WESTERLY WIND BURSTS AFFECT AVALANCHE CYCLES
Bibliographic record
Abstract
Recent climatology and basic physics provide new incite into how major westerly wind bursts contribute to avalanche cycles. A more complete understanding of these westerly wind events can help improve our weather forecasting for winter seasons in western Canada. This study examines the role the major storm events in the years 2006-2008 played in mountain snow accumulation, snow redistribution, freezing level fluctuations and snow stability across western North America. Severe weather systems that deepen rapidly are the focus of this paper. These storms produced unusually heavy snowfalls and have recently been linked to strong westerly wind bursts (WWB's). Our analyses show that a quasi 40-60 day oscillation circumnavigates the globe and is associated with westerly wind bursts at 500 mb (for mid latitudes between 35 o N and 55 o N). These mid latitude WWB oscillations at 500 mb occur in a fashion similar to the well known Madden-Julian Oscillations (MJO) oscillations which occur at 850 and 200 mb. Preliminary results suggest the a Wheeler-type (phase, longitude strength) diagram for the entire globe (or the pacific segment) may allow better tracking of these WWB impulses. The 500 mb wind anomalies may be derived from both model forecasts and direct observation (such as satellite cloud motion data). Using this information we can better understand factors controlling recent major avalanche cycles in western Canada, and better track WWB cycles that appear to be related.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".