Synoptic Typing and Precursors of Heavy Warm-Season Precipitation Events at Montreal, Québec
Bibliographic record
Abstract
Abstract A precipitation climatology is compiled for warm-season events at Montreal, Québec, Canada, using 6-h precipitation data. A total of 1663 events are recorded and partitioned into three intensity categories (heavy, moderate, and light), based on percentile ranges. Heavy (top 10%) precipitation events (n = 166) are partitioned into four types, using a unique manual synoptic typing based on the divergence of Q-vector components. Type A is related to cyclones and strong synoptic-scale quasigeostrophic (QG) forcing for ascent, with high-θe air being advected into the Montreal region from the south. Types B and C are dominated by frontogenesis (mesoscale QG forcing for ascent). Specifically, type B events are warm frontal and feature a near-surface temperature inversion, while type C events are cold frontal and associated with the largest-amplitude synoptic-scale precursors of any type. Finally, type D events are associated with little synoptic or mesoscale QG forcing for ascent and, thus, are deemed to be convective events triggered by weak shortwave vorticity maxima moving through a long-wave ridge environment, in the presence of an anomalously warm, humid, and unstable air mass that is conducive to convection. In general, types A and B feature the strongest dynamical forcing for ascent, while types C and D feature the lowest atmospheric stability. Systematic higher precipitation amounts are not preferential to any event type, although a handful of the largest warm-season precipitation events appear to be slow-moving type C (stationary front) cases.
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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 | 0.002 | 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) | 0.006 | 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".