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NASA NTRS2022

NASA Enterprise Digital Transformation Initiative Strategic Framework & Implementation Approach

Since 1958, NASA has delivered its enduring bold purpose, characterized in the 2022 NASA Strategic Plan as a mission to discover, explore, innovate, and advance solutions to the problems of flight, within and outside the Earth’s atmosphere, for the benefit of humankind. The NASA Strategic Plan also recognizes that this mission will be delivered differently as it looks to a future marked by radical global change, which is in part being driven by digital advances. For this reason, in late 2020 NASA established an Enterprise Digital Transformation (DT) agency-level strategic initiative to accelerate and coordinate leveraging digital advances to transform the way the Agency works, the experience of its workforce and the agility of its workplace. This paper documents NASA’s DT strategic framework and associated implementation approach, with the DT strategic initiative serving to ignite, connect, integrate, and facilitate DT progress across a federated organizational operating model.

digital transformation↗

NASA NTRS2024

Parameterizing Spectral Surface Reflectance Relationships for the Dark Target Aerosol Algorithm Applied to A Geostationary Imager

Originally developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) in polar, sun-synchronous low-earth orbit (LEO), the Dark Target (DT) aerosol retrieval algorithm relies on the assumption of a Surface Reflectance Parameterization (SRP) over land surfaces. Specifically for vegetated and dark-soiled surfaces, values of surface reflectance in blue and red visible-wavelength bands are assumed to be nearly linearly related to each other and to the value in a shortwave infrared (SWIR) wavelength band. This SRP also includes dependencies on scattering angle and a normalized difference vegetation index computed from two SWIR bands (NDVISWIR). As the DT retrieval algorithm is being ported to new sensors to continue and expand the aerosol data record, we assess whether the MODIS-assumed SRP can be used for these sensors. Here, we specifically assess SRP for the Advanced Baseline Imager (ABI) aboard, the Geostationary Operational Environmental Satellite (GOES)-16/East (ABIE). First, we find that using MODIS-based SRP leads to higher biases and artificial diurnal signatures in aerosol optical depth (AOD)retrievals from ABIE. The primary reason appears to be that geostationary orbit (GEO) encounters an entirely different set of observation geometry than does LEO, primarily with regards to solar angles coupled with fixed view angles. Therefore, we have developed a new SRP for GEO that draws the angular shape of the surface bidirectional reflectance. We also introduce modifications to the parametrization of both red-SWIR and blue-red spectral relationships to include additional information.The revised Red-SWIR SRP includes solar zenith angle, NDVISWIR, and land-type percentage from an ancillary database. The blue-red SRP adds dependencies on the scattering angle and NDVISWIR. The new SRPs improve the AOD retrieval of ABIE in terms of overall less bias and mitigation of the overestimation around local noon. The average bias of DT AOD compared to AERONET AOD shows a reduction from 0.08 to 0.03, while the bias of local solar noon decreases from 0.12 to 0.03.The agreement between DT and AERONET AOD is established through regression slope of 1.06 and y-intercept of 0.01with correlation coefficient is0.74. By using the new SRP, the percentage of data falling within the expected error range (±0.05 + 15%) is notably risen from 54% to 78%.

Mijin Kim↗

NASA NTRS1993

Convective and stratiform rain: Multichannel microwave sensing over oceans

Measurements made by the Special Sensor Microwave/Imager (SSM/I) radiometer over the oceans, at 19, 37, and 85 GHz in dual polarization, are used to develop a model to classify rain into light-stratiform, moderately convective, and heavy convective types in the mesoscale convective systems (MCS). It is observed that the bulk of the 19- and 37-GHz data are linearly correlated with respect to one another, and generally increase together in brightness as the mean rain rate in the field of view (FOV) of the radiometer increases. However, a significant fraction of the data from these channels departs from this linear relationship, reflecting the nonuniform rain that is convective vs. the relatively light stratiform rain. It is inferred from the SSM/I data, in a MCS, when the slope dT sub 3/dT sub 19 is greater than unity there are optically thin clouds which produce light uniform rain. On the other hand, when dT sub 3/dT sub 19 is close to unity, the rain cells have an open structure and correspond to the convective type of rain. The openings between the cells are apparently a result of the downdrafts and/or entrainment. Relatively low values of 85-GHz brightness temperatures that are present when dT sub 37/dT sub 19 is close to unity support these views and, in addition, leads us to conclude that when the convection is heavy this brightness temperature decreases due to scattering by hydrometeors. On the basis of this explanation of the SSM/I data, an empirical rain retrieval algorithm is developed. Radar backscatter observations over the Atlantic Ocean next to Florida are used to demonstrate the applicability of this method. Three monthly mean maps of rainfall over the oceans from 50 degrees N to 50 degrees S, are presented to illustrate the ability of this method to sense seasonal and interannual variations of rain.

Prabhakara, C.↗

NASA NTRS1993

Double troughs in broad absorption line quasars and Ly-alpha-N V line-locking

It was investigated whether the double trough (DT) structure in the mean C IV BAL trough reported by Weymann et al. (1991) is real or due to statistical fluctuations of BAL troughs over random outflow velocities in a limited sample. A sample of 72 BAL QSOs with C IV BAL troughs was analyzed. It is found that only 22 percent of the sample explicitly exhibits the DT feature; when present the DTs are deep. A Monte Carlo simulation of the mean C IV BAL suggests that the DT feature is real at only the 95-98 percent level.

Korista, Kirk T.↗

NASA NTRS2002

A Novel Approach to Rotorcraft Damage Tolerance

Damage-tolerance methodology is positioned to replace safe-life methodologies for designing rotorcraft structures. The argument for implementing a damage-tolerance method comes from the fundamental fact that rotorcraft structures typically fail by fatigue cracking. Therefore, if technology permits prediction of fatigue-crack growth in structures, a damage-tolerance method should deliver the most accurate prediction of component life. Implementing damage-tolerance (DT) into high-cycle-fatigue (HCF) components will require a shift from traditional DT methods that rely on detecting an initial flaw with nondestructive inspection (NDI) methods. The rapid accumulation of cycles in a HCF component will result in a design based on a traditional DT method that is either impractical because of frequent inspections, or because the design will be too heavy to operate efficiently. Furthermore, once a HCF component develops a detectable propagating crack, the remaining fatigue life is short, sometimes less than one flight hour, which does not leave sufficient time for inspection. Therefore, designing a HCF component will require basing the life analysis on an initial flaw that is undetectable with current NDI technology.

Forth, Scott C.↗

NASA NTRS2007

Recent Advances in Durability and Damage Tolerance Methodology at NASA Langley Research Center

Durability and damage tolerance (D&DT) issues are critical to the development of lighter, safer and more efficient aerospace vehicles. Durability is largely an economic life-cycle design consideration whereas damage tolerance directly addresses the structural airworthiness (safety) of the vehicle. Both D&DT methodologies must address the deleterious effects of changes in material properties and the initiation and growth of damage that may occur during the vehicle s service lifetime. The result of unanticipated D&DT response is often manifested in the form of catastrophic and potentially fatal accidents. As such, durability and damage tolerance requirements must be rigorously addressed for commercial transport aircraft and NASA spacecraft systems. This paper presents an overview of the recent and planned future research in durability and damage tolerance analytical and experimental methods for both metallic and composite aerospace structures at NASA Langley Research Center (LaRC).

Ransom, J. B.↗

NASA NTRS2010

Middle Atmosphere Dynamics with Gravity Wave Interactions in the Numerical Spectral Model: Tides and Planetary Waves

As Lindzen (1981) had shown, small-scale gravity waves (GW) produce the observed reversals of the zonal-mean circulation and temperature variations in the upper mesosphere. The waves also play a major role in modulating and amplifying the diurnal tides (DT) (e.g., Waltersheid, 1981; Fritts and Vincent, 1987; Fritts, 1995a). We summarize here the modeling studies with the mechanistic numerical spectral model (NSM) with Doppler spread parameterization for GW (Hines, 1997a, b), which describes in the middle atmosphere: (a) migrating and non-migrating DT, (b) planetary waves (PW), and (c) global-scale inertio gravity waves. Numerical experiments are discussed that illuminate the influence of GW filtering and nonlinear interactions between DT, PW, and zonal mean variations. Keywords: Theoretical modeling, Middle atmosphere dynamics, Gravity wave interactions, Migrating and non-migrating tides, Planetary waves, Global-scale inertio gravity waves.

Mayr, Hans G.↗

NASA NTRS2014

Model-Based Estimation of Sampling-Caused Uncertainty in Aerosol Remote Sensing for Climate Research Applications

To evaluate the effect of sampling frequency on the global monthly mean aerosol optical thickness (AOT), we use 6 years of geographical coordinates of Moderate Resolution Imaging Spectroradiometer (MODIS) L2 aerosol data, daily global aerosol fields generated by the Goddard Institute for Space Studies General Circulation Model and the chemical transport models Global Ozone Chemistry Aerosol Radiation and Transport, Spectral Radiationtransport Model for Aerosol Species and Transport Model 5, at a spatial resolution between 1.125 deg × 1.125 deg and 2 deg × 3◦: the analysis is restricted to 60 deg S-60 deg N geographical latitude. We found that, in general, the MODIS coverage causes an underestimate of the global mean AOT over the ocean. The long-term mean absolute monthly difference between all and dark target (DT) pixels was 0.01-0.02 over the ocean and 0.03-0.09 over the land, depending on the model dataset. Negative DT biases peak during boreal summers, reaching 0.07-0.12 (30-45% of the global long-term mean AOT). Addition of the Deep Blue pixels tempers the seasonal dependence of the DT biases and reduces the mean AOT difference over land by 0.01-0.02. These results provide a quantitative measure of the effect the pixel exclusion due to cloud contamination, ocean sun-glint and land type has on the MODIS estimates of the global monthly mean AOT. We also simulate global monthly mean AOT estimates from measurements provided by pixel-wide along-track instruments such as the Aerosol Polarimetry Sensor and the Cloud-Aerosol LiDAR with Orthogonal Polarization. We estimate the probable range of the global AOT standard error for an along-track sensor to be 0.0005-0.0015 (ocean) and 0.0029-0.01 (land) or 0.5-1.2% and 1.1-4% of the corresponding global means. These estimates represent errors due to sampling only and do not include potential retrieval errors. They are smaller than or comparable to the published estimate of 0.01 as being a climatologically significant change in the global mean AOT, suggesting that sampling density is unlikely to limit the use of such instruments for climate applications at least on a global, monthly scale.

long-term variability↗

NASA NTRS2016

Combing Visible and Infrared Spectral Tests for Dust Identification

The MODIS Dark Target aerosol algorithm over Ocean (DT-O) uses spectral reflectance in the visible, near-IR and SWIR wavelengths to determine aerosol optical depth (AOD) and Angstrom Exponent (AE). Even though DT-O does have "dust-like" models to choose from, dust is not identified a priori before inversion. The "dust-like" models are not true "dust models" as they are spherical and do not have enough absorption at short wavelengths, so retrieved AOD and AE for dusty regions tends to be biased. The inference of "dust" is based on postprocessing criteria for AOD and AE by users. Dust aerosol has known spectral signatures in the near-UV (Deep blue), visible, and thermal infrared (TIR) wavelength regions. Multiple dust detection algorithms have been developed over the years with varying detection capabilities. Here, we test a few of these dust detection algorithms, to determine whether they can be useful to help inform the choices made by the DT-O algorithm. We evaluate the following methods: The multichannel imager (MCI) algorithm uses spectral threshold tests in (0.47, 0.64, 0.86, 1.38, 2.26, 3.9, 11.0, 12.0 micrometer) channels and spatial uniformity test [Zhao et al., 2010]. The NOAA dust aerosol index (DAI) uses spectral contrast in the blue channels (412nm and 440nm) [Ciren and Kundragunta, 2014]. The MCI is already included as tests within the "Wisconsin" (MOD35) Cloud mask algorithm.

aerosol optical depth↗

NASA NTRS2020

A Digital Twin Feasibility Study (Part II):Non-Deterministic Predictions of Fatigue Life Using In-Situ Diagnostics and Prognostics

The Digital Twin (DT) concept has the potential to revolutionize the way systems and their components are designed, managed, maintained, and operated across a vast number of fields from engineering to healthcare. The focus of this work is the implementation of DT for the health management of fatigue critical structures. This paper is the second part of a two-part series. The first of the series demonstrated the use of multi-scale, initiation-to-failure crack growth modeling to form non-deterministic predictions of fatigue life. In this second part, a general method for reducing uncertainty in fatigue life predictions is presented that couples in-situ diagnostics and prognostics in a probabilistic framework. Monte Carlo methods and high-fidelity finite element models are used to (i) generate probabilistic estimates of crack state throughout the life of the same geometrically complex test specimen and (ii) predict fatigue life with decreasing uncertainty as more of these diagnoses are obtained. The ability to predict accurately and in the presence of uncertainty is demonstrated, suggesting that the proposed DT method is feasible for fatigue life prognosis and should be pursued further with a focus on increasing application realism.

Patrick E Leser↗

NASA NTRSDate not supplied

The Dark Target aerosol retrieval algorithm applied to Low Earth Orbit and GEOstationary imagers: progress towards an integrated LEO-GEO view of global aerosol

The relatively simple dark-target (DT) aerosol retrieval algorithm provides products of spectral aerosol optical depth (AOD) from measurements of multi-spectral reflectance in visible, near-infrared and shortwave infrared wavelength bands. Originally developed for Moderate-resolution Imaging Spectroradiometer (MODIS aboard Terra and Aqua) in Low-Earth Orbit (LEO), DT has been ported to Visible Infrared Imaging Suite (VIIRS aboard Suomi-NPP and NOAA-20, also in LEO), to enhanced-MODIS Airborne Simulator (eMAS, on an airborne platform), and now to sensors in GEOstationary orbit (Advanced Himawari Imager - AHI aboard Himawari-8 and Advanced Baseline Imagers – ABI aboard GOES-16 and 17). Together, these new datasets not only extend upon the 20+ year MODIS aerosol record, but also expand the temporal sampling and/or spatial resolution. Between July and October of 2019, NASA participated in two field experiments on opposite sides of the globe. These included FIREX-AQ which focused on fire and smoke in the Western U.S., and then CAMP2EX which targeted aerosol/cloud interactions around the Philippines. We have performed DT aerosol retrievals on all images from all sensors during these three months, validated against ground observations from stationary and mobile sunphotometer sites, and have begun to develop a synergy that represents semi-global observations every half hour. The resulting aerosol products are being used as context and for model assimilation, thus providing the framework for more complete characterization of global aerosol transport and lifecycle. Here, we report on progress, as well as remaining challenges such as data management, computer processing, and accounting for differences between GEO and LEO observation geometry and surface reflectance parameterization.

dark target↗

NASA NTRS2021

First retrieval of AOD at fine-resolution over shallow and turbid coastal waters from MODIS

The widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Dark-Target (DT) aerosol product fails to accurately retrieve Aerosol Optical Depth (AOD) over shallow and turbid Coastal Waters (CWs). To fill in gaps, and to improve land to ocean AOD continuity, we developed a coastal water retrieval algorithm at a spatial resolution of 1 km (CW-1km). CW-1km relies on observed top-of-atmosphere reflectance at 2.1 μm (ρ2.1), both to derive AOD and to perform a spatial variation test that enhances the existing DT masks for clouds and land. We show that the CW-1km improves spatial continuity of AOD between land, coast, and open ocean, while also increasing AOD product availability by 47.0%. Comparing with 15 years of marine aerosol network measurements, CW-1km AODs are validated to have a normalized mean bias of 1.0%, which is much smaller than 17.6% for the original DT product.

MODIS AOD↗

NASA NTRS2020

FOSSIL—Finding Our Cosmic Roots

FOSSIL (Fragments from the Origins of the Solar System and our Interstellar Locale) is a concept to explore the largest solar system object visible to the unaided eye—the zodiacal cloud. The cloud’s many dust particles are each a tiny time capsule from a comet or asteroid. The FOSSIL concept is for the in situ compositional analysis of interplanetary dust particles, and also of the particles passing through the solar system from interstellar space. By measuring the zodiacal and interstellar particles’ velocity vectors and compositions, the approach resolves fundamental questions about the solar system’s origins. The FOSSIL concept is to: (1) Discover whether today’s local interstellar dust matches the composition of the feedstock from which the solar system formed; (2) Determine whether comets’ fine-grained component preserves unprocessed pre-solar dust or shows signs of processing in the early solar system; and (3) Learn whether comets’ and asteroids’ organic material share a common source or formed from distinct reservoirs. The FOSSIL concept is based on the use of a Dust Telescope (DT) with the capability to measure the composition and the velocity vector of dust particles to unambiguously separate interstellar from interplanetary particles, and identify a subset of zodiacal particles that are exclusively cometary. In the DT, particles pass through the trajectory sensor unharmed and impact a target plate, where they are vaporized and partially ionized. The ions are electrostatically focused onto a detector where the time-of-flight mass spectrum is recorded, enabling measurement of the composition of the interstellar solids, detection of cometary minerals altered by high temperatures or exposure to liquid water in the early solar system, and the characterization of the organic materials from the comets and asteroids that are still being delivered to the Earth today. FOSSIL’s objectives crosscut several disciplines with planetary science. Astrophysics interests lie in understanding interstellar solar matter and the only debris disk accessible in situ. Heliophysics interest is in the verification of large-scale heliospheric magnetic field models by the measured effect on the motion of electrically charged interstellar dust. Reporting the makeup of interplanetary dust that ablates in our atmosphere is valuable to Earth sciences. FOSSIL’s DT is scalable and can be accommodated to a variety of mission opportunities, without restriction on launch dates, and for a large number of possible orbits.

Turner, Neal↗

NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

machine learning↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗

NASA NTRSDate not supplied

arcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

machine learning↗

NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

Recession tracking↗