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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

The SEASAT altimeter wet tropospheric range correction revisited

An expanded set of radiosonde observations was used to calculate the wet tropospheric range correction for the brightness temperature measurements of the SEASAT scanning multichannel microwave radiometer (SMMR). The accuracy of the conventional algorithm for wet tropospheric range correction was evaluated. On the basis of the expanded observational data set, the algorithm was found to have a bias of about 1.0 cm, and a standard deviation 2.8 cm. In order to improve the algorithm, the exact linear, quadratic and logarithmic relationships between brightness temperatures and range corrections were determined. Various combinations of measurement parameters were used to reduce the standard deviation between SEASAT SMMR and radiosonde observations to about 2.1 cm. The performance of various range correction formulas is compared in a table.

Tapley, D. B.↗
NASA NTRS1993

On the accuracy of the Eddington approximation for radiative transfer in the microwave frequencies

The paper examines how well an Eddington approximation can reproduce brightness temperatures obtained from a more complete, N-stream discrete ordinate solution in the microwave regime. Radiation propagation through a plane parallel medium is considered. Although model discrepancies are complicated functions of the cloud constituents, the differences between an eight-stream discrete ordinate solution and an analytical Eddington solution were found to be generally small, ranging from 0 to 6 K when only one uniform layer of hydrometeors was considered. When realistic multilayered cloud hydrometeor profiles were used, the differences between these two models never exceeded 3 K over the entire range of microwave frequencies considered (6.6-183 GHz). The models agreed to within 0.2 K in the absence of scattering constituents.

Kummerow, Christian↗
NASA NTRS2006

Impact of Surface Roughness on AMSR-E Sea Ice Products

This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate AMSR-E Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea ice algorithms, namely: 1) the National Aeronautics and Space Administration Team (NT) algorithm and 2) the enhanced NT algorithm, as well as the impact of roughness on the AMSR-E snow depth algorithm. Surface snow and ice data collected during the AMSR-Ice03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over or underestimation of the true amount of sea ice in the pixel area viewed. For example, this paper showed that if the sea ice areas modeled in this paper mere assumed to be completely smooth, sea ice concentrations were underestimated by nearly 14% using the NT sea ice algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7,18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled ice. Using the PR at 10.7 GHz to derive an "effective" viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea ice concentration for both algorithms. The AMSR-E snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and 18.7 GHz to these factors to improve snow depth retrievals from spaceborne passive microwave sensors.

Stroeve, Julienne C.↗
NASA NTRS1984

Microwave emissions from snow

The radiation emitted from dry and wet snowpack in the microwave region (1 to 100 GHz) is discussed and related to ground observations. Results from theoretical model calculations match the brightness temperatures obtained by truck mounted, airborne and spaceborne microwave sensor systems. Snow wetness and internal layer structure complicate the snow parameter retrieval algorithm. Further understanding of electromagnetic interaction with snowpack may eventually provide a technique to probe the internal snow properties

Chang, A. T. C.↗
NASA NTRS1991

Microwave polarization inversion observed

Observations of an inversion of solar-active-region microwave polarization are described as they occurred during the Coronal Magnetic Structures Observing Campaign. Data regarding the microwave frequencies, soft X-ray emissions, brightness temperatures, and column emissions are obtained with the observations. The data are employed in the potential-field extrapolation procedure by Sakurai (1982) to calculate the coronal magnetic-field vector, and the microwave polarization observations yield reasonable coronal densities and evidence of an inversion.

Brosius, Jeffrey W.↗
NASA NTRS1977

Geological applications of Nimbus radiation data in the Middle East

Large plateaus of Eocene limestone and exposed limestone escarpments, in Egypt and Saudi Arabia, respectively, were indicated by cool brightness temperatures recorded by the Nimbus-5 electrically scanning microwave radiometer (ESMR), over a 2-year period. Nubian sandstone, desert eolian sand, and igneous-metamorphic rock of the Pliocene, Miocene, Oligocene, and Cretaceous periods were differentiated from these limestone areas by warm T sub B values. These brightness temperature differences are a result of seasonal in-situ ground temperatures and differential emissivity of limestone and sand, sandstone and granite, whose dielectric constants are 6 to 8.9 and 2.9 and 4.2 to 5.3, respectively, at 19.35 GHz.

Allison, L. J.↗
NASA NTRS1989

Atmospheric moisture and cloud structure determined from SSM/I and global gridpoint analyses

Data from the Special Sensor Microwave Imager/I on the DMSP satellite are used to study atmospheric moisture and cloud structure. Column-integrated water vapor and total liquid water retrievals are obtained using an algorithm based on a radiative model for brightness temperature (Wentz, 1983). The results from analyzing microwave and IR measurements are combined with independent global gridpoint analyses to study the distribution and structure of atmospheric moisture over oceanic regions.

Robertson, Franklin R.↗
NASA NTRS2012

Advances in Assimilation of Satellite-Based Passive Microwave Observations for Soil-Moisture Estimation

Satellite-based microwave measurements have long shown potential to provide global information about soil moisture. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS, [1]) mission as well as the future National Aeronautics and Space Administration (NASA) Soil Moisture Active and Passive (SMAP, [2]) mission measure passive microwave emission at L-band frequencies, at a relatively coarse (40 km) spatial resolution. In addition, SMAP will measure active microwave signals at a higher spatial resolution (3 km). These new L-band missions have a greater sensing depth (of -5cm) compared with past and present C- and X-band microwave sensors. ESA currently also disseminates retrievals of SMOS surface soil moisture that are derived from SMOS brightness temperature observations and ancillary data. In this research, we address two major challenges with the assimilation of recent/future satellite-based microwave measurements: (i) assimilation of soil moisture retrievals versus brightness temperatures for surface and root-zone soil moisture estimation and (ii) scale-mismatches between satellite observations, models and in situ validation data.

De Lannoy, Gabrielle J. M.↗
NASA NTRS1975

A radiative transfer model for microwave emissions from bare agricultural soils

A radiative transfer model for microwave emissions from bare, stratified agricultural soils was developed to assist in the analysis of data gathered in the joint soil moisture experiment. The predictions of the model were compared with preliminary X band (2.8 cm) microwave and ground based observations. Measured brightness temperatures at vertical and horizontal polarizations can be used to estimate the moisture content of the top centimeter of soil with + or - 1 percent accuracy. It is also shown that the Stokes parameters can be used to distinguish between moisture and surface roughness effects.

Burke, W. J.↗
NASA NTRS1981

Dependence of sea-surface microwave emissivity on friction velocity as derived from SMMR/SASS

The sea-surface microwave emissivity is derived using SMMR brightness temperatures and SASS inferred friction velocities for three North Pacific Seasat passes. The results show the emissivity increasing linearly with friction velocity with no obvious break between the foam-free and foam regimes up to a friction velocity of about 70 cm/sec (15 m/sec wind speed). For horizontal polarization the sensitivity of emissivity to friction velocity greatly increases with frequency, while for vertical polarization the sensitivity is much less and is independent of frequency. This behavior is consistent with two-scale scattering theory. A limited amount of high friction velocity data above 70 cm/sec suggests an additional increase in emissivity due to whitecapping.

Wentz, F. J.↗
NASA NTRS1982

The utilization of Nimbus-7 SMMR measurements to delineate rainfall over land

Based on previous theoretical calculations, an empirical statistical approach to use satellite multifrequency dual polarized passive microwave data to detect rainfall areas over land was initiated. The addition of information from a lower frequency channel (18.0 or 10.7 GHz) was shown to improve the discrimination of rain from wet ground achieved by using a single frequency dual polarized (37 GHz) channel alone. The algorithm was developed and independently tested using data from the Nimbus-7 Scanning Multichannel Microwave Radiometer. Horizontally and vertically polarized brightness temperature pairs at 37, 18, 10.7 GHz were sampled for raining areas over land (determined from ground base radar), wet ground areas (adjacent and upwind from rain areas determined from radar), and dry land regions (areas where rain had not fallen in a 24h period) over the central and eastern United States. Surface thermodynamic temperatures were both above and below 15 deg C.

Rogers, E.↗
NASA NTRS1993

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).

Lure, Y. M. Fleming↗
NASA NTRS2005

The GPM Common Calibrated Brightness Temperature Product

The Global Precipitation Measurement (GPM) project will provide a core satellite carrying the GPM Microwave Imager (GMI) and will use microwave observations from a constellation of other satellites. Each partner with a satellite in the constellation will have a calibration that meets their own requirements and will decide on the format to archive their brightness temperature (Tb) record in GPM. However, GPM multi-sensor precipitation algorithms need to input intercalibrated Tb's in order to avoid differences among sensors introducing artifacts into the longer term climate record of precipitation. The GPM Common Calibrated Brightness Temperature Product is intended to address this problem by providing intercalibrated Tb data, called "Tc" data, where the "c" stands for common. The precipitation algorithms require a Tc file format that is both generic and flexible enough to accommodate the different passive microwave instruments. The format will provide detailed information on the processing history in order to allow future researchers to have a record of what was done. The format will be simple, including the main items of scan time, latitude, longitude, and Tc. It will also provide spacecraft orientation, spacecraft location, orbit, and instrument scan type (cross-track or conical). Another simplification is to store data in real numbers, avoiding the ambiguity of scaled data. Finally, units and descriptions will be provided in the product. The format is built on the concept of a swath, which is a series of scans that have common geolocation and common scan geometry. Scan geometry includes pixels per scan, sensor orientation, scan type, and incidence angles. The Tc algorithm and data format are being tested using the pre-GPM Precipitation Processing System (PPS) software to generate formats and 1/0 routines. In the test, data from SSM/I, TMI, AMSR-E, and WindSat are being processed and written as Tc products.

Stout, John↗
NASA NTRS1975

On the determination of atmospheric path length by passive microwave radiometer

Microwave radiometer techniques were evaluated for use in atmospheric path length correction of Pacific Plate Motion Experiment interferometer measurements. It is shown that passive microwave radiometry allows precise measurement of the brightness temperature of the sky. It is also noted that the technological requirements of radiometers are very different from the requirements of radio astronomy. The technology was used in the construction of radiometers which are sufficient for use in the path length correction problem. A simulation study shows that, when combined with surface meteorology data, passive microwave radiometer data would allow a determination of the path length correction to better than 2 cm at the zenith. By a careful choice of frequencies, a dual frequency system would allow a measurement of the path length correction to better than 4 cm at zenith angles as great as 60 deg. Because of the wide range of weather conditions to be expected for the PPME sites (which include Alaska, Hawaii and Massachusetts), it will probably be necessary to use a separate correction algorithm for each site.

Webster, W. J., Jr.↗
NASA NTRS1984

Severe storm identification with satellite microwave radiometry: An initial investigation with Nimbus-7 SMMR data

The severe weather characteristics of convective storms as observed by the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) are investigated. Low 37 GHz brightness temperatures (due to scattering of upwelling radiation by precipitation size ice) are related to the occurrence of severe weather (large hail, strong winds or wind damage, tornadoes and funnel clouds) within one hour of the satellite observation time. During 1979 and 1980 over the United States there were 263 storms which had very cold 37 GHz signatures. Of these storms 15% were severe. The SMMR detected hail, wind, and tornadic storms equally well. Critical Success Indices (CSI's) of 0.32, 0.48, and 0.38 are achieved for the thresholding of severe vs. nonsevere low brightness temperature events during 1979, 1980, and the two years combined, respectively. Such scores are comparable to skill scores for early radar detection methods. These results suggest that a future geostationary passive microwave imaging capability at 37 GHz, with sufficient spatial and temporal resolution, would allow the detection of severe convective storms. This capability would provide a useful complement to radar, especially in areas not covered by radar.

Spencer, R. W.↗
NASA NTRS1980

Theoretical and experimental studies of microwave radiation from a natural snow field

The brightness temperature of a natural snow field in northern Europe was studied theortically and experimentally at 5, 12, and 37 GHz for satellite remote sensing applications. A snow model consisting of ice spheres covered by a water shell was used in calculation, taking into account scattering and absorption. The brightness temperature of a natural snow field as a function of view angle was measured from a tower in 1978 and 1979. The measured brightness temperature curves can be fitted with calculated ones by assuming reasonable values for the wetness and the particle size of snow. Experimental results also show that relatively small changes in the snow conditions cause large changes in the brightness temperature. In order to obtain a more controlled situation, experiments were continued in 1980 using a measuring site covered with aluminum sheets and determining the wetness and the particle size in addition to the density and physical temperature.

Tiuri, M.↗
NASA NTRS2021

Comparison of soil dielectric mixing models for Soil Moisture Retrieval using SMAP Brightness Temperature over croplands in India

The accurate estimation of soil moisture (SM) using microwave remote sensing depends mostly on careful selection of retrieval parameters among which the soil dielectric mixing model is the important one. These models are often categorized into empirical, semi-empirical or volumetric based on their methodologies and input data requirements. To study in detail, the comparative performance of four dielectric mixing models -- Wang & Schmugge model, Hallikainen model, Dobson model and Mironov model were used with Soil Moisture Active Passive (SMAP) L-band brightness temperature and Single Channel Algorithm for SM retrieval over agricultural landscapes in India. The highest performance statistics combination in terms of Root Mean Square Error (RMSE), correlation coefficient (R^(2)) and percentage bias (PBIAS) against the concurrent in-situ SM measurements were calculated at the selected validation sites. The overall results indicate that the best performance was given by the Mironov model (RMSE = 0.07 cu. m/cu. m), followed by Wang & Schmugge model (RMSE = 0.08 cu. m3/cu. m), Hallikainen model (RMSE = 0.09 cu. m/cu. m), Dobson model (RMSE = 0.10 cu. m/cu. m) and original SMAP radiometer SM (RMSE = 0.12 cu. m/cu. m). Findings of this study provides important insights into application and performance of dielectric mixing models in mapping surface SM variations. This study also underlines the pivotal role of local conditions for SM retrieval which should be carefully included in the algorithms.

Swati Suman↗