Forecasting and modelling ice layer formation on the snowpack due to freezing precipitation in the Pyrenees
Bibliographic record
Abstract
In the Pyrenees, freezing precipitation at high elevations is quite frequent in winter, leading to the formation of an ice layer on the surface of the snowpack. It may cause many accidents amongst mountaineers and skiers, with sometimes more fatalities per winter than avalanches. Such events are not predicted by current operational systems for snow and avalanche hazard forecasting. A crowd-sourced database of surface ice layer occurrences was first built up, using reports from Internet mountaineering and ski-touring communities, to mitigate the lack of observations from conventional observation networks. Simple diagnostics of freezing precipitation were then developed, based on the cloud water content and 2-m temperature forecast by the Numerical Weather Prediction model AROME, operating at 2.5-km resolution. An evaluation over five winters gave a probability of detection reaching 81% and a false alarm ratio of 23% compared to occurrences reported in the observation database. A new modelling of ice formation on the surface of the snowpack due to impinging supercooled water was added to the detailed snowpack model Crocus. It was combined with the atmospheric diagnostic of freezing precipitation. Resulting snowpack simulations over five winters captured the formation of the main observed ice layers. The performance of the diagnostic associated with the ice formation modelling was assessed for the event of 5–6 January 2012, with altitudinal and spatial distributions of the ice layer matching the observations. These simple methods enable to forecast the occurrence of surface ice layer formations and to simulate their evolution within the snowpack, even if an accurate estimation of the amount of freezing precipitation remains the main challenge.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".