In 2024, there were four hurricanes tracked in the Atlantic basin, up from ***** recorded a year earlier. 2020 had recorded the second most active hurricane season in the displayed period. It only ranked behind 2005, when ** hurricanes were recorded in the region. Between 1990 and 2021, there were on average ***** hurricanes tracked per year in the Atlantic. In the same period, ** hurricanes made landfall in the U.S.

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.

The 2020 hurricane season had a record-breaking 30 named storms. This map symbolizes the Atlantic hurricane tracks based on whether they made landfall at the United States coast. Data source:Tropical Cyclone Best Track: https://www.nhc.noaa.gov/gis/archive_besttrack.php?year=2020

The National Assessment of Coastal Change Hazards project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the Florida coast and attributed to coastal processes during [Atlantic Basin] Hurricane Michael, which made landfall in the U.S. on October 10, 2018.

Hurricane Laura is the twelfth named storm, fourth hurricane, and first major hurricane of the 2020 Atlantic hurricane season. It is the strongest hurricane on record to make landfall in the state of Louisiana on August 27, 2020. This is the storm track from the NHC Tropical Cyclone Best Track.

This dataset provides spatially aboveground biomass density (AGBD) maps from 2022 to 2024, created to assess the impacts of Hurricane Ian on forest ecosystems in Florida. The Hurricane Ian made landfall in Florida on September 28, 2022, as a powerful Category 4 and was the second major storm of the 2022 Atlantic hurricane season.Field data were collected in situ during the spring of 2023 and 2024 across 27 plots, each measuring 25 × 25 meters. These plots were strategically distributed to represent a full gradient of AGBD. Within each plot, tree species were identified, and diameter at breast height (DBH) and height measurements were recorded for all individuals with DBH greater than 10 cm. AGBD was estimated for each tree based on species-specific allometric equations, incorporating species identity, DBH, and tree height.To generate the AGBD maps, we integrated multiple remote sensing datasets, including GEDI L4A AGBD estimates, Harmonized Landsat-Sentinel (HLS) optical imagery, and Sentinel-1C radar data, along with other ancillary layers, totaling 245 predictor variables. A random forest regression model was trained using field data and predictor layers to estimate AGBD, which was then upscaled to create a continuous AGBD map for the study area.The final AGBD prediction for each year was obtained by averaging results from multiple bootstrap iterations.. Additionally, uncertainty maps were derived by calculating the standard deviation of predictions across these iterations, providing insight into spatial variability and model confidence.Resources in this dataset:AGBD predictions for consecutive years: RapidFEM4D_AGBD_prediction_2022.tif; RapidFEM4D_AGBD_prediction_2023.tif, and RapidFEM4D_AGBD_prediction_2024.tifMap uncertainties for the respective AGBD predictions: RapidFEM4D_AGBD_uncertainty_2022.tif; RapidFEM4D_AGBD_uncertainty_2023.tif, and RapidFEM4D_AGBD_uncertainty_2024.tifField data used for technical validation: RapidFEM4D_field_data.xlsx

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

As of 2024, there were more than *** million single-family homes at risk of storm surges from hurricanes in the Atlantic and Gulf coasts in the United States. Hurricanes of category * and * alone could put around *** million homes at risk. Between 1851 and 2022, more than *** Category * and * hurricanes made landfall in the U.S.

Hurricane Sally is the eighteenth named storm, and seventh hurricane of the extremely active 2020 Atlantic hurricane season. It is the first hurricane to make landfall in the state of Alabama since Ivan in 2004, coincidentally on the same day (September 16, 2020). This is the storm track from the NHC Tropical Cyclone Best Track.

These data sets contain information on the probabilities of hurricane-induced erosion (collision, inundation and overwash) for each 1-km section of the Northeast Atlantic coast for category 1-4 hurricanes. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the direct landfall of category 1-4 hurricanes. Hurricane-induced water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of three types of coastal change: collision (dune erosion), overwash, and inundation. Data on dune morphology (dune crest and toe elevation) and hydrodynamics (storm surge, wave setup and runup) are also included in this data set. As new beach morphology observations and storm predictions become available, this analysis will be updated to describe how coastal vulnerability to storms will vary in the future. The data presented here include the dune morphology observations, as derived from lidar surveys taken from May to July, 2010.

The National Assessment of Coastal Change Hazards project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida and Alabama coast and attributed to coastal processes during [Atlantic Basin] Hurricane Sally, which made landfall in the U.S. on September 16, 2020.

The imagery posted on this site is of the Atlantic coast of Florida after Hurricane Jeanne made landfall. The regions photographed range along a 100-mile stretch from Melbourne to Palm Beach, Florida. The flights to collect the Florida detailed imagery were conducted between September 26 and October 1. The images were acquired from an altitude of 7,000 feet, using an Emerge/Applanix Digital Sensor System (DSS). Over 1,200 images of the Florida coastline affected by Hurricane Jeanne are available to view online and download.

The 2005 Significant U.S. Hurricane Strikes poster is one of two special edition posters for the Atlantic Hurricanes. This beautiful poster contains two sets of images of five hurricanes that impacted the United States in 2005, namely Katrina, Ophelia, Rita and Wilma. The images were created from NOAA's geostationary and polar-orbiting environmental satellites. In addition to the images, the poster has a map depicting the general track of each storm, a color temperature scale to read the hurricane cloud top temperatures, high level information on each storm, the category at time of landfall; as well as, a Saffir-Simpson Hurricane Scale. Poster size is 36"x32".

Hurricane Ian was the ninth named storm of the 2022 Atlantic hurricane season. It was a Category 4 hurricane that made landfall twice in the United States, in Florida on 09/28/2022 and in South Carolina on 09/30/2022. This is the storm track from the NHC Tropical Cyclone Best Track. Layers are symbolized using the Updated Arcade Code for Scaling Symbology by Emily Meriam.

Hurricane Hanna is the eighth named storm in the 2020 Atlantic hurricane season. It is the first Atlantic hurricane to make landfall in Texas in July 25, 2020. This is the storm track from the NHC Tropical Cyclone Best Track. Layers are symbolized using the Updated Arcade Code for Scaling Symbology by Emily Meriam.

description: The Combined Hurricane Model data is a collaboration between the National Renewable Energy Laboratory (NREL) and researchers at the University of Miami and University of Texas at Austin. The model is unique in that it offers time-aligned predictions of full field atmospheric data (WRF), ocean current data (HYCOM), and wave data (UMWM) for two major storms: Hurricane Ike, a Category 4 storm which made landfall in Galveston, Texas in 2008, and Hurricane Sandy, a category 3 storm with a landfall north of Atlantic City.; abstract: The Combined Hurricane Model data is a collaboration between the National Renewable Energy Laboratory (NREL) and researchers at the University of Miami and University of Texas at Austin. The model is unique in that it offers time-aligned predictions of full field atmospheric data (WRF), ocean current data (HYCOM), and wave data (UMWM) for two major storms: Hurricane Ike, a Category 4 storm which made landfall in Galveston, Texas in 2008, and Hurricane Sandy, a category 3 storm with a landfall north of Atlantic City.

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida, Georgia, North Carolina,and South Carolina coasts and attributed to coastal processes during [Atlantic Basin] Hurricane Matthew, which made landfall in the U.S. on October 8, 2018.

The National Assessment of Coastal Change Hazards project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida coast and attributed to coastal processes during [Atlantic Basin] Hurricane Irma, which made landfall in the U.S. on September 9, 2017.

These data sets contain information on the probabilities of hurricane-induced erosion (collision, inundation and overwash) for each 1-km section of the Southeast Atlantic coast for category 1-5 hurricanes. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the direct landfall of category 1-5 hurricanes. Hurricane-induced water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of three types of coastal change: collision (dune erosion), overwash, and inundation. Data on dune morphology (dune crest and toe elevation) and hydrodynamics (storm surge, wave setup and runup) are also included in this data set. As new beach morphology observations and storm predictions become available, this analysis will be updated to describe how coastal vulnerability to storms will vary in the future. The data p ...

The National Assessment of Coastal Change Hazards project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the southeast coast of the United States from North Carolina to Virginia and attributed to coastal processes during [Atlantic Basin] Hurricane Florence, which made landfall in the U.S. on September 14, 2018.