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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Assessing Arctic low-level clouds and precipitation from above – a radar perspective

Most Arctic clouds occur below 2 km altitude as revealed by CloudSat satellite observations. However, recent studies suggest that the relatively coarse spatial resolution, low sensitivity, and blind zone of the radar installed on CloudSat may not enable it to comprehensively document low-level clouds. We investigate the impact of these limitations on the Arctic low- level cloud fraction, which is the amount of cloudy points with respect to all points as a function of height, derived from CloudSat radar observations. For this purpose, we leverage highly resolved vertical profiles of low-level cloud fraction derived from downlooking Microwave Radar/radiometer for Arctic Clouds (MiRAC) radar reflectivity measurements. MiRAC has been operated during four aircraft campaigns taking place in the vicinity of Svalbard during different times of the year and covering more than 25,000 km. This allows us to study the dependence of CloudSat limitations on different synoptic and surface conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Constraints on the rate of discharge and duration of the Mangala Valles flood

Interest in Mangala Valles remains high within the planetary science community. This is justified because the survey mission images provide us with nearly complete coverage of the system at high resolution. Upcoming high resolution topography from the Mars Observer Laser Altimeter will enable the application of flood discharge models with an unprecedented level of detail. Previous work on the Mangala Valles problem has relied on the available low resolution topography. This has limited the ability of workers to constrain discharge calculations to only within several-order-of-magnitude estimates. Local determinations of channel depths via shadow length measurements and photoclinometric profiling are much more accurate, but can only be applied to steep slopes (and/or low sun elevations) in the case of shadow measurements, or across relatively short distances (to avoid changes in albedo along asymmetric photoclinometric profiles). We are taking stereo parallax measurements from medium resolution Viking Orbiter images, which provide a valuable intermediate check of the topography between those measurements made thus far and the upcoming Mars Observer data. The images used are from orbits 034A and 637A, and cover Mangala Valles from the source graben in Memnonia Fossae to the beginning of the bifurcated reach (at 9.5 deg lat., 151.5 deg lon.). These images are about 300 m/pixel and 250 m/pixel, respectively. Both sets of images were orthographically projected to 250 m/pixel. The separation angle between left-right pairs is approximately 52 degrees. This results in a vertical accuracy on the order of plus or minus 260 m. Though this is still somewhat coarse, the channel relief is clearly resolved. Preliminary profiles across Mangala Valles and the large topographic ridge to the east are shown on the following page. An east-west regional tilt that resulted from slight scaling differences in the digital data has been 'removed' by visually estimating a regional datum on the stereo pairs. North-south variations in scale have not yet been adjusted for, so the zero datum for each profile was simply taken to be the high water line of the channel itself. Our depth measurement for the source breach of about 750 m agrees reasonably well with values of 700-1000 m determined from shadow measurements.

Parker, T. J.↗
DOE OSTI2020

Simulated Precipitation Diurnal Variation With a Deep Convective Closure Subject to Shallow Convection in Community Atmosphere Model Version 5 Coupled With CLUBB

In order to improve the physical consistency between shallow and deep convection, we modify the deep convective closure in the Community Atmosphere Model version 5 (CAM5) coupled with a third-order turbulence closure parameterization (i.e., Cloud Layers Unified by Binormals; CLUBB). The revised closure reserves a portion of the total convective available potential energy for shallow convection via utilizing the heating and moistening profiles from CLUBB to distribute moisture and energy between shallow and deep convection. Simulations at two resolutions (i.e. 2° and 0.5°, respectively) are conducted to investigate the impacts of convective closure on the simulated precipitation diurnal variations. Results from low-resolution simulations show that the revised closure suppresses deep convection until the lower troposphere is sufficiently moistened by shallow convection, which improves the precipitation diurnal variation simulations compared to the default closure, with the precipitation diurnal peak over tropical lands delayed from 12LST to 19LST. The revised closure better simulates the diurnal variations for precipitation over the Asian monsoon region, such as the delayed precipitation onset, but still fails to well capture the nighttime peak for precipitation there. This deficiency is alleviated to some extent when applying the revised closure in highresolution simulations, but nighttime precipitation is still underestimated probably because key processes responsible for nighttime convection are missing. Altogether, our results indicate that establishing the consistency between shallow and deep convection is critical for the precipitation diurnal cycle simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A HPC Theory-Guided Machine Learning Cyberinfrastructure for Communicating Hydrometeorological Data Across Scales

High-resolution predictions of hydrometeorological variables are critical for supporting hydropower generation decisions and flood control at hydroelectric power plants. Traditional climate and hydrologic models rely on the numerical simulation of detailed physical processes. Therefore, running these simulations is time-, labor-, and computation-intensive. Improving the spatial and temporal resolution in these modeling outputs could lead to cubic increases in both the simulation time and computational demands, rendering high-resolution hydrometeorological predictions expensive and impractical. Many past studies apply the super resolution (SR) technique to downscale climate models using deep learners. However, deep learners are deemed “black-boxes,” as their derivation processes from low-resolution outputs to high-resolution outputs are often hidden. Their results are difficult for domain scientists to interpret and validate. Thus, there is a need for an exploratory machine learning approach that can partially integrate domain-specific theory and knowledge into the data-driven mapping process between simulation outputs of different spatial scales. The domain-specific theory and knowledge can be incorporated into the data model through an inductive approach in which process-related environmental variables are used and analyzed as key drivers (i.e., environmental surrogates) to reflect the complex physical processes. Many of these variables, such as land use land cover, soil types, topography, digital elevation, air temperature, and various watershed characteristics, can be directly measured through sensors or remote sensing techniques. Additionally, SR applications that can downscale hydrological and hydrodynamics models to efficiently produce high-resolution (1 m) flood depth grids are still rare. Since the flood depth grid can be used to support critical decisions for flood control operation at hydroelectric power plants, it is crucial to enable an SR-based capability for interpolating high-resolution flood inundation maps.

13 HYDRO ENERGY↗
DOE OSTI2023

Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model

Abstract. Convection-permitting regional climate models (RCMs) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource-critical mountain regions, where precipitation is highly variable in space, observations are sparse, and the societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; v3.8.1), using the Climate Forecast System Reanalysis (CFS; CFSv2) initial and lateral boundary conditions and a 1 km × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River basin, with a focus on the high-elevation, 750 km2 East River watershed (ERW), where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (Natural Resources Conservation Service Snow Telemetry or SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed mean precipitation conditioned on streamflow and high-resolution snow remote-sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x^ = 0.25 %, s = 13.63 %) and that WRF has a higher percent error during the warm season (x^ = 10.37 %, s = 12.79 %). Warm-season bias manifests as a high number of low-precipitation days, though the low-resolution or SNOTEL gauges limit some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of ± 20 %, particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region and demonstrates that leveraging additional hydrologic information (streamflow and snow remote-sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging the RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly 1 km × 1 km precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

Geology↗
DOE OSTI2025

Artificial Intelligence-Enhanced CMIP6 Climate Projections Across the Conterminous United States

This dataset comprises high-resolution climate projections at 1/24 degree grid (~4km) over the conterminous United States (CONUS) based on ten Global Climate Models (GCMs) that are part of the Coupled Models Intercomparison Project phase 6 (CMIP6). The CMIP6 GCMs are downscaled using two artificial intelligence (AI) techniques, primarily based on the computer vision approach called super-resolution. We train two separate networks: super-resolution convolutional neural network (SRCNN) and super-resolution generative adversarial network (SRGAN). The networks are trained using Daymet observations, originally available at a 1 km resolution. For training purposes, the Daymet data is interpolated to 1/24 degree (~4km), 0.25 degree and 1 degree, which serve as high, intermediate and low-resolution inputs respectively. For each of the SRCNN and SRGAN network, we use a two-step resolution enhancement, the first step generates 4x refinement from 1 degree to 0.25 degree and the second step generates 6x refinement from 0.25 degree to 1/24 degree (~4km). We downscale daily scale precipitation, maximum temperature and minimum temperature for the six CMIP6 GCMs for 1980 to 2019 in the historical period and 2020 to 2059 in the near-term future under the shared socioeconomic pathway 585 and 245 (SSP585 and SSP245) emission scenarios. We also perform double bias-correction with Daymet observations using a quantile mapping approach, first for GCMs prior to making predictions at 1 degree grid and second after making final predictions at ~4km.

13 HYDRO ENERGY↗
NASA NTRS1990

Measurement of the solar UV flux in the stratosphere

Measurements of the direct solar flux from balloons at an altitude of 40 km are used to determine the effective cross sections of the Schumann-Runge bands. Transmission in these bands, which lie between 180 and 200 nm, allows the Sun's radiation in this region of the spectrum to penetrate into the lower mesosphere. Measurements by a high resolution scanning spectrometer (0.02 nm) are used to measure the transmission in the Schumann-Runge bands. Since ozone absorbs in this wavelength region, a low resolution scanning spectrometer (0.25 nm) measures the transmission between 220 and 260 nm, allowing the column ozone to be determined. Absorption due to ozone can then be calculated and the data corrected for this effect.

Mentall, James E.↗
NASA NTRS1999

AIRES: An Airborne Infra-Red Echelle Spectrometer for SOFIA

SOFIA will enable astronomical observations with unprecedented angular resolution at infrared wavelengths obscured from the ground. To help open this new chapter in the exploration of the infrared universe, we are building AIRES, an Airborne Infra-Red Echelle Spectrometer. AIRES will be operated as a first generation, general purpose facility instrument by USRA, NASA's prime contractor for SOFIA. AIRES is a long slit spectrograph operating from 17 - 210 microns. In high resolution mode the spectral resolving power is approx. 10(exp 6) microns/A or approx. 10(exp 4) at 100 microns. Unfortunately, since the conference, a low resolution mode with resolving power about 100 times lower has been deleted due to budgetary constraints. AIRES includes a slit viewing camera which operates in broad bands at 18 and 25 microns.

Dotson, Jessie J.↗
DOE OSTI2020

The chemical nature of the young 120-Myr-old nearby Pisces–Eridanus stellar stream flowing through the Galactic disc

Recently, a new cylindrical-shaped stream of stars up to 700 pc long was discovered hiding in the Galactic disc using kinematic data enabled by the Gaia mission. This stream of stars, dubbed Pisces–Eridanus (Psc–Eri), was initially thought to be as old as 1 Gyr, yet its stars shared a rotation period distribution consistent with a population that was 120 Myr old. Here, we explore the detailed chemical nature of this stellar stream. We carried out high-resolution spectroscopic follow-up of 42 Psc–Eri stars using McDonald Observatory and combined these data with information for 40 members observed with the low-resolution LAMOST spectroscopic survey. Together, these data enabled us to measure the abundance distribution of light/odd-Z (Li, Na, Al, Sc, V), α (Mg, Si, Ca, Ti), Fe-peak (Cr, Mn, Fe, Co, Ni, Zn), and neutron capture (Sr, Y, Zr, Ba, La, Nd, Eu) elements along the Psc–Eri stream. We find that the stream is (1) near-solar metallicity with [Fe/H] = –0.03 dex and (2) has a metallicity spread of 0.07 dex (or 0.04 dex when outliers are excluded). We also find that (3) the abundance of Li indicates that Psc–Eri is ~120 Myr old, consistent with its gyrochronology age. Additionally, Psc–Eri has (4) [X/Fe] abundance spreads that are just larger than the typical uncertainty in most elements, (5) it is a cylindrical-like system whose outer edges rotate about the centre, and (6) no significant abundance gradients along its major axis except a potentially weak gradient in [Si/Fe]. These results show that Psc–Eri is a uniquely close young chemically interesting laboratory for testing our understanding of star and planet formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Effect of image resolution on automated classification of chest X-rays

Deep learning (DL) models have received much attention lately for their ability to achieve expert-level performance on the accurate automated analysis of chest X-rays (CXRs). Recently available public CXR datasets include high resolution images, but state-of-the-art models are trained on reduced size images due to limitations on graphics processing unit memory and training time. As computing hardware continues to advance, it has become feasible to train deep convolutional neural networks on high-resolution images without sacrificing detail by downscaling. This study examines the effect of increased resolution on CXR classification performance. We used the publicly available MIMIC-CXR-JPG dataset, comprising 377,110 high resolution CXR images for this study. We applied image downscaling from native resolution to 2048 × 2048 pixels, 1024 × 1024 pixels, 512 × 512 pixels, and 256 × 256 pixels and then we used the DenseNet121 and EfficientNet-B4 DL models to evaluate clinical task performance using these four downscaled image resolutions. We find that while some clinical findings are more reliably labeled using high resolutions, many other findings are actually labeled better using downscaled inputs. We qualitatively verify that tasks requiring a large receptive field are better suited to downscaled low resolution input images, by inspecting effective receptive fields and class activation maps of trained models. Lastly, we show that stacking an ensemble across resolutions outperforms each individual learner at all input resolutions while providing interpretable scale weights, indicating that diverse information is extracted across resolutions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Fabrication of model ultrafiltration membranes with uniform, high aspect ratio pores

Here, in this manuscript, we report the facile fabrication of large-area model membranes with highly uniform and high aspect ratio pores with diameters <20 nm. These membranes are useful for fundamental investigations of separation by size exclusion in the ultrafiltration regime, where species to be separated from solution have dimensions of 1–100 nm. Such investigations require membranes with narrow pores and high aspect ratios such that the Hagen–Poiseuille equation is followed, enabling well-known models such as the hindered transport model to be evaluated and other affecting factors to be ignored. We demonstrate that the sub-20 nm pores in the membrane are of sufficiently high aspect ratio such that water flux through the membrane is consistent with the Hagen–Poiseuille equation. The fabrication relies on self-assembling block copolymers to form uniform, densely packed patterns with sub-20 nm resolution, sequential infiltration synthesis to convert the block copolymer in situ into a mask with adequate contrast to etch pores with an aspect ratio >5, and low-resolution photolithography to transfer the pattern over a large area into a silicon nitride membrane. Model membranes with narrow pore-size distribution fabricated in this way provide the means to investigate parameters that impact size-selective ultrafiltration separations such as the relationships between solute or particle size and pore size, their distributions, and rejection profiles, and, therefore, test the validity or limits of separation models.

Chen, Wen↗
DOE OSTI2024

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Abstract Superresolution (SR) aims to increase the resolution of images by recovering detail. Compared to standard interpolation, deep learning-based approaches learn features and their relationships to leverage prior knowledge of what low-resolution patterns look like in higher resolution. Deep neural networks can also perform image cross-calibration by learning the systematic properties of the target images. While SR for natural images aims to create perceptually convincing results, SR of scientific data requires careful quantitative evaluation. In this work, we demonstrate that deep learning can increase the resolution and calibrate solar imagers belonging to different instrumental generations. We convert solar magnetic field images taken by the Michelson Doppler Imager (resolution ∼2″ pixel −1 ; space based) and the Global Oscillation Network Group (resolution ∼2.″5 pixel −1 ; ground based) to the characteristics of the Helioseismic and Magnetic Imager (resolution ∼0.″5 pixel −1 ; space based). We also establish a set of performance measurements to benchmark deep-learning-based SR and calibration for scientific applications.

Muñoz-Jaramillo, Andrés (ORCID:0000000247160840)↗
NASA NTRS1980

Remote sensing for water quality and biological measurements in coastal waters

Recent remote sensing experiments in the United States' coastal waters indicate that certain biological and water quality parameters have distinctive spectral characteristics. Data outputs from remote sensors, to date, include: (1) high resolution measurements to determine concentrations and distributions of total suspended particulates, temperature, salinity, chlorophyll a, and phytoplankton color group associations from airborne and/or satellite platforms, and (2) low resolution measurements of total suspended solids, temperature, ocean color, and possibly chlorophyll from satellite platforms. A summary of platforms, sensors and parameters measured is given. Remote sensing, especially when combined with conventional oceanographic research methods, can be useful in such high priority research areas as estuarine and continental shelf sediment transport dynamics, transport and fate of marine pollutants, marine phytoplankton dynamics, and ocean fronts.

Johnson, R. W.↗
NASA NTRS1994

High-resolution mid-infrared observations of NGC 7469

We present a high-resolution 11.7 micrometer image of the starburst/Seyfert hybrid galaxy NGC 7469 using the Hale 5 m telescope at Palomar Observatory. Our map, with diffraction limited spatial resolution of 0.6 sec, shows a 3 sec diameter ring of emission around an unresolved nucleus. The map is similar to the Very Large Array (VLA) 6 cm map of this galaxy made with 0.4 sec resolution by Wilson et al. (1991). About half of the mid-infrared flux in our map emerges from the unresolved nucleus. We also present spatially resolved low resolution spectra that show that the 11.3 micrometer polycyclic aromatic hydrocarbon (PAH) feature comes from the circumnuclear ring but not from the nucleus of the galaxy.

Miles, J. W.↗
NASA NTRS2000

Visible Wavelength Spectroscopy of Ferric Minerals: A Key Tool for Identification of Ancient Martian Aqueous Environments

The mineralogic signatures of past aqueous alteration of a basaltic Martian crust may include iron oxides and oxyhydroxides, zeolites, carbonates, phyllosilicates, and silica. The identities, relative abundances, and crystallinities of the phases formed in a particular environment depend on physicochemical conditions. At one extreme, hot spring environments may be characterized by smectite-chlorite to talc-kaolinite silicate assemblages, plus crystalline ferric oxides dominated by hematite. However, most environments, including cold springs, pedogenic layers, and ponded surface water, are expected to deposit iron oxides and oxyhydroxides, carbonates, and smectite-dominated phyllosilicates. A substantial fraction of the ferric iron is expected to occur in nanophase form, with the exact mineralogy strongly influenced by Eh-pH conditions. Detection of these phases has been an objective of a large body of terrestrial telescopic, Mars orbital, and landed spectral investigations and in situ compositional measurements. However, clear identifications of many of these phases is lacking. Neither carbonate nor silica has been unequivocally detected by any method. Although phyllosilicates may occur near the limit of detection by remote sensing, in general they appear to occur in only poorly crystalline form. In contrast, compelling evidence for ferric iron minerals has been gathered by recent telescopic investigations, the Imager for Mars Pathfinder (IMP), and the Thermal Emission Spectrometer (TES) on the Mars Global Surveyor (MGS). These data yield two crucial findings: (1) In the global, high spatial resolution TES data set, highly crystalline ferric iron (as coarse-grained 'gray' hematite) has been recognized but with only very limited spatial occurrence and (2) Low-resolution telescopic reflectance spectroscopy, very limited orbital reflectance spectroscopy, and landed multispectral imaging provide strong indications that at least two broad classes of ferric iron minerals are commonplace in non-dust covered regions.

Murchie, Scott L.↗
NASA NTRS2001

Ultraviolet Broad Absorption Features and the Spectral Energy Distribution of the QSO PG 1351+641

We present a moderate-resolution (approximately 20 km/s) spectrum of the broad-absorption line QSO PG 1351+64 between 915-1180 angstroms, obtained with the Far Ultraviolet Spectroscopic Explorer (FUSE). Additional low-resolution spectra at longer wavelengths were also obtained with the Hubble Space Telescope (HST) and ground-based telescopes. Broad absorption is present on the blue wings of C III lambda977, Ly-beta, O VI lambda-lambda-1032,1038, Ly-alpha, N V lambda-lambda-1238,1242, Si IV lambda-lambda-1393,1402, and C IV lambda-lambda-1548,1450. The absorption profile can be fitted with five components at velocities of approximately -780, -1049, -1629, -1833, and -3054 km/s with respect to the emission-line redshift of z = 0.088. All the absorption components cover a large fraction of the continuum source as well as the broad-line region. The O VI emission feature is very weak, and the O VI/Ly-alpha flux ratio is 0.08, one of the lowest among low-redshift active galaxies and QSOs. The ultraviolet continuum shows a significant change in slope near 1050 angstroms in the restframe. The steeper continuum shortward of the Lyman limit extrapolates well to the observed weak X-ray flux level. The absorbers' properties are similar to those of high-redshift broad absorption-line QSOs. The derived total column density of the UV absorbers is on the order of 10(exp 21)/s, unlikely to produce significant opacity above 1 keV in the X-ray. Unless there is a separate, high-ionization X-ray absorber, the QSO's weak X-ray flux may be intrinsic. The ionization level of the absorbing components is comparable to that anticipated in the broad-line region, therefore the absorbers may be related to broad-line clouds along the line of sight.

Zheng, W.↗
DOE OSTI2024

2.5D Super-Resolution Approaches for X-Ray Computed Tomography-Based Inspection of Additively Manufactured Parts

X-ray computed tomography (XCT) is a key tool in non-destructive evaluation of additively manufactured (AM) parts, allowing for internal inspection and defect detection. Despite its widespread use, obtaining high-resolution CT scans can be extremely time consuming. This issue can be mitigated by performing scans at lower resolutions; however, reducing the resolution compromises spatial detail, limiting the accuracy of defect detection. Super-resolution algorithms offer a promising solution for overcoming resolution limitations in XCT reconstructions of AM parts, enabling more accurate detection of defects. While 2D super-resolution methods have demonstrated state-of-the-art performance on natural images, they tend to under-perform when directly applied to XCT slices. On the other hand, 3D super-resolution methods are computationally expensive, making them infeasible for large-scale applications. To address these challenges, we propose a 2.5D super-resolution approach tailored for XCT of AM parts. Our method enhances the resolution of individual slices by leveraging multi-slice information from neighboring 2D slices without the significant computational overhead of full 3D methods. Specifically, we use neighboring low-resolution slices to super-resolve the center slice, exploiting inter-slice spatial context while maintaining computational efficiency. This approach bridges the gap between 2D and 3D methods, offering a practical solution for high-throughput defect detection in AM parts.

Sullivan, Haley↗