Latest scientific and technical evolutions in the Crocus snowpack model
Bibliographic record
Abstract
This contribution presents an overview of the last stable release of the Crocus detailed snowpack model in the SURFEX opensource modelling platform. It gathers numerous recent scientific and technical developments in a common code version. An explicit representation of the evolution of light absorbing particles mass in snow (e.g. black carbon, mineral dust) allows representing their impact on solar radiation absorption in the snowpack in the visible and near-infrared spectrum through the TARTES optical scheme, and the consequences on all snowpack properties. Crocus is now coupled to the MEB (Multiple Energy Balance) vegetation scheme and can therefore be applied on forested areas. A module of snow management including grooming and snow making can also be optionally activated to simulate the snowpack on ski slopes. Developments used in the French operational system in support of avalanche hazard forecasting were also fully integrated in SURFEX: the SYTRON module for snow erosion and accumulation by the wind and the expert system MEPRA which analyses the mechanical stability of the simulated snowpacks. Finally, an ensemble multiphysics version of the model (ESCROC) was also developed by implementing from 2 to 4 parameterizations from the literature for each physical process represented by an uncertain empirical parameterization. The different combinations enable the quantification of simulations uncertainty required in various applications: future projections of snow cover; sensitivity analyses of a given process ; data assimilation of snow observations. Crocus and ESCROC are included in the ESM-SnowMIP model intercomparison and exhibit a robust skill in various climates and environments. Several running-time optimizations were also implemented in the latest release. We present an overview of the current numerical cost with a comparison to more classical snow schemes used in NWP and climate model applications. The code is provided through a git repository and with a simple visualization software to help users to display snowpack internal properties at local scale. On-going works are focused on the implementation of new data assimilation algorithms well suited to the numerical specificities of this scheme. An externalized version for coupling with other surface schemes is currently under development.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".