Wave Evolution in Off‐Ice Wind Conditions
Bibliographic record
Abstract
Abstract The reduction of the sea ice coverage increases the importance of wind waves in the Arctic. Updates to the standard spectral wave models in recent years have included many aspects of wave‐ice interaction. Here we use high‐resolution wave parameters retrieved from TerraSAR‐X images and in situ SWIFT buoy observations to evaluate the performance of WAVEWATCH III® in ice‐free waters in the western Arctic over a 7‐week period including the fall freeze‐up. About two thirds of the analyzed data sets show a good agreement between observations and model results. In other cases, more accurate representation of the wind input fields and the ice coverage could improve the model predictions. Two data sets with larger discrepancy are discussed in more detail. In these two cases of low model skill, with off‐ice wind conditions, we show that the model wind‐sea growth is too weak to match TerraSAR‐X observations, and this situation is improved only slightly by including the effects of atmospheric stability using existing methods. Application of an effective fetch parameterization, which allows for reduced wave generation within the marginal ice zone prior to the open ocean, provides the best estimation of wave growth during off‐ice winds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".