On the localization in strongly coupled ensemble data assimilation using a two-scale Lorenz model
Bibliographic record
Abstract
Abstract. In the data assimilation of coupled models, the stongly coupled data assimilation (SCDA) is much more complicated than the weakly coupled data assimilation (WCDA), since it involves the cross-domain error covariances which could be very inaccurate when the ensemble size is small. In this study, the SCDA experiments are conducted using a two-scale Lorenz '96 model, which is a coupled system composed by two Lorenz '96 models in two domains have different temporal and spatial scales. A localization strategy is specially designed for the cross-domain error covariances when the ensemble adjustment Kalman filter (EAKF) is used for the coupled data assimilation (CDA) experiments. The formulas for computing the localization factors that can deal with multiple spatial scales and provide essential information are developed to imporve the quality of analyses. The result shows that the SCDA can provides much more accurate estimation of the states than the WCDA when the localization for the cross-domain error covariances is used. Moreover, it is found that the advantage of the SCDA over the WCDA for this model is attributed to the assimilation of small scale observations into the coupled system, whereas the contribution of the assimilation of the large-scale observations to the coupled system is not obvious. This current study provides a possible strategy or idea for developing operational CDA using realistic coupled 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".