Estimation of precipitation and air temperature over western Canada using a regional climate model
Bibliographic record
Abstract
The Mackenzie River basin (MRB), which covers a geographic region from central Alberta of Canada to the coast of Arctic Ocean, has an area of approximately 1.8 × 106 km2. In this study, the regional climate of MRB, in particular the precipitation and air temperature from the late spring to the fall (May–October), was simulated using a regional climate model Weather Research and Forecasting (WRF). In the open water season of May–October, Mackenzie River provides a critical corridor for Canada's Arctic transportation network. With nine sets of physical parameterizations of WRF selected, we simulated the climate of MRB over representative dry, normal, and wet years to identify the best setting prior to performing the 27‐year simulation for 1979–2005. The major physical parameterizations considered were shortwave (Dudhia, CAM, and RRTMG) and longwave radiation scheme (RRTM, CAM, and RRTMG), microphysics scheme (WRF Double‐Moment 6‐class, WRF Single‐Moment 5‐class, Stony Brook University, and Morrison double‐moment), and cumulus parameterization (Kain–Fritsch, Betts–Miller–Janjic, Tiedtke, and New Simplified Arakawa–Schubert). The simulation for MRB by each set of parameterizations of WRF is assessed using root‐mean‐square error (RMSE), absolute error percentage, and bias maps. After a detailed fine tuning process, and from verifying the 1979–2005 long‐term simulations of WRF, WRF generally simulated realistic air temperature and precipitable water over MRB, but precipitation is over‐simulated, especially at Canadian Rockies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".