| 12 Jun 2026
A Bayesian Maximum Entropy Framework Using Vertical Profiles to Improve Surface Ozone Estimation From IASI+GOME2, OMI/MLS, And Cris Satellite Ozone Observations
Abstract. Satellite observations are essential for global tropospheric ozone monitoring, but their ability to estimate ground-level ozone remains limited because of weak sensitivity and substantial uncertainty near the surface. In this study, we develop new methods for adjusting satellite ozone observations (IASI+GOME2, OMI/MLS, and CrIS) through chemistry-transport reanalysis and in situ ozone vertical profile measurements. Using these methods, we create global maps of ground-level ozone concentrations based on satellite observations. We use the Bayesian Maximum Entropy framework to horizontally interpolate the vertical profiles from ozonesondes and IAGOS and improve the accuracy of both the satellite column measurements and the surface-to-column ratios from a chemical reanalysis. This is done for monthly average maximum daily 8-hr ozone concentrations over several years. For the three satellites, surface ozone estimated from the BME-adjusted column-to-surface conversion showed improved agreement with TOAR-II observations. For IASI+GOME2 (2017–2020), global R2 increased from 0.25 to 0.51, and RMSE was reduced from 10.74 to 9.44 ppb. For OMI/MLS tropospheric column (2005–2022), global R2 increased from 0.26 to 0.57, and RMSE decreased from 22.21 to 7.79 ppb. For the CrIS 0–3 km ozone (2022), global R2 increased from 0.30 to 0.56, and RMSE decreased from 16.48 to 9.45 ppb. The method's efficacy was found to be highest within 6° of a vertical profile station and most impactful when the original satellite data quality was low. The resulting satellite-based monthly ground-level ozone estimates can be used further as an independent input to data fusion methods.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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