MISR research-aerosol-algorithm refinements for dark water retrievals

Abstract

Abstract. We explore systematically the cumulative effect of many assumptions made in the Multi-angle Imaging SpectroRadiometer (MISR) research aerosol retrieval algorithm with the aim of quantifying the main sources of uncertainty over ocean, and correcting them to the extent possible. A total of 1129 coincident, surface-based sun photometer spectral aerosol optical depth (AOD) measurements are used for validation. Based on comparisons between these data and our baseline case (similar to the MISR standard algorithm, but without the "modified linear mixing" approximation), for 558 nm AOD < 0.10, a high bias of 0.024 is reduced by about one-third when (1) ocean surface under-light is included and the assumed whitecap reflectance at 672 nm is increased, (2) physically based adjustments in particle microphysical properties and mixtures are made, (3) an adaptive pixel selection method is used, (4) spectral reflectance uncertainty is estimated from vicarious calibration, and (5) minor radiometric calibration changes are made for the 672 and 866 nm channels. Applying (6) more stringent cloud screening (setting the maximum fraction not-clear to 0.50) brings all median spectral biases to about 0.01. When all adjustments except more stringent cloud screening are applied, and a modified acceptance criterion is used, the Root-Mean-Square-Error (RMSE) decreases for all wavelengths by 8–27% for the research algorithm relative to the baseline, and is 12–36% lower than the RMSE for the Version 22 MISR standard algorithm (SA, with no adjustments applied). At 558 nm, 87% of AOD data falls within the greater of 0.05 or 20% of validation values; 62% of the 446 nm AOD data, and > 68% of 558, 672, and 866 nm AOD values fall within the greater of 0.03 or 10%. For the Ångström exponent (ANG), 67% of 1119 validation cases for AOD > 0.01 fall within 0.275 of the sun photometer values, compared to 49% for the SA. ANG RMSE decreases by 17% compared to the SA, and the median absolute error drops by 36%.