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DOE OSTI2026

BNF Radar b1 Data Processing Report: Spring 2025

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility supports atmospheric science through an integrated network of fixed and mobile observatories. These facilities collect continuous and campaign-based observations of atmospheric properties, with the goal of improving the representation of clouds, aerosols, precipitation, and radiation in Earth system models. The Bankhead National Forest (BNF) site, established as an ARM Mobile Facility (AMF) on 1 October 2024, is situated in a forested region of northern Alabama. Its strategic location in a southeastern U.S. environment characterized by complex terrain, diverse land cover, and frequent convective storms provides a valuable opportunity to examine coupled land-atmosphere processes under natural variability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Interpreting and reporting 40 Ar/ 39 Ar geochronologic data

The 40Ar/39Ar dating method is among the most versatile of geochronometers, having the potential to date a broad variety of K-bearing materials spanning from the time of Earth’s formation into the historical realm. Measurements using modern noble-gas mass spectrometers are now producing 40Ar/39Ar dates with analytical uncertainties of ~1%, thereby providing precise time constraints for a wide range of geologic and extraterrestrial processes. Analyses of increasingly smaller subsamples have revealed age dispersion in many materials, including some minerals used as neutron fluence monitors. Accordingly, interpretive strategies are evolving to address observed dispersion in dates from a single sample. Moreover, inferring a geologically meaningful “age” from a measured “date” or set of dates is dependent on the geological problem being addressed and the salient assumptions associated with each set of data. We highlight requirements for collateral information that will better constrain the interpretation of 40Ar/39Ar data sets, including those associated with single-crystal fusion analyses, incremental heating experiments, and in situ analyses of microsampled domains. To ensure the utility and viability of published results, we emphasize previous recommendations for reporting 40Ar/39Ar data and the related essential metadata, with the amendment that data conform to evolving standards of being findable, accessible, interoperable, and reusable (FAIR) by both humans and computers. Our examples provide guidance for the presentation and interpretation of 40 Ar/ 39 Ar dates to maximize their interdisciplinary usage, reproducibility, and longevity.

58 GEOSCIENCES↗

DOE OSTI2022

Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models

Representation of soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. The magnitude of this uncertainty can be reduced by accurate representation of environmental controllers of SOC stocks in ESMs. In this study, we used data of environmental factors, field SOC observations, ESM projections and machine learning approaches to identify dominant environmental controllers of SOC stocks and derive functional relationships between environmental factors and SOC stocks. Our derived functional relationships predicted SOC stocks with similar accuracy as the machine learning approach. We used the derived relationships to benchmark the coupled model intercomparison project phase six ESM representation of SOC stocks. We found divergent environmental control representation in ESMs in comparison to field observations. Representation of SOC in ESMs can be improved by including additional environmental factors and representing their functional relationships with SOC consistent with observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Simulation of radon-222 with the GEOS-Chem global model: Emissions, seasonality, and convective transport

Radon-222 (222Rn) is a short-lived radioactive gas naturally emitted from land surface, and has long been used to assess convective transport in atmospheric models. In this study, we simulate 222Rn using the GEOS-Chem chemical transport model with aims to improve our understanding of 222Rn emissions and surface concentration seasonality, and to characterize convective transport associated with two Goddard Earth Observing System (GEOS) meteorological products, MERRA and GEOS-FP. We evaluate four available 222Rn emission scenarios by comparing model results with surface observations at 51 global surface sites. The default emission scenario in GEOS-Chem yields a moderate agreement with global surface observations (< 70% data within a factor of 2) and a large underestimate of wintertime surface 222Rn concentrations at Northern Hemisphere mid- and high-latitudes due to an oversimplified formulation of 222Rn emission fluxes (1 atom cm-2 s-1 over land with a reduction by a factor of 3 under freezing conditions). We compose a new global 222Rn emission scenario based on Zhang et al. (2011) and show its potential to improve simulated surface 222Rn concentrations and seasonality. The regional components of this emission scenario include spatially and temporally varying emission fluxes derived from previous measurements of soil radium content and soil exhalation models, which are key factors to 222Rn emission flux rates. However, large model underestimates of surface 222Rn concentrations still exist in Asia, suggesting unusually high regional 222Rn emissions. We propose a conservative up-scaling factor of 1.2 for 222Rn emission fluxes in China, as also constrained by the observed deposition fluxes of 210Pb (decay daughter of 222Rn). With this modification, the model shows better agreement with the observations in Europe and North America (>80% data within a factor of 2), and reasonable agreement in Asia (close to 70%). Further constraints on 222Rn emissions would require additional observations of surface 222Rn concentrations and emission fluxes in central U.S., Canada, Africa, and Asia. We also compare and assess convective transport in model simulations driven by MERRA and GEOS-FP using observed 222Rn vertical profiles during northern mid-latitude summertime and from two short-term airborne campaigns. While the simulations with both GEOS products are able to capture the observed vertical gradient of 222Rn concentrations in the lower troposphere (0-4 km), neither correctly represents the level of convective detrainment, resulting in biases in the middle and upper troposphere. Compared to GEOS-FP, MERRA leads to stronger convective transport of 222Rn, which is partially compensated by its weaker large-scale vertical advection, resulting in similar global vertical distributions of 222Rn concentrations between the two simulations.

Zhang, Bo↗

DOE OSTI2026

Multi-resolution Arctic Shrub Cover Dataset Derived from UAS and Airborne SfM and LiDAR (2013-2025)

We synthesized 177 unoccupied aerial system flights and 77 airborne flights across the Arctic and created a multi-resolution benchmark data of low-to-tall shrub fractional cover leveraging Structure-from-Motion and Light Detection and Ranging. The resulting dataset covered a total of 1899 km2 across Alaska, Western Canada, Sweden, and Siberian Arctic, including key sites from the Oro Arctic to the High Arctic. The dataset is organized into 6 primary data collection directories (“Abisko,” “AWI,” “ERE,” “Fairbanks,” “NGEE,” “Toolik”), each containing site and flight subdirectories. Flight directories include shrub cover rasters (*.tifs) at 1 m, 5 m, and 30 m resolution, the canopy height model at 1 m resolution (*.tifs), and a bounding box *.kml file. For the AWI, Abisko, NGEE, and Fairbanks collections, we also include the GCC raster at 1 m resolution (*.tif). Files are organized by Collection > Site > Flight Name > Data Files. Flight rasters are in the local UTM zone and the .kml files are in the geographic coordinate system EPSG 4326. We also include a .csv file that details the source datasets for every flight. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

canopy height model↗

DOE OSTI2020

The Breakthrough Listen Search for Intelligent Life: A 3.95–8.00 GHz Search for Radio Technosignatures in the Restricted Earth Transit Zone

We report on a search for artificial narrowband signals of 20 stars within the restricted Earth Transit Zone (rETZ) as a part of the ten-year Breakthrough Listen (BL) search for extraterrestrial intelligence (ETI). The rETZ is the region of the sky from which an observer would see Earth transit the Sun with an impact parameter of less than 0.5. This region of the sky is geometrically unique, providing a potential way for an ETI to discover the solar system. The targets were nearby (7–143 pc) and the search covered an electromagnetic frequency range of 3.95–8.00 GHz. We used the Robert C. Byrd Green Bank Telescope to perform these observations with the standard BL data recorder. We searched these data for artificial narrowband (∼Hz) signals with Doppler drift rates of ±20 Hz s{sup −1}. We find no evidence for radio technosignatures from ETI in our observations. The observing campaign would have allowed for detections of emissions that were 10{sup −3} to 0.88 times as powerful as the signaling capability of the Arecibo radar transmitter. We conclude that at least 0.6% of the systems in the rETZ within 150 pc do not possess the type of transmitters searched in this survey. To our knowledge, this is the first targeted search for ETI of the rETZ. All data used in this paper are publicly available via the BL Public Data Archive (http://seti.berkeley.edu/bldr2).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

The SuperCam Instrument Suite on the Mars 2020 Rover: Science Objectives and Mast-Unit Description

On the NASA 2020 rover mission to Jezero crater, the remote determination of the texture, mineralogy and chemistry of rocks is essential to quickly and thoroughly characterize an area and to optimize the selection of samples for return to Earth. As part of the Perseverance payload, SuperCam is a suite of five techniques that provide critical and complementary observations via Laser-Induced Breakdown Spectroscopy (LIBS), Time-Resolved Raman and Luminescence (TRR/L), visible and near-infrared spectroscopy (VISIR), high-resolution color imaging (RMI), and acoustic recording (MIC). SuperCam operates at remote distances, primarily 2–7 m, while providing data at sub-mm to mm scales. We report on SuperCam’s science objectives in the context of the Mars 2020 mission goals and ways the different techniques can address these questions. The instrument is made up of three separate subsystems: the Mast Unit is designed and built in France; the Body Unit is provided by the United States; the calibration target holder is contributed by Spain, and the targets themselves by the entire science team. This publication focuses on the design, development, and tests of the Mast Unit; companion papers describe the other units. The goal of this work is to provide an understanding of the technical choices made, the constraints that were imposed, and ultimately the validated performance of the flight model as it leaves Earth, and it will serve as the foundation for Mars operations and future processing of the data.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial‐Aquatic Interfaces

Interconnected landscape features such as terrestrial‐aquatic interfaces play an outsized role in biogeochemical cycles as ecosystem control points, but it is notoriously challenging to characterize these. Here, we document a synoptic sensor network design that is (a) flexible to accommodate diverse ecosystem interfaces and gradients, (b) adaptable to monitoring and modeling needs of small and large projects alike, (c) standardized for intercomparability across sites and field experiments, and (d) adequately replicated to capture heterogeneity of each parameter monitored. This real‐time monitoring of surface water, groundwater, soil, and vegetation supports configuration and evaluation of models that span upland, wetland, open water strata, and transitions between them. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including large‐scale flood manipulation experiments in both regions. A central design element is “one data logger program to rule them all”—a collection of sensor‐specific modules deployed on 40 loggers controlling ∼2,000 sensors, with the goal of streamlining maintenance, debugging, and reproducible data processing. The network generates ∼6 M observations per month, capturing system dynamics at the broad spatial and fine temporal scales needed to initialize and benchmark models; measurement frequency can be modified remotely to capture events. This network design has also revealed behaviors not represented in Earth system models, such as transient groundwater oxygen pulses. Completely documented and open source, this standardized, flexible, and efficient sensor network design can reduce barriers to understanding environmental changes and ecosystem responses across systems and scales.

Ward, Nicholas D. [Pacific Northwest National Labo↗

DOE OSTI2022

Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"

This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning

Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of the Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

Jiang, Peishi↗

DOE OSTI2021

Stellar Activity Manifesting at a One-year Alias Explains Barnard b as a False Positive

Barnard’s star is among the most studied stars given its proximity to the Sun. It is often considered the radial velocity (RV) standard for fully convective stars due to its RV stability and equatorial decl. Recently, an Msin i=3.3M{sub ⊕} super-Earth planet candidate with a 233 day orbital period was announced by Ribas et al. New observations from the near-infrared Habitable-zone Planet Finder (HPF) Doppler spectrometer do not show this planetary signal. We ran a suite of experiments on both the original data and a combined original + HPF data set. These experiments include model comparisons, periodogram analyses, and sampling sensitivity, all of which show the signal at the proposed period of 233 days is transitory in nature. The power in the signal is largely contained within 211 RVs that were taken within a 1000 day span of observing. Our preferred model of the system is one that features stellar activity without a planet. We propose that the candidate planetary signal is an alias of the 145 day rotation period. This result highlights the challenge of analyzing long-term, quasi-periodic activity signals over multiyear and multi-instrument observing campaigns.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Estimating soil thermal inertia profiles from the passive equilibration of a temperature probe: Supporting data and code

This dataset contains measurements of soil thermal properties and organic layer thickness at three field sites on the Seward Peninsula near Nome, Alaska, USA. Code is also provided for estimation of soil thermal properties from temperature data, including a forward heat transfer mode, a library of simulated temperature data, and an inversion scheme to retrieve thermal properties from temperature measurements (Lamb et al., 2025). This dataset and code is published in support of the manuscript “Estimating Soil Thermal Inertia Profiles from the Passive Equilibration of a Temperature Probe” by Lamb et al., 2025, in which an experimental thermal measurements technique is developed. The dataset contains measurements of soil thermal properties and organic layer thickness obtained with an industry standard thermal properties analyzer and direct observations, as well as data generated using the experimental technique to estimate thermal properties and soil structure from temperature observations. All data is reported in .csv format, and all code is written in Python 3.7 using standard packages. This research was performed as a part of the NGEE Arctic project, which aims to advance model predictions of arctic carbon cycle responses to a changing climate over the 21st Century.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 OSTI2026

Contrasting Carbon–Water–Energy Dynamics in Perennial and Annual Bioenergy Agroecosystems Using Eddy Covariance and Interpretable Machine Learning

Understanding how agroecosystems respond to environmental variability is fundamental to predicting productivity and sustainability under a changing climate. We analyzed 55 site-years of high-frequency eddy covariance observations from five agroecosystems—two perennial grasses (miscanthus and switchgrass), two annual rotation systems (maize–soybean and sorghum–soybean), and a restored native prairie—to examine ecosystem-scale carbon, water, and energy fluxes. Using an interpretable machine-learning framework with regression tree ensembles, Shapley Additive Explanations, and Accumulated Local Effects, we quantified how environmental and temporal factors regulate gross primary productivity (GPP), evapotranspiration (ET), water-use efficiency, and the Bowen ratio. Perennials exhibited stronger physiological buffering and maintained fluxes across a broader range of temperature and moisture conditions, reflecting deeper rooting and persistent canopy cover. Annuals, in contrast, showed greater short-term variability and stronger coupling to atmospheric demand, with GPP and ET declining rapidly under low humidity or soil moisture. Differences in temperature sensitivity of Bowen ratio further revealed that perennials sustained proportionally greater sensible heat flux under cool conditions, whereas annuals exhibited constrained energy exchange when evaporative demand was low. Together, these results demonstrate that crop life cycle and canopy structure are fundamental determinants of ecosystem-scale carbon–water–energy coupling. By integrating long-term flux observations with interpretable machine learning, this study identifies the environmental drivers that shape agroecosystem function and highlights how conversion from annual to perennial feedstocks can enhance climatic resilience and alter land–atmosphere energy feedbacks. These findings provide a data-driven basis for improving crop and Earth-system models and for guiding bioenergy landscape design under future climate scenarios.

Accumulated Local Effects↗

DOE OSTI2021

Pro‐ L * ‐ A Probabilistic L * Mapping Tool for Ground Observations

Abstract Both ground and space observations are used extensively in the modeling of space weather processes within the Earth’s magnetosphere. In radiation belt physics modeling, one of the key phase‐space coordinates is L *, which indicates the location of the drift paths of energetic electrons. Global magnetic field models allow a subset of locations on the ground (mainly subauroral) to be mapped along field lines to a location in space and transformed into L *, provided that the initial ground location maps to a closed drift path. This allows observations from ground, or low‐altitude space‐based platforms to be mapped into space in order to inform radiation belt modeling. Many data‐based magnetic field models exist; however, these models can significantly disagree on mapped L * values for a single point on the ground, during both quiet times and storms. We present a state of the art probabilistic L * mapping tool, Pro‐ L *, which produces probability distributions for L * corresponding to a given ground location. Pro‐ L * has been calculated for a high resolution magnetic latitude by magnetic local time grid in the Earth’s Northern Hemisphere. We have developed the probabilistic model using 11 years of L * calculations for seven widely used magnetic field models. Usage of the tool is highlighted for both event studies and statistical models, and we demonstrate a number of potential applications.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Estimation of the orientation of stress in the Earth’s crust without earthquake or borehole data

Abstract Mechanical stress acting in the Earth’s crust is a fundamental property that is important for a wide range of scientific and engineering applications. The orientation of maximum horizontal compressive stress can be estimated by inverting earthquake source mechanisms and measured directly from borehole-based measurements, but large regions of the continents have few or no observations. Here we present an approach to determine the orientation of maximum horizontal compressive stress by measuring stress-induced anisotropy of nonlinear susceptibility, which is the derivative of elastic modulus with respect to strain. Laboratory and Earth experiments show that nonlinear susceptibility is azimuthally dependent in an anisotropic stress field and is maximum in the orientation of maximum horizontal compressive stress. We observe this behavior in the Earth—in Oklahoma and New Mexico, U.S.A, where maximum nonlinear susceptibility coincides with the orientation of maximum horizontal compressive stress measured using traditional methods. Our measurements use empirical Green’s functions and solid-earth tides and can be applied at different temporal and spatial scales.

58 GEOSCIENCES↗

DOE OSTI2025

Data and scripts associated with “Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics within Water-Tagging Enabled Hydrologic Models” (v3)

This data package is associated with the publication “Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics within Water-Tagging Enabled Hydrologic Models” submitted to Journal of Advances in Modeling Earth Systems (Butler et al. 2025). This study developed the Sequential Precipitation Input Tagging (SPIT) framework to tag input precipitation and estimate water transit times and hydrologic tracers. SPIT tags all precipitation events at regular intervals over an extended period (monthly tags over seven years) in a hydrologic model from 2016-2022. SPIT is applied at six National Ecological Observatory Network (NEON) sites across the continental United States to calculate transit time distributions (TTD) and derive from these mean transit times (MTT), fractions of young water (Fyw), and hydrologic tracer concentrations in stream water (δ18O) within a water-tagging enabled version of the Weather Research and Forecast (WT-WRF-Hydro) model with national water model (NWM) configurations. We go on to validate WT-WRF-Hydro estimates against Butler et al. (2023), who analyzed the same NEON sites using stable water isotope data to estimate water transit times. This new tracking method provides a detailed picture of water movement and helps improve predictions about water availability in the future. This data package was originally published in January 2025. It was updated May 2025 (v2; new and modified files) and October 2025 (v3; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. This data package contains the data and scripts used to develop the SPIT framework WT-WRF-Hydro (Water Tagging Weather Research and Forecasting Hydrologic) model and is associated with the following GitHub repository: https://github.com/zbutler33/SPIT-Framework. This data package contains five parent folders: (1) “Manipulated_outputs”, (2) “Metadata”, (3) “Observed”, (4) “Outputs”, and (5) “Scripts”. Each of these parent folders contains additional subfolders and files. Please see the FLMD (“v*_Butler_2024_WT_WRF_Hydro_flmd.csv”) for a list of all the files contained in this data package and descriptions for each. See the data dictionary (“v*_Butler_2024_WT_WRF_Hydro_dd.csv”) for definitions and units of all of the tabular (files ending in “.csv” and ".tsv") column headers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Benefits of using multiple Raman laser wavelengths for characterizing defects in a UO 2 matrix

Abstract Raman spectroscopy is one of the most useful techniques for studying the structure of UO 2 and changes due to specific defects caused by doping, changes in stoichiometry, irradiation, or heating under oxidizing conditions. In this paper, we illustrate several aspects of the application of Raman techniques to the study of UO 2 , including the use of wavelength‐dependent excitation (455, 532, and 785 nm) to assess the effects of doping (Nd, Th, and Zr), ion irradiation, and in situ heating and oxidation (UO 2 to U 3 O 8 ). Additionally, we show examples of how correlative microscopy is possible using electron backscatter diffraction combined with Raman maps of specific vibration bands or of laser‐induced luminescence generated by rare‐earth dopants in the matrix. For each of these applications, we suggest optimal excitation wavelengths that vary depending on the desired data. Blue (455 nm) excitation tends to promote oxidation even at low powers, but because Raman spectra change little with doping, irradiation‐induced changes are easier to observe. Green (532 nm) excitation is optimal for observing electron–phonon resonance effects in UO 2 and offers a good compromise for high‐temperature oxidation experiments, delivering high‐quality spectra for both UO 2 and U 3 O 8 . Infrared (785 nm) excitation is best for observing “defect” bands associated with doping in UO 2 , as changes with irradiation are small. Raman spectroscopy is particularly suited for studying the stability of UO 2 towards oxidation in the presence of dopants simulating fission products, where electron–phonon resonant effects, dopant ion luminescence, and mapping can be used together to investigate structural rearrangement as a function of temperature. These techniques can offer insight into microstructural changes in UO 2 fuels at higher burnups envisioned in future reactors.

42 ENGINEERING↗

DOE OSTI2021

Evaluation of precipitation indices in suites of dynamically and statistically downscaled regional climate models over Florida

Abstract The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA’s Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices.

54 ENVIRONMENTAL SCIENCES↗