Abstract

This study gives a systematic comparison of the Tropospheric Monitoring Instrument (TROPOMI) version 1.2 and Ozone Monitoring Instrument (OMI) QA4ECV tropospheric NO2 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° N−60° S during April−May 2018 led to larger reductions in the global root mean square error (RMSE) against the assimilated NO2 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 NO2 and by 7−40 % for ozone), which were more obvious than those by OMI DA for many cases (by 2−70 % for NO2 and by 1−22 % for ozone). The estimated global total NOx 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 NO2 and ozone, which would also benefit studies on detailed spatial and temporal variations in ozone and nitrate aerosols and the evaluation of bottom-up NOx emission inventories.

Highlights

  • Satellite measurements from the Global Ozone Monitoring Experiment (GOME) (Burrows et al, 1999), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) (Bovensmann et al, 1999), the Ozone Monitoring Instrument (OMI) (Levelt et al, 2006), and GOME-2 (Callies et al, 2000) have provided long-term global pictures of tropospheric NO2 columns since 1996

  • The estimated global total NOx emissions were 15% lower in Tropospheric Monitoring Instrument (TROPOMI) data assimilation (DA), with 2–23% smaller 10 regional total emissions, in line with the observed negative bias of the TROPOMI version 1.2 product compared to the OMI QA4ECV product

  • We compared DA analyses of NO2, ozone concentrations, and NOx emissions derived from the assimilation of the TROPOMI and OMI tropospheric NO2 column retrievals

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Summary

Introduction

Satellite measurements from the Global Ozone Monitoring Experiment (GOME) (Burrows et al, 1999), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) (Bovensmann et al, 1999), the Ozone Monitoring Instrument (OMI) (Levelt et al, 2006), and GOME-2 (Callies et al, 2000) have provided long-term global pictures of tropospheric NO2 columns since 1996. A multi-constituent chemical DA system developed by our group assimilates multiple satellite measurements simultaneously to improve emissions and concentrations of various species (e.g., Miyazaki et al, 2017, 2020a; Sekiya et al, 2021), which allows us to evaluate the relative value of TROPOMI and OMI retrievals in a consistent framework. We compared concentration and emission analyses derived from the assimilation of TROPOMI and OMI tropospheric NO2 retrievals, which simultaneously optimize tropospheric NO2, ozone concentrations, and NOx emissions at 45 0.56◦ resolution for the globe. This resolution is still insufficient to resolve point source to urban scales, it has the advantage of providing globally–consistent analyses on a megacity scale (Sekiya et al, 2021).

TROPOMI and OMI satellite observations of tropospheric NO2 for assimilation
Independent observations for validation
NASA ATom aircraft-campaign observations
Surface in-situ observations
Ozonesonde observations
CHASER chemical transport model
Ensemble Kalman filter data assimilation
Super-observation approach
Experimental setup
Data characteristics
Self-consistency
ATom aircraft-campaign data
Surface in-situ data
Regional performance over Los Angeles
Impact of OMI instrumental degradation
NOx emission estimates
Validation using surface in-situ data
Validation using ozonesonde data
Findings
Summary and conclusion

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