Dry-Air Entrainment and Advection during Alpine Blowing Snow Events
Bibliographic record
Abstract
Abstract. Blowing snow transport has considerable impact on the hydrological cycle in alpine regions both through the redistribution of the seasonal snowpack and through sublimation back into the atmosphere. Alpine energy and mass balances are typically modelled with time-averaged approximations of sensible and latent heat fluxes. This oversimplifies non-stationary turbulent mixing in complex terrain and may overlook important exchange processes for hydrometeorological prediction. To determine if warm and dry air advection during blowing snow events from atmospheric sweep and ejection motions can provide such exchange mechanisms, they were investigated at an alpine site in the Canadian Rockies and found to supply substantial sensible heat to blowing snow flows. These motions were responsible for temperature fluctuations of up to 1 °C, a considerable change for energy balance estimation. A simple scaling relation was derived that related the frequency of turbulent sweeps and ejections to the event magnitude. This allows the first parameterization of entrained or advected energy for time-averaged representations of blowing snow sublimation and suggests that advection can strongly reduce thermodynamic feedbacks between blowing snow sublimation and the near-surface atmosphere. The recurrence model modeled described provides a significant step towards a more physically-based blowing snow sublimation model. Additionally, calculations of return frequencies and event durations provide a field-measurement context for recent findings of non-stationarity impacts on sublimation rates.
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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 | 0.000 |
| 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".