scholarly journals A research program on radiative transfer model development in support of the ARM program

1992 ◽  
Author(s):  
S.A. Clough
Keyword(s):  
Radiative Transfer ◽  
Research Program ◽  
Model Development ◽  
Radiative Transfer Model ◽  
Transfer Model

scholarly journals Gauss–Seidel limb scattering (GSLS) radiative transfer model development in support of the Ozone Mapping and Profiler Suite (OMPS) limb profiler mission

2015 ◽  
Vol 15 (6) ◽  
pp. 3007-3020 ◽  
Author(s):  
R. Loughman ◽  
D. Flittner ◽  
E. Nyaku ◽  
P. K. Bhartia
Keyword(s):  
Radiative Transfer ◽  
Model Development ◽  
Model Atmosphere ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Process Data ◽  
Variable Surface ◽  
Height Dependence ◽  
Minimal Effort

Abstract. The Gauss–Seidel limb scattering (GSLS) radiative transfer (RT) model simulates the transfer of solar radiation through the atmosphere and is imbedded in the retrieval algorithm used to process data from the Ozone Mapping and Profiler Suite (OMPS) limb profiler (LP), which was launched on the Suomi NPP satellite in October 2011. A previous version of this model has been compared with several other limb scattering RT models in previous studies, including Siro, MCC++, CDIPI, LIMBTRAN, SASKTRAN, VECTOR, and McSCIA. To address deficiencies in the GSLS radiance calculations revealed in earlier comparisons, several recent changes have been added that improve the accuracy and flexibility of the GSLS model, including 1. improved treatment of the variation of the extinction coefficient with altitude, both within atmospheric layers and above the nominal top of the atmosphere; 2. addition of multiple-scattering source function calculations at multiple solar zenith angles along the line of sight (LOS); 3. introduction of variable surface properties along the limb LOS, with minimal effort required to add variable atmospheric properties along the LOS as well; 4. addition of the ability to model multiple aerosol types within the model atmosphere. The model improvements 1 and 2 are verified by comparison to previously published results (using standard radiance tables whenever possible), demonstrating significant improvement in cases for which previous versions of the GSLS model performed poorly. The single-scattered radiance errors that were as high as 4% in earlier studies are now generally reduced to 0.3%, while total radiance errors generally decline from 10% to 1–3%. In all cases, the tangent height dependence of the GSLS radiance error is greatly reduced.

A LONG TERM FIELD EXPERIMENT FOR RADIATIVE TRANSFER MODEL DEVELOPMENT AND LAND SURFACE PROCESSES REMOTE SENSING

2008 ◽  
Vol 52 ◽  
pp. 13-18
Author(s):  
Hui LU ◽  
Toshio KOIKE ◽  
Hiroyuki TSUTSUI ◽  
David Ndegwa KURIA ◽  
Tobias GRAF ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Field Experiment ◽  
Land Surface ◽  
Model Development ◽  
Surface Processes ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Term Field

scholarly journals The Successive-Order-of-Interaction Radiative Transfer Model. Part I: Model Development

2006 ◽  
Vol 45 (10) ◽  
pp. 1388-1402 ◽  
Author(s):  
Andrew K. Heidinger ◽  
Christopher O’Dell ◽  
Ralf Bennartz ◽  
Thomas Greenwald
Keyword(s):  
Radiative Transfer ◽  
Monte Carlo Model ◽  
Model Development ◽  
Microwave Remote Sensing ◽  
Accurate Solution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Iterative Approximation ◽  
Acceleration Technique ◽  
Wide Range

Abstract This study, the first part of a two-part series, develops the method of “successive orders of interaction” (SOI) for a computationally efficient and accurate solution for radiative transfer in the microwave spectral region. The SOI method is an iterative approximation to the traditional adding and doubling method for radiative transfer. Results indicate that the approximations made in the SOI method are accurate for atmospheric layers with scattering properties typical of those in the infrared and microwave regions. In addition, an acceleration technique is demonstrated that extends the applicability of the SOI approach to atmospheres with greater amounts of scattering. A comparison of the SOI model with a full Monte Carlo model using the atmospheric profiles given by Smith et al. was used to determine the optimal parameters for the simulation of microwave top-of-atmosphere radiances. This analysis indicated that a four-stream model with a maximum initial-layer optical thickness of approximately 0.01 was optimal. In the second part of this series, the accuracies of the SOI model and its adjoint are demonstrated over a wide range of microwave remote sensing scenarios.

A dorsiventral leaf radiative transfer model: Development, validation and improved model inversion techniques

2009 ◽  
Vol 113 (12) ◽  
pp. 2560-2573 ◽  
Author(s):  
Jan Stuckens ◽  
Willem W. Verstraeten ◽  
Stephanie Delalieux ◽  
Rony Swennen ◽  
Pol Coppin
Keyword(s):  
Radiative Transfer ◽  
Model Development ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Inversion ◽  
Improved Model ◽  
Inversion Techniques

scholarly journals Gauss-Seidel Limb Scattering (GSLS) radiative transfer model development in support of the Ozone Mapping and Profiler Suite (OMPS) Limb Profiler mission

2014 ◽  
Vol 14 (13) ◽  
pp. 19315-19356
Author(s):  
R. Loughman ◽  
D. Flittner ◽  
E. Nyaku ◽  
P. K. Bhartia
Keyword(s):  
Radiative Transfer ◽  
Model Development ◽  
Model Atmosphere ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Process Data ◽  
Variable Surface ◽  
Height Dependence ◽  
Minimal Effort

Abstract. The Gauss-Seidel Limb Scattering (GSLS) radiative transfer (RT) model simulates the transfer of solar radiation through the atmosphere, and is imbedded in the retrieval algorithm used to process data from the Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP), which was launched on the Suomi NPP satellite in October 2011. A previous version of this model has been compared with several other limb scattering RT models in previous studies, including Siro, MCC++, CDIPI, LIMBTRAN, SASKTRAN, VECTOR, and McSCIA. To address deficiencies in the GSLS radiance calculations revealed in earlier comparisons, several recent changes have been added that improve the accuracy and flexibility of the GSLS model, including: 1. Improved treatment of the variation of the extinction coefficient with altitude, both within atmospheric layers and above the nominal top of the atmosphere (TOA). 2. Addition of multiple scattering source function calculations at multiple zeniths along the line of sight (LOS). 3. Re-introduction of the ability to simulate vector (polarized) radiances. 4. Introduction of variable surface properties along the limb LOS, with minimal effort required to add variable atmospheric properties along the LOS as well. 5. Addition of the ability to model multiple aerosol types within the model atmosphere. The model improvements numbered 1–3 above are verified by comparison to previously published results (using standard radiance tables whenever possible), demonstrating significant improvement in cases for which previous versions of the GSLS model performed poorly. The single-scattered radiance errors that were as high as 4% in earlier studies are now generally reduced to < 0.5%, while total radiance errors generally decline from > 10% to 1–2%. In all cases, the height dependence of the GSLS radiance error is greatly reduced.

Towards improving radiometry-derived snow depths with SNOWPACK and SMRT

2020 ◽  
Author(s):  
Robbie Mallett ◽  
Julienne Stroeve ◽  
Michel Tsamados ◽  
Glen Liston
Keyword(s):  
Radiative Transfer ◽  
Sea Ice ◽  
Model Development ◽  
Light Flux ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Gradient Ratio ◽  
Strong Control

&lt;p&gt;The depth of overlying snow on sea ice exerts a strong control on atmosphere-ocean heat and light flux and introduces major uncertainties in the remote sensing of sea ice thickness. Satellite-mounted microwave radiometers have enabled retrieval of snow depths over first year ice, but such retrievals are subject to a wide margin of error due to spatial variation in snow stratigraphy and roughness.&lt;/p&gt;&lt;p&gt;Here we model the microwave signature of snow on sea ice using a recently released sea ice variant of the snowpack evolution model, SNOWPACK (Wever et al., 2020). By advecting parcels of sea ice using ice motion vectors and exposing them to the relevant atmospheric forcing using ERA5 reanalysis, we model the accumulation of snow and the development of snowpack stratigraphy.&lt;/p&gt;&lt;p&gt;We then pass these modelled snowpacks to the Snow Microwave Radiative Transfer model (Picard et al., 2018) to estimate their microwave emission characteristics. By using relationships from the literature relating the ratios of the 37GHz and 19GHz channels, we calculate whether the traditional &amp;#8220;gradient ratio&amp;#8221; method (Markus and Cavalieri, 1998) over- or underestimates the depth of snow at a particular point based on our modelling. We then adjust the observed gradient ratio based on the model results in an attempt to better characterise snow depths.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;Wever, Nander, et al. &quot;Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model.&quot; &lt;em&gt;Geoscientific Model Development&lt;/em&gt; 13.1 (2020): 99-119.&lt;/p&gt;&lt;p&gt;Picard, Ghislain, Melody Sandells, and Henning L&amp;#246;we. &quot;SMRT: An active&amp;#8211;passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1. 0).&quot; &lt;em&gt;Geoscientific Model Development &lt;/em&gt;11.7 (2018): 2763-2788.&lt;/p&gt;&lt;p&gt;Markus, Thorsten, and Donald J. Cavalieri. &quot;Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data.&quot; &lt;em&gt;Antarctic sea ice: physical processes, interactions and variability &lt;/em&gt;74 (1998): 19-39.&lt;/p&gt;

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