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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

First-principles electron-phonon interactions and polarons in the parent cuprate La 2 CuO 4

Understanding electronic interactions in high-temperature superconductors is an outstanding challenge. In the widely studied cuprate materials, experimental evidence points to strong electron-phonon ( e -ph) coupling and broad photoemission spectra. Yet, the microscopic origin of this behavior is not fully understood. Here, we study e -ph interactions and polarons in a prototypical parent (undoped) cuprate, La 2 CuO 4 (LCO), by means of first-principles calculations. Leveraging parameter-free Hubbard-corrected density functional theory, we obtain a ground state with the band gap and Cu magnetic moment in nearly exact agreement with experiments. This enables a quantitative characterization of e -ph interactions. Our calculations reveal two classes of longitudinal optical (LO) phonons with strong e -ph coupling to hole states. These modes consist of bond stretching and bond bending in the Cu-O plane as well as vibrations of apical O atoms. The hole spectral functions, obtained with a cumulant method that can capture strong e -ph coupling, exhibit broad quasiparticle peaks with a small spectral weight ( Z ≈ 0.25 ) and pronounced LO-phonon sidebands characteristic of polaron effects. Our calculations predict features observed in photoemission spectra, including a 40-meV peak in the e -ph coupling distribution function not explained by existing models. These results show that the universal strong e -ph coupling found experimentally in doped lanthanum cuprates is also present in the parent compound, and elucidate its microscopic origin. Published by the American Physical Society 2025

Chang, Benjamin K. (ORCID:0000000313049324)↗
NASA NTRS1972

The near ultraviolet spectrum of B and A type main sequence stars

Scans of lambda Lep, eta UMa, xi Dra, epsilon Dor, alpha Leo, phi Dra, upsilon Dra, beta Aur, alpha Pic and alpha Dor have been obtained with scanner OAO-2 over the range 1800-3600 A. These scans have been reduced to tracings of F sub lambda in the 20 A pass band vs. wavelength using a wavelength conversion scale and a relative spectral sensitivity function. The general trend of the relative flux plots compares well with the continua predicted by simple model atmospheres.

Underhill, A. B.↗
NASA NTRS1975

The size dependence of sublimation rates for interplanetary ice particles

The sublimation rates for water ice have been computed as a function of particle size for various solar distances. Because of the size dependence of the absorption and emission properties of the particles, a sublimation-rate minimum evolves whose depth and position are sensitive to the spectral-absorption properties of the particle in combination with the spectral distribution of solar radiation. As a consequence, a quasistable size of interplanetary ice particles is predicted which is independent of solar distance.

Patashnick, H.↗
NASA NTRS1981

Spatial and Temporal scales of time-averaged 700 MB height anomalies

The monthly and seasonal forecasting technique is based to a large extent on the extrapolation of trends in the positions of the centers of time averaged geopotential height anomalies. The complete forecasted height pattern is subsequently drawn around the forecasted anomaly centers. The efficacy of this technique was tested and time series of observed monthly mean and 5 day mean 700 mb geopotential heights were examined. Autocorrelation statistics are generated to document the tendency for persistence of anomalies. These statistics are compared to a red noise hypothesis to check for evidence of possible preferred time scales of persistence. Space-time spectral analyses at middle latitudes are checked for evidence of periodicities which could be associated with predictable month-to-month trends. A local measure of the average spatial scale of anomalies is devised for guidance in the completion of the anomaly pattern around the forecasted centers.

Gutzler, D.↗
NASA NTRS1987

The ultraviolet spectral albedo of planet earth

The solar backscattered ultraviolet spectral radiometer on the Nimbus 7 satellite provides a unique set of radiation measurements which allows an evaluation of the spectral albedo of the earth and its atmosphere in the wavelength range 300 to 340 nm. Near 340 nm, the derived spectral albedo expressed as a function of latitude and month exceeds that in the visible part of the spectrum, with values near 45 percent existing equatorward of 30 deg and an increase to 60-80 percent toward the poles. At middle to high latitudes, the monthly mean spectral albedo exceeds the contribution from Rayleigh scattering alone by factors of 1.4 to 2.2. At wavelengths from 300 to 310 nm, where absorption by stratospheric ozone is significant, the daylight averaged spectral albedos receive negligible contribution from scattering by tropospheric clouds, yet the derived values exceed those predicted for Rayleigh scattering from a clean stratosphere. These observations are consistent with the presence of an atmospheric scattering layer, distinct from cloudiness, located at an altitude above the tropopause.

Frederick, John E.↗
NASA NTRS1990

Spanwise measurements of vertical components of atmospheric turbulence

Correlation and spectrum magnitude estimates are computed for vertical gust velocity measurements at the nose and wing tips of a NASA B-57B aircraft for six level flight, low speed and low altitude runs and are compared with those of the von Karman atmospheric turbulence model extended for spanwise relationships. The distance between the wing tips was 62.6 ft. Airspeeds ranged from about 330 to 400 ft/sec, heights above the ground ranged from near ground level to about 5250 ft. and gust velocity standard deviations ranged from 4.10 to 8.86 ft/sec. Integral scale lengths, determined by matching measured autocorrelation estimates with those of the model, ranged from 410 to 2050 ft. Digital signals derived from piezoelectric sensors provided continuous pressure and airspeed measurements. Some directional acceleration sensitivity of the sensors was eliminated by sensor orientation, and their performance was spectrally verified for the higher frequencies with supplemental onboard piezoresistive sensors. The model appeared to satisfactorily predict the trends of the measured cross-correlations and cross-spectrum magnitudes, particularly between the nose and wing tips. However, the measured magnitude estimates of the cross-spectra between the wing tips exceeded the predicted levels at the higher frequencies. Causes for the additional power across the wing tips were investigated. Vertical gust velocity components evaluated along and lateral to the flight path implied that the frozen-turbulence-field assumption is a suitable approximation.

Sleeper, Robert K.↗
NASA NTRS1994

Analysis of the 3d(sup 6)4s((sup 6)D)4f-5g supermultiplet of Fe I in laboratory and solar infrared spectra

The combined laboratory and solar analysis of the highly excited subconfigurations 3d(sup 6)4s((sup 6)D)4f and 3d(sup 6)4s((sup 6)D)5g of Fe I has allowed us to classify 87 lines of the 4f-5g supermultiplet in the spectral region 2545-2585 per cm. The level structure of these JK-coupled configurations is predicted by semiempirical calculations and the quardrupolic approximation. Semiempirical gf-values have been calculated and are compared to gf-values derived from the solar spectrum. The solar analysis has shown that these lines, which should be much less sensitive than lower excitation lines to departures from Local Thermal Equilibrium (LTE) and to temperature uncertanties, lead to a solar abundance of iron which is consistent with the meteoritic value (A(sub Fe) = 7.51).

Johansson, S.↗
NASA NTRS2003

MODIS Retrieval of Dust Aerosol

The MODerate resolution Imaging Spectroradiometer (MODIS) currently aboard both the Terra and Aqua satellites produces a suite of products designed to characterize global aerosol distribution, optical thickness and particle size. Never before has a space-borne instrument been able to provide such detailed information, operationally, on a nearly global basis every day. The three years of Terra-MODIS data have been validated by comparing with co-located AERONET observations of aerosol optical thickness and derivations of aerosol size parameters. Some 8000 comparison points located at 133 AERONET sites around the globe show that the MODIS aerosol optical thickness retrievals are accurate to within the pre-launch expectations. However, the validation in regions dominated by desert dust is less accurate than in regions dominated by fine mode aerosol or background marine sea salt. The discrepancy is most apparent in retrievals of aerosol size parameters over ocean. In dust situations, the MODIS algorithm tends to under predict particle size because the reflectances at top of atmosphere measured by MODIS exhibit the stronger spectral signature expected by smaller particles. This pattern is consistent with the angular and spectral signature of non-spherical particles. All possible aerosol models in the MODIS Look-Up Tables were constructed from Mie theory, assuming a spherical shape. Using a combination of MODIS and AERONET observations, in regimes dominated by desert dust, we construct phase functions, empirically, with no assumption of particle shape. These new phase functions are introduced into the MODIS algorithm, in lieu of the original options for large dust-like particles. The results will be analyzed and examined.

Remer, Lorraine A.↗
NASA NTRS2012

Numerical Analysis Using WRF-SBM for the Cloud Microphysical Structures in the C3VP Field Campaign: Impacts of Supercooled Droplets and Resultant Riming on Snow Microphysics

Two distinct snowfall events are observed over the region near the Great Lakes during 19-23 January 2007 under the intensive measurement campaign of the Canadian CloudSat/CALIPSO validation project (C3VP). These events are numerically investigated using the Weather Research and Forecasting model coupled with a spectral bin microphysics (WRF-SBM) scheme that allows a smooth calculation of riming process by predicting the rimed mass fraction on snow aggregates. The fundamental structures of the observed two snowfall systems are distinctly characterized by a localized intense lake-effect snowstorm in one case and a widely distributed moderate snowfall by the synoptic-scale system in another case. Furthermore, the observed microphysical structures are distinguished by differences in bulk density of solid-phase particles, which are probably linked to the presence or absence of supercooled droplets. The WRF-SBM coupled with Goddard Satellite Data Simulator Unit (G-SDSU) has successfully simulated these distinctive structures in the three-dimensional weather prediction run with a horizontal resolution of 1 km. In particular, riming on snow aggregates by supercooled droplets is considered to be of importance in reproducing the specialized microphysical structures in the case studies. Additional sensitivity tests for the lake-effect snowstorm case are conducted utilizing different planetary boundary layer (PBL) models or the same SBM but without the riming process. The PBL process has a large impact on determining the cloud microphysical structure of the lake-effect snowstorm as well as the surface precipitation pattern, whereas the riming process has little influence on the surface precipitation because of the small height of the system.

lake-effect↗
NASA NTRS2015

Tropospheric Emissions: Monitoring of Pollution Overview

TEMPO is now well into its implementation phase, having passed both its Key Decision Point C and the Critical Design Review (CDR) for the instrument. The CDR for the ground systems will occur in March 2016 and the CDR for the Mission component at a later date, after the host spacecraft has been selected. TEMPO is on schedule to measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO measures from Mexico City to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution. TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry. Measurements are from geostationary (GEO) orbit, to capture the inherent high variability in the diurnal cycle of emissions and chemistry. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve O3, NO2, SO2, H2CO, C2H2O2, H2O, aerosols, cloud parameters, and UVB radiation. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions by 50 percent. TEMPO quantifies and tracks the evolution of aerosol loading. It provides near-real-time air quality products that will be made widely, publicly available. TEMPO provides much of the atmospheric measurement capability recommended for GEO-CAPE in the 2007 National Research Council Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. Instruments from Europe (Sentinel 4) and Asia (GEMS) will form parts of a global GEO constellation for pollution monitoring later this decade, with a major focus on intercontinental pollution transport. TEMPO will launch at a prime time to be a component of this constellation.

Chance, Kelly↗
NASA NTRS2016

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

TEMPO (Tropospheric Emissions: Monitoring of Pollution) was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (approximately 2.1 kilometers N/S by 4.4 kilometers E/W at 36.5 degrees N, 100 degrees W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), bromine monoxide (BrO), IO (iodine monoxide),water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

tropospheric measurement suite↗
NASA NTRS2021

Hydrothermal History of (162173) Ryugu’s Parent Body Inferred from Remote-Sensing Data

Small rubble pile asteroids record the thermal evolution of their much larger parent bodies. However, recent space weathering and/or solar heating create ambiguities between the uppermost layer observable by remote-sensing and the pristine material from the parent body. Hayabusa2 remote-sensing observations find that on the asteroid (162173) Ryugu both north and south pole regions preserve the least space-weathered material, which is spectrally blue carbonaceous chondritic material with a 0 – 3% deep 0.7-μm band absorption, indicative of Fe-bearing phyllosilicates . We report that spectrally blue Ryugu’s parent body experienced intensive aqueous alteration and subsequent thermal metamorphism at 570 – 670 K (300 – 400 ˚C), suggesting that Ryugu’s parent body was heated by radioactive decay of short-lived radionuclides possibly because of its early formation 2-2.5 Ma. The samples being brought to Earth by Hayabusa2 will give us our first insights into this epoch in solar system history. Moreover, we found the NUV-VIS spectral similarity between Ryugu and Polana–Eulalia family members, suggesting plausible origin from inner main belt predicted by the dynamical simulation.

Eri Tatsumi↗
NASA NTRS2021

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional scale due to varying parameters among LiDAR data acquisitions and the availability of sufficient calibration data. Here, we present a model using a 3-D convolutional neural network (CNN), a form of deep learning capable of scanning a LiDAR point cloud, combined with coincident satellite data (spectral, phenology, and disturbance history). We compared this approach to traditional modeling used for making forest predictions from LiDAR data (height metrics and random forest) and found that the CNN had consistently lower uncertainty. We then applied the CNN to public data over six New England states in the USA, generating maps of 14 forest attributes at a 10 m resolution over 85% of the region. Aboveground biomass estimates produced a root mean square error of 36 Mg ha−1 (44%) and were within the 97.5% confidence of independent county-level estimates for 33 of 38 or 86.8% of the counties examined. CNN predictions for stem density and percentage of conifer attributes were moderately successful, while predictions for detailed species groupings were less successful. The approach shows promise for improving the prediction of forest attributes from regional LiDAR data and for combining disparate LiDAR datasets into a common framework for large-scale estimation.

Elias Ayrey↗
NASA NTRSDate not supplied

Principal Component and Machine Learning Approach to Gap Fill Hyperspectral Ocean Color Satellite Retrievals

Retrievals of ocean color properties from space are important for monitoring the health of the ocean ecosystem but such retrievals can be limited spatially due to conditions such as clouds, aerosols, and sun glint. Gap filling of ocean color retrievals is typically performed by combining retrievals from multiple satellites or temporally averaging multiple days of retrievals. Despite these techniques large gaps still exist posing challenges for near real time monitoring of events like harmful algae blooms. To address these limitations, we developed a spatial gap filling approach applying machine learning approach to hyperspectral instruments to learn how to perform an atmospheric correction under challenging retrieval conditions. In this approach a principal component analysis is used to decompose the hyperspectral measurements into spectral components that describe the scattering and absorption of the atmosphere mixed with the surface spectral signatures. The coefficients of the principal components are used to train a neural network to predict ocean color properties derived from a standard MODIS ocean color algorithm. We apply the approach to two hyperspectral UV/VIS sensors, the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can be used to estimate ocean color properties such as chlorophyll, remote sensing reflectance, and fluorescence line height. This method could be used as a gap-filling technique for the future Ocean Color Instrument (OCI) onboard upcoming NASA's Plankton, Aerosol Cloud, ocean Ecosystem (PACE) satellite to provide additional information for monitoring the health of our global oceans. Additionally, it could be applied to the first NASA and Smithsonian geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) spectrometer to better understand diurnal variability in inland and coastal ocean ecology.

Zachary Fasnacht↗
DOE OSTI2025

Hyper Spectral Anomaly Detection

Anomaly detection is a common machine learning (ML) task with growing importance in the fields of imaging, quality assurance, and multiple security related disciplines. Anomaly detection is more difficult than traditional machine learning methods due to the inherent unlabeled nature of the datasets. Existing anomaly detection architectures commonly face challenges with explainability, retaining information related to the relational structure of the data, and false positive rates. Hyperspectral Imaging Anomaly Detection (HSI) is a statistical model that employs vertex and edge weighted graphs to preserve the data’s relationships on different topographical scales. The model is able to generalize from anomaly detection in 2D images to novel datasets related to cyber-security. Furthermore, the use of multi-spectral and other filtering methods results in fewer false positives and increases the explainability of model predictions. When applying HSI to cyber-security datasets, we are able to successfully detect malicious activity with a relatively high degree of accuracy.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS1972

Prediction of directional reflectance of a corn field under stress

A vegetative canopy model concept is developed and used to calculate the directional spectral reflectance of a corn field under stress. The problem has been idealized by assuming that the canopy is represented in several layers of uniform but randomly distributed biological components; horizontal and vertical projection of leaves, for example, replaces the actual leaf. An accumulative horizontal projection leads to a quantitative horizontal leaf area index while accumulated vertical projections provide the vertical leaf index area for each layer. The spectral properties of the projections are obtained from the spectral properties of the biological components. These techniques together with soil reflectance data provide a physical description for the vegetative canopy and adequately interprete multispectral scanning data.

Suits, G. H.↗
NASA NTRS1993

Predicting LANDSAT MSS endmember signatures from corresponding higher resolution TM fraction images

The approach to predicting Landsat Multispectral Scanner System (MSS) endmember signatures from fraction images derived from high resolution Landsat Thematic Mapper (TM) data is presented. The purpose, conducted in the Mogi-Guacu study site located in Sao Paulo State, Brazil, was to determine if information derived from a mixture model applied to higher resolution TM data could serve as ground truth for the lower resolution MSS sensor. The Constrained Least Squares (CLS) method was used to generate vegetation, soil, and shade fraction images from the TM data. The resulting images were then used to estimate the endmember spectral response for MSS data by regressing each MSS spectral channel against the corresponding proportion values estimated for the same resolution cells from the TM mixture model. The evaluation of the predicted signatures was performed by comparing the corresponding fraction images derived from both the MSS and TM data acquired on 14 Sep. 1986. The techniques serves as a potential tool for integrating information in global studies where remote sensors with different spectral and spatial resolutions were used.

Shimabukuro, Yosio E.↗