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At least 289 records · Page 16
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NASA NTRSDate not supplied

166 GHz Ice Scattering Signal in Cold-Season Precipitating Cloud Systems

Surface snowfall can be produced by a variety of forcing mechanisms that are accompanied by different in-cloud physical processes and microphysical composition. For instance, shallow cumuliform snowfall forced by marine cold air outbreaks shows distinct geographic and seasonal signatures and plays in important role in annual snowfall production in many regions worldwide. Coincident CloudSat-CALIPSO observations confirm that oceanic regions exhibiting frequent shallow cumuliform snow are typically composed of mixed-phase cloud and precipitation particles and display passive microwave brightness temperature behavior that is dominated by cloud liquid water emission, while combined Global Precipitation Measurement (GPM) radiometer and surface radar studies of intense shallow cumuliform events indicate significant ice particle scattering signals at the highest GPM radiometer frequencies. Passive microwave imager channels in the 37 to 89 GHz range have long been used to detect precipitation-sized ice particle scattering signatures in clouds. Dual polarization plays an important role in distinguishing between scattering signatures and other background variability. The GPM Microwave Imager (GMI) is the first satellite radiometer to possess dual-polarized channels at 166 GHz. These high-frequency channels are more susceptible to emission and attenuation by cloud liquid water and water vapor than lower-frequency channels, but are also more sensitive to ice scattering. This research studies the ice scattering information contained in the 166 GHz channels compared to the historically used 85/89 GHz channels on previous sensors. A 166 GHz scattering index (SI) will be developed to understand how its correlation with non-precipitating ice scattering at higher altitudes also affects the 166 GHz scattering signature in cold-season precipitating cloud systems. The 166 GHz SI will be used to possibly isolate precipitation scattering signature for different snowfall modes (shallow convective vs. deep stratiform). The supercooled liquid water affecting shallow convective systems and drastically reducing the scattering signal could be detected and quantified, providing additional information for convective snow detection and quantification in solid precipitation retrieval algorithms.

Lisa Milani↗
DOE OSTI2025

Drivers and impacts of sediment deposition in Amazonian floodplains

Abstract The Amazon River carries enormous amounts of sediment from the Andes mountains, much of which is deposited in its floodplains. However, accurate quantification of the sediment sink at fine spatiotemporal scales is still challenging. Here, we present a high-resolution hydrodynamic-sediment model to simulate sediment deposition in a representative Amazon/Solimões floodplain. The process is found to be jointly driven by inundation, suspended sediment concentration in the Amazon River, and floodplain hydrodynamics and only weakly correlated with inundation level. By upscaling the sediment deposition rate (1.33 ± 0.24 kg m −2 yr −1 ), we estimate the trapping of 77.3 ± 13.9 Mt (or 6.1 ± 1%) of the Amazon River sediment by the Amazon/Solimões floodplains every year. Widespread deforestation would reduce the trapping efficiency of the floodplains over time, exacerbating downstream river aggradation. Additionally, we show that the deposition of sediment-associated organic carbon plays a minor role in fueling carbon dioxide and methane emissions in the Amazon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Diagnosing the DARHT Electron Beam-Target Interaction and Hydrodynamic Expansion [Dissertation]

Quantitative electron beam-target interaction studies on the Dual-Axis Radiographic Hydrodynamic Test (DARHT) electron linear induction accelerators have only been performed recently. This includes characterization of the temperature, density, pressure, extent, and expansion velocity of the plasma plume. The results presented in this dissertation present a detailed and unified overview of the accelerator systems, target heating physics, beam transport, and diagnostic tools. Additional information includes calibration sources, radiation hydrodynamics, spectroscopic-quality radiation transport modeling, experimental measurements, and analyses of electron beam driven aluminum experiments. The first set of spatially and temporally resolved spectroscopic measurements of electron beam driven aluminum are presented. Contamination quantification analyses are used to understand the origin of the strong Na-I 3p-3s lines that are observed in absorption within the aluminum plasma continuum. These results inform the creation of the first spectroscopic-quality radiation transport model that links several atomic physics codes to interpret the conditions from which the Na-I lines originate. A good agreement is found between the surface analysis results and the model which confirms the concentration of the sodium present within the aluminum alloy foil material. It also demonstrates, for the first time on electron beam-driven target experiments, the ability to interpret plasma conditions from measured absorption lines. In a second experimental campaign which focuses on pure aluminum, the Al-I 3p-4s and 3p3d doublets are both measured in emission. A detailed analysis of the Al-I 3p-4s doublet reveals that the lines undergo moderate self-absorption. A simple model of the self-absorption effect is successfully used to match measured spectra at various temperature/density/plasma scale length combinations. These measurements led to the realization of the minimum density that can be resolved by the spectrometers for the Al-I 3p-4s lines due to the large slit width required to observe a signal on aluminum. These measurements also demonstrate the sensitivity of visible and long wave UV spectroscopy to minor changes in both temperature and density. The simple self-absorption model will be useful for analysis of other beam-target interaction experiments with spectra exhibiting either self-absorption or full self-reversal. Substantial headway has been made on the modeling front by linking together several codes needed to model both the energy deposition, hydrodynamic motion, and atomic kinetics to produce synthetic spectral calculations that are compared with experimental measurements. There exists ample space for improvement, especially with benchmarking the hydrodynamics codes and equation-of-state tables with experimental measurements. The X-ray diagnostics required to make these new measurements along with the simulation capabilities required to interpret the results are under development and will be the subject of future studies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Performance and Cost Potential for Direct-Fired Supercritical CO2 Natural Gas Power Plants

Direct-fired supercritical CO2 (sCO2) power cycles are being explored as an attractive alternative to natural gas combined cycle (NGCC) plants with carbon capture and storage (CCS). Therefore, understanding their performance and cost potential is important for the commercialization of the technology. This study presents the techno-economic optimization results of natural gas-fired, utility-scale power plants based on the direct sCO2 power cycle, which are lacking in public literature. To identify the optimum plant configuration, the study considered multiple cases with varying levels of thermal integration with the plant air separation unit (ASU). Several design variables for each power cycle configuration were identified and optimized to minimize the levelized cost of electricity (LCOE) for each case. The optimization design variables include the sCO2 cooler outlet temperatures, recuperator approach temperatures, and pressure drops. High fidelity models for recuperators, coolers, and turbines were developed and used to capture the impact of design variables on plant efficiency and capital costs. The optimization was conducted using a combination of manual sensitivity analyses and automated derivative-free optimization algorithms available under NETL’s Framework for Optimization and Quantification of Uncertainty and Sensitivity platform. The optimized direct sCO2 power plants offered similar or slightly higher plant efficiencies than the reference NGCC plants based on the F-class gas turbine with CCS. The LCOE of the optimized direct sCO2 plants is 13 to 17% higher than the reference NGCC plants with CCS due to high capital costs associated with the ASU and sCO2 power block, though there is significant room for improvement due to the high uncertainty in component capital costs for these new plants. Recuperators make up over 50% of the sCO2 power block costs. Consequently, any research and development efforts to reduce the recuperator capital costs will benefit the technology’s commercialization. The study also presents preliminary results showing the impact of co-firing landfill gas and natural gas on plant efficiency, LCOE, and CO2 emissions.

Pidaparti, Sandeep↗
DOE OSTI2023

Performance and Cost Potential for Direct-Fired Supercritical CO2 Natural Gas Power Plants

Direct-fired supercritical CO2 (sCO2) power cycles are being explored as an attractive alternative to natural gas combined cycle (NGCC) plants with carbon capture and storage (CCS). Therefore, understanding their performance and cost potential is important for the commercialization of the technology. This study presents the techno-economic optimization results of natural gas-fired, utility-scale power plants based on the direct sCO2 power cycle, which are lacking in public literature. To identify the optimum plant configuration, the study considered multiple cases with varying levels of thermal integration with the plant air separation unit (ASU). Several design variables for each power cycle configuration were identified and optimized to minimize the levelized cost of electricity (LCOE) for each case. The optimization design variables include the sCO2 cooler outlet temperatures, recuperator approach temperatures, and pressure drops. High fidelity models for recuperators, coolers, and turbines were developed and used to capture the impact of design variables on plant efficiency and capital costs. The optimization was conducted using a combination of manual sensitivity analyses and automated derivative-free optimization algorithms available under NETL’s Framework for Optimization and Quantification of Uncertainty and Sensitivity platform. The optimized direct sCO2 power plants offered similar or slightly higher plant efficiencies than the reference NGCC plants based on the F-class gas turbine with CCS. The LCOE of the optimized direct sCO2 plants is 13 to 17% higher than the reference NGCC plants with CCS due to high capital costs associated with the ASU and sCO2 power block, though there is significant room for improvement due to the high uncertainty in component capital costs for these new plants. Recuperators make up over 50% of the sCO2 power block costs. Consequently, any research and development efforts to reduce the recuperator capital costs will benefit the technology’s commercialization. The study also presents preliminary results showing the impact of co-firing landfill gas and natural gas on plant efficiency, LCOE, and CO2 emissions.

Pidaparti, Sandeep↗
DOE OSTI2022

Full 3D Kinetic Modeling and Quantification of Positive Streamer Evolution in an Azimuthally Swept Pin-to-Plane Wedge Geometry

Cathode-directed streamer evolution in near atmospheric air is modeled in 3D pin-to-plane geometries using a 3D kinetic Particle-In-Cell (PIC) code that simulates particle-particle collisions via the Direct Simulation Monte Carlo (DSMC) method. Due to the computational challenges associated with a complete 360° volumetric domain, a practical alternative was achieved using a wedge domain and a range of azimuthal angles was explored (5°, 15°, 30°, and 45°) to study possible effects on the streamer growth and propagation due to the finite wedge angle. A DC voltage of 6 kV is administered to a hemispherical anode of radius 100 μm, with a planar cathode held at ground potential, generating an over-volted state with an electric field of 4 MV/m across a 1500 μm gap. The domain is seeded with an initial ion and electron density of 10 18 m -3 at 1 eV temperature confined to a spherical region of radius 100 μm centered at the tip of the anode. The air chemistry model [1] includes standard Townsend breakdown mechanisms (electron-neutral elastic, excitation, ionization, attachment, and detachment collision chemistry and secondary electron emission) as well as streamer mechanisms (photoionization and ion-neutral collisions) via tracking excited state neutrals which can then either quench via collisions or spontaneously emit a photon based on specific Einstein-A coefficients [2, 3]. In this work, positive streamer dynamics are formally quantified for each wedge angle in terms of electron velocity and density as temporal functions of coordinates r, Φ, and z. Applying a random plasma seed for each simulation, particles of interest are tracked with near femtosecond temporal resolution out to 1.4 ns and spatially binned. This process is repeated six times and results are averaged. Prior 2D studies have shown that the reduced electric field, E/n, can significantly impact streamer evolution [4]. We extend the analysis to 3D wedge geometries, to limit computational costs, and examine the wedge angle’s effect on streamer branching, propagation, and velocity. Results indicate that the smallest wedge angle that produced an acceptably converged solution is 30°. The potential effects that a mesh, when under-resolved with respect to the Debye length, can impart on streamer dynamics and numerical heating were not investigated, and we explicitly state here that the smallest cell size was approximately 10 times the minimum λ D in the streamer channel at late times. This constraint on cell size was the result of computational limitations on total mesh count.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction

Carbon capture and storage (CCS) is one approach being studied by the U.S. Department of Energy to help mitigate global warming. The process involves capturing CO 2 emissions from industrial sources and permanently storing them in deep geologic formations (storage reservoirs). However, CCS projects generally target “green field sites,” where there is often little characterization data and therefore large uncertainty about the petrophysical properties and other geologic attributes of the storage reservoir. Consequently, ensemble-based approaches are often used to forecast multiple realizations prior to CO 2 injection to visualize a range of potential outcomes. In addition, monitoring data during injection operations are used to update the pre-injection forecasts and thereby improve agreement between forecasted and observed behavior. Thus, a system for generating accurate, timely forecasts of pressure buildup and CO 2 movement and distribution within the storage reservoir and for updating those forecasts via monitoring measurements becomes crucial. This study proposes a learning-based prediction method that can accurately and rapidly forecast spatial distribution of CO 2 concentration and pressure with uncertainty quantification without relying on traditional inverse modeling. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO 2 storage site operators with an effective tool for timely and informative decision making based on limited simulation and monitoring data.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Using Mineralogy to Interpret Martian Geologic History

The surface mineralogy of Mars is key to interpreting the planet’s geologic history and constraining when and where Mars may have been habitable to microbial life. Orbital mineralogical data from infrared spectrometers collected over the last two decades have demonstrated that Mars is more than a basaltic planet. Thermal infrared (TIR) emission spectrometers have identified regional variations in igneous minerals, iron oxides, and chloride salts, showing evidence for magmatic evolution and aqueous alteration. Orbital visible/shortwave infrared reflectance spectra provide detailed information about the aqueous history of Mars through the identification of clay minerals, sulfates, carbonates, zeolites, and amorphous materials in ancient ~3-4-billion-year-old terrains. Mineralogical data from rovers allow us to better characterize mineral formation mechanisms by identifying mineral assemblages and placing them in geologic context using outcrop- to grain-scale images. TIR and Mössbauer data from the Mars Exploration Rovers helped identify an ancient dune-interdune playa environment with multiple episodes of groundwater at Meridiani Planum and volcanism-induced hydrothermalism at Gusev crater. The CheMin X-ray diffractometer on the Mars Science Laboratory Curiosity rover in Gale crater is the first fully quantitative mineralogical instrument sent to another planetary surface. CheMin data allow the quantification of minerals and X-ray amorphous materials with a mineral detection limit of <1 wt.% and provide crystal chemistry of major phases from refined unit-cell parameters. CheMin has revealed local mineralogical changes in ancient sedimentary rocks not seen from orbit and helped identify habitable environments. Throughout the 600+ m of vertical stratigraphy studied so far, changes in clay mineralogy, Fe-oxides/oxyhydroxides, sulfates, and carbonates demonstrate a long history of surface and groundwater at Gale crater with variable salinity, pH, Eh, and temperature. Recent CheMin data document a decrease in clay minerals and an increase in sulfate minerals in younger strata that corresponds with a change from fluvio-lacustrine to eolian depositional environments, potentially documenting a change to a drier climate across the planet.

E. B. Rampe↗
DOE OSTI2024

Enhancing analytical merits of laser-induced breakdown spectroscopy of hydrogen isotopes using an orthogonal double-pulsing scheme

Accurate detection and quantification of hydrogen isotopes in solid materials are vital for diverse applications, including fusion energy, hydrogen storage, and tritium production. Laser-induced breakdown spectroscopy (LIBS) is a well established, rapid, standoff method for this purpose, but it faces challenges related to the analytical merits required for isotopic analyses. In this study, we enhance the analytical and detection capabilities of traditional single-pulse LIBS by implementing an orthogonal double pulsing approach, focusing on the analysis of a range of 2 H concentrations in Zircaloy-4 substrates (acting as a proxy for 3 H). The double-pulse experiments employed an orthogonal re-heating configuration with two nanosecond Nd:YAG lasers. We systematically evaluated critical parameters affecting the signal intensity in double-pulse LIBS, including interpulse delay, ambient gas pressure, and heating laser energy. Finally, our results demonstrate that employing an orthogonal double-pulse scheme significantly enhances 2 H α emission while minimizing line broadening and self-absorption, ultimately improving the technique’s analytical capabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Single-ion magnetism in the extended solid-state: insights from X-ray absorption and emission spectroscopy

Large single-ion magnetic anisotropy is observed in lithium nitride doped with iron. The iron sites are two-coordinate, putting iron doped lithium nitride amongst a growing number of two coordinate transition metal single-ion magnets (SIMs). Uniquely, the relaxation times to magnetisation reversal are over two orders of magnitude longer in iron doped lithium nitride than other 3d-metal SIMs, and comparable with high-performance lanthanide-based SIMs. To understand the origin of these enhanced magnetic properties a detailed characterisation of electronic structure is presented. Access to dopant electronic structure calls for atomic specific techniques, hence a combination of detailed single-crystal X-ray absorption and emission spectroscopies are applied. Together K-edge, L 2,3 -edge and Kβ X-ray spectroscopies probe local geometry and electronic structure, identifying iron doped lithium nitride to be a prototype, solid-state SIM, clean of stoichiometric vacancies where Fe lattice sites are geometrically equivalent. Extended X-ray absorption fine structure and angular dependent single-crystal X-ray absorption near edge spectroscopy measurements determine Fe I dopant ions to be linearly coordinated, occupying a D 6h symmetry pocket. The dopant engages in strong 3dπ-bonding, resulting in an exceptionally short Fe–N bond length (1.873(7) Å) and rigorous linearity. It is proposed that this structure protects dopant sites from Renner–Teller vibronic coupling and pseudo Jahn–Teller distortions, enhancing magnetic properties with respect to molecular-based linear complexes. The Fe ligand field is quantified by L 2,3 -edge XAS from which the energy reduction of 3d z 2 due to strong 4s mixing is deduced. Quantification of magnetic anisotropy barriers in low concentration dopant sites is inhibited by many established methods, including far-infrared and neutron scattering. We deduce variable temperature L 3 -edge XAS can be applied to quantify the J = 7/2 magnetic anisotropy barrier, 34.80 meV (~280 cm –1 ), that corresponds with Orbach relaxation via the first excited, M J = ±5/2 doublet. The results demonstrate that dopant sites within solid-state host lattices could offer a viable alternative to rare-earth bulk magnets and high-performance SIMs, where the host matrix can be tailored to impose high symmetry and control lattice induced relaxation effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt

Abstract. Antarctica's Lambert Glacier drains about one-sixth of the ice from the East Antarctic Ice Sheet and is considered stable due to the strong buttressing provided by the Amery Ice Shelf. While previous projections of the sea-level contribution from this sector of the ice sheet have predicted significant mass loss only with near-complete removal of the ice shelf, the ocean warming necessary for this was deemed unlikely. Recent climate projections through 2300 indicate that sufficient ocean warming is a distinct possibility after 2100. This work explores the impact of parametric uncertainty on projections of the response of the Lambert–Amery system (hereafter “the Amery sector”) to abrupt ocean warming through Bayesian calibration of a perturbed-parameter ice-sheet model ensemble. We address the computational cost of uncertainty quantification for ice-sheet model projections via statistical emulation, which employs surrogate models for fast and inexpensive parameter space exploration while retaining critical features of the high-fidelity simulations. To this end, we build Gaussian process (GP) emulators from simulations of the Amery sector at a medium resolution (4–20 km mesh) using the Model for Prediction Across Scales (MPAS)-Albany Land Ice (MALI) model. We consider six input parameters that control basal friction, ice stiffness, calving, and ice-shelf basal melting. From these, we generate 200 perturbed input parameter initializations using space filling Sobol sampling. For our end-to-end probabilistic modeling workflow, we first train emulators on the simulation ensemble and then calibrate the input parameters using observations of the mass balance, grounding line movement, and calving front movement with priors assigned via expert knowledge. Next, we use MALI to project a subset of simulations to 2300 using ocean and atmosphere forcings from a climate model for both low- and high-greenhouse-gas-emission scenarios. From these simulation outputs, we build multivariate emulators by combining GP regression with principal component dimension reduction to emulate multivariate sea-level contribution time series data from the MALI simulations. We then use these emulators to propagate uncertainty from model input parameters to predictions of glacier mass loss through 2300, demonstrating that the calibrated posterior distributions have both greater mass loss and reduced variance compared to the uncalibrated prior distributions. Parametric uncertainty is large enough through about 2130 that the two projections under different emission scenarios are indistinguishable from one another. However, after rapid ocean warming in the first half of the 22nd century, the projections become statistically distinct within decades. Overall, this study demonstrates an efficient Bayesian calibration and uncertainty propagation workflow for ice-sheet model projections and identifies the potential for large sea-level rise contributions from the Amery sector of the Antarctic Ice Sheet after 2100 under high-greenhouse-gas-emission scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantification of regional net CO 2 flux errors in the Orbiting Carbon Observatory-2 (OCO-2) v10 model intercomparison project (MIP) ensemble using airborne measurements

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO 2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO 2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO 2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO 2 concentrations and airborne observations and then isolate the CO 2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO 2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO 2 flux estimates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Measurement of needle and armature dynamics in a gasoline direct injector by high-speed neutron imaging

In modern spark ignition engines, precise delivery of fuel with gasoline direct injection has become increasingly important in the effort to meet ever stricter efficiency and emissions regulations. Use of multiple small close-coupled injections has become more common in attempt to precisely control fuel distribution in the cylinder, but these strategies are hindered by nonlinear injection effects due to operation in the ballistic region of the solenoid-operated valve and due to armature bounce at the end of injection. Understanding the internal dynamics of the injector is crucial to minimizing and controlling non-linearity and shot-to-shot variation, but the experimental techniques available to date are capable only of tracking the position of either the top of the needle (via laser sensors or by monitoring current and voltage in the solenoid coil) or the bottom of the needle (via transparent nozzles or high-speed x-ray imaging). Furthermore, a complete picture of the axial and radial motion of valve needle has until now remained elusive. In this work, we present high-speed ensemble neutron transmission imaging of an entire 8-hole gasoline direct injector operating at 200 bar, allowing for both visualization and quantification of the actuation dynamics including lift and wobble of the valve ball, oscillation and bending of the valve needle, lift, rocking, and bounce of the armature, compression of the springs, and radial swelling of the solenoid during energization. Because neutrons offer high penetration through the metal injector while also being sensitive to the 1 H nuclei in fuel molecules, it is also possible to simultaneously see the fluid dynamics of the injection process, including filling of the sac volume, emanation of the spray through the nozzle holes and into the downstream gas, and the formation and evolution of fuel films on the tip of the injector and the walls of the spray container.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2019

Further Characterization of Aerosols Sampled on the International Space Station

Spacecraft cabin air quality is of fundamental importance to crew health, with concerns encompassing both gaseous contaminants and airborne particles. Quantification of spacecraft indoor aerosols will increase our understanding of crew exposure and cabin cleanliness. Aerosols on the International Space Station (ISS) have been sampled and brought back to Earth for analysis to characterize the airborne particulate matter in the cabin. Microscopic analyses have been performed to determine morphology and particle size information, and Energy Dispersive X-ray Spectroscopy (EDS) provides information on the chemical elements present in the particles. With the use of IntelliSEM software for computer-controlled scanning electron microscopy (CCSEM), this data provides particle size distribution information and statistics on particle materials. Many of the particles collected were made up of multiple elements and had uncommon morphologies compared to typical indoor aerosols on Earth. These characteristics are thought to be from unique formation mechanisms in the microgravity environment. Several notable particle types are examined further in this work. Bromine-containing particles and cadmium-containing particles are discussed as they constitute a health hazard to crew members. Humans in indoor living and working spaces are typically the single largest particle emission source, and this was observed in the sampled aerosols in ISS as well.

Sampling↗
NASA NTRS1998

Enhanced Sea Ice Concentration and Ice Temperature Algorithms for AMSR

Accurate quantification of sea ice concentration and ice temperature from satellite passive microwave data is important because they provide the only long term, spatially detailed and consistent data set needed to study the climatology of the polar regions. Sea ice concentration data are used to derive large-scale daily ice extents that are utilized in trend analysis of the global sea ice cover. They are also used to quantify the amount of open water and thin ice in polynya and divergence regions which together with ice temperatures are in turn needed to estimate vertical heat and salinity fluxes in these regions. Sea ice concentrations have been derived from the NASA Team and Bootstrap algorithms while a separate technique for deriving ice temperature has been reported. An integrated technique that will utilizes most of the channels of AMSR (Advanced Microwave Scanning Radiometer) has been developed. The technique uses data from the 6 GHz and 37 GHz channels at vertical polarization obtain an initial estimate of sea ice concentration and ice temperature. The derived ice temperature is then utilized to estimate the emissivities for the corresponding observations at all the other channels. A procedure for calculating the ice concentration similar to the Bootstrap technique is then used but with variables being emissivities instead of brightness temperatures to minimizes errors associated with spatial changes in ice temperatures within the ice pack. Comparative studies of ice concentration results with those from other algorithms, including the original Bootstrap algorithm and those from high resolution satellite visible and infrared data will be presented. Also, results from a simulation study that demonstrates the effectiveness of the technique in correcting for spatial variations in ice temperatures will be shown. The ice temperature results are likewise compared with satellite infrared and buoy data with the latter adjusted to account for the effects of the snow cover.

Comiso, Josefino C.↗
DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

SPRUCE Anaerobic oxidation of methane mitigates net methane production and responds to long-term experimental warming in a northern bog: Supporting data

This data set contains measurements of anaerobic oxidation of methane (AOM), net methane (CH4) production, carbon dioxide (CO2) production, and gross CH4 production measurements from the SPRUCE experiment (Hanson et al. 2017). CH4 is greenhouse gas with 45 times the sustained global warming potential of CO2 over 100 years, which may have significant implications on global climate change. The process of AOM plays a large role in the extent of CH4 emissions from wetland environments like those at the SPRUCE experiment site. This study conducted two on different dates. In experiment 1, net CH4 and CO2 production were measured with a gas chromatograph through the peat profile at in situ temperatures of the SPRUCE plots four times from May to October, 2016 (2016-05 to 2016-10). Two treatments were examined, a Porewater Treatment that was a 1:1 volumetric peat:porewater slurry and a No Porewater Treatment that was field-moist peat with no porewater added. Additionally, net CH4 production were measured at frequent intervals (from 4 hr to 9 d) in peat from selected SPRUCE plots from soil collected on October 16, 2016 (2016-10-16). Samples were collected from the SPRUCE plots, immediately made anaerobic, and transported to the University of Oregon for rate measurements within several days of collection. In experiment 2, AOM, net CH4 production, CO2 production, and gross CH4 production were measured through the peat profile at in situ field temperatures once from samples collected in July, 2021 (2021-07). The same two porewater treatments were examined. AOM was measured with a 13CH4 tracer method using a Picarro G2201-i analyzer for isotopic CO2/CH4 quantification with a small stable isotope module attached. This data set contains three data files in comma separate (*.csv) format. This data set is associated with the following publication: Barney, M, AM Hopple, LL Gregory, JK Keller, and SD Bridgham. 2024. Anaerobic oxidation of methane mitigates net methane production and responds to long-term experimental warming in a northern bog. Soil Biology and Biochemistry, 190, 109316. https://doi.org/10.1016/j.soilbio.2024.109316.

SPRUCE Experiment, Marcell Experimental Forest, Me↗