Ontario Winter Lake-effect Systems (OWLeS): Bulk Characteristics and Kinematic Evolution of Misovortices in Long-Lake-Axis-Parallel Snowbands
Bibliographic record
Abstract
Abstract During the Ontario Winter Lake-effect Systems (OWLeS) field campaign, 12 long-lake-axis-parallel (LLAP) snowband events were sampled. Misovortices occurred in 11 of these events, with characteristic diameters of ~800 m, differential velocities of ~11 m s−1, and spacing between vortices of ~3 km. A detailed observational analysis of one such snowband provided further insight on the processes governing misovortex genesis and evolution, adding to the growing body of knowledge of these intense snowband features. On 15–16 December 2013, a misovortex-producing snowband was exceptionally well sampled by ground-based OWLeS instrumentation, which allowed for integrated finescale dual-Doppler and surface thermodynamic analyses. Similar to other studies, horizontal shearing instability (HSI), coupled with stretching, was shown to be the primary genesis mechanism. The HSI location was influenced by snowband-generated boundaries and location of the Arctic front relative to the band. Surface temperature observations, available for the first time, indicated that the misovortices formed along a baroclinic zone. Enhanced mixing, higher radar reflectivity, and increased precipitation rate accompanied the vortices. As the snowband came ashore, OWLeS participants indicated an increase in snowfall and white out conditions with the passage of the snowband. A sharp, small-scale pressure drop, coupled with winds of ~16 m s−1, marked the passage of a misovortex and may be typical of snowband misovortices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".