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1. Introduction The National Oceanic and Atmospheric Administration’s Storm Prediction Center (NOAA SPC) provides a 1–8-day lead-time severe weather forecast, including tornado watches. This severe weather forecast is based on synoptic-scale atmospheric instability [e.g., convective available potential energy (CAPE) and low-level wind shear (LLWS)] from numerical weather forecast models and observations. To extend the current forecast lead time for tornadogenesis to subseasonal time scales (i
1. Introduction The National Oceanic and Atmospheric Administration’s Storm Prediction Center (NOAA SPC) provides a 1–8-day lead-time severe weather forecast, including tornado watches. This severe weather forecast is based on synoptic-scale atmospheric instability [e.g., convective available potential energy (CAPE) and low-level wind shear (LLWS)] from numerical weather forecast models and observations. To extend the current forecast lead time for tornadogenesis to subseasonal time scales (i
intensity distribution. The latter issues were the main motivation for the present paper. In section 2 , we address the question of how the observed c–b correlation is related to a climatological invariant. Using the large U.S. database and by introducing a detection efficiency function for tornadoes, a climatological Weibull intensity distribution for presumably supercell-dominated tornadogenesis is derived. Section 3 considers the problem of F0 underreporting and rating problems and how the
intensity distribution. The latter issues were the main motivation for the present paper. In section 2 , we address the question of how the observed c–b correlation is related to a climatological invariant. Using the large U.S. database and by introducing a detection efficiency function for tornadoes, a climatological Weibull intensity distribution for presumably supercell-dominated tornadogenesis is derived. Section 3 considers the problem of F0 underreporting and rating problems and how the
regarding the relative influence of large-scale climatic signals, the importance of changes in global radiative forcing, and the need to delineate the role of the underlying atmospheric processes at the appropriate spatiotemporal scale ( Trapp et al. 2007 ; Diffenbaugh et al. 2013 ; Tippett et al. 2014 ). Addressing these questions can be quite challenging because our understanding of the complex set of processes leading to tornadogenesis is arguably incomplete. Numerical weather prediction and global
regarding the relative influence of large-scale climatic signals, the importance of changes in global radiative forcing, and the need to delineate the role of the underlying atmospheric processes at the appropriate spatiotemporal scale ( Trapp et al. 2007 ; Diffenbaugh et al. 2013 ; Tippett et al. 2014 ). Addressing these questions can be quite challenging because our understanding of the complex set of processes leading to tornadogenesis is arguably incomplete. Numerical weather prediction and global
region of strong rising motion downstream from the cyclone (due to differential vorticity advection) and thus sets up a favorable environment for tornadogenesis (e.g., Doswell and Bosart 2001 ). Additionally, the lower-tropospheric vertical wind shear is increased over the central and eastern United States during a negative phase of the PNA due to the increased upper-level westerly and lower-level southwesterly flow. Although the PNA is a naturally occurring atmospheric phenomenon driven by
region of strong rising motion downstream from the cyclone (due to differential vorticity advection) and thus sets up a favorable environment for tornadogenesis (e.g., Doswell and Bosart 2001 ). Additionally, the lower-tropospheric vertical wind shear is increased over the central and eastern United States during a negative phase of the PNA due to the increased upper-level westerly and lower-level southwesterly flow. Although the PNA is a naturally occurring atmospheric phenomenon driven by
“other features,” seven items of information are recorded: Classification of the vortex according to the place of tornadogenesis (tornado, waterspout, tornado of waterspout origin); Existence of a detailed map of the damage path; Eyewitness evidence (funnel cloud, electrical phenomena associated with the funnel, existence of video or photographs); Aural phenomena (roaring sound, feeling accompanied with the sudden pressure change); Damage to special structures such as ships, power poles, trees, and
“other features,” seven items of information are recorded: Classification of the vortex according to the place of tornadogenesis (tornado, waterspout, tornado of waterspout origin); Existence of a detailed map of the damage path; Eyewitness evidence (funnel cloud, electrical phenomena associated with the funnel, existence of video or photographs); Aural phenomena (roaring sound, feeling accompanied with the sudden pressure change); Damage to special structures such as ships, power poles, trees, and
induce tornadogenesis), these indices are the meteorological variables most closely associated with severe storm development. They have not previously been used for temporal climatological studies for the United States. A stability index is designed to measure the ease with which an air parcel will rise through the atmosphere, using the parcel-to-environment temperature difference, a comparison of surface and upper-air temperatures, or the moisture content of the boundary layer (i.e., the moisture
induce tornadogenesis), these indices are the meteorological variables most closely associated with severe storm development. They have not previously been used for temporal climatological studies for the United States. A stability index is designed to measure the ease with which an air parcel will rise through the atmosphere, using the parcel-to-environment temperature difference, a comparison of surface and upper-air temperatures, or the moisture content of the boundary layer (i.e., the moisture
question theoretically, statistically, or numerically is highly challenging for the following reasons: the dynamics of tornadogenesis is highly complex and incompletely understood; a long-term, high-quality homogeneous tornado report record is unavailable; and numerical models that resolve climate signals do not currently resolve tornadoes. On weather time scales, information about the environmental “ingredients” associated with severe weather and tornadic storms has proved useful to forecasters in
question theoretically, statistically, or numerically is highly challenging for the following reasons: the dynamics of tornadogenesis is highly complex and incompletely understood; a long-term, high-quality homogeneous tornado report record is unavailable; and numerical models that resolve climate signals do not currently resolve tornadoes. On weather time scales, information about the environmental “ingredients” associated with severe weather and tornadic storms has proved useful to forecasters in
United States occur in slightly lower precipitable water compared to weaker tornadoes, which may be linked to excessive cold pools being less supportive of tornadogenesis ( Markowski et al. 2002 ). Conversely, enhanced lower tropospheric moisture can reduce evaporative cooling, and improve low-level buoyancy. Low-level (0–3 km) lapse rates ( Fig. 3c ) are considerably higher over Europe, consistent with results for 0–3-km CAPE ( Fig. 2b ). Although it may be concluded that large hail events occur
United States occur in slightly lower precipitable water compared to weaker tornadoes, which may be linked to excessive cold pools being less supportive of tornadogenesis ( Markowski et al. 2002 ). Conversely, enhanced lower tropospheric moisture can reduce evaporative cooling, and improve low-level buoyancy. Low-level (0–3 km) lapse rates ( Fig. 3c ) are considerably higher over Europe, consistent with results for 0–3-km CAPE ( Fig. 2b ). Although it may be concluded that large hail events occur
outbreaks, though the authors themselves note the weak statistical strength of this relationship. Both Thompson and Roundy (2013) and Barrett and Gensini (2013) suggested that certain phases of the Madden–Julian oscillation (MJO) modulate large-scale circulations in ways that favor or impede tornadogenesis during the spring, though the phases they deem favorable vary depending on the month chosen for analysis. Tippett (2018) agreed that tornado likelihood seems to vary by MJO phase, but also noted
outbreaks, though the authors themselves note the weak statistical strength of this relationship. Both Thompson and Roundy (2013) and Barrett and Gensini (2013) suggested that certain phases of the Madden–Julian oscillation (MJO) modulate large-scale circulations in ways that favor or impede tornadogenesis during the spring, though the phases they deem favorable vary depending on the month chosen for analysis. Tippett (2018) agreed that tornado likelihood seems to vary by MJO phase, but also noted
