Wind retrieval over the china seas using satellite synthetic aperture radar
Bibliographic record
Abstract
The high spatial resolution and large coverage of synthetic aperture radar (SAR) offers a good opportunity to retrieve detailed wind vector information over the oceans, especially in the coast areas. In the work presented here, the wind speeds estimate is based on the data from ScanSAR aboard the Canadian satellite RADARSAT, operating at C-band with horizontal polarization, and the empirically derived CMOD4 model. Because the CMOD4 model was originally developed for the C-band, W-polarized scatterometer data, polarization ratio should be applied to process RADARSAT ScanSAR data. In order to get an estimate of a suitable polarization ratio, wind directions from the collocated QuikSCAT data in the China Seas were taken as input to CMOD4 model for wind speeds retrieval from RADARSAT ScanSAR data, the results of the comparison of wind speeds from QuikSCAT versus the RADARSAT ScanSAR derived wind speeds indicate that Kirchhoff polarization ratio is suitable for wind speeds retrieval using horizontal polarization RADARSAT ScanSAR data. The corresponding mean bias and standard deviation is 0.37m/s and 1.54m/s, respectively.
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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) | 0.000 | 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 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".