Year-to-year correlation, record length, and overconfidence in wind resource assessment
Bibliographic record
Abstract
Abstract. Wind resource assessments predict future production levels from historical data. To characterize how year-to-year variability in past wind speeds affects the certainty of future predictions, we analyze 62-year wind speed records of 60 weather stations in Canada, and compare the actual levels of each station's final 20 years to "predictions" made from previous periods of varying duration. We estimate both median (P50) and 10 % quantile (P90) levels using historical means and standard deviations, validating estimator performance on statistically-independent "control" sequences made by randomly permuting the 62 annual values of each station's record. Errors of estimates made from the control sequences always decline with record length; the central half of the stations’ exceedances falls within ranges of 44–55 % (P50) and 85–95 % (P90) for 42-year estimates. For the actual chronological records, on the other hand, error is lowest when estimates were made from short records (4–5 years) and increases with length after 15 years; for 42-year estimates the corresponding ranges are 0–45 % (P50) and 36–100 % (P90). The strong biases reflect a nearly nationwide downward trend in recorded wind speeds, but even a near-zero-trend subset of 30 stations exhibits interquartile ranges of 24–73 % (P50) and 80–100 % (P90), both twice as large as expected. These findings show that serial correlation in wind speeds can persist across decades, and, if ignored, results in substantial overconfidence in estimated resource levels.
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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 | 0.022 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".