Do GEMS geostationary satellite observations of tropospheric NO₂ always improve NO_x emission estimates and related air quality modelling?

Abstract. Satellite observations of atmospheric composition from low Earth orbit (LEO) have significantly advanced our understanding of global tropospheric chemistry; however, their 12-hour overpass cadence limits the attribution of rapid compositional changes. The launch of the Korean Geostationary Environment Monitoring Spectrometer (GEMS) in 2020 heralded the beginning of continuous spaceborne monitoring of atmospheric composition during sunlit hours across Asia, allowing researchers to track atmospheric variability in real-time from a geostationary perspective. We assess the added value of GEMS observations of tropospheric NO₂ to estimate monthly emissions of NO_x across Asia compared with the information provided by the equivalent instrument in LEO. We use the adjoint of the GEOS-Chem atmospheric chemistry transport model to infer NO_x emissions, comparing estimates using the full set of GEMS tropospheric NO₂ data against a surrogate LEO dataset created by subsampling the GEMS data at 13:45 local time (Korea Standard Time, KST). We find that the benefits of assimilating high-frequency GEMS observations are most significant during non-summer months (September−May), when elevated NO₂ concentrations and pronounced diurnal variability provide strong constraints on emission estimates. During this period, NO_x emission estimates derived from the full GEMS record deviate substantially from LEO-proxy results, with differences of 0.2−52.6 GgN month⁻¹, corresponding to 0.02−5.06 % of the a priori emissions. These differences further propagate into widespread adjustments in modelled ozone, hydroxyl radicals, and other secondary species, with evaluation against independent in situ measurements showing that GEMS-inferred emission estimates offer comparable or superior performance particularly in regions where the differences are most pronounced. In contrast, we find that during summer months (June−August), low NO₂ levels likely introduce retrieval uncertainties that challenge the data assimilation framework in which only anthropogenic NO_x sources are optimised, leading to negligible or even detrimental impacts on our ability to estimate NO_x emissions.