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Tomography aims to recover a three-dimensional (3D) density map of a mediumor an object. In medical imaging, it is extensively used for diagnostics viaX-ray computed tomography (CT). Optical diffusion tomography is an alternativeto X-ray CT that uses multiply scattered light to deliver coarse density mapsfor soft tissues. We define and derive tomography of cloud dropletdistributions via passive remote sensing. We use multi-view polarimetric imagesto fit a 3D polarized radiative transfer (RT) forward model. Our motivation is3D volumetric probing of vertically-developed convectively-driven clouds thatare ill-served by current methods in operational passive remote sensing. Thesetechniques are based on strictly 1D RT modeling and applied to a single cloudypixel, where cloud geometry is assumed to be that of a plane-parallel slab.Incident unpolarized sunlight, once scattered by cloud-droplets, changes itspolarization state according to droplet size. Therefore, polarimetricmeasurements in the rainbow and glory angular regions can be used to infer thedroplet size distribution. This work defines and derives a framework for a full3D tomography of cloud droplets for both their mass concentration in space andtheir distribution across a range of sizes. This 3D retrieval of keymicrophysical properties is made tractable by our novel approach that involvesa restructuring and differentiation of an open-source polarized 3D RT code toaccommodate a special two-step optimization technique. Physically-realisticsynthetic clouds are used to demonstrate the methodology with rigorousuncertainty quantification.

Multi-view polarimetric scattering cloud tomography and retrieval of droplet size