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DOE OSTI2016

Assessing the ability of $\mathrm{MODIS}$ $\mathrm{EVI}$ to estimate terrestrial ecosystem gross primary production of multiple land cover types

Terrestrial ecosystem gross primary production (GPP) is the largest component in the global carbon cycle. The enhanced vegetation index (EVI) has been proven to be strongly correlated with annual GPP within several biomes. However, the annual GPP-EVI relationship and associated environmental regulations have not yet been comprehensively investigated across biomes at the global scale. In this report we explored relationships between annual integrated EVI (iEVI) and annual GPP observed at 155 flux sites, where GPP was predicted with a log-log model: 1n(GPP) = a x 1n(iEVI) + b. iEVI was computed from MODIS monthly EVI products following removal of values affected by snow or cold temperature and without calculating growing season duration. Through categorisation of flux sites into 12 land cover types, the ability of iEVI to estimate GPP was considerably improved (R 2 from 0.62 to 0.74, RMSE from 454.7 to 368.2 g C m -2 yr -1 ). The biome-specific GPP-iEVI formulae generally showed a consistent performance in comparison to a global benchmarking dataset (R 2 = 0.79, RMSE = 387.8 g C m -2 yr -1 ). Specifically, iEVI performed better in cropland regions with high productivity but poorer in forests. The ability of iEVI in estimating GPP was better in deciduous biomes (except deciduous broadleaf forest) than in evergreen due to the large seasonal signal in iEVI in deciduous biomes. Likewise, GPP estimated from iEVI was in a closer agreement to global benchmarks at mid and high-latitudes, where deciduous biomes are more common and cloud cover has a smaller effect on remote sensing retrievals. Across biomes, a significant and negative correlation (R 2 = 0.37, p < 0.05) was observed between the strength (R 2 ) of GPP-iEVI relationships and mean annual maximum leaf area index (LAI max ), and the relationship between the strength and mean annual precipitation followed a similar trend. LAI max also revealed a scaling effect on GPP-iEVI relationships. Our results suggest that iEVI provides a very simple but robust approach to estimate spatial patterns of global annual GPP whereas its effect is comparable to various light-use-efficiency and data-driven models. The impact of vegetation structure on accuracy and sensitivity of EVI in estimating spatial GPP provides valuable clues to improve EVI-based models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2013

NASA Tech Briefs, April 2013

Topics covered include: Fully Integrated, Miniature, High-Frequency Flow Probe Utilizing MEMS Leadless SOI Technology; Nanoscale Surface Plasmonics Sensor With Nanofluidic Control; Advanced Dispersed Fringe Sensing Algorithm for Coarse Phasing Segmented Mirror Telescopes; Neural Network Back-Propagation Algorithm for Sensing Hypergols; Bulk Moisture and Salinity Sensor; Change-Based Satellite Monitoring Using Broad Coverage and Targetable Sensing; Circularly Polarized Microwave Antenna Element with Very Low Off-Axis Cross-Polarization; Ultra-Low Heat-Leak, High-Temperature Superconducting Current Leads for Space Applications; Flash Cracking Reactor for Waste Plastic Processing; An Automated Safe-to-Mate (ASTM) Tester; Wireless Chalcogenide Nanoionic-Based Radio-Frequency Switch; Compute Element and Interface Box for the Hazard Detection System; DOT Transmit Module; Composite Aerogel Multifoil Protective Shielding; Li-Ion Electrolytes with Improved Safety and Tolerance to High-Voltage Systems; Polymer-Reinforced, Non-Brittle, Lightweight Cryogenic Insulation; Controlled, Site-Specific Functionalization of Carbon Nanotubes with Diazonium Salts; Regenerable Sorbent for CO2 Removal; Sprayable Aerogel Bead Compositions With High Shear Flow Resistance and High Thermal Insulation Value; Lexan Linear Shaped Charge Holder with Magnets and Backing Plate; Robotic Ankle for Omnidirectional Rock Anchors; Wind, Wave, and Tidal Energy Without Power Conditioning; An Active Heater Control Concept to Meet IXO Type Mirror Module Thermal-Structural Distortion Requirement; Waterless Clothes-Cleaning Machine; Integrated Electrical Wire Insulation Repair System; LVGEMS Time-of-Flight Mass Spectrometry on Satellites; Surface Inspection Tool for Optical Detection of Surface Defects; Per-Pixel, Dual-Counter Scheme for Optical Communications; Certification-Based Process Analysis; Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization; Smart-Divert Powered Descent Guidance to Avoid the Backshell Landing Dispersion Ellipse; Estimating Foreign-Object-Debris Density from Photogrammetry Data; Adaptive Sampling of Spatiotemporal Phenomena with Optimization Criteria; Building a 2.5D Digital Elevation Model From 2D Imagery; Eyes on the Earth 3D; Target Trailing With Safe Navigation for Maritime Autonomous Surface Vehicles; Adams-Based Rover Terramechanics and Mobility Simulator - ARTEMIS; ISTP CDF Skeleton Editor; Uplink Summary Generator (ULSGEN) Version 1.0; Robotics On-Board Trainer (ROBoT); Software Engineering Tools for Scientific Models; Automatic Data Filter Customization Using a Genetic Algorithm; Tracker Toolkit; Towards Efficient Scientific Data Management Using Cloud Storage; On a Formal Tool for Reasoning About Flight Software Cost Analysis; A Nanostructured Composites Thermal Switch Controls Internal and External Short Circuit in Lithium Ion Batteries; Spacecraft Crew Cabin Condensation Control; and Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex.

Source record↗

NASA NTRS2000

Distribution of Dust from Kuiper Belt Objects

Using an efficient computational approach, we have reconstructed the structure of the dust cloud in the Solar system between 0.5 and 100 AU produced by the Kuiper belt objects. Our simulations offer a 3-D physical model of the 'kuiperoidal' dust cloud based on the distribution of 280 dust particle trajectories produced by 100 known Kuiper belt objects; the resulting 3-D grid consists of 1.9 x 10' cells containing 1.2 x 10" particle positions. The following processes that influence the dust particle dynamics are taken into account: 1) gravitational scattering on the eight planets (neglecting Pluto); 2) planetary resonances; 3) radiation pressure; and 4) the Poynting-Robertson (P-R) and solar wind drags. We find the dust distribution highly non-uniform: there is a minimum in the kuiperoidal dust between Mars and Jupiter, after which both the column and number densities of kuiperoidal dust sharply increase with heliocentric distance between 5 and 10 AU, and then form a plateau between 10 and 50 AU. Between 25 and 45 AU, there is an appreciable concentration of kuiperoidal dust in the form of a broad belt of mostly resonant particles associated with Neptune. In fact, each giant planet possesses its own circumsolar dust belt consisting of both resonant and gravitationally scattered particles. As with the cometary belts simulated in our related papers, we reveal a rich and sophisticated resonant structure of the dust belts containing families of resonant peaks and gaps. An important result is that both the column and number dust density are more or less flat between 10 and 50 AU, which might explain the surprising data obtained by Pioneers 10 & 11 and Voyager that the dust number density remains approximately distance-independent in this region. The simulated kuiperoidal dust, in addition to asteroidal and cometary dust, might represent a third possible source of the zodiacal light in the Solar system.

Gorkavyi, Nick N.↗

DOE OSTI2024

Assessment and Coordination of EVSE Cybersecurity Standards

Cybersecurity certification programs for Electric Vehicle Supply Equipment (EVSE) are fragmented due to no single certification covering all aspects of the device and additionally the existence of multiple programs and under different levels of regulation. These devices are also confronted by the intricate assembly of product software, firmware, and hardware. Devices contain both logical and physical interfaces. These multifaceted devices have vulnerabilities at many levels and interconnect with other potentially vulnerable systems including the electric vehicle, the cloud where data and payment information are stored, and the electric grid and electric grid equipment including utilities. Of the EVSE certification programs that are found, none are directly for the cybersecurity of EVSE. Many standards are for safety, specifically battery safety, some are cybersecurity standards for other types of equipment and can be modeled for EVSE. In specific, ISA/IEC 62443 is found to be significantly in line with EVSE security needs and will be used in future testing to certify EVSE and help guide the project to demonstrate where gaps exist, where strengths lie in the standard and how this can be used to lead the certification efforts in harmonizing EVSE cybersecurity standards. In addition, there are multiple efforts that are currently seeking to build EVSE standards or revise existing standards to address gaps. This effort is seeking to establish a cybersecurity program for EVSE that will inform customers and help increase the level of security across products and state EVSE procurements to achieve consistency across different jurisdictions.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRSDate not supplied

TPSAS-NF1676L-10735-DND

The Clouds and the Earth's Radiant Energy System (CERES) project has now surpassed the 10-year mark, has combined rigorous calibration, improved scene identification, and multi-satellite data fusion to produce a climate-accuracy data set of global 1° gridded radiative surface and TOA fluxes, and cloud properties. CERES provides the climate community with four observed TOA flux products. The ERBE-like product uses the same algorithms as ERBE in order to be compared with the ERBE time period. The SSF product uses the improved scene identification and radiance to flux conversion. The SYN product uses GEO derived clouds and fluxes to provide diurnally complete TOA fluxes. The EBAF product provides net balanced fluxes tied to the ocean heat storage term. This product is suited for climate modelers estimating the global mean energy budget. Each successive CERES flux product applies the improvements over the previous product. This presentation will focus on the diurnal averaging improvements by the inclusion of imager radiances from 5 geostationary (GEO) satellites at 3-hourly intervals to estimate the flux in between CERES observations. The regional monthly mean flux differences based on SYN (CERES/GEO) and SSF (CERES-only) can exceed 20 Wm^-2. CERES shortwave and longwave fluxes have been rigorously validated and are key elements in evaluating the ability of climate models to predict both past and future climate states. The CERES normalization of GEO derived fluxes in order to maintain the CERES instrument calibration and are consistent globally within 0.1 Wm^-2, even if the GEO calibration is artificially altered by ±5%, twice the anticipated calibration error. The predicted GEO TOA fluxes normalized to Terra have been compared at Aqua times to Aqua flux observations, as well as with GERB hourly fluxes over the METEOSAT domain. Similarly ground based radiometers have been used to validate the CERES radiative transfer computed surface fluxes and have confirmed that the CERES/GEO fluxes have improved the computed surface flux product. Although the GEO based fluxes are of inferior quality then the CERES observed fluxes, they capture the diurnal signal more effectively than using constant meteorology between CERES measurements. The validation of the GEO derived fluxes and clouds will be shown.

D. R. Doelling↗

NASA NTRS2017

Ablation and Heating During Atmospheric Entry and Its Effect on Airburst Risk

Large meteoroids and asteroids entering the atmosphere endure tremendous heating from the shock heated air, and thereby lose a significant fraction of their mass during atmospheric entry a process known as ablation. The predicted evolution of the asteroids mass as it passes through the atmosphere can affect both the predicted energy deposition profile relevant to an airburst event, or the residual mass that strikes the ground in the case of an impact event. This presentation is divided roughly into two parts. In the first part, an overview of traditional models for heat transfer and ablation that are historically used in the meteor physics community is presented, and the validity in the asteroid entry regime discussed. Sensitivity analyses performed using the recently developed Fragment-Cloud Model (FCM) will be presented which show illustrate the range of sizes and entry parameters for which the predicted asteroid threat is most sensitive to the models for ablation and heat transfer. The second part of the presentation shall focus on recent work done under NASAs Asteroid Threat Assessment Project (ATAP) to develop new models for heat transfer and ablation using high-fidelity numerical simulation in concert with state-of-the-art experiments. Coupled computational fluid dynamics (CFD)radiation transport simulations preformed using the state-of-the-art entry modeling tools at NASA show that, for large meteoroids and asteroids, there can significant attenuation of the heat transfer to the surface (95 in some cases) by the products of ablation. In addition to the heat transfer, new models for the material response and ablation of asteroidal material have been developed [cite]. In the current work, we present finding from recent novel experiments performed in the arc jet facility at NASA Ames, which allows us to, in part, simulate the extreme environment experienced by the asteroid during entry. Briefly, the experimental set-up was comprised of a 1.5 conical article of machined H5 chondrite, which was exposed to a high-enthalpy flow resulting in approximately 4 kWcm2 of heating to the surface. A still frame capture from high-speed video taken during this experiment can be seen in Figure 1. In this figure, we can observe some of the major mechanisms for meteoroid ablation, such as melt flow, spallation (mechanical removal of material), and vaporization. Major findings from this, and other experiments will be discussed, as well progress on utilizing the data from the experiments to inform and develop improved models for ablation.

Asteroids↗

DOE OSTI2013

Novel biofuel formulations for enhanced vehicle performance

This interdisciplinary research program at Michigan State University, in collaboration with Ford Motor Company, has explored the application of tailored or designed biofuels for enhanced vehicle performance and reduced emissions. The project has included a broad range of experimental research, from chemical and biological formation of advanced biofuel components to multicylinder engine testing of blended biofuels to determine engine performance parameters. In addition, the project included computation modeling of biofuel physical and combustion properties, and simulation of advanced combustion modes in model engines and in single cylinder engines. Formation of advanced biofuel components included the fermentation of five-carbon and six-carbon sugars to n-butanol and to butyric acid, two four-carbon building blocks. Chemical transformations include the esterification of the butyric acid produced to make butyrate esters, and the esterification of succinic acid with n-butanol to make dibutyl succinate (DBS) as attractive biofuel components. The conversion of standard biodiesel, made from canola or soy oil, from the methyl ester to the butyl ester (which has better fuel properties), and the ozonolysis of biodiesel and the raw oil to produce nonanoate fuel components were also examined in detail. Physical and combustion properties of these advanced biofuel components were determined during the project. Physical properties such as vapor pressure, heat of evaporation, density, and surface tension, and low temperature properties of cloud point and cold filter plugging point were examined for pure components and for blends of components with biodiesel and standard petroleum diesel. Combustion properties, particularly emission delay that is the key parameter in compression ignition engines, was measured in the MSU Rapid Compression Machine (RCM), an apparatus that was designed and constructed during the project simulating the compression stroke of an internal combustion engine under highly instrumented conditions. Simulation of and experimentation on combustion in single and multicylinder engines was carried out in detail throughout the project. The combustion behavior of biofuel blends neat and in petroleum were characterized in the MSU optical engine, in part to validate results obtained in the RCM and to provide data for comparison with simulations. Simulation of in- cylinder, low-temperature combustion included development of an extensive fuel injection model that included fuel spray breakup, evaporation, and ignition, along with prediction of cylinder temperature, pressure, and work produced. Single cylinder and multicylinder engine tests under advanced low-temperature combustion conditions conducted at Ford Motor Company validated experimental and simulation results obtained in the MSU engine and in MSU simulations. Single cylinder engine tests of an advanced biofuel containing biodiesel and dibutyl succinate, carried out under low-temperature combustion conditions, showed similar power generation and gas-phase emissions (CO, HC, NOx), but a reduction in particulates of as much as 60% relative to neat biodiesel and 95% relative to petroleum diesel at the same operating conditions. This remarkable finding suggests that biofuels may be able to play a role in eliminating the need for particulate removal systems in diesel vehicles. The multicylinder engine tests at Ford, carried out using butyl nonanoate as an advanced biofuel, also gave promising results, showing a strong decline in particulate emissions and simultaneously a modest decrease in NOx emissions relative to standard petroleum diesel at the same conditions. In summary, this project has shown that advanced biofuels and their blends are capable of maintaining performance while reducing emissions, particularly particulates (soot), in 3 compression ignition engines. The interdisciplinary nature of biofuel production and testing has identified fuel properties that are capable of producing such performance, thus providing direction for the implementation of renewable fuels for U.S. transportation. The testing and simulation studies have deepened our understanding of combustion 1) by advancing the rigor with which simulations can be carried out and 2) by illustrating that differences in biofuel and petroleum fuel properties can be used to predict differences in combustion behavior in engines. The future viability of biofuels for compression ignition (diesel) engines is now subject to economic (cost) uncertainty more so than to technical barriers, as the advanced biofuel blends developed here can improve cold-weather fuel properties, provide similar engine performance, and reduce emissions.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2016

Combined High Spectral Resolution Lidar and Millimeter Wavelength Radar Measurement of Ice Crystal Precipitation

The goal of this research has been to improve measurements of snowfall using a combination of millimeter-wavelength radar and High Spectral Resolution Lidar (HSRL) Observations. Snowflakes are large compared to the 532nm HSRL wavelength and small compared to the 3.2 and 8.6 mm wavelength radars used in this study. This places the particles in the optical scattering regime of the HSRL, where extinction cross-section is proportional to the projected area of the particles, and in the Rayleigh regime for the radar, where the backscatter cross-section is proportional to the mass-squared of the particles. Forming a ratio of the radar measured cross-section to the HSRL measured cross section eliminates any dependence on the number of scattering particles, yielding a quantity proportional to the average mass-squared of the snowflakes over the average area of the flakes. Using simultaneous radar measurements of particle fall velocities, which are dependent particle mass and cross-sectional area it is possible to derive the average mass of the snow flakes, and with the radar measured fall velocities compute the snowfall rate. Since this retrieval requires the optical extinction cross-section we began by considering errors this quantity. The HSRL is particularly good at measuring the backscatter cross-section. In previous studies of snowfall in the high Arctic were able to estimate the extinction cross-section directly as a fixed ratio to the backscatter cross-section. Measurements acquired in the STORMVEX experiment in Colorado showed that this approach was not valid in mid-latitude snowfalls and that direct measurement of the extinction cross-section is required. Attempts to measure the extinction directly uncovered shortcomings in thermal regulation and mechanical stability of the newly deployed DOE HSRL systems. These problems were largely mitigated by modifications installed in both of the DOE systems. We also investigated other sources of error in the HSRL direct measurement of extinction (see appendix II of this report). We also developed improved algorithms to extract extinction from the HSRL data. These have been installed in the standard HSRL data processing software and are now available to all users of HSRL data. Validation of snowfall measurements has proven difficult due to the unreliability of conventional snowfall measurements coupled with the complexity of considering the vast variety of snowflake geometries. It was difficult to tell how well the algorithm’s approach to accommodating differences in snowflakes was working without good measurements for comparison. As a result, we decided to apply this approach to the somewhat simpler, but scientifically important, problem of drizzle measurement. Here the particle shape is known and the conventional measurement are more reliable. These algorithms where successfully applied to drizzle data acquired during the ARM MAGIC study of marine stratus clouds between California and Hawaii (see Appendix I). This technique is likely to become a powerful tool for studying lifetime of the climatically important marine stratus clouds.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Impact of dry intrusion events on the composition and mixing state of particles during the winter Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA)

Abstract. Long-range transport of continental emissions has a far-reaching influence over remote regions, resulting in substantial change in the size, morphology, and composition of the local aerosol population and cloud condensation nuclei (CCN) budget. Here, we investigate the physicochemical properties of atmospheric particles collected on board a research aircraft flown over the Azores during the winter 2018 Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA) campaign. Particles were collected within the marine boundary layer (MBL) and free troposphere (FT) after long-range atmospheric transport episodes facilitated by dry intrusion (DI) events. Chemical and physical properties of individual particles were investigated using complementary capabilities of computer-controlled scanning electron microscopy and X-ray spectromicroscopy to probe particle external and internal mixing state characteristics. Furthermore, real-time measurements of aerosol size distribution, cloud condensation nuclei (CCN) concentration, and back-trajectory calculations were utilized to help bring into context the findings from offline spectromicroscopy analysis. While carbonaceous particles were found to be the dominant particle type in the region, changes in the percent contribution of organics across the particle population (i.e., external mixing) shifted from 68 % to 43 % in the MBL and from 92 % to 46 % in FT samples during DI events. This change in carbonaceous contribution is counterbalanced by the increase in inorganics from 32 % to 57 % in the MBL and 8 % to 55 % in FT. The quantification of the organic volume fraction (OVF) of individual particles derived from X-ray spectromicroscopy, which relates to the multi-component internal composition of individual particles, showed a factor of 2.06 ± 0.16 and 1.11 ± 0.04 increase in the MBL and FT, respectively, among DI samples. We show that supplying particle OVF into the κ-Köhler equation can be used as a good approximation of field-measured in situ CCN concentrations. We also report changes in the κ values in the MBL from κMBL, non-DI=0.48 to κMBL, DI=0.41, while changes in the FT result in κFT, non-DI=0.36 to κFT, DI=0.33, which is consistent with enhancements in OVF followed by the DI episodes. Our observations suggest that entrainment of particles from long-range continental sources alters the mixing state population and CCN properties of aerosol in the region. The work presented here provides field observation data that can inform atmospheric models that simulate sources and particle composition in the eastern North Atlantic.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Major improvements on the longwave radiative interactions between surface and clouds in the polar regions in atmospheric global circulation model

Motivations: One of the most important tasks in the study of physical and socioeconomic aspects of climate change is the simulation of polar climate and how it changes in response to global warming. One import component of the polar energy budget (and thus of polar climate) is the radiative interaction between surface and clouds. As of today, the vast majority of state-of-the-art global climate models (GCMs), including all three US flagship GCMs (NCAR CESM, GFDL CM3, and GISS E2-H/E2-R), still assume constant surface emissivity overall spectral bands in their longwave radiation treatment. Similarly, a majority of state-of-the-art GCMs assume non-scattering clouds in the longwave spectrum, including both the NCAR CESM and GFDL CM3. The issues of ignoring spectral variation in surface emissivity and scattering by clouds manifest themselves in the far-infrared (IR) over the polar continents because (1) there is so little water vapor in such areas that surface far-IR emission can reach clouds; and (2) the imaginary part of the refractive index of ice has a local minimum over 350-550 cm-1, which implies possible strong scattering effects over this spectral region both within and between the snow surface and ice clouds. Our sensitivity studies using CloudSat-retrieved hydrometeor profiles over the high-elevation Antarctic continent show that, in winter, an appropriate inclusion of snow surface spectral emissivity and ice cloud scattering in radiative transfer calculations can noticeably reduce the monthly-mean surface net downward far-IR flux and net atmospheric far-IR emission over the entire region. For the far-IR alone, the magnitude of these effects is ~1Wm-2 or even larger. Projection Objectives: We propose to carry out the following studies: (1) Develop or update schemes that can be used in the CESM to compute the optical properties of the snow surface and ice clouds consistently across the longwave and the shortwave spectrum; (2) Modify the longwave radiation scheme in the CESM such that it can incorporate surface spectral emissivity and scattering of clouds; (3) Validate schemes developed for (1) and (2) against measurements or benchmark models; (4) Evaluate the effects of these new treatments on the simulated polar energy budget and climate by the CESM. Technical Approaches:1.Use a detailed snowpack radiative transfer model to simulate spectrally resolved emissivity of snow surfaces as a function of snow effective radius. The model will be validated against available high spectral-resolution measurements of snow spectral emissivity (ASTER spectra library v2.0). Currently the CESM prognoses vertically-resolved snow effective radius, and snow shortwave albedo is parameterized based on snow effective radius. Our treatment of snow spectral emissivity here will be consistent with the treatment of snow albedo in the shortwave and seamlessly connected with the snow physical module in the CESM. 2. Update the parameterization of ice cloud optical properties in the CESM, for both longwave and shortwave, based on the latest ice-cloud scattering database developed by co-I Prof. Yang. 3. Modify RRTMG_LW, the current longwave radiation scheme in the CESM, to include the surface spectral emissivity and the scattering of clouds in the longwave calculation. Validate modified scheme against a line-by-line radiative transfer model with multiple scattering capability (LBLRTM/CHARTS). 4. Implement products from Steps 1-3 into the CESM and conduct simulations to evaluate their effects on the simulated polar climate and energy budget. These runs will be compared to control runs and evaluated using routine model-observation diagnostics, including data from the ARM NSA (North Slope of Alaska) site. Moreover, a novel aspect in this step is that we will evaluate the simulated longwave band-by-band fluxes and cloud radiative effect against observational counterparts over both polar continents, using a product recently derived by the lead PI Prof. Huang from collocated AIRS and CERES satellite observations from 2002 to 2013. Benefits and outcomes: This project will significantly improve the longwave radiation scheme in the CESM and its successors and provide more realistic treatment of radiative interactions between polar surfaces and clouds. In the future, it will also be straightforward to extend this framework to include spectral emissivity of all surface types. This is responsive to identified DoE Earth System Modeling Science Drivers to improve the performance of the fully coupled climate system modeling and to help address questions on how rapid changes in cryospheric systems interact with the climate system.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Enhancing Long-Term Trend Simulation of OH Through the Synergy of Model Simulations and Aura Ozone Monitoring Instrument (OMI) NO 2 and HCHO Retrievals

During the last few years, tremendous progress has been made to develop an efficient parameterization module using agile machine learning techniques. The aim of this module is to provide dynamic response of the tropospheric hydroxyl radical (OH) to its major drivers, including trace gases, aerosols, clouds, and meteorology. This module, named ECCOH (pronounced “echo”) and implemented in NASA’s GEOS-5 global model, offers an unrealized opportunity to unravel the convoluted response of OH to its underlying drivers while approaching the accuracy of full-chemistry without incurring excessive computational costs, making it suitable for climate models. However, the accurate representation of OH in ECCOH poses challenges due to the lack of representation of some of its critical inputs such as the abundance of NO 2 and HCHO concentrations. As such, we leverage the well-characterized satellite observations of NO2 and HCHO columns from Aura OMI to enhance their representation in ECCOH using an optimal interpolation method for the time period of 2005 - present. We show how the inclusion of OMI information can affect the spatiotemporal variability and long-term trends of OH, CO, and CH 4 across the globe. Additionally, we underscore the necessity of obtaining high-fidelity information regarding tropospheric ozone from the southern hemisphere from space, a region currently lacking full verification in models, posing a challenge to get a reasonable amount of chemical sink for CH 4 .

OH↗

NASA NTRS2008

NASA Tech Briefs, December 2008

Topics covered include: Crew Activity Analyzer; Distributing Data to Hand-Held Devices in a Wireless Network; Reducing Surface Clutter in Cloud Profiling Radar Data; MODIS Atmospheric Data Handler; Multibeam Altimeter Navigation Update Using Faceted Shape Model; Spaceborne Hybrid-FPGA System for Processing FTIR Data; FPGA Coprocessor for Accelerated Classification of Images; SiC JFET Transistor Circuit Model for Extreme Temperature Range; TDR Using Autocorrelation and Varying-Duration Pulses; Update on Development of SiC Multi-Chip Power Modules; Radio Ranging System for Guidance of Approaching Spacecraft; Electromagnetically Clean Solar Arrays; Improved Short-Circuit Protection for Power Cells in Series; Electromagnetically Clean Solar Arrays; Logic Gates Made of N-Channel JFETs and Epitaxial Resistors; Improved Short-Circuit Protection for Power Cells in Series; Communication Limits Due to Photon-Detector Jitter; System for Removing Pollutants from Incinerator Exhaust; Sealing and External Sterilization of a Sample Container; Converting EOS Data from HDF-EOS to netCDF; HDF-EOS 2 and HDF-EOS 5 Compatibility Library; HDF-EOS Web Server; HDF-EOS 5 Validator; XML DTD and Schemas for HDF-EOS; Converting from XML to HDF-EOS; Simulating Attitudes and Trajectories of Multiple Spacecraft; Specialized Color Function for Display of Signed Data; Delivering Alert Messages to Members of a Work Force; Delivering Images for Mars Rover Science Planning; Oxide Fiber Cathode Materials for Rechargeable Lithium Cells; Electrocatalytic Reduction of Carbon Dioxide to Methane; Heterogeneous Superconducting Low-Noise Sensing Coils; Progress toward Making Epoxy/Carbon-Nanotube Composites; Predicting Properties of Unidirectional-Nanofiber Composites; Deployable Crew Quarters; Nonventing, Regenerable, Lightweight Heat Absorber; Miniature High-Force, Long-Stroke SMA Linear Actuators; "Bootstrap" Configuration for Multistage Pulse-Tube Coolers; Reducing Liquid Loss during Ullage Venting in Microgravity; Ka-Band Transponder for Deep-Space Radio Science; Replication of Space-Shuttle Computers in FPGAs and ASICs; Demisable Reaction-Wheel Assembly; Spatial and Temporal Low-Dimensional Models for Fluid Flow; Advanced Land Imager Assessment System; Range Imaging without Moving Parts.

Source record↗

DOE OSTI2026

Custom surface reflectance, shade mask, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study (2025)

This dataset contains land surface reflectance estimates and additional derived products generated from NEON Imaging Spectrometer (NIS) data collected in the Upper Gunnison river basin during June and July of 2025. Data was collected over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). These products were derived from radiance and LiDAR data collected by the NEON Airborne Observation Platform (AOP) campaign funded by the Colorado Headwaters Ecological Spectroscopy Study (CHESS) (doi:10.15485/3017965). Products include per-pixel surface reflectance (rfl) and reflectance uncertainty (rfl_unc), observational data (obs), canopy equivalent water thickness (ewt), and shade masks. Atmospheric correction was performed per flightline using the ISOFIT (Imaging Spectrometer Optimal FITting) optimal estimation framework to estimate surface reflectance and the associated per-band reflectance uncertainty. Reflectance retrievals achieved a mean absolute error of 1.5% across diverse validation surfaces (see validation report.pdf). Equivalent water thickness was calculated from surface reflectance using the Beer–Lambert absorption of liquid water. Shade masks were generated based on the geometry between the sun angle, ground surface, and sensor at the time of flight. Data products are provided per-flightline and as mosaics for each domain. Flightline data products are provided as ENVI-formatted binary files (rfl, rfl_unc, ewt) and GeoTIFFs (shade). Reflectance and uncertainty mosaics are provided as tiled NetCDFs, while all other mosaicked products are provided as cloud-optimized GeoTIFFs. These formats are supported by common geospatial software (e.g., QGIS, ArcGIS, ENVI) and programmatic libraries in Python (e.g., rasterio, xarray, spectral, netCDF4) and R (e.g., terra, ncdf4). Processing workflows were designed to be equivalent to those used to generate the 2018 CHESS campaign airborne imaging spectroscopy data products (doi:10.15485/3013527). All outputs were co-registered to a common spatial grid to support time series analyses. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). Computational research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗

NASA NTRS2012

SRT Evaluation of AIRS Version-6.02 and Version-6.02 AIRS Only (6.02 AO) Products

Version-6 contains a number of significant improvements over Version-5. This report compares Version-6 products resulting from the advances listed below to those from Version-5. 1. Improved methodology to determine skin temperature (T(sub s)) and spectral emissivity (Epsilon(sub v)). 2. Use of Neural-net start-up state. 3. Improvements which decrease the spurious negative Version-5 trend in tropospheric temperatures. 4. Improved QC methodology. Version-6 uses separate QC thresholds optimized for Data Assimilation (QC=0) and Climate applications (QC=0,1) respectively. 5. Channel-by-channel clear-column radiances R-hat(sub tau) QC flags. 6. Improved cloud parameter retrieval algorithm. 7. Improved OLR RTA. Our evaluation compared V6.02 and V6.02 AIRS Only (V6.02 AO) Quality Controlled products with those of Version-5.0. In particular we evaluated surface skin temperature T(sub s), surface spectral emissivity Epsilon(sub v), temperature profile T(p), water vapor profile q(p), OLR, OLR(sub CLR), effective cloud fraction alpha-Epsilon, and cloud cleared radiances R-hat(sub tau) . We conducted two types of evaluations. The first compared results on 7 focus days to collocated ECMWF truth. The seven focus days are: September 6, 2002; January 25, 2003; September 29, 2004; August 5, 2005; February 24, 2007; August 10, 2007; and May 30, 2010. In these evaluations, we show results for T(sub s), Epsilon(sub v), T(p), and q(p) in terms of yields, and RMS differences and biases with regard to ECMWF. We also show yield trends as well as bias trends of these quantities relative to ECMWF truth. We also show yields and accuracy of channel by channel QC d values of R-hat(sub tau) for V6.02 and V6.02 AO. Version-5 did not contain channel by channel QC d values of R-hat(sub tau). In the second type of evaluation, we compared V6.03 monthly mean Level-3 products to those of Version-5.0, for four different months: January, April, July, and October; in 3 different years 2003, 2007, and 2011. In particular, we compared V6.03 and V5.0 trends of T(p), q(p), alpha-Epsilon, OLR, and OLR(sub CLR) computed based on results for these 12 time periods

Susskind, Joel↗

DOE OSTI2023

Smart Mobility in the Cloud: Enabling Real-Time Situational Awareness and Cyber-Physical Control Through a Digital Twin for Traffic

This article presents the design, implementation, and use cases of the Chattanooga Digital Twin (CTwin) towards the vision for next-generation smart city applications for urban mobility management. CTwin is an end-to-end web-based platform that incorporates various aspects of the decision-making process for optimizing urban transportation systems in Chattanooga, Tennessee, to reduce traffic congestion, incidents, and vehicle fuel consumption. The platform serves as a cyberinfrastructure to collect and integrate multi-domain urban mobility data from various online repositories and Internet of Things (IoT) sensors, covering multiple urban aspects (e.g., traffic, natural hazards, weather, and safety) that are relevant to urban mobility management. The platform enables advanced capabilities for: (a) real-time situational awareness on traffic and infrastructure conditions on highways and urban roads, (b) cyber-physical control for optimizing traffic signal timing, and (c) interactive visual analytics on big urban mobility data and various metrics for traffic prediction and transportation performance evaluation. The platform is designed using a multi-level componentization paradigm and is implemented using modular and adaptive architecture, rendering it as a generalizable and extendable prototype for other urban management applications. We present several use cases to demonstrate CTwin's core capabilities for supporting decision-making in smart urban mobility management.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

Reducing Data Center Peak Cooling Demand and Energy Costs with Underground Thermal Energy Storage (UTES)

By recent estimates, data center energy demands are projected to consume between 6.7% and 12% of U.S. annual electricity generation by the year 2028, driven primarily by expanded demands from cloud services, big data analytics, and Artificial Intelligence (AI) (Shehabi et al., 2024). As much as 40% of data center total energy consumption are loads associated with the site infrastructure cooling systems, and these are often highly water consumptive (Aljbour et al., 2024). For energy system planners, this presents significant challenges to meeting and managing the anticipated loads, and especially the peak loads of projected data center deployments. Geothermal technologies offer two unique solutions to these challenges: 1) by serving loads through the deployment of new conventional and/or next-generation geothermal power technologies such as EGS and 2) through an often-overlooked opportunity to reduce data center peak cooling loads. The latter is the focus of this paper which explores Cold Underground Thermal Energy Storage ("Cold UTES") as an emerging industrial-scale geothermal cooling solution. This cooling solution is energy efficient, non-water-consumptive, and utilizes long duration energy storage (LDES) on both diurnal and seasonal time scales. Cold UTES has the potential to also function as a virtual power plant (VPP). The US Department of Energy's Geothermal Technologies Office is supporting R&D to understand the grid and system-wide value, costs, and impacts of deploying this emergent cooling solution at scale.

AI↗