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NASA NTRS2017

A Framework for Extending the Science Traceability Matrix: Application to the Planned Europa Mission

One of the most critical functions of the systems engineering requirements process for a large multi-instrument science-driven space mission is to successfully communicate customer expectations into a comprehensive and traceable science requirements flowdown. These requirements are essential to communicating the constraints on the scope of the science investigations and clarifying how multiple instruments contribute to a given science goal. They also provide insight into how the science goals of the whole mission are affected by design choices. There is little specific guidance available on best practices for developing this science-driven flowdown. A unified Science Traceability Matrix (USTM) contains a significant amount of information that can be leveraged for that purpose, but the USTM was not designed to directly produce a complete science requirements flowdown. Thus, starting with the principles codified in a USTM, the authors propose a framework that directly maps into the requirements flowdown and supports broader systems engineering processes while retaining its meaning to the science team. This Science Traceability and Alignment Framework, or STAF, defines a set of common definitions and valid relationships to structure communication across the project. In addition, STAF populates a network of information that can be useful to support complex mission analysis activities such as fault protection. This work discusses the highest-level implementation of the STAF, the project-domain or P-STAF, which describes an approach to decomposing customer requirements into science requirements. The planned Europa Mission is used as a case study for the implementation of this framework and its potential benefits to a project.

Susca, Sara↗

NASA NTRS2017

A Framework for Writing Science Measurement Requirements and its Application to the Europa Multiple Flyby Mission

Science-engineering communication is critical to the success of any science-driven mission. The process of building this understanding relies on a shared language for communicating science needs and engineering results, which can be particularly difficult on large space-science missions where many different institutions contribute to the science team. The Science Traceability Matrix can be used to formalize this communication pathway, but it has limited use in the development of the science requirements flow down, and vary in format, scope and content from mission to mission. There are many guidelines on developing well-constructed requirements in general, but very little is published on how to actually write these science-driven requirements in a systematic way. This paper discusses the measurement-domain science traceability and alignment framework, or M-STAF, which was developed to help frame the conversation between scientists and engineers in the development of science measurement requirements. The MSTAF provides a common language that can be used to ensure consistency across instruments, completeness in the coverage of the requirements, and traceability of the engineering work to the science objectives of the project. This work discusses the framework in the context of other communication tools, how it can be implemented on a flight project, and provides examples of how it might be used to improve the measurement requirements set for a project. The general framework is presented through the lens of its potential application on the planned Europa Mission.

Oaida, Bogdan V.↗

NASA NTRS1999

TRMM Realtime Data Production, Transfer, and Future Plans

The Tropical Rainfall Measuring Mission, a joint US NASA and Japan NASDA mission, was launched on November 27, 1997 from Tanegashima Island, Japan on a NASDA provided H-II launch vehicle. The TRMM objectives are: to obtain and study multi-year science data sets of tropical and sub- tropical rainfall measurement; to understand how interactions between the sea, air, and land masses produce changes in global rainfall and climate; to help improve modeling of tropical rainfall processes; to test, evaluate, and improve the performance of satellite rainfall estimates measurements and techniques. The routine production and quick distribution of standard products provides an important underpinning to these objectives. The determination of standard TRMM algorithms and the requirements for routine production and distribution fall within the authority of the joint US-Japan Science Team. In April of 1998, the Joint TRMM Science team requested that TRMM data be made available in near real-time to approved international agencies and modelers. This request was an addition to the routine production of the standard TRMM data products. The science team requested that near-realtime products be as small as possible to facilitate their transfer via networks. Indeed, it was determined that only network transfer would be available. This presentation provides an overview of the type of products available from the realtime system and how the TRMM system was developed at the lowest cost in the shortest amount of time. It then describes the real-time data flow. The presentation also summarizes how the science quality of real-time data is maintained. It describes how interested groups can get access to this data and the importance of networks for the future of real-time data expansion. The presentation concludes real-time possibilities and improvements possible as part of the proposed Global Precipitation Mission. High speed network access availability is an important catalyst to real-time data flow during the proposed mission.

Stocker, Erich Franz↗

NASA NTRS1996

NASA Science Institutes Plan. Report of the NASA Science Institutes Team: Final Publication (Incorporating Public Comments and Revisions)

This NASA Science Institute Plan has been produced in response to direction from the NASA Administrator for the benefit of NASA Senior Management, science enterprise leaders, and Center Directors. It is intended to provide a conceptual framework for organizing and planning the conduct of science in support of NASA's mission through the creation of a limited number of science Institutes. This plan is the product of the NASA Science Institute Planning Integration Team (see Figure A). The team worked intensively over a three-month period to review proposed Institutes and produce findings for NASA senior management. The team's activities included visits to current NASA Institutes and associated Centers, as well as approximately a dozen non-NASA research Institutes. In addition to producing this plan, the team published a "Benchmarks" report. The Benchmarks report provides a basis for comparing NASA's proposed activities with those sponsored by other national science agencies, and identifies best practices to be considered in the establishment of NASA Science Institutes. Throughout the team's activities, a Board of Advisors comprised of senior NASA officials (augmented as necessary with other government employees) provided overall advice and counsel.

Source record↗

NASA NTRS2020

Operations Concept for Responding to Urgent Requests for NASA-ISRO Synthetic Aperture Radar (NISAR)

The NASA-ISRO Synthetic Aperture Radar, or NISAR, mission is an Earth-mapping radar observatory to be launched from Sriharikota (India) in 2022. This mission is a collaboration between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO). This spacecraft will carry two instruments that will operate at radar wavelengths (L and Sband) and will provide data for understanding changes in the Earth’s land surface. The scientific data from this mission will revolutionize our understanding of the causes and consequences of land surface changes on Earth, ranging from Solid Earth Deformation in the form of natural hazards like earthquakes, volcanic eruptions and landslides, to ecosystem disturbances, to changes in the cryosphere (measurements of polar ice caps, ice sheets and sea ice). A nominal Reference Observation Plan, that repeats roughly every 12-24 days, developed prior to launch by the NISAR Mission Planning team, in consultation with the Science Team, will form the basis of science data collection by the payload instruments onboard the NISAR observatory after launch. Scheduling of science observations for the mission requires accounting for limited spacecraft resources like onboard data storage, downlink capacity, energy/power, thermal limits and instrument duty cycles. In addition to nominal science data collection, the project has a Level 1 requirement to respond to requests for urgent data acquisition over disaster sites (natural or anthropogenic) by scheduling new acquisitions within 24 hrs of notification and delivering science data within 5 hours of data acquisition. This capability is to be exercised on a ‘best-efforts basis’. While the definition of what constitutes an ‘urgent request’, and how such requests would be submitted to the project, is within the domain of the Science Team, the Mission System team is responsible for developing the baseline operations concept and implementation approach for responding to such requests. Given the ‘best-efforts’ nature of this requirement, a few highlevel guidelines have been developed to help guide the formulation of the operations concept, and are presented in this paper. Requests for urgent response data will be accommodated following the guiding principle of minimal to no impact on nominal science and planned engineering activities. No change in satellite orbit or attitude will be made for urgent response. Restricting response approaches to only changing the downlink and/or ground processing priority for existing observations, and adding new observations only in areas where NISAR will not be nominally imaging, allows for minimal impact on the Reference science Observation Plan. No instrument mode changes will be allowed for urgent response (except for high-priority requests), and no new observations that impact either planned science or engineering activities will be scheduled. Additionally, data requests must fit within the available project resource margins (both spacecraft and ground resources are to be evaluated). Both JPL and ISRO will be involved at various steps of the implementation, irrespective of whether the urgent request is for L-SAR (NASA instrument) or S-SAR (ISRO instrument) or a joint dataset.

Sharma, Priyanka↗

NASA NTRS2019

Using Science Operations Tools and Methods from NASA for Remote Presence Ocean Exploration on Earth: E/V Nautilus Cruise NA108

NASA Ames' Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade.In the last two years, we have participated in SUBSEA, a multi-institution collaborative project. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC). In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule.The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team's ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA's Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island's Graduate School of Oceanography (GSO). ISC is GSO's telepresence operations facility.

Lees, David S.↗

NASA NTRS2020

Conducting Efficient Remote Science and Planning Operations for Ocean Exploration Using Exploration Ground Data Systems (xGDS)

NASA Ames’ Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade. In the last two years, we have participated in SUBSEA, a multi-institution collaborative project*. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC)**. In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule. The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team’s ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. * SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA’s Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island’s Graduate School of Oceanography (GSO).

Cohen, Tamar E.↗

NASA NTRS1992

Design definition of the Laser Atmospheric Wind Sounder (LAWS), phase 2. Volume 2: Final report

Lockheed personnel, along with team member subcontractors and consultants, have performed a preliminary design for the LAWS Instrument. Breadboarding and testing of a LAWS class laser have also been performed. These efforts have demonstrated that LAWS is a feasible Instrument and can be developed with existing state-of-the-art technology. Only a commitment to fund the instrument development and deployment is required to place LAWS in orbit and obtain the anticipated science and operational forecasting benefits. The LAWS Science Team was selected in 1988-89 as were the competing LAWS phase 1/2 contractor teams. The LAWS Science Team developed requirements for the LAWS Instrument, and the NASA/LAWS project office defined launch vehicle and platform design constraints. From these requirements and constraints, the lockheed team developed LAWS Instrument concepts and configurations. A system designed to meet these requirements and constraints is outlined. The LAWS primary subsystem and interfaces - laser, optical, and receiver/processor - required to assemble a lidar are identified. Also identified are the support subsystems required for the lidar to function from space: structures and mechanical, thermal, electrical, and command and data management. The Lockheed team has developed a preliminary design of a LAWS Instrument System consisting of these subsystems and interfaces which will meet the requirements and objectives of the Science Team. This final report provides a summary of the systems engineering analyses and trades of the LAWS. Summaries of the configuration, preliminary designs of the subsystems, testing recommendations, and performance analysis are presented. Environmental considerations associated with deployment of LAWS are discussed. Finally, the successful LAWS laser breadboard effort is discussed along with the requirements and test results.

Wilson, D. J.↗

DOE OSTI2024

Preparing an on-Demand Cloud Processing Workflow for NISAR Ecosystems Science Products

In preparation for the NISAR launch and data collection in 2024, the NISAR Project Science Team is building workflows for each Science Team discipline (Ecosystems, Cryosphere, and Solid Earth). This abstract focuses on the Ecosystem disciplines and the development of on-demand cloud-processing workflows for wetlands inundation, forest biomass, agricultural active crop area, and forest disturbance. The workflow simulates NISAR data using UAVSAR or ALOS-2 Single Look Complex data, which are processed to Level 2 geocoded polarimetric covariance matrix products using InSAR Scientific Computing Environment 3.0 software and to Level 3 science products using the Algorithm Theoretical Basis Documents. In this presentation, we describe these workflows and efforts to improve efficiency and data accessibility by using a cloud processing system. We present preliminary sample products from each Ecosystem discipline: inundation, forest biomass, crop area, and forest disturbance.

Christensen, Alexandra↗

NASA NTRS2015

CHIMAERA System for Cloud Retrievals v 6.0.85

Organizers of the MODIS-VIIRS Science Team Meeting, held May 18-22, 2015 in Silver Spring, MD plan to post the presentations and posters to the NASA MODIS website: http:modis.gsfc.nasa.govsci_teammeetings201505index.php. The MODIS Science Team Meeting is held twice a year, so that the members of the science team may assemble and discuss data they have collected, ideas they have formed, and future issues that apply to the MODIS Mission.

CHIMAERA↗

NASA NTRS2015

Effect of Cloud Fraction on Near-Cloud Aerosol Behavior Based on MODIS and CALIPSO Observations

aerosol retrieval↗

NASA NTRS1996

Market-Based Approaches to Managing Science Return from Planetary Missions

The return of science is the fundamental objective of any planetary mission. However, which constellation of science observations constitute the best return of science is hard to evaluate. Past approaches toward planning science observations have been based on co-location of payload scientists who debate the merits of which investigation had the stronger science. This advocacy approach is time-consuming and does not provide appropriate incentives for science teams to reveal their trade-offs. An alternative approach, currently under evaluation by the Cassini Mission to Saturn, is one based on providing better incentives to the science teams. Incentives can produce better tradeoffs because the individuals who can make the best decisions about which science observations to propose, what resources are required to implement the observations, and which observations are most important are the science team's Principle Investigators (PI) themselves.

mission↗

NASA NTRS2004

SIM PlanetQuest: Science with the Space Interferometry Mission

SIM - the Space Interferometry Mission - will perform precision optical astrometry on objects as faint as R magnitude 20. It will be the first space-based astrometric interferometer, operating in the optical band with a 10-m baseline. The Project is managed by the Jet Propulsion Laboratory, California Institute of Technology, in close collaboration with two industry partners, Lockheed Martin Missiles and Space, and TRW Inc., Space and Electronics Group. Launch of SIM is currently planned for 2009. In its wide-angle astrometric mode, SIM will yield 4 microarcsecond absolute position and parallax measurements. Astrometric planet searches will be done in a narrow-angle mode, with an accuracy of 4 microarcseconds or better in a single measurement. As a pointed rather than a survey instrument, SIM will maintain.its astrometric accuracy down to the faintest, magnitudes, opening up the opportunity for astrometry of active galactic nuclei to better than 10 pas. SIM will define a new astrometric reference frame, using a grid of approximately 1500 stars with positions accurate to 4 microarcseconds. The SIM Science Team comprises the Principal Investigators of ten Key Projects, and five Mission Scientists contributing their expertise to specific areas of the mission. Their science programs cover a wide range of topics in Galactic and extragalactic astronomy. They include: searches for low-mass planets - including analogs to our own solar system - tlie formation and dynamics of our Galaxy, calibration of the cosmic distance scale, and fundamental stellar astrophysics. All of the science observing on SIM is competitively awarded; the Science Team programs total about 40% of the total available, and the remainder will be assigned via future NASA competitions. This report is a compilation of science summaries by members of the Science Team, and it illustrates the wealth of scientific problems that microarcsecond-precision astrometry can contribute to. More information on SIM, including copies of this report, may be obtained from the project web site, at http://sim. jpl.nasa.gov.

Unwin, Stephen↗

NASA NTRS2001

Gridded Hourly Text Products: A TRMM Data Reduction Approach

The quantity of precipitation data from satellite-based observations is a blessing and a curse. The sheer volume of the data makes it difficult for many researchers to use in targeted applications. This volume increases further as algorithm improvements lead to the reprocessing of mission data. In addition to the overall volume of data, the size and format complexity of orbital granules contribute to the difficulty in using all the available data. Finally, the number of different instruments available to measure rainfall and related parameters further contributes to the volume concerns. In summary, we have an embarrassment of riches. The science team of the Tropical Rainfall Measuring Mission (TRMM) recognized this dilemma and has developed a strategy to address it. The TRMM Science Data and Information System (TSDIS) produces, at the direction of the Joint TRMM Science Team, a number of instantaneous rainfall products. The TRMM Microwave Imager (TMI), the Precipitation Radar and a Combined TMI/PR are the key "instruments" used in this production. Each of these products contains an entire orbit of data. The algorithm code computes not just rain rates but a large number of other physical parameters as well as information needed for monitoring algorithm performance. That makes these products very large. For example, a single orbit of TMI rain rate product is 99 MB, a single orbit of the combined product yields a granule that is 158 MB, while the 80 vertical levels of rain information from the PR yields an orbital product of 253 MB. These are large products that are often difficult for science users to electronically transfer to their sites especially if they want a large period of time. Level 3 gridded products are much smaller, but their 5 or 30 day temporal resolution is insufficient for many researchers. In addition, TRMM standard products are produced in the HDF format. While a large number of user-friendly tools are available to hide the details of the format (including a toolkit developed at TSDIS for the TRMM science team), many potential users shy away

Stocker, Erich↗

NASA NTRS2009

Data Assimilation Experiments Using Quality Controlled AIRS Version 5 Temperature Soundings

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing and reprocessing of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains a number of significant improvements over Version 4. Two very significant improvements are described briefly below. 1) The AIRS Science Team Radiative Transfer Algorithm (RTA) has now been upgraded to accurately account for effects of non-local thermodynamic equilibrium on the AIRS observations. This allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval algorithm during both day and night. Following theoretical considerations, the,AIRS Version 5 temperature profile retrieval step uses only 15 micron CO2 radiances for those channels sensitive to atmospheric emission in the stratosphere. Tropospheric temperature profile information is obtained almost exclusively from clear column radiances in the 4.3 micron CO2 band. These clear column radiances are a derived product that are indicative of radiances AIRS channels would have seen if the field of view were completely clear. Tropospheric sounding 15 micron CO2 observations are used heavily in the determination of the parameters necessary to generate for all sounding channels. This approach allows for the generation of accurate values of and T(p) under most cloud conditions.

Susskind, Joel↗

NASA NTRS2009

Data Assimilation Experiments Using Quality Controlled AIRS Version 5 Temperature Soundings

The AIRS Science Team Version 5 retrieval algorithm has been finalized and is now operational at the Goddard DAAC in the processing (and reprocessing) of all AIRS data. The AIRS Science Team Version 5 retrieval algorithm contains a number of significant improvements over Version 4. Two very significant improvements are described briefly below. 1) The AIRS Science Team Radiative Transfer Algorithm (RTA) has now been upgraded to accurately account for effects of non-local thermodynamic equilibrium on the AIRS observations. This allows for use of AIRS observations in the entire 4.3 micron CO2 absorption band in the retrieval algorithm during both day and night. Following theoretical considerations, tropospheric temperature profile information is obtained almost exclusively from clear column radiances in the 4.3 micron CO2 band in the AIRS Version 5 temperature profile retrieval step. These clear column radiances are a derived product that are indicative of radiances AIRS channels would have seen if the field of view were completely clear. Clear column radiances for all channels are determined using tropospheric sounding 15 micron CO2 observations. This approach allows for the generation of accurate values of clear column radiances and T(p) under most cloud conditions. 2) Another very significant improvement in Version 5 is the ability to generate accurate case-by-case, level-by-level error estimates for the atmospheric temperature profile, as well as for channel-by-channel clear column radiances. These error estimates are used for quality control of the retrieved products. Based on error estimate thresholds, each temperature profiles is assigned a characteristic pressure, pg, down to which the profile is characterized as good for use for data assimilation purposes. We have conducted forecast impact experiments assimilating AIRS quality controlled temperature profiles using the NASA GEOS-5 data assimilation system, consisting of the NCEP GSI analysis coupled with the NASA FVGCM, at a spatial resolution of 0.5 deg by 0.5 deg. Assimilation of Quality Controlled AIRS temperature profiles down to pg resulted in significantly improved forecast skill compared to that obtained from experiments when all data used operationally by NCEP, except for AIRS data, is assimilated. These forecasts were also significantly better than to those obtained when AIRS radiances (rather than temperature profiles) are assimilated, which is the way AIRS data is used operationally by NCEP and ECMWF.

Susskind, Joel↗

NASA NTRS2008

Evolution of the Earth Observing System (EOS) Data and Information System (EOSDIS)

One of the strategic goals of the U.S. National Aeronautics and Space Administration (NASA) is to "Develop a balanced overall program of science, exploration, and aeronautics consistent with the redirection of the human spaceflight program to focus on exploration". An important sub-goal of this goal is to "Study Earth from space to advance scientific understanding and meet societal needs." NASA meets this subgoal in partnership with other U.S. agencies and international organizations through its Earth science program. A major component of NASA s Earth science program is the Earth Observing System (EOS). The EOS program was started in 1990 with the primary purpose of modeling global climate change. This program consists of a set of space-borne instruments, science teams, and a data system. The instruments are designed to obtain highly accurate, frequent and global measurements of geophysical properties of land, oceans and atmosphere. The science teams are responsible for designing the instruments as well as scientific algorithms to derive information from the instrument measurements. The data system, called the EOS Data and Information System (EOSDIS), produces data products using those algorithms as well as archives and distributes such products. The first of the EOS instruments were launched in November 1997 on the Japanese satellite called the Tropical Rainfall Measuring Mission (TRMM) and the last, on the U.S. satellite Aura, were launched in July 2004. The instrument science teams have been active since the inception of the program in 1990 and have participation from Brazil, Canada, France, Japan, Netherlands, United Kingdom and U.S. The development of EOSDIS was initiated in 1990, and this data system has been serving the user community since 1994. The purpose of this chapter is to discuss the history and evolution of EOSDIS since its beginnings to the present and indicate how it continues to evolve into the future. this chapter is organized as follows. Sect. 7.2 provides a discussion of EOSDIS, its elements and their functions. Sect. 7.3 provides details regarding the move towards more distributed systems for supporting both the core and community needs to be served by NASA Earth science data systems. Sect. 7.4 discusses the use of standards and interfaces and their importance in EOSDIS. Sect. 7.5 provides details about the EOSDIS Evolution Study. Sect. 7.6 presents the implementation of the EOSDIS Evolution plan. Sect. 7.7 briefly outlines the progress that the implementation has made towards the 2015 Vision, followed by a summary in Sect. 7.8.

Ramapriyan, Hampapuram K.↗

NASA NTRSDate not supplied

Summary of Apollo Next Generation Sample Analysis (ANGSA) and Insights for Artemis Preliminary Examination Activities

Analyses of Apollo samples have provided fundamental insights into the origin and history of the Earth-Moon system and the solar system broadly. With great foresight, a subset of samples from Apollo were left unprocessed so that they could be studied by future generations with their modern technology. To prepare for the return of samples from the Moon by the Artemis Program, NASA initiated the Apollo Next Generation Sample Analysis Program (ANGSA) to analyze a subset of the previously unprocessed Apollo samples. The ANGSA consortium consisted of 9 original teams funded by NASA that combined into a single science team referred to as the ANGSA Science Team. ANGSA was designed to function like the sample analysis phase of a sample return mission with a goal to investigate the lower portion of a double drive tube previously sealed on the lunar surface (73001), the upper portion of that drive tube that had remained unopened (73002), and a variety of Apollo 17 samples that had remained stored at -20 ºC for approximately 50 years.

Francis M. McCubbin↗