The Canadian Global Environmental Multiscale model on the Yin‐Yang grid system
Bibliographic record
Abstract
Abstract At the Canadian Meteorological Center (CMC), we are currently developing the future global forecasting Yin‐Yang model. In the horizontal we use spherical coordinates on the overset Yin‐Yang grid, while in the vertical we use a log‐hydrostatic‐pressure coordinate on the Charney–Phillips grid. The parametrization of physical processes is kept the same as in the current Global Environmental Multiscale (GEM) operational model. The Yin‐Yang global forecast is performed by considering a domain decomposition (a two‐way coupling method) between two limited‐area models (LAMs) discretized on the two panels of the Yin‐Yang grid and using the same time step. Each panel of the Yin‐Yang grid system is extended by a static halo region and uses the same fully implicit semi‐Lagrangian method as in the GEM operational model to solve its own dynamic core. The spatial and time discretizations are implemented independently on each quasi‐uniform latitude–longitude subgrid. The static halo region plays the same role as the piloting region in limited‐area modelling. Since the two subgrids of the Yin‐Yang grid do not match, the update of the variables in the pilot region is done by cubic Lagrange interpolation. For our model validation, we ran 42 winter and 42 summer cases using analysis from 2008–2009 and we compared five‐day forecast results against observations. No noise is seen in the overlap regions during the simulations. Preliminary results presented in this article are encouraging and demonstrate that in comparison with observations the new Yin‐Yang system performs as well as the GEM global model. © 2011 Crown in the right of Canada. Published by John Wiley & Sons Ltd.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".