Sizing the largest ocean waves using the SWOT mission
Bibliographic record
Abstract
gives a more peaky spectrum. 35Observations of ocean waves show that both Hs and Tp grow 36 as the wave field develops.The two variables are correlated 37 and this is generally well reproduced by numerical models 38 for average conditions, for which we have measurements, but 39 it also holds for modeled extremes (Fig. 1C).For a given 40 value of Tp, Hs may vary by ±10%, with larger wave heights 41 corresponding to stronger wind forcing.Toba (12) proposed 42 an empirical law that predicts Hs from the wind speed and 43 peak period, that is well reproduced by models (Fig. 1D).44 For example, tropical cyclones (TCs) have the highest wind 45 speeds and for a given wave height their peak periods can be 46 10% shorter than those of extratropical storms (ETSs): the 47 modeled wave spectra in TCs are more peaky than those in 48 ETSs. 49 Significance StatementSwells travel across ocean basins, retaining precious information about extreme storm waves that elude direct observation.Using precise sea level measurements from the SWOT satellite, we measure swell heights and lengths, and find that the long waves in the largest storms draw their energy from steep shorter waves, enabling storm waves to reach phenomenal heights.These long waves then radiate outward as swell.Swell measurements thus reveal properties of extreme storm waves, including their dominant period.Our analysis corrects a 20-fold overestimation in empirical expressions for the energies of the longest ocean waves, providing a new awareness of ocean wave properties, with immediate applications to marine meteorology, ocean and coastal engineering, and the interpretation of ocean-generated seismic signals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".