May 22 - July 30
Possible Influences on National Weather Service Tornado Warnings
Trey J. Holiday, Michael E. Baldwin, Harold E. Brooks, Kristin M. Calhoun, and Thea Sandmæl
What is already known:
- Tornado warnings issued by forecasters provide life-saving information, giving communities the crucial minutes needed to seek shelter.
- Meteorologists issue warnings using radar and environmental data, but uncertainty can lead to shorter lead times and false alarms.
- Numerous studies have shown that forecasters’ decision-making when issuing warnings can be influenced by meteorological thresholds or external factors; these can vary greatly across forecasters and environments.
What this study adds:
- Provides verification-based guidance on thresholds for different radar and machine learning algorithms to aid in decision making.
- TORP 30–40%, Azimuthal Shear 0.010 s-1, and Rotational Velocity 30 kts thresholds can be used as a recommendation for forecasters.
- Evidence suggests that population density is an external factor in tornado warning decision-making, with lower false alarm rates in highly populated areas compared to sparsely populated ones.
Abstract:
Tornado warnings issued by meteorologists rely on radar and environmental data to provide life-saving information, but uncertainties can result in shorter lead times and occasional false alarms. Hence, this study evaluates the performance of tornado warnings issued by the National Weather Service in 2018 using probability of detection, false alarm ratio, success ratio, bias, and critical success index. Both radar data and values from a machine learning-based tornado probability algorithm (TORP) are analyzed for each tornado warning before or near the warning to identify factors contributing to accurate warnings or false alarms. TORP detections are based on a 0.006 s¹ azimuthal shear threshold, which uses 0.5° tilt radar data to display a tornado probability for forecasters to use. Thresholds for TORP, rotational velocity, and azimuthal shear were created to provide forecasters with recommendations to aid in the decision-making process. Additionally, population density was examined as a potential factor affecting warning performance, where the success ratio was generally low for sparsely populated areas and increased for densely populated areas. Together, this work contributes to enhancing the accuracy and effectiveness of future warning systems and operational forecasting.