Understanding Heavy Lake-Effect Snowfall: The Vertical Structure of Radar Reflectivity in a Deep Snowband over and downwind of Lake Ontario
Bibliographic record
Abstract
Abstract The distribution of radar-estimated precipitation from lake-effect snowbands over and downwind of Lake Ontario shows more snowfall in downwind areas than over the lake itself. Here, two nonexclusive processes contributing to this are examined: the collapse of convection that lofts hydrometeors over the lake and allows them to settle downwind; and stratiform ascent over land, due to the development of a stable boundary layer, frictional convergence, and terrain, leading to widespread precipitation there. The main data sources for this study are vertical profiles of radar reflectivity and hydrometeor vertical velocity in a well-defined, deep long-lake-axis-parallel band, observed on 11 December 2013 during the Ontario Winter Lake-effect Systems (OWLeS) project. The profiles are derived from an airborne W-band Doppler radar, as well as an array of four K-band radars, an X-band profiling radar, a scanning X-band radar, and a scanning S-band radar. The presence of convection offshore is evident from deep, strong (up to 10 m s −1 ) updrafts producing bounded weak-echo regions and locally heavily rimed snow particles. The decrease of the standard deviation, skewness, and peak values of Doppler vertical velocity during the downwind shore crossing is consistent with the convection collapse hypothesis. Consistent with the stratiform ascent hypothesis are (i) an increase in mean vertical velocity over land; and (ii) an increasing abundance of large snowflakes at low levels and over land, due to depositional growth and aggregation, evident from flight-level and surface particle size distribution data, and from differences in reflectivity values from S-, X-, K-, and W-band radars at nearly the same time and location.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".