Simulation of Wave‐Current Interactions Under Hurricane Conditions Using an Unstructured‐Grid Model: Impacts on Ocean Waves
Bibliographic record
Abstract
Abstract The effect of wave‐current interactions on ocean waves under hurricane conditions is investigated through application of the unstructured‐grid finite‐volume community ocean model (FVCOM) coupled to the unstructured‐grid surface wave model (SWAVE) in the North Atlantic Ocean. We study wave‐current interactions during the life cycles of extratropical hurricanes Juan (2003) and Bill (2009) as they propagate from subtropical waters to midlatitudes in the Northwest Atlantic. Simulations of wave parameters in each hurricane are shown to compare well with buoy and satellite altimeter observations, in terms of winds, significant wave heights, wave energy spectra, and wave directions. This is partially achieved by restricting the drag coefficient. It is well known that the maximum intensity of tropical cyclones depends on the ratio of the enthalpy coefficient to the drag coefficient. The latter increases with wind speed, levels off with category one hurricanes and may drop for even higher winds. In our study, we find that setting a limiting value on the drag coefficient improves the simulations of waves at peak storm intensities. Simulation of wave‐current interactions is also shown to improve the simulation of the wave heights and wave energy spectra. This is notable at the peak of the storms, in comparisons with observations from buoys in areas of both deep water and relatively shallow water. The effect of currents on significant wave heights is shown to reach 0.4 m for hurricane Juan and 1.0 m for hurricane Bill, or as much as about ±10% for wave heights distributed along the storm track.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".