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David J. Kellenbenz
,
Thomas J. Grafenauer
, and
Jonathan M. Davies

evolution and mesocyclone structure as related to tornadogenesis. Mon. Wea. Rev. , 107 , 1184 – 1197 . 10.1175/1520-0493(1979)107<1184:STEAMS>2.0.CO;2 Markowski, P. M. , Straka J. M. , and Rasmussen E. N. , 2002 : Direct surface thermodynamic observations within rear-flank downdrafts of nontornadic and tornadic supercells. Mon. Wea. Rev. , 130 , 1692 – 1721 . 10.1175/1520-0493(2002)130<1692:DSTOWT>2.0.CO;2 Raddatz, R. L. , 1998 : Anthropogenic vegetation transformation and the

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Branden Katona
,
Paul Markowski
,
Curtis Alexander
, and
Stanley Benjamin

, 1669 – 1694 , doi: 10.1175/MWR-D-15-0242.1 . Bosart, L. F. , Seimon A. , LaPenta K. D. , and Dickinson M. J. , 2006 : Supercell tornadogenesis over complex terrain: The Great Barrington, Massachusetts, tornado on 29 May 1995 . Wea. Forecasting , 21 , 897 – 922 , doi: 10.1175/WAF957.1 . Bunkers, M. J. , Klimowski B. A. , Zeitler J. W. , Thompson R. L. , and Weisman M. L. , 2000 : Predicting supercell motion using a new hodograph technique . Wea. Forecasting , 15 , 61 – 79

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John R. Lawson
,
John S. Kain
,
Nusrat Yussouf
,
David C. Dowell
,
Dustan M. Wheatley
,
Kent H. Knopfmeier
, and
Thomas A. Jones

1. Introduction The Warn-on-Forecast program (WoF; Stensrud et al. 2009 ) is addressing the challenge of creating numerical weather prediction (NWP) models that can predict specific thunderstorm-induced hazards such as large hail, flood-producing rainfall, strong wind, and proxies for tornadogenesis. The vision for implementing WoF technologies in NWS operations calls for on-demand activation of horizontal grid-spacing WoF ensemble prediction systems (EPSs) of O (1) km, within a much larger

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Paul R. Desrochers
and
Ralph J. Donaldson Jr.

Colorado: Similarities to wa terspout formation. Mon. Wea. Rev., 117, 843-856.Brandes, E. A., 1978: Mesocyclone evolution and tornadogenesis: Some observations. Mon. Wea. Rev., 106, 995-1011.--, 1981: Finestructure of the Del City-Edmond tornadic meso circulation. Mon. Wea. Rev., 109, 635-647.Brown, R. A., and L. R. Lemon, 1976: Single Doppler radar vortex recognition: Part II--Tornadic vortex signatures. Preprints, 17th Conf. on Radar Meteorology, Seattle, Amer. Meteor. Soc., 104 109

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Matthew J. Bunkers
,
Mark R. Hjelmfelt
, and
Paul L. Smith

. In summary, long-lived supercells, when compared with short-lived supercells, have a much greater tendency to be isolated and discrete, thereby limiting adjacent storm interactions and potentially destructive mergers. Because of this, the production of F2–F5 tornadoes appears to be enhanced for long-lived supercells [although other factors such as low-level shear and cloud-base height are equally or even more important for tornadogenesis; Thompson et al. (2003) ]. Consequently, the less isolated

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Kevin Gray
and
Jeffrey Frame

its south, which likely preserved vertical wind shear and horizontal vorticity near the surface ( Figs. 2 and 4b ), allowing for more relative vertical vorticity ζ to develop through tilting. Markowski et al. (1998b) and Rasmussen et al. (2000) found that the likelihood of tornadogenesis increases as a supercell travels from the warm to the cold side of a pre-existing mesoscale baroclinic boundary such as a differential heating boundary, further suggesting that the proximity of the

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Matthew D. Parker

-organizing hodograph maps (their nodes 5 and 8, and to a lesser extent nodes 3, 4, 6, and 9) included at least weak backing aloft yet were commonly associated with supercells (many of them tornadic); their exemplar cases for two significant tornado outbreaks (see Fig. 6 in Nowotarski and Jensen 2013 ) had very pronounced CCW hodograph kinks as well. It is also worth noting that several of the best-known supercell tornadogenesis studies were initialized with hodographs having a layer of CCW turning with height

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Jordan J. Laser
,
Michael C. Coniglio
,
Patrick S. Skinner
, and
Elizabeth N. Smith

, University of Oklahoma, the National Severe Storms Laboratory (NSSL), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) 3 and is motivated by improving the current conceptual model of supercell thunderstorms. The primary aim of TORUS is to reveal the four-dimensional character of storm-generated boundaries and coherent structures that are crucial to tornadogenesis and the evolution of supercells. A plethora of observational platforms were used for TORUS: Doppler wind lidar

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Jason M. Davis
and
Matthew D. Parker

(FFC); Charleston, SC (CHS); Columbia, SC (CAE); Greenville–Spartanburg, SC (GSP); Wilmington, NC (ILM); Raleigh, NC (RAH); Newport–Morehead City, NC (MHX); Blacksburg, VA (RNK); Wakefield, VA (AKQ); and Baltimore, MD–Washington, DC (LWX). Fundamentally, there are still many aspects of tornadogenesis that are not fully understood, and many storm-scale processes that currently cannot be well observed operationally. Environmental factors are important, but not all storms in favorable environments are

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Casey E. Davenport

zero percent changes ( Fig. 14b ). Thus, there are fairly equivalent numbers of tornadoes produced in environments where increases or decreases of CAPE are occurring, respectively, prior to tornadogenesis, suggesting that shifts in buoyancy are less of a contributing factor. For convective inhibition, no substantial trends in values occur for tornadoes early versus later in a supercell’s lifetime ( Fig. 14c ). Changes in MLCIN leading up to tornado production were largely centered around zero

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