Dual-curve-fitting-based wind parameter extraction from shipborne nautical X-band radar data
Bibliographic record
Abstract
In this paper, techniques for retrieving wind direction and speed from shipborne X-band nautical radar images are presented. First, by analyzing the radar backscatter intensity histogram and zero-pixel percentage, each individual image is designed to go through a data quality control process. With this processing, the rain cases and all-black images can be recognized. Then, a harmonic function that is least-squares fitted to the radar backscatter intensity as a function of antenna look direction is applied to determine the wind direction. Furthermore, for wind speed retrieval, an empirical third-order polynomial model is derived using the average radar backscatter intensity and the reference wind speed. To improve the accuracy of wind retrieval, a modified technique, which involves a dual-curve-fitting approach, is implemented. For the data presented in this paper, it was found that the second stage of the curve-fitting performed optimally when the data at angles of 60° to the left and right of the first-guess upwind direction were used. Also, only the data for the dual-curve-fitting were used to calculate the average intensity of the radar images for wind speed estimation. The modified method is applied to the radar data and the results are compared with the reference data measured by a ship-based anemometer. It is shown that the dual-curve-fitting algorithm produces improvements in the mean differences between the radar and the anemometer results for wind direction and speed of about 7° and 0.4 m/s, respectively, under low sea state.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".