Multi-scale snowdrift-permitting modelling of mountain snowpack
Bibliographic record
Abstract
Abstract. The interaction of mountain terrain with meteorological processes causes substantial temporal and spatial variability in snow accumulation and ablation. Processes impacted by complex terrain include large-scale orographic enhancement of snowfall, small-scale processes such as gravitational and wind-induced transport of snow, and variability in the radiative balance such as through terrain shadowing. In this study, a multi-scale modelling approach is proposed to simulate the temporal and spatial evolution of high-mountain snowpacks. The multi-scale approach combines atmospheric data from a numerical weather prediction system at the kilometre scale with process-based downscaling techniques to drive the Canadian Hydrological Model (CHM) at spatial resolutions allowing for explicit snow redistribution modelling. CHM permits a variable spatial resolution by using the efficient terrain representation by unstructured triangular meshes. The model simulates processes such as radiation shadowing and irradiance to slopes, blowing-snow transport (saltation and suspension) and sublimation, avalanching, forest canopy interception and sublimation, and snowpack melt. Short-term, kilometre-scale atmospheric forecasts from Environment and Climate Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic Prediction System (HRDPS) drive CHM and are downscaled to the unstructured mesh scale. In particular, a new wind-downscaling strategy uses pre-computed wind fields from a mass-conserving wind model at 50 m resolution to perturb the mesoscale HRDPS wind and to account for the influence of topographic features on wind direction and speed. HRDPS-CHM was applied to simulate snow conditions down to 50 m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (∼1000 km2) in the Canadian Rockies. Simulations were evaluated using high-resolution airborne light detection and ranging (lidar) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both wind-induced and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of windblown snow on leeward slopes and associated snow cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture lee-side flow recirculation and associated wind speed decreases. A terrain-based metric helped to identify these lee-side areas and improved the wind field and the associated snow redistribution. An overestimation of snow redistribution from windward to leeward slopes and subsequent avalanching was still found. The results of this study highlight the need for further improvements of snowdrift-permitting models for large-scale applications, in particular the representation of subgrid topographic effects on snow transport.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".