Semi-Lagrangian Advection of Stratospheric Ozone on a Yin–Yang Grid System
Bibliographic record
Abstract
Abstract The lack of formal mass conservation that is inherent to the standard semi-Lagrangian transport scheme represents a significant model limitation that needs to be addressed. The magnitude of this impact depends on the nature of the advected quantity and particularly on the strength of species spatiotemporal variability. In this study, this issue is examined in the context of two configurations of the Environment Canada Global Environmental Multiscale (GEM) model. The first configuration (GEM Lat–Lon) is based on a global latitude–longitude grid system with the Arakawa C grid in the horizontal. The second configuration (GEM Yin–Yang) uses the overset Yin–Yang grid, which is singularity free and has quasi-uniform resolution. Both model versions have been used for studying the mass conservation property of passive and nonpassive tracers such as stratospheric ozone using different shape-preserving schemes and a global mass fixer. Experiments with idealized tracers indicate that the implementation of a global mass fixer and a conservative shape-preserving scheme reduces the error field in both 2D and 3D configurations. In the case of stratospheric ozone, the study demonstrates that the mass conservation error is significantly reduced with the use of the Yin–Yang grid. This is attributed to the quasi-uniform nature of the grid that contributes to improve the accuracy of the computation particularly in high-latitude regions where most of the ozone mass resides. The study demonstrates the potential benefits of using a quasi-uniform Yin–Yang grid system and shows that chemical constituents can serve as a useful diagnostic for the evaluation of numerical weather prediction (NWP) models.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".