A new dynamical core of the Global Environmental Multiscale (GEM) model\n with a height-based terrain-following vertical coordinate
Bibliographic record
Abstract
A new dynamical core of Environment and Climate Change Canada's Global\nEnvironmental Multiscale (GEM) atmospheric model is presented. Unlike the\nexisting log-hydrostatic-pressure-type terrain-following vertical coordinate,\nthe proposed core adopts a height-based approach. The move to a height-based\nvertical coordinate is motivated by its potential for improving model stability\nover steep terrain, which is expected to become more prevalent with the\nincreasing demand for very high resolution forecasting systems. A dynamical\ncore with height-based vertical coordinate generally requires an iterative\nsolution approach. In addition to a three-dimensional iterative solver, a\nsimplified approach has been devised allowing the use of a direct solver for\nthe new dynamical core that separates a three-dimensional elliptic boundary\nvalue problem into a set of two-dimensional independent Helmholtz problems. The\nnew dynamical core is evaluated using numerical experiments that include\ntwo-dimensional nonhydrostatic theoretical cases as well as 25-km resolution\nglobal forecasts. For a wide range of horizontal grid resolutions---from a few\nmeters to up to 25 km---the results from the direct solution approach is found\nto be equivalent to the iterative approach for the new dynamical core.\nFurthermore, results from the numerical experiments confirm that the new\nheight-based dynamical core leads to results that are equivalent to the\nexisting pressure-based core.\n
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".