多视角偏振散射云层析成像及雾滴尺寸反演
Multi-view polarimetric scattering cloud tomography and retrieval of droplet size
摘要：
断层摄影旨在恢复介质或对象的三维（3D）密度图。在医学成像中，它广泛用于通过X射线计算机断层成像（CT）进行诊断。光学扩散断层扫描是X射线CT的一种替代方法，它使用多重散射光来提供软组织的粗略密度图。我们定义并推导出通过被动遥感的云滴分布层析成像。我们使用多视角偏振图像来拟合三维偏振辐射转移（RT）正演模型。我们的动机是三维体积探测垂直发展的对流驱动的云，目前的方法在业务被动遥感服务不良。这些技术是基于严格的一维RT建模和应用到一个单一的多云像素，云的几何形状被假定为一个平面平行板。入射的非偏振太阳光，一旦被云滴散射，其偏振状态就会根据云滴的大小而改变。因此，在彩虹和荣耀角区域的偏振测量可以用来推断液滴的尺寸分布。这项工作定义并推导出一个完整的3D层析成像的云滴在空间中的质量浓度和它们在一系列大小的分布的框架。这种3D检索的关键微物理特性是由我们的新方法，涉及到一个开源的极化3D RT代码的重组和分化，以适应一个特殊的两步优化技术，易于处理。物理逼真的合成云被用来证明与严格的不确定性量化的方法。
英文摘要：
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). Optical diffusion tomography is an alternative to X-ray CT that uses multiply scattered light to deliver coarse density maps for soft tissues. We define and derive 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. These techniques are based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometry is assumed to be 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.
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