Global Environmental Multiscale Model (GEM-LAM), and Canadian Regional Climate
Bibliographic record
Abstract
[1] Knowledge of the probability distributions of surface wind speeds (SWS) is essential for surface flux estimation, wind power estimation, and wind risk assessments. The two-parameter Weibull distribution is the most widely used empirical distribution for SWS. This study considers the probability density function (PDF) of 3-hourly observations from 720 weather stations over North America for the period 1979–1999. The PDF of SWS is classified by season, time of day, and land surface type. The Weibull PDF is characterized by a particular relationship between the mean, standard deviation, and skewness. While the moments of the observed daytime SWS PDF are found to collapse around this Weibull relationship, the observed nighttime PDF has a broader range of values and is significantly more skewed than the Weibull PDF over rough surfaces. An idealized model shows that SWS skewness has a much greater rate of change with both the mean and standard deviation of surface buoyancy flux under conditions of stable stratification than that of unstable stratification. This result suggests that surface buoyancy flux plays an important role in generating diurnal variation of SWS PDF. Two global reanalyses products (ERA-40 and NCEP-NCAR) and three regional climate models (RCMs) (Rossby Centre Atmospheric Model version 3 (RCA3), limited area version of
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".