Tornado Probability of Detection and Lead Time as a Function of Convective Mode and Environmental Parameters

Abstract

The ability to provide advanced warning on tornadoes can be impacted by variations in storm mode. This research evaluates 2 yr of National Weather Service (NWS) tornado warnings, verification reports, and radar-derived convective modes to appraise the ability of the NWS to warn across a variety of convective modes and environmental conditions. Several specific hypotheses are considered: (i) supercell morphologies are the easiest convective modes to warn for tornadoes and yield the greatest lead times, while tornadoes from more linear, nonsupercell convective modes, such as quasi-linear convective systems, are more difficult to warn for; (ii) parameters such as tornado distance from radar, population density, and tornado intensity (F scale) introduce significant and complex variability into warning statistics as a function of storm mode; and (iii) tornadoes from stronger storms, as measured by their mesocyclone strength (when present), convective available potential energy (CAPE), vertical wind shear, and significant tornado parameter (STP) are easier to warn for than tornadoes from weaker systems. Results confirmed these hypotheses. Supercell morphologies caused 97% of tornado fatalities, 96% of injuries, and 92% of damage during the study period. Tornado warnings for supercells had a statistically higher probability of detection (POD) and lead time than tornado warnings for nonsupercells; among supercell storms, tornadoes from supercells in lines were slightly more difficult to warn for than tornadoes from discrete or clusters of supercells. F-scale intensity and distance from radar had some impact on POD, with less impact on lead times. Higher mesocyclone strength (when applicable), CAPE, wind shear, and STP values were associated with greater tornado POD and lead times.

Diagnosing the Conditional Probability of Tornado Damage Rating Using Environmental and Radar Attributes

“…Boldface differences are statistically significant at a , 0.001, and boldface and italic differences are considered to be sufficiently large to be of operational significance (i.e.,V rot . Statistically significant differences in STP 80km values (Table 2) were found between EF41, EF3, and EF2 supercell classes using a two-sample tailed difference of means Student's t test, which complements findings by Brotzge et al (2013) from a similar, independent dataset. 4 gridpoint value or the neighborhood maximum value (i.e., STP 80km ) for the preceding 40-km grid hour, STP increases as tornado damage classifications increase (Fig.…”

“…Magsig (2008) discussed techniques for diagnosing radar-based storm attributes and integrating environmental information into the warning decision-making process. Recent work by Brotzge et al (2013) revealed NWS tornado warning performance, as measured by probability of detection, was maximized for the more intense tornado events [i.e., higher enhanced Fujita (EF)-scale damage ratings] when the tornadoes were produced by discrete supercells with strong mesocyclones, close to the radar site, and in environments strongly supportive of tornadic supercells. Real-time utilization of the multicomponent datasets described in T12 and Brotzge et al (2013) may contribute to the improved situational awareness of tornado potential.…”

Techniques and Thresholds of Significance for Using WSR-88D Velocity Data to Anticipate Significant Tornadoes

“…This process is based on a decades-long foundation of scientific research [e.g., the initial discovery by Brown et al (1978) of tornadic vortex signatures (TVSs) in velocity data from Doppler radars]. Our understanding of severe thunderstorm and supercell structure and the role of the mesocyclone in tornadogenesis was further refined by Lemon and Doswell (1979).…”

Wind gust warning verification

“…(Schaefer, 1990;Barnes et al, 2007). These measures have been used by many meteorological centers, such us Met Office (Sharpe, 2010), the Austrian National Weather Service (Wittman, 2009) or NOAA in the USA (Brotzge et al, 2013). The German Meteorological Service ("Deutscher Wetterdienst", DWD) uses these scores, among other warning verifications, to verify thunderstorm warnings and to compare different nowcasting systems (Wapler et al, 2012).…”