Wave-Induced Stokes Drift Measurement by High-Frequency Radars: Preliminary Results
Bibliographic record
Abstract
High frequency (HF) radars measure ocean surface currents by sending electromagnetic (EM) waves in the HF radio band (3-30 MHz) and recording the EM waves backscattered by ocean surface gravity waves. The recorded signal is dominated by EM waves backscattered from ocean surface waves with half the EM wavelength, called Bragg waves. Since their phase velocity is affected not only by wave-current interactions with mean Eulerian currents, but also by wave-wave interactions with all the other waves present at the sea surface, the question arises as to whether HF radars measure a quantity related to the wave-induced Stokes drift in addition to mean Eulerian currents. However, the literature is inconsistent on the expression and even on the existence of the contribution of the wave-induced Stokes drift to the HF radar measurements. Three different expressions have been proposed in the literature: (1) the filtered surface Stokes drift, (2) half of the surface Stokes drift, and (3) the weighted depth-averaged Stokes drift. We evaluate these expressions for directional wave spectra measured by a bottom-mounted Acoustic Wave and Current (AWAC) profiler in the Lower St. Lawrence estuary, Canada. We then compare the Eulerian surface currents measured by the AWAC with the radial currents measured by two HF radars: one Wellen Radar (WERA) and one Coastal Ocean Dynamics Applications Radar (CODAR). The AWAC and HF-radar currents are surprisingly not correlated, but when subtracting the wave-induced Stokes drift contributions from the radar measurements, moderate but significant correlations are obtained. The highest correlations are obtained when subtracting the filtered surface Stokes drift, suggesting that HF radars measure the latter in addition to mean Eulerian currents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".