Cross‐polarization geophysical model function for C‐band radar backscattering from the ocean surface and wind speed retrieval
Bibliographic record
Abstract
Abstract The wind speed sensitivity of cross‐polarization (cross‐pol) radar backscattering cross section (VH) from the ocean surface increases toward high winds. The signal saturation problem of VH, if it exists, occurs at a much higher wind speed compared to the copolarization (copol: VV or HH) sea returns. These properties make VH a better choice over VV or HH for monitoring severe weather. Combined with high spatial resolution of the synthetic aperture radar (SAR), the development of hurricane wind retrieval using VH is advancing rapidly. This paper describes a cross‐pol C‐band radar backscattering geophysical model function (GMF) with incidence angle dependence for the full wind speed range in the available data sets (up to 56 m/s). The GMF is derived from RADARSAT‐2 (R2) dual‐polarization (dual‐pol) ScanSAR modes with 300 and 500 km swaths. The proposed GMF is compared to other published algorithms. The result shows that the simulated VH cross section and the retrieved wind speed with the proposed GMF is in better agreement with measurements. With careful treatment of noise, the VH‐retrieved wind speeds may extend to mild or moderate conditions. The higher fraction of non‐Bragg contribution in VH can be exploited for analysis of surface wave breaking.
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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) | 0.001 | 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".