Synergistic measurements of ocean winds and waves from <scp>SAR</scp>
Bibliographic record
Abstract
Abstract In this study we present a synergistic method to retrieve both ocean surface wave and wind fields from spaceborne quad‐polarization (QP) synthetic aperture radar (SAR) imaging mode data. This algorithm integrates QP‐SAR wind vector retrieval model and the wave retrieval model, with consideration to the nonlinear mapping relationship between ocean wave spectra and SAR image spectra, in order to synergistically retrieve wind fields and wave directional spectra. The method does not require a priori information on the sea state. It combines the observed VV‐polarized SAR image spectra with the retrieved wind vectors from the VH‐polarized SAR image, to estimate the wind‐generated wave directional spectra. The differences between the observed SAR spectra and optimal SAR image spectra associated with the wind waves are interpreted as the contributions from the swell waves. The retrieved ocean wave spectra are used to estimate the integrated spectral wave parameters such as significant wave heights, wavelengths, wave directions and wave periods. The wind and wave parameters retrieved by QP‐SAR are validated against those measured by the National Data Buoy Center (NDBC) directional wave buoys under different sea states. The validation results show that the QP‐SAR SAR has potential to simultaneously measure the ocean surface waves and wind fields from space.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".