This is an annual edition poster showing all of the hurricanes having impacted the continental U.S. from 1950 to 2022. This 36x28 inch glossy poster gives a quick look of the location and strength of each hurricane which impacted the continental United States. The poster is also available to download as a PDF file. The map includes the name, category strength, year, and approximate strike location of each hurricane. For the 2022 edition two new hurricanes were added: Hurricane Ian, a Category-4 Hurricane hitting the western Florida Peninsula with a secondary landfall in South Carolina, and Hurricane Nicole, a Category-1 hurricane hitting the east coast of Florida.

The State of the Climate is a collection of periodic summaries recapping climate-related occurrences on both a global and national scale. The State of the Climate Monthly Overview - Hurricanes & Tropical Storms report focuses primarily on storms and conditions that affect the U.S. and its territories, in Atlantic and Pacific basins. The report places each basin's tropical cyclone activity in a climate-scale context. Key statistics (dates, strengths, landfall, energy, etc.) for major cyclone activity in other basins is occasionally presented. Reports began in June 2002. The primary Atlantic hurricane season (June-November) is covered each year; other months are included as storm events warrant. An annual summary is available from 2002. These reports are not updated in real time.

Historical Hurricane TracksThis feature layer, utilizing data from the National Oceanic and Atmospheric Administration (NOAA), displays global hurricane tracks from 1842-2024. According to NOAA, "a tropical cyclone is a rotating low-pressure weather system that has organized thunderstorms but no fronts (a boundary separating two air masses of different densities). Tropical cyclones with maximum sustained surface winds of less than 39 miles per hour (mph) are called tropical depressions. Those with maximum sustained winds of 39 mph or higher are called tropical storms. When a storm's maximum sustained winds reach 74 mph, it is called a hurricane."Hurricane Andrew (1992)Data currency: December 31, 2024Data source: International Best Track Archive for Climate Stewardship (IBTrACS)Data modification: Field added - Hurricane DateFor more information: International Best Track Archive for Climate Stewardship (IBTrACS)Support documentation: IBTrACS v04 column documentationFor feedback, please contact: ArcGIScomNationalMaps@esri.comNational Oceanic and Atmospheric Administration (NOAA)Per 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."

Note: This is a real-time dataset. If you do not see any data on the map, there may not be an event taking place. The Atlantic hurricane season begins on June 1 and ends on November 30, and the eastern Pacific hurricane season begins on May 15 and ends on November 30.Hurricanes, also known as typhoons and cyclones, fall under the scientific term tropical cyclone. Tropical cyclones that develop over the Atlantic and eastern Pacific Ocean are considered hurricanes.Meteorologists have classified the development of a tropical cyclone into four stages: tropical disturbance, tropical depression, tropical storm, and tropical cyclone. Tropical cyclones begin as small tropical disturbances where rain clouds build over warm ocean waters. Eventually, the clouds grow large enough to develop a pattern, where the wind begins to circulate around a center point. As winds are drawn higher, increasing air pressure causes the rising thunderstorms to disperse from the center of the storm. This creates an area of rotating thunderstorms called a tropical depression with winds 62 kmph (38 mph) or less. Systems with wind speeds between 63 kmph (39 mph) and 118 kmph (73 mph) are considered tropical storms. If the winds of the tropical storm hit 119 kmph (74 mph), the storm is classified as a hurricane. Tropical cyclones need two primary ingredients to form: warm water and constant wind directions. Warm ocean waters of at least 26 degrees Celsius (74 degrees Fahrenheit) provide the energy needed for the storm to become a hurricane. Hurricanes can maintain winds in a constant direction at increasing speeds as air rotates about and gathers into the hurricane’s center. This inward and upward spiral prevents the storm from ripping itself apart. Hurricanes have distinctive parts: the eye, eyewall, and rain bands. The eye is the calm center of the hurricane where the cooler drier air sinks back down to the surface of the water. Here, winds are tranquil, and skies are partly cloudy, sometimes even clear. The eyewall is composed of the strongest ring of thunderstorms and surrounds the eye. This is where rain and winds are the strongest and heaviest. Rain bands are stretches of rain clouds that go far beyond the hurricane’s eyewall, usually hundreds of kilometers. Scientists typically use the Saffir-Simpson Hurricane Wind Scale to measure the strength of a hurricane’s winds and intensity. This scale gives a 1 to 5 rating based on the hurricane’s maximum sustained winds. Hurricanes rated category 3 or higher are recognized as major hurricanes. Category 1: Wind speeds are between 119 and 153 kmph (74 and 95 mph). Although this is the lowest category of hurricane, category 1 hurricanes still produce dangerous winds and could result in damaged roofs, power lines, or fallen tree branches. Category 2: Wind speeds are between 154 and 177 kmph (96 and 110 mph). These dangerous winds are likely to cause moderate damage; enough to snap or uproot small trees, destroy roofs, and cause power outages. Category 3: Wind speeds are between 178 and 208 kmph (111 and 129 mph). At this strength, extensive damage may occur. Well-built homes could incur damage to their exterior and many trees will likely be snapped or uprooted. Water and electricity could be unavailable for at least several days after the hurricane passes. Category 4: Wind speeds are between 209 and 251 kmph (130 and 156 mph). Extreme damage will occur. Most of the area will be uninhabitable for weeks or months after the hurricane. Well-built homes could sustain major damage to their exterior, most trees may be snapped or uprooted, and power outages could last weeks to months. Category 5: Wind speeds are 252 kmph (157 mph) or higher. Catastrophic damage will occur. Most of the area will be uninhabitable for weeks or months after the hurricane. A significant amount of well-built, framed homes will likely be destroyed, uprooted trees may isolate residential areas, and power outages could last weeks to months. This map is built with data from the NOAA National Hurricane Center (NHC) and the Joint Typhoon Warning Center (JTWC). The map shows recent, observed, and forecasted hurricane tracks and positions, uncertainties, wind speeds, and associated storm watches and warnings. This is a real-time dataset that is programed to check for updates from the NHC and JTWC every 15 minutes. If you are in an area experiencing a tropical cyclone, tune into local sources for more up-to-date information and important safety instructions. This map includes the following information: Forecast position points: These points mark the locations where the NHC predict the tropical cyclone will be at 12, 24, 36, 48, 72, 96, and 120 hours in the future.Observed position points: These points mark the locations where the tropical cyclone has been.Forecast track: This is the line that connects the forecast points and marks the expected path of the hurricane.Observed track: This line marks the path the tropical cyclone has already taken.Cone of uncertainty: Due to the complexity of ocean atmospheric interactions, there are many different factors that can influence the path of a hurricane. This uncertainty is represented on the map by a cone. The further into the future the forecast is, the wider the cone due to the greater uncertainty in the precise path of the storm. Remember rain, wind, and storm surge from the hurricane will likely impact areas outside the cone of uncertainty. This broader impact of wind can be seen if you turn on or off Tropical Storm Force (34 Knots) 5-Day Wind Probability, Strong Tropical Storm Force (50 Knots) 5-Day Wind Probability, or Hurricane Force (64 Knots) 5-Day Wind Probability map layers.Watches and warnings: Storm watches or warnings depend on the strength and distance from the location of the forecasted event. Watches indicate an increased risk for severe weather, while a warning means you should immediately move to a safe space.Tropical storm watch: The NHC issues this for areas that might be impacted by tropical cyclones with wind speeds of 34 to 63 knots (63 to 119 kilometers per hour or 39 to 74 miles per hour) in the next 48 hours. In addition to high winds, the region may experience storm surge or flooding.Tropical storm warning: The NHC issues this for places that will be impacted by hurricanes with wind speeds of 34 to 63 knots (63 to 119 kilometers per hour or 39 to 74 miles per hour) in the next 36 hours. As with the watch, the area may also experience storm surge or flooding.Hurricane watch: The NHC issues this watch for areas where a tropical cyclone with sustained wind speeds of 64 knots (119 kilometers per hour or 74 miles per hour) or greater in the next 48 hours may be possible. In addition to high winds, the region may experience storm surge or flooding.Hurricane warning: The NHC issues this warning for areas where hurricanes with sustained wind speeds of 64 knots (119 kilometers per hour or 74 miles per hour) or greater in the next 36 hours are expected. As with the watch, the region may experience storm surge or flooding. This warning is also posted when dangerously high water and waves continue even after wind speeds have fallen below 64 knots.Recent hurricanes: These points and tracks mark tropical cyclones that have occurred this year but are no longer active.

Want to learn more about how hurricanes form? Check out Forces of Nature or explore The Ten Most Damaging Hurricanes in U.S. History story.

About this dataset

Context

The dataset tabulates the Hurricane population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Hurricane. The dataset can be utilized to understand the population distribution of Hurricane by age. For example, using this dataset, we can identify the largest age group in Hurricane.

Key observations

The largest age group in Hurricane, UT was for the group of age 30-34 years with a population of 1,714 (8.79%), according to the 2021 American Community Survey. At the same time, the smallest age group in Hurricane, UT was the 85+ years with a population of 284 (1.46%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Hurricane is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Hurricane total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hurricane Population by Age. You can refer the same here

This data reflects areas with a risk of storm tide flooding from hurricanes, based on potential storm tide heights calculated by the National Weather Service's SLOSH (Sea, Lake, and Overland Surge from Hurricanes) Model. The SLOSH Basin used for mapping was Chesapeake Bay (CP5), released in 2014. This data was prepared by the U.S. Army Corps of Engineers, Baltimore District, Planning Division in January 2016. SLOSH storm tide elevations used for this mapping are based on the Maximum of Maximums (MOM) SLOSH output dataset. The MOM output elevations represent the highest calculated storm tide values based on thousands of SLOSH simulations using different combinations of approach direction, forward speed, landfall point, astronomical tide, and intensity (Category 1 through Category 4). Categories 1 through 4 refer to the Saffir-Simpson scale of hurricane intensity. This map does not reflect the expected storm tide flooding for every hurricane, or for any one particular type of hurricane. This map shows the overall footprint of the area that has some risk of storm tide flooding from hurricanes, based on the MOM output dataset.

The intensity and spatial extent of tropical cyclone precipitation (TCP) often shapes the risk posed by landfalling storms. Here we provide a comprehensive climatology of landfalling TCP characteristics as a function of tropical cyclone strength, using daily precipitation station data and Atlantic US landfalling tropical cyclone tracks from 1900-2017. We analyze the intensity and spatial extent of ≥ 1 mm/day TCP (Z1) and ≥ 50 mm/day TCP (Z50) over land. We show that the highest median intensity and largest median spatial extent of Z1 and Z50 occur for major hurricanes that have weakened to tropical storms, indicating greater flood risk despite weaker wind speeds. We also find some signs of TCP change in recent decades. In particular, for major hurricanes that have weakened to tropical storms, Z50 intensity has significantly increased, indicating possible increases in flood risk to coastal communities in more recent years.

Methods 1. Station precipitation and tropical cyclone tracks

We use daily precipitation data from the Global Historical Climatology Network (GHCN)-Daily station dataset (Menne et al., 2012) and TC tracks archived in the revised HURricane DATabase (HURDAT2) database (Landsea & Franklin, 2013). HURDAT2 is a post-storm reanalysis that uses several datasets, including land observations, aircraft reconnaissance, ship logs, radiosondes, and satellite observations to determine tropical cyclone track locations, wind speeds and central pressures (Jarvinen et al., 1984; Landsea & Franklin, 2013). We select 1256 US stations from the GHCN-Daily dataset that have observations beginning no later than 1900 and ending no earlier than 2017 (though most station records are not continuous throughout that period). These 1256 land-based stations are well distributed over the southeastern US and Atlantic seaboard (see Supporting Figure S1).

We use the HURDAT2 Atlantic database to select locations and windspeeds of TC tracks that originated in the North Atlantic Ocean, Gulf of Mexico and Caribbean Sea, and made landfall over the continental US. Though tracks are determined at 6-hourly time steps for each storm (with additional timesteps that indicate times of landfall, and times and values of maximum intensity), we limit our analysis to track points recorded at 1200 UTC, in order to match the daily temporal resolution and times of observation of the GHCN-Daily precipitation dataset (Menne et al., 2012), as well as the diurnal cycle of TCP (Gaona & Villarini, 2018). Although this temporal matching technique may omit high values of precipitation from the analysis, it reduces the possibility of capturing precipitation that is not associated with a TC.

1. Tropical cyclone and Lifetime Maximum Intensity (LMI) categories

For each daily point in the tropical cyclone track, we use the maximum sustained windspeed to place the storm into one of three Extended Saffir-Simpson categories: tropical storms (“TS”; 34-63 knots), minor hurricanes (“Min”; categories 1 and 2; 64-95 knots), and major hurricanes (“Maj”; categories 3 to 5; > 96 knots) (Schott et al., 2012). Additionally, for each track, we record the category of the lifetime maximum intensity (LMI), based on the maximum windspeed found along the whole lifetime of the track (i.e., using all available track points). LMI is a standard tropical cyclone metric, and is considered a robust measure of track intensity through time and across different types of data integrated into the HURDAT2 reanalysis (Elsner et al., 2008; Kossin et al., 2013, 2014). Therefore, for each track point, a dual category is assigned: the first portion of the classification denotes the category of the storm for a given point (hereafter “point category”), while the second denotes the LMI category. The combination of the two can thus be considered a “point-LMI category”. For example, the point on August 27, 2017 at 1200 UTC along Hurricane Harvey’s track is classified as TS-Maj because it is a tropical storm (TS) at this point but falls along a major hurricane LMI track (see starred location in Supporting Figure S2a). Given that the LMI category for a given point cannot be weaker than the point category itself, the set of possible point-LMI category combinations for each track point is TS-TS, TS-Min, TS-Maj, Min-Min, Min-Maj, and Maj-Maj. This dual classification allows us to explore climatological TCP spatial extents and intensities during the tropical cyclone lifetime. Our dual classification does not account for the timing of the point category relative to the LMI category for a given point along a track (i.e., the time-lag between the LMI and point in consideration). However, the majority of points selected in our analysis occur after the TC has reached its LMI and are in the weakening stage (see Supporting Table S1 for more details). This could be expected, as our analysis is focused on land-based precipitation stations, and TCs weaken over land. However, a small fraction of TC points analyzed occur over the ocean before making landfall, but are close enough to land for precipitation gauges to be impacted.

1. Moving neighborhood method for TCP spatial extent and intensity

   We first find the distribution of tropical cyclone precipitation (TCP) intensity using all daily land precipitation values from all available stations in a 700 km-radius neighborhood around each point over land on each tropical cyclone track (Figure 1a and Supporting Figure S2). We then create two new binary station datasets, Z1(x) and Z50(x), which indicate whether or not a station meets or exceeds the 1 mm/day or 50 mm/day precipitation threshold, respectively, on a given day. The 50 mm/day threshold is greater than the 75th percentile of TCP across all tropical cyclone categories (Figure 1a), allowing us to capture the characteristics of heavy TCP while retaining a robust sample size. The 1 mm/day threshold captures the extent of the overall TCP around the TC track point.

We use the relaxed moving neighborhood and semivariogram framework developed by Touma et al. (2018) to quantify the spatial extent of Z1 and Z50 TCP for each track point. Using a neighborhood with a 700 km radius around each track point, we select all station pairs that meet two criteria: at least one station has to exhibit the threshold precipitation on that given day (Z(x) = 1; blue and pink stations in Supporting Figure S2b), and at least one station has to be inside the neighborhood (black and pink stations in Supporting Figure S2b). We then calculate the indicator semivariogram, g(h), for each station pair selected for that track point (Eq. 1):

γh=0.5*[Z(x+h)-Z(x)]2, Eq. 1

where h is the separation distance between the stations in the station pair. The indicator semivariogram is a function of the separation distance, and has two possible outcomes: all pairs with two threshold stations (Z(x) = Z(x+h) = 1) have a semivariogram value of 0, and all pairs with one threshold station and one non-threshold station (Z(x) = 1 and Z(x+h) = 0) have a semivariogram value of 0.5.

We then average the semivariogram values for all station pairs for equal intervals of separation distances (up to 1000 km) to obtain the experimental semivariogram (Supporting Figure S2c). To quantify the shape of the experimental semivariogram, we fit three parameters of the theoretical spherical variogram (nugget, partial sill, and practical range) to the experimental semivariogram (Eq. 2):

γ(h) = 0, for h=0

γ(h) = c+b*((3/2)(h/α)-(1/2)(h/α)3), for 0<h≤α

γ(h) = c+b, for h≥α, Eq. 2

where c is the nugget, b is the partial sill, and a is the practical range (Goovaerts, 2015). The nugget quantifies measurement errors or microscale variability, and the partial sill is the maximum value reached by the spherical semivariogram (Goovaerts, 2015). The practical range is the separation distance at which the semivariogram asymptotes (Supporting Figure S2c). At this separation distance, station pairs are no longer likely to exhibit the threshold precipitation (1 mm/day or 50 mm/day) simultaneously (Goovaerts, 2015; Touma et al., 2018). Therefore, as in Touma et al. (2018), we define the length scale – or spatial extent – of TCP for that given track point as the practical range.

There are some subjective choices of the moving neighborhood and semivariogram framework, including the 700 km radius of neighborhood (Touma et al. 2018). Previous studies found that 700 km is sufficient to capture the extent to which tropical cyclones influence precipitation (e.g., Barlow, (2011), Daloz et al. (2010), Hernández Ayala & Matyas (2016), Kim et al. (2014), Knaff et al. (2014), Knutson et al. (2010) and Matyas (2010)). Additionally, Touma et al. (2018) showed that although the neighborhood size can slightly impact the magnitude of length scales, it has little impact on their relative spatial and temporal variations.

1. Analysis of variations and trends

We use Mood’s median test (Desu & Raghavarao, 2003) to test for differences in the median TCP intensity and spatial extent among point-LMI categories, adjusting p-values to account for multiple simultaneous comparisons (Benjamini & Hochberg, 1995; Holm, 1979; Sheskin, 2003). To test for changes in TCP characteristics over time, we divide our century-scale dataset into two halves, 1900-1957 and 1958-2017. First, the quartile boundaries are established using the distributions of the earlier period (1900-1957), with one-quarter of the distribution falling in each quartile. Then, we find the fraction of points in each quartile in the later period (1958-2017) to determine changes in the distribution. We also report the p-values of the Kolmogorov-Smirnov

About this dataset

Context

The dataset tabulates the Hurricane population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Hurricane across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Hurricane was 6,846, a 1.16% decrease year-by-year from 2021. Previously, in 2021, Hurricane population was 6,926, a decline of 0.55% compared to a population of 6,964 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Hurricane increased by 759. In this period, the peak population was 6,964 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Hurricane is shown in this column.
• Year on Year Change: This column displays the change in Hurricane population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hurricane Population by Year. You can refer the same here

Time series of tropical cyclone "best track" position and intensity data are provided for all ocean basins where tropical cyclones occur. Position and intensity data are available at 6-hourly intervals over the duration of each cyclone's life. The general period of record begins in 1851, but this varies by ocean basin. See the inventories [http://rda.ucar.edu/datasets/ds824.1/inventories/] for data availability specific to each basin. This data set was received as a revision to an NCDC tropical cyclone data set, with data generally available through the late 1990s. Since then, the set is being continually updated from the U.S. NOAA National Hurricane Center and the U.S. Navy Joint Typhoon Warning Center best track archives. For a complete history of updates for each ocean basin, see the dataset documentation [http://rda.ucar.edu/datasets/ds824.1/docs/].

This layer features tropical storm (hurricanes, typhoons, cyclones) tracks, positions, and observed wind swaths from the past hurricane season for the Atlantic, Pacific, and Indian Basins. These are products from the National Hurricane Center (NHC) and Joint Typhoon Warning Center (JTWC). They are part of an archive of tropical storm data maintained in the International Best Track Archive for Climate Stewardship (IBTrACS) database by the NOAA National Centers for Environmental Information.Data SourceNOAA National Hurricane Center tropical cyclone best track archive.Update FrequencyWe automatically check these products for updates every 15 minutes from the NHC GIS Data page.The NHC shapefiles are parsed using the Aggregated Live Feeds methodology to take the returned information and serve the data through ArcGIS Server as a map service.Area CoveredWorldWhat can you do with this layer?Customize the display of each attribute by using the ‘Change Style’ option for any layer.Run a filter to query the layer and display only specific types of storms or areas.Add to your map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools like ‘Enrich Data’ on the Observed Wind Swath layer to determine the impact of cyclone events on populations.Visualize data in ArcGIS Insights or Operations Dashboards.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

National Risk Index Version: March 2023 (1.19.0)A Hurricane is a tropical cyclone or localized, low-pressure weather system that has organized thunderstorms but no front (a boundary separating two air masses of different densities) and maximum sustained winds of at least 74 miles per hour (mph). Annualized frequency values for Hurricanes 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.

Hurricane tracks and positions provide information on where the storm has been, where it is currently located, and where it is predicted to go. Each storm location is depicted by the sustained wind speed, according to the Saffir-Simpson Scale. It should be noted that the Saffir-Simpson Scale only applies to hurricanes in the Atlantic and Eastern Pacific basins, however all storms are still symbolized using that classification for consistency.Data SourceThis data is provided by NOAA National Hurricane Center (NHC) for the Central+East Pacific and Atlantic, and the Joint Typhoon Warning Center for the West+Central Pacific and Indian basins. For more disaster-related live feeds visit the Disaster Web Maps & Feeds ArcGIS Online Group.Sample DataSee Sample Layer Item for sample data during inactive Hurricane Season!Update FrequencyThe Aggregated Live Feeds methodology checks the Source for updates every 15 minutes. Tropical cyclones are normally issued every six hours at 5:00 AM EDT, 11:00 AM EDT, 5:00 PM EDT, and 11:00 PM EDT (or 4:00 AM EST, 10:00 AM EST, 4:00 PM EST, and 10:00 PM EST).Public advisories for Eastern Pacific tropical cyclones are normally issued every six hours at 2:00 AM PDT, 8:00 AM PDT, 2:00 PM PDT, and 8:00 PM PDT (or 1:00 AM PST, 7:00 AM PST, 1:00 PM PST, and 7:00 PM PST).Intermediate public advisories may be issued every 3 hours when coastal watches or warnings are in effect, and every 2 hours when coastal watches or warnings are in effect and land-based radars have identified a reliable storm center. Additionally, special public advisories may be issued at any time due to significant changes in warnings or in a cyclone. For the NHC data source you can subscribe to RSS Feeds.North Pacific and North Indian Ocean tropical cyclone warnings are updated every 6 hours, and South Indian and South Pacific Ocean tropical cyclone warnings are routinely updated every 12 hours. Times are set to Zulu/UTC.Scale/ResolutionThe horizontal accuracy of these datasets is not stated but it is important to remember that tropical cyclone track forecasts are subject to error, and that the effects of a tropical cyclone can span many hundreds of miles from the center.Area CoveredWorldGlossaryForecast location: Represents the official NHC forecast locations for the center of a tropical cyclone. Forecast center positions are given for projections valid 12, 24, 36, 48, 72, 96, and 120 hours after the forecast's nominal initial time. Click here for more information.

Forecast points from the JTWC are valid 12, 24, 36, 48 and 72 hours after the forecast’s initial time.Forecast track: This product aids in the visualization of an NHC official track forecast, the forecast points are connected by a red line. The track lines are not a forecast product, as such, the lines should not be interpreted as representing a specific forecast for the location of a tropical cyclone in between official forecast points. It is also important to remember that tropical cyclone track forecasts are subject to error, and that the effects of a tropical cyclone can span many hundreds of miles from the center. Click here for more information.The Cone of Uncertainty: Cyclone paths are hard to predict with absolute certainty, especially days in advance.

The cone represents the probable track of the center of a tropical cyclone and is formed by enclosing the area swept out by a set of circles along the forecast track (at 12, 24, 36 hours, etc). The size of each circle is scaled so that two-thirds of the historical official forecast errors over a 5-year sample fall within the circle. Based on forecasts over the previous 5 years, the entire track of a tropical cyclone can be expected to remain within the cone roughly 60-70% of the time. It is important to note that the area affected by a tropical cyclone can extend well beyond the confines of the cone enclosing the most likely track area of the center. Click here for more information. Now includes 'Danger Area' Polygons from JTWC, detailing US Navy Ship Avoidance Area when Wind speeds exceed 34 Knots!Coastal Watch/Warning: Coastal areas are placed under watches and warnings depending on the proximity and intensity of the approaching storm.Tropical Storm Watch is issued when a tropical cyclone containing winds of 34 to 63 knots (39 to 73 mph) or higher poses a possible threat, generally within 48 hours. These winds may be accompanied by storm surge, coastal flooding, and/or river flooding. The watch does not mean that tropical storm conditions will occur. It only means that these conditions are possible.Tropical Storm Warning is issued when sustained winds of 34 to 63 knots (39 to 73 mph) or higher associated with a tropical cyclone are expected in 36 hours or less. These winds may be accompanied by storm surge, coastal flooding, and/or river flooding.Hurricane Watch is issued when a tropical cyclone containing winds of 64 knots (74 mph) or higher poses a possible threat, generally within 48 hours. These winds may be accompanied by storm surge, coastal flooding, and/or river flooding. The watch does not mean that hurricane conditions will occur. It only means that these conditions are possible.Hurricane Warning is issued when sustained winds of 64 knots (74 mph) or higher associated with a tropical cyclone are expected in 36 hours or less. These winds may be accompanied by storm surge, coastal flooding, and/or river flooding. A hurricane warning can remain in effect when dangerously high water or a combination of dangerously high water and exceptionally high waves continue, even though winds may be less than hurricane force.RevisionsMar 13, 2025: Altered 'Forecast Error Cone' layer to include 'Danger Area' with updated symbology.Nov 20, 2023: Added Event Label to 'Forecast Position' layer, showing arrival time and wind speed localized to user's location.Mar 27, 2022: Added UID, Max_SS, Max_Wind, Max_Gust, and Max_Label fields to ForecastErrorCone layer.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The Continental U.S. Hurricane Strikes Poster is our most popular poster which is updated annually. The poster includes all hurricanes that affected the U.S. since 1950 with hurricane force winds, although some did not necessarily make landfall. This 32"x34" glossy poster will give you a quick look of the location and strength of each hurricane which struck the continental Unites States. The map includes the name, category strength, year, and approximate strike location of each hurricane.

This dataset is described and explored in Rice et al. 2025, "Projected Increases in Tropical Cyclone-induced U.S. Electric Power Outage Risk", published in Environmental Research Letters (doi.org/10.1088/1748-9326/adad85)

This dataset collects peak outage levels modeled for 900,000 synthetic tropical cyclones (TCs; also commonly known as hurricanes) representative of a modeled historical (1980-2015) and future (2066-2100) period under SSP5-8.5 warming. Synthetic TCs are generated with the Risk Analysis Framework for Tropical Cyclones (RAFT; see Xu et al. 2024 and Balaguru et al. 2023), forced by climate simulation data from the Coupled Model Intercomparison Project phase 6 (CMIP6; see Eyring et al. 2016). Outages are modeled with the newly introduced Electric Power Outages from Cyclone Hazards (EPOCH) model, which was trained on county-level outage data from 23 historical TC events in the EAGLE-I dataset (Brelsford et al. 2024).

The EPOCH model predicts outages based on county population and the maximum wind speed and rainfall rate experienced during the TC. Predicted outage levels are provided in the form of peak outage fraction: the maximum fraction of electricity customers expected to experience an outage at any one time during the storm's lifetime. Although we do not model outage duration, other research suggests peak outage level is strongly correlated with duration (Jamal and Hasan, 2023).

Data Format

The data is provided in NetCDF4 files, one for each CMIP6 model and time period. Each NetCDF4 files has the following:

Dimensions:

• ncounties = 2715. The counties in the study domain
• ntracks = 50000. The number of storms

Variables:

• int pseudofips(ncounties). The FIPS code for each county. Puerto Rico data is not available at county level, but instead for six utility-defined regions. We assign "pseudo-FIPS" codes to these region starting at 100000
• double centroid_lons(ncounties). Longitude of approximate center of county, in the range [-180, 0].
• double centroid_lats(ncounties). Latitude of approximate center of county, in the range [0, 90].
• float outage_prediction(ntracks, ncounties). The predicted peak outage fraction for each county, for each storm. Due to the particularities of ensemble models, some predictions may be slightly below zero or above one; we clip these values to the range [0,1] before any analysis in our study.
• ubyte prediction_complete_flag(ntracks). A verification flag used during dataset generation. This flag should equal 1 everywhere for complete data.

Each file also contains the raw predictors at a county level for every storm, inside the 'predictors' group, for feature analysis.

Also provided for convenience is 'counties_pseudofips.csv', which maps the pseudo-FIPS codes to the the name and spatial extent (WKT format) of each county. It can be read easily by Python GeoPandas, or other software.

• 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 TRMM Cyclone Precipitation Feature (TCPF) Database - Level 1 provides Tropical Rainfall Measuring Mission (TRMM)-based tropical cyclone data in a common framework for hurricane science research. This dataset aggregated observations from each of the TRMM instruments for each satellite orbit that was coincident with a tropical cyclone in any of the six TC-prone ocean basins. These swath data were co-located and subsetted to a 20-degree longitude by 20-degree latitude bounding box centered on the tropical storm, which is typically large enough to observe the various sizes of TCs and their immediate environments. The TCPF Level 1 dataset was created by researchers at Florida International University (FIU) and the University of Utah (UU) from the UU TRMM Precipitation Feature database. The TCPF database was built by extracting those precipitation features that are identified as tropical cyclones (TC) using the TC best-track data provided by National Hurricane Center or the US Navy's Joint Typhoon Warning Center.

In late September 2017, intense precipitation associated with Hurricane Maria caused extensive landsliding across Puerto Rico. Much of the Lares municipality in central-western Puerto Rico was severely impacted by landslides., Landslide density in this region was mapped as greater than 25 landslides/km2 (Bessette-Kirton et al., 2019). In order to better understand the controlling variables of landslide occurrence and runout in this region, three 2.5-km2 study areas were selected and all landslides within were mapped in detail using remote-sensing data. Included in the data release are five separate shapefiles: geographic areas representing the mapping extent of the four distinct areas (map areas, filename: map_areas), initiation location polygons (source areas, filename: SourceArea), polygons of the entire impacted area consisting of source, transport, and deposition (affected areas, filename: AffectArea), points on the furthest upslope extent of the landslide source areas (headscarp point, filename: HSPoint), and lines reflecting the approximate travel paths from the furthest upslope extent to the furthest downslope extent of the landslides (runout lines, filename: RunoutLine). These shapefiles contain a number of attributes, some subjective (including general geomorphic setting and impact of human activity), some geometric (including length, width, and depth), and others on the underlying geology and soil of the landslides. A table detailing each attribute, attribute abbreviations, the possible choices for each attribute, and a short description of each attribute is provided as a table in the file labeled AttributeDescription.docx. The headscarp point shapefile attribute tables contain closest distance between headscarp and paved road (road_d_m; road data from U.S. Census Bureau, 2015). The runout line shapefile attribute table reflects if the landslide was considered independently unmappable past a road or river (term_drain), the horizontal length of the runout (length_m), the fall height from the headscarp to termination (h_m), the ratio of fall height to runout length (hlratio), distance to nearest paved road (road_d_m), and the watershed area upslope from the upper end of the runout line (wtrshd_m2). All quantitative metrics were calculated using tools available in ESRI ArcMap v. 10.6. The source area shapefile attribute table reflects general source area vegetation (vegetat) and land use (land_use), whether the slide significantly disaggregated during movement (flow), the failure mode (failmode), if the slide was a reactivation of a previous one (reactivate), if the landslide directly impacted the occurrence of another slide (ls_complex), the proportion of source material that left the source area (sourc_evac), the state of the remaining material (remaining), the curvature of the source area (sourc_curv), potential human impact on landslide occurrence (human_caus), potential landslide impact on human society (human_effc), if a building exists within 10 meters of the source area (buildng10m), if a road exists within 50 meters of the source area (road50m), the planimetric area of the source area (area_m2), the dimension of the source area perpendicular to the direction of motion (width_m), the dimension of the source area parallel to the direction of motion (length_m), the geologic formation of the source area (FMATN; from Bawiec, W.J., 1998), the soil type of the source area (MUNAME; from Acevido, G., 2020), the root-zone (0-100 cm deep) soil moisture estimated by the NASA SMAP mission for 9:30 am Atlantic Standard Time on 21 September 2017 (the day after Hurricane María) (smap; NASA, 2017), the average precipitation amount in the source area for the duration of the hurricane (pptn_mm; from Ramos-Scharrón, C.E., and Arima, E., 2019), the source area mean slope (mn_slp_d), the source area median slope (mdn_slp_d), the average depth change of material from the source area after the landslide (mn_dpth_m), the median depth change of material from the source area after the landslide (mdn_dpt_m), the sum of the volumetric change of material in the source area after the landslide (ldr_sm_m3), the major geomorphic landform of the source (maj_ldfrm), and the landcover of the source area (PRGAP_CL; from Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004). The affected area shapefile attribute table reflects the general affected area vegetation type (vegetat), the major geomorphic landform on which the landslide occurred (maj_ldfrm), whether the slide disaggregated during movement (flow), the general land use (land_use), the planimetric area of the affected area (area_m2), the dominant geologic formation of the affected area (FMATN; from Bawiec, W.J., 1998), the dominant soil type of the affected area (MUNAME; from Acevido, G., 2020), the sum of the volumetric change of material in all the contributing source areas for the affected area (Sum_ldr_sm), the average volumetric change of material in all the contributing source areas for the affected area (Avg_ldr_sm), if the landslide was considered independently unmappable past a road or river (term_drain), the number of contributing source areas to the affected area (num_srce), and the dominant landcover of the affected area (PRGAP_CL; from Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004). Mapping was conducted using aerial imagery collected between 9-15 October 2017 at 25-cm resolution (Quantum Spatial, Inc., 2017), a 1-m-resolution pre-event lidar digital elevation model (DEM) (U.S. Geological Survey, 2018), and a 1-m-resolution post-event lidar DEM (U.S. Geological Survey, 2020). In order to accurately determine the extent of the mapped landslides and to verify the georeferencing of the aerial imagery, aerial photographs were overlain with each DEM as well as a pre- and post-event lidar difference (2016-2018), and corrections were made as needed. Additional data sources described in the AttributeDescription document and metadata were used to extract spatial data once mapping was complete and results were appended to the shapefile attribute tables. Data in this release are provided as ArcGIS point (HSPoint), line (RunoutLine), and polygon (AffectArea and SourceArea) feature class files. Bessette-Kirton, E.K., Cerovski-Darriau, C., Schulz, W.H., Coe, J.A., Kean, J.W., Godt, J.W, Thomas, M.A., and Hughes, K. Stephen, 2019, Landslides Triggered by Hurricane Maria: Assessment of an Extreme Event in Puerto Rico: GSA Today, v. 29, doi:10.1130/GSATG383A.1 U.S. Census Bureau, 2015, 2015 TIGER/Line Shapefiles, State, Puerto Rico, primary and secondary roads State-based Shapefile: United States Census Bureau, accessed September 12, 2019, at http://www2.census.gov/geo/tiger/TIGER2015/ PRISECROADS/tl_2015_72_prisecroads.zip. Bawiec, W.J., 1998, Geology, geochemistry, geophysics, mineral occurrences and mineral resource assessment for the Commonwealth of Puerto Rico: U.S. Geological Survey Open-File Report 98-38, https://pubs.usgs.gov/of/1998/of98-038/ (accessed May 2020). Acevido, G., 2020, Soil Survey of Arecibo Area of Norther Puerto Rico: United States Department of Agriculture, Soil Conservation Service. National Aeronautics and Space Administration [NASA], 2017, SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture Analysis Update, Version 4: National Snow & Ice Data Center web page, accessed September 12, 2019, at https://nsidc.org/data/SPL4SMAU/versions/4. Ramos-Scharrón, C.E., and Arima, E., 2019, Hurricane María’s precipitation signature in Puerto Rico—A conceivable presage of rains to come: Scientific Reports, v. 9, no. 1, article no. 15612, accessed February 28, 2020, at https://doi.org/10.1038/ s41598-019-52198-2. Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan, 2004, Development of a 2001 National Landcover Database for the United States: Photogrammetric Engineering and Remote Sensing, Vol. 70, No. 7, July 2004, pp. 829-840. Quantum Spatial, Inc., 2017, FEMA PR Imagery: https://s3.amazonaws.com/fema-cap-imagery/Others/Maria (accessed October 2017). U.S. Geological Survey, 2018, USGS NED Original Product Resolution PR Puerto Rico 2015: http://nationalmap.gov/elevation.html (accessed October 2018). U.S. Geological Survey, 2020, USGS NED Original Product Resolution PR Puerto Rico 2018: http://nationalmap.gov/elevation.html (accessed June 2020). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Hurricane Evacuation RoutesThis feature layer, utilizing data from Homeland Infrastructure Foundation Level Data (HIFLD), displays the locations of hurricane evacuation routes in the United States. Per HIFLD, "A hurricane evacuation route is a designated route used to direct traffic inland in case of a hurricane threat. This dataset is based on supplied data from Gulf Coast and Atlantic Seaboard states. The GIS data was gathered from states willing to share such data. Three (3) states (ME, NY, and NH) indicated that they do not maintain public maps showing hurricane evacuation routes and were unable or unwilling to share GIS files depicting such routes."Houston, Texas area hurricane evacuation routesData currency: Caution should be exercised by the user of this feature layer. This data is over seventeen years old and has not been updated since creation. HIFLD is inquiring with agencies to determine whether they would be willing to be data stewards for this layer going forward. (As of January 2018)Data source: Hurricane Evacuation RoutesData modification: None

For more information: Homeland Infrastructure Foundation- Level Data Subcommittee

Support documentation: MetadataFor feedback, please contact: ArcGIScomNationalMaps@esri.comHomeland Infrastructure Foundation-Level Data (HIFLD) SubcommitteePer HIFLD, "The Homeland Infrastructure Foundation-Level Data (HIFLD) Subcommittee was established…to address improvements in collection, processing, sharing, and protection of homeland infrastructure geospatial information across multiple levels of government, and to develop a common foundation of homeland infrastructure data to be used for visualization and analysis on all classification domains."

Names and descriptions of hurricanes near St. John USVI. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Based on Tsounis and Edmunds (In press), Ecosphere:\u00a0

Physical environmental conditions were characterized using three features that are well-known to affect coral reef community dynamics (described in Glynn 1993, Rogers 1993, Fabricius et al. 2005): seawater temperature, rainfall, and hurricane intensity. Together, these were used to generate seven dependent variables describing physical environmental features. Seawater temperature was recorded at each site every 15-30 min using a variety of logging sensors (see Edmunds 2006 for detailed information on the temperature measurement regime). Seawater temperature was characterized using five dependent variables calculated for each calendar year: mean temperature, maximum temperature, and minimum temperature (all averaged by day and month for each year), as well as the number of days hotter than 29.3 deg C (\u201chot days\u201d), and the number of days with temperatures greater than or equal to 26.0 deg C (\u201ccold days\u201d). The temperature defining "hot days" was determined by the coral bleaching threshold for St. John ("%5C%22http://www.coral.noaa.gov/research/climate-change/coral-%0Ableaching.html%5C%22">http://www.coral.noaa.gov/research/climate-change/coral- bleaching.html), and the temperature defining "cold days" was taken as 26.0 deg C which marks the lower 12th percentile of all daily temperatures between 1989 and 2005 (Edmunds, 2006). The upper temperature limit was defined by the local bleaching threshold, and the lower limit defined the 12th\u00a0percentile of local seawater temperature records (see Edmunds 2006 for details). Rainfall was measured at various locations around St. John (see http://www.sercc.com) but often on the north shore (courtesy of R.\u00a0Boulon) (see Edmunds and Gray 2014). To assess the influence of hurricanes, a categorical index of local hurricane impact was employed, with the index based on qualitative estimates of wave impacts in Great Lameshur Bay as a function of wind speed, wind direction, and distance of the nearest approach of each hurricane to the study area (see Gross and Edmunds 2015). Index values of 0 were assigned to years with no hurricanes, 0.5 to hurricanes with low impacts, and 1 for hurricanes with high impacts, and years were characterized by the sum of their hurricane index values. awards_0_award_nid=55191 awards_0_award_number=DEB-0841441 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=0841441&HistoricalAwards=false awards_0_funder_name=National Science Foundation awards_0_funding_acronym=NSF awards_0_funding_source_nid=350 awards_0_program_manager=Saran Twombly awards_0_program_manager_nid=51702 awards_1_award_nid=562085 awards_1_award_number=OCE-1332915 awards_1_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1332915 awards_1_funder_name=NSF Division of Ocean Sciences awards_1_funding_acronym=NSF OCE awards_1_funding_source_nid=355 awards_1_program_manager=David L. Garrison awards_1_program_manager_nid=50534 awards_2_award_nid=562593 awards_2_award_number=DEB-1350146 awards_2_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=1350146 awards_2_funder_name=NSF Division of Environmental Biology awards_2_funding_acronym=NSF DEB awards_2_funding_source_nid=550432 awards_2_program_manager=Betsy Von Holle awards_2_program_manager_nid=701685 cdm_data_type=Other comment=Hurricane Data G. Tsounis and P. Edmunds, PIs Version 10 November 2016 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.664760 infoUrl=https://www.bco-dmo.org/dataset/664267 institution=BCO-DMO metadata_source=https://www.bco-dmo.org/api/dataset/664267 param_mapping={'664267': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/664267/parameters people_0_affiliation=California State University Northridge people_0_affiliation_acronym=CSU-Northridge people_0_person_name=Peter J. Edmunds people_0_person_nid=51536 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=California State University Northridge people_1_affiliation_acronym=CSU-Northridge people_1_person_name=Dr Georgios Tsounis people_1_person_nid=565353 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Hannah Ake people_2_person_nid=650173 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=St. John LTREB,VI Octocorals projects_0_acronym=St. John LTREB projects_0_description=Long Term Research in Environmental Biology (LTREB) in US Virgin Islands: From the NSF award abstract: In an era of growing human pressures on natural resources, there is a critical need to understand how major ecosystems will respond, the extent to which resource management can lessen the implications of these responses, and the likely state of these ecosystems in the future. Time-series analyses of community structure provide a vital tool in meeting these needs and promise a profound understanding of community change. This study focuses on coral reef ecosystems; an existing time-series analysis of the coral community structure on the reefs of St. John, US Virgin Islands, will be expanded to 27 years of continuous data in annual increments. Expansion of the core time-series data will be used to address five questions: (1) To what extent is the ecology at a small spatial scale (1-2 km) representative of regional scale events (10's of km)? (2) What are the effects of declining coral cover in modifying the genetic population structure of the coral host and its algal symbionts? (3) What are the roles of pre- versus post-settlement events in determining the population dynamics of small corals? (4) What role do physical forcing agents (other than temperature) play in driving the population dynamics of juvenile corals? and (5) How are populations of other, non-coral invertebrates responding to decadal-scale declines in coral cover? Ecological methods identical to those used over the last two decades will be supplemented by molecular genetic tools to understand the extent to which declining coral cover is affecting the genetic diversity of the corals remaining. An information management program will be implemented to create broad access by the scientific community to the entire data set. The importance of this study lies in the extreme longevity of the data describing coral reefs in a unique ecological context, and the immense potential that these data possess for understanding both the patterns of comprehensive community change (i.e., involving corals, other invertebrates, and genetic diversity), and the processes driving them. Importantly, as this project is closely integrated with resource management within the VI National Park, as well as larger efforts to study coral reefs in the US through the NSF Moorea Coral Reef LTER, it has a strong potential to have scientific and management implications that extend further than the location of the study. The following publications and data resulted from this project: 2015 Edmunds PJ, Tsounis G, Lasker HR (2015) Differential distribution of octocorals and scleractinians around St. John and St. Thomas, US Virgin Islands. Hydrobiologia. doi: 10.1007/s10750-015-2555-zoctocoral - sp. abundance and distributionDownload complete data for this publication (Excel file) 2015 Lenz EA, Bramanti L, Lasker HR, Edmunds PJ. Long-term variation of octocoral populations in St. John, US Virgin Islands. Coral Reefs DOI 10.1007/s00338-015-1315-xoctocoral survey - densitiesoctocoral counts - photoquadrats vs. insitu surveyoctocoral literature reviewDownload complete data for this publication (Excel file) 2015 Privitera-Johnson, K., et al., Density-associated recruitment in octocoral communities in St. John, US Virgin Islands, J.Exp. Mar. Biol. Ecol. DOI 10.1016/j.jembe.2015.08.006octocoral recruitmentDownload complete data for this publication (Excel file) 2014 Edmunds PJ. Landscape-scale variation in coral reef community structure in the United States Virgin Islands. Marine Ecology Progress Series 509: 137–152. DOI 10.3354/meps10891. Data at MCR-VINP. Download complete data for this publication (Excel file) 2014 Edmunds PJ, Nozawa Y, Villanueva RD. Refuges modulate coral recruitment in the Caribbean and Pacific. Journal of Experimental Marine Biology and Ecology 454: 78-84. DOI: 10.1016/j.jembe.2014.02.00 Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Edmunds PJ, Gray SC. The effects of storms, heavy rain, and sedimentation on the shallow coral reefs of St. John, US Virgin Islands. Hydrobiologia 734(1):143-148. Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Levitan, D, Edmunds PJ, Levitan K. What makes a species common? No evidence of density-dependent recruitment or mortality of the sea urchin Diadema antillarum after the 1983-1984 mass mortality. Oecologia. DOI 10.1007/s00442-013-2871-9. Data at MCR-VINP.Download complete data for this publication (Excel file) 2014 Lenz EA, Brown D, Didden C, Arnold A, Edmunds PJ. The distribution of hermit crabs and their gastropod shells on shallow reefs in St. John, US Virgin Islands. Bulletin of Marine Science 90(2):681-692. https://dx.doi.org/10.5343/bms.2013.1049 Data at MCR-VINP.Download complete data for this publication (Excel file) 2013 Edmunds PJ. Decadal-scale changes in the community structure of coral reefs in St. John, US Virgin Islands. Marine Ecology Progress Series 489: 107-123. Data at MCR-VINP.Download complete data for this publication (zipped Excel files) 2013 Brown D, Edmunds PJ. Long-term changes in the population dynamics of the

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."