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

Abstract. 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 which have been used to represent plume rise were based on observations conducted nearly 50 years ago (the semi-empirical dimensional modelling framework of Briggs, 1984), and more recent measurement techniques are available which can be used to generate new data, against which pollutant plume rise theories may be evaluated. 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). In this work, a previously developed deep convolutional neural network (Deep Plume Rise Network, DPRNet) for determining plume rise from visible RGB images was applied to images taken of a facility in the Athabasca oil sands and compared to the theoretical estimates of Briggs parameterizations as formulated in GEM-MACHv2. On average, the Briggs parameterizations tend to predict plume rise in stable and neutral conditions within 30 %, 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 parameterizations could be improved reducing dimensionless constants by factors of 2 and 6 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.