May 22 - July 28
Illustrating Predictability for Nocturnal Tornado Events in the Southeastern United States
Ryan Bunker, Ariel Cohen, Alan Gerard, Kim Klockow, and John Hart
What is already known:
- Substantial vulnerabilities to severe weather and tornadoes exist during the night when visibility is limited and when people are asleep.
- Previous work using the Statistical Severe Convective Risk Assessment Model (SSCRAM) has shown the utility of environmental parameters to predict severe weather occurrence.
What this study adds:
- This study utilized SSCRAM to show that strong vertical shear is useful in predicting nocturnal tornado events in the southeast United States.
- Furthermore, the coastal region subset within the southeast U.S. offers worse predictability when compared to the rest of the CONUS.
Abstract:
Nocturnal tornado events can create societal vulnerabilities when visibility is extremely limited, when people are asleep, and when people are in weak-infrastructure buildings. Understanding these high-impact events is a crucial step for forecasters to improve lead times for the public. Previous studies have assessed the ability for parameters to distinguish severe thunderstorm environments. This study uses the Statistical Severe Convective Risk Assessment Model (SSCRAM) to help assess what parameters can be linked to tornado potential in the southeast United States. This study shows that several parameters have statistically significantly different distributions between the Southeast and everywhere else in the contiguous United States, and between the coastal region subset of the Southeast and everywhere else in the contiguous United States. By adding a constraint of at least knots of effective bulk shear, the predictability for tornadoes in the southeast U.S. is generally better than everywhere else. Overall, the coastal region subset offers worse predictability than everywhere else when no constraints are added. This approach to predictability can contribute to the warn-on-forecast initiatives and current-day operational forecasting.