A Statistical Model to Forecast Tornadoes and Reconstruct Their Climatology and Trends Globally
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".