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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Large Engine Technology (LET) Task XXXVII Low-Bypass Ratio Mixed Turbofan Engine Subsonic Jet Noise Reduction Program Test Report

NASA Glenn Research Center supported a three year effort to develop the technology for reducing jet noise from low-bypass ratio engines. This effort concentrated on both analytical and experimental approaches using various mixer designs. CFD and MGB predictions are compared with LDV and noise data, respectively. While former predictions matched well with data, experiment shows a need for improving the latter predictions. Data also show that mixing noise can be sensitive to engine hardware upstream of the mixing exit plane.

Hauser, Joseph R.↗
NASA NTRS2000

Stennis Space Center Environmental Geographic Information System

As NASA's lead center for rocket propulsion testing, the John C. Stennis Space Center (SSC) monitors and assesses the off-site impacts of such testing through its Environmental Office (SSC-EO) using acoustical models and ancillary data. The SSC-EO has developed a geographical database, called the SSC Environmental Geographic Information System (SSC-EGIS), that covers an eight-county area bordering the NASA facility. Through the SSC-EGIS, the Enivronmental Office inventories, assesses, and manages the nearly 139,000 acres that comprise Stennis Space Center and its surrounding acoustical buffer zone. The SSC-EGIS contains in-house data as well as a wide range of data obtained from outside sources, including private agencies and local, county, state, and U.S. government agencies. The database comprises cadastral/geodetic, hydrology, infrastructure, geo-political, physical geography, and socio-economic vector and raster layers. The imagery contained in the database is varied, including low-resolution imagery, such as Landsat TM and SPOT; high-resolution imagery, such as IKONOS and AVIRIS; and aerial photographs. The SSC-EGIS has been an integral part of several major projects and the model upon which similar EGIS's will be developed for other NASA facilities. The Corps of Engineers utilized the SSC-EGIS in a plan to establish wetland mitigation sites within the SSC buffer zone. Mississippi State University employed the SSC-EGIS in a preliminary study to evaluate public access points within the buffer zone. The SSC-EO has also expressly used the SSC-EGIS to assess noise pollution modeling, land management/wetland mitigation assessment, environmental hazards mapping, and protected areas mapping for archaeological sites and for threatened and endangered species habitats. The SSC-EO has several active and planned projects that will also make use of the SSC-EGIS during this and the coming fiscal year.

Lovely, Janette↗
NASA NTRS1948

Preliminary Results of Altitude-Wind-Tunnel Investigation of X24C-4B Turbojet Engine. II - Engine Performance: Engine Performance - II

An investigation was conducted in the NACA Cleveland altitude wind tunnel to evaluate the performance characteristics of the X24C-4B turbojet engine over a range of simulated altitudes from 5000 to 45,000 feet,simulated flight Mach numbers from 0 to 1.08, and engine speeds from 4000 to 12,500 rpm. Performance data are presented to show graphically the effects of altitude at a flight Mach number of 0.25 and of flight Mach number at an altitude of 25,000 feet. The performance data are generalized to show the applicability of methods used to determine performance at any altitude from data obtained at a given altitude. A complete tabulation of performance data, as well as lubrication- and fuel- system data, is presented.

Meyer, Carl L.↗
NASA NTRS2004

Air Separation Using Hollow Fiber Membranes

The NASA Glenn Research Center in partnership with the Ohio Aerospace Institute provides internship programs for high school and college students in the areas of science, engineering, professional administrative, and other technical areas. During the summer of 2004, I worked with Dr. Clarence T. Chang at NASA Glenn Research Center s combustion branch on air separation using hollow fiber membrane technology. . In light of the accident of Trans World Airline s flight 800, FAA has mandated that a suitable solution be created to prevent the ignition of fuel tanks in aircrafts. In order for any type of fuel to ignite, three important things are needed: fuel vapor, oxygen, and an energy source. Two different ways to make fuel tanks less likely to ignite are reformulating the fuel to obtain a lower vapor pressure for the fuel and or using an On Board Inert Gas Generating System (OBIGGS) to inert the Central Wing Tank. goal is to accomplish the mission, which means that the Air Separation Module (ASM) tends to be bulky and heavy. The primary goal for commercial aviation companies is to transport as much as they can with the least amount of cost and fuel per person, therefore the ASM must be compact and light as possible. The plan is to take bleed air from the aircraft s engines to pass air through a filter first to remove particulates and then pass the air through the ASM containing hollow fiber membranes. In the lab, there will be a heating element provided to simulate the temperature of the bleed air that will be entering the ASM and analysis of the separated air will be analyzed by a Gas Chromatograph/Mass Spectrometer (GC/MS). The GUMS will separate the different compounds in the exit streams of the ASM and provide information on the performance of hollow fiber membranes. Hopefully I can develop ways to improve efficiency of the ASM. different types of jet fuel were analyzed and data was well represented on SAE Paper 982485. Data consisted of the concentrations of over 300 different hydrocarbons commonly found in JP- 8, Jet A, and JP-5 fuels. I researched the major hydrocarbons that has a concentration of greater than 50 parts per million and found the vapor pressure data coefficients for a specific temperature range. The coefficients were applied to Antoine s Equation and Riedel's Equation to calculate the vapor pressures for that specific hydrocarbon in the specific temperature range. With the vapor pressure data scientists can formulate a fuel composition that has a lower vapor pressure profile, therefore making jet fuels less flammable. work, learn how to operate and examine the data from Gas Chromatograph and Mass Spectrometer, and develop new ways in applying hollow fiber membrane technology to other areas of environmental engineering. The United States military currently uses air separation technology and their primary The other side of making air travel safer is to reformulate the fuel. Analyses of three My goal this summer is to learn about hollow fiber membrane technologies and how they

Huang, Stephen E.↗
NASA NTRS2002

NASA planetary data: applying planetary satellite remote sensing data in the classroom

NASA supports several data archiving and distribution mechanisms that provide a means whereby scientists can participate in education and outreach through the use of technology for data and information dissemination. The Planetary Data System (PDS) is sponsored by NASA's Office of Space Science. Its purpose is to ensure the long-term usability of NASA data and to stimulate advanced research. In addition, the NASA Regional Planetary Image Facility (RPIF), an international system of planetary image libraries, maintains photographic and digital data as well as mission documentation and cartographic data.

Regional Plaentary Image Facility RPIF Planetary D↗
NASA NTRS2006

Temporal Decrease in Upper Atmospheric Chlorine

We report a steady decrease in the upper stratospheric and lower mesospheric abundances of hydrogen chloride (HCl) from August 2004 through January 2006, as measured by the Microwave Limb Sounder (MLS) aboard the Aura satellite. For 60(deg)S to 60(deg)N zonal means, the average yearly change in the 0.7 to 0.1 hPa (approx.50 to 65 km) region is -27 +/- 3 pptv/year, or -0.78 +/- 0.08 percent/year. This is consistent with surface abundance decrease rates (about 6 to 7 years earlier) in chlorine source gases. The MLS data confirm that international agreements to reduce global emissions of ozone-depleting industrial gases are leading to global decreases in the total gaseous chlorine burden. Tracking stratospheric HCl variations on a seasonal basis is now possible with MLS data. Inferred stratospheric total chlorine (CITOT) has a value of 3.60 ppbv at the beginning of 2006, with a (2-sigma) accuracy estimate of 7%; the stratospheric chlorine loading has decreased by about 43 pptv in the 18-month period studied here. We discuss the MLS HCl measurements in the context of other satellite-based HCl data, as well as expectations from surface chlorine data. A mean age of air of approx. 5.5 years and an age spectrum width of 2 years or less provide a fairly good fit to the ensemble of measurements.

chlorine↗
NASA NTRS1989

Fiber optic voice/data network

An asynchronous, high-speed, fiber optic local area network originally developed for tactical environments with additional benefits for other environments such as spacecraft, and the like. The network supports ordinary data packet traffic simultaneously with synchronous T1 voice traffic over a common token ring channel; however, the techniques and apparatus of this invention can be applied to any deterministic class of packet data networks, including multitier backbones, that must transport stream data (e.g., video, SAR, sensors) as well as data. A voice interface module parses, buffers, and resynchronizes the voice data to the packet network employing elastic buffers on both the sending and receiving ends. Voice call setup and switching functions are performed external to the network with ordinary PABX equipment. Clock information is passed across network boundaries in a token passing ring by preceeding the token with an idle period of non-transmission which allows the token to be used to re-establish a clock synchronized to the data. Provision is made to monitor and compensate the elastic receiving buffers so as to prevent them from overflowing or going empty.

Bergman, Larry A.↗
NASA NTRS2006

Analysis of ISS Plasma Interaction

To date, the International Space Station (ISS) has been one of the largest objects flown in lower earth orbit (LEO). The ISS utilizes high voltage solar arrays (160V) that are negatively grounded leading to pressurized elements that can float negatively with respect to the plasma. Because laboratory measurements indicate a dielectric breakdown potential difference of 80V, arcing could occur on the ISS structure. To overcome the possibility of arcing and clamp the potential of the structure, two Plasma Contactor Units (PCUs) were designed, built, and flown. Also a limited amount of measurements of the floating potential for the present ISS configuration were made by a Floating Potential Probe (FPP), indicating a minimum potential of 24 Volts at the measurement location. A predictive tool, the ISS Plasma Interaction Model (PIM) has been developed accounting for the solar array electron collection, solar array mast wire and effective conductive area on the structure. The model has been used for predictions of the present ISS configuration. The conductive area has been inferred based on available floating potential measurements. Analysis of FPP and PCU data indicated distribution of the conductive area along the Russian segment of the ISS structure. A significant input to PIM is the plasma environment. The International Reference Ionosphere (IRI 2001) was initially used to obtain plasma temperature and density values. However, IRI provides mean parameters, leading to difficulties in interpretation of on-orbit data, especially at eclipse exit where maximum charging can occur. This limits our predicative capability. Satellite and Incoherent Scatter Radar (ISR) data of plasma parameters have also been collected. Approximately 130,000 electron temperature (Te) and density (Ne) pairs for typical ISS eclipse exit conditions have been extracted from the reduced Langmuir probe data flown aboard the NASA DE-2 satellite. Additionally, another 18,000 Te and Ne pairs of ISR data from several radar locations around the globe were used to assure consistency of the satellite data. PIM predictions for ISS charging made with this data correlated very well with FPP data, indicating that the general physics of spacecraft charging with high voltage solar arrays have been captured. The predictions also provided the probabilities of occurrences for ISS charging. These probabilities give a numerical measure of the number of times when the ISS will approach or exceed the vehicle plasma hazard conditions for each configuration. In this paper we shall present the interaction mechanisms between the ISS and the surrounding plasma and give an overview of the PIM components. PIM predictions are compared with available data followed by a discussion of the variability of plasma parameters and the conductive area on the ISS. The ISS PIM will be further tested and verified as data from the Floating Potential Measurement Unit become available, and construction of the ISS continues.

Reddell, Brandon↗
NASA NTRS2012

IMAGESEER - IMAGEs for Education and Research

IMAGESEER is a new Web portal that brings easy access to NASA image data for non-NASA researchers, educators, and students. The IMAGESEER Web site and database are specifically designed to be utilized by the university community, to enable teaching image processing (IP) techniques on NASA data, as well as to provide reference benchmark data to validate new IP algorithms. Along with the data and a Web user interface front-end, basic knowledge of the application domains, benchmark information, and specific NASA IP challenges (or case studies) are provided.

Le Moigne, Jacqueline↗
NASA NTRS2012

Synchrotron Spectral Curvature from 22 MHZ to 23 GHZ

We combine surveys of the radio sky at frequencies 22 MHz to 1.4 GHz with data from the ARCADE-2 instrument at frequencies 3 GHz to 10 GHz to characterize the frequency spectrum of diffuse synchrotron emission in the Galaxy. The radio spectrum steepens with frequency from 22 MHz to 10 GHz. The projected spectral index at 23 GHz derived from the low-frequency data agrees well with independent measurements using only data at frequencies 23 GHz and above. Comparing the spectral index at 23 GHz to the value from previously published analyses allows extension of the model to higher frequencies. The combined data are consistent with a power-law index beta = -2.64 +/-= 0.03 at 0.31 GHz, steepening by an amount of Delta-beta = 0.07 every octave in frequency. Comparison of the radio data to models including the cosmic-ray energy spectrum suggests that any break in the synchrotron spectrum must occur at frequencies above 23 GHz.

Kogut, Alan J.↗
NASA NTRS2014

Comparison of Computational Approaches for Rapid Aerodynamic Assessment of Small UAVs

Computational Fluid Dynamic (CFD) methods were used to determine the basic aerodynamic, performance, and stability and control characteristics of the unmanned air vehicle (UAV), Kahu. Accurate and timely prediction of the aerodynamic characteristics of small UAVs is an essential part of military system acquisition and air-worthiness evaluations. The forces and moments of the UAV were predicted using a variety of analytical methods for a range of configurations and conditions. The methods included Navier Stokes (N-S) flow solvers (USM3D, Kestrel and Cobalt) that take days to set up and hours to converge on a single solution; potential flow methods (PMARC, LSAERO, and XFLR5) that take hours to set up and minutes to compute; empirical methods (Datcom) that involve table lookups and produce a solution quickly; and handbook calculations. A preliminary aerodynamic database can be developed very efficiently by using a combination of computational tools. The database can be generated with low-order and empirical methods in linear regions, then replacing or adjusting the data as predictions from higher order methods are obtained. A comparison of results from all the data sources as well as experimental data obtained from a wind-tunnel test will be shown and the methods will be evaluated on their utility during each portion of the flight envelope.

Shafer, Theresa C.↗
NASA NTRS2016

Monitoring Drought Conditions in the Navajo Nation Using NASA Earth Observations

The Navajo Nation, a 65,700 sq km Native American territory located in the southwestern United States, has been increasingly impacted by severe drought events and changes in climate. These events are coupled with a lack of domestic water infrastructure and economic resources, leaving approximately one-third of the population without access to potable water in their homes. Current methods of monitoring drought are dependent on state-based monthly Standardized Precipitation Index value maps calculated by the Western Regional Climate Center. However, these maps do not provide the spatial resolution needed to illustrate differences in drought severity across the vast Nation. To better understand and monitor drought events and drought regime changes in the Navajo Nation, this project created a geodatabase of historical climate information specific to the area, and a decision support tool to calculate average Standardized Precipitation Index values for user-specified areas. The tool and geodatabase use Tropical Rainfall Monitoring Mission (TRMM) and Global Precipitation Monitor (GPM) observed precipitation data and Parameter-elevation Relationships on Independent Slopes Model modeled historical precipitation data, as well as NASA's modeled Land Data Assimilation Systems deep soil moisture, evaporation, and transpiration data products. The geodatabase and decision support tool will allow resource managers in the Navajo Nation to utilize current and future NASA Earth observation data for increased decision-making capacity regarding future climate change impact on water resources.

Ly, Vickie↗
NASA NTRS2014

Interpreting the Helioseismic and Magnetic Imager (HMI) Multi-Height Velocity Measurements

The Solar Dynamics Observatory Helioseismic and Magnetic Imager (SDOHMI) filtergrams, taken at six wavelengths around the Fe I 6173.3 Å line, contain information about the line-of-sight velocity over a range of heights in the solar atmosphere. Multiheight velocity inferences from these observations can be exploited to study wave motions and energy transport in the atmosphere. Using realistic convection-simulation datasets provided by the STAGGER and MURaM codes, we generate synthetic filtergrams and explore several methods for estimating Dopplergrams.We investigate at which height each synthetic Dopplergram correlates most strongly with the vertical velocity in the model atmospheres. On the basis of the investigation, we propose two Dopplergrams other than the standard HMI-algorithm Dopplergram produced from HMI filtergrams: a line-center Dopplergram and an average-wing Dopplergram. These two Dopplergrams correlate most strongly with vertical velocities at the heights of 30 40 km above (line center) and 30 40 km below (average wing) the effective height of the HMI-algorithm Dopplergram. Therefore, we can obtain velocity information from two layers separated by about a half of a scale height in the atmosphere, at best. The phase shifts between these multi-height Dopplergrams from observational data as well as those from the simulated data are also consistent with the height-difference estimates in the frequency range above the photospheric acoustic-cutoff frequency.

velocity fields↗
NASA NTRS2019

Sherlock Data Warehouse

This slide deck provides an overview of the data and resources available in the Sherlock Data Warehouse. Sherlock was developed and is currently maintained by the Aviation Systems Division at NASA Ames Research Center. Sherlock contains a valuable collection of flight, air traffic management, and weather data. But Sherlock is not just a data archive. Sherlock also includes tools and resources to access, download, and visualize data, as well as resources to process the data. This overview summarizes Sherlock data sources, demonstrates data analytics and visualization with MicroStrategy, illustrates disparate data integration using the ATM Knowledge graph, and presents a machine learning use case using the Big Data system.

data warehouse↗
NASA NTRS2019

Simplifying NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns, are large, complex and evolving. Such characteristics pose great challenges for end users (e.g., Earth science and applied science users, students, citizen scientists), particularly for those who are unfamiliar with NASA's EOSDIS and thus unable to access and utilize datasets effectively. For example, a novice user may simply ask: what is the total rainfall for a flooding event in my county yesterday? For an experienced user (e.g., algorithm developer), a question can be: how did my rainfall product perform, compared to ground observations, during a flooding event? Nonetheless, with rapid information technology development such as natural language processing, it is possible to develop simplified Web interfaces and back-end processing components to handle such questions and deliver answers in terms of text, data, or graphic results directly to users.In this presentation, we describe the main challenges for end users with different levels of expertise in accessing and utilizing NASA Earth science data. Surveys reveal that most non-professional users normally do not want to download and handle raw data as well as conduct heavy-duty data processing tasks. Often they just want some simple graphics or data for various purposes. To them, simple and intuitive user interfaces are sufficient because complicated ones can be difficult and time-consuming to learn. Professionals also want such interfaces to answer many questions from datasets. One solution is to develop a natural language based search box like Google and the search results can be text, data, graphics and more. Now the challenge is, with natural language processing, can we design a system to process a scientific question typed in by a user? In this presentation, we describe our plan for such a prototype. The workflow is: 1) extract needed information (e.g., variables, spatial and temporal information, processing methods, etc.) from the input, 2) process the data in the backend, and 3) deliver the results (data or graphics) to the user.

Liu, Zhong↗
NASA NTRS2019

TPSAS-NF1676L-32493-DND

The Committee on Earth Observation Satellites (CEOS) System Engineering Office (SEO) has supported the Open Data Cube (ODC) initiative to provide a data architecture solution that has value to its global users and increases the impact of EO satellite data. ODC is an open-source platform for processing satellite data. We have developed software products and tools around the core ODC that would help users perform machine learning on EO satellite data. The recent United Nations (UN) Sustainable Development Agenda provides a shared blueprint for peace and prosperity for people and for the planet, considering our current situation and helping to create a plan. The core of this agenda is a set of seventeen Sustainable Development Goals (SDGs), which represent an urgent call for action by all countries - both developed and developing - in a global partnership. The CEOS SEO team has recently developed and released a set of innovative Jupyter notebooks addressing UN SDGs 6.6.1 (spatial extents of water-related ecosystems), 11.3.1 (ratio of land consumption rate to population growth rate), and 15.3.1 (proportion of land that is degraded over total land area). These notebooks empower users by providing features that will assist with streamlining analysis ready data retrieval, processing, and visualization. We have recently incorporated several machine learning techniques in these notebooks. In this paper, we present the lessons learned from our experience on classifying land using supervised and unsupervised machine learning techniques using ODC framework for UN SDGs. We identify the current limitations of ODC to seamlessly support machine learning techniques. We propose features that would help machine learning, specifically within the ODC framework. We propose a thematic indexing/loading of data for both unsupervised learning as well as data annotation/labeling pipeline. Currently, ODC supports machine learning by separating data-management from the analysis process. It works as a mechanism to load cubes of data. ODC does not natively support features that are vital in machine learning such as validation splits, fair/balanced sampling, establishing load size constraints, etc. We believe that our proposed features will empower users by providing features that bring machine learning techniques closed to ODC. Enhancements to ODC to better accommodate machine learning techniques can assist in fulfilling UN SDGs such as 6.3.2, 6.4.2, 6.6.1, 11.3.1, 14.1.1, 15.1.1, 15.3.1, and 15.4.2.

Syed R Rizvi↗
NASA NTRS2020

Improved SIFTER v2 algorithm for long-term GOME-2A satellite retrievals of fluorescence with a correction for instrument degradation

Solar-induced fluorescence (SIF) data from satellites are increasingly used as a proxy for photosynthetic activity by vegetation, and as a constraint on gross primary production. Here we report on improvements in the algorithm to retrieve mid-morning (09:30 hrs local time) SIF estimates on the global scale from GOME-2 sensor on the Metop-A satellite (GOME-2A) for the period 2007-2019. Our new SIFTER (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval) v2 algorithm improves over a previous version by using a narrower spectral window that avoids strong oxygen absorption and is less sensitive to water vapour absorption, by constructing stable reference spectra from a 6-year period (2007-2012) of atmospheric spectra over the Sahara, and by applying a latitude-dependent zero-level adjustment that accounts for biases in the data product. We generated stable, good-quality SIF retrievals between January 2007 and June 2013, when GOME-2A degradation in the near infrared was still limited. After the narrowing of the GOME-2A swath in July 2013, we characterized the throughput degradation of the level-1 data in order to derive reflectance corrections and apply these for the SIF retrievals between July 2013 and December 2018. SIFTER v2 data compares well with the independent NASA v2.8 data product. Especially in the evergreen tropics, SIFTER v2 no longer shows the underestimates against other satellite products that were seen in SIFTER v1. The new data product includes uncertainty estimates for individual observations, and is best used for mostly clear-sky scenes, and when spectral residuals remain below a certain spectral autocorrelation threshold. Our results support the use of SIFTER v2 data to be used as an independent constraint on photosynthetic activity on regional to global scales.

Solar-induced fluorescence (SIF)↗
NASA NTRSDate not supplied

Biomass Harmonization and SAR Analysis with the Multi-mission Algorithm and Analysis Platform (MAAP)

The Multi‐mission Algorithm and Analysis Platform (MAAP) is a collaborative effort between NASA and the European Space Agency (ESA) to support above ground biomass (AGB) research in an open science framework. MAAP brings together relevant data, algorithms, and computing capabilities in a common cloud environment to address the challenges of sharing and processing data from field, airborne and satellite measurements. MAAP was publicly released in October 2021, providing computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of data. MAAP has allowed scientists from both North America and Europe to collaborate on the generation and analysis/visualization of data derived from multiple, discipline-adjacent missions in an open, collaborative environment that has reached beyond traditional scientific investigation. MAAP has been used to support multiple scientific activities. To date, existing LiDAR data from multiple platforms has been calibrated with field measurements and combined for more comprehensive and accurate estimates of above ground biomass AGB; these LiDAR platforms include airborne (e.g. LVIS), the International Space Station (NASA’s Global Ecosystem Dynamics Investigation (GEDI), and satellites (e.g. ICESat-2). The current challenge is to effectively and seamlessly combine the aforementioned LiDAR-based data with new data sources such as P-band RADAR from ESA’s upcoming BIOMASS mission, existing ESA Sentinel-1 C-band SAR, and the 30 PB/yr of high cadence global coverage L-band SAR data from the upcoming NASA-ISRO SAR (NISAR) mission. Recent analysis using MAAP merged ICESat-2 and optical data (Harmonized Landsat Sentinel) produced the most comprehensively precise estimate of boreal-wide AGB to date. Another effort using MAAP is the production and open distribution of global comparisons of AGB map estimates, including from ICESat-2 and GEDI, to bolster stakeholder uptake for policy applications. These map estimates will feed into the Intergovernmental Panel on Climate Change (IPCC) database, likely aiding the next Global Carbon Stocktake of the UNFCCC. Furthermore, the biomass retrieval intercomparison exercise BRIX-2 could benefit from the MAAP providing standardized test cases (based on airborne campaign and spaceborne data) allowing the community to develop and apply retrieval algorithms based on these test cases, while forthcoming SAR data training curricula could also use the MAAP as a teaching and learning platform. The MAAP is meeting the challenges inherent in international, open science collaboration and large scale computing with a platform that is entirely open source and cloud native, using open standards for data access, manipulation, protocols, and formats. The MAAP data system consists of a dedicated data store whose data is indexed in an online catalog conforming to established metadata, application programmatic interfaces (APIs), and service interface standards, using an implementation of the open sourced NASA Common Metadata Repository. Federation of user identities allows users from either NASA or ESA to access and consume services from the other using a unified metadata catalog for the data utilized across the ESA and NASA MAAP platforms. Similarly, we are exploring how to increase interoperability to achieve a common approach to packaging, orchestrating and executing algorithms, with interoperable access to data for subsetting, fast browse, and cloud-optimized access, all using interoperable standards such as those from the Open Geospatial Consortium (OGC). Designed for interoperability, ESA and NASA utilize a common architecture for the software platform. It provides a cloud-based algorithm development environment (ADE) that enables scientists to develop algorithms collaboratively with access to the MAAP data catalog as well as other data archives. MAAP provides an Eclipse Che-based ADE supporting both Python and R languages, popular in this biomass community. Algorithms developed and containerized within the ADE can be deployed to run to thousands of computational nodes in the MAAP’s data processing system (DPS), dramatically speeding up processing and giving scientists a rapid, iterative turnaround of results. NASA’s implementation of the DPS is based on the Hybrid Science Data System (HySDS) framework, used by NASA flight projects to produce Earth science standard products.

cloud computing↗