Development and Application of a Global Satellite Database of Wind and Wave Conditions
Bibliographic record
Abstract
Oceanographic satellites have now been in operation for almost 30 years, collecting global data on oceanic winds and waves. During this period, a variety of satellites have been operational. These include altimeters (wind speed and wave height), SSMI radiometers (wind speed), scatterometers (wind speed and direction) and Synthetic Aperture Radar, SAR (full directional wave spectrum). Data from these instruments potentially represents an invaluable resource for offshore engineering design and facilities operation. This paper describes the development of a unique database containing data from all these instruments over their full periods of operation. The paper will describe the calibration and cross-validation of all instruments. This analysis shows the limitations of individual instruments and the relative accuracies. Instruments are calibrated against a very comprehensive buoy data set from the United States, Canada, UK, France, Spain, Australia and New Zealand. The extensive buoy dataset means that it is possible to have individual calibration buoys and independent validation sites. Further validation is provided by examining cross-over points between different satellite instruments where they image the same region of ocean at the same time. The paper will also demonstrate the application of this database. These applications include the evaluation of seasonal wind and wave climate on a global scale, the determination of extreme value statistics (100 year return values) for wind speed and wave height, long term trends in wind speed and wave height and potential trends in extreme values.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".