Airborne Radar Observations of Lake-Effect Snowbands over the New York Finger Lakes
Bibliographic record
Abstract
Abstract The vast majority of lake-effect snow research throughout the years has focused on the North American Great Lakes since they are often associated with strong lake-effect events that produce heavy downstream snowfall. This study investigates a lake-effect snow event that instead occurred over two smaller lakes, the New York Finger Lakes, which are just O(5) km wide and O(50) km long. A pair of well-defined snowbands that formed over Seneca and Cayuga Lakes, the two largest of the Finger Lakes, were sampled from above by a vertically pointing Doppler radar and lidar on board the University of Wyoming King Air (UWKA). With typical widths matching the widths of the lakes, and depths of less than 1000 m, the long-lake-axis-parallel bands were actually quite intense for their size. For example, updrafts of 2–3 m s−1 or greater within the band cores were common, and reflectivity occasionally exceeded 5 dBZ. Airborne dual-Doppler data show that both bands were sometimes accompanied by a well-defined thermally driven secondary circulation. Lidar data reveal that the Cayuga Lake band contained significantly more liquid water than the band over Seneca Lake, which was composed mainly of ice. Dissipating lake-effect ice clouds, carried downstream from Lake Ontario toward Seneca Lake, likely seeded the emerging convection over Seneca Lake, resulting in an accelerated depletion of liquid in the Seneca Lake band via more efficient snow growth.
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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 | 0.000 | 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) | 0.001 | 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".