Wind direction determination from rain-contaminated X-band radar images
Bibliographic record
Abstract
A two-dimensional ensemble empirical mode decomposition (2D-EEMD)-based method is presented to improve wind direction retrieval from rain-contaminated X-band nautical radar sea surface images. 2D-EEMD is first implemented to decompose each rain-contaminated radar image into several intrinsic mode function (IMF) components. Then, a harmonic function that is least-squares fitted to the standard deviation of the first IMF component as a function of azimuth is used to retrieve the wind direction. Radar and anemometer data acquired in a sea trial off the east coast of Canada under rain conditions are employed to test the algorithm. The result shows that, compared to the traditional curve fitting method, the proposed method improves the wind direction results in rain events, showing a reduction of 35.9° in the root-mean-square (RMS) difference with respect to the reference.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".