Variational data‐assimilation experiments using flow‐dependent dynamical constraints
Bibliographic record
Abstract
Abstract Mass–wind and vorticity–divergence balance constraints based on the linearized Charney and quasi‐geostrophic omega equations, respectively, are assessed in a developmental version of the global, three‐dimensional variational data‐assimilation system at Environment Canada. Unlike traditional balance constraints, which are averaged in time, the new constraints are flow‐dependent and reflect a more complete set of dynamics. Single observation experiments demonstrate that the new covariance model leads to asymmetrical increments that are qualitatively aligned with the instantaneous background wind field. Data‐assimilation experiments using real observations are performed for a period of five weeks during two different seasons, employing the control and experimental constraints. Subsequent forecast verification against radiosondes shows a definite benefit of the new covariances in the Tropics; however, the impact in the Extratropics is neutral or slightly negative. Verifications against analysis show virtually no change in the troposphere; however, a significant improvement is observed in the stratosphere at all lead times. Compared with the Charney mass–wind balance, the contribution of the quasi‐geostrophic omega constraint is rather minimal, at least in its current adiabatic form. The new balance scheme requires a considerable amount of computational time in the context of our 3D‐Var system, although the relative cost in a 4D‐Var setting may be far less significant. Moreover, the present experiments are useful in elucidating several important aspects of covariance modelling, particularly the dependence of balance dynamics on spatial scale. © 2012 Crown in the right of Canada. Published by John Wiley & Sons Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".