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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
DOE OSTI2016

Google Earth Locations of USA and Seafloor Hydrothermal Vents with Associated Rare Earth Element Data

Google Earth .kmz files that contain the locations of geothermal wells and thermal springs in the USA, and seafloor hydrothermal vents that have associated rare earth element data. The file does not contain the actual data, the actual data is available through the GDR website in two tier 3 data sets entitled "Compilation of Rare Earth Element Analyses from US Geothermal Fields and Mid Ocean Ridge (MOR) Hydrothermal Vents" and "Rare earth element content of thermal fluids from Surprise Valley, California"

15 GEOTHERMAL ENERGY↗
DOE OSTI2013

ParNCL and ParGAL: Data-parallel Tools for Postprocessing of Large-scale Earth Science Data

Earth science high-performance applications often require extensive analysis of their output in order to complete the scien- tific goals or produce a visual image or animation. Often this analysis cannot be done in situ because it requires calculating time-series statistics from state sampled over the entire length of the run or analyzing the relationship between similar time series from previous simulations or observations. Many of the tools used for this postprocessing are not themselves high- performance applications, but the new Parallel Gridded Analysis Library (ParGAL) provides high-performance data-parallel versions of several common analysis algorithms for data from a structured or unstructured grid simulation. The library builds on several scalable systems, including the Mesh Oriented DataBase (MOAB), a library for representing mesh data that sup- ports structured, unstructured finite element, and polyhedral grids; the Parallel-NetCDF (PNetCDF) library; and Intrepid, an extensible library for computing operators (such as gradient, curl, and divergence) acting on discretized fields. We have used ParGAL to implement a parallel version of the NCAR Command Language (NCL) a scripting language widely used in the climate community for analysis and visualization. The data-parallel algorithms in ParGAL/ParNCL are both higher performing and more flexible than their serial counterparts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats

Abstract Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats—instructions, templates, and tools for consistently formatting data within a discipline—can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Challenges and alternatives to empirical orthogonal functions for earth system data

Empirical orthogonal functions (EOFs) applied to gridded Earth system data enables users to diagnose modes of variability with relative ease. Yet, many challenges to interpretation exist such that they must be used with awareness and intention when applied to gridded climate data, especially with large ensembles. Utilizing data from two different Earth system modelling large ensemble frameworks, the Energy Exoscale Earth System Model and the Community Earth System Model, as well as reanalysis data, common EOF pitfalls are summarized and discussed. Challenges include erroneous mode swapping, sign flipping, and the temporal variability of the centers of action. For modes of variability with similar contribution to variance, mode swapping is not uncommon. Sign flipping can occur with almost any mode where the pattern is correct, but the sign is arbitrary. Although the variability of the center of action is not necessarily problematic, it potentially complicates interpretation over multi-century timescales. A wide variety of alternative methods to EOFs exist, but fitness-for-purpose must be evaluated. Additionally, illustrations of alternative methods and examples of proper use are provided. Alternative methods fit into three categories: EOF variants, linear methods, and multilinear methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Aligning NASA Earth Science Data Stewardship with FAIR Principles: Outcomes, Recommendations, and Future Directions

The FAIR Principles—Findable, Accessible, Interoperable, and Reusable—offer a widely accepted framework for improving the sharing and reuse of digital scientific data by both human and machine users. Following these principles is critical for effective scientific data stewardship, broader scientific collaboration, and compliance with federal and agency data policies. This paper, based on the work of NASA’s Open, Free, and FAIR Working Group (O’FAIR WG) under the Earth Science Data Systems Program, presents an overview of how FAIR is being applied within NASA’s Earth science data landscape. It highlights ongoing progress and challenges, identifies FAIR-enabling resources, and offers recommendations and strategic actions to enhance the FAIRness of NASA-funded open and free Earth science data products. The FAIR-enabling resources identified underscore the vital role of NASA's existing enterprise processes, standards, tools, and infrastructures in supporting FAIR implementation. Our findings show strong performance in making NASA Earth science data more findable and accessible. However, further work is needed—especially in enhancing interoperability, so that different systems and tools can better understand and exchange data. This is especially important for enabling machine-driven discovery and analysis. We emphasize the importance of a balanced strategy that combines a centralized, top-down approach—focused on building enterprise-level capabilities and processes—with a decentralized, bottom-up approach driven by discipline-specific needs and community practices. We advocate for coordinated efforts to enhance (meta)data interoperability to facilitate seamless data and information sharing and exchange of Earth science data both within NASA and across other agencies managing Earth science data.

Data Product↗
DOE OSTI2022

Data from: “Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats”

This dataset contains supplementary information for a manuscript describing the ESS-DIVE (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) data repository's community data and metadata reporting formats. The purpose of creating the ESS-DIVE reporting formats was to provide guidelines for formatting some of the diverse data types that can be found in the ESS-DIVE repository. The 6 teams of community partners who developed the reporting formats included scientists and engineers from across the Department of Energy National Lab network. Additionally, during the development process, 247 individuals representing 128 institutions provided input on the formats. The primary files in this dataset are 10 data and metadata crosswalk for ESS-DIVE’s reporting formats (all files ending in _crosswalk.csv). The crosswalks compare elements used in each of the reporting formats to other related standards and data resources (e.g., repositories, datasets, data systems). This dataset also contains additional files recommended by ESS-DIVE’s file-level metadata reporting format. Each data file has an associated dictionary (files ending in _dd.csv) which provide a brief description of each standard or data resource consulted in the data reporting format development process. The flmd.csv file describes each file contained within the dataset.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Semantics-enabled Spatio-Temporal Modeling of Earth Observation Data: An application to Flood Monitoring

Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatiotemporal analysis of such disastrous events is important for understanding the resiliency of an urban system during these events. Remote Sensing (RS) data is one of the crucial earth observation (EO) data sources that can facilitate such spatiotemporal analysis due to its wide spatial coverage and high temporal availability. In this paper, we propose a discrete mereotopology (DM) based approach to enable representation and querying of spatiotemporal information from a series of multitemporal RS images that are acquired during a flood disaster event. We represent this spatiotemporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatiotemporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t1 have been demonstrated with promising results.

Kurte, Kuldeep↗
DOE OSTI2016

Satellite Earth observation data to identify climate and anthropogenic pressures on Bucharest periurban forests

Satellite Earth observation data in the visible and near-infrared (VNIR) wavelengths represent a useful source of information for forest systems monitoring through derived biogeophysical parameters (vegetation index, leaf area index, canopy cover, fraction of absorbed photosynthetically active radiation, chlorophyll content, net primary production, canopy water stress, etc.). Use of satellite remote sensing data to assess forest spatio-temporal changes due to climatic or anthropogenic stressors is an excellent example of the value of multispectral and multitemporal observations. Fusion technique was applied to time-series multispectral and multitemporal satellite imagery (NOAA AVHRR, MODIS Terra/Aqua, Landsat ETM and IKONOS satellite data) for periurban forest areas Cernica-Branesti, placed in the neighboring of Bucharest town, Romania, over 2002-2014 period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

CMIP7 data request: Earth system priorities and opportunities

This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data pertaining to Earth systems science, and provides justification for the resources needed to produce this data. Topics within the CMIP7 Earth System (CMIP7-ES) theme centre around tracking of flows of energy, carbon, water and other fluxes across domains, and constraining feedbacks between these cycles and the climate system. These topics are summarized in this paper as scientific “opportunities” describing specific model intercomparison experiments and use cases for next-generation Earth System Model (ESM) output. These opportunities were submitted by modelling groups and scientific consortia following an extended public consultation process. Contained within each opportunity are requests for groups of Climate & Forecasting (CF) variables, which are bundled into variable groups representing all data required to address the opportunities' needs. Novel opportunities in CMIP7 compared with previous phases will include running `emissions-driven' simulations that integrate carbon emissions and removal scenarios with updated representations of the global carbon cycle, expanded variable groups needed to model marine trophic interactions and biogeochemistry, and data needed to understand the risk of global tipping points, among others. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and support the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). We argue that CMIP7-ES data will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making. As an author group we also reflect on the evolution of the CMIP7-ES data request as a part of a deliberative process in support of the global CMIP program.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Using machine learning and artificial intelligence to improve model-data integrated earth system model predictions of water and carbon cycle extremes

The research proposed here focuses on improving the predictive power of the land component of earth system models (ESMs) using (1) model-data fusion enabled by machine learning (ML) and artificial intelligence (AI), (2) predictive modeling through the combination of ML, AI, and big-data (comprising both model output and observations), and (3) insight of ESM structure and process mechanisms gleaned from complex data using ML and AI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data

Abstract Earth system models (ESMs) have been widely used for projecting global vegetation carbon dynamics, yet how well ESMs performed for simulating vegetation carbon density remains untested. We compiled observational data of vegetation carbon density from literature and existing data sets to evaluate nine ESMs at site, biome, latitude, and global scales. Three variables—root (including fine and coarse roots), total vegetation carbon density, and the root:total vegetation carbon ratios (R/T ratios), were chosen for ESM evaluation. ESM models performed well in simulating the spatial distribution of carbon densities in root ( r = 0.71) and total vegetation ( r = 0.62). However, ESM models had significant biases in simulating absolute carbon densities in root and total vegetation biomass across the majority of land ecosystems, especially in tropical and arctic ecosystems. Particularly, ESMs significantly overestimated carbon density in root (183%) and total vegetation biomass (167%) in climate zones of 10°S–10°N. Substantial discrepancies between modeled and observed R/T ratios were found: the R/T ratios from ESMs were relatively constant, approximately 0.2 across all ecosystems, along latitudinal gradients, and in tropic, temperate, and arctic climatic zones, which was significantly different from the observed large variations in the R/T ratios (0.1–0.8). There were substantial inconsistencies between ESM‐derived carbon density in root and total vegetation biomass and the R/T ratio at multiple scales, indicating urgent needs for model improvements on carbon allocation algorithms and more intensive field campaigns targeting carbon density in all key vegetation components.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Downscaled Earth System Model Data for Resilient Energy System Planning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. In this presentation, we explore the output characteristics of the dataset and various validation analyses. We also present and discuss plans for the integration of this data into power system planning models using a decision-making under deep uncertainty (DMDU) methodology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A global coupled ensemble data assimilation system using the Community Earth System Model and the Data Assimilation Research Testbed

In this paper, we present a description of the CESM/DART ensemble coupled data assimilation (DA) system based on the Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) assimilation software. The CESM/DART should be viewed as a flexible system to support the DA needs of the CESM research community and not as a static reanalysis product. In this implementation of the CESM/DART, conventional insitu observations of the ocean and atmosphere are assimilated into the respective component models of the CESM using a 30-member ensemble adjustment Kalman filter (EAKF). CESM/DART is run in a “weakly coupled” configuration wherein observations native to each climate system component only directly impact the state vector for that component. Information is passed between components indirectly through the short-term coupled model forecasts that provide the EAKF background ensemble. This system leverages previous ensemble DA development for the Community Atmosphere Model and Parallel Ocean Program models using the DART EAKF. The CESM/DART project is a step towards providing increasingly useful DA capabilities for the CESM research community. Results are presented for our prototype 12-year reanalysis, run from 1970 to mid 1982. Multiple lines of evidence demonstrate that the system is capable of constraining the CESM coupled model to simulate the historical variability of the climate system in the well-observed Northern Hemisphere. A collection of monthly average variables, climate mode indices, observation diagnostics and snapshots of synoptic weather in the ocean and atmosphere are compared to established datasets, showing especially good agreement in the Northern Hemisphere. A discussion of the CESM/DART as a modular, community facility and the benefits and challenges associated with this vision is also included.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Second-generation downscaled earth system model data using generative machine learning

The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory

Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘p-theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p-theory describes the probability (p-value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empirically-based CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate p-values for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Utah FORGE: Earth Model Mesh Data for Selected Surfaces

This submission contains a number of data files with vertices of meshed/interpolated surfaces used in the Phase 2B earth model. Examples include land surface (based on 10-meter DEM), the granitoid-basin fill contact, several faults, and also interpolated temperature isosurfaces for 175 and 225 degrees C. All data are georeferenced to UTM, zone 12N, NAD 83, NAVD 88.

15 GEOTHERMAL ENERGY↗