Logo image
Back
Improving Three‐Dimensional NO x Emission Estimates Through Combined Assimilation of Surface and Satellite Observations
Journal article   Peer reviewed

Improving Three‐Dimensional NO x Emission Estimates Through Combined Assimilation of Surface and Satellite Observations

Lei Kong, Xiao Tang, Zifa Wang, Jiang Zhu, Hang Su, Jie Li, Junhua Wang, Qibo Xu and Gregory R. Carmichael
Journal of geophysical research. Atmospheres, Vol.131(14), e2025JD046183
07/28/2026
DOI: 10.1029/2025JD046183

View Online

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. Chemical data assimilation is a widely used way to improve the estimation of NO x emissions. Previous inversion studies mainly assimilated only surface or satellite observations. Few studies have been conducted to jointly assimilate these two data sources. In this study, we developed a multi‐source data assimilation system that can simultaneously assimilate surface and satellite observations. Based on this system, we compared the inversion results of NO x emissions in China derived from these two data sources under the same assimilation framework, which indicated a nearly two‐fold discrepancy between their inversion results. Consequently, assimilating one type of observation often degrades performance for the other and direct combined assimilation only works moderately. The main reason lies in that the model has contrast errors for surface and column concentrations, which may be related to the contrast emission errors between near‐surface and upper layers. Based on this, we proposed a three‐dimensional combined inversion method which helps reconcile the conflicts in single‐source data assimilation. The method also reveals potential biases in single‐source inversion results and suggests that China has a larger share of NO x emission in the surface. These findings highlight the need for multi‐source data assimilation and consideration of vertical emission uncertainties in future emission inversion studies. A nearly two‐fold discrepancy is identified between inversion results of NO x emission derived from surface and satellite assimilation A vertical sensitivity‐aware multi‐source data assimilation method is proposed that can correct three‐dimensional NO x emission The method reveals potential biases in single‐source inversion results and suggests a larger fraction of surface NO x emissions in China
 multi-source data assimilation NOx emission three-dimensional correction emission vertical profile surface data assimilation satellite data assimilation

Details

Metrics

1 Record Views
Logo image