Influence of Large-Scale Flow Regimes on Cool-Season Precipitation in the Northeastern United States
Bibliographic record
Abstract
Abstract The influence of large-scale flow regimes on cool-season (November–April) northeastern U.S. (Northeast) precipitation is investigated for the period 1948–2003 from statistical and synoptic perspectives. These perspectives are addressed through (i) a statistical analysis of cool-season Northeast precipitation associated with the North Atlantic Oscillation (NAO) and Pacific–North American (PNA) regimes (one standard deviation or greater NAO or PNA daily index anomalies persisting several days), and (ii) a composite analysis of the synoptic signatures of major (two standard deviation) 24-h cool-season Northeast precipitation events occurring during NAO and PNA regimes. The statistical analysis reveals that negative PNA regimes are associated with above-average cool-season Northeast precipitation and an above-average frequency of light and moderate precipitation events, whereas the opposite associations are true for positive PNA regimes. In comparison with PNA regimes, NAO regimes are found to have relatively little influence on the amount and frequency of cool-season Northeast precipitation. The composite analysis indicates that a surface cyclone flanked by an upstream trough over the Ohio Valley and downstream ridge over eastern Canada and upper- and lower-level jets in the vicinity of the Northeast are characteristic signatures of major cool-season Northeast precipitation events occurring during NAO and PNA regimes. Negative NAO and positive PNA precipitation events, however, are associated with a more amplified trough–ridge pattern and greater implied Atlantic moisture transport by a low-level jet into the Northeast than positive NAO and negative PNA precipitation events. Furthermore, a signature of lateral upper-level jet coupling is noted only during positive and negative PNA precipitation events.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".