Factors leading to the formation of tornadoes: statistical links emerging from a large dataset
Bibliographic record
Abstract
The dynamics of tornadoes include large vorticity in the lower troposphere and an intense updraft, whose combination may result in their formation. In this study we investigate the possibility of using a statistical relation for their description. In fact, the nonlinearity, complexity and fine scale of these processes presently prevents their simulation in the atmospheric circulation models currently used for weather forecasts and climate projections. Here we use a large dataset of tornadoes observed in the USA and Europe and the data of ERA5 (ECMWF ReAnalysis 5) to establish a statistical link between the occurrence of tornadoes and factors whose values can be extracted from atmospheric circulation models. The values of CAPE (convective available potential energy), WS (wind shear in the lower troposphere), SRH (storm relative helicity) and LCL (lifting condensation level) of the high resolution (about 30km) ERA5 data have been considered. The analysis shows all these variables are significantly linked to the formation of tornadoes with WS and CAPE being the most relevant ones. The analysis is an extension of a former study (Ingrosso et al., 2020, 10.3390/atmos11030301) based on a dataset of tornadoes events much larger than previously, on higher resolution atmospheric data, and more prognostic variables. The results provide a new expression for the probability of occurrence of tornadoes that can be used for forecasting their likelihood with potential applications to their predictions and future changes of their frequency.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".