Impact of Cyclone‐Cyclone Interaction on Lake‐Effect Snowbands: A False Alarm
Bibliographic record
Abstract
Abstract In this study, the impacts of two cyclone interactions with the environmental flow and with each other on a heavy winter snowfall event over the Great Lakes region are examined by applying a recently‐developed diagnostic tool to a lake‐effect snowstorm. This winter snowfall event was a false alarm by the Canadian operational model, in which the predicted lake‐effect snowfall at the lee side of Lake Erie doubles the observed. The false alarm involves the interactions of two cyclones, whose relative locations are poorly predicted, leading to inaccurate prediction of their induced winds and interactions over Lake Erie at both low and upper levels. In particular, at 1,000 hPa, the cyclone‐induced winds and their interactions over Lake Erie caused colder and drier air over warm‐moist open‐water lake surface that is favorable for more latent heat flux deriving from the lake in the model than in the real atmosphere. At 500 hPa, the location errors of the model predicted cyclones reversed the cyclone‐induced winds over Lake Erie from mainly southerly to northerly, especially during the intense snowfall period. An analysis of the conditional symmetric instability (CSI) index over Lake Erie reveals that the operational model systematically over‐predicts CSI, more significantly during the intense snowfall period. As a result, the operational model prediction makes a false alarm of a lake‐effect snowfall event with large errors in precipitation intensity. Future improvements in operational model predictions are also suggested.
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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 | 0.004 |
| 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 | 0.001 |
| 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".