Abstract

Tropical cyclones pose a significant threat to life and property along coastal regions of the United States. As these systems move inland and dissipate, they can also pose a threat to life and property, through heavy rains, high winds, and other severe weather such as tornadoes. While many studies have focused on the impacts from tropical cyclones on coastal counties of the United States, this study goes beyond the coast and examines the impacts caused by tropical cyclones on inland locations. Using geographical information system software, historical track data are used in conjunction with the radial maximum extent of the maximum sustained winds at 34-, 50-, and 64-kt (1 kt ≈ 0.5 m s−1) thresholds for all intensities of tropical cyclones and overlaid on a 30-km equal-area grid that covers the eastern half of the United States. The result is a series of maps with frequency distributions and an estimation of return intervals for inland tropical storm– and hurricane-force winds. Knowing where the climatologically favored areas are for tropical cyclones, combined with a climatological expectation of the inland penetration frequency of these storms, can be of tremendous value to forecasters, emergency managers, and the public.

1. Introduction

In the United States, the impacts from tropical cyclone winds often extend well inland after making landfall along the coast. For example, after the passage of Hurricane Camille (1969), more than 150 deaths occurred in the state of Virginia, some 1300 km inland from where the storm originally made landfall along the Louisiana coast (Emanuel 2005). According to Rappaport (2000), a large portion of fatalities often occurs inland, associated with a decaying tropical cyclone’s winds (falling trees, collapsed roofs, etc.) and heavy flooding rains. In the 1970s, 1980s, and 1990s, freshwater floods accounted for 59% of the recorded deaths from tropical cyclones (Rappaport 2000), and such floods are often a combination of meteorological and hydrological factors. Moisture from tropical cyclones occasionally merges with eastward-moving continental low pressure systems, producing copious amounts of rainfall inland from the coast. Tropical cyclone–induced flooding has devastated communities located many hundreds of miles from the coast (Gibney 2000). Recent examples of storms that caused significant inland impacts include Hurricane Hugo (wind and rain) in 1989, Hurricane Floyd and Tropical Storm (TS) Allison (flooding) in 1999 and 2001, respectively, and Hurricane Fran (wind) in 1996. Each of these storms reveal how the destructive forces of tropical cyclones can impact areas far from the initial landfall point.

Strong winds associated with tropical systems usually diminish quickly once they move ashore, primarily because of the frictional effects of land-based obstructions (topography, forests, urbanized areas) and thermodynamically through a loss of heat energy from the ocean’s surface (Friedman 1975). Emanuel (2005) suggests that the decay rate of a land falling hurricane is rapid, losing half its wind speed value in roughly 7 h, 75% in 15 h, and nearly 90% after just 1 day inland. In numerical simulations, Tuleya et al. (1984) found that more intense hurricanes decay faster upon landfall than do weaker storms. For these reasons, the primary impact areas of tropical cyclones are generally found along coastal (or near coastal) regions.

The National Oceanic and Atmospheric Administration’s (NOAA) National Hurricane Center (NHC) currently develops and maintains a database that tracks those coastal counties that have been impacted by hurricane strikes (see http://www.nhc.noaa.gov/aboutpubs.shtml?#MEMO), based on historical landfall records, storm surge reports, and the tropical cyclones’ wind field (Jarrell et al. 1992). In another study, Keim et al. (2007) produced return periods for hurricane strikes along the U.S. coastline from Texas to Maine. A similar dataset can also be found in Elsner and Kara (1999, their Fig. 8.15), where the coastline east of the Rocky Mountains was subdivided into 2.5° grid boxes and frequency counts of hurricanes appearing in each grid box were tallied. These datasets are limited to the single coastal county or grid box bordering the Gulf of Mexico and/or the Atlantic Ocean. Therefore it is the intent of this study to fill this large gap in our knowledge concerning the inland impacts from historical tropical cyclones.

Most previous studies involving the inland extent of tropical cyclones have generally focused on their expected or modeled rate of decay postlandfall (e.g., Tuleya et al. 1984; Kaplan and DeMaria 1995, 2001), while others have focused on recurrence thresholds or probabilities of landfalls along a given portion of the U.S. coastline (e.g., Bove et al. 1998; Elsner and Bossak 2001; Gray and Klotzbach 2009; Saunders and Lea 2005). Results from Kaplan and DeMaria (1995) showed an idealized scenario for the maximum possible inland wind speed of a decaying tropical cyclone based on both intensity at landfall and forward motion for the Gulf Coast and southeastern United States, and for the New England area (Kaplan and DeMaria 2001). In general they found that for inland locations, the effect of storm motion speed is just as critical as the storm intensity upon landfall, such that a fast-moving category-3 storm on the Saffir–Simpson Scale [hereinafter the Saffir–Simpson Hurricane Wind Scale (SSHWS); Simpson 1974] will have additional inland impacts than a slow-moving category-5 storm.

More recently, Zandbergen (2009) examined the inland exposure of U.S. counties to tropical storm– and hurricane-force winds. It was shown that the inland impacts from tropical cyclones extended across much of the Deep South and into portions of the Ohio valley and New England. Local maxima were evident across much of Florida, eastern North Carolina, and the southern Gulf Coast states. While these results appear realistic at first glance, further investigation reveals various concerns with the methodology that was used to derive the results—namely the use of a symmetric rather than an asymmetric wind swath to estimate the total spatial envelope of a storm’s impact. A more thorough discussion on the differences in methodologies between the Zandbergen (2009) article and those expressed herein follows in section 2.

Knowledge of the climatologically favored areas for hurricanes, as provided in the aforementioned studies, combined with a climatological expectation of the inland penetration frequency of these storms can be of tremendous value. Thus, there is a need to accurately document and understand the inland extent of winds associated with tropical cyclones to enhance public awareness, improve forecasting of hazardous conditions, and in turn save lives. To that end, a comprehensive climatology of the inland extent of tropical cyclone winds over the eastern United States was developed at NOAA’s National Climatic Data Center (NCDC). Section 2 describes the data and methods used to construct the climatology, while section 3 discusses the results of the new climatology. The paper concludes with a summary and discussion of the results.

2. Data and methodology

Historical track data for the North Atlantic were obtained from the International Best Track Archive for Climate Stewardship (IBTrACS; Knapp et al. 2010; Kruk et al. 2010). The IBTrACS dataset is a comprehensive collection of all recorded tropical cyclones worldwide, and the Atlantic basin portion of the record is derived from NOAA’s Atlantic Hurricane Database (HURDAT; Jarvinen et al. 1984). In addition, the so-called extended best-track data were also obtained for the North Atlantic (Demuth et al. 2006). Data for the extended best track begin in 1988 and contain not only the time, position, and intensity of the tropical cyclone at 6-h intervals, but also the maximum extent of winds (MEW) of a given threshold (34-, 50-, and 64-kt (1 kt ≈ 0.5 m s−1) sustained wind averaged over 1 min at 10-m height) in four quadrants: northeast, southeast, southwest, and northwest. These wind radii values are the result of operational estimates and became part of the official North Atlantic best-track data after 2004.

The first step toward producing an inland tropical cyclone wind impact climatology was to compute the average distance for each MEW threshold (wind radius) using the extended best-track dataset. This was done by differentiating those storms that were solely tropical (denoted by an asterisk in the dataset) or extratropical1 (denoted by an “E” in the dataset). Then, for each wind radius and quadrant, a mathematical average was computed and binned by the SSHWS, and included post-tropical storms. This resulted in a matrix of average distances (nm) for each wind radii, SSHWS intensity, and quadrant. However, this matrix was unsuitable for computing the average inland extent of tropical cyclone winds for two reasons.

First, using all data provided in the extended best-track dataset produces an average wind radii distance that is composed of mostly overwater observations and fewer inland observations. Applying this biased result to inland locations would result in inflated frequency counts since the lack of surface friction at sea produces larger wind swaths than those over land. Therefore, it was necessary to recalculate the average wind radii distances by extracting only those portions of storm tracks that were located over land. This also included an extension of the coastline 150 nm out to sea to ensure that coastal counties were included when the spiraling winds around an approaching tropical cyclone were already impacting the coastline. The use of the 150-nm extension is consistent with the approach as used by Ho et al. (1987) and Schwerdt et al. (1979) to assess the climatological characteristics of decaying tropical cyclones.

The results of the average wind radii distances over land by SSHWS intensity for each quadrant and MEW threshold are shown in Table 1. In short, the largest sample size was associated with tropical storms (386), while category-4 and category-5 storms had the fewest number (22). Interestingly, the standard deviation in the computed average wind radii distances (not shown) was highest in the northeast direction, regardless of intensity. For example, for the 34-kt threshold in the northeasterly direction, the standard deviation for post-tropical storms was 139 nm, while it was 64 nm in the southwesterly direction. At the same time, for the 64-kt threshold in the northeasterly direction, the standard deviation for category-1 storms was 28 nm, while it was 11 nm in the southwesterly direction. Reasons for this pattern across the range of intensities are unknown and are beyond the scope of this paper.

Table 1.

Results of the overland averages of the wind radii distances from the extended best-track dataset; the remaining categories are according to the Saffir–Simpson Scale. Units are nautical miles.

Results of the overland averages of the wind radii distances from the extended best-track dataset; the remaining categories are according to the Saffir–Simpson Scale. Units are nautical miles.
Results of the overland averages of the wind radii distances from the extended best-track dataset; the remaining categories are according to the Saffir–Simpson Scale. Units are nautical miles.

Second, geographical information system (GIS) software was used to obtain results, owing to its capability to graphically depict hurricane tracks and the availability of a number of analysis tools, including the use of a “buffering” utility. A GIS is “an integrated collection of computer software and data used to view and manage information about geographic places, analyze spatial relationships and model spatial processes. A GIS provides a framework for gathering and organizing spatial data and related information so that it can be displayed and analyzed” (Wade and Sommer 2006). Buffering in GIS refers to the construction of a polygon surrounding a point, line, or polygon at a specified distance. In our case however, the buffering operation requires that the tropical cyclones’ wind swath around a best-track data point be represented as a “left side” and “right side” only (relative to the storm’s forward motion) and not in four distinct quadrants. To fit this model, a simple average was computed on the distance estimates in Table 1 to derive the left and right buffers for each SSHWS intensity, time step, and wind radius. The right-front and right-rear quadrants of the tropical cyclone compose the right component of the buffer, while the left-front and left-rear quadrants are assumed to be the left component. Table 2 shows the result of this conversion process.

Table 2.

Results of the conversion of the data from Table 1 into left and right components. Units are nautical miles.

Results of the conversion of the data from Table 1 into left and right components. Units are nautical miles.
Results of the conversion of the data from Table 1 into left and right components. Units are nautical miles.

To accurately depict storm asymmetry, a general assumption was applied such that the left side is an average of the northwest and southwest quadrants, and the right side is the average of the northeast and southeast quadrants. This method was selected because it was a straightforward way to obtain results that adequately reflected the climatological mean. Note that the average distances are quite large for post-tropical storms at the 34-kt threshold, but drop off rapidly at the 50- and 64-kt thresholds. This is due to the very small number of post-tropical storms in the extended best-track dataset that had an average wind speed greater than 50 kt (n = 5). The category-3 hurricane, on average, has the largest wind radii of any storm intensity. Category-4 and category-5 storms were lumped together because of the relatively small sample size over land in the extended best-track dataset. The net result of this derivation is that a climatological asymmetry to the tropical cyclone wind field is realized.

As stated earlier, a related study on the exposure of U.S. counties to tropical storms and hurricanes for the Atlantic basin was conducted by Zandbergen (2009). In that article, GIS was also used in which a symmetrical buffer around the track of the tropical cyclone, as derived from an inland wind decay model, was used to deduce those counties that were influenced by the tropical cyclone. While the use of symmetrical buffers in GIS is convenient, the typical asymmetry of tropical cyclones (with, in the absence of storm motion, the strongest winds occurring in the right-rear quadrant; Elsner and Kara 1999) demands a more robust technique than offered above. In addition, Zandbergen (2009) used a period of record of 1851–2003 for hurricane tracks, while the methodology discussed above includes a period of record beginning in 1900 through the 2008 season. Although the historical best-track data prior to 1900 have been updated by Fernandez-Partagas and Diaz (1996), Zandbergen (2009) removed the overwater portions of all hurricane tracks prior to 1944, yet kept the landfall segments of those early tracks. As Ludlum (1963) points out, the intensity and location of hurricanes at landfall were highly subjective in the early portion of the record since instruments to measure wind speeds were not widespread until the late 1870s. Therefore, our use of the period 1900–2008 removes the early portion of the record and includes more recent memorable storms such as Katrina (2005), Rita (2005), and Ike (2008), all of which produced significant inland impacts.

a. Applying GIS software

There are several ways to obtain a frequency distribution for those overland locations impacted by tropical cyclone winds. First, a simple county-by-county tabulation can be done where the frequency incrementally increases when a buffered wind swath overlaps any portion of the county. This technique exaggerates (diminishes) the counts for larger (smaller) counties, since large counties are more likely to be affected by a nearby tropical cyclone wind swath. To account for the heterogeneous county size distribution across the eastern United States, an equal-area grid was constructed at resolutions of 5, 15, and 30 km. A sensitivity analysis was performed to determine which grid resolution should be used to estimate the inland wind impacts from tropical cyclones.

The results of the sensitivity analysis are shown in Table 3, where the maximum frequency count in the entire grid domain is provided for three separate periods and each MEW threshold. The first period, 1988–2008, matches the period of record currently available in the extended best-track dataset (Demuth et al. 2006). The remaining two periods, 1965–2008 and 1900–2008, were selected based on (i) satellite availability and (ii) as mentioned, the somewhat more reliable historical hurricane tracks in the North Atlantic basin, respectively.

Table 3.

Maximum frequency count for each MEW threshold for three distinct periods and equal-area grid projections.

Maximum frequency count for each MEW threshold for three distinct periods and equal-area grid projections.
Maximum frequency count for each MEW threshold for three distinct periods and equal-area grid projections.

Interestingly, the sensitivity analysis revealed little change in the maximum frequency count despite the change in grid size. A potential reason why this occurs is that all the swaths (34, 50, and 64 kt) are large relative to the grid cells and thus tend to intersect a similar number of adjoining cells regardless of grid size. Furthermore, the buffers intersect the largest grid cells (30 km) only slightly more often than the smaller ones, owing to the general large buffer sizes (Table 2), which results in higher-frequency counts at 30 km than at 5 and 15 km. In recognizing that the mean absolute distance error in the extended best-track dataset is roughly 25 km (Demuth et al. 2006), the 30-km grid was selected to derive the inland tropical cyclone wind climatology for the eastern United States (Fig. 1). The period of record for this study was also set at 1900–2008. While Zandbergen (2009) attempts to normalize county size through the use of a novel “shape index,” the application of a 30-km grid can be applied in any tropical cyclone–prone basin and altogether eliminates the dependency on counties.

Fig. 1.

The 30-km grid used to construct the inland wind climatology.

Fig. 1.

The 30-km grid used to construct the inland wind climatology.

Frequency counts for each 30-km grid cell were obtained by executing a custom python script within the Environmental Systems Research Institute (ESRI) ArcInfo Desktop GIS environment. The script was developed to automate the process of selecting best-track segments by intensity and buffering them to the left and right for each wind radius using data from Table 2. The wind buffers were oriented parallel to each storm track and rounded ends (versus flat) were incorporated to simulate the circular nature of tropical cyclones. A rounded-end buffer on a line means that both ends of the line will be in the shape of a half-circle. A flat-end buffer generates rectangular line endings with the middle of the short side of the rectangle coincident with the endpoint of the line. Figure 2 demonstrates this process for a category-4 hurricane that made landfall along the southeastern Texas coast in 1932. Figure 2a shows the track of the storm with each segment color coded by storm intensity (SSHWS). Figure 2b overlays the storm’s track on the 30-km equal-area grid and shows the aerial extent of the inland impacts if only the center point and no wind swath around the storm track were used. The buffers were then merged and dissolved into a single wind swath with rounded ends (Fig. 2c). The resulting inland impact is thus apparent after applying the asymmetrical buffer (Fig. 2d). This process was repeated for each storm in the historical best-track record from 1900 to 2008 (i.e., a unique buffer at each MEW threshold was generated for each storm using the climatological average distances based on SSHWS intensity as provided in Table 2). Once all the buffers were constructed, they were overlaid onto an equal-area grid (Fig. 1) and intrinsic ArcGIS operations (spatial join and join count) were used to determine the cumulative frequency of the 34-, 50-, and 64-kt winds from tropical cyclones. Frequency maps for these thresholds are shown in Fig. 3. It is equally important to note that these wind buffers represent the average maximum extent of the winds in each quadrant, and that not all grid cells falling within the corresponding radius will actually receive the indicated winds. Rather, the buffer is simply used as a guide to conservatively estimate the maximum radius of influence from a given tropical cyclone. Finally, the method presented here is a climatological mean rather than a storm-by-storm analysis. While the authors were unable to identify other asymmetric methods in the existing literature, alternative methods may be possible to address either storm-relative or cardinal-relative wind radii for determining inland impacts.

Fig. 2.

(a) A 1932 hurricane making landfall along the Texas coast, (b) only grid cells (30 km) that intersected with the track, (c) the estimated extent of the 34-, 50-, and 64-kt wind swaths, and (d) the resulting inland impact after applying the asymmetrical buffers. Pink shading indicates those counties that were covered by all wind swaths; blue indicates areas that were only affected by the 34- and 50-kt wind swaths; and yellow shading denotes those areas that were impacted solely by the 34-kt wind swath.

Fig. 2.

(a) A 1932 hurricane making landfall along the Texas coast, (b) only grid cells (30 km) that intersected with the track, (c) the estimated extent of the 34-, 50-, and 64-kt wind swaths, and (d) the resulting inland impact after applying the asymmetrical buffers. Pink shading indicates those counties that were covered by all wind swaths; blue indicates areas that were only affected by the 34- and 50-kt wind swaths; and yellow shading denotes those areas that were impacted solely by the 34-kt wind swath.

Fig. 3.

Frequency distributions of the (a) 34-, (b) 50-, and (c) 64-kt winds for all storms irrespective of intensity.

Fig. 3.

Frequency distributions of the (a) 34-, (b) 50-, and (c) 64-kt winds for all storms irrespective of intensity.

3. Results

While the use of frequencies is beneficial in ascertaining how often a particular region was impacted by tropical storm– or hurricane-force winds, return intervals are often referred to instead. In this regard, rather than acknowledging a single grid cell having 20 “hits” over the course of the 108-yr period, or 0.185 storms per year, it is often more meaningful to say that grid cell experiences tropical storm–force winds once every 5.4 yr (simply the inverse of the ratio of frequency count over the period of interest). In the discussion that follows, return intervals will be used to summarize the historical inland wind climatology.

Return intervals for post-tropical winds greater than or equal to 34 kt are shown in Fig. 4. The map shows that nearly every state east of the Rocky Mountains has experienced winds associated with a post-tropical cyclone. The most favored areas include the Carolinas northeastward through Virginia and into New England. In these locations, return intervals are one event every 2–5 yr. In more inland areas, the return intervals drop off rapidly northwestward from the coasts. For example, the Ohio valley generally experiences post-tropical storm–force 34-kt winds once every 6–10 yr, while parts of northwestern Missouri do once every 50–100 yr. Other minimums in post-tropical storm-force 34-kt winds are the Louisiana and south Florida coastlines, where such events occur once every 20–30 yr.

Fig. 4.

Return intervals in years for PT cyclones with 34-kt winds.

Fig. 4.

Return intervals in years for PT cyclones with 34-kt winds.

Figure 5 is similar to that of Fig. 4, except that it is an examination of 34-kt winds solely from tropical (and not post-tropical) cyclones (Fig. 5a), and all hurricanes with winds greater than 64 kt (Fig. 5b). The figure shows that much of the southern Gulf Coast and Eastern seaboard is regularly affected by 34-kt winds from a tropical cyclone (i.e., one event every 2–5 yr), and 64-kt winds from hurricanes once every 3–5 yr. The return intervals decrease in the New England area, where tropical storm–force (hurricane force) winds are experienced once every 6–10 yr (11–20 yr). The map shows that, in general, warm-core tropical cyclones rarely make it farther north and west than the intersection of 40°N and 85°W.

Fig. 5.

Return intervals in years for (a) tropical storm–force winds >34 kt and (b) all hurricane-force winds >64 kt.

Fig. 5.

Return intervals in years for (a) tropical storm–force winds >34 kt and (b) all hurricane-force winds >64 kt.

The distribution of hurricane-force winds is further dissected, splitting them into nonmajors, category-1–2 hurricanes (Fig. 6a), and majors, category-3–5 hurricanes (Fig. 6b). For the nonmajor hurricanes, several distinct maxima are evident, particularly along eastern North Carolina, southeastern Texas, and much of the southernmost Gulf Coast states including nearly the entire state of Florida. In these areas, return intervals are one nonmajor hurricane every 3–10 yr. The return intervals drop off markedly with inland extent, again signaling the often rapid decay associated with landfalling tropical cyclones. For the major hurricanes, the areal distribution is less, owing to their relative low frequency of occurrence (at landfall), however several important inferences can be made. The highest return intervals exist largely across the southern portions of Florida (one major hurricane every 5–10 yr), with a large area of return intervals between 11 and 20 yr blanketing a good portion of the southern Gulf Coast states. Extreme eastern Maine and Massachusetts also experience hurricane-force winds from major hurricanes once every 51–108 yr. This return interval also extends southward into the Carolina piedmont regions.

Fig. 6.

Return intervals in years for (a) category-1–2 hurricanes and (b) category-3–5 (major) hurricanes.

Fig. 6.

Return intervals in years for (a) category-1–2 hurricanes and (b) category-3–5 (major) hurricanes.

For those locations that are not shaded, the maps imply that, in the recorded history, these regions have not experienced tropical storm- or hurricane-force winds. For example, in Fig. 6b, southern Georgia is not shaded (i.e., no major hurricanes have crossed those inland areas in the last 108 yr), but this is realistic owing to the tendency for storms to either recurve away from the coast (Colon 1953; George and Gray 1977; Hodanish and Gray 1993; Evans and McKinley 1998; Elsner et al. 2000) or rapidly dissipate after landfall. However, this does not suggest tropical cyclone winds cannot occur in the future. Moreover, while the areas shaded in dark blue may have only experienced one storm over the course of the 108-yr period used in this study, the inferred risk in these areas is nonzero, owing to the long-term climatological mean that indicates that inland impacts are possible, though rare, events. Again, these maps are a climatological guide for inland wind impacts from tropical cyclones, and a zero percent occurrence does not mean “never going to happen,” but that it is “highly unlikely.”

To some extent, geography helps explain the heightened exposure of certain regions of the U.S. mainland. Areas that jut out into the Atlantic and Gulf of Mexico are more likely to be affected by North Atlantic and Gulf tropical cyclones. Florida, eastern North Carolina, and Massachusetts are examples of areas that frequently find themselves in the path of tropical storms and hurricanes simply because of the seaward extent of their land area (Gibney 2000).

a. Potential applications

The data derived in this study are valuable to myriad sectors of society. For example, the presented climatology of the frequency of inland winds from tropical cyclones may be useful to city and county government officials when considering the design and construction of public buildings and may even serve as justification for improving building codes and strengthening code enforcement. Additionally, for existing infrastructure built in areas that might experience hurricane-force winds on a more frequent basis, officials may want to consider employing various retrofitting and/or strengthening strategies. The mayor or county council could use the data, in conjunction with other available hazards information, to determine which areas are susceptible to the most destructive hazards to determine where to concentrate and fund hazard mitigation measures (e.g., developing property protection ordinances or encouraging development in less hazard-prone areas). Businesses might use the data for continuity planning, particularly to help determine if their buildings are wind resistant or if their insurance policy coverage limits are adequate. Emergency managers at the federal, state, and local levels might use the data to help prioritize the allocation of resources for disaster mitigation, as well as assist in various evacuation planning and preparedness efforts. Emergency managers might also use the data for public education regarding the potential threat of tropical cyclones in areas away from the coast where many residents may not even be aware that hurricanes are possible. One final consideration on applications of the inland winds climatology is aimed toward government or private forecasters who may need to be trained in anticipating and recognizing such events, especially for those locations much farther inland where return intervals are longer than 20 yr. In addition to the potential applications discussed here, there are likely others that fall under other specific sectors, including arborists, telephone- or power-line repair, and crop losses. The discussion herein is not meant to be all-inclusive, but merely a starting point for discussion on the uses of the inland winds climatology.

4. Summary

Tropical cyclones pose a significant threat to life and property along coastal regions of the United States. As these systems move inland and dissipate, they can also pose a threat to life and property, through heavy rains, high winds, and tornadoes. While many studies have focused on the impacts from tropical cyclones on coastal counties of the United States, there was a need for a detailed climatology of the inland penetration of tropical cyclone wind fields.

NOAA/NCDC has developed a comprehensive climatology of the inland winds from tropical cyclones for the eastern United States. This was done by using the historical Atlantic basin track data (Jarvinen et al. 1984) in concert with the historical extended best-track dataset (Demuth et al. 2006) to compose an average maximum extent of the winds at 34-, 50-, and 64-kt thresholds according to storm intensity. A unique storm-relative asymmetrical buffer was generated for each storm and was overlaid on a 30-km equal-area grid using GIS to depict those regions of the eastern United States that have historically been impacted by tropical cyclones.

The results indicate that nearly every state east of the Rocky Mountains has experienced 34-kt winds associated with a post-tropical cyclone. The most favored areas include the Carolinas northeastward through Virginia and into New England. However, for warm-core tropical systems, much of the southern Gulf Coast and Eastern seaboard states are regularly affected by 34-kt winds from a tropical cyclone (one event every 2–5 yr). The return intervals decrease in the New England area, where tropical storm–force 34-kt winds are experienced once every 6–10 yr. For hurricane-force 64-kt winds, several distinct maxima were apparent, primarily eastern North Carolina, central and northwestern Florida, and southeastern Texas. There is an apparent minimum in hurricane-force winds in central Georgia as well as the states of Pennsylvania and New York. The relative minimum regarding the inland penetration of 64-kt winds from these strong hurricanes implies that they decay rapidly after landfall. The results indicate that warm-core tropical cyclones rarely make it farther north and west of the intersection of 40°N and 85°W.

Future work should examine the computation and addition of storm movement to the extended best-track data. Then each of the four radii could be more precisely classified as left or right halves. Thereafter, statistics such as the mean and variance could be computed for the left and right sides. This would be compatible with the left and right buffers in GIS since the software indicates the storm direction of movement. The variance would provide an idea of the uncertainty or quality of the wind swath model.

Additional future work may focus on the creation of a shapefile for each record in the extended best-track dataset. Each shapefile would contain four features, the four quadrant radii, and would be broken into the 34-, 50-, or 64-kt intervals. Once this is known, the GIS software analyst can draw a contour for speed and convert to a polygon, after which all polygons can be merged together to derive a single storm wind swath. This technique should result in wind swaths that would match the original extended best-track dataset.

There are, however, some limiting factors to efficiently applying both concepts as there are a number of underlying complexities that must be worked out prior to their implementation. Each storm has its own unique set of behaviors, wind swaths, and caveats that demand manual intervention at each step of the process. In addition, any verification of the actual wind data as compared to the wind swath will most likely be done using surface wind reports, which are not always sited in the best possible locations for capturing the true force of the wind (at 10-m height). Ideally each storm and corresponding wind swath would be verified through some sort of in situ wind observing network, either satellite derived or surface based.

Finally, while the results are shown as return intervals, the focus of this study was to produce a comprehensive climatology of the extent of inland winds from tropical cyclones in the eastern United States. While some locations east of the Rocky Mountains have never experienced such winds, the analysis presented here is purely climatological in nature and no predictive trends or assumptions are provided or inferred. However, for those locations with higher return intervals, knowledge of where tropical storm–force winds or hurricane-force winds have most commonly occurred can better prepare local forecasters, emergency managers, county planners, and others to be even more vigilant against the myriad of threats tropical cyclones pose and recognize that their impacts often extend well inland from the coast.

Acknowledgments

The authors acknowledge several members at the National Hurricane Center in Miami, FL, for their insight into the methodology, including Richard Pasch, Chris Landsea, Jack Beven, and James Franklin. Special thanks are given to Phil Klotzbach, James Franklin, Karin Gleason, Scott Applequist, Tamara Houston, and several anonymous reviewers for providing valuable comments toward improving this manuscript.

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Footnotes

Corresponding author address: Michael C. Kruk, STG, Inc., 151 Patton Ave., Asheville, NC 28801. Email: michael.kruk@noaa.gov

1

Hereinafter, the term post-tropical (PT) will be used instead of extratropical as to distinguish between traditional baroclinic midlatitude cyclones and those that are the transformational result of tropical cyclone decay.