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NASA NTRS2017

Access NASA Satellite Global Precipitation Data Visualization on YouTube

Since the satellite era began, NASA has collected a large volume of Earth science observations for research and applications around the world. The collected and archived satellite data at 12 NASA data centers can also be used for STEM education and activities such as disaster events, climate change, etc. However, accessing satellite data can be a daunting task for non-professional users such as teachers and students because of unfamiliarity of terminology, disciplines, data formats, data structures, computing resources, processing software, programming languages, etc. Over the years, many efforts including tools, training classes, and tutorials have been developed to improve satellite data access for users, but barriers still exist for non-professionals. In this presentation, we will present our latest activity that uses a very popular online video sharing Web site, YouTube (https://www.youtube.com/), for accessing visualizations of our global precipitation datasets at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). With YouTube, users can access and visualize a large volume of satellite data without the necessity to learn new software or download data. The dataset in this activity is a one-month animation for the GPM (Global Precipitation Measurement) Integrated Multi-satellite Retrievals for GPM (IMERG). IMERG provides precipitation on a near-global (60 deg. N-S) coverage at half-hourly time interval, providing more details on precipitation processes and development compared to the 3-hourly TRMM (Tropical Rainfall Measuring Mission) Multisatellite Precipitation Analysis (TMPA, 3B42) product. When the retro-processing of IMERG during the TRMM era is finished in 2018, the entire video will contain more than 330,000 files and will last ~3.6 hours. Future plans include development of flyover videos for orbital data for an entire satellite mission or project. All videos, including the one-month animation, will be uploaded and available at the GES DISC site on YouTube (https://www.youtube.com/user/NASAGESDISC).

precipitation↗
NASA NTRS2017

Investigating Access Performance of Long Time Series with Restructured Big Model Data

Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services

reanalysis↗
NASA NTRS2018

FUN3D Manual: 13.3

This manual describes the installation and execution of FUN3D version 13.3, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

Biedron, Robert T.↗
NASA NTRS2018

Seeking the Signs of Life: Assessing the Presence of Biosignatures in the Returned Sample Suite

A biosignature (''definitive biosignature'' or DBS) is an object, substance and/or pattern whose origin specifically requires a biological agent. One category of DBS are complex organic molecules and/or structures whose formation and abundances relative to other compounds are virtually unachievable in the absence of life. A potential biosignature (PBS) is an object, substance and/or pattern that might have a biological origin and thus compels investigators to gather more data before reaching a conclusion as to the presence or absence of life. The usefulness of a PBS is determined not only by the probability that life created it but also by the improbability that nonbiological processes produced it. Because habitable planetary environments create nonbiological features that can mimic biosignatures, these environments must be characterized to the extent necessary to provide a context that is essential for confirming the presence of DBS. Environmental conditions also must have allowed biosignatures to be preserved and amenable to detection. Categories of biosignatures and their measurement requirements are indicated below.

Source record↗
NASA NTRS2018

FUN3D Manual: 13.4

This manual describes the installation and execution of FUN3D version 13.4, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

Biedron, Robert T.↗
NASA NTRS2019

FUN3D Manual: 13.5

This manual describes the installation and execution of FUN3D (Fully-UNstructured three-dimensional CFD (Computational Fluid Dynamics) code) version 13.5, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

Biedron, Robert T.↗
NASA NTRS2019

FUN3D Manual: 13.6

This manual describes the installation and execution of FUN3D version 13.6, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver enables efficient gradient-based design and grid adaptation to reduce estimated discretization error. FUN3D is available with and without a reacting, real-gas capability. This generic gas option is available only for those persons that qualify for its beta release status.

Robert T Biedron↗
NASA NTRS2020

A Fresh Look at the Stratigraphy of Northern Australe

The roughly circular collection of mare deposits centered at ~38.9°S, 93°E is often re- ferred to as Mare Australe. It is located outside of the Procellarum KREEP Terrain. The circular arrangement of Australe’s mare patches has suggested an ancient, heavily degraded or relaxed impact basin roughly 900 km in diameter. The mare deposits are generally thought to have erupted into smaller post- basin craters. The type, volume, and distribution of mare eruptions potentially resembles the early stages of basin-filling mare events, but which are preserved in Australe and some farside locations. Gravity data suggest that if there was a basin, it is much smaller than originally proposed (now ~600 km) and located in the northern part of Mare Australe, between Humboldt, Milne, and Jenner craters. As a whole, Mare Australe lacks the topography typically associated with a basin; however, northern Australe has a slight topographic depression that roughly corresponds to the basin-like Bouguer gravity signature in the same area. The compositions exposed in Humboldt crater suggest that a preexisting basin might have excavated deeper crustal material. However, the underlying cause of the circularity of Mare Australe’s deposits, particularly those extending outside of the potential impact basin setting, is not yet understood. Thus, Australe may preserve fundamental information about mare volcanism potentially uncoupled from basin formation and structure. The objectives of this study are to use new high- resolution data (images, gravity, topography, and com- position) to reassess Australe’s mare deposits, deter- mine the timing and style of volcanism, identify discrete basalt deposits, and to further characterize the evolution of magmatism and subsurface structure in this area. Here, we focus on the northern Australe deposits (between Humboldt, Jenner, and Milne). As originally noted by Whitford-Stark (1979), Humboldt crater and its ejecta make an excellent stratigraphic marker that can be traced across much of the Australe region. The ejecta serves as a stratigraphic constraint for absolute model ages (AMAs) derived from crater size-frequency distributions (CSFDs).

Stopar, J. D.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-16833-DND

Semantic Infrastructure is central to realizing the first goal of the ASDC's Strategic Plan: expanding the ASDC's customer base by improving access to ASDC data. ASDC data comprises a widely heterogeneous set of complex products which presents two significant challenges in data access: Helping customers discover, among many available options, the most suitable data products for their purpose; and Guiding customers to easily and appropriately use products. Data products differ significantly in terms of how the data was collected and processed, even with similar subject matter. Understanding differences is critical to using data effectively. To reach a broader customer range, the ASDC must provide prospective users with enough information to quickly and meaningfully compare and evaluate data products. Data formats and structures also differ among products. Applications displaying and analyzing data need access to federated and semantically disambiguated data. Semantic technologies offer functionality for addressing this issue. Ontologies can provide robust, stable domain models serving as common schema for discovering, evaluating, comparing, and integrating data from disparate products. Reasoning engines and triple stores can leverage ontologies to support intelligent search applications allowing users to discover, query, retrieve, and easily reformat data from a broad spectrum of sources.

Beth Huffer↗
NASA NTRS2020

FUN3D Manual: 13.7

This manual describes the installation and execution of FUN3D version 13.7, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver may be used for formal design optimization, error estimation, and mesh adaptation. FUN3D also offers a reacting, real-gas capability and provides GPU acceleration of many common simulation options.

FUN3D↗
NASA NTRS2020

TOI-481 b and TOI-892 b: Two Long-period Hot Jupiters from the Transiting Exoplanet Survey Satellite

We present the discovery of two new 10 day period giant planets from the Transiting Exoplanet Survey Satellite mission, whose masses were precisely determined using a wide diversity of ground-based facilities. TOI-481 b and TOI-892 b have similar radii (0.99 ± 0.01 R(J) and 1.07 ± 0.02 R(J), respectively), and orbital periods (10.3311 days and 10.6266 days, respectively), but significantly different masses (1.53 ± 0.03 M(J) versus 0.95 ± 0.07 M(J), respectively). Both planets orbit metal-rich stars ([Fe/HG] = +0.26 ± 0.05 dex and [Fe/H] = +0.24 ± 0.05 for TOI-481 and TOI-892, respectively) but at different evolutionary stages. TOI-481 is a M(*) = 1.14 ± 0.02 Mꙩ, R(*) = 1.66 ± 0.02 Rꙩ G-type star (T(eff) = 5735 ± 72 K), that with an age of 6.7 Gyr, is in the turn-off point of the main sequence. TOI-892 on the other hand, is a F-type dwarf star (T(eff) = 6261 ± 80 K), which has a mass of M(*) = 1.28 ± 0.03 Mꙩ and a radius of R(*) = 1.39 ± 0.02 Rꙩ. TOI-481 b and TOI-892 b join the scarcely populated region of transiting gas giants with orbital periods longer than 10 days, which is important to constrain theories of the formation and structure of hot Jupiters.

Rafael Brahm↗
NASA NTRSDate not supplied

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗
NASA NTRSDate not supplied

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized. Video is an mp4 download, with a play time of 9 min 35 secs.

Natural Language Processing↗
NASA NTRSDate not supplied

A Newly Developing Community-Oriented Data System from NASA GES DISC

Data services are essential to facilitate data access and to aid efficiency of conducting research and application activities. With emerging technologies such as cloud computing and AI/ML (Artificial Intelligence/Machine Learning) leading the pace of the data world, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), home to the permanent archive for multidisciplinary Earth Observation (EO) geospatial data to study atmospheric composition, weather and climate variability, and water and energy cycles is no exception.Interfacing directly with users as part of data center work, we understand the challenges for the required time and effort to discover, visualize, and analyze large varieties and quantities of Earth Observation information for research, monitoring, and decision-making, largely due to the existing data and information systems aim to support experienced users, but has been proved difficult for non-earth scientists and new users that are unfamiliar with the variety of formats and structures in which data, metadata, and information are stored, as well as the required methods to use them. To address these challenges, I will update our latest activities with regard to water-and energy-related products and community-oriented and user-friendly services at the GES DISC, including our plans for the emerging technologies.

Jennifer Wei↗
NASA NTRS2022

FUN3D Manual: 14.0

This manual describes the installation and execution of FUN3D version 14.0,including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver may be used for for-mal design optimization, error estimation, and mesh adaptation. FUN3D also offers a reacting, real-gas capability and provides GPU acceleration of many common simulation options.1

William K Anderson↗
NASA NTRS2023

FUN3D Manual: 14.0.2

This manual describes the installation and execution of FUN3D version 14.0.2, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver may be used for formal design optimization, error estimation, and mesh adaptation. FUN3D also offers a reacting, real-gas capability and provides GPU acceleration of many common simulation options.

William K Anderson↗
NASA NTRS2024

FUN3D Manual: 14.1

This manual describes the installation and execution of FUN3D version 14.1, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver may be used for formal design optimization, error estimation, and mesh adaptation. FUN3D also offers a reacting, real-gas capability and provides GPU acceleration of many common simulation options.

William K Anderson↗