The Statistical Predictability of Surface Winds over Western and Central Canada
Bibliographic record
Abstract
Abstract Multivariate linear regression is used to downscale reanalysis-based midtropospheric predictors (wind components and speed, temperature, and geopotential height) to historical wind observations at 44 surface weather stations during the four calendar seasons. The model performance is assessed as a function of statistical feature of the wind, averaging time scale of the wind statistics, and wind regime (as defined by how variable the vector wind is relative to its mean amplitude). Despite large differences in predictability characteristics between sites, several systematic results are observed: consistent with recent studies, a strong anisotropy of predictability for vector quantities is often observed, although no obvious relation is found between large-scale topographic features and the anisotropy orientation or magnitude. The predictability of time-averaged quantities increases with decreasing averaging time scale. In general, the predictability of mean vector wind components is superior to that of mean wind speeds or subaveraging time scale vector wind variability. These results are interpreted through empirically and theoretically based analyses of the sensitivity of mean wind speed to changes in the vector wind statistics. On longer averaging time scales, the statistical features of the wind speed are found to be highly sensitive to subaveraging time-scale vector wind variability, which is poorly predicted. On shorter averaging time scales, the mean wind speed is found to be highly sensitive to the magnitude of the mean vector wind, a quantity whose predictability can be much lower than the individual mean vector wind components. These results demonstrate limitations to the statistical downscaling of wind speed and suggest that deterministic models that resolve the short-time-scale variability may be necessary for successful predictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".