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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

OSOAA: A Vector Radiative Transfer Model of Coupled Atmosphere-Ocean System for a Rough Sea Surface Application to the Estimates of the Directional Variations of the Water Leaving Reflectance to Better Process Multi-angular Satellite Sensors Data Over the Ocean

In this study, we present a radiative transfer model, so-called OSOAA, that is able to predict the radiance and degree of polarization within the coupled atmosphere-ocean system in the presence of a rough sea surface. The OSOAA model solves the radiative transfer equation using the successive orders of scattering method. Comparisons with another operational radiative transfer model showed a satisfactory agreement within 0.8%. The OSOAA model has been designed with a graphical user interface to make it user friendly for the community. The radiance and degree of polarization are provided at any level, from the top of atmosphere to the ocean bottom. An application of the OSOAA model is carried out to quantify the directional variations of the water leaving reflectance and degree of polarization for phytoplankton and mineral-like dominated waters. The difference between the water leaving reflectance at a given geometry and that obtained for the nadir direction could reach 40%, thus questioning the Lambertian assumption of the sea surface that is used by inverse satellite algorithms dedicated to multi-angular sensors. It is shown as well that the directional features of the water leaving reflectance are weakly dependent on wind speed. The quantification of the directional variations of the water leaving reflectance obtained in this study should help to correctly exploit the satellite data that will be acquired by the current or forthcoming multi-angular satellite sensors.

Ocean surface↗
NASA NTRS2021

Understanding Uncertainty of Snow Radiative Transfer Modeling Within a Mixed Deciduous and Evergreen Forest

Satellite-based passive microwave observations provide the best available continuous observational estimates of global snow water storage due to their broad geographic footprint and low sensitivity to clouds and precipitation. However, these observations are subject to substantial uncertainty due to the complex radiative properties of snow and from interference in forested areas. Physical radiative transfer models can be leveraged to improve the fidelity of these observations and as a data-assimilation tool. In this article, the Dense Media Radiative Transfer model with Multiple Layers (DMRT-ML) is used to simulate snow brightness temperatures from data collected from snow pits excavated during a two-day-long field study performed a temperate forest in the Northeast United States. The simulations are evaluated against surface-based radiometer observations collected at the snow pits. The DMRT-ML is configured with varying complexity to determine the snowpack characteristics most essential toward simulating brightness temperature within a temperate forest with complicated snow stratigraphy. In general, the single-layer configurations were not sufficiently complex to accurately simulate snow brightness temperature without significant tuning. The most accurate simulation was a two-layer configuration with a prescribed ice layer separating the snow layers. This simulation had a root-mean-square error <;15 K for the 37-GHz frequency. More complicated snowpack stratigraphy configurations did not substantively improve the results over the two-layer model configuration. The DMRT-ML was also used to examine differences between redundant datasets of density and grain size. It was determined that similar snow data collection and radiative transfer model configuration techniques are critical to ensure cross-study comparability.

Theodore Letcher↗
NASA NTRS2023

Developing a Radar Signal Simulator for the Community Radiative Transfer Model

Active radar instruments provide vertically resolved clouds and precipitation measurements that cannot be provided by the passive instruments. These active measurements are not conventionally assimilated into the data assimilation systems because of the lack of fast forward radiative transfer models and also difficulties in the error modelling of the measurements. This paper describes the development, evaluation, and sensitivity analysis for a forward radar model implemented in the Community Radiative Transfer Model (CRTM). The scattering properties required by the forward model are provided by the hydrometeor lookup tables that were generated using the discrete dipole approximation. The model is able to calculate both the reflectivity and the attenuated reflectivity for any given radar instrument at any given zenith angles as long as CRTM instrument specific coefficients are available. The evaluation using CloudSat measurements shows a very good agreement between the simulations and measurements as long as the input profiles of hydrometeors are consistent with the measured reflectivity profiles. Major sources contributing to the differences between the measured and simulated reflectivities are input hydrometeor profiles, scattering lookup tables, lack of melting layer in the forward model, CRTM scattering solvers, and attenuation calculations. In addition to the forward model, both Tangent Linear and Adjoint of the model are also implemented and tested within CRTM. These components may be required by some data assimilation systems for the assimilation of radar measurements.

radar↗
NASA NTRS1990

Multiparameter radar and microwave radiative transfer modeling of nonspherical atmospheric ice particles

Multiparameter radar and polarized microwave radiative transfer models for arbitrarily shaped particles are developed. The ice crystals modeled are horizontally oriented hexagonal plates, columns, and needles. The ice water content is fixed at 0.1 g/cu m, and a realistic size distribution is used. The radar modeling is done for S through K-band, and the passive microwave calculations are at 37, 85, and 157 GHz. The modeling results show that particle shape is important for both multiparameter radar and passive microwave radiometry. Radar reflectivity and upwelling microwave brightness temperatures depend strongly on the individual particle volume, which in turn depends on the ice crystal shape. Radar differential reflectivity is high for the plates and lower for needles and columns. Linear depolarization calculations indicate that oblate ice crystals such as plates can be distinguished from prolate crystals such as columns. At 85 and 157 GHz, significant polarization brightness temperature differences are calculated for plates and columns. The particle bulk density strongly affects the radar and radiometer observables. Recent measurements of ice crystals are discussed to show that the model results have practical applicability.

Evans, K. Franklin↗
NASA NTRS2011

An Efficient Monte Carlo Method for Modeling Radiative Transfer in Protoplanetary Disks

Monte Carlo methods have been shown to be effective and versatile in modeling radiative transfer processes to calculate model temperature profiles for protoplanetary disks. Temperatures profiles are important for connecting physical structure to observation and for understanding the conditions for planet formation and migration. However, certain areas of the disk such as the optically thick disk interior are under-sampled, or are of particular interest such as the snow line (where water vapor condenses into ice) and the area surrounding a protoplanet. To improve the sampling, photon packets can be preferentially scattered and reemitted toward the preferred locations at the cost of weighting packet energies to conserve the average energy flux. Here I report on the weighting schemes developed, how they can be applied to various models, and how they affect simulation mechanics and results. We find that improvements in sampling do not always imply similar improvements in temperature accuracies and calculation speeds.

radiative transfer↗
NASA NTRS1993

Radiative transfer modelling of discontinuous tree canopies at microwave frequencies

Development of a first-order radiative transfer model for predicting backscatter from tree canopies has been underway at the University of Michigan Radiation Laboratory for some time. This model is known as the Michigan Microwave Canopy Scattering (MIMICS) model. This article presents the second-generation MIMICS model (MIMICS II) which accounts for canopies with discontinuous (open) crown layer geometries. MIMICS II models open crown layers by treating the location, size, and shape of the individual tree crowns as random variables. The backscattering coefficients for the canopy are then determined by introducing statistics derived from these parameters into the radiative transfer solution. Application of the radiative transfer equations to the discontinuous canopy geometry is presented. The resulting model is a robust fully polarimetric solution that is applicable over a wide variety of canopy architectures. Model simulations are compared to results generated with the continuous canopy model. The effect of the open crown geometry is found to be most significant at shallow incidence angles and at high frequencies for trees with well-developed crowns. Under these conditions, the gaps in the crown layer give rise to a notable increase in crown layer transmissivity which allows the radar to see through to the lower layers of the canopy more easily, thereby directly affecting the backscatter contribution of the trunks and ground.

Mcdonald, K. C.↗
NASA NTRS2016

Development of a Fast and Accurate PCRTM Radiative Transfer Model in the Solar Spectral Region

A fast and accurate principal component-based radiative transfer model in the solar spectral region (PCRTMSOLAR) has been developed. The algorithm is capable of simulating reflected solar spectra in both clear sky and cloudy atmospheric conditions. Multiple scattering of the solar beam by the multilayer clouds and aerosols are calculated using a discrete ordinate radiative transfer scheme. The PCRTM-SOLAR model can be trained to simulate top-of-atmosphere radiance or reflectance spectra with spectral resolution ranging from 1 cm(exp −1) resolution to a few nanometers. Broadband radiances or reflectance can also be calculated if desired. The current version of the PCRTM-SOLAR covers a spectral range from 300 to 2500 nm. The model is valid for solar zenith angles ranging from 0 to 80 deg, the instrument view zenith angles ranging from 0 to 70 deg, and the relative azimuthal angles ranging from 0 to 360 deg. Depending on the number of spectral channels, the speed of the current version of PCRTM-SOLAR is a few hundred to over one thousand times faster than the medium speed correlated-k option MODTRAN5. The absolute RMS error in channel radiance is smaller than 10(exp −3) mW∕cm)exp 2)∕sr∕cm(exp −1) and the relative error is typically less than 0.2%.

Liu, Xu↗
NASA NTRS1998

Improvement and Application of Atmospheric Radiative Transfer Models for Prediction of the Climatic Effects of Aerosol

This paper presents a radiative transfer model that has been developed to accurately predict the atmospheric radiant flux in both the infrared and the solar spectrum with a minimum of computational effort. The model is designed to be included in numerical climate models. To assess the accuracy of the model, the results are compared to other more detailed models for several standard cases in the solar and thermal spectrum. As the thermal spectrum has been treated in other publications, we focus here on the solar part of the spectrum. We perform several example calculations focussing on the question of absorption of solar radiation by gases and aerosols.

Bergstrom, Robert W.↗
NASA NTRS1998

Improvement and Application of Atmospheric Radiative Transfer Models for Prediction of the Climatic Effects of Aerosol

This paper presents a radiative transfer model that has been developed to accurately predict the atmospheric radiant flux in both the infrared and the solar spectrum with a minimum of computational effort. The model is designed to be included in numerical climate models. To assess the accuracy of the model, the results are compared to other more detailed models for several standard cases in the solar and thermal spectrum. As the thermal spectrum has been treated in other publications we focus here on the solar part of the spectrum. We perform several example calculations focussing on the question of absorption of solar radiation by gases and aerosols.

Bergstrom, Robert W.↗
NASA NTRS2018

Global Soil Moisture Estimation from L-Band Satellite Data: The Impact of Radiative Transfer Modeling in Assimilation and Retrieval Systems

The SMOS and SMAP missions have collected a wealth of global L-band Brightness temperature (Tb) observations. The retrieval of surface Soil moisture estimates, and the estimation of other geophysical Variables, such as root-zone soil moisture and temperature, via data Assimilation into land surface models largely depends on accurate Radiative transfer modeling (RTM). This presentation will focus on various configuration aspects of the RTM (i) for the inversion of SMOS Tb to surface soil moisture, and (ii) for the forward modeling as part of a SMOS Tb data assimilation System to estimate a consistent set of geophysical land surface Variables, using the GEOS-5 Catchment Land Surface Model.

De Lannoy, Gabrielle↗
NASA NTRS2023

Unifying radiative transfer models in computer graphics and remote sensing, Part I: A Survey

The constellation of Earth-observing satellites continuously collects measurements of scattered radiance, which must be transformed into geophysical parameters in order to answer fundamental scientific questions about the Earth. Retrieval of these parameters requires highly flexible, accurate, and fast forward and inverse radiative transfer models. Existing forward models used by the remote sensing community are typically accurate and fast, but sacrifice flexibility by assuming the atmosphere or ocean is composed of plane-parallel layers. Monte Carlo forward models can handle more complex scenarios such as 3D spatial heterogeneity, but are relatively slower. We propose looking to the computer graphics community for inspiration to improve the statistical efficiency of Monte Carlo forward models and explore new approaches to inverse models for remote sensing. In Part 1 of this work, we examine the evolution of radiative transfer models in computer graphics and highlight recent advancements that have the potential to push forward models in remote sensing beyond their current periphery of realism.

Radiative transfer↗
NASA NTRS2023

Modeling Sea Ice Albedo and Transmittance Measurements With A Fully-Coupled Radiative Transfer Model

A rigorous treatment of the sea ice medium has been incorporated in the advanced Coupled Ocean-Atmosphere Radiative Transfer (COART) model. The inherent optical properties (IOPs) of brine pockets and air bubbles over the 0.25-4.0 μm spectral region are parameterized as a function of the vertical profile of the sea ice physical properties (temperature, salinity and density). We test the model performance using available albedo and transmittance measurements collected during the Impacts of Climate on the Ecosystems and Chemistry of the Arctic Pacific Environment (ICESCAPE) and the Surface Heat Budget of the Arctic Ocean (SHEBA) field campaigns. The observations are adequately simulated when at least three layers are used to represent bare (first-year and multi-year) ice, including a thin top layer characterized by low density and high scattering. Two layers can be sufficient to model isolated cases of multi-year ice, and apply well to ponded ice except for shallow ponds over thick ice. The albedo and transmittance of ponded ice in the visible are mainly determined by the optical properties of the ice underlying the water layer used to model the pond. Sensitivity results indicate that the air volume or ice density has the largest impact on the simulated fluxes. Possible contamination from light-absorbing impurities, such as black carbon or ice algae, is also implemented in the model and is able to effectively reduce the albedo and transmittance in the visible spectrum to further improve the model-observation agreement. The agreement between the observed and modeled spectra validates the parameterization of the sea ice IOPs, and endorses COART as an accurate tool for radiation studies in the cryosphere.

Coupled Ocean-9 Atmosphere Radiative Transfer (COA↗
NASA NTRS1980

A radiative transfer model for remote sensing of laser induced fluorescence of phytoplankton in non-homogeneous turbid water

A radiative transfer computer model was developed to characterize the total flux of chlorophyll a fluoresced or backscattered photons when laser radiation is incident on turbid water that contains a non-homogeneous suspension of inorganic sediments and phytoplankton. The radiative transfer model is based on the Monte Carlo technique and assumes that: (1) the aquatic medium can be represented by a stratified concentration profile; and (2) that appropriate optical parameters can be defined for each layer. The model was designed to minimize the required computer resources and run time. Results are presented for an anacystis marinus culture.

Venable, D. D.↗
NASA NTRS1977

Extending radiative transfer models by use of Bayes rule

This paper presents a procedure that extends some existing radiative transfer modeling techniques to problems in atmospheric science where curvature and layering of the medium and dynamic range and angular resolution of the signal are important. Example problems include twilight and limb scan simulations. Techniques that are extended include successive orders of scattering, matrix operator, doubling, Gauss-Seidel iteration, discrete ordinates and spherical harmonics. The procedure for extending them is based on Bayes' rule from probability theory.

Whitney, C.↗
NASA NTRS2013

Uncertainty Quantification of GEOS-5 L-band Radiative Transfer Model Parameters Using Bayesian Inference and SMOS Observations

Uncertainties in L-band (1.4 GHz) radiative transfer modeling (RTM) affect the simulation of brightness temperatures (Tb) over land and the inversion of satellite-observed Tb into soil moisture retrievals. In particular, accurate estimates of the microwave soil roughness, vegetation opacity and scattering albedo for large-scale applications are difficult to obtain from field studies and often lack an uncertainty estimate. Here, a Markov Chain Monte Carlo (MCMC) simulation method is used to determine satellite-scale estimates of RTM parameters and their posterior uncertainty by minimizing the misfit between long-term averages and standard deviations of simulated and observed Tb at a range of incidence angles, at horizontal and vertical polarization, and for morning and evening overpasses. Tb simulations are generated with the Goddard Earth Observing System (GEOS-5) and confronted with Tb observations from the Soil Moisture Ocean Salinity (SMOS) mission. The MCMC algorithm suggests that the relative uncertainty of the RTM parameter estimates is typically less than 25 of the maximum a posteriori density (MAP) parameter value. Furthermore, the actual root-mean-square-differences in long-term Tb averages and standard deviations are found consistent with the respective estimated total simulation and observation error standard deviations of m3.1K and s2.4K. It is also shown that the MAP parameter values estimated through MCMC simulation are in close agreement with those obtained with Particle Swarm Optimization (PSO).

MCMC↗
NASA NTRS2012

Validation of Aquarius Measurements Using Radiative Transfer Models at L-Band

Aquarius/SAC-D was launched in June 2011 by NASA and CONAE (Argentine space agency). Aquarius includes three L-band (1.4 GHz) radiometers dedicated to measuring sea surface salinity. We report detailed comparisons of Aquarius measurements with radiative transfer model predictions. These comparisons were used as part ofthe initial assessment of Aquarius data. In particular, they were used successfully to estimate the radiometer calibration bias and stability. Further comparisons are being performed to assess the performance of models in the retrieval algorithm for correcting the effect of sources of geophysical "noise" (e.g. the galactic background, atmospheric attenuation and reflected signal from the Sun). Such corrections are critical in bringing the error in retrieved salinity down to the required 0.2 practical salinity unit (psu) on monthly global maps at 150 km by 150 km resolution. The forward models making up the Aquarius simulator have been very useful for preparatory studies in the years leading to Aquarius' launch. The simulator includes various components to compute effects ofthe following processes on the measured signal: 1) emission from Earth surfaces (ocean, land, ice), 2) atmospheric emission and absorption, 3) emission from the Sun, Moon and celestial Sky (directly through the antenna sidelobes or after reflection/scattering at the Earth surface), 4) Faraday rotation, and 5) convolution of the scene by the antenna gain patterns. Since the Aquarius radiometers tum-on in late July 2011, the simulator has been used to perform a first order validation of the data. This included checking the order of magnitude ofthe signal over ocean, land and ice surfaces, checking the relative amplitude of signal at different polarizations, and checking the variation with incidence angle. The comparisons were also used to assess calibration bias and monitor instruments calibration drift. The simulator is also being used in the salinity retrieval. For example, initial assessments of the salinity retrieved from Aquarius data showed degradation in accuracy at locations where glint from the galactic sky background was important. This was traced to an inaccurate correction for the Sky glint. We present comparisons of the simulator prediction to the Aquarius data in order to assess the performances of the models of various physical processes impacting the measurements, such as the effect of sea surface roughness, the impact of the celestial Sky and the Sun emission scattered at the rough ocean surface. We discuss what components of the simulator appear reliable and which ones need improvements. Improved knowledge on the radiative transfer models at L-band will not only lead to better salinity retrieved from Aquarius data, it will also allow be beneficial for SMOS or the upcoming SMAP mission.

Dinnat, E.↗
NASA NTRS1978

A Radiative Transfer Model for Microwave Emission from Soils

The radiative transfer equation for an absorbing medium is used to develop a model for microwave emission from soils. The model calculates the microwave emission intensity in terms of the brightness temperature as a function of the soil moisture and temperature. The consistency of the model is verified by a comparative study of the present model with other microwave emission models. The effect of surface roughness on the brightness temperature is studied by modifying the Fresnel reflection coefficient and by including the surface depolarization effect. The quantitative effect of the surface roughness is studied and it is demonstrated that the brightness temperatures observed for a natural agricultural terrain can be explained through the inclusion of the roughness effect. The surface roughness effect is further analyzed to obtain formulae which are useful in the analysis of the brightness temperatures, particularly, to obtain soil moisture information from the brightness. temperatures. The sensitivity of the brightness temperature to the soil temperature variations is studied both qualitatively and quantitatively. A simple formula is obtained to calculate the soil temperature dependence of the brightness temperature in terms of the surface and the deep soil temperatures. An algorithm to normalize the brightness temperatures so as to compare microwave observations under different soil temperature conditions is also discussed. A program listing of the model is given in the appendix.

Choudhury, B. J.↗