Complex Meteorology over a Complex Mining Facility: Assessment of Topography, Land Use, and Grid Spacing Modifications in WRF
Bibliographic record
Abstract
Abstract The performance of the Weather Research and Forecasting (WRF) Model is evaluated in predicting the meteorological conditions over a complex open-pit mining facility in northern Canada in support of more accurate operational reporting of area-fugitive greenhouse gas emission fluxes from such facilities. WRF is studied in a series of sensitivity tests by varying topography, land use, and horizontal and vertical grid spacings to arrive at optimum configurations for reducing modeling biases in comparison with field meteorological observations. Overall, WRF shows a better performance when accounting for the mine topography and modified land use. As a result, the model biases reduce from 1.10 to 0.08 m s−1, from 1.04 to 0.50 m s−1, from 0.98 to 0.32 K, and from 45.7 to 17.3 W m−2, for near-surface wind speed, boundary layer wind speed, near-surface potential temperature, and turbulent sensible heat flux, respectively. Refining the model horizontal and vertical grid spacings results in bias reductions from 3.31 to 0.08 and from 0.80 to −0.11 m s−1 for near-surface and boundary layer wind speeds, respectively. The simulation results also agree with previous observations of meteorological effects on enclosed Earth depressions, characterized by formation of a cool pool of air, reduced wind speeds, and horizontal wind circulations at the bottom of the depression under thermally stable conditions. The results suggest that such configurations for WRF are necessary to arrive at more accurate meteorological predictions over complex open-pit mining terrains with similar features.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".