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Abstract
Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging, it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomography of cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3D polarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developed convectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Current techniques are based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometry defaults to that of a plane-parallel slab. Incident unpolarized sunlight, once scattered by cloud-droplets, changes its polarization state according to droplet size. Therefore, polarimetric measurements in the rainbow and glory angular regions can be used to infer the droplet size distribution. This work defines and derives a framework for a full 3D tomography of cloud droplets for both their mass concentration in space and their distribution across a range of sizes. This 3D retrieval of key microphysical properties is made tractable by our novel approach that involves a restructuring and differentiation of an open-source polarized 3D RT code to accommodate a special two-step optimization technique. Physically-realistic synthetic clouds are used to demonstrate the methodology with rigorous uncertainty quantification.
Highlights
Clouds play a significant role at local and global scales, affecting weather, the water cycle, solar power generation, and impacting the Earth’s energy balance [1]
Common retrieval of cloud droplet characteristics use two optical bands simultaneously [9]: a visible band, where reflected radiance increases with cloud optical thickness, and a shortwave infra-red (SWIR) band, where absorption by condensed water depends on cloud droplet size
Motivated by the CloudCT mission formulation—only the first of many to come in innovative passive cloud remote sensing—we develop a novel framework for 3D remote sensing of cloud properties using multi-view polarimetric measurements
Summary
There is an additional caveat in common retrievals, which rely on SWIR absorption [9]. Multiple scattering diminishes sensitivity to droplet microphysics. High sensitivity to microphysics is embedded in single-scattering events. Polarization signals of scattered light are dominated by single-scattering events, and are highly sensitive to the type and size specifications of scatters. Polarization provides a significant signal for retrieval of droplet size distributions. The intensity signal, which undergoes multiple-scattering events before reaching the sensor, is insensitive to the droplet size and provides complementary information about optical densities within the cloud. Increased interest in polarimetric sensing capabilities has led to the development of 1D and 3D polarized (or “vector”) RT codes [17,18] with an aim of improving retrieval algorithms. Motivated by the CloudCT mission formulation—only the first of many to come in innovative passive cloud remote sensing—we develop a novel framework for 3D remote sensing of cloud properties using multi-view polarimetric measurements
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