Excitation of Rossby-wave trains: optimal growth of forecast errors
Bibliographic record
Abstract
Singular vectors (SVs) with appropriate norms and time scales are used in this study to capture the excitation of Rossby-wave trains (RWTs). Using global analysis data provided by the Canadian Meteorological Centre, a long-lasting RWT is selected beginning on 19 Nov 2002. Ten SVs are calculated using an optimization time interval of 48 h and a tangent linear model including a complete set of simplified physical parameterizations. At initial time, the global total-energy norm is used. For the final time, the total energy (TE) and the rotational kinetic energy (RKE) norms over three different restricted horizontal and vertical domains are used. These different configurations are examined to enable an appropriate configuration to be selected for our future work on using SVs to study the excitation of RWTs. The SVs are computed and non-linear forecasts are performed using the Canadian Global Environment Multiscale model. Results obtained show that when the analysis is perturbed with the pseudo-inverse of the 48 h forecast error in the SV subspace, the perturbation propagates with the group velocity of the RWT and the error in the non-linear forecast over the life of the wave train (6 days) is significantly reduced. Comparison between using a final-time norm based on either TE or RKE showed little impact.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".