Sanam Vardag and Robert Maiwald employ the atmospheric transport model GRAMM/GRAL in a Bayesian inversion process to assess urban CO2 emissions at a neighborhood level. They examine how changes in the number, accuracy, and placement of CO2 sensors impact the estimation of CO2 flux. Additionally, they test incorporating co-emitted species and correlation into the inversion process. The study highlights the overall effectiveness of GRAMM/GRAL in designing measurement networks.
Vardag, S. N. and Maiwald, R.: Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL, Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, 2024.