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DOE OSTI2016

2% Yield Increase (HH2), All Energy Crops scenario of the 2016 Billion Ton Report

Scientific reason for data generation: to serve as an alternate high-yield scenario for the BT16 volume 1 agricultural scenarios to compare these projections of potential biomass supplies against a reference case (agricultural baseline 10.11578/1337885). The simulation runs from 2015 through 2040; a starting year of 2014 is used but not reported. Date the data set was last modified: 02/02/2016 How each parameter was produced (methods), format, and relationship to other data in the data set: This exogenous price simulations (also referred to as “specified-price” simulations) introduces a farmgate price, and POLYSYS solves for biomass supplies that may be brought to market in response to these prices. In specified-price scenarios, a specified farmgate price is offered constantly in all counties over all years of the simulation. This simulation begins in 2015 with an offered farmgate price for primary crop residues only between 2015 and 2018 and long-term contracts for dedicated crops beginning in 2019. Expected mature energy crop yield grows at a compounding rate of 2% beginning in 2016. The yield growth assumptions are fixed after crops are planted such that yield gains do not apply to crops already planted, but new plantings do take advantage of the gains in expected yield growth. Instruments used: Policy Analysis System –POLYSYS (version POLYS2015_V10_alt_JAN22B), an agricultural policy modeling system of U.S. agriculture (crops and livestock), supplied by the University of Tennessee Institute of Agriculture, Agricultural Policy Analysis Center.

09 BIOMASS FUELS↗

DOE OSTI2016

3% Yield Increase (HH3), All Energy Crops scenario of the 2016 Billion Ton Report

Scientific reason for data generation: to serve as an alternate high-yield scenario for the BT16 volume 1 agricultural scenarios to compare these projections of potential biomass supplies against a reference case (agricultural baseline 10.11578/1337885). The simulation runs from 2015 through 2040; a starting year of 2014 is used but not reported. Date the data set was last modified: 02/02/2016 How each parameter was produced (methods), format, and relationship to other data in the data set: This exogenous price simulations (also referred to as “specified-price” simulations) introduces a farmgate price, and POLYSYS solves for biomass supplies that may be brought to market in response to these prices. In specified-price scenarios, a specified farmgate price is offered constantly in all counties over all years of the simulation. This simulation begins in 2015 with an offered farmgate price for primary crop residues only between 2015 and 2018 and long-term contracts for dedicated crops beginning in 2019. Expected mature energy crop yield grows at a compounding rate of 3% beginning in 2016. The yield growth assumptions are fixed after crops are planted such that yield gains do not apply to crops already planted, but new plantings do take advantage of the gains in expected yield growth. Instruments used: Policy Analysis System –POLYSYS (version POLYS2015_V10_alt_JAN22B), an agricultural policy modeling system of U.S. agriculture (crops and livestock), supplied by the University of Tennessee Institute of Agriculture, Agricultural Policy Analysis Center.

09 BIOMASS FUELS↗

DOE OSTI2016

4% Yield Increase (HH4), All Energy Crops scenario of the 2016 Billion Ton Report

Scientific reason for data generation: to serve as an alternate high-yield scenario for the BT16 volume 1 agricultural scenarios to compare these projections of potential biomass supplies against a reference case (agricultural baseline 10.11578/1337885). The simulation runs from 2015 through 2040; a starting year of 2014 is used but not reported. Date the data set was last modified: 02/02/2016. How each parameter was produced (methods), format, and relationship to other data in the data set: This exogenous price simulations (also referred to as “specified-price” simulations) introduces a farmgate price, and POLYSYS solves for biomass supplies that may be brought to market in response to these prices. In specified-price scenarios, a specified farmgate price is offered constantly in all counties over all years of the simulation. This simulation begins in 2015 with an offered farmgate price for primary crop residues only between 2015 and 2018 and long-term contracts for dedicated crops beginning in 2019. Expected mature energy crop yield grows at a compounding rate of 4% beginning in 2016. The yield growth assumptions are fixed after crops are planted such that yield gains do not apply. Instruments used: Policy Analysis System –POLYSYS (version POLYS2015_V10_alt_JAN22B), an agricultural policy modeling system of U.S. agriculture (crops and livestock), supplied by the University of Tennessee Institute of Agriculture, Agricultural Policy Analysis Center.

09 BIOMASS FUELS↗

DOE OSTI2013

IL-12Rβ2 is critical for survival of primary Francisella tularensis LVS infection

Abstract IL-12Rβ2 is critical in parenteral and mucosal host resistance to primary Francisella tularensis LVS infection, and controls liver damage. Using a panel of vaccines that provided different degrees of protection, we previously identified the IL-12 receptor subunit β2 as a mediator, whose relative expression correlated with strength of protection against secondary lethal challenge of vaccinated mice with an intracellular bacterium, the LVS of Francisella tularensis. The present study therefore tested the hypothesis that IL-12Rβ2 is an important mediator in resistance to LVS by directly examining its role during infections. IL-12Rβ2 KO mice were highly susceptible to LVS primary infection, administered i.d. or i.n. The LD50 of LVS infection of KO mice were 2 logs lower than those of WT mice, regardless of route. Five days after infection with LVS, bacterial organ burdens were significantly higher in IL-12Rβ2 KO mice. IL-12Rβ2 KO mice infected with lethal doses of LVS had more severe liver pathology, including significant increases in the liver enzymes ALT and AST. Despite decreased levels of IFN-γ, LVS-vaccinated IL-12Rβ2 KO mice survived large lethal LVS secondary challenge. Consistent with in vivo protection, in vitro intramacrophage LVS growth was well-controlled in cocultures containing WT or IL-12Rβ2 KO LVS-immune splenocytes. Thus, survival of secondary LVS challenge was not strictly dependent on IL-12Rβ2. However, IL-12Rβ2 is important in parenteral and mucosal host resistance to primary LVS infection and in the ability of WT mice to clear LVS infection and serves to restrict liver damage.

Melillo, Amanda A.↗

DOE OSTI2015

Novel apo E-derived ABCA1 agonist peptide (CS-6253) promotes reverse cholesterol transport and induces formation of preβ-1 HDL in vitro

Apolipoprotein (apo) mimetic peptides replicate some aspects of HDL function. We have previously reported the effects of compound ATI-5261 on its ability to replicate many functions of native apo A-I in the process of HDL biogenesis. ATI-5261 induced muscle toxicity in wild type C57Bl/6 mice, increased CPK, ALT and AST and increase in triglyceride (Tg) levels. Aromatic phenylalanine residues on the non-polar face of ATI-5261, together with positively charged arginine residues at the lipid-water interface were responsible for these effects. This information was used to create a novel analog (CS-6253) that was non-toxic. We evaluated this peptide designed from the carboxyl terminus of apo E, in its ability to mimic apo A-I functionality. Our data shows that the lipidated particles generated by incubating cells overexpressing ABCA1 with lipid free CS-6253 enhances the rate of ABCA1 lipid efflux with high affinity interactions with native ABCA1 oligomeric forms and plasma membrane micro-domains. Interaction between ABCA1 and lipid free CS-6253 resulted in formation of nascent HDL-CS-6253 particles that are actively remodeled in plasma. Mature HDL-CS-6253 particles deliver cholesterol to liver cells via SR-BI in-vitro . CS-6253 significantly increases cholesterol efflux in murine macrophages and in human THP-1 macrophage-derived foam cells expressing ABCA1. Addition of CS-6253 to plasma dose-dependently displaced apo A-I from α-HDL particles and led to de novo formation of preβ-1 HDL that stimulates ABCA1 dependent cholesterol efflux efficiently. When incubated with human plasma CS-6253 was also found to bind with HDL and LDL and promoted the transfer of cholesterol from HDL to LDL predominantly. Our data shows that CS-6253 mimics apo A-I in its ability to promote ABCA1-mediated formation of nascent HDL particles, and enhances formation of preβ-1 HDL with increase in the cycling of apo A-I between the preβ and α-HDL particles in-vitro . These mechanisms are potentially anti-atherogenic.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

New Platform Provides Innovation

Sky Bridge is the primary unclassified scientific computing platform at Sandia, serving all Mission Areas within the Laboratories. At the formal ribbon cutting in December 2014, Laboratories Director Paul Hommert emphasized the importance of the Institutional Computing Program in providing a critical core Laboratories capability. The deployment of Sky Bridge more than doubled the computing cycles available in Sandia’s restricted computing environment, while incorporating the same underlying architecture as the existing ASC/TLCC2 compute clusters, thus allowing many existing computer codes to run on the system without any major modifications. Sky Bridge immediately addressed the shortfall in projected computing cycles for Sandia’s programs of energy and environment, nuclear weapons life expectancy program (NW LEP), and Applied Laboratory Technology (ALT). From a technological perspective, Sky Bridge is contributing to the readiness of the data center facility and operations personnel for a transition to liquid cooling in future HPC systems and other high-density systems. By comparing the performance and energy costs of Sky Bridge to similar systems (e.g., Dark Bridge) that are air-cooled rather than liquid-cooled, it will also provide valuable data for making return on investment decisions for future purchases. Installation of Sky Bridge enables decommissioning of the Red Sky supercomputer, reduce energy and support costs, and make room for future HPC systems already planned through the ASC and Institutional Computing programs. Sky Bridge’s use of liquid cooling (cold plate to the systems processors) and more efficient power distribution (480 V direct to the rack) saved over $\$$700, 000 in construction costs while reducing annual operating costs by $\$$120,000.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

The Dark Energy Spectrographic Instrument (DESI) Guide-Focus Assembly

The specific research of the DESI project is to study in detail the expansion history of the Universe over the past 10 billion years. In order to do this, the project designed, fabricated, tested and commissioned the DESI instrument which has been deployed at the Mayall Telescope at the Kitt Peak National Observatory near Tucson, Arizona. The DESI project was conducted by approximately 30 US and foreign national laboratories. Our CRADA with the The Institut de Física d’Altes Energies (IFAE) in Barcelona (Spain) involved both research planning for eventual observing on the DESI telescope; and in providing hardware jointly designed by LBNL and IFAE; ten guide and focus sensor systems (GFAs) that either provide feedback to the Mayall telescope tracking system or provide focus position information to the DESI corrector hexapod. The key major goals of this CRADA were achieved.For survey planning, Drs. Fernandez, Fosalba, Garcia-Bellido, and Gaztañaga were all important contributors to the working groups to which they were assigned. Their application of redshift techniques, development of simulations to support target selection, exploration of the use of alternative observables to extract cosmological information like AP and void statistics, and the use of PAU data to assist in the optimization of targeting selections have been vital in the early planning for targets during commissioning. The major role in the project involved Drs. Castander, Miquel, Padilla, and Sànchez. They were all directly involved with the development of the Guide-Focus Assembly (GFA) hardware, software and filter validation. In order to accomplish the goals of the DESI Project, the Mayall telescope must track DESI targeted galaxies for up to 15 minutes and the DESI corrector must be repositioned on its hexapod system for different telescope orientations. The guide and focus assemblies (GFA) provide in this information. There are ten GFA modules on the focal plane, six providing tracking information and four providing focus information. A common GFA design supports both functions by a simple switching of its operating mode. IFAE designed, fabricated, tested and delivered twelve GFAs. Once received at LBNL, the GFAs were integrated with the focal plane. This was an iterative process as the GFA’s were delivered as they were made. Our working relationship with the IFAE scientists and engineers was remarkable given the distance and time zone issues. The GFAs have performed extremely well during the commissioning phase of the instrument.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Ductile Fracture - Gradient Regularization

Nuclear weapons alteration (ALT) and life extension programs (LEP) are of primary interest to the mission of Sandia National Laboratories. These programs continue to require experimental exploration and computational simulation of ductile failure scenarios to address qualification. Therefore, we invest in generating understanding about ductile failure as demonstrated though experimental procedures and computational simulation of engineering environments. In particular, we study an approach to ductile failure that incorporates the notion of phase-field fracture into our models of inelasticity appropriate for structural alloys. This report covers the formulations of the constitutive model and fracture models used within the phase-field approach and provides some numerical examples highlighting features and the state of the capability.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

Pan-Arctic Permafrost Subsidence [Slides]

The Arctic is warming twice as fast as the rest of the globe, resulting in dramatic loss of ice across the Arctic system. Models project 7% to 90% of permafrost could be lost by the end of the century. Permafrost thaw is driving landscape degradation and infrastructure disruption through thermokarst and thermal erosion. Previous pan-arctic modeling shows potential for significant subsidence (Lee et al. 2014). CLM4.5 pan-Arctic simulations from PCNMIP were used to calculate change in projected ALT from 2010-2030.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2010

Electron beam-based sources of ultrashort x-ray pulses.

A review of various methods for generation of ultrashort x-ray pulses using relativistic electron beam from conventional accelerators is presented. Both spontaneous and coherent emission of electrons is considered. The importance of the time-resolved studies of matter at picosecond (ps), femtosecond (fs), and atttosecond (as) time scales using x-rays has been widely recognized including by award of a Nobel Prize in 1999 [Zewa]. Extensive reviews of scientific drivers can be found in [BES1, BES2, BES3, Lawr, Whit]. Several laser-based techniques have been used to generate ultrashort x-ray pulses including laser-driven plasmas [Murn, Alte, Risc, Rose, Zamp], high-order harmonic generation [Schn, Rund, Wang, Arpi], and laser-driven anode sources [Ande]. In addition, ultrafast streak-camera detectors have been applied at synchrotron sources to achieve temporal resolution on the picosecond time scale [Wulf, Lind1]. In this paper, we focus on a different group of techniques that are based on the use of the relativistic electron beam produced in conventional accelerators. In the first part we review several techniques that utilize spontaneous emission of electrons and show how solitary sub-ps x-ray pulses can be obtained at existing storage ring based synchrotron light sources and linacs. In the second part we consider coherent emission of electrons in the free-electron lasers (FELs) and review several techniques for a generation of solitary sub-fs x-ray pulses. Remarkably, the x-ray pulses that can be obtained with the FELs are not only significantly shorter than the ones considered in Part 1, but also carry more photons per pulse by many orders of magnitude.

43 PARTICLE ACCELERATORS↗

DOE OSTI2019

Self-Doped Conjugated Polymeric Binders Improve the Capacity and Mechanical Properties of V 2 O 5 Cathodes

Polymeric binders serve to stabilize the morphology of electrodes by providing adhesion and binding between the various components. Successful binders must serve multiple functions simultaneously, including providing strong adhesion, improving conductivity, and providing electrochemical stability. A tradeoff between mechanical integrity and electrochemical performance in binders for lithium-ion batteries is one of the many challenges of improving capacity and performance. In this paper, we demonstrate a self-doped conjugated polymer, poly(9,9-bis(4'-sulfonatobutyl)fluorene-alt-co-1,4-phenylene) (PFP), which not only provides mechanical robustness but also improves electrode stability at temperatures as high as 450 °C. The self-doped PFP polymer is comprised of a conjugated polyfluorene backbone with sulfonate terminated side-chains that serve to dope the conjugated polymer backbone, resulting in stable conductivity. Composite electrodes are prepared by blending PFP with V 2 O 5 in water, followed by casting and drying. Structural characterization with X-ray diffraction and wide-angle X-ray scattering shows that PFP suppresses the crystallization of V 2 O 5 at high temperatures (up to 450 °C), resulting in improved electrode stability during cycling and improved rate performance. This study demonstrates the potential of self-doped conjugated polymers for use as polymeric binders to enhance mechanical, structural, and electrochemical properties.

36 MATERIALS SCIENCE↗

DOE OSTI2024

The Most Distant H i Galaxies Discovered by the 500 m Dish FAST

Abstract Neutral hydrogen (Hi) is the primary component of the cool interstellar medium (ISM) and is the reservoir of fuel for star formation. Owing to the sensitivity of existing radio telescopes, our understanding of the evolution of the ISM in galaxies remains limited, as it is based on only a few hundred galaxies detected in Hibeyond the local Universe. With the high sensitivity of the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we carried out a blind Hisearch, the FAST Ultra-Deep Survey, which extends to redshifts up to 0.42 and a sensitivity of 50μJy beam −1 . Here, we report the first discovery of six galaxies in Hi atz> 0.38. For these galaxies, the FAST angular resolution of ∼4′ corresponds to a mean linear size of ∼ 1.3 h 70 − 1 Mpc. These galaxies are among the most distant Hiemission detections known, with one having the most massive Hicontent ( 10 10.93 ± 0.04 h 70 − 2 M ⊙ ). Using recent data from the DESI survey and new observations with the Hale, Big Telescope Alt-azimuth, and Keck telescopes, optical counterparts are detected for all galaxies within the 3σpositional uncertainty ( 0.5 h 70 − 1 Mpc) and 200 km s −1 in recession velocity. Assuming that the dominant source of Hiis the identified optical counterpart, we find evidence of evolution in the Hicontent of galaxies over the last 4.2 Gyr. Our new high-redshift Higalaxy sample provides the opportunity to better investigate the evolution of cool gas in galaxies. A larger sample size in the future will allow us to refine our knowledge of the formation and evolution of galaxies.

Astronomy & Astrophysics↗

DOE OSTI2016

Eyes can See What Mind Can’t see

A 55 year old Caucasian female with a history of Iron deficiency anemia with hemoglobin of 6.1 gm/dl was admitted from her hematologist office for a Venofer infusion. She had profound confusion on presentation. Physical examination was pertinent for fever of 101οF, and laboratory data pertinent for elevated blood sugars of 590 mg/dl, leukocytosis of 10.3 K/UL with bandemia of 13%, elevated AST 170 U/L, ALT 184 U/L, and normal alkaline phosphate and total bilirubin. She was subsequently found to have Strep. Viridian bacteremia and CT scan of Chestabdomen- pelvis revealed multiple hepatosplenic masses consistent with malignancy or abscesses. Liver biopsy showed benign liver cells with a background of acute and chronic inflammatory cells and abscess (*image shown). She was treated with long term course of Ceftriaxone via PICC line.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

Simulated high-latitude soil thermal dynamics during the past 4 decades

Soil temperature ( T s ) change is a key indicator of the dynamics of permafrost. On seasonal and interannual timescales, the variability of T s determines the active-layer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing T s not only drives permafrost thaw/retreat but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960–2000, to characterize the warming rate of T s in permafrost regions. There is a large spread of T s trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 °C yr -1 . Most models show smaller increase in T s with increasing depth. Air temperature ( T sub>a ) and longwave downward radiation (LWDR) are the main drivers of T s trends, but their relative contributions differ amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in T s trends, while trends of T a only explain 5 % of the differences in T s trends. Uncertain climate forcing contributes a larger uncertainty in T s trends (0.021 ± 0.008 °C yr -1 , mean ± standard deviation) than the uncertainty of model structure (0.012 ± 0.001 °C yr -1 ), diagnosed from the range of response between different models, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active-layer thickness (ALT) is less than 3 m loss rate, is found to be significantly correlated with the magnitude of the trends of T s at 1 m depth across the models ( R = -0.85, P = 0.003), but not with the initial total near-surface permafrost area ( R = -0.30, P = 0.438). The sensitivity of the total boreal near-surface permafrost area to T s at 1 m is estimated to be of -2.80 ± 0.67 million km 2 °C -1 . Finally, by using two long-term LWDR data sets and relationships between trends of LWDR and T s across models, we infer an observation-constrained total boreal near-surface permafrost area decrease comprising between 39 ± 14 × 10 3 and 75 ± 14 × 10 3 km 2 yr -1 from 1960 to 2000. This corresponds to 9–18 % degradation of the current permafrost area.

58 GEOSCIENCES↗

DOE OSTI2016

Effect of soil property uncertainties on permafrost thaw projections: A calibration-constrained analysis

Here, the effect of soil property uncertainties on permafrost thaw projections are studied using a three-phase subsurface thermal hydrology model and calibration-constrained uncertainty analysis. The Null-Space Monte Carlo method is used to identify soil hydrothermal parameter combinations that are consistent with borehole temperature measurements at the study site, the Barrow Environmental Observatory. Each parameter combination is then used in a forward projection of permafrost conditions for the 21$^{st}$ century (from calendar year 2006 to 2100) using atmospheric forcings from the Community Earth System Model (CESM) in the Representative Concentration Pathway (RCP) 8.5 greenhouse gas concentration trajectory. A 100-year projection allows for the evaluation of intra-annual uncertainty due to soil properties and the inter-annual variability due to year to year differences in CESM climate forcings. After calibrating to borehole temperature data at this well-characterized site, soil property uncertainties are still significant and result in significant intra-annual uncertainties in projected active layer thickness and annual thaw depth-duration even with a specified future climate. Intra-annual uncertainties in projected soil moisture content and Stefan number are small. A volume and time integrated Stefan number decreases significantly in the future climate, indicating that latent heat of phase change becomes more important than heat conduction in future climates. Out of 10 soil parameters, ALT, annual thaw depth-duration, and Stefan number are highly dependent on mineral soil porosity, while annual mean liquid saturation of the active layer is highly dependent on the mineral soil residual saturation and moderately dependent on peat residual saturation. By comparing the ensemble statistics to the spread of projected permafrost metrics using different climate models, we show that the effect of calibration-constrained uncertainty in soil properties, although significant, is less than that produced by structural climate model uncertainty for this location.

58 GEOSCIENCES↗

DOE OSTI2015

Effect of soil property uncertainties on permafrost thaw projections: A calibration-constrained analysis

The effect of soil property uncertainties on permafrost thaw projections are studied using a three-phase subsurface thermal hydrology model and calibration-constrained uncertainty analysis. The Null-Space Monte Carlo method is used to identify soil hydrothermal parameter combinations that are consistent with borehole temperature measurements at the study site, the Barrow Environmental Observatory. Each parameter combination is then used in a forward projection of permafrost conditions for the 21st century (from calendar year 2006 to 2100) using atmospheric forcings from the Community Earth System Model (CESM) in the Representative Concentration Pathway (RCP) 8.5 greenhouse gas concentration trajectory. A 100-year projection allows for the evaluation of intra-annual uncertainty due to soil properties and the inter-annual variability due to year to year differences in CESM climate forcings. After calibrating to borehole temperature data at this well-characterized site, soil property uncertainties are still significant and result in significant intra-annual uncertainties in projected active layer thickness and annual thaw depth-duration even with a specified future climate. Intra-annual uncertainties in projected soil moisture content and Stefan number are small. A volume and time integrated Stefan number decreases significantly in the future climate, indicating that latent heat of phase change becomes more important than heat conduction in future climates. Out of 10 soil parameters, ALT, annual thaw depth-duration, and Stefan number are highly dependent on mineral soil porosity, while annual mean liquid saturation of the active layer is highly dependent on the mineral soil residual saturation and moderately dependent on peat residual saturation. By comparing the ensemble statistics to the spread of projected permafrost metrics using different climate models, we show that the effect of calibration-constrained uncertainty in soil properties, although significant, is less than that produced by structural climate model uncertainty for this location.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

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 (°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 “0” 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 “time warp” 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, 1046–1053, 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, 2503–2524, 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, 100–103. 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., 278–283. 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↗

DOE OSTI2015

Calibrated Hydrothermal Parameters, Barrow, Alaska, 2013

A model-observation-experiment process (ModEx) is used to generate three 1D models of characteristic micro-topographical land-formations, which are capable of simulating present active layer thickness (ALT) from current climate conditions. Each column was used in a coupled calibration to identify moss, peat and mineral soil hydrothermal properties to be used in up-scaled simulations. Observational soil temperature data from a tundra site located near Barrow, AK (Area C) is used to calibrate thermal properties of moss, peat, and sandy loam soil to be used in the multiphysics Advanced Terrestrial Simulator (ATS) models. Simulation results are a list of calibrated hydrothermal parameters for moss, peat, and mineral soil hydrothermal parameters drawn from literature and field observations at the NGEE-Arctic site. Find more details in the associated publication by Atchley, et.al, 2015.There is one data file as both a .xlsx and .csv. The datafile was transformed into a csv for interoperability, however some information might have been lost in the transformation.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗