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At least 253 records · Page 14

An Open Source Web Map Server Implementation For California and the Digital Earth: Lessons Learned

This paper describes an Open Source implementation of the Open GIS Consortium's Web Map interface. It is based on the very popular Apache WWW Server, the Sun Microsystems Java ServIet Development Kit, and a C language shared library interface to a spatial datastore. This server was initially written as a proof of concept, to support a National Aeronautics and Space Administration (NASA) Digital Earth test bed demonstration. It will also find use in the California Land Science Information Partnership (CaLSIP), a joint program between NASA and the state of California. At least one WebMap enabled server will be installed in every one of the state's 58 counties. This server will form a basis for a simple, easily maintained installation for those entities that do not yet require one of the larger, more expensive, commercial offerings.

Sullivan, D. V.↗

InSAR Scientific Computing Environment

This computing environment is the next generation of geodetic image processing technology for repeat-pass Interferometric Synthetic Aperture (InSAR) sensors, identified by the community as a needed capability to provide flexibility and extensibility in reducing measurements from radar satellites and aircraft to new geophysical products. This software allows users of interferometric radar data the flexibility to process from Level 0 to Level 4 products using a variety of algorithms and for a range of available sensors. There are many radar satellites in orbit today delivering to the science community data of unprecedented quantity and quality, making possible large-scale studies in climate research, natural hazards, and the Earth's ecosystem. The proposed DESDynI mission, now under consideration by NASA for launch later in this decade, would provide time series and multiimage measurements that permit 4D models of Earth surface processes so that, for example, climate-induced changes over time would become apparent and quantifiable. This advanced data processing technology, applied to a global data set such as from the proposed DESDynI mission, enables a new class of analyses at time and spatial scales unavailable using current approaches. This software implements an accurate, extensible, and modular processing system designed to realize the full potential of InSAR data from future missions such as the proposed DESDynI, existing radar satellite data, as well as data from the NASA UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), and other airborne platforms. The processing approach has been re-thought in order to enable multi-scene analysis by adding new algorithms and data interfaces, to permit user-reconfigurable operation and extensibility, and to capitalize on codes already developed by NASA and the science community. The framework incorporates modern programming methods based on recent research, including object-oriented scripts controlling legacy and new codes, abstraction and generalization of the data model for efficient manipulation of objects among modules, and well-designed module interfaces suitable for command- line execution or GUI-programming. The framework is designed to allow users contributions to promote maximum utility and sophistication of the code, creating an open-source community that could extend the framework into the indefinite future.

Rosen, Paul A.↗

The NASA Severe Thunderstorm Observations and Regional Modeling (NASA STORM) Project

The NASA Severe Storm Thunderstorm Observations and Regional Modeling(NASA STORM) project enhanced NASA’s severe weather research capabilities, building upon existing Earth Science expertise at NASA Marshall Space Flight Center (MSFC). During this project, MSFC extended NASA’s ground-based lightning detection capacity to include a readily deployable lightning mapping array (LMA). NASA STORM also enabled NASA’s Short-term Prediction and Research Transition (SPoRT) to add convection allowing ensemble modeling to its portfolio of regional numerical weather prediction (NWP) capabilities. As a part of NASA STORM, MSFC developed new open-source capabilities for analyzing and displaying weather radar observations integrated from both research and operational networks. These accomplishments enabled by NASA STORM are a step towards enhancing NASA’s capabilities for studying severe weather and positions them for any future NASA related severe storm field campaigns.

modeling↗

Program Options to Explore Ocean Worlds

Including Earth, roughly a dozen water ocean worlds exist in the solar system: the relict worlds Ceres and Mars, vast oceans inside most of the large Jovian and Saturnian icy moons, and Kuiper Belt Objects like Triton, Charon, and Pluto whose geologies are dominated by water and ammonia. Key pieces of the ocean-world science puzzle – which when completed may reveal whether life is widespread in the cosmos, why it exists where it does, and how it originates – are distributed among them. The eventual exploration of all these worlds will yield humanity’s total tangible knowledge about life in the universe, essentially forever. Thus, their exploration has existential significance for humanity’s self-regard, and indeed perhaps of our place in the natural scheme. The matter of planning how to pursue such a difficult and unprecedented exploration opportunity is therefore historic. The technical challenges are formidable, far harder than at Mars: missions to the Jovian and Saturnian ocean worlds are severely power-limited; trip times can be as much as a half decade and decade, respectively. And the science targets are global-scale oceans beneath kilometers of cryogenic ice. Reaching and exploring them would be a multi-generational undertaking, so again it is essential to plan and prepare. Today, we lack the instrumentation, subsystems, and remote operational-intelligence technologies needed to build and use exploration avatars as good as what we can envision needing. Each ocean world holds a piece of the puzzle, but the three priority targets are Europa at Jupiter, and Enceladus and Titan at Saturn. As with the systematic exploration of Mars, exploring these diverse worlds poses a complex technical and programmatic challenge – a strategic challenge – that needs to be designed and managed if each generation is to see its work bear fruit, and if the space science community is to make most effective use of the public money devoted to the quest. Strategic programs benefit from coherence. In only 15 years, the Mars Exploration Program (MEP) has transformed humanity’s view of Mars as a once and future habitable place, a world quite possibly holding relict evidence of life. Finding such evidence, we would study it to know if that life shared an origin common with Earth life. However, life in the ocean worlds could not have shared our origin, so exploring them opens another level in our quest to understand life in the universe: not only to places with vast salt-water seas known to contain organics and hydrothermal seafloors active today, but to places where anything alive cannot be related to us. MEP’s success – from its presence in the public consciousness to its rewriting of planetary habitability – make it an obvious template and source of lessons learned for a viable ocean worlds exploration program (OWEP). Six attributes of the MEP are analyzed for application to a potential OWEP. From this, five hypothetical programmatic scenarios are compared to the default case, and conclusions drawn. A coherent OWEP should have several parts: first, dedicated continuous investment in enabling technologies; and second, two directed-purpose, medium-class (~$1B) missions per decade that conduct pivotal investigations on a documented roadmap. Science could start in 2035, informing development of decadal flagship missions after Europa Clipper, to the places revealed to hold the most promise. The fastest pace of scientific discoveries would require access to high-performance propulsion infrastructure, e.g., the Space Launch System, Falcon Heavy, and high-power in-space solar electric propulsion, all capable of halving trip time. Not including these boosts, such a program would cost about a half-billion dollars more per year than NASA’s existing mission portfolio; the program architecture funded today cannot deliver a strategic OWEP while also sustaining balance among other solar system exploration priorities and opportunities.

Naderi, F.↗

Demystifying Kepler Data: A Primer for Systematic Artifact Mitigation

The Kepler spacecraft has collected data of high photometric precision and cadence almost continuously since operations began on 2009 May 2. Primarily designed to detect planetary transits and asteroseismological signals from solar-like stars, Kepler has provided high quality data for many areas of investigation. Unconditioned simple aperture time-series photometry are however affected by systematic structure. Examples of these systematics are differential velocity aberration, thermal gradients across the spacecraft, and pointing variations. While exhibiting some impact on Kepler's primary science, these systematics can critically handicap potentially ground-breaking scientific gains in other astrophysical areas, especially over long timescales greater than 10 days. As the data archive grows to provide light curves for 10(exp 5) stars of many years in length, Kepler will only fulfill its broad potential for stellar astrophysics if these systematics are understood and mitigated. Post-launch developments in the Kepler archive, data reduction pipeline and open source data analysis software have occurred to remove or reduce systematic artifacts. This paper provides a conceptual primer for users of the Kepler data archive to understand and recognize systematic artifacts within light curves and some methods for their removal. Specific examples of artifact mitigation are provided using data available within the archive. Through the methods defined here, the Kepler community will find a road map to maximizing the quality and employment of the Kepler legacy archive.

Kinemuchi, K.↗

Challenges in Obtaining and Visualizing Satellite Level 2 Data in GIS

Satellite data products are important for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if the satellite data are well utilized and interpreted. Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite data products provided by NASA and other organizations. One way to help users better understand the satellite data is to provide data along with Images, including accurate pixel coverage area delineation, and science team recommended quality screening for individual geophysical parameters. However, there are challenges of visualizing remote sensed non-gridded products: (1) different geodetics of space-borne instruments (2) data often arranged in a long-track and a cross-track axes (3) spatially and temporally continuous data chunked into granule files: data for a portion (or all) of a satellite orbit (4) no general rule of resampling or interpolations to a grid (5) geophysical retrieval only based on pixel center location without shape information. In this presentation, we will unravel a new Goddard Earth Sciences Data and Information Services Center (GES DISC) Level 2 (L2) visualization on-demand service. The service's front end provides various visualization and data accessing capabilities, such as overlay and swipe of multiply variables and subset and download of data in different formats. The backend of the service consists of Open Geospatial Consortium (OGC) standard-compliant Web Mapping Service (WMS) and Web Coverage Service. The infrastructure allows inclusion of outside data sources served in OGC compliant protocols and allows other interoperable clients, such as ArcGIS clients, to connect to our L2 WCS/WMS.

GI↗

Data Management as a Cluster Middleware Centerpiece

Through earth and space modeling and the ongoing launches of satellites to gather data, NASA has become one of the largest producers of data in the world. These large data sets necessitated the creation of a Data Management System (DMS) to assist both the users and the administrators of the data. Halcyon Systems Inc. was contracted by the NASA Center for Computational Sciences (NCCS) to produce a Data Management System. The prototype of the DMS was produced by Halcyon Systems Inc. (Halcyon) for the Global Modeling and Assimilation Office (GMAO). The system, which was implemented and deployed within a relatively short period of time, has proven to be highly reliable and deployable. Following the prototype deployment, Halcyon was contacted by the NCCS to produce a production DMS version for their user community. The system is composed of several existing open source or government-sponsored components such as the San Diego Supercomputer Center s (SDSC) Storage Resource Broker (SRB), the Distributed Oceanographic Data System (DODS), and other components. Since Data Management is one of the foremost problems in cluster computing, the final package not only extends its capabilities as a Data Management System, but also to a cluster management system. This Cluster/Data Management System (CDMS) can be envisioned as the integration of existing packages.

Zero, Jose↗

The Stratospheric Observatory for Infrared Astronomy (SOFIA) - Current Status, Recent Results, Future Plans, and Synergies with the AKARI Archive

The Stratospheric Observatory for Infrared Astronomy comprises a 2.7m diameter telescope mounted in a heavily modified B747SP aircraft. The SOFIA program is a joint US NASA and German DLR program, with the development and operations costs split roughly 80%:20%, respectively. Although the observatory is funded by these two nations, its observing time is open to proposals from astronomers of any nationality. The observatory has been flying and taking scientific data since 2010 and currently observes astronomical targets from the stratosphere for approximately 800 research flight hours per year. Seven science instruments (with an eighth coming online in 2020) cover the visible to sub-millimeter wavelengths with a variety of spectral resolutions ranging up to 1e8. The AKARI Archive with its 1.7 to 180 micron wavelength coverage is a natural complementary source for follow-up observations with SOFIA. This presentation will cover the current SOFIA technical capabilities and will present a few recent science highlights that demonstrate the SOFIA/AKARI complementarity. The presentation will also cover the SOFIA proposal process and will summarize other partnership opportunities for additional observing time on SOFIA.

Astronomy↗

Math Description Engine Software Development Kit

The Math Description Engine Software Development Kit (MDE SDK) can be used by software developers to make computer-rendered graphs more accessible to blind and visually-impaired users. The MDE SDK generates alternative graph descriptions in two forms: textual descriptions and non-verbal sound renderings, or sonification. It also enables display of an animated trace of a graph sonification on a visual graph component, with color and line-thickness options for users having low vision or color-related impairments. A set of accessible graphical user interface widgets is provided for operation by end users and for control of accessible graph displays. Version 1.0 of the MDE SDK generates text descriptions for 2D graphs commonly seen in math and science curriculum (and practice). The mathematically rich text descriptions can also serve as a virtual math and science assistant for blind and sighted users, making graphs more accessible for everyone. The MDE SDK has a simple application programming interface (API) that makes it easy for programmers and Web-site developers to make graphs accessible with just a few lines of code. The source code is written in Java for cross-platform compatibility and to take advantage of Java s built-in support for building accessible software application interfaces. Compiled-library and NASA Open Source versions are available with API documentation and Programmer s Guide at http:/ / prim e.jsc.n asa. gov.

Shelton, Robert O.↗

Data Analysis Challenges for Multi-Messenger Astrophysics

Recent multi-messenger observations of gravitational-wave and high-energy neutrino sources together with electromagnetic signatures have opened new ways of observing the Universe. These promise a future in which physics and astronomy will be advanced by combining observations and data from across the electromagnetic spectrum with gravitational waves and neutrinos. We consider the challenges the field is facing in fully utilizing data for multi-messenger astrophysics. Such data come from heterogeneous detector networks and standards, and their analysis is often time-critical to guide further observations. In this area, science capabilities depend on the interplay among observation, theory and computational/modeling work. Advances in data science and computing present additional opportunities and considerations in analyzing such data. We invited ADASS participants to a Birds of a Feather session to engage in discussion on the challenges and opportunities in data analysis for multimessenger astrophysics.

Peter S Shawhan↗

Community Involvement in Enhancing the Global Change Master Directory (GCMD) Controlled Vocabularies (Keywords)

NASA's Global Change Master Directory (GCMD) develops and expands a hierarchical set of controlled vocabularies (keywords) covering the Earth sciences and associated information (data centers, projects, platforms, instruments, etc.). The purpose of the keywords is to describe Earth science data and services in a consistent and comprehensive manner, allowing for the precise searching of metadata and subsequent retrieval of data and services. The keywords are accessible in a standardized SKOSRDFOWL representation and are used as an authoritative taxonomy, as a source for developing ontologies, and to search and access Earth Science data within online metadata catalogues. The keyword development approach involves: (1) receiving community suggestions, (2) triaging community suggestions, (3) evaluating the keywords against a set of criteria coordinated by the NASA ESDIS Standards Office, and (4) publication/notification of the keyword changes. This approach emphasizes community input, which helps ensure a high quality, normalized, and relevant keyword structure that will evolve with users changing needs. The Keyword Community Forum, which promotes a responsive, open, and transparent processes, is an area where users can discuss keyword topics and make suggestions for new keywords. The formalized approach could potentially be used as a model for keyword development.

governance↗

Evaluating Machine Learning Approaches to Plume Tracking

On July 15, 2022, the Hunga Tonga-Hunga Ha’apai (HTHH) submarine volcano erupted, propelling trace gasses and ash through the troposphere and up into the stratosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using imagery from NASA’s Earth Observing System, including MODIS aerosol products and OMI sulfur dioxide products, this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline, establishes a framework for systematically and rapidly studying natural disasters, including additional volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of NASA’s Earth Observation and remote sensing data, this work shows how AI and open science can accelerate research and generate actionable results, even for unprecedented events. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions, and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters in a changing world.

machine learning↗

The Deep Space Network: An instrument for radio science research

Doppler and ranging data routinely generated at the Deep Space Stations of the California Institute of Technology-Jet Propulsion Laboratory Deep Space Network serve as an excellent source of radio science information. Important radio science experiments based on Deep Space Network generated radio metric data have included confirmation of Einstein's Theory of Relativity, measurement of the masses and gravitational harmonics of the planets out to Saturn, and measurement of electron density distribution and turbulence in the solar corona. In response to an increased level of radio science requirements, the Deep Space Network chose in 1976 to implement a new radio science system, which was completed in late 1978. Key features include (1) highly phase stable open loop receivers, (2) reduction of recorded data bandwidth through use of programmed local oscillators, and (3) real time digitization and recording on computer compatible tape.

Renzetti, N. A.↗

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

On January 15, 2022, the Hunga Tonga-Hunga Ha’apai (hereafter, Hunga Tonga) submarine volcano had an explosive eruption that thrusted ash, gases, and water vapor through the troposphere into the stratosphere and mesosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using data retrieved from low earth orbiting satellite instruments (e.g., OMPS, OMI, and CALIPSO), this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), with prompt engineering can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline using NASA Earthdata and Openscapes, establishes a framework for systematically and rapidly studying extreme events, including volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of remote sensing data, this work demonstrates how AI and open science can accelerate research and generate actionable results. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions (e.g., the Atmosphere Observing System (AOS)), and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters and extreme events in a changing world.

David M. Giles↗

Preparing GMAT for Operational Maneuver Planning of the Advanced Composition Explorer (ACE)

The General Mission Analysis Tool (GMAT) is an open-source space mission design, analysis and trajectory optimization tool. GMAT is developed by a team of NASA, private industry, public and private contributors. GMAT is designed to model, optimize and estimate spacecraft trajectories in flight regimes ranging from low Earth orbit to lunar applications, interplanetary trajectories and other deep space missions. GMAT has also been flight qualified to support operational maneuver planning for the Advanced Composition Explorer (ACE) mission. ACE was launched in August, 1997 and is orbiting the Sun-Earth L1 libration point. The primary science objective of ACE is to study the composition of both the solar wind and the galactic cosmic rays. Operational orbit determination, maneuver operations and product generation for ACE are conducted by NASA Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF). This paper discusses the entire engineering lifecycle and major operational certification milestones that GMAT successfully completed to obtain operational certification for the ACE mission. Operational certification milestones such as gathering of the requirements for ACE operational maneuver planning, gap analysis, test plans and procedures development, system design, pre-shadow operations, training to FDF ACE maneuver planners, shadow operations, Test Readiness Review (TRR) and finally Operational Readiness Review (ORR) are discussed. These efforts have demonstrated that GMAT is flight quality software ready to support ACE mission operations in the FDF.

Trajectory analysis↗

Framework for Processing Citizens Science Data for Applications to NASA Earth Science Missions

Citizen science (or crowdsourcing) has drawn much high-level recent and ongoing interest and support. It is poised to be applied, beyond the by-now fairly familiar use of, e.g., Twitter for natural hazards monitoring, to science research, such as augmenting the validation of NASA earth science mission data. This interest and support is seen in the 2014 National Plan for Civil Earth Observations, the 2015 White House forum on citizen science and crowdsourcing, the ongoing Senate Bill 2013 (Crowdsourcing and Citizen Science Act of 2015), the recent (August 2016) Open Geospatial Consortium (OGC) call for public participation in its newly-established Citizen Science Domain Working Group, and NASA's initiation of a new Citizen Science for Earth Systems Program (along with its first citizen science-focused solicitation for proposals). Over the past several years, we have been exploring the feasibility of extracting from the Twitter data stream useful information for application to NASA precipitation research, with both "passive" and "active" participation by the twitterers. The Twitter database, which recently passed its tenth anniversary, is potentially a rich source of real-time and historical global information for science applications. 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. 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. Mining the Twitter stream could augment these validation programs and, potentially, help tune existing algorithms. Our ongoing work, though exploratory, has resulted in key components for processing and managing tweets, including the capabilities to filter the Twitter stream in real time, to extract location information, to filter for exact phrases, and to plot tweet distributions. The key step is to process the "precipitation" tweets to be compatible with satellite-retrieved precipitation data. These key components for processing and managing "precipitation" tweets (and additional ones to be developed) are not limited to precipitation, nor are they limited to the Twitter social medium. Indeed, to maximize the value of our work for NASA earth science programs, these components should be generalized and be part of an overall framework for processing citizen science data for science research. In this paper, we outline such a framework.

earth science satellite data↗

Preparing General Mission Analysis Tool for Operational Maneuver Planning of the Advanced Composition Explorer Mission

The General Mission Analysis Tool (GMAT) is an open-source space mission design, analysis and trajectory optimization tool. GMAT is developed by a team of NASA, private industry, public and private contributors. GMAT is designed to model, optimize and estimate spacecraft trajectories in flight regimes ranging from low Earth orbit to lunar applications, interplanetary trajectories and other deep space missions. GMAT has also been flight qualified to support operational maneuver planning for the Advanced Composition Explorer (ACE) mission. ACE was launched in August, 1997 and is orbiting the Sun-Earth L1 libration point. The primary science objective of ACE is to study the composition of both the solar wind and the galactic cosmic rays. Operational orbit determination, maneuver operations and product generation for ACE are conducted by NASA Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF). This paper discusses the entire engineering lifecycle and major operational certification milestones that GMAT successfully completed to obtain operational certification for the ACE mission. Operational certification milestones such as gathering of the requirements for ACE operational maneuver planning, gap analysis, test plans and procedures development, system design, pre-shadow operations, training to FDF ACE maneuver planners, shadow operations, Test Readiness Review (TRR) and finally Operational Readiness Review (ORR) are discussed. These efforts have demonstrated that GMAT is flight quality software ready to support ACE mission operations in the FDF.

mission design↗

The Herschel ATLAS

The Herschel ATLAS is the largest open-time key project that will be carried out on the Herschel Space Observatory. It will survey 570 sq deg of the extragalactic sky, 4 times larger than all the other Herschel extragalactic surveys combined, in five far-infrared and submillimeter bands. We describe the survey, the complementary multiwavelength data sets that will be combined with the Herschel data, and the six major science programs we are undertaking. Using new models based on a previous submillimeter survey of galaxies, we present predictions of the properties of the ATLAS sources in other wave bands.

Eales, S.↗