Effects of Convection‐Permitting Grid Resolution on Cold‐Season Precipitation Simulated by the Canadian Regional Climate Model Version 6 Over the Province of Quebec, Canada
Bibliographic record
Abstract
Abstract This study assesses the performance of the sixth‐generation Canadian Regional Climate Model (CRCM6) in simulating the amount and phase of cold‐season precipitation as well as 2‐m air temperature. It also examines the added value of finer grid spacing, which enables the explicit representation of deep convection. Simulations were conducted at grid spacings of 0.11° (≈12 km) and 0.0225° (≈2.5 km), and the results were compared with surface observations from 35 hydrometeorological stations across Quebec over two cold seasons (October to April in 2020–21 and 2021–22). The analysis was further supported by atmospheric sounding data from two stations, and several gridded reference products. Both simulations exhibited similar large‐scale 2‐m air temperature spatial patterns with the coarser‐resolution simulation generating consistently colder values as well as a larger total precipitation amount and bias compared to station observations. The finer‐resolution simulation reduced the total precipitation bias by a factor of three. Both simulations overestimated liquid precipitation and underestimated solid precipitation with the finer resolution better capturing liquid precipitation and the coarser resolution better capturing solid precipitation. Mixed precipitation remained a challenge, being simulated at colder temperatures than observed, particularly near 0°C. The 50% rain‐snow temperature threshold ( T 50 ), which indicates when liquid and solid phases occur equally, was 0.5°C for the finer resolution and 0.8°C for the coarser resolution both below the observed 2.1°C. This study highlights the need to refine the model's representation of mixed‐phase precipitation and rain‐snow transitions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".