Snow Studies. Part II: Average Relationship between Mass of Snowflakes and Their Terminal Fall Velocity
Bibliographic record
Abstract
Abstract This study uses a dataset of low-density snow aggregates measurements collected by a ground-based optical disdrometer that provides particle size and terminal fall speed for each size interval from which the velocity–size and area ratio–size relationships can be derived. From these relationships and relations between the Best and Reynolds numbers proposed in the literature, the mass power-law coefficients are obtained. Then, an approximate average relation between the coefficients in the experimentally determined velocity–size power law (with exponent fixed at 0.18) and the coefficients in the estimated mass power law (with exponent fixed at 2) is obtained. The validation of the retrieved relation is made by comparing, for each snowfall event, the time series of the reflectivity factor calculated from the derived mass–size relationship for a snowflake and from the size distribution measured by the optical disdrometer, with the reflectivity obtained from measurements. Using the measured snow size distribution and the retrieved mass–velocity relationship, a few useful relations between the bulk quantities of snow are derived. This study considers relations suitable for the microphysical modeling consistent with radar measurements of precipitating snow composed of unrimed or lightly rimed aggregate snowflakes.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".