Estimation of ocean wave height from grazing incidence microwave backscatter
Bibliographic record
Abstract
When waves on the ocean surface are viewed with a ship-borne marine radar, their height is not directly related to either the magnitude of the microwave backscatter field or the magnitude of the modulation of this field in this grazing incidence regime. The authors have performed some validation tests of a model, L. B. Wetzel (1990), which theoretically related the statistics of the ratio of illuminated to shadowed areas through the viewing geometry to the significant height of the waves causing the shadowing. Using a subset of the data collected by two different digital marine radars on the Sea Truth and Radar Systems Experiment of December, 1994, the present authors calibrated the model against measured wave heights, and then validated it using the remaining data from the radars and other independent wave height estimations. Significant wave heights in these experiments ranged from less than one to greater than 5 metres. They found reasonable agreement between their inferred wave height extracted from radar imagery and heights estimated by other means when the sea conditions were moderate and not too complex. Scatter in the results was not inconsistent with the statistical variability expected from the sampling statistics. The model failed under conditions of crossing seas, where it overpredicted the wave height, and when the measured wave height was a significant fraction of the radar scanner height, where the model exhibited asymptotic behaviour and underpredicted the actual wave height.
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 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) | 0.000 | 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) | 0.000 | 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 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".