Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size

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

Learned 3D volumetric recovery of clouds and its uncertainty for climate analysis

Significant uncertainty in climate prediction and cloud physics is tied to observational gaps relating to shallow scattered clouds. Addressing these challenges requires remote sensing of their three-dimensional (3D) heterogeneous volumetric scattering content. This calls for passive scattering computed tomography (CT). We design a learning-based model (ProbCT) to achieve CT of such clouds, based on noisy multi-view spaceborne images. ProbCT infers - for the first time - the posterior probability distribution of the heterogeneous extinction coefficient, per 3D location. This yields arbitrary valuable statistics, e.g., the 3D field of the most probable extinction and its uncertainty. ProbCT uses a neural-field representation, making essentially real-time inference. ProbCT undergoes supervised training by a new labeled multi-class database of physics-based volumetric fields of clouds and their corresponding images. To improve out-of-distribution inference, we incorporate self-supervised learning through differential rendering. We demonstrate the approach in simulations and on real-world data, and indicate the relevance of 3D recovery and uncertainty to precipitation and renewable energy

Expanding the solvent chemical space for self-assembly of dipeptide nanostructures.

Nanostructures composed of short, noncyclic peptides represent a growing field of research in nanotechnology due to their ease of production, often remarkable material properties, and biocompatibility. Such structures have so far been almost exclusively obtained through self-assembly from aqueous solution, and their morphologies are determined by the interactions between building blocks as well as interactions between building blocks and water. Using the diphenylalanine system, we demonstrate here that, in order to achieve structural and morphological control, a change in the solvent environment represents a simple and convenient alternative strategy to the chemical modification of the building blocks. Diphenylalanine (FF) is a dipeptide capable of self-assembly in aqueous solution into needle-like hollow micro- and nanocrystals with continuous nanoscale channels that possess advantageous properties such as high stiffness and piezoelectricity and have so emerged as attractive candidates for functional nanomaterials. We investigate systematically the solubility of diphenylalanine in a range of organic solvents and probe the role of the solvent in the kinetics of self-assembly and the structures of the final materials. Finally, we report the crystal structure of the FF peptide in microcrystalline form grown from MeOH solution at 1 Å resolution and discuss the structural changes relative to the conventional materials self-assembled in aqueous solution. These findings provide a significant expansion of the structures and morphologies that are accessible through FF self-assembly for existing and future nanotechnological applications of this peptide. Solvent mediation of molecular recognition and self-association processes represents an important route to the design of new supramolecular architectures deriving their functionality from the nanoscale ordering of their components.We thank the Newman Foundation (T.O.M., T.P.J.K.), the FEBS and the Tel Aviv University Center for Nanoscience and Nanotechnology (A.L.), the BBSRC (T.P.J.K.), and the Leverhulme Trust and Magdalene College (A.K.B.) for financial support. A.L. thanks Or Berger for his assistance with the HR-SEM imaging. The X-ray diffraction data collection experiments were performed in the crystallographic X-ray facility at the Department of Biochemistry, University of Cambridge. The authors thank Pavel Afonin for help with PHENIX software suite in the refinement of the structures.This is the accepted manuscript for a paper published in ACS Nano, 2014, 8 (2), pp 1243–1253 DOI: 10.1021/nn404237f , Publication Date (Web): January 14, 201