Verification of eddy properties in operational oceanographic analysis systems
Bibliographic record
Abstract
Mesoscale eddy features are found ubiquitously throughout the world’s oceans. Many needs exist for numerical products from operational oceanographic systems to provide information on eddy properties. While numerous eddy identification and tracking methods have been developed for oceanic eddies, specific methods and metrics tailored to verify the skill of ocean analyses and forecasts in capturing these features are lacking. Here we introduce a novel feature-based verification methodology for operational oceanographic systems. This methodology builds on previous efforts at eddy tracking and applies open-source software to provide a robust method to evaluate the skill of operational oceanographic systems in terms of representing observed eddies. We demonstrate that an eddy tracking methodology can discern clear improvements in analyses produced using a regional analysis system (RIOPS; 1/12° grid-resolution) over a global system (GIOPS; 1/4° grid resolution). For eddies with amplitudes greater than 10 cm, RIOPS has a probability of detection 10%–30% higher than GIOPS with a false alarm ratio 5%–10% lower. A significant improvement in the spatial properties of simulated eddies in RIOPS is also found. In particular, results show a marked improvement in radius and separation distance errors (by 25% and 21% respectively), with fewer occurrences of errors above 20 km in radius and 40 km in separation distance. This basic demonstration opens the door for a more detailed examination of eddy features in ocean prediction systems.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".