Mesoscale Wind Fields Retrieved from RADARSAT-1 ScanSAR
Bibliographic record
Abstract
In this paper an algorithm is presented and verified to retrieve mesoscale wind fields from the ScanSAR aboard the Canadian satellite RADARSAT-i, which operates at C-band with horizontal polarization in transmit and receive. The wind directions are extracted from wind induced streaks e.g. from boundary layer rolls, Langmuir cells, or wind shadowing, which are approximately in line with the mean wind direction. The algorithm defines wind direction as normal to the gradient of the amplitude image, which is approximated by finite differences over an appropriate length. The resulting wind direction is normal to the best and most frequent values of the retrieved local gradients. Wind speeds are derived from the normalized radar cross section (NRCS) and image geometry of the calibrated ScanSAR images, together with the local wind direction. Therefore semi empirical C-band models, e.g. CMOD4 or CMOD_IFR2, which describe the dependency of the NRCS on wind and image geometry are inverted. These models were developed for vertical and have to be modified for horizontal polarization, which is performed by considering several C-band polarization ratios including theoretical and empirical forms.
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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 | 0.001 | 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 | 0.001 |
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".