Sensitivity of PARASOL multi-angle photopolarimetric aerosol retrievals to cloud contamination

Abstract

An important problem in satellite remote sensing of aerosols is related to the need to perform an adequate cloud screening. If a cloud screening is applied that is not strict enough, the ground scene has the probability of residual cloud cover which causes large errors on the retrieved aerosol parameters. On the other hand, if the cloud-screening procedure is too strict, too many clear sky cases, especially near-cloud scenes, will falsely be flagged cloudy. The detrimental effects of cloud contamination as well as the importance of aerosol cloud interactions that can be studied in these near-cloud scenes call for new approaches to cloud screening. Multi-angle multi-wavelength photopolarimetric measurements have a unique capability to distinguish between scattering by (liquid) cloud droplets and aerosol particles. In this paper the sensitivity of aerosol retrievals from multi-angle photopolarimetric measurements to cloud contamination is investigated and the ability to intrinsically filter the cloud-contaminated scenes based on a goodness-of-fit criteria is evaluated. Hereto, an aerosol retrieval algorithm is applied to a partially clouded over-ocean synthetic data set as well as non-cloud-screened over-ocean POLDER-3/PARASOL observations. It is found that a goodness-of-fit filter, together with a filter on the coarse mode refractive index (m(r)(coarse) > 1.335) and a cirrus screening, adequately rejects the cloud-contaminated scenes. No bias or larger SD are found in the retrieved parameters for this intrinsic cloud filter compared to the parameters retrieved in a priori cloud-screened data set (using MODIS/AQUA cloud masks) of PARASOL observations. Moreover, less high-aerosol load scenes are misinterpreted as cloud contaminated. The retrieved aerosol optical thickness, single scattering albedo and Angstrom exponent show good agreement with AERONET observations. Furthermore, the synthetic retrievals give confidence in the ability of the algorithm to correctly retrieve the micro-physical aerosol parameters.