Urban regions emit large amounts of CO2, are drivers of national inventory uncertainty, and have a direct impact on the day-to-day life of their inhabitants. Therefore, an independent assessment of urban emissions supports climate action and governance. This work presents a complete pipeline for improving urban emissions estimates. It includes setting up, optimizing, and evaluating atmospheric transport as well as CO2 and CO simulations and estimating emissions based on aircraft measurements. We optimize the physics configuration of the WRF atmospheric transport model and show that its performance exceeds previous studies. We use this optimized configuration to generate MACRO-2018, a 1 km resolved dataset of meteorology as well as CO2 and CO concentrations in German metropolitan areas for 2018. This dataset provides two different physics configurations and biogenic models. We evaluate its performance against high-precision CO2 and CO measurements and find mean absolute biases of 5.2 ppm to 6.4 ppm for CO2, exceeding the quality of comparable datasets. For CO, we find mean absolute biases of 25.4 ppb to 28.6 ppb. Finally, based on this dataset we set up a Bayesian inversion framework which assimilates aircraft measurements to optimize the emissions of Berlin and the background CO2 concentration simultaneously. Using this approach, we reduce the emissions estimate uncertainty from a simpler and widely-used mass balance approach by a factor of five.
Lukas Nicolai Pilz (Thu,) studied this question.