Wind Direction Estimation From Rain-Contaminated Marine Radar Data Using the Ensemble Empirical Mode Decomposition Method
Bibliographic record
Abstract
Two ensemble empirical mode decomposition (EEMD)-based methods are presented to retrieve wind direction from rain-contaminated X-band nautical radar sea surface images. Each radar image is first decomposed into disparate intrinsic mode function (IMF) components using 1-D EEMD or 2-D EEMD. Then, the standard deviation of one IMF component or the combination of several IMF components as a function of azimuth is least-squares fitted to a harmonic function to determine the wind direction. Tests of the proposed algorithms are conducted by employing radar and anemometer data collected in a sea trial during rain events off the east coast of Canada. The results show that compared with the 1-D discrete-Fourier-transform-based method, both the 1-D- and 2-D-EEMD-based algorithms improve the wind direction results in rain events, showing a reduction of 7.4° and 8.7°, respectively, in the root-mean-square difference with respect to the reference.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".