An air mass‐derived cool season climatology of synoptically forced Appalachian cold‐air damming
Bibliographic record
Abstract
ABSTRACT An air mass approach was used to identify episodes of cool season cold‐air damming (CAD) within the central Appalachian Mountains region of the eastern United States. Daily air mass type data were used to identify days on which moist polar (MP) air was regionally evident east of the mountains, while non‐MP air was in place at nearby stations west of the mountains. Over a 35‐year study period, 219 CAD days were identified (>6 per year) with the annual frequency exhibiting no trend but suggesting that El Niño (La Niña) coincides with a greater (lesser) frequency of CAD days in winter (December–February). Synoptic atmospheric composites reveal west‐to‐east migration of a parent anticyclone to a classic position along the border of the northeastern United States and southeastern Canada. This coincides with a pattern of amplifying and slowly eastward‐moving 500 hPa height anomalies characterized by positive (negative) values over eastern (western) North America that are signalled a few days in advance by the index representing the Pacific‐North American teleconnection pattern. Confinement of the CAD below the 850 hPa level is evident in the synoptic wind field, while the composite vertical profile of the atmosphere within the CAD environment further depicts the shallow nature of the surface‐based cool, moist air. Northeasterly winds at the surface veer to southeasterly within a few hundred metres above the surface, and then southwesterly at less than one km above the surface, at the 850 hPa level. The air mass approach to CAD identification appears to successfully identify regional occurrences of synoptically forced CAD, although it likely does not detect local and/or diabatically forced CAD.
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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".