LLAP Band Structure and Intense Lake-Effect Snowfall Downwind of Lake Ontario: Insights from the OWLeS 7–9 January 2014 Event
Bibliographic record
Abstract
Abstract Modeling and observational studies stemming from the 2013–14 Ontario Winter Lake-Effect Systems (OWLeS) field campaign have yielded much insight into the structure and development of long-lake-axis-parallel (LLAP) lake-effect systems over Lake Ontario. This study uses airborne single- and dual-Doppler radar data obtained during two University of Wyoming King Air flights, as well as a high-resolution numerical model simulation, to examine and contrast two distinctly different LLAP band structures observed within a highly persistent lake-effect system on 7–9 January 2014. On 7 January, a very cold air mass accompanied by strong westerly winds and weak capping aloft resulted in a deep, intense LLAP band that produced heavy snowfall well inland. In contrast, weaker winds, weaker surface heat fluxes, and stronger capping aloft resulted in a weaker LLAP band on 9 January. This band was blocked along the downwind shore and produced only light snowfall closer to the shoreline. Although the two structures examined here represent opposite ends of a spectrum of LLAP bands, both cases reveal a well-organized mesoscale secondary circulation composed of two counterrotating horizontal vortices positioned on either side of a narrow updraft within the band. In both cases, this circulation traces back to a shallow, baroclinic land-breeze front originating along a bulge in the lake’s southern shoreline. As the band extends downstream and the low-level baroclinity weakens, buoyancy increases within the band—driven in part by cloud latent heating—leading to band intensification and a deeper, stronger, and more symmetric secondary circulation over the lake.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".