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detect, is not a sufficient condition for tornado occurrence ( Trapp et al. 1999 , 2005 ; Wakimoto et al. 2004 ). In fact, Trapp et al. (2005) estimated that only 15% of storms with midlevel mesocyclones produce tornadoes. Assuming modeled storms possess a similar relationship, then midlevel UH alone is not an appropriate surrogate for tornado occurrence, and an additional surrogate that is more closely related to tornadogenesis is needed. While directly sampling CAM output for the presence of
detect, is not a sufficient condition for tornado occurrence ( Trapp et al. 1999 , 2005 ; Wakimoto et al. 2004 ). In fact, Trapp et al. (2005) estimated that only 15% of storms with midlevel mesocyclones produce tornadoes. Assuming modeled storms possess a similar relationship, then midlevel UH alone is not an appropriate surrogate for tornado occurrence, and an additional surrogate that is more closely related to tornadogenesis is needed. While directly sampling CAM output for the presence of
). While UH is a good predictor for severe hazards, it is not necessarily a good proxy for tornadoes when used alone. Like in reality, simulated mesocyclones often form in environments unfavorable for tornadogenesis ( Clark et al. 2012b ). Therefore, if generating tornado probabilities from UH alone, large areas of false alarms will occur in regions with unfavorable environments. However, adding environmental criteria for probability generation could reduce the false alarm area, increasing the
). While UH is a good predictor for severe hazards, it is not necessarily a good proxy for tornadoes when used alone. Like in reality, simulated mesocyclones often form in environments unfavorable for tornadogenesis ( Clark et al. 2012b ). Therefore, if generating tornado probabilities from UH alone, large areas of false alarms will occur in regions with unfavorable environments. However, adding environmental criteria for probability generation could reduce the false alarm area, increasing the
; Thompson et al. 2003 ; Markowski et al. 2003 ; Nowotarski and Jensen 2013 ; C19 ; Coffer et al. 2020 ; Warren et al. 2021 ). The dynamics that connect large low-level wind shear/SRH to supercell mesocyclones and associated dynamic lifting that favor tornadogenesis are fairly well understood ( Rotunno 1981 ; Davies-Jones 1984 ; Droegemeier et al. 1993 ; Markowski and Richardson 2014 ; Davies-Jones 2015 ; Coffer and Parker 2018 ; Peters et al. 2020 ; Goldacker and Parker 2021 ). But still
; Thompson et al. 2003 ; Markowski et al. 2003 ; Nowotarski and Jensen 2013 ; C19 ; Coffer et al. 2020 ; Warren et al. 2021 ). The dynamics that connect large low-level wind shear/SRH to supercell mesocyclones and associated dynamic lifting that favor tornadogenesis are fairly well understood ( Rotunno 1981 ; Davies-Jones 1984 ; Droegemeier et al. 1993 ; Markowski and Richardson 2014 ; Davies-Jones 2015 ; Coffer and Parker 2018 ; Peters et al. 2020 ; Goldacker and Parker 2021 ). But still
WEATHER AND INIS =where .and tornadoes. Numerical modeling work by Weismanand Klemp (1982; 1984) indicates that thunderstormtype is dependent on the magnitude of the vertical windshear. Low shears produced short-lived single cells,moderate shears, multicell storms, and high shears supercells. Recent theories of tornadogenesis (e.g., Klempand Rotunno 1983) have emphasized the importanceof vertical wind shear generated horizontal vorticity,which is tilted into the vertical by a storm. Experimental
WEATHER AND INIS =where .and tornadoes. Numerical modeling work by Weismanand Klemp (1982; 1984) indicates that thunderstormtype is dependent on the magnitude of the vertical windshear. Low shears produced short-lived single cells,moderate shears, multicell storms, and high shears supercells. Recent theories of tornadogenesis (e.g., Klempand Rotunno 1983) have emphasized the importanceof vertical wind shear generated horizontal vorticity,which is tilted into the vertical by a storm. Experimental
Weisman and Klemp (1982) who showed that much of the relationship between storm type, wind shear, and buoyancy could be represented in the form of a bulk Richardson number (BRN) using various combinations of parameters that relate instability and vertical wind shear to mesoscyclogenesis and tornadogenesis. Their modeling results and calculation of BRN for a series of storms suggested that multicellular growth occurs most readily for BRN > 30 and the supercellular growth is confined to magnitudes of
Weisman and Klemp (1982) who showed that much of the relationship between storm type, wind shear, and buoyancy could be represented in the form of a bulk Richardson number (BRN) using various combinations of parameters that relate instability and vertical wind shear to mesoscyclogenesis and tornadogenesis. Their modeling results and calculation of BRN for a series of storms suggested that multicellular growth occurs most readily for BRN > 30 and the supercellular growth is confined to magnitudes of
the existence of hail. The WER could be recognized up to a height of approximately 3.5 km. This three-dimensional structure of the radar reflectivity is similar to that of hook echoes within well-developed supercell thunderstorms in Lemon and Doswell (1979) and Markowski (2002) . A WER is an indicator implying the existence of strong updrafts, cyclonic rotation, and hail aloft, which consequently describe the storm severity. In addition, WER is frequently indicative of tornadogenesis ( Rotunno
the existence of hail. The WER could be recognized up to a height of approximately 3.5 km. This three-dimensional structure of the radar reflectivity is similar to that of hook echoes within well-developed supercell thunderstorms in Lemon and Doswell (1979) and Markowski (2002) . A WER is an indicator implying the existence of strong updrafts, cyclonic rotation, and hail aloft, which consequently describe the storm severity. In addition, WER is frequently indicative of tornadogenesis ( Rotunno
. For tornado warnings in particular, it is possible that event-only studies could eventually provide a reasonably comprehensive understanding of tornadogenesis, and the operational application of that understanding might be both straightforward in terms of what variables to look for in the diagnosis and practical in terms of permitting accurate decisions based on routinely available data. Such an understanding could make it clear why most situations involving supercells either fail to be tornadic
. For tornado warnings in particular, it is possible that event-only studies could eventually provide a reasonably comprehensive understanding of tornadogenesis, and the operational application of that understanding might be both straightforward in terms of what variables to look for in the diagnosis and practical in terms of permitting accurate decisions based on routinely available data. Such an understanding could make it clear why most situations involving supercells either fail to be tornadic
EMLs over the Northeast. The complex topography of the Northeast has been shown to influence the development of severe weather. Wasula et al. (2002) linked the spatial distribution of severe events over New England to the influence of terrain under southwesterly and northwesterly 700-hPa flow. More specifically, it has been suggested that terrain channeling plays an important role in tornadogenesis for a number of events in New England by accelerating the surface flow and thus increasing shear in
EMLs over the Northeast. The complex topography of the Northeast has been shown to influence the development of severe weather. Wasula et al. (2002) linked the spatial distribution of severe events over New England to the influence of terrain under southwesterly and northwesterly 700-hPa flow. More specifically, it has been suggested that terrain channeling plays an important role in tornadogenesis for a number of events in New England by accelerating the surface flow and thus increasing shear in
location data would have little predictive value for tornadoes,” when CG rates peaked near the time of tornado dissipation in each case examined. MacGorman et al. (1989) documented a tornadic storm in Oklahoma where the an increase in the IC flash rate occurs prior to tornadogenesis, while the peak CG rate occurs 15 min after the increase in IC lightning and several minutes after the tornado had already touched down. Kane (1991) found two examples where CG lightning rates peaked prior to severe
location data would have little predictive value for tornadoes,” when CG rates peaked near the time of tornado dissipation in each case examined. MacGorman et al. (1989) documented a tornadic storm in Oklahoma where the an increase in the IC flash rate occurs prior to tornadogenesis, while the peak CG rate occurs 15 min after the increase in IC lightning and several minutes after the tornado had already touched down. Kane (1991) found two examples where CG lightning rates peaked prior to severe
1. Introduction Discriminating a tornado threat from an overall severe convective threat poses a unique forecast challenge. Forecasters incorporate knowledge of internal storm dynamics and environments conducive to tornadogenesis, a thorough understanding of current observations, and numerical weather prediction (NWP) guidance to forecast tornadoes. Until very recently, NWP guidance has been too coarse to depict specific storm modes, but recent expansion of computational resources has enabled
1. Introduction Discriminating a tornado threat from an overall severe convective threat poses a unique forecast challenge. Forecasters incorporate knowledge of internal storm dynamics and environments conducive to tornadogenesis, a thorough understanding of current observations, and numerical weather prediction (NWP) guidance to forecast tornadoes. Until very recently, NWP guidance has been too coarse to depict specific storm modes, but recent expansion of computational resources has enabled
