Search NASA⌕ Search

SEARCH · Search NASA

Results for “data harmonization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Maximizing Earth Science Observations with Data Harmonization: Harmonized Landsat/Sentinel-2

In August 2021, NASA released science quality harmonized Landsat/Sentinel-2 products for both cloud-based access and direct download from the Land Processes Distributed Active Archive Center (LP DAAC). These 30-meter products, HLSS30 (Sentinel-2 component) and HLSL30 (Landsat component) are placed on the same grid and are generated and distributed fully in the cloud. Similarly, ESA is prototyping harmonized Landsat Sentinel-2 products at 10-meter resolution. The data production and science teams from NASA and ESA will present technical details on the data production system, data product status and availability, and the benefit of harmonizing instruments with similar sensing characteristics between the agencies.

Earth Science↗
NASA NTRS1983

Measurements of mantle wave velocities and inversion for lateral heterogeneity and anisotropy. I - Analysis of great circle phase velocities

The global lateral heterogeneity of the upper mantle is investigated using the classical Fourier-transform method of Sato(1958) and IDA/GDSN data from 25 1980 earthquakes. The great-circle phase velocities of 200 Love and 250 Rayleigh 100-330-sec-period fundamental-mode wave paths are determined and interpreted in terms of regional phase-velocity variation, using additional data on surface tectonics to extrapolate odd-harmonic information from the even-harmonic data. The results are presented in extensive tables, maps, and graphs. Regionalized inversion using the seven-region model of Okal (1977) is found to give maximum variance reductions of 65 percent for Love waves and 85 percent for Rayleigh waves, compared to 60 and 90 percent for l(max) = 2 inversion. Significant interregion differences are found in the regionalized Love-wave phase velocities.

Nakanishi, I.↗
NASA NTRS2022

An Advanced Open-Source Platform for Air Quality Analysis, Visualization, and Prediction

Ambient air pollution is the largest environmental health risk factor, leading to several million premature deaths globally per year. The challenge of combating poor air quality is exacerbated by growing urban populations, changing emissions, and a warming climate. While there have been many advances monitoring and modeling of atmospheric composition, reflected in the dramatic increase in archived Earth Observations, there is no single measurement or method that alone can provide an accurate depiction of the entire atmosphere. The rapidly growing collections of observational and modeling data require us to be smarter about what data to include, and how such data is used. In recent years, NASA has invested significantly in advancing the concepts for Analytics Collaborative Framework (ACF) [5] and New Observing Strategies (NOS) [4] to tackle our software infrastructure need for harmonized data management and dynamic acquisition of diverse measurements for on-demand, interactive, multivariate analysis, and access [3]. It is not enough to have a big data, standalone analytics solution; it is critical that we start integrating data from remote sensing, modeling, and in-situ networks in a harmonized manner that enables timely and data-driven decision-making for air quality management. This work presents the design and development of an Air Quality Analytics Collaborative Framework (AQ ACF), as part of NASA’s Advanced Information Systems Technology (AIST) effort, to establish a data, machine-learning, and numerically driven platform for air quality analysis, visualization, and prediction.

Liu, Qian↗
NASA NTRS2019

An Integrated Data Analytics Platform

An Integrated Science Data Analytics Platform is an environment that enables the confluence of resources for scientific investigation. It harmonizes data, tools and computational resources which subsequently enable the research community to focus on the investigation rather than spending time on security, data preparation, management, etc. OceanWorks is a NASA technology integration project to establish a cloud-based Integrated Ocean Science Data Analytics Platform at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) for big ocean science. It focuses on advancement and maturity by bringing together several NASA open-source, big data projects for parallel analytics, anomaly detection, in-situ to satellite data matchup, quality-screened data subsetting, search relevancy, and data discovery. Our communities are relying on data distributed through data centers such as the PO.DAAC, COAPS, NCAR, and many others to conduct their research. In typical investigations, scientists would engage in: search for data, evaluate the relevance of that data, download it, and then apply algorithms to identify trends. Such workflow cannot scale if the research involves a massive amount of data or multi-variate measurements. NASA’s Surface Water and Ocean Topography (SWOT) mission is expected to produce massive amount of observational data during its 3-year nominal mission. Collections like SWOT challenges all existing Earth Science data archival, distribution and analysis paradigms. In this paper, we will discuss how OceanWorks enhances the analysis of physical ocean data where the computation is done on an elastic cloud platform next to the archive to deliver fast, web-accessible services for working with oceanographic measurements.

Yang, Chaowei↗
NASA NTRS1991

Fourier functional analysis for unsteady aerodynamic modeling

A method based on Fourier analysis is developed to analyze the force and moment data obtained in large amplitude forced oscillation tests at high angles of attack. The aerodynamic models for normal force, lift, drag, and pitching moment coefficients are built up from a set of aerodynamic responses to harmonic motions at different frequencies. Based on the aerodynamic models of harmonic data, the indicial responses are formed. The final expressions for the models involve time integrals of the indicial type advocated by Tobak and Schiff. Results from linear two- and three-dimensional unsteady aerodynamic theories as well as test data for a 70-degree delta wing are used to verify the models. It is shown that the present modeling method is accurate in producing the aerodynamic responses to harmonic motions and the ramp type motions. The model also produces correct trend for a 70-degree delta wing in harmonic motion with different mean angles-of-attack. However, the current model cannot be used to extrapolate data to higher angles-of-attack than that of the harmonic motions which form the aerodynamic model. For linear ramp motions, a special method is used to calculate the corresponding frequency and phase angle at a given time. The calculated results from modeling show a higher lift peak for linear ramp motion than for harmonic ramp motion. The current model also shows reasonably good results for the lift responses at different angles of attack.

Lan, C. Edward↗
NASA NTRS1991

Fourier functional analysis for unsteady aerodynamic modeling

A method based on Fourier analysis is developed to analyze the force and moment data obtained in large amplitude forced oscillation tests at high angles of attack. The aerodynamic models for normal force, lift, drag, and pitching moment coefficients are built up from a set of aerodynamic responses to harmonic motions at different frequencies. Based on the aerodynamic models of harmonic data, the indicial responses are formed. The final expressions for the models involve time integrals of the indicial type advocated by Tobak and Schiff. Results from linear two- and three-dimensional unsteady aerodynamic theories as well as test data for a 70-degree delta wing are used to verify the models. It is shown that the present modeling method is accurate in producing the aerodynamic responses to harmonic motions and the ramp type motions. The model also produces correct trend for a 70-degree delta wing in harmonic motion with different mean angles-of-attack. However, the current model cannot be used to extrapolate data to higher angles-of-attack than that of the harmonic motions which form the aerodynamic model. For linear ramp motions, a special method is used to calculate the corresponding frequency and phase angle at a given time. The calculated results from modeling show a higher lift peak for linear ramp motion than for harmonic ramp motion. The current model also shows reasonably good results for the lift responses at different angles of attack.

Chin, Suei↗
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↗
NASA NTRS1985

Characteristics of cosmic ray pole-equator anisotropy derived from spherical harmonic analysis of neutron monitor data

The spherical harmonic analysis of cosmic ray neutron data from the worldwide network neutron monitor stations during the years, 1966 to 1969 was carried out. The second zonal harmonic component obtained from the analysis corresponds to the Pole-Equator anisotropy of the cosmic ray neutron intensity. Such an anisotropy makes a semiannual variation. In addition to this, it is shown that the Pole-Equator anisotropy makes a variation depending on the interplanetary magnetic field (IMF) sector polarities around the passages of the IMF sector boundary. A mechanism to interpret these results is also discussed.

Takahashi, H.↗
NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship. 1. Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. 2. National Academies of Sciences, E. and Medicine, Open Science by Design: Realizing a Vision for 21st Century Research. 2018, Washington, DC: The National Academies Press. 232. 3. Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5.

Life Sciences data↗
NASA NTRSDate not supplied

NASA Life Sciences Portal (NLSP): Supporting Scientific Transparency and Reproducibility

NASA’s Life Sciences Ports (NLSP) serves the scientific community by providing curated data from space life science experiment. The Human Research Program (HRP) with the help of NLSP is currently transforming their life sciences data archive systems and processes to improve compliance with the FAIR principles [1]. Some of these improvements will at the same time support the twin pillars of Open Science [2]: transparency of methods and reproducibility of results. Scientific transparency is marked by the easily intelligible communication of what has been investigated: what were the procedures for collecting sample and the characteristics of samples collected? what kinds of measurements were made, what were the environmental conditions of the measurements? What were the analysis techniques of the collected data? Reproducibility of the results and findings from the investigation requires a high level of transparency for all but the simplest investigations; the slightest deviation in communicating and replicating complex experimental procedures or data analyses can often yield quite different data and even findings, thwarting their validation. One of the ways the NLSP is aiming to improve the communication of scientific information is through the use of ontology-driven metadata. Ontologies are powerful, graph-based knowledge representation structures, which can be leveraged to increase data interoperability, the area of the FAIR principles in which many data systems most lack compliance. Over the past decade, there has been a concerted effort in the biomedical community to develop modular and narrowly focused domain and application-specific ontologies in a common, open-source framework, the Open Biological and Biomedical Ontology (OBO) Foundry [3]. The open sharing and modular nature of this effort promises huge increases in harmonized data sharing for systems that leverage these models. Which is in line with the FAIR Data Principles of Findability, Accessibility, Interoperability, and Reuse for scientific data management and stewardship.

Life Sciences data↗
NASA NTRS1980

Experimental unsteady aerodynamics of conventional and supercritical airfoils

Experimental data on the unsteady aerodynamics of oscillating airfoils in transonic flow are presented. Two 0.5 m-chord airfoil models - an NACA 64A010 and an NLR 7301 - were tested in the NASA-Ames 11 by 11 foot Transonic Wind Tunnel at Mach numbers to 0.85, at chord Reynolds numbers to 12 million and at mean angles of attack to 4 deg. The airfoils were subjected to both pitching and plunging motions at reduced frequencies to 0.3 (physical frequencies to 53 Hz). The new hardware and the extensive use of computer-experiment integration developed for this test are described. The geometrical configuration of the model and the test arrangement are described in detail. Mean and first harmonic data are presented in both tabular and graphical form to aid in comparisons with other data and with numerical computations.

Davis, S. S.↗
NASA NTRS2021

Mapping Global Forest Age from Forest Inventories, Biomass and Climate Data

Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. However, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. This study provides a new global distribution of forest age circa 2010, estimated using a machine learning approach trained with more than 40 000 plots using forest inventory, biomass and climate data. First, an evaluation against the plot-level measurements of forest age reveals that the data-driven method has a relatively good predictive capacity of classifying old-growth vs. non-old-growth (precision = 0.81 and 0.99 for old-growth and non-old-growth, respectively) forests and estimating corresponding forest age estimates (NSE = 0.6 – Nash–Sutcliffe efficiency – and RMSE = 50 years – root-mean-square error). However, there are systematic biases of overestimation in young- and underestimation in old-forest stands, respectively. Globally, we find a large variability in forest age with the old-growth forests in the tropical regions of Amazon and Congo, young forests in China, and intermediate stands in Europe. Furthermore, we find that the regions with high rates of deforestation or forest degradation (e.g. the arc of deforestation in the Amazon) are composed mainly of younger stands. Assessment of forest age in the climate space shows that the old forests are either in cold and dry regions or warm and wet regions, while young–intermediate forests span a large climatic gradient. Finally, comparing the presented forest age estimates with a series of regional products reveals differences rooted in different approaches and different in situ observations and global-scale products. Despite showing robustness in cross-validation results, additional methodological insights on further developments should as much as possible harmonize data across the different approaches. The forest age dataset presented here provides additional insights into the global distribution of forest age to better understand the global dynamics in the forest water and carbon cycles. The forest age datasets are openly available at https://doi.org/10.17871/ForestAgeBGI.2021 (Besnard et al., 2021).

Simon Besnard↗
NASA NTRS1983

Loads and performance data from a wind-tunnel test of model articulated helicopter rotors with 2 different blade torsional stiffnesses

A passive means of tailoring helicopter rotor blades to improve performance and reduce loads was evaluated. The parameters investigated were blade torsional stiffness, blade section camber, and distance between blade structural elastic axis and blade tip aerodynamic center. This offset was accomplished by sweeping the tip. The investigation was conducted at advance ratios of 0.20, 0.30, and 0.40. Data are presented without analysis; however, cross referencing of performance data and harmonic loads data may be useful to the analyst for validating aeroelastic theories and design methodologies as well as for evaluating passive aeroelastic tailoring or rotor blade parameters.

Yeager, W. T., Jr.↗
NASA NTRS2009

Satellite Observations for Detecting and Tracking Changes in Atmospheric Composition

The satellite observations provide constraints on detailed atmospheric modeling, including emissions inventories, indications of transport, harmonized data over vast areas suitable for trends analysis, and a link between spatial scales ranging from local to global, and temporal scales from diurnal to interannual. 1 The National Oceanic and Atmospheric Administration's (NOAA) long-term commitments help provide these observations in cooperation with international meteorological organizations. NASA s long-term commitments will advance scientifically important observations as part of its Earth Science Program, and will assist the transition of the science measurements to applied analyses through the Applied Science Program. Both NASA and NOAA have begun to provide near realtime data and tools to visualize and analyze satellite data,2 while maintaining data quality, validation, and standards. Consequently, decision-makers can expect satellite data services to support air quality decision making now and in the future. The international scientific community's Integrated Global Atmosphere Chemistry Observation System Report3 outlined a plan for ground-based, airborne and satellite measurements and models to integrate the observations into a four-dimensional representation of the atmosphere (space and time) to support assessment and policy information needs. This plan is being carried out under the Global Earth Observation System of Systems (GEOSS). Demonstrations of such an integrated capability4 provide new understanding of the changing atmosphere and link policy decisions to benefits for society. In this article, we highlight the use of satellite data to constrain biomass burning emissions, to assess oxides of nitrogen (NO(x)) emission reductions, and to contribute to state implementation plans, as examples of the use of satellite observations for detecting and tracking changes in atmospheric composition.

Neil, Doreen O.↗