Complex Meteorology over a Complex Mining Facility: Assessment of Topography, Land Use, and Grid Spacing Modifications in WRF

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.

WRF wind field assessment under multiple forcings using spatialized aircraft data

“…Similar results were also obtained by Nahian et al . (2020) when improving the land‐use and topographical data set, highlighting the importance of considering land use as a physical driver of the biosphere atmosphere interaction rather than a static variable.…”

Machine Learning to Predict Area Fugitive Emission Fluxes of GHGs from Open-Pit Mines

“…Nahian et al (2020) [20] investigated that, by means of utilizing the latest modified land-use configurations and topography, the computations of atmospheric state variables at the same facility would agree better with the measurements. The representation of land-use according to the Moderate Resolution Imaging Spectroradiometer (MODIS) 30s dataset (updated 21-class IGBP-MODIS land use dataset) by Friedl et al (2010) [88] was utilized with categorizations for the pond (21), the two mines (16), mine processing facilities and housing (13), and cleared forest (10).…”

“…Atmospheric state variables can vary, caused by the influences of changing aerodynamic roughness length scale when higher terrain and land-use resolution information are used [62]. The thermodynamic variables show improved agreement with measurements when detailed terrain information is utilized [20,63].…”

“…Large estimation differences of methane emission fluxes from such facilities are the result of ignoring the meteorological effects that complicate pollutant transport in complex topographies [14]. While orographic complexities confound atmospheric boundary layer (ABL) flows compared to ABL flows over flat and homogeneous terrains [15][16][17][18][19][20][21][22][23], only a handful of investigations are dedicated to ABL flows over open-pit mines [24,25], primarily due to operational, risk, access, economic, and other concerns [25,26]. Open-pit mines represent an unusual topographic feature.…”

“…Open-pit mines represent an unusual topographic feature. The meteorological patterns within and near these mines can be quite different from flat terrain [20][21][22]27,28], and understanding these patterns is important given the environmental impact of mining activities. The release of GHGs from such environments is associated with multiple steps of the resource extraction process, starting from land modification to the exportation of the mined goods [4,14].…”