Data assimilation with the Canadian middle atmosphere model
Bibliographic record
Abstract
A data assimilation scheme has been coupled to the Canadian Middle Atmosphere Model, providing, for the first time, the capability of assimilating data from the ground to the top of the mesosphere (about 95 km). This model is a full general circulation model with on‐line fully interactive chemistry involving 127 gas‐phase and heterogeneous reactions. Thus, feedback between dynamics, chemistry and radiation occurs in every model time step. In this work, validation of the system for tropospheric and lower stratospheric analyses is undertaken with the standard observation set used in operational weather forecasting. Results are found to agree reasonably well with radiosonde observations and with Met Office (UK) analyses. Although ozone is not assimilated, ozone fields match total column observations well in terms of synoptic patterns. However, due to model biases, total column values are too large at mid‐latitudes and too small in the tropics. Since the assimilation scheme was designed for tropospheric weather prediction, its application to a middle atmosphere model can help to identify the challenges of assimilating data from this region of the atmosphere.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".