Impact of windflow calculations on simulations of alpine snow accumulation, redistribution and ablation
Bibliographic record
Abstract
Abstract Wind redistribution, radiation and turbulent heat fluxes determine seasonal snow accumulation and melt patterns in alpine environments. Mathematical representations of windflow vary in complexity and introduce uncertainty to snow modelling. To characterize this uncertainty, a spatially distributed snow model that considers the physics of blowing snow transport and sublimation and the energy fluxes contributing to snowpack ablation were evaluated for its ability to simulate seasonal snow patterns around a windy alpine ridge in the Canadian Rockies. The model was forced with output from three windflow models of varying computational complexity and physical realism: (i) a terrain‐based empirical interpolation of station observations, (ii) a simple turbulence model and (iii) a computational fluid dynamics model. Compared with wind measurements, the windflow simulations produced similar and relatively accurate (biases lower than ±1.1 m s−1) wind speed estimates. However, the snow mass budget simulated by the snow model was highly sensitive to the windflow simulation used. Compared with measurements, distributed snow model depth and water equivalent errors were smallest using either of the two turbulence models, with the best representation of downwind drifts by the computational fluid dynamics model. Sublimation was an important mass loss from the ridge, and windflow model choice resulted in cumulative seasonal sublimation differences ranging from 10.5% to 19.0% of seasonal snowfall. When aggregated to larger scales, differences in cumulative snowmelt and snow transport were negligible, but persistent differences in sublimation and snow‐covered area suggest that windflow model choice can have significant implications at multiple scales. Uncertainty can be reduced by using physically based windflow models to drive distributed snow models. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".