Enhancement of Optically Thick to Thin Line Intensities in Solar and Stellar Coronal Plasmas through Radiative Transfer Effects: An Angularly Resolved Study

2004 ◽  
Vol 613 (2) ◽  
pp. L181-L184 ◽  
Author(s):  
F. M. Kerr ◽  
S. J. Rose ◽  
J. S. Wark ◽  
F. P. Keenan
Keyword(s):  
Radiative Transfer ◽  
Transfer Effects ◽  
Line Intensities ◽  
Thin Line

scholarly journals Performance of the line-by-line radiative transfer model (LBLRTM) for temperature and species retrievals: IASI case studies from JAIVEx

2009 ◽  
Vol 9 (19) ◽  
pp. 7397-7417 ◽  
Author(s):  
M. W. Shephard ◽  
S. A. Clough ◽  
V. H. Payne ◽  
W. L. Smith ◽  
S. Kireev ◽  
...  
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Trace Gas ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Line Intensities ◽  
Remote Sounding ◽  
Instrument Noise ◽  
Large Peak ◽  
Line Coupling

Abstract. Presented here are comparisons between the Infrared Atmospheric Sounding instrument (IASI) and the "Line-By-Line Radiative Transfer Model" (LBLRTM). Spectral residuals from radiance closure studies during the IASI JAIVEx validation campaign provide insight into a number of spectroscopy issues relevant to remote sounding of temperature, water vapor and trace gases from IASI. In order to perform quality IASI trace gas retrievals, the temperature and water vapor fields must be retrieved as accurately as possible. In general, the residuals in the CO2 ν2 region are of the order of the IASI instrument noise. However, outstanding issues with the CO2 spectral regions remain. There is a large residual ~−1.7 K in the 667 cm−1 Q-branch, and residuals in the CO2 ν2 and N2O/CO2 ν3 spectral regions that sample the troposphere are inconsistent, with the N2O/CO2 ν3 region being too negative (warmer) by ~0.7 K. Residuals on this lower wavenumber side of the CO2 ν3 band will be improved by line parameter updates, while future efforts to reduce the residuals reaching ~−0.5 K on the higher wavenumber side of the CO2 ν3 band will focus on addressing limitations in the modeling of the CO2 line shape (line coupling and duration of collision) effects. Brightness temperature residuals from the radiance closure studies in the ν2 water vapor band have standard deviations of ~0.2–0.3 K with some large peak residuals reaching ±0.5–1.0 K. These are larger than the instrument noise indicating that systematic errors still remain. New H2O line intensities and positions have a significant impact on the retrieved water vapor, particularly in the upper troposphere where the water vapor retrievals are 10% drier when using line intensities compared with HITRAN 2004. In addition to O3, CH4, and CO, of the IASI instrument combined with an accurate forward model allows for the detection of minor species with weak atmospheric signatures in the nadir radiances, such as HNO3 and OCS.

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
Sign in / Sign up

Export Citation Format

Share Document