Predicting Seasonal Tornado Activity

How climate change might impact severe convective storms remains an open question. Given the large gaps in knowledge of how climate influences severe weather and the dearth of methods to forecast severe weather on the seasonal scale, basic and applied research is needed that focuses on statistical modeling, diagnostic understanding, and methods to predict. Potentially useful skill at predicting tornado activity prior to the start of the season has been noted recently (Elsner and Widen, 2014; Allen et al. 2015). While these studies are a first step toward seasonal forecasting more work is needed to quantify the skill geographically. In this talk I discuss a probabilistic approach to seasonal tornado forecasting and address the following questions; (1) how can regional tornado climatology be recovered from a heterogeneous database of rare, clustered events? (2) how can that climatology by conditioned on climate variables and used for probabilistic seasonal forecasting? I argue that developing such models we will improve our understanding of the relationships between severe weather and climate, contribute methods for forecasting, and increase public awareness of the risk tornadoes pose to life and property. An added benefit will be a new generation of climate researchers trained in applied spatial statistics.

References

Allen, J. T., M. K. Tippett, and A. H. Sobel, 2015: Influence of the El Nino/Southern Oscillation on tornado and hail frequency in the United States. Nature Geosciences, 8, 278–283.

Elsner, J. B., and H. M. Widen, 2014: Predicting spring tornado activity in the central Great Plains by March 1st. Mon. Wea. Rev., 142, 259–267.