Some wind and instability parameters associated with strong and violent tornadoes: 2. Variations in the combinations of wind and instability parameters

Johns, Robert H.; Davies, Jonathan M.; Leftwich, Preston W.

doi:10.1029/gm079p0583

Cited by 79 publications

(79 citation statements)

References 9 publications

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“…This result supports previous conclusions that thermodynamic quantities such as CAPE and CIN are unable to distinguish outbreak type (e.g., Doswell and Evans 2003;Johns et al 1993). The Table 5, bottom section) was optimal for both methods.…”

Section: Discussionsupporting

confidence: 91%

“…This grouping only included one covariate that contained information on the thermodynamic instability in the atmosphere [0-1-km energyhelicity index (EHI)]. This result is expected (Johns et al 1993), because thermodynamic instability magnitude varies considerably between outbreak type (i.e., CAPE is a necessary but not sufficient condition for a tornado outbreak). This result confirms previous conclusions (Stensrud et al 1997;Johns and Hart 1993;Johns and Doswell 1992) that thermodynamic variables are not crucial for distinguishing storm type or outbreak type, although they can be useful for differentiating storm cases from those without storms.…”

Section: A 24-h Resultsmentioning

confidence: 99%

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Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks

Mercer

Shafer

Doswell

et al. 2009

Monthly Weather Review

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Tornadoes often strike as isolated events, but many occur as part of a major outbreak of tornadoes. Nontornadic outbreaks of severe convective storms are more common across the United States but pose different threats than do those associated with a tornado outbreak. The main goal of this work is to distinguish between significant instances of these outbreak types objectively by using statistical modeling techniques on numerical weather prediction output initialized with synoptic-scale data. The synoptic-scale structure contains information that can be utilized to discriminate between the two types of severe weather outbreaks through statistical methods. The Weather Research and Forecast model (WRF) is initialized with synopticscale input data (the NCEP-NCAR reanalysis dataset) on a set of 50 significant tornado outbreaks and 50 nontornadic severe weather outbreaks. Output from the WRF at 18-km grid spacing is used in the objective classification. Individual severe weather parameters forecast by the model near the time of the outbreak are analyzed from simulations initialized at 24, 48, and 72 h prior to the outbreak. An initial candidate set of 15 variables expected to be related to severe storms is reduced to a set of 6 or 7, depending on lead time, that possess the greatest classification capability through permutation testing. These variables serve as inputs into two statistical methods, support vector machines and logistic regression, to classify outbreak type. Each technique is assessed based on bootstrap confidence limits of contingency statistics. An additional backward selection of the reduced variable set is conducted to determine which variable combination provides the optimal contingency statistics. Results for the contingency statistics regarding the verification of discrimination capability are best at 24 h; at 48 h, modest degradation is present. By 72 h, the contingency statistics decline by up to 15%. Overall, results are encouraging, with probability of detection values often exceeding 0.8 and Heidke skill scores in excess of 0.7 at 24-h lead time.

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Section: Discussionsupporting

confidence: 91%

Section: A 24-h Resultsmentioning

confidence: 99%

Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks

Mercer

Shafer

Doswell

et al. 2009

Monthly Weather Review

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“…The low-level SRH, can vary dramatically over short distances, due to topographically channeled flow (e.g., Braun and Monteverdi, 1991;Bosart et al, 2006). The selection of these parameters is consistent with the approach adopted in previous studies (Droegemeier et al, 1993;Johns et al, 1993;Rasmussen and Blanchard, 1998;Brooks et al, 2003;Doswell and Evans, 2003;Thompson et al, 2003;Shafer et al, 2009;Matsangouras et al, 2011aMatsangouras et al, , 2012Sioutas et al, 2012). Table 1.…”

Section: T Matsangouras Et Al: Effect Of Topography On Tornadogesupporting

confidence: 69%

Numerical modeling and analysis of the effect of complex Greek topography on tornadogenesis

Matsangouras

Pytharoulis

2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Tornadoes have been reported in Greece over recent decades in specific sub-geographical areas and have been associated with strong synoptic forcing. While it has been established that meteorological conditions over Greece are affected at various scales by the significant variability of topography, the Ionian Sea to the west and the Aegean Sea to the east, there is still uncertainty regarding topography's importance on tornadic generation and development.The aim of this study is to investigate the role of topography in significant tornadogenesis events that were triggered under strong synoptic scale forcing over Greece. Three tornado events that occurred over the last years in Thebes (Boeotia, 17 November 2007), Vrastema (Chalkidiki, 12 February 2010) and Vlychos (Lefkada, 20 September 2011) were selected for numerical experiments. These events were associated with synoptic scale forcing, while their intensities were T4-T5 (on the TORRO scale), causing significant damage. The simulations were performed using the non-hydrostatic weather research and forecasting model (WRF), initialized by European Centre for Medium-Range Weather Forecasts (ECMWF) gridded analyses, with telescoping nested grids that allow for the representation of atmospheric circulations ranging from the synoptic scale down to the mesoscale. In the experiments, the topography of the inner grid was modified by: (a) 0 % (actual topography) and (b) −100 % (without topography), making an effort to determine whether the occurrence of tornadoes -mainly identified by various severe weather instability indices -could be indicated by modifying topography. The principal instability variables employed consisted of the bulk Richardson number (BRN) shear, the energy helicity index (EHI), the storm-relative environmental helicity (SRH), and the maximum convective available potential energy (MCAPE, for parcels with maximum θ e ). Additionally, a model verification was conducted for every sensitivity experiment accompanied by analysis of the absolute vorticity budget.Numerical simulations revealed that the complex topography constituted an important factor during the

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“…Uncertainty exists concerning the links between synoptic-scale processes and the likelihood of tornado outbreaks (e.g., Johns and Doswell 1992;Doswell et al 1993;Doswell and Bosart 2001). These relatively recent assessments come after years of research focused on finding these links.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of WRF Forecasts of Tornadic and Nontornadic Outbreaks When Initialized with Synoptic-Scale Input

Shafer

Mercer

Doswell

et al. 2009

Monthly Weather Review

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Uncertainty exists concerning the links between synoptic-scale processes and tornado outbreaks. With continuously improving computer technology, a large number of high-resolution model simulations can be conducted to study these outbreaks to the storm scale, to determine the degree to which synoptic-scale processes appear to influence the occurrence of tornado outbreaks, and to determine how far in advance these processes are important. To this end, 50 tornado outbreak simulations are compared with 50 primarily nontornadic outbreak simulations initialized with synoptic-scale input using the Weather Research and Forecasting (WRF) mesoscale model to determine if the model is able to distinguish the outbreak type 1, 2, and 3 days in advance of the event. The model simulations cannot resolve tornadoes explicitly; thus, the use of meteorological covariates (in the form of numerous severe-weather parameters) is necessary to determine whether or not the model is predicting a tornado outbreak. Results indicate that, using the covariates, the WRF model can discriminate outbreak type consistently at least up to 3 days in advance. The severe-weather parameters that are most helpful in discriminating between outbreak types include low-level and deep-layer shear variables and the lifting condensation level. An analysis of the spatial structures and temporal evolution, as well as the magnitudes, of the severe-weather parameters is critical to diagnose the outbreak type correctly. Thermodynamic instability parameters are not helpful in distinguishing the outbreak type, primarily because of a strong seasonal dependence and convective modification in the simulations.

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Some wind and instability parameters associated with strong and violent tornadoes: 2. Variations in the combinations of wind and instability parameters

Cited by 79 publications

References 9 publications

Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks

Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks

Numerical modeling and analysis of the effect of complex Greek topography on tornadogenesis

Evaluation of WRF Forecasts of Tornadic and Nontornadic Outbreaks When Initialized with Synoptic-Scale Input

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