The Stokes drift in ocean surface drift prediction
Bibliographic record
Abstract
The importance of explicitly resolving the Stokes drift in ocean surface drift modelling is demonstrated by comparing four models with 58,612 observational data points obtained from undrogued drifting buoys in the Estuary and Gulf of St. Lawrence, Canada. Drift model inputs are obtained from regional atmosphere and ocean circulation, and spectral wave models. The control drift model considers near-surface currents provided by the top grid cell of the ocean circulation model, which is 5-m thick, and a correction term proportional to the near-surface wind. The three other drift models account for the unresolved near-surface current shear by extrapolating the near-surface currents to the surface assuming Ekman dynamics. Two of these models consider explicitly the Stokes drift, with and without a wind correction term. Proposed models reduce the mean separation distance between observed and predicted trajectories by 34–40% relative to the control model, on average, for forecast times ranging from 3 to 72 h. The best improvement with respect to all metrics used is, however, obtained for the model that takes into account the near-surface shear correction and the Stokes drift, without any wind correction term (skill score of 0.93 after 3 h and 0.81 after 72 h).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".