Bridging the gap between cyclone wind and wave by <scp>C</scp>‐band <scp>SAR</scp> measurements
Bibliographic record
Abstract
Abstract Active microwave remote sensing of hurricane‐strength wind is a challenging task due to the saturation of col‐polarization backscattering signal under such condition. Here we take advantage of the fact that wind sea wave growth does not saturate at high wind and the intrinsic relationship among wind‐wave triplets (sea surface wind speed, significant wave height, and peak wave period) within a tropical storm to derive the wind speed. Three Sentinel‐1 (S‐1) and nine RADARSAT‐2 (R‐2) C‐band synthetic aperture radar (SAR) images acquired between 20 and 40 m/s winds are collected in this study. The S‐1 and R‐2 SAR‐derived winds are compared with those measured by coincident National Oceanic and Atmospheric Administration Stepped‐Frequency Microwave Radiometer (SFMR) and simulated by Symmetric Hurricane Estimates for Wind (SHEW) model. Validations against SFMR winds for S‐1 show Root‐Mean‐Square Error (RMSE) of 1.7 m/s with a 0.2 m/s bias at the left side of cyclone centers and RMSE of 2.9 m/s RMSE with a 0.56 bias at the backside of cyclone centers. R‐2 SAR‐derived winds against SHEW model results show a RMSE of 2.4 m/s with a 0.3 m/s bias and 2.6 m/s with a 0.35 m/s bias at the right side and the left side of cyclone centers, while the RMSE is 3.9 m/s with a 0.1 m/s bias at the backside of cyclone centers. The wave‐information‐based wind retrieval method works well at the left side and right side, but less accurately at the backside of a tropical storm when wind wave and swell are mixed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".