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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

A physical interpretation of brightness temperatures observed by the microwave sounding units based upon raobs

Ten years (1979-1988) of Microwave Sounding Unit (MSU) data from five satellites were calibrated and processed using procedures described by Spence and Christy (1990) and Spenser et al. (1990). The data are averaged at one month and 2.5-deg grid resolution, and the monthly anomalies in channel 2 are computed from annual cycles measured at each gridpoint. The anomalies at gridpoints are then compared with anomalies computed from radiosonde data.

Spencer, Roy W.↗
NASA NTRS2014

Using a Support Vector Machine and a Land Surface Model to Estimate Large-Scale Passive Microwave Temperatures over Snow-Covered Land in North America

A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the Advanced Microwave Scanning Radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [14]. The results suggest the SVM outperforms the ANN at 10.65 GHz, 18.7 GHz, and 36.5 GHz for both vertically and horizontally-polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root mean squared error in the SVM output is 8 K or less while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the root mean squared error was reduced by more than 18 percent while the anomaly correlation coefficient was increased by more than 52 percent. Further, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.

Modeling↗
NASA NTRS1983

Study of tropopause height estimate from TOMS total ozone data from Nimbus-7 and from the microwave regression temperature retrieval of simulated brightness temperatures

The use of TOMS total ozone data obtained by the Nimbus-7 satellite in order to improve satellite microwave retrieval in the tropopause is investigated. Attention is given to the determination of the tropopause by means of TOMS data, the character of the effect of tropopause errors on microwave MSU retrievals, and the use of air mass to stratify regression retrievals.

Munteanu, M.-J.↗
NASA NTRS2018

Assimilating GCOM-W AMSR2 Radiance Data in Future GEOS Reanalysis

Surface and atmospheric radiation are being measured by space-borne conical scanning microwave radiometers with high accuracy and spatial resolution since 1980s'. These radiometers include Special Sensor Microwave Imager (SSM/I) onboard various NOAA's polar-orbiting satellites, Special Sensor Microwave Imager Sounder (SSMIS) onboard Defense Meteorological Satellite Program (DMSP) satellites, and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), Advanced Microwave Scanning Radiometer 2 (AMSR2), Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI), and Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard NASA and JAXA's satellites. Atmospheric temperature, humidity, clouds, and precipitation are retrieved using brightness temperature data at frequencies between 22 GHz – 183 GHz. Some of the brightness temperature and retrieved rain rate data made by these imagers are assimilated in NASA Global Modelling and Assimilation Office (GMAO)'s weather and climate data sets including the Modern-Era Retrospective analysis for Research and Applications (MERRA) and its version 2 (MERRA-2) data sets. However, those radiance data are assimilated with the Goddard Earth Observing System model, version 5 (GEOS-5) only in clear-sky conditions. Recently, we started assimilate GPM/GMI radiance data with GEOS-5's atmospheric data assimilation system GSI under all-sky conditions in order to have better constraints in analyzed hydrological properties. GMAO plans to assimilate more microwave radiometers' brightness temperature data in its analyses. In this talk, we will discuss TMI and AMSR2 data's impact in temperature, humidity, clouds and precipitation under all-sky conditions in future GEOS reanalysis.

Jin, Jianjun↗
NASA NTRS1980

Effect of soil texture on the microwave emission from soils

The intensity brightness temperature of the microwave emission from the soil is determined primarily by its dielectric properties. The large difference between the dielectric constant of water and that of dry soil produces a strong dependence of the soil's dielectric constant on its moisture content. This dependence is effected by the texture of the soil because the water molecules close to the particle surface are tightly bound and do not contribute significantly to the dielectric properties. Since this surface area is a function of the particle size distribution (soil texture), being larger for clay soils with small particles, and smaller for sandy soils with larger particles; the dielectric properties will depend on soil texture. Laboratory measurements of the dielectric constant for soils are summarized. The dependence of the microwave emission on texture is demonstrated by measurements of brightness temperature from an aircraft platform for a wide range of soil textures. It is concluded that the effect of soil texture differences on the observed values can be normalized by expressing the soil moisture values as a percent field capacity for the soil.

Schmugge, T. J.↗
NASA NTRS1974

Signatures of various earth surfaces measured by the Nimbus-5 microwave spectrometer

The Nimbus-5 Meteorological Satellite is equipped with a 5-channel microwave spectrometer. The two lowest channels (22.2 and 31.4 GHz) provide information on surface brightness temperature. Distinctive microwave signatures can be observed for snow, land ice, and sea ice in both polar regions. Some information on subsurface temperatures can be gained for desert areas.

Kunzi, K. F.↗
NASA NTRS1989

Aircraft microwave observations and simulations of deep convection at 18-183 GHz

A radiative transfer model is used to simulate the aircraft multichannel microwave observations and to study a storm system observed on June 29, 1986 during the Cohmex program. The model, which is a revision of the model used by Wilheit et al. (1982), is described. The microwave upwelling brightness temperatures at the top of the atmosphere are calculated from hydrometeor profiles derived from ground-based radar data. The model is used to study various vertical hydrometeor phase profiles and particle size distributions. The results are compared with aircraft data from the Multispectral Cloud Radiometer and the Microwave Precipitation Radiometer. The results show that the brightness temperatures are directly influenced by the complex nature of hydrometeors, including size distribution and the mixture of ice and water phases.

Adler, R. F.↗
NASA NTRS1996

Passive Microwave Remote Sensing of Rainfall Considering the Effects of Wind and Nonprecipitating Clouds

It has long been shown both in theory and in observation that emission from rain drops in a raining cloud results in upwelling brightness temperature above that caused by the sea surface alone. High brightness temperatures at microwave frequencies (e.g. 37 and 19 GHz) have usually been quantitatively associated with rainfall using physical or statistical models. By comparing concurrent special sensor microwave/imager and radar data, however, we noticed many cases where there is no appreciable rainfall in a field of view (FOV) which exhibits high brightness temperature (T(sub B)) at 37 and 19 GHz. On the basis of calculations and past literature it is shown that such high brightness temperatures can be caused by nonprecipitating clouds and by wind. The effect of the wind is to create wave and high-emissivity foam on the sea surface. A model is developed to relate T(sub B) to the fractional coverage of rain, f, within a FOV. The parameters of the model are calibrated by fitting the model to the observed brightness temperature and fractional rain coverage data. The critical parameter of the model, T(sub B min.), which is the threshold brightness temperature for the presence of rain, depends on the strength of the storm. The strength of the storm is characterized by the fraction of the FOVs within a large area that have T(sub B) higher than 240 K, which is readily obtainable from satellite data alone. The instantaneous FOV rain rate R can then be obtained through the f approximately R relationship which is empirically derived using radar data. An algorithm has been proposed based on the T(sub B) approximately f and f approximately R relationship. Application of the algorithm to TOGA-COARE and Darwin storms results in reasonable instantaneous FOV rain estimate. When averaged over the entire radar scan, a more accurate and unbiased areal rain estimate can be achieved.

Qihang Li.↗
NASA NTRS1973

Passive microwave images of lake ice

Photographic data and microwave emission images from the Great Lake ice formation are compared for their applicability to commercial shipping interests. A synoptic view of the microwave radiation from the lake area ice shows a large variation in brightness temperature. The snow ice appears to have the highest microwave brightness temperatures, whereas the thick clear ice shows up some 30 degrees kelvin colder, and the thin clear ice is colder still with a 1.55 cm radiation.

Gloersen, P.↗
NASA NTRS2023

A Comparison of Passive Microwave Emission Models for Estimating Brightness Temperature at L- and P-band Under Bare and Vegetated Soil Conditions

P-band radiometry has been demonstrated to have a deeper sensing depth than at L-band, making the consideration of multi-layer microwave interactions necessary. Additionally, the scattering and phase interference effects are different at P-band, requiring a re-consideration of the need for coherent models. However, the impact remains to be clarified, and understanding the validity and limitations of these models at both L-band and P-band is crucial for their refinement and application. Therefore, two general categories of microwave emission models, including two stratified coherent models (Njoku and Wilhite) and four incoherent models (conventional tau-omega model and three multi-layer models being zero-order, first-order, and incoherent solution), were intercompared for the first time on the same dataset. This evaluation utilized observations of L-band and P-band radiometry under different land cover conditions from a tower-based experiment in Victoria, Australia. Model estimations of brightness temperature (TB) were consistent with measurements, with the lowest root mean square error (RMSE) at P-band V-polarization under corn (2 K) and the highest RMSE at L-band H-polarization under bare soil (13 K). Coherent models performed slightly better than incoherent models under bare soil (3 K less RMSE), while the opposite was true under vegetated soil conditions (1 K less RMSE). Coherent and incoherent models showed maximum differences (3 K at P-band, 2 K at L-band), correlating strongly with soil moisture variations at 0-10 cm. Findings suggest that coherent and incoherent models perform similarly; thus, incoherent models may be preferable for estimating TB at L- and P-band due to reduced computational complexity.

Soil moisture profile↗
NASA NTRS1991

Homogeneity of spatial correlation statistics of tropical oceanic rainfall

The possibility of uniform horizontal correlation scales for tropical oceanic rainfall has been examined by a study of satellite-observed microwave data as a proxy measure of rain rates. From the brightness temperatures from the electrically scanning microwave radiometer on Nimbus 5 near nadir during the year 1974, the mean spatial autocorrelation function as a function of simultaneous pixel separation was calculated in each 5 by 5 deg grid box over the tropical Pacific and Atlantic for each season. The equal-time spatially lagged correlations were compared for geographical dependence to investigate the hypothesis of homogeneity. A simple model of the spatial statistics of the microwave brightness temperatures was used, consisting of a mixture of uncorrelated spatial white noise incoherently superimposed on a spatially correlated field (spatial red noise). The red noise signals are presumed to be generated by convective activity in the tropical atmosphere. The parameters of the red noise are consistent with this scheme over the tropical oceans, yielding a uniform spatial scale of about 50 km throughout.

Shin, Kyung-Sup↗
NASA NTRS1983

Atmospheric water distribution in cyclones as seen with Scanning Multichannel Microwave Radiometers (SMMR)

Passive microwave measurements are used to study the distribution of atmospheric water in midlatitude cyclones. The integrated water vapor, integrated liquid water, and rainfall rate are deduced from the brightness temperatures at microwave frequencies measured by the Scanning Multichannel Microwave Radiometer (SMRR) flown on both the Seasat and Nimbus 7 satellites. The practical application of locating fronts by the cyclone moisture pattern over oceans is shown, and the relationship between the quantity of coastal rainfall and atmospheric water content is explored.

Katsaros, K. B.↗
NASA NTRS2024

Classifying Microwave Radiometer Observations Over the Netherlands Into Dry, Shallow-, and Non-Shallow Precipitation Using A Random Forest Model

Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures, Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using space-borne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest model (RF) to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands - a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF is trained on five years of data (2016-2020) and tested with two independent years (2015, 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM’s Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA-5 2-meter temperature and freezing level reanalysis and/or Dual Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels (≥85 GHz) with lower Tb-values as non-shallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb-values, likely resulting from the presence of ice particles in non-precipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles.

Linda Bogerd↗
NASA NTRS1989

A model for atmospheric brightness temperatures observed by the special sensor microwave imager (SSM/I)

A closed-form mathematical model for the atmospheric contribution to microwave the absorption and emission at the SSM/I frequencies is developed in order to improve quantitative interpretation of microwave imagery from the Special Sensor Microwave Imager (SSM/I). The model is intended to accurately predict upwelling and downwelling atmospheric brightness temperatures at SSM/I frequencies, as functions of eight input parameters: the zenith (nadir) angle, the integrated water vapor and vapor scale height, the integrated cloud water and cloud height, the effective surface temperature, atmospheric lapse rate, and surface pressure. It is shown that the model accurately reproduces clear-sky brightness temperatures computed by explicit integration of a large number of radiosonde soundings representing all maritime climate zones and seasons.

Petty, Grant W.↗
NASA NTRS2006

Diurnal Variability of Vertical Structure from a TRMM Passive Microwave "Virtual Radar" Retrieval

Robust description of the diurnal cycle from TRMM observations is complicated by the limitations of Low Earth Orbit (LEO) sampling; from a 'climatological' perspective, sufficient sampling must exist to control for both spatial and seasonal variability, before tackling an additional diurnal component (e.g., with 8 additional 3-hourly or 24 1-hourly bins). For documentation of vertical structure, the narrow sample swath of the TRMM Precipitation Radar limits the resolution of any of these components. A neural-network based 'virtual radar" retrieval has been trained and internally validated, using multifrequency / multipolarization passive microwave(TM1) brightness temperatures and textures parameters and lightning (LIS) observations, as inputs, and PR volumetric reflectivity as targets (outputs). By training the algorithms (essentially highly multivariate, nonlinear regressions) on a very large sample of high-quality co-located data from the center of the TRMM swath, 3D radar reflectivity and derived parameters (VIL, IWC, Echo Tops, etc.) can be retrieved across the entire TMI swath, good to 8-9% over the dynamic range of parameters. As a step in the retrieval (and as an output of the process), each TMI multifrequency pixel (at 85 GHz resolution) is classified into one of the 25 archetypal radar profile vertical structure "types", previously identified using cluster analysis. The dynamic range of retrieved vertical structure appears to have higher fidelity than the current (Version 6) experimental GPROF hydrometeor vertical structure retrievals. This is attributable to correct representation of the prior probabilities of vertical structure variability in the neural network training data, unlike the GPROF cloud-resolving model training dataset used in the V6 algorithms. The LIS lightning inputs are supplementary inputs, and a separate offline neural network has been trained to impute (predict) LIS lightning from passive-microwave-only data. The virtual radar retrieval is thus, in principle, extensible to Aqua/AMSR-E and NPOESS/CMIS passive microwave instruments. The virtual radar approach yields a threefold increase in effective sampling from the mission, albeit of lower-quality "retrieved" data, reducing the variance of local estimates by one third (or the standard deviation by-0.57). In this talk, the variance reduction is leveraged to more finely resolve global diurnal variability in both space and time (local hour).

Boccippio, Dennis J.↗
NASA NTRS1972

The brightness temperature of the air-sea interface at microwave frequencies

Available results of observation have shown that at nadir the brightness temperature of the sea surface at 19.35 GHz increases linearly with increasing wind speed. The computational results of the modified theoretical model presented are in good agreement with the measurement results both at nadir and other angles. The model depicts that, for a fully developed sea driven by the wind with speed above 5 m/sec, the air in the transitional zone immediately above the air-sea interface is mixed with sea water droplets from bursting air bubbles. The droplet concentration has a profile tapering off to zero at a certain height. The dielectric constant of the inhomogeneous droplet profile is thus both a function of the height above the interface and the wind speed. Both the inhomogeneity effect and the possible attenuation effect of the droplet concentration have been considered.

Tang, C. C. H.↗
NASA NTRS2021

Error Propagation in Microwave Soil Moisture and Vegetation Optical Depth Retrievals

Satellite soil moisture and vegetation optical depth (VOD; related to the total vegetation water mass per unit area) are increasingly being used to study water relations in the soil-plant continuum across the globe. However, soil moisture and VOD are typically jointly estimated, where errors in the optimization approach can cause compensation between both variables and confound such studies. It is thus critical to quantify how satellite microwave measurement errors propagate into soil moisture and VOD. Such a study is especially important for VOD given limited investigations of whether VOD reflects in-situ plant physiology. Furthermore, despite new approaches that constrain (or regularize) VOD dynamics to reduce soil moisture errors, there is limited study of whether regularization reduces VOD errors without obscuring true vegetation temporal dynamics. Here, we find that, across the globe, VOD is less robust to measurement error (more difficult for optimization methods to find the true solution) than soil moisture in their joint estimation. However, a moderate degree of regularization (via time-constrained VOD) reduces errors in VOD to a greater degree than soil moisture and reduces spurious soil moisture-VOD coupling. Furthermore, despite constraining VOD time dynamics, regularized VOD variations on sub-weekly scales are both closer to simulated true VOD time series and have global VOD post-rainfall responses with reduced error signatures compared to VOD retrievals without regularization. Ultimately, we recommend moderately regularized VOD for use in large scale studies of soil-plant water relations because it suppresses noise and spurious soil moisture-VOD coupling without removing the physical signal.

Vegetation optical depth↗
NASA NTRS1979

A spectral filter for ESMR's sidelobe errors

Fourier analysis was used to remove periodic errors from a series of NIMBUS-5 electronically scanned microwave radiometer brightness temperatures. The observations were all taken from the midnight orbits over fixed sites in the Australian grasslands. The angular dependence of the data indicates calibration errors consisted of broad sidelobes and some miscalibration as a function of beam position. Even though an angular recalibration curve cannot be derived from the available data, the systematic errors can be removed with a spectral filter. The 7 day cycle in the drift of the orbit of NIMBUS-5, coupled to the look-angle biases, produces an error pattern with peaks in its power spectrum at the weekly harmonics. About plus or minus 4 K of error is removed by simply blocking the variations near two- and three-cycles-per-week.

Chesters, D.↗