Seasonal prediction of springtime tornado activity in the United States using a hybrid model

Graber, Matthew; Wang, Zhuo; Trapp, Robert J.

doi:10.5194/wcd-7-1349-2026

Articles | Volume 7, issue 3

https://doi.org/10.5194/wcd-7-1349-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wcd-7-1349-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 7, issue 3

Research article

|

28 Jul 2026

Research article |

| 28 Jul 2026

Seasonal prediction of springtime tornado activity in the United States using a hybrid model

Matthew Graber, Zhuo Wang, and Robert J. Trapp

Supplement

https://doi.org/10.5194/wcd-7-1349-2026-supplement

Data sets

ERA5 hourly data on pressure levels from 1940 to present Hans Hersbach et al. https://doi.org/10.24381/cds.bd0915c6

ERA5 hourly data on single levels from 1940 to present Hans Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

ECMWF Seasonal Forecasts Copernicus Climate Change Service 2018 https://doi.org/10.24381/cds.50ed0a73

US billion-dollar weather and climate disasters Adam B. Smith https://doi.org/10.25921/stkw-7w73

Model code and software

Springtime Prediction Code Matthew Graber https://github.com/Matt0604/Springtime-Prediction-Manuscript

Short summary

This study aims to seasonally predict springtime tornado activity using a weather-regime-based hybrid model and to identify the physical sources of predictability to explain the results. Tornado outbreaks, days with several tornadoes, exhibit model skill and should be a primary focus of future work given their societal impacts. Low-frequency climate modes are important sources of predictability for weather regimes, providing forecasts of opportunity for springtime tornado outbreaks.