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1. Introduction Despite major advances in our understanding of supercell tornadoes over the past two decades, skillful, short-term (0–1 h) forecasting (i.e., “nowcasting”) of tornadogenesis remains elusive owing to a lack of understanding of the complicated processes involved and a dearth of observations at the spatiotemporal scales commensurate with the process. Work to distinguish differences between tornadic and nontornadic supercells are ongoing using both observations and numerical
1. Introduction Despite major advances in our understanding of supercell tornadoes over the past two decades, skillful, short-term (0–1 h) forecasting (i.e., “nowcasting”) of tornadogenesis remains elusive owing to a lack of understanding of the complicated processes involved and a dearth of observations at the spatiotemporal scales commensurate with the process. Work to distinguish differences between tornadic and nontornadic supercells are ongoing using both observations and numerical
1. Introduction The currently most prevalent conceptual model of supercell tornadogenesis is based on the evolution of a single, discrete supercell (e.g., Lemon 1976 ; Davies-Jones 2015 ). However, it is generally acknowledged among researchers and forecasters that storm-external factors can also have an impact on tornado formation. As discussed in this section, these external factors typically come in two forms; (i) supercell interaction with another thunderstorm, broadly referred to
1. Introduction The currently most prevalent conceptual model of supercell tornadogenesis is based on the evolution of a single, discrete supercell (e.g., Lemon 1976 ; Davies-Jones 2015 ). However, it is generally acknowledged among researchers and forecasters that storm-external factors can also have an impact on tornado formation. As discussed in this section, these external factors typically come in two forms; (i) supercell interaction with another thunderstorm, broadly referred to
reflectivity isopleth observed by DOW7 also is overlaid at 0108, 0116, 0124, and 0132 UTC (blue contours). The locations of the photographs that appear in Figs. 11a–c are indicated by the green, black, and purple camera icons, respectively (the photograph time are indicated beside the icons). Following a brief explanation of the available data and the analysis methods in section 2 , a detailed description of the chain of events that led to tornadogenesis are presented in sections 3 and 4 . The
reflectivity isopleth observed by DOW7 also is overlaid at 0108, 0116, 0124, and 0132 UTC (blue contours). The locations of the photographs that appear in Figs. 11a–c are indicated by the green, black, and purple camera icons, respectively (the photograph time are indicated beside the icons). Following a brief explanation of the available data and the analysis methods in section 2 , a detailed description of the chain of events that led to tornadogenesis are presented in sections 3 and 4 . The
1. Introduction Tornadic storms, and tornadogenesis in supercellular storms have been observed visually, with surface observations, and with radars for decades (e.g., Stout and Huff 1953 ; Ludlam 1963 ; Fujita 1975 ; Ray et al. 1975 , 1981 ; Brandes 1977 , 1978 , 1981 , 1984a ; Fujita and Wakimoto 1982 ; Brandes et al. 1988 ; Dowell and Bluestein 1997 , 2002a , b ; Wakimoto and Liu 1998 ; Trapp 1999 ; Trapp et al. 1999 ; Wakimoto and Cai 2000 ; Bluestein
1. Introduction Tornadic storms, and tornadogenesis in supercellular storms have been observed visually, with surface observations, and with radars for decades (e.g., Stout and Huff 1953 ; Ludlam 1963 ; Fujita 1975 ; Ray et al. 1975 , 1981 ; Brandes 1977 , 1978 , 1981 , 1984a ; Fujita and Wakimoto 1982 ; Brandes et al. 1988 ; Dowell and Bluestein 1997 , 2002a , b ; Wakimoto and Liu 1998 ; Trapp 1999 ; Trapp et al. 1999 ; Wakimoto and Cai 2000 ; Bluestein
1. Introduction Significant progress has been made in our understanding of supercell storms through Doppler radar measurements and three-dimensional numerical simulations. However, our knowledge of the dynamics of tornadogenesis in supercell storms is still limited because of difficulties in collecting detailed observational data with good spatial and temporal resolutions and in numerically simulating a tornado that is more than two orders of magnitude smaller than a supercell storm. The
1. Introduction Significant progress has been made in our understanding of supercell storms through Doppler radar measurements and three-dimensional numerical simulations. However, our knowledge of the dynamics of tornadogenesis in supercell storms is still limited because of difficulties in collecting detailed observational data with good spatial and temporal resolutions and in numerically simulating a tornado that is more than two orders of magnitude smaller than a supercell storm. The
LMCs ( Markowski et al. 2002 , 2003 , 2008 ; Straka et al. 2007 ). Because this horizontal vorticity and convective updraft are intensified by environmental low-level vertical shear and water vapor, respectively, the preexisting low-level environment is especially important for tornadogenesis ( Thompson et al. 2003 ; Craven and Brooks 2004 ; Markowski and Richardson 2014 ; Parker and Dahl 2015 ). Even in the presence of MMCs and LMCs, however, tornadoes are not necessarily generated. Trapp
LMCs ( Markowski et al. 2002 , 2003 , 2008 ; Straka et al. 2007 ). Because this horizontal vorticity and convective updraft are intensified by environmental low-level vertical shear and water vapor, respectively, the preexisting low-level environment is especially important for tornadogenesis ( Thompson et al. 2003 ; Craven and Brooks 2004 ; Markowski and Richardson 2014 ; Parker and Dahl 2015 ). Even in the presence of MMCs and LMCs, however, tornadoes are not necessarily generated. Trapp
1. Introduction To understand the mechanism of supercell tornadogenesis, identifying the responsible vorticity sources is a vitally important issue. Despite several decades of study on this issue, a complete understanding remains ambiguous. Previous studies included field observations and idealized numerical simulations. It was suggested that the baroclinic effects are prominent in generating the vorticity of tornadoes (e.g., Davies-Jones and Brooks 1993 ; Adlerman et al. 1999 ; Straka et al
1. Introduction To understand the mechanism of supercell tornadogenesis, identifying the responsible vorticity sources is a vitally important issue. Despite several decades of study on this issue, a complete understanding remains ambiguous. Previous studies included field observations and idealized numerical simulations. It was suggested that the baroclinic effects are prominent in generating the vorticity of tornadoes (e.g., Davies-Jones and Brooks 1993 ; Adlerman et al. 1999 ; Straka et al
radar near the damage paths of the corresponding tornadoes ( Fig. 1a ). Since the radar was not in full operational mode, and had performed only three scans at the lower elevation angles around the time of the tornadogenesis, they were unable to clarify the detailed relationship between the mesovortices and tornadogenesis. A maximum surface wind of 35 m s −1 ( Fig. 1b ) with rapid change in wind direction from east to west was observed by an anemometer located at the southern end of the runway at
radar near the damage paths of the corresponding tornadoes ( Fig. 1a ). Since the radar was not in full operational mode, and had performed only three scans at the lower elevation angles around the time of the tornadogenesis, they were unable to clarify the detailed relationship between the mesovortices and tornadogenesis. A maximum surface wind of 35 m s −1 ( Fig. 1b ) with rapid change in wind direction from east to west was observed by an anemometer located at the southern end of the runway at
; Wicker and Wilhelmson 1993 ; Lewellen and Lewellen 2007a , b ; Davies-Jones 2015 ). A growing body of literature has indicated that the near-ground horizontal vorticity generated by surface drag may play an important role in tornadogenesis if it can be tilted into the vertical and sufficiently concentrated ( Schenkman et al. 2014 ; Markowski 2016 ; Roberts et al. 2016 ; Roberts and Xue 2017 ; Roberts et al. 2020 ; Fischer and Dahl 2022 ). Other studies have shown that surface drag may have
; Wicker and Wilhelmson 1993 ; Lewellen and Lewellen 2007a , b ; Davies-Jones 2015 ). A growing body of literature has indicated that the near-ground horizontal vorticity generated by surface drag may play an important role in tornadogenesis if it can be tilted into the vertical and sufficiently concentrated ( Schenkman et al. 2014 ; Markowski 2016 ; Roberts et al. 2016 ; Roberts and Xue 2017 ; Roberts et al. 2020 ; Fischer and Dahl 2022 ). Other studies have shown that surface drag may have
. 3. Unedited logarithmic radar reflectivity factor (dB Z ) observed by the KCYS WSR-88D (0.5° elevation angle) and DOW7 radars (1° elevation angle) at (a) 2107:04, (b) 2116:14, (c) 2125:23, (d) 2132:26, (e) 2140:06, and (f) 2148:07 UTC (“ t − X min” indicates X min prior to tornadogenesis). The DOW7 reflectivity is uncalibrated. Fig . 4. Large-scale depiction of the track of the Goshen County storm on 5 Jun 2009 as evidenced by the 40-, 50-, and 60-dB Z isopleths of logarithmic reflectivity
. 3. Unedited logarithmic radar reflectivity factor (dB Z ) observed by the KCYS WSR-88D (0.5° elevation angle) and DOW7 radars (1° elevation angle) at (a) 2107:04, (b) 2116:14, (c) 2125:23, (d) 2132:26, (e) 2140:06, and (f) 2148:07 UTC (“ t − X min” indicates X min prior to tornadogenesis). The DOW7 reflectivity is uncalibrated. Fig . 4. Large-scale depiction of the track of the Goshen County storm on 5 Jun 2009 as evidenced by the 40-, 50-, and 60-dB Z isopleths of logarithmic reflectivity
