Validation and intercomparison of ocean surface circulation analyses and forecasts
Bibliographic record
Abstract
Operational systems provide daily surface velocities that reproduce as closely as possible the state of the ocean, a field that lays at the base of many diverse applications such as routing or search and rescue. There’s a growing need to assess different systems’ ability to reproduce ocean dynamical processes covering a range of spatio-temporal scales so to inform on their suitability for use in a vast range of users’ applications.Here we present an initiative for putting in place a multi-metric validation platform for the comparison of ocean currents making use of a number of service evolution developments and concepts (MEDSUB py_eddy_tracker, HIVE…). Such tool is aimed at being operable in any region of interest, applicable to any Copernicus Marine products on the Wekeo DIAS cloud access service.We show an intercomparison of the mesoscale eddy field of different Copernicus Marine systems in the IBI area, together with a range of statistical metrics on surface currents (Eulerian and Lagrangian) comparing against drifting buoys’ velocity measurements. The use of this platform is illustrated with the case of the Grande America catastrophe within the Bay of Biscay in March 2019 to analyse the currents and in fine to help decision-making. Such approach is shown to provide relevant user-oriented uncertainty information for a range of applications.
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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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".