Calibrated Probabilistic Hub-Height Wind Forecasts in Complex Terrain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This work evaluates the use of a WRF ensemble for short-term, probabilistic, hub-height wind speed forecasts in complex terrain. Testing for probabilistic-forecast improvements is conducted by increasing the number of planetary boundary layer schemes used in the ensemble. Additionally, several prescribed uncertainty models used to derive forecast probabilities based on knowledge of the error within a past training period are evaluated. A Gaussian uncertainty model provided calibrated wind speed forecasts at all wind farms tested. Attempts to scale the Gaussian distribution based on the ensemble mean or variance values did not result in further improvement of the probabilistic forecast performance. When using the Gaussian uncertainty model, a small-sized six-member ensemble showed equal skill to that of the full 48-member ensemble. A new uncertainty model called the pq distribution that better fits the ensemble wind forecast error distribution is introduced. Results indicate that the gross attributes (central tendency, spread, and symmetry) of the prescribed uncertainty model are more important than its exact shape.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it