winds with speeds between 138 and 177 kmph (86-110 mph) EF-2: Significant winds with speeds between 178 and 217 kmph (111-135 mph) EF-3: Severe winds with speeds between 218 and 266 kmph (136-165 mph) EF-4: Devastating winds with speeds between 267 and 322 kmph (166-200 mph) EF-5: Incredible winds with speeds over 322 kmph (200 mph)

Do you have tornadoes where you live? Learn How to Stay Safe from Tornadoes!

This map layer features U.S. tornado track data from the National Oceanic and Atmospheric Administration between 1980 and 2022. This very large dataset has been filtered to visualize large and violent tornado tracks from EF-3 to EF-5 tornadoes that occurred between 2000 and 2017.

Want to learn more about tornadoes? Check out Forces of Nature.

TornadoesThis feature layer, utilizing data from the National Oceanic and Atmospheric Administration (NOAA), displays tornadoes in the United States, Puerto Rico and U.S. Virgin Islands between 1950 and 2024. Per NOAA, "A tornado is a narrow, violently rotating column of air that extends from a thunderstorm to the ground. Because wind is invisible, it is hard to see a tornado unless it forms a condensation funnel made up of water droplets, dust and debris. Tornadoes can be among the most violent phenomena of all atmospheric storms we experience. The most destructive tornadoes occur from supercells, which are rotating thunderstorms with a well-defined radar circulation called a mesocyclone. (Supercells can also produce damaging hail, severe non-tornadic winds, frequent lightning, and flash floods.)"EF-5 Tornado (May 22, 2011) near Joplin, MissouriData currency: December 30, 2024Data source: Storm Prediction CenterData modifications: Added field "Date_Calc"For more information: Severe Weather 101 - Tornadoes; NSSL Research: TornadoesSupport documentation: SPC Tornado, Hail, and Wind Database Format SpecificationFor feedback, please contact: ArcGIScomNationalMaps@esri.comNational Oceanic and Atmospheric AdministrationPer NOAA, its mission is "To understand and predict changes in climate, weather, ocean, and coasts, to share that knowledge and information with others, and to conserve and manage coastal and marine ecosystems and resources."

A database of verified tornado tracks across Canada has been created covering the 30-year period from 1980 to 2009. The tornado data have undergone a number of quality control checks and represent the most current knowledge of past tornado events over the period. However, updates may be made to the database as new or more accurate information becomes available. The data have been converted to a geo-referenced mapping file that can be viewed and manipulated using GIS software.

Naturgewalt Tornado - Eine Übersicht: Tornados als sich heftig drehende Wirbel gehören mit Windgeschwindigkeiten bis zu 500 km/h zu den schlimmsten Naturgewalten. In den USA treten etwa 1.000 Tornados/Jahr auf; in Europa sind sie halb so häufig. Knapp 90% gehören zur Kategorie »schwach«. Sie können entstehen, wenn sich Schauer- oder Gewitterwolken in Anwesenheit warm-feuchter Luftmassen bilden und instabilen Bedingungen herrschen. Die Entstehung eines Tornados kann noch nicht genau vorhergesagt werden. Wetterdienste verbreiten jedoch kurzfristige Warnungen, wenn ein Verdacht auf Entstehung von Tornados vorliegt. Sie sind kleinräumig sowie meist kurzlebig und daher sehr schwer zu registrieren. Durch die Zusammenarbeit zwischen nationalen Wetterdiensten und Skywarn können die Warnungen und der Schutz der Bevölkerung verbessert werden. Eine Zunahme der Tornados infolge des Klimawandels ist bisher nicht nachgewiesen. Force of nature Tornado - A overview: Tornadoes as violently rotating columns of air with wind speeds up to 500 km/h belong to the strongest forces of nature. More than 1000 tornadoes per year occur in the USA; and in Europa 500-600. Nearly 90% belong to the category »weak«. They can arise when deep convective clouds form. Tornadoes cannot yet be predicted. Weather services issue short-term warnings when tornado development is probable. Tornadoes have small scales and are short-lived and thus not all are detected. Therefore, the co-operation between National Weather Services and the NGO Skywarn is very important to improve the short-term warnings for better public protection measures. An increase in tornadoes due to climate change cannot be proven yet.

• Update Frequency: Weekly

Data from this dataset can be downloaded/accessed through this dataset page and Kaggle's API.

Context

Severe weather is defined as a destructive storm or weather. It is usually applied to local, intense, often damaging storms such as thunderstorms, hail storms, and tornadoes, but it can also describe more widespread events such as tropical systems, blizzards, nor'easters, and derechos.

The Severe Weather Data Inventory (SWDI) is an integrated database of severe weather records for the United States. The records in SWDI come from a variety of sources in the NCDC archive. SWDI provides the ability to search through all of these data to find records covering a particular time period and geographic region, and to download the results of your search in a variety of formats. The formats currently supported are Shapefile (for GIS), KMZ (for Google Earth), CSV (comma-separated), and XML.

Content

The current data layers in SWDI are:
- Filtered Storm Cells (Max Reflectivity >= 45 dBZ) from NEXRAD (Level-III Storm Structure Product)
- All Storm Cells from NEXRAD (Level-III Storm Structure Product)
- Filtered Hail Signatures (Max Size > 0 and Probability = 100%) from NEXRAD (Level-III Hail Product)
- All Hail Signatures from NEXRAD (Level-III Hail Product)
- Mesocyclone Signatures from NEXRAD (Level-III Meso Product)
- Digital Mesocyclone Detection Algorithm from NEXRAD (Level-III MDA Product)
- Tornado Signatures from NEXRAD (Level-III TVS Product)
- Preliminary Local Storm Reports from the NOAA National Weather Service
- Lightning Strikes from Vaisala NLDN

Disclaimer:
SWDI provides a uniform way to access data from a variety of sources, but it does not provide any additional quality control beyond the processing which took place when the data were archived. The data sources in SWDI will not provide complete severe weather coverage of a geographic region or time period, due to a number of factors (eg, reports for a location or time period not provided to NOAA). The absence of SWDI data for a particular location and time should not be interpreted as an indication that no severe weather occurred at that time and location. Furthermore, much of the data in SWDI is automatically derived from radar data and represents probable conditions for an event, rather than a confirmed occurrence.

Acknowledgements

Dataset Source: NOAA. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Cover photo by NASA on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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.

Currently filtered for Storm Date is after 12/1/2023Purpose: This is a feature layer of tornado swaths for the NWS Damage Assessment Toolkit.The National Weather Service (NWS) Damage Assessment Toolkit (DAT) has been utilized experimentally since 2009 to assess damage following tornadoes and convective wind events. The DAT is a GIS-based framework for collecting, storing, and analyzing damage survey data, utilizing the Enhanced Fujita (EF) scale for the classification of damage. Data collected from individual locations via mobile device are transmitted to a central geospatial database where they are quality controlled and analyzed to assign the official EF rating. In addition to the individual point, the data are analyzed to generate track centerlines and damage swaths. High resolution satellite imagery and radar data, through partnership with the NASA Short-term Prediction Research and Transition Center, are also available to aid in the analysis. The subsequent dataset is then made available through a web-based graphical interface and GIS services.Here is the full REST service: https://services.dat.noaa.gov/arcgis/rest/services/nws_damageassessmenttoolkitGeoplatform website: https://communities.geoplatform.gov/disasters/noaa-damage-assessment-toolkit-dat/More InformationWelcome to the National Weather Service Damage Assessment Toolkit. Data on this interface is collected during NWS Post-Event Damage Assessments. While the data has been quality controlled, it is still considered preliminary. Official statistics for severe weather events can be found in the Storm Data publication, available from the National Centers for Environmental Information (NCEI) at: https://www.ncdc.noaa.gov/IPS/sd/sd.html Questions regarding this data can be addressed to: parks.camp@noaa.gov.

5-minute event-based volumes of radar reflectivity, differential reflectivity, specific differential phase, correlation coefficient, velocity spectrum width, azimuthal shear, and radial divergence data. This data encompasses ~100 of the most severe events in the US each year based on tornadoes, hail, and wind from 2010 to the Present, with domains that vary to encompass each event. The storm track data are included for each of the events. Data for 2010-2012 do not include differential reflectivity, specific differential phase, and correlation coefficient, which are only observed following the dual-polarization upgrade to the NEXRAD network that was completed in early 2013.

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. 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. # Catastrophic storms, forest disturbance, and the natural history of Swainson’s warbler https://doi.org/10.5061/dryad.gmsbcc2w8 ## Description of the data and file structure The spreadsheet presents the song recording field number, location (state and county or parish), date, and geographic coordinates of 1717 territorial Swainson's warblers (Limnothlypis swainsonii) documented from 1986 to 2014 in the southeastern United States. Records are ordered by state, county or parish, date, and song recording field number. All recordings were made by the author.

On behalf of the Press and Information Office of the Federal Government, the opinion research institute Kantar investigates attitudes towards climate protection and the assessment of political measures for climate protection on a quarterly basis. In the 4th quarter of 2021, questions on climate impacts formed a thematic focus. Concerns about possible consequences of climate change; degree to which different people are affected by the consequences of climate change (respondent personally, population in Germany, population worldwide); assessment of current political measures for climate protection; informedness about possibilities to contribute to climate protection themselves; opinion on the conversion of a large car park into a seepage area for heavy rainfall; concerns about the occurrence of various disasters in one´s own region (floods, forest fires, storms/tornadoes, earthquakes, new pathogens/pandemics, terrorist attacks, chemical or reactor accidents, power outages lasting several days). Demography: sex; age; highest level of education; occupation; household size; number of persons in the household aged 14 and over; party preference; voter eligibility; household net income (grouped); survey by mobile vs. landline. Additionally coded: respondent ID; weighting factor; interview date; city size (BIK regions); federal state; survey area west/east. Im Auftrag des Presse- und Informationsamts der Bundesregierung untersucht das Meinungsforschungsinstitut Kantar quartalsweise die Einstellungen zum Klimaschutz und die Bewertung politischer Maßnahmen zum Klimaschutz. Im 4. Quartal 2021 bildeten Fragen zu Klimafolgen einen thematischen Schwerpunkt. Sorgen über mögliche Folgen des Klimawandels; Grad des Betroffenseins verschiedener Personen von den Folgen des Klimawandels (Befragter persönlich, Bevölkerung in Deutschland, Bevölkerung weltweit); Beurteilung der aktuellen politischen Maßnahmen zum Klimaschutz; Informiertheit über Möglichkeiten selbst etwas zum Klimaschutz beizutragen; Meinung zur Umwandlung eines großen Parkplatzes in eine Sickerfläche für Starkregenfälle; Sorgen über das Eintreten verschiedener Katastrophenfälle in der eigenen Region (Hochwasser/ Überschwemmungen, Waldbrände, Stürme/ Tornados, Erdbeben, neue Krankheitserreger/ Pandemien, Terroranschläge, Chemie- oder Reaktorunfälle, mehrtägige Stromausfälle). Demographie: Geschlecht; Alter; höchster Bildungsabschluss; Berufstätigkeit; Haushaltsgröße; Anzahl der Personen im Haushalt ab 14 Jahren; Parteipräferenz; Wahlberechtigung; Haushaltsnettoeinkommen (gruppiert); Erhebung per Mobilfunk vs. Festnetz. Zusätzlich verkodet wurde: Befragten ID; Interviewdatum; Ortsgröße (BIK-Regionen); Bundesland; Befragungsgebiet West/ Ost; Gewichtungsfaktor.

Post-disaster field observations of the built environment are critical for advancing fundamental research that links hazard data to structural performance, cascading community impacts, and the development of effective mitigation strategies. Yet, data collection efforts remain fragmented across hazard types and infrastructure systems due to varying objectives, methodologies, protocols, and standards among investigators and organizations. To address this, a Unified Assessment Framework has been developed for standardized post-disaster hazard and structural assessment data and metadata collection across multiple natural hazards (earthquake, windstorm, coastal events) and infrastructure typologies. The framework encompasses a tiered performance assessment of infrastructure with increasing rigor and fidelity levels: Basic Assessment (BA), Load Path Assessment (LPA), and Detailed Component Assessment (DCA). The framework has been implemented as an open-access mobile application, the Structural Extreme Events Reconnaissance Network’s “StEER Unified App”, hosted on Fulcrum, a commercial data collection platform by Spatial Networks Inc. Along with unification of data fields, preliminary mapping rules were developed to map out existing hazard-specific damage rating scales (e.g., wind, surge/flood, rainwater ingress) to the European Macroseismic Scale (EMS-98) compatible unified damage scale, enabling consolidation of global damage ratings into a common data field, facilitating the unification of multiple hazards within a single app. In the mapping process, care was taken to retain the overarching damage level definitions (e.g., slight, moderate, severe damage) while customizing the specific descriptors to reflect hazard-specific damage mechanisms. Two use cases are presented to demonstrate the application of this framework through the StEER Unified App: a supervised pilot after the 2022 Hurricane Ian, Florida and an unsupervised deployment for the 2023 Turkey earthquake sequence. These deployments highlight the framework’s flexible and scalable nature, demonstrate the feasibility of standardized assessments, and offer insights into how data quality is influenced by assessor pre-deployment training and assessment tier, particularly for more complex tasks such as load path evaluation. This work advances the field by providing a scalable, standardized, and hazard-agnostic approach to structural field reconnaissance, enabling more consistent and coordinated data collection across events. The open-access framework and app not only support real-time deployments but also allow integration of legacy datasets into a unified platform—laying the foundation for longitudinal analyses, cross-hazard comparisons, and expanded data reuse within the Natural Hazards Engineering community.

The NCDP U.S. Natural Hazards Climate Change Projections project provides an interactive map-based tool, with a downloadable dataset, to explore county-level future natural hazard projections for Wildfires, Tropical Cyclones (Hurricanes), Tornadoes, and Sea Level Rise under one or more climate change scenarios in the United States. This tool provides a new dimension of hazard data to supplement NCDPs Natural Hazards Index v2.0. This new tool is a collaborative effort between multiple academic and public institutions that bring together the most up-to-date science to anticipate future hazards to visualize mid- and end-century hazard indicator estimates under one or more climate change scenarios (i.e., Shared Socioeconomic Pathways 2 (SSP2) and 5 (SSP5)) or RCP 8.5) allowing users to compare each time period and scenario to a historical baseline and to see the anticipated direction and magnitude of change for each hazard. The product documentation provides source data links, individual dataset corresponding authors, and details on the use, limitations, and interpretations. All datasets included in this map application have their limitations. As such, interpretation of the data should be only one piece of information to determine risk, which may vary depending on local and regional mitigation and adaptation actions.

A violent tornado outbreak occurred on December 10-11, 2021 in the Midwest US. One of the tornadoes, known as the Quad-State tornado, tracked across four states and devastated the downtown area of Mayfield, KY, producing high-end EF-4 damage. The data here provides a series of wind speed and direction time histories of the Quad-State tornado for 44 damaged residential houses in Mayfield, KY, which can be useful for detailed forensic analysis of the residential building damage. The data was generated using a software that performs a treefall pattern analysis method, developed by the first author. In addition to the many structural damage, the tornado damaged a large number of trees in the Mayfield area. The fall direction of the damaged trees displayed a converging pattern, caused by a rotational wind flow, which is a typical indicator of a tornado. The converging treefall pattern then can be analyzed to characterize the tornadic flow and estimate the wind field (i.e., treefall pattern analysis method). The treefall pattern analysis method simulates a series of tornadoes using an idealized Rankine vortex model and generates a virtual treefall pattern, which is used to compare to the treefall pattern observed in the field and iterated until the "best-matching" pattern is found. In order to reduce the uncertainty in the estimates, the translational speed of the tornado was estimated based on tracking the motion of the vortex signature from the nearest NEXRAD radar, and the Radius of Maximum Wind (RMW) and decay exponent of the Rankine vortex model were estimated based on the structural damage. Then, the software was used to estimate the rest of the vortex parameters and wind time history (e.g., wind speed and direction) at selected locations. More detailed description on the parameter estimation and software will be published later in the NIST Technical Note.

The GIS database contains the data of stand-replacing windthrow areas in the forest zone of the European Russia (ER) for the 1986–2017 period, and the ArcGIS 10.1/10.2 and Qgis 3* projects to view and analyze the data. The database contains three shapefiles (.shp), corresponding to three hierarchical levels of the data:

• “Elementary damaged area”, that is a single-part polygon of wind-damaged forest;

• “Windthrow”, that represent a group of closely spaced forest disturbances, i.e., a multipart polygon, associated with one storm event;

• “Storm event track”, that is a cluster of windthrow areas with identical direction and having the same date (or date range) of occurrence, which was most likely induced by one convective or non-convective storm.

The key fields ID and storm_ID associates each damaged area with the features in the datasets of windthrows and storm event tracks respectively using one-to-many relation. A single value “‒9999” was used for storing NoData in all fields of the attribute table. All data and projects have WGS 1984 coordinate system (without projection).

Delineation of windthrow areas was based on the full archive of Landsat images and two Landsat-derived products on forest cover change, namely the Global Forest Change and the Eastern’ Europe Forest Cover Change. Subsequent verification and analysis of each windthrow was carried out to determine a type of storm events with a degree of event certainty, dates (date ranges) and time (time ranges) of an event, windthrow geometrical characteristics. The database contains 102,747 elementary areas of damaged forest that were combined into 700 windthrow areas caused by 486 convective or non-convective storm events. The database includes stand-replacing windthrow only, which an area > 5 ha or > 25 ha for events caused by tornadoes or other storms, respectively. Additional information contained weather station reports and event description from media sources is also provided..

The compiled database provides a valuable source of spatial and temporal information on windthrows in the ER and can be successfully used both in forestry and severe storm studies.

This data set contains Tornadoes that occurred in Tennessee between 1950 and 2017. The data was downloaded from the NWS Storm Prediction Center.Column Names and Definitions from the NWS (pdf)om - Tornado number - A count of tornadoes during the y ear: Prior to 2007, these numbers were assigned to the tornado as the information arrived in the NWS database. Since 2007, the numbers may have been assigned in sequential (temporal) order after event date/times are converted to CST. However, do not use "om" to count the sequence of tornadoes through the year as sometimes new entries have come in late, or corrections are made, and the data are not re-sequenced.NOTE: Tornado segments that cross state borders and/or more than 4 counties will have same OM number. See information about fields 22-24 below.yr - Year, 1950-2017mo - Month, 1-12dy - Day, 1-31date - Date - in format yyyy-mm-dd formattime - Time - in format HH:MM:SStz - Time Zone - All t imes, except for ?=unkown and 9=GMT, were converted to 3=CST. This should be accounted for when building queries for GMT summaries such as 12z- 12z.st - State - Two letter postal abbreviation (PR=Puerto Rico. VI=Virgin Islands)stf - State FIPS Number - Note some Puerto Rico codes are incorrectstn - State Number - number of this tornado, in this state, in this year: May not be sequential in some years. Note: discontinued in 2008. This number can be calculated in a spreadsheet by sorting and after accounting for border crossing tornadoes and 4+ county segments.f - F-Scale - F-scale (EF-scale after Jan. 2007): values -9, 0, 1, 2, 3, 4, 5 (-9=unknown).inj - Injuries - when summing for state totals use sn=1, not sg=1 (see below)fat - Fatalities - when summing for state totals use sn=1, not sg=1 (see below)loss - Estimated property loss information - Prior to 1996 this is a categorization of tornado damage by dollar amount (o or blank-unknown; 1<$50, 2=$50-$500, 3=$500-$5,000, 4=$5,000-$50,000; 5=$50,000-$500,000, 6=$500,000-$5,000,000, 7=$5,000,000-$50,000,000, 8=$50,000,000-$500,000,000; 9=$5,000,000,000) When summing for state total use sn= 1, not Sg=1 (see below). From 1996, this is tornado property damage in millions of dollars. Note: this may change to whole dollar amounts in the future. Entry of 0 does not mean $0.closs - Estimated crop loss in millions of dollars (started in 2007). Entry of 0 does not mean 0$Tornado database file updated to add "fc" field for estimated F-scale rating in 2016. Valid for records altered between 1950-1982. slat - Starting latitude in decimal degreesslong - Starting longitude in decimal degreeselat - Ending latitude in decimal degreeselon - Ending longitude in decimal degreeslen - Length in mileswid - Width in yardsns, sn, sg - Understanding these fields is critical to counting state tornadoes, totaling state fatalities/losses. The tornado segment information can be thought of as follows:ns - Number of States affected by this tornado: 1, 2, or 3.sn - State Number 1 or 0 (1=entire track info in this state)sg - Tornado Segment number: 1, 2, or -9 (1 = entire track info)1,1,1 = Entire record for the track of the tornado (unless all 4 fips codes are non -zero).1,0,-9 = Continuing county fips code information only from 1,1,1 record, above (same om).2,0,1 = A two-state tornado (st=state of touchdown, other fields summarize entire track).2,1,2 = First state segment for a two-state (2,0,1) tornado (state same as above, same om).2,1,2 = Second state segment for two-state (2,0,1) tornado (state tracked into, same om).2,0,-9 = Continuing county fips for a 2,1,2 record that exceeds 4 counties (same om).3,0,1 = A three-state (st=state of touchdown, other fields summarize entire track).3,1,2 = First state segment for a three-state (3,0,1) tornado (state same as 3,0,1, same om).3,1,2 = Second state segment for three-state (3,0,1) tornado (2nd state tracked into, same om as 3,0,1 record).3,1,2 = Third state segment for a three-state (3,0,1) tornado (3rd state tracked into, same om as the initial 3,0,1 record).f1 - 1st county FIPS codef2 - 2nd county FIPS codef3 - 3rd county FIPS codef4 - 4th county FIPS codefc - fc = 0 for unaltered (E)F - scale rating. fc = 1 if previous rating was -9 (unknown)

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.

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.

A severe line of thunderstorms moved across southeast Texas on Tuesday, January 24th, producing damaging winds and multiple tornadoes across the region during the day. Strong to severe thunderstorms began to develop in the morning hours, producing strong winds of up to 60 mph and heavy downpours. As the day progressed, the environment became more favorable for tornadoes as a warm front slowly moved northward into Southeast TX. Radar indicated a few mesovortices developing just southwest of the Houston metro area by late morning. An hour later (1:45 pm) an EF-0 tornado touched down in Fort Bend County, roughly from Needville to Thompsons. Another tornado touched down 25 minutes later in Brazoria County, near southwest Pearland producing EF-0 damage. Severe weather continued as a stronger cell developed across Southeast Harris County and produced a strong tornado around 2:15 PM near El Franco Lee Park, east of Brookside Village. A near-continuous path of damage extended east-northeast, then northeast from there, across portions of Southeast Harris County, which includes the cities of Pasadena, Deer Park, and Baytown.