Turbulent characteristics of momentum flux in the marine atmospheric boundary layer of North Bay of Bengal
Bibliographic record
Abstract
We use a 16-month-long, 20 Hz wind data from a mooring deployed in the Bay of Bengal (BoB) to study the characteristics of turbulent wind stress ( $${u}{\prime}{w}{\prime})$$ events in the marine atmospheric boundary layer (MABL). Quadrant analysis of the motion-corrected $${u}{\prime}$$ and $${w}{\prime}$$ suggests that sweep and ejections, representing downward stress transfer into the ocean, dominate the $${u}{\prime}{w}{\prime}$$ (~ 140%). In comparison, outward and inward interactions representing an upward stress transfer into the atmosphere provide the counter-contribution (~ 40%). We found a wind speed (ws) dependency on stress transfer for ws > 3 m/s, while for low ws, the swell-dominated ocean state modulates the $${u}{\prime}{w}{\prime}$$ with a significant reverse stress transfer into the atmosphere, especially during intermonsoon periods. It is found that for weak winds ( $$ws$$ < 3 m/s), the number of turbulent events (N) is less, but they frequently repeat with more considerable flux per event ( $$\widehat{f})$$ , with outward and inward interactions (sweeps and ejections) dominating during intermonsoon periods (monsoon periods). For medium to strong winds, sweeps and ejections dominate $${u}{\prime}{w}{\prime}.$$ Ejections are found to be the most efficient method of stress transfer in the BoB, contributing 80% of $${u}{\prime}{w}{\prime}$$ , compared to sweeps contributing ~ 60% and interaction processes contributing ~ − 20% each to the $${u}{\prime}{w}{\prime}$$ . Though the duration of sweep events is larger than ejections and with comparable flux energy per event ( $$\widehat{f}$$ ), the larger number N of ejection events makes it the dominant stress transfer mechanism in the Bay in all seasons.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".