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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Evaluation of SO2, SO4(2−) and an Updated SO2 Dry Deposition Parameterization in the United Kingdom Earth System Model

In this study we evaluate simulated surface SO2 and sulphate (SO4(2-)) concentrations from the United Kingdom Earth System Model (UKESM1) against observations from ground based measurement networks in the USA and Europe for the period 1987 to 2014. We find that UKESM1 captures the historical trend for decreasing concentrations of atmospheric SO2 and SO4(2-) in both Europe and the USA over the period 1987 to 2014. However, in the polluted regions of the eastern USA and Europe, UKESM1 over-predicts surface SO2 concentrations by a factor of 3, while under-predicting surface SO4(2-) concentrations by 25-35%. In the cleaner western USA, the model over-predicts both surface SO2 and SO4(2-) concentrations by a factor of 12 and 1.5 respectively. We find that UKESM1’s bias in surface SO2 and SO4(2-) concentrations is variable according to region and season. We also evaluate UKESM1 against total column SO2 from the Ozone Monitoring Instrument (OMI) using an updated data product. This comparison provides information about the model’s global performance, finding that UKESM1 over predicts total column SO2 over much of the globe, including the large source regions of India, China, the USA and Europe as well as over outflow regions. Finally, we assess the impact of a more realistic treatment of the model’s SO2 dry deposition parameterization. This change increases SO2 dry deposition to the land and ocean surfaces, thus reducing the atmospheric loading of SO2 and SO(sup 2-)(sub 4). In comparison with the ground-based and satellite observations, we find that the modified parameterization reduces the models over prediction of surface SO2 concentrations and total column SO2. Relative to the ground-based observations the simulated surface SO4(2-) concentrations are also reduced, while the simulated SO2 dry deposition fluxes increase.

SO2↗
NASA NTRSDate not supplied

The Virginia Earth System Science Scholars (VESSS) Experience: Inspiring High School Students in Stem Through Mentorship and Collaborative Skills Development

Since 2016, the Virginia Earth System Science Scholars (VESSS) program offered by the Virginia Space Grant Consortium (VSGC) has provided an interactive science, technology, engineering, and mathematics learning experience for Virginia high school juniors and seniors. The VESSS program was created through a partnership with VSGC, NASA Langley Research Center (LaRC), and Hampton University to engage students in learning about Earth’s climate change and its global impacts. High school students from all areas of Virginia learn about Earth science and climate change in a 16 week online course focused on the Earth’s surface and interior, atmospheric composition, weather, water energy cycles, and climate variability. Students that complete the online course are invited to a no-cost seven-day residential summer academy at NASA LaRC where students are divided into teams based on four of the major spheres of study for Earth: atmosphere, biosphere, hydrosphere, and lithosphere. Each team is guided by a Master Teacher and teams of undergraduate student interns who were also previous VESSS scholars. Students interview for team positions and are given a role reflecting an actual satellite design team position such as project scientist, research scientist, budget analyst, launch and orbit analyst, contamination analyst, and education and public outreach specialist. The students spend a week mentored by NASA and industry scientists and engineers as they learn how to select science goals and objectives, select science instruments, balance a mission budget, determine the mission launch location and orbit, and communicate the importance of the mission to the public. Each team produces a mission proposal, and at the end of the week, each team presents their portion of the mission to a panel of experts, followed by a question and answer period. Through this week students learn teamwork, communication, time management, meeting management, and research skills. Project work is supplemented by team-building activities, NASA facility tours, and lectures by acclaimed guest speakers such as Dr. Christine Darden, a NASA “Hidden Figure.” Students have conveyed through course evaluations and testimonials not only the vast personal learning and growth they achieved, but also the new perspectives they gained on future STEM career fields and opportunities to explore.

Elaine Seasly↗
DOE OSTI2022

Reconciling and Improving Formulations for Thermodynamics and Conservation Principles in Earth System Models (ESMs)

Abstract This paper provides a comprehensive derivation of the total energy equations for the atmospheric components of Earth System Models (ESMs). The assumptions and approximations made in this derivation are motivated and discussed. In particular, it is emphasized that closing the energy budget is conceptually challenging and hard to achieve in practice without resorting to ad hoc fixers. As a concrete example, the energy budget terms are diagnosed in a realistic climate simulation using a global atmosphere model. The largest total energy errors in this example are spurious dynamical core energy dissipation, thermodynamic inconsistencies (e.g., coupling parameterizations with the host model) and missing processes/terms associated with falling precipitation and evaporation (e.g., enthalpy flux between components). The latter two errors are not, in general, reduced by increasing horizontal resolution. They are due to incomplete thermodynamic and dynamic formulations. Future research directions are proposed to reconcile and improve thermodynamics formulations and conservation principles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Surrogate multi-fidelity data and model fusion for scientific discovery and uncertainty quantification in Earth System Models

This whitepaper addresses the Earth and Environmental Systems Sciences Division (EESSD)’s predictability challenges in modeling the integrated water cycle and data-model integration. Specifically, it focuses on reducing and characterizing the uncertainty in the representation of process models for unresolved physics, either due to model resolution or limited by the physical under standing or computational efficiency, and the use of observational data for in-situ process parameter optimization within ESM. The described methods may also be used to determine the nature of responses (e.g. strength and direction), and hence to identify critical processes that drive the overall ESM responses to perturbation in the forcing

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

The Crew Earth Observations Experiment: Earth System Science from the ISS

This viewgraph presentation reviews the use of Astronaut Photography (AP) as taken from the International Space Station (ISS) in Earth System Science (ESS). Included are slides showing basic remote sensing theory, data characteristics of astronaut photography, astronaut training and operations, crew Earth observations group, targeting sites and acquisition, cataloging and database, analysis and applications for ESS, image analysis of particular interest urban areas, megafans, deltas, coral reefs. There are examples of the photographs and the analysis.

Stefanov, William L.↗
NASA NTRSDate not supplied

Publishing Variables Archived at GES DISC to Earth System Grid Federation (ESGF)

We present a straightforward and low-cost approach to publish variables archived at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) to the Earth System Grid Federation (ESGF). An ESGF publication requires a single standard-name variable aggregated over time to facilitate data inter-comparison. It also contains significant metadata to enable searching in ESGF. We look up standard names on high demand in ESGF search history, and using OPeNDAP and NcML technologies we aggregate the corresponding variables available in the GES DISC archive with augmented metadata required by CMIP6 and obs4MIPs Data Specification version 2.1. At this writing 10 variables from a standard product of the Atmospheric Infrared Sounder along with the Tech Notes are published in ESGF by NASA Center for Climate Simulation (NCCS). Users can view, analyze, and subset remotely, and download these aggregated variables via links in any ESGF node after searching. We plan to work on and publish more variables and data from different NASA missions and experiments in our archive.

Fan Fang↗
DOE OSTI2022

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

In October 2021, the U.S. Department of Energy (DOE) welcomed participants to the Artificial Intelligence for Earth System Predictability (AI4ESP) Workshop, hosted by the Office of Biological and Environmental Research (BER)—Advanced Scientific Computing Research (ASCR). The workshop is part of BER-ASCR’s ambition to more radically and aggressively advance prediction capabilities in the climate, Earth, and environmental sciences through the use of modern data analytics and artificial intelligence (AI). Advances in these capabilities are needed to improve predictions of climate change and extreme events that provide actionable information for planning and building resilience to their impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Systematic Underestimation of Canopy Conductance Sensitivity to Drought by Earth System Models

The response of vegetation canopy conductance (g c ) to changes in moisture availability ($γ^{m}_{gc}$) is a major source of uncertainty in climate projections. While vegetation typically reduces stomatal conductance during drought, accurately modeling how and to what degree stomata respond to changes in moisture availability at global scales is particularly challenging, because no global scale g c observations exist. Here, we leverage a collection of satellite, reanalysis and station-based near-surface air and surface temperature estimates, which are physically and statistically linked to $γ^{m}_{gc}$ due to the local cooling effect of g c through transpiration, to develop a novel emergent constraint of $γ^{m}_{gc}$ in an ensemble of Earth System Models (ESMs). We find that ESMs systematically underestimate $γ^{m}_{gc}$ by ~33%, particularly in grasslands, croplands, and savannas in semi-arid and bordering regions of the Central United States, Central Europe, Southeastern South America, Southern Africa, Eastern Australia, and parts of East Asia. We show that this underestimation occurs because ESMs inadequately reduce g c when soil moisture decreases. As g c controls carbon, water and energy fluxes, the misrepresentation of modeled $γ^{m}_{gc}$ contributes to biases in ESM projections of gross primary production, transpiration, and temperature during droughts. Our results suggest that the severity and duration of droughts may be misrepresented in ESMs due to the impact of sustained g c on both soil moisture dynamics and the biosphere-atmosphere feedbacks that affect local temperatures and regional weather patterns.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

The Hydrosphere State (Hydros) Satellite Mission: An Earth System Pathfinder for Global Mapping of Soil Moisture and Land Freeze/Thaw

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39 with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.

Entekhabi, D.↗
DOE OSTI2026

Modeling Large Dust Aerosols in the Community Earth System Model Version 2 (CESM2)

Dust aerosols have a wide size distribution from less than 0.1 to over 100 μm and dominate Earth's atmospheric aerosol mass. However, most Earth system models (ESMs) inadequately represent dust aerosols larger than 10 μm in diameter, limiting the accuracy of the simulated dust cycle and climate impacts. Here, we introduce a new modeling framework that captures the full observed size distribution of dust aerosols, incorporating recent advances into a mineral-resolved version of the Community ESM, while addressing known issues in previous versions. Comprehensive evaluation against diverse observations of bulk dust and component minerals demonstrates that the model reproduces the observed dust cycle across particle sizes. Incorporating the previously unrepresented large-dust fractions substantially alters dust budget estimates, highlighting potential changes in simulated climate impacts and underscoring the importance of comprehensive size-resolved dust modeling. Despite these advancements, uncertainties persist. Our results indicate that a size-dependent reduction in settling velocity is required to reproduce the observed dust size distribution downwind of source regions. Specifically, in the new model, the gravitational settling velocity of dust particles larger than 10 μm in diameter must be reduced by as much as 85% to achieve agreement with observations. This empirical reduction serves as a constraint on physics-based models of dust settling. Future developments should address misrepresented physical processes that hinder accurate modeling of the large dust aerosol transport. Expanding observational data sets covering the full-size distribution is also essential to better constrain the dust cycle and improve the representation of dust optical properties and climate effects.

Li, Longlei [Cornell Univ., Ithaca, NY (United Sta↗
NASA NTRSDate not supplied

Artificial Intelligence for Future Earth Science Information Systems and Earth System Digital Twins

NASA's Advanced Information Systems Technology (AIST) Program is one of several Technology programs managed by the Earth Science Technology Office (ESTO) in the Earth Science Division (ESD). The AIST Program focuses on advanced information systems and novel computer science technologies that will be needed by NASA Earth Science in the next 5 to 10 years. The three main thrusts of the AIST Program deal with Novel Observing Strategies (NOS), Analytic Collaborative Frameworks (ACF) and Earth System Digital Twins (ESDT). This presentation summarizes the AIST Program with a special focus on the Artificial Intelligence (AI) and Machine Learning (ML) technologies included in its funded projects.

Earth Science Remote Sensing; Information Systems↗
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↗
NASA NTRS2011

Towards a Standard for Provenance and Context for Preservation of Data for Earth System Science

Long-term data sets with data from many missions are needed to study trends and validate model results that are typical in Earth System Science research. Data and derived products originate from multiple missions (spaceborne, airborne and/or in situ) and from multiple organizations. During the missions as well as well past their termination, it is essential to preserve the data and products to support future studies. Key aspects of preservation are: preserving bits and ensuring data are uncorrupted, preserving understandability with appropriate documentation, and preserving reproducibility of science with appropriate documentation and other artifacts. Computer technology provides adequate standards to ensure that, with proper engineering, bits are preserved as hardware evolves. However, to ensure understandability and reproducibility, it is essential to plan ahead to preserve all the relevant data and information. There are currently no standards to identify the content that needs to be preserved, leading to non-uniformity in content and users not being sure of whether preserved content is comprehensive. Each project, program or agency can specify the items to be preserved as a part of its data management requirements. However, broader community consensus that cuts across organizational or national boundaries would be needed to ensure comprehensiveness, uniformity and long-term utility of archived data. The Federation of Earth Science Information Partners (ESIP), a diverse network of scientists, data stewards and technology developers, has a forum for ESIP members to collaborate on data preservation issues. During early 2011, members discussed the importance of developing a Provenance and Context Content Standard (PCCS) and developed an initial list of content items. This list is based on the outcome of a NASA and NOAA meeting held in 1998 under the auspices of the USGCRP, documentation requirements from NOAA and our experience with some of the NASA Earth science missions. The items are categorized into the following 8 high level categories: Preflight/Pre-Operations, Products (Data), Product Documentation, Mission Calibration, Product Software, Algorithm Input, Validation, Software Tools.

Ramaprian, Hampapuram K.↗
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 OSTI2021

Estimating Submicron Aerosol Mixing State at the Global Scale With Machine Learning and Earth System Modeling

Abstract This study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance ( χ a ), the mixing of optically absorbing and nonabsorbing species ( χ o ), and the mixing of hygroscopic and nonhygroscopic species ( χ h ). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χ a , between 38% and 94% for χ o , and between 20% and 87% for χ h with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Distributed Resources for the Earth System Grid Advanced Management (DREAM). Final Report

The DREAM project was funded more than 3 years ago to design and implement a next generation ESGF (Earth System Grid Federation) architecture which would be suitable for managing and accessing data and services resources on a distributed and scalable environment. In particular, the project intended to focus on the computing and visualization capabilities of the stack, which at the time were rather primitive. At the beginning, the team had the general notion that a better ESGF architecture could be built by modularizing each component, and redefining its interaction with other components by defining and exposing a well defined API. Although this was still the high level principle that guided the work, the DREAM project was able to accomplish its goals by leveraging new practices in IT that started just about 3 or 4 years ago: the advent of containerization technologies (specifically, Docker), the development of frameworks to manage containers at scale (Docker Swarm and Kubernetes), and their application to the commercial Cloud. Thanks to these new technologies, DREAM was able to improve the ESGF architecture (including its computing and visualization services) to a level of deployability and scalability beyond the original expectations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Energy Exascale Earth System Model Simulations With High Vertical Resolution in the Lower Troposphere

Abstract General circulation models (GCMs) are typically run with coarse vertical resolution. For example, the Energy Exascale Earth System Model (E3SM) has a vertical resolution of about 200 m in the boundary layer, which is far too coarse to resolve sharp gradients often found in the thermodynamic fields capping subtropical marine stratocumulus. In this article, we present a series of multiyear atmosphere only simulations of E3SM version 1 where we progressively increase the vertical resolution in the lower troposphere to scales approaching those often used in large eddy simulation (LES). We report marginal impacts in regards to the simulation of boundary layer clouds when vertical resolution is moderately increased, yet find significant positive impacts when the vertical resolution approaches that typically used in LES (∼10 m). In these experiments, there is a marked change in the simulated turbulence and thermodynamics which leads to more abundant marine stratocumulus. However, these simulations are burdened with excessive computational cost. They are also subject to degradations in overall climate metrics due to time step sensitivities and because some processes and parameterizations are sensitive to changes in the vertical resolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Priorities, opportunities, and challenges for integrating microorganisms into Earth system models for climate change prediction

ABSTRACT Climate change jeopardizes human health, global biodiversity, and sustainability of the biosphere. To make reliable predictions about climate change, scientists use Earth system models (ESMs) that integrate physical, chemical, and biological processes occurring on land, the oceans, and the atmosphere. Although critical for catalyzing coupled biogeochemical processes, microorganisms have traditionally been left out of ESMs. Here, we generate a “top 10” list of priorities, opportunities, and challenges for the explicit integration of microorganisms into ESMs. We discuss the need for coarse-graining microbial information into functionally relevant categories, as well as the capacity for microorganisms to rapidly evolve in response to climate-change drivers. Microbiologists are uniquely positioned to collect novel and valuable information necessary for next-generation ESMs, but this requires data harmonization and transdisciplinary collaboration to effectively guide adaptation strategies and mitigation policy.

Microbiology↗