Using snow depth observation to provide insight into the quality of regional-scale snowpack simulations for avalanche forecasting
Bibliographic record
Abstract
Abstract. The combination of numerical weather prediction and snowpack models has potential to provide valuable information about snow avalanche conditions in remote areas. However, the output of snowpack models is sensitive to precipitation inputs, which can be difficult to verify in mountainous regions. To examine how existing observation networks can help interpret the accuracy of snowpack models, we compared snow depths predicted by a weather-snowpack model chain with data from automated weather stations and manual observations. Data from the 2020–21 winter were compiled for 21 avalanche forecast regions across western Canada covering a range of climates and observation networks. To perform regional-scale comparisons, snowpack model simulations were run at select grid points from the HRDPS numerical weather prediction model to represent conditions at treeline elevations and observed snow depths were interpolated to the same locations. Snow depths in the Coast Mountain range were systematically overpredicted, while snow depths in many parts of the interior Rocky Mountain range were underpredicted. The impact of these biases had a greater impact on the simulated avalanche conditions in the interior ranges, where faceting was more sensitive to snow depth. To put the comparisons in context, the quality of the observations were assessed with uncertainties in the interpolations and by checking whether snow depth increases during stormy periods were consistent with the forecast avalanche hazard. While some regions had high quality observations, many regions had large uncertainties, suggesting in some situations the modelled snow depths could be more reliable than the observations. The analysis provides insights into the potential for validating weather and snowpack models with readily available observations, and for how avalanche forecasters can better interpret the accuracy of snowpack simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".