A Statistical Model to Forecast Tornadoes and Reconstruct Their Climatology and Trends Globally

Additive Logistic Regression Models (AR-CHaMo) to predict the occurrence of tornadoes of intensity > (E)F1 were developed using lightning observations from the Arrival Time Difference Network (ATDnet), tornado reports from the European Severe Weather Database (ESWD), the Storm Prediction Centre (SPC) and the Australian Bureau of Meteorology (BOM) and environmental predictors from the ERA5 reanalysis. The models output the probability of a tornado as a function of environmental predictors from ERA5 and can be used both for forecasting and climate analysis. By applying the models to ERA5 for the period 1992-2023, we were able to map the modelled climatological occurrence of tornadoes > (E)F1 on a global scale. According to AR-CHaMo, tornadoes are most common across the Plains and the Southeast of the US, but also across Uruguay, Paraguay, and southern Brazil. Local hotspots are also modelled across southeastern South Africa, southeastern Australia, as well as southeastern and northeastern China. Conditions favouring tornadoes are climatologically less frequent in Europe, but local hotspots are present across coastal regions of the Mediterranean. Although a ground-based verification is impossible due to the lack of a globally consistent tornado reports database, the modelled spatial distribution from AR-CHaMo is in agreement with local climatologies from regions where reports are collected, such as the US and South America. Using 31 years of time series, we were able to detect long-term trends in modelled tornado frequency. In North America, AR-CHaMo indicates that tornadoes have increased in frequency across the US Southeast (most strongly) and the Upper Midwest, while they have locally decreased in the Great Plains. Large relative increases are also present in southeastern Canada. Trends are negative across South America, southern China, and Australia, while the occurrence has increased across southeastern Asia and locally in southern Europe. As part of the presentation, we will also report on the forecasting applications of the AR-CHaMo models, while focusing on a few recent tornado outbreaks across Europe and the US.