Modeling of Two Northwest Atlantic Storms with Third-Generation Wave Models
Bibliographic record
Abstract
Abstract In this study, three state-of-the-art operational forecast wave models are implemented on nested grids in order to achieve fine-resolution wave simulations (0.1°) in the Gulf of Maine and related northwest Atlantic waters. These models are the Simulating Waves Nearshore (SWAN) model, the Wave Action Model (WAM), and WAVEWATCH-III (hereafter WW3). Model performance is evaluated through comparisons with field measurements. Four composite model systems are compared: WAM and WW3 implemented on three nested domains, SWAN nested within WAM, and SWAN nested within WW3. Storm case studies include two intense midlatitude winter storms from January 2000 and January 2002. Although the models are comparable in terms of their overall performance and skill, it is found that WW3 provides a better statistical fit to the observed wave data compared with the other models, and that SWAN gives slightly better results if nested within WW3, rather than within WAM.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".