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, type 1). Conversely, type 2 tornado reports tended to occur less than 1 h after the development of cores ( Fig. 22 , type 2). Thus, type 1 events took longer to reach tornadogenesis than type 2 events. Fig . 22. Timelines of each QLCS tornado outbreak event, indicating the time periods of the QLCS (black bar), precipitation cores that develop within the QLCS (orange bar) and tornado reports associated with each event (gray bar). However, both the type 2 case of 29 May 2015 and the unassigned case
, type 1). Conversely, type 2 tornado reports tended to occur less than 1 h after the development of cores ( Fig. 22 , type 2). Thus, type 1 events took longer to reach tornadogenesis than type 2 events. Fig . 22. Timelines of each QLCS tornado outbreak event, indicating the time periods of the QLCS (black bar), precipitation cores that develop within the QLCS (orange bar) and tornado reports associated with each event (gray bar). However, both the type 2 case of 29 May 2015 and the unassigned case
; Markowski et al. 2008 , 2012 ; Schenkman et al. 2014 ; Dahl 2015 ; Parker and Dahl 2015 ; Markowski 2016 ; Roberts et al. 2016 ; Roberts and Xue 2017 ; Roberts et al. 2020 ). However, Coffer and Parker (2017) showed that both nontornadic and tornadic supercells generate ample pretornadic ζ sfc . The step that “makes or breaks” the mechanism of tornadogenesis is likely step 3: the ability for ζ sfc to be contracted into a tornado. Step 3 is most strongly favored when overlying rotation
; Markowski et al. 2008 , 2012 ; Schenkman et al. 2014 ; Dahl 2015 ; Parker and Dahl 2015 ; Markowski 2016 ; Roberts et al. 2016 ; Roberts and Xue 2017 ; Roberts et al. 2020 ). However, Coffer and Parker (2017) showed that both nontornadic and tornadic supercells generate ample pretornadic ζ sfc . The step that “makes or breaks” the mechanism of tornadogenesis is likely step 3: the ability for ζ sfc to be contracted into a tornado. Step 3 is most strongly favored when overlying rotation
was concluded that the volatility associated with downdraft position and strength, which affect the baroclinic vorticity generation within the storm, may explain the failure of many supercells to produce tornadoes in seemingly favorable environments. Yokota et al. (2018) investigated the dynamics of tornadogenesis (50-m horizontal grid spacing) in a 33-member ensemble of supercell simulations for a case that occurred in a landfalling tropical cyclone in Japan. The initial conditions and
was concluded that the volatility associated with downdraft position and strength, which affect the baroclinic vorticity generation within the storm, may explain the failure of many supercells to produce tornadoes in seemingly favorable environments. Yokota et al. (2018) investigated the dynamics of tornadogenesis (50-m horizontal grid spacing) in a 33-member ensemble of supercell simulations for a case that occurred in a landfalling tropical cyclone in Japan. The initial conditions and
tornadogenesis. Intense Atmospheric Vortices , L. Bengtsson and J. Lighthill, Eds., Springer, 175–189 . Davies-Jones , R. , 2000 : A Lagrangian model for baroclinic genesis of mesoscale vortices. Part I: Theory . J. Atmos. Sci. , 57 , 715 – 736 , https://doi.org/10.1175/1520-0469(2000)057<0715:ALMFBG>2.0.CO;2 . Davies-Jones , R. , 2017 : Roles of streamwise and transverse partial-vorticity components in steady inviscid isentropic supercell-like flows . J. Atmos. Sci. , 74 , 3021 – 3041 , https
tornadogenesis. Intense Atmospheric Vortices , L. Bengtsson and J. Lighthill, Eds., Springer, 175–189 . Davies-Jones , R. , 2000 : A Lagrangian model for baroclinic genesis of mesoscale vortices. Part I: Theory . J. Atmos. Sci. , 57 , 715 – 736 , https://doi.org/10.1175/1520-0469(2000)057<0715:ALMFBG>2.0.CO;2 . Davies-Jones , R. , 2017 : Roles of streamwise and transverse partial-vorticity components in steady inviscid isentropic supercell-like flows . J. Atmos. Sci. , 74 , 3021 – 3041 , https
characteristics of the environment within the hook echo. In turn, many past studies have discussed the important influence that the thermodynamic attributes of supercell RFDs may have on supercell evolution, including, but not limited to, tornadogenesis (e.g., Markowski et al. 2002 ; Grzych et al. 2007 ) and tornado maintenance (e.g., Marquis et al. 2012 ). Information about PSDs has been obtained from convective storms mainly through two different methods: direct PSD data from disdrometers and estimated
characteristics of the environment within the hook echo. In turn, many past studies have discussed the important influence that the thermodynamic attributes of supercell RFDs may have on supercell evolution, including, but not limited to, tornadogenesis (e.g., Markowski et al. 2002 ; Grzych et al. 2007 ) and tornado maintenance (e.g., Marquis et al. 2012 ). Information about PSDs has been obtained from convective storms mainly through two different methods: direct PSD data from disdrometers and estimated
1. Introduction A major milestone was reached in the operational detection of severe weather when the hook echo was first observed by radar and shown to be associated with tornadogenesis ( Stout and Huff 1953 ; Forbes 1981 ). Subsequent studies examining radial velocities based on Doppler radars measurements were able to resolve the mesocyclone and the tornadic-vortex signature (TVS), which can be associated with the parent circulation of the tornado and the tornado, respectively (e.g., Brown
1. Introduction A major milestone was reached in the operational detection of severe weather when the hook echo was first observed by radar and shown to be associated with tornadogenesis ( Stout and Huff 1953 ; Forbes 1981 ). Subsequent studies examining radial velocities based on Doppler radars measurements were able to resolve the mesocyclone and the tornadic-vortex signature (TVS), which can be associated with the parent circulation of the tornado and the tornado, respectively (e.g., Brown
taken at the same location as the radar deployment site. Elevation- and azimuth-angle grids were created using photogrammetric techniques and superimposed on each image. These grids correspond to the radar scanning angles since the photographer and radar were collocated. A description of the photogrammetry analysis used in the current study has been presented in Wakimoto et al. (2015) . a. Tornadogenesis and the tornadic debris signature The approximate time of tornadogenesis (2303:14 UTC) is shown
taken at the same location as the radar deployment site. Elevation- and azimuth-angle grids were created using photogrammetric techniques and superimposed on each image. These grids correspond to the radar scanning angles since the photographer and radar were collocated. A description of the photogrammetry analysis used in the current study has been presented in Wakimoto et al. (2015) . a. Tornadogenesis and the tornadic debris signature The approximate time of tornadogenesis (2303:14 UTC) is shown
. Discussion An interesting question is how the above analysis relates to tornadogenesis. As discussed in section 3b , we have rather limited faith in the treatment of near-ground trajectories once they descend below the bottom scalar model level, so we cannot describe vortex genesis faithfully. However, when animated, the vertical vorticity field at the lowest scalar model level clearly shows how the rivers and lobes of positive ζ move downstream and feed into the developing vortex (see also Fig. 5
. Discussion An interesting question is how the above analysis relates to tornadogenesis. As discussed in section 3b , we have rather limited faith in the treatment of near-ground trajectories once they descend below the bottom scalar model level, so we cannot describe vortex genesis faithfully. However, when animated, the vertical vorticity field at the lowest scalar model level clearly shows how the rivers and lobes of positive ζ move downstream and feed into the developing vortex (see also Fig. 5
the available statistical guidance for predicting outbreak characteristics particularly when combined with other models. Fig . 1. Example tornado clusters. Each point is the tornadogenesis location shaded by EF rating. The black line is the spatial extent of the tornadoes occurring on that convective day and is defined by the minimum convex hull encompassing the set of locations. In this paper, we focus on outbreaks rather than on individual tornadoes. The larger space and time scales associated
the available statistical guidance for predicting outbreak characteristics particularly when combined with other models. Fig . 1. Example tornado clusters. Each point is the tornadogenesis location shaded by EF rating. The black line is the spatial extent of the tornadoes occurring on that convective day and is defined by the minimum convex hull encompassing the set of locations. In this paper, we focus on outbreaks rather than on individual tornadoes. The larger space and time scales associated
development of tornado–cyclone-scale vorticity maxima at the lowest model level. That is, only the seed near-ground rotation for possible tornadogenesis is addressed herein. Whether or not this vorticity is actually concentrated into a strong tornado-like vortex by vertical stretching is a separate problem not considered in this study [but it is discussed elsewhere, e.g., by Markowski and Richardson (2014) ]. 2. Methods a. Experimental design The goal is to produce simulations of a supercell that
development of tornado–cyclone-scale vorticity maxima at the lowest model level. That is, only the seed near-ground rotation for possible tornadogenesis is addressed herein. Whether or not this vorticity is actually concentrated into a strong tornado-like vortex by vertical stretching is a separate problem not considered in this study [but it is discussed elsewhere, e.g., by Markowski and Richardson (2014) ]. 2. Methods a. Experimental design The goal is to produce simulations of a supercell that
