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 indeed 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 gridded 3D retrieval of key microphysical properties is made tractable by our novel approach that involves a restructuring and partial linearization of an open-source polarized 3D RT code to accommodate a special two-step iterative optimization technique. Physically-realistic synthetic clouds are used to demonstrate the methodology with rigorous uncertainty quantification, while a real-world cloud imaged by AirMSPI is processed to illustrate the new remote sensing capability.
When observing a spatially complex mix of aerosols and clouds in a single relatively large field-of-view, nature entangles their signals non-linearly through polarized radiation transport processes that unfold in the 3D position and direction spaces. In contrast, any practical forward model in a retrieval algorithm will use only 1D vector radiative transfer (vRT) in a linear mixing technique. We assess the difference between the observed and predicted signals using synthetic data from a high-fidelity 3D vRT model with clouds generated using a Large Eddy Simulation model and an aerosol climatology. We find that this difference is signal—not noise—for the Aerosol Polarimetry Sensor (APS), an instrument developed by NASA. Moreover, the worst case scenario is also the most interesting case, namely, when the aerosol burden is large, hence hase the most impact on the cloud microphysics and dynamics. Based on our findings, we formulate a mitigation strategy for these unresolved cloud adjacency effects assuming that some spatial information is available about the structure of the clouds at higher resolution from “context” cameras, as was planned for NASA’s ill-fated Glory mission that was to carry the APS but failed to reach orbit. Application to POLDER (POLarization and Directionality of Earth Reflectances) data from the period when PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) was in the A-train is briefly discussed.
This paper surveys the need for oxygen A-band spectroscopy to improve our understanding of clouds and their key role in the climate system. We then report on a novel holographic A-band substrate-guided spectrometer device recently developed at Luminit. This A-band spectrometer prototype is based on an innovative structure of two thick reflection substrate-guided wave-based holograms (SGWHs) that act as dispersive and/or imaging elements to enable a sufficient spectral resolution. The technology is made very attractive by its significantly lower cost compared to currently available systems/devices with similar A-band capability, while providing higher light throughput, a better out-of-band rejection ratio, higher resolution at a smaller size, and better stability and reliability.
I survey the theoretical foundations of the slowly-but-surely emerging field of multiple scattering lidar, which has already found applications in atmospheric and cryospheric optics that I also discuss. In multiple scattering lidar, returned pulses are stretched far beyond recognition, and there is no longer a one-to-one connection between range and return-trip timing. Moreover, one can exploit the radial profile of the diffuse radiance field excited by the laser source that, by its very nature, is highly concentrated in space and collimated in direction. One needs, however, a new class of lidar equations to explore this new phenomenology. A very useful set is derived from radiative diffusion theory, which is found at the opposite asymptotic limit of radiative transfer theory than the conventional (single-scattering) limit used to derive the standard lidar equation. In particular, one can use it to show that, even if the simple time-of-flight-to-range connection is irretrievably lost, multiply-scattered lidar light can be used to restore a unique profiling capability with coarser resolution but much deeper penetration into a wide variety of optical thick media in nature. Several new applications are proposed, including a laser bathymetry technique that should work for highly turbid coastal waters.
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