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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

A Geostatistical Data Fusion Technique for Merging Remote Sensing and Ground-Based Observations of Aerosol Optical Thickness

Particles in the atmosphere reflect incoming sunlight, tending to cool the Earth below. Some particles, such as soot, also absorb sunlight, which tens to warm the ambient atmosphere. Aerosol optical depth (AOD) is a measure of the amount of particulate matter in the atmosphere, and is a key input to computer models that simulate and predict Earth's changing climate. The global AOD products from the Multi-angle Imaging SpectroRadiometer (MISR) and the MODerate resolution Imaging Spectroradiometer (MODIS), both of which fly on the NASA Earth Observing System's Terra satellite, provide complementary views of the particles in the atmosphere. Whereas MODIS offers global coverage about four times as frequent as MISR, the multi-angle data makes it possible to separate the surface and atmospheric contributions to the observed top-of-atmosphere radiances, and also to more effectively discriminate particle type. Surface-based AERONET sun photometers retrieve AOD with smaller uncertainties than the satellite instruments, but only at a few fixed locations. So there are clear reasons to combine these data sets in a way that takes advantage of their respective strengths. This paper represents an effort at combining MISR, MODIS and AERONET AOD products over the continental US, using a common spatial statistical technique called kriging. The technique uses the correlation between the satellite data and the "ground-truth" sun photometer observations to assign uncertainty to the satellite data on a region-by-region basis. The larger fraction of the sun photometer variance that is duplicated by the satellite data, the higher the confidence assigned to the satellite data in that region. In the Western and Central US, MISR AOD correlation with AERONET are significantly higher than those with MODIS, likely due to bright surfaces in these regions, which pose greater challenges for the single-view MODIS retrievals. In the east, MODIS correlations are higher, due to more frequent sampling of the varying AOD. These results demonstrate how the MISR and MODIS aerosol products are complementary. The underlying technique also provides one method for combining these products in such a way that takes advantage of the strengths of each, in the places and times when they are maximal, and in addition, yields an estimate of the associated uncertainties in space and time.

Chatterjee, Abhishek↗
NASA NTRS2011

Mapping Canopy Damage from Understory Fires in Amazon Forests Using Annual Time Series of Landsat and MODIS Data

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars less than 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (greater than 500 ha) burn scars that accounted for the majority of all fire-damaged forest in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 square kilometers) were an order of magnitude higher than during the 1997-1998 El Nino event (124 square kilometers and 39 square kilometers, respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.

Morton, Douglas C.↗
DOE OSTI2024

Integration of fixed-frequency and FM-CW (frequency-modulated continuous-wave) reflectometers for coincident turbulence measurements on LTX- β (Lithium Tokamak eXperiment- β )

The fixed-frequency and frequency-modulated continuous-wave (FM-CW) reflectometers on LTX-β (Lithium Tokamak eXperiment-β) have been configured to use the same transmission lines and antenna arrays for coincident views of the core and edge plasma. The fixed-frequency channels (13.1–20.5 and 20–40 GHz, tunable between discharges) provide time-resolved measurements of density fluctuations, while the FM-CW channels (13.1–20.2 and 19.5–33.5 GHz) measure the density profile and fluctuations, with high spatial resolution and a sampling rate determined by the frequency sweep interval (5 μs). Data from both reflectometers are synchronously acquired to simultaneously leverage the wide bandwidth and high spatial resolution of the respective systems. Experiments showed that mutual crosstalk interference is momentary and does not diminish the capability of either system. Spectral analysis indicated broad power spectra (several hundreds of kHz) and suggests that the signals from the FM-CW system are consistent with under-sampled fixed-frequency signals. Radial correlations were explored using data from the two reflectometers, as well as from the FM-CW system alone. The core channels showed high levels of agreement between these two comparisons, suggesting that the data from the reflectometers are interchangeable for statistical estimates. For the edge channels, comparisons using data from the FM-CW reflectometer alone showed significant decorrelation due to time lag caused by the finite frequency up-sweep duration. Alternatively, this effect is eliminated when cross-correlating data from the different reflectometers. Finally, these results highlight the advantages of operating the fixed-frequency and FM-CW reflectometers in this manner, where the combined system can overcome the limitations of each separate system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Early-stage dynamics of chloride ion–pumping rhodopsin revealed by a femtosecond X-ray laser

Chloride ion–pumping rhodopsin (ClR) in some marine bacteria utilizes light energy to actively transport Cl – into cells. How the ClR initiates the transport is elusive. Here, we show the dynamics of ion transport observed with time-resolved serial femtosecond (fs) crystallography using the Linac Coherent Light Source. X-ray pulses captured structural changes in ClR upon flash illumination with a 550 nm fs-pumping laser. High-resolution structures for five time points (dark to 100 ps after flashing) reveal complex and coordinated dynamics comprising retinal isomerization, water molecule rearrangement, and conformational changes of various residues. Combining data from time-resolved spectroscopy experiments and molecular dynamics simulations, this study reveals that the chloride ion close to the Schiff base undergoes a dissociation–diffusion process upon light-triggered retinal isomerization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Physics-constrained superresolution diffusion for six-dimensional phase space diagnostics

Adaptive physics-constrained superresolution diffusion is developed for noninvasive virtual diagnostics of the six-dimensional (6D) phase space density of charged particle beams. An adaptive variational autoencoder embeds initial beam condition images and scalar measurements to a low-dimensional latent space from which a 32 6 pixel 6D tensor representation of the beam's 6D phase space density is generated. Projecting from a 6D tensor generates physically consistent two-dimensional projections. Physics-guided superresolution diffusion transforms low-resolution images of the 6D density to high resolution 256 × 256 pixel images. Unsupervised adaptive latent space tuning enables tracking of time-varying beams without knowledge of time-varying initial conditions. The method is demonstrated with experimental data and multiparticle simulations at the HiRES UED. The general approach is applicable to a wide range of complex dynamic systems evolving in high-dimensional phase space. The method is shown to be robust to distribution shift without retraining. Published by the American Physical Society 2025

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗
DOE OSTI2023

First flight performance of the Micro-X microcalorimeter X-ray sounding rocket

The flight of the Micro-X sounding rocket on July 22, 2018, marked the first operation of transition-edge sensors and their superconducting quantum interference device readouts in space. Here, the instrument combines the microcalorimeter array with an imaging mirror to take high-resolution spectra from extended X-ray sources. The first flight target was the Cassiopeia A supernova remnant. Although a rocket pointing malfunction led to no time on-target, data from the flight were used to evaluate the performance of the instrument and demonstrate the flight viability of the payload. The instrument successfully achieved a stable cryogenic environment, executed all flight operations, and observed X-rays from the on-board calibration source. The flight environment did not significantly affect the performance of the detectors compared with ground operation. The flight provided an invaluable test of the impact of external magnetic fields and the instrument configuration on detector performance. This flight provides a milestone in the flight readiness of these detector and readout technologies, both of which have been selected for future X-ray observatories.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1980

The utilization of spaceborne microwave radiometers for monitoring snowpack properties

Snow accumulation and depletion at specific locations can be monitored from space by observing related variations in microwave brightness temperatures. Using vertically and horizontally polarized brightness temperatures from the Nimbus 6 electrically scanning microwave radiometer, a discriminant function can be used to separate snow from no snow areas and map snowcovered area on a continental basis. For dry snow conditions on the Canadian high plains, significant relationships between snow depth or water equivalent and microwave brightness temperature were developed which could permit remote determination of these snow properties after acquisition of a wider range of data. The presence of melt water in the snowpack causes a marked increase in brightness temperature which can be used to predict snowpack priming and timing of runoff. As the resolutions of satellite microwave sensors improve the application of these results to snow hydrology problems should increase.

Rango, A.↗
NASA NTRS1981

Deconvolution of wide field-of-view radiometer measurements of earth-emitted radiation. II - Analysis of first year of Nimbus 6 ERB data

The theory of deconvolution considered by Smith and Green (1981) is applied to Nimbus 6 data in order to interpret the data with the maximum obtainable resolution. Attention is given to the data sample, sun-contaminated measurements, the measurement model, the deconvolution, the degree variance, the spherical harmonic coefficients, the geographical distribution of longwave radiation, time histories of zonal coefficients, and the effect of a grid system. Degree variance plots for 12 months of longwave radiation data show that the limit for a spherical harmonic representation of the Nimbus 6 wide field-of-view longwave data is 12th degree. The degree variance plots also reveal that most of the power is in the lower degree terms. Contour maps of the radiation field show the geographical distribution of earth emitted radiant exitance for each month.

Bess, T. D.↗
NASA NTRS1981

A systematic program of cometary spectroscopy

Some early results of a systematic program of observing the spectroscopic behavior of comets as a function of heliocentric distance are presented. An ultraviolet sensitive microchannel plate intensifier spectrograph was used to record the 3000-5000A spectrum of comets brighter than magnitude 17 with a spectral resolution of 8 or 16A, followed by direct image for better interpretation of the spatial distribution of spectral features. Although the goals of the program require much more time and data, some interesting results from Comets Schwassmann-Wachmann, 1, Bradfield and Bowell were obtained.

Larson, S. M.↗
NASA NTRS1984

Solar modulation of cosmic ray electrons 1978-1983

The ISEE 3 spacecraft has provided over 4 years of continuous data from nearby interplanetary space. By using these data together with prelaunch calibration data and precise electron spectra from balloon flights in 1979 and 1982 at Thompson, Manitoba, Canada, the flux level of approximately 1 GeV cosmic ray electrons with 1 month time resolution is monitored. The step-like onset of modulation observed for positive particles in 1979 is also seen in electrons, confirming that the dominant modulation mechanism is not sensitive to the charge of the particle. Hysteresis effects observed in the previous solar maximum by Burger and Swanenburg (1973) are not reproduced at the present solar maximum. A return to the relatively higher electron/proton ratios of the 1960s is a strong possibility. Conclusions must be drawn with caution, however, since the overall time structure of the present solar maximum is significantly different from that of the previous one.

Evenson, P.↗
NASA NTRS1990

BATSE software for the analysis of the gamma ray burst spatial distribution

The Burst and Transient Source Experiment (BATSE) on the Gamma Ray Observatory (GRO) is designed to study astronomical gamma ray sources and to provide better positional, spectral, and time resolution about these objects than has previously been possible from one experiment. The procedure to be used in the analysis of the gamma ray burst spatial distribution is presented. Data is input from BATSE via the Gamma Ray Burst Catalog (listing individual burst positions, flux values, and associated errors) and the Sky Sensitivity Map (which summarizes observational selection effects in table format). A FORTRAN program generates Monte Carlo burst catalogs, which are models to be compared to the actual distribution. The Monte Carlo models are then filtered through the Sky Sensitivity Map so that they suffer from the same selection effects as the actual catalog data. Additionally, each burst position is converted into a probability distribution to mimic BATSE positional sensitivity. The Burst Catalog, Monte Carlo burst catalog, and Sky Sensitivity Map are then passed onto an IDL program that compares the catalogs for statistical significance. The Sky Sensitivity Map is used to estimate how often each sky area is observed above the minimum flux level in question. Each burst found in this sky area is then weighted according to the frequency with which this sky area is observed. The catalogs are then compared via tests of homogeneity (based on their radial distributions) and isotropy (based upon their angular distributions). The results of the statistical comparisons along with graphs and charts of the summaries, are output from the IDL program for study.

Hakkila, Jon↗
NASA NTRS2000

The High Resolution Chandra X-Ray Spectrum of 3C273

The bright quasar 3C273 was observed by Chandra in January 2000 for 120 ksec as a calibration target. It was observed with all detector- plus-grating combinations (ACIS+HETG, ACIS+LETG, and HRC+LETG) yielding an X-ray spectrum across the entire 0.1-10 keV band with unprecedented spectral resolution. At about 10 arcsec from the nucleus, an X-ray jet is also clearly visible and resolved in the Oth order images. While the jet is much fainter than the nuclear source, the Chandra spatial resolution allows, for the first time, spectral analysis of both components separately. We will present detailed spectral analysis with particular emphasis on possible absorption features and comparison with simultaneous BeppoSAX data.

Fruscione, Antonella↗
NASA NTRS2002

Forcing a Global, Offline Land Surface Modeling System with Observation-Based Fields

The Global Land Data Assimilation System (GLDAS) drives multiple uncoupled land surface models in order to produce optimal output fields of surface states in near-real time, globally, at 1/4 degree spatial resolution. These fields are then made available for coupled atmospheric model initialization and further research. One of the unique aspects of GLDAS is its ability to ingest both modeled and observation-derived forcing for running global scale land surface models. This paper compares results of runs forced by modeled and observed precipitation and shortwave radiation fields. Differences are examined and the impact of the observations on model skill is assessed.

Rodell, Matthew↗
NASA NTRS2004

Application of Remote Sensing to Assess the Impact of Short Term Climate Variability on Coastal Sedimentation

The purpose of this joint University of Wisconsin (UW) and Louisiana State University (LSU) project has been to relate short term climate variation to response in the coastal zone of Louisiana in an attempt to better understand how the coastal zone is shaped by climate variation. Climate variation in this case largely refers to variation in surface wind conditions that affect wave action and water currents in the coastal zone. The primary region of focus was the Atchafalaya Bay and surrounding bays in the central coastal region of Louisiana. Suspended solids in the water column show response to wind systems both in quantity (through resuspension) and in the pattern of dispersement or transport. Wind systems associated with cold fronts are influenced by short term climate variation. Wind energy was used as the primary signature of climate variation in this study because winds are a significant influence on sediment transport in the micro-tidal Gilf of Mexico coastal zone. Using case studies, the project has been able to investigate the influence of short term climate variation on sediment transport. Wind energy data, collected daily for National Weather Service (NWS) stations at Lake Charles and New Orleans, LA, were used as an indicator of short term climate variation influence on seasonal time scales. A goal was to relate wind energy to coastal impact through sediment transport. This goal was partially accomplished by combining remote sensing and wind energy data. Daily high resolution remote sensing observations are needed to monitor the complex coastal zone environment, where winds, tides, and water level all interact to influence sediment transport. The NASA Earth Observing System (EOS) era brings hope for documenting and revealing response of the complex coastal transport mosaic through regular high spatial resolution observations from the Moderate resolution Imaging Spectrometer (MODIS) instrument. MODIS observations were sampled in this project for information content and should continue to be viewed as a resource for coastal zone monitoring. The project initialized the effort to transfer a suspended sediment concentration (SSC) algorithm to the MODIS platform for case 2 waters. MODIS enables monitoring of turbid coastal zones around the globe. The MODIS SSC algorithm requires refinements in the atmospheric aerosol contribution, sun glint influence, and designation of the sediment inherent optical properties (IOPs); the framework for continued development is in place with a plan to release the algorithm to the MODIS direct broadcast community.

Menzel, W. Paul↗
NASA NTRS2004

Use of EO-1 Hyperion Data for Inter-Sensor Calibration of Vegetation Indices

Numerous satellite sensor systems useful in terrestrial Earth observation and monitoring have recently been launched and their derived products are increasingly being used in regional and global vegetation studies. The increasing availability of multiple sensors offer much opportunity for vegetation studies aimed at understanding the terrestrial carbon cycle, climate change, and land cover conversions. Potential applications include improved multiresolution characterization of the surface (scaling); improved optical-geometric characterization of vegetation canopies; improved assessments of surface phenology and ecosystem seasonal dynamics; and improved maintenance of long-term, inter-annual, time series data records. The Landsat series of sensors represent one group of sensors that have produced a long-term, archived data set of the Earth s surface, at fine resolution and since 1972, capable of being processed into useful information for global change studies (Hall et al., 1991).

Huete, Alfredo↗
NASA NTRS2006

The Character and Variability of the Eta Carinae Wind Lines

The binarity of Eta Carinae has been debated for a long time. We have searched for more evidence for a companion star in a spectroscopic investigation of the Eta Carinae stellar wind lines, using moderate spectral and high angular resolution HST/STIS data. Over Eta Carinae's 5.54 year spectroscopic period many of the observable wind lines in the NUV/Optical spectral region exhibit peculiar line profiles with unusual velocity shifts relative to the system velocity. Some of the lines are exclusively blue-shifted over the entire cycle. Their ionization/excitation imply formation not in the stellar wind but rather in the interface between the two massive stars. We have analyzed velocity and intensity variations over the spectroscopic period and interpreted what the variations tell us about the geometry of the nebular structure close to Eta Carinae.

Nielsen, K. E.↗