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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

MAPPER: A personal computer map projection tool

MAPPER is a set of software tools designed to let users create and manipulate map projections on a personal computer (PC). The capability exists to generate five popular map projections. These include azimuthal, cylindrical, mercator, lambert, and sinusoidal projections. Data for projections are contained in five coordinate databases at various resolutions. MAPPER is managed by a system of pull-down windows. This interface allows the user to intuitively create, view and export maps to other platforms.

Bailey, Steven A.↗
NASA NTRS2000

Earth Science Enterprise Scientific Data Purchase Project: Verification and Validation

This paper presents viewgraphs on the Earth Science Enterprise Scientific Data Purchase Project's verification,and validation process. The topics include: 1) What is Verification and Validation? 2) Why Verification and Validation? 3) Background; 4) ESE Data Purchas Validation Process; 5) Data Validation System and Ingest Queue; 6) Shipment Verification; 7) Tracking and Metrics; 8) Validation of Contract Specifications; 9) Earth Watch Data Validation; 10) Validation of Vertical Accuracy; and 11) Results of Vertical Accuracy Assessment.

Jenner, Jeff↗
NASA NTRS2006

The NPOESS Preparatory Project Science Data Segment: Brief Overview

The NPOESS Preparatory Project (NPP) provides remotely-sensed land, ocean, atmospheric, ozone, and sounder data that will serve the meteorological and global climate change scientific communities while also providing risk reduction for the National Polar-orbiting Operational Environmental Satellite System (NPOESS), the U.S. Government s future low-Earth orbiting satellite system monitoring global weather and environmental conditions. NPOESS and NPP are a new era, not only because the sensors will provide unprecedented quality and volume of data but also because it is a joint mission of three federal agencies, NASA, NOAA, and DoD. NASA's primary science role in NPP is to independently assess the quality of the NPP science and environmental data records. Such assessment is critical for making NPOESS products the best that they can be for operational use and ultimately for climate studies. The Science Data Segment (SDS) supports science assessment by assuring the timely provision of NPP data to NASA s science teams organized by climate measurement themes. The SDS breaks down into nine major elements, an input element that receives data from the operational agencies and acts as a buffer, a calibration analysis element, five elements devoted to measurement based quality assessment, an element used to test algorithmic improvements, and an element that provides overall science direction. This paper will describe how the NPP SDS will leverage on NASA experience to provide a mission-reliable research capability for science assessment of NPP derived measurements.

Schweiss, Robert J.↗
NASA NTRSDate not supplied

Increasing Discovery and Usability of Earth Science Satellite Data with My NASA Data

For 20 years, the My NASA Data project at NASA Langley Research Center has developed innovative approaches to increase the use of NASA’s satellite data by learners. My NASA Data offers a variety of authentic Earth Science datasets and a data visualization tool, eliminating the need for educators and/or learners to obtain specialized knowledge of GIS data formats and software to access and use authentic Earth Science data. While there is no shortage of available data, as federal government agencies such as NASA house petabytes of freely accessible Earth Science datasets, much of the data are only available for download and visualization in specialized formats and software, limiting their accessibility to educators and learners, especially those in primary and secondary school. Using the Google Earth Engine platform, the My NASA Data team has recently reinvented their data visualization tool, called the Earth System Data Explorer (ESDE). The ESDE gives users the capability to explore over 60 Earth Science satellite datasets in a multitude of formats such as maps, graphs, and data table Its new and improved user interface design was developed based on the preferences of educators, whom the My NASA Data project has over 20 years’ experience working with. Earth Science and GIS Subject Matter Experts (SMEs) structured the data in a professional and scientific manner. During Fiscal Year 2023, the My NASA Data website received over 1 million digital engagements, with over one-third being visitors to the data visualization tool. These metrics highlight the interest in a visualization tool that is simple and free to use with reliable and trusted datasets. The ESDE empowers users to readily relate and analyze NASA Earth Science data within their area of interest. The team used a user-centered design (UCD) framework to receive and incorporate feedback into the application’s design. Core requested features include the ability to create time series graphs, comparative analysis of maps, and download the data as CSV file. Responses indicate that advances in data visualization tools such as the ESDE make authentic Earth Science data more accessible. This presentation will cover how the My NASA Data project develops tools to enhance data discovery and accessibility, as well as how SME and user suggestions are incorporated.

Desiray Wilson↗
NASA NTRS1998

Application of Parametrized Post-Newtonian Methods to the Gravitational IS of Satellite Energy Exchange Data

Project Satellite Energy Exchange (SEE) is a free-flying, high altitude satellite that utilizes space to construct a passive, low-temperature, nano-g environment in order to accurately measure the poorly known gravitational constant G plus other gravitational parameters that are difficult to measure in an earth-based laboratory. Eventually data received from SEE must be analyzed using a model of the gravitational interaction including parameters that describe deviations from general relativity and experiment. One model that can be used to fit tile data is the Parametrized post- Newtonian (PPN) approximation of general relativity (GR) which introduces ten parameters which have specified values in (GR). It is the lowest-order, consistent approximation that contains non linear terms. General relativity predicts that the Robertson parameters, gamma (light deflection), and beta (advance of the perihelion), are both 1 in GR. Another eight parameters, alpha(sub k), k=1,2,3 and zeta(sub k), k=1,2,3,4 and Xi are all zero in GR. Non zero values for alpha(sub k) parameters predict preferred frame effects; for zeta(sub k) violations of globally conserved quantities such as mass, momentum and angular momentum; and for Xi a contribution from the Whitehead theory of gravitation, once thought to be equivalent to GR. In addition, there is the possibility that there may be a preferred frame for the universe. If such a frame exists, then all observers must measure the velocity omega of their motion with respect to this universal rest frame. Such a frame is somewhat reminiscent of the concept of the ether which was supposedly the frame in which the velocity of light took the value c predicted by special relativity. The SEE mission can also look for deviations from the r(exp -2) law of Newtonian gravity, adding parameters alpha and lamda for non Newtonian behavior that describe the magnitude and range of the r(exp -2) deviations respectively. The foundations of the GR supposedly agree with Newtonian gravity to first order so that the parameters alpha and lamda are zero in GR. More important, however, GR subsequently depends on this Newtonian approximation to build up the non linear higher-order terms which forms the basis of the PPN frame work.

Smalley, Larry L.↗
DOE OSTI2021

Dilution impacts on smoke aging: evidence in Biomass Burning Observation Project (BBOP) data

Abstract. Biomass burning emits vapors and aerosols into the atmosphere that can rapidly evolve as smoke plumes travel downwind and dilute, affecting climate- and health-relevant properties of the smoke. To date, theory has been unable to explain observed variability in smoke evolution. Here, we use observational data from the Biomass Burning Observation Project (BBOP) field campaign and show that initial smoke organic aerosol mass concentrations can help predict changes in smoke aerosol aging markers, number concentration, and number mean diameter between 40–262 nm. Because initial field measurements of plumes are generally >10 min downwind, smaller plumes will have already undergone substantial dilution relative to larger plumes and have lower concentrations of smoke species at these observations closest to the fire. The extent to which dilution has occurred prior to the first observation is not a directly measurable quantity. We show that initial observed plume concentrations can serve as a rough indicator of the extent of dilution prior to the first measurement, which impacts photochemistry, aerosol evaporation, and coagulation. Cores of plumes have higher concentrations than edges. By segregating the observed plumes into cores and edges, we find evidence that particle aging, evaporation, and coagulation occurred before the first measurement. We further find that on the plume edges, the organic aerosol is more oxygenated, while a marker for primary biomass burning aerosol emissions has decreased in relative abundance compared to the plume cores. Finally, we attempt to decouple the roles of the initial concentrations and physical age since emission by performing multivariate linear regression of various aerosol properties (composition, size) on these two factors.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1985

Activities of the Pilot Land Data System project

The University of Maryland's Remote Sensing Systems Laboratory submitted to NASA/Goddard an interim progress report on the work being conducted within its Pilot Land Data System IPLDS project. The Remote Sensing Systems Laboratory addressed the following tasks: (1) identify data types and data sources needed to describe the selected test sites in collaboration with Goddard's Hydrological Sciences Branch; (2) define the procedures necessary to access/acquire this data; (3) conduct meetings with the PLDS Systems Engineering Group to identify functional specification priorities for PLDS development; (4) assemble documentation on historical remotely sensed imagery and transfer of such information to the PLDS Data Management Group; (5) collect data identified by Goodard's Hydrological Sciences Branch for data set inventory in PLD; (6) develop a Workstation-PLDS system interface over high speed lines, (7) develop and test through a Phase 1 demonstration of a micro workstation to access PLDS; and (8) establish interdepartmental agreement of development of computer link for electronic access of water resources data from USGS.

Sircar, J. K.↗
NASA NTRS2008

The NPOESS Preparatory Project Science Data Segment (SDS) Data Depository and Distribution Element (SD3E) System Architecture

The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) Science Data Segment (SDS) will make daily data requests for approximately six terabytes of NPP science products for each of its six environmental assessment elements from the operational data providers. As a result, issues associated with duplicate data requests, data transfers of large volumes of diverse products, and data transfer failures raised concerns with respect to the network traffic and bandwidth consumption. The NPP SDS Data Depository and Distribution Element (SD3E) was developed to provide a mechanism for efficient data exchange, alleviate duplicate network traffic, and reduce operational costs.

Ho, Evelyn L.↗
DOE OSTI2025

IM3 + EPRI Data Center Load Projections

This dataset contains scenarios of hourly total electricity demand with and without projected loads from data centers over the period 2022-2040. The root projections without data center demands are identical to those documented in Burleyson et al. 2024. In short, those projections encompass hourly electricity demands for 54 Balancing Authorities (BAs) in the United States across a range of eight of weather and socioeconomic scenarios. Refer to the root dataset and accompanying publication, Burleyson et al. 2025, for information about how those projections were generated. For this derivative dataset we used the base loads from the following scenarios: rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 The root load projections did not reflect the drastic expansion of data centers that has occurred in the last several years to support artificial intelligence and cloud computing. To reflect growth in data center demand, a second set of load projections were created in which we layered in additional data center load projections based on the data center load growth scenarios described in a 2024 report by the Electric Power Research Institute (EPRI): "Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption". The EPRI projections from the report are included in this dataset (EPRI_2024_Projections.xlsx). That report contained annual state-level data center load projections for four year-over-year growth rates for data center demands: Low (3.71% annual growth) Moderate (5% annual growth) High (10% annual growth) Higher (15% annual growth) To homogenize the load projections with and without data centers we had to get them to a common scale. The first step was to take the EPRI annual state-level data center energy consumption values and convert them to 8760-hr loads for each year. We did that by assuming a flat (e.g., not weather- or time-sensitive) load profile and distributing the data center loads in each state evenly across all hours in a year. From there the loads were downscaled from the state-level to the county-level using 2019 county-level populations as weights. Finally, the county-level hourly data center loads were summed to the BA-level using the county-to-BA mapping underpinning the root load projections. The net result is 16 (4 weather and socioeconomic scenarios crossed with 4 data center load growth scenarios) unique load projections for the period 2022-2040. The file format follows that of the root dataset with a single additional column "Scaled_TELL_BA_Load_with_DC_MWh" that contains the hourly loads with the added data center loads for a given BA-year-scenario combination. Please refer to the readme file in the root dataset for more information on the file format.

Burleyson, Casey [Pacific Northwest National Labor↗
NASA NTRSDate not supplied

An Introduction to NASA DEVELOP & Project Applications of Airborne Data

DEVELOP projects often explore how NASA airborne data can aid partners in understanding the feasibility of using EO to support decisions. The use of NASA airborne EO has been a strong capacity building tool for many DEVELOP participants. A key to success in the DEVELOP projects was NASA scientists with expert knowledge of the airborne sensor data. NASA scientists will have to continue in this role until the community knowledge/capacity is built up as for many of the spaceborne EO data. NASA airborne sensors used in the last decade include AirSWOT, AVIRIS, AVIRIS-NG, HyTES, and UAVSAR.

Airborne remote sensing↗
NASA NTRS2015

Geocuration Lessons Learned from the Climate Data Initiative Project

Curation is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest and typically occurs in museums, art galleries, and libraries. The task of organizing data around specific topics or themes is a vibrant and growing effort in the biological sciences but to date this effort has not been actively pursued in the Earth sciences. This presentation will introduce the concept of geocuration, which we define it as the act of searching, selecting, and synthesizing Earth science data/metadata and information from across disciplines and repositories into a single, cohesive, and useful compendium. We also present the Climate Data Initiative (CDI) project as an prototypical example. The CDI project is a systematic effort to manually curate and share openly available climate data from various federal agencies. CDI is a broad multi-agency effort of the U.S. government and seeks to leverage the extensive existing federal climate-relevant data to stimulate innovation and private-sector entrepreneurship to support national climate change preparedness. The geocuration process used in the CDI project, key lessons learned, and suggestions to improve similar geocuration efforts in the future will be part of this presentation.

climate↗
NASA NTRS1992

Seasonal variation of surface radiation budget derived from International Satellite Cloud Climatology Project C1 data

Surface radiation budget data are presented for the midseasonal months of July and October of 1983 and January and April of 1984. These data allow the examination of geographical and seasonal variations of the entire surface radiation budget from pole to pole. The latest flux estimation techniques have been used along with data from the International Satellite Cloud Climatology Project and the Earth Radiation Budget Experiment. Regional, zonal, and hemispheric distributions of the downward and net components of both shortwave and longwave fluxes and of the net total surface fluxes are determined. Seasonal flux variation per region, expressed as flux range, is illustrated for these components also. The estimated fluxes appear to be accurate to about 16 W/sq m on a global average, based on sensitivity analyses and comparisons with ground data. An analysis of flux errors showed that most of the error was attributable to errors in input data.

Darnell, Wayne L.↗
NASA NTRS2018

Status of and Scientific Results from the ISIS-I Topside Digital Ionogram Data Enhancement Project

Selected original analog telemetry tapes from three of the topside-sounder satellites of the International Satellites for Ionospheric Studies (ISIS) program, namely Alouette 2, ISIS I, and ISIS II, were used in an earlier project to produce more than million digital topside ionograms; the resulting digital topside ionograms from ISIS II were used to produce morethan 86,000 globally-distributed vertical topside ionospheric electron density profiles Ne(h)that cover a time span of more than a solar cycle. These Ne(h) were produced using the TOPIST auto-scaling software. Before attempting to automatically process Alouette-2 orISIS-I ionograms a data-enhancement project was initiated so as to increase the auto processing success rate. These enhancements were mainly to correct problems that often occurred during the analog-to-digital conversion of the original telemetry tapes. Here we present the status of, and results from, this ongoing enhancement effort.

topside ionosphere↗
DOE OSTI2020

Planning green infrastructure placement based on projected precipitation data

Continued urbanization has led to tremendous changes on the landscape. These changes have exacerbated the effects of extreme climatic events such as flooding because of constrained water infiltration and increased surface flow. Typical runoff control measures involve sophisticated gray infrastructure that guide excess surface flow into storage and disposal sites. In a dynamic climate system, these measures are not sustainable since they cannot be easily modified to accommodate large volumes of runoff. Green Infrastructure (GI) is an adaptable technique that can be used to minimize runoff, in addition to offering an array of additional benefits (urban heat regulation, aesthetics, improved air quality etc.). Strategic placement of GI is key to achieving maximum utility. While physical site characteristics play a major role in determining suitable GI placement sites, knowledge of future precipitation patterns is crucial to ensure successful flood mitigation. Here, suitable GI sites within the city of Knoxville, Tennessee, were determined based on potential impact of an extreme flood event as indicated by site characteristics. Then, the relative potential likelihood of a flood event was determined based on projected precipitation data and knowledge of existing flood zones. By combining potential impact with likelihood information, low, medium, and high priority GI implementation sites were established. Results indicate that high priority sites are in the central parts of the city with priority decreasing outward. The GI prioritization scheme presented here, offers valuable guidance to city planners and policy makers who wish to exploit the GI approach for flood mitigation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Seasat data utilization project

During the three months of orbital operations, the satellite returned data from the world's oceans. Dozens of tropical storms, hurricanes and typhoons were observed, and two planned major intensive surface truth experiments were conducted. The utility of the Seasat-A microwave sensors as oceanographic tools was determined. Sensor and geophysical evaluations are discussed, including surface observations, and evaluation summaries of an altimeter, a scatterometer, a scanning multichannel microwave radiometer, a synthetic aperture radar, and a visible and infrared radiometer.

Born, G. H.↗
DOE OSTI2024

Projected income data under different shared socioeconomic pathways for Washington state

Abstract High-resolution income projections under different Shared Socioeconomic Pathways (SSPs) are essential for the climate change research communities to devise climate change adaptation and mitigation strategies. To generate income projections for Washington state, we obtain state-level GDP per capita projections and convert them into projected annual household income. The resulting state-level income projections are subsequently downscaled to the census block-level based on the Longitudinal Origin-Destination Employment Statistics (LODES) dataset. For accuracy assessment, we downscale historical income data from state- level to block- and block group-level and compare the downscaled results against the actual income data from LODES. County-level accuracy assessment is also conducted based on American Community Survey. The results demonstrate a good agreement (Average R 2 of 0.67, 0.8, and 0.99 for block-, block group-, and county-level, respectively) between the downscaled income data and the reference data, thereby validating the methodology employed. Our approach is applicable to other states for income projections, which can be utilized by a broader audience, including those involved in demographic analysis, economic research, and urban planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1990

CCSDS - An approach to the definition of common standards for understanding space-related data

International cooperation for space data projects requires common data processing goals and unique data exchange mechanisms. The absence of standards has led to project unique interface definitions and special software on diverse systems. To address this problem, CCSDS Panel 2 is creating a set of standards to support self description of data using standard structures. The proposed standard data interchange mechanism - the Standard Formatted Data Unit (SFDU) - reduces information loss in data transfers, increases automated information exchange, and extends the lifetime of data. Data interchange structures, languages, and services are being developed with necessary control functions to provide these benefits. A set of recommendations for agency review has been issued this year to be the basis for future data exchange and to enhance access to older mission data sets. Space agencies and other space data handling facilities are being encouraged to implement the SFDU concept for ongoing and planned projects.

Drexler, Manfred↗
DOE OSTI2022

Quality Analysis of Baseline Time-Lapse CSEM Data CarbonSAFE Project, North Dakota

Conference presentation at International Meeting for Applied Geoscience & Energy (IMAGE), Houston, TX, August 28 – September 2, 2022. When a series of time-lapse CSEM surveys are designed to measure the often-small variations in signal seen in CCUS projects, the primary concern must be collecting the highest-quality data and reducing as much noise as possible. This includes optimization during planning and feasibility, careful and consistent quality checks in the field, and transparent and repeatable postprocessing steps. We apply this practice to a baseline (prior to CO 2 injection) CSEM data set collected for a time-lapse survey in Center, North Dakota, as a part of the North Dakota CarbonSAFE project, and describe the rigorous quality control and assessment methods used, including noise removal and data validation with 1D and 3D inversion to tie results to borehole logs. The final result is an accurate and representative CSEM data set and information that stakeholders can use to inform future time-lapse survey costs and designs.

20 FOSSIL-FUELED POWER PLANTS↗