MUCCnet visiting Vienna: refining inverse model prior information with tall-tower flux measurements

More than two thirds of global anthropogenic greenhouse gas (GHG) emissions originate from cities. Urban mitigation policies need a reliable emission data basis to effectively reduce emissions and given inventory uncertainties at the level of single cities, there is growing interest in measurement-based methods to support urban GHG emissions monitoring. Inverse modelling is a measurement-based approach that integrates atmospheric observations with emission inventories, whereby the inventories serve as prior estimates that are subsequently constrained against the observations. While such inverse systems rely on modelling frameworks that typically utilise in situ and/or remote measurements of atmospheric GHG mixing ratios, there is scope for city-scale inverse frameworks to utilise other types of GHG observations, such as flux measurements.

In this study, we investigate such an approach based on a two month field campaign between 15th of May and 20th of July in 2022 in Vienna, Austria. In particular, for the prior information, we use tall-tower eddy covariance observations to constrain the CH₄ emissions within the tower's flux footprint and combine the measurements with 1km x 1km inventory data of the larger city area of Vienna. This refined and measurement-supported inventory serves as a-priori information for both, a Bayesian- and a Phillips-Tikhonov based inversion approach. The observational input for the inversion methods is delivered by MUCCnet (Munich Urban Carbon Column network) instruments consisting of four ground-based, sun-viewing FTIR spectrometers (EM27/SUN), with three of these instruments located on the outskirts of Vienna and one instrument located at the bottom of the tall-tower close to the city center. 

This study investigates the synergetic aspects of two different measurement systems: the eddy-covariance system is particularly sensitive to near field emissions with a range of hundreds of meters upwind of the tower, whereas the ground-based remote sensing instruments observe the differential total column concentration and are therefore sensitive to emissions originating several Kilometers upwind. Applying both measurement systems within a city inversion framework may indeed represent a viable option for further constraining city emissions and improving urban GHG emissions monitoring.