1. Introduction
Hail is responsible for most severe storm-induced property and agricultural loss each year in the United States and elsewhere, producing up to $10 billion (U.S. dollars) per year in the United States alone (Gunturi and Tippett 2017). Supercell storms (those with a column of rotation—a mesocyclone—that is broadly collocated with the storm’s updraft) are argued to be responsible for a majority of severe hail observed in the United States, especially for the largest sizes [≥2-in. maximum dimension (1 in. ≈ 2.5 cm); e.g., Thompson et al. 2003; Duda and Gallus 2010; Blair et al. 2011; Smith et al. 2012; Blair et al. 2017; Murphy et al. 2023]. Such attribution to supercells and much of what is known about severe hail–producing storms has been possible because of ground reports, especially in the United States. However, these reports have unique limitations, including providing only a minimal assessment of the overall severe hail fall associated with a storm (i.e., reporting from primarily population-dense regions), timing errors commonly up to ±5 min, and an underestimation of hail size or otherwise large uncertainty of up to 1-in. in maximum dimension due to reliance on reference objects in reporting practices and delayed measurement of hailstones after they have reached the surface and begun melting (e.g., Kelly et al. 1985; Witt et al. 1998b; Doswell et al. 2005; Ortega et al. 2009; Allen and Tippett 2015; Blair et al. 2017). As a result, the use of remote sensing to better understand and estimate severe hail occurrence, maximum hail size, and broader storm characteristics has increased during the past several decades.
Radar observations have been the primary remote sensing tool used to study severe hail events and storms. Early work using single-polarization radar revealed a broad relationship between radar reflectivity (Z, which depends on particle concentration, size, and liquid water content) and hail size, as well as the maximum altitude (absolute and/or above the environmental 0°C level) reached by high-Z (≥45 dBZ) radar echoes (e.g., Donaldson 1958, 1959; Geotis 1963; Mather et al. 1976; Dye and Martner 1978; Waldvogel et al. 1979). While most early studies used an echo-top Z threshold of 45 dBZ, more recent work has suggested that even higher thresholds (e.g., 50 dBZ) may be more successful at estimating the maximum hail size in a storm (e.g., Donavon and Jungbluth 2007). Although broad relationships exist between Z and hail size, there is no direct relationship due to complex backscattering behavior for hail sizes approaching or exceeding the radar wavelength, which depends on radar wavelength, hail size, and the amount and distribution of liquid water on/within hailstones. These effects are often referred to as resonance scattering, which is unfortunately common in volumes containing severe hail (e.g., Herman and Battan 1961; Atlas and Wexler 1963; Bohren and Battan 1982; Aydin et al. 1984; Kumjian et al. 2018; Jiang et al. 2019). Beginning shortly after the establishment of operational radar networks in the United States and elsewhere, various metrics involving vertical integration of Z have been used for hail size estimation, with varying success. These include vertically integrated liquid (VIL) and VIL density (e.g., Amburn and Wolf 1997; Edwards and Thompson 1998), and the severe hail index (SHI) and the maximum expected size of hail (MESH) relationships based on it (e.g., Witt et al. 1998a; Murillo and Homeyer 2019). Such approaches aim to assess the abundance of large scatterers aloft as an indication of maximum hail size expected at the ground and are a popular choice for radar-based climatologies of severe hail occurrence in the United States (e.g., Cintineo et al. 2012; Murillo et al. 2021; Wendt and Jirak 2021).
The emergence of operational Doppler radar observations in the 1990s led to novel kinematic techniques for estimating hail size. Most studies leverage the Doppler (or radial) velocity, with derived metrics including storm-top divergence (e.g., Witt and Nelson 1991; Boustead 2008) and midaltitude rotation inferred from azimuthal shear (primarily between the 0° and −30°C isotherms—the hail growth region; e.g., Witt 1998; Blair et al. 2011; Witt et al. 2018; Gutierrez and Kumjian 2021) being the most documented indicators of maximum hail size. In particular, both metrics have been shown to broadly increase in magnitude with increasing hail size, indicating an increase in the strength of the updraft (storm-top divergence) and mesocyclone (midaltitude rotation) within supercell storms. Such relationships between storm-top divergence and midaltitude rotation with hail size are broadly consistent with the strong updrafts required to support hailstone residence time in the hail growth region, especially for increasingly large hailstones (e.g., Johnson and Sugden 2014; Gutierrez and Kumjian 2021), and increasing likelihood of favorable pathways for growth (e.g., Kumjian et al. 2021), respectively.
More recently, the operational implementation of polarimetric (dual-polarization) radar, which was completed in the United States in 2013, has enabled hail size estimation from a suite of polarimetric variables. Combination of radar reflectivity at horizontal polarization (ZH) and differential radar reflectivity (ZDR, the difference between reflectivity at horizontal and vertical polarizations) for hail size estimation, often in the form of a composite hail differential reflectivity (HDR), has been most common (e.g., Aydin et al. 1986; Hubbert et al. 1998; Depue et al. 2007; Kumjian and Ryzhkov 2008; Picca and Ryzhkov 2012; Murillo and Homeyer 2019). Notably, ZDR often decreases to values near or below 0 dB with increasing hail size (and ZH), indicating the presence of isotropically scattering particles (tumbling hail, spherical hail, and/or a spectrum of hailstone shapes with various orientations) dominating backscatter in the sample volume, because ZDR is a Z-weighted measure of particle shape. The combination of ZH and specific differential phase (KDP, which provides information on the total mass of nonspherical particles) has also been explored, but exhibits less pronounced/discernible relationships1 given the isotropic scattering behavior of large hail (e.g., Balakrishnan and Zrnić 1990a; Smyth et al. 1999). Finally, the copolar correlation coefficient (ρHV, a measure of the diversity of particles’ ZDR in the sampling volume) has been less studied, but often exhibits a substantial decrease at low- to mid-altitudes with increasing hail size (e.g., Balakrishnan and Zrnić 1990b; Kumjian and Ryzhkov 2008; Picca and Ryzhkov 2012; Witt et al. 2018). Efforts relating polarimetric signatures and hail size have culminated in the development and operational use of hail size discrimination algorithms based on a combination of ZH, ZDR, and ρHV (e.g., Heinselman and Ryzhkov 2006; Ryzhkov et al. 2013; Ortega et al. 2016). However, despite the growing body of radar-based indicators for hail detection and sizing, accurate and/or reliable radar-based estimates of hail size remain an important challenge for the research and operational communities (Allen et al. 2020; Kumjian et al. 2024). This ultimately stems from limitations in the observational constraints used for algorithm development and evaluation (i.e., report quality, sample size, etc.), variations in radar sampling, and complications from resonance scattering and attenuation of the radar beam that enhance storm-to-storm variability. The lack of comprehensive evaluation of all the above radar metrics for a large population of storms also hinders progress.
Supercell physics and dynamics conducive to severe hail growth have been increasingly studied with numerical models over the past several decades. Such studies often leverage convection-resolving simulations (real or ideal) of supercell storms and/or a trajectory model to elucidate hail growth pathways, source locations of hail embryos, and key dynamical features supporting hail growth to various sizes (e.g., Kumjian and Lombardo 2020, and references therein). Recent findings include the importance of the vertical profile of storm-relative wind, which significantly affects the updraft width and mesocyclone structure that can lead to increased maximum hail size (e.g., Nelson 1983; Dennis and Kumjian 2017; Peters et al. 2019; Kumjian and Lombardo 2020; Kumjian et al. 2021), and a more nuanced dependence of maximum hail size on convective available potential energy, or CAPE (Lin and Kumjian 2022).
Complementary studies combining hail size reports from severe weather databases and environmental information from proximity soundings and/or reanalyses have also suggested the importance of the storm-relative wind profiles (e.g., Johnson and Sugden 2014; Gutierrez and Kumjian 2021). Additionally, these studies have found a general increase in maximum reported hail size and CAPE (Edwards and Thompson 1998; Johnson and Sugden 2014; Taszarek et al. 2020), though most of the environmental parameters studied display significant overlap for adjacent hail-size categories. Reporting limitations and biases have been an important constraint on progress in understanding relationships between observed hail size and storm environments (e.g., Allen and Tippett 2015; Blair et al. 2017). Moreover, the vast majority (if not all) of the extensive environmental analyses in past work have focused on relating bulk metrics like CAPE, midlevel temperature lapse rates, 0–6-km wind shear, 0–3-km storm-relative helicity, and others to observed severe hail size. Some studies leverage a combination of these and related fixed-layer measurements as composite parameters to assess hail potential, which have been more successful than evaluation of the bulk metrics alone (e.g., Johnson and Sugden 2014). Thus, improved understanding of hail size and environment relationships may be possible with more comprehensive analysis of the atmospheric profile near well-characterized severe hail storms.
With the increasingly extensive archive of polarimetric and/or Doppler radar data available in the United States and the more recent stability of hail reporting (Allen and Tippett 2015), it is now possible to more comprehensively evaluate radar observations of severe hail–producing storms based on their maximum reported hail size. This study leverages NEXRAD WSR-88D data from 2010 to 2019 to identify common characteristics of thousands of hail-producing supercell storms grouped into three categories based on the maximum hail size they produce: marginally severe (1–1.5-in.), significant severe (2–3-in.), and giant/gargantuan (≥4-in.). This work aims to both reevaluate the utility of radar-based hail size indicators identified in previous studies from much smaller storm populations and reveal new insights into radar-diagnosed storm intensity and structure that can be utilized in the development of future hail size algorithms. Finally, to complement the radar-based analyses, profiles of the near-storm environment are extracted for our large set of well-characterized storms to better understand relationships between maximum hail size and environmental factors such as buoyancy, humidity, and wind shear, and to compare to prior studies.
2. Data and methods
a. GridRad-Severe data
To evaluate radar-observed storm characteristics of hail-producing supercells, we leverage the recently created GridRad-Severe dataset (School of Meteorology/University of Oklahoma 2021). GridRad-Severe is a long-term record of 5-min Gridded NEXRAD WSR-88D Radar (GridRad) data for ∼100 of the most severe events (1200–1200 UTC days) per year in the contiguous United States (CONUS; Murphy et al. 2023). In this study, we employ the first 10 years of the record (2010–19), which consists of 1010 total events. The longitude, latitude, and time bounds of GridRad-Severe events vary to encompass ∼90% of tornado, severe hail, and/or severe wind reports during the event day and the relative annual and spatial distribution of events and reports captured mirrors that for all reported severe weather. Events excluded from the GridRad-Severe dataset are days with mostly isolated reports of one or more types (tornado, hail, or wind) and amount to approximately one-third or less of the annual total severe report data. More detail on event definition can be found in Murphy et al. (2023).
GridRad-Severe radar volumes are created using version 4.2 of the GridRad algorithm (Homeyer and Bowman 2022), resulting in up to 7 variables on a grid with ∼0.02° × ∼0.02° longitude–latitude resolution (48 grid points per degree) and a vertical resolution of 0.5 km for altitudes below 7 km above mean sea level (AMSL) and 1 km for altitudes between 7 and 22 km: ZH, Doppler velocity spectrum width (σV), azimuthal shear of the radial velocity (rotation), radial divergence of the radial velocity, ZDR, KDP, and ρHV. The polarimetric variables (ZDR, KDP, and ρHV) are only available in years 2013 and later, after the completed upgrade of the entire NEXRAD WSR-88D radar network to dual-polarization capabilities, and illustrate important details about the physics of hydrometeors within each storm [including type, shape, size, and concentration; see, e.g., Kumjian (2013a,b,c)]. The remaining variables (ZH and the kinematic fields: azimuthal shear, radial divergence, and σV) are available in all years. The GridRad algorithm is a space- and time-weighted binning procedure that merges all individual NEXRAD WSR-88D radar volumes within an analysis domain. Observations made at shorter distances from a radar and smaller offsets from the analysis time are weighted more in the resulting binned average volumes, thereby reducing offsets in storm observations resulting from timing differences between measurements that commonly introduce artificial storm tilt in single-radar volumes. This is frequently accomplished since most locations are observed by four or more radars, each nominally providing at least two volumes with 14 elevation scans when scanning convection (Homeyer 2014; Solomon et al. 2016; Cooney et al. 2018; Homeyer and Bowman 2021, 2022). Examples of minimal artificial storm tilt in GridRad deduced from comparisons with independent vertically scanning measurements can be found in Homeyer and Kumjian (2015) and Homeyer and Bowman (2022). The derived kinematic fields, azimuthal shear and radial divergence, are computed using a centered differencing method following multiple radial velocity quality-control steps including dealiasing, smoothing, and neighborhood median filtering, which is outlined extensively in Sandmæl et al. (2019) and is similar to the commonly used linear least squares derivative approach (Smith and Elmore 2004). While the horizontal grid spacing of GridRad volumes is somewhat coarse relative to near-range single radar observations, it is finer than the resolved scales of most contributing observations and retains much of their underlying detail (e.g., see Homeyer and Bowman 2022, and references therein).
In addition to the 5-min radar volumes, objective storm tracks from a ZH = 30-dBZ echo-top tracking algorithm [following Homeyer et al. (2017), with revisions to resolve storm splits and mergers as in Lagerquist et al. (2020)] are also available and include severe reports (tornadoes, hail, and wind) from the NOAA Storm Events Database (NCEI/NOAA 2023) matched to the closest storm that falls within 30 km of the report location. The storm tracks follow the local echo-top maximum of each storm, such that any resulting analyses are approximately centered on the storm updraft location. The start and end times of the tracks capture the majority of the storm life cycle—any consecutive period with a 30-dBZ echo top exceeding an altitude of 4 km. Only storms that span at least three consecutive 5-min GridRad analysis times are retained by the tracking algorithm. Storm splits and mergers are identified after the initial tracking process and combined when there is minimal spatial offset and temporal overlap, which routinely blends cyclic supercell updrafts into a singular track. Additional detail on GridRad-Severe storm tracks is available in Murphy et al. (2023). These storm tracks allow for objective identification of severe hail–producing supercell storms and storm motion in this study, which we use for our radar and environmental analyses.
To identify hail-producing supercell storms with differing maximum hail size, we first add several parameters from the GridRad volumes to the storm tracks (considering only observations within 30 km of storm center) and population density estimates valid in the year 2015 from the U.S. Census as captured in version 4 of the Gridded Population of the World database (SEDAC 2018). The GridRad parameters extracted along each storm track are used to objectively identify right-moving supercell storms, following that done previously and well demonstrated to be reliable in Sandmæl et al. (2019) and Homeyer et al. (2020). Thus, left-moving supercells are expected to be excluded from analysis in this study. The only exception to our application of the right-moving supercell classification is that we remove a previously used echo-top criterion to avoid exclusion of shallow supercell storms, which we found to be common in months outside of May–September. That is, we objectively define storms as supercells if the following criteria are met: 1) maximum midlevel (4–7 km AMSL) azimuthal shear > 4 × 10−3 s−1 for at least 40 min, 2) storm-maximum midlevel azimuthal shear ≥ 5 × 10−3 s−1 and column-maximum azimuthal shear ≥ 7 × 10−3 s−1, 3) storm-maximum radial divergence at any altitude ≥ 1 × 10−2 s−1, and 4) storm-maximum σV at any altitude ≥ 13 m s−1.
Once supercells are identified, they are examined for hail reports and retained for analysis if one or more of several conditions are met. Namely, to ensure that hail-producing supercell storms are analyzed at a time when the maximum hail size produced has the best chance of being observed and reported, we employ a population density filter for marginally and significant severe hail–producing storms [after Murillo and Homeyer (2019), ≥25 persons per square mile]. Thus, for marginally and significant severe hail–producing supercell storms, we only consider times at which a storm was over a population-dense location and extract the radar observations for analysis at the time the maximum hail size was reported in such locations, whereas for giant/gargantuan hail–producing supercells, no population density requirement is applied since the category encompasses all hail sizes ≥ 4 in. in maximum dimension. The hail size populations include a size buffer between categories to account for uncertainty in the reports. The resulting number of qualifying severe hail-producing supercell storms identified in the GridRad-Severe data are summarized in Table 1, and their locations are shown in Fig. 1. There are a substantial number of storms identified in each category during both the single-polarization (2010–12) and dual-polarization (2013–19) records, which allows for analysis of all seven radar variables for each storm type. Storm locations are primarily concentrated in the eastern two-thirds of the CONUS, with evident clusters near population-dense regions for the marginally and significant severe hail–producing supercell populations (as required by the methods employed).
The number of hail-producing supercell storms analyzed in this study, sorted by maximum reported hail size linked with each storm and analysis period (2010–12, before the dual-polarization upgrade of the NEXRAD network; 2013–19, after the dual-polarization upgrade; and the total). The fraction of storms in each population that are linked to tornado reports and the maximum EF rating of tornadic storms is also listed.
Maps of analyzed hail-producing supercell storm locations that are (a) marginally severe, (b) significant severe, and (c) giant/gargantuan.
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
b. ERA5
The fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis, ERA5, is used for examination of near-storm environments. ERA5 provides hourly updates of the global atmospheric state on a grid with 31-km horizontal resolution and 137 unevenly spaced vertical model levels (Hersbach et al. 2020). The vertical grid resolution ranges from ∼20 m near the ground up to ∼300 m throughout the middle and upper troposphere. We use model-level data on a 1.0° longitude–latitude grid, extracting profiles from one grid point removed to the east of that most closely coinciding with each storm analyzed and from the closest hour preceding and at least 30 min prior to the storm analysis time. These offsets in profile location and time aim to avoid storm outflow contamination in the lower troposphere, but the results discussed are largely insensitive to the precise near-storm grid profile used (within one grid point). Resultant ERA5 profiles of temperature, pressure, humidity, and winds are then used for analysis.
c. Probability-matched means
To enable identification of the key differences between hail-producing supercell storms of varying maximum hail size, we apply an increasingly used composite mean technique to rotated, storm-centered radar volumes: the probability-matched mean (PMM; Ebert 2001). Our approach is consistent with that recently used in Homeyer et al. (2020), which is summarized briefly here. Namely, PMMs leverage the cumulative distribution of values from individual events (supercell storms in this study) to rescale the smooth composite means such that the range of values typically observed in an individual event are adequately represented. As outlined extensively in Homeyer et al. (2020), the advantage of a PMM is that it helps to highlight common characteristics of an event with values adjusted to match typical instantaneous magnitudes, rather than muted signals and broadening of features common in traditional composite means.
To produce PMMs of each radar variable consistently in this study, we extract volumes at the 5-min radar analysis time closest to the time of maximum reported hail size, centered on the objectively tracked storm location, and rotate the volume so that the 30-min-average storm motion vector is aligned with the positive x dimension. The extracted volumes span a grid 60 km × 60 km wide and encompass all altitudes of the native GridRad column. Since the GridRad vertical grid is in altitude relative to mean sea level, terrain heights from the National Geophysical Data Center (1993) are used to redefine the vertical grid to altitude above ground level (AGL) for analysis, so that all PMMs are made at constant altitudes AGL. Similar to Homeyer et al. (2020), the influence of extremes on the PMM result is reduced by trimming observations below the 0.1th percentile and above the 99.9th percentiles of the complete cumulative distribution of each variable before computing the final cumulative distribution used in the PMM (thereby eliminating outliers in the data that can occur from radar miscalibration, random errors, or artifacts). One step that we introduce in addition to the procedure originally used in Homeyer et al. (2020) is to sample the composite means by the number of times each grid point was observed at each altitude for probability matching of the cumulative distributions, so that the scaled PMM fields are more appropriately representative of an individual storm. In doing so, the area of extreme values decreases slightly, but we expect this approach to be more accurate than treating all grid points in the composite means equally since the number of observations contributing to the mean varies across the storm-relative grid.
Note that extracting the closest 5-min GridRad volume to the report time introduces a timing uncertainty of ±2.5 min in the radar analysis, but the reports typically have a larger time uncertainty of up to ±5 min (Witt et al. 1998b). GridRad data at additional times up to 10 min removed from the report time were also analyzed, but the PMM results were insensitive to such time changes; thus, only those at the 5-min time nearest to the report are shown.
3. Results
In this section, we first present constant-altitude plots at five levels (1, 3, 5, 7, and 9 km AGL) to review the key spatial characteristics (storm-relative location and magnitude) of each radar variable for each population. Following the broad storm-relative analysis enabled by the constant-altitude plots, vertical cross sections of the PMM fields are shown to provide a complimentary perspective of identified distinguishing features and better characterize the vertical structure of the storm types. For each of the PMM analyses, we first examine the traditional single-polarization radar variables that are available for the entire data record (ZH, azimuthal shear, radial divergence, and σV) and subsequently the polarimetric variables available after the upgrade of the radars to dual polarization in 2013 (ZDR, KDP, and ρHV). Finally, to complement the PMM analyses, near-storm environments for each severe hail–producing supercell population from ERA5 are aggregated to determine potential drivers of the observed differences in storm characteristics.
a. PMM constant-altitude plots
For all PMM plots shown in this subsection, the density of hail report locations on the storm-relative grid is superimposed to provide context for the radar signatures and is valid for the entire storm population in each case. ZH is historically the most commonly used radar variable for estimating hail size given its dependence on hydrometeor concentration and size and its ubiquity in historical observations. PMM constant-altitude plots of ZH (Fig. 2) reveal well-established trends of differing hail potential that have been leveraged in algorithms such as VIL and MESH. In particular, two notable characteristics are observed: (i) the magnitudes of ZH increase incrementally with increasing maximum hail size, with storm-maximum PMM values of 59.9 for marginally severe storms, 61.8 for significant severe storms, and 64.8 dBZ for giant/gargantuan hail storms, respectively; and (ii) the volume of high ZH (≥50 dBZ) increases more rapidly with increasing maximum hail size, with values of 720, 1037, and 1711 km3, respectively. The maximum ZH found within the storm-relative domain coincides with storm center aloft (as expected given echo-top storm tracking), but is found increasingly left of storm motion with decreasing altitude, consistent with the common occurrence of a weak-echo region in the storm updraft and the hail fallout or “cascade” (e.g., Browning and Foote 1976). Moreover, the greatest ZH at the lowest altitudes does not coincide with the most common location of largest hail fall, which is centered slightly right of storm motion and rear of storm center in each population [as similarly discussed for gargantuan hail events in Gutierrez and Kumjian (2021) and seen in hail trajectory simulations by Kumjian and Lombardo (2020) and Kumjian et al. (2021)]. Apart from these differences in ZH magnitude and variation with altitude, ZH ≥ 35 dBZ extends farther into the rear of hail-producing supercells with smaller maximum hail sizes (i.e., the lower-left quadrant of plots at 1 and 3 km AGL), indicating an increased extent of heavy precipitation in the rear flank of these storms.
Probability matched composite mean (PMM) constant-altitude plots at (from top to bottom) 9, 7, 5, 3, and 1 km AGL of radar reflectivity at horizontal polarization (ZH) for (left) marginally severe (1–1.5 in.) hail–producing supercell storms, (center) significant severe (2–3 in.) hail–producing supercell storms, and (right) giant/gargantuan (4 + in.) hail–producing supercell storms. Thick black solid contours atop each map show the normalized density of reports on the storm-relative grid and represent values of 30%, 60%, and 90% of the maximum report count after application of 2σ Gaussian smoothing. Thinner “downhill” contours (dashes point toward low values) indicate the number of observations (i.e., storms) contributing to the mean and double starting at 25 (i.e., 25, 50, 100, 200, etc.).
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
PMM constant-altitude plots of azimuthal shear (Fig. 3) reveal the vertical structure and strength of the mesocyclone for each storm population. Regardless of maximum hail size, hail-producing supercell storms are characterized by a deep mesocyclone and mesoanticyclone that are present at nearly all altitudes (evidenced by the circular, highly positive and negative azimuthal shear features near storm center in each plot). Such a mesocyclone–anticyclone couplet at altitudes above 1 km AGL is expected from tilting of environmental low-level horizontal vorticity into the vertical by the storm updraft, with rotation magnitudes that are impacted in part by stretching from strong updrafts and downdrafts (Klemp 1987, and references therein). Consistent with prior work, the midaltitude, specifically 3–7 km AGL here, azimuthal shear magnitude (rotation) within the mesocyclone increases with increasing maximum hail size, with maximum PMM values of 4.74, 4.95, and 6.10 × 10−3 s−1, respectively. In comparison, differences between mesoanticyclone minima are relatively minor. The orientation of the mesocyclone–mesoanticyclone couplet relative to storm motion rotates with increasing altitude, consistent with that found in nontornadic and tornadic supercell PMM analysis by Homeyer et al. (2020) and indicative of the well-known dynamic variations in supercells induced by directional wind shear (e.g., Rotunno and Klemp 1982; Klemp 1987). There are also incremental increases in the area of strong positive rotation within the mesocyclone with increasing hail size, which maximize near 5 km AGL. Namely, areas of values in excess of 1.5 × 10−3 s−1 at 5 km AGL increase from ∼120 km2 in marginally severe storms to ∼130 km2 in significant severe storms to ∼165 km2 in giant/gargantuan storms. Differences in the area of the mesoanticyclone are more variable and less consistent across altitudes, especially at the lowest altitude (1 km AGL) where only a strong mesocyclone is expected in right-moving supercells as a result of baroclinically generated vorticity (Davies-Jones et al. 2001, and references therein).
As in Fig. 2, but for azimuthal shear (rotation).
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
An unexpected result in Fig. 3 is the differing vertical alignment of the mesocyclone between storms with increasing hail size production. In particular, the low-level (1–3 km AGL) mesocyclone is increasingly displaced toward the front of storm center and away from the mid- to upper-level mesocyclone as maximum hail size decreases. This result is also consistent with one of the primary outcomes of the nontornadic and tornadic supercell PMM analysis by Homeyer et al. (2020), where tornadic supercells were characterized by a more vertically aligned mesocyclone than that in nontornadic supercells. Motivated by this similarity, we examined the fraction of each storm population that was also linked with a tornado report at some point in its lifetime (Table 1). Indeed, we find that the frequency of tornadic storms in each population roughly doubles with each increase in maximum hail size for our supercell populations. However, we also find that the severity of tornadoes (maximum EF rating) changes in a less consistent fashion. In particular, while high-end EF2 and greater tornadic storms increase in frequency from marginally to significant severe hail–producing supercells, weak EF0 tornadic storms increase at the expense of EF1 and EF2+ storms for giant/gargantuan hail–producing supercells (potentially facilitated by broadening of the storm updraft and more fortuitous stretching of weaker vortices—see remaining analyses). Regardless, these results convincingly demonstrate that tornadic potential and severe hail potential (size) collectively increase in supercell storms, which is difficult to assess from conventional reports alone given the numerous limitations outlined in section 1.
Radial divergence PMM constant-altitude plots (Fig. 4) reveal the common location of the supercell updraft in each storm population, evidenced by vertically overlapping circular features of strong low- to midlevel convergence and strong upper-level divergence. Given the limitations of single-Doppler velocities, the magnitude of the updraft cannot be reliably detected via radial divergence magnitudes, so we focus solely on the location of this feature. In each storm population, the updraft roughly coincides with the location of hail fall (as expected). There is a slight indication that the low-level updraft (∼1 km AGL) is displaced slightly right of storm motion for the marginally severe hail–producing supercells and that low-level convergence transitions to upper-level divergence at lower altitudes with increasing hail size (suggesting a lowering in the altitude of maximum vertical velocity), which is evidenced by the emergence of a convergence-divergence dipole at 5–7 km AGL. Additional insight into the transition from low-level convergence to upper-level divergence is provided in section 3b. Last, there is increasingly noticeable “background” convergence and divergence at decreasing hail size toward the edges of the storm-relative domain that likely represents contamination from environmental wind shear in the radial divergence observations.
As in Fig. 2, but for radial divergence.
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
The final kinematic field analyzed, σV, is the standard deviation of radial velocity observed within a sample volume by the radars contributing to the GridRad-Severe data. σV is impacted by multiple sources of velocity variance (both speed and direction), including wind shear (especially true at lower altitudes), rotation, diverse hydrometeor fall speeds, and turbulence (Doviak and Zrnić 1993). The magnitude of σV aloft is a good indicator of vertical velocity magnitude given that it is typically dominated by turbulence and storm-driven wind shear near the updraft edge where vertical motion is most efficiently converted to strong horizontal divergence (e.g., Feist et al. 2019). PMM constant-altitude plots of σV for the severe hail–producing supercell storms (Fig. 5) show that the area of large magnitudes (≥5 m s−1) and maximum σV increase with increasing altitude and increasing hail size. Low-level (1–3 km AGL) σV maxima increase approximately 0.35 m s−1 between storm populations, with high-σV areas in giant/gargantuan storms being roughly twice that in the smaller hail size populations (150–210 versus 60–100 km2). Increases in σV magnitude and the area of large values are most noticeable at mid- to upper levels (7–9 km AGL), with incremental changes from marginally severe to giant/gargantuan storms, likely indicating that the vertical velocity increases with increasing hail size (as expected). Finally, low-level enhanced σV is more closely connected to severe hail report locations with increasing size, suggesting that increases in σV due to contamination from extreme hail fall speeds (and horizontal motion) become important at these altitudes.
As in Fig. 2, but for radial velocity spectrum width (σV).
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
The polarimetric variables provide additional insight about distributions of hydrometeor phase, shape, size, and concentration in these supercell storms. ZDR provides a reflectivity-weighted observation of average particle shape, orientation, and phase composition (Kumjian 2013a). PMM constant-altitude plots of ZDR (Fig. 6) exhibit known patterns in hail-producing supercell storms. Namely, a broadly circular ZDR “hole” aloft, where values approach or fall below 0 dB within and near the storm updraft, signifying isotropically scattering particles in hail growth regions, is found in each storm population. At midlevels ∼1 km above the environmental 0°C level (commonly 3–4.5 km AGL here), a ZDR dipole is present, where a circular feature of positive ZDR (>1 dB) representing lofting of supercooled rain drops and/or wet ice particles within the storm updraft–commonly referred to as a ZDR column given its vertical extent (e.g., Kumjian et al. 2014, and references therein)–lies adjacent to the ZDR hole. Also consistent with the tornadic-nontornadic supercell PMM analysis of Homeyer et al. (2020), the ZDR dipole at midlevels is oriented increasingly parallel to storm motion with increasing hail size (and tornado potential). Notably, the ZDR column is least apparent in the PMMs of the giant/gargantuan hail–producing supercell storm population, perhaps impacted by greater hail fall through the updraft in those cases. While differences in the environmental 0°C level could also partly explain these differences, no substantial differences in this level were found between the storm populations analyzed here (see section 3c). Finally, ZDR at low levels (1–3 km AGL) is reduced to near-zero across a greater area and to lower altitudes with increasing hail size. While this is true, the lack of a stronger signal at the lowest altitudes of marginally and significant severe hail storms is somewhat surprising and expected to be muted due to the averaging inherent to GridRad-Severe data creation (i.e., binned averaging).
As in Fig. 2, but for differential radar reflectivity (ZDR).
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
PMM constant-altitude plots of KDP (Fig. 7) appear to provide the least differentiation between the three hail-producing supercell types, consistent with prior work. KDP provides information on the total mass of nonspherical particles, which is especially useful for characterizing rain rates and/or smaller melting hail. Two minor distinguishing characteristics exist in this analysis. First, there is a slight increase (from 2 to 5 km) in the offset between the location of greatest KDP at low levels and hail report density with increasing size, potentially related to decreased melting and shedding of liquid from increasingly large hail stones. It could also indicate greater offsets in the locations of small hail from the large hail captured by the report distributions since the rotational shears (and thus, horizontal advection) increase with increasing maximum hail size. Second, there is an apparent weakening of the KDP column [a signature analogous to the ZDR column; see van Lier-Walqui et al. (2016)] with increasing hail size to the right and rear of storm center from 3 to 5 km AGL.
As in Fig. 2, but for specific differential phase (KDP).
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
Finally, PMM constant-altitude plots of ρHV (Fig. 8) help to assess the diversity of hydrometeor intrinsic ZDR observed by the radars. The largest differences between storm type are found at and below 5 km AGL, where low ρHV (≤0.92) in the hail fall region occupies a larger area with increasing hail size, indicating increasing hydrometeor shape and/or ZDR diversity. This is demonstrated well quantitatively as an increase in low ρHV volume with increasing hail size, which is 52, 67, and 142 km3 in marginally severe, significant severe, and giant/gargantuan hail–producing supercell storms, respectively. Furthermore, the coincidence of minimum ρHV with the densest region of observed hail solidifies its usefulness as an indicator of hail size (at least in a mean sense). The precise relationship has been demonstrated to be a complicated function of resonance scattering, amount of liquid coating on the hail stones, and hailstone shape irregularity in electromagnetic scattering calculations (e.g., Jiang et al. 2019; Mirkovic et al. 2022). Aloft, a ρHV minimum persists mostly rear of storm center in each population, largely revealing mixed-phase or otherwise diverse hydrometeor distributions common to the storm updraft, and is of consistent scale and magnitude across storm type. The appearance of differences among the hail size classes only below 5 km presumably highlights the importance of melting and/or wet hail: adding liquid water to the population of hailstones within the radar sampling volume increases the population’s ZDR diversity, and thus noticeably decreases ρHV, which is sensitive to this diversity (Kumjian 2018).
As in Fig. 2, but for copolar correlation coefficient (ρHV).
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
b. PMM vertical sections
To complement the PMM constant-altitude analysis in section 3a, PMM vertical sections perpendicular (Fig. 9) and parallel (Fig. 10) to storm motion for all radar variables are shown here and help further reveal the detailed vertical structure of key distinguishing storm features. In each case, the vertical sections are taken along the path that intersects the densest storm-relative point of observed hail fall, which is nearly identical across the three storm types. Several of the differences highlighted in the constant-altitude plots are also obvious in these vertical cross sections, especially the variation in vertical alignment and intensity of the mesocyclone (see azimuthal shear row in Fig. 10), the volume of large ZH (contours ≥ 50 dBZ), the coincidence of the storm updraft with hail fall location (columns with strong upper-level divergence and strong low-level convergence), and the strong polarimetric indicators of increasingly large hail descending toward the ground in ZDR and ρHV (low values of each).
Probability matched composite mean (PMM) vertical cross sections perpendicular to storm motion of (from top to bottom) radar reflectivity at horizontal polarization (ZH), azimuthal shear, radial divergence, velocity spectrum width, differential radar reflectivity (ZDR), specific differential phase (KDP), and copolar correlation coefficient (ρHV) for (left) marginally severe (1–1.5 in.) hail–producing supercell storms, (middle) significant severe (2–3 in.) hail–producing supercell storms, and (right) giant/gargantuan (4+ in.) hail–producing supercell storms. Only grid volumes where at least 25% of all contributing storms were observed are shown. Thick black lines within the boxes at the base of each cross section show the normalized density of hail reports on the storm-relative grid where the vertical range spans 0%–100% of the maximum report count after application of 2σ Gaussian smoothing (as in the PMM constant-altitude plots). Sections for marginally severe storms were taken 3 km rear of storm center and, for significant severe and giant/gargantuan storms, 2 km rear of storm center to intersect the location of densest hail reports.
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
As in Fig. 9, but taken parallel to storm motion. The section location is 1 km to the right of storm center for each storm type.
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
One unique outcome from this vertical cross-section analysis is the indication of increasing updraft width from marginally severe to giant/gargantuan hail–producing supercell storms in σV. This is evidenced by the expanding σV “hole” near storm center aloft (a local minimum above 9 km AGL), the extremes at the periphery of which should indicate both turbulence associated with entrainment of environmental air into the storm updraft and transition of strong ascent to horizontal detrainment. From inspection of additional high-altitude constant-altitude plots (not shown), a resulting high-σV “donut” (common at altitudes ≥ 13 km) is approximately 12–15 km wide for giant/gargantuan hail storms, 8–12 km wide for significant severe hail storms, and 5–8 km wide for marginally severe hail storms. The larger inferred updraft widths for the giant/gargantuan hail cases are consistent with the widest simulated updrafts in Peters et al. (2019) and in line with estimates based on BWER areas from gargantuan hail cases in Gutierrez and Kumjian (2021). Vertical sections of radial divergence also support such a conclusion, evidenced by the increasing width of strong vertical gradients from convergence to divergence and width of high (>3 × 10−3 s−1) storm-top divergence. The increasing area and magnitude of storm-top divergence with increasing hail size is consistent with prior work (e.g., Witt and Nelson 1991; Blair et al. 2011). One radial divergence feature that is more evident in the cross-sections (especially right of storm motion in Fig. 9) is the decreasing altitude of the level of nondivergence within the updraft as hail size increases, implying that vertical velocities are maximized at lower altitudes in supercells with larger hail. Finally, increases in maximum PMM echo-top height with increasing hail size are evident in the vertical cross sections. This reflects differences in the distribution of storm-top heights with increasing hail size, where we find that only 29% of 30-dBZ echo-top maxima exceed heights of 15 km AGL in marginally severe hail–producing supercell storms, compared to 41% and 55% in significant severe and giant/gargantuan hail–producing storms, respectively.
c. Storm environments
The carefully curated severe hail–producing supercell populations in this study afford a unique opportunity to explore differences in the near-storm environment that may be helpful to assessing or forecasting maximum hail size potential. Here, we first examine traditional bulk parameters of severe storm environments, then examine related parameters throughout the environmental profile. Given the different population sizes, with the 242 giant/gargantuan storm population being the smallest (Table 1), we collect subsamples for the smaller hail size populations by randomly selecting 242 profiles from each. The results based on these subsamples are broadly consistent with analyses using the full populations (not shown) and this approach is preferred to provide roughly equivalent characterization of the uncertainty for each population and ultimately identify meaningful differences between them.
Figure 11 shows frequency distributions of most unstable CAPE (MUCAPE), 0–6 km AGL bulk wind shear, and precipitable water (PWAT), which are commonly used to assess differences in hail environments. Similar to prior work, there is broad overlap between these distributions with slight indications of increased MUCAPE and increased bulk shear with increasing hail size. These bulk parameters, however, provide limited understanding on how exactly such differences arise (i.e., where in the vertical profile differences are most substantial) and ultimately contribute little-to-no practical utility for forecasting or nowcasting applications.
Frequency distributions of (a) MUCAPE, (b) 0–6 km AGL bulk wind shear, and (c) PWAT from near-storm environments. Tan, pink, and dark blue lines of increasing thickness show distributions for marginally severe, significant severe, and giant/gargantuan hail–producing supercell storm populations, respectively.
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
To best assess where differences exist between environments of supercell storms with differing hail production, we evaluate distributions as a function of altitude in Fig. 12. Temperature, humidity, and wind speeds at all altitudes are mostly identical among the three storm populations (Figs. 12a–c). However, comparing profiles of the difference between most unstable parcel and environmental temperature (Fig. 12d) and storm-relative wind speed (Fig. 12e) leads to the emergence of clear incremental increases in both quantities throughout much of the troposphere with increasing hail size (also consistent with prior work; e.g., see Johnson and Sugden 2014; Taszarek et al. 2020). For example, low-level storm-relative wind speeds for giant/gargantuan cases increase to at least 1 m s−1 greater than other hail-size classes by just a few hundred meters AGL. Such an increase in storm-relative wind speeds in the inflow layer leads to wider updrafts (e.g., Peters et al. 2019). The storm-relative wind profiles also demonstrate that low-level (0–2 km AGL) wind shear is ∼0.5 m s−1 km−1 weaker in the giant/gargantuan hail–producing supercell environment. Substantial overlap in the environments remain for these quantities (as for the bulk parameters), but differences in the hail growth layer (4–10 km AGL or temperatures of ∼0° to −40°C) are most evident. Note that these profiles were also made in altitude relative to the 0°C level, but results found are broadly insensitive to this choice since the altitudes of a given tropospheric isotherm typically differ by ≤1 km in these environments (identifiable in Fig. 12a as the altitude span of the distributions at a select threshold temperature). In summary, the practical use of detailed profile information likely remains as low as the bulk parameters given the overlap between hail size categories.
Vertical profiles of near-storm environments at altitude above ground level. (a)–(e) Profiles of temperature, specific humidity, ground-relative wind speed, the difference between most unstable parcel temperature and the environmental temperature (i.e., potential buoyancy), and storm-relative wind speed, respectively. Solid lines show the average profile, while dashed lines with dotted ends show the 10th–90th-percentile range of values. Colors are as in Fig. 11.
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
Because the environmental ground-relative wind profiles differ little between the supercell storm populations, whereas the storm-relative wind speed shows incremental differences, statistics on the speed and direction of radar-observed storm motion were investigated (Fig. 13). This analysis demonstrates that both the speed and direction of supercell motion change with increasing hail size potential and are the primary source of identified differences in storm-relative wind. There is a slight slowing of storm motion and more rightward (i.e., deviant) motion with increasing hail size, especially at the most extreme hail sizes analyzed. Thus, both elements of storm motion contribute to the incremental increases in storm-relative wind speed found with increasing hail size (and tornado potential), in agreement with recent studies by Bunkers (2018), Gutierrez and Kumjian (2021), Lin and Kumjian (2022), and Bunkers et al. (2022).
As in Fig. 11, but for the (left) speed and (right) direction of storm motion.
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
The final environmental analysis presented here (Fig. 14) is a pair of storm-mean hodographs for each population. One hodograph is calculated based on the u and υ components of the ground-relative environmental wind profiles (Fig. 14a; as done operationally using radiosonde observations or forecast model output) and the other is calculated using the storm-relative u and υ component winds, with the entire profile rotated based on the observed direction of storm motion so that it aligns with the positive x dimension prior to computing the mean (Fig. 14b). Rotating the profiles prior to calculating the average storm-relative component wind speeds ensures that the means represent purely parallel and perpendicular storm-relative flow.2 The greatest differences between supercell environments emerge from this hodograph analysis, particularly using the storm-relative, storm-rotated approach. While conventional hodographs suggest slightly greater 0–3-km directional shear and slightly greater deep-layer speed shear for increasing hail size potential, the storm-relative, storm-rotated hodographs partially de-emphasize the importance of differences in low-level directional shear and more substantially indicate that storm-relative wind speed perpendicular to storm motion above 2 km AGL is an important environmental constraint on hail size. This result is consistent with recent machine learning efforts by Gensini et al. (2021) that identified midlevel storm-relative helicity as the most important environmental predictor of severe hail size.
Mean 0–10 km AGL hodographs of ERA5 winds computed (a) conventionally such that u- and υ-component winds are aligned with the x and y dimensions, respectively, and (b) in magnitude relative to storm motion and rotated prior to computing the mean such that the storm motion vector is aligned with the positive x dimension. Colors indicate the corresponding hail size population, as in Fig. 11, and circles superimposed along each storm profile indicate altitudes every 1 km AGL beginning at 0 km.
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
4. Conclusions and discussion
This study leveraged 10 years of recent NEXRAD WSR-88D data, facilitated by the GridRad-Severe record, to identify distinguishing characteristics of severe hail–producing supercell storms with varying maximum reported hail size. It was demonstrated that increasing hail size is well correlated with increasing volume and maximum altitude of high ZH (Figs. 2, 9, and 10), as has been repeatedly documented for nearly six decades. Further, it was found that supercell storms exhibit increasing midlevel mesocyclone rotation with increasing hail size (Figs. 3 and 9), decreasing ZDR and low ρHV volume at low- to midlevels with increasing hail size (Figs. 6, 8, 9, and 10), and signs of increasing updraft width and strength with increasing hail size (Figs. 9 and 10). These results are in agreement with a growing list of observational and modeling studies and provide additional insight on the discriminatory potential of each radar variable for hail size. In particular, PMM analyses support the use of the following metrics for hail size discrimination:
- 1)The maximum height and volume of the ZH = 50-dBZ echo.
- 2)Mesocyclone rotation magnitude at 3–7 km AGL (∼5° to −25°C).
- 3)Storm-top divergence (area/volume and magnitude).
- 4)The volume of
within ZH ≥ 40 dBZ (especially below the 0°C level). - 5)The volume of ρHV ≤ 0.92 within ZH ≥ 40 dBZ (primarily at altitudes below 5 km AGL).
The larger differences in ZH echo top and volume between hail size categories for a higher ZH threshold than convention (i.e., 50 vs 40 dBZ) suggests that the constraints employed for integrated ZH metrics such as SHI and MESH should be updated to be more restrictive for hail size discrimination. However, to enable the greatest progress, it is imperative that comprehensive severe and nonsevere hail swath datasets be collected to further develop and validate algorithms based on these and similar analyses. Past analyses with such hail swath data (e.g., Blair et al. 2011) have found broad overlap between 50-dBZ echo-top distributions from significant severe and giant/gargantuan hail–producing storms. While clear findings were possible in this study by using strictly and carefully defined maximum hail size categories, the results likely do not enable accurate hail size discrimination across the full spectrum of hail produced in individual storms due to substantial storm-to-storm variability.
This study also uncovered some previously undocumented differences in radar observations of supercell storms and in their near-storm environments (summarized via illustration in Fig. 15). Novel findings of the present study include:
- 1)The increasing vertical alignment of the mesocyclone center with increasing maximum hail size (Fig. 10).
- 2)The identification of a σV minimum near storm center aloft that increases in diameter with increasing hail size (Figs. 9 and 10) and is argued to indicate increasing updraft width.
- 3)The increasing storm-relative environmental wind speed perpendicular to storm motion above 2 km AGL with increasing hail size (Fig. 14b). Note that this differs from the increasing magnitude of deep-layer wind shear with increasing hail size identified in prior work, which was not found in this study.
Summary schematic of the novel results of this study. Black contours are near-surface radar reflectivity, red cylinders indicate mesocyclone structure, blue arrows indicate midlevel storm-relative wind, and green vertical arrows indicate the width and strength of the storm updraft. The maximum hail size produced increases from left to right.
Citation: Monthly Weather Review 151, 10; 10.1175/MWR-D-23-0019.1
These new results suggest that a promising path forward for radar estimation of maximum hail size in supercells is to measure the vertical displacement of the mesocyclone center and width of the σV minimum aloft (potentially through vertically integrated quantities like that commonly done for ZH). Given the somewhat limited coverage of Doppler fields relative to physical quantities (i.e., ZH, ZDR, KDP, ρHV), finite volume scan update times coupled with storm motion, the recent operational decisions to emphasize update time at low levels at the expense of midlevel scans (e.g., Kingfield and French 2022), and quality-control techniques required for quantification of rotation/divergence (i.e., dealiasing), such integrated velocity metrics may be difficult to implement operationally. On the other hand, the diagnosed hodograph differences from the ERA5 near-storm environment analysis in this study can be used immediately for operations. Similar to prior storm environment analyses, we found minor variations in bulk metrics (particularly in MUCAPE; see Fig. 11) and in profiles of most unstable parcel-environment temperature differences and storm-relative wind speed (Figs. 12d,e) between hail size categories. While these results likely offer limited practical use given the broad overlap between supercell storms with varying hail size potential, the greater separation in storm-relative, storm-rotated hodographs is an encouraging finding and differs in nature from the storm-relative wind sensitivities explored in prior work (i.e., increasing storm-relative wind perpendicular to storm motion at low levels or deep-layer wind shear parallel to storm motion; see, e.g., Johnson and Sugden 2014; Dennis and Kumjian 2017; Kumjian and Lombardo 2020). As noted above, substantial differences in deep-layer shear for supercell storms with differing maximum hail size were not found in this study.
While the observational indications of increasing updraft width with increasing hail potential in supercell storms are not unique to this study, the variable tilt of the mesocyclone has (to the authors’ knowledge) not been previously recognized as an important constraint. Such a change in the tilt of the mesocyclone (and therefore, the degree to which it is coincident with the storm updraft) would logically support increasingly favorable growth trajectories for hailstones. Specifically, a vertically aligned mesocyclone collocated with the storm updraft would encourage hailstones to be confined within the most advantageous environment for growth as they ascend (i.e., increasing vertical velocity and high concentrations of supercooled liquid droplets)—envisioned as a “corkscrew” pathway analogous to the “full circuit” large hailstone trajectories in the model simulations of Kumjian et al. (2021). Conversely, a vertically displaced mesocyclone that is not well-aligned with the updraft would result in divergent hailstone trajectories that stymie growth by shortening the hailstone residence time within the updraft (since particles within the mesocyclone and updraft at a lower altitude would exit the mesocyclone as they ascend and be advected horizontally away from the storm updraft, or vice versa). Such a pathway is supported by the near collocation of the most frequent hail fall and the gradient between low- to midlevel mesocyclone and mesoanticyclone in the 1–1.5-in. hail-producing supercell population analyzed here (Fig. 3). It is noted that some degree of artificial storm tilt may be present in the GridRad volumes, since no attempt was made to apply advection correction to the underlying single-radar observations. However, because the differences in storm motion between the differing supercell groups are small and the weighting procedure employed by GridRad aims to mitigate such bias, no one storm population analyzed is expected to be significantly more prone to artificial tilt than another. Thus, we expect this result and other storm differences noted here to be largely insensitive to such bias.
The differing tilt of the mesocyclone is complementary to our recent related work comparing radar observations of tornadic and nontornadic supercells (Homeyer et al. 2020), for which the mesocyclone was demonstrated to be less tilted in tornadic supercells. Given the similarity in result, we examined the frequency that severe hail–producing supercells were also tornadic at some point in their lifetime (Table 1). It was found that supercell storms with increasing maximum hail size exhibited a greater propensity for tornadoes, in agreement with the underlying similarity in mesocyclone tilt changes. This result is uniquely enabled by the GridRad-Severe dataset and methods employed in this study and, perhaps for the first time, enables confident co-evaluation of tornado potential and hail severity within observed supercell storms. The reason(s) for the observed change in mesocyclone tilt between supercell types is (are) not yet known, but the differences in the near-storm environment identified here may be a contributing factor. Further study is needed to elucidate the mechanism(s) responsible.
Our findings motivate several related foci of future work. One of the more obvious pathways moving forward would be a comprehensive evaluation of radar observations and near-storm environments for other hail-producing storm types (e.g., multicell, quasi-linear convective systems). Another immediate path could be similar analyses to that here using radar and environmental data from other countries/regions. As previously mentioned, improving upon the results of this study and ultimately advancing radar-based hail size discrimination requires more comprehensive ground truth of hail-producing storms (supercell or otherwise). Field projects that acquire such data and include a suite of remote sensing systems like polarimetric Doppler radars are desperately needed. Finally, the differences in storm-relative wind profiles found here motivates exploration of the sensitivity of hail production in supercells to the wind speed perpendicular to storm motion aloft using idealized simulations (as recently carried out and documented for CAPE and deep-layer wind shear).
Acknowledgments.
We are thankful for the efforts of A. Murphy and K. Allen toward creating the GridRad-Severe dataset. This work was sponsored in part by NOAA Weather Program Office Joint Technology Transfer Initiative Grant NA20OAR4590356 to the University of Oklahoma. MRK was supported by NSF Grant AGS-1855063 and an award from the Insurance Institute for Business and Home Safety.
Data availability statement.
All of the data used in this study are publicly available. GridRad-Severe data were downloaded from the NCAR Research Data Archive (School of Meteorology/University of Oklahoma 2021). ERA5 output on model levels were downloaded from https://apps.ecmwf.int/data-catalogues/era5/.
Footnotes
An exception is for storms that produce large quantities of small melting hail, which tend to have extremely large ZH and KDP values (Kumjian et al. 2019).
Note that this is different from the rotation relative to the deep-layer shear vector performed in other studies (e.g., Parker 2014; Kumjian et al. 2019; Gutierrez and Kumjian 2021). Owing to the availability of observed storm motions, the approach here assesses what the observed storms experienced in a more interpretable framework (parallel or perpendicular to storm motion).
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