US East Coast synthetic aperture radar wind atlas for offshore wind energy
Bibliographic record
Abstract
Abstract. We present the first synthetic aperture radar (SAR)-based offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from Radarsat-1, Envisat, Sentinel-1A, and Sentinel-1B are processed to wind maps using the Geophysical Model Function (GMF) CMOD5.N. Extensive comparisons with 6,008 collocated buoy observations revealed that biases of the individual system range from −0.8 to 0.6 m/s. Unbiased wind retrievals are crucial for producing an accurate wind atlas and intercalibration for correcting these biases by adjusting the normalized radar cross section is applied. The intercalibrated SAR observations show biases in the range of to −0.2 to 0.0 m/s, while at the same time improving the root mean squared error from 1.67 to 1.46 m/s. These intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas. Monthly averages are used to correct artefacts from seasonal biases. The SAR wind atlas is used as a reference to study wind resources derived from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation of wind resources and show that model results estimate lower coastal wind speed gradients than those from SAR. At sites designated for offshore wind development by the Bureau of Ocean Energy Management, mean wind speeds typically vary between 0.3 and 0.5 m/s for SAR and less than 0.2 m/s for the WRF model within each site. Findings indicate that wind speed gradients and variation might be underestimated in mesoscale model outputs along US East Coast.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 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".