PISTE: A Snow-Physics Model Incorporating Human Factors for Groomed Ski Slopes*
Bibliographic record
Abstract
Abstract Accurately calculating snow-surface temperature and liquid water content for a groomed ski run, known as a ski piste, is crucial to the preparation of fast skis for alpine racing. Ski technicians can use forecasts of these variables to reduce ski–snow friction by applying layers of wax ahead of time. A new one-dimensional numerical Lagrangian snowpack model, Prognostic Implementation for Snow Temperature Estimation (PISTE), is presented that solves the heat-, liquid water–, and ice-budget equations to calculate these snow variables. In addition, the human effects of skiing and grooming are modeled. Meteorological measurements from a 5-day, clear-sky case study at a ski piste on Whistler Mountain, British Columbia, Canada, are prescribed to PISTE as boundary conditions. Because of a lack of interior snowpack measurements, PISTE was spun up from a dry, isothermal snowpack using repeated boundary conditions from 1 day of measurements. Initial conditions for the main model run that used the subsequent 4 days were taken from this spinup. Simulated and measured snow-surface temperatures show very good agreement, with slight cold daytime and warm nighttime biases (averaging 0.5° and 1°C, respectively). The modeled behavior of snowpack temperature and liquid water content profiles is consistent with previous literature having similar radiative boundary conditions. The case study indicates that PISTE is useful under simple conditions. It shows the potential to be developed into a more sophisticated model that can incorporate complex boundary conditions such as cloudiness and precipitation and can be driven by numerical weather prediction output.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".