Regional Wave Modeling and Evaluation for the North Atlantic Coast Comprehensive Study
Bibliographic record
Abstract
Accurate estimation of storm surge along the coasts subject to extreme storm conditions requires proper wind and pressure forcing and quantification of the wind waves resulting from local and far-field energy sources. This paper summarizes the steps involved in accurately representing the offshore wave climate for the North Atlantic Coast Comprehensive Study (NACCS) domain, defined from the United States–Canadian border in Maine to the Virginia–North Carolina border. The motivation of the regional wave modeling is to provide offshore boundary conditions for the simulation of extreme extratropical and synthetic tropical events to drive the nearshore wave and surge modeling efforts within the NACCS. The offshore wave conditions were estimated using the third-generation WAve Modelling (WAM) model. Value-added wind fields were defined for each of the four wave model grids (North Atlantic Ocean Basin, U.S. Coastal Regional scale, and two subregional-scale grid systems covering the NACCS coastal domain). Five tropical events (Hurricanes Sandy, Irene, Isabel, and Gloria and Tropical Storm Josephine) and 17 extratropical events were simulated to evaluate WAM’s performance. Model results were compared with 30 point-source measurements available during these storm events. Time, scatter, and quartile-quartile plots; Taylor diagrams; and a battery of statistical tests were used in the evaluation process. The WAM provided quality zero-moment wave height estimates, with biases in the range of −0.07 to −0.14 m, RMS errors (RMSEs) of about 0.40 m, scatter indexes (SIs) around 25%, and a correlation of 0.95 compared with the measurements. The wave period results contained the greatest errors with peak period biases of −0.26 to 0.06 s, RMSEs from 2.4 to 2.7 s, SIs near 25%, and a correlation between 0.47 and 0.59. The mean period biases were about −0.70s, RMSEs were about 1.5 s, and there was a correlation of 0.6–0.7. The mean wave direction biases ranged from 4.5 to −0.34° with RMSEs of 55°.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".