Quantification of the impact of latent heat associated with the freezing of supercooled drops at the surface during freezing rain over Eastern Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The formation of winter precipitation is driven by ice-phase and liquid-phase processes, with the energy required for melting and freezing affecting both temperature and precipitation type. A major freezing rainstorm occurred in early April 2023 over Eastern Canada, causing damage to infrastructure and impacting the economy. The goal of this study is to investigate the impact of the latent heat release associated with freezing rain on the 2-m air temperature and the type of precipitation that reaches the surface. To illustrate the impacts of latent heat, the April storm was simulated using the Global Environmental Multiscale (GEM) model with the modified Predicted Particle Properties (P3) scheme. It was observed that the release of latent heat from freezing rain led to a rise in the 2-m air temperature, with rain recorded when temperatures exceeded 0 °C. The median cumulative freezing rain showed a 34.4 % decrease, while time for the median temperature to reach 0 °C decreased by 2.5 h. The results from the model suggest that temperature advection played a role in balancing the precipitation phase change. This study contributes to our knowledge of processes associated with maintaining or stopping freezing rain and improves our ability to mitigate its hazards. • Latent heat associated with freezing rain contributes to the increase of the low-level air temperature. • The latent heat associated with freezing rain may limit freezing rain amount. • Thermal advection and freezing rain onset temperature can also impact precipitation transition from freezing rain to rain.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it