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NASA NTRS2014

Analyzing a 35-Year Hourly Data Record: Why So Difficult?

At the Goddard Distributed Active Archive Center, we have recently added a 35-Year record of output data from the North American Land Assimilation System (NLDAS) to the Giovanni web-based analysis and visualization tool. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) offers a variety of data summarization and visualization to users that operate at the data center, obviating the need for users to download and read the data themselves for exploratory data analysis. However, the NLDAS data has proven surprisingly resistant to application of the summarization algorithms. Algorithms that were perfectly happy analyzing 15 years of daily satellite data encountered limitations both at the algorithm and system level for 35 years of hourly data. Failures arose, sometimes unexpectedly, from command line overflows, memory overflows, internal buffer overflows, and time-outs, among others. These serve as an early warning sign for the problems likely to be encountered by the general user community as they try to scale up to Big Data analytics. Indeed, it is likely that more users will seek to perform remote web-based analysis precisely to avoid the issues, or the need to reprogram around them. We will discuss approaches to mitigating the limitations and the implications for data systems serving the user communities that try to scale up their current techniques to analyze Big Data.

computational performance↗

NASA NTRS2022

Coevolution of Machine Learning and Process-Based Modelling to Revolutionize Earth and Environmental Sciences: A Perspective

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support. In this perspective, we assert that the cultural barriers between the ML and PBM communities limit the potential of ML, and even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often ignored, differences between ML and PBM are discussed as well as their strengths and weaknesses in light of three overarching modelling objectives in EES, (1) nowcasting and prediction, (2) scenario analysis, and (3) diagnostic learning. The paper ponders over a ‘coevolutionary’ approach to model building, shifting away from a borrowing to a co-creation culture, to develop a generation of models that leverage the unique strengths of ML such as scalability to big data and high-dimensional mapping, while remaining faithful to process-based knowledge base and principles of model explainability and interpretability, and therefore, falsifiability.

Saman Razavi↗

NASA NTRS2016

Decision Manifold Approximation for Physics-Based Simulations

With the recent surge of success in big-data driven deep learning problems, many of these frameworks focus on the notion of architecture design and utilizing massive databases. However, in some scenarios massive sets of data may be difficult, and in some cases infeasible, to acquire. In this paper we discuss a trajectory-based framework that quickly learns the underlying decision manifold of binary simulation classifications while judiciously selecting exploratory target states to minimize the number of required simulations. Furthermore, we draw particular attention to the simulation prediction application idealized to the case where failures in simulations can be predicted and avoided, providing machine intelligence to novice analysts. We demonstrate this framework in various forms of simulations and discuss its efficacy.

Wong, Jay Ming↗

NASA NTRS2017

Mining Twitter Data to Augment NASA GPM Validation

The Twitter data stream is an important new source of real-time and historical global information for potentially augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. There have been other similar uses of Twitter, though mostly related to natural hazards monitoring and management. The validation of satellite precipitation estimates is challenging, because many regions lack data or access to data, especially outside of the U.S. and in remote and developing areas. The time-varying set of "precipitation" tweets can be thought of as an organic network of rain gauges, potentially providing a widespread view of precipitation occurrence. Twitter provides a large source of crowd for crowdsourcing. During a 24-hour period in the middle of the snow storm this past March in the U.S. Northeast, we collected more than 13,000 relevant precipitation tweets with exact geolocation. The overall objective of our project is to determine the extent to which processed tweets can provide additional information that improves the validation of GPM data. Though our current effort focuses on tweets and precipitation, our approach is general and applicable to other social media and other geophysical measurements. Specifically, we have developed an operational infrastructure for processing tweets, in a format suitable for analysis with GPM data; engaged with potential participants, both passive and active, to "enrich" the Twitter stream; and inter-compared "precipitation" tweet data, ground station data, and GPM retrievals. In this presentation, we detail the technical capabilities of our tweet processing infrastructure, including data abstraction, feature extraction, search engine, context-awareness, real-time processing, and high volume (big) data processing; various means for "enriching" the Twitter stream; and results of inter-comparisons. Our project should bring a new kind of visibility to Twitter and engender a new kind of appreciation of the value of Twitter by the science research communities.

validatio↗

NASA NTRS1995

New generation lidar systems for eye safe full time observations

The traditional lidar over the last thirty years has typically been a big pulse low repetition rate system. Pulse energies are in the 0.1 to 1.0 J range and repetition rates from 0.1 to 10 Hz. While such systems have proven to be good research tools, they have a number of limitations that prevent them from moving beyond lidar research to operational, application oriented instruments. These problems include a lack of eye safety, very low efficiency, poor reliability, lack of ruggedness and high development and operating costs. Recent advances in solid state laser, detectors and data systems have enabled the development of a new generation of lidar technology that meets the need for routine, application oriented instruments. In this paper the new approaches to operational lidar systems will be discussed. Micro pulse lidar (MPL) systems are currently in use, and their technology is highlighted. The basis and current development of continuous wave (CW) lidar and potential of other technical approaches is presented.

Spinhirne, James D.↗

NASA NTRS2013

Scalable Adaptive Graphics Environment (SAGE) Software for the Visualization of Large Data Sets on a Video Wall

The use of collaborative scientific visualization systems for the analysis, visualization, and sharing of "big data" available from new high resolution remote sensing satellite sensors or four‐dimensional numerical model simulations is propelling the wider adoption of ultra‐resolution tiled display walls interconnected by high speed networks. These systems require a globally connected and well‐integrated operating environment that provides persistent visualization and collaboration services. This abstract and subsequent presentation describes a new collaborative visualization system installed for NASA's Shortterm Prediction Research and Transition (SPoRT) program at Marshall Space Flight Center and its use for Earth science applications. The system consists of a 3 x 4 array of 1920 x 1080 pixel thin bezel video monitors mounted on a wall in a scientific collaboration lab. The monitors are physically and virtually integrated into a 14' x 7' for video display. The display of scientific data on the video wall is controlled by a single Alienware Aurora PC with a 2nd Generation Intel Core 4.1 GHz processor, 32 GB memory, and an AMD Fire Pro W600 video card with 6 mini display port connections. Six mini display‐to‐dual DVI cables are used to connect the 12 individual video monitors. The open source Scalable Adaptive Graphics Environment (SAGE) windowing and media control framework, running on top of the Ubuntu 12 Linux operating system, allows several users to simultaneously control the display and storage of high resolution still and moving graphics in a variety of formats, on tiled display walls of any size. The Ubuntu operating system supports the open source Scalable Adaptive Graphics Environment (SAGE) software which provides a common environment, or framework, enabling its users to access, display and share a variety of data‐intensive information. This information can be digital‐cinema animations, high‐resolution images, high‐definition video‐teleconferences, presentation slides, documents, spreadsheets or laptop screens. SAGE is cross‐platform, community‐driven, open‐source visualization and collaboration middleware that utilizes shared national and international cyberinfrastructure for the advancement of scientific research and education.

Jedlovec, Gary↗

NASA NTRSDate not supplied

Towards Automated Analytics of Research Publications

For readers of scientific publications it remains a big challenge to unambiguously relate the published research with the data used. To a substantial degree it is attributed to authors, journals, editors, and reviewers not prioritizing correct data citation, which impacts traceability, repeatability, and giving credits to published authors and their funding sources. Furthermore, uniform classification of the content of the published research is hampered by journals using journal specific topics and letting authors to assign free text keywords to their papers. We demonstrate automated analytics methods for extracting and relating datasets used and the research application areas by processing 1,300 research papers that referenced the NASA Giovanni service (but probably not the datasets in particular) as supporting their publication process. This presentation was given during the 2022 ESIP January meeting held virtually in January 2022.

Irina Gerasimov↗

NASA NTRS2017

NASA Soil Moisture Active Passive Mission Status and Science Highlights

The Soil Moisture Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational during April 2015. This paper provides a summary of the quality assessment of its baseline soil moisture and freeze/thaw products as well as an overview of new products. The first new product explores the Backus Gilbert optimum interpolation based on the oversampling characteristics of the SMAP radiometer. The second one investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic aperture radar (SAR) data to obtain soil moisture products at about 1 to 3 km resolution. In addition, SMAPs L-band data have been found useful for many scientific applications, including depictions of water cycles, vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.The SMAP soil moisture, freeze/taw state and SSSprovide a synergistic view of water cycle. For example, Fig.7 illustrates the transition of freeze/thaw state, change of soilmoisture near the pole and SSS in the Arctic Ocean fromApril to October in 2015 and 2016. In April, most parts ofAlaska, Canada, and Siberia remained frozen. Melt onsetstarted in May. Alaska, Canada, and a big part of Siberia havebecome thawed at the end of May; some freshwater dischargecould be found near the mouth of Mackenzie in 2016, but notin 2015. The soil moisture appeared to be higher in the Oband Yenisei river basins in Siberia in 2015. As a result,freshwater discharge was more widespread in the Kara Seanear the mouths of both rivers in June 2015 than in 2016. TheNorth America and Siberia have become completely thawedin July. After June, the freshwater discharge from other riversinto the Arctic, indicated by blue, also became visible. Thefreeze-up started in September and the high latitude regionsin North America and Eurasia became frozen. Comparing thespread of freshwater in August 2015 and 2016 suggests thatthere was more discharge from Ob and Yenisei in 2015,which appeared to correspond to a higher soil moisturecontent in the Ob and Yenisei basins. In contrast, Mackenzieappeared to have more discharge in September 2016.

soil moisture↗

NASA NTRS2008

Proceedings of the 39th Lunar and Planetary Science Conference

Sessions with oral presentations include: A SPECIAL SESSION: MESSENGER at Mercury, Mars: Pingos, Polygons, and Other Puzzles, Solar Wind and Genesis: Measurements and Interpretation, Asteroids, Comets, and Small Bodies, Mars: Ice On the Ground and In the Ground, SPECIAL SESSION: Results from Kaguya (SELENE) Mission to the Moon, Outer Planet Satellites: Not Titan, Not Enceladus, SPECIAL SESSION: Lunar Science: Past, Present, and Future, Mars: North Pole, South Pole - Structure and Evolution, Refractory Inclusions, Impact Events: Modeling, Experiments, and Observations, Mars Sedimentary Processes from Victoria Crater to the Columbia Hills, Formation and Alteration of Carbonaceous Chondrites, New Achondrite GRA 06128/GRA 06129 - Origins Unknown, The Science Behind Lunar Missions, Mars Volcanics and Tectonics, From Dust to Planets (Planetary Formation and Planetesimals):When, Where, and Kaboom! Astrobiology: Biosignatures, Impacts, Habitability, Excavating a Comet, Mars Interior Dynamics to Exterior Impacts, Achondrites, Lunar Remote Sensing, Mars Aeolian Processes and Gully Formation Mechanisms, Solar Nebula Shake and Bake: Mixing and Isotopes, Lunar Geophysics, Meteorites from Mars: Shergottite and Nakhlite Invasion, Mars Fluvial Geomorphology, Chondrules and Chondrule Formation, Lunar Samples: Chronology, Geochemistry, and Petrology, Enceladus, Venus: Resurfacing and Topography (with Pancakes!), Overview of the Lunar Reconnaissance Orbiter Mission, Mars Sulfates, Phyllosilicates, and Their Aqueous Sources, Ordinary and Enstatite Chondrites, Impact Calibration and Effects, Comparative Planetology, Analogs: Environments and Materials, Mars: The Orbital View of Sediments and Aqueous Mineralogy, Planetary Differentiation, Titan, Presolar Grains: Still More Isotopes Out of This World, Poster sessions include: Education and Public Outreach Programs, Early Solar System and Planet Formation, Solar Wind and Genesis, Asteroids, Comets, and Small Bodies, Carbonaceous Chondrites, Chondrules and Chondrule Formation, Chondrites, Refractory Inclusions, Organics in Chondrites, Meteorites: Techniques, Experiments, and Physical Properties, MESSENGER and Mercury, Lunar Science Present: Kaguya (SELENE) Results, Lunar Remote Sensing: Basins and Mapping of Geology and Geochemistry, Lunar Science: Dust and Ice, Lunar Science: Missions and Planning, Mars: Layered, Icy, and Polygonal, Mars Stratigraphy and Sedimentology, Mars (Peri)Glacial, Mars Polar (and Vast), Mars, You are Here: Landing Sites and Imagery, Mars Volcanics and Magmas, Mars Atmosphere, Impact Events: Modeling, Experiments, and Observation, Ice is Nice: Mostly Outer Planet Satellites, Galilean Satellites, The Big Giant Planets, Astrobiology, In Situ Instrumentation, Rocket Scientist's Toolbox: Mission Science and Operations, Spacecraft Missions, Presolar Grains, Micrometeorites, Condensation-Evaporation: Stardust Ties, Comet Dust, Comparative Planetology, Planetary Differentiation, Lunar Meteorites, Nonchondritic Meteorites, Martian Meteorites, Apollo Samples and Lunar Interior, Lunar Geophysics, Lunar Science: Geophysics, Surface Science, and Extralunar Components, Mars, Remotely, Mars Orbital Data - Methods and Interpretation, Mars Tectonics and Dynamics, Mars Craters: Tiny to Humongous, Mars Sedimentary Mineralogy, Martian Gullies and Slope Streaks, Mars Fluvial Geomorphology, Mars Aeolian Processes, Mars Data and Mission,s Venus Mapping, Modeling, and Data Analysis, Titan, Icy Dwarf Satellites, Rocket Scientist's Toolbox: In Situ Analysis, Remote Sensing Approaches, Advances, and Applications, Analogs: Sulfates - Earth and Lab to Mars, Analogs: Remote Sensing and Spectroscopy, Analogs: Methods and Instruments, Analogs: Weird Places!. Print Only Early Solar System, Solar Wind, IDPs, Presolar/Solar Grains, Stardust, Comets, Asteroids, and Phobos, Venus, Mercury, Moon, Meteorites, Mars, Astrobiology, Impacts, Outer Planets, Satellites, and Rings, Support for Mission Operations, Analog Education and Public Outreach.

Source record↗

NASA NTRS2024

Big Bend Ecological Forecasting: Integrating Earth Observations Into Invasive Species Management Decisions in Big Bend National Park in Texas

Located in Brewster County Texas, U.S., along the Texas-Mexico border, Big Bend National Park is 3,243 square kilometers of desert, mountains, and rivers. NASA’s MSFC Spring 2024 DEVELOP Team partnered with the National Park Service (NPS), to address the environmental concern of perineal invasive grasses in Big Bend National Park. Buffelgrass (Cenchrus ciliaris), introduced to the park in the 1940’s, poses an ongoing threat, and causes habitat destruction for many of the park’s native ecosystems. Buffelgrass amplifies fire risk in the park, aids in the destruction of historic structures, and alters stream channels. To address the rising concern of Buffelgrass presence, unique advanced spatial technique applications were necessary to construct a habitat suitability model and perform a comprehensive fire risk assessment. A habitat suitability model was developed considering climate, vegetation, and phenological variables, in addition to physical and topographical variables. Subsequently, a fire risk model was developed, taking into account fire history data, accessibility factors, climate trends and predictions, along with developed areas. Multi-Source Land Surface Phenology (LSP) Sentinel-2 and Landsat 8 Operational Land Imager (OLI) imagery were used to predict Buffelgrass hotspot locations throughout the park. These analyses allowed the identification of optimal Buffelgrass habitat and hotspot locations, as well as park zones that reflect the greatest risk for future Buffelgrass invasion and fire risk. The collective results of the habitat suitability model, fire risk assessment, and Buffelgrass hot spot identification will allow the NPS to facilitate efficient mitigation measures and management strategies, and improved resource allocation where it’s most needed.

remote sensing↗

NASA NTRS2024

Combining Large Datasets - Cancer Moonshot Task Group Final Summary

In February 2022, President Biden re-ignited the Cancer Moonshot with bold new goals: to reduce the cancer death rate by half within 25 years and improve the lives of people with cancer and cancer survivors. To achieve these ambitious goals, the White House convened the first-ever Cancer Cabinet, bringing together departments and agencies from across the federal government to end cancer as we know it.The Cancer Cabinet convened three task forces and supporting task groups, including the Data and Innovation Task Force, which supported the Cancer Moonshot priority to “Deliver innovation to patients and communities.” In early 2023, the “Combining Large Datasets” (CoLD) Task Group was created within the Data and Innovation Task Force. The scope of the CoLD Task Group was how federal agencies combine large datasets for broad applications across cancer prevention and control, including nutrition, epidemiology, and military/Veteran health. Within this scope, the group sought to better leverage the immense potential of data and power of data tools to increase our understanding of cancer incidence, causes, mortality, treatments, prevention, outcomes, costs, and all other aspects of the burden of cancer.

data integration↗

NASA NTRS1999

Introduction to this Special Issue on Geostatistics and Scaling of Remote Sensing

The germination of this special PE&RS issue began at the Royal Geographical Society (with the Institute of British Geographers)(RCS-IBC) annual meeting in January, 1997 held at the University of Exeter in Exeter, England. The cold and snow of an England winter were greatly tempered by the friendly and cordial discussions that ensued at the meeting on possible ways to foster both dialog and research across "the Big Pond" between geographers in the US and the UK on the use of geostatistics and geospatial techniques for remote sensing of land surface processes. It was decided that one way to stimulate and enhance cooperation on the application of geostatistics and geospatial methods in remote sensing was to hold parallel sessions on these topics at appropriate meeting venues in 1998 in both the US and the UK Selected papers given at these sessions would be published as a special issue of PE&RS on the US side, and as a special issue of Computers and Geosciences (C&G) on the UK side, to highlight the commonality in research on geostatistics and geospatial methods in remote sensing and spatial data analysis on both sides of the Atlantic Ocean. As a consequence, a session on "Ceostatistics and Geospatial Techniques for Remote Sensing of Land Surface Processes" was held at the Association of American Geographers (AAG) annual meeting in Boston, Massachusetts in March, 1998, sponsored by the AAG's Remote Sensing Specialty Group (RSSG). A similar session was held at the RGS-IBG annual meeting in Guildford, Surrey, England in January 1998, organized by the Modeling and Advanced Techniques Special Interest Group (MAT SIG) of the Remote Sensing Society (RSS). The six papers that in part, comprise this issue of PE&RS, are the US complement to such a dual journal publication effort. Both of us are co-editors of each of the journal special issues, with the lead editor of each journal being from their respective side of the Atlantic where the journals are published. The special issue of C&G that constitutes the other half of this co-edited journal series will be published in early 1999, with 3 papers by US authors being published along with 6 papers authored by individuals from the UK and other places in Europe.

Quattrochi, Dale A.↗

NASA NTRS2019

Earth Science Technology Office (ESTO) New Observing Strategies (NOS) and NOS-Testbed (NOS-T)

With the advancement of space hardware technologies such as smaller spacecraft, component and instrument miniaturization and high performance space processors, and with the advancement of software technologies in artificial intelligence, big data analysis and autonomous decision making, Earth Science is looking at novel ways to observe phenomena that previously could not have been studied or would have been too expensive to study with traditional missions. In particular, the New Observing Strategies (NOS) component of the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program aims at leveraging these novel technologies as well as low cost and easy access to space to acquire multi-temporal or simultaneous multi-angular, multi-locations, multi-resolution and multi-spectral observations that will provide better multi-source measurements and will build a more dynamic and comprehensive picture of Earth Science phenomena that need to be studied and analyzed. For applications such as water resources management, air quality monitoring, biodiversity studies or disaster management, NOS will integrate the use of small instruments, small spacecraft, constellations of spacecraft and networks of sensors to design new missions that will provide the necessary measurements to improve future forecast and science modeling systems.Measurement acquisition will therefore be approached as a system of systems rather than on a mission basis, and a system of this complexity should not be expected to work without full integration and experimental characterization. Although most of the individual technologies enabling to link and coordinate multi-source observations are more or less mature, a few technologies need to be developed and all of them need to be integrated and tested as a system. In order for this validation to occur, the AIST Program is developing the NOS Testbed that includes 3 main goals:1.Validate novel NOS technologies, independently and as a system2.Demonstrate novel distributed operations concepts3.Socialize new Distributed Spacecraft Mission (DSM) and SensorWeb (SW) technologies and concepts to the science community by significantly retiring the risk of integrating these new technologies.The NOS Testbed will consist of multiple sensing nodes, simulated or actual, representing space, air and/or ground measurements, that are interconnected by a communications fabric (infrastructure that permits nodes to transmit and receive data between one another and interact with each other). Each node will be supported by hardware capabilities required to perform nodes monitoring and command & control, as well as intelligent "onboard" computing. The nodes will work together in a collaborative manner to demonstrate optimal science capabilities. The testbed will enable to validate technologies such as inter-node communication models, techniques and protocols; inter-node coordination; real-time data fusion and understanding; planning; sensor re-targeting; etc. Additionally, the testbed will have the capability to interact with various mission design tools, OSSEs and one or several forecast models. More details about the NOS Testbed will be presented at the confererence.

Earth Science missions; Advanced information Syste↗

NASA NTRS2001

TT and C - First TDRSS, Then Commercial GEO and Big LEO and Now through LEO

The advent of low earth orbit (LEO) commercial communications satellites provides an opportunity to dramatically reduce Telemetry Tracking and Control (TT&C) costs of launch vehicles and Unpiloted Aerial Vehicles (UAVs) by reducing or eliminating ground infrastructure. Personnel from the Goddard Space Flight Center Wallops Flight Facility (GSFC/WFF) in Virginia have successfully used commercial GEO & Big LEO communications satellites for Long Duration Balloon flight TT&C. In addition, TDRSS capability for these balloons has been developed by WFF for the Ultra Long Duration Balloons with the first test flight launch in January 2001 for one global circumnavigation at 120,000 feet altitude launched from Alice Springs. Australia. Numerous other low cost applications can new utilize the commercial LEO satellites for TT&C. The Flight Modern became a GSFC/WFF Advanced Range Technology Initiative (ARTI) in an effort to streamline TT&C capability to the user community at low cost. Phase I ground tests of The Flight Modem verified downlink communications quality of service and measured transmission latencies. These tests were completed last year, Phase II consisting of aircraft flight tests provide much of the data presented in this paper. Phase III of the Flight Modern baseline test program is a demonstration of the ruggedized version of the WFF Flight Modem flown on one sounding rocket launched from Sweden. Flights of opportunity have been and are being actively pursued with other centers, ranges and users at universities. The WFF goal is to reduce TT&C costs by providing a low cost COTS Flight Modem with a User Handbook containing system capability and limitation descriptions. Additionally, since data transmission is by packetized Internet Protocol (IP), data can be received and commands initialed from practically any location with no infrastructure. The WFF, like most ranges, has been using GPS receivers on sounding rockets and long duration balloons for several years, The WFF Flight Modem contains a GPS receiver to provide vehicle position for tracking and vehicle recovery. The system architecture which integrates antennas, GPS receiver, commercial satellite packet data modem. and a single board computer with custom software is described and a number of technical challenges are discussed along with the plan for their resolution. These include antenna development, high Doppler rates, reliability, environmental ruggedness, hand over between satellites and data security. An aggressive test plan is included which in addition to environmental Testing measures bit error rate latency and antenna patterns. Additional flight tests are planned far the near future on aircraft, long duration balloons and sounding rockets and these results as well as the current status of the project arc reported. Use of the WFF Flight Modem on small satellites is also being pursued. The LEO satellite constellation altitude above 1400 km is not an obstacle because most spacecraft do not require continuous Communications. The challenge is scheduling where store and forward techniques for command are required and downlink when the communications link allows connection (above 60 percent of the time depending on the satellite altitude). Sophisticated scheduling techniques utilizing 2-line orbital element sets available on the NASA/NORAD Internet site could be implemented for rare special cases. The current 9600 baud rate of the LEO communications link may be increased With special techniques that are planned for development in the WFF Flight Modem project.

Morgan, Dwayne↗

NASA NTRS2014

Data-Intensive Science meets Inquiry-Driven Pedagogy: Interactive Big Data Exploration, Threshold Concepts, and Liminality

Threshold concepts in any discipline are the core concepts an individual must understand in order to master a discipline. By their very nature, these concepts are troublesome, irreversible, integrative, bounded, discursive, and reconstitutive. Although grasping threshold concepts can be extremely challenging for each learner as s/he moves through stages of cognitive development relative to a given discipline, the learner's grasp of these concepts determines the extent to which s/he is prepared to work competently and creatively within the field itself. The movement of individuals from a state of ignorance of these core concepts to one of mastery occurs not along a linear path but in iterative cycles of knowledge creation and adjustment in liminal spaces - conceptual spaces through which learners move from the vaguest awareness of concepts to mastery, accompanied by understanding of their relevance, connectivity, and usefulness relative to questions and constructs in a given discipline. For example, challenges in the teaching and learning of atmospheric science can be traced to threshold concepts in fluid dynamics. In particular, Dynamic Meteorology is one of the most challenging courses for graduate students and undergraduates majoring in Atmospheric Science. Dynamic Meteorology introduces threshold concepts - those that prove troublesome for the majority of students but that are essential, associated with fundamental relationships between forces and motion in the atmosphere and requiring the application of basic classical statics, dynamics, and thermodynamic principles to the three dimensionally varying atmospheric structure. With the explosive growth of data available in atmospheric science, driven largely by satellite Earth observations and high-resolution numerical simulations, paradigms such as that of dataintensive science have emerged. These paradigm shifts are based on the growing realization that current infrastructure, tools and processes will not allow us to analyze and fully utilize the complex and voluminous data that is being gathered. In this emerging paradigm, the scientific discovery process is driven by knowledge extracted from large volumes of data. In this presentation, we contend that this paradigm naturally lends to inquiry-driven pedagogy where knowledge is discovered through inductive engagement with large volumes of data rather than reached through traditional, deductive, hypothesis-driven analyses. In particular, data-intensive techniques married with an inductive methodology allow for exploration on a scale that is not possible in the traditional classroom with its typical problem sets and static, limited data samples. In addition, we identify existing gaps and possible solutions for addressing the infrastructure and tools as well as a pedagogical framework through which to implement this inductive approach.

Ramachandran, Rahul↗

NASA NTRSDate not supplied

Development of an Airspace Simulation and Modeling Tool for Enhanced Spectrum Management

The emergence of new aerial vehicles into the National Airspace System creates an increased demand for aeronautical communications to support aviation operations. However, the issue of spectrum scarcity remains an ever-present concern, and the growing demand cannot be supported using existing spectrum allocation strategies. As a result, a new spectrum management approach is required, and the National Aeronautics and Space Administration (NASA) is investigating advanced concepts to modernize the management and use of aviation spectrum by leveraging the latest advancements in wireless communications, big data and machine learning. This research proposes an autonomous spectrum allocation concept, which allocates communications resources, such as spectrum and power, based on the predicted communications and air traffic demands throughout the airspace, as opposed to the use of fixed allocations as is done today. This approach will result in improved spectrum utilization efficiency and enhanced airspace capacity. The autonomous spectrum allocation concept decomposes into three research areas: demand prediction, resource allocation, and use case evaluation. As part of the use case evaluation effort, a modeling and simulation capability is currently under development. This simulation capability includes the implementation of various features, including visualization of both live or virtually-generated airspace traffic, simulation scenario development, simulation management with data collection, and flight plan creation with corresponding trajectory generation. This modeling and simulation capability will continue to evolve as new and advanced airspace applications are introduced into existing and emerging operational environments.

Eric J. Knoblock↗

NASA NTRS2003

Combining Multiple Gyroscope Outputs for Increased Accuracy

A proposed method of processing the outputs of multiple gyroscopes to increase the accuracy of rate (that is, angular-velocity) readings has been developed theoretically and demonstrated by computer simulation. Although the method is applicable, in principle, to any gyroscopes, it is intended especially for application to gyroscopes that are parts of microelectromechanical systems (MEMS). The method is based on the concept that the collective performance of multiple, relatively inexpensive, nominally identical devices can be better than that of one of the devices considered by itself. The method would make it possible to synthesize the readings of a single, more accurate gyroscope (a virtual gyroscope) from the outputs of a large number of microscopic gyroscopes fabricated together on a single MEMS chip. The big advantage would be that the combination of the MEMS gyroscope array and the processing circuitry needed to implement the method would be smaller, lighter in weight, and less power-hungry, relative to a conventional gyroscope of equal accuracy. The method (see figure) is one of combining and filtering the digitized outputs of multiple gyroscopes to obtain minimum-variance estimates of rate. In the combining-and-filtering operations, measurement data from the gyroscopes would be weighted and smoothed with respect to each other according to the gain matrix of a minimum- variance filter. According to Kalman-filter theory, the gain matrix of the minimum-variance filter is uniquely specified by the filter covariance, which propagates according to a matrix Riccati equation. The present method incorporates an exact analytical solution of this equation.

Bayard, David S.↗

NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗