Bak, J., Liu, X., Kim, J. H., Chance, Kelly V., and Haffner, D. P. 2014. "Validation of OMI total ozone retrievals from the SAO ozone profile algorithm and three operational algorithms with Brewer measurements." Atmospheric Chemistry & Physics Discussions 14:4051-4087. https://doi.org/10.5194/acpd-14-4051-2014
The accuracy of total ozone computed from the Smithsonian Astrophysical Observatory (SAO) optimal estimation (OE) ozone profile algorithm (SOE) applied to the Ozone Monitoring Instrument (OMI) is assessed through comparisons with ground-based Brewer spectrometer measurements from 2005 to 2008. We also make comparisons with the three OMI operational ozone products, derived from the NASA Total Ozone Mapping Spectrometer (TOMS), KNMI Differential Optical Absorption Spectroscopy (DOAS), and KNMI OE (KOE) algorithms. Excellent agreement is observed between SAO and Brewer, with a mean difference of less than ±1% at most individual stations. The KNMI OE algorithm systematically overestimates Brewer total ozone by 2% at low/mid latitudes and 5% at high latitudes while the TOMS and DOAS algorithms underestimate it by ~1.65% on average. Standard deviations of ~1.8% are found for both SOE and TOMS, but DOAS and KOE have scatters of 2.2% and 2.6%, respectively. The stability of the SOE algorithm is found to have insignificant dependence on viewing geometry, cloud parameters, total ozone column. In comparison, the KOE differences to Brewer values are significantly correlated with solar and viewing zenith angles, with a significant deviation depending on cloud parameters and total ozone amount. The TOMS algorithm exhibits similar stability to SOE with respect to viewing geometry and total column ozone, but stronger cloud parameter dependence. The dependence of DOAS on the algorithmic variables is marginal compared to KOE, but distinct compared to the SOE and TOMS algorithms. Comparisons of All four OMI products with Brewer show no apparent long-term drift but a seasonally affected feature, especially for KOE and TOMS. The substantial differences in the KOE vs. SOE algorithm performance cannot be sufficiently explained by the use of soft calibration (in SOE) and the use of different a priori error covariance matrix, but other algorithm details cause larger fitting residuals by a factor of 2-3 for KOE.