Evaluation of an operational SAR wind field retrieval algorithm for ENVISAT ASAR
Bibliographic record
Abstract
The operational algorithm WiSAR is introduced, which enables to extract high-resolution ocean surface wind fields from satellite borne synthetic aperture radars (SARs) on a fully operational basis. WiSAR can be applied to SAR data acquired in C-band at either vertical (VV) or horizontal (HH) polarization in transmit and receive from the European satellites ERS-1/2 and ENVISAT as well as the Canadian satellite RADARSAT-1. SAR wind field retrieval is a two step process. In the first step wind directions are extracted from wind induced streaks that are visible in the SAR images at scales above 200 m and that are assumed to be approximately in line with the mean surface wind direction. The orientations of these streaks are derived by a method based on investigation of local gradients of the SAR intensity image. The SAR retrieved wind directions are used in the second step, where wind speeds are derived from the normalized radar cross sections of the SAR data under consideration of the wind direction and local SAR imaging geometry. Therefore, the empirical model CMOD4, is used, which was developed for the C-band VV polarized scatterometer aboard ERS-1/2. CMOD4 has been extended to HH polarization considering the polarization ratio and its dependency on incidence angle. To show WiSARs applicability it is applied to a set of 32 ENVISAT ASAR data from the North Sea. The resulting wind fields are compared to the results of the operational numerical model of the German Weather Service.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".