Predicting Turbulent Vertical Velocity in the Ocean Surface Layer under Mixed Convective and Wind/Wave Forcing
Bibliographic record
Abstract
Abstract Turbulence in the ocean surface layer is forced by a mixture of buoyancy, wind, and wave processes that evolves over time scales from the diurnal scale of buoyancy forcing, through storm time scales, to the annual cycle. This study seeks a predictor for root-mean-square w (rmsw), a time and surface layer average of turbulent vertical velocity w measured by bottom-mounted vertical-beam acoustic Doppler current profilers, in terms of concurrently measured surface forcing fields. Data used are from two coastal sites, one shallow (LEO, 15-m depth) and one deeper (R2, 26-m depth). The analysis demonstrates that it is possible to predict observed rmsw with a simple linear combination of two scale velocities, one the convective scale velocity familiar from the atmospheric literature, the other a scale velocity w S representing combined wind and wave effects. Three variants are considered for this latter scale velocity, the wind stress velocity alone and two forms using both and U S , a Stokes velocity characteristic of the surface wave field. At both sites, the two-parameter fit using alone is least accurate, while fits using the other two variants are essentially indistinguishable. At both sites, the coefficient multiplying is the same, within error bounds, and within the range of previous observations. At the deeper site, the coefficient multiplying the wind/wave scale velocity w S is approximately half that at the shallow site, a difference here attributed to difference in wave character.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".