Synoptic Typing of Extreme Cool-Season Precipitation Events at St. John’s, Newfoundland, 1979–2005
Bibliographic record
Abstract
Abstract Quantitative precipitation forecasting (QPF) continues to be a significant challenge in operational forecasting, particularly in regions susceptible to extreme precipitation events. St. John’s, Newfoundland, Canada (CYYT), is affected frequently by such events, particularly in the cool season (October–April). The 50 median events in the extreme (>33.78 mm during a 48-h period) precipitation event category are selected for further analysis. A manual synoptic typing is performed on these 50 events, using two separate methodologies to partition events. The first method utilizes a Lagrangian backward air parcel trajectory analysis and the second method utilizes the evolution of dynamically relevant variables, including 1000–700-hPa horizontal temperature advection, 1000–700-hPa (vector) geostrophic frontogenesis, and 700–400-hPa absolute vorticity advection. Utilizing the first partitioning method, it is found that south cases are characterized by a strong anticyclone downstream of St. John’s, southwest events are synoptically similar to the overall extreme composite and are marked by a strong cyclone that develops in the Gulf of Mexico, while west events are characterized by a weak Alberta clipper system that intensifies rapidly upon reaching the Atlantic Ocean. The second partitioning method suggests that while cyclone events are dominated by the presence of a rapidly developing cyclone moving northeastward toward St. John’s, frontal events are characterized by the presence of a strong downstream anticyclone and deformation zone at St. John’s. It is the hope of the authors that the unique methodology and results of the synoptic typing in this paper will aid forecasters in identifying certain characteristics of future precipitation events at St. John’s and similar stations.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".