Articles | Volume 19, issue 18
https://doi.org/10.5194/gmd-19-8855-2026
https://doi.org/10.5194/gmd-19-8855-2026
Development and technical paper
 | 
21 Sep 2026
Development and technical paper |  | 21 Sep 2026

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

Kevin M. Axelrod, Mark Gordon, Mohammad Koushafar, Jingliang Hao, Paul Makar, Sepehr Fathi, and Gunho Sohn

Data sets

Smokestack Plume Images and Plume Identification Masks Mark Gordon et al. https://doi.org/10.20383/103.01448

Model code and software

Code and data for the calculation of plume rise Mark Gordon et al. https://doi.org/10.5683/SP3/WZVZBV

Download
Short summary
The is a study of the plumes that rise from smokestacks. Knowing how these plume behave helps predict downwind pollutant concentrations. We use photos over a 2-year period to investigate how these plumes rise under different conditions and compare this to a commonly used model parameterization. It is found that the equations used to model plume rise in current models do well for some condition, but these equations can over-predict the plume rise, typically during the day when it is hot.
Share