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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.

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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A hierarchical stochastic modeling approach for representing point bar geometries and petrophysical property variations

The flow of fluids in point bars is affected by the existence of heterogeneities like shale drapes that are found on the surfaces of inclined heterolithic stratifications. In fact, these shale drapes can act as fluid flow baffles; therefore, developing a framework for modeling point bars and their associated heterogeneities is vital. In this study, a stochastic process-based modeling approach is presented for capturing the main point bar heterogeneities: accretion surfaces (i.e., the aerial heterogeneity) and inclined heterolithic stratifications (i.e., the vertical heterogeneity). The former was modeled using a sine-generation function and the latter, with a sigmoidal function after which they were combined into a 3D point bar model. To ensure proper modeling of petrophysical properties, we developed a more representative gridding scheme which generates curvilinear grids representative of the point bar geometry. This grid was then transformed into a rectilinear grid to allow for geostatistical simulation after which all petrophysical properties were mapped back into the original curvilinear grid. An essential element of this modeling approach is the stochastic representation of shale drapes at the interface between successive accretion surfaces. The workflow was tested using a real field dataset for the Cranfield field, Mississippi. The constructed model was then subjected to a flow simulation study mimicking a CO 2 storage scenario. Various sensitivities were simulated to evaluate the effect of heterogeneities on CO 2 flow within the point-bar. Results demonstrate the importance of representing point-bar related heterogeneity and the spatial distribution of shale drapes on CO 2 plume migration and storage.

58 GEOSCIENCES↗
DOE OSTI2025

IoT-based retrofit information diffusion in future smart communities

Community-scale building retrofits are not merely scaled-up versions of single-building retrofits. They involve complex challenges, such as reconciling individual interests with collective goals and managing the dynamic interplay between buildings through mechanisms like power grids and social connections. Internet of Things (IoT) connectivity holds the potential to leverage these interplays to balance individual and collective interests effectively in smart communities. One critical aspect of this interplay is information diffusion, which shapes how retrofit decisions spread among neighbors, influencing individual choices and ultimately impacting community-level retrofit outcomes. In other words, IoT-based smart devices automatically push tailored retrofit notifications to homeowners, which completely changes the format of information diffusion in the future. To investigate this influence by such information diffusion, the study used CityBES to simulate energy performance for different retrofits and applied an information diffusion model to analyze how decisions spread in a networked community of 192 buildings. The diffusion process was modeled on a weighted, directed network, capturing the dynamics of information flow and decision-making across 16 scenarios. Individual retrofit benefits were evaluated through payback years, while community-level retrofit outcomes were assessed using greenhouse gas (GHG) emission reductions. The results demonstrate that easier information diffusion among neighbors encourages households to prioritize retrofit measures that align with the majority’s optimal choices, even at the expense of individual financial benefits. In this case, such collective prioritization enhanced community-level retrofit performance, increasing GHG emission reductions by up to 29.4 %. However, this improvement came with trade-offs, as the average payback period for households extended by approximately 1.74 years. These findings highlight the potential of IoT-based information diffusion in future smart communities to coordinate individual interests with collective goals, ultimately accelerating community-level building retrofits.

Shu, Lei↗
DOE OSTI2022

The Terrestrial Biosphere Model Farm

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901–2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Tools for estimating fake/non-prompt lepton backgrounds with the ATLAS detector at the LHC

Measurements and searches performed with the ATLAS detector at the CERN LHC often involve signatures with one or more prompt leptons. Such analyses are subject to 'fake/non-prompt' lepton backgrounds, where either a hadron or a lepton from a hadron decay or an electron from a photon conversion satisfies the prompt-lepton selection criteria. These backgrounds often arise within a hadronic jet because of particle decays in the showering process, particle misidentification or particle interactions with the detector material. As it is challenging to model these processes with high accuracy in simulation, their estimation typically uses data-driven methods. Three methods for carrying out this estimation are described, along with their implementation in ATLAS and their performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Novel Process for Converting Coal to High-Value Polyurethane Products

Battelle has demonstrated a patented process for making high-value, polyurethane (PU) foam from coal, based on preparing liquefied coal via direct liquefaction, converting it to polyols as an intermediate via ozonation, and then making PU foams from these polyols. This process represents a breakthrough in innovative utilization of U.S. coals, and is applicable to bituminous as well as sub-bituminous coals. The resulting PU foam products are projected to have an extremely high value (i.e., over $\$5,000$ /ton), with nearly 100% of carbon utilization from coalderived liquid feedstock, and 31.5% to 43.5% of the carbon in the PU foam polyol product being bio-based. The targeted products represent an extremely large (i.e., over $80 billion/year), existing PU foam market, which could expand into making coatings and adhesives. The process can further help reduce petroleum imports, while improving the economics of PU foam production. This work was completed with funding from the National Energy Technology Laboratory (NETL), with cost share from the State of Ohio’s Ohio Development Services Agency (ODSA) and others, and has advanced the process to 10 kg/day continuous scale and thus to Technology Readiness Level (TRL) 5. A total of 48 coal-based polyols were prepared and evaluated. The initial 28 polyols focused on range finding for ideal conditions. The later 20 polyols were produced as part of process optimizations. These optimizations were targeted around a continuous ozonolysis process to evaluate extended time reactions and to create the necessary intermediate for production of 1-gallon samples of polyol. The most unique attribute of Battelle’s polyol is in the utilization of coal’s aromaticity to gain final foam rigidity. Typically, polyols depend on the isocyanate fraction and cross-linking to gain rigidity. By utilizing coal, we were able to maintain rigidity while reducing the overall hydroxyl value of the polyol. This is important as lower hydroxyl value leads to greater percent weight of the coal-based polyol because less isocyanate is required for foaming. This leads to greater foam cost savings. This report provides the details, process, and process cost models of the conversion of coal to polyols and further to PU foams. Battelle’s process begins with coal liquids. These liquids can be obtained by two processes: coal coking or pyrolysis to produce coal tar, and Battelle’s biobased coal-to-liquids (CTL) process to produce heavy syncrude after liquifying >85% coal. After liquification, Battelle utilizes ozonolysis to create functionalization on the polyaromatic coal structure. The functionalization is then converted to the final polyester polyol through transesterification, or to hydroxyamide polyol through amidification. Equivalent or better standard properties have been obtained for 2 lb/ft 3 density rigid, water and freon-alternative blown foams, including compressive strength, density, R-value, and dimensional stability. Target applications for these foams are insulation, packaging, and energy-absorbing foams. Some exploratory testing also showed promise for adhesives applications. A detailed economic analysis showed that Battelle’s polyol process is economical, at a 140 metric tons per day (MTD) polyol production scale. An attractive return on investment (ROI) at competitive pricing validates the process is ready for a pilot-plant demonstration. A scale-up plan is provided.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Resilient Distributed Frequency Regulation of Renewable Generators under Communication Interruptions

Modern power systems (MPSs) face significant challenges due to the high penetration of renewable energy sources (RESs) and new types of loads such as electric vehicles (EVs). Traditional load frequency control (LFC) methods struggle with the intermittent, stochastic nature of RESs, the near-zero inertia of power-electronics-based generators, and the mobility of controllable loads and battery systems. This paper introduces a novel resilient distributed frequency regulation method to address these issues. The proposed method employs a state space model to represent the dynamic behavior of participating power sources while accounting for stochastic switching processes to model structural and parameter variations caused by disruptions such as generator connection/disconnection, communication interruptions, and physical faults. By integrating these dynamic and stochastic components, the method treats power grids as a comprehensive stochastic hybrid system. Our method enhances conventional frequency control by incorporating local stability control, neighborhood control decoupling, and coordination feedback. Theoretical analyses establish the stability, convergence, and resilience of the proposed method, and its effectiveness is validated through case studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

PDF Entity Annotation Tool (PEAT)

While different text mining approaches – including the use of Artificial Intelligence (AI) and other machine based methods - continue to expand at a rapid pace, the tools used by researchers to create the labeled datasets required for training, modeling, and evaluation remain rudimentary. Labeled datasets contain the target attributes the machine is going to learn; for example, training an algorithm to delineate between images of a car or truck would generally require a set of images with a quantitative description of the underlying features of each vehicle type. Development of labeled textual data that can be used to build natural language machine learning models for scientific literature is not currently integrated into existing manual workflows used by domain experts. Published literature is rich with important information, such as different types of embedded text, plots, and tables that can all be used as inputs to train ML/natural language processing (NLP) models, when extracted and prepared in machine readable formats. Currently, both normalized data extraction of use to domain experts and extraction to support development of ML/NLP models are labor intensive and cumbersome manual processes. Automatic extraction of data and information from formats such as PDFs that are optimized for layout and human readability, not machine readability. The PDF (Portable Document Format) Entity Annotation Tool (PEAT) was developed with the goal of allowing users to annotate publications within their current print format, while also allowing those annotations to be captured in a machine-readable format. One of the main issues with traditional annotation tools is that they require transforming the PDF into plain text to facilitate the annotation process. While doing so lessens the technical challenges of annotating data, the user loses all structure and provenance that was inherent in the underlying PDF. Also, textual data extraction from PDFs can be an error prone process. Challenges include identifying sequential blocks of text and a multitude of document formats (multiple columns, font encodings, etc.). As a result of these challenges, using existing tools for development of NLP/ML models directly from PDFs is difficult because the generated outputs are not interoperable. We created a system that allows annotations to be completed on the original PDF document structure, with no plain text extraction. The result is an application that allows for easier and more accurate annotations. In addition, by including a feature that grants the user the ability to easily create a schema, we have developed a system that can be used to annotate text for different domain-centric schemas of relevance to subject matter experts. Different knowledge domains require distinct schemas and annotation tags to support machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Data for Microbial-Explicit Processes and Refined Perennial Plant Traits Improve Modeled Ecosystem Carbon Dynamics

Globally, soils hold approximately half of ecosystem carbon and can serve as a source or sink depending on climate, vegetation, management, and disturbance regimes. Understanding how soil carbon dynamics are influenced by these factors is essential to evaluate proposed natural climate solutions and policy regarding net ecosystem carbon balance. Soil microbes play a key role in both carbon fluxes and stabilization. However, biogeochemical models often do not specifically address microbial-explicit processes. Here, we incorporated microbial-explicit processes into the DayCent biogeochemical model to better represent large perennial grasses and mechanisms of soil carbon formation and stabilization. We also take advantage of recent model improvements to better represent perennial grass structural complexity and life-history traits. Specifically, this study focuses on: 1) a plant sub-model that represents perennial phenology and more refined plant chemistry with downstream implications for soil organic matter (SOM) cycling though litter inputs, 2) live and dead soil microbe pools that influence routing of carbon to physically protected and unprotected pools, 3) Michaelis-Menten kinetics rather than first-order kinetics in the soil decomposition calculations, and 4) feedbacks between decomposition and live microbial pools. We evaluated the performance of the plant sub-model and two SOM cycling sub-models, Michaelis-Menten (MM) and first-order (FO), using observations of net ecosystem production, ecosystem respiration, soil respiration, microbial biomass, and soil carbon from long-term bioenergy research plots in the mid-western United States. The MM sub-model represented seasonal dynamics of soil carbon fluxes better than the FO sub-model which consistently overestimated winter soil respiration. While both SOM sub-models were similarly calibrated to total, physically protected, and physically unprotected soil carbon measurements, the models differed in future soil carbon response to disturbance and climate, most notably in the protected pools. Adding microbial-explicit mechanisms of soil processes to ecosystem models will improve model predictions of ecosystem carbon balances but more data and research are necessary to validate disturbance and climate change responses and soil pool allocation.

Field Data↗
DOE OSTI2020

Modeling the Sources and Transport Processes During Extreme Ammonia Episodes in the U.S. Corn Belt

Abstract Atmospheric ammonia (NH 3 ) is the primary form of reactive nitrogen (N r ) and a precursor of ammonium (NH 4 + ) aerosols. Ammonia has been linked to adverse impacts on human health, the loss of ecosystem biodiversity, and plays a key role in aerosol radiative forcing. The midwestern United States is the major NH 3 source in North America because of dense livestock operations and the high use of synthetic nitrogen fertilizers. Here, we combine tall‐tower (100 m) observations in Minnesota and Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) modeling to investigate high and low NH 3 emission episodes within the U.S. Corn Belt to improve our understanding of the distribution of emission sources and transport processes. We examined observations and performed model simulations for cases in February through November of 2017 and 2018. The results showed the following: (1) Peak emissions in November 2017 were enhanced by above‐normal air temperatures, implying a Q 10 (i.e., the change in NH 3 emissions for a temperature increase of 10°C) of 2.5 for emissions. (2) The intensive livestock emissions rom northern Iowa, approximately 400 km away from the tall tower, accounted for 17.6% of the abundance in tall‐tower NH 3 mixing ratios. (3) Ammonia mixing ratios in the innermost domain 3 frequently (i.e., 336 hr, 48% of November 2017) exceeded 5.3 ppb, an important air quality health standard. (4) In November 2017, simulated NH 3 net ecosystem exchange (the difference between NH 3 emissions and dry deposition) accounted for 60–65% of gross NH 3 emissions for agricultural areas and was 2.8–3.1 times the emissions of forested areas. (5) We estimated a mean annual NH 3 net ecosystem exchange of 1.60 ± 0.06 nmol · m −2 · s −1 for agricultural lands and −0.07 ± 0.02 nmol · m −2 · s −1 for forested lands. These results imply that future warmer fall temperatures will enhance agricultural NH 3 emissions, increase the frequency of dangerous NH 3 episodes, and enhance dry NH 3 deposition in adjacent forested lands.

Hu, Cheng↗
DOE OSTI2020

Status of the Mu2e experiment at Fermilab

The Mu2e experiment aims to improve, by four orders of magnitude, current sensitivity in the search for the charged-lepton flavor violating (cLFV) neutrino-less conversion of a negative muon into an electron. The conversion process will be identified by a distinctive signature of a mono-energetic electron with energy slightly below the muon rest mass. In the Standard Model this process has a negligible rate. However, in many Beyond the Standard Model scenarios its rate is within the reach of Mu2e sensitivity. In this paper, we explain the Mu2e design guidelines and summarize the status of the experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Optimization of leaf morphology in relation to leaf water status: A theory

The leaf economic traits such as leaf area, maximum carbon assimilation rate, and venation are all correlated and related to water availability. Furthermore, leaves are often broad and large in humid areas and narrower in arid/semiarid and hot and cold areas. We use optimization theory to explain these patterns. We have created a constrained optimization leaf model linking leaf shape to vein structure that is integrated into coupled transpiration and carbon assimilation processes. The model maximizes net leaf carbon gain (NPPleaf) over the loss of xylem water potential. Modeled relations between leaf traits are consistent with empirically observed patterns. As the results of the leaf shape-venation relation, our model further predicts that a broadleaf has overall higher NPPleaf compared to a narrowleaf. In addition, a broadleaf has a lower stomatal resistance compared to a narrowleaf under the same level of constraint. With the same leaf area, a broadleaf will have, on average, larger conduits and lower total leaf xylem resistance and thus be more efficient in water transportation but less resistant to cavitation. By linking venation structure to leaf shape and using water potential as the constraint, our model provides a physical explanation for the general pattern of the covariance of leaf traits through the safety-efficiency trade-off of leaf hydraulic design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗
DOE OSTI2021

A Computational Information Criterion for Particle-Tracking with Sparse or Noisy Data

Traditional probabilistic methods for the simulation of advection-diffusion equations (ADEs) often overlook the entropic contribution of the discretization, e.g., the number of particles, within associated numerical methods. Many times, the gain in accuracy of a highly discretized numerical model is outweighed by its associated computational costs or the noise within the data. Herein, we address the question of how many particles are needed in a simulation to best approximate and estimate parameters in one-dimensional advective-diffusive transport. To do so, we use the well-known Akaike Information Criterion (AIC) and a recently-developed correction called the Computational Information Criterion (COMIC) to guide the model selection process. Random-walk and mass-transfer particle tracking methods are employed to solve the model equations at various levels of discretization. Numerical results demonstrate that the COMIC provides an optimal number of particles that can describe a more efficient model in terms of parameter estimation and model prediction compared to the model selected by the AIC even when the data is sparse or noisy, the sampling volume is not uniform throughout the physical domain, or the error distribution of the data is non-IID Gaussian.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Joint estimation of biogeochemical model parameters from multiple experiments: A bayesian approach applied to mercury methylation

Here, to characterize complex biogeochemical systems, results from multiple experiments, where each targets a specific subprocess, are commonly combined. The resulting datasets are interpreted through the calibration of biogeochemical models for process inference and predictions. Commonly used calibration approaches of fitting datasets from individual experiments to subprocess models one at a time is prone to missing information shared between datasets and incomplete uncertainty propagation. We propose a Bayesian joint-fitting scheme addressing the above-mentioned concerns by jointly fitting all the available datasets, thus calibrating the entire biogeochemical model in one go using Markov Chain Monte Carlo (MCMC). The identification of null spaces in the parameter distributions from MCMC guided the simplification of certain subprocess models. For example, fast kinetic sorption was replaced by equilibrium sorption, and Monod demethylation was replaced by first-order demethylation. Joint fitting of datasets resulted in complete uncertainty propagation with parameter estimates informed by all available data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Advanced Performance Metrics and their Application to the Sensitivity Analysis for Model Validation and Calibration

High-quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMUs, measurement-based approach for model validation has gained significant prominence. In this approach, the quality of a model is analyzed by visually comparing measured generator response with the model-based simulated response for large system disturbances. This paper proposes a new set of performance metrics to assess the model validation results to facilitate automation of the model validation process. In the proposed methodology, first, the slow governor response and comparatively faster oscillatory response are separated, and then a separate set of performance metrics is calculated for each of these two components. These proposed metrics quantify the mismatch between the actual and model-based response in a comprehensive manner without missing any information enabling automation of the process. Furthermore, in this paper, we are also proposing that the sensitivity analysis for model calibration be performed with respect to the proposed metrics for the systematic identification of key parameters. In this work, results obtained using both simulated and real-world case-studies validate the effectiveness of the proposed performance metrics for model validation and their application to the sensitivity analysis for model calibration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Modeling Molten Salt Reactor Fission Product Removal with SCALE

Liquid-fuel Molten Salt Reactors (MSRs) allow for continuous online fuel treatment and processing using a variety of subsystems. Among these subsystems, the off-gas system (OGS) continuously removes fission products from the primary fuel salt through a helium sparging process. Accurately modeling fission product behavior is paramount for the development of MSRs; while there are a variety of depletion modeling and simulation tools that allow for removal of material from a defined system, most of them work in a semicontinuous, batch-wise manner. The new tools implemented into TRITON, in the SCALE suite for reactor analysis and design, aim to address this issue by implementing truly continuous material feeds and removals. In addition, material accountancy is enhanced in these tools, which enable tracking of removed material in user-specified mixtures. These continuous depletion tools provide a way to model material transport throughout systems more efficiently and accurately. This report validates the new SCALE MSR modeling capabilities using data from the Molten Salt Reactor Experiment (MSRE). After a framework was established for deriving realistic removal rates based on design specifications, these rates were used by TRITON to continuously remove specific elements while performing burnup calculations. Results were compared to those obtained in MSRE, with a focus on the 135 Xe poison fraction. After these capabilities were validated, the learned concepts and best practices were applied to model the Molten Salt Breeder Reactor (MSBR) and the Molten Salt Demonstration Reactor (MSDR) to study the effects of continuous fission product removal in fuel salt reactivity, as well as the lifetime effects.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions

Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.

54 ENVIRONMENTAL SCIENCES↗