Influence of the Model Horizontal Resolution on Atmospheric Conditions Leading to Freezing Rain in Regional Climate Simulations
Bibliographic record
Abstract
Freezing rain occurs in complex atmospheric conditions when the temperature is close to 0°C. To better understand how its occurrence will change in the future, there is a need to assess how well regional climate models can reproduce those conditions. The goal of the present study is to investigate the influence of horizontal resolution on the simulation of freezing rain using the fifth generation of the Canadian Regional Climate Model (CRCM5). Three CRCM5 simulations driven by the European Centre for Medium-range Weather Forecasts interim reanalysis (ERA-Interim) over eastern North America at resolutions of 0.11°, 0.22°, and 0.44° were conducted over a period of 36 years (1979–2014). Freezing rain is diagnosed using an in-line diagnostic method for precipitation partitioning. A climatological study of annual and seasonal accumulated freezing rain was conducted. In addition, the ability of the three simulations to reproduce individual freezing rain events was evaluated. Our analyses include frequency and partitioning of different precipitation types and comparisons with observations. All simulations reproduced the climatology of freezing rain sufficiently well and show similar large-scale patterns. The number of freezing rain events tends to be overestimated at higher resolution and underestimated at lower resolution. Despite the overestimation, detailed maxima associated with freezing rain are well defined and located at higher resolution, notably in regions of the St. Lawrence River Valley. Overall, this study is consistent with other added-value studies, generally showing a mix of improvement and deterioration in the precipitation fields by the higher resolution simulations.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".