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NASA NTRSDate not supplied

GeoNEX-SUBSETS: Cutouts of geostationary satellite data over long-term monitoring sites

Satellite remote sensing data are important tool for extrapolating the knowledge obtained at the Fluxnet towers. We introduce our GEO-NEX geostationary satellite subset products, which make it easy to compare between ground observation and Geo-NEX products. The MODIS subset has been frequently used for validating ecosystem models developed at flux towers and upscaling the observed flux data to regional scales. However, MODIS on the polar orbiting satellites can observe target regions only once a day, while the Fluxnet eddy-covariance data are compiled as sub-hourly datasets. As a results, summarizing the sub-hourly flux data into daily statistics is necessary for the comparison between MODIS and Flux data. Here, the new generation geostationary satellite sensors (GOES-16/17 ABI and Himawari-8/9 AHI) has the high-frequent observation feature (10 minutes) in addition to similar spectral band and spatial resolution with MODIS. The high frequent observation allows us to compare the flux diurnal cycle with geostationary satellite sensor data. Some studies have already shown the effective utilization of time series of geostationary satellite data for ecosystem modeling. We are producing NEX Level-1G products, which is the gridded Top-of-Atmosphere reflectance and brightness temperature data of geostationary satellite sensors. We cut out the NEX Level-1G data using the same file format with the MODIS subset except for the projection. The other data products (e.g., surface reflectance, land surface temperature, vegetation indices and climate data) will be also added upon their availability. We selected the ground observation sites from Fluxnet, Phenocam, and AERONET networks. The NEX subset data will be provided through NASA NEX data portal.

GeoNEX↗
NASA NTRS2021

Research for Climate Adaptation

Adaptation to climate change must be ramped up urgently. We propose three avenues to transform ambition to action: improve tracking of actions and progress, upscale investment especially in critical areas, and accelerate learning through practice.

Climate-change adaptation↗
NASA NTRS2021

Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: Structural impacts in geometrically explicit vegetation canopies

Solar-induced fluorescence (SIF) is a subtle but informative optical signal of vegetation photosynthesis. Remotely sensed SIF integrates environmental, physiological and structural changes that alter photosynthesis at leaf, plant and canopy scales. Radiative transfer models are ideally suited to investigate the complex sources of variability in the SIF signal to guide the interpretation of SIF retrievals from airborne and space-borne platforms. Here, we coupled the Fluspect-Cx model of leaf optical properties and chlorophyll-a fluorescence with the Discrete Anisotropic Radiative Transfer (DART) model to upscale SIF from individual leaves to three-dimensional (3D) structurally explicit canopies. For one-dimensional homogeneous (turbid-like) canopies, DART-SIF was nearly identical to SIF simulated in two existing models, SCOPE and mSCOPE (RMSE <0.221 W./sq.m.μm.sr). DART simulations in geometrically explicit 3D canopies offered four important insights regarding the influence of vegetation structure on the multi-angular top-of-canopy SIF signal. First, changes in the 3D canopy architecture of maize crops, represented by leaf density (leaf area index), and plant clumping (canopy closure) had a larger impact on SIF than the modelled photosynthetic efficiency distinction between sun-adapted and shade-adapted foliage. Second, clumping of leaves at the crop and stand levels was identified as one of the key driving factors of multi-angular anisotropy of red and far-red SIF (686 and 740 nm) for both maize and eucalyptus canopies. Third, non-photosynthetic woody material had a significant impact on top-of-canopy SIF in modelled 3D forest stands. Wood shadowing decreased the photosynthetically active radiation absorbed by green leaves, and consequently the SIF emissions, by 10% in sparse and 17% in dense eucalyptus stands. The wood obstruction (blocking) effect, quantified as a relative difference of SIF escape probabilities from canopies with and without wood in the nadir viewing direction, decreased far-red SIF by 4–6% but it had a smaller and sometimes positive influence (by less than 2%) on red SIF. Fourth, DART 3D radiative budget profiles revealed that the majority of the SIF signal from a dense eucalyptus stand originated from the top 25% of the simulated canopy. Interestingly, the introduction of bark-covered woody elements did not alter the simulated balance and omnidirectional escape factor of red SIF in this upper canopy part but did raise significantly both of them in case of far-red SIF. These results demonstrate the importance of 3D radiative transfer and radiative budget simulations for investigating SIF interactions in structurally complex plant canopies and for a better understanding of spatiotemporal and multi-angular remote sensing SIF observations.

Zbyněk Malenovský↗
NASA NTRS2020

Mapping tree canopy cover and canopy height with L-band SAR using LiDAR data and Random Forests

Light detection and ranging (LiDAR) data can provide direct measurements of vegetation structures but are limited by the sparse spatial coverage. Polarimetric synthetic aperture radar (SAR) can perform large-scale high-resolution mapping without weather constraints but the information about vegetation and ground subsurface are mixed in the backscatter data. In this paper, we adopted the Random Forests algorithm to train an upscaling function using tree canopy cover (TCC) and canopy height model (CHM) derived from Goddard’s LiDAR, Hyperspectral and Thermal Imager (G-LiHT) data. The regression model is then applied to the L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired during the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) airborne campaign to map the TCC and CHM over the Delta Junction area in interior Alaska.

Moghaddam, Mahta↗
NASA NTRS2021

Simulation of Radon-222 with the GEOS-Chem Global Model: Emissions, Seasonality, and Convective Transport

Radon-222 (Rn-222) is a short-lived radioactive gas naturally emitted from land surfaces and has long been used to assess convective transport in atmospheric models. In this study, we simulate Rn-222 using the GEOS-Chem chemical transport model to improve our understanding of Rn-222 emissions and surface concentration seasonality and characterize convective transport associated with two Goddard Earth Observing System (GEOS) meteorological products, the Modern-Era Retrospective analysis for Research and Applications (MERRA) and GEOS Forward Processing (GEOS-FP). We evaluate four global Rn-222 emission scenarios by comparing model results with observations at 51 surface sites. The default emission scenario in GEOS-Chem yields a moderate agreement with surface observations globally (68.9 % of data within a factor of 2) and a large underestimate of winter surface Rn-222 concentrations at Northern Hemisphere midlatitudes and high latitudes due to an oversimplified formulation of Rn-222 emission fluxes (1 atom cm−2 s−1 over land with a reduction by a factor of 3 under freezing conditions). We compose a new global Rn-222 emission scenario based on Zhang et al. (2011) and demonstrate its potential to improve simulated surface Rn-222 concentrations and seasonality. The regional components of this scenario include spatially and temporally varying emission fluxes derived from previous measurements of soil radium content and soil exhalation models, which are key factors in determining Rn-222 emission flux rates. However, large model underestimates of surface Rn-222 concentrations still exist in Asia, suggesting unusually high regional Rn-222 emissions. We therefore propose a conservative upscaling factor of 1.2 for Rn-222 emission fluxes in China, which was also constrained by observed deposition fluxes of 210Pb (a progeny of Rn-222). With this modification, the model shows better agreement with observations in Europe and North America (> 80 % of data within a factor of 2) and reasonable agreement in Asia (close to 70 %). Further constraints on Rn-222 emissions would require additional concentration and emission flux observations in the central United States, Canada, Africa, and Asia. We also compare and assess convective transport in model simulations driven by MERRA and GEOS-FP using observed Rn-222 vertical profiles in northern midlatitude summer and from three short-term airborne campaigns. While simulations with both GEOS products are able to capture the observed vertical gradient of Rn-222 concentrations in the lower troposphere (0–4 km), neither correctly represents the level of convective detrainment, resulting in biases in the middle and upper troposphere. Compared with GEOS-FP, MERRA leads to stronger convective transport of Rn-222, which is partially compensated for by its weaker large-scale vertical advection, resulting in similar global vertical distributions of Rn-222 concentrations between the two simulations. This has important implications for using chemical transport models to interpret the transport of other trace species when these GEOS products are used as driving meteorology.

Bo Zhang↗
NASA NTRS2022

Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle

The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, RS), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and RS are irreconcilable. When we estimate GPP based on RS measurements and some assumptions about RS:GPP ratios, we found the resulted global GPP values (bootstrap mean 149^(+29)_(−23) Pg C/yr) are significantly higher than most GPP estimates reported in the literature (113^(+18)_(−18) Pg C/yr). Similarly, historical GPP estimates imply a soil respiration flux (Rs(_GPP), bootstrap mean of 68^(+10)_(−8) Pg C/yr) statistically inconsistent with most published RS values (87^(+9)_(−8) Pg C/yr), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global R_(S):GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.

Jinshi Jian↗
NASA NTRS2023

Observational Constraints Reduce Model Spread but Not Uncertainty in Global Wetland Methane Emission Estimates

The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Kuang-Yu Chang↗
NASA NTRSDate not supplied

Progress Towards SiC ASICs for Extreme Temperature and Radiation Environments

This presentation describes development and demonstrations of semiconductor integrated circuits (ICs) and ceramic packaging that are arguably the most environmentally durable transistor electronics ever demonstrated. Silicon carbide (SiC) junction field effect transistor-resistor (JFET-R) ICs fabricated by NASA Glenn Research Center with two-level interconnect have successfully operated for over 1 year in 500 °C air-ambient, 60 days in 460 °C and 9.3 MPa pressure caustic Venus surface environment test chamber, and radiation exposure through 7 Mrad(Si) total ionizing dose (TID) and 86 MeV-cm2/mg heavy ion strikes. Furthermore, these ICs have also demonstrated operation from -190 °C to +812 °C (over 1000 °C temperature span) without significant change in signal (input /output) or power supply voltages. While the operating frequency and functional complexity is far below silicon-based ICs, these SiC application specific ICs (ASICs) are nevertheless becoming capable of providing unique and advantageous harsh-environment circuit functionality without cooling/sheltering overhead. With modest adjustments, the SiC JFET-R fabrication process is compatible with semiconductor mass-production tools and materials. As an initial step towards manufacture, a majority of processing steps to realize the next SiC JFET-R IC prototype wafer run have been outsourced to commercial foundry. It is expected that further upscaling combined with technology transfer to commercial production will lower investment and risk barriers to useful application deployment.

high temperature↗
NASA NTRSDate not supplied

Multiscale Modeling of Fracture Strength in Fibrous Thermal Protection System Materials

This work presents a multiscale modeling approach to predict the fracture strength of fibrous Thermal Protection System (TPS) materials. The model assumes that system failure is initiated at the joints between individual fibers. We investigated three distinct TPS compositions: amorphous silica, alumina and aluminosilicate fibers. Molecular dynamics (MD) simulations were employed to determine the fracture strength values of these fiber joints for both material systems. These fracture strength values were then integrated into simulations of 3D randomly populated fiber structures, where tensile load transfer occurs through the fiber joints. These microscale properties are upscaled through a renormalization approach [1] to predict macroscale tensile strength of 3D random fiber networks, accounting for joint-dominated failure and effective load-bearing area. The study concludes by demonstrating the resulting strength variation as a function of material composition, fiber density, and morphology. We also show validation of results by comparing them against explicit fiber finite element (FE) modeling [2] where fiber joint fracture is represented by cohesive elements.

Jaehyun Cho↗
DOE OSTI2020

Single step solution combustion synthesis of crystalline transuranic-doped rare earth zirconate pyrochlores

One-step solution combustion synthesis (SCS) methods for fabricating durable crystalline transuranic-doped rare earth zirconium pyrochlores are described. Methods are fast, amenable to upscaling, and present a simple strategy for producing crystalline ceramic materials that meet the minimum attractiveness criteria for special nuclear material. The methods include analysis of reactants and reaction conditions to select proper fuel as well as proper fuel content so as to encourage formation of the crystalline product in a single-step synthesis procedure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Impact of sub-core scale heterogeneity on CO2/brine multiphase flow for geological carbon storage in the Minnelusa sandstone

CO2 geological storage in deep saline aquifers is a mitigation option for CO2 emissions due to its large storage capacity and immediate accessibility. Accurately determining the CO2-brine relative permeability curves is key to the evaluation of CO2 injectivity and sweep efficiency in reservoir simulation as well as the CO2 injection in the field. This study highlights the remarkable effects of sub-core scale heterogeneity on the CO2-brine multiphase flow properties of the Minnelusa Sandstone in Wyoming. Two unsteady state CO2-brine drainage experiments were performed on the two samples. The first sample exhibits slanted laminated structure, while the second one represents a more homogeneous sandstone system. The CO2 saturation distributions during drainage reveals that the main variation in multiphase flow properties of two core samples were attributed to the porosity distribution that leads to the capillary pressure heterogeneity. Assisted history matching was used to obtain the respective relative permeability curves, which suggests heterogeneity-dependent behavior. In addition, sensitive and uncertainty analyses indicate that physical and petro-physical properties of low-porosity and low-permeability bedding layers exert marked effects on CO2 front breakthrough time and brine production. The results presented in this study help to gain insight into the CO2-brine multiphase flow properties in heterogeneous sandstones and can pave the way for the upscaling of CO2 migration and field-scale simulation accurately. This work is funded under the Department of Energy CarbonSAFE program (awards DE-FE0031624 DE0031891).

Kou, Zuhao↗
DOE OSTI2023

Grand Challenges for Small Wind: Results of the European Academy Study

While modern wind turbines have become by far the largest rotating machines on Earth with further upscaling planned for the future, a renewed interest in small wind turbines (SWTs) is fostering energy transition and smart grid development. Small machines have traditionally not received the same level of aerodynamic refinement as their larger counterparts, resulting in lower efficiency, lower capacity factors, and therefore a higher cost of energy. In an effort to reduce this gap, research programs are developing worldwide. With this background, the scope of the present study is 2-fold. In the first part of this paper, an overview of the current status of the technology is presented in terms of technical maturity, diffusion, and cost. The second part of the study proposes five grand challenges that are thought to be key to fostering the development of small wind turbine technology in the near future, i.e. (1) improving energy conversion of modern SWTs through better design and control, especially in the case of turbulent wind; (2) better predicting long-term turbine performance with limited resource measurements and proving reliability; (3) improving the economic viability of small wind energy; (4) facilitating the contribution of SWTs to the energy demand and electrical system integration; (5) fostering engagement, social acceptance, and deployment for global distributed wind markets. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Based on them, improvement areas are suggested, for which 10 key enabling actions are finally proposed.

ENGINEERING,WIND ENERGY↗
DOE OSTI2024

Membrane-based carbon capture process optimization using CFD modeling

Carbon capture is a promising option to mitigate CO2 emissions from existing coal-fired power plants, cement and steel industries, and petrochemical complexes. Among the available technologies, membrane-based carbon capture presents the lowest energy consumption, operating costs, and carbon footprint. In addition, membrane processes have important operational flexibility and response times. On the other hand, the major challenges to widespread application of this technology are related to reducing capital costs and improving membrane stability and durability. To upscale the technology into stacked flat sheet configurations, high fidelity computational fluid dynamics (CFD) that describes the separation process accurately are required. High fidelity simulations have been shown to be effective in studying the complex transport phenomena in membrane systems. In addition, obtaining high CO2 recovery percentages and product purity requires a multi-stage membrane process, where the optimal network configuration of the membrane modules must be studied in a systematic way. In order to address the design problem at process scale, we formulate a superstructure for the membrane-based carbon capture, including up to three separation stages. In the formulation of the optimization problem, we include reduced models, based on rigorous CFD simulations of the membrane modules. Numerical results indicate that the optimal design includes three membrane stages, and the capture cost is 45.4 $/t-CO2.

Pedrozo, Hector A.↗
DOE OSTI2022

An Anthradithiophene Donor Polymer for Organic Solar Cells with a Good Balance between Efficiency and Synthetic Accessibility

Over the past few years, the organic photovoltaics technology (OPV) has reached remarkable power conversion efficiencies (PCEs), mostly thanks to the advent of nonfullerene acceptors as well as to a high level of materials engineering. Yet, the complex materials syntheses behind these results ultimately limit technological readiness. The quest for scalable organic compounds offering high PCE and reasonably low synthetic complexity is a must to close the gap between laboratory R&D and commercial products. The synthesis and full characterization of a new conjugated polymer called PATTD is reported herein, based on a novel anthradithiophene as an electron–rich building block coupled with a commercially available dithienylbenzodithiophenedione as an electron–withdrawing comonomer. Its photovoltaic properties are studied in blends with IT–4F and IDIC as acceptors. PATTD:IT–4F–based photovoltaic devices exhibit a PCE approaching 10% and over 2300 h shelf–life stability. Finally, the PATTD scalability factor (SF), together with the PATTD–based photovoltaic performances, lead to a PCE/SF value equal to 0.297, placing such devices into the innermost circle of OPV materials, achieving one of the best compromises between efficiency and synthetic complexity and at the same time offering promising industrial perspectives.

14 SOLAR ENERGY↗
DOE OSTI2023

Quantitative Representativeness and Constituency of the Long-Term Agroecosystem Research Network and Analysis of Complementarity with Existing Ecological Networks

Abstract Studies conducted at sites across ecological research networks usually strive to scale their results to larger areas, trying to reach conclusions that are valid throughout larger enclosing regions. Network representativeness and constituency can show how well conditions at sampling locations represent conditions also found elsewhere and can be used to help scale-up results over larger regions. Multivariate statistical methods have been used to design networks and select sites that optimize regional representation, thereby maximizing the value of datasets and research. However, in networks created from already established sites, an immediate challenge is to understand how well existing sites represent the range of environments in the whole area of interest. We performed an analysis to show how well sites in the USDA Long-Term Agroecosystem Research (LTAR) Network represent all agricultural working lands within the conterminous United States (CONUS). Our analysis of 18 LTAR sites, based on 15 climatic and edaphic characteristics, produced maps of representativeness and constituency. Representativeness of the LTAR sites was quantified through an exhaustive pairwise Euclidean distance calculation in multivariate space, between the locations of experiments within each LTAR site and every 1 km cell across the CONUS. Network representativeness is from the perspective of all CONUS locations, but we also considered the perspective from each LTAR site. For every LTAR site, we identified the region that is best represented by that particular site—its constituency—as the set of 1 km grid locations best represented by the environmental drivers at that particular LTAR site. Representativeness shows how well the combination of characteristics at each CONUS location was represented by the LTAR sites’ environments, while constituency shows which LTAR site was the closest match for each location. LTAR representativeness was good across most of the CONUS. Representativeness for croplands was higher than for grazinglands, probably because croplands have more specific environmental criteria. Constituencies resemble ecoregions but have their environmental conditions “centered” on those at particular existing LTAR sites. Constituency of LTAR sites can be used to prioritize the locations of experimental research at or even within particular sites, or to identify the extents that can likely be included when generalizing knowledge across larger regions of the CONUS. Sites with a large constituency have generalist environments, while those with smaller constituency areas have more specialized environmental combinations. These “specialist” sites are the best representatives for smaller, more unusual areas. The potential of sharing complementary sites from the Long-Term Ecological Research (LTER) Network and the National Ecological Observatory Network (NEON) to boost representativeness was also explored. LTAR network representativeness would benefit from borrowing several NEON sites and the Sevilleta LTER site. Later network additions must include such specialist sites that are targeted to represent unique missing environments. While this analysis exhaustively considered principal environmental characteristics related to production on working lands, we did not consider the focal agronomic systems under study, or their socio-economic context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

τ-SIMPLE Algorithm for the closure problem in homogenization of stokes flows

Estimation of the permeability tensor is crucial for many natural and industrial porous media applications. Homogenization provides a rigorous framework to calculate effective parameters from pore-scale images of a representative unit cell of the porous medium by solving a boundary value problem, also known as closure problem, subject to global constraints. However, the latter are hard to satisfy for arbitrarily complex geometries. We have developed a novel computational algorithm to calculate rigorously the permeability tensor. The approach, here referred to as τ-SIMPLE, is based on introducing an artificial time scale τ to satisfy the global constraint within the SIMPLE iteration. Here, we show that the proposed algorithm has high accuracy for both two-dimensional and three-dimensional periodically arranged geometries.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enforcing global constraints for the dispersion closure problem: τ 2 -SIMPLE algorithm

Permeability and effective dispersion tensors are critical parameters to characterize flow and transport in porous media at the continuum scale. Homogenization theory defines a framework in which such effective properties are first computed from solving a closure problem in a repeating unit cell of the periodic microstructure and then used in a macroscopic formulation for efficient computation. The closure problem is formulated as a local boundary value problem subjected to global constraints, which guarantee the uniqueness of the solution and can be difficult to satisfy for complex geometries and at high flow conditions. These constraints also ensure that pore-scale pressure, velocity, and concentration fields can be accurately reconstructed from the closure variable. Building on a previous work, here we present a framework that allows to satisfy global constraints associated to both the permeability and the dispersion closure problems by introducing two artificial time scales. The algorithm, called τ 2 -SIMPLE, computes both permeability and effective dispersion given an arbitrarily complex geometry and flow condition. Furthermore, this algorithm is demonstrated to be accurate for both 2D and 3D geometries across varying flow conditions, and thus it can be used to quickly characterize effective properties from porous media images in many applications.

97 MATHEMATICS AND COMPUTING↗