1. Introduction
There have been numerous documented cases of quasi-concentric multiple wind field maxima in a variety of vortex configurations observed at finescale (i.e., with horizontal data spacing 100 m or less) with mobile Doppler radars. Figure 1 illustrates an outstanding example of prominent triple-maxima Doppler azimuthal wind and reflectivity fields collected by the Doppler-on-Wheels (DOW; Wurman et al. 1997) radar during VORTEX 95 (Rasmussen et al. 1994). The triple-maxima rotational couplets in the Doppler velocity signatures of vortex circulations of different scales were distinguished by three inbound (negative value toward the radar) and three outbound (positive value away from the radar) Doppler velocity peaks located at different radii from the signature centers. There was evidence of smaller areas of strong rotation (black circle) with spatial scales of a few hundred meters surrounded by the vortex signature of a medium-sized rotational velocity couplet (blue circle), which in turn was embedded in a larger background, convergent velocity couplet (white curves). Mean rotational velocity maxima of the smaller-, medium-, and larger-scale rotational couplets, respectively, were 49, 41, and 31 m s−1 (Fig. 2). The estimated diameters of the couplets, respectively, were 185, 1438, and 2316 m (separated by the black, white, and gray arrows) at a constant range of 4 km from the DOW radar.
DOW scans of (a) storm-relative Doppler velocity (m s−1) and (b) reflectivity (dBZ) of the Wheeler, TX, tornado at 0027:45 UTC 9 Jun 1995 during VORTEX 95. (a) Positive (negative) Doppler velocities represent flow away from (toward) the radar. The zero Doppler velocity band represents flow perpendicular to the radar viewing direction. The tornado center (black circle) was located 4.05 km from the radar. At an elevation angle of 1.0°, the beam altitude at this range was 71 m AGL. The black, dashed range rings are marked every 1 km, with dashed spokes provided every 30° in azimuth. The black and blue circles, respectively, represent the approximate vortex sizes of the smaller- and medium-scale vortex circulations. The white curves outside the blue circle denote a possible Doppler velocity couplet of a larger scale but weak vortex circulation pattern. Doppler velocity and reflectivity scales are indicated at the bottom. Storm motion is 9 m s−1 from 169°. (Radar image courtesy of J. Wurman of the Center for Severe Weather Research.)
Citation: Monthly Weather Review 145, 5; 10.1175/MWR-D-16-0191.1
Azimuthal profile of storm-relative Doppler velocities across the smaller-, medium-, and larger-scale vortex signatures approximately centered at 4 km from the DOW radar at 0027:45 UTC 9 Jun 1995 during VORTEX 95. The dotted curve represents the azimuthal profile of data points. The black and white arrows denote Doppler velocity signatures of smaller- and medium-scale vortex circulations, respectively, as shown in Fig. 1a. The gray arrow indicates the Doppler velocity signatures of a possible larger-scale vortex circulation pattern. At an elevation angle of 1.0°, the beam altitude at this range was 71 m AGL. (Based on Doppler velocity data provided by J. Wurman of the Center for Severe Weather Research.)
Citation: Monthly Weather Review 145, 5; 10.1175/MWR-D-16-0191.1
Many other examples of concentric tangential wind maxima in various vortex circulations were provided in Marquis et al. (2008), Wurman and Kosiba (2013), Wurman et al. (2014), and Wakimoto et al. (2015), among others, which revealed Doppler velocity signatures of vortex circulations of different scales. To the best of the authors’ knowledge, there have been no documented cases of more than three concentric tangential wind maxima in vortices on a spatial scale of 10 km or less.
Atmospheric vortices with dual-maximum tangential winds have been revealed in high-resolution numerical simulations of a near-surface tornado-like vortex centered within a larger-scale background vortex aloft (Wicker and Wilhelmson 1993; Lewellen et al. 2000; Lewellen and Lewellen 2007; Davies-Jones 2008; Lewellen 2014). Lewellen et al. (2000) and Lewellen and Lewellen (2007) noted that vortex surface pressure and wind distributions depended on both the structure of the vortex aloft and the detailed structure of the corner flow region. Additionally, they observed that double-pressure minima responded to the dual-maxima tangential velocities at some low altitudes.
As of this writing, no feasible means exists to obtain pressure measurements within the core flow region of a tornado above the boundary layer (i.e., where the effects of surface friction become vanishingly small). However, many studies have been published in which in situ probes made near-ground pressure and wind measurements within close range of an intercepted tornado (Bluestein 1983; Winn et al. 1999; Markowski et al. 2002; Lee et al. 2004; Samaras and Lee 2004; Wurman and Samaras 2004; Blair et al. 2008; Kato et al. 2015). It is unclear in these cases whether researchers using the probe measurements recognize whether the measurements result from the tornado itself or from the tornado being embedded in the supercell, low-level mesocyclone. Karstens et al. (2010) documented recorded pressure deficits in the pressure time series plots, thereby yielding some clues on how tornado pressure drops varied with time. On the basis of seven of their nine documented cases, the intercepted tornado occurred within the region of the radar-indicated or visually identified mesocyclone. Augmented with videography and eyewitness accounts, Karstens et al. attempted to surmise vortex structure ranging from low-swirl single-celled vortices through medium-swirl two-celled vortices to high swirl-ratio two-celled multiple vortex tornadoes by scrutinizing and interpreting the shape profiles of recorded pressure drops. Nevertheless, it is not possible to untangle the exact contributions of the measurements from the vortices of different scales. It is our belief that the current generation of in situ probes simply lacks the spatiotemporal resolution for researchers to effectively decouple the pressure deficit contribution of the mesocyclone from that of the tornado.
Since the relative contribution of a parent mesocyclone to the low pressure at the center of a tornado is not well understood, a couple of questions are raised:
How do varying radial profiles of dual-maxima tangential velocities of the mesocyclone and tornado affect the total radial pressure profiles?
How much does the mesocyclone surrounding the tornado contribute to a central pressure minimum?
2. Multiple concentric vortex structure
3. Cyclostrophic speed equation for a single vortex
A simple Rankine1 (1882) vortex model supplemented with the cyclostrophic approximation has been widely used by numerous investigators to provide a diagnostic tool for analyzing and interpreting the observed tangential wind and deduced pressure structures in such convective vortices as dust devils (Sinclair 1973), waterspouts (Leverson et al. 1977), tornadoes (Hoecker 1961 among others), and misocyclones (Inoue et al. 2011). Rennó et al. (1998) and Rennó and Bluestein (2001) used the cyclostrophic speed equation to develop a simple thermodynamical theory for dust devils and waterspouts. Compared to the Rankine vortex, the parametrically constructed non-Rankine2 vortices have a larger central pressure deficit (Wood and White 2013).
4. New cyclostrophic schematization for dual concentric vortices
5. A parametric tangential wind model
In this study, we chose a parametric tangential wind model of WW13, not only because of its utility and simplicity but because the one-dimensional model is valid above the tornado boundary layer, where the main swirling flow is not in cyclostrophic balance (Snow 1982; Davies-Jones 1986; Bluestein 2013; Bluestein et al. 2014). The model can be useful for parametrically constructing varying tangential velocities for different applications. Wood and Brown (2011) have shown utility in creating different parametric tangential wind profiles to investigate how the simulated Doppler tornadic vortex signatures might change as the simulated radar scanned azimuthally across the tornado structure consisting of one or two cells. For tropical cyclone applications, Wood et al. (2013) tailored the WW13 model to depict representative surface pressure profiles deduced from single-, dual-, and triple-maximum concentric-eyewall wind profiles associated with the primary (inner), secondary (first outer), and tertiary (second outer) complete rings of enhanced radar reflectivity. Kato et al. (2015) used mean anemometer data of a near-surface, winter tornadic vortex to compare radial profiles of observed tangential winds to those of the Rankine, Burgers–Rott, and WW13 models.
a. Parametric vortex with a single-maximum tangential velocity profile
b. Parametric vortices with dual-maxima tangential velocity profiles
6. Comparative results
This section uses Eq. (11) to construct and superposition two different radial profiles of the tangential winds aloft in the outer-flow region. To provide what each input parameter may be able to deduce about the primary and secondary tangential wind and deduced pressure deficit structures, Table 1 lists the selected input parameter values for creating each Rankine tangential wind profile for the primary and secondary circulations in eight experiments. In each experiment (except for experiment A), we perform comparative cases by varying one parameter (boldface number) while keeping other parameters unchanged in the radial distributions of the tangential velocities. Since these parameters are not known in reality, the input parameters for this and other simulations are purely subjective in order to keep the one-dimensional Rankine and non-Rankine models and a mathematical analysis as simple as possible for the sake of interpretation. In all experiments, the constant 
Profile parameters that produced the radial profiles of the primary (p) and secondary (s) tangential velocities (m s−1) for eight experiments (A–H). The units of vortex radii (
a. Experiment A
A simple experiment A is presented in Fig. 3, wherein a smaller-scale, primary vortex representing a tornado is located at the center of a larger-scale, secondary vortex representing a supercell mesocyclone in the outer-flow region. The radial profiles of Rankine tangential velocity, deduced pressure deficit, and radial pressure gradient force associated with the primary and secondary Rankine vortices are constructed from the selected parameters given in Table 1, via Eqs. (5)–(11), and are plotted in Fig. 3. To represent some of the variety found in nature, we assume a strong tornado characteristic of its maximum tangential velocity (
Radial distributions of (a) primary (
Citation: Monthly Weather Review 145, 5; 10.1175/MWR-D-16-0191.1
Central primary (
We explore the role of the centrifugal force components in Eqs. (7a) and (7b) in the physical behavior of the radial pressure gradient force components in the primary and secondary vortices. The individual centrifugal acceleration components corresponding to the solid curves of 
The magnitudes of three individual centrifugal force terms of 
Assessment of the magnitudes of three individual centrifugal force terms (m s−2) associated with the primary (secondary) Rankine tangential velocity maximum at 
b. Experiment B
What would happen to a central pressure minimum if the mesocyclone experiences increasing intensity from its maximum tangential wind speed of 25–50 m s−1 (magenta, upward-pointing arrow in Fig. 4a)? As expected, an increase in 
As in Fig. 3, but (a) with the magenta arrow showing that 
Citation: Monthly Weather Review 145, 5; 10.1175/MWR-D-16-0191.1
As the mesocyclone’s maximum tangential wind speed is increased, the vertical dashed line separating the region where 
c. Experiment C
Lee and Wurman (2005), Knaff et al. (2011), and WW13 showed that for a given maximum tangential wind speed, a central pressure minimum was relatively insensitive to variations in a single vortex’s vortex radius at which the maximum occurs. This insensitivity raises an important question about whether the vortex radius variation affects a central pressure minimum when a primary (tornado) vortex is centered within a secondary (mesocyclone) vortex. To answer the question, we ran another experiment (experiment C) in which the primary and secondary wind structures remained unchanged (Table 1), except that the secondary vortex is contracted by halving 
As in Fig. 4, but for experiment C. In (b), the vertical dashed line is described in text.
Citation: Monthly Weather Review 145, 5; 10.1175/MWR-D-16-0191.1
d. Experiment D
In the last paragraph, we demonstrated that only the secondary vortex was displaced inward toward the vortex center, while, at the same time, the primary vortex’s position remained fixed. Now, we conduct another experiment (experiment D) by varying both the primary and secondary vortex radii to determine whether the change in vortex radii can impact a central pressure minimum. If we reduce 
As in Fig. 4, but for experiment D.
Citation: Monthly Weather Review 145, 5; 10.1175/MWR-D-16-0191.1
e. Experiment E
Based on proximity radar observations of tornadoes by mobile, high-resolution Doppler radars, the modified Rankine tangential winds approximated the outer radial profiles of tangential velocities outside the radius of maximum winds, using 
Now, we begin to explore the role of the decay parameter (
As in Fig. 4, but for experiment E.
Citation: Monthly Weather Review 145, 5; 10.1175/MWR-D-16-0191.1
f. Experiment F
The azimuthal profile of the Doppler velocities (Fig. 2) reveals that there are two types of tangential velocity segments straddling the velocity maximum: 1) sharply peaked and 2) broadly peaked. To demonstrate how 
As in Fig. 4, but for experiment F.
Citation: Monthly Weather Review 145, 5; 10.1175/MWR-D-16-0191.1
The salient feature in Fig. 8c is the change from the sharply to the broadly peaked profiles of centrifugal forces in association with the transitioning secondary vortex, when compared with Fig. 4c. The vertical dashed line is slightly shifted from 0.9 to 0.75 km. Contributions in calculated percentages and values of 
g. Experiment G
In the examples presented thus far, we have shown how the 
As in Fig. 4, but for experiment G. In (a), a green rectangle illustrating the radial width of the tangential velocity profile straddling the velocity maximum is enlarged for ease of readability.
Citation: Monthly Weather Review 145, 5; 10.1175/MWR-D-16-0191.1
h. Experiment H
In the last several subsections, we have discussed the influences of larger-scale, secondary vortex variability on tornado pressure minima when the primary (tornado) vortex’s wind structure remained unchanged. Now, we reverse all the previous experiments A–G by stating that in another and final experiment H, a strong mesocyclone is now the primary vortex and a weak tornado is now the secondary vortex. In this experiment, we explore the effects of smaller-scale vortex variability on tornado pressure minima when the tornado, as a smaller-scale, separate vortex, is centered within the steady-state mesocyclone. Tanamachi et al. (2013) described the collection of high-resolution, W-band Doppler radar data in a sub-tornado-strength, convective vortex (SCV) that occurred near Prospect Valley, Colorado, on 26 May 2010 as part of the Second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2; Wurman et al. 2012) field campaign. The SCV never attained wind speeds meriting designation as a tornado with a rating of EF0 on the enhanced Fujita (EF) scale (McDonald et al. 2004; WSEC 2006), but its vortex structure and evolution were suggestive of a tornado.
We used combinations of one low-level mesocyclone and two tornadoes having different velocity strengths as listed in Table 1 for experiment H. When the SCV’s tangential velocity peak was assumed to be 20 m s−1 at its vortex radius of 100 m, the radial distributions of the tangential wind and pressure deficit in association with the primary (mesocyclone) and secondary (SCV) vortices, respectively, are indicated by the dashed curves in Figs. 10a and 10b. The red dashed curve showing 
As in Fig. 4, but for experiment H.
Citation: Monthly Weather Review 145, 5; 10.1175/MWR-D-16-0191.1
Suppose that the SCV intensified into an EF0 tornado by increasing its maximum tangential wind up to 30 m s−1 (a magenta upward-pointing arrow in Fig. 10). Figure 10 presents the resultant profiles of tangential wind, centrifugal forces, and central pressure deficits in association with the primary and secondary vortices. The secondary vortex’s central pressure deficit (
The centrifugal force associated with the transitioning secondary vortex is dominant between the vortex center and 
7. Summary and discussion
The Wood and White (2013) parametric tangential wind profile model supplemented with cyclostrophic balance and quasi-steady-state assumptions offers a diagnostic tool for depicting tangential wind and deducing pressure deficit structures in a primary vortex (e.g., tornado) centered within a secondary vortex (e.g., mesocyclone) above the tornado boundary layer. Both vortices were assumed to be concentric and axisymmetric, thus facilitating a simple mathematical analysis using the Rankine and non-Rankine vortex models. The partitioned cyclostrophic wind equations allowed us to analyze and determine the effect of one varying segment of the tangential wind profile on the deduced pressure deficit structures. The main conclusions of this study are as follows:
A change in one key velocity-controlling parameter that shapes a segment of the tangential wind profile in a tornado (mesocyclone) vortex yields significant contributions to the total central pressure deficit, even though the mesocyclone (tornado) vortex’s tangential wind structure remains unchanged.
In all experiments except for experiment H, the largest
Compared to the central pressure minimum of a single tornado alone, a strong tornado centered within a parent mesocyclone has a larger central pressure deficit, as demonstrated in experiments A–G. On the other hand, a weak tornado in its developing stage embedded in a strong, steady-state mesocyclone, as shown in experiment H, experiences a larger central pressure deficit. It is suggested that the “observed” central pressure deficit due to the tornado alone may be overestimated.
The relative contribution of the parent mesocyclone to the low pressure at the center of a tornado is unknown. The significance of this analytical study is that one can isolate the impacts of the mesocyclone and the tornado and thus gain an understanding, in a variety of situations, of the relative contributions of each to the tornadoes’ minimum pressure. Although our parametric model, complemented with cyclostrophic balance and quasi-steady-state assumptions, is quite useful for understanding the variability in the tangential velocity and deduced pressure deficit fields for the given range of input parameters, our knowledge, scrutiny, and interpretation of the effects of larger-scale vortex variability on tornado pressure minima are hypothetical, owing to the limited utility of the parametric model and also to the lack of in situ wind and pressure data from real tornadoes that would be most helpful for validating the model representation. It is recommended that a credible investigation of the model representation be involved using ultra-high-resolution numerical model output (e.g., Lewellen 2014 or Orf et al. 2014) of the wind and pressure fields in a tornado-like vortex centered within a larger-scale, background vortex.
Accurate estimates of real-life tornado wind speed maxima and pressure minima are very difficult to measure, owing to several factors. First, estimates of near-ground wind speed peaks and central pressure minima provided by Davies-Jones and Kessler (1974) are based mostly on damage and thus have a large degree of uncertainty given known complexities in estimating wind speed from damage (Doswell et al. 2009). Second, the tornadoes change intensity quite rapidly and are not necessarily steady state (Bluestein 2013). Also, they have very complex structures such as wind asymmetries, multiple vortices, vortex breakdown, turbulent boundary layers, transient phenomena, etc. Additional pressure deficits could be associated with a different vortex structure, deviations from cyclostrophic balance (e.g., Wakimoto et al. 2003), or, to a lesser extent, to the asymmetric part of the wind field (e.g., Bluestein et al. 2003). Wurman et al. (2007) showed that different tornadoes have substantially different tangential wind field structures, and both the horizontal and vertical distributions of winds differed markedly from one tornado to another. Third, the sampling frequency of deployable instrumentation within close range of an intercepted tornado (e.g., Karstens et al. 2010) was critical in determining whether true pressure deficit and maximum winds were captured. High (low) frequencies in the sampling would sufficiently (inadequately) resolve the characteristics of a tornado if the tornado’s vortex passed directly over or nearby in situ probes. It is possible that some pressure deficit measurements were erroneous as a result of instrument damping and sluggish responses (Davies-Jones 1986), because pressure gradients were very intense in tornadoes. Fourth, surface roughness can make recorded velocity and pressure data near the intercepted tornado more difficult to interpret, because surface roughness significantly affected the near-surface turbulence (Karstens et al. 2010). Fifth, it is not straightforward to surmise thee vortex structure by scrutinizing the shape profiles of recorded pressure drops, because the current generation of in situ probes, in our belief, is lacking the spatiotemporal resolution for investigators to effectively disengage the exact pressure deficit contributions from vortices of different scales.
Anemometers are typically mounted in an exposed position and are therefore more susceptible to failure in tornadoes than are hardened barometers that measure the dynamic pressure change due to airflow. State-of-the-art observing systems with the capacity to measure the surface layer of intercepted tornadoes typically measure pressure (e.g., Bluestein and Golden 1993; Schroeder and Weiss 2008; Weiss and Schroeder 2008; Karstens et al. 2010) rather than wind speed, with a few exceptions (Bedard and Ramzy 1983; Wurman et al. 2012). The same is true of current instruments that take meteorological measurements at heights corresponding to the intercepted tornadoes’ inertial layer [e.g., drones, radiosondes, pitot tubes on aircraft; Elston et al. (2015)]. As a result, formulating any analytical or parametric model (such as the one in this study) in terms of pressure field allows for more straightforward comparison with anticipated future observations.
Acknowledgments
The authors appreciate the constructive comments on an earlier version of the manuscript provided by Qin Xu, Corey Potvin, and Louis Wicker of NSSL and Alan Shapiro of University of Oklahoma. Rodger Brown, Don Burgess, Chris Karstens, and Jeff Snyder of NSSL; Howie Bluestein, Brian Fiedler, and Jerry Straka of University of Oklahoma; and Dave Lewellen of West Virginia University provided helpful discussion. The authors are grateful to Joshua Wurman of the Center for Severe Weather Research for offering us his DOW radar images illustrated in Fig. 1. The DOW radars are an NSF Lower Atmospheric Observing Facility supported by NSF-AGS-1361237. Our study is independent of this NSF grant. The authors also acknowledge Yvette Richardson of The Pennsylvania State University and the three anonymous reviewers of this manuscript, whose comments and suggestions greatly improved its scope and clarity.
REFERENCES
Bedard, A. J., Jr., and C. Ramzy, 1983: Surface meteorological observations in severe thunderstorms. Part I: Design details of TOTO. J. Climate Appl. Meteor., 22, 911–918, doi:10.1175/1520-0450(1983)022<0911:SMOIST>2.0.CO;2.
Blair, S. F., D. R. Deroche, and A. E. Pietrycha, 2008: In situ observations of the 21 April 2007 Tulia, Texas tornado. Electron. J. Severe Local Storms Meteor., 3 (3). [Available online at http://www.ejssm.org/ojs/index.php/ejssm/article/view/39.]
Bluestein, H. B., 1983: Surface meteorological observations in severe thunderstorms. Part II: Field experiments with TOTO. J. Climate Appl. Meteor., 22, 919–930, doi:10.1175/1520-0450(1983)022<0919:SMOIST>2.0.CO;2.
Bluestein, H. B., 2013: Severe Convective Storms and Tornadoes: Observations and Dynamics. Springer-Verlag, 456 pp.
Bluestein, H. B., and J. H. Golden, 1993: A review of tornado observations. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 319–352.
Bluestein, H. B., W.-C. Lee, M. Bell, C. C. Weiss, and A. L. Pazmany, 2003: Mobile Doppler radar observations of a tornado in a supercell near Bassett, Nebraska, on 5 June 1999. Part II: Tornado-vortex structure. Mon. Wea. Rev., 131, 2968–2984, doi:10.1175/1520-0493(2003)131<2968:MDROOA>2.0.CO;2.
Bluestein, H. B., J. B. Houser, M. M. French, J. C. Snyder, G. D. Emmitt, I. PopStefanija, C. Baldi, and R. T. Bluth, 2014: Observation of the boundary layer near tornadoes and in supercells using a mobile, collocated, pulsed Doppler lidar and radar. J. Atmos. Oceanic Technol., 31, 302–325, doi:10.1175/JTECH-D-13-00112.1.
Brooks, E. M., 1949: The tornado cyclone. Weatherwise, 2 (2), 32–33, doi:10.1080/00431672.1949.9930047.
Burgers, J. M., 1948: A mathematical model illustrating the theory of turbulence. Adv. Appl. Mech., 1, 197–199.
Davies-Jones, R. P., 1986: Tornado dynamics. Thunderstorm Morphology and Dynamics, 2nd ed. E. Kessler, Ed., University of Oklahoma Press, 197–236.
Davies-Jones, R. P., 2008: Can a descending rain curtain in a supercell instigate tornadogenesis barotropically? J. Atmos. Sci., 65, 2469–2497, doi:10.1175/2007JAS2516.1.
Davies-Jones, R. P., and E. Kessler, 1974: Tornadoes. Weather and Climate Modification, W. N. Hess, Ed., John Wiley and Sons, 552–595.
Doswell, C. A., III, H. E. Brooks, and N. Dotzek, 2009: On the implementation of the enhanced Fujita scale in the USA. Atmos. Res., 93, 554–563, doi:10.1016/j.atmosres.2008.11.003.
Elston, J., B. Argrow, M. Stachura, D. Weibel, D. Lawrence, and D. Pope, 2015: Overview of small fixed-wing unmanned aircraft for meteorological sampling. J. Atmos. Oceanic Technol., 32, 97–115, doi:10.1175/JTECH-D-13-00236.1.
Hoecker, W. H., Jr., 1961: Three-dimensional pressure pattern of the Dallas tornado and some resultant implications. Mon. Wea. Rev., 89, 533–542, doi:10.1175/1520-0493(1961)089<0533:TPPOTD>2.0.CO;2.
Inoue, H. Y., and Coauthors, 2011: Finescale Doppler radar observations of a tornado and low-level misocyclones within a winter storm in the Japan Sea coastal region. Mon. Wea. Rev., 139, 351–369, doi:10.1175/2010MWR3247.1.
Karstens, C. D., T. M. Samaras, B. D. Lee, W. A. Gallus Jr., and C. A. Finley, 2010: Near-ground pressure and wind measurements in tornadoes. Mon. Wea. Rev., 138, 2570–2588, doi:10.1175/2010MWR3201.1.
Kato, R., and Coauthors, 2015: Analysis of the horizontal two-dimensional near-surface structure of a winter tornadic vortex using high-resolution in situ wind and pressure measurements. J. Geophys. Res. Atmos., 120, 5879–5894, doi:10.1002/2014JD022878.
Knaff, J. A., C. R. Sampson, P. J. Fitzpatrick, Y. Jin, and C. M. Hill, 2011: Simple diagnosis of tropical cyclone structure via pressure gradients. Wea. Forecasting, 26, 1020–1031, doi:10.1175/WAF-D-11-00013.1.
Kosiba, K. A., P. Robinson, P. W. Chan, and J. Wurman, 2014: Wind field of a nonmesocyclone anticyclonic tornado crossing the Hong Kong International Airport. Adv. Meteor., 2014, 597378, doi:10.1155/2014/597378.
Lee, J. J., T. M. Samaras, and C. R. Young, 2004: Pressure measurements at the ground in an F-4 tornado. 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 15.3. [Available online at https://ams.confex.com/ams/11aram22sls/techprogram/paper_81700.htm.]
Lee, W.-C., and J. Wurman, 2005: Diagnosed three-dimensional axisymmetric structure of the Mulhall tornado on 3 May 1999. J. Atmos. Sci., 62, 2373–2393, doi:10.1175/JAS3489.1.
Leverson, V. H., P. C. Sinclair, and J. H. Golden, 1977: Waterspout wind, temperature, and pressure structure deduced from aircraft measurements. Mon. Wea. Rev., 105, 725–733, doi:10.1175/1520-0493(1977)105<0725:WWTAPS>2.0.CO;2.
Lewellen, D. C., 2014: Local roughness effects on tornado dynamics. 27nd Conf. on Severe Local Storms, Madison, WI, Amer. Meteor. Soc., 15A.1. [Available online at https://ams.confex.com/ams/27SLS/webprogram/Paper254357.html.]
Lewellen, D. C., and W. S. Lewellen, 2007: Near-surface intensification of tornado vortices. J. Atmos. Sci., 64, 2176–2194, doi:10.1175/JAS3965.1.
Lewellen, D. C., W. S. Lewellen, and J. Xia, 2000: The influence of a local swirl ratio on tornado intensification near the surface. J. Atmos. Sci., 57, 527–544, doi:10.1175/1520-0469(2000)057<0527:TIOALS>2.0.CO;2.
Markowski, P. M., J. M. Straka, and E. N. Rasmussen, 2002: Direct surface thermodynamic observations within the rear-flank downdrafts of nontornadic and tornadic supercells. Mon. Wea. Rev., 130, 1692–1721, doi:10.1175/1520-0493(2002)130<1692:DSTOWT>2.0.CO;2.
Marquis, J., Y. Richardson, J. Wurman, and P. Markowski, 2008: Single- and dual-Doppler analysis of a tornadic vortex and surrounding storm-scale flow in the Crowell, Texas, supercell of 30 April 2000. Mon. Wea. Rev., 136, 5017–5043, doi:10.1175/2008MWR2442.1.
McDonald, J. R., G. S. Forbes, and T. P. Marshall, 2004: The enhanced Fujita (EF) scale. Preprints, 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 3B.2. [Available online at http://ams.confex.com/ams/pdfpapers/81090.pdf.]
Orf, L., R. B. Wilhelmson, L. J. Wicker, B. D. Lee, and C. A. Finley, 2014: Genesis and maintenance of a long-track EF5 tornado embedded within a simulated supercell. 27th Conf. on Severe Local Storms, Madison, WI, Amer. Meteor. Soc., 3B.3. [Available online at https://ams.confex.com/ams/27SLS/webprogram/Paper255451.html.]
Rankine, W. J. M., 1882: A Manual of Applied Physics. 10th ed. Charles Griff and Co., 663 pp.
Rasmussen, E. N., J. M. Straka, R. Davies-Jones, C. A. Doswell III, F. H. Carr, M. D. Eilts, and D. R. MacGorman, 1994: Verification of the Origins of Rotation in Tornadoes Experiment: VORTEX. Bull. Amer. Meteor. Soc., 75, 995–1006, doi:10.1175/1520-0477(1994)075<0995:VOTOOR>2.0.CO;2.
Rennó, N. O., and H. B. Bluestein, 2001: A simple theory for waterspouts. J. Atmos. Sci., 58, 927–932, doi:10.1175/1520-0469(2001)058<0927:ASTFW>2.0.CO;2.
Rennó, N. O., M. L. Burkett, and M. P. Larkin, 1998: A simple thermodynamical theory for dust devils. J. Atmos. Sci., 55, 3244–3252, doi:10.1175/1520-0469(1998)055<3244:ASTTFD>2.0.CO;2.
Rott, N., 1958: On the viscous vortex of a line vortex. Z. Angew. Math. Phys., 9, 543–553, doi:10.1007/BF02424773.
Samaras, T. M., and J. J. Lee, 2004: Pressure measurements within a large tornado. Eighth Symp. on Integrated Observing and Assimilation for Atmosphere, Oceans, and Land Surface, Seattle, WA, Amer. Meteor. Soc., 4.9. [Available online at https://ams.confex.com/ams/84Annual/techprogram/paper_74267.htm.]
Schroeder, J. L., and C. C. Weiss, 2008: Integrating research and education through measurement and analysis. Bull. Amer. Meteor. Soc., 89, 793–798, doi:10.1175/2008BAMS2287.1.
Sinclair, P. C., 1973: The lower structure of dust devils. J. Atmos. Sci., 30, 1599–1619, doi:10.1175/1520-0469(1973)030<1599:TLSODD>2.0.CO;2.
Snow, J. T., 1982: A review of recent advances in tornado vortex dynamics. Rev. Geophys. Space Phys., 20, 953–964, doi:10.1029/RG020i004p00953.
Tanamachi, R. L., H. B. Bluestein, M. Xue, W.-C. Lee, K. A. Orzel, S. J. Frasier, and R. M. Wakimoto, 2013: Near-surface vortex structure in a tornado and in a sub-tornado-strength, convective-storm vortex, observed by a mobile, W-band radar during VORTEX2. Mon. Wea. Rev., 141, 3661–3690, doi:10.1175/MWR-D-12-00331.1.
Wakimoto, R. M., and J. W. Wilson, 1989: Non-supercell tornadoes. Mon. Wea. Rev., 117, 1113–1140, doi:10.1175/1520-0493(1989)117<1113:NST>2.0.CO;2.
Wakimoto, R. M., H. V. Murphey, D. C. Dowell, and H. B. Bluestein, 2003: The Kellerville tornado during VORTEX: Damage survey and Doppler radar analyses. Mon. Wea. Rev., 131, 2197–2221, doi:10.1175/1520-0493(2003)131<2197:TKTDVD>2.0.CO;2.
Wakimoto, R. M., N. T. Atkins, K. M. Butler, H. B. Bluestein, K. Thiem, J. Snyder, and J. Houser, 2015: Photogrammetric analysis of the 2013 El Reno tornado combined with mobile X-band polarimetric radar data. Mon. Wea. Rev., 143, 2657–2683, doi:10.1175/MWR-D-15-0034.1.
Weiss, C. C., and J. L. Schroeder, 2008: StickNet—A new portable, rapidly deployable, surface observing system. 25th Int. Conf. on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology, New Orleans, LA, Amer. Meteor. Soc., P4A.1. [Available online at https://ams.confex.com/ams/88Annual/techprogram/paper_134047.htm.]
Wicker, L. J., and R. B. Wilhelmson, 1993: Numerical simulation of tornadogenesis within a supercell thunderstorm. The Tornado: Its Structure, Dynamics, Prediction, and Hazards, Geophys. Monogr., Vol. 79, Amer. Geophys. Union, 75–88.
Winn, W. P., S. J. Hunyady, and G. D. Aulich, 1999: Pressure at the ground in a large tornado. J. Geophys. Res., 104, 22 067–22 082, doi:10.1029/1999JD900387.
Wood, V. T., and R. A. Brown, 2011: Simulated tornadic vortex signatures of tornado-like vortices having one- and two-celled structures. J. Appl. Meteor. Climatol., 50, 2338–2342, doi:10.1175/JAMC-D-11-0118.1.
Wood, V. T., and L. W. White, 2013: A parametric wind–pressure relationship for Rankine versus non-Rankine cyclostrophic vortices. J. Atmos. Oceanic Technol., 30, 2850–2867, doi:10.1175/JTECH-D-13-00041.1.
Wood, V. T., L. W. White, H. E. Willoughby, and D. P. Jorgensen, 2013: A new parametric tropical cyclone tangential wind profile model. Mon. Wea. Rev., 141, 1884–1909, doi:10.1175/MWR-D-12-00115.1.
WSEC, 2006: A recommendation for an enhanced Fujita scale (EF-scale). Wind Science and Engineering Center Tech. Rep., Texas Tech University, 15 pp + 3 appendixes. [Available online at http://www.depts.ttu.edu/nwi/Pubs/FScale/EFScale.pdf.]
Wurman, J., 2002: The multiple-vortex structure of a tornado. Wea. Forecasting, 17, 473–505, doi:10.1175/1520-0434(2002)017<0473:TMVSOA>2.0.CO;2.
Wurman, J., and S. Gill, 2000: Finescale radar observations of the Dimmitt, Texas (2 June 1995), tornado. Mon. Wea. Rev., 128, 2135–2164, doi:10.1175/1520-0493(2000)128<2135:FROOTD>2.0.CO;2.
Wurman, J., and T. Samaras, 2004: Comparison of in-situ pressure and DOW Doppler winds in a tornado and RHI vertical slices through 4 tornadoes during 1996-2004. 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 15.4. [Available online at https://ams.confex.com/ams/11aram22sls/techprogram/paper_82352.htm.]
Wurman, J., and C. R. Alexander, 2005: The 30 May 1998 Spencer, South Dakota, storm. Part II: Comparison of observed damage and radar-derived winds in the tornadoes. Mon. Wea. Rev., 133, 97–119, doi:10.1175/MWR-2856.1.
Wurman, J., and K. Kosiba, 2013: Finescale radar observations of tornado and mesocyclone structures. Wea. Forecasting, 28, 1157–1174, doi:10.1175/WAF-D-12-00127.1.
Wurman, J., J. Straka, E. Rasmussen, M. Randall, and A. Zahrai, 1997: Design and deployment of a portable, pencil-beam, pulsed, 3-cm Doppler radar. J. Atmos. Oceanic Technol., 14, 1502–1512, doi:10.1175/1520-0426(1997)014<1502:DADOAP>2.0.CO;2.
Wurman, J., C. R. Alexander, P. Robinson, and Y. Richardson, 2007: Low-level winds in tornadoes and potential catastrophic tornado impacts in urban areas. Bull. Amer. Meteor. Soc., 88, 31–46, doi:10.1175/BAMS-88-1-31.
Wurman, J., D. Dowell, Y. Richardson, P. Markowski, E. Rasmussen, D. Burgess, L. Wicker, and H. Bluestein, 2012: The Second Verification of the Origins of Rotation in Tornadoes Experiment: VORTEX2. Bull. Amer. Meteor. Soc., 93, 1147–1170, doi:10.1175/BAMS-D-11-00010.1.
Wurman, J., K. Kosiba, P. Robinson, and T. Marshall, 2014: The role of multiple-vortex tornado structure in causing storm researcher fatalities. Bull. Amer. Meteor. Soc., 95, 31–45, doi:10.1175/BAMS-D-13-00221.1.
The Rankine vortex consists of a core of solid-body rotation surrounded by an outer region of potential flow, where the tangential wind is inversely proportional to the distance from the center of circulation.
In contrast to the Rankine vortex, the “non-Rankine vortex” may be defined as a viscous vortex that exhibits a smooth transition between solid-body rotation and potential flow, which encompasses the annular zone of the velocity maximum, resembling the Burgers–Rott (Burgers 1948; Rott 1958) tangential velocity profile.