Regional Thermodynamic Characteristics Distinguishing Long- and Short-Duration Freezing Rain Events over North America
Bibliographic record
Abstract
Abstract Freezing rain is an especially hazardous winter weather phenomenon that remains particularly challenging to forecast. Here, we identify the salient thermodynamic characteristics distinguishing long-duration (six or more hours) freezing rain events from short-duration (2–4 h) events in three regions of the United States and Canada from 1979 to 2016. In the northeastern United States and southeastern Canada, strong surface cold-air advection is not common during freezing rain events. Colder onset temperatures at the surface and in the near-surface cold layer support longer-duration events there, allowing heating mechanisms (e.g., the release of latent heat of fusion when rain freezes at the surface) to act for longer periods before the surface reaches 0°C and precipitation transitions to rain. In the south-central United States, cold air at the surface is replenished via continuous cold-air advection, reducing the necessity of cold onset surface temperatures for event persistence. Instead, longer-duration events are associated with warmer and deeper >0°C warm layers aloft and stronger advection of warm and moist air into this layer, delaying its erosion via cooling mechanisms such as melting. Finally, in the southeastern United States, colder and especially drier onset conditions in the cold layer are associated with longer-duration events, with evaporative cooling crucial to maintaining the subfreezing surface temperatures necessary for freezing rain. Through an improved understanding of the regional conditions supporting freezing rain event persistence, we hope to provide useful information to forecasters in their attempt to predict these potentially damaging events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".