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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Fully Automated Profile-based Calibration Strategy for Airborne and Terrestrial Mobile LiDAR Systems with Spinning Multi-beam Laser Units

LiDAR-based mobile mapping systems (MMS) are rapidly gaining popularity for a multitude of applications due to their ability to provide complete and accurate 3D point clouds for any and every scene of interest. However, an accurate calibration technique for such systems is needed in order to unleash their full potential. In this paper, we propose a fully automated profile-based strategy for the calibration of LiDAR-based MMS. The proposed technique is validated by comparing its accuracy against the expected point positioning accuracy for the point cloud based on the used sensors’ specifications. The proposed strategy was seen to reduce the misalignment between different tracks from approximately 2 to 3 m before calibration down to less than 2 cm after calibration for airborne as well as terrestrial mobile LiDAR mapping systems. In other words, the proposed calibration strategy can converge to correct estimates of mounting parameters, even in cases where the initial estimates are significantly different from the true values. Furthermore, the results from the proposed strategy are also verified by comparing them to those from an existing manually-assisted feature-based calibration strategy. The major contribution of the proposed strategy is its ability to conduct the calibration of airborne and wheel-based mobile systems without any requirement for specially designed targets or features in the surrounding environment. The above claims are validated using experimental results conducted for three different MMS – two airborne and one terrestrial – with one or more LiDAR unit.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Prediction of Distributed River Sediment Respiration Rates Using Community-Generated Data and Machine Learning

River sediment microbial respiration is a key indicator of ecosystem functioning and the biogeochemical fluxes across this critical zone link surface and subsurface waters. As such, there is tremendous interest in measuring and mapping these respiration rates. Respiration observations are expensive and labor intensive; there is limited data available to the community. An open science, collaborative initiative is collecting samples for respiration rate analysis and multi-scale metadata; this evolving data set is being used for making machine learning (ML) predictions at unsampled sites to help inform continued community engagement. However, it is a challenge to find an optimum configuration for ML models to work with this feature-rich (i.e., 100+ possible input variables) data set. Here, we present results from a two-tiered approach to managing the analysis of this complex data set: (a) a stacked ensemble of models that automatically optimizes hyperparameters and manages the training of many models and (b) feature permutation importance to detect the most important features in the models. The major elements of this workflow are modular, portable, open, and cloud-based thus making this implementation a potential template for other applications. The models developed here predict that sediment organic matter chemistry is one of the most important features for predicting sediment respiration rate. Other larger-scale, important features fall into the categories of climatic, ecological, geological, and fluvial settings. Leveraging these larger-scale features to generate data-driven estimates of river sediment respiration rates reveals spatially consistent but heterogeneous patterns across the river network of the Columbia River Basin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A Play-Based Exploration of CO 2 Storage in the Illinois Basin: Community Baseline Assessment for Carbon Storage Engagement Planning in the Illinois Basin

This report presents a stakeholder mapping analysis conducted as part of the Play-Based Exploration of CO 2 Storage in the Illinois Basin project (DOE Cooperative Agreement DE-FE0032366). The analysis was undertaken to systematically identify, categorize, and characterize stakeholder groups and community conditions relevant to the planning and deployment of carbon capture and storage (CCS) infrastructure across the Illinois Basin. Its purpose is to establish a contextual baseline that informs targeted engagement strategies, supports equitable project planning, and strengthens the social dimensions of CCS site screening.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Geostatistical Mapping of Salinity Conditioned on Borehole Logs, Montebello Oil Field, California

We present a geostatistics-based stochastic salinity estimation framework for the Montebello Oil Field that capitalizes on available total dissolved solids (TDS) data from groundwater samples as well as electrical resistivity (ER) data from borehole logging. Data from TDS samples (n = 4924) was coded into an indicator framework based on falling below four selected thresholds (500, 1000, 3000, and 10,000 mg/L). Collocated TDS-ER data from the surrounding groundwater basin were then employed to produce a kernel density estimator to establish conditional probabilities for ER data (n = 8 boreholes) falling below the selected TDS thresholds within the Montebello Oil Field area. Directional variograms were estimated from these indicator coded data, and 500 TDS realizations from conditional indicator simulation were generated for the subsurface region above the Montebello Oil Field reservoir. Simulations were summarized as 3D maps of median TDS, most likely salinity class, and probability for exceeding each of the specified TDS thresholds. Results suggested TDS was below 500 mg/L in most of the study area, with a trend toward higher values (500 to 1000 mg/L) to the southwest; consistent with the average regional groundwater flow direction. Discrete localized zones of TDS greater than 1000 mg/L were observed, with one of these zones in the greater than 10,000 mg/L range; however, these areas were not prevalent. The probabilistic approach used here is adaptable and is readily modified to include additional data and types and can be employed in time-lapse salinity modeling through Bayesian updating.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Surface Variability Mapping and Roughness Analysis of the Moon Using a Coarse–Graining Decomposition

The lunar surface contains a wide variety of topographic shapes and features, each with different distributions and scales, and any analysis technique to objectively measure roughness must respect these qualities. Coarse-graining is a naturally scale-dependent filtering technique that preserves scale-dependent symmetries and produces coarse elevation maps that gradually erase the smaller features from the original topography. In this study of the lunar surface, we present two surface variability metrics obtained from coarse-graining lunar topography: fine elevation and coarse curvature. Both metrics are isotropic, deterministic, slope-independent, and coordinate-agnostic. Fine (detrended) elevation is acquired by subtracting the coarse elevation from the original topography and contains features that are smaller than the coarse-graining length-scale. Coarse curvature is the Laplacian of coarsened topography, and naturally quantifies the curvature at any scale and indicates whether a location is elevated or depressed relative to its neighborhood at that scale. We find that highlands and maria have distinct roughness characteristics at all length-scales. Our topographic spectra reveal four scale-breaks that mark characteristic shifts in surface roughness: 100, 300, 1,000, and 4,000 km. Comparing fine elevation distributions between maria and highlands, we show that maria fine elevation is biased toward smaller-magnitude elevations and that the maria–highland discrepancies are more pronounced at larger length-scales. Here, we also provide local examples of selected regions to demonstrate that these metrics can successfully distinguish geological features of different length-scales.

58 GEOSCIENCES↗
DOE OSTI2023

Dating submarine landslides using the transient response of gas hydrate stability

Abstract Submarine landslides are prevalent on the modern-day seafloor, yet an elusive problem is constraining the timing of past slope failure. We present a novel age-dating technique based on perturbations to underlying gas hydrate stability caused by slide-impacted seafloor changes. Using three-dimensional (3-D) seismic data, we mapped an irregular bottom simulating reflection (BSR) underneath a submarine landslide in the Orca Basin, Gulf of Mexico. The irregular BSR mimics the pre-slide seafloor geometry rather than the modern bathymetry. Therefore, we suggest that the gas hydrate stability zone (GHSZ) is still adjusting to the post-slide sediment temperature. We applied transient conductive heat-flow modeling to constrain the response of the GHSZ to the slope failure, which yielded a most likely age of ca. 8 ka, demonstrating that gas hydrate can respond to landslides even on multimillennial time scales. We further provide a generalized analytical solution that can be used to remotely date submarine slides in the absence of traditional dating techniques.

Geology↗
DOE OSTI2024

Performance evaluation of the USGS velocity model for the San Francisco Bay Area

In this study, we evaluated the performance of the United States Geological Survey velocity model developed for the San Francisco Bay Area (SFBA), version 21.1. The evaluation was performed through high-resolution three-dimensional physics-based ground motion simulations of seven small-magnitude earthquakes (ranging from magnitude 3.8 to 4.4) that occurred on the eastern side of the San Francisco Bay. The simulations were performed in the frequency range from 0 to 5 Hz with a minimum shear-wave velocity of 250 m/s, which allowed the capture of wave propagation effects of the near-surface soft materials that characterize local basins. Based on the direct comparison of Fourier amplitude spectra between recorded and simulated ground motions for more than 250 stations, we found that the velocity model generally performs well in the frequency range of 0.2–5 Hz. The median value of the Fourier amplitude residuals was found to be near zero for all seven earthquakes. The slight over-prediction of 0.2 log-natural units at frequencies above 3 Hz in our simulations was attributed to the potentially inaccurate representation of the source radiation pattern by a double-couple point source model, and simple representation of shallow small-scale underground structural complexity in the velocity model. Maps of spectral amplitude differences between the simulated and recorded data were used to identify areas responsible for systematic ground motion over-predictions or under-predictions. For example, while some sub-domains over soft sediments show over-prediction patterns, the block east of the Hayward fault is prone to exhibit patterns of under-prediction. These maps can be used to guide future refinements of the SFBA velocity model. Since our simulation methodology allows for the decoupling of the source and wave propagation effects, the ground motion data generated by our simulations can also be used to quantify the epistemic uncertainty due to the velocity model, in empirically based ground motion estimates for the SFBA.

58 GEOSCIENCES↗
DOE OSTI2021

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO 2 sequestration accelerated with machine learning

Successful geologic CO 2 storage projects depend on numerical simulations to predict reservoir performance during site selection, injection verification, and post-injection monitoring phases of the project. These numerical simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence, individual simulations often require tens to hundreds of hours to complete on high-performance computing clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative permeability exhibit substantial variability, even within the same rock type. This combination of computational expense and wide-ranging parametric variability means that there remains substantial uncertainty in the behavior of multi-phase CO 2 -water systems, particularly in the context of feedbacks between relative permeability and capillary pressure. To bridge this knowledge gap, here we develop a novel workflow that utilizes physics-based numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate parameter space that governs both capillary pressure and relative permeability. With this approach, the ANN is trained to emulate both fluid pressure distribution and CO 2 saturation, which are then interrogated quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this study initially show that capillary entry pressure is the dominant control on both CO 2 plume geometry and fluid pressure propagation when considering the combined effects of capillary pressure and relative permeability, particularly when phase interference is low and residual CO 2 saturation is high. Moreover, the ANN emulator provides tremendous computational speed-up by computing 2691 individual simulations in several minutes; whereas, the same simulation ensemble would have required ~3 years of simulation time using only physics-based simulation methods (25,000 times speed up).

58 GEOSCIENCES↗
DOE OSTI2026

A Play-Based Exploration of CO 2 Storage in the Illinois Basin (Final Technical Report)

This report documents the results of "A Play-Based Exploration of CO₂ Storage in the Illinois Basin" (DE-FE0032366), a project funded by the U.S. Department of Energy Office of Fossil Energy and Carbon Management and conducted by the Illinois State Geological Survey (ISGS) at the University of Illinois Urbana-Champaign in partnership with Visage Energy. The project adapted play-based exploration (PBE), a systematic basin-scale evaluation methodology from the petroleum industry, to screen areas of Illinois for commercial geologic carbon storage (GCS) in Cambro-Ordovician strata. The traditional play concept was expanded to encompass three play element groups, subsurface geologic factors, surface features, and societal factors, yielding 24 play elements with defensible suitability criteria applied through a five-tier classification scheme. An integrated geospatial database was assembled from ISGS, MGSC, MRCI, NATCARB, and public data sources, supported by significant data-improvement work including correction of legacy well locations, digitization of more than 1,300 well construction records using the DOE CATALOG team's OGRRE tool, compilation of a statewide 2D seismic database, and production of a refined fault and fold geodatabase.

58 GEOSCIENCES↗
DOE OSTI2022

Legacy surface change analyses of the 1993 Rock Valley earthquake sequence for direct comparison to planned NA-22 underground conventional high-explosive experiments (Source Physics Experiment 3 (SPE3) - RV/DC Task 1.3 FY22 Final Report)

Recent work under two previous phases of the NNSA NA-22 Source Physics Experiment (SPE) have shown that underground chemical high-explosive experiments can produce detectable surface changes that differ in spatial extent and vertical magnitude depending on the geologic media at the site (Schultz-Fellenz et al., 2018; 2020; Crawford et al., 2021). Neither of these two prior phases of SPE identified natural earthquake-related surface effects in the same region that occurred at a similar depth as the explosive experiments for direct comparison. While earthquakes can also produce surface changes that are detectable using remote sensing data analyses, it is expected that the pattern and spatial extent of surface changes would vary between earthquakes and explosions. However, no direct-observed surface-change signature comparison between earthquakes and explosions has ever been performed. The SPE Phase 3 Rock Valley/Direct Comparison (RV/DC) program presents a unique opportunity to investigate and characterize co-occurrence of both earthquakes and explosions. In this project, we worked to address five tasks in a workflow, as follows: 1. Identify and obtain existing high-resolution legacy satellite and aerial imagery as close in time before and after the 1993 Rock Valley earthquake sequence to temporally constrain the analyses. 2. Transform these pre-earthquake and post-earthquake datasets into digital elevation models (DEMs) using geospatial analysis software packages (e.g., ArcGIS, Agisoft Metashape, and Google Earth Engine). 3. Perform DEM differencing analyses to assess and quantify earthquake-related changes from the 1993 sequence, and develop map products that visualize these analyses. 4. Use the analyses from (3) to: (a) assess spatial distribution and magnitude of surface changes due to the 1993 Rock Valley earthquake sequence, and (b) determine parameters of forthcoming, planned explosion-related surface change data collection from sensors mounted on unmanned aerial vehicles (UAVs) (e.g., spatial extent of collection, design and density of survey control, sensors to deploy, forward speed and line spacing of UAV flight lines, flight altitude). 5. Develop a summary report on the analyses, including how the analyses define parameters and identify focus areas for any future surface change analytical field campaigns related to the explosive experiment. Analyzing these legacy data and identifying whether they can detect any surface changes related to the earthquake sequence facilitates opportunities for direct signature comparison of surface change from explosions at one location, which has never previously been performed. Comparing the surface change signatures from a co-located and depth-equivalent earthquake and an explosion could help to advance remote sensing event discrimination techniques. This report summarizes the work completed toward this ambitious goal.

42 ENGINEERING↗
DOE OSTI2026

Distributed Mafic Rock Resources for Carbon Mineralization in Arizona

Ex-situ carbon mineralization is a process by which CO2 is reacted with alkaline silicate minerals and rocks to produce stable carbonate materials, which can be used for other industrial processes. Arizona, U.S.A., hosts abundant surficial mafic rocks in three young volcanic fields, Geronimo-San Bernardino, San Francisco, and Springerville, and other distributed locations throughout the state. We created a Mafic Rock Resource Inventory (MRRI) that categorizes geochemical, physical, and textural characteristics of a diverse suite of surficial mafic rock samples and provide a benchmark reaction dataset parameterizing the temperature, pressure, and pH conditions best suited ex-situ mineralization in different rock types. MRRI data is publicly available online via a map-viewer. We establish two reaction condition sets, varied in temperature, pressure, and pH, where crystal-rich and glassy rocks reach maximum reaction extent and different carbonate phases are formed. Systematic ex-situ mineralization experiments on 21 diverse rock types show trends in geochemical, mineralogical, and reactivity behavior and establish maximum effective capture capacity. From this, scoria cones in three Arizona volcanic fields have a ~62 Gt effective CO₂ storage capacity with one of the fields having a ~42 Gt storage capacity in lava flows. Reactivity results have applications to alkaline mafic rock resources exposed globally, including producing additional effective storage capacity estimates and scaled commercialization of mafic rock ex-situ mineralization, should reaction extents be improved through advances in mineralization techniques. MRRI data were used to create a Direct Air Capture to Mineralization (DACM) systems model, technoeconomic analysis, and life-cycle assessment. These documents are presented as three appendices.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

HFTS-1 (Final Report)

Despite the long history of hydraulic fracturing, the optimal number of fracturing stages during multi-stage fracture stimulation in horizontal wells can be a complex subject. In addition to the increased expense of multistage fracturing in horizontal wells, increasing the number of fracturing stages does not always correlate with a commensurate increase in production. The problem is the application of a uniform fracture stimulation design to all stages with no consideration for geological variations along the wellbore. The result is an inefficient use and costly waste of energy and water. Optimization of the fracturing process requires an understanding of the cause-and-effect relationship between fracturing parameters and local geological properties at a given location along the wellbore. Realizing that the generalized rock mechanics theories and hypotheses are not truly applicable to fractured and laminated shales, quantifiable impacts of a shale’s geomechanical and depositional features are a prerequisite for design and implementation of optimized hydraulic fractures. The overarching goal of this project is to understand and define the relationships of shale geology and fracture dynamics using detailed field data that includes coring of the fracture domain. Finally, resource recovery from shale formations is estimated to be less than fifteen percent. Research proposed in this project will establish the foundations for investigating enhanced recovery techniques for increased resource recovery in existing fracture treated wells. Natural gas as an EOR fluid is in the initial stages of being used broadly in the oil and gas industry and as such, many aspects of the process need to be researched and addressed prior to widespread acceptance. In addition, re-fracturing techniques, particularly using cemented liners also need to be further investigated. This project explores several of these EOR techniques in greater detail, not just at the theoretical level, but through actual field test sites. This report delves into the research program, identified tasks, several studies, observations, results, and conclusions under the Hydraulic Fracturing Test Site (HFTS) project. This test project is subdivided into three distinct phases and each phase is discussed in detail under separate sections. The original test site for Phase 1 as well as the Phase 2 site are in Midland Basin in Texas and operated by Laredo Petroleum. In the first two phases, the program targeted the Wolfcamp formation in Midland Basin. Phase 3 of the study targeted the EagleFord play in South Texas with Devon Energy as the operator. Significant conclusions from this study include: 1) lateral and vertical fracture growth mapping as well as proppant transport behavior, 2) Use of field gas and its applicability for huff n puff EOR injection in the Midland Basin, 3) Development and validation of proppant log, both at core scale as well as in-fill well implementation by analyzing drilling mud return samples, 4) Applicability of liner refracturing including best practices for enhanced unit level productivity. This report will detail all these studies as well as technical results and conclusions from said studies. These include general subsurface characterization work, completion designs as implemented, studies looking at fracture geometry, high resolution microseismic study, advanced crosswell strain mapping studies including drainage characterization, core characterization results, proppant analysis (core and observation wells), geochemistry, etc. to name a few.

02 PETROLEUM↗
DOE OSTI2024

Spatial variability in Arctic–boreal fire regimes influenced by environmental and human factors

Abstract Wildfire activity in Arctic and boreal regions is rapidly increasing, with severe consequences for climate and human health. Regional long-term variations in fire frequency and intensity characterize fire regimes. The spatial variability in Arctic–boreal fire regimes and their environmental and anthropogenic drivers, however, remain poorly understood. Here we present a fire tracking system to map the sub-daily evolution of all circumpolar Arctic–boreal fires between 2012 and 2023 using 375 m Visible Infrared Imaging Radiometer Suite active fire detections and the resulting dataset of the ignition time, location, size, duration, spread and intensity of individual fires. We use this dataset to classify the Arctic–boreal biomes into seven distinct ‘pyroregions’ with unique climatic and geographic environments. We find that these pyroregions exhibit varying responses to environmental drivers, with boreal North America, eastern Siberia and northern tundra regions showing the highest sensitivity to climate and lightning density. In addition, anthropogenic factors play an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic–boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events.

Geology↗
DOE OSTI2021

Displacement Analysis of Geothermal Field Based on PSInSAR And SOM Clustering Algorithms A Case Study of Brady Field, Nevada—USA

The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluation of global teleconnections in CMIP6 climate projections using complex networks

In climatological research, the evaluation of climate models is one of the central research subjects. As an expression of large-scale dynamical processes, global teleconnections play a major role in interannual to decadal climate variability. Their realistic representation is an indispensable requirement for the simulation of climate change, both natural and anthropogenic. Therefore, the evaluation of global teleconnections is of utmost importance when assessing the physical plausibility of climate projections. We present an application of the graph-theoretical analysis tool δ-MAPS, which constructs complex networks on the basis of spatio-temporal gridded data sets, here sea surface temperature and geopotential height at 500 hPa. Complex networks complement more traditional methods in the analysis of climate variability, like the classification of circulation regimes or empirical orthogonal functions, assuming a new non-linear perspective. While doing so, a number of technical tools and metrics, borrowed from different fields of data science, are implemented into the δ-MAPS framework in order to overcome specific challenges posed by our target problem. Those are trend empirical orthogonal functions (EOFs), distance correlation and distance multicorrelation, and the structural similarity index. δ-MAPS is a two-stage algorithm. In the first place, it assembles grid cells with highly coherent temporal evolution into so-called domains. In a second step, the teleconnections between the domains are inferred by means of the non-linear distance correlation. We construct 2 unipartite and 1 bipartite network for 22 historical CMIP6 climate projections and 2 century-long coupled reanalyses (CERA-20C and 20CRv3). Potential non-stationarity is taken into account by the use of moving time windows. The networks derived from projection data are compared to those from reanalyses. Our results indicate that no single climate projection outperforms all others in every aspect of the evaluation. But there are indeed models which tend to perform better/worse in many aspects. Differences in model performance are generally low within the geopotential height unipartite networks but higher in sea surface temperature and most pronounced in the bipartite network representing the interaction between ocean and atmosphere.

58 GEOSCIENCES↗
DOE OSTI2021

Tree Canopies Reflect Mycorrhizal Composition

Mycorrhizae alter global patterns of CO 2 fertilization, carbon storage, and elemental cycling, yet knowledge of their global distributions is currently limited by the availability of forest inventory data. Here, we show that maps of tree-mycorrhizal associations (hereafter “mycorrhizal maps”) can be improved by the novel technology of imaging spectroscopy because mycorrhizal signatures propagate up from plant roots to impact forest canopy chemistry. We analyzed measurements from 143 airborne imaging spectroscopy surveys over 112,975 individual trees collected across 13 years. Results show remarkable accuracy in capturing ground truth observations of mycorrhizal associations from canopy signals across disparate landscapes (R 2 = 0.92, p < 0.01). Additionally, upcoming imaging spectroscopy satellite missions can reveal new insights into landscape-scale variations in water, nitrogen, phosphorus, carotenoid/anthocyanin, and cellulose/lignin composition. Applied globally, this approach could improve the spatial precision of mycorrhizal distributions by a factor of roughly 104 and facilitate the incorporation of dynamic shifts in forest composition into Earth system models.

58 GEOSCIENCES↗
DOE OSTI2023

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

GIScience in the era of Artificial Intelligence: a research agenda towards Autonomous GIS

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

Autonomous GI↗