Enhanced method for multiscale wind simulations over complex terrain for wind resource assessment
Bibliographic record
Abstract
Due to the natural variability of the wind, it is necessary to conduct thorough wind resource assessments to determine how much energy can be extracted at a given site. Lately, important advancements have been achieved in numerical methods of multiscale models used for high resolution wind simulations over steep topography. As a contribution to this effort, an enhanced numerical method was devised in the mesoscale compressible community (MC2) model of the Meteorological Service of Canada, adapting a new semi-implicit scheme with its imbedded large-eddy simulation (LES) capability for mountainous terrain. This implementation has been verified by simulating the neutrally stratified atmospheric boundary layer (ABL) over flat terrain and a Gaussian ridge. These preliminary results indicate that the enhanced MC2-LES model reproduces efficiently the results reported by other researchers who use similar models with more sophisticated sub-grid scale turbulence schemes. The proposed multiscale method also provides a new wind initialization scheme and additional utilities to improve numerical accuracy and stability. The resulting model can be used to assess the wind resource at meso- and micro-scales, reducing significantly the wind speed overestimation in mountainous areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".