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of the various parameters is compared in an objective manner in section 8 . The results of this investigation are summarized in terms of tornadogenesis and supercell-favoring environments, and tornadogenesis failure modes, in section 9 . 2. Methods a. Sounding database The soundings evaluated here are contained in Rawinsonde Data for North America, 1946–1992 (Forecast Systems Laboratory and National Climatic Data Center 1993) and were all made at 0000 UTC nominal sounding time from the U
of the various parameters is compared in an objective manner in section 8 . The results of this investigation are summarized in terms of tornadogenesis and supercell-favoring environments, and tornadogenesis failure modes, in section 9 . 2. Methods a. Sounding database The soundings evaluated here are contained in Rawinsonde Data for North America, 1946–1992 (Forecast Systems Laboratory and National Climatic Data Center 1993) and were all made at 0000 UTC nominal sounding time from the U
convective available potential energy (MUCAPE)], and the bulk Richardson number. The purpose of this paper is to further explore which characteristic sounding features are supportive for tornadogenesis, and which sounding parameters could help to make a probabilistic distinction between the occurrence of weak and strong tornadoes. The results presented in Part I indicate that significant tornadic storms tended to have larger shear values than both weak tornadic and nontornadic severe storms. In
convective available potential energy (MUCAPE)], and the bulk Richardson number. The purpose of this paper is to further explore which characteristic sounding features are supportive for tornadogenesis, and which sounding parameters could help to make a probabilistic distinction between the occurrence of weak and strong tornadoes. The results presented in Part I indicate that significant tornadic storms tended to have larger shear values than both weak tornadic and nontornadic severe storms. In
, with the maximum frequency occurring between May and August. There is a peak in July, due to the seasonal lag in the availability of low-level moisture for this flow regime ( Johns 1982 ). During periods of northwest flow, there is an average of ~15° of directional shear between 850 and 500 hPa over the northeast United States ( Johns 1984 ). For the other wind patterns, tornadoes develop with little (<5°) directional shear in this layer. Most of the analysis of tornadogenesis over the northeast
, with the maximum frequency occurring between May and August. There is a peak in July, due to the seasonal lag in the availability of low-level moisture for this flow regime ( Johns 1982 ). During periods of northwest flow, there is an average of ~15° of directional shear between 850 and 500 hPa over the northeast United States ( Johns 1984 ). For the other wind patterns, tornadoes develop with little (<5°) directional shear in this layer. Most of the analysis of tornadogenesis over the northeast
the right turn, respectively. The final column shows the differences between the pre- and post-turn speed and direction. The time of tornadogenesis is probably more strongly influenced by storm-scale details or environmental inhomogeneities than the time of the right turn, but some general characteristics of the time of tornadogenesis were examined. Of the 90 tornadic supercells that exhibited a right turn, the mean time from initial cell development to tornadogenesis was about 99 min with a
the right turn, respectively. The final column shows the differences between the pre- and post-turn speed and direction. The time of tornadogenesis is probably more strongly influenced by storm-scale details or environmental inhomogeneities than the time of the right turn, but some general characteristics of the time of tornadogenesis were examined. Of the 90 tornadic supercells that exhibited a right turn, the mean time from initial cell development to tornadogenesis was about 99 min with a
1. Introduction Tornadic thunderstorm outbreaks are frequently attended by interactions between storms, such as cell mergers. A few studies (e.g., Lee et al. 2006b ), along with anecdotal evidence, suggest that cell mergers may affect the occurrence and timing of subsequent tornadogenesis. On the other hand, there are also documented instances in which tornado production appears to slow or cease following a merger (e.g., Lindsey and Bunkers 2005 ), or in which a merger is associated with the
1. Introduction Tornadic thunderstorm outbreaks are frequently attended by interactions between storms, such as cell mergers. A few studies (e.g., Lee et al. 2006b ), along with anecdotal evidence, suggest that cell mergers may affect the occurrence and timing of subsequent tornadogenesis. On the other hand, there are also documented instances in which tornado production appears to slow or cease following a merger (e.g., Lindsey and Bunkers 2005 ), or in which a merger is associated with the
mesoscale environment a. Synoptic environment The Funing tornado occurred in a typical convection-producing synoptic environment. At 1400 local standard time (LST; LST = UTC + 8 h), ~10 min before the tornadogenesis, Yancheng was located in front of a shallow 500-hPa trough extending from a quasi-stationary cold vortex in northeast China ( Fig. 2a ). A cold vortex is characterized by a synoptic cyclonic vortex with a local cold core in the middle and upper troposphere ( Xie and Bueh 2015 ). It is
mesoscale environment a. Synoptic environment The Funing tornado occurred in a typical convection-producing synoptic environment. At 1400 local standard time (LST; LST = UTC + 8 h), ~10 min before the tornadogenesis, Yancheng was located in front of a shallow 500-hPa trough extending from a quasi-stationary cold vortex in northeast China ( Fig. 2a ). A cold vortex is characterized by a synoptic cyclonic vortex with a local cold core in the middle and upper troposphere ( Xie and Bueh 2015 ). It is
environments may vary locally around smaller-scale terrain features. Tornadogenesis may be more likely as storms encounter some of these smaller-scale features ( Lyza and Knupp 2018 ). This increase in tornado likelihood near a plateau system in northeastern Alabama was attributed to enhanced low-level wind shear atop the plateau associated with modifications to the low-level flow on days where flow is largely perpendicular to the plateau’s long axis ( Lyza et al. 2020 ). It is not known how frequently
environments may vary locally around smaller-scale terrain features. Tornadogenesis may be more likely as storms encounter some of these smaller-scale features ( Lyza and Knupp 2018 ). This increase in tornado likelihood near a plateau system in northeastern Alabama was attributed to enhanced low-level wind shear atop the plateau associated with modifications to the low-level flow on days where flow is largely perpendicular to the plateau’s long axis ( Lyza et al. 2020 ). It is not known how frequently
convergence and significant frontogenesis focus along the outflow boundary between 1200 and 1800 UTC indicating at least lower-tropospheric upward forcing from midmorning into the early afternoon. However, tornadogenesis and heaviest rainfall ensue only after middle-tropospheric upward motion, associated with a rising branch of a direct thermal circulation induced by an upper-tropospheric jet streak, becomes collocated over the surface boundaries. From this perspective, this event concurs with previous
convergence and significant frontogenesis focus along the outflow boundary between 1200 and 1800 UTC indicating at least lower-tropospheric upward forcing from midmorning into the early afternoon. However, tornadogenesis and heaviest rainfall ensue only after middle-tropospheric upward motion, associated with a rising branch of a direct thermal circulation induced by an upper-tropospheric jet streak, becomes collocated over the surface boundaries. From this perspective, this event concurs with previous
horizontal vorticity necessary for low-level mesocyclogenesis, while the horizontal vorticity associated with storm-generated boundaries played only a minor role. Numerical modeling studies have also suggested a variety of methods by which tornadogenesis occurs following the development of the low-level mesocyclone, including a downward-building vortex via the “dynamic pipe effect” ( Trapp and Davies-Jones 1997 ), two-celled vortex instabilities within the low-level mesocyclone ( Rotunno 1986 ), and
horizontal vorticity necessary for low-level mesocyclogenesis, while the horizontal vorticity associated with storm-generated boundaries played only a minor role. Numerical modeling studies have also suggested a variety of methods by which tornadogenesis occurs following the development of the low-level mesocyclone, including a downward-building vortex via the “dynamic pipe effect” ( Trapp and Davies-Jones 1997 ), two-celled vortex instabilities within the low-level mesocyclone ( Rotunno 1986 ), and
temporal observations can afford valuable insights into storm evolution by indicating, for example, the changing positions of surface gust fronts ( Bluestein et al. 2010 ), the formation of downbursts and microbursts ( Heinselman et al. 2008 ; Willingham et al. 2011 ; Kuster et al. 2015b ), the intensification of low-level winds ( Bluestein et al. 2010 ; Bowden et al. 2015 ), the intensification of mid- and low-level rotation that may indicate imminent tornadogenesis ( Zrnić et al. 2007 ; Wurman et
temporal observations can afford valuable insights into storm evolution by indicating, for example, the changing positions of surface gust fronts ( Bluestein et al. 2010 ), the formation of downbursts and microbursts ( Heinselman et al. 2008 ; Willingham et al. 2011 ; Kuster et al. 2015b ), the intensification of low-level winds ( Bluestein et al. 2010 ; Bowden et al. 2015 ), the intensification of mid- and low-level rotation that may indicate imminent tornadogenesis ( Zrnić et al. 2007 ; Wurman et
