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DOE OSTI2024

ELM-Wet: Inclusion of a wetland landunit with sub-grid representation of eco-hydrological patches and hydrological forcing in E3SM Land Model (ELM)

Wetlands emit the most biogenic methane (CH4) and present the greatest uncertainty in the global CH4 budget. Modeling these emissions is challenging due to the temporal and spatial variability in wetland structure and CH4 flux rates, along with complex interactions among hydrological, ecological, meteorological, and microbial processes that govern CH4 dynamics. To address these issues, we aim to enhance the accuracy of wetland representation in the U.S. Department of Energy’s Exascale Earth System Model (E3SM) Land Model, ELM. This effort led to the development of ELM-Wet, which incorporates a dedicated wetland landunit with subgrid representation of eco-hydrological patch types. We implement wetland-specific hydrology by imposing site-specific constraints on surface water levels, thereby allowing different patches to sustain varying depths of inundation. Additionally, we refined the calculation of aerenchyma transport diffusivity based on observed conductance across different vegetation types. We validated these enhancements through site-specific simulations of a coastal freshwater wetland, the Salvador WMA Freshwater Marsh (Ameriflux, site ID US-LA2), located in the coast of Louisiana (29.85N,90.29W) at an elevation of 0 m. The site was simulated with ELM-Wet and the default version ELMv1. We use Bayesian Optimization to parameterize CO2 and CH4 fluxes. Eddy covariance observations of CO2 and CH4 fluxes from 2012-2013 were used to train the model and data from 2021 were used for validation. In this repository, we include all the output of all the simulations performed using different versions of ELMv1 and ELM-Wet, all input required to the model, and the Matlab scripts we used to processed the output data. ELM-Wet_flmd.csv includes a detailed description of the datasets files.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Final Report: Process Models of the Equilibrium Size & State of Organic/Inorganic Aerosols for the Development of Large Scale Atmospheric Models & the Analysis of Field Data

Our work addressed the following elements of the Call for Proposals: (i) “to improve the theoretical representation of aerosol processes studied in ASP laboratory or field studies”, (ii) “to enhance the incorporation of aerosol process information into modules suitable for large-scale or global atmospheric models”, and (iii) “provide systematic experimental validation of process model predictions ... using data from targeted laboratory and field experiments”. Achievements to the end of 2012 are described in four previous reports, and include: new models of densities and surface tensions of pure (single solute) and mixed aqueous solutions of typical aerosol composition under all atmospheric conditions (0 to 100% RH and T > 150 K); inclusion of these models into the widely used Extended Aerosol Inorganics model (E-AIM, http://www.aim.env.uea.ac.uk/aim/aim.php); the addition of vapor pressure calculators for organic compounds to the E-AIM website; the ability of include user-defined organic compounds and/or lumped surrogates in gas/aerosol partitioning calculations; the development of new equations to represent the properties of soluble aerosols over the entire concentration range (using methods based upon adsorption isotherms, and derived using statistical mechanics), including systems at close to zero RH. These results are described in publications 1-6 at the end of this report, and on the “News” page of the E-AIM website (http://www.aim.env.uea.ac.uk/aim/info/news.html). During 2012 and 2013 we have collaborated in a combined observation and lab-based study of the water uptake of the organic component of atmospheric aerosols (PI Gannet Hallar, of the Desert Research Institute). The aerosol samples were analyzed using several complementary techniques (GC/MS, FT-ICR MS, and ion chromatography) to produce a very complete organic “speciation” including both polar and non-polar compounds. Hygroscopic growth factors of the samples were measured, and we have just completed comparisons of the data with our process model predictions based upon the inorganic and organic composition of the samples.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

TMI-2 - A Case Study for PWR Instrumentation Performance during a Severe Accident

The accident at the Three Mile Island Unit 2 (TMI-2) reactor provided a unique opportunity to evaluate sensors exposed to severe accident conditions. Conditions associated with the release of coolant and the hydrogen burn that occurred during this accident exposed instrumentation to harsh conditions, including direct radiation, radioactive contamination, and high humidity with elevated temperatures and pressures. As part of a program initiated in 2012 by the Department of Energy Office of Nuclear Energy (DOE-NE), a review was completed to gain insights from prior TMI-2 sensor survivability and data qualification efforts. This new effort focused upon a set of sensors that provided critical data to TMI-2 operators for assessing the condition of the plant and the effects of mitigating actions taken by these operators. In addition, the effort considered sensors providing data required for subsequent accident simulations. Over 100 references related to instrumentation performance and post-accident evaluations of TMI-2 sensors and measurements were reviewed. Insights gained from this review are summarized within this report. For each sensor, a description is provided with the measured data and conclusions related to the sensor’s survivability, and the basis for conclusions about its survivability. As noted within this document, several techniques were invoked in the TMI-2 post-accident evaluation program to assess sensor status, including comparisons with data from other sensors, analytical calculations, laboratory testing, and comparisons with sensors subjected to similar conditions in large-scale integral tests and with sensors that were similar in design but more easily removed from the TMI-2 plant for evaluations. Conclusions from this review provide important insights related to sensor survivability and enhancement options for improving sensor performance. In addition, this document provides recommendations related to the sensor survivability and data evaluation process that could be implemented in upcoming Fukushima Daiichi recovery efforts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

The Application of Quaternions to Strap-Down MEMS Sensor Data

We describe the mathematical transformations required to convert the data recorded using typical 6-axis microelectromechanical systems (MEMS) sensor packages (3-axis rate gyroscopes and 3-axis accelerometers) when attached to an object undergoing a short duration loading event, such as blast loading, where inertial data alone are sufficient to track the object motion. By using the quaternion description, the complex object rotations and displacements that typically occur are translated into the more convenient earth frame of reference. An illustrative example is presented where a large and heavy object was thrown by the action of a very strong air blast in a complex manner. The data conversion process yielded an accurate animation of the object’s subsequent motion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Doppler LIDAR Motion-corrected Wind Speed and Direction Profiles VAP

The dlmcprof-wind VAP contains profiles of wind speed and direction that are computed using motion-corrected Doppler lidar data from the dlmc VAP. The dlmcprof-wind VAP was specifically developed to process scanning data acquired by the ARM Doppler lidar aboard the Polarstern during the MOSAiC campaign.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

University of Nebraska unmanned aerial system (UAS) profiling during the LAPSE-RATE field campaign

Abstract. This paper describes the data collected by the University of Nebraska-Lincoln (UNL) as part of the field deployments during the Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) flight campaign in July 2018. The UNL deployed two multirotor unmanned aerial systems (UASs) at multiple sites in the San Luis Valley (Colorado, USA) for data collection to support three science missions: convection initiation, boundary layer transition, and cold air drainage flow. We conducted 172 flights resulting in over 21 h of cumulative flight time. Our novel design for the sensor housing onboard the UAS was employed in these flights to meet the aspiration and shielding requirements of the temperature and humidity sensors and to separate them from the mixed turbulent airflow from the propellers. Data presented in this paper include timestamped temperature and humidity data collected from the sensors, along with the three-dimensional position and velocity of the UAS. Data are quality-controlled and time-synchronized using a zero-order-hold interpolation without additional post-processing. The full dataset is also made available for download at https://doi.org/10.5281/zenodo.4306086 (Islam et al., 2020).

Islam, Ashraful (ORCID:000000022826269X)↗

DOE OSTI2020

Featureless adaptive optimization accelerates functional electronic materials design

Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Machine Learning Algorithms for Matching Theories, Simulations, and Observations in Cosmology (Final Project)

The search for simple and elegant equations to explain and predict observable natural phenomena has been a driving force in scientific theory for several centuries. The great successes of Newton's laws, Maxwell's equations, the Schrodinger equation, and the theory of General Relativity have encouraged this perspective, and perhaps a beautiful equation comprising a “theory of everything” remains to be discovered. But now scientists are grappling with systems of ever-increasing complexity, fraught with nonlinearity, and with the quantities of interest often only indirectly observed. Nature may resist a simple description, and the most important discoveries of the next century may be complex theories with countless variables and parameters. The era of big data opens up a promising new approach to scientific discovery in this setting. The predictions of modern theories, even if complex or nonlinear, can be examined through detailed and computationally-intensive simulations that take days or weeks to run and fill petabytes of disk space. The upcoming generation of scientific instruments will provide petabytes of observations at unprecedented resolution and depth. The challenge is to compare data to simulations so as to test theories and identify those that best match the observations. The data are numerous and rich but are subject to noise and various systematic biases. The simulations are large and detailed but are so costly that the effective sample size is small. Moreover, the mapping from data to simulation to the parameters of interest is typically complicated and ill-conditioned. Although these challenges, and the solutions we propose, apply across the sciences, we will ground our efforts by focusing on cosmology. Here, we are truly entering the era of big data in several senses. Ever larger experiments survey larger and larger cosmological volumes, resulting in enormous amounts of pixel data to process and extract the information of cosmological interest. These include large imaging surveys, which collect all the light in broad wavelength ranges over significant fractions of the sky, and spectroscopic surveys, which measure the light as a function of wavelength to map out 3D distances. These experiments are designed to shed light on the big unanswered questions in cosmology. Distilling the petabytes of data into observed quantities for comparison with theory is a daunting task. But these large experiments do not only result in large amounts of data to analyze: if we wish to compare to theories of physics, then we need increasingly extensive predictions for the observed quantities as well, which requires ever-larger cosmological simulations that take substantial -- and at times prohibitive -- computational resources to produce. Our goal is to develop statistical and machine learning methods for using observed and simulated data to advance machine learning with applications cosmology. In particular, we focus our research efforts on the following tasks: The challenge for automated science is that it is computationally impossible and statistically dangerous to consider every possible model in order to find the best one. We will develop Bayesian Optimization based active-learning methods that accelerate both the execution of the simulations and the search for best-fitting parameters. The key idea is to make the simulations adaptive -- across resolutions, time, and parameters -- using the data to search as the simulation runs; Most ML methods operate on simple finite dimensional feature vectors. However, many cosmology and other science applications require ML methods that can operate on more complex objects as inputs or outputs such as functions, distributions, or sets and point clouds. Our goal is to develop efficient ML methods for this problem and demonstrate their applicability in Cosmology, Astrophysics, and other science problems.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

TU-AB-204-02: Advances in C-Arm CBCT for Cardiac Interventions

This symposium highlights advanced cone-beam CT (CBCT) technologies in four areas of emerging application in diagnostic imaging and image-guided interventions. Each area includes research that extends the spatial, temporal, and/or contrast resolution characteristics of CBCT beyond conventional limits through advances in scanner technology, acquisition protocols, and 3D image reconstruction techniques. Dr. G. Chen (University of Wisconsin) will present on the topic: Advances in C-arm CBCT for Brain Perfusion Imaging. Stroke is a leading cause of death and disability, and a fraction of people having an acute ischemic stroke are suitable candidates for endovascular therapy. Critical factors that affect both the likelihood of successful revascularization and good clinical outcome are: 1) the time between stroke onset and revascularization; and 2) the ability to distinguish patients who have a small volume of irreversibly injured brain (ischemic core) and a large volume of ischemic but salvageable brain (penumbra) from patients with a large ischemic core and little or no penumbra. Therefore, “time is brain” in the care of the stroke patients. C-arm CBCT systems widely available in angiography suites have the potential to generate non-contrast-enhanced CBCT images to exclude the presence of hemorrhage, time-resolved CBCT angiography to evaluate the site of occlusion and collaterals, and CBCT perfusion parametric images to assess the extent of the ischemic core and penumbra, thereby fulfilling the imaging requirements of a “one-stop-shop” in the angiography suite to reduce the time between onset and revascularization therapy. The challenges and opportunities to advance CBCT technology to fully enable the one-stop-shop C-arm CBCT platform for brain imaging will be discussed. Dr. R. Fahrig (Stanford University) will present on the topic: Advances in C-arm CBCT for Cardiac Interventions. With the goal of providing functional information during cardiac interventions, significant effort has been expended to improve the quantitative accuracy of C-arm CBCT reconstructions. The challenge is to improve image quality while providing very short turnaround between data acquisition and volume data visualization. Corrections for x-ray scatter, view aliasing and patient motion that require no more than 2 iterations keep processing time short while reducing artifact. Fast, multi-sweep acquisitions can be used to permit assessment of left ventricular function, and visualization of radiofrequency lesions created to treat arrhythmias. Workflows for each imaging goal have been developed and validated against gold standard clinical CT or histology. The challenges, opportunities, and limitations of the new functional C-arm CBCT imaging techniques will be discussed. Dr. W. Zbijewski (Johns Hopkins University) will present on the topic: Advances in CBCT for Orthopaedics and Bone Health Imaging. Cone-beam CT is particularly well suited for imaging of musculoskeletal extremities. Owing to the high spatial resolution of flat-panel detectors, CBCT can surpass conventional CT in imaging tasks involving bone visualization, quantitative analysis of subchondral trabecular structure, and visualization and monitoring of subtle fractures that are common in orthopedic radiology. A dedicated CBCT platform has been developed that offers flexibility in system design and provides not only a compact configuration with improved logistics for extremities imaging but also enables novel diagnostic capabilities such as imaging of weight-bearing lower extremities in a natural stance. The design, development and clinical performance of dedicated extremities CBCT systems will be presented. Advanced capabilities for quantitative volumetric assessment of joint space morphology, dual-energy image-based quantification of bone composition, and in-vivo analysis of bone microarchitecture will be discussed, along with emerging applications in the diagnosis of arthritis and osteoporosis and assessment of novel therapies. Finally, Dr. J. Boone (UC Davis) will present on the topic: Advances in CBCT for Breast Imaging. Breast CT has been studied as an imaging tool for diagnostic breast evaluation and for potential breast cancer screening. The breast CT application lends itself to CBCT because of the small dimensions of the breast, the tapered shape of the breast towards higher cone angle, and the fact that there are no bones in the breast. The performance of various generations of breast CT scanners developed in recent years will be discussed, focusing on advances in spatial resolution and image noise characteristics. The results will also demonstrate the results of clinical trials using both computer and human observers. Learning Objectives: Understand the challenges, key technological advances, and emerging opportunities of CBCT in: Brain perfusion imaging, including assessment of ischemic stroke Cardiac imaging for functional assessment in cardiac interventions Orthopedics imaging for evaluation of musculoskeletal trauma, arthritis, and osteoporosis Breast imaging for screening and diagnosis of breast cancer. Work presented in this symposium includes research support by: Siemens Healthcare (Dr. Chen); NIH and Siemens Healthcare (Dr. Fahrig); NIH and Carestream Health (Dr. Zbijewski); and NIH (Dr. Boone)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

AI Automated Discovery of New Climate Water System Knowledge from Models and Observations

This paper addresses focus area 3, “Insight gleaned from complex data.” Science Challenge: The climate-water system is highly complex, containing a multitude of positive and negative feedbacks, time scales stretching many orders-of-magnitudes, and non-linear, connected processes. Powerful artificial intelligence (AI) methods can revolutionize and automate the discovery of new knowledge and relationships in the climate-water system, which will improve understanding and predictability of extreme hydrological events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Makah Tribe Climate Assessment, Adaptation, and Resilience Planning

Our aim was to develop a Climate Adaptation and Resilience Plan through gathering community input through personal interviews and individual surveys, building from our Climate Impacts Assessment work completed in 2018. This project has collected traditional, cultural, and local knowledge to support the ongoing multi-departmental environmental planning processes of the Makah Tribe. This information was then used to identify specific human wellbeing indicators to inform and enhance Makah tribal policy, management, and decision-making processes. Combined our tribal environmental planning to include the work of the Tribal Resilience Work Group (TRWG) and Oil Spill Work Group (OSWG), which relied on publicly available Western science-based data. While this information provided a strong basis for tribal environmental planning, it does not capture the unique traditional, cultural needs, and local knowledge and tribal voices of the Makah community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

North Slope Alaska and Tethered Balloon Systems: ARM Facilities Monthly Status Update

In support of the DOE Office of Science, Sandia National Labs is one of nine national laboratories which oversees the Atmospheric Radiation Measurement (ARM) Program. The Sandia ARM team has an obligation to fulfill its mission to provide the nation with data to improve the understanding of climate processes and the representation of those processes in climate models. Sandia’s ARM team’s ability to provide detailed and accurate descriptions of the Earth’s atmosphere in diverse climate regimes assists the DOE in development of sustainable solutions to the nation’s energy and environmental challenges. Sandia Labs manages ARM atmospheric facilities along the North Slope of Alaska (NSA) at Utqiagvik as well as the Tethered Balloon System (TBS). Activities conducted at NSA and with the TBS aide in data collection for the ARM data archive. An overview of these activities for the month of December follows.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Elucidating Abnormal Grain Growth in Thermomagnetic Processed Materials with Transfer Learning and Reinforcement Learning

The goal of this research program is to establish the mechanism governing local grain boundary motion, which is needed to design and process desirable microstructures for better performance, by identifying the relative contributions of grain boundary (GB) energy and mobility to grain growth. Classical models for grain growth assume that the primary mechanism for reducing the total interfacial energy is area reduction and that GB restructuring is not significant. This assumption implies that grain growth is locally driven by curvature. However, recent experimental observations using new non-destructive 3D x-ray diffraction microscopy techniques (3D-XRM) reveal that classic descriptors (i.e., curvature, number of neighbors, grain size) do not predict real grain growth. Instead, local GB motion appears to be governed by its energy relative to its neighbors such that low-energy boundaries replace those of higher energy. However, simulations that incorporate GB energy anisotropy still fail to reproduce these observations. These discrepancies suggest that the common assumption for grain growth theory must be re-examined to predict and, thus, control microstructure evolution in real polycrystals. A significant challenge to testing this assumption is due to anisotropic GB mobility. Mobility may cause abnormal grain growth or affect the final grain shapes or growth rate but its true contributions are unknown because it is difficult to measure. For example, observations in Fe have found that grains associated with high energy and high mobility boundaries tend to experience abnormal grain growth, whereas abnormal grain growth is associated with low energy and high mobility boundaries in alumina. As mobility and energy both control GB motion, it is challenging to isolate the local driving forces necessary to test the common assumption that the primary mechanism is area reduction. The novelty of this work is the use of machine learning tools to capture GB mobility and energy from 3D-XRM measurements in polycrystals to test the common assumption used in grain growth models. Machine learning can capture high-order correlations in dynamic systems like those found in the evolving GB topology. The PIs have developed a physics-regularized interpretable machine learning microstructure evolution (PRIMME) model that accurately replicates the grain growth behavior of its trained data set.

36 MATERIALS SCIENCE↗

DOE OSTI2016

DOE Network 2025: Network Research Problems and Challenges for DOE Scientists (Workshop Report)

The growing investments in large science instruments and supercomputers by the US Department of Energy (DOE) hold enormous promise for accelerating the scientific discovery process. They facilitate unprecedented collaborations of geographically dispersed teams of scientists that use these resources. These collaborations critically depend on the production, sharing, moving, and management of, as well as interactive access to, large, complex data sets at sites dispersed across the country and around the globe. In particular, they call for significant enhancements in network capacities to sustain large data volumes and, equally important, the capabilities to collaboratively access the data across computing, storage, and instrument facilities by science users and automated scripts and systems. Improvements in network backbone capacities of several orders of magnitude are essential to meet these challenges, in particular, to support exascale initiatives. Yet, raw network speed represents only a part of the solution. Indeed, the speed must be matched by network and transport layer protocols and higher layer tools that scale in ways that aggregate, compose, and integrate the disparate subsystems into a complete science ecosystem. Just as important, agile monitoring and management services need to be developed to operate the network at peak performance levels. Finally, these solutions must be made an integral part of the production facilities by using sound approaches to develop, deploy, diagnose, operate, and maintain them over the science infrastructure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

CROCUS Weather Data at Argonne National Laboratory Prairie Site

Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements. These measurements are collected at the Argonne Testbed for Multiscale Observational Science (ATMOS), a prairie field site at Argonne National Laboratory in Lemont, Illinois. Data is available in the netCDF data format, we encourage data users review documentation through Project Pythia to understand how to work with netCDF data https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html. The data is aggregated into daily frequency to make it easier to process multiple days, and compress the higher-resolution fields. Each file contains one day's worth of data (24 hours, starting at 0000 UTC). File naming convention includes the project (CROCUS), location (atmos), data level (raw, a1), date (year, month, day), and hour (0000).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

BLOC Site - ASSIST Thermodynamic Retrievals TROPoe v0.18 / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). This is a post-processed dataset and recommended for use. The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Michaud-Belleau et al. 2025) operated by NOAA Physical Sciences Laboratory (PSL) on Block Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a collocated surface tower operated by NOAA PSL. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY. The TROPoe docker container (version 0.18) is available from Docker Hub at https://hub.docker.com/r/davidturner53/tropoe/tags, and the source code code is available in the GitHub repository https://github.com/OAR-atmospheric-observations/TROPoe.

17 WIND ENERGY↗

DOE OSTI2026

NANT Site - ASSIST Thermodynamic Retrievals TROPoe v0.18 / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). This is a post-processed dataset and recommended for use. The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Michaud-Belleau et al. 2025) operated by NOAA Physical Sciences Laboratory (PSL) on Nantucket Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a collocated surface tower operated by NOAA PSL. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY. The TROPoe docker container (version 0.18) is available from Docker Hub at https://hub.docker.com/r/davidturner53/tropoe/tags, and the source code code is available in the GitHub repository https://github.com/OAR-atmospheric-observations/TROPoe.

17 WIND ENERGY↗

DOE OSTI2025

High-count rate effects in event processing for XRISM/ Resolve X-ray microcalorimeter: I. Ground test

The spectroscopic performance of an X-ray microcalorimeter is compromised at high count rates. We utilize the Resolve X-ray microcalorimeter onboard the XRISM satellite to examine the effects observed during high-count rate measurements and propose modeling approaches to mitigate them. We specifically address the following instrumental effects that impact performance: CPU limit, pile-up, and untriggered electrical cross-talk. Experimental data at high count rates were acquired during ground testing using the flight model instrument and a calibration X-ray source. In the experiment, data processing not limited by the performance of the onboard CPU was run in parallel, which cannot be done in orbit. This makes it possible to access the data degradation caused by limited CPU performance. We use these data to develop models that allow for a more accurate estimation of the aforementioned effects. To illustrate the application of these models in observation planning, we present a simulated observation of GX 13+1. Understanding and addressing these issues is crucial to enhancing the reliability and precision of X-ray spectroscopy in situations characterized by elevated count rates.

47 OTHER INSTRUMENTATION↗