Search NASA⌕ Search

SEARCH · Search NASA

Results for “microwave brightness temperature”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

An Analytical Calibration Approach for the Polarimetric Airborne C Band Radiometer

Passive microwave remote sensing is sensitive to the quantity and distribution of water in soil and vegetation. During summer 2000, the Microwave Geophysics Group at the University of Michigan conducted the 7th Radiobrightness Energy Balance Experiment (REBEX-7) over a corn canopy in Michigan. Long time series of brightness temperatures, soil moisture and micrometeorology on the plot scale were taken. This paper addresses the calibration of the NASA GSFC polarimetric airborne C band microwave radiometer (ACMR) that participated in REBEX-7. Passive polarimeters are typically calibrated using an end-to-end approach based upon a standard artificial target or a well-known geophysical target. Analyzing the major internal functional subsystems offers a different perspective. The primary goal of this approach is to provide a transfer function that not only describes the system in its entirety but also accounts for the contributions of each subsystem toward the final modified Stokes parameters. This approach also serves as a realistic instrument simulator, a useful tool for future designs. The ACMR architecture can be partitioned into several functional subsystems. Each subsystem was extensively measured and the estimated parameters were imported into the overall system model. We will present the results of polarimetric antenna measurements, the instrument model as well as four Stokes observations from REBEX-7 using a first order inversion.

Pham, Hanh↗
NASA NTRS2004

Microphysical Characterization of Ice in Hurricane Erin for Wideband Passive Microwave Comparisons

In order to better understand the characteristics of frozen hydrometeors in hurricane systems, computed brightness temperatures were compared with observations of Hurricane Erin from CAMEX-4 radiometers on the ER-2 aircraft. These wideband observations ranged from 10.7 to 183 plus or minus 10 GHz. In order to initialize the cloud profiles used in the radiative transfer calculations, radar reflectivities from an ER-2 instrument were converted to mass contents at specified height levels. Dropsonde data from the ER-2 and modeled Hurricane Andrew data were used to generate profiles of temperature, pressure, and relative humidity and to provide surface wind speed and ocean temperature. Four different ice (size and density) parameterizations were used to compute brightness temperature values. The comparisons between the calculated and observed brightness temperatures show that in general the Grenfell and Warren particle size distribution does the best job of matching the observations for most of the high frequencies. These high frequencies (greater than or equal to 85 GHz) are particularly sensitive to frozen particles. The Sehkon and Srivastava particle size distribution produced brightness temperatures that were too cold in most cases. On the other hand, the fluffy snow particle parameterizations with and without a melting layer produced brightness temperatures that were much warmer than the observations at these high frequencies. All low frequency calculations track the observations well except in the eye of Hurricane Erin where the surface wind speed and/or near surface temperatures were not properly adjusted to warm the brightness temperature calculations to values closer to the observations. Overall, this work shows that selecting a proper ice particle parameterization can be difficult and additional in situ measurements are needed to define appropriate parameterizations.

Skofronick-Jackson, G.↗
NASA NTRS2019

An L-Band Brightness Temperature Disaggregation Method Using S-Band Radiometer Data for the Water Cycle Observation Mission (WCOM)

The Water Cycle Observation Mission (WCOM) will build upon previous L and C band passive microwave soil moisture satellite missions. WCOM will consist of a passive microwave synthetic aperture radiometer operating at L, S, and C bands. The WCOM requirements for passive soil moisture are to estimate soil moisture in the top 5 cm of soil layer with an error less than 0.04 cu m/cu m, at 15 km resolution and with a 3-day revisit. A new set of algorithms for these multi-frequency platforms will need to be developed for estimating the data products at the desired resolution. To accomplish this, a brightness temperature (TB) downscaling methodology is developed that uses passive S-band TB (30 km) to downscale L-band TB (50 km) and to estimate soil moisture at a 30 km resolution, based on the linear relationships between the passive signals of L-band and S-band. To test this downscaling method, analysis was performed using PALS data from the Soil Moisture Experiments in 2002 (SMEX02). For this study, 4 km L-band observations were downscaled to 800 m. The root mean square errors (RMSE) between the downscaled TBL at 800m with the observed TBL at 800m are 2.63 K and 1.60 K for H and V polarizations respectively. The results also showed that it was possible to use these disaggregated TB to estimate soil moisture to meet the mission requirement of 0.04 cu m/cu m. These results showed that we can obtain higher resolution soil moisture from L band passive TB with a high accuracy (<0.04 cu m/cu m) by using S-band information.

Disaggregation↗
NASA NTRS2010

Evaluating the Impact of Vegetation Cover and Atmospheric Characteristics on the Estimation of Snow Water Equivalent from Spaceborne Microwave Radiometry

A radiative transfer model for estimating snow water equivalent (SWE, mm) from satellite-observed brightness temperature (K) at 19 and 37 GHz (respectively, T(sub B(sub, sat,19)) and T(sub B(sub, sat,37)) over partially forested area is presented, as an extension of a previously published model, by considering scattering of radiation within the canopy. For the specific case of dense vegetation covering fractional area f, the model can be written as, SWE = alpha{ A. delta (T(sub B(sub, sat)) + B - C. f}/(l f), where delta T(sub B(sub, sat)), is the difference of T(sub B(sub, sat,19)) and T(sub B(sub, sat,37)), alpha(mm/K) is the slope of SWE vs. brightness temperature difference at 19 and 37 GHz that would be obtained by ignoring the presence of atmosphere, delta(T(sub B)sub g)), for a homogeneous snow cover (which varies with grain size). The parameters A, B, and C, are determined primarily by atmospheric characteristics, and for a likely range of atmospheric conditions appear to be in the range of, respectively, 1.15-1.63, 0.69-2.84 K and 0.59-2.39 K. Ignoring atmospheric correction would introduce bias towards underestimation of SWE (and also, snow cover area and snow depth). Increasing cloud liquid water path (L) has the effect of increasing A, and ignoring this variation of A with L would have the impact of biasing the estimate of SWE (and snow extent). Such biasing is further exacerbated with increasing f, because of the appearance of term (l-f) in the denominator. The impact of ignoring the intercept parameters (B and C) would be noticeable at low values of SWE (appearing as a bias towards underestimation of SWE), which has been determined to be about 6 mm for average environmental conditions. The uncertainty in estimating SWE due to variations in the atmospheric characteristics is likely to be less than 15%, but could be up to 25% for non-vegetated snow-covered areas. Better estimates of SWE (and snow extent) would be obtained by adjusting the parameters of the above model to regional differences in the atmospheric characteristics. The biases in determining SWE arising due to variations in atmospheric conditions and due to changes in fractional forest cover are not independent, since they interact as {A/(l-f)}. The present calculations also show that improvement in determining snow cover area from the microwave data is likely to occur when these data are corrected for atmospheric effects, as demonstrated by a specific case study.

Choudhury, Bhaskar J.↗
NASA NTRS1972

Microwave measurements of sea state

Passive microwave measurements were studied for determining sea state. It was found that the brightness temperature increases due to the foam produced by winds, and that the surface wind can be determined by the brightness temperature.

Wilheit, T. T.↗
NASA NTRS1975

Application of the Nimbus 5 ESMR to rainfall detection over land surfaces

The ability of the Nimbus 5 Electrically Scanning Microwave Radiometer (ESMR) to detect rainfall over land surfaces was evaluated. The ESMR brightness temperatures (Tb sub B) were compared with rainfall reports from climatological stations for a limited number of rain events over portions of the U.S. The greatly varying emissivity of land surfaces precludes detection of actively raining areas. Theoretical calculations using a ten-layer atmospheric model showed this to be an expected result. Detection of rain which had fallen was deemed feasible over certain types of land surfaces by comparing the Tb sub B fields before and after the rain fell. This procedure is reliable only over relatively smooth terrain having a substantial fraction of bare soil, such as exists in major agricultural regions during the dormant or early growing seasons. Soil moisture budgets were computed at selected sites to show how the observed emissivity responded to changes in the moisture content of the upper soil zone.

Meneely, J. M.↗
NASA NTRS1982

Microwave radiometric observations near 19.35, 92 and 183 GHz of precipitation in tropical storm Cora

Observations of rain cells in the remains of a decaying tropical storm were made by Airborne Microwave Radiometers at 19.35,92 and three frequencies near 183 GHz. Extremely low brightness temperatures, as low as 140 K were noted in the 92 and 183 GHz observations. These can be accounted for by the ice often associated with raindrop formation. Further, 183 GHz observations can be interpreted in terms of the height of the ice. The brightness temperatures observed suggest the presence of precipitation sized ice as high as 9 km or more.

Wilheit, T. T.↗
NASA NTRS1989

Merging AVHRR and SMMR data for remote sensing of ice and cloud in polar regions

Multispectral data from the Advanced Very High Resolution Radiometer (AVHRR) were digitally processed and merged with Scanning Multichannel Microwave Radiometer (SMMR) imagery. Five channels of AVHRR data, four channels of SMRR brightness temperatures and SMMR-derived ice concentration and ice type were co-registered to a polar stereographic grid. The merged data sets are currently being used in combination with meteorological information for integrated studies of clouds and sea ice.

Maslanik, J. A.↗
NASA NTRS1990

Multifrequency observations of a solar microwave burst with two-dimensional spatial resolution

Frequency-agile interferometry observations using three baselines and the technique of frequency synthesis were used to obtain two-dimensional positions of multiple microwave sources at several frequency ranges in a solar flare. Source size and brightness temperature spectra were obtained near the peak of the burst. The size spectrum shows that the source size decreases rapidly with increasing frequency, but the brightness temperature spectrum can be well-fitted by gyrosynchrotron emission from a nonthermal distribution of electrons with power-law index of 4.8. The spatial structure of the burst showed several characteristics in common with primary/secondary bursts discussed by Nakajima et al. (1985). A source of coherent plasma emission at low frequencies is found near the secondary gyrosynchrotron source, associated with the leader spots of the active region.

Gary, Dale E.↗
NASA NTRS1993

Airborne imaging of tropospheric emission at millimeter and submillimeter wavelengths

In September 1993, the NASA Millimeter-wave Imaging Radiometer (MIR) flew on board the NASA ER-2 high-altitude aircraft during CAMEX, and obtained the first wideband millimeter- and submillimeter-wavelength images of tropospheric emission. The MIR is a cross-track radiometer with channels at 89, 150, 183 +/- 1, 3, 7, 220, and 325 +/- 1, 3, 8 GHz. This set provides upwelling brightness information at the two strong rotational water vapor lines at 183.310 and 325.153 GHz and three nearby atmospheric transmission windows. The wideband MIR images of convective raincells reveal unique cloud and precipitation mapping capabilities that are not available from lower frequency microwave channels. Comparisons between the 183 and 325 GHz spectra also reveal differential brightness temperature modes that are related to cloud water.

Wang, J. R.↗
NASA NTRS2015

Python-Based Scientific Analysis and Visualization of Precipitation Systems at NASA Marshall Space Flight Center

At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python‐based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre‐existing Python modules as well as to be object‐oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25‐year history into a common data structure suitable for user‐defined analyses. It features rapid, simplified (i.e., one line of code) production of quick‐look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication‐ready figures. Deconvolution of the polarization‐varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real‐time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May‐June 2014. The second software package is the Marshall Multi‐Radar/Multi‐Sensor (MRMS) Mosaic Python Toolkit, or MMM‐Py for short. MMM‐Py was designed to read, analyze, and display three‐dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM‐Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM‐Py can easily stitch multiple tiles together to provide a larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM‐Py can also write to the native MRMS binary format, and sub‐sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM‐Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM‐Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python‐based analysis routines with the U.S. Department of Energy's Py‐ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC‐based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective‐scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py‐ART's radar data ingest routines and data model to rapidly produce aviation‐relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py‐ART framework will be highlighted. Python‐based analysis within the Py‐ART framework is also being done for new research related to intercomparison of ground‐based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

Lang, Timothy J.↗
NASA NTRS1984

Radiometric correction of atmospheric path length fluctuations in interferometric experiments

To support very long baseline interferometric experiments, a system has been developed for estimating atmospheric water vapor path delay. The system consists of dual microwave radiometers, one operating at 20.7 GHz and the other at 31.4 GHz. The measured atmospheric brightness temperatures at these two frequencies yield the estimate of the precipitable water present in both vapor and droplets. To determine the accuracy of the system, a series of observations were undertaken, comparing the outputs of two water vapor radiometers with the phase variation observed with two connected elements of the very large array (VLA). The results show that: (1) water vapor fluctuations dominate the residual VLA phase and (2) the microwave radiometers can measure and correct these effects. The rms phase error after correction is typically 15 deg at a wavelength of 6 cm, corresponding to an uncertainty in the path delay of 0.25 cm. The residual uncertainty is consistent with the stability of the microwave radiometer but is still considerably larger than the stability of the VLA. The technique is less successful under conditions of heavy cloud.

Resch, G. M.↗
NASA NTRS1987

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

The use of satellite passive microwave observations for the detection of severe thunderstorms is investigated. Nimbus 7 SMMR data obtained from 1979 to 1980 over the U.S. east of 105 deg W are analyzed. The relative temperature brightnesses of the severe storms are examined; it is observed that temperature brightness decreases as the storm severity increases. The temperature brightness data were transformed into a quantitative measure of storm detection ability using the critical success index of Donaldson et al. (1975). Critical success indices of 0.32, 0.48, and 0.38 were obtained for the low-brightness-temperature thresholding of severe versus nonsevere storms during 1979, 1980, and 1979 and 1980 combined, respectively. The data reveal that the geostationary passive microwave imaging capability at 37 GHz is applicable to the detection and monitoring of severe convective storms.

Howland, Michael R.↗
NASA NTRS1990

The effect of convective life cycle stage on microwave brightness temperature/rainrate relations as determined from 3-D cloud model results

The relationship between the rain rate and the brightness temperature (Tb) was investigated using a cloud model/microwave radiative transfer model combination to obtain the rain-rate/Tb relations for four different frequencies: 10, 19, 37, and 86 GHz. The results at 19, 37, and 86 GHz were found to be significantly affected by ice in the modeled convective system, while the results at 10 GHz showed very little effect. Nonprecipitating cloud water was found to affect Tb in two ways. First, at low rain rates, the presence of significant cloud water produced higher Tb values than in cases with little cloud water. The second effects occurs at 19, 37, and 86 GHz at higher rainrates associated with significant ice formation; the scattering by ice lowered the Tb.

Adler, Robert F.↗
NASA NTRS1982

The evolution of Florida thunderstorms on 23 September 1979 as observed by an airborne passive microwave radiometer

The observations made by the aircraft are examined in order to determine sensing parameters for future satellite missions. Minimum brightness temperatures substantially below atmospheric temperatures are revealed in the data. Scattering by an ice layer within the clouds is postulated as the cause. A one-dimensional cloud model initialized with a representative local sounding gives reasonable values for the height and thickness of this ice layer. The model cloud is similar to the observed cloud in both cloud top height and radar reflectivity. A 'mystery' feature observed by the microwave sensors as an area of enhanced scattering, suggesting an updraft core of graupel that in turn is indicative of a convective tower is considered of particular interest. It is noted that this feature was masked in the visible and infrared sensors by the presence of upper-level semitransparent cirrus. The detection of this mystery feature is seen as suggesting that passive microwave sensing of convective areas may provide information regarding the location and/or strength of the more active towers which cannot be detected now with conventional visible or infrared instruments because of a uniform higher-level cloud cover.

Hakkarinen, I. M.↗
NASA NTRS1991

Remote sensing of earth terrain

In remote sensing, the encountered geophysical media such as agricultural canopy, forest, snow, or ice are inhomogeneous and contain scatters in a random manner. Furthermore, weather conditions such as fog, mist, or snow cover can intervene the electromagnetic observation of the remotely sensed media. In the modelling of such media accounting for the weather effects, a multi-layer random medium model has been developed. The scattering effects of the random media are described by three-dimensional correlation functions with variances and correlation lengths corresponding to the fluctuation strengths and the physical geometry of the inhomogeneities, respectively. With proper consideration of the dyadic Green's function and its singularities, the strong fluctuation theory is used to calculate the effective permittivities which account for the modification of the wave speed and attenuation in the presence of the scatters. The distorted Born approximation is then applied to obtain the correlations of the scattered fields. From the correlation of the scattered field, calculated is the complete set of scattering coefficients for polarimetric radar observation or brightness temperature in passive radiometer applications. In the remote sensing of terrestrial ecosystems, the development of microwave remote sensing technology and the potential of SAR to measure vegetation structure and biomass have increased effort to conduct experimental and theoretical researches on the interactions between microwave and vegetation canopies. The overall objective is to develop inversion algorithms to retrieve biophysical parameters from radar data. In this perspective, theoretical models and experimental data are methodically interconnected in the following manner: Due to the complexity of the interactions involved, all theoretical models have limited domains of validity; the proposed solution is to use theoretical models, which is validated by experiments, to establish the region in which the radar response is most sensitive to the parameters of interest; theoretically simulated data will be used to generate simple invertible models over the region. For applications to the remote sensing of sea ice, the developed theoretical models need to be tested with experimental measurements. With measured ground truth such as ice thickness, temperature, salinity, and structure, input parameters to the theoretical models can be obtained to calculate the polarimetric scattering coefficients for radars or brightness temperature for radiometers and then compare theoretical results with experimental data. Validated models will play an important role in the interpretation and classification of ice in monitoring global ice cover from space borne remote sensors in the future. We present an inversion algorithm based on a recently developed inversion method referred to as the Renormalized Source-Type Integral Equation approach. The objective of this method is to overcome some of the limitations and difficulties of the iterative Born technique. It recasts the inversion, which is nonlinear in nature, in terms of the solution of a set of linear equations; however, the final inversion equation is still nonlinear. The derived inversion equation is an exact equation which sums up the iterative Neuman (or Born) series in a closed form and, thus, is a valid representation even in the case when the Born series diverges; hence, the name Renormalized Source-Type Integral Equation Approach.

Yueh, Herng-Aung↗
NASA NTRS1973

The moon as a proposed radiometric standard for microwave and infrared observations of extended sources.

Measured values of the average midnight and morning terminator infrared brightness temperatures of the central portion of the lunar disk can quite accurately determine the mean surface temperature despite likely horizontal and vertical inhomogeneities of the thermal properties of the lunar soil. These data together with laboratory measurements on lunar soil and in situ temperature measurements in and on the lunar surface lead to a mean surface temperature of 220.5 K plus or minus 2.5 per cent and a mean temperature 35 K hotter at a depth of 1 m. The monthly average brightness temperature is then estimated as a function of wavelength between 10 microns and 100 cm taking into account likely temperature dependencies of the thermal conductivity and loss tangent, and the variation of emissivity with wavelength.

Linsky, J. L.↗
NASA NTRS1981

A parameterization of effective soil temperature for microwave emission

A parameterization of effective soil temperature is discussed, which when multiplied by the emissivity gives the brightness temperature in terms of surface (T sub o) and deep (T sub infinity) soil temperatures as T = T sub infinity + C (T sub o - T sub infinity). A coherent radiative transfer model and a large data base of observed soil moisture and temperature profiles are used to calculate the best-fit value of the parameter C. For 2.8, 6.0, 11.0, 21.0 and 49.0 cm wavelengths. The C values are respectively 0.802 + or - 0.006, 0.667 + or - 0.008, 0.480 + or - 0.010, 0.246 + or - 0.009, and 0,084 + or - 0.005. The parameterized equation gives results which are generally within one or two percent of the exact values.

Choudhury, B. J.↗