Forecasting Tornadic Thunderstorm Potential in Alberta Using Environmental Sounding Data. Part II: Helicity, Precipitable Water, and Storm Convergence
Bibliographic record
Abstract
Abstract Sounding parameters are examined to determine whether they can help distinguish between Alberta, Canada, severe thunderstorms that spawn significant tornadoes (F2–F4), weak tornadoes (F0–F1), or nontornadic severe storms producing large hail. Parameters investigated included storm-relative helicity (SRH), precipitable water (PW), and storm convergence. The motivation for analyzing these parameters is that, in theory, they might affect the rate of change of vertical vorticity generation through vortex stretching, vortex tilting, and baroclinic effects. Precipitable water showed statistically significant differences between significant tornadic storms and those severe storms that produced weak tornadoes or no tornadoes. All significant tornadic cases in the dataset used had PW values exceeding 22 mm, with a median value of 24 mm. Values of PW between 19 and 23 mm were generally associated with weak tornadic storms. Computed values of storm convergence, height of the lifted condensation level, and normalized most unstable CAPE did not discriminate between any of the three storm categories. The SRH showed discrimination of significant tornadoes from both weak tornadic and nontornadic severe storm groups. The Alberta data suggest that significant tornadoes tended to occur with SRH > 150 m2 s−2 computed for the 0–3-km layer whereas weak tornadoes were typically formed for values between 30 and 150 m2 s−2. Threshold values of SRH were lower than those suggested in studies based on storm observations throughout much of the United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".