Influence of Lake Erie on a Lake Ontario Lake-Effect Snowstorm
Bibliographic record
Abstract
Abstract Annual lake-effect snowstorms, which develop through surface buoyant instability and upward moisture transport from the Laurentian Great Lakes, lead to important local increases in snowfall to the south and east. Surface wind patterns during cold-air outbreaks often result in areas where the air is modified by more than one Great Lake. While it is known that boundary layer air that has crossed multiple lakes can produce particularly intense snow, few observations are available on the process by which this occurs. This study examines unique observations taken during the Ontario Winter Lake-effect Systems (OWLeS) field project to document the process by which Lake Erie influenced snowfall that was produced over Lake Ontario on 28 January 2014. During the event, lake-effect clouds and snow that developed over Lake Erie extended northeastward toward Lake Ontario. OWLeS and operational observations showed that the clouds from Lake Erie disappeared (and snow greatly decreased) as they approached the Lake Ontario shoreline. This clear-air zone was due to mesoscale subsidence, apparently due to the divergence of winds moving from land to the smoother lake surface. However, the influence of Lake Erie in producing a deeper lake-effect boundary layer, thicker clouds, increased turbulence magnitudes, and heavier snow was identified farther downwind over Lake Ontario. It is hypothesized that the combination of a low-stability, high-moisture boundary layer as well as convective eddies and limited snow particles crossing the mesoscale subsidence region locally enhanced the lake-effect system over Lake Ontario within the plume of air originating over Lake Erie.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".