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Volume 10, issue 12 | Copyright
Atmos. Meas. Tech., 10, 4747-4759, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 05 Dec 2017

Research article | 05 Dec 2017

Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

Rintaro Okamura et al.
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Cited articles
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Cornet, C., Isaka, H., Guillemet, B., and Szczap, F.: Neural network retrieval of cloud parameters of inhomogeneous clouds from multispectral and multiscale radiance data: Feasibility study, J. Geophys. Res.-Atmos., 109, D12203,, 2004.
Cornet, C., Buriez, J.-C., Riédi, J., Isaka, H., and Guillemet, B.: Case study of inhomogeneous cloud parameter retrieval from MODIS data, Geophys. Res. Lett., 32, L13807,, 2005.
Evans, K. F., Marshak, A., and Várnai, T.: The potential for improved boundary layer cloud optical depth retrievals from the multiple directions of MISR, J. Atmos. Sci., 65, 3179–3196, 2008.
Faure, T., Isaka, H., and Guillemet, B.: Neural network retrieval of cloud parameters of inhomogeneous and fractional clouds: Feasibility study, Remote Sens. Environ., 77, 123–138,, 2001.
Publications Copernicus
Short summary
Three-dimensional (3-D) radiative transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. Multi-pixel, multispectral approaches based on deep learning are proposed for retrieval of cloud optical thickness and droplet effective radius. A feasibility test shows that proposed retrieval methods are effective to obtain accurate cloud properties. Use of the convolutional neural network is effective to reduce 3-D radiative transfer effects.
Three-dimensional (3-D) radiative transfer effects are a major source of retrieval errors in...