RADIATIVE TRANSFER MODEL PARAMETERS FOR CARBON MONOXIDE AT HIGH TEMPERATURE

1998 ◽  
Author(s):  
Vladimir P. Solovjov ◽  
Brent W. Webb
Keyword(s):  
Carbon Monoxide ◽  
High Temperature ◽  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Model Parameters ◽  
Transfer Model

scholarly journals Radiative Transfer Model parametrization for simulating the reflectance of meadow vegetation

2014 ◽  
Vol 18 (2) ◽  
pp. 5-9 ◽  
Author(s):  
Anna M. Jarocińska
Keyword(s):  
Radiative Transfer ◽  
Field Measurements ◽  
Radiative Transfer Model ◽  
Model Parameters ◽  
Transfer Model ◽  
Biophysical Parameters ◽  
Input Parameters ◽  
Vegetation Characteristics ◽  
Field Reflectance ◽  
Lower Water Content

Abstract Natural vegetation is complex and its reflectance is not easy to model. The aim of this study was to adjust the Radiative Transfer Model parameters for modelling the reflectance of heterogeneous meadows and evaluate its accuracy dependent on the vegetation characteristics. PROSAIL input parameters and reference spectra were collected during field measurements. Two different datasets were created: in the first, the input parameters were modelled using only field measurements; in the second, three input parameters were adjusted to minimize the differences between modelled and measured spectra. Reflectance was modelled using two datasets and then verified based on field reflectance using the RMSE. The average RMSE for the first dataset was equal to 0.1058, the second was 0.0362. The accuracy of the simulated spectra was analysed dependent on the value of the biophysical parameters. Better results were obtained for meadows with higher biomass value, greater LAI and lower water content.

scholarly journals Seeking edge-on galaxies with substantial extraplanar dust using a radiative transfer model: determination of the model parameter uncertainties for EON_10.477_41.954 (FGC 79)

2019 ◽  
Vol 489 (4) ◽  
pp. 4690-4704 ◽  
Author(s):  
Jong-Ho Shinn
Keyword(s):  
Radiative Transfer ◽  
Confidence Intervals ◽  
Three Dimensional ◽  
Radiative Transfer Model ◽  
Model Parameters ◽  
Transfer Model ◽  
Target Classification ◽  
Parameter Uncertainties ◽  
Model Parameter ◽  
Galaxy Model

ABSTRACT We have revisited the target EON_10.477_41.954 in order to determine more accurately the uncertainties in the model parameters that are important for target classification (i.e. galaxies with or without substantial extraplanar dust). We performed a Markov chain Monte Carlo (MCMC) analysis for the 15 parameters of the three-dimensional radiative-transfer galaxy model we used previously for target classification. To investigate the convergence of the MCMC sampling – which is usually neglected in the literature but should not be – we monitored the integrated autocorrelation time (τint), and we achieved effective sample sizes >5650 for all the model parameters. The confidence intervals are unstable at the beginning of the iterations where the values of τint are increasing, but they become stable in later iterations where those values are almost constant. The final confidence intervals are ∼5–100 times larger than the nominal uncertainties used in our previous study (the standard deviation of three best-fitting results). Thus, those nominal uncertainties are not good proxies for the model-parameter uncertainties. Although the position of EON_10.477_41.954 in the target-classification plot (the scale height to diameter ratio of dust versus that of light source) decreases by about 20–30 per cent when compared to our previous study, its membership in the ‘high-group’ – i.e. among galaxies with substantial extraplanar dust – nevertheless remains unchanged.

scholarly journals Inverse Modeling of Radiative Transfer by Two-Stream Approximation using the Luus-Jaakola Method

2021 ◽  
Vol 22 (2) ◽  
pp. 325-340
Author(s):  
F. F. Ferreira ◽  
J. M. Krieger ◽  
G. B. Lyra ◽  
W. R. Telles ◽  
J. L. Souza ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Terrestrial Ecosystem ◽  
Early Morning ◽  
Radiative Transfer Model ◽  
Tropical Region ◽  
Model Parameters ◽  
Transfer Model ◽  
Local Conditions ◽  
Sugarcane Crop ◽  
Adequate Representation

The simulation of terrestrial ecosystem processes, using numerical biosphere-atmosphere models that can be coupled to the Atmospheric Models, assist in a better diagnosis and forecast of climate and weather. To be able to represent a particular region, biome or ecosystem, the model parameters need to be adjusted for local conditions. This work aims to assess the Luus-Jaakola (LJ) method in the optimization of the parameters in a two-stream radiative transfer model applied to a vegetation canopy. Solar radiation components (incident, S↓, and reflected, S↑) were measured above a sugarcane crop in a Tropical region from February 17 to 24, 2006. Among the combinations of internal and external iterations evaluated for Luss-Jaakola method, 60/30 (external/internal) iterations presented more precise albedo (∝ = S↑/S↓) simulated (r^2 = 0.7386) and, for the accuracy of the simulated ∝, even though the 60/40 combination had the smallest percentual error (6.40%), the 60/30 combination was 0.03% higher. The precision and accuracy of S↑ was greater with the parameters obtained by the inverse problem with the combination of 60/30 (external/internal) iterations respectively. In general, the behavior of simulated S↑ at the top of the canopy was  underestimated compared to the observed S↑, especially in the early morning. For the simulated ∝ at the top of the canopy, the model's overestimation was observed at the lowest values of albedo. When the largest albedos are observed, only at the beginning of the day the model underestimated the values.  As shown by the tests result, the parameters optimized by Luus-Jaakola method  have an adequate representation of the observed data.

Probing clouds in planets with a simple radiative transfer model: the Jupiter case

2012 ◽  
Vol 33 (6) ◽  
pp. 1611-1624 ◽  
Author(s):  
Iñigo Mendikoa ◽  
Santiago Pérez-Hoyos ◽  
Agustín Sánchez-Lavega
Keyword(s):  
Radiative Transfer ◽  
Radiative Transfer Model ◽  
Transfer Model

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

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