Forecasting Tornadic Thunderstorm Potential in Alberta Using Environmental Sounding Data. Part I: Wind Shear and Buoyancy
Bibliographic record
Abstract
Abstract This study investigates, for Alberta, Canada, whether observed sounding parameters such as wind shear and buoyant energy can be used to help distinguish between thunderstorms with significant (F2–F5) tornadoes, thunderstorms with weak (F0–F1) tornadoes, and nontornadic severe thunderstorms. The observational dataset contains 87 severe convective storms, all of which occurred within 200 km of the upper-air site at Stony Plain, Alberta, Canada. Of these storms, 13 spawned significant (F2–F5) tornadoes, 61 spawned weak (F0–F1) tornadoes, and 13 had no reported tornadoes yet produced 3 cm or larger hailstones. The observations suggest that bulk shear contained information about the probability of tornado formation and the intensity of the tornado. Significant tornadic storms tended to have stronger shear values than weak tornadic or nontornadic severe storms. All significant tornado cases had a wind shear magnitude in the 900–500-mb layer exceeding 3 m s−1 km−1. Combining the 900–500-mb shear with the 900–800-mb shear increased the probabilistic guidance for the likelihood of significant tornado occurrence. The data suggest that buoyant energy alone (quantified by the most unstable convective available potential energy) provided no skill in discriminating between tornadic and nontornadic severe storms, or between significant and weak tornadoes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".