Modifications to the Near-Storm Environment Induced by Simulated Supercell Thunderstorms

Christopher J. Nowotarski Department Atmospheric Sciences, Texas A&M University, College Station, Texas

Search for other papers by Christopher J. Nowotarski in
Current site
Google Scholar
PubMed
Close
and
Paul M. Markowski Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Paul M. Markowski in
Current site
Google Scholar
PubMed
Close
Full access

Abstract

This study investigates the changes that simulated supercell thunderstorms impart on their surroundings. Supercells are simulated in a strongly sheared convective boundary layer comprising horizontal roll vortices. In sensitivity tests, the effects of cloud shading on the near-storm environment are explored through the removal of cloud ice, water, and hydrometeor effects on parameterized radiation. All of the simulated supercells increase the low-level shear in their proximal environment; however, this effect is more pronounced when cloud shading is included. Shading stabilizes the boundary layer beneath the cirrus anvil, diminishes boundary layer rolls and their attendant thermodynamic perturbations, and reduces the intensity of resolved turbulent mixing in the convective boundary layer. Anvil shading also acts to reduce the buoyancy of inflow air and the horizontal buoyancy gradient along the forward-flank outflow boundary.

Corresponding author address: Christopher J. Nowotarski, Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX 77843-3150. E-mail: cjnowotarski@tamu.edu

Abstract

This study investigates the changes that simulated supercell thunderstorms impart on their surroundings. Supercells are simulated in a strongly sheared convective boundary layer comprising horizontal roll vortices. In sensitivity tests, the effects of cloud shading on the near-storm environment are explored through the removal of cloud ice, water, and hydrometeor effects on parameterized radiation. All of the simulated supercells increase the low-level shear in their proximal environment; however, this effect is more pronounced when cloud shading is included. Shading stabilizes the boundary layer beneath the cirrus anvil, diminishes boundary layer rolls and their attendant thermodynamic perturbations, and reduces the intensity of resolved turbulent mixing in the convective boundary layer. Anvil shading also acts to reduce the buoyancy of inflow air and the horizontal buoyancy gradient along the forward-flank outflow boundary.

Corresponding author address: Christopher J. Nowotarski, Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX 77843-3150. E-mail: cjnowotarski@tamu.edu

1. Introduction

There is an ample body of evidence linking aspects of the large-scale environment to the structure and evolution of supercell thunderstorms, particularly as related to tornadogenesis. Present-day skill in forecasting supercells and tornadoes is largely due to advances in our understanding of the dependence of these phenomena on bulk environmental quantities derived from proximity sounding datasets (e.g., Beebe 1958; Darkow 1969; Maddox 1976; Rasmussen and Blanchard 1998; Markowski et al. 2003; Thompson et al. 2003, 2007) and idealized modeling studies (e.g., Weisman and Klemp 1982,1984; McCaul and Weisman 2001; Naylor et al. 2012). There have been limited studies on the effects of mesoscale environmental variability on supercells (e.g., Maddox et al. 1980; Weaver and Purdom 1995; Rasmussen et al. 2000; Ziegler et al. 2010; Davenport and Parker 2015), and even fewer investigating the effects supercells have on their own environments (Brooks et al. 1994; Weisman et al. 1998; Potvin et al. 2010; Parker 2014). In the forecasting context, it is generally desirable to remove storm-induced variability from climatologies so as to improve prediction of hazards based on information about the prestorm environment, undisturbed by the convection itself.

From the perspective of clarifying the physical processes by which supercells and their environments evolve to produce tornadoes, and perhaps even improving short-range forecasts of tornadogenesis in existing storms, mesoscale heterogeneity and storm–environment feedbacks remain important considerations. Toward this end, Nowotarski et al. (2015, hereafter NMRB15) investigated the effects of boundary layer rolls on low-level mesocyclones in simulated supercell thunderstorms. Using the same simulation technique for developing a fully resolved convective boundary layer (CBL) in the simulation domain (Nowotarski et al. 2014, hereafter NMRB14), this study explores potential mechanisms by which supercells may modify their own environments.

Apart from heterogeneity due to airmass boundaries or CBL turbulence, observations (Markowski et al. 1998a; Potvin et al. 2010; Parker 2014) and simulations (Brooks et al. 1994; Weisman et al. 1998) have revealed considerable variability in the bulk thermodynamic and kinematic properties between the far-field and near-supercell thunderstorms, much of which may be induced by the storms themselves. In simulations without radiation, Brooks et al. (1994) showed substantial changes in CAPE and helicity within the near-storm inflow sector of supercell thunderstorms. Weisman et al. (1998) also simulated supercells in a range of homogeneous large-scale environments, with storm-induced decreases in CAPE and increases in hodograph length in closer proximity to all the simulated storms. Notably, the degree of environmental modification was dependent on the large-scale environment, with some modifications extending as far as 60 km from the simulated storms. In a statistical analysis of a climatology of observed proximity soundings, Potvin et al. (2010) largely corroborated the findings of Weisman et al. (1998) in showing that both low-level and deep-layer shear tend to increase in closer proximity to significantly tornadic supercells whereas CAPE decreases. Yet, their results diverged within 40 km of storms where mean shear was found to locally decrease in the Potvin et al. study. They attributed these changes, in part, to the effects of increased low-level inflow winds in the convergent region beneath strong updrafts and anvil shading. Both Potvin et al. (in terms of statistical significance in near-storm versus far-field values) and Weisman et al. (in terms of percent difference) concluded that the storm-induced kinematic modifications were larger than thermodynamic modifications. It remains unknown if similar modifications to bulk environmental properties would be seen in simulations with more realistic physical processes included.

Perhaps our clearest picture of storm-induced modifications to the local environment comes from composite analyses of recent sounding observations from the VORTEX2 field project by Parker (2014). In that study, near-storm CAPE (CIN) within the inflow region was observed to decrease (increase) by approximately 150 (40) J kg−1 close to the storm, which was attributed to shallow surface potential temperature deficits of 0.5–1.0 K beneath the anvil shadow and/or inflow clouds. Large low-level (0–1 km) hodograph differences were observed between the near-storm and far-field environments of all supercells in the study. Despite low-level shear increases near the storm, Parker found that 0–6-km shear was relatively uniform [in contrast to simulations by Weisman et al. (1998) where shear increased over a deep layer in close proximity to supercells]. Interestingly, though nontornadic storms tended to have less favorable far-field environments, and low-level winds were more backed overall, these storms modified their local environment more, such that composite near-storm low-level shear in nontornadic cases was within 10%–20% of that for tornadic supercells.

None of these studies were able to conclusively ascertain the physical mechanisms by which storms modify their environment, particularly the characteristics of boundary layer convection in the vicinity of storms. The aforementioned simulation studies did not include boundary layer convection or anvil-shading effects, and the observation-based studies could not isolate the roles of anvil shading effects from those induced by increased inflow in proximity to the updraft on the vertical wind profile (i.e., accelerations due to the “inflow low”). The organization of boundary layer convection is dictated by the wind profile within the boundary layer, such that storm-induced changes to the ambient wind profile may affect the characteristics of boundary layer rolls near the storm. For instance, inflow accelerations resulting in changes in the direction or magnitude of the boundary layer shear vector might be expected to alter the orientation or structure of boundary layer rolls. Because these rolls (and the environmental heterogeneity associated with them) are driven largely by the convective instability that results from daytime surface heating, it is likely that variations in temperature resulting from the presence of a supercell, particularly its anvil cloud, may affect the evolution of the CBL. Moreover, it is likely that storm-induced effects on boundary layer rolls may vary for differing orientations of rolls relative to the storms.

The radiative influence of cirrus anvils on convective storms and their environments has been the topic of several recent studies in the context of squall lines (Oberthaler and Markowski 2013) and supercells (Markowski et al. 1998b; Markowski and Harrington 2005; Frame and Markowski 2010, 2013). These investigations studied the effects of the low-level cooling that results from decreased shortwave radiation incident on the surface in the anvil shadow. The degree of cooling depends on the optical thickness, size, and ground-relative speed of the anvil, the characteristics of the underlying land surface (e.g., vegetation and soil type, soil moisture), and the amount of time near-surface air parcels are shaded. In the three cases documented by Markowski et al. (1998b), near-surface air temperature deficits beneath supercell anvils of up to 5 K were observed. Smaller temperature deficits developed in the simulations of Markowski and Harrington (2005) and Frame and Markowski (2010, 2013).

Cooling effects of anvil shading have been linked to dynamical aspects of supercell evolution. Markowski et al. (1998b) observed the development of baroclinic zones along the edges of the anvil shadows. They speculated that, along certain trajectories, horizontal vorticity generated within these baroclinic zones could augment inflow parcels’ horizontal vorticity, thereby potentially enhancing mesocyclone rotation. Later simulations, however, dismissed this mechanism; Markowski and Harrington (2005) as well as Frame and Markowski (2010, 2013) found cooling beneath the anvil to be generally detrimental to supercells. In simulations including anvil shading, Frame et al. (2009) found that the shading of the surface led to a reversal of the surface sensible heat flux and cooling of the boundary layer. Frame and Markowski (2013) found that decreased (parameterized) vertical mixing beneath the storm anvil was responsible for increased easterly shear in the inflow, encouraging the rear-flank outflow to undercut the main updraft. The robustness of these findings for more realistic, resolved vertical mixing is tested in this study.

These previous simulation efforts did not explicitly simulate boundary layer rolls and could not determine the effects of anvil shading on a fully resolved CBL. It is the goal of this study to address these limitations and better understand storm-induced modifications to the near-storm environment for two hodographs in which the orientation of the low-level shear vector and boundary layer rolls relative to storm motion differ. This is achieved using three-dimensional, nonhydrostatic numerical simulations using the Cloud Model, version 1 (CM1; Bryan 2002; Bryan and Fritsch 2002), with radiation and land surface parameterizations to simulate supercell thunderstorms in a CBL with resolved boundary layer rolls. In addition to storm-induced modifications of bulk environmental properties through processes such as local inflow accelerations and the direct thermodynamic effects of anvil shading, we also seek to assess the validity of previous findings regarding the effects of suppressed boundary layer vertical mixing on low-level wind shear in anvil-shaded inflow regions when boundary layer turbulence is resolved (not only parameterized on the subgrid scale). Because of their possible effects on supercell evolution (as demonstrated by NMRB15), another objective of this study is to investigate modifications to the structure of boundary layer rolls caused by the supercells for two regimes of roll orientation relative to the storm motion.

Section 2 briefly revisits the methods and experiment design for the simulations. Storm-induced modifications to the environment, including boundary layer convection, are presented and discussed in section 3. Finally, we offer concluding remarks and avenues for future research regarding supercell–CBL interactions in section 4.

2. Methods and experiment design

The results presented here are drawn from the simulations presented in NMRB14; NMRB15). The full details regarding the model configuration and initialization methods are described at length in those works; however, for convenience we provide a short summary here. These simulations were performed using the cloud model CM1, release 15, over a domain spanning 250 km × 200 km × 18 km with 200-m horizontal grid spacing and a vertically stretched grid with a minimum vertical grid spacing of 50 m over the lowest 3 km of the domain and a maximum spacing of 500 m above 8 km. Horizontal boundary conditions are periodic in all directions with rigid lower and upper boundaries. Rayleigh damping was applied above 14 km. In each supercell simulation, deep moist convection was triggered using a warm thermal perturbation of 3 K, and the compressible governing equations were integrated for 2 h with a large time step of 0.75 s and a smaller time step for acoustic wave terms (Klemp and Wilhelmson 1978) of 0.125 s. Single-moment ice microphysics (Lin et al. 1983; Tao and Simpson 1993) is used in all simulations. Parameterizations for shortwave and longwave radiation (Tao et al. 1996; Chou et al. 1998; Chou and Suarez 1999; Chou et al. 1999), and surface fluxes of heat, moisture, and momentum (see Grell et al. 1994) were included in all but the CONTROL simulation.

Two suites of four simulations were performed. Each set has an identical experiment design, with the exception of the base-state hodograph, in order to generate boundary layer rolls that are either perpendicular or parallel to the motion of the right-moving supercell (Fig. 2 in NMRB15). In this way, the sensitivity of the results on both the low-level shear and roll orientation may be explored. The initial base-state thermodynamic sounding and hodographs for both the parallel- and perpendicular-roll simulations are shown in Fig. 1. The members of each set of simulations are as follows:

  • BASE: This simulation is used to develop the CBL to be used as the base state for subsequent simulations of deep convection, as described in NMRB14. It is initialized from a horizontally homogeneous environment with a stable boundary layer including random, weak thermal perturbations. Influences of radiation on the surface fluxes cause a destabilization with time that mimics the morning boundary layer transition. By three hours into the BASE simulation, the random thermal perturbations have evolved into boundary layer rolls in the presence of vertical shear and static instability.

  • CBL FRAD: A supercell is initiated using the restart files from BASE once rolls form (2.75 h into the BASE simulation). This simulation includes full radiation and land surface parameterizations. As the most realistic simulation, CBL FRAD includes both boundary layer rolls and anvil-shading effects.

  • CBL INVRAD: Identical to CBL FRAD, but clouds have been made “invisible” to both long and shortwave radiation such that anvil shading effects are removed. This is accomplished by setting the mixing ratios of liquid and ice phase particles to zero only in the radiation scheme.

  • CONTROL: A supercell is initiated with a horizontally homogeneous base state equal to the average of the BASE simulation at initialization time in the CBL simulations. Because radiation and land surface fluxes are omitted, the base-state temperature, water vapor, and wind profiles are nudged toward the horizontal average base state of the other simulations by adding a constant tendency at each large time step. This tendency is derived from the average change in the BASE simulation over the same time period. More details on the nudging technique are provided in NMRB15. Surface drag is included with a constant drag coefficient (, representative of the average coefficient in the CBL simulations).

Fig. 1.
Fig. 1.

(a) Skew T–logp diagram showing the temperature (red) and dewpoint temperature (green) profiles used to initialize each supercell simulation. Hodographs for the initial BASE (blue) and initial CONTROL, CBL FRAD, and CBL INVRAD (black) simulations in the (b) perpendicular-roll and (c) parallel-roll simulations. Heights are labeled in kilometers on the hodographs. (From NMRB15.)

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

3. Results

a. Anvil shading effects

In this study, the CBL INVRAD and CBL FRAD simulations were designed such that the radiative effects of anvil shading on the underlying CBL might be discerned. The simulated supercells and convection to their north produce large anvil clouds eastward or downstream (relative to the upper-level winds) of the updrafts by 120 min (the end of the simulation period). The mature anvils spread as far as 120 km to the east and, to a lesser extent, westward (upstream) of the convection, as diagnosed by ice water mixing ratio at z = 12 km, the tropopause height (Fig. 2). The maximum ice water mixing ratio at this level is approximately 5 × 10−3 kg kg−1 and is located in the main updraft of the right-moving supercell. Over the greater portion of the anvil, ice water mixing ratios range from ~1 to 2 × 10−3 kg kg−1. The anvils are similar in both the parallel-roll and perpendicular-roll simulations, owing to similar storm-relative upper-level wind profiles; the perpendicular-roll anvils, however, move about 13 m s−1 faster relative to the ground than the parallel-roll anvils. This difference in anvil translation speed is likely to account for some of the following differences in results between simulations.

Fig. 2.
Fig. 2.

Plan view of ice water mixing ratio at z = 12 km (shaded) and vertical velocity greater than 0.5 m s−1 at z = 225 m for the (a) parallel-roll and (b) perpendicular-roll CBL FRAD as well as the (c) parallel-roll and (d) perpendicular-roll CBL INVRAD simulations at 120 min. The 10-dBZ simulated reflectivity contour at z = 1 km is shown in thick black. Axes are labeled in km.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

Beneath the cirrus outflow, incoming shortwave radiation decreases from 800 W m−2 to as little as 50 W m−2 under the optically thickest portion of the anvil to the northeast of the right-moving supercell in the CBL FRAD simulations. Emission from the anvil increases downward longwave radiation at the surface in this region by a maximum of 30 W m−2, which is not enough to offset the large shortwave deficit. The result is a surface soil temperature deficit (relative to the average over a portion of the unshaded environment) of as much as 6 K in the parallel-roll CBL FRAD anvil shadow (Fig. 3a) and 5 K in the perpendicular-roll anvil shadow (Fig. 3b). Potential temperature at the lowest grid level decreases by a maximum of 2.0 K beneath the anvil in the parallel-roll simulation. The perpendicular-roll simulation, owing to a faster ground-relative storm and anvil motion (i.e., a shorter period of shading) has somewhat smaller surface temperature deficits with a maximum deficit of 1.5 K. Observations have found maximum surface temperature deficits beneath supercell anvils ranging from ~1 K (Parker 2014, virtual temperature) to as much as 5 K (Markowski et al. 1998b). The deficits in our simulations are on the small end of this range, which may reflect a noted warm bias in simulated anvil shading (Frame and Markowski 2010; Oberthaler and Markowski 2013)1 or the relatively short simulation duration. Smaller θ deficits in our simulations may also be due, in part, to the relatively low Bowen ratio of the land surface condition. Consequently, we might expect some of the forthcoming effects on the near-storm environment to be amplified in nature, particularly at later stages of storm evolution. In the CBL INVRAD simulations (Figs. 3c,d), a small surface soil temperature deficit (1 K) exists east of the deep convection owing largely to increased surface heat fluxes in the near-storm environment driven by stronger near-surface winds in the storm inflow region, but these surface soil temperature deficits do not cause appreciable cooling of the surface-layer air.

Fig. 3.
Fig. 3.

Plan view of surface soil temperature perturbation (shaded) and at z =25 m (black lines with the −0.5-, −1-, −1.5-, −2-, −4-, −6-, −8-, and −10-K contours shown) for the (a) parallel-roll and (b) perpendicular-roll CBL FRAD as well as the (c) parallel-roll and (d) perpendicular-roll CBL INVRAD simulations at 120 min. The 10-dBZ simulated reflectivity contour at z = 1 km is shown in thick black. Line segments AB and CD represent the plane of vertical cross sections and paths for traces of thermodynamic and kinematic variables in later figures. The boxes represent the area over which near-storm and environment average wind profiles are computed. Axes are labeled in km.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

Vertical cross sections of the CBL across the southern boundary of the parallel-roll anvil shadow (along line segment AB shown in Figs. 3a and 3c) reveal the vertical extent of the anvil shadow’s influence. Potential temperature deficits of ~1 K are evident through the entire depth of the boundary layer in the CBL FRAD simulation (Fig. 4a), but the greatest deficits ( 298 K) are confined to the lowest few hundred meters in closest proximity to the supercell (nearest point B). In the anvil-cooled region, vertical velocity w associated with dry boundary layer convection is greatly diminished compared to the CBL INVRAD simulation (Fig. 4b). Consequently, surface θ and the height of the base of the capping inversion have less horizontal variability within the less turbulent flow beneath the anvil.

Fig. 4.
Fig. 4.

Vertical cross section along line AB in Fig. 3 of w (shaded) and θ [colored contours, 0.5-K increments from 298 (purple) to 305 K (magenta)] for the parallel-roll (a) CBL FRAD and (b) CBL INVRAD simulations at 120 min. Axes are labeled in km.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

Average profiles of potential temperature , moisture, and wind were computed over 10 km × 10 km areas near the storm (northern boxes in Fig. 3) and in the far-field environment (southern boxes in Fig. 3).2 Comparison of over the lowest 3 km confirm the cooling effect of anvil shading in both the parallel-roll and perpendicular-roll simulations (Fig. 5). Away from the storm (solid lines) the CBL FRAD and CBL INVRAD simulations have near-identical profiles of with a superadiabatic surface layer below a neutral mixed layer capped by a strong inversion. Near the storm (dashed lines) the CBL FRAD and CBL INVRAD profiles are similar above 750 m, with some warming of the capping inversion above the mixed layer relative to the far-field environment. Low-level cooling has created a stable surface layer in both CBL FRAD simulations with slightly stronger and deeper cooling in the parallel-roll case (cf. Figs. 5a,b). Despite a shallower depth, the near-storm boundary layer in the CBL INVRAD simulations is nearly identical to the far-field environment. There is not an appreciable difference in boundary layer moisture between the CBL FRAD and CBL INVRAD simulations; anvil shading does not significantly alter the evaporation of surface moisture in these simulations (not shown).

Fig. 5.
Fig. 5.

Vertical profiles of in the boxes near the storm (dashed) and in the far-field environment (solid) shown in Fig. 3 for the (a) parallel-roll and (b) perpendicular-roll CBL INVRAD (red) and CBL FRAD (blue) simulations at 120 min.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

Plan views of surface-based CAPE are shown for each perpendicular- and parallel-roll simulation in Fig. 6. CAPE is generally slightly higher in the far-field environment of the parallel-roll storms, which we attribute to stronger surface winds increasing the surface heat flux for this hodograph (Fig. 1). In both control simulations (Figs. 6a,b) CAPE is relatively homogeneous in the far-field environment, with slight increases and some inhomogeneity in closer proximity to the storm due to storm-induced temperature perturbations at mid- and upper levels. In all the simulations there is significant variability of CAPE in and near the storms’ precipitation regions with minimal CAPE in the cold pools. When boundary layer convection is included, there is notable horizontal inhomogeneity in CAPE in the far-field environment associated with the rolls. In the most realistic cases where anvil shading is included (CBL FRAD), CAPE decreases beneath the storm anvil in the near-storm environment, regardless of hodograph (Figs. 6e,f)

Fig. 6.
Fig. 6.

SBCAPE (J kg−1) at 120 min in the vicinity of the parallel-roll (a) control, (c) CBL INVRAD, and (e) CBL FRAD simulations and the perpendicular-roll (b) control, (d) CBL INVRAD, and (f) CBL FRAD simulations. The gray contour is the 10-dBZ simulated reflectivity 1 km above the surface. Axes are labeled in km.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

To further quantify these trends, CAPE and CIN were also computed along the line segments in Fig. 3 crossing the anvil shadow edge and averaged near the storm and in the far-field environment as reported in Table 1. Surface-based CAPE and CIN are largely similar between the CBL FRAD and CBL INVRAD simulations for each wind profile away from the storm anvil (Fig. 7) with horizontal variations associated with rolls in the environment. Underneath the anvil, CAPE (CIN) is decreased (increased) in the CBL FRAD simulation relative to the CBL INVRAD simulation for both the perpendicular- and parallel-roll simulations. The persistence of horizontal variations in CAPE and CIN is likely due to fluctuations in the capping inversion height, and, to a lesser extent, remaining variations in surface temperature. Between the far-field environment and near the storm, average CAPE decreases by 170 J kg−1 (6%) in the parallel-roll CBL FRAD simulation and 504 J kg−1 (18%) in the perpendicular-roll CBL FRAD simulation. The change in average CAPE in both CBL INVRAD simulations is smaller (5%). Differences in the near-storm CAPE deficit between the perpendicular- and parallel-roll FRAD simulations are likely due to differences in the average depth of the boundary layer cooling within the anvil shadow as well as subsidence warming above the boundary layer between simulations (Fig. 5). CIN increases closer to the storm in all of the simulations, but the magnitude of CIN increase in both CBL FRAD simulations is over twice as large as that in the corresponding CBL INVRAD simulations. The LCL is more than 100 m lower near the storm in both perpendicular-roll and parallel-roll CBL FRAD simulations, with no decrease in the LCL in either CBL INVRAD simulation (not shown). The modifications in CAPE and CIN are somewhat stronger in these simulations than observed by Parker (2014), though modifications to the LCL are similar. The disparity in thermodynamic storm-induced environmental modifications between CBL INVRAD and CBL FRAD suggests that much (though not all) of the near-storm thermodynamic modifications are due to anvil shading.

Table 1.

Average (1 standard deviation, when relevant) thermodynamic and kinematic parameters over regions (defined in section 2a) in the far-field environment and near the storm.

Table 1.
Fig. 7.
Fig. 7.

(a) SBCAPE and (b) SBCIN along line segments AB (CD) from Fig. 3 for the parallel-roll (perpendicular roll) CBL INVRAD and CBL FRAD simulations at 120 min.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

Low-level cooling in the anvil shadow results in a decrease in buoyancy in the near-storm inflow region with detrimental effects on baroclinic generation of horizontal vorticity along the supercells’ outflow boundaries. Without cloud shading, near-surface ( 25 m) inflow is generally neutrally buoyant, with areas of weak positive and negative buoyancy associated with rolls in the CBL INVRAD simulations (Figs. 8a,b). Inflow air is negatively buoyant in both CBL FRAD simulations, but is more negatively buoyant in the parallel-roll CBL FRAD simulations. The result is generally weaker horizontal buoyancy gradients and less baroclinic generation of streamwise horizontal vorticity in the simulations with cloud shading along the forward flank (cf. Figs. 8b,d). In the parallel-roll simulation, the rear-flank baroclinic zone is weaker in the CBL FRAD simulation (Fig. 8c) than the CBL INVRAD simulation (Fig. 8a) owing to a diffuse buoyancy gradient. In the perpendicular-roll simulations, the CBL FRAD and CBL INVRAD rear-flank baroclinic zones are of similar strength, because of both more negatively buoyant outflow in the CBL FRAD simulation (analysis of cold-pool strength differences between simulations is beyond the scope of this study) and weaker cooling and negative buoyancy in the inflow region (when compared with the inflow region of the parallel-roll CBL FRAD simulation).

Fig. 8.
Fig. 8.

Plan views of buoyancy (shaded) and baroclinic generation of horizontal vorticity (black contours at 10−4 s−2 intervals) and storm-relative winds (vectors) at z = 25 m in the parallel-roll (a) CBL INVRAD and (c) CBL FRAD simulations as well as the perpendicular-roll (b) CBL INVRAD and (d) CBL FRAD simulations at 120 min. Axes are labeled in km.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

Because the rolls are driven by thermal instability, anvil-shadow stabilization of the boundary layer (Fig. 5) suppresses boundary layer turbulence. Figure 9 shows the effects of anvil shading on boundary layer vertical velocity w and vertical vorticity ζ. In the parallel-roll CBL FRAD simulation (Fig. 9a) updraft and downdraft magnitudes diminish below 0.5 m s−1 within 10 km north of the anvil shadow boundary. Widespread maxima of ζ 0.01 s−1 found south of the anvil3 do not occur in the region where vertical motion has weakened, and there are no areas of 0.005 s−1 north of y = 70 km (with the exception of vorticity associated with the supercell itself in the upper-left corner of the subdomain). In contrast, boundary layer vertical motions and ζ extrema extend throughout the inflow when cloud shading is not considered (Fig. 9c). Resolved turbulence kinetic energy (TKE) values at z = 225 m are similar in the unperturbed environment of the two parallel-roll CBL simulations (cf. Figs. 10a,c). Nearer to the supercell, average resolved TKE at this level drops by 57% in CBL FRAD but increases by 14% in CBL INVRAD (Table 1). In this case, decreased vertical mixing counteracts the increase in TKE due to stronger perturbation winds due to inflow acceleration in the vicinity of the updraft, leading to a net decrease in resolved TKE. The decrease in resolved TKE in CBL FRAD indicates less vertical mixing of momentum in the anvil shadow, resulting in stronger (+74%) vertical wind shear over the depth of the CBL (Fig. 10b; Table 1). Previous simulation studies found parameterized TKE near this height level (300 m) to decrease to near zero [0.5 m2 s−2, Frame and Markowski (2010); 0.25 m2 s−2, Oberthaler and Markowski (2013)]. The near-storm resolved TKE in these simulations (0.6 m2 s−2) is slightly higher than those results; Oberthaler and Markowski, however, simulated more realistic low-level cooling, whereas Frame and Markowski diagnosed TKE 3 hours into the simulation. Given more time or greater surface cooling, we suspect that resolved boundary layer TKE in our simulations would further decrease to similar levels.

Fig. 9.
Fig. 9.

Plan views of vertical velocity at z = 225 m (shaded) and vertical vorticity at z = 25 m (black contours) at selected intervals of 0.001, 0.005, 0.01, 0.02, 0.03, 0.04, and 0.05 s−1 in the parallel-roll (a) CBL FRAD and (c) CBL INVRAD and the perpendicular-roll (b) CBL FRAD and (d) CBL INVRAD simulations at 120 min. Axes are labeled in km.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

Fig. 10.
Fig. 10.

Turbulent kinetic energy (shaded) at z = 225 m and bulk wind difference over the height of the boundary layer in the boxes shown in Fig. 3 for CBL FRAD (a) unperturbed environment and (b) near the storm, and for CBL INVRAD (c) unperturbed environment and (d) near the storm in the parallel-roll simulations at 120 min. Axes are labeled in km.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

The rolls in the perpendicular-roll CBL FRAD similarly weaken in w and ζ beneath the anvil (Fig. 9b). In the CBL INVRAD simulation (Fig. 9d), the rolls increase in linearity as they approach the storm from the south, but do not otherwise intensify in w or ζ. Unlike the TKE decrease in the parallel-roll case, average TKE increases by 13% near the storm in the perpendicular-roll CBL FRAD simulation; this change, however, is smaller than the 63% increase in TKE near the supercell in the CBL INVRAD simulation (Table 1). With this hodograph, increased TKE in proximity to both storms is due to widespread positive perturbation winds as inflow is accelerated in the vicinity of the updraft, regardless of anvil shading. Though vertical wind shear increases approaching the storm over the depth of the CBL in both perpendicular-roll simulations, the near-storm shear is stronger in the CBL FRAD simulation with anvil shading (Fig. 11, Table 1).

Fig. 11.
Fig. 11.

As in Fig. 10, but for the perpendicular-roll simulations. Axes are labeled in km.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

Storm-relative helicity (SRH) from 0 to 1 km in the inflow is also affected by anvil shading. Approaching the storm along line segments AB and CD (see Fig. 3), 0–1-km SRH is similar between the parallel-roll CBL simulations outside of the anvil shadow (Fig. 12a). Without anvil shading, 0–1-km SRH is generally consistent in the near-storm and far-field environment with the parallel-roll hodograph (Figs. 13a,c). Underneath the anvil, 0–1- km SRH is generally stronger in the CBL FRAD simulation than the CBL INVRAD simulation (Figs. 12a and 13c,e). The amplitude of ~2-km-wavelength fluctuations in SRH is reduced in the CBL FRAD simulation relative to the CBL INVRAD simulation. Interestingly, in all parallel-roll simulations SRH decreases to slightly negative values in the direct vicinity of the storm updraft.

Fig. 12.
Fig. 12.

As in Fig. 7, but for 0–1-km storm-relative helicity for the (a) parallel-roll and (b) perpendicular-roll simulations.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

Fig. 13.
Fig. 13.

As in Fig. 6, but for 0–1-km SRH (m2 s−2).

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

In the perpendicular-roll simulations (Figs. 12b and 13b,d,f), 0–1-km SRH increases in all simulations closer to the supercell, but is more often stronger in the CBL FRAD simulation within the anvil shadow compared with the unshaded CBL INVRAD simulation. In general, when comparing average SRH values near the storm and in the far-field environment both for 0–1- and 0–3-km SRH, the increase is always greater in the simulations with anvil shading and SRH is more uniform (i.e., smaller standard deviation) beneath the anvil (Table 1). The increase in near-storm SRH is notably larger in the perpendicular-roll simulations than the parallel-roll simulations.

b. Near-storm wind profile

Hodographs from the average profiles of the near-storm and far-field environment regions (horizontal averages are computed over the same regions discussed in the previous section) are shown in Fig. 14. In the far field, the hodographs in both CBL simulations for each roll orientation are nearly identical. Near the storm in both the parallel- and perpendicular-roll simulations, the hodographs lengthen over the lowest 3 km, reflecting the increase in ground-relative flow toward the main updraft (located to the northwest of the average hodograph location for both sets of simulations) throughout the depth of the inflow layer. Storm-relative helicity increases closer to the storm in every case, but the largest changes (in terms of percent difference) occur over the 0–1-km layer in the anvil-shaded cases (Table 1). In the parallel-roll CBL FRAD simulation (Fig. 14a), the hodograph lengthens (easterly shear increases) more than in the CBL INVRAD simulation, likely because of the decrease in vertical mixing caused by the anvil shadow. Without anvil shading, continued vertical mixing of higher momentum air from aloft leads to stronger wind speeds at the surface and less vertical shear, particularly below 500 m. Consequently, 0–1- and 0–3-km SRH increase more in the near-storm environment with anvil shading than without it. The increase in hodograph length with anvil shading is less pronounced for the perpendicular-roll simulations (Fig. 14b). In both of these simulations, 0–1- and 0–3-km SRH increase by over 75% and 50%, respectively; there are stronger increases in CBL FRAD SRH, however, because resolved TKE near the storm is weaker than in the CBL INVRAD case. Similar changes in the 0–1-km hodographshape and SRH between the near-storm and far-field environments are also apparent in observations; however, our simulations and those of Weisman et al. (1998) show greater modifications aloft (1–3 km) than observations (Parker 2014). Flow acceleration toward the updraft aloft is consistent with previous work showing that simulated supercell updrafts may ingest air parcels well above the boundary layer (~2–4 km; Nowotarski et al. 2011).

Fig. 14.
Fig. 14.

Average near-storm (dashed) and far-field environment (solid) hodographs over the lowest 3 km for the (a) parallel-roll and (b) perpendicular-roll CBL INVRAD (red) and CBL FRAD (blue) simulations at 120 min. Hodographs are averaged over boxes in Fig. 3.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

c. Reorientation of boundary layer rolls

In close proximity to the simulated supercells, there is a reorientation of the wind shear vector in the boundary layer toward the low-level updraft in the perpendicular-roll simulations. Figure 15 shows streamlines of vertical wind shear over the depth of the CBL (~750 m at this time) and w at z = 225 m in the perpendicular-roll CONTROL (Fig. 15b) and CBL INVRAD (Fig. 15d) simulations. Regardless of the presence of rolls, the supercell causes a deflection of the shear ~30°–40° toward the storm in close proximity to the updraft and under the forward-flank precipitation region. Because rolls are aligned parallel to the boundary layer shear vector, rolls in these regions bend with the shear toward the updraft. A similar modification to roll orientation was observed in close proximity to sea-breeze fronts by Atkins et al. (1995). The increase in low-level inflow (and shear) discussed in the previous section also tends to increase the linearity of the rolls in close proximity to the low-level updraft. These two effects are only observed in the CBL INVRAD simulation, because rolls are essentially nonexistent in close proximity to the updraft and precipitation regions in the CBL FRAD simulations owing to anvil shading.

Fig. 15.
Fig. 15.

Plan view of vertical velocity (shaded) at z = 225 m and streamlines of 0–1-km vertical wind shear in the parallel-roll (a) CONTROL and (c) CBL INVRAD as well as the perpendicular-roll (b) CONTROL and (d) CBL INVRAD simulations at 120 min. Boldface dashed lines in (d) denote roll axes. Axes are labeled in km.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

The reorientation of low-level shear and the rolls is not notable in the parallel-roll simulations (Figs. 15a,c). This is because the low-level shear in the parallel-roll case is mostly speed shear (as opposed to directional shear), such that the low-level winds are largely unidirectional and oriented toward the storm updraft in the near-storm inflow environment. Thus, accelerations of the low-level flow in the vicinity of the parallel-roll updrafts do not result in a noteworthy reorientation of the shear vector. However, there is some indication that roll linearity also increases in proximity to the updraft in the parallel-roll CBL INVRAD simulation (Fig. 15c).

d. Discussion

The foregoing simulation results indicate that supercells have a substantial effect on the bulk properties and environmental perturbations in their vicinity that could influence subsequent storm evolution. Thermodynamic indices (CAPE and CIN) become less favorable for deep convection closer to the supercell when anvil shading effects are included. Though anvil shading is responsible for the largest decreases in CAPE in close proximity to the storm, CIN increases regardless of radiative influences. Figure 5 shows a warming above the near-storm boundary layer even in the CBL INVRAD simulations. This lowering and strengthening of the capping inversion is caused by subsidence in the near-storm environment. Figure 16 shows subsidence at z = 3 km surrounding the main updraft in both the CBL FRAD and CBL INVRAD simulations for both the parallel- and perpendicular-roll hodographs. This subsidence gives way to broadscale weak ascent south of the storm coincident with the edge of the anvil. At earlier times, these features are less prominent, but become a quasi-steady feature of the mature supercell environments.

fig. 16.
fig. 16.

Plan view of vertical velocity at z = 3 km (shaded) and cloud water mixing ratio at z = 1 km greater than 0.1 g kg−1 (thin black contours) in the parallel-roll (a) CBL FRAD and (c) CBL INVRAD as well as the perpendicular-roll (b) CBL FRAD and (d) CBL INVRAD simulations at 120 min. Simulated reflectivity greater than 10 dBZ at z = 1 km is outlined in thick black contours. The approximate position of the edge of the anvil at 12 km is shown with a dashed black line. Axes are labeled in km.

Citation: Monthly Weather Review 144, 1; 10.1175/MWR-D-15-0247.1

Within the region of ascent, shallow cumulus clouds develop atop the boundary layer (shown in Fig. 16 at z = 1 km). These clouds deepen in closer proximity to the storm, but are abruptly stifled under the region of subsidence along the anvil boundary in both simulations. Frame and Markowski (2013) noted the commonly observed cessation of shallow cumulus underneath the storm anvil, which they attributed to the boundary layer stabilization from anvil shading. Anvil shading does suppress boundary layer convection, but considering cumulus clouds erode near the storm both with and without anvil shading in this study, another factor must be at play. Indeed, without cloud shading boundary layer convection remains vigorous under the anvil in these simulations, yet cumulus clouds are still suppressed by a stronger capping inversion near the storm. The lowering of the boundary layer top prevents boundary layer convection from lifting parcels to their LCL (800 m) near the storm—this height is attainable in the deeper boundary layer in the far-field environment. In the CBL FRAD simulations, the LCL decreases underneath the anvil, but the erosion of boundary layer convection removes the mechanism by which parcels may be lifted to this lower LCL.

In these simulations, there is a general tendency for the supercells to increase low-level shear and SRH in their near-storm environment, regardless of anvil shading (i.e., the presence of resolved boundary layer turbulence). Frame and Markowski (2010) found that decreased vertical mixing led to an increase in low-level wind shear in anvil shadows. In the present study, anvil shading results in stronger boundary layer shear and more SRH (relative to the unshaded simulations) near the storm regardless of roll orientation, but the effect was more prominent for parallel rolls because boundary layer turbulence (TKE) was more diminished in this case. This is likely because the anvil motion relative to the ground was slower and the easterly low-level winds suggest a longer path for inflow parcels through the anvil shadow than for the perpendicular-roll hodograph. Both effects are responsible for the stronger and deeper boundary layer cooling in the parallel-roll case (cf. Figs. 5a,b). The net result is a greater lengthening of the low-level shear vector in the parallel-roll CBL FRAD simulation than in the perpendicular-roll CBL FRAD simulation, relative to the corresponding CBL INVRAD simulations.

The modification of bulk shear by rolls (or their removal) is likely susceptible to decreases in the surface heat flux beyond anvil shading. For instance, decreased insolation in the evening hours would likely lead to a weakening of rolls similar to what is seen in anvil shadows. Changes in the land surface condition encountered by a supercell (e.g., moving over a body of water, a region with different vegetation, or an area recently rained upon) might be similarly detrimental to boundary layer convection. Regardless of the cause, low-level shear is expected to increase when boundary layer turbulence decreases. Whether or not changes in shear associated with cessation of boundary layer convection improve or hinder the chances of tornadogenesis would depend, in part, on the hodograph. Many supercell environments (particularly tornadic supercell environments) are characterized by strong southerly low-level shear beneath a low-level jet with a hodograph similar to that in the perpendicular-roll cases. In these cases, it would seem that an increase in low-level shear because of decreased boundary layer turbulence would be favorable for supercells and tornadoes. In other cases, increased easterly shear (with an eastward-moving supercell) could possibly worsen conditions for low-level rotation through the density current dynamics discussed by Frame and Markowski (2013). Indeed, Parker (2014) found that in a small set of observed cases, low-level shear in nontornadic near-storm environments had a greater easterly component than tornadic near-storm environments.

Though there seems to be a clear increase in low-level wind shear when anvil shading reduces boundary layer turbulence, the effects on SRH and the potential for net updraft rotation seem to be dependent on the hodograph shape. For instance, SRH increases near the storm regardless of anvil shading in the perpendicular-roll cases, but does not increase as much (and in fact decreases to negative values in the direct vicinity of the updraft) in the near-storm environment with the parallel-roll hodograph (cf. Figs. 13a,c,e and 13b,d,f). This further supports the notion that the potential favorability of storm-induced modifications on storm evolution is dependent on the background wind profile.

Finally, the direct effects of rolls on supercell thunderstorms discussed in NMRB15 may be modulated by the findings presented here. For instance, in the early stages of supercell development, it was found that the strongest low-level cyclonic ζ was associated with intensifying ζ maxima in the inflow. It was also found that perpendicular rolls tend to disorganize the low-level mesocyclone later in supercell evolution. When anvil shading suppresses rolls in the near-storm inflow, both of these effects are limited. The reorientation and increase in linearity/organization of unshaded rolls as they approach the low-level updraft shown in Fig. 15 might prolong the period of time that portions of the storm encounter the same roll. For example, as roll vorticity structures become more linear nearer to the storm, misocyclones along the outflow boundaries of the storm might continually encounter a band of cyclonic vorticity or increased convergence such that they are more likely to intensify. Alternatively, if anticyclonic vorticity in the environment is indeed detrimental to low-level circulation associated with the low-level mesocyclone, the mesocyclone might be exposed to adverse conditions for a longer period of time as rolls become more organized nearer to the storm. In any case, it appears that the extent to which a supercell may modify the surrounding boundary layer and, consequently, the way in which these modifications might influence subsequent supercell characteristics, are dependent on the shape of the hodograph and not easily generalized.

4. Summary and conclusions

Supercell thunderstorms were simulated in an idealized environment containing a convective boundary layer (CBL) composed of boundary layer rolls in order to investigate the interactions between mature supercells and more realistic, turbulent boundary layers. The effects of rolls oriented either perpendicular or parallel to the motion of the right-moving supercell on the storms were compared against simulations with a horizontally homogeneous environment. Additional simulations tested the sensitivity of the results to the radiative effects of anvil shading. Whereas Nowotarski et al. (2015) described the effects of boundary layer heterogeneity on supercell low-level mesocyclones, in this paper we documented storm-induced effects on the near-storm environment.

The results presented in section 3 support the following conclusions:

  1. When boundary layer convection and cloud shading are included, the magnitude of CIN increases, whereas CAPE and the LCL generally decrease in proximity to supercell thunderstorms, largely due to the storm-induced effects of anvil shading and compensating subsidence.

  2. Low-level shear increases in proximity to supercell thunderstorms, largely due to low-level inflow acceleration by the storm updraft. The extent of this effect and its influence on SRH depend on the base-state hodograph shape.

  3. Though low-level shear increases near the storm regardless of the presence of boundary layer convection or cloud shading, anvil shading stabilizes the boundary layer, decreasing horizontal buoyancy gradients along outflow boundaries, suppressing boundary layer convection, and increasing low-level shear relative to nonshaded simulations.

  4. Reorientation of the boundary layer shear vector near the storm results in the reorientation of roll axes toward the main updraft when not already oriented in that direction.

  5. Shallow cumulus clouds in the inflow region are suppressed near the storm, regardless of anvil shading, owing to a region of compensating subsidence around the main updraft.

Uncertainty exists regarding the robustness of these results in diverse, real-world scenarios. For instance, the degree of boundary layer modification through anvil shading depends on details regarding anvil characteristics and the vertical wind profile (i.e., orientation of anvil-relative low-level inflow). Moreover, future work is warranted investigating the effects of storm maturity (i.e., anvil size and thickness) over a longer simulation period, upper-level storm-relative winds, as well as microphysics, radiation, and land surface parameterizations on the results shown here. It remains unclear if storm-induced modifications to the bulk characteristics of the near-storm environment play an active role in tornadogenesis or if consideration of such modifications can add predictive skill. Whereas most have sought to eliminate storm-induced effects from proximity soundings in search of a representative measure of the prestorm environment, neglect of these modifications or the assumption that all supercells similarly modify their environments may be unwise. Observations by Parker (2014) and the simulations herein confirm that the far-field environment (particularly hodograph shape) contributes to the amount of modification a storm may induce. Furthermore, as Frame and Markowski (2013) showed, even if low-level shear increases in proximity to all storms, such increases may actually be detrimental to the tornadic potential of the storm, depending on the orientation of the background low-level winds. Thus, future work should test these conclusions over a greater range of thermodynamic and wind profiles supportive of boundary layer convection and tornadic supercells.

Acknowledgments

We thank George Bryan for his continued development and support of CM1 (including implementation of the land surface and radiation schemes used in this study) as well as his comments on this work. We are also grateful to Yvette Richardson, Nels Shirer, Lyle Long, Morris Weisman, Jim Marquis, and Ryan Hastings for many helpful suggestions throughout the course of this project. Suggestions from three anonymous reviewers improved the clarity of the manuscript. This work was supported by NSF Grant AGS-0644533 and the College of Geosciences at Texas A&M University. Computational resources and travel support also were provided by the National Center for Atmospheric Research, which is funded by NSF. Many of the figures in this manuscript were created using the Grid Analysis and Display System (GrADS) developed by the Center for Ocean–Land–Atmosphere Studies.

REFERENCES

  • Atkins, N. T., R. M. Wakimoto, and T. M. Weckwerth, 1995: Observations of the sea-breeze front during CaPE. Part II: Dual-Doppler and aircraft analysis. Mon. Wea. Rev., 123, 944969, doi:10.1175/1520-0493(1995)123<0944:OOTSBF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Beebe, R. G., 1958: Tornado proximity soundings. Bull. Amer. Meteor. Soc., 39, 195201.

  • Brooks, H. E., C. A. Doswell III, and J. Cooper, 1994: On the environments of tornadic and nontornadic mesocyclones. Wea. Forecasting, 9, 606618, doi:10.1175/1520-0434(1994)009<0606:OTEOTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., 2002: An investigation of the convective region of numerically simulated squall lines. Ph.D. thesis, The Pennsylvania State University, 181 pp.

  • Bryan, G. H., and J. M. Fritsch, 2002: A benchmark simulation for moist nonhydrostatic numerical models. Mon. Wea. Rev., 130, 29172928, doi:10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. NASA Tech. Rep. 15, 38 pp.

  • Chou, M. D., M. J. Suarez, C. H. Ho, M. M. Yan, and K. T. Lee, 1998: Parameterizations for cloud overlapping and shortwave single-scattering properties for use in general circulation and cloud ensemble models. J. Climate, 11, 202214, doi:10.1175/1520-0442(1998)011<0202:PFCOAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chou, M. D., K. T. Lee, S. C. Tsay, and Q. Fu, 1999: Parameterization for cloud longwave scattering for use in atmospheric models. J. Climate, 12, 159169, doi:10.1175/1520-0442-12.1.159.

    • Search Google Scholar
    • Export Citation
  • Darkow, G. L., 1969: An analysis of over sixty tornado proximity soundings. Preprints, Sixth Conf. on Severe Local Storms, Chicago, IL, Amer. Meteor. Soc., 218–221.

  • Davenport, C. E., and M. D. Parker, 2015: Observations of the 9 June 2009 dissipating supercell from VORTEX2. Wea. Forecasting, 30, 368388, doi:10.1175/WAF-D-14-00087.1.

    • Search Google Scholar
    • Export Citation
  • Frame, J. W., and P. M. Markowski, 2010: Numerical simulations of radiative cooling beneath the anvils of supercell thunderstorms. Mon. Wea. Rev., 138, 30243047, doi:10.1175/2010MWR3177.1.

    • Search Google Scholar
    • Export Citation
  • Frame, J. W., and P. M. Markowski, 2013: Dynamical influences of anvil shading on simulated supercell thunderstorms. Mon. Wea. Rev., 141, 28022820, doi:10.1175/MWR-D-12-00146.1.

    • Search Google Scholar
    • Export Citation
  • Frame, J. W., J. L. Petters, P. M. Markowski, and J. Y. Harrington, 2009: An application of the tilted independent pixel approximation to cumulonimbus environments. Atmos. Res., 91, 127136, doi:10.1016/j.atmosres.2008.05.005.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 121 pp.

    • Search Google Scholar
    • Export Citation
  • Klemp, J. B., and R. B. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35, 10701096, doi:10.1175/1520-0469(1978)035<1070:TSOTDC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lin, Y. L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 10651092, doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., 1976: An evaluation of tornado proximity wind and stability data. Mon. Wea. Rev., 104, 133142, doi:10.1175/1520-0493(1976)104<0133:AEOTPW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., L. R. Hoxit, and C. F. Chappell, 1980: A study of tornadic thunderstorm interactions with thermal boundaries. Mon. Wea. Rev., 108, 322336, doi:10.1175/1520-0493(1980)108<0322:ASOTTI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., and J. Harrington, 2005: A simulation of a supercell thunderstorm with emulated radiative cooling beneath the anvil. J. Atmos. Sci., 62, 26072617, doi:10.1175/JAS3497.1.

    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., J. M. Straka, E. N. Rasmussen, and D. O. Blanchard, 1998a: Variability of storm-relative helicity during VORTEX. Mon. Wea. Rev., 126, 29592971, doi:10.1175/1520-0493(1998)126<2959:VOSRHD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., E. N. Rasmussen, J. M. Straka, and D. C. Dowell, 1998b: Observations of low-level baroclinity generated by anvil shadows. Mon. Wea. Rev., 126, 29422958, doi:10.1175/1520-0493(1998)126<2942:OOLLBG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., C. Hannon, J. Frame, E. Lancaster, A. Pietrycha, R. Edwards, and R. L. Thompson, 2003: Characteristics of vertical wind profiles near supercells obtained from the Rapid Update Cycle. Wea. Forecasting, 18, 12621272, doi:10.1175/1520-0434(2003)018<1262:COVWPN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McCaul, E. W., and M. L. Weisman, 2001: The sensitivity of simulated supercell structure and intensity to variations in the shapes of environmental buoyancy and shear profiles. Mon. Wea. Rev., 129, 664687, doi:10.1175/1520-0493(2001)129<0664:TSOSSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Naylor, J., M. A. Askelson, and M. S. Gilmore, 2012: Influence of low-level thermodynamic structure on the downdraft properties of simulated supercells. Mon. Wea. Rev., 140, 25752589, doi:10.1175/MWR-D-11-00200.1.

    • Search Google Scholar
    • Export Citation
  • Nowotarski, C. J., P. M. Markowksi, and Y. P. Richardson, 2011: The characteristics of numerically simulated supercell storms situated over statically stable boundary layers. Mon. Wea. Rev., 139, 31393162, doi:10.1175/MWR-D-10-05087.1.

    • Search Google Scholar
    • Export Citation
  • Nowotarski, C. J., P. M. Markowksi, Y. P. Richardson, and G.H. Bryan, 2014: Properties of a simulated convective boundary layer in an idealized supercell thunderstorm environment. Mon. Wea. Rev., 142, 39552976, doi:10.1175/MWR-D-13-00349.1.

    • Search Google Scholar
    • Export Citation
  • Nowotarski, C. J., P. M. Markowksi, Y. P. Richardson, and G.H. Bryan, 2015: Supercell low-level mesocyclones in simulations with a sheared convective boundary layer. Mon. Wea. Rev., 143, 272297, doi:10.1175/MWR-D-14-00151.1.

    • Search Google Scholar
    • Export Citation
  • Oberthaler, A. J., and P. M. Markowski, 2013: A numerical simulation study of the effects of anvil shading on quasi-linear convective systems. J. Atmos. Sci., 70, 767793, doi:10.1175/JAS-D-12-0123.1.

    • Search Google Scholar
    • Export Citation
  • Parker, M. D., 2014: Composite VORTEX2 supercell environments from near-storm soundings. Mon. Wea. Rev., 142, 508529, doi:10.1175/MWR-D-13-00167.1.

    • Search Google Scholar
    • Export Citation
  • Potvin, C. K., K. L. Elmore, and S. J. Weiss, 2010: Assessing the impacts of proximity sounding criteria on the climatology of significant tornado environments. Wea. Forecasting, 25, 921930, doi:10.1175/2010WAF2222368.1.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. N., and D. O. Blanchard, 1998: A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea. Forecasting, 13, 11481164, doi:10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. N., S. Richardson, J. M. Straka, P. M. Markowski, and D. O. Blanchard, 2000: The association of significant tornadoes with a baroclinic boundary on 2 June 1995. Mon. Wea. Rev., 128, 174191, doi:10.1175/1520-0493(2000)128<0174:TAOSTW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tao, W. K., and J. Simpson, 1993: The Goddard Cumulus Ensemble Model. Part I: Model description. Terr. Atmos. Oceanic Sci., 4, 1951.

  • Tao, W. K., S. Lang, J. Simpson, C. H. Sui, B. Ferrier, and M. D. Chou, 1996: Mechanisms of cloud-radiation interaction in the tropics and midlatitudes. J. Atmos. Sci., 53, 26242651, doi:10.1175/1520-0469(1996)053<2624:MOCRII>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Thompson, R. L., R. Edwards, J. A. Hart, K. L. Elmore, and P. Markowski, 2003: Close proximity soundings within supercell environments obtained from the Rapid Update Cycle. Wea. Forecasting, 18, 12431261, doi:10.1175/1520-0434(2003)018<1243:CPSWSE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Thompson, R. L., C. M. Mead, and R. Edwards, 2007: Effective storm-relative helicity and bulk shear in supercell thunderstorm environments. Wea. Forecasting, 22, 102115, doi:10.1175/WAF969.1.

    • Search Google Scholar
    • Export Citation
  • Weaver, J. F., and J. F. W. Purdom, 1995: An interesting mesoscale storm–environment interaction observed just prior to changes in severe storm behavior. Wea. Forecasting, 10, 449453, doi:10.1175/1520-0434(1995)010<0449:AIMSEI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504520, doi:10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and J. B. Klemp, 1984: The structure and classification of numerically simulated convective storms in directionally varying wind shears. Mon. Wea. Rev., 112, 24792498, doi:10.1175/1520-0493(1984)112<2479:TSACON>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., M. S. Gilmore, and L. J. Wicker, 1998: The impact of convective storms on their local environment: What is an appropriate ambient sounding? Preprints, 19th Conf. on Severe Local Storms, Minneapolis, MN, Amer. Meteor. Soc., 238–241.

  • Ziegler, C. L., E. R. Mansell, J. M. Straka, D. R. MacGorman, and D. W. Burgess, 2010: The impact of spatial variations of low-level stability on the life cycle of a simulated supercell storm. Mon. Wea. Rev., 138, 17381766, doi:10.1175/2009MWR3010.1.

    • Search Google Scholar
    • Export Citation
1

Oberthaler and Markowski (2013) corrected for this bias by artificially increasing the optical depth of anvil clouds in their simulation. For simplicity, we have chosen not to employ this method considering the surface temperature deficits are within the range of those found in observations.

2

“Far field” refers to areas away from the updraft, outflow, and anvil, where effects from the storm are small, whereas “near storm” refers to inflow regions within 10–20 km of the main updraft where the storm is expected to have the greatest thermodynamic and kinematic influences on its surroundings.

3

The origin of vertical vorticity in the CBL is discussed in NMRB14.

Save
  • Atkins, N. T., R. M. Wakimoto, and T. M. Weckwerth, 1995: Observations of the sea-breeze front during CaPE. Part II: Dual-Doppler and aircraft analysis. Mon. Wea. Rev., 123, 944969, doi:10.1175/1520-0493(1995)123<0944:OOTSBF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Beebe, R. G., 1958: Tornado proximity soundings. Bull. Amer. Meteor. Soc., 39, 195201.

  • Brooks, H. E., C. A. Doswell III, and J. Cooper, 1994: On the environments of tornadic and nontornadic mesocyclones. Wea. Forecasting, 9, 606618, doi:10.1175/1520-0434(1994)009<0606:OTEOTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., 2002: An investigation of the convective region of numerically simulated squall lines. Ph.D. thesis, The Pennsylvania State University, 181 pp.

  • Bryan, G. H., and J. M. Fritsch, 2002: A benchmark simulation for moist nonhydrostatic numerical models. Mon. Wea. Rev., 130, 29172928, doi:10.1175/1520-0493(2002)130<2917:ABSFMN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. NASA Tech. Rep. 15, 38 pp.

  • Chou, M. D., M. J. Suarez, C. H. Ho, M. M. Yan, and K. T. Lee, 1998: Parameterizations for cloud overlapping and shortwave single-scattering properties for use in general circulation and cloud ensemble models. J. Climate, 11, 202214, doi:10.1175/1520-0442(1998)011<0202:PFCOAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chou, M. D., K. T. Lee, S. C. Tsay, and Q. Fu, 1999: Parameterization for cloud longwave scattering for use in atmospheric models. J. Climate, 12, 159169, doi:10.1175/1520-0442-12.1.159.

    • Search Google Scholar
    • Export Citation
  • Darkow, G. L., 1969: An analysis of over sixty tornado proximity soundings. Preprints, Sixth Conf. on Severe Local Storms, Chicago, IL, Amer. Meteor. Soc., 218–221.

  • Davenport, C. E., and M. D. Parker, 2015: Observations of the 9 June 2009 dissipating supercell from VORTEX2. Wea. Forecasting, 30, 368388, doi:10.1175/WAF-D-14-00087.1.

    • Search Google Scholar
    • Export Citation
  • Frame, J. W., and P. M. Markowski, 2010: Numerical simulations of radiative cooling beneath the anvils of supercell thunderstorms. Mon. Wea. Rev., 138, 30243047, doi:10.1175/2010MWR3177.1.

    • Search Google Scholar
    • Export Citation
  • Frame, J. W., and P. M. Markowski, 2013: Dynamical influences of anvil shading on simulated supercell thunderstorms. Mon. Wea. Rev., 141, 28022820, doi:10.1175/MWR-D-12-00146.1.

    • Search Google Scholar
    • Export Citation
  • Frame, J. W., J. L. Petters, P. M. Markowski, and J. Y. Harrington, 2009: An application of the tilted independent pixel approximation to cumulonimbus environments. Atmos. Res., 91, 127136, doi:10.1016/j.atmosres.2008.05.005.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 121 pp.

    • Search Google Scholar
    • Export Citation
  • Klemp, J. B., and R. B. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35, 10701096, doi:10.1175/1520-0469(1978)035<1070:TSOTDC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lin, Y. L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 10651092, doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., 1976: An evaluation of tornado proximity wind and stability data. Mon. Wea. Rev., 104, 133142, doi:10.1175/1520-0493(1976)104<0133:AEOTPW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., L. R. Hoxit, and C. F. Chappell, 1980: A study of tornadic thunderstorm interactions with thermal boundaries. Mon. Wea. Rev., 108, 322336, doi:10.1175/1520-0493(1980)108<0322:ASOTTI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., and J. Harrington, 2005: A simulation of a supercell thunderstorm with emulated radiative cooling beneath the anvil. J. Atmos. Sci., 62, 26072617, doi:10.1175/JAS3497.1.

    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., J. M. Straka, E. N. Rasmussen, and D. O. Blanchard, 1998a: Variability of storm-relative helicity during VORTEX. Mon. Wea. Rev., 126, 29592971, doi:10.1175/1520-0493(1998)126<2959:VOSRHD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., E. N. Rasmussen, J. M. Straka, and D. C. Dowell, 1998b: Observations of low-level baroclinity generated by anvil shadows. Mon. Wea. Rev., 126, 29422958, doi:10.1175/1520-0493(1998)126<2942:OOLLBG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Markowski, P. M., C. Hannon, J. Frame, E. Lancaster, A. Pietrycha, R. Edwards, and R. L. Thompson, 2003: Characteristics of vertical wind profiles near supercells obtained from the Rapid Update Cycle. Wea. Forecasting, 18, 12621272, doi:10.1175/1520-0434(2003)018<1262:COVWPN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • McCaul, E. W., and M. L. Weisman, 2001: The sensitivity of simulated supercell structure and intensity to variations in the shapes of environmental buoyancy and shear profiles. Mon. Wea. Rev., 129, 664687, doi:10.1175/1520-0493(2001)129<0664:TSOSSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Naylor, J., M. A. Askelson, and M. S. Gilmore, 2012: Influence of low-level thermodynamic structure on the downdraft properties of simulated supercells. Mon. Wea. Rev., 140, 25752589, doi:10.1175/MWR-D-11-00200.1.

    • Search Google Scholar
    • Export Citation
  • Nowotarski, C. J., P. M. Markowksi, and Y. P. Richardson, 2011: The characteristics of numerically simulated supercell storms situated over statically stable boundary layers. Mon. Wea. Rev., 139, 31393162, doi:10.1175/MWR-D-10-05087.1.

    • Search Google Scholar
    • Export Citation
  • Nowotarski, C. J., P. M. Markowksi, Y. P. Richardson, and G.H. Bryan, 2014: Properties of a simulated convective boundary layer in an idealized supercell thunderstorm environment. Mon. Wea. Rev., 142, 39552976, doi:10.1175/MWR-D-13-00349.1.

    • Search Google Scholar
    • Export Citation
  • Nowotarski, C. J., P. M. Markowksi, Y. P. Richardson, and G.H. Bryan, 2015: Supercell low-level mesocyclones in simulations with a sheared convective boundary layer. Mon. Wea. Rev., 143, 272297, doi:10.1175/MWR-D-14-00151.1.

    • Search Google Scholar
    • Export Citation
  • Oberthaler, A. J., and P. M. Markowski, 2013: A numerical simulation study of the effects of anvil shading on quasi-linear convective systems. J. Atmos. Sci., 70, 767793, doi:10.1175/JAS-D-12-0123.1.

    • Search Google Scholar
    • Export Citation
  • Parker, M. D., 2014: Composite VORTEX2 supercell environments from near-storm soundings. Mon. Wea. Rev., 142, 508529, doi:10.1175/MWR-D-13-00167.1.

    • Search Google Scholar
    • Export Citation
  • Potvin, C. K., K. L. Elmore, and S. J. Weiss, 2010: Assessing the impacts of proximity sounding criteria on the climatology of significant tornado environments. Wea. Forecasting, 25, 921930, doi:10.1175/2010WAF2222368.1.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. N., and D. O. Blanchard, 1998: A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea. Forecasting, 13, 11481164, doi:10.1175/1520-0434(1998)013<1148:ABCOSD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. N., S. Richardson, J. M. Straka, P. M. Markowski, and D. O. Blanchard, 2000: The association of significant tornadoes with a baroclinic boundary on 2 June 1995. Mon. Wea. Rev., 128, 174191, doi:10.1175/1520-0493(2000)128<0174:TAOSTW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tao, W. K., and J. Simpson, 1993: The Goddard Cumulus Ensemble Model. Part I: Model description. Terr. Atmos. Oceanic Sci., 4, 1951.

  • Tao, W. K., S. Lang, J. Simpson, C. H. Sui, B. Ferrier, and M. D. Chou, 1996: Mechanisms of cloud-radiation interaction in the tropics and midlatitudes. J. Atmos. Sci., 53, 26242651, doi:10.1175/1520-0469(1996)053<2624:MOCRII>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Thompson, R. L., R. Edwards, J. A. Hart, K. L. Elmore, and P. Markowski, 2003: Close proximity soundings within supercell environments obtained from the Rapid Update Cycle. Wea. Forecasting, 18, 12431261, doi:10.1175/1520-0434(2003)018<1243:CPSWSE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Thompson, R. L., C. M. Mead, and R. Edwards, 2007: Effective storm-relative helicity and bulk shear in supercell thunderstorm environments. Wea. Forecasting, 22, 102115, doi:10.1175/WAF969.1.

    • Search Google Scholar
    • Export Citation
  • Weaver, J. F., and J. F. W. Purdom, 1995: An interesting mesoscale storm–environment interaction observed just prior to changes in severe storm behavior. Wea. Forecasting, 10, 449453, doi:10.1175/1520-0434(1995)010<0449:AIMSEI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and J. B. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504520, doi:10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., and J. B. Klemp, 1984: The structure and classification of numerically simulated convective storms in directionally varying wind shears. Mon. Wea. Rev., 112, 24792498, doi:10.1175/1520-0493(1984)112<2479:TSACON>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., M. S. Gilmore, and L. J. Wicker, 1998: The impact of convective storms on their local environment: What is an appropriate ambient sounding? Preprints, 19th Conf. on Severe Local Storms, Minneapolis, MN, Amer. Meteor. Soc., 238–241.

  • Ziegler, C. L., E. R. Mansell, J. M. Straka, D. R. MacGorman, and D. W. Burgess, 2010: The impact of spatial variations of low-level stability on the life cycle of a simulated supercell storm. Mon. Wea. Rev., 138, 17381766, doi:10.1175/2009MWR3010.1.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    (a) Skew T–logp diagram showing the temperature (red) and dewpoint temperature (green) profiles used to initialize each supercell simulation. Hodographs for the initial BASE (blue) and initial CONTROL, CBL FRAD, and CBL INVRAD (black) simulations in the (b) perpendicular-roll and (c) parallel-roll simulations. Heights are labeled in kilometers on the hodographs. (From NMRB15.)

  • Fig. 2.

    Plan view of ice water mixing ratio at z = 12 km (shaded) and vertical velocity greater than 0.5 m s−1 at z = 225 m for the (a) parallel-roll and (b) perpendicular-roll CBL FRAD as well as the (c) parallel-roll and (d) perpendicular-roll CBL INVRAD simulations at 120 min. The 10-dBZ simulated reflectivity contour at z = 1 km is shown in thick black. Axes are labeled in km.

  • Fig. 3.

    Plan view of surface soil temperature perturbation (shaded) and at z =25 m (black lines with the −0.5-, −1-, −1.5-, −2-, −4-, −6-, −8-, and −10-K contours shown) for the (a) parallel-roll and (b) perpendicular-roll CBL FRAD as well as the (c) parallel-roll and (d) perpendicular-roll CBL INVRAD simulations at 120 min. The 10-dBZ simulated reflectivity contour at z = 1 km is shown in thick black. Line segments AB and CD represent the plane of vertical cross sections and paths for traces of thermodynamic and kinematic variables in later figures. The boxes represent the area over which near-storm and environment average wind profiles are computed. Axes are labeled in km.

  • Fig. 4.

    Vertical cross section along line AB in Fig. 3 of w (shaded) and θ [colored contours, 0.5-K increments from 298 (purple) to 305 K (magenta)] for the parallel-roll (a) CBL FRAD and (b) CBL INVRAD simulations at 120 min. Axes are labeled in km.

  • Fig. 5.

    Vertical profiles of in the boxes near the storm (dashed) and in the far-field environment (solid) shown in Fig. 3 for the (a) parallel-roll and (b) perpendicular-roll CBL INVRAD (red) and CBL FRAD (blue) simulations at 120 min.

  • Fig. 6.

    SBCAPE (J kg−1) at 120 min in the vicinity of the parallel-roll (a) control, (c) CBL INVRAD, and (e) CBL FRAD simulations and the perpendicular-roll (b) control, (d) CBL INVRAD, and (f) CBL FRAD simulations. The gray contour is the 10-dBZ simulated reflectivity 1 km above the surface. Axes are labeled in km.

  • Fig. 7.

    (a) SBCAPE and (b) SBCIN along line segments AB (CD) from Fig. 3 for the parallel-roll (perpendicular roll) CBL INVRAD and CBL FRAD simulations at 120 min.

  • Fig. 8.

    Plan views of buoyancy (shaded) and baroclinic generation of horizontal vorticity (black contours at 10−4 s−2 intervals) and storm-relative winds (vectors) at z = 25 m in the parallel-roll (a) CBL INVRAD and (c) CBL FRAD simulations as well as the perpendicular-roll (b) CBL INVRAD and (d) CBL FRAD simulations at 120 min. Axes are labeled in km.

  • Fig. 9.

    Plan views of vertical velocity at z = 225 m (shaded) and vertical vorticity at z = 25 m (black contours) at selected intervals of 0.001, 0.005, 0.01, 0.02, 0.03, 0.04, and 0.05 s−1 in the parallel-roll (a) CBL FRAD and (c) CBL INVRAD and the perpendicular-roll (b) CBL FRAD and (d) CBL INVRAD simulations at 120 min. Axes are labeled in km.

  • Fig. 10.

    Turbulent kinetic energy (shaded) at z = 225 m and bulk wind difference over the height of the boundary layer in the boxes shown in Fig. 3 for CBL FRAD (a) unperturbed environment and (b) near the storm, and for CBL INVRAD (c) unperturbed environment and (d) near the storm in the parallel-roll simulations at 120 min. Axes are labeled in km.

  • Fig. 11.

    As in Fig. 10, but for the perpendicular-roll simulations. Axes are labeled in km.

  • Fig. 12.

    As in Fig. 7, but for 0–1-km storm-relative helicity for the (a) parallel-roll and (b) perpendicular-roll simulations.

  • Fig. 13.

    As in Fig. 6, but for 0–1-km SRH (m2 s−2).

  • Fig. 14.

    Average near-storm (dashed) and far-field environment (solid) hodographs over the lowest 3 km for the (a) parallel-roll and (b) perpendicular-roll CBL INVRAD (red) and CBL FRAD (blue) simulations at 120 min. Hodographs are averaged over boxes in Fig. 3.

  • Fig. 15.

    Plan view of vertical velocity (shaded) at z = 225 m and streamlines of 0–1-km vertical wind shear in the parallel-roll (a) CONTROL and (c) CBL INVRAD as well as the perpendicular-roll (b) CONTROL and (d) CBL INVRAD simulations at 120 min. Boldface dashed lines in (d) denote roll axes. Axes are labeled in km.

  • fig. 16.

    Plan view of vertical velocity at z = 3 km (shaded) and cloud water mixing ratio at z = 1 km greater than 0.1 g kg−1 (thin black contours) in the parallel-roll (a) CBL FRAD and (c) CBL INVRAD as well as the perpendicular-roll (b) CBL FRAD and (d) CBL INVRAD simulations at 120 min. Simulated reflectivity greater than 10 dBZ at z = 1 km is outlined in thick black contours. The approximate position of the edge of the anvil at 12 km is shown with a dashed black line. Axes are labeled in km.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1450 353 40
PDF Downloads 460 134 13