Evaluation of plume rise parameterizations in GEM-MACHv2 with analysis of image data using a deep convolutional neural network

The study of plume rise from smokestacks and other pollutant point sources is extremely important for the estimation and modelling of the dispersion of pollutants on regional scales via atmospheric modelling platforms. However, the algorithms currently used to represent plume rise were validated using observations made nearly 50 years ago. Advances in measurement technology now allow the collection of long-term continuous observations. These data sets can be used to investigate seasonal and diurnal variability, and to evaluate pollutant plume rise theories. A key result of the theoretical formulations based on these past observations is the height reached by the plumes (the process by which they reach that height is known as plume rise). This study applies a previously developed deep convolutional neural network (Deep Plume Rise Network, DPRNet) to visible RGB images acquired at an oil extraction facility in the Athabasca oil sands. The resulting plume-rise image-based observations are compared to theoretical estimates generated using the Briggs parameterizations within the three-dimensional multi-scale model, GEM-MACHv2. On average, the Briggs parameterizations tend to predict plume rise in stable and neutral conditions within 30 % of the observed heights, but consistently overpredict plume rise during unstable conditions by more than 100 %. Further, while Briggs parameterizations predicted diurnal variations in plume rise, no such variation was observed by the image analysis. The agreement between the plume rise calculated from observations and the Briggs parameterizations could be improved by increasing the assumed entrainment parameters in the Briggs equations by factors of 1.45 and 2.1 in neutral and unstable conditions, respectively. The plume height data have been shown to provide a significant resource for plume rise theory evaluation and development.