Improved Detection Using Negative Elevation Angles for Mountaintop WSR-88Ds. Part III: Simulations of Shallow Convective Activity over and around Lake Ontario
Bibliographic record
Abstract
Abstract During the winter, lake-effect snowstorms that form over Lake Ontario represent a significant weather hazard for the populace around the lake. These storms, which typically are only 2 km deep, frequently can produce narrow swaths (20–50 km wide) of heavy snowfall (2–5 cm h−1 or more) that extend 50–75 km inland over populated areas. Subtle changes in the low-altitude flow direction can mean the difference between accumulations that last for 1–2 h and accumulations that last 24 h or more at a given location. Therefore, it is vital that radars surrounding the lake are able to detect the presence and strength of these shallow storms. Starting in 2002, the Canadian operational radars on the northern side of the lake at King City, Ontario, and Franktown, Ontario, began using elevation angles of as low as −0.1° and 0.0°, respectively, during the winter to more accurately estimate snowfall rates at the surface. Meanwhile, Weather Surveillance Radars-1988 Doppler in New York State on the southern and eastern sides of the lake—Buffalo (KBUF), Binghamton (KBGM), and Montague (KTYX)—all operate at 0.5° and above. KTYX is located on a plateau that overlooks the lake from the east at a height of 0.5 km. With its upward-pointing radar beams, KTYX’s detection of shallow lake-effect snowstorms is limited to the eastern quarter of the lake and surrounding terrain. The purpose of this paper is to show—through simulations—the dramatic increase in snowstorm coverage that would be possible if KTYX were able to scan downward toward the lake’s surface. Furthermore, if KBUF and KBGM were to scan as low as 0.2°, detection of at least the upper portions of lake-effect storms over Lake Ontario and all of the surrounding land area by the five radars would be complete. Overlake coverage in the lower half (0–1 km) of the typical lake-effect snowstorm would increase from about 40% to about 85%, resulting in better estimates of snowfall rates in landfalling snowbands over a much broader area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".