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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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".