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

Abstract. Accurate global aerosol single scattering albedo (SSA) data is 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 OMI and POLDER with AERONET observations respectively, namely Merged-OMI and Merged-POLDER dataset. The background ensemble is constructed with 231/106 members using all monthly mean OMI/POLDER SSA available to represent the variability of 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 AERONET results. Cross validation using independent AERONET observations shows an average increase of 70 % in correlation, 15 % reduction in RMSE and 14 % reduction in MAB for Merged-OMI dataset, and 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 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.