Data assimilation of forecasted errors in hydrodynamic models using inter‐model correlations
Bibliographic record
Abstract
Abstract Data‐assimilation techniques of the Kalman filter type are considered to be the state‐of‐the‐art approach for combining data information and deterministic numerical models with the objective of operational forecasting. This paper introduces, as an alternative, a faster and simpler data‐assimilation technique that exploits inter‐model correlations to distribute predicted errors. This scheme is performed in two steps: (i) prediction of the deterministic model errors at observation points using so‐called local linear models and (ii) distribution of the forecasted errors over the computational domain employing a scheme based on deterministic inter‐model correlations which describe the spatial nature of error structure. The method's advantage is that systematic error can be predicted by the error correction scheme, while the dynamics remain described by the deterministic model, which also establishes a basis for the spatial error distribution scheme. This relatively simple approach is inspired by original Kalman filter techniques but distinguishes error prediction and distribution in two different stages, hence allowing for data‐driven error forecasting and off‐line correction. In order to test the scheme's performance, a deterministic model of an artificial bay was constructed and run. The system was driven by specific forcing conditions and characterized by physical parameters that, in subsequent simulations, were deliberately manipulated to introduce errors into the model and test the scheme's capability. Copyright © 2007 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 | 0.000 | 0.000 |
| 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".