A Stochastic Statistical Model for U.S. Outbreak-level Tornado Occurrence based on the Large-scale Environment

Abstract

Tornado outbreaks — when multiple tornadoes occur within a short period of time — are rare yet impactful events. Here we developed a two-part stochastic tornado outbreak index for the contiguous United States (CONUS). The first component produces a probability map for outbreak tornado occurrence based on spatially-resolved values of convective precipitation, storm relative helicity (SRH), and convective available potential energy. The second part of the index provides a probability distribution for the total number of tornadoes given the outbreak tornado probability map. Together these two components allow stochastic simulation of location and number of tornadoes that is consistent with environmental conditions. Storm report data from the Storm Prediction Center for 1979–2021 period are used to train the model and evaluate its performance. In the first component, the probability of an outbreak-level tornado is most sensitive to SRH changes. In the second component, the total number of CONUS tornadoes depends on the sum and grid point maximum of the probability map. Overall, the tornado outbreak index represents the climatology, seasonal cycle, and interannual variability of tornado outbreak activity well, particularly over regions and seasons when tornado outbreaks occur most often. We found that the El Niño-Southern Oscillation (ENSO) modulates the tornado outbreak index such that La Niña is associated with enhanced U.S. tornado outbreak activity over the Ohio River Valley and Tennessee River Valley regions during January through March, similar to the behavior seen in storm report data. We also found an upward trend in U.S. tornado outbreak activity during winter and spring for the 1979–2021 period using both observations and the index.