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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/amt-2017-154
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
30 Jun 2017
Review status
This discussion paper is under review for the journal Atmospheric Measurement Techniques (AMT).
Retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning
Rintaro Okamura1, Hironobu Iwabuchi1, and K. Sebastian Schmidt2 1Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, 6-3 Aoba Aramaki-aza, Sendai, Miyagi 980-8578, Japan
2Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
Abstract. Three-dimensional (3D) radiative transfer effects are a major source of retrieval errors in satellite-based optical re- mote sensing of clouds. In this study, we present two retrieval methods based on deep learning. We use deep neural networks (DNNs) to retrieve multipixel estimates of cloud optical thickness and column-mean cloud droplet effective radius simultane- ously from multispectral, multipixel radiances. Cloud field data are obtained from large-eddy simulations, and a 3D radiative transfer model is employed to simulate upward radiances from clouds. The cloud and radiance data are used to train and test the DNNs. The proposed DNN-based retrieval is shown to be more accurate than the existing look-up table approach that assumes plane-parallel, homogeneous clouds. By using convolutional layers, the DNN method estimates cloud properties robustly, even for optically thick clouds, and can correct the 3D radiative transfer effects that would otherwise affect the radiance values.

Citation: Okamura, R., Iwabuchi, H., and Schmidt, K. S.: Retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2017-154, in review, 2017.
Rintaro Okamura et al.
Rintaro Okamura et al.
Rintaro Okamura et al.

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Short summary
Three-dimensional (3D) radiative transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. A feasibility test shows that deep learning-based retrieval method is effective to simultaneously retrieve multipixel estimates of cloud optical thickness and column-mean cloud droplet effective radius from multispectral, multipixel radiances. By using convolutional layers, the deep learning method can correct the 3D radiative transfer effects.
Three-dimensional (3D) radiative transfer effects are a major source of retrieval errors in...
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