Near-surface snow particle dynamics from particle tracking velocimetry and turbulence measurements during alpine blowing snow storms
Bibliographic record
Abstract
Abstract. Many blowing snow conceptual and predictive models have been based on simplified two-phase flow dynamics derived from time-averaged observations of bulk flow conditions in blowing snow storms. Measurements from the first outdoor application of particle tracking velocimetry (PTV) of near-surface blowing snow yield new information on mechanisms for blowing snow initiation, entrainment, and rebound, whilst also confirming some findings from wind tunnel observations. Blowing snow particle movement is influenced by complex surface flow dynamics, including saltation development from creep that has not previously been measured for snow. Comparisons with 3-D atmospheric turbulence measurements show that blowing snow particle motion immediately above the snow surface responds strongly to high-frequency turbulent motions. Momentum exchange from wind to the dense near-surface particle-laden flow appears significant and makes an important contribution to blowing snow mass flux and saltation initiation dynamics. The more complete and accurate description of near-surface snow particle motions observable using PTV may prove useful for improving blowing snow model realism and accuracy.
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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 | 0.000 |
| 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) | 0.001 | 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".