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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method

A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this study, a new monthly gridded Arctic SAT dataset dated back to 1979 was reconstructed with a deep learning method by combining surface air temperatures from multiple data sources. The source data include the observations from land station of GHCN (Global Historical Climatology Network), ICOADS (International Comprehensive Ocean-Atmosphere Data Set) over the oceans, drifting ice station of Russian NP (North Pole), and buoys of IABP (International Arctic Buoy Programme). The last two are crucial for improving the representation of the in-situ observed temperatures within the Arctic. The newly reconstructed dataset includes monthly Arctic SAT beginning in 1979 and daily Arctic SAT beginning in 2011. This dataset would represent a new improvement in developing observational temperature datasets and can be used for a variety of applications.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

Natural resources information system.

A computer-based Natural Resources Information System was developed for the Bureaus of Indian Affairs and Land Management. The system stores, processes and displays data useful to the land manager in the decision making process. Emphasis is placed on the use of remote sensing as a data source. Data input consists of maps, imagery overlays, and on-site data. Maps and overlays are entered using a digitizer and stored as irregular polygons, lines and points. Processing functions include set intersection, union and difference and area, length and value computations. Data output consists of computer tabulations and overlays prepared on a drum plotter.

Leachtenauer, J. C.↗
NASA NTRS1977

Integration of land-use data and soil survey data

Approaches are discussed for increasing the utility of remotely sensed interpretations through the use of a computer-assisted process which provides capabilities for merging several types of data of varying formats. The resulting maps and summary data are used for planning and zoning in a rapidly developing area (34,000 ha) adjacent to the Black Hills in South Dakota. Attention is given to the data source, data digitization, and aspects of data handling and analysis.

Cox, T. L.↗
NASA NTRS1983

Mapping project on land use changes in the carboniferous region of Santa Catarina

The utilization of remote sensing data for monitoring land use changes by means of digital image analysis is described. The following data were utilized: LANDSAT data from September 4, 1975, April 24, 1978, and September 8, 1981; LANDSAT paper photography data; area IV color photographs; IBGE topography maps, and auxiliary data about the Brazilian state of Santa Catarina. Three kinds of analyses of digital images were carried out. The project identified and mapped major classes of land use areas including urban areas, coal deposits, agricultural areas, forests, lakes, and flood plains. Five areas directly affected by coal exploration southeast of Santa Catarina are identified and described. In addition, the classification system used for organizing data about land cover in a hierarchical arrangement is presented. The project made use of two remote sensing data sources: data of MSS spectral (Mulitspectral Scanner System)/LANDSAT on a scale of 1:100,000 with approximately 80 m resolution, and infrared color aerial photographs on a scale of 1:45,000 with approximately 5 m resolution. Therefore, the classification system included three levels, two selected to be compatible with aerial photography data and the third to conform to the resolution of MSS/LANDSAT.

Valeriano, D. D.↗
NASA NTRS2001

A Metadata Action Language

The data management problem comprises data processing and data tracking. Data processing is the creation of new data based on existing data sources. Data tracking consists of storing metadata descriptions of available data. This paper addresses the data management problem by casting it as an AI planning problem. Actions are data-processing commands, plans are dataflow programs and goals are metadata descriptions of desired data products. Data manipulation is simply plan generation and execution, and a key component of data tracking is inferring the effects of an observed plan. We introduce a new action language for data management domains, called ADILM. We discuss the connection between data processing and information integration and show how a language for the latter must be modified to support the former. The paper also discusses information gathering within a data-processing framework, and show how ADILM metadata expressions are a generalization of Local Completeness.

Golden, Keith↗
NASA NTRS2007

All Source Solution Decision Support Products Created for Stennis Space Center in Response to Hurricane Katrina

In the aftermath of Hurricane Katrina and in response to the needs of SSC (Stennis Space Center), NASA required the generation of decision support products with a broad range of geospatial inputs. Applying a systems engineering approach, the NASA ARTPO (Applied Research and Technology Project Office) at SSC evaluated the Center's requirements and source data quality. ARTPO identified data and information products that had the potential to meet decision-making requirements; included were remotely sensed data ranging from high-spatial-resolution aerial images through high-temporal-resolution MODIS (Moderate Resolution Imaging Spectroradiometer) products. Geospatial products, such as FEMA's (Federal Emergency Management Agency's) Advisory Base Flood Elevations, were also relevant. Where possible, ARTPO applied SSC calibration/validation expertise to both clarify the quality of various data source options and to validate that the inputs that were finally chosen met SSC requirements. ARTPO integrated various information sources into multiple decision support products, including two maps: Hurricane Katrina Inundation Effects at Stennis Space Center (highlighting surge risk posture) and Vegetation Change In and Around Stennis Space Center: Katrina and Beyond (highlighting fire risk posture).

Ross, Kenton W.↗
NASA NTRS2020

A Systematic Review of Local to Regional Yield Forecasting Approaches and Frequently Used Data Resources

Forecasting crop yields, or providing an expectation of ex-ante harvest amounts, is highly relevant to the whole agricultural production chain. Farmers can adapt their management, traders or insurers their pricing schemes, suppliers their stocks, logistic companies their routes, national authorities their food balance sheets to guide import or export and, finally, international aid organizations can mobilize reliefs. Evidence has grown in the literature that such forecasts with a meaningful lead time are possible on various geographic scales and for a broad range of crops. Here, we present a systematic review of the methods applied in end-of-season yield forecasting and three frequently used data sources: weather data, satellite data and crop masks. Our literature database comprises 362 studies (2004–2019) which were evaluated regarding methods, crops, regions, data sources, lead time and performance. Moreover, we present 24 sources of real-time and predictive weather data, 21 sources of remote sensing data and 16 crop masks. Yield forecasting in our literature sample has been performed for 44 crops in 71 countries, also including many non-staple crops, but with an apparent bias in regions and crops. Forecasting performance depends on various factors, including crop, region, method, lead time to harvest and input diversity. Our systematic review supports a broader application of locally successful approaches at larger scales by providing a comprehensive, accessible compendium of necessary information for yield forecasting. We discuss improvement potentials with respect to methodological approaches and available data sources. We additionally suggest standardization procedures for future forecasting studies and encourage studying additional crops and geographic regions. Implications of forecasts for different target groups on different scales and the adaptation towards climate change are also discussed.

Seasonal crop yield forecasting↗
DOE OSTI2020

Urban-scale Energy Modeling: Scaling Beyond Tax Assessor Data

In an attempt to attain building-specific characteristics for urban-scale building energy models, county-specific tax assessors’ data is often an initial data source. This data source can contain valuable information such as year built, area, height, HVAC type, and roof/wall descriptions.We will show examples of 2,000 fields from Hamilton County in Tennessee with examples of many fields which are not relevant to urban-scale building energy modeling, are incorrect compared to other data sources, and highlight some lessons learned working with such a data source.There are currently 3,142 counties in the United States, each with their own data format, field definitions, and data access policy. As urban-scale involves city-scale analysis potentially covering multiple counties and matures toward state- or nation-scale analysis, county-by-county approaches are not scalable. While there are efforts to unify these datasets, there is an increasing proliferation of data and algorithms that can cover wider areas and provide more accurate inputs for urban-scale models. This paper summarizes computer vision of imagery, cartographic layers, building type assessment, and model generation used to achieve scalable detection and analysis of buildings.

New, Joshua↗
DOE OSTI2021

Linking resource availability to pantropical forest canopy resistance and resilience to cyclone disturbance

Statement of purpose: Tropical cyclones are intensifying and occurring at higher latitudes in recent decades, but the mechanisms underpinning the resistance (ability to withstand disturbance-induced change) and resilience (pace of return to pre-disturbance reference values) of tropical forests to cyclones remains largely unexplored at the pantropical scale. We conducted a meta-analysis to investigate the role of soil resource availability (i.e., total soil phosphorus concentration) in mediating site-level forest canopy resistance and resilience to cyclones pan-tropically. We evaluated cyclone-induced and post-cyclone litterfall mass (g/m2/day), phosphorus (P) and nitrogen (N) fluxes (mg/m2/day), as well as concentrations (mg/g) across 73 case studies in Australia, Guadeloupe, Hawaii, Mexico, Puerto Rico, and Taiwan. The dataset zip file includes three data and two metadata files: - The compiled Litterfall Mass Flux data from tropical forests across the globe prior to and after varying tropical cyclone disturbances are provided in Litterfall_Mass.csv. This data file also includes site location, geographical characteristics, elevation, soil phosphorus concentration, geology, and several variables related to each tropical cyclone disturbance. - The compiled Litterfall Nitrogen and Phosphorus Flux data from tropical forests across the globe prior to and after varying tropical cyclone disturbances are provided in Litterfall_Nutrients.csv. This data file also includes site location, geographical characteristics, elevation, soil phosphorus concentration, geology, and several variables related to each tropical cyclone disturbance. - Tropical cyclone track data compiled from HURDAT2 and IBTrACS databases and used as input in the HURRECON model (https://github.com/hurrecon-model/HurreconR) to generate wind data is provided in hurdat2-1851-2019-052520.txt. - The metadata file (Metadata_Meta-analysis_Litterfall-Mass.pdf) has the complete information on each variable included in the Litterfall_Mass.csv dataset, the data sources, and data processing information. - The metadata file (Metadata_Meta-analysis_Litterfall-Nutrients.pdf) has the complete information on each variable included in the Litterfall_Nutrients.csv dataset, the data sources, and data processing information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Riverine Plastic Pollution: Sampling and Analysis Methods

Riverine plastic pollution has been found in all major U.S. rivers, but the exact amount of plastic being released to the oceans has not been quantified. Field studies conducted in U.S. rivers have used a range of sampling and analysis techniques and rarely measured the mass of the plastic collected. Measurements of riverine plastic pollution are needed to calibrate and validate models used to estimate the U.S. riverine plastic emissions to the oceans. This report surveys measurement methods used to quantify riverine pollution and current estimates of U.S. riverine plastic pollution from measurements and models. Measurement methods include field sampling and laboratory analysis. Field sampling methods are described for large (macro) and small (micro) plastic particles. Laboratory analysis methods are described for macro and microplastic with an emphasis on the detailed characterization processes of microplastics. Waterborne leachate analysis is also briefly described. Three models are described that estimate plastic pollution based on mismanaged plastic waste in the river catchment basins. The models were validated and calibrated with global data sources. The data sources were predominantly outside of the U.S., where the magnitude and composition of plastic pollution is different than what is found in U.S. rivers. Comprehensive measurements of riverine plastics are needed not only to characterize the riverine plastic pollution, but also parameterize and validate models of plastic fate and transport. This report also describes five key U.S. rivers that span a range of sizes and environmental conditions that could be sampled to obtain data to support characterization and model development of plastic pollution from rivers to oceans. Sampling and analysis protocol recommendations are made to ensure the highest quality of data are collected in the five rivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Open Source Scalable Data Services and Data Fusion for Biological and Environmental Sciences (SBIR Phase I Final Scientific/ Technical Report)

The overarching goal of the project is to develop an integrated open-source scientific data management system (Apache V2 license), ResonantEco, that meets the need of biological and environmental researchers and developers for data management, curation, and data processing for analyses with a wide range of scale and complexity. ResonantEco will provide web enabled data services with features such as unified data interfaces and federated views of data and metadata for heterogeneous data sources with an interactive web client for data exploration. Our use of the term fusion is taken from geospatial (GIS) domain where data fusion is often synonymous with data integration. In particular, data integration in ResonantEco involves combining data residing in different sources and providing users with a unified view of them.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1972

Remote sensing as a source of data for outdoor recreation planning

Specific data needs for outdoor recreation planning and the ability of tested remote sensors to provide sources for these data are examined. Data needs, remote sensor capabilities, availability of imagery, and advantages and problems of incorporating remote sensing data sources into ongoing planning data collection programs are discussed in detail. Examples of the use of imagery to derive data for a range of common planning analyses are provided. A selected bibliography indicates specific uses of data in planning, basic background materials on remote sensing technology, and sources of information on environmental information systems expected to use remote sensing to provide new environmental data of use in outdoor recreation planning.

Reed, W. E.↗
NASA NTRS1994

An investigation of current and future satellite and in-situ data for the remote sensing of the land surface energy balance

This final report from the University of Wisconsin-Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS) summarizes a research program designed to improve our knowledge of the water and energy balance of the land surface through the application of remote sensing and in-situ data sources. The remote sensing data source investigations to be detailed involve surface radiometric ('skin') temperatures and also high-spectral-resolution infrared radiance data from atmospheric sounding instruments projected to be available at the end of the decade, which have shown promising results for evaluating the land-surface water and energy budget. The in-situ data types to be discussed are measurements of the temporal changes of the height of the planetary boundary layer and measurements of air temperature within the planetary boundary layer. Physical models of the land surface, planetary boundary layer and free atmosphere have been used as important tools to interpret the in-situ and remote sensing signals of the surface energy balance. A prototype 'optimal' system for combining multiple data sources into a three-dimensional estimate of the surface energy balance was developed and first results from this system will be detailed. Potential new sources of data for this system and suggested continuation research will also be discussed.

Diak, George R.↗
DOE OSTI2020

Assessing United States County-Level Exposure for Research on Tropical Cyclones and Human Health

Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm studies. Further, an understanding of patterns in and between exposure metrics that are based on specific hazards of the storm can help in designing tropical cyclone epidemiological research. a) Provide an open-source data set for tropical cyclone exposure assessment for epidemiological research; and b) investigate patterns and agreement between county-level assessments of tropical cyclone exposure based on different storm hazards. We created an open-source data set with data at the county level on exposure to four tropical cyclone hazards: peak sustained wind, rainfall, flooding, and tornadoes. The data cover all eastern U.S. counties for all land-falling or near-land Atlantic basin storms, covering 1996–2011 for all metrics and up to 1988–2018 for specific metrics. We validated measurements against other data sources and investigated patterns and agreement among binary exposure classifications based on these metrics, as well as compared them to use of distance from the storm’s track, which has been used as a proxy for exposure in some epidemiological studies. Our open-source data set was typically consistent with data from other sources, and we present and discuss areas of disagreement and other caveats. Over the study period and area, tropical cyclones typically brought different hazards to different counties. Therefore, when comparing exposure assessment between different hazard-specific metrics, agreement was usually low, as it also was when comparing exposure assessment based on a distance-based proxy measurement and any of the hazard-specific metrics. Our results provide a multihazard data set that can be leveraged for epidemiological research on tropical cyclones, as well as insights that can inform the design and analysis for tropical cyclone epidemiological research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

BAMCensus (The Behavior and Advanced Mobility Census Dataset Aggregator) [SWR-25-120]

This software is a high-performance tool developed in Rust for downloading and processing large-scale geospatial datasets, specifically focusing on US Census data. It is designed to address scaling limitations found in existing tools, such as R's [tidycensus](https://walker-data.com/tidycensus/), by providing performant streaming dataset JOIN operations between various US Census datasets (like ACS and LEHD) and their corresponding geometries stored on the TIGER/Lines web server. The tool automates the process of joining these data sources, returning aggregated data to the user based on a specified census GEOID type. The tool automates the process of joining these data sources, returning aggregated data to the user based on a specified census GEOID type. Its primary motivation stems from the need for a high-performance solution to combine spatial datasets with graph traversals within the context of mobility analysis tooling being developed at NREL's Behavior and Advanced Mobility (BAM) group.

Fitzgerald, Robert [National Renewable Energy Labo↗
NASA NTRSDate not supplied

The Use of the BSRN Data as A Benchmark for the POWER Hourly DHI and DNI and In Validating Derived Hourly GTI

The satellite-based CERES SYN1deg hourly data is the source data of the POWER GIS solar data that covers 2001 to near present. The SYN1deg(Ed4.1) hourly GHI agrees well with the BSRN data, but the hourly DHI and DirHI (Direct Horizontal Irradiance) are positively and negatively, respectively, biased with appreciable magnitudes. The hourly DNI, derived by dividing the DirHI by cos(SZA), or the cosine of the solar zenith angle, is therefore negatively biased. Based on the statistics of comparisons with the BSRN data, we performed bias corrections on the hourly DHI and DNI. The corrections were executed in the 3-D phase space of latitude, cos(SZA), and cloud fraction (CLFR). The isotropic model is then used to derive the hourly global tilted irradiance (GTI). For validation purpose, we applied the isotropic model to the BSRN data at the original 1-, 2-, 3- or 5-minute interval. The satellite-based hourly GTI shows good agreement with their BSRN counterpart. We also examined two monthly-mean-based methods that empirically derive monthly mean GTI and DNI from monthly mean GHI and from both monthly mean GHI and DHI. The monthly-mean-based results compare favorably with the hourly-mean-based results. The GEWEX SRB (V4-IP) provides POWER with daily mean GHI for the years before the CERES era, and the data were corrected using quantile mapping by referencing the CERES SYN1deg data. We used the Kolmogorov -Smirnov test (K-S test) and Cramer-von Mises test to examine how well the results agree with the BSRN data. We found that if we set the lower limit for the daily mean GHI to 30 W m-2, the data can pass the K-S test at 0.01 significance level and the Cramer-von Mises test at 0.001 significance level. If no lower limit is set on the daily means, the data fail both tests. The satellite-based CERES SYN1deg hourly data is the source data of the POWER GIS solar data that covers 2001 to near present. The SYN1deg(Ed4.1) hourly GHI agrees well with the BSRN data, but the hourly DHI and DirHI (Direct Horizontal Irradiance) are positively and negatively, respectively, biased with appreciable magnitudes. The hourly DNI, derived by dividing the DirHI by cos(SZA), or the cosine of the solar zenith angle, is therefore negatively biased. Based on the statistics of comparisons with the BSRN data, we performed bias corrections on the hourly DHI and DNI. The corrections were executed in the 3-D phase space of latitude, cos(SZA), and cloud fraction (CLFR). The isotropic model is then used to derive the hourly global tilted irradiance (GTI). For validation purpose, we applied the isotropic model to the BSRN data at the original 1-, 2-, 3- or 5-minute interval. The satellite-based hourly GTI shows good agreement with their BSRN counterpart. We also examined two monthly-mean-based methods that empirically derive monthly mean GTI and DNI from monthly mean GHI and from both monthly mean GHI and DHI. The monthly-mean-based results compare favorably with the hourly-mean-based results. The GEWEX SRB (V4-IP) provides POWER with daily mean GHI for the years before the CERES era, and the data were corrected using quantile mapping by referencing the CERES SYN1deg data. We used the Kolmogorov -Smirnov test (K-S test) and Cramer-von Mises test to examine how well the results agree with the BSRN data. We found that if we set the lower limit for the daily mean GHI to 30 W m-2, the data can pass the K-S test at 0.01 significance level and the Cramer-von Mises test at 0.001 significance level. If no lower limit is set on the daily means, the data fail both tests.

Taiping Zhang↗