Impact of satellite observations on assimilation inversion of high-resolution urban-scale carbon dioxide fluxes

Abstract. Cities are major sources of global carbon. Accurately quantifying urban-scale carbon dioxide (CO₂) fluxes is essential for supporting targeted emission reduction policies and effective monitoring. To address the limitations in the accuracy of current urban carbon emission estimates, we developed FEISSO, an urban-scale CO₂ flux inversion system that integrates a Lagrangian atmospheric transport model with a Bayesian assimilation framework. FEISSO was used to systematically explore the feasibility of retrieving high-resolution flux distributions from satellite-based XCO₂ observations. Sensitivity experiments were conducted in Weifang, Chengdu, and Xining (China) to identify key influencing factors in CO₂ flux inversion. Results show that the resolution of meteorological drivers substantially affects the accuracy of simulated transport trajectories, with higher resolution (0.25°) improving the spatial fidelity of flux retrieval. Sensitivity analysis indicates that the column-averaged CO₂ observation error and the total error for the inversion domain are the dominant factors affecting the total flux estimates, and they induce a "seesaw" effect in the spatial distribution of emissions. In contrast, prior flux error and spatial correlation length for land have limited influence on the total emissions but primarily affect the spatial pattern of weak emission regions and the smoothness of flux fields, respectively. Differences in topography and meteorological conditions across cities govern the temporal response of flux estimates to observations. With optimized parameter settings, the system successfully retrieved 10-days of total CO₂ emissions for Weifang, Chengdu, and Xining, showing overall consistency with EDGAR and local inventory data. The retrieved emissions correspond to 85.8 %, 190.42 %, and 86.4 % of the EDGAR estimates for the three cities, respectively, while the relative differences from local inventories are 2.3 % for Weifang and 10.9 % for Xining. The results from this study demonstrate the applicability and scalability of the FEISSO system for urban CO₂ flux estimation. In this study, the frequency of urban-scale inversions was limited by the current orbital coverage of the OCO-2 satellite. With future improvements in satellite observation capabilities, particularly in spatial resolution and revisit frequency, FEISSO is expected to play a pivotal role in global urban carbon emission monitoring and in the evaluation of emission reduction policy.