Convection-permitting WRF simulation of extreme winds in Canada: Present and future scenarios
Bibliographic record
Abstract
This study investigates extreme wind events across southern Canada using 4-km convection-permitting WRF simulations under present (CTRL) and future (PGW) climate scenarios. The high resolution allows explicit representation of convective processes and complex terrain, improving local-scale wind prediction. We analyze three distinct regions—the central Prairies, Rocky Mountains, and southern Ontario—and find strong spatial and seasonal contrasts. Under future conditions, summer wind extremes intensify notably in the Prairies and southern Ontario, while winter winds decrease in the Prairies but increase in Ontario, Quebec, and mountainous areas. A conditional probability analysis based on Convective Available Potential Energy (CAPE) reveals that the likelihood of destructive winds (>20 m/s) rises significantly in convectively unstable environments. In southern Ontario, the probability under strong instability (CAPE > 2500 J/kg) increases from nearly zero to 0.4. We also apply the Peaks-over-Threshold (POT) method to estimate 50-year return period wind speeds, which show substantial future increases, up to 6 m/s in some areas during summer. These changes indicate a rising threat from convectively driven wind extremes. This study highlights the value of convection-permitting models in resolving local wind features and emphasizes the need for region-specific adaptation strategies. The findings critically impact wind hazard assessment, infrastructure design, and climate resilience planning across southern Canada.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".