Scatterometer observations of wind variations induced by oceanic islands: Implications for wind-driven ocean circulation
Bibliographic record
Abstract
Scatterometer data at 25-km resolution are used to investigate the effects of the Hawaiian and Cabo Verde islands on the mean atmospheric flow. A wake of weak winds, flanked by accelerated winds, appears for each major island of both archipelagos. The resulting wind stress curl displays dipole-like structures, with positive values on the northern side and negative values on the southern side of the lee, extending several island diameters downwind. These curl anomalies reach a magnitude of 2 10‐6 Pa·m‐1 and correspond to Ekman pumping velocities of 3 m·day‐1 for Hawaii and 4 m·day‐1 for Cabo Verde. They spin up cyclonic eddies on the north side and anticyclonic eddies on the south side of the lee of each island. The response of the ocean circulation is investigated using a simple Sverdrup balance. Two counter-rotating Sverdrup gyres are spun up west of the island of Hawaii and extend to the western boundary of the Pacific Ocean. They result in an eastward zonal transport confined between 19° and 20°N. East of 170°W, the surface expression of this transport coincides with the Hawaiian Lee Counter Current. Similar gyres are anticipated to form in the Atlantic Ocean, but remain to be observed. These results suggest that strong mesoscale patterns in the wind field occurring in the lee of high-topography features must be resolved to force global ocean circulation 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".