A probabilistic prediction of rogue waves
Bibliographic record
Abstract
Rogue waves are individual ocean surface waves with a height greater than 2.2 times the significant wave height. They can pose a danger to marine operations, onshore and offshore structures, and beachgoers, especially when encountered in high sea states. The prediction of bulk sea state parameters like significant wave height, period, direction, and swell components is satisfactorily addressed in current operational wave models. Individual wave heights cannot be predicted by those spectral models, and the prediction of rogue wave occurrence has to be in a probabilistic sense.Previous attempts on such a prediction are based on the Benjamin Feir Index (BFI), which reflects the nonlinear process of modulation instability as the dominant generation mechanism for rogue waves. However, there is increasing evidence that BFI has limited predictive power in the real ocean. Recent studies established the average crest-trough correlation as the strongest single variable to correlate with rogue wave probability.We demonstrate that crest-trough correlation can be forecast by an operational WAVEWATCHIII wave model with moderate accuracy. Using multi-year wave buoy observations from the northeast Pacific we establish the functional relation between crest-trough correlation and rogue wave occurrence rate, thus calibrating predicted crest-trough correlations into probabilistic rogue wave predictions. Combined with the predicted significant wave heights we can identify regions of enhanced rogue wave risk. Results from a case study of a large storm off Canada’s west coast are presented to evaluate the regional wave model at high seas, and to present the rogue wave probability forecast based on crest-trough correlation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".