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

• URL: http://arxiv.org/abs/2403.05932v1
• Date: Sat, 9 Mar 2024 14:57:03 GMT
• Title: Learned 3D volumetric recovery of clouds and its uncertainty for climate analysis
• Authors: Roi Ronen and Ilan Koren and Aviad Levis and Eshkol Eytan and Vadim Holodovsky and Yoav Y. Schechner
• Abstract summary: Uncertainty in climate prediction and cloud physics is tied to observational gaps relating to shallow scattered clouds. We design a learning-based model (ProbCT) to achieve CT of such clouds, based on noisy multi-view spaceborne images. 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.
• Score: 16.260663741590253
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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, 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.

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