An Empirical Mode Decomposition Method for Sea Surface Wind Measurements From X-Band Nautical Radar Data
Bibliographic record
Abstract
In this paper, sea surface wind direction and speed are obtained from X-band nautical radar images. A data control strategy is proposed to distinguish rain-free and rain-contaminated radar data. The radar data are decomposed by an ensemble empirical mode decomposition method into several intrinsic mode functions (IMFs) and a residual. A normalization scheme is applied to the first IMF to obtain the amplitude modulation (AM) component. Wind direction is determined from the residual for the rain-free and high-wind-speed rain-contaminated data, and from the AM portion of the first IMF for the low-wind-speed rain-contaminated data, based on curve fitting a harmonic function. Wind speed is determined from a combination of the residual and the AM part of the first IMF for both rain-free and rain-contaminated data using a logarithmic relationship. Results employing ship-borne radar and anemometer data collected in a sea trial off the east coast of Canada are presented. The root-mean-square differences for wind direction and speed measurements are 11.5° and 1.31 m/s, respectively, compared with reference values from anemometers.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".