The 15‐km version of the Canadian regional forecast system
Bibliographic record
Abstract
Abstract A new mesoscale version of the regional forecast system became operational at the Canadian Meteorological Centre on 18 May 2004. The main changes to the regional modelling system include an increase in both the horizontal and vertical resolutions (15‐km horizontal resolution and 58 vertical levels instead of 24‐km resolution and 28 levels) as well as major upgrades to the physics package. The latter consist of a new condensation package, with an improved formulation of the cloudy boundary layer, a new shallow convection scheme based on a Kuo‐type closure, and the Kain and Fritsch deep convection scheme, together with a subgrid‐scale orography parametrization scheme to represent gravity wave drag and low‐level blocking effects. The new forecast system also includes a few changes to the regional data assimilation such as additional radiance data from satellites. Objective verifications using a series of cases and parallel runs, along with subjective evaluations by CMC meteorologists, indicate significantly improved performance using the new 15‐km resolution forecast system. We can conclude from these verifications that the model exhibits a marked reduction in errors, improved predictability by about 12 hours, better forecasts of precipitation, a significant reduction in the spin‐up time, and a different implicit‐explicit partitioning of precipitation. A number of other features include: sharper precipitation patterns, better representation of trace precipitation, and general improvements of deepening lows and hurricanes. In mountainous regions, several aspects are better represented due to combined higher‐resolution orography and the low‐level blocking term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".