Nontornadic Supercells Research Articles

Impacts of Sampling and Storm-Motion Estimates on RUC/RAP-Based Discriminations of Nontornadic and Tornadic Supercell Environments

Abstract This study explores reasons for differences in discriminations of nontornadic and tornadic supercell environments between a recent study of field project (FP) radiosonde observations and RUC/RAP-based studies. Two differences are explored: 1) differences in the relative skill between near-ground and deeper-layer storm-relative helicity (SRH) and 2) differences in skill for storm-relative winds (SRWs) seen in observed soundings that are not seen in RUC/RAP-based analyses. Results show that RUC/RAP-derived near-ground SRH continues to show larger skill than deeper-layer SRH for springtime, afternoon/evening cases over the plains (the “FP” domain), although 0–1-km SRH becomes more skillful than 0–500-m SRH. The skill of kinematic variables decreases over the FP domain, as the skill of mixed-layer convective available potential energy (MLCAPE) and the percent of the low-level horizontal vorticity that is streamwise increase for significant tornadoes. Large skill is found for mean ground-relative winds (GRWs) over all layers tested, but the skill of SRWs using Bunkers motion is relatively small. The field project dataset is shown to be biased toward particularly high-end nontornadic supercells, with more tornado-favorable mixed-layer lifted condensation levels (MLLCLs), lapse rates, and low-level shear/SRH compared to the nontornadic cases in the RUC/RAP dataset over the FP domain. The skill of deeper-layer SRH, GRWs, SRWs, and MLCAPE is unusually large in the field project sample, which highlights variables that may increase the likelihood of tornadoes when other variables that relate to the supercell tornado production (low-level shear/SRH and MLLCLs) are already in a tornado-favorable range. The skill of deeper-layer kinematic variables is particularly evident when observed storm motions are used instead of Bunkers motion. Significance Statement Researchers continue to explore reasons why some storms produce tornadoes and others do not. Some recent studies show conflicting results, including the best layers to analyze the environmental wind shear and the ability of wind speeds relative to the storm to distinguish nontornadic from tornadic storms. One study is rooted in observations taken for field projects that targeted tornadic storms and the others in model analyses. Restricting model analyses to a spatiotemporal region that emulates field projects does not explain the disagreement but reveals how the skill of some other variables changes. The tendency for field projects to target particularly strong storms and the use of actual storm motions versus estimates of storm motions both contribute to the conflicting results.

Investigating Temporal Characteristics of Polarimetric and Electrical Signatures in Three Severe Storms: Insights from the VORTEX-Southeast Field Campaign

Abstract The dual-polarization radar characteristics of severe storms are commonly used as indicators to estimate the size and intensity of deep convective updrafts. In this study, we track rapid fluctuations in updraft intensity and size by objectively identifying polarimetric fingerprints such as ZDR and KDP columns, which serve as proxies for mixed-phase updraft strength. We quantify the volume of ZDR and KDP columns to evaluate their utility in diagnosing temporal variability in lightning flash characteristics. Specifically, we analyze three severe storms that developed in environments with low-to-moderate instability and strong 0–6-km wind shear in northern Alabama during the 2016–17 VORTEX-Southeast field campaign. In these three cases (a tornadic supercell embedded in stratiform precipitation, a nontornadic supercell, and a supercell embedded within a quasi-linear convective system), we find that the volume of the KDP columns exhibits a stronger correlation with the total flash rate. The higher covariability of the KDP column volume with the total flash rate suggests that the overall electrification and precipitation microphysics were dominated by cold cloud processes. The lower covariability with the ZDR column volume indicates the presence of nonsteady updrafts or a less prominent role of warm rain processes in graupel growth and subsequent electrification. Furthermore, we observe that the majority of cloud-to-ground (CG) lightning strikes a carried negative charge to the ground. In contrast to findings from a tornadic supercell over the Great Plains, lightning flash initiations in the Alabama storms primarily occurred outside the footprint of the ZDR and KDP column objects. Significance Statement This study quantifies the correlation between mixed-phase updraft intensity and total lightning flash rate in three severe storms in northern Alabama. In the absence of direct updraft velocity measurements, we use polarimetric signatures, such as ZDR and KDP columns, as proxies for updraft strength. Our analysis of polarimetric radar and lightning mapping array data reveals that the lightning flash rate is more highly correlated with the KDP column volume than with the ZDR column volume in all three storms examined. This contrasts with previous findings in storms over the central Great Plains, where the ZDR column volume showed higher covariability with flash rate. Interestingly, lightning initiation in the Alabama storms mainly occurred outside the ZDR and KDP column areas, contrary to previous findings.

Comparing Polarimetric Signatures of Proximate Pretornadic and Nontornadic Supercells in Similar Environments

Abstract While prior research has shown that characteristics of the supercell environment can indicate the likelihood of tornadogenesis, it is common for tornadic and nontornadic supercells to coexist in seemingly similar environments. Thus, some small-scale factors must support tornadogenesis in some supercells and not in others. In this study we examined polarimetric radar signatures of proximate pretornadic and nontornadic supercells in seemingly similar environments to determine if these radar signatures can indicate which proximate supercells are pretornadic and which are nontornadic. We gathered a collection of proximity supercell groups and developed a method to quantify environmental similarity between storms. Using this method, we selected pretornadic–nontornadic supercell pairs in close proximity in space and time having the most similar environments. These pairs were run through an automated tracking algorithm that quantifies polarimetric signatures in each supercell. Supercells with larger differential reflectivity (ZDR) column areas were more likely to become tornadic within the next 30 min compared to neighboring supercells with smaller ZDR column areas. In about two-thirds of pairs, the pretornadic supercell had a larger ZDR column area than the nontornadic supercell prior to its maximum low-level rotation, which is consistent with much prior work. The ZDR arcs could not discriminate between pretornadic and nontornadic supercells, and hailfall area was larger in pretornadic supercells. The separation distance between the specific differential phase (KDP) foot and the ZDR arc was larger in pretornadic supercells, yet was a limited result due to the small sample size used for comparison. Significance Statement Atmospheric conditions often indicate whether certain thunderstorms will produce tornadoes. However, sometimes multiple thunderstorms exist in a similar environment, and some produce tornadoes while others do not. Weather radar can identify signatures within thunderstorms that may give some indication of vertical motion, size sorting, and precipitation distributions. When multiple thunderstorms exist in a similar environment, there may be differences in these radar signatures that may indicate which thunderstorms are most likely to become tornadic. The key finding from this study is that pretornadic storms have larger radar-inferred updraft areas than neighboring nontornadic storms.

The Influence of Convection Initiation Strength on Subsequent Simulated Supercell Evolution

Abstract Recent studies have shown how very small differences in the background environment of a supercell can yield different outcomes, particularly in terms of tornado production. In this study, we use a novel convection initiation technique to simulate six supercells with a focus on their early development. Each experiment is identical, except for the strength of thermal forcing for the initial convection initiation. Each experiment yields a mature supercell, but differences in storm-scale characteristics like updraft speed, cold pool temperature deficit, and vertical vorticity development abound. Of these, the time when the midlevel updraft strengthens is most strongly related to initiation strength, with stronger thermal forcing favoring quicker updraft development. The same is true for the low-level updraft, with the additional relationship that stronger thermal forcing also tends to yield stronger low-level updrafts for around the first 2 h of the simulations. The experiments with faster updraft development tend to be associated with more rapid surface vortex intensification; however, cold pool evolution differs between simulations with weaker versus stronger thermal forcing. Stronger thermal forcing also yields deviant rightward storm motion earlier in the supercell’s life cycle that remains more consistent for the duration of the simulation. These results highlight the range of supercellular outcomes that are possible across a background environment due to differences in storm-scale initiation strength. They are also of potential importance for predicting the paths and tornado potential of supercells in real time. Significance Statement Despite a better understanding of processes related to tornado production in supercell thunderstorms, forecasters still have difficulty discriminating between tornadic and nontornadic supercells in close proximity to each other within the same severe weather event. In this study, we use six simulations of supercells to examine how these different outcomes can occur. Our results show that, given the same background environment, a storm that is more strongly initiated will exhibit faster updraft development and, possibly, quicker tornado production. The opposite can be said for storms that are more weakly initiated. Differences in initiation strength are also associated with different storm motions. These findings inspire future work to better relate supercell evolution to characteristics of initiation and the environment.

Investigating the Relationship between Polarimetric Radar Signatures of Hydrometeor Size Sorting and Tornadic Potential in Simulated Supercells

Abstract The national upgrade of the operational weather radar network to include polarimetric capabilities has led to numerous studies focusing on polarimetric radar signatures commonly observed in supercells. One such signature is the horizontal separation of regions of enhanced differential reflectivity (ZDR) and specific differential phase (KDP) values due to hydrometeor size sorting. Recent observational studies have shown that the orientation of this separation tends to be more perpendicular to storm motion in supercells that produce tornadoes. Although this finding has potential operational utility, the physical relationship between this observed radar signature and tornadic potential is not known. This study uses an ensemble of supercell simulations initialized with tornadic and nontornadic environments to investigate this connection. The tendency for tornadic supercells to have a more perpendicular separation orientation was reproduced, although to a lesser degree. This difference in orientation angles was caused by stronger rearward storm-relative flow in the nontornadic supercells, leading to a rearward shift of precipitation and, therefore, the enhanced KDP region within the supercell. Further, this resulted in an unfavorable rearward shift of the negative buoyancy region, which led to an order of magnitude less baroclinic generation of circulation in the nontornadic simulations compared to tornadic simulations.

Using the Supercell Polarimetric Observation Research Kit (SPORK) to Examine a Large Sample of Pretornadic and Nontornadic Supercells

Previous work examining dual-polarization (dual-pol) radar signatures of supercells has shown that differential reflectivity (ZDR) column area, hailfall area and specific differential phase (KDP)–ZDR separation angles may differ between tornadic and nontornadic storms. However, these signatures often can be difficult to quantify quickly enough to enable their use in forecasting operations, and little work has been done examining how these characteristics vary with environmental parameters in large samples of observed storms. This paper introduces the Supercell Polarimetric Observation Research Kit (SPORK) as an update to our automated ZDR arc-detection algorithm. This update adds the capability to identify ZDR column and inferred hailfall signatures in supercells automatically, and quickly quantify their characteristics. Dual-pol metrics calculated by SPORK are compared to manually calculated dual-pol metrics from previous work. SPORK is run on a large sample of supercells to examine whether SPORK-calculated dual-pol metrics exhibit the same differences between tornadic and nontornadic supercells seen in manual analyses. Storm-mean dual-pol metrics obtained from SPORK also are used to evaluate how supercell dual-pol metrics vary with different environmental parameters. Results from SPORK support previous findings that tornadic supercells have larger ZDR column areas, smaller hailfall areas, and larger KDP–ZDR separation angles than nontornadic storms. Additionally, ZDR columns tend to be larger and deeper in more conditionally unstable environments. Hailfall areal extents are larger in environments with lower environmental 0ºC levels, higher LCLs and LFCs, and less SRH. Separation angles are larger in environments with larger low-level shear vectors, SRH and lower lifted condensation levels (LCLs) and levels of free convection (LFCs). However, none of these correlations exceed r = 0.52. Overall, our results indicate that SPORK can quantify supercell dual-pol signatures accurately enough to detect potentially useful differences between dual-pol signatures of pretornadic and nontornadic supercells, and provide a first look at how dual-pol signatures vary with environmental characteristics in a large sample of supercells.

Scan-by-Scan Storm-Motion Deviations for Concurrent Tornadic and Nontornadic Supercells

Abstract In this exploratory study, storm-motion deviations are examined for concurrent tornadic and nontornadic supercells using 171 cases. This deviation, or “delta,” is defined as the shear-orthogonal distance between the observed supercell motion and a baseline supercell-motion prediction. Larger deltas—representing supercells moving farther right (in a shear-relative sense) compared to the baseline prediction—are hypothesized as more likely to be associated with tornadoes than nearby supercells with smaller deltas, consistent with recent research. Automated radar tracking is used to calculate supercell motion every scan, which then is compared to a model-derived hourly supercell-motion prediction to calculate the deltas. Tornadic supercells have larger average deltas (by 1.9–2.0 m s−1) than nearby nontornadic supercells when using 20- and 30-min storm-motion calculations, and the deltas are larger for the tornadic versus nontornadic supercells ∼80% of the time. Average delta trends also are positive 62%–70% of the time prior to tornadogenesis. The supercell-motion deltas show a modest positive correlation with EF-scale damage rating, indicating a possible relationship between tornado rating and storm deviation. The relative delta differences between tornadic and nontornadic supercells appear more meaningful than the absolute delta magnitudes (i.e., about 70% of tornadic cases with negative average deltas had deltas that were less negative compared to concurrent nontornadic supercells). This concept shows promise as a potential tool to assist operational forecasters in tornado warning decisions. Significance Statement Supercells are rotating thunderstorms, and these storms produce the most destructive tornadoes. However, it has been challenging to forecast which supercells will produce tornadoes. In this exploratory study to help better forecast supercell tornadoes, we looked at how the observed supercell motion compared to the predicted motion, based on a commonly used method. We found tornadic supercells tend to move somewhat differently from the predicted motion—compared to nearby nontornadic supercells. This unusual movement often starts prior to tornadogenesis, potentially providing lead time to tornado formation. Pending further validation, development, and testing of real-time analysis tools, this storm-motion behavior could be used by operational forecasters as a factor to help determine when (or when not) to issue a tornado warning for a supercell thunderstorm, thus providing better information to the public.

SPC Mesoscale Analysis Compared to Field-Project Soundings: Implications for Supercell Environment Studies

Abstract A total of 257 supercell proximity soundings obtained for field programs over the central United States are compared with profiles extracted from the SPC mesoscale analysis system (the SFCOA) to understand how errors in the SFCOA and in its baseline model analysis system—the RUC/RAP—might impact climatological assessments of supercell environments. A primary result is that the SFCOA underestimates the low-level storm-relative winds and wind shear, a clear consequence of the lack of vertical resolution near the ground. The near-ground (≤500 m) wind shear is underestimated similarly in near-field, far-field, tornadic, and nontornadic supercell environments. The near-ground storm-relative winds, however, are underestimated the most in the near-field and in tornadic supercell environments. Underprediction of storm-relative winds is, therefore, a likely contributor to the lack of differences in storm-relative winds between nontornadic and tornadic supercell environments in past studies that use RUC/RAP-based analyses. Furthermore, these storm-relative wind errors could lead to an under emphasis of deep-layer SRH variables relative to shallower SRH in discriminating nontornadic from tornadic supercells. The mean critical angles are 5°–15° larger and farther from 90° in the observed soundings than in the SFCOA, particularly in the near field, likely indicating that the ratio of streamwise to crosswise horizontal vorticity is often smaller than that suggested by the SFCOA profiles. Errors in thermodynamic variables are less prevalent, but show low-level CAPE to be too low closer to the storms, a dry bias above the boundary layer, and the absence of shallow near-ground stable layers that are much more prevalent in tornadic supercell environments. Significance Statement A total of 257 radiosonde observations taken close to supercell thunderstorms during field programs over the last 25 years are compared with a model-based analysis system (the SFCOA), which is often used for studying supercell thunderstorm environments. We present error characteristics of the SFCOA as they relate to tornado production and distance to the storm to clarify interpretations of environments favorable for tornado production made from past studies that use the SFCOA. A primary result is that the SFCOA underpredicts the speed and shear of the air flowing toward the storm in many cases, which may lead to different interpretations of variables that are most important for discriminating tornadic from nontornadic supercell thunderstorms. These results help to refine our understanding of the conditions that support tornado formation, which provides guidance on environmental cues that can improve the prediction of supercell tornadoes.

Infrasound signals in simulated nontornadic and pre-tornadic supercells.

There has been increased interest in improving severe weather detection by supplementing the conventional operational radar network with an infrasound observation network, which may be able to detect distinct sub-audible signatures from tornadic supercells. While there is evidence that tornadic thunderstorms exhibit observable infrasound signals, what is not well-understood is whether these infrasound signals are unique to tornadic supercells (compared to nontornadic supercells) or whether there is useful signal prior to tornadogenesis, which would be most relevant to forecasters. Using simulations of supercells, tailored to represent acoustic waves with frequencies from 0.1 to 2 Hz, spectral analysis reveals that both nontornadic and pre-tornadic supercells produce strikingly similar sound pressure levels at the surface, even in close spatial proximity to the storms (less than 20 km). Sensitivity tests employing varying microphysics schemes also show similar acoustic emissions between supercells. Riming of supercooled water droplets in the upper-troposphere is the sole mechanism generating high-frequency pressure waves in supercells prior to tornadogenesis or during tornadogenesis-failure; however, riming occurs continuously in mature nontornadic and tornadic supercells. Our simulations found no clear evidence that infrasound produced by supercells prior to tornado formation (compared to nontornadic supercells) is sufficiently distinct to improve lead-time of tornado warnings.

Tornado Formation and Intensity Prediction Using Polarimetric Radar Estimates of Updraft Area

AbstractA sample of 198 supercells are investigated to determine if a radar proxy for the area of the storm midlevel updraft may be a skillful predictor of imminent tornado formation and/or peak tornado intensity. A novel algorithm, a modified version of the Thunderstorm Risk Estimation from Nowcasting Development via Size Sorting (TRENDSS) algorithm is used to estimate the area of the enhanced differential radar reflectivity factor (ZDR) column in Weather Surveillance Radar – 1988 Doppler data; the ZDR column area is used as a proxy for the area of the midlevel updraft. The areas of ZDR columns are compared for 154 tornadic supercells and 44 non-tornadic supercells, including 30+ supercells with tornadoes rated EF1, EF2, and EF3; nine supercells with EF4+ tornadoes also are analyzed. It is found that (i) at the time of their peak 0-1 km azimuthal shear, non-tornadic supercells have consistently small (< 20 km2) ZDR column areas while tornadic cases exhibit much greater variability in areas, and (ii) at the time of tornadogenesis, EF3+ tornadic cases have larger ZDR column areas than tornadic cases rated EF1/2. In addition, all nine violent tornadoes sampled have ZDR column areas > 30 km2 at the time of tornadogenesis. However, only weak positive correlation is found between ZDR column area and both radar-estimated peak tornado intensity and maximum tornado path width. Planned future work focused on mechanisms linking updraft size and tornado formation and intensity is summarized and the use of the modified TRENDSS algorithm, which is immune to ZDR bias and thus ideal for real-time operational use, is emphasized.

Characteristics of the Wind Field in Three Nontornadic Low-Level Mesocyclones Observed by the Doppler On Wheels Radars

The three-dimensional wind fields within three nontornadic supercell thunderstorms are retrieved from dual-Doppler radar observations obtained by a pair of Doppler on Wheels (DOW) radars. The observations focus on the low-level mesocyclone regions of the storms near the time of strongest low-level rotation. All three storms display strong low-level rotation (e.g., the vertical vorticity maxima exceed 0.05 s-1 in the lowest 1000 m AGL in each storm). A principal finding is that the nontornadic mesocyclones possess many of the same signatures found in tornadic supercells, even those viewed in similarly fine resolution; e.g., rear-flank gust fronts wrapping around the circulation centers, multiple cyclonic vertical vorticity maxima along the gust front that spiral inward toward the circulation center, and arching vortex lines joining the cyclonic vorticity maxima to regions of anticyclonic vertical vorticity on the opposite side of the hook echo. The nontornadic mesocyclones possess less circulation than most of the tornadic mesocyclones that have been observed by the DOW radars, particularly within 1 km of the axis of rotation. Another finding is that the trajectories of air parcels passing through the near-surface vertical vorticity maxima have relatively shallow upward vertical excursions, suggesting that these parcels do not enter the overlying midlevel updraft and mesocyclone.

Discriminating between Tornadic and Non-Tornadic Supercells: A New Hodograph Technique

Thompson and Edwards (2000) first noted a prominent hodograph kink separating primarily speed shear from primarily directional shear in the environments of some supercells producing significant tornadoes. Responding to this observation, we compared similar thermodynamic and shear environments between the Moore, Oklahoma tornado of 3 May 1999 and non-tornadic supercell thunderstorms occurring in north Texas on 23 April 2003. The results suggest that certain characteristics of the kink could discriminate between tornadic and non-tornadic supercells. This combination of features consisted of a strong (> 10 m s-1) nearly straight-line hodograph below 500 m above ground level (AGL) and storm-relative inflow orthogonal to the base of this hodograph segment at 10 m, yielding almost purely streamwise storm-relative inflow.
 We evaluated this hypothesis by analyzing 67 severe convective events, 65 of which were supercells, in Oklahoma from 1997-2004, and dividing the events into non-tornadic, weak-tornadic (F0-­F1), and significant-tornadic (F2-­F5) storm classes. The results show improved discrimination between storm classes for 10-­500 m storm-relative helicity and bulk shear magnitude when compared to 10-­1000 m calculations of the same. Also, histograms of the critical angle (defined by the storm-relative inflow vector at 10 m and 10-­500 m shear vector) revealed that the tornadic storms, and in particular the significant tornadic storms, tended to be characterized by angles near 90°, whereas the non-tornadic storms were not. Although the results are based on a relatively small sample, they suggest that a careful consideration of the evolution of the low-level hodograph in both time and space in relation to the storm motion can potentially be a valuable aid in forecasting supercell tornadoes.

Observed Bulk Hook Echo Drop Size Distribution Evolution in Supercell Tornadogenesis and Tornadogenesis Failure

AbstractThe time preceding supercell tornadogenesis and tornadogenesis “failure” has been studied extensively to identify differing attributes related to tornado production or lack thereof. Studies from the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX) found that air in the rear-flank downdraft (RFD) regions of non- and weakly tornadic supercells had different near-surface thermodynamic characteristics than that in strongly tornadic supercells. Subsequently, it was proposed that microphysical processes are likely to have an impact on the resulting thermodynamics of the near-surface RFD region. One way to view proxies to microphysical features, namely drop size distributions (DSDs), is through use of polarimetric radar data. Studies from the second VORTEX used data from dual-polarization radars to provide evidence of different DSDs in the hook echoes of tornadic and non-tornadic supercells. However, radar-based studies during these projects were limited to a small number of cases preventing result generalizations. This study compiles 68 tornadic and 62 non-tornadic supercells using Weather Surveillance Radar–1988 Doppler (WSR-88D) data to analyze changes in polarimetric radar variables leading up to, and at, tornadogenesis and tornadogenesis failure. Case types generally did not show notable hook echo differences in variables between sets, but did show spatial hook echo quadrant DSD differences. Consistent with past studies, differential radar reflectivity factor (ZDR) generally decreased leading up to tornadogenesis and tornadogenesis failure; in both sets, estimated total number concentration increased during the same times. Relationships between DSDs and the near-storm environment, and implications of results for nowcasting tornadogenesis, also are discussed.

Polarimetric Radar Characteristics of Tornadogenesis Failure in Supercell Thunderstorms

Many nontornadic supercell storms have times when they appear to be moving toward tornadogenesis, including the development of a strong low-level vortex, but never end up producing a tornado. These tornadogenesis failure (TGF) episodes can be a substantial challenge to operational meteorologists. In this study, a sample of 32 pre-tornadic and 36 pre-TGF supercells is examined in the 30 min pre-tornadogenesis or pre-TGF period to explore the feasibility of using polarimetric radar metrics to highlight storms with larger tornadogenesis potential in the near-term. Overall the results indicate few strong distinguishers of pre-tornadic storms. Differential reflectivity (ZDR) arc size and intensity were the most promising metrics examined, with ZDR arc size potentially exhibiting large enough differences between the two storm subsets to be operationally useful. Change in the radar metrics leading up to tornadogenesis or TGF did not exhibit large differences, though most findings were consistent with hypotheses based on prior findings in the literature.

Cold Pool Characteristics of Tornadic Quasi-Linear Convective Systems and Other Convective Modes Observed during VORTEX-SE

AbstractMany numerical studies have focused on the importance of baroclinically generated vorticity at the edge of cold pools in supercellular tornadogenesis, and observational work has consistently found that strongly tornadic supercells have less dense, more buoyant cold pools than weakly or nontornadic supercells. However, there is a lack of observational studies that consider potential relationships between cold pool characteristics (e.g., density) and tornado production within linear systems, such as mesoscale convective system (MCS) or quasi-linear convective system (QLCS) events. This study presents two tornadic QLCS events that were observed during the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) field project in 2016 and 2017. Supercell and hybrid modes were also observed and compared to the observations from the linear systems. No obvious differences in the thermodynamic deficits of the tornadic and nontornadic samples were found, likely due to the weakness of the produced tornadoes (≤EF1) and the small tornadic sample size (five cold pools). Comparison across storm mode did find some differences, with QLCS cold pools producing larger virtual potential temperature and psuedoequivalent potential temperature deficits than those observed in supercells. More importantly, our findings suggest that, in a QLCS, the magnitude of density gradients along the leading edge of the cold pool may be related to tornadogenesis by virtue of the implied baroclinic vorticity generation.

Distinguishing Characteristics of Tornadic and Nontornadic Supercell Storms from Composite Mean Analyses of Radar Observations

AbstractAn improved understanding of common differences between tornadic and nontornadic supercells is sought using a large set of observations from the operational NEXRAD WSR-88D polarimetric radar network in the contiguous United States. In particular, data from 478 nontornadic and 294 tornadic supercells during a 7-yr period (2011–17) are used to produce probability-matched composite means of microphysical and kinematic variables. Means, which are centered on echo-top maxima and in a horizontal coordinate system rotated such that storm motion points in the positive x dimension, are created in altitude relative to ground level at times of peak echo-top altitude and peak midlevel rotation for nontornadic supercells and times at and prior to the first tornado in tornadic supercells. Robust differences between supercell types are found, with consistent characteristics at and preceding tornadogenesis in tornadic storms. In particular, the mesocyclone is found to be vertically aligned in tornadic supercells and misaligned in nontornadic supercells. Microphysical differences found include a low-level radar reflectivity hook echo aligned with and ~10 km right of storm center in tornadic supercells and displaced 5–10 km down-motion in nontornadic supercells, a low-to-midlevel differential radar reflectivity dipole that is oriented more parallel to storm motion in tornadic supercells and more perpendicular in nontornadic supercells, and a separation between enhanced differential radar reflectivity and specific differential phase (with unique displacement-relative correlation coefficient reductions) at low levels that is more perpendicular to storm motion in tornadic supercells and more parallel in nontornadic supercells.

Infrasound characteristics of nontornadic and tornadic thunderstorms using high-resolution simulations

There has been increased interest in improving severe weather detection by supplementing NOAA's WSR-88D radar network with an infrasound observation network, which may be able to detect distinct sub-audible signatures from tornadic thunderstorms. While there is evidence that tornadic thunderstorms exhibit observable infrasound signals, what is not well-understood is whether these infrasound signals are unique to tornadic storms compared to nontornadic storms or whether there is any useful signal prior to tornadogenesis (which would be most relevant to NWS forecasters). In this presentation, we will present the results of high-resolution simulations of severe thunderstorms, specifically tailored to represent acoustic wave propagation with frequencies of 0.1 to 1 Hz. The spectral analysis of pressure perturbations generated by a nontornadic and tornadic supercell thunderstorm will be compared against each other. Two additional sensitivity tests that employ different microphysics schemes (i.e., the representation of precipitation within the storm) will be discussed, since prior work has indicated that the melting of precipitation is a dominant contributor to infrasound generation in thunderstorms. So far, preliminary results suggest that there are no discernible differences in infrasound observed in the vicinity of tornadic versus nontornadic supercell thunderstorms prior to tornadogenesis.

Modes of Storm-Scale Variability and Tornado Potential in VORTEX2 Near- and Far-Field Tornadic Environments

AbstractSome supercellular tornado outbreaks are composed almost entirely of tornadic supercells, while most consist of both tornadic and nontornadic supercells sometimes in close proximity to each other. These differences are related to a balance between larger-scale environmental influences on storm development as well as more chaotic, internal evolution. For example, some environments may be potent enough to support tornadic supercells even if less predictable intrastorm characteristics are suboptimal for tornadogenesis, while less potent environments are supportive of tornadic supercells given optimal intrastorm characteristics. This study addresses the sensitivity of tornadogenesis to both environmental characteristics and storm-scale features using a cloud modeling approach. Two high-resolution ensembles of simulated supercells are produced in the near- and far-field environments observed in the inflow of tornadic supercells during the second Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2). All simulated supercells evolving in the near-field environment produce a tornado, and 33% of supercells evolving in the far-field environment produce a tornado. Composite differences between the two ensembles are shown to address storm-scale characteristics and processes impacting the volatility of tornadogenesis. Storm-scale variability in the ensembles is illustrated using empirical orthogonal function analysis, revealing storm-generated boundaries that may be linked to the volatility of tornadogenesis. Updrafts in the near-field ensemble are markedly stronger than those in the far-field ensemble during the time period in which the ensembles most differ in terms of tornado production. These results suggest that storm-environment modifications can influence the volatility of supercellular tornadogenesis.

Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction

Abstract This paper describes the development of convolutional neural networks (CNN), a type of deep-learning method, to predict next-hour tornado occurrence. Predictors are a storm-centered radar image and a proximity sounding from the Rapid Refresh model. Radar images come from the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) and Gridded NEXRAD WSR-88D Radar dataset (GridRad), both of which are multiradar composites. We train separate CNNs on MYRORSS and GridRad data, present an experiment to optimize the CNN settings, and evaluate the chosen CNNs on independent testing data. Both models achieve an area under the receiver-operating-characteristic curve (AUC) well above 0.9, which is considered to be excellent performance. The GridRad model achieves a critical success index (CSI) of 0.31, and the MYRORSS model achieves a CSI of 0.17. The difference is due primarily to event frequency (percentage of storms that are tornadic in the next hour), which is 3.52% for GridRad but only 0.24% for MYRORSS. The best CNN predictions (true positives and negatives) occur for strongly rotating tornadic supercells and weak nontornadic cells in mesoscale convective systems, respectively. The worst predictions (false positives and negatives) occur for strongly rotating nontornadic supercells and tornadic cells in quasi-linear convective systems, respectively. The performance of our CNNs is comparable to an operational machine-learning system for severe weather prediction, which suggests that they would be useful for real-time forecasting.

Differentiating Between Tornadic and Nontornadic Supercells Using Polarimetric Radar Signatures of Hydrometeor Size Sorting

AbstractSupercell storms are the most prolific producers of violent tornadoes, though only a fraction of supercells produce tornadoes. Past research into the differences between tornadic and nontornadic supercells have provided some insights but are of little utility to a real‐time warning decision process. Operational weather radars provide consistent observations in real time, but conventional radar techniques have not been able to effectively distinguish between tornadic and nontornadic supercells. After the national radar network upgrade to polarimetric capabilities in 2013, a polarimetric signature frequently observed in supercells is the separation of low‐level enhanced differential reflectivity ZDR and specific differential phase KDP regions. We analyzed this signature in tornadic and nontornadic supercell cases and found that, although the separation distances are similar, the separation orientations are statistically significantly different. Tornadic supercells have orientations more orthogonal to storm motion and nontornadic supercells have more parallel orientations. Possible reasons for these differences are discussed.