Spaceborne observations serve as input to models and retrieval algorithms of various atmospheric properties. Specifically, we seek 3D volumetric scattering tomography of clouds. Towards this, cloud-fields are to be imaged simultaneously from multiple directions. The CloudCT project aims to demonstrate this by a formation of ten nanosatellites. Based on this data, scattering tomography will seek the 3D volumetric distribution of cloud microphysical properties. We present constraints and fundamental considerations for spaceborne formation-based cloud tomography. We quantitatively compare visible polarized imagers, visible unpolarized imagers, and short-wave infra-red unpolarized imagers. Each possibility is considered using a large eddy simulation of clouds. We study tomographic quality in the presence of sensor and photon noise, calibration errors and stray light. We find that a polarized imager of a red waveband is preferable.

We introduce a comprehensive method for space-borne 3D volumetric scattering-tomography of cloud micro-physics, developed for the CloudCT mission. The retrieved micro-physical properties are the liquid-water-content and effective droplet radius within a cloud. We include a model for a perspective polarization imager, and an assumption of 3D variation of there. Elements of our work include computed tomography initialization by a parametric horizontally-uniform micro-physical model. This results in smaller errors than the prior art. The mean absolute errors of the retrieved liquid-water-content and effective-radius are reduced from 62% and 28% to 40% and 9%, respectively. The parameters of this initialization are determined by a grid search of a cost function. Furthermore, we add viewpoints in the cloudbow region, to better sample the polarized scattering phase function. The suggested advances are evaluated by retrieval of a set of clouds generated by large-eddy simulations.

We derive computed tomography (CT) of a time-varying volumetric scattering object, using a small number of moving cameras. We focus on passive tomography of dynamic clouds, as clouds have a major effect on the Earth’s climate. State of the art scattering CT assumes a static object. Existing 4D CT methods rely on a linear image formation model and often on significant priors. In this paper, the angular and temporal sampling rates needed for a proper recovery are discussed. Spatiotemporal CT is achieved using gradient-based optimization, which accounts for the correlation time of the dynamic object content. We demonstrate this in physics-based simulations and on experimental real-world data.

We present 3DeepCT, a deep neural network for computed tomography, which performs 3D reconstruction of scattering volumes from multi-view images. The architecture is dictated by the stationary nature of atmospheric cloud fields. The task of volumetric scattering tomography aims at recovering a volume from its 2D projections. This problem has been approached by diverse inverse methods based on signal processing and physics models. However, such techniques are typically iterative, exhibiting a high computational load and a long convergence time. We show that 3DeepCT outperforms physics-based inverse scattering methods, in accuracy, as well as offering orders of magnitude improvement in computational run-time. We further introduce a hybrid model that combines 3DeepCT and physics-based analysis. The resultant hybrid technique enjoys fast inference time and improved recovery performance.