Modernization of Atmospheric Physics Parameterization in Canadian NWP
Bibliographic record
Abstract
Abstract Atmospheric physics is represented in numerical models by parameterizations that use resolved‐scale information to estimate the effects of physical processes on the atmospheric state. Over time, our understanding of these processes improves, new techniques are introduced to represent physics in a numerical model, and increased resolution changes the relative importance of different parameterizations within the system. As a result, the physical parameterization packages of numerical weather prediction (NWP) models undergo regular updates as older schemes are replaced with newer ones that offer an improved, and often more complex, depiction of relevant physical processes. Such changes are typically combined with a rebalancing of the physics suite because of strong interactions between parameterization schemes and the presence of compensating errors in the system. In this study, a major update to the package of physical parameterizations used in Canadian operational NWP is introduced. The primary goals of this effort were to improve the global energy budget and to facilitate an increase in the vertical resolution of operational configurations. Both of these objectives were achieved, along with a significant improvement in guidance quality for global and regional prediction systems.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".