The role of blowing snow in the hydrometeorology of the Mackenzie River Basin /
Bibliographic record
Abstract
Despite being ubiquitous in the Mackenzie River Basin (MRB) of Canada, the role of snow in its energy and water budgets are still open to much speculation. This thesis presents a multi-scale analysis of the contribution of blowing snow to the hydrometeorology of the MRB. A climatology of adverse wintertime weather events is first presented and demonstrates that blowing snow events are rare within the forested sections of the MRB but become more frequent in the northern parts of the basin covered by Arctic tundra. It is these areas which experience the largest impacts of blowing snow transport and sublimation due to large-scale processes. To further assess the mesoscale and microscale effects of blowing snow to the northern regions of the MRB, the development of a bulk blowing snow model is then described. The single- and double-moment versions of the PIEKTUK blowing snow model are shown to produce equivalent results as a previous spectral version of the numerical model while operating about 100 times faster. The application of the double-moment PIEKTUK model (PIEKTUK-D) to a Canadian Arctic tundra site near the northern tip of the MRB reveals that blowing snow sublimation depletes ≈3 mm snow water equivalent (swe) from the snowpack over a period of 210 days during the winter of 1996/1997 at Trail Valley Creek, Northwest Territories. Various assumptions on the state of the background thermodynamic profiles and their evolution during blowing snow, however, can yield significantly higher (>300%) rates of sublimation over the same period. PIEKTUK-D is then coupled to the Mesoscale Compressible Community (MC2) model for an interactive simulation of a ground blizzard at Trail Valley Creek. This coupled mesoscale simulation reveals that moistening and cooling of near-surface air associated with blowing snow sublimation is observed but mitigated in part by advective and entrainment processes. Combined, blowing snow sublimation and mass divergence are then shown to rem
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".