Performance Evaluation of Parameterizations for Wind Input and Wave Dissipation in the Spectral Wave Model for the Northwest Atlantic Ocean
Bibliographic record
Abstract
An ocean wave model for the northwest Atlantic Ocean based on WAVEWATCH III is used to evaluate four different source term packages (known as ST2, ST3, ST4, and ST6) for the wind input and wave dissipation. The performance of ST2, ST3, ST4, and ST6 is assessed using available measurements from buoy stations and satellite altimeters. The model results for significant wave height (Hs), mean wave period (Tm02), wave spectrum, wind input, and wave dissipation are examined during two periods: (i) winter storms in February and (ii) Hurricane Ophelia in September/October 2011. Analyses of model results demonstrate that ST4 and ST6 have the best performance with an average scatter index within 19.0% for Hs and Tm02 in the presence of strong currents and sea ice. These four packages perform differently under different sea states. Package ST6 generally overestimates Hs under the wind-wave-dominated sea states because of strong wind input and fast wave growth but underestimates Hs under swell-dominated sea states because of strong swell dissipation. The effects of ocean surface currents and sea ice on the wave model performance are also investigated. The linear kinematic effects of surface currents on waves can cause non-linear dynamic effects, which can differ among the four packages. Wave scattering in sea ice increases the wave directional spread and may cause an increase in Hs. In the presence of sea ice, wind input is reduced and shifted to higher frequencies and wave dissipation is further suppressed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".