The United States experienced a significant surge in tornado activity in 2024, with 1,910 reported across the country. This marked a substantial increase from previous years, highlighting the unpredictable nature of these violent atmospheric phenomena. Fatalities and economic impact While tornado frequency increased, the death toll from such events remained relatively low compared to historical peaks. In 2023, 86 fatalities were reported due to tornadoes, a notable increase from the 23 deaths in 2022 but far below the 553 lives lost in 2011. Moreover, the economic impact of these storms was substantial, with tornado damage in 2023 amounting to approximately 1.38 billion U.S. dollars, nearly doubling from the previous year. However, this pales in comparison to the record-setting damage of 9.5 billion U.S. dollars in 2011. Comparison to other extreme weather events While tornadoes pose significant risks, hurricanes have historically caused more extensive damage and loss of life in the United States. Hurricane Katrina in 2005 remains the costliest tropical cyclone in recent decades, with damages totaling 200 billion U.S. dollars when adjusted to 2024 values. The impact of such extreme weather events extends beyond immediate destruction, as evidenced by the 1,518 hurricane-related fatalities recorded in 2005. As climate change continues to influence weather patterns, both tornado and hurricane activity may see further shifts in frequency and intensity in the years to come.

The graph illustrates the number of tornado-related fatalities in the United States from 2008 to 2024. The x-axis represents the years, abbreviated from ’08 to ’24, while the y-axis shows the number of deaths each year. Fatalities range from a low of 10 in 2018 to a peak of 553 in 2011. Most years have fatalities between 18 and 126, with notable exceptions in 2020 (76 deaths), 2021 (101 deaths), and 2023 (83 deaths). The data is presented in a bar graph format, highlighting the significant spike in fatalities in 2011 and the overall variability in tornado-related deaths over the 16-year period.

In 2023, there were a total of 86 fatalities reported due to tornadoes in the United States, up from 23 fatalities in the previous year. This was the lowest figure reported in the North American country since 2018, when a total of 10 lives were lost due to tornadoes. On the other hand, the highest figure reported in the U.S. since 1995 was in 2011, when tornadoes caused 553 fatalities.

In 2023, tornadoes resulted in approximately 1.38 billion U.S. dollars worth of damage across the United States. This was an increase of almost 95 percent in comparison to the previous year. The North American country's economic damage caused by tornadoes peaked in 2011, at nearly 9.5 billion U.S. dollars. That same year, the number of fatalities due to tornadoes in the United States was also the highest.

Tornado Warnings are issued to enable the public to get out of harm’s way and mitigate preventable loss. NWS forecasters issue approximately 2,900 Tornado Warnings per year, primarily between the Rockies and Appalachian Mountains. Tornado Warning statistics are based on a comparison of warnings issued and weather spotter observations of tornadoes and/or storm damage surveys from Weather Forecast Offices in the United States. Lead Time (LT) for a Tornado Warning is the difference between the time the warning was issued and the time the tornado occurred (based on certified reports) in minutes, assuming the tornado tracked within the bounds of the warned area. Lead Times for all tornado occurrences within the U.S. are averaged to get this statistic for a given fiscal year. This average includes all warned events with zero lead times and all unwarned events. Lead Time is calculated down to the minute for individual Tornado Warnings and tornadic events. Although the timing of the warning transmission is recorded to the nearest second, typically there is only an estimate to the nearest minute of when a tornado touches down. Additionally, even though we can compute the average tornado warning lead time to a precision of 30 second increments or less, the reporting of this value implies greater accuracy in the data based on scientific and logistical restrictions on tornado reporting and surveying. Most tornadoes cannot be visually tracked from beginning to end and post-storm damage surveying is the official method with which the NWS categorizes tornado characteristics (intensity, path length & width) but must rely on radar data to estimate the timing of the tornado track.

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.

Tornadoes cause loss of life and damage to property each year in the United States and around the world. The largest impacts come from ‘outbreaks’ consisting of multiple tornadoes closely spaced in time. Here we find an upward trend in the annual mean number of tornadoes per US tornado outbreak for the period 1954–2014. Moreover, the variance of this quantity is increasing more than four times as fast as the mean. The mean and variance of the number of tornadoes per outbreak vary according to Taylor’s power law of fluctuation scaling (TL), with parameters that are consistent with multiplicative growth. Tornado-related atmospheric proxies show similar power-law scaling and multiplicative growth. Path-length-integrated tornado outbreak intensity also follows TL, but with parameters consistent with sampling variability. The observed TL power-law scaling of outbreak severity means that extreme outbreaks are more frequent than would be expected if mean and variance were independent or linearly related.

Q: Where is severe weather likely at this time of year? A: Shading on each map reflects how often severe weather occurred within 25 miles during a 30-year base period. The darker the shading, the higher the number of severe weather reports near that date. For this map, severe weather encompasses tornadoes, thunderstorm winds over 58 miles per hour, and hail larger than three-quarters of an inch in diameter. Q: How were these maps produced? A: For each day of the year, scientists plotted reports of severe weather from 1982 to 2011 on a gridded map. To reveal the long-term patterns of these events, they applied mathematical filters to smooth the counts in time and space. Keep in mind that severe weather is possible at any location on any day of the year. Q: What do the colors mean? A: Shaded areas show the historical probability of severe weather occurring within 25 miles. Meteorologists estimated these probabilities from severe weather reports submitted from 1982-2011. For each day of the year, scientists plotted reports of severe events onto a map marked with grid cells 50 miles on a side. For each grid cell, they counted the number of years with at least one report, and divided by the total number of years. To reveal the long-term patterns suggested by this relatively small dataset, they used statistical methods to smooth the data. For instance, to smooth clusters of events in time, a mathematical filter replaced the value in every grid cell with a 15-day average. Another filter extended report locations over a 25-mile-wide circle to indicate the probability that the event could have occurred at other points within that area. Q: Why do these data matter? A: Knowing when and where severe weather tends to occur through the year promotes preparedness. Residents who are alert to the possibility of severe weather are better able to respond in ways that keep them safe. These data can also help emergency response personnel plan for when and where their services may be necessary. Q: How did you produce these snapshots? A: Data Snapshots are derivatives of existing data products: to meet the needs of a broad audience, we present the source data in a simplified visual style. NOAA's National Weather Service Storm Prediction Center produced the Severe Weather Climatology files. To produce our images, we obtained the climatology data as a numpy array, and ran a set of scripts to display the mapped areas on our base maps with a custom color bar. Additional information Data for these images represents an update and extension of work first put forth by Dr. Harold Brooks of the National Severe Storms Laboratory. References Brooks, H. E., C. A. Doswell, III, and M. P. Kay, (2003) Climatological estimates of local daily tornado probability, Wea. Forecasting, 18, 626-640.Source: https://www.climate.gov/maps-data/data-snapshots/data-source/historic-probability-severe-weather This upload includes two additional files:* Historic Probability of Severe Weather _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/historic-probability-severe-weather )* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.

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)

Scroll down to view all tornado events or use the navigation above to view a particular tornado day. This overview map on the right shows all tornadoes events for the entire year. The shadings and numbers within the counties and parishes indicate how many tornadoes were records in those individual counties and parishes during the year.Click on a rating location within the map to see details of the ratings and pictures of the damage where available.

Between 1950 and 2018, over 8800 tornadoes are known to have occurred in the state of Texas, an average of around 130 per year. While official records from before 1950 are not available, reconstruction from local press reports indicate at least 500 tornadoes occurred during 1900-1949; undoubtedly the true number is far higher.

On the evening of 28 March 2000, two tornados struck Fort Worth, Arlington, and Grand Prairie, Texas. The Fort Worth Tornado touched down west of the city, and moved through the downtown area. The tornado was rated an F2 on the Fujita scale at its strongest point. The Arlington tornado started as an F3, and varied from F2 to F0 throughout its 6.5 mile track. The damages from these tornados was estimated at $450 million in the Fort Worth area. 5 F2's, and 8 F0-F1's. While southern Louisiana's annual average for tornados is 13 (1950-1995), it hosted 12 tornados on 1-2 January. All of the tornados were indicated by WSR-88D radars in Lake Charles and Fort Polk,

Louisiana. The average lead time was an impressive 24 minutes. There was one fatality in Texas, but, given the severity of the outbreak and the fact that it happened overnight, it is fortunate that there were not more people injured or killed.

For more information, see: http://data.eol.ucar.edu/codiac/projs?COMET_CASE_028

In 2023, storms caused nearly 15,000 deaths across the globe. the third-largest figure recorded since 1990. In the past three decades, the highest annual deathtoll due to storms was registered in 1991, when storm events were responsible for the death of more than 146 thousand people worldwide. That year, a massive cyclone hit Bangladesh, becoming one of the deadliest storms of the century. The death count due to storms was also remarkably high in 2008, mainly associated with a cyclone which hit Myanmar in May.

On 1-2 January 1999, southeast Texas and southwest Louisiana experienced a major tornado outbreak which featured 1 F3, 5 F2's, and 8 F0-F1's. While southern Louisiana's annual average for tornados is 13 (1950-1995), it hosted 12 tornados on 1-2 January. All of the tornados were indicated by WSR-88D radars in Lake Charles and Fort Polk, Louisiana. The average lead time was an impressive 24 minutes. There was one fatality in Texas, but, given the severity of the outbreak and the fact that it happened overnight, it is fortunate that there were not more people injured or killed.

For more information, see: http://data.eol.ucar.edu/codiac/projs?COMET_CASE_027

OverviewThe multiple hazard index for the United States Counties was designed to map natural hazard relating to exposure to multiple natural disasters. The index was created to provide communities and public health officials with an overview of the risks that are prominent in their county, and to facilitate the comparison of hazard level between counties. Most existing hazard maps focus on a single disaster type. By creating a measure that aggregates the hazard from individual disasters, the increased hazard that results from exposure to multiple natural disasters can be better understood. The multiple hazard index represents the aggregate of hazard from eleven individual disasters. Layers displaying the hazard from each individual disaster are also included.

The hazard index is displayed visually as a choropleth map, with the color blue representing areas with less hazard and red representing areas with higher hazard. Users can click on each county to view its hazard index value, and the level of hazard for each individual disaster. Layers describing the relative level of hazard from each individual disaster are also available as choropleth maps with red areas representing high, orange representing medium, and yellow representing low levels of hazard.Methodology and Data CitationsMultiple Hazard Index

The multiple hazard index was created by coding the individual hazard classifications and summing the coded values for each United States County. Each individual hazard is weighted equally in the multiple hazard index. Alaska and Hawaii were excluded from analysis because one third of individual hazard datasets only describe the coterminous United States.

Avalanche Hazard

University of South Carolina Hazards and Vulnerability Research Institute. “Spatial Hazard Events and Losses Database”. United States Counties. “Avalanches United States 2001-2009”. < http://hvri.geog.sc.edu/SHELDUS/

Downloaded 06/2016.

Classification

Avalanche hazard was classified by dividing counties based upon the number of avalanches they experienced over the nine year period in the dataset. Avalanche hazard was not normalized by total county area because it caused an over-emphasis on small counties, and because avalanches are a highly local hazard.

None = 0 AvalanchesLow = 1 AvalancheMedium = 2-5 AvalanchesHigh = 6-10 Avalanches

Earthquake Hazard

United States Geological Survey. “Earthquake Hazard Maps”. 1:2,000,000. “Peak Ground Acceleration 2% in 50 Years”. < http://earthquake.usgs.gov/hazards/products/conterminous/

. Downloaded 07/2016.

Classification

Peak ground acceleration (% gravity) with a 2% likelihood in 50 years was averaged by United States County, and the earthquake hazard of counties was classified based upon this average.

Low = 0 - 14.25 % gravity peak ground accelerationMedium = 14.26 - 47.5 % gravity peak ground accelerationHigh = 47.5+ % gravity peak ground acceleration

Flood Hazard

United States Federal Emergency Management Administration. “National Flood Hazard Layer”. 1:10,000. “0.2 Percent Annual Flood Area”. < https://data.femadata.com/FIMA/Risk_MAP/NFHL/

. Downloaded 07/2016.

Classification

The National Flood Hazard Layer 0.2 Percent Annual Flood Area was spatially intersected with the United States Counties layer, splitting flood areas by county and adding county information to flood areas. Flood area was aggregated by county, expressed as a fraction of the total county land area, and flood hazard was classified based upon percentage of land that is susceptible to flooding. National Flood Hazard Layer does not cover the entire United States; coverage is focused on populated areas. Areas not included in National Flood Hazard Layer were assigned flood risk of Low in order to include these areas in further analysis.

Low = 0-.001% area susceptibleMedium = .00101 % - .005 % area susceptibleHigh = .00501+ % area susceptible

Heat Wave Hazard

United States Center for Disease Control and Prevention. “National Climate Assessment”. Contiguous United States Counties. “Extreme Heat Events: Heat Wave Days in May - September for years 1981-2010”. Downloaded 06/2016.

Classification

Heat wave was classified by dividing counties based upon the number of heat wave days they experienced over the 30 year time period described in the dataset.

Low = 126 - 171 Heat wave DaysMedium = 172 – 187 Heat wave DaysHigh = 188 – 255 Heat wave Days

Hurricane Hazard

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Atlantic Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Pacific Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

Classification

Atlantic and Pacific datasets were merged. Tropical storm and disturbance tracks were filtered out leaving hurricane tracks. Each hurricane track was assigned the value of the category number that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as being more hazardous. Values describing each hurricane event were aggregated by United States County, normalized by total county area, and the hurricane hazard of counties was classified based upon the normalized value.

Landslide Hazard

United States Geological Survey. “Landslide Overview Map of the United States”. 1:4,000,000. “Landslide Incidence and Susceptibility in the Conterminous United States”. < https://catalog.data.gov/dataset/landslide-incidence-and-susceptibility-in-the-conterminous-united-states-direct-download

. Downloaded 07/2016.

Classification

The classifications of High, Moderate, and Low landslide susceptibility and incidence from the study were numerically coded, the average value was computed for each county, and the landslide hazard was classified based upon the average value.

Long-Term Drought Hazard

United States Drought Monitor, Drought Mitigation Center, United States Department of Agriculture, National Oceanic and Atmospheric Administration. “Drought Monitor Summary Map”. “Long-Term Drought Impact”. < http://droughtmonitor.unl.edu/MapsAndData/GISData.aspx >. Downloaded 06/2016.

Classification

Short-term drought areas were filtered from the data; leaving only long-term drought areas. United States Counties were assigned the average U.S. Drought Monitor Classification Scheme Drought Severity Classification value that characterizes the county area. County long-term drought hazard was classified based upon average Drought Severity Classification value.

Low = 1 – 1.75 average Drought Severity Classification valueMedium = 1.76 -3.0 average Drought Severity Classification valueHigh = 3.0+ average Drought Severity Classification value

Snowfall Hazard

United States National Oceanic and Atmospheric Administration. “1981-2010 U.S. Climate Normals”. 1: 2,000,000. “Annual Snow Normal”. < http://www1.ncdc.noaa.gov/pub/data/normals/1981-2010/products/precipitation/

. Downloaded 08/2016.

Classification

Average yearly snowfall was joined with point location of weather measurement stations, and stations without valid snowfall measurements were filtered out (leaving 6233 stations). Snowfall was interpolated using least squared distance interpolation to create a .05 degree raster describing an estimate of yearly snowfall for the United States. The average yearly snowfall raster was aggregated by county to yield the average yearly snowfall per United States County. The snowfall risk of counties was classified by average snowfall.

None = 0 inchesLow = .01- 10 inchesMedium = 10.01- 50 inchesHigh = 50.01+ inches

Tornado Hazard

United States National Oceanic and Atmospheric Administration Storm Prediction Center. “Severe Thunderstorm Database and Storm Data Publication”. 1: 2,000,000. “United States Tornado Touchdown Points 1950-2004”. < https://catalog.data.gov/dataset/united-states-tornado-touchdown-points-1950-2004-direct-download

. Downloaded 07/2016.

Classification

Each tornado touchdown point was assigned the value of the Fujita Scale that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as more hazardous. Values describing each tornado event were aggregated by United States County, normalized by total county area, and the tornado hazard of counties was classified based upon the normalized value.

Volcano Hazard

Smithsonian Institution National Volcanism Program. “Volcanoes of the World”. “Holocene Volcanoes”. < http://volcano.si.edu/search_volcano.cfm

. Downloaded 07/2016.

Classification

Volcano coordinate locations from spreadsheet were mapped and aggregated by United States County. Volcano count was normalized by county area, and the volcano hazard of counties was classified based upon the number of volcanoes present per unit area.

None = 0 volcanoes/100 kilometersLow = 0.000915 - 0.007611 volcanoes / 100 kilometersMedium = 0.007612 - 0.018376 volcanoes / 100 kilometersHigh = 0.018377- 0.150538 volcanoes / 100 kilometers

Wildfire Hazard

United States Department of Agriculture, Forest Service, Fire, Fuel, and Smoke Science Program. “Classified 2014 Wildfire Hazard Potential”. 270 meters. < http://www.firelab.org/document/classified-2014-whp-gis-data-and-maps

. Downloaded 06/2016.

Classification

The classifications of Very High, High, Moderate, Low, Very Low, and Non-Burnable/Water wildfire hazard from the study were numerically coded, the average value was computed for each county, and the wildfire hazard was classified based upon the average value.

This ranking shows the ten deadliest tornadoes in U.S. history, ranked by the death toll of their victims. The deadliest tornado of all time in the United States was the Tri-State Tornado on March 18, 1925 in Missouri, Illinois and Indiana. It killed 695 people and injured over 2,000.

ArcGIS layer of all of Florida's F/EF-0 tornado points from 1950-2022, data from NOAA/NWS SPC tornado archive found at https://www.spc.noaa.gov/wcm/#data.

This database provides data (in NetCDF format) and code for reproducing figures in the manuscript of Chavas&Li (2022).

The number of emergency situations caused by storms, hurricanes, tornadoes, or twisters in Russia increased in the latest year observed. There were 27 disasters of that type in the country in 2021, compared to two catastrophes recorded in 2019.

Climate affects many aspects of our daily lives. Transportation, industrial processes, tourism, environment, and agriculture are closely tied to climatic factors. Even our recreation and general lifestyles are influenced by the climate of our region. Because of this, one of the primary objectives of the SCO is to provide accurate and thorough climate information to all of North Carolina's citizens, including private industry, community economic development authorities, state agencies, schools, community colleges, universities, and other community organizations. The SCO provides a wealth of information which is available to everyone.

Climate Monitoring Services Available include:

• Climate Retrieval and Observations Network Of the Southeast (CRONOS)

enables the public to quickly and easily retrieve archived weather observations from 216 stations in and around North Carolina.

• AgNet

a network of automated weather stations located at most of the outlying research stations and field laboratories.

• Drought

Links to web sites with drought monitoring data and information.

• NC ECONet

North Carolina Environment and Climate Observing Network

• Station Density

  Analysis of Monitoring Station Density of the North Carolina Environment and Climate Observing System (NC ECONet)

• Storm Events Database

Access the National Climatic Data Center's Storm Events Database.

Access All Products: https://climate.ncsu.edu/cronos

[Summary Extracted from the State Climate Office of North Carolina Home Page]