SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
Bibliographic record
Abstract
Abstract. Wind-induced snow transport has a strong influence on snow spatial variability, especially at spatial scales between 1 and 500 m in alpine environments. Thus, the evolution of operational snow modelling systems towards 100–500 m resolutions requires representing this process at these resolutions over large domains and entire snow seasons. We developed SnowPappus, a parsimonious blowing-snow model coupled to the state-of-the-art Crocus snow model able to cope with these requirements. SnowPappus simulates blowing-snow occurrence, horizontal transport flux and sublimation rate at each grid cell as a function of 2D atmospheric forcing and snow surface properties. Then, it computes a mass balance using an upwind scheme to provide eroded or accumulated snow amounts to Crocus. Parameterizations used to represent the different processes are described in detail and discussed against existing literature. A point-scale evaluation of blowing-snow fluxes was conducted, mainly at the Col du Lac Blanc observatory in the French Alps. Evaluations showed that SnowPappus performs as well as the currently operational scheme SYTRON in terms of blowing-snow occurrence detection, while the latter does not give access to spatialized information. Evaluation of the simulated suspension fluxes highlighted a strong sensitivity to the suspended particle's terminal fall speed. Proper calibrations allow the model to reproduce the correct order of magnitude of the mass flux in the suspension layer. Numerical performances of gridded simulations of Crocus coupled with SnowPappus were assessed, showing the feasibility of using it for operational snow forecast at the scale of the entire French Alps.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".