Wind Fields From C- and X-Band SAR Images at VV Polarization in Coastal Area (Gulf of Oristano, Italy)
Bibliographic record
Abstract
This work deals with the spatial characteristics of the wind fields evaluated from synthetic aperture radar (SAR) images and simulated by the weather research and forecasting (WRF) atmospheric model in the Gulf of Oristano, a small coastal area about 10 km × 18 km wide in western coast of Sardinia (Western Mediterranean Sea). The SAR-derived wind fields have been obtained analyzing images of the COSMO-SkyMed, Radarsat-2, and Sentinel-1A satellites through a fully two-dimensional continuous wavelet transform (2-D-CWT) method. The analysis of the wind directions has shown that the model variability is limited if compared to that inferred by 2-D-CWT method, which mostly respects the variability evidenced by in situ data. As the use of model directions to compute the SAR wind fields is a standard in many studies, the impact on the SAR wind speed retrieval of using the model instead of the SAR-derived directions has been assessed: differences of wind speed greater than ±10% occur for about the 20% of data. The spatial variability of the SAR and model wind speed fields results quite different at both local and domain scales. The knowledge of the spatial variations of the surface wind fields can be very important for the oceanographic applications and constitutes the added value brought by SAR in the description of the coastal wind. For this reason, the SAR-derived wind fields should be taken as reference in many kind of applications.
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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 | 0.000 | 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".