Wind Retrieval over the Ocean using synthetic aperture radar with C-band HH polarization
Bibliographic record
Abstract
The high spatial resolution and large coverage of satellite-based synthetic aperture radars (SAR) offers a unique opportunity to derive mesoscale wind fields over the ocean surface, providing high resolution wind fields near the shore. For this purpose, algorithms were developed and tested using the ScanSAR aboard the Canadian satellite RADARSAT-1, operating at C-band with horizontal polarization in transmit and receive. Wind directions are extracted from wind-induced streaks visible on most SAR images. Wind speeds are derived from normalized radar cross sections (NRCS) using empirical models. The models were developed for scatterometers (SCAT) operating at C-band with vertical polarization and must be modified for horizontal polarization. Several available C-band polarization ratios were considered, including theoretical and empirical forms. To verify and improve the algorithm, wind speeds were computed from several RADARSAT-1 ScanSAR images and compared to colocated measurements from the SCAT aboard the European remote sensing satellite ERS-2 and to the results of the Danish high resolution limited area model (HIRLAM). Using the colocated measurements, the polarization ratio was estimated and applied to improve the wind retrieval algorithm. In addition, the main error sources in SAR wind field extraction are discussed with respect to the RADARSAT-1 ScanSAR data. Sensitivity studies were performed under different atmospheric situations using the modified C-band model to compute the errors due to wind direction and inaccuracies in NRCS.
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 | 0.000 | 0.000 |
| 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) | 0.004 | 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".