Climatology and Recent Changes in the Occurrence of Freezing Rain throughout the Laurentian Great Lakes Region
Bibliographic record
Abstract
Abstract Freezing rain (FZRA) events have expensive and sometimes deadly effects on major population centers in the Great Lakes region (GLR) of North America. This paper investigates changes in the spatiotemporal nature of FZRA. The work extends the spatial and temporal record of a previously published, but now dated, Great Lakes FZRA climatology (1976–90) to analyze trends and their underlying reasons by utilizing observations made through 2020. To isolate regional trends and determine changes in the synoptic-scale processes driving them, a k -means objective clustering algorithm is applied to pressure anomaly maps and records of FZRA observations to create three archetypal synoptic weather patterns associated with FZRA. A northward shift in FZRA incidence is found across the GLR on an annual basis, with an increase in FZRA occurrence in January and April and a decrease during March. Regionally, the largest trends were a decrease since 1979 throughout Pennsylvania, upstate New York, and the St. Lawrence Lowlands of Canada and an increase in the southern reach of Manitoba, Ontario, and Quebec and the low-lying Atlantic Coastal Plain of New York. The geographic location of the low three synoptic patterns associated with FZRA events remained similar between 1979 and 1999 and 2000–20, though changes to their intensity allowed for enhanced warm air advection. We suggest that this, combined with rising temperatures in a nonstationary climate and a subtle northward shift in some cyclones, was responsible for an observed northward shift in FZRA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".