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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Training Evaluation: A Case Study of Measuring Impact and ROI

The purpose of this case study was to evaluate the Compliance Officer/Coordinator (CO/CC) training program using Phillips’ approach to training evaluation. INL Learning Performance Consultants (LPCs) applied the ROI Methodology to measure the effectiveness of the CO/CC training program. INL CO/CCs participated in focus groups to provide feedback on their learning, enablers and barriers to applying what they learned, and opportunities to improve the CO/CC learning experience. With collaboration from the CO/CC training program owner, the LPCs demonstrated the business impact and return on investment (ROI) of the CO/CC training program. Included are training program recommendations and lessons learned using the ROI Methodology.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2011

US Spacesuit Knowledge Capture

The ability to learn from both the mistakes and successes of the past is vital to assuring success in the future. Due to the close physical interaction between spacesuit systems and human beings as users, spacesuit technology and usage lends itself rather uniquely to the benefits realized from the skillful organization of historical information; its dissemination; the collection and identification of artifacts; and the education of those in the field. The National Aeronautics and Space Administration (NASA), other organizations and individuals have been performing United States (U.S.) Spacesuit Knowledge Capture since the beginning of space exploration. Avenues used to capture the knowledge have included publication of reports; conference presentations; specialized seminars; and classes usually given by veterans in the field. More recently the effort has been more concentrated and formalized whereby a new avenue of spacesuit knowledge capture has been added to the archives in which videotaping occurs engaging both current and retired specialists in the field presenting technical scope specifically for education and preservation of knowledge. With video archiving, all these avenues of learning can now be brought to life with the real experts presenting their wealth of knowledge on screen for future learners to enjoy. Scope and topics of U.S. spacesuit knowledge capture have included lessons learned in spacesuit technology, experience from the Gemini, Apollo, Skylab and Shuttle programs, hardware certification, design, development and other program components, spacesuit evolution and experience, failure analysis and resolution, and aspects of program management. Concurrently, U.S. spacesuit knowledge capture activities have progressed to a level where NASA, the National Air and Space Museum (NASM), Hamilton Sundstrand (HS) and the spacesuit community are now working together to provide a comprehensive closed-looped spacesuit knowledge capture system which includes

Chullen, Cinda↗
NASA NTRS2024

Integrated Systems Engineering, Safety, Reliability and Risk Management – Minimizing Black Swan Events

This paper examines key barriers that can possibly inhibit safe and reliable mission execution and, in the worst case, result in loss of human life due to many unknown contributory factors that can lead to Black Swan events. Some of the representative contributory factors include decision errors, overconfidence and a host of common causes including cultural and human factors. Decisions are always easy to criticize in hindsight when more information is available after a major accident. Depending on the type and complexity of the project and/or mission, the catastrophic risks of drifting into failure can be alleviated by implementing uniquely and strategically tailored Integrated-System-of-Systems, dynamic, risk-informed decision management processes. This paper presents some of the lessons learned from James Webb Space Telescope (JWST), NASA’s Human Space Flight program, and industry that provide motivation to organizations working on mega-complex missions to prudently accomplish targeted mission success. These lessons are important for future human Lunar, Mars and Beyond missions planned to be pursued by NASA through a public-private partnership using nimble but effective safety-conscious, proven sound engineering practices including implementation of integrated risk mitigation practices.

SLS↗
NASA NTRS2004

Planning Mars Memory: Learning from the Mer Mission

Knowledge management for space exploration is part of a multi-generational effort at recognizing, preserving and transmitting learning. Each mission should be built on the learning, of both successes and failures, derived from previous missions. Knowledge management begins with learning, and the recognition that this learning has produced knowledge. The Mars Exploration Rover mission provides us with an opportunity to track how learning occurs, how it is recorded, and whether the representations of this learning will be optimally useful for subsequent missions. This paper focuses on the MER science and engineering teams during Rover operations. A NASA team conducted an observational study of the ongoing work and learning of the these teams. Learning occurred in a wide variety of areas: how to run two teams on Mars time for three months; how to use the instruments within the constraints of the martian environment, the deep space network and the mission requirements; how to plan science strategy; how best to use the available software tools. This learning is preserved in many ways. Primarily it resides in peoples memories, to be carried on to the next mission. It is also encoded in stones, in programming sequences, in published reports, and in lessons learned activities, Studying learning and knowledge development as it happens allows us to suggest proactive ways of capturing and using it across multiple missions and generations.

Linde, Charlotte↗
NASA NTRS2010

A National Partnership-Based Summer Learning Initiative to Engage Underrepresented Students with Science, Technology, Engineering and Mathematics

In response to the White House Educate to Innovate campaign, NASA developed a new science, technology, engineering, and mathematics (STEM) education program for non-traditional audiences that also focused on public-private partnerships and nationwide participation. NASA recognized that summer break is an often overlooked but opportune time to engage youth in STEM experiences, and elevated its ongoing commitment to the cultivation of diversity. The Summer of Innovation (SoI) is the resulting initiative that uses NASA's unique missions and resources to boost summer learning, particularly for students who are underrepresented, underserved and underperforming in STEM. The SoI pilot, launched in June 2010, is a multi-faceted effort designed to improve STEM teaching and learning through partnership, multi-week summer learning programs, special events, a national concluding event, and teacher development. The SoI pilot features strategic infusion of NASA content and educational resource materials, sustainability through STEM Learning Communities, and assessments of effectiveness of SoI interventions with other pilot efforts. This paper examines the inception and development of the Summer of Innovation pilot project, including achievements and effectiveness, as well as lessons learned for future efforts.

Melvin, Leland↗
DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗
NASA NTRS2019

Overcoming the Challenges of Data Integration and Automation

Artificial Intelligence (AI) and machine learning (ML) are gaining increased attention as a way to leverage the world's data to solve problems. Although AI and ML offer much potential, there are often misconceptions about the application of such techniques. This panel discussion includes speakers from airlines and the research community who will present machine learning approaches they have developed on a variety of aviation data including digital flight data, safety reporting data, and traffic flow data. They will explain the purpose of the application, the data used, and the lessons learned in the development and deployment of their solutions. The panel discussion will focus on common pitfalls in developing and AI solution, the dangers of the current hype around AI, tips for gaining value from a machine learning solution, how to determine whether a machine learning approach is appropriate for a problem, and more.

Matthews, Bryan L.↗
NASA NTRS2019

Artificial Intelligence in Aviation Safety Applications - Exploring Myths and Truths of AI and ML

Artificial Intelligence (AI) and machine learning (ML) are gaining increased attention as ways to leverage the world's data to solve problems. Although AI and ML offer much potential, there are often misconceptions about the application of such techniques.Panel speakers will present machine learning approaches they have developed on a variety of aviation data, including digital flight data, safety reporting data, and voice communications data. They will discuss the purpose of the application, the data used, and the lessons learned in the development and deployment of their solutions. The panel will also discuss common pitfalls in developing an AI solution, the dangers of the current hype around AI, tips for gaining value from a ML solution, how to determine whether a ML approach is appropriate for a problem, and more.

Reeves, Scott (Capt.)↗
DOE OSTI2023

Medical Physics Leadership Academy Journal Club (Leadership Club) program: Two-year review in building a community of leaders

The American Association of Physicists in Medicine began the Medical Physics Leadership Academy Journal Club in the fall of 2020. The initiative was launched to provide a forum for medical physicists to learn about leadership topics using published material, discuss and reflect on the material, and consider incorporating the discussed skills into their professional practice. This report presents the framework for the MPLA Journal Club program, describes the lessons learned over the last 2 years, summarizes the data collected from attendees, and highlights the roadmap for the program moving forward.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Scalable training of trustworthy and energy-efficient predictive graph foundation models for atomistic materials modeling: a case study with HydraGNN

We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGNN expands the boundaries of graph neural network (GNN) computations in both training scale and data diversity. It abstracts over message passing algorithms, allowing both reproduction of and comparison across algorithmic innovations that define nearest-neighbor convolution in GNNs. This work discusses a series of optimizations that have allowed scaling up the GFMs training to tens of thousands of GPUs on datasets consisting of hundreds of millions of graphs. Our GFMs use multitask learning (MTL) to simultaneously learn graph-level and node-level properties of atomistic structures, such as energy and atomic forces. Using over 154 million atomistic structures for training, we illustrate the performance of our approach along with the lessons learned on two state-of-the-art US Department of Energy (US-DOE) supercomputers, namely the Perlmutter petascale system at the National Energy Research Scientific Computing Center and the Frontier exascale system at Oak Ridge Leadership Computing Facility. The HydraGNN architecture enables the GFM to achieve near-linear strong scaling performance using more than 2000 GPUs on Perlmutter and 16,000 GPUs on Frontier.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Commercially Hosted Government Payloads: Lessons from Recent Programs

In a commercially hosted operational mode, a scientific instrument or operational device is attached to a spacecraft but operates independently from the spacecraft s primary mission. Despite the expected benefits of this arrangement, there are few examples of hosted payload programs actually being executed by government organizations. The lack of hosted payload programs is largely driven by programmatic challenges, both real and perceived, rather than by technical challenges. Partly for these reasons, NASA has not sponsored a hosted payload program, in spite of the benefits and visible community interest in doing so. In the interest of increasing the use of hosted payloads across the space community, this paper seeks to alleviate concerns about hosted payloads by identifying these programmatic challenges and presenting ways in which they can be avoided or mitigated. Despite the challenges, several recent hosted payload programs have been successfully completed or are currently in progress. This paper presents an assessment of these programs, with a focus on acquisition, costs, schedules, risks, and other programmatic aspects. The hosted payloads included in this study are the Federal Aviation Administration's Wide Area Augmentation System (WAAS) payloads, United States Coast Guard's Automatic Identification System (AIS) demonstration payload, Department of Defense's IP Router In Space (IRIS) demonstration payload, the United States Air Force's Commercially Hosted Infrared Payload (CHIRP), and the Australian Defence Force's Ultra High Frequency (UHF) payload. General descriptions of each of these programs are presented along with issues that have been encountered and lessons learned from those experiences. A set of recommended approaches for future hosted payload programs is presented, with a focus on addressing risks or potential problem areas through smart and flexible contracting up front. This set of lessons and recommendations is broadly applicable to future hosted payload programs, whether they are technology demonstrations, communications systems, or operational sensors. Additionally, we present a basic cost model for commercial access to space for hosted payloads as a function of payload mass

Andraschko, Mark A.↗
NASA NTRSDate not supplied

Informing NLP Learning Tasks by Tracking User Features: An ASRS Use Case using Kaona

There has been growing interest in utilizing natural language processing (NLP) algorithms in Aviation Safety. This interest has extended to leveraging the decades of records publicly available on the Aviation Safety Reporting System (ASRS). While related literature has given more emphasis in lessons learned from the narratives, our prior work has focused on using NLP to support narrative search in the ASRS. Specifically, we evaluated if the use of alternative search mechanisms to keyword search, such as the retrieval of related narratives even without matching keywords could improve narrative discovery. A difficulty in experimenting alternative search mechanisms in any information retrieval task is the lack of ground truth. To address this limitation, we propose Kaona, a lightweight interface which enables the prototyping of alternative search retrieval tasks, by tracking user experience both explicitly (user-specified feedback), or implicitly (user navigation through interface affordances). Differently from distracting requests for feedback during user navigation, Kaona collects explicit feedback from users by mapping them to affordances which support the user workflow, while obtaining ground truth information for learning tasks.

human-computer-interaction↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
DOE OSTI2024

SQMS Quantum R&D in Machine Learning, Optimization and Sensing beyond Fundamental Physics Applications

This newly formed team at SQMS under the Ecosystem Thrust is looking to develop capabilities impacting societal advances outside the core domain of HEP and condensed matter physics. We explicitly leverage the experimental and algorithmic innovations developed across all groups as well as connect to broad-scope external projects of the diverse team of PIs. As the inaugural set of projects, we are studying numerically quantum machine learning models inspired by efficiently trainable echo-state and orthogonal neural networks and developing designs for related experiments to be performed on quantum processors based on SQMS SRF cQED technology and Rigetti s transmon arrays. Investigated models exploit ideas and lessons learned from multiple prior work by SQMS team members in a variety of internal and external activities [R1]. Target initial applications include noisy signal processing, potentially captured by quantum sensors or noisy QPUs, as well as simulation and classification of healthcare data. For instance, image reconstruction of the brain s electrical properties by solving the inverse Maxwell equation problem with uncertainty [R2] through a hybrid quantum-classical physics-informed architecture for time-dependent processes [R3]. The group is also investigating the application and development of novel quantum sensors based on magnetic levitation of a superconducting sphere coupled to a superconducting qubit. This coupling enables high-precision measurements of the position of the sphere, which can be used for sensitive detection of forces, enabling practical applications such as gravimetry for geophysics analysis, or accelerometry for GPS-denied navigation [R4] [R1] Rieffel, Eleanor G., Ata Akbari Asanjan, M. Sohaib Alam, Namit Anand, David E. Bernal Neira, Sophie Block, Lucas T. Brady et al. "Assessing and advancing the potential of quantum computing: A NASA case study." Future Generation Computer Systems (2024). [R2] Yu, X., Serrall s, J.E., Giannakopoulos, I.I., Liu, Z., Daniel, L., Lattanzi, R. and Zhang, Z., 2023. Pifon-ept: Mr-based electrical property tomography using physics-informed fourier networks. IEEE Journal on Multiscale and Multiphysics Computational Techniques. [R3] Wudarski, Filip, Daniel OConnor, Shaun Geaney, Ata Akbari Asanjan, Max Wilson, Elena Strbac, P. Aaron Lott, and Davide Venturelli. "Hybrid quantum-classical reservoir computing for simulating chaotic systems." arXiv preprint arXiv:2311.14105 (2023). [R4] Higgins, Gerard, Saarik Kalia, and Zhen Liu. "Maglev for dark matter: Dark-photon and axion dark matter sensing with levitated superconductors." Physical Review D 109.5 (2024): 055024.

Venturelli, Davide↗
NASA NTRS2008

Space Human Factors: Research to Application

Human Factors has been instrumental in preventing potential on-orbit hazards and increasing overall crew safety. Poor performance & operational learning curves on-orbit are mitigated. Human-centered design is applied to optimize design and minimize potentially hazardous conditions, especially with larger crew sizes and habitat constraints. Lunar and Mars requirements and design developments are enhanced, based on ISS Lessons Learned.

Woolford, Barbara↗
NASA NTRSDate not supplied

How Do Different Knowledge Frameworks Help Us Learn From Aviation Line Observations?

Human performance includes actions that increase safety, as well as actions that can reduce safety. Ensuring safety in complex dynamic operations like commercial aviation depends on the ability to institute appropriate responses based on what is learned from flightcrew performance and the contexts in which it occurs. To do this systemically at the organization level requires collecting data on flightcrew performance, developing effective approaches to analyzing those data, and understanding how to translate what has been learned into policies, procedures, and practice. Systematic observation of front-line operators is a vital source of human performance data. Much has been learned from such observations, including methodological principles. Most observations have been based on a framework focused on managing safety challenges and the ensuing unsafe events. A complementary perspective focuses on flexibility and actions that promote continued safe and effective operation. We consider lessons learned about observational methods from an established framework focused on undesired actions and how these might be extended for a framework focused on desired actions.

safety↗
DOE OSTI2023

Development of a Machine-Learned Cruise Guide Indicator for Rotorcraft

This paper presents a machine-learned virtual cruise guide indicator (vCGI) for Chinook helicopters. Two temporal neural networks were trained and evaluated on measured data from 55 flight tests, one for the fore rotor and another for the aft rotor, to predict a vCGI value, which protects 23 components from fatigue damage during steady-state conditions. Three different classes of machine learning architectures were evaluated for prediction of the vCGI from time sequences: a temporal convolutional neural network with 1D dilated causal convolutions, a long short-term memory recurrent neural network, and an attention-based transformer architecture. The final average model accuracy on unseen flight data is currently greater than 93% for CGI values which could result in fatigue damage and 90% for normal operation CGI values. Model accuracy was improved through a series of advancements in:(1) selection of optimal training data using temporal collective variables and unsupervised learning, (2) dataset augmentation with maximum-entropy temporal collective variables, and (3) implementation of a mixture-of-experts classification- regression approach using an adversarial classification approach to assign maneuver labels. The results are presented for each advancement in model development along with lessons learned in training machine learning models on real- world, time-dependent rotorcraft data.

Boyer, Mathew↗
NASA NTRS2004

An Overview of MODIS Calibration and Characterization

The MODerate Resolution Imaging Spectroradiometer (MODIS) is one of the key instruments for the NASA s Earth Observing System (EOS). It is currently operating on both EOS Terra and Aqua satellites. The MODIS is a major advance over its heritage sensors in terms of its spectral, spatial, and temporal resolutions with frequent global observations and a broad range of science applications. There are 20 reflective solar bands (RSB) with center wavelengths ranging from 0.41 to 2.l microns and 16 thermal emissive bands (TEB) from 3.7 to 14.4 microns. The absolute radiometric accuracy requirements (lsigma) at the typical spectral radiance levels are plus or minus 2% for the RSB reflectance factors and plus or minus 5% for the RSB radiance products. With few exceptions, the TEB requirements are plus or minus 1%. To verify that the instruments met their specified design requirements both Terra and Aqua MODIS underwent extensive pre-launch calibration and characterization at various levels, including system-level thermal vacuum testing. On-orbit calibration and characterization are performed by the on-board calibrators: a solar diffuser (SD) and a solar diffuser stability monitor (SDSM), a V-groove flat panel blackbody (BB), and a spectro-radiometric calibration assembly (SRCA). In this paper, we present an overview of MODIS calibration and characterization activities, methodologies, and lessons learned from pre-launch testing and on-orbit operations. Key issues to be discussed include our on-orbit efforts of monitoring detectors noise characterization, tracking solar diffuser and optics degradation, and updating sensor s response versus scan-angle. The MODIS experience has provided invaluable lessons that are being used in designing and testing the Visible Infrared Imaging Radiometer Suite (VIIRS), a direct follow-on to the MODIS that will be flown on the National Polar-Orbit Operational Environmental Satellite System (NPOESS) missions.

Xiong, X.↗