A comparison of the impact of TROPOMI and OMI tropospheric NO₂ on global chemical data assimilation

<p>This study gives a systematic comparison of the Tropospheric Monitoring Instrument (TROPOMI) version 1.2 and Ozone Monitoring Instrument (OMI) QA4ECV tropospheric <span class="inline-formula">NO₂</span> column through global chemical data assimilation (DA) integration for the period April–May 2018. DA performance is controlled by measurement sensitivities, retrieval errors, and coverage. The smaller mean relative observation errors by 16 % in TROPOMI than OMI over 60<span class="inline-formula">^∘</span> N–60<span class="inline-formula">^∘</span> S during April–May 2018 led to larger reductions in the global root-mean-square error (RMSE) against the assimilated <span class="inline-formula">NO₂</span> measurements in TROPOMI DA (by 54 %) than in OMI DA (by 38 %). Agreements against the independent surface, aircraft-campaign, and ozonesonde observation data were also improved by TROPOMI DA compared to the control model simulation (by 12 %–84 % for <span class="inline-formula">NO₂</span> and by 7 %–40 % for ozone), which were more obvious than those by OMI DA for many cases (by 2 %–70 % for <span class="inline-formula">NO₂</span> and by 1 %–22 % for ozone) due to better capturing spatial and temporal variability by TROPOMI DA. The estimated global total <span class="inline-formula">NO_<i>x</i></span> emissions were 15 % lower in TROPOMI DA, with 2 %–23 % smaller regional total emissions, in line with the observed negative bias of the TROPOMI version 1.2 product compared to the OMI QA4ECV product. TROPOMI DA can provide city-scale emission estimates, which were within 10 % differences with other high-resolution analyses for several limited areas, while providing a globally consistent analysis. These results demonstrate that TROPOMI DA improves global analyses of <span class="inline-formula">NO₂</span> and ozone, which would also benefit studies on detailed spatial and temporal variations in ozone and nitrate aerosols and the evaluation of bottom-up <span class="inline-formula">NO_<i>x</i></span> emission inventories.</p>