Ocean wind field mapping from synthetic aperture radar and its application to research and applied problems
Bibliographic record
Abstract
pL and the office of research and Applications of the national oceanic and Atmospheric Administration have developed a system to use near-real-time satellite synthetic aperture radar (sAr) data from the radarsat-1 and envisat satellites to produce high-resolution (subkilometer) maps of the ocean surface wind field in coastal areas.These maps have shown diverse meteorological phenomena, from gap flows to atmospheric roll vortices.in this article, we describe how sAr can measure wind over the ocean surface and then present examples illustrating how such measurements may be applied.The first application is a scientific one in which sAr wind fields are used to understand the dynamics and spatial variability of barrier jets off the west coast of canada and the southern coast of Alaska.The second application is a practical one in which high-resolution sAr wind maps are used to determine the optimal placement of offshore wind turbines for generating electric power.Johns hopkins ApL TechnicAL DigesT, VoLume 26, number 2 (2005) F. m. monALDo et al.The use of high-resolution sAr wind mapping is just beginning to be recognized.A major objective of this article is to illustrate its capability through examples from both research and application.Although much work remains to be done in the validation and refinement of sAr wind mapping techniques, the potential payoff is well worth continued effort.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".