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Lon Curtis

consistent with the “large outbreak” definition found in Galway (1975 , 1977) as applied to all tornado outbreaks. 1 Eleven cases were found to offer clear evidence of a dry intrusion at midlevels over the outbreak area. The outbreaks all occurred when the favored area for tornadogenesis (the right-front or northeast quadrant of the storm) coincided with a pronounced gradient of relative humidity, with the gradient best reflected at the 700- and/or 500-hPa level(s). Two distinct patterns were

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Brice E. Coffer
,
Mateusz Taszarek
, and
Matthew D. Parker

VORTEX2 composite than the nontornadic (159 versus 80 m 2 s −2 ). In simulations based on the Parker (2014) composite nontornadic supercell sounding, Coffer and Parker (2017 , 2018) found near-ground crosswise horizontal vorticity to be unfavorable for steady low-level mesocylones and thus tornadogenesis. These results led C19 to focus on the forecast skill of a particular component of the STP, the lower-tropospheric SRH, specifically asking whether progressively shallower layers of SRH would

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Harold E. Brooks
,
Charles A. Doswell III
, and
Jeremy Cooper

, storm-relative environmental helicity, and low-level absolute humidity to develop longlived tomadic mesocyclones. In the absence of that balance, such storms should be rare. The failure of earlierforecast efforts to discriminate between tornadic and nontornadic severe storms is discussed in the context ofa physical understanding of supercell tornadogenesis. Finally, it is shown that attempts to gather large datasetsof proximity soundings associated with rare weather events are likely to take many

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M. Toth
,
R. J. Trapp
,
J. Wurman
, and
K. A. Kosiba

1. Introduction Visual observations of tornadoes and/or the existence of tornado damage currently provides the sole evidence of tornadogenesis in association with a mesocyclone or other radar-detected storm-scale vortex. The severity of the tornado damage, as expressed in terms of the Fujita (F)—and now enhanced Fujita (EF)—scales ( Fujita 1971 ; McDonald and Mehta 2006 ), is currently the only means of estimating the intensity of tornadoes, radar detected or otherwise. The limitations of the

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Luke J. LeBel
,
Brian H. Tang
, and
Ross A. Lazear

instability in the region where the 31 May 1998 Mechanicville, New York, tornado occurred. Similarly, Bosart et al. (2006) found that terrain-channeled southerly flow was present in the Hudson Valley on 29 May 1995 in the region where the Great Barrington, Massachusetts, tornado developed. They hypothesized that terrain channeling resulted in increased 0–1-km storm-relative helicity (SRH) and shear within the valley, increasing tornadogenesis potential. Bosart et al. (2006) further proposed that

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Matthew C. Brown
,
Christopher J. Nowotarski
,
Andrew R. Dean
,
Bryan T. Smith
,
Richard L. Thompson
, and
John M. Peters

daytime environments) and have potential ramifications for storm maintenance and tornadogenesis (e.g., Maddox 1993 ; Markowski et al. 1998 ; Parker 2014 ). These and other related factors have been offered up as explanation for the peak in near-sunset tornado counts noted in the literature (e.g., Kelly et al. 1978 ; Mead and Thompson 2011 ). Understanding how these environmental features and their impact on accompanying convection evolve with time are vitally important for determining the ability

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Kelsey C. Britt
,
Patrick S. Skinner
,
Pamela L. Heinselman
, and
Kent H. Knopfmeier

, long-track tornadoes owing to circulations occluding so quickly that tornadogenesis is hindered ( Dowell and Bluestein 2002a , b ; Beck et al. 2006 ; French et al. 2008 ). Thus, predicting the presence and cycling frequency of cyclic supercells may provide more specific guidance of severe thunderstorm threats to forecasters and the public by identifying which storms have the potential for long-track tornadoes and which do not. The frequency at which supercells cycle is potentially dependent on

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Bruce D. Lee
,
Brian F. Jewett
, and
Robert B. Wilhelmson

between storms mergers and tornadogenesis. 2. Data sources The primary Doppler radar datasets used in the analysis presented in this paper included Weather Surveillance Radar-1988 Doppler (WSR-88D; Klazura and Imy 1993 ) level II data from Lincoln, Illinois (KILX); St. Louis, Missouri (KLSX); and Davenport, Iowa (KDVN). The data were analyzed using the WSR-88D Algorithm Testing and Display System (WATADS; NSSL 2000 ) version 10.2. Where possible, gaps (missing data) in the archived WSR-88D radar

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Zack Byko
,
Paul Markowski
,
Yvette Richardson
,
Josh Wurman
, and
Edwin Adlerman

tornadogenesis. In a companion study, Kennedy et al. (2007a) examined a larger sample of DRCs observed in 64 isolated supercells using WSR-88D data. Of these, 39 (60%) produced at least one DRC, with 19 out of 39 producing multiple DRCs. Kennedy et al. found that 30% (41%) of the DRCs appeared within tornadic supercells on the lowest elevation scan of the radar within the period beginning 10 (30) min prior to tornadogenesis and ending 5 (15) min after tornadogenesis. Kennedy et al. argued that the presence

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Scott M. Spratt
,
David W. Sharp
,
Pat Welsh
,
Al Sandrik
,
Frank Alsheimer
, and
Charlie Paxton

technology is essential. This can be achieved by further defining the structure and evolution of TC–tornadoes through model simulation efforts (e.g., McCaul 1990 ; Weisman and McCaul 1995 ) and through case study analyses (e.g., McCaul 1987 ; Snell and McCaul 1993 ; Zubrick and Belville 1993 ; Cammarata et al. 1996 ). Specific to outer band tornadogenesis, McCaul (1987) , McCaul et al. (1993) , and Snell and McCaul (1993) have made significant contributions via the analysis of data from several

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