Precipitation Type Distribution and Microphysical Processes During the 1998 Ice Storm Simulated Under Pseudo‐Warmer Conditions
Bibliographic record
Abstract
Abstract In the future, the intensity, phases, and frequency of precipitation are expected to change due to global warming, in particular during colder seasons when temperatures are near 0°C. To investigate the impacts of warmer atmospheric conditions on the microphysical processes that lead to several precipitation types, the extreme 1998 Ice Storm was simulated using the Weather Research and Forecasting (WRF) model, with and without a pseudo‐global warming. The pseudo‐global warming approach simulates similar large‐scale conditions but in warmer conditions, which allows for the assessment of thermodynamic feedback from cloud and precipitation microphysics. For both simulations, WRF was coupled with the Predicted Particle Properties (P3) bulk microphysics scheme that predicts the liquid fraction of mixed‐phase particles. Results of the pseudo‐global warming simulation show an increase of ∼828 m in the upper 0°C level and a northeastward migration (∼60 km) of the rain‐snow transition region. The results also show a 20% decrease in domain‐averaged freezing rain amounts, but with an increased maximum amount of 50%. The horizontal distance associated with a melting aloft and a refreezing layer near the surface is 105 km longer in southern Quebec due to the combined effects of the pseudo‐warming and the presence of the Appalachian Mountains. The microphysical processes that lead to precipitation are impacted as well; the increased ice mass and riming conditions aloft in warmer temperatures result in higher liquid precipitation rates. This study contributes to our understanding of the changes in the fine‐scale processes of an extreme storm, simulated with pseudo‐global warming conditions.
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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 | 0.001 |
| 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 | 0.001 | 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".