Summary of Learned 3d Volumetric Recovery Of Clouds and Its Uncertainty For Climate Analysis, by Roi Ronen and Ilan Koren and Aviad Levis and Eshkol Eytan and Vadim Holodovsky and Yoav Y. Schechner
Learned 3D volumetric recovery of clouds and its uncertainty for climate analysis
by Roi Ronen, Ilan Koren, Aviad Levis, Eshkol Eytan, Vadim Holodovsky, Yoav Y. Schechner
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel machine learning-based model called ProbCT that enables passive scattering computed tomography (CT) for shallow scattered clouds. The goal is to remotely sense the three-dimensional (3D) heterogeneous volumetric scattering content of these clouds, which is crucial for improving climate prediction and cloud physics. ProbCT infers the posterior probability distribution of the heterogeneous extinction coefficient per 3D location using noisy multi-view spaceborne images. This yields valuable statistics such as the 3D field of the most probable extinction and its uncertainty. The model uses a neural-field representation, allowing for real-time inference. ProbCT is trained on a new labeled database of physics-based volumetric fields of clouds and their corresponding images. To enhance out-of-distribution performance, self-supervised learning through differential rendering is incorporated. The approach is demonstrated in simulations and on real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a machine that can see inside clouds from far away. Clouds are important for understanding the weather and climate, but they’re hard to study because we can’t see what’s going on inside them. The researchers developed a new way to use computer images of clouds to figure out what’s happening inside them. This helps us understand how clouds will behave in the future and how we might be able to use them for renewable energy. |
Keywords
» Artificial intelligence » Inference » Machine learning » Probability » Self supervised
