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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

A Comparison of Satellite Based, Modeled Derived Daily Solar Radiation Data with Observed Data for the Continental US

Many applications of simulation models and related decision support tools for agriculture and natural resource management require daily meteorological data as inputs. Availability and quality of such data, however, often constrain research and decision support activities that require use of these tools. Daily solar radiation (SRAD) data are especially problematic because the instruments require electronic integrators, accurate sensors are expensive, and calibration standards are seldom available. The Prediction Of Worldwide Energy Resources (NASA/POWER; power.larc.nasa.gov) project at the NASA Langley Research Center estimates daily solar radiation based on data that are derived from satellite observations of outgoing visible radiances and atmospheric parameters based upon satellite observations and assimilation models. The solar data are available for a global 1 degree x 1 degree coordinate grid. SRAD can also be estimated based on attenuation of extraterrestrial radiation (Q0) using daily temperature and rainfall data to estimate the optical thickness of the atmosphere. This study compares daily solar radiation data from NASA/POWER (SRADNP) with instrument readings from 295 stations (SRADOB), as well as with values that were estimated with the WGENR solar generator. WGENR was used both with daily temperature and precipitation records from the stations reporting solar data and records from the NOAA Cooperative Observer Program (COOP), thus providing two additional sources of solar data, SRADWG and SRADCO. Values of SRADNP for different grid cells consistently showed higher correlations (typically 0.85 to 0.95) with SRADOB data than did SRADWG or SRADCO for sites within the corresponding cells. Mean values of SRADOB, SRADWG and SRADNP for sites within a grid cell usually were within 1 MJm-2d-1 of each other, but NASA/POWER values averaged 1.1 MJm-2d-1 lower than SRADOB. The magnitude of this bias was greater at lower latitudes and during summer months and may be at least partially explained by assumptions in ambient aerosol properties. Overall, the NASA/POWER solar radiation data are a promising resource for regional modeling studies where realistic accounting of historic variation is required.

White, Jeffrey W.↗
NASA NTRSDate not supplied

Improving Boundary Layer Data Assimilation Using Observation Data from Multiple Observing Systems in the NASA GEOS System

The Planetary Boundary Layer (PBL) is a complex interface that mediates energy and moisture exchanges between the Earth’s surface and atmosphere. Accurate simulation and observation of PBL characteristics, such as PBL height and thermodynamic structure, have proven to be a challenge. In our latest efforts, we have focused on improving PBL thermodynamic structure using data from multiple observing systems in the Goddard Earth Observing System (GEOS), developed by the NASA Global Modeling and Assimilation Office (GMAO). We present strategies and results from assimilating PBL height data derived from radiosondes, GNSS radio occultation, space-based lidar (CALIPSO, CATS, IceSat-2), ground-based lidar (MPLNET), and radar wind profilers, including a novel global PBL height analysis dataset. We also discuss the impacts of better representing capping inversions by using PBL height data together with other observations in GEOS through adjustments to the background error covariance. Long-term statistics of the impact of assimilating and utilizing PBL height data in GEOS are presented. In addition, we explore an innovative approach to assimilate GNSS-RO refractivity data in the lower troposphere.

Eun-Gyeong Yang↗
DOE OSTI2017

Hunting Data Rogues at Scale: Data Quality Control for Observational Data in Research Infrastructures

Data quality control is one of the most time consuming activities within Research Infrastructures (RIs), especially when involving observational data and multiple data providers. In this work we report on our ongoing development of data rogues, a scalable approach to manage data quality issues for observational data within RIs. The motivation for this work started with the creation of the FLUXNET2015 dataset, which includes carbon, water, and energy fluxes plus micrometeorological and ancillary data measured in over 200 sites around the world. To create an uniform dataset, including derived data products, extensive work on data quality control was needed. The unpredictable nature of observational data quality issues makes the automation of data quality control inherently difficult. Developed based on this experience, the data rogues methodology allows for increased automation of quality control activities by systematically identifying, cataloging, and documenting implementations of solutions to data issues. We believe this methodology can be extended and applied to others domains and types of data, making the automation of data quality control a more tractable problem.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2015

Rapid, High-Resolution Detection of Environmental Change over Continental Scales from Satellite Data - the Earth Observation Data Cube

The effort and cost required to convert satellite Earth Observation (EO) data into meaningful geophysical variables has prevented the systematic analysis of all available observations. To overcome these problems, we utilise an integrated High Performance Computing and Data environment to rapidly process, restructure and analyse the Australian Landsat data archive. In this approach, the EO data are assigned to a common grid framework that spans the full geospatial and temporal extent of the observations - the EO Data Cube. This approach is pixel-based and incorporates geometric and spectral calibration and quality assurance of each Earth surface reflectance measurement. We demonstrate the utility of the approach with rapid time-series mapping of surface water across the entire Australian continent using 27 years of continuous, 25 m resolution observations. Our preliminary analysis of the Landsat archive shows how the EO Data Cube can effectively liberate high-resolution EO data from their complex sensor-specific data structures and revolutionise our ability to measure environmental change.

Lewis, Adam↗
NASA NTRS1981

Objective analysis of observational data from the FGGE observing systems

An objective analysis procedure for updating the GLAS second and fourth order general atmospheric circulation models using observational data from the first GARP global experiment is described. The objective analysis procedure is based on a successive corrections method and the model is updated in a data assimilation cycle. Preparation of the observational data for analysis and the objective analysis scheme are described. The organization of the program and description of the required data sets are presented. The program logic and detailed descriptions of each subroutine are given.

Baker, W.↗
DOE OSTI2024

Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics

A large and ever-growing body of geophysical information is measured in campaigns and at specialized observatories as a part of scientific expeditions and experiments. These collections of observed data include many essential climate variables (as defined by the Global Climate Observing System) but are often distinguished by a wide range of additional non-routine measurements that are designed to not only document the state of the environment but also the drivers that contribute to that state. These field data are used not only to further understand environmental processes through observation-based studies but also to provide baseline data to test model performance and to codify understanding to improve predictive capabilities. To address the considerable barriers and difficulty in utilizing these diverse and complex data for observation–model research, the Merged Observatory Data File (MODF) concept has been developed. A MODF combines measurements from multiple instruments into a single file that complies with well-established data format and metadata practices and has been designed to parallel the development of corresponding Merged Model Data Files (MMDFs). Using the MODF and MMDF protocols will facilitate the evolution of model intercomparison projects into model intercomparison and improvement projects by putting observation and model data “on the same page” in a timely manner. The MODF concept was developed especially for weather forecast model studies in the Arctic. The surprisingly complex process of implementing MODFs in that context refined the concept itself. Thus, this article explains the concept of MODFs by providing details on the issues that were revealed and resolved during that first specific implementation. Detailed instructions are provided on how to make MODFs, and this article can be considered a MODF creation manual.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

The I4 Online Query Tool for Earth Observations Data

The NASA Earth Observation System Data and Information System (EOSDIS) delivers an average of 22 terabytes per day of data collected by orbital and airborne sensor systems to end users through an integrated online search environment (the Reverb/ECHO system). Earth observations data collected by sensors on the International Space Station (ISS) are not currently included in the EOSDIS system, and are only accessible through various individual online locations. This increases the effort required by end users to query multiple datasets, and limits the opportunity for data discovery and innovations in analysis. The Earth Science and Remote Sensing Unit of the Exploration Integration and Science Directorate at NASA Johnson Space Center has collaborated with the School of Earth and Space Exploration at Arizona State University (ASU) to develop the ISS Instrument Integration Implementation (I4) data query tool to provide end users a clean, simple online interface for querying both current and historical ISS Earth Observations data. The I4 interface is based on the Lunaserv and Lunaserv Global Explorer (LGE) open-source software packages developed at ASU for query of lunar datasets. In order to avoid mirroring existing databases - and the need to continually sync/update those mirrors - our design philosophy is for the I4 tool to be a pure query engine only. Once an end user identifies a specific scene or scenes of interest, I4 transparently takes the user to the appropriate online location to download the data. The tool consists of two public-facing web interfaces. The Map Tool provides a graphic geobrowser environment where the end user can navigate to an area of interest and select single or multiple datasets to query. The Map Tool displays active image footprints for the selected datasets (Figure 1). Selecting a footprint will open a pop-up window that includes a browse image and a link to available image metadata, along with a link to the online location to order or download the actual data. Search results are either delivered in the form of browse images linked to the appropriate online database, similar to the Map Tool, or they may be transferred within the I4 environment for display as footprints in the Map Tool. Datasets searchable through I4 (http://eol.jsc.nasa.gov/I4_tool) currently include: Crew Earth Observations (CEO) cataloged and uncataloged handheld astronaut photography; Sally Ride EarthKAM; Hyperspectral Imager for the Coastal Ocean (HICO); and the ISS SERVIR Environmental Research and Visualization System (ISERV). The ISS is a unique platform in that it will have multiple users over its lifetime, and that no single remote sensing system has a permanent internal or external berth. The open source I4 tool is designed to enable straightforward addition of new datasets as they become available such as ISS-RapidSCAT, Cloud Aerosol Transport System (CATS), and the High Definition Earth Viewing (HDEV) system. Data from other sensor systems, such as those operated by the ISS International Partners or under the auspices of the US National Laboratory program, can also be added to I4 provided sufficient access to enable searching of data or metadata is available. Commercial providers of remotely sensed data from the ISS may be particularly interested in I4 as an additional means of directing potential customers and clients to their products.

Stefanov, William L.↗
NASA NTRS2017

Re-Organizing Earth Observation Data Storage to Support Temporal Analysis of Big Data

The Earth Observing System Data and Information System archives many datasets that are critical to understanding long-term variations in Earth science properties. Thus, some of these are large, multi-decadal datasets. Yet the challenge in long time series analysis comes less from the sheer volume than the data organization, which is typically one (or a small number of) time steps per file. The overhead of opening and inventorying complex, API-driven data formats such as Hierarchical Data Format introduces a small latency at each time step, which nonetheless adds up for datasets with O(10^6) single-timestep files. Several approaches to reorganizing the data can mitigate this overhead by an order of magnitude: pre-aggregating data along the time axis (time-chunking); storing the data in a highly distributed file system; or storing data in distributed columnar databases. Storing a second copy of the data incurs extra costs, so some selection criteria must be employed, which would be driven by expected or actual usage by the end user community, balanced against the extra cost.

data storage↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

A photometric study of the Orion OB 1 association. 1: Observational data

An extensive catalog of observational data is presented for stars in the region of the young stellar association Orion OB 1. In addition to new photoelectric observations obtained on the uvbyB and UBV systems, photoelectric and spectroscopic data were compiled for the stars observed and for several bright members of the association having available photometric indices. Mean weighted values were computed for the uvbyB and UBV data and are tabulated in summary tables which include all references for individual values. These tables are expected to be reasonably complete for association members earlier than spectral type A0. From an analysis of currently available proper motion, radial velocity, and photometric data, membership criteria were derived and qualitative membership probabilities for 526 stars were summarized. A set of charts is included for assistance in identification of the program stars in all regions of the association.

Warren, W. H., Jr.↗
NASA NTRS2017

Towards a Preservation Content Standard for Earth Observation Data

Information from Earth observing missions (remote sensing with airborne and spaceborne instruments, and in situ measurements such as those from field campaigns) is proliferating in the world. Many agencies across the globe are generating important datasets by collecting measurements from instruments on board aircraft and spacecraft, globally and constantly. The data resulting from such measurements are a valuable resource that needs to be preserved for the benefit of future generations. These observations are the primary record of the Earths environment and therefore are the key to understanding how conditions in the future will compare to conditions today. Earth science observational data, derived products and models are used to answer key questions of global significance. In the near-term, as long as the missions data are being used actively for scientific research, it continues to be important to provide easy access to the data and services commensurate with current information technology. For the longer term, when the focus of the research community shifts toward new missions and observations, it is essential to preserve the previous mission data and associated information. This will enable a new user in the future to understand how the data were used for deriving information, knowledge and policy recommendations and to repeat the experiment to ascertain the validity and possible limitations of conclusions reached in the past and to provide confidence in long term trends that depended on data from multiple missions. Organizations that collect, process, and utilize Earth observation data today have a responsibility to ensure that the data and associated content continue to be preserved by them or are gathered and handed off to other organizations for preservation for the benefit of future generations. In order to ensure preservation of complete content necessary for understanding and reusing the data and derived digital products from todays missions, it is necessary to develop a specification of such preservation content. While there are existing standards that address archival and preservation in general, there are no existing international standards or specifications today to address what content should be preserved. The purpose of this paper is to outline briefly the existing standards that apply to preservation, describe a recent effort in getting an international standard in place for specifying preservation content for Earth observation data and derived digital data products and the remaining work needed to arrive at a standard.

ISO Standards↗
DOE OSTI2020

Comparison of Social Media, Syndromic Surveillance, and Microbiologic Acute Respiratory Infection Data: Observational Study

Internet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study. This study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset. This study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users’ tweets. Of 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants’ tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P≤.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P≤.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19). To our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2017

Transforming Science Data for GIS: How to Find and Use NASA Earth Observation Data Without Being a Rocket Scientist

NASAs Earth Observing System Data Information System (EOSDIS) manages Earth Observation satellites and the Distributed Active Archive Centers (DAACs), where the data is stored and processed. The challenge is that Earth Observation data is complicated. There is plenty of data available, however, the science teams have had a top-down approach: define what it is you are trying to study -select a set of satellite(s) and sensor(s), and drill down for the data.Our alternative is to take a bottom-up approach using eight environmental fields of interest as defined by the Group on Earth Observations (GEO) called Societal Benefit Areas (SBAs): Disaster Resilience (DR) Public Health Surveillance (PHS) Energy and Mineral Resource Management (EMRM) Water Resources Management (WRM) Infrastructure and Transport Management (ITM) Sustainable Urban Development (SUD) Food Security and Sustainable Agriculture (FSSA) Biodiversity and Ecosystems Sustainability (BES).

DAAC↗
NASA NTRSDate not supplied

Feasibility Study to Interactive Workshop: Building End-user Capacity to Integrate Earth Observation Data into Federally Endangered Atlantic Salmon (Salmo salar) Habitat Monitoring in Main

Changes in temperature and precipitation patterns, along with alterations in land cover threaten ongoing conservation efforts for Federally Endangered Atlantic salmon (Salmo salar) in Maine. Earth observation data offers a unique perspective for habitat monitoring that can complement habitat restoration and conservation activity on the ground. As a dual capacity building program, the NASA DEVELOP National Program strives to build the capacity of program participants by leveraging Earth observation data to address environmental concerns across the globe, while also building capacity in partner organizations to integrate Earth observation data into their decision making practices. Between September 2021 and August 2022, three NASA DEVELOP teams demonstrated the feasibility of utilizing NASA Earth observations including Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Terra MODIS, Landsat 5 Thematic Mapper (TM), Landsat 8 Operation Land Imager (OLI), and Shuttle Radar Topography Mission (SRTM) in conjunction with Sentinel-2 MultiSpectral Instrument (MSI) to assess temperature, precipitation, and land use land cover (LULC) over time throughout salmon habitat in Maine. While the first two teams completed projects that were categorized as NASA DEVELOP’s traditional feasibility projects, the third and final project team generated resources and planned an interactive workshop to transfer project methods to end-user organizations. Ultimately, the goal of this work was to not only inform the partner’s ongoing salmon population recovery and habitat restoration initiatives but provide tools that allow partner organizations to continue integrating Earth observation data into their work beyond their partnership with the program. This project serves as a case study within the NASA DEVELOP Program and provides lessons learned for moving beyond traditional feasibility studies to more interactive partner engagement and knowledge transfer practices.

Nicole Ramberg-Pihl↗
DOE OSTI2016

Confronting Weather and Climate Models with Observational Data from Soil Moisture Networks over the United States

Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Finally, data from models and observations should be put on the same spatial and temporal scales before comparison.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

A photometric study of the Orion OB 1 association. I - Observational data. II - Photometric analysis

A catalog of observational data is presented for stars in the region of the young stellar association Orion OB 1. Photoelectric observations obtained in the uvby-beta and UBV systems are compiled along with previous photoelectric and spectroscopic data for all these stars as well as for several bright members of the association with available photometric indices. Mean weighted values are computed for the photometric data and summarized in tables expected to be reasonably complete for association members earlier than spectral type A0. Membership criteria are derived, and qualitative membership probabilities summarized, for the 526 stars in the final program. The analytical procedures are discussed for association stars of B, intermediate, and AF types. Effects of the nebular environment and various calibrations of Balmer-line and four-color indices are considered for the determination of absolute magnitudes for the B-type stars.

Warren, W. H., Jr.↗