Multiscale Analysis of a Flash Freeze Event
Bibliographic record
Abstract
Abstract A high-impact flash freeze event is analyzed from synoptic-scale, mesoscale, and microscale perspectives. The 28–29 February 2024 cold front produced rapidly decreasing temperatures of 12°C over 3 h and gusty winds that primarily impacted regions from the Great Lakes eastward to Quebec and portions of northern New England. A dynamical distinction between synoptic-scale and mesoscale features in the Great Lakes and Saint Lawrence River Valley region is examined. This event presents a remarkable example of an idealized cold frontal profile through upper-air data, whereby a shallow depth of 40 hPa was associated with a 15°C temperature difference. This near-surface frontal inversion is coincident with first-order temperature discontinuities at the surface. A Doppler lidar wind profiler is used to measure the time evolution of the wind field as the cold front passes over Montreal. Horizontal wind speed gusts neared 35 m s −1 with an average gust factor of 1.83 found throughout the event. Analysis of turbulence properties and intensity shows the strong winds that can often be associated with flash freeze events. Lidar measurements are also combined and compared with local upper-air data to investigate evolving atmospheric static stability during the event. Significance Statement Flash freeze events are often associated with hazardous conditions. Yet, relatively few case studies exist to provide a thorough analysis of the associated weather dynamics. This study is the first of its kind to present a multiscale analysis of a remarkable flash freeze event. The case to be addressed is that of 28–29 February 2024 in southeastern Canada and portions of New York and New England. This event demonstrates that it is the concurrence of multiple processes—a rapid 12°C temperature drop in Montreal within 3 h, a transition from above to below freezing during precipitation, and extreme winds of 35 m s −1 —that defines this flash freeze as distinct from an ordinary cold frontal passage. These results can inform a follow-up study that would seek to quantitatively define a flash freeze, providing important insight to operational forecasting.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".