The OWLeS IOP2b Lake-Effect Snowstorm: Shoreline Geometry and the Mesoscale Forcing of Precipitation
Bibliographic record
Abstract
Long-lake-axis-parallel (LLAP) lake-effect precipitation systems that form when the flow is parallel to the long axis of an elongated body of water frequently produce intense, highly localized snowfall. Conceptual models of these LLAP systems typically emphasize the role of thermally forced land breezes from the flanking shorelines, with low-level convergence and ascent centered near the lake axis. In reality, other factors such as shoreline geometry and differential surface roughness can strongly influence LLAP systems. Here a WRF Model simulation is used to examine the mesoscale forcing of lake-effect precipitation over Lake Ontario during IOP2b of the Ontario Winter Lake-effect Systems (OWLeS) field campaign. In the simulation, the large-scale flow, shoreline geometry, and differential surface heating and roughness contribute to the development of three major airmass boundaries. The first is a land-breeze front that forms along a bulge in the south shoreline between St. Catharines, Ontario, Canada, and Thirty Mile Point, New York; extends downstream over eastern Lake Ontario; and plays a primary role in the LLAP system development. The second is a land-breeze front that forms along the southeast shoreline near Oswego, New York; extends downstream and obliquely across the LLAP system near Tug Hill; and influences inland precipitation processes. The third is a convergence zone that extends downstream from the north shoreline near Point Petre, Ontario, Canada; and contributes to the intermittent development of lake-effect precipitation north of the primary LLAP system. These results highlight the multifaceted nature of LLAP system development over Lake Ontario, especially the contributions of shoreline geometry and mesoscale airmass boundaries.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".