Trends in Wind Speed at Wind Turbine Height of 80 m over the Contiguous United States Using the North American Regional Reanalysis (NARR)
Bibliographic record
Abstract
Abstract The trends in wind speed at a typical wind turbine hub height (80 m) are analyzed using the North American Regional Reanalysis (NARR) dataset for 1979–2009. A method, assuming the wind profile in the lower boundary layer as power-law functions of altitude, is developed to invert the power exponent (in the power-law equation) from the NARR data and to compute the following variables at 80 m that are needed for the estimation and interpretation of the trend in wind speed: air density, zonal wind u, meridional wind υ, and wind speed. Statistically significant and positive annual trends are found to be predominant over the contiguous United States, with spring and winter being the two largest contributing seasons. Positive trends in surface wind speed are generally smaller than those at 80 m, with less spatial coverage, reflecting stronger increases in wind speed at altitudes above the 80-m level. Large and positive trends in winds over the southeastern region and high-mountain region are primarily due to the increasing trend in southerly wind, while the trends over the northern states (near the Canadian border) are primarily due to the increasing trend in westerly wind. Trends in the 90th percentile of the annual wind speed, a better indicator for the trend in wind power recourses, are 40%–50% larger than but geographically similar to the trends in the annual mean wind speed. The probable climatic drivers for change in wind speed and direction are discussed, and further studies are needed to evaluate the fidelity of wind speed and direction in the NARR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".