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NASA NTRS1993

Airborne brightness temperature measurements of the polar winter troposphere as part of the Airborne Arctic Stratosphere Experiment 2 and the effect of brightness temperature variations on the diabatic heating in the lower stratosphere

In this paper we report radiometric measurements of tropospheric brightness temperatures obtained during the AASE 2 experiment. These measurements represent the first attempt to characterize effective radiative temperatures as seen from above the troposphere during the Arctic winter. The reported measurements include brightness temperatures at 6.7 and 10.5 microns as seen from the NASA DC-8 aircraft flying at about 11 km altitude. We also present radiative transfer calculations to estimate the effect of tropospheric brightness temperature on the lower stratospheric heating rates. Because of the recent massive eruption of the Pinatubo volcano, we also discuss the effects of a volcanic aerosol layer. It is concluded that small particles like the volcanic aerosol or polar stratospheric clouds (PSCs) type 1 do not affect stratospheric heating rates by much; on the other hand, larger particles, PSCs types 2 and 3, may have significant effects on heating rates and consequently on dynamics of the lower stratosphere. The dynamical effects of local stratospheric temperature variations are briefly discussed.

Valero, Francisco P. J.↗

NASA NTRS1993

Airborne Brightness Temperature Measurements of the Polar Winter Troposphere as Part of the Airborne Arctic Stratosphere Experiment 2 and the Effect of Brightness Temperature Variations on the Diabatic Heating in the Lower Stratosphere

In this paper we report radiometric measurements of tropospheric brightness temperatures obtained during the AASE 2 experiment. These measurements represent the first attempt to characterize effective radiative temperatures as seen from above the troposphere during the Arctic winter. The reported measurements include brightness temperatures at 6.7 and 10.5 micrometers as seen from the NASA DC-8 aircraft flying at about 11 km altitude. We also present radiative transfer calculations to estimate the effect of tropospheric brightness temperature on the lower stratospheric heating rates. Because of the recent massive eruption of the Pinatubo volcano, we also discuss the effects of a volcanic aerosol layer. It is concluded that small particles like the volcanic aerosol or PSCs type 1 do not affect stratospheric heating rates by much; on the other hand, larger particles, PSCs types 2 and 3, may have significant effects on heating rates and consequently on dynamics of the lower stratosphere. The dynamical effects of local stratospheric temperature variations are briefly discussed.

Valero, Francisoco P. J.↗

NASA NTRS1995

Considerations for modeling thin cirrus effects via brightness temperature differences

Brightness temperature difference (BTD) values are calculated for selected Geostationary Operational Environmental Satellite (GOES-6) channels (3.9, 12.7 micrometer) and Advanced Very High Resolution Radiometer channels (3.7, 12.0 micrometer). Daytime and nighttime discrimination of particle size information is possible given the infrared cloud extinction optical depth and the BTD value. BTD values are presented and compared for cirrus clouds composed of equivalent ice spheres (volume, surface area) versus randomly oriented hexagonal ice crystals. The effect of the hexagonal ice crystals is to increase the magnitude of the BTD values calculated relative to equivalent ice sphere (volume, surface area) BTDs. Equivalent spheres (volume or surface area) do not do a very good job of modeling hexagonal ice crystal effects on BTDs; however, the use of composite spheres improves the simulation and offers interesting prospects. Careful consideration of the number of Legendre polynomial coefficients used to fit the scattering phase functions is crucial to realistic modeling of cirrus BTDs. Surface and view-angle effects are incorporated to provide more realistic simulation.

Schmidt, E. O.↗

NASA NTRSDate not supplied

Empirical Hydrometeor Type Identification from GMI Brightness Temperature Measurements

Brightness temperatures (TB) from GMI and related sensors contain information about the types and amounts of precipitation particles in a column. Approaches like GPROF or machine learning are good for providing precipitation estimates and other quantified information, but it can be difficult to understand why / how a given set of TBs traces to a given retrieval solution. Outlier TBs that are not well-represented in a training sample can lead to dubious solutions. Our goal is to use the scattering signatures at multiple frequencies to determine whether hail is present, or otherwise graupel, or otherwise snow, or otherwise liquid rain without a substantial precipitation ice component. (A hierarchical approach is employed, so a prediction of hail can include the presence of all other categories, but a prediction of snow implies a lack of graupel or hail, and a prediction of liquid rain implies a lack of any detectable precipitation ice.)

Daniel J. Cecil↗

NASA NTRS1980

The influence of snow depth and surface air temperature on satellite-derived microwave brightness temperature

Areas of the steppes of central Russia, the high plains of Montana and North Dakota, and the high plains of Canada were studied in an effort to determine the relationship between passive microwave satellite brightness temperature, surface air temperature, and snow depth. Significant regression relationships were developed in each of these homogeneous areas. Results show that sq R values obtained for air temperature versus snow depth and the ratio of microwave brightness temperature and air temperature versus snow depth were not as the sq R values obtained by simply plotting microwave brightness temperature versus snow depth. Multiple regression analysis provided only marginal improvement over the results obtained by using simple linear regression.

Foster, J. L.↗

NASA NTRS1995

Modeling of Wind Direction Signals in Polarimetric Sea Surface Brightness Temperatures

Sea surface brightness temperatures are the radiometric power measure of blackbody radiation from sea water. This radiation is the electromagnetic waves excited by the random thermal motion of charged particles in the sea water. The energy transmitted through the air- water interface produces a scattering of electromagnetic waves into the atmosphere. Polarimetric microwave emissions are investigated.

ocean wind remote sensing polarimetric microwave r↗

NASA NTRS1976

The solar brightness temperature at millimeter wavelengths

Measurements of the brightness temperature of the sun near 36 GHz and 93 GHz were made using the new moon as a calibration source. Provided the brightness temperature of the moon is known and all measurements are reduced to the same zenith angle, a simple expression can be used for the sun-to-new moon ratio which is independent of antenna gain, atmospheric absorption and reemission, and radiometer calibration constants. This ratio was measured near 36 GHz and at two frequencies near 93 GHz with a Dicke switched superheterodyne radiometer system and a 2.4 m Cassegrain antenna. The slopes of the solar brightness temperature spectrum based on these ratios were measured. The absolute solar brightness spectrum derived from all current available measurements supplemented by the present ones is also plotted and discussed.

Kuseski, R. A.↗

NASA NTRS2012

Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

Reichle, Rolf H.↗

NASA NTRS2015

Extremely Low Passive Microwave Brightness Temperatures Due to Thunderstorms

Extreme events by their nature fall outside the bounds of routine experience. With imperfect or ambiguous measuring systems, it is appropriate to question whether an unusual measurement represents an extreme event or is the result of instrument errors or other sources of noise. About three weeks after the Tropical Rainfall Measuring Mission (TRMM) satellite began collecting data in Dec 1997, a thunderstorm was observed over northern Argentina with 85 GHz brightness temperatures below 50 K and 37 GHz brightness temperatures below 70 K (Zipser et al. 2006). These values are well below what had previously been observed from satellite sensors with lower resolution. The 37 GHz brightness temperatures are also well below those measured by TRMM for any other storm in the subsequent 16 years. Without corroborating evidence, it would be natural to suspect a problem with the instrument, or perhaps an irregularity with the platform during the first weeks of the satellite mission. Automated quality control flags or other procedures in retrieval algorithms could treat these measurements as errors, because they fall outside the expected bounds. But the TRMM satellite also carries a radar and a lightning sensor, both confirming the presence of an intense thunderstorm. The radar recorded 40+ dBZ reflectivity up to about 19 km altitude. More than 200 lightning flashes per minute were recorded. That same storm's 19 GHz brightness temperatures below 150 K would normally be interpreted as the result of a low-emissivity water surface (e.g., a lake, or flood waters) if not for the simultaneous measurements of such intense convection. This paper will examine records from TRMM and related satellite sensors including SSMI, AMSR-E, and the new GMI to find the strongest signatures resulting from thunderstorms, and distinguishing those from sources of noise. The lowest brightness temperatures resulting from thunderstorms as seen by TRMM have been in Argentina in November and December. For SSMI sensors carried on five DMSP satellites examined so far, the lowest thunderstorm-related brightness temperatures have been from Argentina in November - December and from Minnesota in June-July. The Minnesota cases were associated with spotter reports of large hail, significant severe wind, and tornadoes. Those locations have the record-holders for each satellite. The lowest AMSR-E 36.5 GHz brightness temperatures associated with deep convection have been in Argentina; the lowest 89.0 GHz brightness temperatures were from Typhoon Bolaven in the Philippine Sea. This paper will show examples of cases with the lowest brightness temperatures, and map the locations of these and other storms with brightness temperatures nearly as low. The study is largely motivated by the new GMI sensor on the Global Precipitation Mission core satellite, launched in February 2014, with its high resolution expected to reveal unprecedented low brightness temperatures when extreme events are encountered.

Cecil, Daniel J.↗

NASA NTRS1983

A model function for ocean microwave brightness temperatures

A relatively simple, yet accurate, relationship between the microwave brightness temperature of the ocean and conventional oceanographic and meteorological parameters is derived. The equation for the brightness temperature upwelling from the sea surface through the intervening atmosphere is obtained, considering radiative emission and scattering by the sea surface along with radiative absorption and emission by the atmosphere. A number of approximations are applied to the integral brightness temperature equation and its supporting equations in order to obtain a simple equation for the brightness temperature that does not contain integrals. Values for a number of atmospheric parameters are determined, including temperature sensitivities, oxygen opacity, water vapor and liquid water normalized absorption coefficients, and effective columnar height. The sea surface emissivity model is then considered, modelling the sea surface as a composite of foam-free rough water and foam patches.

Wentz, F. J.↗

NASA NTRS2004

Landcover Based Optimal Deconvolution of PALS L-band Microwave Brightness Temperature

An optimal de-convolution (ODC) technique has been developed to estimate microwave brightness temperatures of agricultural fields using microwave radiometer observations. The technique is applied to airborne measurements taken by the Passive and Active L and S band (PALS) sensor in Iowa during Soil Moisture Experiments in 2002 (SMEX02). Agricultural fields in the study area were predominantly soybeans and corn. The brightness temperatures of corn and soybeans were observed to be significantly different because of large differences in vegetation biomass. PALS observations have significant over-sampling; observations were made about 100 m apart and the sensor footprint extends to about 400 m. Conventionally, observations of this type are averaged to produce smooth spatial data fields of brightness temperatures. However, the conventional approach is in contrast to reality in which the brightness temperatures are in fact strongly dependent on landcover, which is characterized by sharp boundaries. In this study, we mathematically de-convolve the observations into brightness temperature at the field scale (500-800m) using the sensor antenna response function. The result is more accurate spatial representation of field-scale brightness temperatures, which may in turn lead to more accurate soil moisture retrieval.

Limaye, Ashutosh S.↗

NASA NTRS1984

Calculations of atmospheric transmittance in the 11 micrometer window for estimating skin temperature from VISSR infrared brightness temperatures

An algorithm for calculating the atmospheric transmittance in the 10 to 20 micro m spectral band from a known temperature and dewpoint profile, and then using this transmittance to estimate the surface (skin) temperature from a VISSR observation in the 11 micro m window is presented. Parameterizations are drawn from the literature for computing the molecular absorption due to the water vapor continuum, water vapor lines, and carbon dioxide lines. The FORTRAN code is documented for this application, and the sensitivity of the derived skin temperature to variations in the model's parameters is calculated. The VISSR calibration uncertainties are identified as the largest potential source of error.

Chesters, D.↗

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 NTRS1982

Effects of subsurface volume scattering on the lunar microwave brightness temperature spectrum

The effects of volumetric scattering on the lunar microwave brightness temperature are examined for a broad range of feasible lunar rock population distributions. Mie-scattering phase functions and the radiative transfer method are utilized. Surveyor and Apollo data relevant to lunar rock size distributions are discussed, and parameters are chosen for nine scattering models which liberally cover the range of studied rock population distributions. Scattering model brightness temperature predictions are analyzed in terms of the lunar disk center emission averaged over a lunation for wavelengths of 3-30 cm. The effects of scattering on the amplitude of disk center brightness temperature variations and resultant deductions of regolith electrical loss are examined. Constraints on the global scale variability of subsurface scatterers imposed by microwave brightness temperature maps are considered.

Keihm, S. J.↗

NASA NTRS1993

An alternative representation of the ice canopy for calculating microwave brightness temperatures over a thunderstorm

Passive microwave brightness temperatures (TBs) at 92 and 183 GHz from an aircraft thunderstorm overflight are compared with values calculated from radar-derived hydrometeor profiles and a modified proximity sounding. Two methods for modeling particles in the ice canopy are contrasted. The first is a 'traditional' approach employing Marshall-Palmer ice spheres. The second, or 'alternative', method partitions 20 percent of the ice water content into a Marshall-Palmer component for graupel and hail, and 80 percent into a modified gamma spherical particle size distribution function representing ice crystals. Results from the alternative approach are superior to those from the traditional method in the anvil and mature convective core. In the decaying convective region, the traditional approach yields better agreement with observed magnitudes. Neither method, however, matches the geometry of the observed TB depression associated with the decaying convective core. This is likely due to the presence of graupel, which is not detected as a special signature in radar reflectivity, but does diminish TBs through scattering. Brightness temperatures at the relatively high microwave frequencies considered are shown to be very sensitive to the ice-particle size distribution.

Muller, Bradley M.↗

NASA NTRS1984

The prediction of tropopause height from clusters of brightness temperatures and its application in the stratified regression temperature retrievals using microwave and infrared satellite measurements

A total of 1575 radiosondes and the corresponding simulated brightness temperatures were used in an effort to derive a temperature retrieval based on the clusters of brightness temperatures. The 8 simulated channels, namely, 3 MSU and 5 IR of the TIROS-N satellite are used by the GLAS temperature retrieval method. The 3 MSU and 5 IR brightness temperatures were clustered into 17 cluster groups and a regression for the prediction of the tropopause height in mb was generated. The overall r.m.s. for the tropopause prediction is excellent, namely, around 16 mb for the summer and 23 mb for the winter. The correct cluster of brightness temperatures can be identified 98% of the time by the method of discriminatory classification if it is approximately a normal distribution or, in general, by the method of the nearest neighbor.

Munteanu, M. J.↗

NASA NTRS1989

Comparison of theoretical and measured brightness temperature of a soybean canopy

A model for the brightness temperature of a canopy which is based on the physical characteristics of the vegetation is being developed. This model uses the Peake model to relate the scattering coefficient of the vegetation to its radiometric brightness temperature and a discrete scattering model to computer the scattering coefficient. A distorted Born approximation is used to provide a tractable solution to the scattering problem. To verify the theory a comparison has been made with experiments. Radiometric measurements of brightness temperature were made at L- and C-bands over a crop of mature soybeans, along with ground truth measurements. All model input parameters were measured; there were no free parameters to adjust to obtain agreement with the measurements. Results showed that agreement between theory and measurements was reasonable for horizontal polarization at both L- and C-bands with dry underlying soil.

Lang, R. H.↗

NASA NTRS1993

IRTM brightness temperature maps of the Martian south polar region during the polar night: The cold spots don't move

A series of infrared thermal mapper (IRTM) south polar brightness temperature maps obtained by Viking Orbiter 2 during a 35-day period during the southern fall season in 1978 was examined. The maps show a number of phenomena that have been identified in previous studies, including day to day brightness temperature variations in individual low temperature regions and the tendency for IRTM 11-micron channel brightness temperatures to also decrease in regions where low 20-micron channel brightness temperatures are observed. The maps also show new phenomena, the most striking of which is a clear tendency for the low brightness temperature regions to occur at fixed geographic regions. During this season, the coldest low brightness temperatures appear to be concentrated in distinct regions, with spatial scales ranging from 50 to 300 km. There are approximately a dozen of these concentrations, with the largest centered near the location of the south residual polar cap. Other concentrations are located at Cavi Angusti and close to the craters Main, South, Lau, and Dana. Broader, less intense regions appear to be well correlated with the boundaries of the south polar layered deposits and the Mountains of Mitchell. No evidence for horizontal motion of any of these regions has been detected.

Paige, D. A.↗