Ensemble Sensitivity of Precipitation Type to Initial Conditions for a Major Freezing Rain Event in Montreal
Bibliographic record
Abstract
Abstract The predictability of precipitation type in a January 2017 winter storm over the northeastern United States and southeastern Canada is examined using a convective-scale initial-condition ensemble with the Weather Research and Forecasting (WRF) Model. Real-time forecasts of the event by Environment and Climate Change Canada predicted 15–25 cm of snow accumulation in Montreal, Quebec, Canada. However, the initial 4 h of the event had 5–8 mm of freezing rain instead, followed by 7 cm of snow. While the total liquid-equivalent precipitation was consistent with the forecast, the unexpected freezing rain caused significant disruption in the Montreal region. The fraction of freezing precipitation (freezing rain and/or ice pellets) over the initial 4 h in Montreal varied greatly across the ensemble, with some members producing nearly all snow and others producing nearly all freezing precipitation. In members with larger fractions of freezing precipitation (as opposed to snow), the cyclone’s midlevel trough was displaced slightly to the northwest, and its downstream (eastern) edge was narrower, the latter of which was traced back to model initialization. These differences increased the midlevel southerly flow into southern Quebec, which both enhanced the horizontal warm advection and decreased the vertical cold advection leading up to the event. The consequent midlevel warming over Montreal in these members produced an above-zero layer that melted falling precipitation, leading to freezing upon contact with the ground. This case study highlights the value of convective-scale ensembles for identifying mechanisms by which initial synoptic-scale uncertainties lead to high-impact localized errors in precipitation type.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".