Wind Stress Functions for Blowing Snow Initiation and Transport, in Vegetated Grids
Bibliographic record
Abstract
Adequate representation of snow processes in land surface schemes would improve General Circulation Model (GCM) simulations. The Canadian Land Surface Scheme (CLASS) and other land surface models lack blowing snow codes. A blowing snow sublimation parameterization recently formulated from simplifying assumptions and the PIEKTUK model results, omits micro-scale details, making it suitable for incorporation into CLASS. The parameterization is specific to non-vegetated surfaces and its adaptation for use in vegetated grids is being targeted here. The transport formula for PIEKTUK and the Prairie Blowing Snow Model (PBSM) are similar except for vegetation roughness represented only in the latter. This similarity provides the opportunity to formulate vegetated transport rates on the basis of PBSM results and to fit the formula to the new blowing snow sublimation parameterization to generate vegetated sublimation rates. In this approach, a wind stress function influences threshold, or otherwise effective, velocities for testing blowing snow initiation at various vegetation exposures. For strong-wind situations a wind dependent stress function, formulated from the non-vegetated to vegetated wind stress ratio, generates transport rates comparable to those of the PBSM. These transport rates increase as a power function of the wind speed and are higher for smoother surfaces. With weaker winds
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".