Circulations, Bounded Weak Echo Regions, and Horizontal Vortices Observed within Long-Lake-Axis-Parallel–Lake-Effect Storms by the Doppler on Wheels*
Bibliographic record
Abstract
Abstract The eastern Great Lakes (Erie and Ontario) are often affected by intense lake-effect snowfalls. Lake-effect storms that form parallel to the major axes of these lakes can strongly impact communities by depositing more than 100 cm of snowfall in less than 24 h. Long-lake-axis-parallel (LLAP) storms are significantly different in structure and dynamics compared to the much more studied wind-parallel roll storms that typically form over the western Great Lakes. A Doppler on Wheels (DOW) mobile radar sampled several of these storms at fine spatial and temporal resolutions (and close to the surface) during the winter of 2010–11 over and downwind of Lake Ontario to document and improve understanding of how these storms develop. Over 1100 observations of vortices were catalogued within the 16 December 2010 and 4–5 January 2011 events. The majority of these vortices were less than 1 km in diameter with a statistical modal difference in Doppler velocity (delta-V) value across the vortex of 11 m s−1. Vortices developed along boundaries, which formed within the bands, suggesting horizontal shear instability was the main cause. Other features noted in the DOW observations included bounded weak echo regions, anvils, and horizontal vortices, typically on the south side of west–east-oriented LLAP bands. The reflectivity and velocity structure of LLAP bands were found to be much more complex than previously thought, which may impact localized precipitation amounts and errors in forecast location/intensity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 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".