Search Results
1. Introduction To understand why tornadoes form and how they are related to processes occurring in their parent convective storms, high-resolution, near-surface observations of tornadogenesis are needed. Although numerical simulations can be used to do controlled experiments with varying environmental conditions (e.g., different wind shear and thermodynamic or buoyancy profiles) to assess the impact of such changes on the ability of a storm to produce a tornado, the experiments tend to be
1. Introduction To understand why tornadoes form and how they are related to processes occurring in their parent convective storms, high-resolution, near-surface observations of tornadogenesis are needed. Although numerical simulations can be used to do controlled experiments with varying environmental conditions (e.g., different wind shear and thermodynamic or buoyancy profiles) to assess the impact of such changes on the ability of a storm to produce a tornado, the experiments tend to be
1. Introduction As scientists simulate and observe supercell thunderstorms at smaller scales, research continues to show that tornadogenesis and tornadogenesis failure are significantly influenced by storm-scale processes (e.g., Davies-Jones and Brooks 1993 ; Markowski and Richardson 2009 ; Coffer and Parker 2017 ; Orf et al. 2017 ; Fischer and Dahl 2020 ). Mesoscale environmental characteristics do play an important role in the development of supercell structure, as differences in
1. Introduction As scientists simulate and observe supercell thunderstorms at smaller scales, research continues to show that tornadogenesis and tornadogenesis failure are significantly influenced by storm-scale processes (e.g., Davies-Jones and Brooks 1993 ; Markowski and Richardson 2009 ; Coffer and Parker 2017 ; Orf et al. 2017 ; Fischer and Dahl 2020 ). Mesoscale environmental characteristics do play an important role in the development of supercell structure, as differences in
1. Introduction The most violent tornadic storms tend to produce families of tornadoes, rather than just one tornado, often at strikingly regular intervals ( Darkow and Roos 1970 ; Darkow 1971 ). Fujita et al. (1970) and Fujita (1974) documented these types of storms with detailed damage surveys and classified them according to patterns of tornado tracks. We will refer to the formation of a series of tornadoes in a supercell thunderstorm as “cyclic tornadogenesis” ( Darkow and Roos 1970
1. Introduction The most violent tornadic storms tend to produce families of tornadoes, rather than just one tornado, often at strikingly regular intervals ( Darkow and Roos 1970 ; Darkow 1971 ). Fujita et al. (1970) and Fujita (1974) documented these types of storms with detailed damage surveys and classified them according to patterns of tornado tracks. We will refer to the formation of a series of tornadoes in a supercell thunderstorm as “cyclic tornadogenesis” ( Darkow and Roos 1970
al. 2015 ) and nontornadic supercells (e.g., Skinner et al. 2014 ; Murdzek et al. 2020 ), facilitating comparisons between tornadic and nontornadic supercells ( Klees et al. 2016 ). In the past decade, numerical simulation studies have allowed researchers to investigate numerous aspects of the tornadogenesis process in supercells (e.g., Markowski and Richardson 2014 , 2017 ; Orf et al. 2017 ; Coffer and Parker 2017 , 2018 ; Coffer et al. 2017 ). Unfortunately, dedicated field observations and
al. 2015 ) and nontornadic supercells (e.g., Skinner et al. 2014 ; Murdzek et al. 2020 ), facilitating comparisons between tornadic and nontornadic supercells ( Klees et al. 2016 ). In the past decade, numerical simulation studies have allowed researchers to investigate numerous aspects of the tornadogenesis process in supercells (e.g., Markowski and Richardson 2014 , 2017 ; Orf et al. 2017 ; Coffer and Parker 2017 , 2018 ; Coffer et al. 2017 ). Unfortunately, dedicated field observations and
1. Introduction The ability to understand the processes involved in tornadogenesis, maintenance, and decay is dependent, in large part, on obtaining observations in key regions of supercell thunderstorms that historically have been very difficult to gather. One such area is within roughly 1 km of the tornado or tornadogenesis region that contains the air parcels that ultimately comprise the tornado inflow. With the rapid evolution of mobile Doppler radar ( Wurman et al. 1997
1. Introduction The ability to understand the processes involved in tornadogenesis, maintenance, and decay is dependent, in large part, on obtaining observations in key regions of supercell thunderstorms that historically have been very difficult to gather. One such area is within roughly 1 km of the tornado or tornadogenesis region that contains the air parcels that ultimately comprise the tornado inflow. With the rapid evolution of mobile Doppler radar ( Wurman et al. 1997
the likelihood of tornadogenesis. It is important to consider the means by which environmental variables with documented discriminatory skill in HSLC environments (low-level lapse rates and shear vector magnitudes 1 ; Sherburn and Parker 2014 ; Sherburn et al. 2016 ) could impact each of these features. The sensitivity of vortexgenesis to low-level shear vector magnitude has been documented in both QLCSs and supercells within high-CAPE environments, as reviewed above. The strength and lifetime
the likelihood of tornadogenesis. It is important to consider the means by which environmental variables with documented discriminatory skill in HSLC environments (low-level lapse rates and shear vector magnitudes 1 ; Sherburn and Parker 2014 ; Sherburn et al. 2016 ) could impact each of these features. The sensitivity of vortexgenesis to low-level shear vector magnitude has been documented in both QLCSs and supercells within high-CAPE environments, as reviewed above. The strength and lifetime
domain. Once in the CASA domain, X-band radars provided high spatial and temporal observations of tornadogenesis and evolution. The closest representation of the atmospheric conditions near the tornado was a sounding taken at Norman, Oklahoma (KOUN), at 0000 UTC on 14 May 2009 ( Fig. 3a ). Surface-based convective available potential energy (CAPE) was ~4600 J kg −1 , while mixed layer CAPE (MLCAPE) was ~4900 J kg −1 . Surface-based convective inhibition was ~−10 J kg −1 . Overall, the atmosphere
domain. Once in the CASA domain, X-band radars provided high spatial and temporal observations of tornadogenesis and evolution. The closest representation of the atmospheric conditions near the tornado was a sounding taken at Norman, Oklahoma (KOUN), at 0000 UTC on 14 May 2009 ( Fig. 3a ). Surface-based convective available potential energy (CAPE) was ~4600 J kg −1 , while mixed layer CAPE (MLCAPE) was ~4900 J kg −1 . Surface-based convective inhibition was ~−10 J kg −1 . Overall, the atmosphere
1. Introduction The origin of vertical vorticity in tornadoes is one of the most critical questions about tornadogenesis. A widely used approach to address this problem, both in models and dual-Doppler analyses, is the backward integration of trajectories initialized within the near-surface vortex. Based on these trajectories, vorticity or circulation budgets following individual parcels may be computed. However, these budgets—and the inferred sources of vorticity—critically depend on the
1. Introduction The origin of vertical vorticity in tornadoes is one of the most critical questions about tornadogenesis. A widely used approach to address this problem, both in models and dual-Doppler analyses, is the backward integration of trajectories initialized within the near-surface vortex. Based on these trajectories, vorticity or circulation budgets following individual parcels may be computed. However, these budgets—and the inferred sources of vorticity—critically depend on the
simple climate data and daily measurements of ablation . Ann. Glaciol. , 50 , 9 – 15 , doi: 10.3189/172756409787769726 . Brandes , E. A. , 1978 : Mesocyclone evolution and tornadogenesis: Some observations . Mon. Wea. Rev. , 106 , 995 – 1011 , doi: 10.1175/1520-0493(1978)106<0995:MEATSO>2.0.CO;2 . Dahl , J. M. L. , M. D. Parker , and L. J. Wicker , 2014 : Imported and storm-generated near-ground vertical vorticity in a simulated supercell . J. Atmos. Sci. , 71 , 3027 – 3051 , doi
simple climate data and daily measurements of ablation . Ann. Glaciol. , 50 , 9 – 15 , doi: 10.3189/172756409787769726 . Brandes , E. A. , 1978 : Mesocyclone evolution and tornadogenesis: Some observations . Mon. Wea. Rev. , 106 , 995 – 1011 , doi: 10.1175/1520-0493(1978)106<0995:MEATSO>2.0.CO;2 . Dahl , J. M. L. , M. D. Parker , and L. J. Wicker , 2014 : Imported and storm-generated near-ground vertical vorticity in a simulated supercell . J. Atmos. Sci. , 71 , 3027 – 3051 , doi
, 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
