1. Introduction
Quasi-linear convective systems (QLCSs) represent one of the more common modes of convective organization and are responsible for a significant percentage of severe convective weather events, including high winds, hail, flooding, and tornadoes (e.g., Ashley et al. 2019; Smith et al. 2012; Thompson et al. 2012; Trapp et al. 2005). Often, such severe weather exhibits bow echoes or line-echo wave pattern (LEWP) radar reflectivity configurations, which in turn are associated with strong surface cold pools and straight-line surface winds (e.g., Przybylinski 1995). Mesoscale circulations such as along-line vortices (e.g., Wakimoto et al. 2006; Atkins and St. Laurent 2009; Wheatley and Trapp 2008; Weisman and Trapp 2003; Trapp and Weisman 2003) and line-end (or bookend) vortices (e.g., Weisman and Davis 1998) are also common features of such systems and can contribute to the production of severe surface winds and tornadoes (e.g., Przybylinski and Schmocker 1993; Pfost and Gerard 1997; Atkins et al. 2004).
Convection allowing models (CAMs) with horizontal grid spacing of ∼4 km or less have become highly valued tools for predicting hazardous convective weather (e.g., Kain et al. 2008; Weisman et al. 2008). The ability to forecast differing convective modes, such as supercells versus QLCSs, which help guide the characterization of the severe weather threat, has been an important component of this success. In this regard, both idealized and case study simulations using 3–4-km grid resolutions have generally been found to be sufficient to reasonably represent the mesoscale aspects of QLCSs, including bowing convective lines, rear inflow jets, and mesoscale vortices (e.g., Weisman 1993; Weisman and Davis 1998; Cram et al. 2002; Weisman et al. 2013; Xu et al. 2015).
Still, some deficiencies have also been documented when using 3–4-km grid spacing, presumably due to an inability to properly represent the physical processes critical for some of the convective hazards. Hepper et al. (2016) described two cases where convective winds were underforecast in their 4-km Δx forecasts, suggesting the deficiency was due to the inability to resolve processes related to the production of near-surface convective gusts. In their tornado forecasts, Gallo et al. (2016, 2018, 2019) similarly suggested that 3–4-km Δx CAMs were unable to represent processes leading to intense low-level rotation. Errant storm motions have also been noted in 3–4-km Δx forecasts of both supercells (VandenBerg et al. 2014) and MCSs (Schwartz et al. 2017). Similar deficiencies have been identified in idealized studies using simplified cloud models, for processes such as mesocyclone cycling (Adlerman and Droegemeier 2002), low-level vorticity intensification (Potvin and Flora 2015), and updraft and downdraft strength (e.g., Bryan et al. 2003; Bryan and Morrison 2012).
Increasing grid resolution to 1 km improves the representation of some QLCS features, such as more detailed and realistic reflectivity structures and more realistic updraft and downdraft strengths (e.g., Kain et al. 2008; Schwartz et al. 2017; Bryan et al. 2003; Bryan and Morrison 2012) as well as the tendency to develop smaller scale mesoscale vortices that often develop along the leading edge and ends of such systems (e.g., Weisman and Trapp 2003; Trapp and Weisman 2003; Atkins and St. Laurent 2009, Atkins et al. 2004; Wheatley and Trapp 2008; Xu et al. 2015). However, when considering overall precipitation and severe weather production, some studies of next-day CAM forecasts have documented minimal benefit to moving toward 1-km Δx (e.g., Kain et al. 2008; Schwartz et al. 2009; Clark et al. 2012; Johnson et al. 2013; Loken et al. 2017).
More recently, Schwartz et al. (2017) considered the relative value of using 1- versus 3-km ensembles for forecasting severe convection during the 15 May–15 June 2013 MPEX field campaign (e.g., Weisman et al. 2015). They found that 10-member 1-km probabilistic forecasts produced better precipitation forecasts than 3-km probabilistic forecasts over the first 12 h and for heavier rain rates. Additionally, 1-km cold pool deficits were slightly larger than 3-km deficits, and 1-km MCS centroids were regularly farther southeast than 3-km objects, suggesting that 1-km MCSs moved faster to the east and south than 3-km MCSs. Overall, the 1-km MCS locations agreed better with the observed systems. Schwartz and Sobash (2019) and Sobash et al. (2019) further considered the impact of increasing grid resolutions from 3 to 1 km for 497 cases of severe thunderstorms east of the Rocky Mountains between 2010 and 2017, noting improvements in both next day precipitation characteristics (e.g., timing and location of the convection) and next day tornado guidance, especially for relatively large convective systems. However, they also noted that the more positive results using 1-km Δx as compared to earlier studies might be related to improvements in the initial conditions in these later studies.
Additionally, Thielen and Gallus (2019) simulated 10 nocturnal convective systems in weakly forced environments at 3 and 1 km using WRF-ARW with four different microphysics schemes. The WRF-ARW underpredicted linear modes and overpredicted cellular modes at 3 km for all of the microphysics schemes. The proportion of linear systems increased using 1-km Δx, but this improvement was insufficient to match observations or show more forecast accuracy. Similarly, Squitieri and Gallus (2020) used WRF-ARW to simulate 14 leading line/trailing stratiform MCSs from the Great Plains and Upper Mississippi Valley with 3-, 1-, and 0.33-km grids to study the sensitivity of cold pool behavior and MCS propagation to grid spacing. Cold pools were larger using finer grids. The 1-km grids slightly increased both the 3-h QPF forecast skill and 9-h precipitation swath alignment compared to 3 km, but the 3-km MCS speed was closer to observed than for the 1-km MCS. The observed cold pool strengths, however, were not documented.
In the present paper, we present a companion study to Schwartz and Sobash (2019) and Sobash et al. (2019), as well as extending the work of Thielen and Gallus (2019) and Squitieri and Gallus (2020), to further investigate the impacts of resolution on QLCS structure, with specific focus on characteristics often associated with severe weather production. For this purpose, 14 severe QLCS cases, considering a wide range of convective system structures and environmental conditions, were chosen from the Schwartz and Sobash (2019) and Sobash et al. (2019) studies for more in-depth analysis. The 1- and 3-km simulations are compared to the observed systems, with emphasis placed on documenting reflectivity features, cold pool and mesovortex characteristics, and the resulting surface wind structure and strength, as related to severe weather production, which was not addressed in the previous studies. Given the importance of cold pools to overall QLCS structure and evolution, we also attempt to validate the simulated cold pool characteristics relative to the observed cold pools, which was also not included in these previous studies.
We begin in section 2 with a description of the methodology used in the present study, followed in section 3 by an overview of four of the more notable cases, emphasizing the range of environments and structural features considered in the present study. Sections 4–6 then present a more detailed analysis of the observed and simulated cold pools, mesoscale vortices, and surface wind characteristics, followed in section 7 by a summary and comparison to the previous studies noted above.
2. Methods
a. Case selection
The set of 14 severe QLCSs that were chosen from the Schwartz and Sobash (2019) and Sobash et al. (2019) studies are listed in Table 1 along with some of their basic environmental characteristics. These cases represent a wide range of synoptic environments, seasons, and geographical locations, and all produced significant severe weather outbreaks. Some of the more noteworthy events include the 27 April 2011 tornado outbreak in the southeastern United States, for which there was a severe QLCS producing several tornadoes and damaging straight line winds early in the morning prior to the main afternoon event (e.g., Knupp et al. 2014), the 29 June 2012 derecho that produced a swath of severe wind damage from Indiana to Washington, D.C., the 26 April 2016 severe weather outbreak in Texas, Oklahoma, and Kansas, for which a highly anticipated supercell tornado outbreak evolved instead into an extensive squall line with more localized tornado and severe wind events, and the 30 June 2014 severe bow echo that passed through Iowa and southern Wisconsin, producing winds over 90 mph along with extensive tree damage and power outages. These four cases are described in more detail to highlight the range of characteristic structural features often associated with such convective systems.
The 14 MCS cases used in the present study, including estimated representative environmental CAPE (J kg−1), 0–6-km vertical wind shear (kt), cold pool strength
b. Model configuration
The 3- and 1-km Δx were produced using version 3.6.1 of the Advanced Research version of the Weather Research and Forecasting (WRF) Model (Skamarock et al. 2008; Powers et al. 2017) as described in Sobash et al. (2019). Both sets of forecasts were initialized at 0000 UTC by interpolating 0000 UTC 0.5° Global Forecast System (GFS) analyses onto the 3- and 1-km domains and used 3-hourly 0.5° GFS forecasts as lateral boundary conditions. The subsequent convective systems of interest developed roughly 9–24 h after initialization, which is generally considered sufficient to avoid model spinup issues. All forecasts used a computational domain spanning the entire CONUS, with 40 vertical levels and a 50-hPa model top, and were run for 36 h. Physical parameterizations included Thompson microphysics (Thompson et al. 2008), the Mellor–Yamada–Janjić (MYJ) planetary boundary layer (Mellor and Yamada 1982; Janjić 1994, 2002), the Noah land surface model (Chen and Dudhia 2001), and the Rapid Radiative Transfer Model for Global Climate Models (RRTMG) for longwave and shortwave radiation (Mlawer et al. 1997; Iacono et al. 2008; Tegen et al. 1997). The time step was set to 4 times Δx for both sets of forecasts (i.e., 4 s for the 1-km Δx forecasts and 12 s for the 3-km Δx forecasts).
c. Cold pool characteristics
Cold pool characteristics were further documented using the equivalent potential temperature θe, which is generally conserved in these convective systems and can be used to identify the source region for the system-scale downdrafts (e.g., the potentially coldest surface outflow in such systems often originates from the midtroposphere, from the level of minimum environmental θe). Thus, differences between the observed and simulated cold pool θe could indicate significant differences in the cold pool source regions.
The
d. Mesoscale vortex characteristics
The common association of embedded and line-end mesoscale vortices with severe weather within convective systems motivates the need to document the capabilities of using 1- versus 3-km Δx to simulate such features. Such mesoscale vortices occur over a fairly large range of scales, from just a few kilometers for many of the leading-line vortices responsible for producing QLCS tornadoes, to tens of kilometers for some line-end vortices that can produce similarly large swaths of damaging straight-line winds. Thus, it is important to document the ability of these simulations to reproduce this full range of observed mesoscale vortex scales.
Mesoscale vortices can often be identified operationally using Doppler winds when the radar is favorably positioned relative to the convective system (e.g., relatively close, and directly downstream or upstream of the convective system relative to system motion). They are also often associated with bow echo or embedded LEWP reflectivity features, which can be identified using radar reflectivity at larger distances and any angle. Thus, both winds and model-generated reflectivity are used to identify comparable features in the simulations.
Many convective mesoscale vortices, especially those that produce QLCS tornadoes, are associated with strong updrafts, which can be readily identified in the simulations using updraft helicity (UH; Kain et al. 2008). The physical model on which UH is based is that of a supercell storm, composed of a midlevel rotating updraft along with a low-level rotating updraft that at times can be tornadic. As such, 2–5 km above ground level (AGL) UH is found to be very helpful in identifying midlevel supercell structures in 1–3-km CAM forecasts, with 0–1-km UH then adding further guidance as to the probability that the supercell may be tornadic (e.g., Kain et al. 2008, 2010; Sobash et al. 2011; Naylor et al. 2012). Extreme values of UH have also been used to produce next-day guidance for the combined threat from all severe hazards [i.e., hail ≥ 1 in., wind gusts ≥ 50 kt (1 kt ≈ 0.51 m s−1), and/or tornadoes, e.g., Sobash et al. 2011, 2016]. Although not all mesoscale vortices within QLCSs are necessarily associated with updrafts and subsequently high values of UH, the apparent association of high UH with many of the severe weather phenomena in such systems (e.g., especially QLCS tornadoes) makes it a useful parameter to help characterize the severe weather potential of such systems.
Herein, we use 0–1 km AGL updraft helicity (UH01) to help identify such potentially significant low-level mesoscale vortices. In WRF, this is computed as a summation of updraft speed times vertical vorticity, multiplied by the layer depth, using model levels between 0 and 1 km AGL. Diagnostics were computed each time step during the WRF integration and stored as hourly maximum values, as in Kain et al. (2010). It should be noted that the magnitudes of UH01 are scale dependent: e.g., based on simple linear scaling of vertical velocity and vertical vorticity one would expect a factor of about 9 times increase in UH01 magnitudes for 1- versus 3-km gridscale resolutions. Consistent with this approximate scaling, Sobash et al. (2019) found critical minimum values of UH01 for severe weather prediction of 4.7 m2 s−2 for 3-km simulations, increasing to 34.6 m2 s−2 for the equivalent 1-km simulations. For the current set of simulations, it was found useful to use a 0–10 m2 s−2 scale for UH01 for the 3-km simulations and a 0–80 m2 s−2 scale for UH01 for the 1-km simulations.
e. Surface wind characteristics
Severe surface winds within QLCSs can be generated by a variety of mechanisms, ranging from most simply developing in a strong mean surface wind environment, to being generated by the accelerations associated with convective and mesoscale downdrafts and associated cold pools, as well as accelerations associated with the development of mesoscale vortices. Such vortices may be associated with supercells embedded within the convective system, but often develop in association with nonsupercellular features, such as bow echoes, LEWPs, etc. (e.g., Trapp and Weisman 2003; Weisman and Trapp 2003; Wakimoto et al. 2006). The most severe winds, however, are often the result of the additive contributions from all these forcing influences. Thus, the ability to predict severe surface winds in such systems depends critically on the ability to resolve these forcing features.
In the following, observed maximum surface winds were documented using both the available 5-min ASOS time series, within a roughly 20-min time period after documented gust front passage (as described in section 2c), as well as National Weather Service (NWS) storm reports. The NWS storm reports were especially helpful given the inherent local nature of severe wind gusts, and the relative sparsity of ASOS reports at times. However, the ASOS observations alone were used for direct comparison with the model results, given the inherent uncertainties in the accuracy of the storm reports at times. Simulated maximum surface winds were similarly diagnosed using the maximum 10 m AGL winds from the 5-min time series. Comparison with the observed maximum winds is compromised somewhat by the lack of gust information from the simulations.
3. Case overviews
The 14 QLCS cases selected for this study (Table 1) all evolved in a classic fashion for such convective systems, developing a leading line trailing stratiform reflectivity structure during their mature phases, with embedded bow echo and/or LEWPs evident at times, as are often associated with the production of the most severe weather within such systems. The mature system reflectivity structure for the 27 April 2011, 29 June 2012, 26 April 2016, and the 30 June 2014 cases are presented in Fig. 1, and are representative of the range of reflectivity characteristics observed with the full set of events. The severe weather associated with these events, as reported by the Storm Prediction Center (SPC), is shown in Fig. 2. Of these four cases, bowing or LEWP structures were most evident in the 27 April 2011 case (Fig. 1a), and were associated with a significant outbreak of QLCS tornadoes in Alabama during the early morning hours (e.g., Knupp et al. 2014). One particularly well-formed bowing feature is also evident within the 30 June 2014 convective system (Fig. 1c) and was associated with an extensive swath of damaging surface winds in eastern Iowa, along with one confirmed tornado.
Figure 3 presents the basic environmental conditions for these four cases, including 500-hPa heights, most-unstable convective available potential energy (CAPE) and 0–6-km AGL vertical wind shear, as representative of the time being portrayed for each case, as taken from the 1-km simulations (the differences in environmental characteristics between the 1- versus 3-km environments were not found to be significant). As also noted for the full set of 14 cases (Table 1), these four cases cover a wide range of environments spanning from extreme CAPE with weak vertical wind shear and weak synoptic scale forcing for the 29 June 2012 derecho (Fig. 3b), to more modest CAPE with strong shear and stronger synoptic scale forcing for the 27 April 2011 event (Fig. 3a).
Figures 4a and 4b through 7a and 7b depict the mature simulated reflectivity structure at 1 and 3 km for each of the four selected cases. As has been noted in many past studies using CAMs, some errors in timing and location are apparent in comparison to the observed systems (Fig. 1). For the present set of cases, the timing and location of convective initiation is very similar in the 1- and 3-km simulations. The forecasts for the 27 April 2011 case are shifted a bit north of the observed system using both 1 and 3 km. This is a relatively common mode of forecast failure for this model setup (seen in 5 of the 14 cases), and work is ongoing to understand this bias. However, the overall morphology of these and the other nine events included in the present study (Table 1) are reproduced quite reasonably. Still, some systematic differences are evident in comparing the 1- and 3-km forecasts, with the 1-km forecasts often depicting a more realistic leading-line trailing-stratiform reflectivity structure, slightly faster propagation, and more numerous smaller scale LEWPs evident along the leading edge of the system. In the following, we will discuss some of the more detailed characteristic associated with the production of severe weather, including cold pool structure and strength and the production of leading-line and line-end vortices, both of which contribute to the production of severe surface winds and potential QLCS tornadoes.
4. Cold pool characteristics
As noted above, cold pools represent one of the critical components distinguishing QLCSs from less organized forms of convection, contributing to their maintenance, propagation, and severe weather potential. In this section, we offer a more detailed presentation of the impact of resolution on cold pool and outflow production for our set of simulations and attempt to validate these system characteristics using surface observations.
Some of the basic differences in cold pool characteristics for the 1- and 3-km simulations are apparent in Figs. 4c and 4d through 7c and 7d, which depict surface θυ for the four sample cases described above during their mature phase. In general, the cold pools are slightly colder and larger at 1 versus 3 km. The only exception among these four sample cases is perhaps 30 June 2014 (e.g., Figs. 6c,d), which, at the analysis time, only depicts an enhanced cold pool for the 1-km simulation in a narrow strip just behind the active convective line in east-central Iowa. However, the 1-km cold pool does clearly become stronger and larger than the 3-km cold pool at later times in the simulation (not shown). All in all, the stronger 1-km cold pools are consistent with the slight increase in propagation speed for the 1-km simulations, as noted above and in previous studies (e.g., Schwartz et al. 2017; Squitieri and Gallus 2020).
The 29 June 2012 event represents an extreme example of such cold pool generation. Figure 8a shows the observed time series of surface observations from Dayton, Ohio, showing a 17-K drop in potential temperature and a 40-K drop in θe along with a 7–8-hPa pressure rise as the cold pool passes by. Model time series for Dayton from the 1-km simulation is presented in Fig. 8b, showing similar magnitudes for
Figure 9 documents the maximum cold pool generated
Figures 11 and 12 document the maximum cold pool generated ΔP and minimum θe, respectively, as observed from available ASOS stations on this day, again overlayed with the equivalent parameters from the 1- and 3-km simulations. A ΔP value of 6–8 hPa, and minimum values of θe near 330 K were observed all along the system’s path.
Although the simulated systems were slightly south of the observed system on this day, both the 1- and 3-km simulations successfully reproduced the observed range of surface conditions. However, the size and speed of propagation of the cold pool is clearly a bit larger for the 1-km case, which also exhibits a 2–3 K larger
The results for all 14 cases are summarized in Figs. 13–16, which compare the observed event cold pool characteristics (
Figure 15 similarly presents the maximum event ΔP observed and simulated for all 14 cases. ΔP magnitudes range from 2 to 8 hPa over this set of cases, and the modeled and observed ΔP generally display good correspondence, ±∼1 hPa, with the one exception from 13 July 2015. Indeed, assuming an average cold pool
Figure 16 shows results for surface θe, again suggesting that the simulations successfully reproduced the observed range of cold pool θe. However, there is now a consistent slight cold bias as compared to the observed θe for both the 1- and 3-km simulations. Since the cold pool θe is often representative of the midlevel origin of the downdraft/cold pool air mass, this suggests that the environmental midlevel θe is either a bit too low, or that the source level for the downdraft air mass is misrepresented in this set of simulations, perhaps due to initialization or other model errors. A comparison of the observed and modeled environmental θe (not shown), however, did not suggest any systematic error in the modeled environmental midlevel θe. Additionally, an inspection of the vertical gradients of θe at midlevels suggests that a couple of degree difference in the simulated θe would not suggest a significant change in the height of origin of the downdraft air mass. Thus, the apparent slight cold bias in cold pool θe for this set of simulations is not considered to be significant.
5. Mesoscale vortices
Figures 17–20 compare the low-level wind, updraft, vertical vorticity structure, and UH01 tracks for the 29 June 2012, 30 June 2014, 27 April 2011, and 27 April 2016 cases at 1 and 3 km. Based on the associated reflectivity characteristics, the overall character of the observed vortices for these cases seems to be reasonably represented by the simulations. However, while both resolutions seem capable of producing larger mesoscale vortices, the 1-km simulations appear generally more apt to produce smaller-scale vortices along the system’s leading line, as can be identified by narrow streaks of large UH01 magnitudes (Figs. 17b, 18b, 19b, and 20b).
The 29 June 2012 case is representative of cold pool dominated events that occur in environments of very large CAPE but weak deep layer shear (Fig. 17). Although embedded supercells and other deep mesoscale vortices would not be expected in such environments, shallow mesoscale vortices still occur along the leading edge of such systems, as is especially evident in the 1-km simulation (Fig. 17b). However, these shallow vortices tend not to be as associated with significant local enhancements of the outflow winds, as can occur with the deeper embedded mesoscale vortices. Likewise, QLCS tornadoes tend to be very rare with such systems, and indeed, despite the large extent of severe outflow winds, no such tornadoes were reported with this case.
The 30 June 2014 case (Fig. 18) is characteristic of stronger shear events, with the 0–6-km AGL environmental vertical wind shear magnitude ranging between 40 and 50 kt. Such magnitudes of vertical wind shear are generally sufficient to produce embedded supercells as well as the nonsupercellular mesoscale vortices. Deep, embedded mesoscale vortices are evident in both the 1- and 3-km simulations, associated with LEWP type reflectivity features (e.g., Fig. 6), and, similar-to the 29 June case, additional smaller-scale vortices appear in the 1-km simulation. These vortices are also clearly coincident with long-lived UH01 tracks, suggesting that embedded supercells may have contributed to mesoscale vortex formation in this case (a tornado was observed with this event). Although the 1-km simulation again produces more smaller vortices, the singular large and strong mesoscale vortex evident in the equivalent 3-km simulation seems more consistent with the observations on this day (e.g., note the singular LEWP reflectivity structure in Fig. 1c).
The 27 April 2011 case (Fig. 19) represents another strong shear event that was especially notable in that the early morning QLCS produced an extensive number of tornadoes associated with both leading line and line-end mesoscale vortex structures (e.g., Knupp et al. 2014). The 1-km simulation produces both a larger-scale embedded mesoscale vortex along the Alabama–Tennessee border, as well as a series of smaller-scale vortices along the leading edge of the system extending south from this larger-scale vortex, as identified by the several narrow UH01 streaks (Fig. 19b). Wide swaths of significant UH01 are also evident extending from the leading edge of both the 1- and 3-km simulations. However, for this case, these features simply reflect the more general 2D correlation of low-level updraft and cyclonic vorticity along the entire edge of the system as opposed to the more isolated mesoscale vortices generally associated with enhanced severe weather and possible tornadoes.
Finally, the 26 April 2016 case (Fig. 20) is quite representative of a moderate shear case, producing more linear reflectivity segments as opposed to more isolated supercell-type configurations, and also producing a fair number of QLCS-type tornadoes, primarily in northeast Oklahoma. Again, although the potentially severe segment in the simulations was a bit further south than the observed system, the 1-km simulation produced several intense low-level updraft helicity tracks embedded with the bow shaped reflectivity features. However, as for the 27 April 2011 case (Fig. 19), only a single larger updraft helicity track is evident in the 3-km simulation, extending along the center of a bow shaped reflectivity segment. Again, this feature seemed more associated with the collocation of low-level cyclonic shear and updraft at the leading of the system, as opposed to the development of a localized vortex that might be associated with enhanced severe weather. For the present case, the existence of multiple QLCS-type tornadoes associated with the observed system would seem to better support the guidance offered by the 1-km simulation.
6. Surface winds
The variety of maximum surface wind patterns for the present set of QLCSs is illustrated in Fig. 21, which depicts 1-h maximum wind swaths during the mature phases of the 29 June 2012, 30 June 2014, and 27 April 2011 cases, as also discussed above. The 29 June 2012 case was the most extensive severe surface wind event of the cases considered herein, with observed wind gusts of 50–70 kt and local maximum wind gusts up to 90 kt in the NWS severe weather reports (Fig. 2b) through much of the system’s path, especially through Ohio and West Virginia. For this case, the primary forcing for the severe surface winds was the intense cold pool and associated rear-inflow jet, with perhaps some local enhancement from small, shallow embedded vortices along its leading edge. The 1-km simulation produced a larger cold pool in this case, as noted above, along with more shallow leading line embedded vortices, but both the 1- and 3-km simulations produced similar strength cold pools and rear-inflow jets. Overall, the 1-km simulation produced slightly stronger surface winds.
The 30 June 2014 system (Figs. 21c,d) also produced widespread observed maximum wind gusts up to 60 kt extending across southern Iowa and north central Illinois, with locally extreme winds up to 90 mph, specifically in association with an embedded bow echo and an apparent associated mesoscale vortex on the Iowa–Illinois border. For this case, the 1-km simulation produced a more extensive region of strong surface winds than the 3-km simulation, consistent with a more extensive and slightly colder cold pool. The 1-km simulation also produced more narrow swaths of enhanced surface winds, largely associated with its more numerous embedded leading line mesoscale vortices (as also depicted by its more numerous UH swaths in Fig. 18). Most notably, though, the strongest simulated surface winds for this case, reaching over 90 kt, were generated in the 3-km simulation, which produced a larger and more coherent swath of high winds associated with a single, large embedded mesoscale vortex associated with a bulge in the reflectivity field, similar to what was observed on this day (e.g., Fig. 1c). This case emphasizes that, although the 1-km simulations seem to consistently produce more embedded mesoscale vortices, 3-km simulations are capable of producing large mesoscale vortices that significantly enhance surface winds under the right environmental conditions.
For the 27 April 2011 case (Figs. 22e,f), the 1-km simulation produces generally stronger and more extensive surface outflow associated with the cold pool as well as more intense narrow swaths of strong surface winds associated with embedded mesoscale vortices, which are largely absent in the 3-km simulation. As such, the 1-km simulation is more accurate in representing the potential for severe surface winds as well as the multiple QLCS tornadoes that were observed for this case. Interestingly, the strongest surface winds in the 1-km simulation were again associated with both a large mesoscale vortex as well as smaller leading-line vortices, although the larger mesoscale vortex was not clearly identified as an enhanced swath of UH.
The maximum surface wind characteristics for all 14 cases is presented in Fig. 22. While there is certainly some correspondence between the simulated and observed maximum winds over all the cases, the correlation does not seem as strong as for the cold pool characteristics (e.g., Figs. 13–16). This is likely because, as discussed above, the embedded mesoscale vortices can also be a significant contributor to surface wind strength. Also, while the surface wind strength is consistently stronger for the 1- versus 3-km simulations, the 3-km simulations appear to match the observed winds a bit better.
7. Summary, discussion, and conclusions
Herein, we have compared the structure of 14 simulated severe QLCS using 1- and 3-km grid spacings, to better document the capabilities of these resolutions to represent the phenomena often associated with the production of hazardous weather within such systems. This study represents an extension of a similar study by Squitieri and Gallus (2020) to consider events over a wider range of environmental conditions, and to better validate the more detailed system features associated with severe weather production. As such, emphasis is placed on documenting overall reflectivity characteristics, objectively validating cold pool characteristics as compared to the observations, and documenting low-level mesovortex and surface wind characteristics, which were not included in this previous study.
As to the overall reflectivity characteristics, the basic leading-line trailing stratiform structure that is commonly observed with mature QLCSs was often better defined at 1 versus 3 km. This result is consistent with many recent studies that find that reflectivity features are better represented using enhanced resolution (e.g., Bryan et al. 2003; Kain et al. 2008; Schwartz et al. 2009; Bryan and Morrison 2012; Schwartz et al. 2017). Additionally, while 3-km grid spacing seems sufficient to reproduce LEWP and bow echo type configurations embedded within larger QLCSs, such features seem more numerous, and are generally of smaller scale using 1-km grid resolutions. The enhanced ability to produce such LEWP type features using 1-km grid spacing did improve the forecast guidance for some cases (e.g., the 27 April 2011 and 27 April 2016 cases). However, it was not as clear whether the enhanced grid resolution, considering the full set of 14 cases, provided a significant improvement more generally in the representation of such reflectivity features.
In comparing the cold pool characteristics, both the 1- and 3-km simulations well replicated the basic variations observed for the differing environments from case to case. As has also been noted in previous studies (e.g., Schwartz et al. 2017; Squitieri and Gallus 2020), the 1-km cold pools were generally slightly colder, larger in area, and slightly faster than those of the 3-km systems. However, for the present set of cases, the peak intensity displayed by the simulated 3-km cold pools seemed a bit more consistent with the observations. More generally, simulated cold pool pressure changes were generally a bit larger than the observed pressure changes, for both resolutions. Also, the simulated cold pool θe was consistently slightly lower than observed, perhaps indicating drier simulated midlevel environmental conditions than observed.
Both the 1- and 3-km simulations were successful in producing embedded low-level mesoscale vortices within the convective systems, although these vortices were generally smaller in scale and more numerous using the 1-km grid. While these vortices using 1-km resolutions generally better matched the observations, in some cases (e.g., 30 June 2014) the scale and number of mesoscale vortices in the 3-km simulations seemed better aligned with the observations. The ability of 3-km simulations to realistically reproduce such larger mesoscale vortices was also highlighted for the 8 May 2009 super derecho event wherein the northern line-end mesoscale vortex developed a warm-core structure along with hurricane force surface winds (e.g., Weisman et al. 2013). However, although many leading-line vortices and subsequent tornadoes were observed with this system, the 3-km forecast produced only a solid band of positive vertical vorticity extending along the gust front, similar to the 3-km simulation of the 27 April 2011 case presented herein. It is also important to note that, while UH01 was quite effective in identifying many of these QLCS mesoscale vortices, some of the mesoscale vortices that contributed to the production of severe winds in the simulations were not collocated with updrafts (e.g., 27 April 2011). Likewise, continuous wide swaths of high UH01 were generated along the leading line of some of the convective systems. However, such features were found to be quite shallow, and were not indicative of the same level of severe weather risk attributed to supercells. Thus, caution must be used when applying UH concepts to QLCS mesoscale vortices.
Simulated convective surface wind strength was related to both the strength of the resulting convective cold pools as well as the occurrence of low-level mesoscale vortices embedded within the leading line of the system, making the predictability of such system attributes more complicated. The inherent difficulty in documenting such small-scale phenomena like wind gusts, especially given the sparsity of surface wind observations at times, makes validating surface winds even more difficult. Indeed, maximum surface winds were only marginally well predicted for the 14 cases. The simulations, however, were able to differentiate the potential mechanisms contributing to severe winds from case to case. For instance, the widespread extreme winds on 30 June 2012 were clearly produced by an especially intense cold pool, while the production of locally extreme winds on 27 April 2011 and 30 June 2014 were more associated with embedded mesoscale vortices. Interestingly, while the 1-km systems generally produced stronger surface winds than the 3-km systems, the 3-km winds seemed a bit more accurate.
The overall results presented here are quite consistent with the previous related studies, suggesting that some improvement in convective system structure and potential hazard prediction is achieved by increasing the grid resolutions from 3 to 1 km, especially in the ability of the 1-km simulations to produce more realistic reflectivity features as well as the smaller, often more realistic leading-line vortices that can be associated with QLCS tornadoes. However, whether such improvements are significant enough to warrant operational resolution increases to represent QLCS type phenomena is still an open question. This, of course, depends strongly on the forecast perspective being taken. While such improvements may not be necessary for longer-term forecasts (e.g., >24 h), a better representation of convective system-scale properties and explicit hazard potential might be considered critical for shorter-term warning applications.
Acknowledgments.
This work was partially supported by NOAA OAR Grant NA17OAR4590182 and the NCAR Short-term Explicit Prediction (STEP) program. We thank Joseph Klemp (NCAR/MMM) for an internal review and three anonymous reviewers for comments that improved the paper. We would also like to acknowledge high-performance computing support from Cheyenne (Computational and Information Systems Laboratory 2017) provided by NCAR’s Computational and Information Systems Laboratory. The National Center for Atmospheric Research is sponsored by the National Science Foundation.
Data availability statement.
The output from our 14 corresponding 3- and 1-km numerical forecasts are available from the corresponding author. ASOS surface observations were obtained from https://mesonet.agron.iastate.edu/ASOS/.
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