Potential retrieval of aerosol properties for aerosol cloud interaction studies using Aerosol Polarimetry Sensor (APS) measurements

Satellite observations of aerosol cloud interactions are usually based on statistical approaches which look for correlations between cloud and aerosol properties averaged over time and space but efforts are now being explored which uses GOES imagery for cloud transport together with polar multispectral observations to quantify aerosols. In these studies, aerosols are primarily quantified from the Aerosol Optical Depth (AOD) but is has been shown that AOD is not the best proxy for aerosol particles as Cloud Condensation Nuclei (CCN). In particular, fine mode AOD has been shown as a better proxy for CCN and further improvements could be expected if the fine mode particle volume could be estimated. In addition, retrieval of complex refractive index (i.e SSA) is also useful as a means of filtering aerosols for their hygroscopic capacity. While multiangle polarization measurements such as POLDER have been used to extract the fine mode AOD, the lack of long wavelength channels make it less suited in separation of atmosphere and ground signals.

With the above limitations in mind, we explore the possibility of retrieving both the fine mode aerosol volume and particulate number as well as the aerosol SSA using the unique potential of the Aerosol Polarimetry Sensor (APS) on the GLORY satellite. In particular, using a Neural Network approach using a wide range of aerosol properties over realistic oceanic surfaces, NN estimators of the fine mode volume and SSA are obtained with correlations > 0.9. Furthermore, comparisons in the retrieval are made against convention intensity measurements from MODIS to better illustrate the advantages of APS. Finally, we explore the potential improvement in retrieval such as those obtained by ingesting the vertical structure obtained from simultaneous Calipso Lidar measurements.