Extraction of wind speed from high frequency ground wave radar oceanic backscatter
Bibliographic record
Abstract
The ability to remotely sense ocean winds has numerous research and commercial applications. High Frequency radar operating in ground wave mode has proven itself to be an effective means of remotely sensing the ocean surface. This is because at the typical operating frequencies (3-30 MHz), the radar signal can travel very large distances. Also, wavelengths in this band interact closely with the most energetic ocean waves. The problem that is dealt with in this thesis is the extraction of the wind speed blowing over a radar-illuminated patch of ocean. -- The Doppler spectra of the returned radar signal contain a wealth of oceanographic information. This is owing to the various complex electromagnetic scattering mechanisms. The radar cross section of the ocean surface that results has many salient features that can be used to extract particular ocean parameters. Based on the existing HF radar theory, an expression is derived that extracts the peak frequency of the ocean spectrum from the radar cross section. This spectral peak frequency is then linked to oceanographic models which dictate the growth of an ocean spectrum to a given wind condition. -- The models are applied to simulated noisy data. In addition, appropriate signal processing techniques are applied to mitigate the effects of noise and to improve the robustness of the models. Finally, the models are applied to sample HF radar data provided by Rutger's University. This data was obtained from a Coastal Ocean Dynamics Applications Radar (CODAR) operating in Breezy Point, NY. The results are then compared to ground truth data provided by the National Oceanic and Atmospheric Administration (NOAA) from a weather station located in the vicinity of the illuminated patch of ocean.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".