Assessing Empirical Components in Wave Breaking and Rollers Through Model Comparison of Radiation Stress and Vortex Force
Bibliographic record
Abstract
Abstract As incoming waves approach shallow water, they break and result in nearshore currents. The assessment of the wave‐induced nearshore currents often relies on the application of radiation stress (RS) and vortex force (VF) in circulation models, both of which incorporate empirical components to describe wave breaking and wave rollers. In this study, we compared a recent 3D RS formulation and a VF formulation by assessing the modeled cross‐shore velocities against the measured counterparts from the LIP11D and Duck’94 experiments. The comparison results indicate that the RS model performs better in LIP11D, whereas the VF model exhibits better with respect to Duck’94. Using momentum balance analysis, we found that the cross‐shore velocities were dominated by the breaking term (representing the RS terms and non‐conservative breaking term in the RS and VF models, respectively) and the roller term. The breaking term generates undertow currents across the entire surf zone. The roller term cooperates with the breaking term in the inner surf zone, but counteracts the breaking term and tends to trigger unrealistic offshore currents at the water surface in the outer surf zone. Further analysis reveals that the RS and VF formulations can simulate almost identical velocity results by adjusting the vertical distribution of the breaking and roller terms. Overall, this study indicates that both the RS and VF formulations are effective in evaluating wave effects on the cross‐shore currents, and empirical components in the breaking and roller terms are important for modeling cross‐shore velocities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".