Wave height and wind direction from the HF coastal ocean surface radar
Bibliographic record
Abstract
The biggest difficulty in extracting wind direction from high-frequency (HF) backscatter ground-wave radar data is in not knowing the fundamental shape of the directional spreading function at the Bragg wavelength for the sea-surface gravity waves. In this paper we present data from a deployment of the HF coastal ocean surface radar (COSRAD) which samples the same patch of water from a range of different angles, allowing us to determine the shape of the spectral spreading function for the Bragg resonant gravity waves. The resulting evaluation of wind direction compares favourably with wind-vane measurements in the vicinity. A routine method for extracting root mean square (rms) wave heights from HF backscatter ground-wave radar spectra has been developed based on the theoretical work of D.E. Barrick. This method is reviewed in the paper and a "best practice" procedure is described for routine production of rms wave heights. Results are shown for a recent deployment of the COSRAD HF radar near Cairns in the Great Barrier Reef Region of northeast Australia. The observed rms wave heights agree reasonably well with those given by the JONSWAP model over the same range of wind speeds. A method for obtaining a measure of the spreading of the directional wave spectrum has been developed. Over the period of observation, the wind speeds varied between 2 and 11 m/s, and the S values for the M.S. Longuet-Higgins et al. spreading function were in the range 1.94 ± 0.62. These S values are less than those given by the JONSWAP model, especially at low wind speeds. A sensitivity study was carried out on the spread of wind directions which would arise from this variability in the wave directional spreading function. For single measurements, the error in wind direction is ±25°, but with spectral averaging over time and space the HF radar errors in wind directions are reduced to about ±10°.
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 | 0.000 | 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) | 0.000 | 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".