Observed Ocean Surface Winds and Mixed Layer Currents Under Tropical Cyclones: Asymmetric Characteristics
Bibliographic record
Abstract
Abstract Tropical cyclones (TC) transfer kinetic energy to the upper ocean and thus accelerate the ocean mixed layer (OML) currents. However, the quantitative link between near‐surface currents and high wind speeds, under extreme weather conditions, remains poorly understood. In this study, we use multi‐mission satellites and drifting‐buoy observations to investigate the connections between TC surface winds and currents, including their spatial distribution characteristics. Observed ageostrophic current speeds in the OML increase linearly with wind speeds (for the range 20–50 m/s). The ratios of the ageostrophic current speeds to the wind speeds are found to vary with TC quadrants. In particular, the mean ratio is around 2% in the left‐front and left‐rear quadrants with relatively small variability, compared to between 2% and 4% in the right‐front and right‐rear quadrants, with much higher variations. Surface winds and currents both exhibit strong asymmetric features, with the largest wind speeds and currents on the TC right side. In the eyewall region of Hurricane Igor, high winds (e.g., about 47 m/s) induce strong currents (about 2 m/s). The directional rotations of surface winds and currents are resonant and dependent on the location within the storm. Wind directions are approximately aligned with current directions in the right‐front quadrant; a difference of about 90° occurs in the left‐front and left‐rear quadrants. The directional discrepancy between winds and currents in the right‐rear quadrant is smaller. Reliable observations of the wind‐current relation, including asymmetric features, support published theories developed in idealized numerical experiments to explain the upper ocean response to TCs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".