Evaluation of several model error schemes in the EnKF assimilation: Applied to Argo profiles in the Pacific Ocean
Bibliographic record
Abstract
[1] The efficacy of several model error schemes in the Ensemble Kalman Filter (EnKF) data assimilation is investigated through a series of sensitivity experiments, in which the Argo and other in situ temperature and salinity profiles are assimilated into an ocean general circulation model (OGCM) for the Pacific Ocean. Different schemes for combining the additive inflation, multiplicative inflation, one-step bias correction and two-stage bias correction are evaluated in the framework of the EnKF. Experimental results indicate that the additive inflation is the key technique that can maintain ensemble spread in an adequate range. When sufficient observations are available, the assimilation system with additive inflation scheme can efficiently reduce both model bias and random errors. The combination of additive inflation and multiplicative inflation can further improve the performance of the assimilation system, in particular when the additive inflation underestimates model error. The bias correction schemes, the one-step method and the persistent bias method are effective in reducing the model bias only within a relatively short initial assimilation period and in some regions. Further improvement from the bias correction schemes is not evident as the assimilation period increases.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".