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

arXiv:2403.05932v1 Announce Type: new
Abstract: 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, …

abstract analysis arxiv challenges climate cloud cs.ai cs.cv design physics prediction recovery sensing three-dimensional type uncertainty