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Exact relativistic expressions for polarization of incoherent Thomson scattering

We present a derivation of the degree of polarization for incoherent Thomson scattering (TS) using Mueller matrix formalism. An exact analytic solution is obtained for spectrum-integrated matrix elements. The solution is valid for the full range of incident polarizations, scattering angles, and electron thermal motion from non-relativistic to ultra-relativistic. It is based on a newly developed theoretical model, a finite transit time (FTT) correction to previous theoretical work on TS polarization. The Mueller matrix elements are substantially different from previous calculations without the FTT correction, even to the lowest linear order in T e =m e c 2 <<1. Mathematically, the derivation is a unique example of fully analytical integration of the 3D scattering operator over a relativistic Maxwellian distribution function; experimentally, the results have application to the use of the polarization properties of Thomson scattered light as a method of electron temperature measurement. The results can also be used as a reliable tool for benchmarking and verification of numerical codes for frequency resolved properties of TS polarization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

GALACTIC COSMIC-RAY MODULATION IN A REALISTIC GLOBAL MAGNETOHYDRODYNAMIC HELIOSPHERE

To understand the behavior of cosmic-ray modulation seen by the two Voyager spacecraft in the region near the termination shock (TS) and in the heliosheath at a distance of >100 AU, a realistic magnetohydrodynamic global heliosphere model is incorporated into our cosmic-ray transport code, so that the detailed effects of the heliospheric boundaries and their plasma/magnetic geometry can be revealed. A number of simulations of cosmic-ray modulation performed with this code result in the following conclusions. (1) Diffusive shock acceleration by the TS can significantly affect the level of cosmic-ray flux and, in particular, its radial gradient profile in the region near the TS and in the inner heliosheath. (2) The radial profile of cosmic-ray flux strongly depends on longitude. There is a slight north-south asymmetry due to an asymmetric TS, but the larger difference in the radial profiles comes from longitudinal variation. Voyager 1 and 2 are separated by {approx} 40 Degree-Sign in longitude. Simulations in these two directions show a large difference in the radial profile of cosmic-ray flux. Thus, it is not appropriate to determine the cosmic-ray radial gradient by directly using the two-point Voyager measurements. Various other simulations are also performed to show how sensitively the modulation level depends on latitude, cosmic-ray energy, and interstellar spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗

Final Technical Report for GO17004 Regulatory Logic: Codes and Standards for the Hydrogen Economy

The objectives of this project are to: develop a robust supporting research and development program to provide critical hydrogen behavior data and a detailed understanding of hydrogen combustion and safety across a range of scenarios, needed to establish setback distances in building codes and minimize the overall data gaps in code development; support and facilitate the completion of technical specifications by the International Organization for Standardization (ISO) for gaseous hydrogen refueling (TS 20012) and standards for on-board liquid (ISO 13985) and gaseous or gaseous blend (ISO 15869) hydrogen storage by 2007; support and facilitate the effort, led by the NFPA, to complete the draft Hydrogen Technologies Code (NFPA 2) by 2008; with experimental data and input from Technology Validation Program element activities, support and facilitate the completion of standards for bulk hydrogen storage (e.g., NFPA 55) by 2008; facilitate the adoption of the most recently available model codes (e.g., from the International Code Council [ICC]) in key regions; complete preliminary research and development on hydrogen release scenarios to support the establishment of setback distances in building codes and provide a sound basis for model code development and adoption; support and facilitate the development of Global Technical Regulations (GTRs) by 2010 for hydrogen vehicle systems under the United Nations Economic Commission for Europe, World Forum for Harmonization of Vehicle Regulations and Working Party on Pollution and Energy Program (ECE-WP29/GRPE); and to Support and facilitate the completion by 2012 of necessary codes and standards needed for the early commercialization and market entry of hydrogen energy technologies.

08 HYDROGEN↗

Hydrogen Quantitative Risk Assessment

DOE has identified consistent safety, codes, and standards as a critical need for the deployment of hydrogen technologies, with key barriers related to the availability and implementation of technical information in the development of regulations, codes, and standards. Advances in codes and standards have been enabled by risk-informed approaches to create and implement revisions to codes, such as National Fire Protection Association (NFPA) 2, NFPA 55, and International Organization for Standardization (ISO) Technical Specification (TS)-19880-1. This project provides the technical basis for these revisions, enabling the assessment of the safety of hydrogen fuel cell systems and infrastructure using QRA and physics-based models of hydrogen behavior. The risk and behavior tools that are developed in this project are motivated by, shared directly with, and used by the committees revising relevant codes and standards, thus forming the scientific basis to ensure that code requirements are consistent, logical, and defensible.

08 HYDROGEN↗

Hydrogen Quantitative Risk Assessment (Annual Progress Report)

DOE has identified consistent safety, codes, and standards as a critical need for the deployment of hydrogen technologies, with key barriers related to the availability and implementation of technical information in the development of regulations, codes, and standards. Advances in codes and standards have been enabled by risk-informed approaches to create and implement revisions to codes, such as National Fire Protection Association (NFPA) 2, NFPA 55, and International Organization for Standardization (ISO) Technical Specification (TS)-19880-1. This project provides the technical basis for these revisions, enabling the assessment of the safety of hydrogen fuel cell systems and infrastructure using QRA and physics-based models of hydrogen behavior. The risk and behavior tools that are developed in this project are motivated by, shared directly with, and used by the committees revising relevant codes and standards, thus forming the scientific basis to ensure that code requirements are consistent, logical, and defensible.

08 HYDROGEN↗

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (&deg;C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a &ldquo;0&rdquo; if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the &ldquo;time warp&rdquo; period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046&ndash;1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503&ndash;2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100&ndash;103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278&ndash;283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

Challenges of SLOWPOKE-2 HEU Irradiated Fuel Transportation from Canada to USA - 19123(2)

The SLOWPOKE-2 (Safe Low-Power Kritical Experiment) was designed by AECL (Atomic Energy of Canada Limited) in the late 1960's. The core consists of HEU (highly enriched uranium) fuel with an enrichment of 93%. The fuel cage typically has approximately 300 fuel pins assembled in a triangular pitch between the two plates. The fuel pins are made from uranium-aluminum alloy cladded with aluminum. The SLOWPOKE-2 reactor is rated at a nominal 20 kW. with varying neutron flux, maximum being 1 E12 n/cm{sup 2}/s. The mass of the fuel cage is 5.06 kg. The life of the core can be extended by adding layers of beryllium reflector. The transportation flask for the irradiated fuel is a Type B(U)F. The shipment classification is UN3328 Class 7. To date, 4 SLOWPOKE-2 reactors have been decommissioned. The fuel from all these reactors was transported to the U.S. The CNSC (Canadian Nuclear Safety Commission) joint Canada-United States guide for approval of Type B(U) and fissile material transportation packages (RD-364) satisfies the requirements stipulated in International Atomic energy Agency (IAEA) TS-R-1 1996 edition (revised) and is acceptable to the DOT (Department of Transportation) and NRC (Nuclear Regulatory Commission) for complying with U.S. code of federal regulations 10 CFR Part 71. The export license is issued by the CNSC. The transportation licence is issued by CNSC after approval from TC (Transport Canada) and also by DOT after approval from NRC. The final home of the HEU SLOWPOKE-2 irradiated fuel is in Savannah River Site of DOE (Department of Energy). The transportation licence requires that the Type B(U)F flask is structurally sound in case of a crash. The US DOT provides a CAC (Competent Authority Certificate) which describes the irradiated fuel specifications that can be transported in the designated flask. The U.S. DOT also requires a leak test certification in accordance with ANSI N14.5-2014 to verify a leakage rate not to exceed 1 E-7 std-cm{sup 3}/s. As most SLOWPOKE-2 facilities are not located on the existing approved routes for nuclear fuel transportation, a route survey needs to be performed and submitted to the NRC. The route survey report is assessed by the NRC and issues a route approval for the transportation of the fissile material. This technical paper will discuss the detailed processes required to obtain approvals in order to transport the HEU SLOWPOKE-2 irradiated fuel from Canada to U.S. based upon recent experiences of decommissioning SLOWPOKE-2 reactors in Canada. It will provide details on the requirements of various regulators in Canada and the U.S. that govern and regulate this shipment including transportation, safety and security. It will also present lessons learned from previous experiences that can be utilized for best practice on other similar projects. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Spectral broadening measurement of the lower hybrid waves during long pulse operation in Tore Supra

On many tokamaks (C-Mod, EAST, FTU, JET, HT-7, TS), a decrease in current drive efficiency of the Lower Hybrid (LH) waves is observed in high electron density plasmas. The cause of this behaviour is believed to be: Parametric Instabilities (PI) and Scattering from Density Fluctuations (SDF). For the ITER LH system, our knowledge must be improved to avoid such effects and to maintain the LH current drive efficiency at high density. The ITPA IOS group coordinates this effort [1] and all experimental data are essential to validate the numerical codes in progress. Usually the broadening of the LH wave frequency spectrum is measured by a probe located in the plasma edge. For this study, the frequency spectrum of a reflected power signal from the LH antenna was used. In addition, the spectrum measurements are compared with the density fluctuations observed on RF probes located at the antenna mouth. Several plasma currents (0.6 to 1.4 MA) and densities up to 5.2 × 10{sup 19} m−3 have been realised on Tore Supra (TS) long pulses and with high injected RF power, up to 5.4 MW-30s. This allowed using a spectrum analyser to make several measurements during the plasma pulse. The side lobe amplitude, shifted by 20-30MHz with respect to the main peak, grows with increasing density. Furthermore, for an increase of plasma current at the same density, the spectra broaden and become asymmetric. Some parametric dependencies are shown in this paper.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Calculation of the temperature in the container unit with a modified design for the production of {sup 99}Mo at the VVR-Ts research reactor facility (IVV.10M)

The temperature regime is calculated for two different designs of containers with uranium-bearing material for the upgraded VVR-Ts research reactor facility (IVV.10M). The containers are to be used in the production of {sup 99}Mo. It is demonstrated that the modification of the container design leads to a considerable temperature reduction and an increase in the near-wall boiling margin and allows one to raise the amount of material loaded into the container. The calculations were conducted using the international thermohydraulic contour code TRAC intended to analyze the technical safety of water-cooled nuclear power units.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

EGS Collab Experiment 2: Shear Stimulation ERT Monitoring Data

This repository contains the electrical resistivity tomography (ERT) monitoring data that was collected before, during, and after shear stimulation attempts were conducted at EGS Collab. These tests were carried out on the SURF 4100 level during Experiment 2 in March, 2022. Flow and stimulation data corresponding to this dataset are available in a different GDR dataset, which is linked below. Also included here are the E4D input files that were used to process the ERT data. E4D is an open source ERT monitoring and inversion code which is linked for use below.

15 GEOTHERMAL ENERGY↗

Energy Exascale Earth System Model v2.0.1

First patch release of v2.0.0 Changes since v2.0.0 [Important change] Fix ocean threading bug seen in debug cases on Chrysalis with Intel 20.0.4. Was introduced around time of v2.0.0 tag. Does not change v2.0.0 answers on Chrysalis because those didn't use threading or debugging. [EAM] Add semi-lagrangian tracer transport for theta-l (F90 and C++), add new algorithm for finding tropopause, add DSCREAM to allow v2 and SCREAM settings in same code such as adjust_ps [EAM-MMF] 60L default, allow C++ back end of RRTMGP (EAM too). [EAMxx] add nu-top functionality, fix forcing functor, add ttype9 and dcmip2012 tests 2.1, 2.2, and 3 HOMME: remove obsolete remap algs, option to specify dynamics alg indep of tracer, new sponge layer, add imex tests [ELM] Add topography-based subgrid (topounits), add FATES-ELM Nitro., Phos. and CH4 coupling, add land-use ts for NARRM, add lulc for SSP3 RCP7, Fix nutrient fertilization exp test and carbon isotope flux, Fix xactive lnd dry deposition, add lake water storage option, fix plant hydraulics 2d params, fix carbon budget calc, fix soil nutrient conc. bug, fix mosart dam bug, add test for new ELM, MOSART features, fix bug in O3 dry dep stomatal resistances, fix plant hydraulics restart BFB error, update mkmapdata. [MOSART] fix bug for reading the latitude from an unstructured input file, fix oversat in bubble test. [MPAS-ocean] Add CFC11, CFC12 tracers, add 2D spherical transport tests, fix del4 tracer mixing, add MARBL ocean tracer mixing, modify harmonic analysis options, add GPU port of vmix routines, fix calc of ML-averaged BV freq. [MPAS-seaice] Change extents of initial polar disks for oRRS18to6v3 grid, fix ice BGC with MARBL, update spherical test cases, fix DON coupling, Remove Cf from sea ice constants. [MPAS-landice] add CRYO1850-4xCO2 compset [CIME] add GCP, ANL GCE, Spock, Perlmutter, deprecate config_compilers.xml, fix and clean-up cmake macros, fix slurm bindings, refactor CIME internal testing, cleanup SCORPIO perf data, allow position independent compset naming, [also] update v2 benchmarking suite, extend e3sm_prod with throughput and memory checks

E3SM Project, DOE↗

Multispecies Hypersonic Flow Simulator

MHYSA is a conservative finite-difference code to solve the 1D, 2D, and 3D multispecies Navier-Stokes equations for hypersonic flow applications. It supports Cartesian grids only and has the option of including immersed boundaries. - Solves 1D Euler and 2D and 3D Navier-Stokes equations. - Solves the PDEs over Cartesian grids. - Written entirely in C and uses the MPICH library. It also uses OpenMP threads but this is a work-inprogress. - Can be compiled with PETSc (http://www.mcs.anl.gov/petsc/), if available, where it can use PETSc's time integration module TS (http://www.mcs.anl.gov/petsc/petsc-current/src/ts/). - For 3-dimensional simulations, the immersed boundaries can be used to solve over non-Cartesian geometries. MHYSA has been developed to be scalable, and apart from the usual functionalities to solve a system of PDEs on distributed architectures, it provides scalable file I/O functions.

Ghosh, Debojyoti↗

Modeling SARS-CoV-2 proteins in the CASP-commons experiment

Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS-CoV-2 genome. Forty-seven research groups submitted over 3000 three-dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure-based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).

59 BASIC BIOLOGICAL SCIENCES↗

Domain Specific Language Support for Exascale Application Development

The University of Oregon has produced a Fortran parser based on an SDF (Syntax Definition Formalism) grammar. Primary partners in this effort were Rice University and Lawrence Livermore National Laboratory. The developed Fortran parser incorporates the full Fortran 2008 standard with extensions from the more recent document, TS 29113, “Further Interoperability of Fortran with C,” (to be included in the Fortran 2018 standard). In the final year of the project, the Fortran parser was updated to comply with the complete 2018 standard, as specified in the ISO/IEC document, JTC 1/SC 22/WG 5/N2146. The research explored in this project provided the expression of the Fortran grammar in SDF form for integration with the Rosebud DSL framework developed by Rice University. The Rosebud framework is a cohesive interface allowing modular DSL plugins to be developed independently and then distributed and combined in HPC applications. These plugins implement a form of generalized DSL which includes both concrete languages used to write application source code and abstract languages used within the compilation process. ROSE’s predefined DSL Building Blocks (for example the Fortran base language) are represented as abstract language plugins and DSL developers can use Rosebud to construct additional building blocks in the same way.

97 MATHEMATICS AND COMPUTING↗

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025

This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: v{x}-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_v{x}-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_v{x}-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_v{x}-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_v{x}-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗