Wind Energy Simulation Toolkit (WEST): A Wind Mapping System for Use by the Wind-Energy Industry
Bibliographic record
Abstract
A state-of-art wind mapping system, the Wind Energy Simulation Toolkit (WEST), was developed in the Meteorological Service of Canada (MSC) for use by the wind energy industry. WEST is based on a statistical-dynamical downscaling approach, i.e. (i) a statistical analysis of climate data to determine the basic atmospheric states, and (ii) a dynamic adaptation of each basic state to high-resolution terrain and surface roughness by using mesoscale and microscale models. The approach has already been used by Frank and Landberg (1997), in their KAMM/WAsP method, to create a numerical wind atlas. The novel part of WEST is the fixed wind-speed interval in classification scheme and the integration of different modules (meso-/micro-scale models and statistical module) into a single toolkit in a more portable form. WEST was built for use by industries not having sophisticated computer facilities. WEST is applied to the Gaspé region of Canada. The mesoscale model MC2 (operated within WEST) is run at 5 km resolution, while the microscale model within WEST is at 200 m resolution. The simulation results are evaluated in comparison with tower observations at a height of 40 m above ground level. The mean of the 29 observed sets of wind data is 6.6 m/s. The mean absolute difference between the observed and simulated sets of wind data is 0.83 m/s with MC2 (meso-component of WEST) and 0.69 with ‘full WEST’ (with both meso- and micro- components). The correlation coefficient of the mean wind-speeds between the simulations and observations for the 29 stations is improved from 0.5 with MC2 to 0.7 with WEST.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".