A triple-moment blowing snow-atmospheric model and its application in computing the seasonal wintertime snow mass budget
Bibliographic record
Abstract
Abstract. Many field studies have shown that surface sublimation and blowing snow transport and sublimation have significant influences on the snow mass budget in many high latitude regions. We developed a coupled triple-moment blowing snow-atmospheric modeling system to study the influence of these processes on a seasonal time scale over the Northern Hemisphere. Two simulations were performed. The first is a 5 month simulation for comparison with snow survey measurements over a Saskatchewan site to validate the modeling system. The second simulation covers the 2006/2007 winter period to study the snow mass budget over the Northern Hemisphere. The results show that surface sublimation is significant in Eurasian Continent and the eastern region of North America, reaching a maximum value of 200 mm SWE (Snow Water Equivalent). Over the Arctic Ocean and Northern Canada, surface deposition with an average value of 30 mm SWE was simulated. Blowing snow sublimation was found to return up to 50 mm SWE back to the atmosphere over the Arctic Ocean, while the divergence of blowing snow transport contributes only a few mm SWE to the change in snow mass budget. The results were further stratified in 10 degree latitudinal bands. The results show that surface sublimation decreases with an increase in latitude while blowing snow sublimation increases with latitude. Taken together, the surface sublimation and blowing snow processes was found to distribute 23% to 52% of winter precipitation over the three month winter season.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".