scholarly journals Three-dimensional radiative transfer effects of clouds in the microwave spectral range

1996 ◽  
Vol 101 (D2) ◽  
pp. 4289-4298 ◽  
Author(s):  
Quanhua Liu ◽  
Clemens Simmer ◽  
Eberhard Ruprecht
Keyword(s):  
Radiative Transfer ◽  
Spectral Range ◽  
Three Dimensional ◽  
Transfer Effects

scholarly journals Retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

2017 ◽  
Author(s):  
Rintaro Okamura ◽  
Hironobu Iwabuchi ◽  
K. Sebastian Schmidt
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Three Dimensional ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Transfer Effects ◽  
Cloud Properties

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.

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