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DOE OSTI2024

Distinct universality classes of diffusive transport from full counting statistics

The hydrodynamic transport of local conserved densities furnishes an effective coarse-grained description of the dynamics of a many-body quantum system. However, the full quantum dynamics contains much more structure beyond the simplified hydrodynamic description. Here we show that systems with the same hydrodynamics can nevertheless belong to distinct dynamical universality classes, as revealed by new classes of experimental observables accessible in synthetic quantum systems, which can, for instance, measure simultaneous site-resolved snapshots of all of the particles in a system. Specifically, we study the full counting statistics of spin transport, whose first moment is related to linear-response transport, but the higher moments go beyond. We present an analytic theory of the full counting statistics of spin transport in various integrable and nonintegrable anisotropic one-dimensional spin models, including the XXZ spin chain. We find that spin transport, while diffusive on average, is governed by a distinct non-Gaussian dynamical universality class in the models considered. We consider a setup in which the left and right half of the chain are initially created at different magnetization densities, and consider the probability distribution of the magnetization transferred between the two half-chains. We derive a closed-form expression for the probability distribution of the magnetization transfer, in terms of random walks on the half-line. We show that this distribution strongly violates the large-deviation form expected for diffusive chaotic systems, and explain the physical origin of this violation. Here, we discuss the crossovers that occur as the initial state is brought closer to global equilibrium. Our predictions can directly be tested in experiments using quantum gas microscopes or superconducting qubit arrays.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2004

Digitally Synthesized Phased Antenna for Multibeam Global Positioning

In a system according to the proposed technique, the signal received by each element of the array antenna would be subjected to downconversion, and spread-spectrum demodulation and correlation as necessary; this processing would be performed separately from, and simultaneously with, similar processing of signals received by the other antenna elements. For the GPS implementation, following downconversion to baseband, the signals would be digitized, and all subsequent processing would be digital. In In the digital process, residual carriers would be removed and each signal would be correlated with a locally generated model pseudo random-noise code, all following normal GPS procedure. As part of this procedure, accumulated values would be added in software and the resulting signals would be phase-shifted in software by the amounts necessary to synthesize the desired antenna directional gain pattern of peaks and nulls. The principal advantage of this technique over the conventional radio-frequency-combining technique is that the parallel digital baseband processing of the signals from the various antenna elements would be a relatively inexpensive and flexible means for exploiting the inherent multiple-peak/multiple-null aiming capability of a phased-array antenna. In the original intended GPS application, the peaks and nulls could be directed independently for each GPS signal being tracked by the GPS receiver. This will improve the SNR simultaneously for each GPS signal being tracked while steering multiple nulls toward sources of interference. The technique could also be applied to other code-division multiple-access communication systems.

Charles E Dunn↗

NASA NTRS2004

Digitally synthesized phased antenna for multibeam global positioning

In a system according to the proposed technique (see figure), the signal received by each element of the array antenna would be subjected to downconversion, and spread-spectrum demodulation and correlation as necessary; this processing would be performed separately from, and simultaneously with, similar processing of signals received by the other antenna elements. For the GPS implementation, following downconversion to baseband, the signals would be digitized, and all subsequent processing would be digital. In the digital process, residual carriers would be removed and each signal would be correlated with a locally generated model pseudo random-noise code, all following normal GPS procedure. As part of this procedure, accumulated values would be added in software and the resulting signals would be phase-shifted in software by the amounts necessary to synthesize the desired antenna directional gain pattern of peaks and nulls. The principal advantage of this technique over the conventional radio-frequency-combining technique is that the parallel digital baseband processing of the signals from the various antenna elements would be a relatively inexpensive and flexible means for exploiting the inherent multiple-peak/multiple-null aiming capability of a phased-array antenna. In the original intended GPS application, the peaks and nulls could be directed independently for each GPS signal being tracked by the GPS receiver. This will improve the SNR simultaneously for each GPS signal being tracked while steering multiple nulls toward sources of interference. The technique could also be applied to other code-division multiple-access communication systems.

Dunn, Charles E.↗

DOE OSTI2019

Relationship of left ventricular global longitudinal strain with cardiac autonomic denervation as assessed by {sup 123}I-mIBG scintigraphy in patients with heart failure with reduced ejection fraction submitted to cardiac resynchronization therapy

Background: Heart failure (HF) is associated with cardiac autonomic denervation (AD), which can be non-invasively assessed by {sup 123}I-metaiodobenzylguanidine ({sup 123}I-mIBG) scintigraphy and has prognostic implications. We aimed to study the relationship between myocardial contractility assessed by global longitudinal strain (GLS) and AD assessed by {sup 123}I-mIBG scintigraphy in advanced HF. Methods/Results: BETTER-HF is a prospective randomized clinical trial including HF patients (pts) submitted to cardiac resynchronization therapy (CRT) who are submitted to a clinical, echocardiographic, and scintigraphic assessment before and 6 months after CRT. 81 pts were included. An echocardiographic response (absolute increase in left ventricular ejection fraction ≥ 10%) was observed in 73.7% of pts. A higher baseline late heart-to-mediastinum ratio (HMR) was associated with a better echocardiographic response. There was a significant association between late HMR and GLS at baseline and 6 months. At baseline, GLS had an AUC of 0.715 for discrimination for a late HMR < 1.6. A GLS cut-off of − 9% maximized the likelihood of correctly classifying a pt as having severe AD (HMR < 1.6). Conclusion: Myocardial contractility as assessed by GLS is moderately correlated with AD as assessed by {sup 123}I-mIBG scintigraphy and has a good discrimination for the identification of severe cardiac denervation. GLS may allow for a more readily accessible estimation of the degree of AD in advanced HF pts.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

NASA NTRS2000

CLUST - EVAP Monte Carlo Simulation Applications for Determining Effective Energy Deposition in Silicon by High Energy Protons

The CLUST-EVAP is a Monte Carlo simulation of the interaction of high energy (25 - 400 MeV) protons with silicon nuclei. The initial nuclear cascade stage is modeled using the CLUST model developed by Indiana University over 30 years ago. The second stage, in which the excited nucleus evaporates particles in random directions, is modeled according to the evaporation algorithm provided by H. H. K. Tang of IBM. Using the CLUST-EVAP code to model fragment produ6tion and the Vavilov-Landau theory to model fluctuations in direct ionization in thin silicon layers, we have predicted energy deposition in silicon components for various geometrical configurations. We have compared actual measurements with model predictions for geometry's such as single, thin silicon particle detectors, telescopic particle detectors flown in space to measure the environment, and thin sensitive volumes of modern micro-electronic components. We have recently compared the model predictions with actual measurements made by the DOSTEL spectrometer flown in the Shuttle payload bay on STS-84. The model faithfully reproduces the features and aids in interpretation of flight results of this instrument. We have also applied the CLUST-EVAP model to determine energy deposition in the thin sensitive volumes of modern micro-electronic components. We have accessed the ability of high energy (200 MeV) protons to induce latch-up in certain devices that are known to latch up in heavy ion environments. However, some devices are not nearly as susceptible to proton induced latch-up as expected according to their measured heavy ion latch-up cross sections. The discrepancy is believed to be caused by the limited range of the proton-silicon interaction fragments. The CLUST-EV AP model was used to determine a distribution of these fragments and their range and this is compared to knowledge of the ranges required based on the known device structure. This information is especially useful in accessing the risk to on-orbit perfonnance in a heavy ion environment based on testing performed with only protons.

ONeill, Pat M.↗

DOE OSTI2026

Transit Rider/Travel Behavior Inventory Survey - Minneapolis-St. Paul Metro - 1990

The 1990 transit on-board survey aimed to update the 1988 survey, which was conducted as part of the Preliminary Engineering Study for the Hennepin County Light Rail Transit System. The 1988 study received Regional Transit Board funding, and survey results could be applied to a mode split model for projecting ridership on the proposed Light Rail Transit System. However, this survey was designed with the 1990 Travel Behavior Inventory in mind. The intention had been to update the 1988 survey in 1990 to be compatible with the 1990 Travel Behavior Inventory data. The results of the 1990 survey were used primarily to create a table of observed transit trips between each of the 1,200 traffic analysis zones in the region. This trip table was used to calibrate a new mode split model, which estimated future year travel by mode. The 1990 update survey focused on new routes and routes that had changed significantly since 1988. To preserve compatibility with the 1988 survey, the same survey questionnaire card was used, together with the same survey procedures for data collection. The procedures randomly sampled bus patrons during the transit trip, asking key questions about the patron and the transit trip. The survey card was intended for patrons to fill out quickly so it could be completed during the transit trip. The questions focused on conditions that have proven over time to significantly influence ridership. In all, a total of 20,126 valid survey records were processed. Adding surrogate trips, the survey data file is composed of a total of 27,159 trip records . About 10% of the records were filled out by persons who had answered more than one questionnaire.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Seroprevalence of Herpes Simplex Virus type-2 (HSV-2) among pregnant women who participated in a national HIV surveillance activity in Haiti

Background: Herpes simplex virus type 2 (HSV-2), one the most common causes of genital ulcers, appears to increase both the risk of HIV acquisition and HIV transmission. HSV-2/HIV co-infection among pregnant women may increase the risk of perinatal transmission of HIV. This study describes rates of HSV-2 among pregnant women in Haiti and HSV-2 test performance in this population. Methods: Unlinked residual serum specimens from the 2012 National HIV and Syphilis Sentinel Surveillance Survey among pregnant women in Haiti were tested using two commercial kits (Focus HerpeSelect, Kalon) for HSV-2 antibodies. We evaluated rates of HSV-2 seropositivity and HSV-2/HIV co-infection, associations between HSV-2 and demographic characteristics using multivariable Cox proportional hazards modeling, and HSV-2 test performance in this population. Results: Serum samples from 1000 pregnant women (all 164 HIV positive and 836 random HIV negative) were selected. The overall weighted prevalence of HSV-2 was 31.4% (95% CI: 27.7–35.4) and the prevalence of HIV-positivity among HSV2 positive pregnant women was five times higher than the prevalence among HSV-2 negative women (4.8% [95% CI: 3. 9–6.0] vs. 0.9% [95% CI: 0.6–1.3], respectively). Factors significantly associated with HSV-2 positivity were HIV-positivity (PR: 2.27 [95% CI: 1.94–2.65]) and older age (PRs: 1.41 [95% CI: 1.05–1.91] for 20–24 years, 1.71 [95% CI:1.13–2.60] for 30– 34 years, and 1.55 [95% CI: 1.10–2.19] for 35 years or greater]), while rural residence was negatively associated with HSV2 positivity (PR 0.83 [95% CI: 0.69–1.00]), after controlling for other covariables. For this study a conservative Focus index cutoff of 3.5 was used, but among samples with a Focus index value ≥2.5, 98.4% had positive Kalon tests. Conclusion: The prevalence of HSV-2 is relatively high among pregnant women in Haiti. Public health interventions to increase access to HSV-2 screening in antenatal services are warranted.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Rescattering photoelectron spectroscopy of the CO 2 molecule: Progress towards experimental discrimination between theoretical target-structure models

Photoelectron momentum distributions (PEMDs) generated in strong-field ionization of randomly oriented CO 2 molecules by intense infrared laser pulses at 1300-, 1450-, and 1650-nm wavelengths are measured experimentally and analyzed theoretically. Here, the experimental PEMDs extracted along the outermost backward rescattering caustic are well reproduced by theoretical calculations based on the recently derived factorization formula with an analytical returning photoelectron wave packet (RWP). The sensitivity of the theoretical results to the target structure models used in the calculations is investigated. It is shown that RWPs obtained in the single-active-electron (SAE) approximation and by the Hartree-Fock method have only minor differences. On the other hand, differential cross sections (DCSs) for elastic scattering of a photoelectron on the parent molecular ion calculated by ab initio , SAE, and independent-atom model methods are considerably different. This difference almost disappears after averaging over molecular orientations, so the present experiment does not enable us to discriminate between the different target structure models. However, we show that such a discrimination should become possible by measuring PEMD with aligned molecules. This will provide an access to the rich target structure information contained in the DCS.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2019

Computational experiments to characterize bubble formation and movement in waste glass foam layer

Computer simulation experiments are a valuable complement to physical experimentation for systems where there is limited access to quantities of interest. The motivation for this work is to characterize the fluid dynamic and heat transfer processes that affect melt rate of waste slurry during vitrification, which can limit the throughput rate of the melter. Heat transfer to the cold cap from the molten glass below this layer is an important factor determining melt rate. In this study, computational fluid dynamics is used to elucidate the behavior of gas-filled cavities that form underneath the cold cap in a laboratory-scale waste glass melter. The cold cap consists of a reacting feed layer above a foam layer. The cavities are a result of coalesced gases from collapsing primary foam and air from forced convection bubblers. Bubbles in the foam layer move horizontally with the circulation pattern of the melt and escape around the edges of the cold cap to the plenum above. The effects of varying thermophysical properties and foam parameters based on the bounds of high-level waste glass formulations were explored. Using the DAKOTA toolkit, a Latin hypercube space-filling design was used to randomly distribute samples in bins to achieve good coverage over the parameter space with minimal points. Using the results from the set of simulations, a Gaussian process model with a linear trend was used to create response surfaces for several key quantities of interest, including temperatures, temperature gradients, velocities, and foam extent beneath the cold cap.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

The Sixth Data Release of the Radial Velocity Experiment (RAVE). I. Survey Description, Spectra, and Radial Velocities

The Radial Velocity Experiment (Rave) is a magnitude-limited (9 < I < 12) spectroscopic survey of Galactic stars randomly selected in Earth’s southern hemisphere. The Rave medium-resolution spectra (R ~ 7500) cover the Ca-triplet region (8410–8795 Å). The sixth and final data release (DR6) is based on 518,387 observations of 451,783 unique stars. Rave observations were taken between 2003 April 12 and 2013 April 4. Here we present the genesis, setup, and data reduction of Rave as well as wavelength-calibrated and flux-normalized spectra and error spectra for all observations in Rave DR6. Furthermore, we present derived spectral classification and radial velocities for the Rave targets, complemented by cross-matches with Gaia DR2 and other relevant catalogs. A comparison between internal error estimates, variances derived from stars with more than one observing epoch, and a comparison with radial velocities of Gaia DR2 reveals consistently that 68% of the objects have a velocity accuracy better than 1.4 km s{sup –1}, while 95% of the objects have radial velocities better than 4.0 km s{sup –1}. Stellar atmospheric parameters, abundances and distances are presented in a subsequent publication. The data can be accessed via the Rave website (http://rave-survey.org) or the Vizier database.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

From laser particle acceleration to the synthesis of extremely neutron rich isotopes via the novel fission-fusion mechanism

High-power, short pulse lasers have emerged in the last decade as attractive tools for accelerating charged particles (electrons, ions) to high energies over mm-scale acceleration lengths, thus promising to rival conventional acceleration techniques in the years ahead. In the first part of the article, the principles of laser-plasma interaction as well as the techniques and the current status of the acceleration of electron and ion beams will be briefly introduced. In particular with the upcoming next generation of multi-PW class laser systems, such as the one under construction for the ELI-Nuclear Physics project in Bucharest (ELI-NP), very efficient acceleration mechanisms for brilliant ion beams like radiation pressure acceleration (RPA) come into reach. Here, ultra-dense ion beams reaching solid-state density can be accelerated from thin target foils, exceeding the density of conventionally accelerated ion beams by about 14 orders of magnitude. This unique property of laser-accelerated ion beams can be exploited to explore the scenario of a new reaction mechanism called ‘fission-fusion’, which will be introduced in the second part of the article. Accelerating fissile species (e.g. {sup 232}Th) towards a second layer of the same material will lead to fission both of the beam-like and target-like particles. Due to the close to solid-state density of the accelerated ion bunches, fusion may occur between neutron-rich (light) fission products. This may open an access path towards extremely neutron-rich nuclides in the vicinity of the N=126 waiting point of the astrophysical r process. ‘Waiting points’ at closed nucleon shells play a crucial role in controlling the reaction rates. However, since most of the pathway of heavy-element formation via the rapid-neutron capture process (r-process) runs in ‘terra incognita’ of the nuclear landscape, in particular the waiting point at N=126 is yet unexplored and will remain largely inaccessible to conventional nuclear reaction schemes even at next-generation radioactive beam facilities, underlining the attractive perspectives offered, e.g., by ELI-NP.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Effect of microstructure and temperature on nuclear graphite oxidation using the 3D Random Pore Model

This work compared the 3D Random Pore Model (3D-RPM) with experimental and characterization data to systematically study the effect of nuclear graphite microstructure and air oxidation temperature. It is well known that oxidation-induced weight loss at elevated temperatures degrades graphite structure and properties; however, a fundamental understanding of the role of graphite microstructure is still unclear. In this work, three diverse grades of nuclear graphite—IG-110, NBG-18, and PCEA—were examined and tested at air oxidation temperatures of 600, 650, 700, and 750 °C following ASTM D7542. Here, the 3D-RPM reproduced the microstructure and temperature dependence of mass loss curves for these grades. For the first time, measurements and modeling were combined to show how bulk density and the amount and spatial distribution of open and closed porosity affects oxidation. IG-110 was less dense and had an open pore network that is finer and more uniformly spread, and showed fastest oxidation. PCEA porosity was less uniform and showed less oxidation, while NBG-18 was more dense, had the least fine and uniform porosity, and showed the slowest oxidation. In general, oxidation proceeds faster if open porosity is more uniformly distributed and can incrementally access closed pore surface area with more ease.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data

Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management. Without accessibility to other data sources, the onsite observed temperatures or the airport temperatures are used in forecast models. In this paper, we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations (PWS) to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework. The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs. Our approach is distinguished from existing literature in various aspects. First, we leverage the crowdsourcing weather data from PWS in addition to public data sources. In this way, the data is at much finer time resolution (e.g., at 5-minute frequency) and spatial resolution (e.g., arbitrary location vs grid). Second, our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance. Here, we demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio, Texas, USA. In general, the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50% compares with persistent model and has 90% chance to outperform airport forecast in short-term forecasting. In a real-time setting, the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon. Moreover, we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance. Finally, we implement our model in other cities such as Syracuse and Chicago to test the model's performance in different landforms and climate types.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Using Deep Learning to Automate Inference of Meteoroid Pre-Entry Properties

Properly assessing the asteroid threat depends on the knowledge of asteroid pre-entry parameters, such as size, velocity, mass, density, and strength. Although a vast number of possible bodies to study exist, such characterization of asteroid populations is currently limited by substantial costs associated with space rendezvous missions and rare meteorite findings. As asteroids fragment, ablate, and decelerate in the atmosphere, they emit light detectable by ground-based and space-borne instruments. Earth’s atmosphere, thus, becomes an accessible laboratory that enables impactor risk assessments by facilitating inference of the pre-entry parameters. These asteroid pre-entry conditions are typically deduced by modeling the entry and breakup physics that best reproduce the observed light or energy deposition curve. However, this process requires extensive manual trial-and-error of uncertain modeling parameters. Automating meteor modeling and inference would improve property distributions used in risk assessments and enable population characterization as more light curves become more readily available through the presence of space assets and ground-based camera networks. We previously developed a genetic algorithm to automate meteor modeling by using the fragment-cloud model (FCM) to search for the values of the FCM input parameters (e.g., diameter) that generate energy deposition profiles that match the observed one. Now, we apply deep learning to infer asteroid diameter, velocity, and density from observed energy deposition curves. We trained and tested our neural network models with synthetic energy deposition curves modeled using the FCM rubble pile implementation. We present an application of a 1D convolutional neural network and compare its performance to other attempted regressors and machine learning techniques, such as a fully connected neural network and Random Forest regression, to demonstrate its capabilities. We validate our model weights and approach using the Chelyabinsk, Tagish Lake, Benešov, Košice, and Lost City meteors.

Tarano, Ana Maria↗

DOE OSTI2020

The Sixth Data Release of the Radial Velocity Experiment (Rave). II. Stellar Atmospheric Parameters, Chemical Abundances, and Distances

We present part 2 of the sixth and final Data Release (DR6) of the Radial Velocity Experiment (Rave), a magnitude-limited (9<I<12) spectroscopic survey of Galactic stars randomly selected in Earth’s southern hemisphere. The Rave medium-resolution spectra (R ∼ 7500) cover the Ca triplet region (8410–8795 Å) and span the complete time frame from the start of Rave observations on 2003 April 12 to their completion on 2013 April 4. In the second of two publications, we present the data products derived from 518,387 observations of 451,783 unique stars using a suite of advanced reduction pipelines focusing on stellar atmospheric parameters, in particular purely spectroscopically derived stellar atmospheric parameters (T{sub eff}, logg, and the overall metallicity), enhanced stellar atmospheric parameters inferred via a Bayesian pipeline using Gaia DR2 astrometric priors, and asteroseismically calibrated stellar atmospheric parameters for giant stars based on asteroseismic observations for 699 K2 stars. In addition, we provide abundances of the elements Fe, Al, and Ni, as well as an overall [α/Fe] ratio obtained using a new pipeline based on the GAUGUIN optimization method that is able to deal with variable signal-to-noise ratios. The Rave DR6 catalogs are cross-matched with relevant astrometric and photometric catalogs, and are complemented by orbital parameters and effective temperatures based on the infrared flux method. The data can be accessed via the Rave website (http://rave-survey.org) or the Vizier database.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Permafrost Thaw, Uneven Subsidence and Projected Drying of Ice-wedge Polygon Tundra: Modeling Archive

This dataset is a model archive of the paper Permafrost Thaw, Uneven Subsidence and Projected Drying of Ice-wedge Polygon Tundra (in prep) to support a modeling study investigating how projected increases in Arctic temperature and precipitation will jointly influence hydrologic conditions in ice-rich tundra landscapes. With this dataset, this study is to address the research question: Will Arctic tundra landscapes become wetter or drier with increasing precipitation and temperature in the future when thaw-induced ground subsidence and associated microtopographic evolution are represented? The simulations focus on ice-wedge polygon tundra, a widespread form of ice-rich permafrost terrain that is highly sensitive to thaw-driven landscape change. This dataset contains model input and output data for four study watersheds in Alaska: Anaktuvuk, Utqiagvik (formerly Barrow), Brooks Foothills, and Prudhoe Bay. Simulations were performed using the Advanced Terrestrial Simulator (ATS, v1.5), a physics-rich integrated surface–subsurface hydrologic model. For each watershed, ten modeling cases were performed representing two landscape evolution conditions (with subsidence and without subsidence) combined with five climate forcing scenarios derived from Shared Socioeconomic Pathways (SSP5, SSP5 with precipitation trend, SSP2, SSP2 with precipitation trend, and SSP2 with double precipitation trend). Particularly, for each watershed under the forcing SSP2 with precipitation trend, there are two additional simulations considering spatially heterogeneous subsidence distributions: one assumes randomly distributed scaling and the other includes elevation dependent distribution scaling. These simulations span 1980–2099 and include spin-up runs (1980–2009) followed by transient projections (2010–2099). To facilitate reproducibility of simulations, all datasets are organized by watershed. For each study watershed, the dataset contains: (1) Pre-partitioned mesh files for 32-core modeling (.par.32.XX), located in EACH_WATERSHED/mesh/basin; and also a non-partitioned mesh file (.exo) located in EACH_WATERSHED/mesh; (2) Climate forcings corresponding to the five SSP scenarios (.h5), located in EACH_WATERSHED/data; (3) Final states (.h5) from column spin-up modeling used to initialize historical watershed-scale spin-up runs from 1980 to 2009, located in EACH_WATERSHED/PreSpinupHistorical; (4) Final states (.h5) of historical watershed-scale spin-up runs from 1980 to 2009 used to initialize projection runs, located in EACH_WATERSHED/Spinup_daymetERA5; (5) ATS modeling input files (.xml), located in EACH_WATERSHED/EACH_SIMULATION_SCENARIO/inputfiles; (6) ATS modeling output files (.dat), located in in EACH_WATERSHED/EACH_SIMULATION_SCENARIO/combined_obs; (7) For the Brooks Foothills watershed, additional spatial model outputs are provided (.h5) for selected years (2033 and 2093) used to generate spatial figures in this study, located in Brooksfoothills/EACH_SIMULATION_SCENARIO/results-WITH/WITHOUT_SUBSIDENCE-year2033/2093. All data files with suffix .h5 can be accessible through Python h5py, and all data files with suffix of .dat can be imported by Python pandas. Mesh file with .exo can be visualized through Paraview or read by Python netCDF. 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.

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION↗

DOE OSTI2011

Opinions of fisheries researchers, managers, and anglers towards recreational fishing issues: an exploratory analysis for North America

There is a need to better understand the perspectives of various recreational fishing stakeholder groups regarding key issues related to fisheries sustainability. To provide a first snapshot and to inform future human dimension studies in this area, we distributed a Web-based open-access survey to fisheries researchers, fisheries managers, and anglers in North America. Attitudes of these respondents towards issues such as overharvest, impacts of catch and release, recreational fisheries management, and research priorities for the future were assessed. We found similar opinions and perspectives by the responding recreational anglers, managers,and researchers on a number of issues, such as the perceived impact of commercial fishing contributing to fish stock declines, the perceived importance of using and promoting gear that minimizes stress and injury to individual fish when fish are to be released, and the belief that conflicts among stakeholders is growing as is the global anti-fishing movement based on animal rights thinking. Differences among responding groups included that researchers tended to be more concerned than anglers and managers with the potential of recreational angling contributing to fish stock declines. Responding anglers were also less content with their involvement in the fisheries management process than were responding managers and researchers, and these anglers also indicated a greater desire for more human dimensions research on understanding angler attitudes and behavior than was evident for responding managers and researchers. This preliminary survey revealed some variation in attitudes among recreational fisheries stakeholders. However, due to lack of random sampling, the study results cannot be extrapolated to the population level. We nevertheless conclude that improved communication and better understanding about the different perspectives among fisheries researchers, managers, and anglers and intrasectorally among different angling groups are needed, particularly when addressing contentious issues of relevance for the entire recreational fishing sector.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

How Should Machine Learning Be Successfully Used for Wind Speed Vertical Extrapolation?

An accurate characterization of the wind resource available at hub-height is required for an efficient and bankable wind farm project. However, direct measurement of wind speed at the constantly increasing height of the hub of commercial wind turbines is oftentimes challenging and expensive, so that it is common practice to vertically extrapolate the wind resource from lower and more easily accessible levels. Conventional techniques for wind speed vertical extrapolation include the use of a power law and a logarithmic profile. While simple, the limits in accuracy of these methods have been shown in various studies. Recently, machine learning has been proposed as a new method to vertically extrapolate winds. All the published studies on the topic assess the performance of machine learning techniques in vertically extrapolating the wind resource at the same location where the algorithm has been trained. However, in real-world applications, the wind resource is measured at the instrument location, but it then needs to be extrapolated at hub height at the location of the wind turbines within the find farm. To be able to fully recommend the use of machine learning techniques over the simple power law and logarithmic law, the spatial variability of the performance improvements of the machine learning approaches needs to be assessed. Here, we propose a round-robin validation of a machine learning-based method for wind speed extrapolation. We use 20 months of observations at four locations spanning a 100 km wide region at the Southern Great Plains (SGP) atmospheric observatory, in north-central Oklahoma. At each location, we train a random forest to predict 30-min average wind speed at 143 m AGL. We use as input features lidar wind speed at 65 m AGL, time of day, sonic anemometer wind speed at 4 m AGL, turbulent kinetic energy, and Obukhov length. First, we perform a same-site comparison of the performance of the proposed random forest against the conventional techniques for wind speed extrapolation (namely power law and logarithmic profile, with widely accepted stability corrections). We find that the random forest outperforms the power law in vertically extrapolating wind speed in all the considered stability regimes, with a 33% reduction in MAE for stable conditions, and a 31% reduction in unstable conditions. Similar results are found when comparing predictions of extrapolated winds from the logarithmic profile and the random forest with the observed values. Next, we propose a round-robin validation, to use the random forest trained at each site to extrapolate wind speed at the remaining three sites. We find that the performance of the random forest approach degrades when the algorithm is tested at a site different than the training one. However, even under those circumstances, the machine learning-based approach still outperforms the conventional techniques for wind speed extrapolation, with, on average, a reduction in mean absolute error between 15 and 20% over the conventional methods, with the largest benefits obtained under stable conditions.

Monte Carlo↗