Climate and climate variability of the wind power resources in the Great Lakes region of the United States
Bibliographic record
Abstract
The climate and climate variability of low‐level winds over the Great Lakes region of the United States is examined using 30 year (1979–2008) wind records from the recently released North American Regional Reanalysis (NARR), a three‐dimensional, high‐spatial and temporal resolution, and dynamically consistent climate data set. The analyses focus on spatial distribution and seasonal and interannual variability of wind speed at 80 m above the ground, the hub height of the modern, 77 m diameter, 1500 kW wind turbines. The daily mean wind speeds exhibit a large seasonal variability, with the highest mean wind speed (∼6.58 m s −1 ) in November through January and the lowest (∼4.72 m s −1 ) in July and August. The spatial variability of the annual mean winds is small across the entire region and is dominated by land‐water contrasts with stronger winds over the lake surface than over land. Larger interannual variability is found during the winter months, whereas smaller variations occur in mid to late summer. The interannual variability appears to have some connections to El Niño‐Southern Oscillation, with lower mean wind speeds and more frequent occurrences of lulls during major El Niño episodes. Above‐normal ice cover of the Great Lakes appears to be associated with slightly lower wind speeds and vice versa. According to NARR data and the criteria established by wind energy industry, the areas over Lake Superior, Michigan, and Ontario appear to be rich in wind resources, but most land areas in the region are either unsuitable or marginal for potential wind energy development.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".