Modelling Hazardous Surface Hoar Layers in the Mountain Snowpack over Space and Time
Bibliographic record
Abstract
Surface hoar layers are a common failure layer in hazardous snow slab avalanches. Surface hoar crystals (frost) initially form on the surface of the snow, and once buried can remain a persistent weak layer for weeks or months. Avalanche forecasters have difficulty tracking the spatial distribution and mechanical properties of these layers in mountainous terrain. This thesis presents numerical models and remote sensing methods to track the distribution and properties of surface hoar layers over space and time. The formation of surface hoar was modelled with meteorological data by calculating the downward flux of water vapour from the atmospheric boundary layer. The timing of surface hoar formation and the modelled crystal size was verified at snow study sites throughout western Canada. The major surface hoar layers over several winters were predicted with fair success. Surface hoar formation was modelled over various spatial scales using meteorological data from weather forecast models. The largest surface hoar crystals formed in regions and elevation bands with clear skies, warm and humid air, cold snow surfaces, and light winds. Field surveys measured similar regional-scale patterns in surface hoar distribution. Surface hoar formation patterns on different slope aspects were observed, but were not modelled reliably. Mechanical field tests on buried surface hoar layers found layers increased in shear strength over time, but had persistent high propensity for fracture propagation. Layers with large crystals and layers overlying hard melt-freeze crusts showed greater signs of instability. Buried surface hoar layers were simulated with the snow cover model SNOWPACK and verified with avalanche observations, finding most hazardous surface hoar layers were identified with a structural stability index. Finally, the optical properties of surface hoar crystals were measured in the field with spectral instruments. Large plate-shaped crystals were less reflective at shortwave infrared wavelengths than other common surface snow grains. The methods presented in this thesis were developed into operational products that model hazardous surface hoar layers in western Canada. Further research and refinements could improve avalanche forecasts in regions prone to hazardous surface hoar layers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".