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Results of the data which were acquired, reduced, and analyzed from the Communications Link Characterization Experiment (CLCE) while utilizing the CTS satellite are presented. The test data obtained from the stations consisted of the results of various TV tests, attenuation and rain rate data. An additional meteorological parameter was measured and it consists of the back scatter returns of the multifrequency weather radar.
Here, in this paper, we study the propagation of data uncertainty through the marching cubes algorithm for isosurface visualization for correlated uncertain data. Consideration of correlation has been shown paramount for avoiding errors in uncertainty quantification and visualization in multiple prior studies. Although the problem of isosurface uncertainty with spatial data correlation has been previously addressed, there are two major limitations to prior treatments. First, there are no analytical formulations for uncertainty quantification of isosurfaces when the data uncertainty is characterized by a Gaussian distribution with spatial correlation. Second, as a consequence of the lack of analytical formulations,existing techniques resort to a Monte Carlo sampling approach, which is expensive and difficult to integrate into visualization tools. To address these limitations, we present a closed-form framework to efficiently derive uncertainty in marching cubes level-sets for Gaussian uncertain data with spatial correlation (MAGIC). To derive closed-form solutions, we leverage the Hinkley's derivation on the ratio of Gaussian distributions. With our analytical framework, we achieve a significant speed-up and enhanced accuracy of uncertainty quantification over classical Monte Carlo methods. We further accelerate our analytical solutions using many-core processors to achieve speed-ups up to 585× and integrability with production visualization tools for broader impact. We demonstrate the effectiveness of our correlation-aware uncertainty framework through experiments on meteorology, urban flow, and astrophysics simulation datasets.
Soil moisture is essential to the terrestrial carbon and water cycles and land–atmosphere interactions. There are various types of soil moisture data, and each type has the distinct spatiotemporal strengths and limitations, depending on the diverse applications and retrieval methodologies of different data types (Li et al., in review; The PNNL-82151 FY23 Report). However, the limitations of different soil moisture data in terms of accuracy and spatiotemporal coverage hinder our ability to further understand the soil moisture dynamics across scales. To have a gap free soil moisture data product with a fine spatiotemporal coverage and vertical profiles, we train extreme gradient boosting (XGBoost) models by using (1) in-situ soil moisture measurements from the International Soil Moisture Network (ISMN), (2) soil moisture from the ECMWF reanalysis (ERA) at the 9 km and sub-daily spatiotemporal resolution, (3) the Daymet meteorological fields, and (4) data products that characterize surface conditions, including soil texture, organic content, topography, vegetation type, and rooting depth. We use the trained XGBoost models that have consistent performance across seven soil layers, i.e., 0–5 cm, 5–10 cm, 10–20 cm, 20–40 cm, 40–60 cm, 60–100 cm, and 100–200 cm, and the gridded model predictors to generate a soil moisture data at the 1 km and daily spatiotemporal resolution for the Continental United States (CONUS) from 2001–2020. This dataset can be broadly used for Earth system model benchmark, monitoring extreme weathers, making informed decisions regarding agriculture, water resource management, climate change mitigation, and ecosystem preservation.
Vegetation canopy structure is a critically important habit characteristic for many threatened and endangered birds and other animal species, and it is key information needed by forest and wildlife managers for monitoring and managing forest resources, conservation planning and fostering biodiversity. Advances in Light Detection and Ranging (LiDAR) technologies have enabled remote sensing-based studies of vegetation canopies by capturing three-dimensional structures, yielding information not available in two-dimensional images of the landscape pro- vided by traditional multi-spectral remote sensing platforms. However, the large volume data sets produced by airborne LiDAR instruments pose a significant computational challenge, requiring algorithms to identify and analyze patterns of interest buried within LiDAR point clouds in a computationally efficient manner, utilizing state-of-art computing infrastructure. We developed and applied a computationally efficient approach to analyze a large volume of LiDAR data and to characterize and map the vegetation canopy structures for 139,859 hectares (540 sq. miles) in the Great Smoky Mountains National Park. This study helps improve our understanding of the distribution of vegetation and animal habitats in this extremely diverse ecosystem.
Addition of [UI 2 (THF) 3 (μ-OMe)] 2 ·THF (2·THF) to THF solutions containing 6 equiv. of K[C 14 H 10 ] generates the heteroleptic dimeric complexes [K(18-crown-6)(THF) 2 ] 2 [U(η 6 -C 14 H 10 )(η 4 -C 14 H 10 )(μ-OMe)] 2 ·4THF (1 18C6 ·4THF) and {[K(THF) 3 ][U(η 6 -C 14 H 10 )(η 4 -C 14 H 10 )(μ-OMe)]} 2 (1 THF ) upon crystallization of the products in THF in the presence or absence of 18-crown-6, respectively. Both 1 18C6 ·4THF and 1 THF are thermally stable in the solid-state at room temperature; however, after crystallization, they become insoluble in THF or DME solutions and instead gradually decompose upon standing. X-ray diffraction analysis reveals 1 18C6 ·4THF and 1 THF to be structurally similar, possessing uranium centres sandwiched between bent anthracenide ligands of mixed tetrahapto and hexahapto ligation modes. Yet, the two complexes are distinguished by the close contact potassium-arenide ion pairing that is seen in 1 THF but absent in 1 18C6 ·4THF, which is observed to have a significant effect on the electronic characteristics of the two complexes. Structural analysis, SQUID magnetometry data, XANES spectral characterization, and computational analyses are generally consistent with U(IV) formal assignments for the metal centres in both 1 18C6 ·4THF and 1 THF , though noticeable differences are detected between the two species. For instance, the effective magnetic moment of 1 THF (3.74 μ B ) is significantly lower than that of 1 18C6 ·4THF (4.40 μ B ) at 300 K. Furthermore, the XANES data shows the U L III -edge absorption energy for 1 THF to be 0.9 eV higher than that of 1 18C6 ·4THF, suggestive of more oxidized metal centres in the former. Of note, CASSCF calculations on the model complex {[U(η 6 -C 14 H 10 )(η 4 -C 14 H 10 )(μ-OMe)] 2 } 2– (1*) shows highly polarized uranium–arenide interactions defined by π-type bonds where the metal contributions are primarily comprised by the 6d-orbitals (7.3 ± 0.6%) with minor participation from the 5f-orbitals (1.5 ± 0.5%). These unique complexes provide new insights into actinide–arenide bonding interactions and show the sensitivity of the electronic structures of the uranium atoms to coordination sphere effects.
The Permian Basin is the highest producing oil and gas reservoir in the United States. Hydrocarbon extraction methods in this region are often associated with frac hits, or interwell communication events where an established well is affected by the pumping of fracture fluid into a new well. Our previous work revealed a unique geochemical signal indicating the presence of frac hits in the Permian Basin. We returned to this area with the overall goal of expanding our understanding of the microbial and geochemical dynamics common in this region. To do so, we collected produced water from 25 unique sites across the Permian Basin, 10 of which had previously been characterized during an active frac hit with the rest being novel. For each sample, we measured the pH, alkalinity, geochemical composition, microbial load (qPCR), and microbial community composition (16S rRNA sequencing). Permian Basin produced water is characterized by higher sulfate and lower total dissolved solids (TDS) concentrations compared to other regions. Interestingly, wells impacted by frac hits have a geochemical profile that resembles that of fracture fluid, with both lowered sulfate and lowered TDS concentrations compared to unaffected wells in this region. Due to the year-long recovery window between sample collection periods, we anticipate that all of our data will be characterized by the typical high sulfate, low TDS concentrations.
The objectives of the Illinois Storage Corridor (ISC) project are to accelerate commercial deployment of carbon capture utilization and storage at two individual sites and receive approvals for Underground Injection Control (UIC) Class VI permits for construction at each site (ISC Project Narrative, 2020). As part of this project, and as part of the subsurface geologic characterization, 2D seismic data was acquired at both sites. This report summarizes the findings from the 2D and 3D seismic interpretation at the One Earth Energy site near Gibson City, Illinois. The seismic data confirms the stratigraphic continuity of the Mt. Simon Arkose Zone storage interval and the Eau Claire confining unit across the project area. The seismic data also indicates that there are faults that transect the Mt. Simon Arkose Zone Sandstone storage reservoir within the modeled CO 2 plume (for more detailed information, see Faults and Fractures section of One Earth Energy Class VI Permit applications). However, the seismic data also shows that there are no faults within the modeled CO 2 plume that transect the confining unit Eau Claire Formation. The faults that transect the Mt. Simon Arkose Zone Sandstone storage reservoir all tip out in the Lower Mt. Simon Formation and do not reach the overlying Eau Claire confining unit. A small 3D survey acquired around the One Earth Energy #1 characterization well confirms these findings.
The high rates of bat mortality caused by operating wind turbines is a concern for wind energy and wildlife stakeholders. One theory that explains the mortality is that bats are not only killed by impact trauma, but also by barotrauma that results from exposure to the pressure variations caused by rotating turbine blades. To date, no published research has calculated the pressure changes that bats may be exposed to when flying near wind turbines and then used these data to estimate the likelihood that turbines cause barotrauma in bats. To address this shortcoming, we performed computational fluid dynamics simulations of a wind turbine and analytical calculations of blade-tip vortices to estimate the characteristics of the sudden pressure changes bats may experience when flying near a utility-scale wind turbine. Because there are no data available that characterize the pressure changes that cause barotrauma in bats, we compared our results to changes in pressure levels that cause barotrauma and mortality in other mammals of similar size. This comparison shows that the magnitude of the low-pressures bats experience when flying near wind turbines is approximately 8 times smaller than the pressure that causes mortality in rats, the smallest mammal for which data are available. The magnitude of the high-pressures that bats may experience are approximately 80 times smaller than the exposure level that causes 50% mortality in mice, which have a body mass similar to several bat species that are killed by wind turbines. Further, our results show that for a bat to experience the largest possible magnitude of low- and high-pressures, they must take very specific and improbable flight paths that skim the surface of the blades. Even a small change in the flight path results in the bat being hit by the blade or experiencing a much smaller pressure change. Accordingly, if bats have a physiological response to rapid low- and high-pressure exposure that is similar to other mammals, we conclude that it is unlikely that barotrauma is responsible for a significant number of turbine-related bat fatalities, and that impact trauma is the likely cause of the majority of wind-turbine-related bat fatalities.
The thermal characterization test of NASA's 12.5-kW Hall Effect Rocket with Magnetic Shielding has been completed. This thruster was developed to support a number of potential Solar Electric Propulsion Technology Demonstration Mission concepts, including the Asteroid Redirect Robotic Mission concept. As a part of the preparation for this characterization test, an infrared-based, non-contact thermal imaging system was developed to measure the temperature of various thruster surfaces that are exposed to high voltage or plasma. An in-situ calibration array was incorporated into the setup to improve the accuracy of the temperature measurement. The key design parameters for the calibration array were determined in a separate pilot test. The raw data from the characterization test was analyzed though further work is needed to obtain accurate anode temperatures. Examination of the front pole and discharge channel temperatures showed that the thruster temperature was driven more by discharge voltage than by discharge power. Operation at lower discharge voltages also yielded more uniform temperature distributions than at higher discharge voltages. When operating at high discharge voltage, increasing the magnetic field strength appeared to have made the thermal loading azimuthally more uniform.
Here, this paper presents an analysis of the wave climate and a characterization of the wave energy resources in American Samoa, a U.S. territory covering seven south central Pacific islands and atolls. A numerical wave model based on WAVEWATCH III® and unstructured SWAN was developed, validated, and executed for 1979–2020 to generate a hindcast dataset suitable for resource characterization. Model-data comparisons were performed with measurements collected in situ and from satellite-based altimeters. The model was found to perform well with a bias in significant wave height of -0.14 m and -0.06 m against buoy and altimeters, respectively. The multimodal sea state of American Samoa is investigated by identifying the sources of energy reaching the islands and partitioning the wave spectrum accordingly. The wave resources characterization follows the International Electrotechnical Commission Technical Specifications to be compatible with studies performed for other U.S. regions. The average omnidirectional wave power at 2 km from shore around Tutuila, the main island of American Samoa, is 14 kW/m. Locations east and west of the islands have a more consistent resource throughout the year because the northwest and southwest swells that are dominant during different seasons complement each other.
The primary science objective for the CHUVA lightning mapping campaign is to combine measurements of total lightning activity, lightning channel mapping, and detailed information on the locations of cloud charge regions of thunderstorms with the planned observations of the CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement) field campaign. The lightning campaign takes place during the CHUVA intensive observation period October-December 2011 in the vicinity of S o Luiz do Paraitinga with Brazilian, US, and European government, university and industry participants. Total lightning measurements that can be provided by ground-based regional 2-D and 3-D total lightning mapping networks coincident with overpasses of the Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS) and the SEVIRI (Spinning Enhanced Visible and Infrared Imager) on the Meteosat Second Generation satellite in geostationary earth orbit will be used to generate proxy data sets for the next generation US and European geostationary satellites. Proxy data, which play an important role in the pre-launch mission development and in user readiness preparation, are used to develop and validate algorithms so that they will be ready for operational use quickly following the planned launch of the GOES-R Geostationary Lightning Mapper (GLM) in 2015 and the Meteosat Third Generation Lightning Imager (LI) in 2017. To date there is no well-characterized total lightning data set coincident with the imagers. Therefore, to take the greatest advantage of this opportunity to collect detailed and comprehensive total lightning data sets, test and validate multi-sensor nowcasting applications for the monitoring, tracking, warning, and prediction of severe and high impact weather, and to advance our knowledge of thunderstorm physics, extensive measurements from lightning mapping networks will be collected in conjunction with electric field mills, field change sensors, high speed cameras and other lightning sensors, dual-polarimetric radars, and aircraft in-situ microphysics which will allow for excellent cross-network inter-comparisons, assessments, and physical understanding.
This project evaluated how high-resolution, spatially distributed field data can be used to refine and validate site-scale hydrodynamic simulations of tidal channels. Use of such spatially-distributed field observations or site-scale hydrodynamic simulations will be needed for producing accurate predictions of tidal energy production over larger arrays of tidal turbines. The data contained in this archive includes field observations of water velocity from a series of short surveys in Agate Pass taken on August 20th, 2020 using station-keeping ADCP (acoustic doppler current profiler) measurements, drifting downward-looking ADCPs, and microFloats, gathered by researchers at APL-UW under a previous project. It also includes high-resolution hydrodynamic model runs for the site during that time generated under TEAMER support. An explanatory file (.pdf format) describes the survey and field data structure. Processed data is stored in .mat format. Survey summary: start time, number of floats, water level. microFloat data: - Cleaned data for each float in survey, - Time series of position (lat., lon.), depth (relative to surface), and horizontal velocity (u, v) Drifting ADCP data: - Cleaned data for each drifting ADCP in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) Stationary ADCP data: - Cleaned data for each station-keeping data set in survey - Time series of position (lat., lon.), sample depth (relative to surface), and observed water velocity (u,v,w) FVCOM (Finite Volume Coastal Ocean Model) model outputs: Water level and velocity, as well as derived variables (e.g., power density) from the final refined model simulations for the Agate Pass subdomain. Contained in NetCDF and ASCII files for the duration of August 18th and 20th 2020 field surveys. Additional scripts for processing the data and generating the figures in the report are included.
Fault injection-based automated testing (FIAT) environment, which can be used to experimentally characterize and evaluate distributed realtime systems under fault-free and faulted conditions is described. A survey is presented of validation methodologies. The need for fault insertion based on validation methodologies is demonstrated. The origins and models of faults, and motivation for the FIAT concept are reviewed. FIAT employs a validation methodology which builds confidence in the system through first providing a baseline of fault-free performance data and then characterizing the behavior of the system with faults present. Fault insertion is accomplished through software and allows faults or the manifestation of faults to be inserted by either seeding faults into memory or triggering error detection mechanisms. FIAT is capable of emulating a variety of fault-tolerant strategies and architectures, can monitor system activity, and can automatically orchestrate experiments involving insertion of faults. There is a common system interface which allows ease of use to decrease experiment development and run time. Fault models chosen for experiments on FIAT have generated system responses which parallel those observed in real systems under faulty conditions. These capabilities are shown by two example experiments each using a different fault-tolerance strategy.
The IEAGHG Weyburn-Midale CO₂ Monitoring and Storage Project (WMP) began in 2000 with the first four years of research that confirmed the suitability of the containment complex of the Weyburn oil field in southeastern Saskatchewan as a storage location for CO₂ injected as part of enhanced oil recovery (EOR) operations. The first half of this report covers research conducted from 2010 to 2012, under the funding of the United States Department of Energy (contract DEFE0002697), the Government of Canada, and various other governmental and industry sponsors. The work includes more in-depth analysis of various components of a measurement, monitoring and verification (MMV) program through investigation of data on site characterization and geological integrity, wellbore integrity, storage monitoring (geophysical and geochemical), and performance/risk assessment. These results then led to the development of a Best Practices Manual (BPM) providing oilfield and project operators with guidance on CO₂ storage and CO₂-EOR. In 2013, the USDOE and Government of Saskatchewan exercised an optional phase of the same project to further develop and deploy applied research tools, technologies, and methodologies to the data and research at Weyburn with the aim of assisting regulators and operators in transitioning CO₂-EOR operations into permanent storage. This work, detailed in the second half of this report, involves seven targeted research projects – evaluating the minimum dataset for confirming secure storage; additional overburden monitoring; passive seismic monitoring; history-matched modelling; developing proper wellbore design; casing corrosion evaluation; and assessment of post CO₂-injected core samples. The results from the final and optional phases of the Weyburn-Midale Project confirm the suitability of CO₂-EOR fields for the injection of CO₂, and further, highlight the necessary MMV and follow-up monitoring required for these operations to be considered permanent storage.
The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone and impossible to scale. With the advent of autonomous robotic scientists or self-driving laboratories, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which outputs probabilistic classifications—rather than absolutes—to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering substantial time savings. It is demonstrated on a diverse set of organic and inorganic materials characterization challenges. This method is directly applicable to inverse design approaches and robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.
Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current earth system models to capture and simulate and-atmosphere feedback. To facilitate its realistic simulation in next generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land-atmosphere (L-A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable. Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive data sets for characterizing L-A feedback independently of any model input. Several new applications are introduced such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain, the testing of Monin-Obukhov similarity theory and turbulence parameterizations, the direct measurement of entrainment fluxes, and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE data set are introduced. We envision the initiation of innovative L-A feedback studies in different climate regions to improve weather forecast, climate, and earth system models worldwide.
SASPDF, a method for characterizing the structure of nanoparticle assemblies (NPAs), is presented. The method is an extension of the atomic pair distribution function (PDF) analysis to the small-angle scattering (SAS) regime. The PDFgetS3 software package for computing the PDF from SAS data is also presented. An application of the SASPDF method to characterize structures of representative NPA samples with different levels of structural order is then demonstrated. The SASPDF method quantitatively yields information such as structure, disorder and crystallite sizes of ordered NPA samples. The method was also used to successfully model the data from a disordered NPA sample. The SASPDF method offers the possibility of more quantitative characterizations of NPA structures for a wide class of samples.
Efficient Biomass Separation Object detection of anatomical parts (Cob, Stalk, Husk) in IR images enables precise separation, improving preprocessing (e.g., drying, grinding) for biofuel production. Detailed Biomass Characterization with Hyperspectral Data Hyperspectral imaging captures spectral signatures of biomass, allowing for the identification of specific traits like moisture content, lignin levels, and nutrient composition, leading to optimized treatments for each biomass part. Enhanced Feedstock Quality By leveraging hyperspectral data, feedstock can be processed based on its chemical composition, improving conversion efficiency and biofuel yield. Automation for Large-Scale Operations Automated object detection and hyperspectral data analysis reduce manual labor, ensuring accurate sorting and faster processing, making large-scale biofuel production more efficient. Maximized Biomass Utilization Accurate identification of biomass properties minimizes waste and ensures that each part is processed according to its highest biofuel potential.