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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

exoALMA. V. Gaseous Emission Surfaces and Temperature Structures

An analysis of the gaseous component in protoplanetary disks can inform us about their thermal and physical structure, chemical composition, and kinematic properties, all of which are crucial for understanding various processes within the disks. By exploiting the asymmetry of the line emission, or via line profile analysis, we can locate the emitting surfaces. Here, we present the emission surfaces of the exoALMA sources in 12 CO J = 3–2, 13 CO J = 3–2, and CS J = 7–6. We find that 12 CO traces the upper disk atmosphere, with mean $\langle$ z/r $\rangle$ values of ≈0.28, while 13 CO and CS trace lower regions of the disk with mean $\langle$ z/r $\rangle$ values of ≈0.16 and ≈0.18, respectively. We find that 12 CO $\langle$ z/r $\rangle$ and the disk mass are positively correlated with each other; this relationship offers a straightforward way to infer the disk mass. We derive 2D r – z temperature distributions of the disks. Additionally, we search for substructure in the surfaces and radial intensity profiles; we find evidence of localized substructure in the emission surfaces and peak intensity profiles of nearly every disk, with this substructure often being coincident between molecular tracers, intensity profiles, and kinematic perturbations. Four disks display evidence of potential photodesorption, implying that this effect may be common even in low far-ultraviolet star-forming regions. For most disks, we find that the physical and thermal structure is more complex than analytical models can account for, highlighting a need for more theoretical work and a better understanding of the role of projection effects on our observations.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2013

Modeling Weather Impact on Airport Arrival Miles-in-Trail Restrictions

When the demand for either a region of airspace or an airport approaches or exceeds the available capacity, miles-in-trail (MIT) restrictions are the most frequently issued traffic management initiatives (TMIs) that are used to mitigate these imbalances. Miles-intrail operations require aircraft in a traffic stream to meet a specific inter-aircraft separation in exchange for maintaining a safe and orderly flow within the stream. This stream of aircraft can be departing an airport, over a common fix, through a sector, on a specific route or arriving at an airport. This study begins by providing a high-level overview of the distribution and causes of arrival MIT restrictions for the top ten airports in the United States. This is followed by an in-depth analysis of the frequency, duration and cause of MIT restrictions impacting the Hartsfield-Jackson Atlanta International Airport (ATL) from 2009 through 2011. Then, machine-learning methods for predicting (1) situations in which MIT restrictions for ATL arrivals are implemented under low demand scenarios, and (2) days in which a large number of MIT restrictions are required to properly manage and control ATL arrivals are presented. More specifically, these predictions were accomplished by using an ensemble of decision trees with Bootstrap aggregation (BDT) and supervised machine learning was used to train the BDT binary classification models. The models were subsequently validated using data cross validation methods. When predicting the occurrence of arrival MIT restrictions under low demand situations, the model was able to achieve over all accuracy rates ranging from 84% to 90%, with false alarm ratios ranging from 10% to 15%. In the second set of studies designed to predict days on which a high number of MIT restrictions were required, overall accuracy rates of 80% were achieved with false alarm ratios of 20%. Overall, the predictions proposed by the model give better MIT usage information than what has been currently provided under current day operations. Traffic flow managers can use these predictions to identify potential MIT restrictions to eliminate (e.g., those occurring during low arrival demand periods), and to determine the days in which a significant number of restrictions may be required

Operation↗
DOE OSTI2023

EI_MS_ML

The unambiguous identification of compounds from their electron ionization mass (EI-MS) spectra remains a significant unsolved problem in the field of metabolomics and analytical chemistry as a whole. Typically EI-MS spectra are compared using various mathematical operations that convert the spectral similarity or differences into a distance-like metric that roughly approximates the similarity of any two spectra. A commonly used metric for this is the cosine similarity metric which has values close to one for very similar spectra and a value of zero for very dissimilar spectra; however, no metric is perfect. Due to the prevalence of structurally-similar compounds such as isomers and the prevalence of certain fragmentation patterns across structurally-dissimilar compounds, the unambiguous assignment of EI-MS spectra compounds remains difficult. Frequently, querying an observed EI-MS spectrum against a large database such as the NIST17 library yields multiple possible assignments requiring the end user to distinguish between multiple high scoring hits, or multiple low scoring hits while keeping in mind that the correct hit may not be in the database at all. Although techniques such as orthogonal information from techniques such as chromatography can greatly aid in unambiguous assignment, this also requires more complicated experimental designs and access to more complicated analytical instrumentation. Substructures can be trivially detected and represented as strings using a previously published technique called node coloring from a known chemical structure. However, for experimentally-derived EI-MS spectra this information must be derived from the spectra itself (i.e., because we do not know what compound it represents). To achieve this, the software uses techniques from the field of machine learning and a large training dataset of EI-MS spectra corresponding to known structures annotated with substructure strings, to build models that can predict the presence of a given chemical substructure from an EI-MS spectrum directly.If these predictions are of high-quality (i.e., are unlikely to be false positives), the presence of one or more predicted substructures can be used to constrain the number of possible hits for a query spectrum. Mathematically, this restriction could be expressed in many forms, but the most straight-forward implementation is to weight the cosine similarity of a query spectrum and a plausible database match with a Tanimoto-like coefficient based on the ratio of the number of substructures predicted to the number of substructures present in the potential database hit. Determining which combination of models best reduces assignment ambiguity will be achieved using a combination of manual curation and optimization techniques such as genetic algorithms. This software will perform all the steps necessary to construct said models from a training dataset and evaluate them using a holdout dataset. Various statistical analyses can be performed to determine if this approach does decrease assignment ambiguity. For example, if this approach works, on average, the rank-order of the correct assignment for the holdout set of EI-MS spectra should decrease and the weighted cosine similarities for most of the possible matches in the database should be better than the unweighted cosine similarities. Furthermore, this same pipeline can be used on real experimental data to generate less ambiguous assignments.

Mitchell, Joshua↗
DOE OSTI2023

Field surveying data of low-cost networked flood sensors in southeast Texas

Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are essential. Southeast Texas is still at significant risk of flooding, and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. Lamar University developed a survey program to measure elevation and coordinates at each sensor site location to make this data more useful for flood monitoring and mapping. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various flood stage thresholds at critical points that flood decision-makers can use for reference at each site. In the first phase of this program, these sensors were deployed throughout a 7-county region spanning nearly 6,000 square miles in Southeast Texas. The latitude and longitude of the sensors and their elevations were determined using survey-grade Global Navigation Satellite System (GNSS) technology. Various Continually Operating Reference Stations (CORS) were utilized for post-processing to achieve sub-inch resolution. The flood stage thresholds, water level sensors elevation, and the elevations and positions of other critical surrounding points are viewable to the public through two online repositories and a web-based sensor management dashboard. The data is used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Hand Calculation Methods for Nuclear Criticality Safety

This primer provides an overview of the most common hand calculation methods used for criticality safety calculations. The most widely used tools available to a nuclear criticality safety (NCS) practitioner are probably the common Monte Carlo or deterministic criticality safety codes, which can be used to model very complex systems. However, use of these codes can obscure the parameters to which a particular fissile system may be sensitive, whereas the hand calculation methods can be used to delve into the ways each parameter may affect the reactivity of a fissile material system. Furthermore, practitioners must avoid using computer codes as devices that take inputs and simply provide outputs (i.e., a “black box”). Many years ago, pioneers such as Joe Thomas, David Smith, and Hugh Paxton, among others in the field of nuclear criticality safety, took the time before the advent of high-speed desktop computers to create simple hand methods for criticality safety analyses. Some of the methods can be used for single fissile units; others are applicable to fissile units arranged into simple array configurations. This primer discusses the applicability of the various methods, illustrates how they are used, and provides an interpretation of the various results. The NCS practitioner will need to spend time to master the methods that could be most useful; however, they can provide the practitioner with fast and accurate answers to criticality safety problems if they are used correctly and if critical data exist for the problem at hand. Hand calculation methods can be used as a starting point for more advanced calculations, and in many circumstances, they can provide sensitivity and perturbation information more quickly than using a criticality code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2021

Intercomparison of Middle Atmospheric Meteorological Analyses for the Northern Hemisphere Winter 2009-2010

Detailed meteorological analyses based on observations extending through the middle atmosphere (~15 to 100 km altitude) can provide key information to whole atmosphere modelling systems regarding the physical mechanisms linking day-to-day changes in ionospheric electron density to meteorological variability near the Earth’s surface. However, the extent to which independent middle atmosphere analyses differ in their representation of wave-induced coupling to the ionosphere is unclear. To begin to address this issue, we present the first intercomparison among four such analyses, JAGUAR-DAS, MERRA-2, NAVGEM-HA, and WACCMX+DART, focusing on the Northern Hemisphere (NH) 2009-2010 winter, which includes a major sudden stratospheric warming (SSW). This intercomparison examines the altitude, latitude, and time dependences of zonal mean zonal winds and temperatures among these four analyses over the 1 December 2009 – 31 March 2010 period, as well as latitude and altitude dependences of monthly mean amplitudes of the diurnal and semidiurnal migrating solar tides, the eastward propagating diurnal zonal wave number 3 nonmigrating tide, and traveling planetary waves associated with the quasi-5-day and quasi-2-day Rossby modes. Our results show generally good agreement among the four analyses up to the stratopause (~50 km altitude). Large discrepancies begin to emerge in the mesosphere and lower thermosphere owing to (1) differences in the types of satellite data assimilated by each system and (2) differences in the details of the global atmospheric models used by each analysis system. The results of this intercomparison provide initial estimates of uncertainty in analyses commonly used to constrain middle atmospheric meteorological variability in whole atmosphere model simulations.

John P Mccormack↗
DOE OSTI2020

A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation Information

Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have differing error models, we seek a theoretically justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. Furthermore, the theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate and illustrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf’s ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed in parallel for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. However, when the observations are not so normalized, the sheaf model still remains valid.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.

Petzold, Christopher [Lawrence Berkeley National L↗
DOE OSTI2023

Radionuclide surrogate aerosolization, resuspension and suppression in hazardous situations

Radioactive fire following nuclear accidents causes resuspension of radioactive contamination that can spread over large areas. Although many papers focus on particle deposition and resuspension, a limited number of studies model its suppression. Even less data is available for the comparison of emissions across particle size, surface, and mode of resuspension. In this study, cerium dioxide surrogate for internal hazards such as radionuclide particle matter was aerosolized into a large chamber and subsequently resuspended after sedimentation. Resuspension was induced by common modes of human movement including walking, wind, and driving on common surfaces including polyvinyl chloride (PVC), artificial grass (turf), and concrete in a 5.8 m long, 3.7 m wide and 3 m high tent. The resuspension was experimentally measured for two particle sizes (1 and 10 μm) at three different heights (0.3 m, 1.2 m, and 2.4 m) and compared across size, surface, and mode of resuspension. Particle size distribution was measured using an aerodynamic particle sizer and the resuspended particles were collected with the wetted wall cyclone aerosol collector and quantitated using inductively coupled plasma mass spectrometry. The testing was repeated with the F-500 fire retardant application to study its effect on resuspension. In this study, artificial grass flooring was found to generate the most resuspension, with walking on turf for the 1 μm particle testing yielding the highest factor overall. Concrete had the lowest resuspension, showing variations in the size distribution of aerosol as a function of height from the source resuspension factors. The application of fire retardant was found to significantly reduce surrogate resuspension, regardless of resuspension method or testing surface. Here, the results provide impactful information regarding the different modes of resuspension including human transport (walking and driving) and wind for the resuspension of radionuclide surrogate particles deposited onto common surfaces and the effect of fire retardant application on their resuspension, leading to higher levels of safety for nuclear energy plants and facilities.

42 ENGINEERING↗
DOE OSTI2020

Conifer water use patterns in the East River Watershed, Colorado US, based on stable water isotopes and cellulose isotopes

This data package contains a series of datasets aimed at understanding the seasonal origins of water used by the dominant conifer species, Abies lasiocarpa and Picea engelmannii, in the East River Watershed. There is a distinct difference in the stable isotopic ratio of summer rain and snowpack and thus by tracking the isotopic ratio of the xylem water in trees, it is possible to separate when a tree is using snow vs. summer rain. Following typical protocols, we sampled and extracted water from soils in the "Research Meadow" near Gothic, CO within the East River Watershed through the 2016 growing season. The stable isotopic ratio of these waters were then measured. Using values of the isotopic ratio of precipitation and snowpack from that season, we develop a mixing model to show how the trees transitioned from use of snowpack to summer in late July. We extended the information on the seasonal origins of water from 2016 back until 1980 by using measurements of the isotopic ratio of cellulose from trees in the Research Meadow. The isotopic ratio of tree ring cellulose records a signal both of the Vapor Pressure Deficit at the leaf-atmosphere boundary and also the source water for the trees. We concluded from this extended analysis that the trees underwent transitions between periods where snow was the dominant water source and summer rain was the dominant water source. More details on this analysis can be found in the publication: Persistence and Plasticity in Conifer Water Use Strategies. JGR Biogeoscience. DOI 10.1029/2018JG004845. The dataset specifically includes stable isotope measurements of xylem and soil water for Spruce and Fir trees at the Rocky Mountain Biological Lab's Research Meadow as well as snowpack and precipitation stable isotopes from nearby National Atmospheric Deposition Program sites. Associated meteorological data is included, which is needed to understand the source water for the trees based on the cellulose measurements.This dataset was updated in June 2021 also include intra-ring measurements of the d18O and d13C of cellulose these same species of trees at the Ziegler Reservoir site in Snowmass, CO. These are listed as files: "Interglaciald18OCellulose.csv", "Interglaciald13OCellulose.csv" and "Modernd13CCellulose.csv". The intent of these measurements was to assess whether common conifers (abies and picea) had undergone ecophysiological or ecohydrological changes during the last period in Earth's history was warmer than today, i.e. the last Interglacial (116,000 to 130,000 years ago). This site was chosen because it is a location where subfossil wood from this critical warm period was discovered. We measured the d18O and d13C of the subfossil trees following the same protocol as the modern samples which included chemical extraction of cellulose and isotope ratio mass spectrometry. We found that the trees showed little difference in water use efficiency but utilized more summer rain during the interglacial period. This analysis and detailed methods are provided in the following manuscript: Wetter summers mitigated temperature stress on Rocky Mountain forests during the last interglacial warm period which is in revision at GRL but is available as a preprint here: 10.1002/essoar.10506740.2

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Canopy position influences the degree of light suppression of leaf respiration in abundant tree genera in the Amazon Forest

Leaf respiration in the dark (R dark ) and light (R day ) is poorly characterized in diverse tropical ecosystems, and little to no information exists on the degree of light suppression in common tree species within the Amazon basin, and their dependences upon plant functional traits and position within the canopy. We quantified R dark and apparent R day using the Kok method and measured key leaf traits in 26 tree individuals of different species distributed in three different canopy positions: canopy, lower canopy, and understory. To explore the relationships between the leaf traits we used the standardized major axis (SMA). We found that canopy trees had significantly higher rates of R dark and R day than trees in the understory. The difference between R dark and R day (the light suppression of respiration) was greatest in the understory (68 ± 9%, 95% CI) and lower canopy (49 ± 9%, 95% CI) when compared to the canopy (37 ± 10%, 95% CI). We also found that R day was significantly and strongly correlated with R dark (p < 0.001) for all the canopy positions. Also, leaf mass per area (LMA) and leaf Phosphorus concentration (P) had a significant relationship with R dark (p < 0.001; p = 0.003), respectively. In addition, a significant relationship was found for LMA in the canopy and lower canopy positions (p = 0.009; p = 0.048) while P was only significant in the canopy (p = 0.044). Finally, no significant relationship was found between R dark and nitrogen, sugars, and starch. Our results highlight the importance of including representation of the light suppression of leaf respiration in terrestrial biosphere models and also of accounting for vertical gradients within forest canopies and connections with functional traits.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

2015 Space Radiation Standing Review Panel

The 2015 Space Radiation Standing Review Panel (from here on referred to as the SRP) met for a site visit in Houston, TX on December 8 - 9, 2015. The SRP met with representatives from the Space Radiation Element and members of the Human Research Program (HRP) to review the updated research plan for the Risk of Radiation Carcinogenesis Cancer Risk. The SRP also reviewed the newly revised Evidence Reports for the Risk of Acute Radiation Syndromes Due to Solar Particle Events (SPEs) (Acute Risk), the Risk of Acute (In-flight) and Late Central Nervous System Effects from Radiation Exposure (CNS Risk), and the Risk of Cardiovascular Disease and Other Degenerative Tissue Effects from Radiation (Degen Risk), as well as a status update on these Risks. The SRP would like to commend Dr. Simonsen, Dr. Huff, Dr. Nelson, and Dr. Patel for their detailed presentations. The Space Radiation Element did a great job presenting a very large volume of material. The SRP considers it to be a strong program that is well-organized, well-coordinated and generates valuable data. The SRP commended the tissue sharing protocols, working groups, systems biology analysis, and standardization of models. In several of the discussed areas the SRP suggested improvements of the research plans in the future. These include the following: It is important that the team has expanded efforts examining immunology and inflammation as important components of the space radiation biological response. This is an overarching and important focus that is likely to apply to all aspects of the program including acute, CVD, CNS, cancer and others. Given that the area of immunology/inflammation is highly complex (and especially so as it relates to radiation), it warrants the expansion of investigators expertise in immunology and inflammation to work with the individual research projects and also the NASA Specialized Center of Research (NSCORs). Historical data on radiation injury to be entered into the Watson “big data” study must be used with caution. The general scientific issues of reproducibility, details of experimental methods and data analysis from preclinical and basic research laboratories have been raised broadly over the last few years (not specific to this work) and indicate that caution must be applied in the ways these data are used. This pertains to preclinical data and also to phase 3 clinical trials in radiation oncology and medical oncology. Of course, appropriate use and analysis of these “big-data” sets also offer the potential of pinpointing limitations and extracting remaining useful information. Emphasis should be placed on the latter possibility. A key target is risk reduction from radiation exposure. Progress of the entire space program, now moving towards the Mars mission, requires timely answers to key components of human risk, which are known to be complex. Periodic review of progress should be conducted with additional resources directed into achieving critical milestones. Turning the long red bars to yellow and green (or for some risks such as CNS possibly to grey) must be high priority. That such progress will require new science and not engineering means that it should be viewed in a knowledge-based light. The technology-based aspects of engineering issues are certainly as important, however, science and knowledge-based problems are solved in a different way than engineering. Timelines for engineering are more predictable, while for science, progress can be methodical with occasional major incremental findings that can rapidly change the rate of progress. As opportunities for rapid incremental changes arise, periodic enhancement of investment is strongly recommended to enable such new knowledge to be quickly and efficiently exploited. Collaborations and linkages with National Institute of Allergy and Infectious Diseases (NIAID), the Biomedical Advanced Research and Development Authority (BARDA) and the Department of Defense (DoD) are in place and more are encouraged, where possible, with the radiation injury and medical countermeasure studies. This could include utilizing some of their animal model testing contracts to facilitate obtaining results using common platforms. Such approach will facilitate the comparison of results among laboratories, and will facilitate and accelerate the development of medical countermeasures. It is particularly noteworthy that the NASA Space Radiation Element is reaching out to the Multidisciplinary European Low Dose Initiative (MELODI) platform coordinating low dose radiation risk research, and to other international agencies that are studying low dose radiation effects in an effort to fill the void generated by the cancelation of the Department of Energy (DOE) low dose radiation program. While NASA is working actively with NIAID and BARDA to integrate their relevant findings of radiation mitigator investigations to NASA programs, the committee notes its disappointment that the United States currently lacks a dedicated low dose radiation program with clear mechanistic orientation and aimed at the quantification and mitigation of human radiation risk on Earth. This void gives to the NASA Space Radiation Program Element special societal value, but also makes its overall design more challenging.

Steinberg, Susan↗
DOE OSTI2021

Detection of photon statistics and multimode field correlations by Raman processes

Glauber’s g (2) -function provides a common measure of quantum field statistics through two-photon coincidence counting in Hanbury Brown–Twiss measurements. Here, we propose to use nonlinear optical signals as a tool for the characterization of quantum light. In particular, we show that Raman measurements provide an alternative direct probe for a different component of the four-point correlation function underlying the g (2) -function. We illustrate this capacity for a specific quantum state obtained from a frequency conversion process. Our work points out how the analysis of controlled optical nonlinear processes can provide an alternative window toward the analysis of quantum light sources.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

First-collision source treatment for ray effect mitigation in discrete-ordinate radiation transport solutions

Deterministic transport codes play a fundamental role in the modelling and simulation of neutron transport. One of the most common deterministic methods is the method of discrete ordinates, also known as the S method. While offering significant advantages over other deterministic methods or stochastic methods like Monte Carlo, the method of discrete ordinates suffers from non-physical artifacts in its local solution due to its discretization of angle. These artifacts, referred to as ray effects because of their ray-like appearance, tend to be worse in problems with small sources in areas with little scattering. Significant effort has gone into developing methods to mitigate ray effects, such as the first-collision source treatment, which separates the angular flux into the uncollided and collided fluxes and solving them using non-traditional techniques such as ray tracing. One such code capable of doing this is Lawrence Livermore National Laboratory's deterministic transport code ARDRA. Current ray tracing methods typically trace to a set of points inside a zone to compute an overall flux. However, this approach has significant drawbacks, such as a low order of convergence and not being conservative. Therefore, a new method has been developed that traces instead to a set of points on each of a zone's surfaces and computing the currents, before using these to obtain the flux. A comparison between these two ray tracing methods showed significant advantages to the new surface method, including inherent conservation, a higher convergence rate, and an increase in calculable information like leakage. This work performed under the auspices of the U.S. Department of Energy by Lawrence Liver- more National Laboratory under Contract DE-AC52-07NA27344. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Future changes in extreme precipitation over the San Francisco Bay Area: Dependence on atmospheric river and extratropical cyclone events

Extreme precipitation poses a major challenge for local governments, including the City and County of San Francisco, California, as flooding can damage and destroy infrastructure and property. As the climate continues to warm, reliable future precipitation projections are needed to provide the best possible information to decision makers. However, future changes in the magnitude of extreme precipitation are uncertain, as current state-of-the-art global climate models are typically run at relatively coarse horizontal resolutions that require the use of convective parameterization and have difficulty simulating observed extreme rainfall rates. Here, we performed ensembles of convection-permitting regional climate model simulations to investigate how five historically impactful extreme precipitation events over the San Francisco Bay Area could change if similar events occurred in future climates. We found that changes in storm-total precipitation depend strongly on storm type. Precipitation associated with an atmospheric river (AR) accompanied by an extratropical cyclone (ETC) is projected to increase at a rate exceeding (by up to 1.5 times) the theoretical Clausius Clapeyron scaling of 6–7% per °C warming. On the other hand, future precipitation changes are weak or negative for events characterized by an AR only, despite increases in precipitable water and integrated vapor transport that are similar to those of the co-occurring AR and ETC events. The differences in the sign of future precipitation change between AR-only events and co-occurring AR and ETC events is instead linked with changes in mid-tropospheric vertical velocity. Given that the majority of observed ARs are associated with an ETC, this research has important implications for future precipitation impacts over the Bay Area, as it indicates that storm-total precipitation associated with the most common type of storm event may increase by up to 26–37% in 2100 relative to historical.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Roles for Countercharge in the Voltage Sensor Domain of Ion Channels

Voltage-gated ion channels share a common structure typified by peripheral, voltage sensor domains. Their S4 segments respond to alteration in membrane potential with translocation coupled to ion permeation through a central pore domain. The mechanisms of gating in these channels have been intensely studied using pioneering methods such as measurement of charge displacement across a membrane, sequencing of genes coding for voltage-gated ion channels, and the development of all-atom molecular dynamics simulations using structural information from prokaryotic and eukaryotic channel proteins. One aspect of this work has been the description of the role of conserved negative countercharges in S1, S2, and S3 transmembrane segments to promote sequential salt-bridge formation with positively charged residues in S4 segments. These interactions facilitate S4 translocation through the lipid bilayer. In this review, we describe functional and computational work investigating the role of these countercharges in S4 translocation, voltage sensor domain hydration, and in diseases resulting from countercharge mutations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗