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NASA NTRS2005

NASA's Earth Observations of the Global Environment

A birds eye view of the Earth from afar and up close reveals the power and magnificence of the Earth and juxtaposes the simultaneous impacts and powerlessness of humankind. The NASA Electronic Theater presents Earth science observations and visualizations in an historical perspective. Fly in from outer space to Africa and Cape Town. See the latest spectacular images from NASA & NOAA remote sensing missions like Meteosat, TRMM, Landsat 7, and Terra, which will be visualized and explained in the context of global change. See visualizations of global data sets currently available from Earth orbiting satellites, including the Earth at night with its city lights, aerosols from biomass burning in the Middle East and Africa, and retreat of the glaciers on Mt. Kilimanjaro. See the dynamics of vegetation growth and decay over Africa over 17 years. New visualization tools allow us to roam & zoom through massive global mosaic images including Landsat and Terra tours of Africa and South America, showing land use and land cover change from Bolivian highlands. Spectacular new visualizations of the global atmosphere & oceans are shown. See massive dust storms sweeping across Africa and across the Atlantic to the Caribbean and Amazon basin. See ocean vortexes and currents that bring up the nutrients to feed tiny phytoplankton and draw the fish, pant whales and fisher- man. See how the ocean blooms in response to these currents and El Nino/La Nifia. We will illustrate these and other topics with a dynamic theater-style presentation, along with animations of satellite launch deployments and orbital mapping to highlight aspects of Earth observations from space.

King, Michael D.↗

NASA NTRS2023

Constructing a Raman Spectroscopy System

The project aims to understand how Raman spectroscopy works by constructing a system based on existing concepts of the technique and utilizing NASA’s available tools and software to analyze the collected data. Once the data is successfully interpreted and proven, other improvements to the assembled system will then be explored. Part of the paper goes through several phases of construction of the system and improvements made. Nevertheless, the data obtained and the results were unsatisfactory as they need to be further analyzed for accuracy. The ultimate ambition was to be able to assist NASA with its objective of implementing a remote and compact Raman spectroscopy system on a rover for future planetary surface characterization such as Mars and lunar surfaces via a Raman spectroscopy system that the agency’s Senior Engineer, Dr. M. Nurul Abedin, has been working on since the early 2000s [2,3,4].

Jake Nguyen↗

NASA NTRSDate not supplied

A Ground-Based Network for Improved Validation of Satellite Carbon Dioxide and Methane Observations Over the Eastern United States

Satellite observations of greenhouse gases (GHGs), notably carbon dioxide and methane, over the Eastern United States, are currently only validated indirectly and/or sporadically. There are only four existing routine, ground-based remote sensing locations in the United States suitable for validation of satellite GHG observations: Edwards and Pasadena, CA, Lamont, OK, and Park Falls, WI as part of the Total Carbon Column Observing Network (TCCON). Among other efforts, e.g. the Network for the Detection of Atmospheric Composition Change (NDACC) and EM27/SUN deployments led by the University of Toronto, the only sites west of the Mississippi River are Park Falls, WI and Toronto, ON. The only remaining validation tools, vicarious calibration and airborne campaigns, are sporadic in space and/or time and thus coincide with only a small subsample of available soundings and conditions. As a result, satellite GHG observations over the east coast of the United States, home to more than half of its population, lack a consistent, widespread means of validation. We describe an ongoing effort to position 8 EM27/SUN spectrometers along the Eastern Seaboard over the next two years. The goals of this effort are to improve both satellite validation and our understanding of human and natural influences on the carbon cycle of the Eastern US, the former enabling the latter. This work is intended to augment past, ongoing, and future inter-agency programs, e.g., the NIST Urban Testbed, routine aircraft and aircore sampling by NOAA, and NASA’s Atmospheric Carbon and Transport (ACT)-America sub-orbital campaign, in particular by offering information on broader time and spatial scales than what is already available while maintaining the high-accuracy constraints of in situ data. We will present early analysis including siting considerations to capture local and/or background conditions and comparison to NASA’s Goddard Earth Observing System (GEOS) modeling and assimilation systems. This includes a 40-day, 3-km horizontal resolution global simulation of early 2020 and a 50-km retrospective analysis of Orbiting Carbon Observatory 2 (OCO-2) observations over 2015-present. Both are valuable tools for analyzing expected and observed signals and are useful boundary conditions for yet higher-resolution studies.

Brad Weir↗

NASA NTRSDate not supplied

ML/AI Applications to the Atmosphere Science Data and Simulations (Demonstration and Vision)

Artificial Intelligence has been recognized as one of the most powerful tools for scientific research. It has a wide range of applications in atmospheric science and plays a significant role in advancing our understanding of the Earth-Atmosphere system, as well as improving our ability to monitor, predict, and respond to atmospheric observations and simulations. Different AI architectures can help us handle various problems according to our research goals. In this presentation, we will introduce several use cases and models for applying computer vision to active remote sensing data from CALIOP, analyzing cloud dynamical relationships for climate model parameterizations, and building surrogate models for lidar simulations. The presentation will highlight the advantages that AI offers compared to traditional analytical methods. By assembling pieces of the multidimensional atmospheric science puzzle, we can integrate our knowledge from observations to simulations.

Shan Zeng↗

DOE OSTI2023

Using Satellite‐Derived Fire Arrival Times for Coupled Wildfire‐Air Quality Simulations at Regional Scales of the 2020 California Wildfire Season

Abstract Wildfire frequency has increased in the Western US over recent decades, driven by climate change and a legacy of forest management practices. Consequently, human structures, health, and life are increasingly at risk due to wildfires. Furthermore, wildfire smoke presents a growing hazard for regional and national air quality. In response, many scientific tools have been developed to study and forecast wildfire behavior, or test interventions that may mitigate risk. In this study, we present a retrospective analysis of 1 month of the 2020 Northern California wildfire season, when many wildfires with varying environments and behavior impacted regional air quality. We simulated this period using a coupled numerical weather prediction model with online atmospheric chemistry, and compare two approaches to representing smoke emissions: an online fire spread model driven by remotely sensed fire arrival times and a biomass burning emissions inventory. First, we quantify the differences in smoke emissions and timing of fire activity, and characterize the subsequent impact on estimates of smoke emissions. Next, we compare the simulated smoke to surface observations and remotely sensed smoke; we find that despite differences in the simulated smoke surface concentrations, the two models achieve similar levels of accuracy. We present a detailed comparison between the performance and relative strengths of both approaches, and discuss potential refinements that could further improve future simulations of wildfire smoke. Finally, we characterize the interactions between smoke and meteorology during this event, and discuss the implications that increases in regional smoke may have on future meteorological conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Connecting speeds, directions and arrival times of 22 coronal mass ejections from the sun to 1 AU

Forecasting the in situ properties of coronal mass ejections (CMEs) from remote images is expected to strongly enhance predictions of space weather and is of general interest for studying the interaction of CMEs with planetary environments. We study the feasibility of using a single heliospheric imager (HI) instrument, imaging the solar wind density from the Sun to 1 AU, for connecting remote images to in situ observations of CMEs. We compare the predictions of speed and arrival time for 22 CMEs (in 2008-2012) to the corresponding interplanetary coronal mass ejection (ICME) parameters at in situ observatories (STEREO PLASTIC/IMPACT, Wind SWE/MFI). The list consists of front- and backsided, slow and fast CMEs (up to 2700 km s{sup –1}). We track the CMEs to 34.9 ± 7.1 deg elongation from the Sun with J maps constructed using the SATPLOT tool, resulting in prediction lead times of –26.4 ± 15.3 hr. The geometrical models we use assume different CME front shapes (fixed-Φ, harmonic mean, self-similar expansion) and constant CME speed and direction. We find no significant superiority in the predictive capability of any of the three methods. The absolute difference between predicted and observed ICME arrival times is 8.1 ± 6.3 hr (rms value of 10.9 hr). Speeds are consistent to within 284 ± 288 km s{sup –1}. Empirical corrections to the predictions enhance their performance for the arrival times to 6.1 ± 5.0 hr (rms value of 7.9 hr), and for the speeds to 53 ± 50 km s{sup –1}. These results are important for Solar Orbiter and a space weather mission positioned away from the Sun-Earth line.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1988

Space-based multifunctional end effector systems functional requirements and proposed designs

The end effector is an essential element of teleoperator and telerobot systems to be employed in space in the next decade. The report defines functional requirements for end effector systems to perform operations that are currently only feasible through Extra-Vehicular Activity (EVA). Specific tasks and functions that the end effectors must be capable of performing are delineated. Required capabilities for forces and torques, clearances, compliance, and sensing are described, using current EVA requirements as guidelines where feasible. The implications of these functional requirements on the elements of potential end effector systems are discussed. The systems issues that must be considered in the design of space-based manipulator systems are identified; including impacts on subsystems tightly coupled to the end effector, i.e., control station, information processing, manipulator arm, tool and equipment stowage. Possible end effector designs are divided into three categories: single degree-of-freedom end effectors, multiple degree of freedom end effectors, and anthropomorphic hands. Specific design alternatives are suggested and analyzed within the individual categories. Two evaluations are performed: the first considers how well the individual end effectors could substitute for EVA; the second compares how manipulator systems composed of the top performers from the first evaluation would improve the space shuttle Remote Manipulator System (RMS) capabilities. The analysis concludes that the anthropomorphic hand is best-suited for EVA tasks. A left- and right-handed anthropomorphic manipulator arm configuration is suggested as appropriate to be affixed to the RMS, but could also be used as part of the Smart Front End for the Orbital Maneuvering Vehicle (OMV). The technical feasibility of the anthropomorphic hand and its control are demonstrated. An evolutionary development approach is proposed and approximate scheduling provided for implementing the suggested manipulator systems in time for space stations operations in the early 1990s.

Mishkin, A. H.↗

NASA NTRS1993

Multivariable control of the Space Shuttle Remote Manipulator System using linearization by state feedback

This paper demonstrates an approach to nonlinear control system design that uses linearization by state feedback to allow faster maneuvering of payloads by the Shuttle Remote Manipulator System (SRMS). A nonlinear feedback law is defined to cancel the nonlinear plant dynamics so that a linear controller can be designed for the SRMS. First a nonlinear design model was generated via SIMULINK. This design model included nonlinear arm dynamics derived from the Lagrangian approach, linearized servo model, and linearized gearbox model. The current SRMS position hold controller was implemented on this system. Next, a trajectory was defined using a rigid body kinematics SRMS tool, KRMS. The maneuver was simulated. Finally, higher bandwidth controllers were developed. Results of the new controllers were compared with the existing SRMS automatic control modes for the Space Station Freedom Mission Build 4 Payload extended on the SRMS.

Gettman, Chang-Ching L.↗

NASA NTRS2002

EZStream: Distributing Live ISS Experiment Telemetry via Internet

This paper will present the high-level architecture and components of the current version of EZStream as well as the product direction & enhancements to be incorporated through a Phase II grant. Security will be addressed such as data encryption and user login. Remote user devices will be discussed including web browsers on PC's and displays on PDA's and smart cell phones. The interaction between EZStream and TReK will be covered as well as the eventuality of EZStream to receive and parse binary data streams directly. This makes EZStream beneficial to both the International Partners and non-NASA applications. The options of developing client-side display web pages will be addressed and the development of new tools to allow creation of display web pages by non-programmers.

Myers, Gerry↗

NASA NTRS2015

Ocean Color Measurements with the Operational Land Imager on Landsat-8: Implementation and Evaluation in SeaDAS

The Operational Land Imager (OLI) is a multispectral radiometer hosted on the recently launched Landsat8 satellite. OLI includes a suite of relatively narrow spectral bands at 30 m spatial resolution in the visible to shortwave infrared, which makes it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of spaceborne multispectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote sensing reflectance (Rrs; sr−1). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents, such as the concentration of the phytoplankton pigment chlorophyll a.

Measurements↗

DOE OSTI2019

Robotic Non-destructive Test of Concrete Structures with GPR, Impact Echo and 3D Vision - 19540

Structural members come across with various internal defects that cannot be evaluated visually. Instead, hand-held nondestructive sensors are used to detect such defects. In many situations, the hand-held NDT is not possible in high-risk structures because of limited access or safety issues. Therefore, Robotic NDT plays major role in assessing such structures. Robot Operating Systems (ROS) is a powerful open source tool, which is used to program and control robots and other devices enabling one to use sensors, devices and new functionalities such as autonomous navigation, visual perception. For the research presented, ROS is used to program the Sawyer robot arm manufactured by Rethink Robotics. The control system for the robot ensures the ability to adapt to different weights of integrated measuring devices and measured force applied against the wall with a wheeled measuring device while making a remote sweep. Twelve reinforced concrete (RC) beams built with characterized defects are evaluated with the robotic arm integrated with the measuring devices. The RC beams are characterized by voids, corrosion and debonding problems of rebars. The Impact Echo and Ground Penetrating Radar (GPR) devices attached to the robot arm are used to measure the material properties of the concrete beams. The robot arm ensures consistency and uniformity of measurement even in obscure locations. Infrared cameras are used to obtain 3D images of the surfaces of the concrete structures. Roughness is analyzed using computer vision techniques prior to the use of other sensors that are in touch with the surfaces. The computer vision is also used to handle the images collected from GPR sensor. Hand-held NDE is also performed to verify the results obtained by robotic examination. (authors)

42 ENGINEERING↗

DOE OSTI2022

Informing Nature-based Climate Solutions for the United States with the best-available science

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. Furthermore, we outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRSDate not supplied

Modeling Atmospheric Science Knowledge from Research Publications

NASA Earth Science Data Centers contain enormous amounts of remote sensing digital data. It is often a significant challenge for users to find data suitable for their research topic in these vast archives. One of the approaches is the usage-driven dataset discovery, where users seek publications on projects similar to their intended study. For this approach to be effective, users need a clear connection between the underlying data in the publications and the study objectives; this is not often apparent to non-expert users. Tools and methodologies that can help facilitate and organize these connections are therefore valuable for creating improved knowledge mappings, which can be further used by search engines to suggest data or publications best tailored to a user’s specific research goal. As an illustration of these challenges, in this work we focus on the atmospheric chemistry processes related to Earth environmental impacts such as ozone depletion, aerosols, smog formation, acid rain, and radiative forcing. We further limit our study to publications that use data from the Microwave Limb Sounder (MLS) instrument flown on the Aura Earth Observing System. To create knowledge representations of science carried out in these publications, we use existing ontologies such as the Global Change Master Directory (GCMD) and Semantic Web for Earth and Environmental Terminology (SWEET). These ontologies together encompass term dictionaries that include measured variables, names of molecules or radicals, mission and instrument names, locations, action words, among many others. Based on these terms acknowledge graph database was populated with the terms retrieved from scientific publications that study atmospheric chemistry. These databases can be used to further enhance the automation of knowledge discovery and facilitate machine learning and artificial intelligence algorithms or applications. These tools and methods can also be extended to apply to content from other related Earth science domains.

Irina Gerasimov↗

DOE OSTI2016

AiiDA: automated interactive infrastructure and database for computational science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA’s design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA’s design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.

Materials Science↗

DOE OSTI2024

High-Throughput Data Processing at FRIB Using ESnet

Real-time or nearly real-time (nearline) data processing methods are critical tools as detector technologies and data acquisition (DAQ) systems allow for higher data rates and volumes. The introduction of the energy sciences network (ESnet), a U.S. Department of Energy (DOE) supported high-speed network for scientific research, creates opportunities to leverage the computing power of DOE facilities like the National Energy Research Scientific Computing Center (NERSC). As a first step toward realizing a DOE Office of Science Integrated Research Infrastructure (IRI) pattern, an automated workflow was developed to remotely process data obtained from a nuclear physics experiment at the Facility for Rare Isotope Beams (FRIB) at NERSC with data transferred between FRIB and NERSC over ESnet. The workflow demonstrated the ability to process one week’s worth of experimental data in approximately 90 min and was used successfully for nearline analysis during a recently completed FRIB experiment. Here, a summary of the workflow development and results of recent demonstrations will be presented.

Data processing↗

NASA NTRS2003

Modeling and Visualizing Uncertainty in Continuous Variables Predicted using Remotely Sensed Data

The use of remotely sensed images to map continuous biophysical variables, such as those related to terrestrial vegetation amount, sea surface temperature, and many other targets of NASA s Earth Observing System (EOS), includes variable, parametric, positional, spatial support and structural sources of uncertainty. A complete description of uncertainty will lead to a probability distribution at each location, allowing the exploration of the spatial dimension of uncertainty, that is, where the field is not well quantified. To achieve this purpose, convenient visualization tools are required. We have produced such a tool, called PDFVis, that facilitates the display of probability density functions (pdfs) on a per-grid-cell basis. The density estimate from Monte-Carlo generated realizations is interactively displayed as well as parametric and non-parametric summaries of the pdf field (such as mean, median, quartiles, standard deviation, number of modes, and locations of modes). Shaded surface renderings of pdfs along a transect can also be projected onto a plane. This tool will become more useful as richer descriptions of spatial uncertainty become available.

Dungan, Jennifer L.↗

NASA NTRS2007

Infrared Camera Characterization of Bi-Propellant Reaction Control Engines during Auxiliary Propulsion Systems Tests at NASA's White Sands Test Facility in Las Cruces, New Mexico

This paper describes the application of a FUR Systems A40M infrared (IR) digital camera for thermal monitoring of a Liquid Oxygen (LOX) and Ethanol bi-propellant Reaction Control Engine (RCE) during Auxiliary Propulsion System (APS) testing at the National Aeronautics & Space Administration's (NASA) White Sands Test Facility (WSTF) near Las Cruces, New Mexico. Typically, NASA has relied mostly on the use of ThermoCouples (TC) for this type of thermal monitoring due to the variability of constraints required to accurately map rapidly changing temperatures from ambient to glowing hot chamber material. Obtaining accurate real-time temperatures in the JR spectrum is made even more elusive by the changing emissivity of the chamber material as it begins to glow. The parameters evaluated prior to APS testing included: (1) remote operation of the A40M camera using fiber optic Firewire signal sender and receiver units; (2) operation of the camera inside a Pelco explosion proof enclosure with a germanium window; (3) remote analog signal display for real-time monitoring; (4) remote digital data acquisition of the A40M's sensor information using FUR's ThermaCAM Researcher Pro 2.8 software; and (5) overall reliability of the system. An initial characterization report was prepared after the A40M characterization tests at Marshall Space Flight Center (MSFC) to document controlled heat source comparisons to calibrated TCs. Summary IR digital data recorded from WSTF's APS testing is included within this document along with findings, lessons learned, and recommendations for further usage as a monitoring tool for the development of rocket engines.

Holleman, Elizabeth↗