Algorithm evaluation for Polarimetric Remote Sensing of Atmospheric Aerosols

Abstract. From a passive satellite remote sensing point-of-view, the richest set of information on aerosol properties can be obtained from instruments that measure both intensity and polarization of back- scattered sun light at multiple wavelengths and multiple viewing angles for one ground pixel. However, it is challenging to exploit this information at a global scale because complex algorithms are needed with many fit parameters (aerosol and land/ocean reflection), based on online radiative transfer models. So far, two of such algorithms have demonstrated capability at a global scale: the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm and the Remote Sensing of Trace gas and Aerosol Products (RemoTAP) algorithm. In this paper, we present a detailed comparison of the most recent versions of RemoTAP and GRASP. We evaluate both algorithms for synthetic observations, for real PARASOL observations against AERONET for common pixels, and for global PARASOL retrievals for the year 2008. Both RemoTAP and GRASP show good agreement against AERONET. For the Aerosol  Optical Depth (AOD) over land, both algorithms show a Root Mean Square Error (RMSE) of 0.10 (at 550 nm). For Single Scattering Albedo (SSA), both algorithms show a good performance in terms of RMSE ( 0.04) but RemoTAP has a smaller bias (0.002) compared to GRASP (0.021). For Angstrom Exponent (AE), GRASP has a smaller RMSE (0.367) than RemoTAP (0.387), mainly caused by a small overestimate of AE at low values (large particles). Over ocean both algorithms perform very well. For AOD, RemoTAP has an RMSE of 0.057 and GRASP an even smaller RMSE of 0.047. For AE, the RMSE  of RemoTAP and GRASP are 0.285 and 0.224, respectively. Based on the AERONET comparison, we conclude that both algorithms show very similar overall performance, where both algorithms have stronger and weaker points. For the global data products, we find a Root Mean Square Difference (RMSD) between RemoTAP and GRASP AOD of 0.12 and 0.038 over land and ocean, respectively. The largest differences occur over the biomass burning region in equatorial Africa. The global mean values are virtually unbiased with respect to each other. For AE the RMSD between RemoTAP and GRASP is 0.33  over land and 0.23 over ocean. For SSA, we find good agreement over land (RMSD=0.043) for retrievals with AOD > 0.2. Over ocean the agreement is poor with a bias of 0.053 (where RemoTAP retrieves higher SSA) and an RMSD of 0.074. As expected, the differences increase towards low AOD, both over land and ocean. We also compared the GRASP and RemoTAP AOD and AE products against MODIS. For AOD over land, the agreement of either GRASP or RemoTAP with MODIS is worse than the agreement between the 2 PARASOL algorithms themselves. Over ocean, the agreement is very similar among  the 3 products for AOD. For AE, the agreement between GRASP and RemoTAP is much better than the agreement of both products with MODIS. Overall, the good agreement between RemoTAP and GRASP, and between the individual algorithms with AERONET, demonstrates high fidelity of both data products for PARASOL. The agreement of the latest product versions with each other and with AERONET improved significantly compared to the previous version of the global products of GRASP and RemoTAP. The results demonstrate that dedicated effort on algorithm development for MAP aerosol retrievals still leads to substantial improvement of the resulting aerosol products and this is still an ongoing process.