Radiative Transfer Effects on the Stability of Sound Waves in a Polytropic Atmosphere

1997 ◽  
Vol 481 (2) ◽  
pp. 963-972 ◽  
Author(s):  
James MacDonald ◽  
Dermott Mullan
Keyword(s):  
Radiative Transfer ◽  
Sound Waves ◽  
Transfer Effects ◽  
The Stability ◽  
Polytropic Atmosphere

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

2017 ◽  
Vol 10 (12) ◽  
pp. 4747-4759 ◽  
Author(s):  
Rintaro Okamura ◽  
Hironobu Iwabuchi ◽  
K. Sebastian Schmidt
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Optical Remote Sensing ◽  
Transfer Effects ◽  
Cloud Properties

Abstract. Three-dimensional (3-D) radiative-transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. The challenge is that 3-D effects manifest themselves across multiple satellite pixels, which traditional single-pixel approaches cannot capture. In this study, we present two multi-pixel retrieval approaches based on deep learning, a technique that is becoming increasingly successful for complex problems in engineering and other areas. Specifically, we use deep neural networks (DNNs) to obtain multi-pixel estimates of cloud optical thickness and column-mean cloud droplet effective radius from multispectral, multi-pixel radiances. The first DNN method corrects traditional bispectral retrievals based on the plane-parallel homogeneous cloud assumption using the reflectances at the same two wavelengths. The other DNN method uses so-called convolutional layers and retrieves cloud properties directly from the reflectances at four wavelengths. The DNN methods are trained and tested on cloud fields from large-eddy simulations used as input to a 3-D radiative-transfer model to simulate upward radiances. The second DNN-based retrieval, sidestepping the bispectral retrieval step through convolutional layers, is shown to be more accurate. It reduces 3-D radiative-transfer effects that would otherwise affect the radiance values and estimates cloud properties robustly even for optically thick clouds.

scholarly journals A study on radiative transfer effects in 3-D cloudy atmosphere using satellite data

2017 ◽  
Vol 122 (1) ◽  
pp. 443-468 ◽  
Author(s):  
M. Okata ◽  
T. Nakajima ◽  
K. Suzuki ◽  
T. Inoue ◽  
T. Y. Nakajima ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Satellite Data ◽  
Transfer Effects ◽  
Cloudy Atmosphere

scholarly journals Radiative transfer effects during primordial helium recombination

2012 ◽  
Vol 423 (4) ◽  
pp. 3227-3242 ◽  
Author(s):  
Jens Chluba ◽  
Jeffrey Fung ◽  
Eric R. Switzer
Keyword(s):  
Radiative Transfer ◽  
Transfer Effects ◽  
Primordial Helium

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 Two Machine Learning Based Schemes for Solving Direct and Inverse Problems of Radiative Transfer Theory

2020 ◽  
pp. paper45-1-paper45-12
Author(s):  
Dmitry Efremenko ◽  
Himani Jain ◽  
Jian Xu
Keyword(s):  
Radiative Transfer ◽  
Inverse Operator ◽  
Tikhonov Regularization Method ◽  
Computational Time ◽  
Optical Parameters ◽  
Discrete Ordinate ◽  
Arbitrary Geometry ◽  
Direct And Inverse Problems ◽  
The Stability ◽  
Level 2

Artificial neural networks (ANNs) are used to substitute computationally expensive radiative transfer models (RTMs) and inverse operators (IO) for retrieving optical parameters of the medium. However, the direct parametrization of RTMs and IOs by means of ANNs has certain drawbacks, such as loss of generality, computations of huge training datasets, robustness issues etc. This paper provides an analysis of different ANN-related methods, based on our results and those published by other authors. In particular, two techniques are proposed. In the first method, the ANN substitutes the eigenvalue solver in the discrete ordinate RTM, thereby reducing the computational time. Unlike classical RTM parametrization schemes based on ANN, in this method the resulting ANN can be used for arbitrary geometry and layer optical thicknesses. In the second method, the IO is trained by using the real measurements (preprocessed Level-2 TROPOMI data) to improve the stability of the inverse operator. This method provides robust results even without applying the Tikhonov regularization method.

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