On the Dynamics, Thermodynamics, and Forecast Model Evaluation of Two Snow-Burst Events in Southern Alberta
Bibliographic record
Abstract
Abstract Two high-impact convective snowband events (“snow bursts”) that affected Calgary, Alberta, Canada, are examined to better understand the dynamics and thermodynamics of heavy snowbands not associated with lake effects or the cold conveyor belt of synoptic-scale cyclones. Such events are typically characterized by brief, but heavy, periods of snow; low visibilities; and substantial hazards to automobile and aviation interests. Previous literature on these events has been limited to a few case studies across North America, including near the leeside foothills of the U.S. Rockies. The large-scale dynamics and thermodynamics are investigated using the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR). Previously, high-resolution convection-explicit Weather Research and Forecasting Model (WRF) simulations have shown some ability to successfully reproduce the dynamics, thermodynamics, and appearance of convective snowbands, with small errors in location and timing. Therefore, WRF simulations are performed for both events, and are evaluated along with the NCEP North American Mesoscale (NAM) model forecasts. Both the NARR and WRF simulations show that while the two snow bursts are similar in appearance, they form as a result of different dynamic and thermodynamic mechanisms. The first event occurs downstream of an upper-tropospheric jet streak in a region of little synoptic-scale ascent, where ageostrophic frontogenesis helps to release conditional, dry symmetric, and inertial instability in an unsaturated environment. The inertial instability is determined to be related to fast flow over upstream high terrain. The second event occurs in a saturated environment in a region of Q-vector convergence (primarily geostrophic frontogenesis), which acts to release conditional, convective, and conditional symmetric instability (CSI).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".