CloudUNet: Adapting UNet for Retrieving Cloud Properties

Abstract

The Earth’s radiation budget relies on cloud properties like Cloud Optical Thickness obtained from cloud radiance observations. Traditional physics-based cloud retrieval methods face challenges due to 3D radiative transfer effects. Deep learning approaches have emerged to address this, but their performance are limited by simple deep neural network architectures and vanilla objective functions. To overcome these limitations, we propose CloudUNet, a modified UNet-style architecture that captures spatial context and mitigates 3D radiative transfer effects. We introduce a cloud-sensitive objective function with regularized L2 and SSIM losses to learn thick cloud regions often underrepresented in input radiance data. Experiments using realistic atmospheric and cloud Large-Eddy Simulation data demonstrate that our proposed CloudUNet obtains 5-fold improvement over the existing state-of-the-art deep learning, and physics-based methods.