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An Analysis of 2016–18 Tornadoes and National Weather Service Tornado Warnings across the Contiguous United States

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  • 1 a NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma
  • | 2 b National Weather Service Forecast Office, Norman, Oklahoma
  • | 3 c NOAA/NWS Warning Decision Training Division, Norman, Oklahoma
  • | 4 d National Weather Service, Peachtree City, Georgia
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Abstract

Previous work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–18) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance. Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment, which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1 n mi; 1 n mi = 1.852 km) circulations in a poor (STP = 0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.

Significance Statement

Recent studies have explored the radar signatures associated with severe storms and tornadoes and recently these radar signatures have been correlated to surveyed damage from tornadoes. However, to date, there is no known research relating the radar signatures that prompt National Weather Service tornado warnings to the verification of those warnings. This research accomplished this goal and showed that the most skillful warning thresholds match current guidance for National Weather Service forecasters. Typically, an increase in POD will result in an increase in FAR and vice versa. However, this research showed there may be opportunities to improve POD and FAR with minimal negative consequences by focusing on the tails of the distribution (poor environment/weak signature and favorable environment and strong signature).

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Evan S. Bentley, evan.bentley@noaa.gov

Abstract

Previous work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–18) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance. Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment, which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1 n mi; 1 n mi = 1.852 km) circulations in a poor (STP = 0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.

Significance Statement

Recent studies have explored the radar signatures associated with severe storms and tornadoes and recently these radar signatures have been correlated to surveyed damage from tornadoes. However, to date, there is no known research relating the radar signatures that prompt National Weather Service tornado warnings to the verification of those warnings. This research accomplished this goal and showed that the most skillful warning thresholds match current guidance for National Weather Service forecasters. Typically, an increase in POD will result in an increase in FAR and vice versa. However, this research showed there may be opportunities to improve POD and FAR with minimal negative consequences by focusing on the tails of the distribution (poor environment/weak signature and favorable environment and strong signature).

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Evan S. Bentley, evan.bentley@noaa.gov
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