Influence of wind direction in the downscaling of wind speeds from numerical weather prediction
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes a refinement of wind speed prediction methods in order to enhance their accuracy for wind energy applications. Specifically, techniques used to downscale raw forecasts from numerical weather prediction models are investigated. Many downscaling techniques have been proposed, however most of these rely on wind speed data while ignoring a potentially valuable source of information, namely wind direction. In this paper, we incorporate wind speed and direction into three downscaling methods: linear model output statistics; feedforward artificial neural network (ANN); and Kalman filter (KF). We apply the techniques to downscale outputs of a global numerical weather prediction model to six test locations in Ireland for which wind speed and direction measurements were available. While classical downscaling methods require large sets of historical data in order to be trained, the KF has the potential to rapidly estimate the bias that needs to be added to the raw forecasts in order to provide the best fit possible to local observations. Comparing the results of the three downscaling methods, it is shown that while the levels of prediction accuracy attainable with the KF are similar to classical techniques, the amount of data required to parameterise the KF is much less than for other techniques. The KF has a further advantage over the ANN in that it does not require offline parameterisation. However, in this study, the ANN performance was more satisfactory in reducing prediction errors.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it