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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Rainfall Estimates from the TMI and the SSM/I

The Tropical Rainfall Measuring Mission (TRMM), which is a joint Japan-U.S. Earth observing satellite, has been successfully launched from Japan on November 27, 1997. The main purpose of the TRMM is to measure quantitatively rainfall over the tropics for the research of climate and weather. One of three rainfall measuring instruments abroad the TRMM is the high resolution TRMM Microwave Imager (TMI). The TMI instrument is essentially the copy of the SSM/I with a dual-polarized pair of 10.7 GHz channels added to increase the dynamic range of rainfall estimates. In addition, the 21.3 GHz water vapor absorption channel is designed in the TMI as opposed to the 22.235 GHz in the SSM/I to avoid saturation in the tropics. This paper will present instantaneous rain rates estimated from the coincident TMI and SSM/I observations. The algorithm for estimating instantaneous rainfall rates from both sensors is the Goddard Profiling algorithm (Gprof). The Gprof algorithm is a physically based, multichannel rainfall retrieval algorithm, The algorithm is very portable and can be used for various sensors with different channels and resolutions. The comparison of rain rates estimated from TMI and SSM/I on the same rain regions will be performed. The results from the comparison and the insight of tile retrieval algorithm will be given.

Hong, Ye↗
NASA NTRS1991

Bulk ice characterization using coupled cloud, multiparameter radar, and radiative transfer models

The study demonstrates the potential utility of employing scattering-based passive microwave channels (not less than 37 GHz) to estimate the amount of ice water path that exists above rain in precipitating clouds. This methodology can be used along with lower-frequency emission-based channels to infer the columnar melt water and ice water paths. Portions of the ice column that coexist with cloud water hide the usually steep brightness temperature cooling through this region and inhibit characterization of the total ice column.

Turk, J.↗
DOE OSTI2020

Parallel high-frequency magnetic sensing with an array of flux transformers and multi-channel optically pumped magnetometer for hand MRI application

Here, we investigate an approach for parallel high-frequency magnetic sensing based on a multi-channel radio frequency (RF) optically pumped magnetometer (OPM) coupled to multiple flux transformers (FTs) with a focus on hand magnetic resonance imaging (MRI) application at ultra-low field (ULF). Multiple RF OPM sensing channels are realized by using a single large-area alkali-metal vapor cell and two laser beams for pumping and probing, shared for all the channels. This design leads to significant cost reduction when multi-channel sensing is desirable, as in the case of ULF MRI. The FT, composed of two connected coils, serves as a transmitter of a target magnetic field to the OPM, while decoupling the OPM from untargeted magnetic fields in the sensing area that can limit the OPM performance. For hand MRI application, theoretical and numerical analysis is performed to determine an optimal geometry for the FT array that could improve signal-to-noise ratio (SNR) and sufficiently reduce crosstalk between FTs. We estimate that the optimized multi-channel FT-OPM sensor can achieve a magnetic field sensitivity of the order of 1 fT / Hz 1 / 2 above 100 kHz, which would be sufficient for 1 mm resolution MRI. In general, the multi-channel capability enables simultaneous magnetic measurements, thus reducing the sensing time and improving the SNR, and we anticipate many applications of the multi-channel FT-OPM sensor beyond the targeted here hand MRI: anatomical parallel ULF MRI of the human brain and other parts of the body, airport security screening, magnetic material imaging, and many others.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1982

Doppler radar echoes of lightning and precipitation at vertical incidence

Digital time series data at 16 heights within two storms were collected at vertical incidence with a 10-cm Doppler radar. On several occasions during data collection, lightning echoes were observed as increased reflectivity on an oscilloscope display. Simultaneously, lightning signals from nearby electric field change antennas were recorded on an analog recorder together with the radar echoes. Reflectivity, mean velocity, and Doppler spectra were examined by means of time series analysis for times during and after lightning discharges. Spectra from locations where lightning occurred show peaks, due to the motion of the lightning channel at the air speed. These peaks are considerably narrower than the ones due to precipitation. Besides indicating the vertical air velocity that can then be used to estimate hydrometeor-size distribution, the lightning spectra provide a convenient means to estimate the radar cross section of the channel. Subsequent to one discharge, we deduce that a rapid change in the orientation of hydrometeors occurred within the resolution volume.

Zrnic, D. S.↗
DOE OSTI2021

On the Reliability of Parameter Inferences in a Multiscale Model for Transport in Stream Corridors

Nonreacting tracer tests capture information about physical processes in transient storage zones including the hyporheic zone (HZ). However, reliably extracting this information from breakthrough curves (BTCs) and distinguishing the effects of in-channel dispersion and transient storage are well-known challenges. Using BTCs from a nonreacting tracer test monitored at multiple locations, we explore ways for reliable parameter estimations. The identifiability of parameters is greatly influenced by the choice of forward and inverse modeling frameworks in addition to the quality of the data. Our forward model is a recently proposed multiscale model that uses subgrid transport models written in the Lagrangian form to represent transport along a diverse set of HZ pathways with a shape-free distribution of travel times. Joint distributions of HZ and channel parameters are estimated using the Markov Chain Monte Carlo technique. Numerical experiments show ambiguity between channel dispersion and HZ transport when the reach length is too short to allow significant solute-HZ interaction, the observation period is too brief to observe the tailing behavior, or the solute source is spread in time. In contrast, we obtained reliable parameter estimates by simultaneously fitting BTCs observed at different locations in the test reach using a single set of HZ parameters and section-specific channel areas and dispersion coefficients. Furthermore, this study demonstrates the estimation of travel time distributions, HZ exchange rates, and channel parameters in a new multiscale approach and offers guidance for extracting reliable parameter estimates from multiple BTCs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

The use of TIMS data to estimate the SO2 concentrations of volcanic plumes: A case study at Mount Etna, Sicily

Thermal Infrared Multispectral Scanner (TIMS) data were acquired over Mount Etna, Sicily, on 29 July 1986. The volcanic activity at that time was characterized by the steady effusion of gas from the Bocca Nuova (BN), Chasm, and Southeast craters. The Northeast crater, quiet at the time of the TIMS overflight, was the site of Strombolian eruptive activity between 31 July and 24 Sep. 1986. In aerial photographs of the Etna summit region acquired during the TIMS overflight, the SO2-rich plume is visible due to the scattering of sunlight by the entrained aerosols. In the TIMS imagery, the plume is revealed by the strong absorption of SO2 between 8 and 9 microns. This absorption feature falls within the first three channels of TIMS, with the strongest absorption falling within Channel 2. Following decorrelation processing, the plume is visible in color-composites of TIMS channels 2, 3, and 5. To estimate the concentration of SO2 within the plume, the LOWTRAN 7 radiative transfer code was used to model the radiance spectra measured by TIMS.

Realmuto, Vincent J.↗
NASA NTRS1994

Procedures to validate/correct calibration error in solar backscattered ultraviolet instruments

The Nimbus 7 SBUV measures the same latitude ozone at widely different sun angle conditions at the ascent and descent part of the orbit during the summer solstice. This situation is used in a particular procedure (Ascent/Descent) to obtain the relative channel-to-channel calibration error for channels 273 nm to 306 nm. These estimated errors are combined with results from the Pair Justification procedure to correct the sun-view diffuser drift in calibration from November 1978 to February 1987 for the shorter wavelength channels that measure upper stratospheric ozone. Some preliminary re-calirated Nimbus 7 SBUV data in 1989 is compared with the first set of SBUV measurements flown on the Space Shuttle.

Taylor, Steven L.↗
NASA NTRS1991

Multi-frequency and polarimetric radar backscatter signatures for discrimination between agricultural crops at the Flevoland experimental test site

We describe the calibration and analysis of multi-frequency, multi-polarization radar backscatter signatures over an agriculture test site in the Netherlands. The calibration procedure involved two stages: in the first stage, polarimetric and radiometric calibrations (ignoring noise) were carried out using square-base trihedral corner reflector signatures and some properties of the clutter background. In the second stage, a novel algorithm was used to estimate the noise level in the polarimetric data channels by using the measured signature of an idealized rough surface with Bragg scattering (the ocean in this case). This estimated noise level was then used to correct the measured backscatter signatures from the agriculture fields. We examine the significance of several key parameters extracted from the calibrated and noise-corrected backscatter signatures. The significance is assessed in terms of the ability to uniquely separate among classes from 13 different backscatter types selected from the test site data, including eleven different crops, one forest and one ocean area. Using the parameters with the highest separation for a given class, we use a hierarchical algorithm to classify the entire image. We find that many classes, including ocean, forest, potato, and beet, can be identified with high reliability, while the classes for which no single parameter exhibits sufficient separation have higher rates of misclassification. We expect that modified decision criteria involving simultaneous consideration of several parameters increase performance for these classes.

Freeman, A.↗
NASA NTRSDate not supplied

CERES FluxByCldTyp NB2BB Fluxes Improvement based on Deep Neural Network

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗
NASA NTRSDate not supplied

Flux Improvement based on Machine Learning for the CERES FluxByCldTyp Data Product

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗
NASA NTRSDate not supplied

Improvement of Radiative Fluxes for the CERES FluxByCldTyp Data Product Based on Machine Learning Technique

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗
NASA NTRSDate not supplied

Improving Radiative Fluxes for the CERES FluxByCldTyp Data Product Using Deep Neural Network

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Preliminary results show significant LW improvement.

Sun, Moguo↗
NASA NTRS1995

Estimation of penetration of forest canopies by Interferometric SAR measurements

In contrast to traditional Synthetic Aperture Radar (SAR), an Interferometric SAR (INSAR) yields two additional measurements: the phase difference and the correlation between the two interferometric channels. The phase difference has been used to estimate topographic height. For homogeneous surfaces, the correlation depends on the system signal-to-noise (SNR) ratio, the interferometer parameters, and the local slope. In the presence of volume scattering, such as that encountered in vegetation canopies, the correlation between the two channels is also dependent on the degree of penetration of the radiation into the scattering medium. In this paper, we propose a method for removing system and slope effects in order to obtain the decorrelation due to penetration alone. The sensitivities and accuracy of the proposed method are determined by Monte Carlo experiments, and we show that the proposed technique has sufficient sensitivity to provide penetration measurements for airborne SAR systems. Next, we provide a theoretical model to estimate the degree of penetration in a way which is independent of the details of the scattering medium. We also present a model for the correlation from non-homogeneous layers. We assess the sensitivity of the proposed inversion technique to these inhomogeneous situations. Finally, we present a comparison of the interferometric results against in situ data obtained by an airborne laser profilometer which provides a direct measurement of tree height and an estimate of the vegetation density profile in the forested areas around Mt. Adams, WA.

Rodriguez, Ernesto↗
NASA NTRS1992

On the estimation of snow depth from microwave radiometric measurements

Multiple-channel microwave radiometric measurements made over Alaska at aircraft (near 90 and 183 GHz) and satellite (at 37 and 85 GHz) altitudes are used to study the effect of atmospheric absorption on the estimation of snow depth. The estimation is based on the radiative transfer calculations using an early theoretical model of Mie scattering of single-size particles. It is shown that the radiometric correction for the effect of atmospheric absorption is important even at 37 GHz for a reliable estimation of snow depth. Under a dry atmosphere and based on single-frequency radiometric measurements, the underestimation of snow depth could amount to 50 percent at 85 GHz and 20-30 percent at 37 GHz if the effect of atmospheric absorption is not taken into account. The snow depths estimated from the 90-GHz aircraft and 85-GHz satellite measurements are found to be in reasonable agreement. However, there is a discrepancy in the snow depths estimated from the 37-GHz (at both vertical and horizontal polarizations) and 85-GHz satellite measurements.

Wang, James R.↗
NASA NTRSDate not supplied

The Impact of Pixel Size on the Characterization of Deep Convective Clouds for Calibration

The NASA CERES project provides the scientific community the observed TOA SW and LW fluxes for climate monitoring and climate model validation. CERES utilizes hourly geostationary imager derived broadband fluxes, which rely on the channel radiances and associated cloud retrievals, are used to estimate the broadband fluxes between CERES observations. This requires stable and consistent cross-platform imager visible channel calibration. The CERES project utilizes deep convective clouds (DCC) as an invariant Earth target to both monitor the stability of sensors and for radiometric scaling. GSICS, an international collaboration, is also evaluating and implementing the DCC invariant target calibration methodology to provide consistent calibration coefficients across geostationary imagers anchored to the AquaMODIS or the NOAA-20 VIIRS calibration reference. Tropical DCC are the brightest, coldest, most Lambertian, top of the atmosphere Earth targets. The DCC invariant target calibration methodology relies on a large ensemble of tropical D CC-identified pixel-level reflectances, which are aggregated as probability density functions (PDF). By assuming the monthly PDF shape is otherwise consistent in time excepting shifts in reflectance caused by changes in the sensor calibration, the imager stability is monitored. Radiometric scaling is accomplished by ratioing the sensor pair DCC PDF reflectance values. The success of the DCC methodology relies on consistent PDF distributions. The goal of this study is to determine the impact of pixel resolution on the DCC reflectance distribution. Single SNPP-VIIRS 750-m and Landsat 8 OLI 30-m granules are aggregated to degrade the pixel resolution from the native level. The DCC pixels are identified using a BT threshold. Most of the brightest DCC pixels are also the coldest, although there are exceptions. It was found that increasing the BT threshold exponentially increased the number of darker pixels. The pixel resolution did not seem to impact the DCC reflectance PDF distribution for pixel resolutions less than 3 km, which suggests that imagers of varying pixel resolutions may be radiometrically scaled to each other using DCC targets.

DCC↗
NASA NTRS1991

Least squares restoration of multichannel images

Multichannel restoration using both within- and between-channel deterministic information is considered. A multichannel image is a set of image planes that exhibit cross-plane similarity. Existing optimal restoration filters for single-plane images yield suboptimal results when applied to multichannel images, since between-channel information is not utilized. Multichannel least squares restoration filters are developed using the set theoretic and the constrained optimization approaches. A geometric interpretation of the estimates of both filters is given. Color images (three-channel imagery with red, green, and blue components) are considered. Constraints that capture the within- and between-channel properties of color images are developed. Issues associated with the computation of the two estimates are addressed. A spatially adaptive, multichannel least squares filter that utilizes local within- and between-channel image properties is proposed. Experiments using color images are described.

Galatsanos, Nikolas P.↗
NASA NTRS1994

Vorticity and Vertical Motions Diagnosed from Satellite Deep-Layer Temperatures

Spatial fields of satellite-measured deep-layer temperatures are examined in the context of quasigeostrophic theory. It is found that midtropospheric geostrophic vorticity and quasigeostrophic vertical motions can be diagnosed from microwave temperature measurements of only two deep layers. The lower- ( 1000-400 hPa) and upper- (400-50 hPa) layer temperatures are estimated from limb-corrected TIROS-N Microwave Sounding Units (MSU) channel 2 and 3 data, spatial fields of which can be used to estimate the midtropospheric thermal wind and geostrophic vorticity fields. Together with Trenberth's simplification of the quasigeostrophic omega equation, these two quantities can be then used to estimate the geostrophic vorticity advection by the thermal wind, which is related to the quasigeostrophic vertical velocity in the midtroposphere. Critical to the technique is the observation that geostrophic vorticity fields calculated from the channel 3 temperature features are very similar to those calculated from traditional, 'bottom-up' integrated height fields from radiosonde data. This suggests a lack of cyclone-scale height features near the top of the channel 3 weighting function, making the channel 3 cyclone-scale 'thickness' features approximately the same as height features near the bottom of the weighting function. Thus, the MSU data provide observational validation of the LID (level of insignificant dynamics) assumption of Hirshberg and Fritsch.

Spencer, Roy W.↗