Machine Learning for Retrieving Cloud Optical Thickness from Observed Reflectance: 3D Effects CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences
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Date
2020
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Citation of Original Publication
Angelof, Kallista; Bergstrom, Kirana; Le, Tianhao; Xu, Chengtao; Rajapakshe, Chamara; Zheng, Jianyu; Zhang, Zhibo; Machine Learning for Retrieving Cloud Optical Thickness from Observed Reflectance: 3D Effects CyberTraining: Big Data + High-Performance Computing + Atmospheric Sciences (2020); http://hpcf-files.umbc.edu/research/papers/CT2020Team5.pdf
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Abstract
Clouds are inherently three dimensional (3D), and simulating radiative transfer (RT) properties accurately requires models that take their 3D effects into account. Because 3D models are
complex and computationally expensive, RT models often use simplified 1D models to retrieve
cloud properties, which suffer from retrieval uncertainty and sometimes significant biases due
to 3D effects. Recent advancements in machine learning may lead to a retrieval algorithm that
is capable of taking these effects into account. We will develop a machine-learning based cloud
property retrieval algorithm that is able to reconstruct the 3D structure of clouds based on
observed cloud radiative signatures.