Elevated Mixed Layers during Great Lake Lake-Effect Events: An Investigation and Case Study from OWLeS
Bibliographic record
Abstract
Abstract Data from rawinsondes launched during intensive observation periods (IOPs) of the Ontario Winter Lake-Effect Systems (OWLeS) field project reveal that elevated mixed layers (EMLs) in the lower troposphere were relatively common near Lake Ontario during OWLeS lake-effect events. Conservatively, EMLs exist in 193 of the 290 OWLeS IOP soundings. The distribution of EML base pressure derived from the OWLeS IOP soundings reveals two classes of EML, one that has a relatively low-elevation base (900–750 hPa) and one that has a relatively high-elevation base (750–500 hPa). It is hypothesized that the former class of EML, which is the focus of this research, is, at times, the result of mesoscale processes related to individual Great Lakes. WRF reanalysis fields from a case study during the OWLeS field project provide evidence of two means by which low-elevation base EMLs can originate from the lake-effect boundary layer convection and associated mesoscale circulations. First, such EMLs can form within the upper-level outflow branches of mesoscale solenoidal circulations. Evacuated Great Lakes–modified convective boundary layer air aloft then lies above ambient air of a greater static stability, forming EMLs. Second, such EMLs can form in the absence of a mesoscale solenoidal circulation when Great Lake–modified convective boundary layers overrun ambient air of a greater density. The reanalysis fields show that EMLs and layers of reduced static stability tied to Great Lakes–modified convective boundary layers can extend downwind for hundreds of kilometers from their areas of formation. Operational implications and avenues for future research are discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".