Climatological Characteristics and Orographic Enhancement of Lake-Effect Precipitation East of Lake Ontario and over the Tug Hill Plateau
Bibliographic record
Abstract
Abstract Lake-effect snowstorms east of Lake Ontario are frequently intense and contribute to substantial seasonal accumulations, especially over the Tug Hill Plateau (hereafter Tug Hill), which rises at a gentle 1.25% slope to ~500 m above lake level. Using a variety of datasets including radar imagery from the KTYX (Fort Drum, New York) WSR-88D, this paper examines the characteristics of lake-effect precipitation east of Lake Ontario over 13 cool seasons (16 September 2001–15 May 2014). During this period, days with at least 2 h of lake effect account for 61%–76% of the mean cool-season snowfall and 24%–37% of the mean cool-season liquid precipitation. Mean monthly lake-effect frequency and snowfall peak in December and January. The highest lake-effect frequency and snowfall occur over the western and upper Tug Hill, with an arm of relatively high lake-effect frequency and snowfall extending to the southeast shore of Lake Ontario. To the east (lee), lake-effect frequency and snowfall decrease abruptly over the Black River valley, although relatively high frequency and snowfall extend downstream into the western Adirondack Mountains. Broad coverage and long-lake-axis-parallel (LLAP) bands dominate the lake-effect morphology throughout the region. There is no diurnal modulation of lake-effect frequency during winter, but weak modulation in fall and spring, especially of LLAP bands. Collectively, these results quantify the role that lake effect plays in the cool-season hydroclimate east of Lake Ontario. The increase in lake-effect frequency and snowfall over Tug Hill suggest an inland/orographic intensification of many lake-effect systems, with evidence for shadowing in the lee.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".