Differences in Near-Storm Parameters Useful for Forecasting Intensity of Nocturnal and Diurnal Bow Echo Winds

William A. Gallus Jr. aDepartment of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa

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Anna C. Duhachek aDepartment of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa

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Abstract

Because bow echoes are often associated with damaging wind, accurate prediction of their severity is important. Recent work by Mauri and Gallus showed that despite increased challenges in forecasting nocturnal bows due to an incomplete understanding of how elevated convection interacts with the nocturnal stable boundary layer, several near-storm environmental parameters worked well to distinguish between bow echoes not producing severe winds (NS), those only producing low-intensity severe winds [LS; 50–55 kt (1 kt ≈ 0.51 m s−1)], and those associated with high-intensity (HS; >70 kt) severe winds. The present study performs a similar comparison for daytime warm-season bow echoes examining the same 43 SPC mesoanalysis parameters for 158 events occurring from 2010 to 2018. Although low-level shear and the meridional component of the wind discriminate well for nocturnal bow severity, they do not significantly differ in daytime bows. CAPE parameters discriminate well between daytime NS events and severe ones, but not between LS and HS, differing from nocturnal events where they discriminate between HS and the other types. The 500–850-hPa layer lapse rate works better to differentiate daytime bow severity, whereas the 500–700-hPa layer works better at night. Composite parameters work well to differentiate between all three severity types for daytime bow echoes, just as they do for nighttime ones, with the derecho composite parameter performing especially well. Heidke skill scores indicate that both individual and pairs of parameters generally are not as skillful at predicting daytime bow echo wind severity as they are at predicting nocturnal bow wind severity.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: William A. Gallus, wgallus@iastate.edu

Abstract

Because bow echoes are often associated with damaging wind, accurate prediction of their severity is important. Recent work by Mauri and Gallus showed that despite increased challenges in forecasting nocturnal bows due to an incomplete understanding of how elevated convection interacts with the nocturnal stable boundary layer, several near-storm environmental parameters worked well to distinguish between bow echoes not producing severe winds (NS), those only producing low-intensity severe winds [LS; 50–55 kt (1 kt ≈ 0.51 m s−1)], and those associated with high-intensity (HS; >70 kt) severe winds. The present study performs a similar comparison for daytime warm-season bow echoes examining the same 43 SPC mesoanalysis parameters for 158 events occurring from 2010 to 2018. Although low-level shear and the meridional component of the wind discriminate well for nocturnal bow severity, they do not significantly differ in daytime bows. CAPE parameters discriminate well between daytime NS events and severe ones, but not between LS and HS, differing from nocturnal events where they discriminate between HS and the other types. The 500–850-hPa layer lapse rate works better to differentiate daytime bow severity, whereas the 500–700-hPa layer works better at night. Composite parameters work well to differentiate between all three severity types for daytime bow echoes, just as they do for nighttime ones, with the derecho composite parameter performing especially well. Heidke skill scores indicate that both individual and pairs of parameters generally are not as skillful at predicting daytime bow echo wind severity as they are at predicting nocturnal bow wind severity.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: William A. Gallus, wgallus@iastate.edu

1. Introduction

Bow echoes (e.g., Fujita 1978), a subset of MCSs where the leading line of convection accelerates in a small region, resulting in a bow configuration in radar imagery, have long been known to pose an enhanced risk of wind damage (Fujita and Wakimoto 1981; Davis et al. 2004; Ashley and Mote 2005; Atkins et al. 2005; Wheatley et al. 2006; Wakimoto et al. 2006; Gallus et al. 2008). Therefore, many studies have examined both the environments that favor bow echo formation, and the mechanisms leading to strong winds within the systems. Weisman (1993) used idealized simulations to find that severe, long-lived bow echoes were supported in environments with large amounts of CAPE (at least 2000 J kg−1) and strong vertical wind shear (at least 20 m s−1 over the lowest 5 km above ground level). Other studies (e.g., Coniglio et al. 2004; James et al. 2006; Guastini and Bosart 2016) found that abundant low-level moisture and relatively dry midlevel conditions were favorable for bow echoes. A modeling study by Mahoney and Lackmann (2011) found that MCSs in drier midlevel environments produced severe winds over larger areas, accompanied by a descending rear-inflow jet. Rear-inflow jets have long been believed to account for damaging winds in bow echoes (e.g., Johns and Hirt 1987; Weisman 1992, 1993).

Although Weisman (1993) found strong 0–5-km shear to be important for bow echo formation, with bows being even more prevalent when most of the shear was confined to the 0–2.5-km layer, Evans and Doswell (2001) and Coniglio et al. (2004) found low-level (0–2.5 km) shear was not skillful in forecasting long-lived bow echoes. Evans and Doswell (2001) and Cohen et al. (2007) found high variation in the ambient shear and instability, suggesting that, alone, they are not sufficient to differentiate long-lived bow echo, or derecho, environments from those associated with nonsevere MCSs. Klimowski et al. (2004) found that about half of the 273 bow echoes they observed in a 7-yr period formed near a synoptic or mesoscale boundary, which might account for some of the large variations in shear and instability.

High variability could be linked to the fact that in some events, storm-scale interactions may strongly influence bow echo evolution. Klimowski et al. (2004) and French and Parker (2012) found that different types of bow echoes often are produced by mergers of the quasi-linear convective systems (QLCS) and supercells under different shear conditions. French and Parker (2014) also found in a modeling study that although strong shear was important for the background environment, their bow echo of interest was strongly influenced by a merger with a supercell in front of the system that locally intensified rear-to-front flow and the strength of the cold pool. Lawson and Gallus (2016) also theorized that the difficulties in accurately simulating some bow echoes, even with 1-km ensembles, was evidence of the important role that storm-scale processes can play in the formation of bowing.

The Cohen et al. (2007) study, which focused on nonsevere MCSs, severe MCSs, and severe derecho-producing MCSs, found that the best discriminators for distinguishing severe wind-producing MCSs from nonsevere MCSs were deep-layer wind shear and low- to upper-level wind speeds, together with 0–1-km system-relative wind speeds and midlevel environmental lapse rates. For their most intense events, the derecho-producing MCSs, they noted that deep-layer shear, particularly in the 0–10-km layer, worked well, along with midlevel lapse rates, to discriminate the derecho events from the others. Despite prior findings that suggested large amounts of CAPE were important, they observed that CAPE and vertical differences in equivalent potential temperature only differentiate well between weak MCS environments and those leading to MCSs classified as either severe or derecho—they do not differentiate well between severe and derecho-producing MCSs. They also found that environments with large downdraft CAPE (DNCP), a measure of how much negative buoyancy a downdraft might acquire, are favorable for severe wind-producing MCSs, a result agreeing with Evans and Doswell (2001).

Because many important bow echo events occur at night (e.g., Johns and Hirt 1987; Bentley and Mote 1998; Bernardet and Cotton 1998; Davis et al. 2004; Wakimoto et al. 2006; Wheatley et al. 2006; Adams-Selin and Johnson 2010; Coniglio et al. 2012; Adams-Selin and Johnson 2013; Guastini and Bosart 2016), despite an environment seemingly more hostile for the occurrence of severe winds at ground level due to a low-level stable layer, Mauri and Gallus (2021, hereafter MG21) focused on weather parameters that might differentiate well the severity of winds in nocturnal bow echoes. They examined 132 events, with roughly one-third having no severe wind reports (referred to as NS), one-third having only low-intensity severe winds of 50–55 kt (LS; 1 kt ≈ 0.51 m s−1), and one-third having high-intensity severe winds exceeding 70 kt (HS). Despite some prior works showing that nocturnal convective systems are more poorly forecast compared to daytime convective systems (Davis et al. 2003; Wilson and Roberts 2006; Clark et al. 2007; Weisman et al. 2008), they found that several weather parameters distinguish well the severity of winds in these nighttime bow echoes. LS and NS events were discriminated well by 10 kinematic based parameters (shear, storm-relative helicity in several layers) and severe composite parameters such as the supercell composite parameter (SCP; Thompson et al. 2004), significant tornado parameter (STP; Thompson et al. 2012), and derecho composite parameter (DCP; Evans and Doswell 2001); while HS and LS events were discriminated well by thermodynamic parameters such as CAPE and the same severe composite parameters. Even though the nocturnal events were typically elevated above a near surface stable layer that did not vary among the different severity types, large values of buoyancy were a distinctive trait of HS bow echoes, especially during the early hours of the night. During these early hours, DNCP was a good discriminator for all severity types, and it remained a good discriminator between severe and nonsevere events later at night. This result is consistent with Parker (2008), Marsham et al. (2011), Hitchcock et al. (2019), Parker et al. (2020), and Hiris and Gallus (2021) that have found that nocturnal convective systems may still produce surface cold pools, and downdrafts that reach the ground, even when a low-level stable layer exists.

The significant differences found by MG21 in many parameters for nocturnal bow echoes with different peak wind severity motivates the present study to determine if similar significant differences exist for daytime bow echoes, and how the best discriminating parameters vary between diurnal and nocturnal bow echoes. A description of the data and methods is presented in section 2. Section 3 discusses single and multiple parameter results while comparing to the findings of MG21 for nocturnal bow echoes. A general summary and conclusions are presented in section 4.

2. Data and methodology

A sample of 158 warm-season diurnal bow echo events was selected to compare near-storm environmental conditions to those found for nocturnal bow echoes in MG21. The events occurred during the months of April–August over the period from 2010 to 2018. All events occurred east of the Rocky Mountains (Fig. 1) as in MG21, but the diurnal events were distributed differently from the nocturnal events. Most of the events in MG21 occurred in the central United States with only a few more easterly cases. The diurnal events were more evenly distributed across the central and eastern United States. Because of this difference in distribution, a second dataset with three-fourths of the cases east of 85°W longitude randomly removed was created and analyzed alongside the original dataset to examine if results differ simply because of differences in the spatial distribution of reports. Differences were relatively small and thus results using the entire dataset are emphasized below.

Fig. 1.
Fig. 1.

Location of the bow echo events examined with severity of the winds colored according to the legend in the upper right. The dots indicate the location of the maximum wind assigned to a storm report for the severe classes and the apex of the bow for the nonsevere.

Citation: Weather and Forecasting 37, 12; 10.1175/WAF-D-21-0213.1

Diurnal bow echoes were defined as a bowing convective line that reached its peak intensity (largest area of bowing echo exceeding 50 dBZ) between 1400 and 0000 UTC. Most of the bow echoes occurred after 1900 UTC, with the peak hour of occurrence being 2300 UTC (Fig. 2). The bow echoes were located by subjectively examining loops of composite reflectivity data from past national radar mosaics from the National Center for Environmental Information (https://gis.ncdc.noaa.gov/maps/ncei/radar). No standard guideline exists for determining that a system is a bow echo, and the process is inherently subjective. For our sample of cases, events were generally chosen if a noticeable amount of curvature existed in the leading line convection and was sustained for at least 30 min. These criteria are similar to those used in Klimowski et al. (2003, 2004) and Adams-Selin and Johnson (2010). Although the subjective nature of bow echo classification is problematic, our focus on prediction of the severity of their winds reduces somewhat the problems, since forecasters and the public care more about the wind severity than whether the amount of curvature in the echo should suffice for it to be called a bow. Bow echoes that met the criteria for this study were added to the list of cases. As in MG21, most relevant bow echoes occurring during 2010–18 that met our wind severity criteria (defined below) were used, but a small number of cases may have been missed.

Fig. 2.
Fig. 2.

Time distribution of cases (peak wind intensity or best bow apex) used in the analysis.

Citation: Weather and Forecasting 37, 12; 10.1175/WAF-D-21-0213.1

The monthly distribution of cases was generally similar between all severity types (defined below) both at night and in the daytime (Fig. 3), although the June peak was more pronounced for HS events occurring at night than for other severity types, and NS events during the daytime were most common in April and August, not in June or July as was the case for other severity types. If one focuses on seasonal distributions, grouping April and May together since those months tend to have stronger synoptic flow and weaker instability than the other three months, differences are small. Thus, it is unlikely most of the results to follow are strongly influenced by the time of year in which the bow echo occurred, although the trend for more of the daytime NS events being in April and less in June than for nocturnal ones is relatively more noticeable, as is the trend for more nocturnal HS events to be in June and less in May than for daytime events.

Fig. 3.
Fig. 3.

Monthly distribution of cases used in this study, expressed as percentage of all events of that severity type for either night (dashed; cases from MG21) or day (solid).

Citation: Weather and Forecasting 37, 12; 10.1175/WAF-D-21-0213.1

The methods used for the analyses presented in the current study closely follow those of MG21, and the following paragraphs are drawn largely from that source for reader convenience. The cases were selected and categorized as nonsevere (NS), low-intensity severe wind (LS), or high-intensity severe wind (HS) using the same criteria as MG21. The criteria were “no severe winds or wind damage reports for at least 6 hours before and after the time of maximum bow echo development (largest area within bow of reflectivity greater than 50 dBZ)” for NS cases, “all wind reports were in the range of 50–55 kt” for LS cases, and “at least one severe wind report with a magnitude greater than 70 kt occurred” for HS cases. The wind reports had to occur within a bowing echo to be included. The dataset contains 37 NS cases, 64 LS cases, and 57 HS cases. Like MG21, the present study relies on estimated wind reports in addition to measured wind reports to create a large enough sample of cases to analyze, and focuses on the maximum reported wind severity instead of the number of wind reports. Prior studies have noted the deficiencies in the storm reports database, including problems with wind estimation (e.g., Doswell et al. 2005; Edwards et al. 2018), nonmeteorological trends in report frequency (Weiss et al. 2002), and impact of population density, land use, and time of day on likelihood of strong winds being reported (e.g., Trapp et al. 2006). Tirone et al. (2020), for example, found that 39% of all estimated thunderstorm wind damage reports during the period 2007–18 have a value of exactly 50 kt, while only 13% of measured values have this value. A sensitivity test was performed where we only used measured reports for the LS cases, reducing the sample size to 32, and measured reports or estimates where the damage was severe enough to justify estimates over 70 kt for the HS cases. This reduced the HS sample to 49. (Changes in results were minimal when the smaller sample was used, with only 1 of the 87 significance tests shown in Table 2 having a change in p value of more than 0.1, and less than 10% of the entries in the table changing relative to significance with 95% confidence.) Thus, to allow a fair comparison to MG21, we chose to include both types of wind reports. It is likely that our approach is generally insensitive to problems with the wind estimates because the three severity categories are distinctly different. Even if winds were overestimated in some LS cases, documented damage did occur in those events, unlike the NS cases, implying stronger winds than in the NS cases. In addition, our HS category uses a threshold (over 70 kt) higher than that used for significantly severe wind (65 kt) by the Storm Prediction Center, so any errors in estimates are unlikely to be large enough to cause overlap between the LS and HS cases. Nonetheless, it is still possible that for thunderstorms occurring in areas with low population density and few trees, especially at night, peak bow echo winds might be stronger than the values listed in the database.

Hourly SPC mesoanalysis sounding output, a dataset with 40-km grid spacing based on the RUC (before May 2012) and RAP (after May 2012) analyses modified by surface observations via a two-pass Barnes objective analysis (Bothwell et al. 2002), provided all weather parameter information that was averaged over the nine grid points nearest to the highest wind report for LS and HS cases and the bow echo apex for NS cases (Table 1). This dataset was used despite having coarser grid spacing than the native RUC and RAP output (13 km) because it is available in real time and commonly used by forecasters anticipating severe weather threats. In addition, a long-term archive is available and numerous parameters that are a combination of other more basic variables are already computed and contained within it. Even though observations are used in these analyses, it is important to remember that the analyses are also influenced by the background model (RUC or RAP) and thus biases are possible (e.g., Thompson et al. 2007; Coniglio 2012; Cohen et al. 2015). The mesoanalysis output was mostly taken from the analysis hour immediately before the timing of the wind report or most pronounced bowing, but the previous hour was used if the report was within 10 min of the analysis time to reduce contamination from the convective event. Because bow echoes typically move rapidly, as would be expected with the 0–6-km pressure-weighted wind speed computed from the data in Table 1 being 16.5 m s−1, upstream convection would be unlikely to impact much of the averaging box. To determine the amount of impact this limited contamination might have, a sensitivity test was performed using only parameters valid at the point closest to the center point instead of the 9-point region, and it was found that statistical significance of differences between parameters for different bow echo wind severity levels (Table 2) changed in less than 10% of the 84 entries in the table, usually because of changes in the p-value magnitudes no greater than 0.05. The only parameter substantially impacted was VMXP, the meridional wind component at the best CAPE level, which no longer has statistically significant differences between NS and the other two types when only a single point is used. This likely reflects large changes in the meridional component of the wind at the level of best CAPE in the vicinity of the bow echoes. Overall, though, this test strongly suggests that convective contamination did not substantially influence our results. It is common to use gridded datasets based on model analyses to represent proximity soundings (Evans and Doswell 2001; Doswell and Evans 2003; Thompson et al. 2003; Coniglio et al. 2004; Cohen et al. 2007; Potvin et al. 2010; Thompson et al. 2012; Reames 2017).

Table 1

Name, description, and mean values of the parameters examined (the highest absolute values for each parameter are in bold), categorized as kinematic, thermodynamic, and composite.

Table 1
Table 2

Results from bootstrapped paired t tests (given in %) for all comparisons between two severity types for daytime events. The p values greater than 5% are not shown. Parameters for which no differences were statistically significant for any of the three pairs are not shown. An asterisk indicates a change from the significance found in MG21 for nocturnal bow echoes, whereby a p value ≤5% here for daytime bows was >5% for nocturnal events, or a value here >5% was ≤5% for nocturnal events.

Table 2

Our study focuses on the same 43 near-storm parameters examined in MG21, which included kinematic properties, thermodynamic properties, and composite indices (Table 1). The mesoanalysis dataset used by MG21 and the present study includes 335 parameters, and the 43 chosen for analysis were not meant to be an exhaustive list of all possible predictors but instead parameters that MG21 believed could differ among the different severity types based on the extensive prior research done on thunderstorm winds. The composite indices are the supercell composite parameter (SCP; Thompson et al. 2004), a function of effective storm relative helicity (SRH; based on Bunkers right supercell motion; Bunkers et al. 2000), most unstable CAPE (MUCP), most unstable CIN (MUCN), and 0–6-km wind shear magnitude (S6MG); significant tornado parameter (STP; Thompson et al. 2012), using surface-based CAPE (SBCP), 0–1-km SRH (SRH1), and S6MG; derecho composite parameter (DCP; Evans and Doswell 2001), a function of downdraft CAPE (DNCP), MUCP, S6MG, and the 0–6-km mean wind; and XTRN, the product of maximum mixing ratio (MXMX) and wind speed at the most unstable parcel level (MUPL). Many of these parameters examined by MG21 have been shown to be useful in distinguishing convective mode and observed severe weather (Johns 1993; Brooks et al. 1994; Evans and Doswell 2001; Doswell and Evans 2003; Thompson et al. 2003; Kuchera and Parker 2006; Thompson et al. 2012; Hampshire et al. 2017; Reames 2017).

The statistical significance of differences among the 43 parameter distributions were determined using bootstrapped paired t tests (Mendenhall and Sincich 2007) and nonparametric Wilcoxon signed rank-sum tests (Wilks 2011). As in MG21, a significance level of p = 5% was used to determine if differences were statistically significant. The bootstrapped paired t tests are emphasized below because the results of the two tests were very similar; however, when noteworthy differences occurred, they are discussed.

The Heidke skill score (hereafter HSS; Heidke 1926) and corresponding threshold values also were calculated to estimate how well each parameter might work as a forecasting aide. It is noted that HSS is just one metric to gauge discriminatory skill in our dataset. HSS is defined as
HSS=2adbc(a+c)(c+d)+(a+b)(b+d),
where a, b, c, and d are the hits, false alarms, misses, and correct nulls, respectively. An HSS of 1 indicates the most skill, 0 indicates no skill, and negative values mean that a chance forecast is better.

With HSS, optimal threshold values xopt,i can be obtained that maximize the HSS (Kuchera and Parker 2006; Reames 2017) for each parameter. As in MG21, an HS event would be forecast if the value of a forecast parameter exceeds xopt,1, and an NS event would be forecast if the parameter value was lower than xopt,2. Two exceptions are for midlevel relative humidity values and the average kinematic vertical velocity between the most unstable parcel level and lifting condensation level (VKLC), for which the reverse is true for both severity types (lower relative humidity is more favorable for strong wind, and the vertical velocity is negative for upward motion). An LS event would be forecast if the parameter value is between the xopt,3 and xopt,4. The same methods were used in the examination of combinations of two parameters, except that the condition had to apply to both parameters.

3. Results

The distribution of the near-storm environmental parameters and thermodynamic soundings among HS, LS, and NS events is analyzed below for the sample of daytime bow echoes, and the results are compared with the nocturnal events studied in MG21. To facilitate comparison, discussion will emphasize those parameters that differed significantly between at least two of the bow echo wind severity categories, and parameters generally will be discussed in the same order as in MG21, with shear-related parameters discussed first, then thermodynamic, and finally composite parameters. Statistical significance of differences between weather parameters for the different severities of bow echo winds during the daytime, and the forecast skill of single and double parameter approaches are also analyzed, along with differences between the forecast skill valid for daytime and nocturnal events.

a. Single-parameter distributions

Since Weisman (1993), among others, have pointed out the important role that vertical wind shear plays in bow echo occurrence, it should not be a surprise that some shear parameters discriminate well between severity levels in both nocturnal (Table 2 in MG21) and diurnal (Table 2) events. However, the parameters which are significantly different and the severity levels which can be discriminated well vary between nocturnal and diurnal bow echo environments (Tables 1 and 2; Fig. 4). For instance, the bootstrapped paired t test showed no low-level shear parameter (0–1- and 0–3-km shear, and 0–3-km SRH) to differ significantly among the three types of wind severity for diurnal events, while the rank-sum tests (not shown) indicated that SRH3 was significantly higher for HS events than LS or NS events, and that 0–3-km zonal wind shear component (U3SV) could significantly discriminate between NS and HS events. This lack of significance with the paired t test for daytime events contrasts with the finding of MG21 that both low-level shear and deep layer shear (0–6, 0–8 km) discriminated well between nonsevere and severe events, with NS environments having significantly weaker shear, while shear did not discriminate between LS and HS events. The lack of discriminatory ability of the low-level shear for wind severity for daytime cases is somewhat consistent with Cohen et al. (2007) who found that deeper layer shear discriminated better between nonsevere MCSs and those that did produce severe wind. The difference in behavior of the low-level shear as a discriminator for nighttime events may be due to the important role the Great Plains nocturnal low-level jet (LLJ) plays for severe convection at night. A strong LLJ typically leads to large 0–1-km shear, because even though its peak intensity often occurs near or below 500 m (e.g., Shapiro et al. 2016; Smith et al. 2019) the LLJ does result in some enhancement of flow at 1 km (e.g., Stensrud 1996; Gebauer et al. 2018). Although the LLJ does not extend up to 3 km, strong LLJ events often occur in association with strong synoptic-scale troughs (e.g., Uccellini 1980) that would have relatively stronger 0–3-km shear than in more quiescent situations.

Fig. 4.
Fig. 4.

Box-and-whisker plots for the full sample of cases of SRH1 and SRH3 (values plotted along the left axis), and S1MG, U3SV, V3SV, U6SV, S6MG, and U8SV (values plotted along the right axis). The color-filled boxes are for NS (blue), LS (green), and HS (orange). The whiskers span the interval from the 10th to 90th percentiles, and the boxes enclose the 25th–75th percentiles. Outliers, or points outside the whiskers, are not plotted.

Citation: Weather and Forecasting 37, 12; 10.1175/WAF-D-21-0213.1

For diurnal environments, shear in a deeper layer significantly discriminates between NS and HS events and LS and HS events. NS and LS events have similar wind shear values while HS events have greater wind shear values. MG21 found that nocturnal HS events have a similar average 0–6-km shear magnitude (S6MG) value (41.1 kt) to derecho producing MCSs (43 kt in Cohen et al. 2007; 40 kt in Coniglio et al. 2004) and a S6MG value not much smaller, 36.9 kt, for LS events. Diurnal HS events also have a similar S6MG value to nocturnal HS events (40.2 kt), but diurnal LS events only have a value of 29.5 kt. The similar value of S6MG for HS events to derecho producing MCSs, both during the night and the day, happens despite only around 10% of our events being derechos during both time periods (not shown). Cohen et al. (2007) did find that deep layer shear, such as in the 0–6- or 0–10-km layer, was among the best discriminators for derecho-producing MCSs. The significantly smaller value of S6MG for LS events is associated with much weaker midtropospheric flow (500-hPa wind speeds of about 12.5 m s−1 for both NS and LS events, increasing to 17.5 m s−1 for HS cases) than in HS events (Fig. 5), and 2.5 m s−1 weaker midtropospheric flow than in the nocturnal LS events examined in MG21. Surface winds differ little among severity types (Table 1) and between nocturnal and diurnal LS events.

Fig. 5.
Fig. 5.

Hodographs constructed by averaging winds for (left) NS, (center) LS, and (right) HS events. Height data shown in meters (color bar on right) for the average profile, with speed indicated by rings every 10 m s−1. Individual hodographs for all cases in each severity category are shown in gray.

Citation: Weather and Forecasting 37, 12; 10.1175/WAF-D-21-0213.1

The decreased value of S6MG for diurnal bow echoes is not due to more of these cases happening in the summer (Fig. 3) when winds are typically weaker in the middle troposphere. It is possible that the much larger values of CAPE present for daytime LS events (cf. Table 1 to Table 1 in MG21) compensate for weaker synoptic forcing implied by much weaker vertical velocities at the LCL of the most unstable parcel (VKLC) and weaker midlevel flow during the day compared to the night to allow marginally severe wind in daytime bow echoes in environments that would be unlikely to result in severe wind at night.

The biggest difference among the average hodographs (Fig. 5) for the three different severity types is in the winds at high levels, generally above 6 km. These winds are much weaker for the LS cases than both NS and HS, with the strongest upper-level winds (generally in the 6–14-km layer) being in the HS cases. In addition, the hodograph in the 2–6-km layer is also larger for the HS events. In all systems, wind directions veer from southerly at low levels to west-southwesterly or westerly at high levels, although individual cases evidence great variability, with strong southwest or south-southwest winds at high levels for some LS and HS events. Unlike nocturnal bow echo environments, average winds in the lower and mid troposphere did not have a stronger southerly component for severe events. The most noticeable difference in wind directions among the daytime severity types may be increasingly backed surface winds in the NS cases compared to the other events, implying that more of these cases may have occurred near or just on the cold side of boundaries. The much greater values of SBCN and lower values of the CAPE variables (Table 1) for NS cases is also consistent with these events being near or just on the cold side of boundaries.

No layer of shear involving the meridional wind component significantly differs among severity types for diurnal events, in contrast with nocturnal events where MG21 found the 0–3-km meridional shear component (V3SV) to significantly discriminate between severe and NS events. The different behavior for V3SV may reflect the presence of a stronger synoptic-scale trough with stronger southerly flow on the downstream side for events producing more intense winds at night. As already discussed above, such synoptic environments often favor the development of strong nocturnal LLJs.

The greater importance of the LLJ in severe nocturnal systems is also implied by the differences in locations of the bow echoes found in the present study for daytime events (Fig. 1) versus those found in MG21 for nocturnal events (Fig. 1 in MG21). Nocturnal bow echoes were more restricted to the central United States where strong nocturnal Great Plains LLJs are more common (e.g., Bonner 1968; Stensrud 1996). Similarly, the differentiating ability at night for VKLC may imply that nocturnal severe bow echoes are more dependent on mesoscale or synoptic-scale upward vertical motion than daytime events, since VKLC did not vary significantly for daytime events. It is possible that differences in the thermodynamic environments of severe daytime bow echoes compared to those at night (discussed below) allow stronger evaporative cooling-driven downdrafts that can produce severe wind even when thunderstorms have formed without mesoscale lift in the region. At night, the low-level stable layer likely makes it more difficult for severe thunderstorms to be present without some source of larger-scale lift.

The wind component and shear values discussed to this point have been relative to the ground. Idealized studies of the importance of wind shear in convective evolution (e.g., Weisman 1993) have focused on line-normal shear. Because the storm-relative reference frame varies not only among cases but also often over time and even location during the life cycle of a single event, line-normal and along-line components of the winds and shear are not available in the mesoanalysis dataset used for the present study. Precise computation of storm motion is difficult for bow echoes as they rapidly evolve and distort the shape of linear systems. For the present study, an analysis was performed to determine the general orientation of the convective systems in which the bow echoes were present to allow some subjective inferences into how the ground-relative winds and shear vectors would manifest as along-line and line-normal components. Because the general orientation was used and not that of the bowing segment, this should be roughly equal to the orientation of the system prior to formation of the bow echo. It was found that for all three severity categories both day and night, the average orientation of the systems was remarkably similar, with no more than a 9° variation, and the average system orientation ran from 259° to 79°, or from a bit south of west to just north of east (not shown). Thus, the shear vectors (Table 1) would have been roughly in the along-line direction, as well as the 0–8-km pressure-weighted wind. The 0–6-km pressure-weighted wind and the winds at lower levels such as the best CAPE level and surface would have had more of a line-normal component, feeding into the system from ahead of it.

Although it is well known that wind shear plays an important role in the organization of severe thunderstorms, the thermodynamic environment also plays a role through its impact on hydrometeor generation, precipitation loading, rear-inflow jet development and behavior, and evaporatively driven downdrafts. A key motivation for the focus on nocturnal bow echoes in MG21 was the uncertainty remaining in understanding how nocturnal MCSs interact with the nocturnal stable boundary layer. Thus, one counterintuitive result in MG21 may have been the finding that CIN was a poor discriminator for the severity of nocturnal events. In other words, the occurrence of intense severe winds did not imply a weaker than normal nocturnal near-ground stable layer. With those results in mind, one might also expect CIN to not discriminate well during the daytime either, when strong solar heating typically leads to a well-mixed boundary layer. However, for diurnal events, the two statistical tests used to evaluate the significance of the CIN-related parameter differences disagreed. The t tests showed the CIN parameters to not differ significantly among severity types, but the rank-sum tests (not shown) found SBCN to significantly discriminate between NS and severe events, and MUCN and M1CN to significantly discriminate between NS and LS events for the dataset with some of the most easterly cases removed. For the full dataset and the one with eastern cases removed, the rank-sum tests found significant differences between NS and HS events. Overall, though, CIN parameters do not work particularly well to discriminate the severity of winds in both diurnal and nocturnal events. This result suggests that the severity of bow echo winds may be primarily a function of the intensity of evaporatively driven downdrafts and/or momentum brought down from above, as will be seen in the discussion of CAPE, lapse rates, and relative humidity to follow. For the bow echoes in our sample and in MG21, the amount of CIN in the different cases likely does not vary enough among cases to play much of a role in preventing the wind from reaching the surface (Fig. 6).

Fig. 6.
Fig. 6.

As in Fig. 4, but for SBCP, MUCP, M1CP, and DNCP (values plotted along the left axis), and SBCN, MUCN, and M1CN (values plotted along the right axis).

Citation: Weather and Forecasting 37, 12; 10.1175/WAF-D-21-0213.1

Despite the MG21 cases happening at night, they found that CAPE parameters did discriminate well between HS and LS/NS environments but not NS and LS environments. CAPE also had some discriminatory ability for diurnal events, with NS bow echo environments having the lowest CAPE values of any severity type (Table 1; Fig. 6) with over 800 J kg−1 less SBCP and MUCP than severe events. It is possible the much lower CAPE value for daytime NS events, and thus the bigger differences compared to LS than was present at night, is influenced some by the greater number of daytime NS events occurring in April (when CAPE is typically lower) than nocturnal ones. These significantly different CAPE values make the three CAPE parameters (surface-based, lowest 100-hPa mixed layer, and most unstable) strong significant discriminators between NS and severe environments (Table 2). The larger value of CIN for daytime NS events also implies more of these may have been elevated than LS or HS events. The CAPE parameters do not discriminate well, however, between diurnal LS and HS environments. Although this result differs from what was found for nocturnal events by MG21, possibly influenced by the larger number of nocturnal HS events happening in June and less in May than for daytime events (which should help increase average CAPE values for nocturnal HS events compared to daytime ones), it is similar to the findings of Cohen et al. (2007) that CAPE discriminates well between MCS environments that produce weak events and severe or derecho events but not between severe and derecho events (i.e., CAPE alone cannot distinguish well how intense the severe winds will be, just that winds are more likely to be severe when CAPE is higher).

The CAPE parameters discussed above provide insight into the thermodynamic instability available which influences the strength of thunderstorm updrafts. Other thermodynamic parameters, however, also influence bow echoes, either by giving another indication of instability (such as lapse rates), or how favorable the environment might be for wind production through evaporative cooling. A key parameter that should correlate well to wind severity is downdraft CAPE, which is an integrated measure of negative buoyancy for a descending parcel that had cooled to its wet bulb temperature. In this SPC dataset, DNCP is computed by finding the minimum equivalent potential temperature pressure level in the lowest 400 hPa of the sounding, and that parcel is then traced downward to the surface assuming saturated descent (following a moist adiabat). Of all the single parameters tested in the present study, DNCP most significantly differs among all three severity types, at the 95% confidence level (Table 2; Fig. 6). It should be noted, however, that for the dataset with some of the more easterly cases removed to better match the geographical distribution of cases in MG21, the differences are only significant at the 90% confidence level (not shown). DNCP also strongly discriminated for nocturnal bow echo wind severity (MG21), despite the common occurrence of stable boundary layers in those events which would tend to reduce DNCP. In fact, the average values for DNCP for all three severity types at night was greater than for all three types during the day (cf. Table 1 in MG21 to Table 1 here). This counterintuitive result is likely related primarily to drier lower levels in nocturnal bow echoes (cf. 3KRH, RH70, and RH80 in Table 1 to the values in Table 1 of MG21).

In addition to DNCP, relative humidity in the lower and middle troposphere should also provide insight into the likelihood that substantial evaporative cooling might occur and lead to strong winds (e.g., Mahoney and Lackmann 2011). MG21 found that RH70 can discriminate between nocturnal HS and LS/NS events, as HS events are associated with drier air at 700 hPa. However, RH70 does not differ significantly between the varying severities for diurnal events (Table 2; Fig. 7). While HS diurnal cases have the lowest average 700-hPa RH values (Table 1), the spread among cases is large (Fig. 7), larger than in nocturnal events (Fig. 4 in MG21), and it is common to have HS events with much moister RH70 values than those for LS or NS events. RH80 significantly discriminates only between diurnal NS and HS events, unlike with nocturnal events where it discriminates between HS and both NS and LS events. RHC5 significantly discriminates between all types of diurnal events while it only discriminates between HS and NS nocturnal events. These results suggest that although dry air in the lower to middle troposphere plays an important role in the amount of evaporative cooling that can occur to develop strong winds in bow echoes, the layer that discriminates best moves to higher levels during the daytime than at night. It is unclear why the higher levels work better during the daytime, although it is possible that some moistening would occur during the daytime at 850 hPa due to the mixing within a deepening boundary layer, and for cases in the High Plains, this moistening could extend through 700 hPa, diminishing the effectiveness of some of the lower-level relative humidity parameters as discriminators.

Fig. 7.
Fig. 7.

As in Fig. 4, but for RH80, RH70, and RHC5 (values plotted along the left axis), and LR85 and LR75 (values plotted along the right axis).

Citation: Weather and Forecasting 37, 12; 10.1175/WAF-D-21-0213.1

Other common parameters for predicting storm severity include the lapse rate over various layers. In MG21, LR75 was found to be one of the best discriminators between all three wind severity types at night. For daytime bows, LR85 works best (Fig. 7), and has almost as low of p values as DNCP (Table 2), which likely reflects the fact that steep lapse rates in low levels can promote large DNCP, and permit strong negative buoyancy if relative humidity is low. LR85 discriminates between severe and nonsevere nocturnal events while it discriminates between all types of diurnal events. LR75 discriminates between all types of nocturnal events while it only discriminates between HS and LS/NS diurnal events. It is possible this difference in different layers between day and night is related to the different distribution of cases with more nocturnal events (MG21) happening in the central Plains than diurnal cases. At higher elevation locations, the nocturnal stable layer is occasionally deep enough to cool the 850-hPa temperatures and reduce LR85. As might be expected with the discriminatory ability of relative humidity and midlevel lapse rates, the maximum equivalent potential temperature difference in the lowest 3 km (TE3K) discriminates between severe and nonsevere events for both nocturnal and diurnal events. Similarly, the surface equivalent potential temperature (STHE) also discriminates between severe and nonsevere bow echoes for diurnal events, but only between HS and NS/LS events for nocturnal events. The much lower STHE value for NS events, as with the greater SBCN shown earlier, strongly suggests more of these events were elevated.

A few other thermodynamic parameters in the SPC mesoanalysis dataset were found to have discriminatory ability. The maximum mixing ratio (MXMX) discriminates between severe and nonsevere diurnal events in contrast to nocturnal events where it was found to discriminate between HS and LS/NS events (MG21). This result may suggest that large mixing ratios are important for daytime events because they are associated with more unstable environments capable of producing severe weather, and the highest values are likely to be found near the surface. At night, the maximum values are likely to be located somewhat aloft, and may play less of a role in storm severity so that differences between LS and NS cases are small. Instead, greater peak mixing ratios may be a needed ingredient only in the bow echoes producing the most intense winds where they may allow greater precipitation loading or more potential for microphysical cooling if larger hydrometeors are produced, to assist the acceleration of flow via downdrafts. XTRN, a product of the maximum mixing ratio and the wind speed at the most unstable parcel level, also discriminates between severe and nonsevere events both day and night. This is not surprising considering the discriminatory ability of MXMX.

Finally, it is worth investigating the performance of several composite parameters that were found to discriminate well among all three wind severity types for nocturnal bow echoes. The discriminatory effectiveness of the severe composite parameters SCP, STP, and DCP varies little between nocturnal and diurnal events (Table 2; Fig. 8; Fig. 5 of MG21). The largest difference is that nocturnal events tend to have greater values of SCP, STP, and DCP than diurnal events for all severity types. HS events have the highest values for all composite parameters while NS events have the lowest values for both nocturnal (MG21) and diurnal events. MG21 theorized that the effectiveness of SCP, STP, and DCP for discriminating the severity of nocturnal events is due to them incorporating at least one parameter that can differentiate between severe and nonsevere and at least one that can differentiate between HS and LS/NS, and the same behavior is true for daytime events even though some differences are present in the discriminatory ability of individual parameters for bow echo severity between nocturnal and diurnal events. Among the parameters used to compute SCP, MUCP differentiates well between LS and NS, and HS and NS events, while S6MG differentiates well between HS and NS, and HS and LS events during the daytime. STP also is computed from S6MG, and uses SBCP instead of MUCP, but SBCP differentiates well the same severity types as MUCP. Finally, DCP is computed from several parameters that discriminate well, including MUCP and S6MG like SCP, but also XTRN that discriminates similarly to MUCP, and DNCP which distinguishes well among all three wind severity categories. The other two composite parameters do not use DNCP, and its inclusion in DCP likely explains why it works so well.

Fig. 8.
Fig. 8.

As in Fig. 4, but for SCP (values plotted along the left axis), and STP and DCP (values plotted along the right axis).

Citation: Weather and Forecasting 37, 12; 10.1175/WAF-D-21-0213.1

b. Single-parameter forecast skill

As in MG21, the predictive skill of individual parameters for determining wind severity was evaluated by finding the thresholds that yielded the best HSS. MG21 found that the four composite parameters, XTRN, SCP, STP, and DCP, were among the most skillful parameters for forecasting the severity of nocturnal events. The utility of parameters as measured by HSS differed in some ways for diurnal events (Table 3), with the most notable change being a general decrease in skill for the best performing parameters for daytime events compared to nighttime ones, except for the top-performing one (DCP) for HS events. XTRN forecast skill differed most dramatically, as its HSS value decreased from 0.51 for nocturnal NS events (Table 5 of MG21) to 0.23 for daytime ones, from 0.33 for nocturnal LS events to 0.13 for daytime ones, and from 0.36 for nocturnal HS events to 0.21 for daytime ones. Because XTRN is simply the product of MXMX and the wind speed at the level of the most unstable parcel, and MXMX alone still showed significant ability to differentiate between severe and nonsevere daytime cases (Table 2), it is likely the poor performance of XTRN for daytime events is related more to smaller variation in low-level flow among severe categories, at least partly because the most unstable parcel for daytime events is more frequently near the ground (Table 1 shows little change in surface wind components among severity types). For nocturnal events, the greater variability among severity types may be influenced by greater variations in the level of the most unstable parcel, implied by the larger differences between SBCAPE and MUCAPE for all three severity categories for nocturnal bow echoes compared to daytime ones, and to the strength of the low-level jet.

Table 3

The 10 highest HSS values and their corresponding optimal threshold range for all single-parameter Heidke skill score tests for each severity type.

Table 3

On the other hand, SCP earned a similar HSS value for predicting both nocturnal and diurnal HS events, but a much lower HSS for predicting both diurnal NS and LS events compared to nocturnal ones. The less skillful values may be related to the S6MG parameter, which is a component of SCP, not differing among these two severity categories for daytime bow echoes (Table 1) but differing greatly for nocturnal ones (Table 1 of MG21). This result is consistent with the idea that severe wind production in nocturnal bow echoes may require stronger synoptic-scale systems with greater deep-layer shear. STP was the most skillful parameter for distinguishing nocturnal NS and HS events with HSS values of 0.6 and 0.45, respectively (MG21). However, while it is still a useful parameter for distinguishing diurnal NS and HS events, its HSS values decrease markedly, likely related to the fact that one of the STP components, SBCP, does not differ much between LS and HS events (Table 1) and another, S6MG, does not really differ between LS and NS events. This contrasts with nocturnal events where the two parameters differ much more between the three wind severity categories. DCP was the only composite parameter for which skill for predicting severity of diurnal events is similar to or greater than its skill for predicting severity of nocturnal events. This is consistent with the idea that evaporative cooling may play a bigger role in generating strong winds in daytime bow echoes when a deeper, well-mixed boundary layer is likely present compared to at night.

Regarding the specific wind severity categories, MG21 found that the most skillful parameters for nocturnal NS events were STP, SCP, and DCP. These three parameters all have lower HSS values for diurnal NS events, although DCP remains one of the most skillful diurnal parameters, along with DNCP which is a component of DCP. MG21 found VMXP and STP to be the most skillful parameters for forecasting nocturnal LS events. This differs from diurnal LS events as diurnal VMXP and STP have relatively low HSS values. The most skillful parameters for diurnal LS events are DCP again, and U8SV. STP and MUCP are the most skillful parameters for nocturnal HS events (MG21), but STP and MUCP are less skillful for daytime events. The most skillful parameters for diurnal HS events are DCP again and LR75. It is worth noting that the HSS value for DCP for HS events is the only HSS value for the top-performing predictor for any wind severity type that is higher for daytime events than nocturnal ones. This result may suggest that in daytime bow echoes, the severity of winds is more dependent on the amount of evaporative cooling that occurs in low levels, which is related to downdraft CAPE, than in nocturnal events. The other parameters used in computing DCP, (MUCP, 0–6-km mean wind, and 0–6-km shear), do not show as much variation among severe types for diurnal events as they do for nocturnal ones (cf. Table 1 to Table 1 in MG21).

c. Two-parameter space analysis

MG21 found that combining two parameters to predict wind severity in nocturnal bow echoes improved skill by roughly 20% over that obtained when a single parameter was used. They found the combination of DCP and SRH3 to have the highest HSS value (0.71) for a nocturnal NS event with a probability of detection (POD) of 0.82 and a false alarm rate (FAR) of 0.2. However, this combination for daytime events, while still more skillful than all the single-parameter forecasts except that of DCP, is less useful for discriminating between severe and nonsevere events (Table 4) as its HSS value drops to 0.54, its POD drops to 0.59, and its FAR increases to 0.31. For nocturnal events, SRH3 discriminates between NS and LS/HS events and has a HSS value of 0.48 (Table 5 of MG21). It is less effective for diurnal NS events because diurnal LS and HS events have much lower SRH3 than nocturnal events which causes their environments to largely overlap with the environments of NS events. SRH3 can no longer significantly discriminate between NS and severe events. It is likely that SRH3 works well at night because it will typically be large when the low-level jet is strong, and other parameters previously discussed have suggested that greater severity of winds happens in nocturnal bow echoes when the low-level jet is stronger. The low-level jet is less common during daytime, and thus SRH3 likely varies less among the severity types for daytime bow echoes, although SRH3 is also a function of storm motion which is influenced by S6MG.

Table 4

The five highest HSS values and their corresponding optimal threshold ranges for all two–two-parameter Heidke skill score tests for each severity type. Parameter 1 is the first item mentioned in column 2, while parameter 2 is the second.

Table 4

DCP remains a skillful predictor for diurnal events, appearing in 12 of the 15 combinations (Table 4) earning the highest HSS values (5 for each severity type). The most skillful combination for diurnal NS events is STP and DCP with a HSS value of 0.57 and a POD of 0.62 and FAR of 0.28. This HSS value is lower than that found for the best combination for predicting nocturnal NS events, which was DCP and SRH3 with an HSS of 0.71. However, both STP and DCP tend to be smaller for diurnal NS events compared to LS and HS events, with little overlap between the values for NS events and severe events, explaining the good discrimination ability. It is understandable that severe wind would not be likely when STP and DCP are small, since both are a function of parameters related to instability and deep-layer wind shear, with DCP also being a function of DNCP.

DCP and VMXP are the most skillful pair of parameters for predicting nocturnal LS events with a HSS value of 0.5, POD of 0.56, and FAR of 0.28 (MG21). For diurnal events, the combination is less skillful as its HSS value drops to 0.35. The POD for DCP and VMXP is actually greater for diurnal events (0.625) than nocturnal events, but the improved POD comes at the expense of a much worse FAR (0.39). DCP remains a useful parameter for predicting diurnal LS events and is part of several of the most highly skilled combinations, but VMXP is less skillful for predicting diurnal events than nocturnal events, not appearing at all in the top five combinations for daytime events (Table 4) but appearing in three of the top five for nocturnal events (Table 6 of MG21). This difference in the discriminatory ability of VMXP could be related to the nocturnal low-level jet playing an important role in nocturnal events while not being present typically for diurnal events. A strong low-level jet would lead to a larger value of VMXP as it would increase the υ component of the wind, especially around the level of most unstable CAPE. The best combination for predicting diurnal LS events is TE3K and U6SV with a HSS value of 0.40, but all five of the combinations with highest HSS have similar values. For the combination of TE3K and U6SV, the POD is 0.49 and the FAR is 0.28. Again, these values, except for FAR, are noticeably worse than for nocturnal events. All five of the most skillful combinations for predicting daytime LS events are functions of some parameter that provides information about the likelihood to create strong evaporatively cooled downdrafts (DCP, TE3K, LR85).

The most skillful combination for nocturnal HS events is SBCP and U6SV with a HSS value of 0.53, a POD of 0.62, and a FAR of 0.23 (MG21). This combination is less skillful for diurnal events with a HSS value of 0.37, a POD of 0.35, and a FAR of 0.13. U6SV is one of the most skillful parameters for anticipating diurnal HS events, likely reflecting a strong zonal component aloft, but SBCP values tend to be similar for diurnal LS and HS events. The reduced ability of SBCP to discern daytime HS events results in less skill when it is combined with other parameters. The most skillful diurnal combination is DCP and U8SV with a HSS value of 0.56, a POD of 0.65, a FAR of 0.23. As with the single parameter skill scores, this is the only combination that earns higher HSS values for predicting daytime bow echo severity than the best combination for nighttime HS events. This differs from NS and LS bow echoes, where the best HSS values for daytime events were worse than those for nocturnal bow echoes. All of the best 10 combinations for diurnal NS and HS events include DCP as one of the parameters. Since DCP is a function of DNCP, MUCP, 0–6-km mean wind, and 0–6-km shear, and MUCP and the wind parameters do not differ substantially among the different severity types, differences in DNCP among the different severity types likely account for the good performance of DCP.

4. Summary and conclusions

This work closely follows MG21, but focuses on daytime bow echoes instead of nocturnal events to determine which near-storm environmental parameters discriminate best between several levels of wind severity. It also examines how these parameters differ from those found to work well for nocturnal events. 158 daytime bow echo events are examined from across the United States, east of the Rocky Mountains, with 37 NS cases (no severe wind reports), 64 LS cases (peak winds of 50–55 kt), and 57 HS (peak wind of at least 70 kt). The same 43 forecast parameters from the SPC mesoanalysis system used in MG21 are evaluated here.

It is found that low-level shear and the meridional component of the wind do not significantly differ among daytime bow echoes, unlike nocturnal events, likely highlighting the important role that the nocturnal low-level jet plays in determining the peak wind speeds in nocturnal bow echoes. Deeper layer shear, such as that in the 0–6- or 0–8-km layer, works better during daytime than at night to discriminate the HS events from the LS and NS events, and averaged soundings show the increase in shear for HS events is due to stronger winds aloft in these HS cases compared to others, suggesting potentially more intense synoptic-scale disturbances playing a role when higher severity winds occur in daytime bows. On the other hand, weaker midlevel winds in daytime LS events compared to nighttime ones, in combination with higher CAPE values for daytime LS cases suggest that marginally severe wind in daytime cases may be facilitated by more favorable thermodynamics than at night, with less need for stronger flow aloft and synoptic forcing.

For daytime events, CAPE parameters discriminated well between NS events and severe ones, but not between LS and HS, differing from nocturnal events where CAPE-related parameters discriminated between HS and the other two types. The daytime results thus are similar to Cohen et al. (2007) which found that CAPE discriminates well between MCS environments that produce weak events and severe or derecho events but not severe and derecho events. The 500–850-hPa layer lapse rate works better to differentiate daytime bow severity, whereas the 500–700-hPa layer worked better at night, possibly because 850-hPa temperatures at night cool within the stable boundary layer in higher elevation regions of the western Plains. Roughly 30% of the cases in MG21 occurred west of 100°W longitude where the nocturnal inversion frequently extended above 850 hPa. DNCP and LR85 had the best discriminatory ability between all three wind severity types, based on p values in significance testing. Both parameters are suggestive of environments with good potential for strong cold pools to form. As with nocturnal events, some relative humidity parameters discriminate well between bow echo severity levels. RH80 significantly discriminates between diurnal NS and HS events, unlike with nocturnal events where it discriminates between HS and both NS and LS events. RHC5 significantly discriminates between all types of diurnal events while it only discriminates between HS and NS nocturnal events. Again, drier air in the lower to middle troposphere favoring more intense severe bow echo wind agrees with prior studies (e.g., Mahoney and Lackmann 2011). Just as had been found for nocturnal events (MG21), CIN-related variables were among the worst discriminators of wind severity in daytime bows. Finally, composite parameters worked well to differentiate between all three severity types for daytime bow echoes, just as they had for nighttime ones, with DCP, the one including DNCP, performing best. The analysis of thermodynamic parameters suggests that both for daytime and nocturnal bow echoes, sufficient instability to produce strong updrafts capable of generating large amounts of hydrometeors, along with steep lapse rates extending to lower levels where dry air is present favors more intense wind, although which specific measure of these parameters works best to distinguish between any two wind severity categories varies between day and night.

For all but one of the single and double parameter combinations found to produce the highest HSS values for daytime bow echo wind severity prediction, the peak skill was substantially less than for nocturnal bow echoes. This result may be counterintuitive since the nocturnal stable layer would seem to offer a challenge for strong thunderstorm winds to reach the surface, which might make forecasting at night more difficult. The one exception was the DCP for prediction of HS events, which alone earned a higher HSS than that of the best-performing parameter for nocturnal events, and likewise earned a higher HSS when combined with U8SV than any two parameter pairs for nocturnal HS bow echoes. DCP ended up being the best performing single parameter for prediction of all three severity levels, and was represented in all of the top five best-performing parameter combinations for NS and HS events, and two of the top five for prediction of LS events. DCP is a function of DNCP, MUCP, 0–6-km mean wind, and 0–6-km shear. Because MUCP and the wind parameters do not differ substantially among the different severity types, and DNCP was found to work well as a single parameter at discriminating all types, it is likely DCP works so well because of its relation to DNCP. This result also suggests daytime bow echo wind severity is a strong function of the amount of evaporative cooling and negative buoyancy that is available to drive a strong downdraft. Although DCP also worked well as a predictor of nighttime bow echoes, some other parameters worked better, and DNCP did not perform as well as for daytime events, likely owing to the presence of the nocturnal stable boundary layer. As in MG21 for nocturnal events, multiparameter forecasting methods produced improved forecast skill compared with single-parameter skill, although the improvement in skill was slightly less than for nocturnal events.

The above findings should help forecasters anticipate the potential severity of bow echo winds in daytime events, as MG21 did for nocturnal ones. However, it is important to be aware of nonmeteorological factors that influence the reporting of severe winds, and to apply the results with caution in regions of low population density. In addition, the present study focused on parameters in the vicinity of the peak wind report or best bow apex, and ignored spatial variability in the vicinity of the bow echoes. Such variability could be important and should be examined in future work. Although the present study did analyze orientation of the bow echoes sampled, future work also should attempt to determine storm motion to analyze the differences in line-normal and along-line wind components and shear for the different severity categories. Finally, more extensive analysis should be performed, possibly using an idealized model like the CM1, to better understand why differences are present between parameters that work well at night, and those that work best during the day. Future work could also examine if the best discriminators vary as a function of the broader convective morphology, or scale of the bowing.

Acknowledgments.

This research was supported by National Science Foundation Grant AGS2022888. The authors thank Ezio Mauri for his extensive help in providing Python codes used during various portions of this work, and Israel Jirak and Kent Knopfmeier for access to the SPC mesoanalysis archive. Nathan Sontag assisted in the collection of some of the bow echo cases for his freshman honors project at Iowa State University. We acknowledge the constructive comments of three anonymous reviewers. We thank Daryl Herzmann and Dave Flory for their assistance with Iowa State University computational resources, and Elizabeth Tirone for some assistance with processing the data. We acknowledge the importance of open source Python code and packages (Matplotlib, NumPy, MetPy), which were used extensively during this study.

Data availability statement.

The data that supports the findings of this work are available from the UCAR Image Archive browser (https://www2.mmm.ucar.edu/imagearchive) and the NOAA/NCEI website (https://www.ncdc.noaa.gov/). The SPC mesoanalysis data (Bothwell et al. 2002) and the list of cases for the period examined in the present study are available from the corresponding author upon request.

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  • Doswell, C. A., III, and J. S. Evans, 2003: Proximity sounding analysis for derechos and supercells: An assessment of similarities and differences. Atmos. Res., 67–68, 117133, https://doi.org/10.1016/S0169-8095(03)00047-4.

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
  • Edwards, R., J. T. Allen, and G. W. Carbin, 2018: Reliability and climatological impacts of convective wind estimations. J. Appl. Meteor. Climatol., 57, 18251845, https://doi.org/10.1175/JAMC-D-17-0306.1.

    • Search Google Scholar
    • Export Citation
  • Evans, J. S., and C. A. Doswell III, 2001: Examination of derecho environments using proximity soundings. Wea. Forecasting, 16, 329342, https://doi.org/10.1175/1520-0434(2001)016<0329:EODEUP>2.0.CO;2.

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  • French, A. J., and M. D. Parker, 2012: Observations of mergers between squall lines and isolated supercell thunderstorms. Wea. Forecasting, 27, 255278, https://doi.org/10.1175/WAF-D-11-00058.1.

    • Search Google Scholar
    • Export Citation
  • French, A. J., and M. D. Parker, 2014: Numerical simulations of bow echo formation following a squall line–supercell merger. Mon. Wea. Rev., 142, 47914822, https://doi.org/10.1175/MWR-D-13-00356.1.

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
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