Abstract

The characteristics of tropical cyclone vertical wind profiles and their associated wind speed peaks below 1.5 km were examined through the use of a large number of GPS dropwindsondes (GPS sondes) and radar-derived velocity–azimuth display (VAD) profiles. Composite wind profiles were generated to document the mean structure of tropical cyclone vertical wind profiles and their changes with storm-relative position. Composite profiles were observed to change as the radius decreased inward toward the radius of maximum winds. Profiles also varied between three azimuthal sectors. At landfall, wind profiles exhibited changes with radial distance and differences were observed between those within offshore and onshore flow regimes. The observations support a general reduction in boundary layer depth with decreasing radial distance. Wind profiles with peaks at low altitudes were typically confined to radii less than 60 km, near and radially inward from the radius of maximum winds. Wind speed maxima, when scaled by a layer mean wind, decreased in magnitude as the radius decreased. At landfall, composite profiles showed a distinct low-level wind speed maximum in the eyewall region with significant differences between the onshore and offshore flow regimes.

1. Introduction

Understanding how wind speed changes with height is of considerable importance with regard to damage potential in landfalling tropical cyclones. As coastal populations increase and monetary losses rise, the need to understand the characteristics of the hurricane wind field both horizontally and vertically has become apparent (Pielke et al. 2008). Additionally, the proliferation of high-rise structures along the immediate shoreline suggests that characterizing the wind speed change with height for a variety of terrain exposures is vital to maintaining appropriate structural design standards and for inclusion in tropical cyclone risk modeling applications.

This study seeks to understand the characteristics of tropical cyclone vertical wind profiles through composite profiles utilizing several variables to stratify observations. Emphasis was placed on understanding changes in the lowest 1.5 km, including the hurricane boundary layer (HBL). Unfortunately, a significant data void exists as tropical cyclones make landfall. A goal of the current study was to modify existing velocity–azimuth display (VAD) techniques to produce a large number of vertical wind profiles of tropical cyclones at landfall to fill this data void. These profiles were used to evaluate the similarities and differences between those profiles from open-ocean conditions versus those at landfall. Additionally, the current study provides a link to understanding changes in near-surface wind gust characteristics beyond those associated with frictional processes.

The deployment of GPS dropwindsondes (GPS sondes), beginning in 1997, has provided a wealth of information regarding the kinematic and thermodynamic structure of the HBL (Hock and Franklin 1999, hereafter HF1999). The current study employs vertical wind profiles from GPS sondes released in deep-water conditions to examine changes in the mean vertical wind profile with storm-relative position. The characteristics of low-level wind speed maxima within vertical wind profiles were also investigated through their associated statistics. A VAD technique was applied to leverage Weather Surveillance Radar-1988 Doppler (WSR-88D) information from several landfalling hurricanes. Through the compositing of profiles, a conceptual understanding of the evolution of the vertical wind profile with changing radius in the open ocean and at landfall was developed.

Historical studies have documented the presence of jetlike features within tropical cyclone vertical wind profiles prior to the implementation of GPS sondes (Wilson 1979; Moss and Merceret 1976; Korolev et al. 1990). The GPS sonde archive has provided a large number of high-resolution profiles, which include data from the lowest 500 m of the HBL. Franklin et al. (2003, hereafter FBV2003) and Powell et al. (2003, hereafter PVR2003) used a large number of GPS sondes to produce composite wind profiles; both found a wind speed maximum near 500 m with a logarithmic decrease below. Both FBV2003 and PVR2003 stratified profiles to examine the changing mean structure. FBV2003 used subjective eyewall and outer-vortex classifications along with radial distance from the radius of maximum winds (RMW) observed at the aircraft flight level. Radial and storm-relative position asymmetries were observed by FBV2003, but were attributed to influences from individual sonde trajectories rather than dynamical processes. PVR2003 used the mean boundary layer (MBL) wind, which is a layer mean below 500 m, to scale a single composite profile for all observations and to group GPS sonde profiles into different mean wind environments. Although the focus was on air–sea interaction, PVR2003 showed a decrease in the height of the mean wind speed maximum with increasing MBL wind and a jetlike profile became evident as the MBL wind increased above 50 m s−1. Vickery et al. (2009) used similar composite profiles to show that the wind profile was logarithmic through the depth of the wind speed maximum, while Amano et al. (1999) used Doppler sodar observations to illustrate the validity of power-law wind profiles during several landfalling typhoons. Blackwell (2000) and Knupp et al. (2006) have also provided observational evidence to support a jetlike wind profile in two weak landfalling tropical cyclones.

Although the observational studies of FBV2003 and PVR2003 provided evidence to support the mean presence of a broad jetlike wind profile, individual GPS sonde wind profiles often departed from the mean structure (FBV2003; Kepert 2006a,b; Schwendike and Kepert 2008). The individual profiles are subjected to a variety of scales of motion during descent, while also moving several kilometers azimuthally and several hundred meters radially. Profiles often contain several local wind speed maxima and minima. Although the motion of the instrument is well understood, it remains unclear what the specific scales of motion the perturbations within individual profiles represent.

Numerical studies have focused on evaluating the departure from gradient balance near the top of the boundary layer, which may be responsible for producing the jetlike profile. Rosenthal (1962), Eliassen and Lystad (1977), and Shapiro (1983) have documented supergradient flow numerically using a slab approach to modeling the HBL. Kepert (2001, hereafter K2001) and Kepert and Wang (2001, hereafter KW2001), through the use of a linear model and complete numerical simulation, provided a mechanism in which the flow could exceed gradient balance, producing profiles that contained low-level jet features. It was found that the forward motion of the tropical cyclone was responsible for an azimuthal asymmetry in the height and strength of the feature. The radial distribution of the feature was dependent on the shape of the radial wind profile and the contribution of angular momentum advection. A flattened radial wind profile, considered stable for radial perturbations, produced a wind maximum comparable in magnitude to the gradient wind speed, which extended radially outward from the RMW. However, a peaked and inertially neutral radial wind profile resulted in a maximum that exceeded the gradient wind but was confined to the region near the RMW. The height of the wind maximum scaled with the inertial stability of the tropical cyclone, which was a function of radial distance and the radial change in wind speed. This result suggested a compression of the boundary layer as the radial distance decreased. It is noted that the wind speed maxima presented in the present study cannot be directly compared to the features described by K2001 and KW2001 as the characteristics of observed wind maxima are not examined as a departure from the gradient wind. However, the observed radial and azimuthal dependencies presented here are similar both over water and at landfall.

2. Data and methodology

a. GPS sonde observations

The GPS sondes are launched from reconnaissance aircraft at altitudes of 1.5–3 km and fall at typical speeds between 10 and 15 m s−1. Measurements of the standard kinematic and thermodynamics variables are made at 0.5-s intervals, yielding a vertical resolution of approximately 2 m. The three-dimensional wind vectors are derived from GPS tracking of the instrument package as it descends. The typical horizontal (vertical) wind measurement error was found to be 0.5–2 m s−1 (±4 m s−1). A 5-s low-pass filter is applied to the profile in order to remove undersampled scales of motion as well as radio frequency noise. Extreme turbulence and intense precipitation can also contribute to the degradation of the telemetry transmitted by the instrument (HF1999).

Between 1997 and 2005, over 2000 GPS sondes were postprocessed by the Hurricane Research Division (HRD) of the National Oceanographic and Atmospheric Administration’s (NOAA) Atlantic Oceanographic and Meteorological Laboratory (AOML; Powell 2007). Wind data quality flags were subjectively assigned to each profile during the review process. Sondes that were flagged more than 3 times were removed and were no longer considered for postprocessing. For each postprocessed GPS sonde, a high-resolution storm track was assimilated using the method described by Willoughby and Chelmow (1982). Storm positions were used to calculate a motion vector in order to compute the storm-relative quantities of azimuth angle and radial distance, as well as the two-dimensional radial and tangential wind components. GPS sondes were also restricted to those which landed within 2–200 km of the storm center in order to limit the influence of GPS sondes launched during synoptic surveillance missions and those released within the eye. The radial and azimuthal distribution of GPS sondes included in the analysis dataset is provided in Fig. 1. Given the study’s focus on the boundary layer wind profile, those GPS sondes that did not transmit data below 200 m were excluded from the analysis dataset. The additional quality control measures resulted in an inventory of 1080 GPS sondes, shown in Table 1.

Fig. 1.

Storm-relative distribution of GPS sondes used in the current study for (a) an unscaled radial distance, where range rings (dashed) represent 20-km intervals, and (b) radius normalized by an estimate of the surface RMW, where range rings (dashed) represent intervals of 1.0. Azimuthal sectors (solid) are denoted in 30° increments.

Fig. 1.

Storm-relative distribution of GPS sondes used in the current study for (a) an unscaled radial distance, where range rings (dashed) represent 20-km intervals, and (b) radius normalized by an estimate of the surface RMW, where range rings (dashed) represent intervals of 1.0. Azimuthal sectors (solid) are denoted in 30° increments.

Table 1.

Summary of GPS sondes used in the current study. All storms were of hurricane intensity at their peak unless noted.

Summary of GPS sondes used in the current study. All storms were of hurricane intensity at their peak unless noted.
Summary of GPS sondes used in the current study. All storms were of hurricane intensity at their peak unless noted.

b. Velocity–azimuth display technique

As a tropical cyclone makes landfall, the ability of reconnaissance and research aircraft to make critical measurements of the vertical wind profile is reduced. The GPS sonde analysis dataset included only 55 GPS sondes within 5 km of the immediate shoreline. To produce a larger number of wind profiles from several landfalling tropical cyclones, a VAD technique was applied to historical WSR-88D velocity data. The horizontal wind vector can be obtained from Doppler radial velocity data acquired from a single Doppler radar (Lhermitte and Atlas 1961). The VAD technique described by Browning and Wexler (1968) was adapted by restricting the horizontal domain to a 3–5-km annulus from the radar site and binning the Doppler velocity data by height to produce wind profiles of the lowest 1500 m. The VAD technique requires that the horizontal wind field is linear and the restriction of the VAD domain minimized the influence of flow curvature. The method was able to produce quality vertical profiles radially inward from the radius of maximum winds. Data within the complete volume were binned by height and the highest tilt angle was 14.9°. For typical WSR-88D velocity coverage patterns (VCP), profiles contained 11 height bins between 50 and 1500 m with a vertical resolution of approximately 100 m below 700-m height.

Historical data were restricted to radar sites within 15 km of the immediate shoreline, bay, or tidal lake and within 150 km of the tropical cyclone center at its closest approach. Fifteen events were selected between 1996 and 2008 using seven different WSR-88Ds, resulting in 384 vertical wind profiles (Table 2). The raw Doppler velocity data were dealiased and decluttered. A Matlab script was developed according to the methodology of Zhang and Wang (2006) to dealias the Doppler velocities and allow for subjective decluttering. The technique gives an estimate of the wind profile using a volumetric and temporal average of Doppler velocity data and it is reasonable to assume that a VAD profile is representative of a mean profile. The residuals from each height bin and their respective Fourier series fits were examined in order to evaluate the possible measurement error. Although the mean departure (3 m s−1) from the VAD-estimated mean wind speed changed little with increasing height, the tails of the distribution became larger as the height increased. This result was likely due to the larger volume of data at higher bins. The envelope of data also contains information regarding transient features that likely pass through the VAD domain while the volumetric scanning strategy is completed.

Table 2.

List of radar sites and events used in the VAD analysis.

List of radar sites and events used in the VAD analysis.
List of radar sites and events used in the VAD analysis.

c. Analysis methodologies and techniques

Composite wind profiles were produced for both GPS sondes and VAD wind profiles. The use of composite profiles allowed for the comparison between open-ocean and landfall conditions and additionally provided information regarding the mean structure of the vertical wind profile. Individual profiles from each platform are vastly different in their measurement characteristics. The profiles were normalized by the MBL wind to produce a composite using all available GPS sonde or VAD observations. Profiles were also grouped using the MBL wind similar to PVR2003. This layer mean was selected to normalize the wind speed observations since it typically contains the wind maximum and is not significantly influenced by local wind speed maxima and minima within the layer. A gradient wind was not used given the difficulty in assigning a gradient height; Willoughby (1990) has shown that this classification may apply to winds as high as 3 km. Additionally, kinematic definitions, such as the height at which the radial wind component is zero, have differed from thermodynamic estimates of HBL depth (Zhang et al. 2011). The radial inflow layer depth was also found to vary by several hundred meters among GPS sondes released in quick succession along radial flight legs, likely a result of the variety of the scales of motion a sonde may encounter. GPS sondes were grouped according to MBL wind speed using 5 m s−1 bin sizes, which is smaller than the 10 m s−1 threshold used by PVR2003 and Vickery et al. (2009). The larger number of GPS sondes used in the current study allowed for the smaller bin size. A 10 m s−1 bin size was applied to VAD profiles due to the relatively smaller total number of available profiles. Composite profiles were generated for each group according to PVR2003 through the application of the ergodic assumption. The lower portion of the GPS sonde profiles was corrected for an acceleration bias induced by the measurement characteristics of GPS sondes (HF1999). A least squares log-linear fit was applied to the lowest 150 m of the profile and height bins below 20 m were adjusted to the fit.

Individual profiles were also stratified by radius, radius scaled by an estimate of the surface RMW (R/Rmax), and storm-relative azimuthal sector. The radius and storm-relative azimuth angle were determined through the method described by Willoughby and Chelmow (1982). An estimate of the surface RMW was used to scale the splash radii in order to remove the influence of storm size.

The storm-relative azimuth sectors were selected according to Black et al. (2007): right = 20°–150°; rear = 151°–240°; and left front = 241°–19°. The sectors are based on differences in the underlying wave characteristics of each zone as described by Wright et al. (2001). VAD profiles were also assigned to onshore, offshore, or alongshore flow regimes based on the track, coastal geometry, and the lowest height bin wind direction.

The following radial (scaled radius) groupings for GPS sondes were used: less than 30 km (<1.0), 30–60 km (1.0–1.34), 60–112 km (1.34–2.10), and greater than 112 km (2.10). The values represent the quartiles of the splash radii (scaled radii) distribution for GPS sondes used in the current study. The quartiles of the VAD radius (scaled radii) distribution were slightly different for smaller radii, with groups of less than 40 (1.0), 40–60 (1–1.53), 60–112 (1.53–2.65), and greater than 112 km (>2.65).

The surface radius of maximum winds was estimated using historical, gridded H*Wind operational wind field analyses (Powell et al. 1998). The analyzed, gridded wind field was rotated into a storm-relative coordinate system and the maximum 1-min marine exposure wind speed was found in each storm-relative sector. The associated radial distance was assigned as the surface RMW for the given sector at the time of the analysis. A linear interpolation was used to provide a continuous estimate of the surface RMW between operational analyses. The RMW for the sector in which the GPS sonde landed was assigned to the individual profile. There were instances where the gridded operational analyses were not available within 24 h of GPS sondes. These profiles were excluded from the analysis using a scaled radius (27 sondes). Given the relatively coarse temporal resolution of H*Wind analyses, rapid storm structural changes would induce an error in the RMW estimate. It is noted that the RMW estimate rarely changed more than 10 km between two consecutive analysis fields. This method differs from that used by Zhang et al. (2011), who used a radar estimate of the RMW at 2-km altitude or the flight-level RMW if radar data were unavailable. As shown by Powell et al. (2009), the outward slope of the RMW with height can result in differences of several kilometers between the flight-level RMW and the surface. Given the current study’s emphasis on the lowest 1.5 km, the estimated surface RMW was chosen. This method was also applied to the VAD wind profiles. It is noted that improvements in measurement platforms (e.g., Stepped-Frequency Microwave Radiometer, Doppler wind retrievals etc.) and various adjustment techniques over the nearly one decade of GPS sonde observations may also induce small errors into the H*Wind analysis fields (Franklin et al. 2003; Aberson et al. 2006; Uhlhorn et al. 2007; Powell et al. 2009).

3. Open-ocean wind profile characteristics

a. GPS sonde composite profiles

Mean profiles were generated for each MBL wind group (Fig. 2) and showed a broad range of wind speed maxima. As expected, the profiles exhibited a logarithmic increase up to the wind speed maximum. The height of the total and tangential wind speed maxima decreased with increasing MBL wind speed, for MBL ≥ 60 m s−1, but the number of sondes available for analysis decreased substantially (Fig. 2). The standard deviations for each profile decreased with height through the wind maximum and then increased above (Fig. 2). The decrease in variance was anticipated, as mechanical production of turbulence is expected to dominate and turbulence intensities should decrease with height. This result was consistent with that shown by PVR2003. The depth of the radial inflow decreased with increasing MBL wind speed for groups below 55 m s−1, but remained nearly constant for the higher MBL wind regimes (Figs. 3a and 3c). Interestingly, a nearly constant inflow layer through a depth of approximately 200 m was observed in each MBL wind group (Figs. 3a and 3c). Standard deviations for the radial wind component for height bins below 500 m approached 5 m s−1. The composite profiles of the tangential wind component revealed a more marked jetlike feature as the MBL wind speed increased (Figs. 3b and 3d). The height of the tangential maximum descended from 900 m for the 35–39.999 m s−1 group to an altitude near 400 m for the 70–74.999 m s−1 group. The decrease in the height of the tangential wind maximum supports a decrease in boundary layer depth with increasing mean wind speed described by Rosenthal (1962) and K2001. The wind maximum contained within the composite profiles is likely representative of a wind maximum near the top of the HBL, as influences from convective- or smaller-scale features are buried within the composite. However, the lack of a significant change in the radial inflow layer depth at high wind speeds creates difficulty in assigning a true top to the HBL, as suggested by PVR2003.

Fig. 2.

Composite wind profiles for MBL wind speed groups. Error bars represent ±1 std dev from the mean. The total number of GPS sondes included in each composite is provided in parentheses.

Fig. 2.

Composite wind profiles for MBL wind speed groups. Error bars represent ±1 std dev from the mean. The total number of GPS sondes included in each composite is provided in parentheses.

Fig. 3.

GPS sonde composite (a) radial composite wind profiles for GPS sondes for MBL wind groups less than 55 m s−1, (b) tangential composite wind profiles for MBL wind groups <55 m s−1, (c) radial composite wind profiles for MBL wind groups greater than 55 m s−1, and (d) tangential composite wind profiles for MBL wind groups greater than 55 m s−1.

Fig. 3.

GPS sonde composite (a) radial composite wind profiles for GPS sondes for MBL wind groups less than 55 m s−1, (b) tangential composite wind profiles for MBL wind groups <55 m s−1, (c) radial composite wind profiles for MBL wind groups greater than 55 m s−1, and (d) tangential composite wind profiles for MBL wind groups greater than 55 m s−1.

b. Mean structure and radial dependencies

A reduction in the height of the wind maximum with decreasing splash radii and scaled radius was observed after the GPS sondes were composited (Figs. 4a and 5a). When normalized by MBL wind speed, the magnitude of the wind maximum decreased with decreasing radius. The result was inherently tied to the decrease in the wind maximum height with increasing MBL wind speed, and larger speeds would be expected to be found at smaller radii. The composite profiles, grouped by scaled radius, exhibited slight differences from that observed using an unscaled radius (Figs. 4a and 5a). The altitude of the wind maximum descended with decreasing scaled radius to a minimum near 400 m radially inward from the surface RMW. The composite profile at the RMW and slightly outward (1–1.34 group) exhibited a wind maximum near 500 m. Unfortunately, the number of GPS sondes did not allow for stratification by both MBL wind and radial distance. The MBL wind was also used to normalize the radial and tangential wind components. The depth of the radial inflow layer was significantly reduced for the composite profile of GPS sondes within 30-km radius but the change not as pronounced when the radius was scaled (Figs. 4b and 5b). A reduction in the scaled magnitude of the radial wind was observed. This result argued that the low-level flow near the eyewall becomes more tangential, which is supported by the composite profiles using a scaled radius (Fig. 5b) and the decrease in surface roughness in high winds as shown by PVR2003.

Fig. 4.

GPS sonde composite of (a) total wind and (b) tangential (right) and radial (left) component profiles for splash radii stratifications of <30, 30–60, 60–112, and >112 km. Error bars represent ±1 std dev from the mean. The total number of GPS sondes included in each composite is provided in parentheses.

Fig. 4.

GPS sonde composite of (a) total wind and (b) tangential (right) and radial (left) component profiles for splash radii stratifications of <30, 30–60, 60–112, and >112 km. Error bars represent ±1 std dev from the mean. The total number of GPS sondes included in each composite is provided in parentheses.

Fig. 5.

GPS sonde composite of (a) total wind and (b) tangential (right) and radial (left) component profiles for splash radius scaled by the surface radius of maximum winds of <1.0, 1.0–1.34, 1.34–2.10, and >2.10. Error bars represent ±1 std dev from the mean. The total number of GPS sondes included in each composite is provided in parentheses.

Fig. 5.

GPS sonde composite of (a) total wind and (b) tangential (right) and radial (left) component profiles for splash radius scaled by the surface radius of maximum winds of <1.0, 1.0–1.34, 1.34–2.10, and >2.10. Error bars represent ±1 std dev from the mean. The total number of GPS sondes included in each composite is provided in parentheses.

c. Mean structure and azimuthal dependencies

Differences were also observed between composite profiles generated following segregation of sondes into storm-relative azimuthal sectors (Fig. 6). Although not as striking as those found within the radial groupings, a 500-m scaled wind maximum was found in the rear-sector composite profile, with the right- and left-front sectors exhibiting similar profiles but with a maximum near 600 m. A reduction in the scaled radial wind component was also found in the rear sector compared to the remaining two. This coincided with a change in the slope of the total wind profile, and suggested a possible reduction in surface roughness in this region. Preliminary results of Powell (2007) found a reduction in aerodynamic drag for the rear sector as wind speeds increased. It is noted that in this study the rear sector contained 217 profiles, which was nearly half of that contained in the other two sectors. The rear sector was characterized by Wright et al. (2001) as having relatively small-wavelength waves (150–200 m), which are less developed than those typically found in the right- and left-front sectors. The orientation of the wave field is along the mean flow, which may contribute to a smoother effective roughness and the differences between the composite profiles of the rear sector compared to the other regions (Wright et al. 2001; Black et al. 2007). A constant 200-m inflow layer was again observed for each sector. It is also noted that the influence of storm translation likely contributes to the observed asymmetries.

Fig. 6.

GPS sonde composite of (a) total wind and (b) tangential (right) and radial (left) component profiles for storm-relative sectors. The number of GPS sondes included in each composite is provided in parentheses.

Fig. 6.

GPS sonde composite of (a) total wind and (b) tangential (right) and radial (left) component profiles for storm-relative sectors. The number of GPS sondes included in each composite is provided in parentheses.

d. GPS sonde wind speed maxima

The underlying GPS sonde profiles were examined in an effort to understand the characteristics of low-level wind maxima, which have been observed in individual profiles (Franklin et al. 2003; Kepert 2006a,b; Schwendike and Kepert 2008). Absolute wind speed maxima within the HBL of individual profiles were prevalent within the dataset as nearly 40% of all GPS sondes contained a peak wind speed below 500 m (Table 3). The height of the absolute wind speed maximum exhibited a radial dependence; profiles with a maximum below 200 m were typically confined to small radii, near and radially inward from the surface RMW (Fig. 7). Maxima that occurred between 200 and 500-m altitudes were typically confined to within 100-km radius whereas maxima found above 500 m were well distributed radially.

Table 3.

Wind maxima summary statistics for all GPS sonde MBL wind groups.

Wind maxima summary statistics for all GPS sonde MBL wind groups.
Wind maxima summary statistics for all GPS sonde MBL wind groups.
Fig. 7.

Plan/storm-relative view of GPS sondes and the altitudes of their associated wind speed maxima (Zmax) for (a) unscaled radius with range rings (dashed) representing intervals of 20 km and (b) radius scaled by the estimate of the surface RMW with range rings (dashed) representing intervals of 1.0. Azimuthal sectors (solid) are denoted in 30° intervals.

Fig. 7.

Plan/storm-relative view of GPS sondes and the altitudes of their associated wind speed maxima (Zmax) for (a) unscaled radius with range rings (dashed) representing intervals of 20 km and (b) radius scaled by the estimate of the surface RMW with range rings (dashed) representing intervals of 1.0. Azimuthal sectors (solid) are denoted in 30° intervals.

The peak wind speed observed in each profile was scaled by the MBL wind speed as well as the layer mean of the lowest 150 m of the profile (WL150; Franklin et al. 2003). It is noted that an eyewall or outer-vortex adjustment was not subsequently applied to the WL150 values. The scaled wind maxima exhibited a radial dependence as large values were typically confined to large radii with a decrease in magnitude found toward the center (Figs. 8 and 9). The mean ratios of the absolute maximum wind speed to the MBL wind and WL150 were found to be 1.19 and 1.24, respectively. Extreme low-level wind maxima (<200-m altitude) appear to be confined to the eyewall region with a large percentage (68%) located radially inward from the surface RMW. Maxima in this region only deviated from the MBL wind speed by 10% or the WL150 layer mean by 15%. The departures from the layer mean were similar to that observed in near-surface gust factors by Schroeder et al. (2009) within high-resolution observations of landfalling tropical cyclones. Mean gust factor values were found to decrease with no change in upstream roughness as the radial distance from the tropical cyclone center decreased. The similarity between the two observations in vastly different surface roughness environments indicated that tropical cyclone dynamics may influence the relative momentum available for vertical transport to the surface and the near-surface wind gust characteristics. The result also suggests that an upper bound for the magnitude of expected gusts may be determined for individual landfalling cyclones through observations of the mean vertical wind profile.

Fig. 8.

GPS sonde absolute wind speed maxima (a) normalized by MBL wind speed and (b) WL150 wind speed shown as a function of splash radius. A least squares linear fit is provided (solid). The mean ratio of the absolute wind speed maximum (Umax) to the MBL and WL150 (dashed) is shown for the radius groups. Error bars represent ±1 std dev from the mean for each group.

Fig. 8.

GPS sonde absolute wind speed maxima (a) normalized by MBL wind speed and (b) WL150 wind speed shown as a function of splash radius. A least squares linear fit is provided (solid). The mean ratio of the absolute wind speed maximum (Umax) to the MBL and WL150 (dashed) is shown for the radius groups. Error bars represent ±1 std dev from the mean for each group.

Fig. 9.

Same as Fig. 8, but shown as a function of radius scaled by the surface radius of maximum winds.

Fig. 9.

Same as Fig. 8, but shown as a function of radius scaled by the surface radius of maximum winds.

Although this general result is tied to the reduction in scaled magnitude of the wind maximum, one cannot assume the absolute wind speed maximum within a GPS sonde profile is representative of the mean profile. A variety of scales are likely superimposed upon the underlying mean profile. The distribution of the scaled magnitudes of observed wind maxima is provided in Fig. 10 for maxima normalized by MBL wind and WL150. The largest perturbation from the WL150, within 80-km radius, was 1.87. This large value was found within a GPS sonde released within the right storm-relative sector (splash storm-relative azimuth of 77°) at 9-km radius from the center and slightly inward from the estimated surface RMW (R/Rmax = 0.85) of Hurricane Irene (1999). The absolute wind maximum was found at 601 m with an MBL wind speed of 47.7 m s−1. The peak in wind speed was associated with a 4.2 m s−1 updraft and the feature may be a result of convective elements; however, it is noted that a statistically significant relationship between the magnitude of scaled wind maxima (scaled by MBL wind and WL150) and its accompanying vertical velocity was not found. The sonde was launched from an U.S. Air Force Reserve reconnaissance mission; therefore, no radar data were available to investigate possible mesoscale features that may have contributed to the significant perturbation (Marks and Black 1990; Black and Marks 1991; Wakimoto and Black 1994; Willoughby and Black 1996; Stewart and Lyons 1996; Stewart et al. 1997; Hasler et al. 1997; Kossin and Schubert 2001; Braun 2002; Kossin et al. 2002; Montgomery et al. 2002; Marks et al. 2008). It is noted that Hurricane Irene during this time was undergoing a brief period of rapid intensification (Avila 1999). The peak perturbation from the MBL wind within 80-km radius was 1.61 and was associated with an absolute wind speed maximum at nearly 2-km altitude and 7-km radius during Hurricane Bonnie (1998).

Fig. 10.

Probability distributions for (a) Umax normalized by MBL wind and (b) Umax normalized by WL150. A fitted lognormal distribution is provided (gray). Bin size is 0.05.

Fig. 10.

Probability distributions for (a) Umax normalized by MBL wind and (b) Umax normalized by WL150. A fitted lognormal distribution is provided (gray). Bin size is 0.05.

4. Characteristics of hurricane vertical wind profiles at landfall

The VAD technique resulted in a large number of vertical wind profiles from 14 landfalling tropical cyclones. A similar composite technique was used to examine the mean structure of the vertical wind profile. Observations were again stratified by several variables in order to understand the influence of storm-relative position.

a. Overland composite profiles

The derived VAD profiles were grouped by MBL wind speed using 10 m s−1 bin sizes. The relatively small number of profiles (compared to GPS sondes) warranted a larger bin size. Differing from the composite technique for GPS sondes, wind speeds were scaled by the MBL wind in order to mitigate the influence of differing surface roughness conditions between radar sites. Below 30 m s−1, the composite profiles increased throughout the depth of the profiles, while the higher mean wind speed profiles exhibited a relatively constant magnitude above 300 m. A pronounced low-level wind speed maximum was not evident in any of the composite profiles (Fig. 11a). The radial and tangential composite profiles were examined and a maximum was evident in the tangential wind near 300 m for the 40–50 m s−1 MBL wind group (Fig. 11b).

Fig. 11.

VAD composited profiles for (a) total wind profiles and (b) tangential and radial wind profiles for MBL wind speed groups. Error bars represent ±1 std dev from the mean. The number of VAD profiles included in each composite is provided in parentheses.

Fig. 11.

VAD composited profiles for (a) total wind profiles and (b) tangential and radial wind profiles for MBL wind speed groups. Error bars represent ±1 std dev from the mean. The number of VAD profiles included in each composite is provided in parentheses.

b. Mean structure and radial dependence

The VAD profiles were stratified according to radius (scaled radius), similar to the GPS sondes, using the quartiles of the distribution of radii (scaled radii). The smaller number of VAD profiles allowed for a subjective eyewall or outer-vortex classification, which was assigned to each based on an examination of the associated reflectivity field. This was considered impractical for GPS sondes given the larger number of profiles.

Similar to the GPS sonde composite profiles, a clear low-level wind speed maximum was evident within the total wind speed and tangential wind component for the representative composite VAD profile within 40-km radius (Fig. 12). The composite profiles provide insight into the evolution of the vertical wind profile, as it transitioned from a well-mixed shape at large radius to a more pronounced low-level wind speed maximum with the approach of the eyewall and the surface RMW. When examined within a scaled radial framework, the jetlike feature was far less evident within total and tangential wind speed profiles (Fig. 13). The differences were to be expected given that K2001 and Kepert (2006a,b) have shown that the wind profile has some dependence upon storm size through the shape of the radial wind profile. The radial stratification likely includes this influence whereas it is removed when scaled by the surface RMW. The composite profiles of the radial wind component were similar in shape to the GPS sonde composite profiles; however, variability in magnitude was present and standard deviations were quite large (not shown). The variance was likely due to the variability in the roughness characteristics from one radar site to another, as well as the significant nonhomogeneity of the underlying terrain characteristics within individual VAD horizontal domains. The roughness characteristics of open-ocean conditions associated with GPS sondes are far more homogeneous. Additional variability was likely associated with small errors in the associated storm track and spline fits (Willoughby and Chelmow 1982).

Fig. 12.

VAD composite profiles for (a) total wind, and (b) tangential and (c) radial components for VAD profiles stratified by radius. Error bars represent ±1 std dev from the mean. The total number of VAD profiles used in the composite is provided in parentheses.

Fig. 12.

VAD composite profiles for (a) total wind, and (b) tangential and (c) radial components for VAD profiles stratified by radius. Error bars represent ±1 std dev from the mean. The total number of VAD profiles used in the composite is provided in parentheses.

Fig. 13.

As in Fig. 12, but for VAD profiles stratified by the ratio of radius to surface radius of maximum winds (R/Rmax). The total number of VAD profiles for each composite is provided in parentheses.

Fig. 13.

As in Fig. 12, but for VAD profiles stratified by the ratio of radius to surface radius of maximum winds (R/Rmax). The total number of VAD profiles for each composite is provided in parentheses.

c. Mean structure, eyewall/outer vortex structure, and azimuthal dependencies

Composite profiles were also generated for eyewall and outer-vortex subjective stratifications as well as for onshore or offshore flow regimes (Fig. 14). Both eyewall profiles for on- and offshore flow contained a low-level wind speed maxima near 350-m altitude, whereas the outer-vortex profiles showed a nearly logarithmic increase in wind speed through the depth. Interestingly, the eyewall/offshore regime yielded a slightly stronger scaled wind speed maximum relative to the eyewall/onshore grouping. The eyewall/offshore flow regime also maintained a higher slope in the lowest portion of the profile, which is likely due to frictional effects associated with a large overland fetch. The result differed from that observed using the scaled radial stratification to produce the composites. This was driven by a large number of eyewall profiles from Hurricane Ike (2008), in which the surface RMW was located radially outward from the eyewall reflectivity maximum. The two outer-vortex profiles were similar above 200 m but with differences in the slope of the lower portion of the profile. Once again, this was likely a result of the differences in overland fetch between the two flow regimes. Unfortunately, evaluating the representative roughness regime of a VAD profile proved to be quite difficult. Within the 5-km domain, significant terrain changes were often present, likely leading to a variety of transitional flow regimes and internal boundary layer development (IBL) within the volume sampled by the WSR-88D. Discontinuities were noted within individual VAD profiles, which were similar to what would be expected from a neutrally stratified IBL development and growth pattern (Arya 1988; Simiu and Scanlan 1996). A detailed investigation of these profile kinks relative to the underlying terrain was not attempted. Estimates of roughness length, using a least squares log-linear fit to the 50–200-m layer, departed significantly from those estimated using aerial photography (Wieringa 1992). The lack of data below 50 m also likely degraded the estimate as IBL influences may occur below this altitude (Echols and Wagner 1972; Howard 2004). Generally, the surrounding terrain of most radar sites was characterized as suburban or forested, with occasional open farmland. The primary exception is the Key West, Florida (KBYX), radar site in which the VAD domain fell entirely over water.

Fig. 14.

VAD composite wind speed profiles for subjective eyewall/outer-vortex stratification and by on- and offshore flow regime. Error bars represent ±1 standard deviation from the mean. The total number of VAD profiles included in each composite is provided in parentheses.

Fig. 14.

VAD composite wind speed profiles for subjective eyewall/outer-vortex stratification and by on- and offshore flow regime. Error bars represent ±1 standard deviation from the mean. The total number of VAD profiles included in each composite is provided in parentheses.

d. VAD wind speed maxima

Absolute wind maxima within the VAD profiles were examined to determine if a similar relationship was found between the maxima and radial distance. As was found within GPS sondes, the scaled magnitude of the absolute wind speed maximum typically increased with larger radial distance (Fig. 15) with a simple linear trend explaining 60% of the variance. A similar relationship was also present when the absolute maxima were presented as a function of a scaled radius. The relationship between the height of the wind speed maximum and radial distance was influenced by the relatively coarse resolution of the VAD wind profiles compared to that of GPS sondes. The lowest absolute maxima were typically found near and radially inward from the surface RMW. However, the passage of rainband features at relatively large radii (well removed from the RMW) produced low-level wind maxima as low as 300 m. There were also instances in which maxima were found below 500 m at significant distances radially outward from the RMW. The variability was possibly associated with dynamical processes, as described by K2001, where a more flattened radial wind profile may lead to a jetlike feature extending well away from the RMW. Although low-level wind maxima described in this study cannot be directly compared to the jet features discussed by K2001, KW2001, Kepert (2006a,b), and Schwendike and Kepert (2008), the mechanisms described influence the wind profiles shown in this study.

Fig. 15.

VAD profile absolute wind speed maxima (Umax) normalized by MBL wind speed shown as (a) a function of radius and (b) radius scaled by the surface radius of maximum winds. The respective least squares linear fits are shown (solid) and the mean for the VAD profiles is grouped by radius and scaled radius (dashed). Error bars represent ±1 std dev from the mean.

Fig. 15.

VAD profile absolute wind speed maxima (Umax) normalized by MBL wind speed shown as (a) a function of radius and (b) radius scaled by the surface radius of maximum winds. The respective least squares linear fits are shown (solid) and the mean for the VAD profiles is grouped by radius and scaled radius (dashed). Error bars represent ±1 std dev from the mean.

5. Conclusions and discussion

A large dataset of vertical wind profiles from tropical cyclones was assembled in an effort to understand the general characteristics and evolution of the wind profile. Over 1000 GPS sondes and 330 VAD wind profiles were included in the analysis. The VAD technique described by Browning and Wexler (1968) was adapted to produce a vertical profile of the lowest 1500 m by altering the domain and binning Doppler velocities by height. Both composite and individual profiles were examined.

The information obtained from this study provides insight into the changes in the vertical wind profile with radial distance and storm-relative azimuth. The diagram shown in Fig. 16 describes the general evolution of the vertical wind profile over water moving toward the cyclone center. Observations supported a general compression of the boundary layer, as discussed by K2001. The wind maximum within each composite profile was simply the peak expected near the top of a neutrally stratified boundary layer. Storm-scale, mesoscale, and convective-scale influences were likely smoothed through the composite profile statistics; however, these scales of motion influence individual profiles. The composite overwater wind profiles also contained an azimuthal asymmetry, as the wind speed maximum was located at a lower altitude within the rear storm-relative sector.

Fig. 16.

Schematic summarizing the general change in the mean vertical wind profile and the ±1 std dev envelope with respect to radial distance for a typical tropical cyclone. The eye is on the left with increasing radial distance to the right. The mean wind profile is scaled by MBL wind speed. Also denoted is the typical inflow layer depth observed by GPS sondes.

Fig. 16.

Schematic summarizing the general change in the mean vertical wind profile and the ±1 std dev envelope with respect to radial distance for a typical tropical cyclone. The eye is on the left with increasing radial distance to the right. The mean wind profile is scaled by MBL wind speed. Also denoted is the typical inflow layer depth observed by GPS sondes.

The VAD-derived landfall composite profiles are similar to the composite GPS sonde profiles and showed a transition from a well-mixed profile to a defined low-level wind speed maximum near the eyewall and radius of maximum winds. The lowest absolute wind speed maxima at landfall are typically found radially inward from the surface RMW. The similar results from two vastly different roughness regimes argue that tropical cyclone dynamics govern the general reduction in boundary layer depth and the height of the mean wind speed maximum. However, it is likely a combination of frictional effects and dynamical processes that shape the vertical profile and its asymmetries. Hirth et al. (2012) has provided limited observational evidence that the low-level wind maximum limits IBL growth and overwhelms the influences of transitional flow regimes. The influences of land are quite evident given the difference in the slope of the VAD composite wind profiles in the offshore and onshore flow regimes. Unfortunately, the difficulty in assessing the true roughness characteristics captured within a VAD profile did not allow for evaluation of the influence of terrain on the shape of the profile.

Upon investigation of individual GPS sonde and VAD profiles and their scaled wind speed maximum, an interesting relationship was found. The magnitude of the scaled wind maxima exhibited a decrease with decreasing radial distance. The largest departures from the MBL wind speed were found at large radii, well removed from the surface RMW. Below 500 m, the wind speed maximum is captured within the layer below 500 m; however, a more steeply sloped profile would still result in a large perturbation from the scaled maximum. The result provides a link to the results of Schroeder et al. 2009, who found extreme near-surface gust factors (>2.0) were confined to large radii (where mean wind speeds were minimal), with a reduction in magnitude toward the tropical cyclone center. Applying the hypothesis put forth by PVR2003 that the wind speed maximum aloft represents the upper bound of the surface gust magnitudes, the reduction in the relative momentum available for vertical transport at small radii near the RMW may contribute to the reduction in gust factors. Well outside the RMW at large radii, the available relative momentum associated with the wind maximum is much larger; therefore, larger surface gust factors are found. These are of little importance within the engineering community as they occur within a relatively low wind speed environment and the probability of such gusts exceeding minimum structural design standards is very low. The results may also explain the rarity of “extreme” surface wind gusts, which are often mentioned within the historical literature. For engineering applications, the presence of low-level wind maxima below 200 m may not be of concern within minimum structural design standards, as their magnitudes do not significantly depart from expected gust envelopes for prescribed wind profiles based on coastal exposure classifications (American Society of Civil Engineers 2007).

The GPS sonde dataset employed for this study was comprehensive and contained profiles from extremely high wind environments (MBL wind > 60 m s−1); sondes were relatively well distributed radially and azimuthally. The continued deployment of GPS sondes by reconnaissance aircraft will allow for more refined stratifications. Unfortunately, data are still lacking at landfall. The total number of VAD profiles generated was comparable to the number of GPS sondes used by PVR2003 and Vickery et al. (2009) but the maximum wind speed within the entire dataset was only 56 m s−1. The need persists for collecting profiles in higher wind speed conditions (MBL wind > 50 m s−1). Additional effort is also needed to understand the representative roughness lengths of VAD profiles and how the dynamical processes that influence the altitude and magnitude of the low-level wind maximum interact with changes in underlying terrain conditions. This is vital to engineering interests as well as hurricane wind field, numerical weather prediction, and risk modeling applications. The limitations described here continue to provide support for continued observational campaigns of tropical cyclones over water and especially at landfall. The coupling of near-surface high-resolution wind speed observations with GPS sonde and radar datasets can provide a more detailed evaluation of the asymmetries described within this study. The collection of such coordinated datasets can also shed light on the dependency of near-surface wind gusts on the structure of the vertical wind profile.

Acknowledgments

Financial support for the various data collection and analysis efforts has been provided by the National Science Foundation (Grant DGE-0221688,) Department of Energy (Cooperative Agreement DE-FG36-06G0B6092), and the Wind Science and Engineering Research Center and Department of Geosciences at Texas Tech University. Special thanks are extended to Russell St. Fleur, Nick Carrasco, and Sonia Otero at NOAA/AOML/HRD for assistance with the GPS dropwindsonde database and H*Wind as well as the men and women of NOAA/Aircraft Operations Center, NOAA/AOML/HRD, and the U.S. Air Force, who collected the GPS sonde data used in this study. The authors thank Dr. Tanya Brown and Dr. Brian Hirth for their review and suggestions to help improve the manuscript’s readability. We also acknowledge two anonymous reviewers for their constructive comments, which have helped improve the manuscript and the presented analyses.

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