Cross-Polarized Synthetic Aperture Radar: A New Potential Measurement Technique for Hurricanes
Bibliographic record
Abstract
We present an empirical C-band Cross-Polarization Ocean (C-2PO) model for wind retrievals from synthetic aperture radar (SAR) data collected by the RADARSAT-2 satellite. The C-2PO model relates normalized radar cross section (NRCS) in cross polarization to wind speed at 10-m height. This wind retrieval model has the characteristic that it is independent of wind direction and radar incidence angle but is quite linear with respect to wind speed. To evaluate the accuracy of the proposed model, winds with a resolution on the scale of 1 km were retrieved from a dual-polarization SAR image of Hurricane Earl on 2 September 2010, using the C-2PO model and compared with CMOD5.N, the newest available C-band geophysical model function (GMF), and validated with collocated airborne stepped-frequency microwave radiometer measurements and National Data Buoy Center data. Results suggest that for winds up to 38 m s−1, C-2PO has a bias of −0.89 m s−1 and a root-meansquare error of 3.23 m s−1 compared to CMOD5.N, which has a bias of −4.14 m s−1 and an rms difference of 6.24 m s−1. Similar results are obtained from Hurricane Ike, comparing wind retrievals from C-2PO and CMOD5.N with H*Wind data. The advantage of C-2PO over CMOD5.N and other GMFs is that it does not need any external wind direction and radar incidence angle inputs. Moreover, in the presently available quad-polarization dataset, C-2PO has the feature that the cross-polarized NRCS linearly increases even for wind speeds up to 26 m s−1 and reproduces the hurricane eye structure well, thereby providing a potential technique for hurricane observations from space.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".