This paper assesses the capability of liquid cloud droplet effective radius (CER) and cloud effective variance (CEV) retrieval from space-borne multi-angular hyperspectral measurements. The capability and sensitivity study is based on a neural network (NN) retrieval approach which is developed for the Spectropolarimeter for Planetary EXploration - one (SPEXone), a multi-angular hyperspectral polarimeter onboard Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite. The synthetic measurements used in NN training and the sensitivity experiments are generated by Remote sensing of Trace gas and Aerosol Products (RemoTAP) forward model, and include variations of cloud, surface and aerosol properties, as well as cloud fraction. On the basic validation set, the NN performs similar over ocean and land with a mean absolute error (MAE) around 2μm on CER and around 0.04 on CEV. The performance over different cloud fraction (CF) and cloud optical thickness (COT) is evaluated, and indicates that most accurate retrievals can be performed for cases where CF >0.6 and COT between 2 and 12. The sensitivity to above-cloud aerosols (both fine-mode-dominated and dust-mode-dominated cases) suggests the retrieval is more sensitive to absorbing fine mode aerosols (CER MAE <2.5μm up to AOT of 0.2 for fully cloudy scene), than to dust aerosols (CER MAE <2.5μm up to AOT of 0.5). Moreover, the retrievals are virtually insensitive to above cloud cirrus over fully cloudy scene, but shows large sensitivity over partly cloudy scenes over land. Finally, synthetic measurements from partly cloudy scenes are generated based on the 3D MYSTIC radiative transfer model. The retrieval on these measurements suggests no significant 3D cloud radiative effect artifacts.

This paper describes an algorithm for above-cloud aerosol (ACA) retrievals from PARASOL (Polarisation and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) Multi-Angle Polarimetric measurements. The algorithm, based on neural networks (NNs), has been trained on synthetic measurements and has been applied to the processing of one-year PARASOL data. The algorithm makes use of three subsequent NNs: (1) for the detection of liquid clouds, (2) for the retrieval of aerosol properties for ACA cases, and (3) an NN forward model to evaluate the goodness-of-fit of the retrieval. The NN’s theoretical capability of retrieval is investigated by several synthetic data studies. It is shown that the NNs retrieve ACAOT₅₅₀ (above cloud aerosol optical thickness, at 550 nm), AE_440–670 (Ångström exponent, between 440 and 670 nm), and SSA₅₅₀ (single scattering albedo, at 550 nm) with an RMSE (root mean squared error) of ∼ 0.1 on ACAOT₅₅₀, ∼ 0.4 on AE_440–670 and ∼ 0.04 on SSA₅₅₀ in synthetic experiments. Finally, comparison between the NN retrievals and adjacent PARASOL-RemoTAP clear-sky retrieval in 2008 shows good agreement within the range expected from the synthetic study.