The snow/snow water equivalent ratio and its predictability across Canada /
Bibliographic record
Abstract
The current practice of snowfall forecasting in Canada is to determine the snow water equivalent (SWE) expected to precipitate using a numerical weather predication model and then multiplying this amount by a snow/SWE ratio to determine the forecast snow depth. The 10:1 "rule of thumb" is still widely used operationally as this ratio, even though it is well-known to introduce error in the forecasts because the density of snow is highly variable. In 2003 Ivan DubE developed a decision tree type algorithm to find the snow/SWE ratio which has subsequently been automated in 2004 by the Meteorological Service of Canada (MSC). The objectives of this study are to explore the behaviour of snow/SWE ratio by developing a Canada-wide climatology of this quantity and examining performance of the MSC algorithm over the winter 2004-2005 using several verification techniques. We found that the mean annual snow/SWE ratio across Canada is 13:1 with large variations temporally and spatially and that the MSC algorithm performed with equal or better skill than the 10:1 algorithm in 84% of the events.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".