Capability of liquid cloud microphysical property retrieval from satellite-borne multi-angle hyperspectral polarimetric measurements

Abstract

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.