Lake-Effect Snowbands in Baroclinic Environments
Bibliographic record
Abstract
Abstract Lake-effect snowstorms are often observed to manifest as dominant bands, commonly produce heavy localized snowfall, and may extend large distances inland, resulting in hazards and high societal impact. Some studies of dominant bands have documented concomitant environmental baroclinity (i.e., baroclinity occurring at a scale larger than the width of the parent lake), but the interaction of this baroclinity with the inland structure of dominant bands has been largely unexplored. In this study, the thermodynamic environment and thermodynamic and kinematic structure of simulated dominant bands are examined using WRF reanalyses at 3-km horizontal resolution and an innovative technique for selecting the most representative member from the WRF ensemble. Three reanalysis periods are selected from the Ontario Winter Lake-effect Systems (OWLeS) field campaign, encompassing 185 simulation hours, including 155 h in which dominant bands are identified. Environmental baroclinity is commonly observed during dominant-band periods and occurs in both the north–south and east–west directions. Sources of this baroclinity are identified and discussed. In addition, case studies are conducted for simulation hours featuring weak and strong along-band environmental baroclinity, resulting in weak and strong inland extent, respectively. These contrasting cases offer insight into one mechanism by which along-band environmental baroclinity can influence the inland structure and intensity of dominant bands: in the case with strong environmental baroclinity, inland portions of this band formed under weak instability and therefore exhibit slow overturning, enabling advection far inland under strong winds, whereas the nearshore portion forms under strong instability, and the enhanced overturning eventually leads to the demise of the inland portion of the band.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".