Topographic Impacts on the Spatial Distribution of Deep Convection over Southern Quebec
Bibliographic record
Abstract
Abstract Observations and numerical simulations reveal pronounced mesoscale variability in deep-convection occurrence over southern Quebec, Canada. A 22-yr climatology from the McGill radar just west of Montreal shows that deep-convection maxima exist (i) within the St. Lawrence valley surrounding Ottawa; (ii) within the Champlain valley of upstate New York, extending north to just east of Montreal; and (iii) in the lee of the Laurentian Mountains northeast of Trois-Rivières. These features are sensitive to the background low- to midlevel geostrophic wind direction, shifting northward as the southerly wind component increases. A meridional axis of suppressed convection also extends from Lake Ontario and the Adirondacks of New York north through Montreal and into the Laurentians. To physically interpret these features, a suite of quasi-idealized convection-permitting simulations is conducted. Analysis of the simulations, which broadly reproduce the observed extrema in convection occurrence, reveals that the maxima develop within pockets of moisture and mass convergence at the junctions of major river valleys and in the lee of prominent mountain ridges. In these locations, enhanced boundary layer humidity and convective available potential energy (CAPE) coincides with minimal convective inhibition (CIN). The minima occur over and downwind of water bodies, where limited surface heat fluxes reduce CAPE and increase CIN, and over the higher terrain, where reduced low-level moisture limits storm intensity.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".