In 2023, there were 45 hurricanes registered worldwide, up from 45 hurricanes a year earlier. This was nevertheless below the average of 47 hurricanes per year registered from 1990 to 2022. The years of 1992 and 2018 tied as the most active in the indicated period, each with 59 hurricanes recorded. The Pacific Northwest basin recorded the largest number of hurricanes in 2023.

Most exposed countries to hurricanes With the Pacific Northwest basin being one of the most active for hurricanes in the world , there is perhaps no surprise that Japan was the country most exposed to tropical cyclones in 2023. It was followed by the Philippines, also a West Pacific nation. Meanwhile, the Bahamas was the most exposed country in the Atlantic Ocean and ranked third most exposed worldwide during the same year.

Effects of tropical cyclones From 1970 to 2019, almost 800,000 deaths due to tropical cyclones have been reported worldwide. In the past decade, the number of such casualties stood at some 19,600, the lowest decadal figure in the last half-century . In contrast to the lower number of deaths, economic losses caused by tropical cyclones has continuously grown since 1970, reaching a record high of more than 570 billion U.S. dollars from 2010 to 2019.

Between 2011 and 2020, 19 hurricanes made landfall in the United States, the same figure reported in the previous decade. This is the highest number recorded for a 10-year timespan since the 1940s, which holds the current record for most landfalls, with 24 hurricanes. In 2023, only hurricane Ian made landfall in the U.S.

Between 1851 and 2023, there were 304 hurricane direct hits in the United States, of which 40 percent were category 1 hurricanes. In the same period, 97 major hurricanes (with a category 3 or higher) made landfall in the country. Hurricane Michael, in 2018, was the latest category 5 hurricane to hit the North American country. Florida was the state most commonly hit by hurricanes.

NCALM Seed. PI: Phillip Lane, Massachusetts Institute of Technology. The survey area consists of a 44.75 square kilometer area covering a portion of Bald Point State Park, Florida. The data were collected to examine a 4,500-year record of hurricane frequency and storm surge magnitude archived in North Florida Sinkholes. Data collection occurred on September 4, 2010. Publications associated with this dataset can be found at NCALM's Data Tracking Center

In 2021, there were 68 fatalities due to hurricanes reported in the United States. Since the beginning of the century, the highest number of fatalities was recorded in 2005, when four major hurricanes – including Hurricane Katrina – resulted in 1,518 deaths.

The worst hurricanes in U.S. history
Hurricane Katrina, which made landfall in August 2005, ranked as the third deadliest hurricane in the U.S. since records began. Affecting mainly the city of New Orleans and its surroundings, the category 3 hurricane caused an estimated 1,500 fatalities. Katrina was also the costliest tropical cyclone to hit the U.S. in the past seven decades, with damages amounting to roughly 186 billion U.S. dollars. Hurricanes Harvey and Maria, both of which made landfall in 2017, ranked second and third, resulting in damage costs of 149 and 107 billion dollars, respectively.

How are hurricanes classified?
According to the Saffir-Simpson scale, hurricanes can be classified into five categories, depending on their maximum sustained wind speed. Most of the hurricanes that have made landfall in the U.S. since 1851 are category 1, the mildest of the five. Hurricanes rated category 3 or above are considered major hurricanes and can cause devastating damage. In 2021, there were 38 hurricanes recorded across the globe, of which 17 were major hurricanes.

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.

A KNOWLEDGE DISCOVERY STRATEGY FOR RELATING SEA SURFACE TEMPERATURES TO FREQUENCIES OF TROPICAL STORMS AND GENERATING PREDICTIONS OF HURRICANES UNDER 21ST-CENTURY GLOBAL WARMING SCENARIOS

CAITLIN RACE*, MICHAEL STEINBACH*, AUROOP GANGULY**, FRED SEMAZZI***, AND VIPIN KUMAR*

Abstract. The connections among greenhouse-gas emissions scenarios, global warming, and frequencies of hurricanes or tropical cyclones are among the least understood in climate science but among the most fiercely debated in the context of adaptation decisions or mitigation policies. Here we show that a knowledge discovery strategy, which leverages observations and climate model simulations, offers the promise of developing credible projections of tropical cyclones based on sea surface temperatures (SST) in a warming environment. While this study motivates the development of new methodologies in statistics and data mining, the ability to solve challenging climate science problems with innovative combinations of traditional and state-of-the-art methods is demonstrated. Here we develop new insights, albeit in a proof-of-concept sense, on the relationship between sea surface temperatures and hurricane frequencies, and generate the most likely projections with uncertainty bounds for storm counts in the 21st-century warming environment based in turn on the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. Our preliminary insights point to the benefits that can be achieved for climate science and impacts analysis, as well as adaptation and mitigation policies, by a solution strategy that remains tailored to the climate domain and complements physics-based climate model simulations with a combination of existing and new computational and data science approaches.

The Hurricane Intensity product contains information about tropical cyclones along their trajectories from the time they are identified to the current time. Tropical cyclone information provided in the product includes its identity, location, maximum wind speed, Dvorak tropical cyclone current intensity number, detailed wind shear, cloud, and eye characteristics, strengthening and weakening state information, and the start, midpoint, and end observation time of the source ABI product image. Data quality information is not included in the product. A hurricane intensity product file is produced for each tropical cyclone. The units of measure for the maximum sustained wind speed value is meters per second. The advanced Dvorak technique tropical cyclone current and tropical intensity numbers are dimensionless quantities. The Hurricane Intensity product is produced using ABI Full Disk coverage region observations. Product data is produced when a tropical cyclone is in the ABI's field of regard for both daytime and nighttime conditions.

This study used computer modeling to study the impacts of hurricanes across the Yucatan Peninsula since 1851. For details on methods and results please see the published paper (Boose, E. R., D. R. Foster, A. Barker Plotkin and B. Hall. 2003. Geographical and historical variation in hurricanes across the Yucatan Peninsula. In Lowland Maya Area: Three Millennia at the Human-Wildland Interface. A. Gomez-Pompa, M. F. Allen, S. Fedick and J. J. Jimenez-Osornio, eds. Haworth Press, New York, NY. In press). The Abstract from the paper is reproduced below. "The ecological impacts of hurricanes across the Yucatan Peninsula over the last 150 years were investigated using a simple meteorological model (HURRECON) developed at Harvard Forest as well as a database of historical hurricane data (HURDAT) maintained by the U. S. National Hurricane Center. All hurricanes over the period 1851-2000 with sustained winds of hurricane force (33 meters/sec) within 300 kilometers of the study region were analyzed (n = 105). Each storm was reconstructed to produce estimates of wind damage on the Fujita scale across the region. Individual reconstructions were then compiled to study cumulative impacts of all 105 storms. "Results showed considerable variation in hurricane activity from year to year, and from decade to decade, while at the half-century scale there was an increase in hurricane intensity since the mid-nineteenth century. Ninety percent of the hurricanes causing F1 damage or higher (on the Fujita scale) occurred in the months of August, September, and October. A strong spatial gradient in hurricane frequency and intensity extended across the region from northeast to southwest, resulting from (1) the greater number of hurricanes to the north, (2) the east to west movement of most hurricanes across the area, and (3) the tendency for most hurricanes to weaken significantly after landfall. For example, during the study period, northeastern parts of the peninsula experienced a minimum of one F3 hurricane, six F2 hurricanes, and thirty F1 hurricanes, while southwestern parts experienced no F2 or F3 damage and fewer than five F1 storms. Though a significant disturbance across much of the Yucatan Peninsula, hurricanes may have shorter-lived and less severe ecological impacts than fire or human land use. The interaction of these factors (e.g., fires following hurricanes), however, may be very significant and deserves further study."

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.

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!

In 2023, there were 78 named storms registered worldwide, down from 87 storms in the previous year. Overall, there was an average of 87 named tropical cyclones registered per year from 1980 to 2023. Japan was the country most exposed to this type of event worldwide.

What is a tropical cyclone?Tropical cyclones are intense rotating storms that form over warm tropical waters, characterized by heavy rain and strong winds. Once a cyclone sustains wind speeds exceeding 63 kilometers per hour, they are considered a tropical storm and receive a name. Named tropical storms can also receive further classification depending on their intensity and location (also known as basin). High-speed cyclones in the Northern Atlantic and Eastern Pacific basins are called hurricanes, while in the Western Pacific they are called typhoons. When the event takes place within the South Pacific and Indian Ocean, it is known as a cyclone.

Frequency of tropical cyclones worldwide

The Northwest Pacific basin is one of the most active for tropical cyclones worldwide. In 2023, there were 16 named storms reported in the region, of which more than half were classified as hurricanes. Meanwhile, the North Indian Ocean represented one of the least active basins for tropical cyclones, with an annual average of five named storms recorded from 1990 to 2023.

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 project used a combination of historical research and computer modeling to study the impacts of hurricanes in New England since 1620. For details on methods and results, please see the published paper (Boose, E. R., K. E. Chamberlin and D. R. Foster. 2001. Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27-48). The Abstract from the paper is reproduced below. "Hurricanes are a major factor controlling ecosystem structure, function and dynamics in many coastal forests and yet their ecological role can be understood only by assessing impacts in space and time over a period of centuries. We present a new method for reconstructing hurricane disturbance regimes using a combination of historical research and computer modeling. Historical data on wind damage for each hurricane in the selected region are quantified using the Fujita scale to produce regional maps of actual damage. A simple meteorological model (HURRECON), parameterized and tested for selected recent hurricanes, provides regional estimates of wind speed, direction, and damage for each storm. Individual reconstructions are compiled to analyze spatial and temporal patterns of hurricane impacts. Long-term effects of topography on a landscape scale are then examined with a simple topographic exposure model (EXPOS). "We applied this method to New England, USA, examining hurricanes since European settlement in 1620. Results showed strong regional gradients in hurricane frequency and intensity from southeast to northwest: average return intervals for F0 damage on the Fujita scale (loss of leaves and branches) ranged from 5 to 85 years, average return intervals for F1 damage (scattered blowdowns, small gaps) ranged from 10 to more than 200 years, and average return intervals for F2 damage (extensive blowdowns, large gaps) ranged from 85 to more than 380 years. On a landscape scale, average return intervals for F2 damage in the town of Petersham MA ranged from 125 years across most sites to more than 380 years on scattered lee slopes. Actual forest damage was strongly dependent on land-use and natural disturbance history. Annual and decadal timing of hurricanes varied widely. There was no clear century-scale trend in the number of major hurricanes. "The historical-modeling approach is applicable to any region with good historical records and will enable ecologists and land managers to incorporate insights on hurricane disturbance regimes into the interpretation and conservation of forests at landscape to regional scales."

The Hurricane and Severe Storm Sentinel (HS3) Statistical Hurricane Intensity Prediction Scheme (SHIPS) Intensity dataset was obtained from March 18, 2014 through September 30, 2014 during the Hurricane and Severe Storm Sentinel (HS3) field campaign. Goals for the HS3 field campaign included assessing the relative roles of large-scale environment and storm-scale internal processes, addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification, and the role of deep convection in the inner-core region of storms. The SHIPS model provides tropical storm intensity forecasts for the Atlantic Ocean and the eastern and central North Pacific Ocean storms and invest areas. SHIPS uses GOES infrared imagery as input to the systems. These SHIPS data are available in ASCII format.

The 2020 Atlantic hurricane season ranked amongst the top six most powerful seasons in the satellite era, based on Accumulated Cyclone Energy (ACE). That year, the 30 named storms (including 14 hurricanes, of which seven major hurricanes) in the basin registered an ACE of 180. All of the top ten most extreme Atlantic hurricane seasons recorded in the satellite era happened since 1990, with 2005 leading the ranking with an ACE of 250.

Nearly 40 percent of all hurricanes that made landfall in the United States between 1851 and 2022 hit Florida. The state was hit by 120 hurricanes in the period, of which 37 were major hurricanes (category 3 or higher). Texas and Louisiana were the second and third most hit states in the country, with 64 and 63 hurricanes, respectively.

Tropical storms and hurricanes are systems with the potential to significantly impact the Gulf region. These storms can produce torrential rain, damaging winds, storm surge, flooding, and tornadoes affecting both coastal and inland areas. The intensity of a hurricane is classified using the Saffir-Simpson scale, which is based on sustained wind speed.Saffir-Simpson Hurricane Wind Scale (NOAA National Weather Service)CategoryWind SpeedTropical depression≤34 knots (≤ 38 mph)Tropical storm35-63 knots (39-73 mph)Hurricane: Category 164-82 knots (74-95 mph)Hurricane: Category 283-95 knots (96-110 mph)Hurricane: Category 396-112 knots (111-129 mph)Hurricane: Category 4113-136 knots (130-156 mph)Hurricane: Category 5≥137 knots (≥157 mph)This dataset is a subset of the NOAA National Hurricane Center (NHC) hurricane databases (HURDAT), which has been maintained since 1851. HURDAT includes six-hourly storm center positions (to the nearest 0.1° latitude and longitude), estimated maximum 1-minute sustained winds at 33 feet above sea level, and, when available, central pressure. The original HURDAT format has been retired and replaced by HURDAT2. Note that the data are smoothed, and do not capture fine-scale fluctuations in storm center position and speed that occur between the six-hour reporting intervals (00, 06, 12, and 18 UTC). Storm tracks also form a subset of the IBTrACS (International Best Track Archive for Climate Stewardship) dataset, with additional data from the NHC for 2012.Data: NCEI (.zip)Metadata: Storm FrequencyThis is a component of the Gulf Data Atlas (V1.0) for the Physical topic area.

Tropical storms and hurricanes are systems with the potential to significantly impact the U.S. Gulf region. These storms can produce torrential rain, damaging winds, storm surge, flooding, and tornadoes affecting both coastal and inland areas. The intensity of a hurricane is classified using the Saffir-Simpson scale, which is based on sustained wind speed.Saffir-Simpson Hurricane Wind Scale (NOAA National Weather Service)CategoryWind SpeedTropical depression≤34 knots (≤ 38 mph)Tropical storm35-63 knots (39-73 mph)Hurricane: Category 164-82 knots (74-95 mph)Hurricane: Category 283-95 knots (96-110 mph)Hurricane: Category 396-112 knots (111-129 mph)Hurricane: Category 4113-136 knots (130-156 mph)Hurricane: Category 5≥137 knots (≥157 mph)This dataset is a subset of the NOAA National Hurricane Center (NHC) hurricane databases (HURDAT), which has been maintained since 1851. HURDAT includes six-hourly storm center positions (to the nearest 0.1° latitude and longitude), estimated maximum 1-minute sustained winds at 33 feet above sea level, and, when available, central pressure. The original HURDAT format has been retired and replaced by HURDAT2. Note that the data are smoothed, and do not capture fine-scale fluctuations in storm center position and speed that occur between the six-hour reporting intervals (00, 06, 12, and 18 UTC). Storm tracks also form a subset of the IBTrACS (International Best Track Archive for Climate Stewardship) dataset, with additional data from the NHC for 2012.Data: NCEI (.zip)Metadata: Storm FrequencyThis is a component of the Gulf Data Atlas (V1.0) for the Physical topic area.

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