Search NASA⌕ Search

SEARCH · Search NASA

Results for “Earth Filter”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Pandora Thermal Subsystem Systems Engineering Class Project

Thousands of planets orbiting stars outside our solar system have been discovered, revealing an incredible diversity of planetary systems. One of the key questions in astrophysics is whether any of these distant worlds have atmospheres that could potentially support life. The presence of life can significantly alter the makeup of our atmosphere (as on Earth), leaving unique spectral fingerprints that can be detected via remote sensing. Life outside of our solar system may leave detectable traces in the atmospheres of distant exoplanets. Exoplanet transmission spectroscopy is a proven technique to probe the atmospheres of exoplanets that cross in front of—or transit—their star. During a transit, starlight filters through the planet’s atmosphere and that spectrum can reveal the atmosphere’s chemical makeup.

42 ENGINEERING↗
DOE OSTI2026

Qualitative Risk Assessment of Legacy Wells within the Estimated Prairie State Generating Company Area of Review

This report details the digitization of a legacy wellbore database, including data processing assumptions, parameter estimation, and risk assessment methodology. The database, comprising 6,454 documents, was provided by ISGS. It includes valuable data from the Prairie State Generating Company (PSGC) and One Earth Energy (OEE) sites of the CarbonSAFE Phase III – Illinois Storage Corridor project. The report focuses on wells within a 15-mile radius from the Lively Grove #1 (LG#1) well at PSGC site, evaluating subsurface conditions and potential risks. A total of 4,386 wellbores within 15 miles of the LG#1 well were filtered based on depth and formation codes. LG#1 is the stratigraphic well at the PSGC site drilled in 2021. Ninety-four (94) wells penetrating the Maquoketa Shale Group (the primary confining unit) within the estimated area-of-review (AoR) for the PSGC site were evaluated using a qualitative risk assessment (QRA) methodology. The QRA developed by Arbad et al. 2022 focuses on legacy wells within the AoR and categorizes them based on well construction details. The QRA identifies wells that need immediate attention by categorizing them based on penetration depth and protection. Wells within the AoR were categorized into nine groups based on penetrations and protections. These categories range from Type 1 wells, with no documentation, to Type 9 wells, which do not penetrate the primary confining unit or storage reservoir (unit). Well accessibility within the AoR varies based on well status, including Dry & Abandoned (DA), Plugged & Abandoned (PA), Injection (INJ), Oil/Gas Producing (PROD), and Observation (Obs) wells. Accessibility levels were determined by well construction, with DA wells being the least accessible and Observation wells the most accessible, impacting gas leakage detection possibilities. Remedial action priority of wells decreases from Type 1 to Type 9 wells. Type 1 to Type 6 wells with status DA and PA require immediate attention, while Type 7 and Type 8 wells are low priority. A risk matrix used to prioritize corrective actions for legacy wells is proposed to categorize wells within an AoR based on penetrations, protections, and accessibility. The methodology involves data acquisition, well categorization into nine types, and determining CO 2 leakage pathways using well schematics and geospatial mapping. This approach is particularly useful for managing the integrity of legacy wells throughout the lifecycle of a Carbon Capture and Storage (CCS) project. A qualitative risk assessment of 94 wells within the AoR of the PSGC site identified 54 wells with high priority for corrective action due to penetration of the primary containment seal. The assessment utilizes color-coded maps to categorize well types and prioritize corrective actions, providing a comprehensive analysis. Schematics of wells penetrating the primary confining unit were drawn, and leakage pathways were identified. Details of all wells penetrating the confining zone are provided in the appendix, including information on well types, plugging, and casing status.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Analyzing LF/VLF Lightning Waveforms to Estimate D-region Electron Density Profiles

Lightning waveforms in the low frequency (LF; 30-300 kHz) and the very low frequency (VLF; 3-30 kHz) bands can be exploited to produce data-driven ionospheric D-region electron density profile (EDP) estimates with significantly higher spatial and temporal coverage than previously available. The lightning waveforms used in this paper are signals detected in the LF/VLF of negative cloud-to-ground lightning by the Earth Networks Total Lightning Detection Network. Each waveform contains a ground wave and a time-delayed ionospheric reflection. The time delay between the ground wave and ionospheric reflection has previously been used to estimate a single specular reflection altitude, where LF/VLF emissions are reflected by the ionosphere. Here, we expand upon previous methods to include filtering and spectral analysis, and account for oblique propagation to produce higher-order estimates for reflection altitudes and corresponding electron densities. Once estimated, reflection altitudes and corresponding electron densities can be used to derive parameters β and h’, which define an EDP for the D-region. In this study, the lightning waveform (LW) analysis is demonstrated using a single representative 24-hour dataset over the Southeast United States, and then extended to a total of 10 separate datasets with varying locations and ionospheric conditions. The LW-derived D-region EDPs are in agreement with predictions made by the Faraday International Reference Ionosphere model, and the LW EDPs β and h’ values are consistent with previous LF/VLF-derived estimates.

D-region ionosphere↗
DOE OSTI2023

Likely accelerated weakening of Atlantic overturning circulation emerges in optimal salinity fingerprint

The long-term response of the Atlantic meridional overturning circulation (AMOC) to anthropogenic forcing has been difficult to detect from the short direct measurements available due to strong interdecadal variability. Here, we present observational and modeling evidence for a likely accelerated weakening of the AMOC since the 1980s under the combined forcing of anthropogenic greenhouse gases and aerosols. This likely accelerated AMOC weakening signal can be detected in the AMOC fingerprint of salinity pileup remotely in the South Atlantic, but not in the classic warming hole fingerprint locally in the North Atlantic, because the latter is contaminated by the “noise” of interdecadal variability. Our optimal salinity fingerprint retains much of the signal of the long-term AMOC trend response to anthropogenic forcing, while dynamically filtering out shorter climate variability. Given the ongoing anthropogenic forcing, our study indicates a potential further acceleration of AMOC weakening with associated climate impacts in the coming decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Robustness of the Ensemble Score Filter to the Type of Assimilated Observation Networks

Recent advances in data assimilation (DA) have focused on developing more flexible approaches that can better accommodate nonlinearities in models and observations. However, it remains unclear how the performance of these advanced methods depends on the observation network characteristics. In this study, we present initial experiments with the surface quasi‐geostrophic model, in which we compare a recently developed ensemble filter using score‐based diffusion models with the standard Local Ensemble Transform Kalman Filter (LETKF). Our results show that the analysis solutions respond differently to the number, spatial distribution, and nonlinear fraction of assimilated observations. We also find notable changes in the multiscale characteristics of the analysis errors. Given that standard DA techniques will eventually be replaced by more advanced methods, we hope this study sets the ground for future efforts to reassess the value of Earth observing systems in the context of newly emerging algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Origin and mitigation of wind noise on balloon-borne infrasound microbarometers

High-altitude monitoring of low-frequency acoustic waves (infrasound) on Earth has regained prominence in recent years, primarily driven by improvements in light-weight sensor technology and advances in scientific ballooning techniques. Balloon-borne infrasound monitoring is also being proposed as a remote sensing technique for planetary exploration. Contrary to ground-based infrasound monitoring, the infrasound noise background in the stratosphere as measured by a balloon remains uncharacterized and the efficacy of wind noise mitigation filters has not been investigated. In this study, an analysis of pressure data collected using infrasound microbarometers during the flight of a long-duration zero pressure balloon is presented. Here, a dramatic reduction of background noise in the stratosphere is demonstrated and it is shown that wind noise mitigation filters are not effective at reducing wind noise under these conditions. Results from this study demonstrate stratospheric balloons as a low-noise platform for infrasound monitoring and motivate the development of improved noise mitigation tools.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Earth System Reanalysis in Support of Climate Model Improvements

Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial timescales. Further model improvements are expected to arise specifically from improved representation of physical processes realized through model-data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet, the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble and ML methods might be what is required in the future. In this context ML could be important to handle non-linear responses, and to better approximate non-Gaussian distributions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

NDVI and Multispectral Images, UAS Flights, Council Road Site, Seward Peninsula, Alaska, June 2022

The dataset contains Normalized Difference Vegetation Index (NDVI) and multispectral images collected on June 18, 2022 using an UAS (Unmanned Aerial System) Yuneec H520, Mapir Survey 3, and Parrot Sequoia cameras. This data was created to accompany the following datasets:- NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Council Road Mile Marker 71, Seward Peninsula, Alaska (https://doi.org/10.5440/1526749)- Sun-induced chlorophyll fluorescence/eddy covariance measurements at NGEE Arctic Council site, Seward Peninsula, Alaska, 2022 (https://doi.org/10.15485/2466175)- NGEE Arctic Meteorological Data from Instrumented PCDC Stations, Council Site, Seward Peninsula, Alaska, 2018 - 2023 (https://doi.org/10.5440/1529604)Survey 3 is RGN (Red, Greeen, Near Infra Red) camera with 87° Horizontal Field of View (HFOV) (19mm) f/2.8 Aperture, 550nm/660nm/850nm (Red+Green+NIR) filter transmission and produced geo referenced jpg and raw format images. Parrot Sequoia is a camera with 16 megapixel RGB (Red, Green, Blue) sensor and 1.2 megapixel monochrome sensors in discrete spectral bands: Green (550nm - 40Nm Bandwidth), Red (660nm - 40nm Bandwidth), Red Edge (735nm - 10nm Bandwidth) and Near Infrared (790nm - 40nm Bandwidth).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks.

58 GEOSCIENCES↗
DOE OSTI2020

Low-Temperature Plasma Treatment for Enhanced Recovery of Highly Valued Critical REEs from Coal (Final Report)

The principle objective of this project was to develop a novel process flowsheet using low-temperature plasma treatment (LTP) integrated with conventional hydrometallurgical processes to recover rare earth elements (REEs), especially highly valued REEs (i.e. scandium and critical REEs), from coal and coal byproducts. LTP treatment was used to pretreat the solid feed material prior to leaching in an effort to improve the leaching characteristics of the REEs and potentially reduce acid consumption. Initially, a detailed experimental program was performed on a laboratory LTP furnace to identify a set of process parameter values to be used for assessing the enhanced leaching performance achieved on four specific gravity fractions of West Kentucky No. 13 and Fire Clay coarse coal refuse materials. High-temperature oxidation (HTO)) was also evaluated as a means for improving REE leachability on the same fractions of both coal sources. The lixiviants used to assess leachability improvements included: i) untreated water, ii) 0.1 M ammonium sulfate solution and iii) 1.2 M sulfuric acid solution at a temperature of 75°C. The laboratory data was used to develop multiple process flow sheets using HTO only to produce a rare earth oxide mix concentrate or utilizing both LTP and HTO treatment processes to generate a high purity heavy REO mix concentrate and a light REO mix concentrate. A techno-economic analysis was performed on each circuit. Low-temperature plasma oxidation was found to enhance REE leaching characteristics for all specific gravity (SG) fractions of the West Kentucky No. 13 coarse refuse material. The leaching recovery improvement was preferentially achieved for the heavy REEs with higher values obtained when treating the lower density fraction material. For example, the leaching recovery for the heavy REEs in the untreated 1.6 SG Float fraction material was 8% in a 0.1 M ammonium sulfate solution. After LTP treatment for 32 hours, heavy REE recovery increased to 33%. The corresponding recovery value for the light REEs was 10%. These findings indicate a strong affinity of the heavy REEs with the organic material, which is the component being reduced in content during the LTP treatment. Scanning electron micrographs showed the development of a honeycomb structure with increased LTP treatment with higher surface area as confirmed from BET measurements. Higher surface area promotes improved leaching efficiency due to enhance chemical interactions. When using 1.2 M sulfuric acid, the LTP treatment was found to provide a heavy REE recovery increase from 23% to 53% for the 1.6 SG Float fraction which corresponded to an ash content increase from around 20% to 35%. However, LTP treatment provided no improvement in leaching characteristic for the 2.2 SG sink material in the acid solution, which was likely due to the low organic content. The LTP treatment of the Fire Clay SG fractions provided no improvement in REE leaching characteristic, which may be due to the association with REE minerals such as monazite and REE-associated mineral matter such as clays. High temperature oxidation using temperatures in the range of 600-750OC was found to significantly enhance the leaching characteristics of both the light and heavy REEs for all density fractions in both coarse coal refuse materials. Generally, the increase in light REE recovery for the West Kentucky material was from 15% for the untreated material to 90% for the HTO treated material at 600°C. Recovery values dropped significantly when using temperatures below 600°C and above 750°C. The impact of the excessive temperatures was most likely a result of sintering. For the heavy REEs, HTO significantly improved recovery in the lighter SG fractions, which may be due to an affinity with the organic content. Only limited heavy REE recovery improvement was obtained for the denser SG fractions. Another benefit of HTO is the conversion of calcium-based minerals to a very soluble calcium oxide, which is removable using a mild acid solution, and the conversion of Fe minerals to relatively insoluble minerals such as hematite and magnetite. The pregnant leaching solution produced from the leaching of all SG fractions obtained from both coal sources was treated using two stages of solvent extraction procedurally modeling a rougher-cleaner circuit followed by oxalic acid precipitation. Rare earth oxide products having a purity greater than 97% was produced from nearly all of the SG fractions. A novel process flowsheet was developed based on the selective leaching of heavy REEs that was made possible through LTP treatment of West Kentucky No. 13 coarse refuse followed by salt leaching. In the flowsheet, coal refuse material is ground to a top size of 1mm and then treated by LTP. The treated material is mixed into a 0.1 M ammonium sulfate solution and then filtered to produce a heavy REE enriched pregnant leach solution. The heavy REEs are concentrated by two stages of selective precipitation to produce a heavy REE mix oxalate and then roasted to obtain a heavy REO mix concentrate. The solid residues remaining after salt leaching is dried and then subjected to HTO treatment followed by concentration using selective precipitation and roasting to produce a light REO mix concentrate. An alternative circuit was developed using HTO treatment only to treat 100% of the coarse refuse material followed by selective precipitation and roasting to generate a high purity REO mix product. A techno-economic analysis performed for the treatment of West Kentucky No. 13 and Fire Clay coarse refuse based on a hypothetical 500-tph plant revealed that the net present value for all scenarios was significantly negative. Primary reasons include: i) a substantial decrease in the basket value of the final product relative to the feed and ii) high chemical costs relative to the amount of product generated. The basket value reduction was primarily due to little to no scandium recovery due to poor leaching characteristics when using low acid concentrations as necessitated by the low feed grades. Operating costs were primarily influenced by the chemical costs relative to the kilograms of concentrate produced and feed grade. For the Fire Clay coarse refuse material, the total operating cost was estimated to be $354 per kg of REO produced which was higher than the basket value of the REO product ($276/kg). Approximately 37% of the total cost was the sulfuric acid used during the leaching process. A comparative analysis performed to assess the economic viability of using low-temperature plasma furnaces found that market value enhancement for the generation of both heavy REO and light REO concentrates will be needed in addition to significant technology advancements to reduce the cost of plasma furnaces.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Migration of 60 Co, 133 Ba, 137 Cs, and 152 Eu from cementitious wasteforms in field lysimeter experiments

Safe and effective storage of radioactive waste is essential to protect environmental health. Due to the potential for accidental releases and the severity of the associated risks, it is imperative to further understand radionuclide transport should an accident occur. This work analyzed the vadose zone migration of radionuclides from cementitious wasteforms at the Savannah River Site after ten years. The observed radionuclides are prominent constituents of radioactive waste or analogs for other groups or series of radionuclides. Lysimeters were first analyzed in 2016 using a collimated high-purity germanium gamma-ray spectrometer to non-destructively measure the concentration of each radionuclide in the sediment column as a function of depth. Following these measurements, the lysimeters were redeployed in the field for another 4 years. All radionuclides in all lysimeters were observed to transport further during the redeployment period; however, the extent of migration varied with the material used for introduction. Except for 137 Cs, migration through the sediment control system increased with decreasing ionic potential (ionic charge/radius); migration order: 152 Eu< 137 Cs< 60 Co< 133 Ba. Overall, the cementitious wasteforms were observed to decrease radionuclide migration extent relative to the filter paper. In both cementitious wasteforms, the migration extent increased in the order 152 Eu< 133 Ba< 60 Co< 137 Cs. However, less migration was measured when the radionuclides were incorporated into a reducing grout wasteform. The novelty of this paper is the demonstration of a technique capable of creating non-destructive measurements over decade time scales. Ultimately, this work provides insight into the long-term migration of alkali, alkali earth, divalent transition metal, and trivalent actinide element isotopes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation

Abstract There is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.

Pawar, Suraj↗
DOE OSTI2022

Correcting for filter-based aerosol light absorption biases at the Atmospheric Radiation Measurement program's Southern Great Plains site using photoacoustic measurements and machine learning

Abstract. Measurement of light absorption of solar radiation by aerosols is vital for assessing direct aerosol radiative forcing, which affects local and global climate. Low-cost and easy-to-operate filter-based instruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosols on a filter and measure light attenuation through the filter are widely used to infer aerosol light absorption. However, filter-based absorption measurements are subject to artifacts that are difficult to quantify. These artifacts are associated with the presence of the filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (Babs). However, the inability of these algorithms to incorporate into their formulations the complex matrix of influencing parameters such as particle asymmetry parameter, particle size, and particle penetration depth results in prediction of particle-phase absorption coefficients with relatively low accuracy. The analytical forms of corrections also suffer from a lack of universal applicability: different corrections are required for rural and urban sites across the world. In this study, we analyzed and compared 3 months of high-time-resolution ambient aerosol absorption data collected synchronously using a three-wavelength photoacoustic absorption spectrometer (PASS) and PSAP. Both instruments were operated on the same sampling inlet at the Department of Energy's Atmospheric Radiation Measurement program's Southern Great Plains (SGP) user facility in Oklahoma. We implemented the two most commonly used analytical correction algorithms, namely, Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)–Bond et al. (1999) as well as a random forest regression (RFR) machine learning algorithm to predict Babs values from the PSAP's filter-based measurements. The predicted Babs was compared against the reference Babs measured by the PASS. The RFR algorithm performed the best by yielding the lowest root mean square error of prediction. The algorithm was trained using input datasets from the PSAP (transmission and uncorrected absorption coefficient), a co-located nephelometer (scattering coefficients), and the Aerosol Chemical Speciation Monitor (mass concentration of non-refractory aerosol particles). A revised form of the Virkkula (2010) algorithm suitable for the SGP site has been proposed; however, its performance yields approximately 2-fold errors when compared to the RFR algorithm. To generalize the accuracy and applicability of our proposed RFR algorithm, we trained and tested it on a dataset of laboratory measurements of combustion aerosols. Input variables to the algorithm included the aerosol number size distribution from the Scanning Mobility Particle Sizer, absorption coefficients from the filter-based Tricolor Absorption Photometer, and scattering coefficients from a multiwavelength nephelometer. The RFR algorithm predicted Babs values within 5 % of the reference Babs measured by the multiwavelength PASS during the laboratory experiments. Thus, we show that machine learning approaches offer a promising path to correct for biases in long-term filter-based absorption datasets and accurately quantify their variability and trends needed for robust radiative forcing determination.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Accelerating full-waveform inversion using source stacking: synthetic experiments at the global scale in a realistic 3-D earth model

SUMMARY The spectral element method is currently the method of choice for computing accurate synthetic seismic wavefields in realistic 3-D earth models at the global scale. However, it requires significantly more computational time, compared to normal mode-based approximate methods. Source stacking, whereby multiple earthquake sources are aligned on their origin time and simultaneously triggered, can reduce the computational costs by several orders of magnitude. We present the results of synthetic tests performed on a realistic radially anisotropic 3-D model, slightly modified from model SEMUCB-WM1 with three component synthetic waveform ‘data’ for a duration of 10 000 s, and filtered at periods longer than 60 s, for a set of 273 events and 515 stations. We consider two definitions of the misfit function, one based on the stacked records at individual stations and another based on station-pair cross-correlations of the stacked records. The inverse step is performed using a Gauss–Newton approach where the gradient and Hessian are computed using normal mode perturbation theory. We investigate the retrieval of radially anisotropic long wavelength structure in the upper mantle in the depth range 100–800 km, after fixing the crust and uppermost mantle structure constrained by fundamental mode Love and Rayleigh wave dispersion data. The results show good performance using both definitions of the misfit function, even in the presence of realistic noise, with degraded amplitudes of lateral variations in the anisotropic parameter ξ. Interestingly, we show that we can retrieve the long wavelength structure in the upper mantle, when considering one or the other of three portions of the cross-correlation time series, corresponding to where we expect the energy from surface wave overtone, fundamental mode or a mixture of the two to be dominant, respectively. We also considered the issue of missing data, by randomly removing a successively larger proportion of the available synthetic data. We replace the missing data by synthetics computed in the current 3-D model using normal mode perturbation theory. The inversion results degrade with the proportion of missing data, especially for ξ, and we find that a data availability of 45 per cent or more leads to acceptable results. We also present a strategy for grouping events and stations to minimize the number of missing data in each group. This leads to an increased number of computations but can be significantly more efficient than conventional single-event-at-a-time inversion. We apply the grouping strategy to a real picking scenario, and show promising resolution capability despite the use of fewer waveforms and uneven ray path distribution. Source stacking approach can be used to rapidly obtain a starting 3-D model for more conventional full-waveform inversion at higher resolution, and to investigate assumptions made in the inversion, such as trade-offs between isotropic, anisotropic or anelastic structure, different model parametrizations or how crustal structure is accounted for.

Geochemistry & Geophysics↗
DOE OSTI2025

BLADE: An Automated Framework for Classifying Light Curves from the Center for Near-Earth Object Studies Fireball Database

Fireballs (bolides) are high-energy luminous phenomena produced when meteoroids and small asteroids enter Earth’s atmosphere at hypersonic speeds, often resulting in fragmentation or complete disintegration accompanied by significant energy release. The resulting bolide light curves capture temporal brightness variations as these objects traverse increasingly dense atmospheric layers, providing essential information on meteoroid entry dynamics, fragmentation behavior, and atmospheric energy deposition processes. The Center for Near-Earth Object Studies’ (CNEOS) continuously expanding fireball database offers a globally comprehensive archive of bolide events, including light curves and associated metadata. Events associated with infrasound detections allow direct correlations between acoustic signatures and light curve features, therefore enabling detailed analyses of fragmentation dynamics and energy deposition. Here, we introduce Bolide Light-curve Analysis and Discrimination Explorer (BLADE), a robust and high-fidelity framework specifically designed to analyze bolide light curves for objects detected from space. BLADE incorporates a processing pipeline integrating Savitzky–Golay filtering, prominence-based peak detection, and gradient analysis, enabling systematic identification and classification of fragmentation events and their associated energy release characteristics. Preliminary results demonstrate that BLADE reliably distinguishes distinct bolide behaviors, providing an objective, scalable methodology for characterization and analysis of large bolide light curve data sets. This foundational work establishes a novel pathway for advanced bolide research, with promising applications in planetary defense and global atmospheric monitoring. Future research should adopt an integrative approach combining CNEOS optical data with complementary infrasound measurements, further clarifying relationships between bolide energy deposition and acoustic signatures, thus refining our understanding of meteoroid and asteroid atmospheric entry processes.

Asteroids↗
DOE OSTI2021

Imaging Complex Subsurface Structures for Geothermal Exploration at Pirouette Mountain and Eleven-Mile Canyon in Nevada

Accurate imaging of subsurface complex structures with faults is crucial for geothermal exploration because faults are generally the primary conduit of hydrothermal flow. It is very challenging to image geothermal exploration areas because of complex geologic structures with various faults and noisy surface seismic data with strong and coherent ground-roll noise. In addition, fracture zones and most geologic formations behave as anisotropic media for seismic-wave propagation. Properly suppressing ground-roll noise and accounting for subsurface anisotropic properties are essential for high-resolution imaging of subsurface structures and faults for geothermal exploration. We develop a novel wavenumber-adaptive bandpass filter to suppress the ground-roll noise without affecting useful seismic signals. This filter adaptively exploits both characteristics of the lower frequency and the smaller velocity of the ground-roll noise than those of the signals. Consequently, this filter can effectively differentiate the ground-roll noise from the signal. We use our novel filter to attenuate the ground-roll noise in seismic data along five survey lines acquired by the U.S. Navy Geothermal Program Office at Pirouette Mountain and Eleven-Mile Canyon in Nevada, United States. We then apply our novel anisotropic least-squares reverse-time migration algorithm to the resulting data for imaging subsurface structures at the Pirouette Mountain and Eleven-Mile Canyon geothermal exploration areas. The migration method employs an efficient implicit wavefield-separation scheme to reduce image artifacts and improve the image quality. Our results demonstrate that our wavenumber-adaptive bandpass filtering method successfully suppresses the strong and coherent ground-roll noise in the land seismic data, and our anisotropic least-squares reverse-time migration produces high-resolution subsurface images of Pirouette Mountain and Eleven-Mile Canyon, facilitating accurate fault interpretation for geothermal exploration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

The Potential Benefits of Handling Mixture Statistics via a Bi-Gaussian EnKF: Tests With All-Sky Satellite Infrared Radiances

The meteorological characteristics of cloudy atmospheric columns can be very different from their clear counterparts. Thus, when a forecast ensemble is uncertain about the presence/absence of clouds at a specific atmospheric column (i.e., some members are clear while others are cloudy), that column's ensemble statistics will contain a mixture of clear and cloudy statistics. Such mixtures are inconsistent with the ensemble data assimilation algorithms currently used in numerical weather prediction. Hence, ensemble data assimilation algorithms that can handle such mixtures can potentially outperform currently used algorithms. In this study, we demonstrate the potential benefits of addressing such mixtures through a bi-Gaussian extension of the ensemble Kalman filter (BGEnKF). The BGEnKF is compared against the commonly used ensemble Kalman filter (EnKF) using perfect model observing system simulated experiments (OSSEs) with a realistic weather model (the Weather Research and Forecast model). Synthetic all-sky infrared radiance observations are assimilated in this study. In these OSSEs, the BGEnKF outperforms the EnKF in terms of the horizontal wind components, temperature, specific humidity, and simulated upper tropospheric water vapor channel infrared brightness temperatures. This study is one of the first to demonstrate the potential of a Gaussian mixture model EnKF with a realistic weather model. Our results thus motivate future research toward improving numerical Earth system predictions though explicitly handling mixture statistics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Climate warming enhances biodiversity and stability of grassland soil phosphorus-cycling microbial communities

Abstract Climate warming poses significant challenges to global phosphorus sustainability, an essential component of Earth biogeochemistry cycling and water-food-energy nexus. Despite the crucial role of polyphosphate-accumulating organism as key functional microbial agents in phosphorus cycling, the impacts of global climate warming on polyphosphate accumulating organism communities remain largely enigmatic. This study investigates the effects of climate warming on the taxonomic, network, and functional profiles of soil bacterial polyphosphate-accumulating organisms, leveraging fluorescence-activated cell sorting and single-cell Raman spectroscopy. Climate warming enhances both taxonomic and functional biodiversity of polyphosphate-accumulating organisms via biotic interactions and environmental filtering, with observed functionality-biodiversity relationships supporting the functional redundancy theory. Furthermore, polyphosphate-accumulating organism network complexity and stability rise under warming with strengthened positive relationships, supporting stress gradient hypothesis and the belief that complexity begets stability. Finally, polyphosphate-accumulating organisms are significantly correlated to key ecosystem functioning in carbon and phosphorus cycling under warming. Our study suggests that preserving polyphosphate-accumulating organism communities is crucial for maintaining soil ecosystem functioning and sustainable phosphorus management in a warming world and opens avenues for predicting the responses of other functional microbial groups to climate change, beneficially or maliciously.

Environmental Sciences & Ecology↗