Inferring and evaluating satellite-based constraints on NO_<i>x</i> emissions estimates in air quality simulations

Abstract. Satellite observations of tropospheric NO2 columns can provide top-down observational constraints on emissions estimates nitrogen oxides (NOx). Mass-balance-based methods are often applied for this purpose but do not isolate near-surface from those aloft, such as lightning emissions. Here, we introduce an inverse modeling framework that couples satellite chemical data assimilation to a transport model. In the framework, satellite-constrained totals inferred using model simulations with and without in iterative finite-difference mass-balance method. The approach improves inversion by isolating increment. We apply separately estimate anthropogenic NOx over Northern Hemisphere 2019. Using overlapping Ozone Monitoring Instrument (OMI) Tropospheric (TROPOMI), compare separate inferences these instruments, well impacts changes modeled O3. OMI consistently lead larger than TROPOMI inferences, attributed low bias retrievals. Updated either improve model's O3 bias. combined lighting updates ability reproduce measured ozone adjusting natural, long-range, local pollution contributions. Thus, informs supports design domestic international control strategies.