Analyzing and Forecasting Rocky Mountain Lee Cyclogenesis Often Associated with Strong Winds
Bibliographic record
Abstract
Since numerical forecast models often err in predicting the timing and location of lee cyclogenesis, a physically based method to diagnose such errors is sought. A case of Rocky Mountain lee cyclogenesis associated with strong winds is examined to explore the transformation from a stationary lee trough to a mobile midlatitude cyclone (hereafter, departure). Up to 12 h before departure, a pronounced surface pressure trough travels eastward across western North America at an average speed of 22 m s21. Several methods are employed to examine the structure and evolution of the pressure field: total sea level pressure, time series at individual stations, isallobars, and bandpass filtering. Bandpass filtering of the observed sea level pressure data is useful for clarifying the movement of the mobile trough through the complex terrain. Quasigeostrophic height-tendency diagnostics show that the mobile pressure trough is related to the traveling mid- to upper-tropospheric vorticity maximum that is responsible for departure. At many stations, surface temperature changes associated with this pressure trough are not consistent with those commonly associated with surface frontal passages. To test the hypothesis that mobile pressure troughs are associated with departure, a five-winter climatology of 111 southern Alberta lee cyclones is constructed. Sixty-two percent of these events feature an upstream pressure minimum 3–9 h prior to departure, in a manner resembling the case study. Seventy-six percent of these 111 events are associated with reports listed in Storm Data, indicating the potential severity of these storms. 1.
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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 | 0.000 | 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) | 0.000 | 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".