Relationship of Low-Level Instability and Tornado Damage Rating Based on Observed Soundings

Hampshire, Nicholas L.; Mosier, Richard M.; Ryan, Ted M.; Cavanaugh, Dennis E.

doi:10.15191/nwajom.2018.0601

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…Low-level SRH ranked in the top 5 for both models, but with different choices of integration bounds (10-500 m for RFC, and 0.01-1 km for LOGIT). A majority (7 of the top 10) of predictors for the RFC model were noted as being kinematic, which agrees with previous research examining the best environmental discriminators for tornadoes of various magnitudes (e.g., Brooks et al 2003a;Rasmussen 2003;Markowski et al 2003;Thompson et al 2003;Nowotarski and Jensen 2013;Hampshire et al 2018;Coffer et al 2019;King and Kennedy 2019;Coffer et al 2020). LOGIT had fewer kinematic variables (5) in the top 10.…”

Section: ) Tornadosupporting

confidence: 87%

Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings

Gensini

Converse

Ashley

et al. 2021

Weather and Forecasting

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Previous studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid-point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality-controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross-validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and -10 to -30 °C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.

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Section: ) Tornadosupporting

confidence: 87%

Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings

Gensini

Converse

Ashley

et al. 2021

Weather and Forecasting

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“…This peculiarity with low-level lapse rates may also further speak to issues with the physical parameterization of surface fluxes within the PBL, as discussed in the appendix. Hampshire et al (2018) showed NWS rawinsonde observations had values of low-level lapse rates 0.758 and 0.638C km 21 higher for violent and significant torndaoes, respectively, compared to the SFCOA. It is possible that PBL schemes struggle in environments with large lower-tropospheric shear combined with steep lapse rates, a condition that violates various similarity theories used for turbulence closures (Stull 1988).…”

Section: Discussionmentioning

confidence: 87%

“…Some of these involve measures of low-level instability, including the 0-3-km CAPE (3CAPE) and 0-3-km lapse rates (3LR). Examples of this include the enhanced stretching potential (ESP; Caruso and Davies 2005), the severe hazards in environments with reduced buoyancy (SHERB; Sherburn and Parker 2014), and the violent tornado parameter (VTP; Hampshire et al 2018). While none of these were specifically designed to work across all environments and forecast scenarios, the SCP and the STP are more skillful at discriminating between significantly tornadic and nontornadic supercells than any of the three (Table 3).…”

Section: Discussionmentioning

confidence: 99%

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Using Near-Ground Storm Relative Helicity in Supercell Tornado Forecasting

Coffer

Parker

Thompson³

et al. 2019

Weather and Forecasting

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This study examines the possibility that supercell tornado forecasts could be improved by utilizing the storm-relative helicity (SRH) in the lowest few hundred meters of the atmosphere (instead of much deeper layers). This hypothesis emerges from a growing body of literature linking the near-ground wind profile to the organization of the low-level mesocyclone and thus the probability of tornadogenesis. This study further addresses the ramifications of near-ground SRH to the skill of the significant tornado parameter (STP), which is probably the most commonly used environmental indicator for tornadic thunderstorms. Using a sample of 20 194 severe, right-moving supercells spanning a 13-yr period, sounding-derived parameters were compared using forecast verification metrics, emphasizing a high probability of detection for tornadic supercells while minimizing false alarms. This climatology reveals that the kinematic components of environmental profiles are more skillful at discriminating significantly tornadic supercells from severe, nontornadic supercells than the thermodynamic components. The effective-layer SRH has by far the greatest forecast skill among the components of the STP, as it is currently defined. However, using progressively shallower layers for the SRH calculation leads to increasing forecast skill. Replacing the effective-layer SRH with the 0–500 m AGL SRH in the formulation of STP increases the number of correctly predicted events by 8% and decreases the number of missed events and false alarms by 18%. These results provide promising evidence that forecast parameters can still be improved through increased understanding of the environmental controls on the processes that govern tornado formation.

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“…Also, a better understanding of predictive sounding parameters and the radar evolution of storms that produce long-track tornadoes could improve forecasts for these extreme events. Based on the success of the significant tornado parameter (STP; Thompson et al 2003;Thompson et al 2004) and the development of a violent tornado parameter (VTP; Hampshire et al 2018) to diagnose environments favorable for "significant (EF2/EF3)" and "violent (EF4/EF5)" tornadoes, severe-storm forecasters might benefit from a long-track tornado parameter (LTTP) that is currently being developed by the authors. Finally, a climatology for long-track tornadoes for 10, 20, 30, and 40+ y periods is also underway to examine, for example, regional changes with time, while trying to account for natural variability.…”

Section: Discussionmentioning

confidence: 99%

Climatology of Long-Track Tornadoes

Straka¹,

Kanak²

2022

EJSSM

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A 40-year climatology (1979–2018) is presented for long-track tornadoes in the United States using the Storm Prediction Center tornado database. Path length (PL) stratification thresholds are defined using characteristics of supercell storm evolution, and are categorized as: all (ALL), PL >0 mi or 0 km; short (S), PL <30 mi or 48.3 km; long (L), [30, 60) mi or [48.3, 96.6) km; very long (V), [60, 90) mi or [96.6, 144.8) km; extremely long (X), PL ≥90 mi or 144.8 km; and long-track sum (LVX), PL ≥30 mi or 48.3 km. Results show LVX tornadoes: a) made up <1% of all tornadoes; b) occurred east of the Rocky Mountains; c) were generally wider; d) caused disproportionate numbers of deaths and injuries; e) typically had damage ratings ≥F/EF2 (peak F/EF3); f) occurred more often with more deaths and injuries in the Southeast than in the Midwest or Great Plains; g) had larger area scale and Destruction Potential Index; h) occurred mostly in April and May versus May and June for S tornadoes; i) occurred primarily from midafternoon to early evening; j) had a peak formation hour at 1600 local solar time (also true for ALL S, V and X), though L tornadoes had a peak and secondary peak at 1800 and 1500, respectively; k) were less common during nighttime than S tornadoes; l) were more frequent during nighttime in the Southeast than nighttime in the Midwest or Great Plains; and m) occurred more often with tornado outbreaks.

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Relationship of Low-Level Instability and Tornado Damage Rating Based on Observed Soundings

Cited by 15 publications

References 18 publications

Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings

Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings

Using Near-Ground Storm Relative Helicity in Supercell Tornado Forecasting

Climatology of Long-Track Tornadoes

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