Search Results
Abstract
An early morning tornado outbreak occurred on 13 April 2020 in the Central Savannah River Area. Multiple significant tornadoes were reported, resulting in fatalities and injuries. While the operational tornado warnings had positive lead times, the convective mode (quasi-linear convective system) increased the warning decision complexity. The timing of the event [0500–0600 local time (LT)] also made NWS-to-public communication difficult. The experimental NSSL Warn-on-Forecast System (WoFS) was run retrospectively for this case. The WoFS consists of 3–6-h ensemble forecasts initialized every 30 min, and the goals of the system are to bridge the gap between severe weather watches and warnings and to increase warning lead times. Multiple WoFS forecasts were initialized leading up to the first tornado report; those initialized prior to tornado warning issuance have high ensemble probabilities of low-level rotation in the appropriate areas based on subsequent tornado reports. This case highlights another example of the usefulness of WoFS before its eventual transition to operations. Using the WoFS forecasts, kinematic and thermodynamic storm–environment relationships are analyzed using ensemble sensitivity analysis (ESA). The analyses suggest variations in the mesoscale environmental vertical wind profile are not as influential on mesovortex intensity as variations in the thermodynamic environment. Surface observations recorded prior to the tornado outbreak reveal subtle temperature and moisture gradients that may be the impetus for mesovortex intensification and tornadogenesis.
Abstract
An early morning tornado outbreak occurred on 13 April 2020 in the Central Savannah River Area. Multiple significant tornadoes were reported, resulting in fatalities and injuries. While the operational tornado warnings had positive lead times, the convective mode (quasi-linear convective system) increased the warning decision complexity. The timing of the event [0500–0600 local time (LT)] also made NWS-to-public communication difficult. The experimental NSSL Warn-on-Forecast System (WoFS) was run retrospectively for this case. The WoFS consists of 3–6-h ensemble forecasts initialized every 30 min, and the goals of the system are to bridge the gap between severe weather watches and warnings and to increase warning lead times. Multiple WoFS forecasts were initialized leading up to the first tornado report; those initialized prior to tornado warning issuance have high ensemble probabilities of low-level rotation in the appropriate areas based on subsequent tornado reports. This case highlights another example of the usefulness of WoFS before its eventual transition to operations. Using the WoFS forecasts, kinematic and thermodynamic storm–environment relationships are analyzed using ensemble sensitivity analysis (ESA). The analyses suggest variations in the mesoscale environmental vertical wind profile are not as influential on mesovortex intensity as variations in the thermodynamic environment. Surface observations recorded prior to the tornado outbreak reveal subtle temperature and moisture gradients that may be the impetus for mesovortex intensification and tornadogenesis.
Abstract
Herein, 14 severe quasi-linear convective systems (QLCS) covering a wide range of geographical locations and environmental conditions are simulated for both 1- and 3-km horizontal grid resolutions, to further clarify their comparative capabilities in representing convective system features associated with severe weather production. Emphasis is placed on validating the simulated reflectivity structures, cold pool strength, mesoscale vortex characteristics, and surface wind strength. As to the overall reflectivity characteristics, the basic leading-line trailing stratiform structure was often better defined at 1 versus 3 km, but both resolutions were capable of producing bow echo and line echo wave pattern type features. Cold pool characteristics for both the 1- and 3-km simulations were also well replicated for the differing environments, with the 1-km cold pools slightly colder and often a bit larger. Both resolutions captured the larger mesoscale vortices, such as line-end or bookend vortices, but smaller, leading-line mesoscale updraft vortices, that often promote QLCS tornadogenesis, were largely absent in the 3-km simulations. Finally, while maximum surface winds were only marginally well predicted for both resolutions, the simulations were able to reasonably differentiate the relative contributions of the cold pool versus mesoscale vortices. The present results suggest that while many QLCS characteristics can be reasonably represented at a grid scale of 3 km, some of the more detailed structures, such as overall reflectivity characteristics and the smaller leading-line mesoscale vortices would likely benefit from the finer 1-km grid spacing.
Significance Statement
High-resolution model forecasts using 3-km grid spacing have proven to offer significant forecast guidance enhancements for severe convective weather. However, it is unclear whether additional enhancements can be obtained by decreasing grid spacings further to 1 km. Herein, we compare forecasts of severe quasi-linear convective systems (QLCS) simulated using 1- versus 3-km grids to document the potential value added of such increases in grid resolutions. It is shown that some significant improvements can be obtained in the representation of many QLCS features, especially as regards reflectivity structure and in the development of small, leading-line mesoscale vortices that can contribute to both severe surface wind and tornado production.
Abstract
Herein, 14 severe quasi-linear convective systems (QLCS) covering a wide range of geographical locations and environmental conditions are simulated for both 1- and 3-km horizontal grid resolutions, to further clarify their comparative capabilities in representing convective system features associated with severe weather production. Emphasis is placed on validating the simulated reflectivity structures, cold pool strength, mesoscale vortex characteristics, and surface wind strength. As to the overall reflectivity characteristics, the basic leading-line trailing stratiform structure was often better defined at 1 versus 3 km, but both resolutions were capable of producing bow echo and line echo wave pattern type features. Cold pool characteristics for both the 1- and 3-km simulations were also well replicated for the differing environments, with the 1-km cold pools slightly colder and often a bit larger. Both resolutions captured the larger mesoscale vortices, such as line-end or bookend vortices, but smaller, leading-line mesoscale updraft vortices, that often promote QLCS tornadogenesis, were largely absent in the 3-km simulations. Finally, while maximum surface winds were only marginally well predicted for both resolutions, the simulations were able to reasonably differentiate the relative contributions of the cold pool versus mesoscale vortices. The present results suggest that while many QLCS characteristics can be reasonably represented at a grid scale of 3 km, some of the more detailed structures, such as overall reflectivity characteristics and the smaller leading-line mesoscale vortices would likely benefit from the finer 1-km grid spacing.
Significance Statement
High-resolution model forecasts using 3-km grid spacing have proven to offer significant forecast guidance enhancements for severe convective weather. However, it is unclear whether additional enhancements can be obtained by decreasing grid spacings further to 1 km. Herein, we compare forecasts of severe quasi-linear convective systems (QLCS) simulated using 1- versus 3-km grids to document the potential value added of such increases in grid resolutions. It is shown that some significant improvements can be obtained in the representation of many QLCS features, especially as regards reflectivity structure and in the development of small, leading-line mesoscale vortices that can contribute to both severe surface wind and tornado production.
Yagi in August 2018, producing 11 tornadoes, and an EC in July 2021, producing 13 tornadoes. Interestingly, these two events both occurred in summer and produced tornadoes mainly in the same area. Some studies have revealed that the environment of TC Yagi was favorable for tornadogenesis, including significant dry air intrusions and the interaction of Yagi with an approaching midlatitude, midlevel trough ( Bai et al. 2020b ); these conditions caused the overlap of high SRH and E-CAPE values in the
Yagi in August 2018, producing 11 tornadoes, and an EC in July 2021, producing 13 tornadoes. Interestingly, these two events both occurred in summer and produced tornadoes mainly in the same area. Some studies have revealed that the environment of TC Yagi was favorable for tornadogenesis, including significant dry air intrusions and the interaction of Yagi with an approaching midlatitude, midlevel trough ( Bai et al. 2020b ); these conditions caused the overlap of high SRH and E-CAPE values in the
in the analyses were averaged over 12-s intervals. Data were plotted relative to radar echoes using time-to-space conversion, assuming that the features being analyzed did not change significantly over the time interval during which the measurements were made (the “Taylor hypothesis”). Supercells are not steady. If they were, then tornadogenesis could not occur. However, steadiness assumptions, at least for short time intervals, are virtually unavoidable in observational studies (e.g., multiple
in the analyses were averaged over 12-s intervals. Data were plotted relative to radar echoes using time-to-space conversion, assuming that the features being analyzed did not change significantly over the time interval during which the measurements were made (the “Taylor hypothesis”). Supercells are not steady. If they were, then tornadogenesis could not occur. However, steadiness assumptions, at least for short time intervals, are virtually unavoidable in observational studies (e.g., multiple
1. Introduction To better understand an unpredictable atmosphere, meteorologists have created scientific conceptual models that aid in identifying favorable severe-weather environments and assist in overall threat recognition, including the potential for supercells and tornadogenesis (e.g., Johns and Doswell 1992 ; Doswell and Burgess 1993 ; Rasmussen 2003 ; Boustead et al. 2013 ). Such scientifically based conceptual models help forecasters anticipate future storm characteristics and
1. Introduction To better understand an unpredictable atmosphere, meteorologists have created scientific conceptual models that aid in identifying favorable severe-weather environments and assist in overall threat recognition, including the potential for supercells and tornadogenesis (e.g., Johns and Doswell 1992 ; Doswell and Burgess 1993 ; Rasmussen 2003 ; Boustead et al. 2013 ). Such scientifically based conceptual models help forecasters anticipate future storm characteristics and
value of the convective available potential energy (CAPE; Moncrieff and Green 1972 ) shows a gradual trend of increasing instability until 1 h before tornadogenesis ( Fig. 2 ), reaching a peak of over 2200 J kg −1 . The CAPE then drops off quickly until tornadogenesis, after which it continues to decrease at a slower rate. Nontornadic supercell environments exhibit a slightly different behavior; the median value of CAPE exhibits a faster increase than the median tornadic environment CAPE, rising
value of the convective available potential energy (CAPE; Moncrieff and Green 1972 ) shows a gradual trend of increasing instability until 1 h before tornadogenesis ( Fig. 2 ), reaching a peak of over 2200 J kg −1 . The CAPE then drops off quickly until tornadogenesis, after which it continues to decrease at a slower rate. Nontornadic supercell environments exhibit a slightly different behavior; the median value of CAPE exhibits a faster increase than the median tornadic environment CAPE, rising
boundaries. 2. Methodology Significant tornadoes for the years 1979–2011 for parts of the central and northern plains were compiled using the National Climatic Data Center (NCDC) publication Storm Data ( Fig. 1 ). For each tornado occurrence, archived surface observations were obtained and plotted using the Digital Atmosphere program for a period of 2 h before to 1 h after tornadogenesis. Hand-drawn analyses of temperature, dewpoint, and pressure were completed for each of the hours. Using the manual
boundaries. 2. Methodology Significant tornadoes for the years 1979–2011 for parts of the central and northern plains were compiled using the National Climatic Data Center (NCDC) publication Storm Data ( Fig. 1 ). For each tornado occurrence, archived surface observations were obtained and plotted using the Digital Atmosphere program for a period of 2 h before to 1 h after tornadogenesis. Hand-drawn analyses of temperature, dewpoint, and pressure were completed for each of the hours. Using the manual
-based straight-line winds, and undergo tornadogenesis ( Lemon and Doswell 1979 ; Markowski and Richardson 2009 ; Duda and Gallus 2010 ; Davies-Jones 2015 ). Though tornadogenesis remains an active area of research, this study specifically addresses the mesocyclone in lieu of tornadic rotation because of the shared dependency of lightning and the mesocyclone on the midlevel updraft. Current tornadogenesis research establishes the role of the mesocyclone as a necessary but insufficient component related to
-based straight-line winds, and undergo tornadogenesis ( Lemon and Doswell 1979 ; Markowski and Richardson 2009 ; Duda and Gallus 2010 ; Davies-Jones 2015 ). Though tornadogenesis remains an active area of research, this study specifically addresses the mesocyclone in lieu of tornadic rotation because of the shared dependency of lightning and the mesocyclone on the midlevel updraft. Current tornadogenesis research establishes the role of the mesocyclone as a necessary but insufficient component related to
hodograph curves in a clockwise direction, while the hodograph in the 2–4-km layer exhibits counterclockwise curvature. This low-level clockwise curvature is favorable for tornadogenesis in the right mover, but unfavorable for the left mover since positive streamwise vorticity would enter the left mover’s updraft near the surface ( Davies-Jones et al. 2001 ). Accordingly, the 0–1-km SRH was 157 m 2 s −2 (−46 m 2 s −2 ) given the observed motion of the tornadic right-moving (nontornadic left moving
hodograph curves in a clockwise direction, while the hodograph in the 2–4-km layer exhibits counterclockwise curvature. This low-level clockwise curvature is favorable for tornadogenesis in the right mover, but unfavorable for the left mover since positive streamwise vorticity would enter the left mover’s updraft near the surface ( Davies-Jones et al. 2001 ). Accordingly, the 0–1-km SRH was 157 m 2 s −2 (−46 m 2 s −2 ) given the observed motion of the tornadic right-moving (nontornadic left moving
of supercellular tornadoes is achieved by identifying known features or ongoing processes in operational remote sensing data that are thought to indicate that tornado formation or evolution is imminent. Many observational studies have investigated the tornado’s life cycle using mobile and airborne radar data with a focus on the tornadogenesis process (e.g., Brandes 1977 ; Dowell and Bluestein 2002a , b ; Bluestein et al. 2003; Wurman et al. 2007 ; Markowski et al. 2012 , 2018 ; Kosiba et al
of supercellular tornadoes is achieved by identifying known features or ongoing processes in operational remote sensing data that are thought to indicate that tornado formation or evolution is imminent. Many observational studies have investigated the tornado’s life cycle using mobile and airborne radar data with a focus on the tornadogenesis process (e.g., Brandes 1977 ; Dowell and Bluestein 2002a , b ; Bluestein et al. 2003; Wurman et al. 2007 ; Markowski et al. 2012 , 2018 ; Kosiba et al
