The Influence of a Lake-to-Lake Connection from Lake Huron on the Lake-Effect Snowfall in the Vicinity of Lake Ontario
Bibliographic record
Abstract
Abstract Lake-effect storms (LES) produce substantial snowfall in the vicinity of the downwind shores of the Great Lakes. These storms may take many forms; one type of LES event, lake to lake (L2L), occurs when LES clouds/snowbands develop over an upstream lake (e.g., Lake Huron), extend across an intervening landmass, and continue over a downstream lake (e.g., Lake Ontario). The current study examined LES snowfall in the vicinity of Lake Ontario and the atmospheric conditions during Lake Huron-to-Lake Ontario L2L days as compared with LES days on which an L2L connection was not present [i.e., only Lake Ontario (OLO)] for the cold seasons (October–March) from 2003/04 through 2013/14. Analyses of snowfall demonstrate that, on average, significantly greater LES snowfall totals occur downstream of Lake Ontario on L2L days than on OLO days. The difference in mean snowfall between L2L and OLO days approaches 200% in some areas near the Tug Hill Plateau and central New York State. Analyses of atmospheric conditions found more-favorable LES environments on L2L days relative to OLO days that included greater instability over the upwind lake, more near-surface moisture available, faster wind speeds, and larger surface heat fluxes over the upstream lake. Last, despite significant snowfalls on L2L days, their average contribution to the annual accumulated LES snowfall in the vicinity of Lake Ontario was found to be small (i.e., 25%–30%) because of the relatively infrequent occurrence of L2L days.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".