Aerosol single-scattering albedo derived by merging OMI/POLDER satellite products and AERONET ground observations

Abstract

Accurate global aerosol single-scattering albedo (SSA) data are critical for assessing aerosol radiative effects and identifying aerosol composition. However, current satellite-based SSA retrievals are both limited and highly uncertain, whereas the more accurate ground-based observations lack global coverage. In this study, we employ an ensemble Kalman filter (EnKF) data synergy technique to construct two monthly mean SSA datasets over land by synergizing the Ozone Monitoring Instrument (OMI) and Polarization and Directionality of the Earth's Reflectance (POLDER) instrument with Aerosol Robotic Network (AERONET) observations, namely, the Merged-OMI and Merged-POLDER datasets. The background ensemble is constructed with 231/106 members using all monthly mean OMI/POLDER SSA available to represent the variability of the SSA field. Then, AERONET measurements are assimilated into each satellite dataset using the EnKF approach. The merged datasets show substantial improvements against the original products, with the correlation coefficient increased by up to 100 % and the mean absolute bias (MAB) and root mean square error (RMSE) reduced by more than 30 % compared with the AERONET results. Cross-validation using independent AERONET observations shows an average increase of 64 % in correlation, an 11 % reduction in RMSE, and a 10 % reduction in MAB for the Merged-OMI dataset, as well as similar - although weaker - improvement for Merged-POLDER mainly due to the smaller sample size. This study confirms the effectiveness of the EnKF technique in extending the information obtained from ground stations to larger regions. The two merged datasets generated in this study, available at 10.5281/zenodo.14294462 , can offer more accurate SSA estimates for assessing aerosol radiative forcing and improving climate modeling, serving as an important resource for advancing global aerosol research.