Improving Three‐Dimensional NO _<i>x</i> Emission Estimates Through Combined Assimilation of Surface and Satellite Observations

Abstract Emission inversion based on surface or satellite data assimilation is an important way to constrain NO x emissions, thereby facilitating the development of effective mitigation strategies and assessment of CO 2 emissions. However, few studies have been conducted to compare their inversion performance under the same assimilation framework, and the efforts to combine these two data sources remain limited. In this study, a multi‐source data assimilation system based on an ensemble Kalman filter and the Community Multiscale Air Quality Modeling System is developed, which can simultaneously assimilate surface and satellite observations. A nearly two‐fold discrepancy is identified between the inversion results of surface and satellite assimilation, where assimilating one type of observation often degrades performance for the other. Although direct combined assimilation of surface and satellite observation simultaneously optimized surface and column concentration simulations, it yields only moderate performance. The inconsistent simulation errors in surface and column concentrations are found to be the main reasons for these issues and may reflect the contrast emission errors between near‐surface and upper layers. To address this, we proposed a three‐dimensional (3D) combined assimilation method by utilizing the varied vertical sensitivity of surface and satellite data to emissions. The method shows capability in reconciling the conflicts inherent in single‐source data assimilation and highlights potential biases in single‐source inversion results and a larger fraction of surface NO x emissions in China was identified. These findings highlight the necessity of multi‐source data assimilation and the consideration of vertical emission uncertainty in future inversion studies.