Medium-Range Quantitative Precipitation Forecasts from Canada’s New 33-km Deterministic Global Operational System
Bibliographic record
Abstract
Abstract The Meteorological Service of Canada (MSC) recently implemented a 33-km version of the Global Environmental Multiscale (GEM) model, with improved physics, for medium-range weather forecasts. Quantitative precipitation forecasts (QPFs) from this new system were compared with those from the previous global operational system (100-km grid size) and with those from MSC’s short-range (48 h) regional system (15-km grid size). The evaluation is based on performance measures that evaluate bias, accuracy, and the value of the QPFs. Results presented in this article consistently show, for these three aspects of the evaluation, that the new global forecast system (GLBNEW) agrees more closely with observations, relative to the performance of the previous global system (GLBOLD). The biases are noticeably smaller with GLBNEW compared with GLBOLD, which severely overpredicts (underpredicts) the frequencies and total amounts associated with weak (strong) precipitation intensities. The accuracy and value scores reveal gains of at least 12 h and even up to 72 h for medium-range QPFs (i.e., day 3 to day 5 predictions). The new global system even performs slightly better than MSC’s operational regional 15-km system for short-range QPFs. In a more absolute manner, results suggest that QPFs from the new global system may still have accuracy and value even at the medium range. This seems to be true at least for the smallest precipitation threshold, related to precipitation occurrence, for which the positive area under curves of relative economic value remains important, even for day 5 QPFs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".