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NASA NTRS1981

Numerical simulation of soil brightness temperatures at wavelength of 21 cm

A simulation model is applied to reproduce some observed brightness temperatures at a wavelength of 21 cm. The simulated results calculated with two different soil textures are compared directly with observations measured over fields in Arizona and South Dakota. It is found that good agreement is possible by properly adjusting the surface roughness parameter. Correlation analysis and linear regression of the brightness temperatures versus soil moistures are also carried out.

Mo, T.↗
NASA NTRSDate not supplied

AN INTRODUCTION TO THE GEONEX LEVEL-1G PRODUCTS: TOP-OF-ATMOSPHERE REFLECTANCE AND BRIGHTNESS TEMPERATURE

This paper introduces the GeoNEX (Geostationary-NASA Earth eXchange) Level-1G products of top-of-atmosphere (TOA) reflectance and brightness temperature. The products use data streams from the latest geostationary (GEO) sensors including the GOES-16/17 ABI and the Himawari-8/9 AHI. The GeoNEX processing pipeline starts by converting digital numbers to physical quantities with the latest radiometric calibration information. It integrates algorithms to automatically detect and remove residual geolocation errors, to estimate the pixel-wise data-acquisition time, and to accurately calculate the solar illumination angles for each pixel in the domain at every time step. The outputs are reprojected to a globally tiled common grid in geographic coordinates designed to facilitate inter-comparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites. Therefore, the GeoNEX L1G products provide accurate and consistent TOA reflectance and brightness temperature datasets for scientific analyses and downstream product development.

Geostationary satellite, GOES-16, Himawari-8, NASA↗
NASA NTRS1975

The correlation of Skylab L-band brightness temperatures with antecedent precipitation

The S194 L-band radiometer flown on the Skylab mission measured terrestrial radiation at the microwave wavelength of 21.4 cm. The terrain emissivity at this wavelength is strongly dependent on the soil moisture content, which can be inferred from antecedent precipitation. For the Skylab data acquisition pass from the Oklahoma panhandle to southeastern Texas on 11 June 1973, the S194 brightness temperatures are highly correlated with antecedent precipitation from the preceding eleven day period, but very little correlation was apparent for the preceding five day period. The correlation coefficient between the averaged antecedent precipitation index values and the corresponding S194 brightness temperatures between 230 K and 270 K, the region of apparent response to soil moisture in the data, was -0.97. The equation of the linear least squares line fitted to the data was: API (cm) = 31.99 -0.114 TB (K), where API is the antecedent precipitation index and TB is the S194 brightness temperature.

Mcfarland, M. J.↗
NASA NTRS1980

Brightness temperatures of Saturn's disk and rings at 400 and 700 micrometers

Observations at wavelengths longer than 300 microns provide temperature measurements at deeper layers in the atmosphere of giant planets because opacities are reduced. Submillimeter observations of Saturn were made in November 1979 when the inclination angle of the rings was 1.2 degrees. A 55 sec focal plane aperture was used along with two filters with spectral passbands of 300 to 800 microns (mean wavelength approximately 400 microns) and 500 to 850 microns (mean wavelength approximately 700). Mean brightness temperatures of 121 + or - 12 K at 400 microns and 139 + or - 15 K at 700 microns are reported. The brightness temperatures fall below predictions of atmospheric models at shorter wavelengths, requiring an opacity source besides the pressure-broadened hydrogen lines. In combination with measurements at larger inclination angles, the results yield a 400 micrometer brightness temperature for the rings of approximately 75 K.

Whitcomb, S. E.↗
NASA NTRS1980

Absolute brightness temperature measurements at 3.5-mm wavelength

Careful observations have been made at 86.1 GHz to derive the absolute brightness temperatures of the sun (7914 + or - 192 K), Venus (357.5 + or - 13.1 K), Jupiter (179.4 + or - 4.7 K), and Saturn (153.4 + or - 4.8 K) with a standard error of about three percent. This is a significant improvement in accuracy over previous results at millimeter wavelengths. A stable transmitter and novel superheterodyne receiver were constructed and used to determine the effective collecting area of the Millimeter Wave Observatory (MWO) 4.9-m antenna relative to a previously calibrated standard gain horn. The thermal scale was set by calibrating the radiometer with carefully constructed and tested hot and cold loads. The brightness temperatures may be used to establish an absolute calibration scale and to determine the antenna aperture and beam efficiencies of other radio telescopes at 3.5-mm wavelength.

Ulich, B. L.↗
NASA NTRS1988

Identification and recovery of discontinuous synoptic features in satellite-retrieved brightness temperatures using a radiative transfer model

An attempt is made to recover and identify discontinuous synoptic features from satellite-retrieved brightness temperatures, with attention to near-discontinuities in temperature and moisture that are typically found in fronts and inversions. Efforts are made to ascertain whether the vectors of satellite channel brightness temperatures can be classified according to synoptic source, and whether those sources are amenable to quantification.

White, G. A., III↗
NASA NTRS2006

Inter-calibrating Brightness Temperatures of a Constellation of Radiometers

In 2010, the National Aeronautics and Space Agency (NASA) of the U.S.A. and the Japanese Exploration Agency (JAXA) of Japan in cooperation with other U.S. and international partners will launch the Global Precipitation Measurement (GPM) mission. The mission center-piece is a core U.S. provided satellite holding a scanning microwave imager provided by the U.S. A. and a dual-frequency precipitation radar provided by Japan. The core satellite is in a 65 deg inclination (the current Tropical Rainfall Measuring Mission -TRMM is in a 35 deg inclination). Joining the core satellite are a constellation of approximately 8 satellites containing scanning radiometers. The purpose of the constellation is to increase the global sampling capability of the mission. One constellation satellite will be provided by NASA. Some of the constellation radiometers will provided by other U.S. agencies with existing (e.g. SSMI/S) or planned (NPOESS) radiometer assets. International groups have also expressed interest in contributing to the GPM mission including providing radiometer data for the GPM constellation. The use of a heterogeneous group of scanning radiometers each with its own unique purpose, characteristics and calibration offers a significant challenge for combining brightness temperatures or rain retrievals to create meaningful combined global radiometer products. However, the availability of active dual precipitation radar on the GPM core in combination with a well-calibrated radiometer on the same platform offers the possibility of inter-calibrating the constellation radiometers using the core satellite as a calibrator. This paper describes a joint NASA/GSFC and Colorado State University prototype effort at inter-calibrating existing radiometers using such a core calibrator approach. In the prototype, existing radiometers (i.e. SSM/I and AMSR-E) are intercalibrated, as required, using the TRMM Precipitation Radar (PR) and TRMM Microwave Imager (TMI) as the calibration core. This prototype includes four basic steps: provide the data in a normalized-common logical radiometer format, convert all data to a granule that stops and starts at the southernmost latitude rather than at the equator, perform basic processing and finally inter-calibrate the radiometers with the TRMM core as appropriate. Basic processing includes quality control, cross-track bias correction as well as the core action of converting T(sub a), to T(sub b). The purpose of the inter-calibration step is to provide consistent brightness temperatures for the various SSMA sensors, TMI and AMSR-E. The C appended to the level of the product indicates common brightness temperature (T(sub c)). The paper provides a summary of each of these actions. The details of the processing as well as data format and tools can be obtained by connecting to the website at mrain.atmos.colostate.eduLEVEL1C .

Stocker, Erich Franz↗
NASA NTRS2010

L Band Brightness Temperature from Forest: Comparison of Approximate Techniques

In this paper, three approximate physical microwave radiometry models have been used to calculate brightness temperatures from a forest canopy at L-band. These models are (1) tau-omega model (zero order scattering approximation to radiative transfer equations), (2) successive order of scattering model up to first order (first order scattering approximation to the radiative transfer equations), and (3) Peak technique utilizing the active solution obtained from the Distorted Born Approximation (DBA). These models are physically-based and treat vegetation as a layer of discrete scatterers over a rough surface. Vegetation components within the canopy are represented by canonical shapes such as dielectric discs and cylinders. The tau-omega model is based on a zero-order solution to the radiative transfer (RT) equations. The model ignores scattering except for the effect of the scatterers in the attenuation of the emission through the vegetation. Application of the tau-omega model to data acquired during airborne and ground-based campaigns over the years has solidified scientific understanding of microwave interactions with different landscapes. In particular, shrubland, grasslands, agricultural crops, and light to moderate vegetation have been investigated. Its applicability to areas with a significant tree fraction is unknown. The first order scattering model is based on an iterative solution of the RT equation up to the first order. The first order solution is obtained by substituting the zeroth-order solution into the scattering source term and then solving the resulting radiative transfer equations. This formulation adds a new scattering term to the tau-omega model. It represents emission by particles in the layer and emission by the ground that is scattered once by particles in the layer. The resulting model represents an improvement over the standard zero-order solution (the tau-omega model) since it accounts for the scattered vegetation and ground radiation that can have a pronounced effect on the observed brightness temperature. The third model is based on the Peake formulation in conjunction with the DBA. The procedure for calculation of forest emission is accomplished by first calculating the bistatic scattering cross section for each type of scatterer, then by using the DBA to calculate specular albedo of the ground and the diffused albedo of the layer. Once the albedos are determined, Peake s principle relating active and passive problems can be used to determine the effective emissivity of the forest layer.

Kurum, Mehmet↗
NASA NTRS1993

Relationships between evaprorative fraction and remotely sensed vegetation index and microwave brightness temperature for semiarid rangelands

Measurements of the microwave brightness temperature (TB) with the Pushbroom Microwave Radiometer (PBMR) over the Walnut Gulch Experiment Watershed were made on selected days during the MONSOON 90 field campaign. The PBMR is an L-band instrument (21-cm wavelength) that can provide estimates of near-surface soil moisture over a variety of surfaces. Aircraft observations in the visible and near-infrared wavelengths collected on selected days also were used to compute a vegetation index. Continuous micrometeorological measurements and daily soil moisture samples were obtained at eight locations during experimental period. Two sites were instrumented with time domain reflectometry probes to monitor the soil moisture profile. The fraction of available energy used for evapotranspiration was computed by taking the ratio of latent heat flux (LE) to the sum of net radiation (Rn) and soil heat flux (G). This ratio is commonly called the evaporative fraction (EF) and normally varies between 0 and 1 under daytime convective conditions with minimal advection. A wide range of environmental conditions existed during the field campaign, resulting in average EF values for the study area varying from 0.4 to 0.8 and values of TB ranging from 220 to 280 K. Comparison between measured TB and EF for the eight locations showed an inverse relationship. Other days were included in the analysis by estimating TB with the soil moisture data. Because transpiration from the vegetation is more strongly coupled to root zone soil moisture, significant scatter in this relationship existed at high values of TB or dry near-surface soil moisture conditions. The variation in EF under dry near-surface soil moisture conditions was correlated to the amount of vegetation cover estimated with a remotely sensed vegetation index. These findings indicate that information obtained from optical and microwave data can be used for quantifying the energy balance of semiarid areas. The microwave data can indicate when soil evaporation is significantly contributing to EF, while the optical data is helpful for quantifying the spatial variation in EF due to the distribution of vegetation cover.

Kustas, W. P.↗
NASA NTRS2008

Titan's Surface Brightness Temperatures and H2 Mole Fraction from Cassini CIRS

The atmosphere of Titan has a spectral window of low opacity around 530/cm in the thermal infrared where radiation from the surface can be detected from space. The Composite Infrared spectrometer1 (CIRS) uses this window to measure the surface brightness temperature of Titan. By combining all observations from the Cassini tour it is possible to go beyond previous Voyager IRIS studies in latitude mapping of surface temperature. CIRS finds an average equatorial surface brightness temperature of 93.7+/-0.6 K, which is close to the 93.65+/-0.25 K value measured at the surface by Huygens HASi. The temperature decreases toward the poles, reaching 91.6+/-0.7 K at 90 S and 90.0+/-1.0 K at 87 N. The temperature distribution is centered in latitude at approximately 12 S, consistent with Titan's season of late northern winter. Near the equator the temperature varies with longitude and is higher in the trailing hemisphere, where the lower albedo may lead to relatively greater surface heating5. Modeling of radiances at 590/cm constrains the atmospheric H2 mole fraction to 0.12+/-0.06 %, in agreement with results from Voyager iris.

Jennings, Donald E.↗
NASA NTRS1987

Satellite-derived ice data sets no. 2: Arctic monthly average microwave brightness temperatures and sea ice concentrations, 1973-1976

A summary data set for four years (mid 70's) of Arctic sea ice conditions is available on magnetic tape. The data include monthly and yearly averaged Nimbus 5 electrically scanning microwave radiometer (ESMR) brightness temperatures, an ice concentration parameter derived from the brightness temperatures, monthly climatological surface air temperatures, and monthly climatological sea level pressures. All data matrices are applied to 293 by 293 grids that cover a polar stereographic map enclosing the 50 deg N latitude circle. The grid size varies from about 32 X 32 km at the poles to about 28 X 28 km at 50 deg N. The ice concentration parameter is calculated assuming that the field of view contains only open water and first-year ice with an ice emissivity of 0.92. To account for the presence of multiyear ice, a nomogram is provided relating the ice concentration parameter, the total ice concentration, and the fraction of the ice cover which is multiyear ice.

Parkinson, C. L.↗
NASA NTRS2017

Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates

Active radar backscatter (s◦) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable s◦ and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0-10 cm) soil moisture simulations over the period 2010-2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating s◦ or TB alone improves the average correlation of seasonal anomalies (Ran) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves Ran to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves Ran from 0.502 to 0.536 and 0.533, respectively for s◦ and TB, whereas the best performance is observed for the joint assimilation (Ran = 0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

GLEAM↗
NASA NTRS1994

Simulations of the effects of water vapor, cloud liquid water, and ice on AMSU moisture channel brightness temperatures

Radiative transfer simulations are performed to determine how water vapor and nonprecipitating cloud liquid water and ice particles within typical midlatitude atmospheres affect brightness temperatures T(sub B)'s of moisture sounding channels used in the Advanced Microwave Sounding Unit (AMSU) and AMSU-like instruments. The purpose is to promote a general understanding of passive top-of-atmosphere T(sub B)'s for window frequencies at 23.8, 89.0, and 157.0 GHz, and water vapor frequencies at 176.31, 180.31, and 182.31 GHz by documenting specific examples. This is accomplished through detailed analyses of T(sub B)'s for idealized atmospheres, mostly representing temperate conditions over land. Cloud effects are considered in terms of five basic properties: droplet size distribution, phase, liquid or ice water content, altitude, and thickness. Effects on T(sub B) of changing surface emissivity also are addressed. The brightness temperature contribution functions are presented as an aid to physically interpreting AMSU T(sub B)'s. Both liquid and ice clouds impact the T(sub B)'s in a variety of ways. The T(sub B)'s at 23.8 and 89 GHz are more strongly affected by altostratus liquid clouds than by cirrus clouds for equivalent water paths. In contrast, channels near 157 and 183 GHz are more strongly affected by ice clouds. Higher clouds have a greater impact on 157- and 183-GHz T(sub B)'s than do lower clouds. Clouds depress T(sub B)'s of the higher-frequency channels by suppressing, but not necessarily obscuring, radiance contributions from below. Thus, T(sub B)'s are less closely associated with cloud-top temperatures than are IR radiometric temperatures. Water vapor alone accounts for up to 89% of the total attenuation by a midtropospheric liquid cloud for channels near 183 GHz. The Rayleigh approximation is found to be adequate for typical droplet size distributions; however, Mie scattering effects from liquid droplets become important for droplet size distribution functions with modal radii greater than 20 micrometers near 157 and 183 GHz, and greater than 30-40 micrometers at 89 GHz. This is due mainly to the relatively small concentrations of droplets much larger than the mode radius. Orographic clouds and tropical cumuli have been observed to contain droplet size distributions with mode radii in the 30-40 micrometers range. Thus, as new instruments bridge the gap between microwave and infrared to frequencies even higher than 183 GHz, radiative transfer modelers are cautioned to explicitly address scattering characteristics of such clouds.

Muller, Bradley M.↗
NASA NTRS1989

VLBI using a telescope in Earth orbit. II - Brightness temperatures exceeding the inverse Compton limit

VLBI observations at 2.3 GHz were conducted using an antenna of the Tracking and Data Relay Satellite System (TDRSS), in geosynchronous orbit, and two ground stations. Twenty-three of 24 sources were detected, on baselines as long as 2.15 earth diameters. These baseline lengths gave the interferometer much better sensitivity to high brightness temperatures than any earth-based observations. Brightness temperatures of 1-4 times the 10 to the 12th K inverse Compton limit were measured for 10 sources, suggesting bulk relativistic motion in these sources. Coherence values of approximately 85 percent for integration times of 360 s were obtained.

Linfield, R. P.↗
NASA NTRS1993

Surface-induced brightness temperature variations and their effects on detecting thin cirrus clouds using IR emission channels in the 8-12 micrometer region

A method for detecting cirrus clouds in terms of brightness temperature differences between narrow bands at 8, 11, and 12 mu m has been proposed by Ackerman et al. (1990). In this method, the variation of emissivity with wavelength for different surface targets was not taken into consideration. Based on state-of-the-art laboratory measurements of reflectance spectra of terrestrial materials by Salisbury and D'Aria (1992), we have found that the brightness temperature differences between the 8 and 11 mu m bands for soils, rocks and minerals, and dry vegetation can vary between approximately -8 K and +8 K due solely to surface emissivity variations. We conclude that although the method of Ackerman et al. is useful for detecting cirrus clouds over areas covered by green vegetation, water, and ice, it is less effective for detecting cirrus clouds over areas covered by bare soils, rocks and minerals, and dry vegetation. In addition, we recommend that in future the variation of surface emissivity with wavelength should be taken into account in algorithms for retrieving surface temperatures and low-level atmospheric temperature and water vapor profiles.

Gao, Bo-Cai↗
NASA NTRS2019

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (TB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and TB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted TB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic TB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

Kwon, Yonghwan↗