Version 3.0 of the Crocus snowpack model
Bibliographic record
Abstract
This article presents a comprehensive description of the 3.0.2 stable release of the Crocus snowpack model in the SURFEX modelling platform. It synthesizes and harmonizes a number of equations disseminated in various previous publications, introduces a number of unpublished parameterizations and includes new developments implemented since 2012. Among the novelties, an explicit representation of the evolution of impurity mass in snow (e.g. black carbon, mineral dust) allows representing their impact on solar radiation absorption in the snowpack at different wavelengths and their feedback on all snowpack properties. The model also allows the formation of surface ice layers due to freezing rain. In addition, Crocus is coupled to the MEB “big-leaf” vegetation scheme and can therefore be applied in forested areas. A module for snow management can also be optionally activated to simulate the snowpack on ski slopes in ski resorts. The model can be coupled with various blowing snow schemes. The MEPRA expert system which analyses the mechanical stability of the simulated snowpack has been implemented directly within SURFEX. For each physical process represented by empirical parameterizations, several new parameterizations from the literature were implemented. The different combinations of these parameterizations constitute the ESCROC multiphysics ensemble model. It allows the quantification of simulations uncertainty for various applications. Finally, a technical solution was proposed for externalized applications allowing the use of the scheme in other Land Surface Models. The paper also reviews the available scientific evaluations and applications of the model. It describes its numerical efficiency and the main scientific and technical challenges providing guidance for the future of snow modelling.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".