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.

This paper presents a novel airborne remote sensing method using thermal imaging to directly georeference and calculate Earth surface temperature with a high spatiotemporal resolution. A tethered balloon is used to elevate an uncooled thermal camera in the field. When deployed, images with oblique view angles of the surrounding Earth surface are collected. Images recorded from a field environmental monitoring campaign in a northern Canadian mining facility are processed with open source software, and it is shown that they successfully represent the diurnal and spatial surface temperature variations within the facility. Furthermore, in comparison to MODerate resolution Imaging Spectroradiometer (MODIS) satellite images, the approach results in a median absolute error of 0.64 K, with a bias and Root Mean Square Error (RMSE) of 0.5 K and 5.45 K, respectively.

This study presents the first environmental monitoring field campaign of a newly developed Tethered Air Blimp (TAB) system to investigate the microclimate over a complex terrain. The use of a tethered balloon in complex terrains such as mines and tailings ponds is novel and the focus of the present study. The TAB system was fully developed and launched at a mining facility in northern Canada in May 2018. This study describes the key design features, the sensor payload on board, calibration, and the observations made by the TAB system. The system measured meteorological conditions including components of wind velocity vector, temperature, relative humidity, and pressure over the first few tens of metres of the atmospheric boundary layer. The measurements were made at two primary locations in the facility: (i) near a tailings pond and (ii) in a mine pit. TAB measured the dynamics of the atmosphere at different diurnal times (e.g. day versus night) and locations (near a tailings pond versus inside the mine). Such dynamics include mean and turbulence statistics pertaining to flow momentum and energy, and they are crucial in the understanding of emission fluxes from the facility in future studies. In addition, TAB can provide boundary conditions and validation datasets to support mesoscale dispersion modelling or computational fluid dynamics simulations for various transport models.

Greenhouse gas (GHG) emissions from open-pit mines pose a global climate challenge, which necessitates appropriate quantification to support effective mitigation measures. This study considers the area-fugitive methane advective flux (as a proxy for emission flux) released from a tailings pond and two open-pit mines, denominated “old” and “new”, within a facility in northern Canada. To estimate the emission fluxes of methane from these sources, this research employed near-surface observations and modeling using the weather research and forecasting (WRF) passive tracer dispersion method. Various machine learning (ML) methods were trained and tested on these data for the operational forecasting of emissions. Predicted emission fluxes and meteorological variables from the WRF model were used as training and input datasets for ML algorithms. A series of 10 ML algorithms were evaluated. The four models that generated the most accurate forecasts were selected. These ML models are the multi-layer perception (MLP) artificial neural network, the gradient boosting (GBR), XGBOOST (XGB), and support vector machines (SVM). Overall, the simulations predicted the emission fluxes with R${}^2$ (-) values higher than 0.8 (-). Considering the bias (Tonnes h${}^{-1}$), the ML predicted the emission fluxes within 6.3%, 3.3%, and 0.3% of WRF predictions for the old mine, new mine, and the pond, respectively.

Greenhouse Gas (GHG) emissions pose a global climate challenge and the mining sector is a large contributor. Diurnal and seasonal variations of area-fugitive methane advective flux, released from an open-pit mine and a tailings pond, from a facility in northern Canada, were simulated in spring 2018 and winter 2019, using the Weather Research and Forecasting (WRF) model. The methane mixing ratio boundary conditions for the WRF model were obtained from the in-situ field measurements, using Los Gatos Research Ultra-Portable Greenhouse Gas Analyzers (LGRs), placed in various locations surrounding the mine pit and a tailings pond. The simulated advective flux was influenced by local and synoptic weather conditions in spring and winter, respectively. Overall, the average total advective flux in the spring was greater than that in the winter by 36% and 75%, for the mine and pond, respectively. Diurnal variations of flux were notable in the spring, characterized by low flux during thermally stable (nighttime) and high flux during thermally unstable (daytime) conditions. The model predictions of the methane mixing ratio were in reasonable agreement with limited aircraft observations ($R^2=0.68$). The findings shed new light in understanding the area-fugitive advective flux from complex terrains and call for more rigorous observations in support of the findings.

Soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Therefore, there is widespread interest in the use of soil moisture retrievals from passive microwave satellites. In the assimilation of satellite soil moisture data into land surface models, two approaches are commonly used. In the first approach brightness temperature (TB) data are assimilated, while in the second approach retrieved soil moisture (SM) data from the satellite are assimilated. However, there is not a significant body of literature comparing the differences between these two approaches, and it is not known whether there is any advantage in using a particular approach over the other. In this study, TB and SM L2 retrieval products from the Soil Moisture and Ocean Salinity (SMOS) satellite are assimilated into the Canadian Land Surface Scheme (CLASS), for improved soil moisture estimation over an agricultural region in Saskatchewan. CLASS is the land surface component of the Canadian Earth System Model (CESM), and the Canadian Seasonal and Interannual Prediction System (CanSIPS). Our results indicated that assimilating the SMOS products improved the soil moisture simulation skill of the CLASS. Near surface soil moisture assimilation also resulted in improved forecasts of root zone soil moisture (RZSM) values. Although both techniques resulted in improved forecasts of RZSM, assimilation of TB resulted in the superior estimates.