Numerical Simulation and Validation of Deepwater Spectral Wind-Waves
Bibliographic record
Abstract
A numerical simulation study is carried out over Indian Ocean using spectral wind-wave model “WAM.” The surface wind analysis data utilized in this study are generated by assimilation of satellite data in numerical weather prediction models. These winds are used for forcing the ocean WAve Model (WAM) and various spectral and significant wave parameters are simulated. The model simulated outputs viz. significant wave height, peak, and mean wave periods; mean wave and wind-wave directions; the swell wave height, frequency, and direction; frictional wind velocity, wave-induced stress, frequency spectrum, and the two-dimensional directional wave spectrum are presented. A detail analysis is performed to these parameters for assessing spatio-temporal variability for rough weather period (July 1–August 24, 1999). Further, the input wind and simulated waves are validated against ocean buoy observations. The time series spectral evolution of wind-wave in central Bay of Bengal is discussed. The comparison results of significant and spectral wave parameters with measured data co-locating in time and space are presented. The result reveals that the performance of third generation wave model is promising over Indian Ocean despite several limitations. Further, the coincidence and departure of simulated and observed waves are critically examined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".