Not all speeds are created equal: investigating the predictability of statistically downscaled historical land surface winds over central Canada.
Bibliographic record
Abstract
A statistical downscaling approach based on multiple linear-regression is used to \ninvestigate the predictability of land surface winds over the Canadian prairies and Ontario. \nThis study's model downscales mid-tropospheric predictors (wind components \nand speed, temperature, and geopotential height) from reanalysis products to predict \nhistorical wind observations at thirty-one airport-based weather surface stations in \nCanada. The model's performance is assessed as a function of: season; geographic \nlocation; averaging timescale of the wind statistics; and wind regime, as defined by \nhow variable the vector wind is relative to its mean amplitude. \nDespite large differences in predictability characteristics between sites, several \nsystematic results are observed. Consistent with recent studies, a strong anisotropy \nof predictability for vector quantities is observed, while some components are generally \nwell predicted, others have no predictability. The predictability of mean quantities is \ngreater on shorter averaging timescales. In general, the predictability of the surface \nwind speeds over the Canadian prairies and Ontario is poor; as is the predictability \nof sub-averaging timescale variability. \nThese results and the relative predictability of vector and scalar wind quantities \nare interpreted with theoretically- and empirically-derived wind speed sensitivities to \nthe resolved and unresolved variability in the vector winds. At most sites, and on longer averaging timescales, the scalar wind quantities are found to be highly sensitive \nto unresolved variability in the vector winds. These results demonstrate limitations to \nthe statistical downscaling of wind speed and suggest that deterministic models which \nresolve the short-timescale variability may be necessary for successful predictions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".