Assessment of SWOT observations for monitoring nearshore and coastal waves
Bibliographic record
Abstract
Coastal zones are considered one of the Earth’s most dynamic environments, hosting more than 35% of the global population within 100 km of the coastline. These regions are more vulnerable and exposed to increasing risks induced by extreme waves during high-energy events and storms, produced at short scales, combined with the long-term effects of sea-level rise. Accurate estimation of wave parameters is crucial for accurate estimation and requires high-resolution monitoring from ocean to nearshore and coastal areas. Although in situ wave buoys and coastal radar systems offer fast and precise wave monitoring, the spatial coverage is strictly limited, and the quality of the Lagrangian measurement of wave buoys relies strongly on the mooring configuration and the absence of biofouling. Numerical modeling addresses this data gap, yet it relies on empirical spectral relationships that are ill-defined for transient water from the deep ocean to shallow nearshore areas, fetch, and ambient conditions. Satellite radar altimetry is therefore one realistic means to provide a quantitative near-global mapping of the wave field, but much effort needs to be devoted to work around the decreasing accuracy closer to the coastline due to land contamination and complex coastal wave states. The Surface Water and Ocean Topography (SWOT) mission significantly advances nearshore observations by providing sea surface height (SSH) and significant wave height (SWH) measurements with unprecedented accuracy. This work investigates the use of SWOT low-rate (LR) and high-rate (HR) observations for monitoring changes in surface gravity wave patterns in nearshore and coastal areas, which are novel capabilities of SWOT compared to previous altimeters. We estimate from SWOT the SWH at different spatial and temporal scales, including interdaily, monthly, and seasonal scales, using a series of statistical and spectral analyses to explore different SWOT LR and HR products and raster maps, together with in situ wave buoys. The obtained results demonstrate the ability of SWOT to capture realistic significant wave height and other key parameters that include peak period, peak direction, and wavelength in nearshore and coastal zones after applying some correction to LR products (example: beta-angle corrections), which significantly enhances the wave estimation. This methodological approach has been applied to the English Channel and coastal areas for the first time. Further work expanded our approach to the Saint Lawrence Gulf and Estuary as well. This study presents the first comprehensive analysis to evaluate the SWOT accuracy for wave monitoring in shallow waters, which is crucial for improving boundary conditions and calibrating hydrodynamic models.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".