Validation of an X-Band SAR Wind Algorithm by SIR-C/X SAR Data
Bibliographic record
Abstract
Space borne radar systems are capable of providing wind field information over the ocean. Radar instruments are of high value for operational applications because of their all weather and daylight capabilities. Synthetic aperture radar (SAR) instruments as flown on the European satellites ERS-2, ENVISAT or the Canadian platform RADARSAT are of particular interest for applications where high resolution two- dimensional information on the near surface wind field is needed. All these operate in C-band. The respective wind field algorithm CMOD was tuned to this wavelength. New research focussed on high speed cases to be able to measure wind speeds above 20 m/sec and on the VV to HH polarisation ratio. For future missions like TerraSAR-X, to be launched in May 2007 new wind field algorithms tuned to X-band are needed. The TerraSAR-X instrument has a spatial resolution of up to 1 m and additional features like multi polarisation which make it a very interesting tool for oceanographic applications. In this paper a new X band wind field algorithm, XMOD1.0 is introduced. The algorithm is based on the detection of wind streaks in the SAR images and scatterometer measurements of [1]. Data from the SRTM mission flown on the shuttle in February 2000 and SIR C/X SAR in 1994 are used to test the algorithm. Results are validated against in situ data and model data from ECMWF. The potential of SAR measurements to support the optimal siting, the design, as well as the operation of offshore wind parks is shown. Applications for offshore wind farming of the TerraSAR-X mission will be discussed. The platform FINO 1 was chosen as a primary test site to calibrate and validate wind fields for X band satellite images. For the development, optimisation and validation of the retrieval algorithms comparisons with in situ data, e.g., acquired at the FINO platform will be carried out. The respective calibration and validations strategies will be summarized.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".