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DOE OSTI2017

MARCUS ARM CN and CCN data reprocessed to remove ship exhaust influence

The data presented in this collection are the cloud condensation nuclei (CCN) and condensation nuclei (CN) from the MARCUS (Measurements of Aerosols, Radiation and CloUds over the Southern Oceans) campaign. This campaign involved the deployment of the ARM Mobile Facility aboard the RSV Aurora Australis between October 2017 and March 2018 during its summer season resupplying Australia’s Antarctic research stations from its port in Hobart, Australia. Routine processing undertaken by ARM does not remove platform exhaust associated with its deployment aboard ships. Consequently, the raw data were obtained and manually filtered to remove the influence of platform exhaust on the measurements by reviewing CCN, CN and BC data simultaneously and flagging all affected periods. Remaining data were then quality controlled for when instrument parameters were out of specification. CCN data are split into different streams for supersaturation settings before statistics for each hour being calculated. All data files contain NaN values for periods when data was removed for QC or exhaust reasons. Original raw data available at: Kulkarni, G. R., Flynn, C. and Koontz, A.: Atmospheric Radiation Measurement (ARM) user facility. 2017, updated hourly. Cloud Condensation Nuclei Particle Counter (AOSCCN100). 2017-10-29 to 2018-03-24, ARM Mobile Facility (MAR) Hobart, AUS to Antarctic Coast - resupply ship Aurora Australis; AMF2 (M1)., Atmospheric Radiation Measurement (ARM) user facility Data Center: Oak Ridge, Tennessee, USA [online] Available from: http://dx.doi.org/10.5439/1227964 (Accessed 9 November 2018), 2018. Kuang, C., Salwen, C., Boyer, M. and Singh, A.: Atmospheric Radiation Measurement (ARM) user facility. 2017, updated hourly. Condensation Particle Counter (AOSCPCF). 2017-10-29 to 2018-03-26, ARM Mobile Facility (MAR) Hobart, AUS to Antarctic Coast - resupply ship Aurora Australis; AMF2 (M1)., Atmospheric Radiation Measurement (ARM) user facility Data Center: Oak Ridge, Tennessee, USA [online] Available from: http://dx.doi.org/10.5439/1046184 (Accessed 9 November 2018), 2018.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Reprocessed MPL data sets

This dataset contains reprocessed micropulse lidar (MPL) data (equivalent to the ARM cloud mask VAP's lidar fields) which were carefully corrected for each site. These reprocessed data were used in Silber et al. (JGR, 2018). The data extend up to a height of 15 km and have vertical and temporal resolutions of 15 m and 30 s, respectively.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Final results on the $$0\nu \beta \beta $$ decay half-life limit of $$^{100}$$Mo from the CUPID-Mo experiment

Abstract The CUPID-Mo experiment to search for 0 $$\nu \beta \beta $$ ν β β decay in $$^{100}$$ 100 Mo has been recently completed after about 1.5 years of operation at Laboratoire Souterrain de Modane (France). It served as a demonstrator for CUPID, a next generation 0 $$\nu \beta \beta $$ ν β β decay experiment. CUPID-Mo was comprised of 20 enriched $$\hbox {Li}_{{2}}$$ Li 2 $$^{100}$$ 100 $$\hbox {MoO}_4$$ MoO 4 scintillating calorimeters, each with a mass of $$\sim 0.2$$ ∼ 0.2 kg, operated at $$\sim 20$$ ∼ 20 mK. We present here the final analysis with the full exposure of CUPID-Mo ( $$^{100}$$ 100 Mo exposure of 1.47 $$\hbox {kg} \times \hbox {year}$$ kg × year ) used to search for lepton number violation via 0 $$\nu \beta \beta $$ ν β β decay. We report on various analysis improvements since the previous result on a subset of data, reprocessing all data with these new techniques. We observe zero events in the region of interest and set a new limit on the $$^{100}$$ 100 Mo 0 $$\nu \beta \beta $$ ν β β decay half-life of $$T_{1/2}^{0\nu }$$ T 1 / 2 0 ν $$> {1.8}\times 10^{24}$$ > 1.8 × 10 24 year (stat. + syst.) at 90% CI. Under the light Majorana neutrino exchange mechanism this corresponds to an effective Majorana neutrino mass of $$\left $$ m β β $$<~{(0.28{-}0.49)} $$ < ( 0.28 - 0.49 ) eV, dependent upon the nuclear matrix element utilized.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

The Role of Premagmatic Rifting in Shaping a Volcanic Continental Margin: An Example From the Eastern North American Margin

Both magmatic and tectonic processes contribute to the formation of volcanic continental margins. Such margins are thought to undergo extension across a narrow zone of lithospheric thinning (~100 km). New observations based on existing and reprocessed data from the Eastern North American Margin contradict this hypothesis. With ~64,000 km of 2-D seismic data tied to 40 wells combined with published refraction, deep reflection, receiver function, and onshore drilling efforts, we quantified along-strike variations in the distribution of rift structures, magmatism, crustal thickness, and early post-rift sedimentation under the shelf of Baltimore Canyon Trough (BCT), Long Island Platform, and Georges Bank Basin (GBB). Results indicate that BCT is narrow (80–120 km) with a sharp basement hinge and few rift basins. The seaward dipping reflectors (SDR) there extend ~50 km seaward of the hinge line. In contrast, the GBB is wide (~200 km), has many syn-rift structures, and the SDR there extend ~200 km seaward of the hinge line. Early post-rift depocenters at the GBB coincide with thinner crust suggesting “uniform” thinning of the entire lithosphere. Models for the formation of volcanic margins do not explain the wide structure of the GBB. We argue that crustal thinning of the BCT was closely associated with late syn-rift magmatism, whereas the broad thinning of the GBB segment predated magmatism. Correlation of these variations to crustal terranes of different compositions suggests that the inherited rheology determined the premagmatic response of the lithosphere to extension.

58 GEOSCIENCES↗

DOE OSTI2018

Performance of the Belle II Conditions Database

The Belle II experiment at KEK observed its first collisions in the summer of 2018. Processing the large amounts of data that will be produced requires conditions data to be readily available to systems worldwide in a fast and efficient manner that is straightforward for both the user and maintainer. This was accomplished by relying on industry-standard tools and methods: the conditions database is built as an HTTP REST service using tools such as Swagger for the API interface development, Payara for the Java EE application server, and Squid for the caching proxy. This article presents the design of the Belle II conditions database production environment as well as details about the capabilities and performance during both Monte Carlo campaigns and data reprocessing.

Wood, Lynn S.↗

DOE OSTI2017

Stability and scalability of the CMS Global Pool: Pushing HTCondor and glideinWMS to new limits

The CMS Global Pool, based on HTCondor and glideinWMS, is the main computing resource provisioning system for all CMS workflows, including analysis, Monte Carlo production, and detector data reprocessing activities. The total resources at Tier-1 and Tier-2 grid sites pledged to CMS exceed 100,000 CPU cores, while another 50,000 to 100,000 CPU cores are available opportunistically, pushing the needs of the Global Pool to higher scales each year. These resources are becoming more diverse in their accessibility and configuration over time. Furthermore, the challenge of stably running at higher and higher scales while introducing new modes of operation such as multi-core pilots, as well as the chaotic nature of physics analysis workflows, places huge strains on the submission infrastructure. This paper details some of the most important challenges to scalability and stability that the CMS Global Pool has faced since the beginning of the LHC Run II and how they were overcome.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Machine Learning Approaches for Nuclear Material Accounting Data from Irradiation and Reprocessing

We are currently exploring data analysis methods for their ability to strengthen the synthesis and evaluation of information generated within domestic and international safeguard regimes. Safeguard data typically includes rich heterogenous datasets amenable to advanced data analytics methods, such as machine learning. We have converted transactional data containing both numerical and categorical attributes from a domestic nuclear material control and accountability (NMC&A) system into a low-dimensional numerical structure via linear principal component analysis. This data representation allows for global structure discovery via cluster analysis, which can characterize the typical behavior of each of the primary types of transaction events. Furthermore, the structure of the top principal components captures the “typical behavior” of the data and is thus amenable to anomaly detection through statistical hypothesis testing. We explored this capability by generating erroneous permutations of the data and computing the Q-residual quantity increase associated with the information loss when these data are projected into the low-dimensional principal component analysis space representative of typical transactions.Future work will focus on identifying data transformations (e.g., graph networks) that more closely align with the inherent structure of these transactions to explain more salient information and disambiguate the underlying nuclear process—in this case, irradiation and reprocessing—from artifacts of NMC&A system transactional record keeping data.

Drescher, Adam↗

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 OSTI2018

Automated data processing architecture for the Gemini Planet Imager Exoplanet Survey

The Gemini Planet Imager Exoplanet Survey (GPIES) is a multiyear direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines (DRPs) together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our DRPs. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Accelerating Machine Learning Inference with GPUs in ProtoDUNE Data Processing

Abstract We study the performance of a cloud-based GPU-accelerated inference server to speed up event reconstruction in neutrino data batch jobs. Using detector data from the ProtoDUNE experiment and employing the standard DUNE grid job submission tools, we attempt to reprocess the data by running several thousand concurrent grid jobs, a rate we expect to be typical of current and future neutrino physics experiments. We process most of the dataset with the GPU version of our processing algorithm and the remainder with the CPU version for timing comparisons. We find that a 100-GPU cloud-based server is able to easily meet the processing demand, and that using the GPU version of the event processing algorithm is two times faster than processing these data with the CPU version when comparing to the newest CPUs in our sample. The amount of data transferred to the inference server during the GPU runs can overwhelm even the highest-bandwidth network switches, however, unless care is taken to observe network facility limits or otherwise distribute the jobs to multiple sites. We discuss the lessons learned from this processing campaign and several avenues for future improvements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

First ARM Aerosol Chemical Speciation Monitor Users’ Meeting Report

The first U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility aerosol chemical speciation monitor (ACSM) users’ meeting was held on April 11-13, 2017, at Aerodyne Research, Inc. in Billerica, Massachusetts, to discuss the Southern Great Plains (SGP) atmospheric observatory ACSM data quality and establish best practices for data collection and processing. The participants examined six years of calibration and processed data. Specific issues raised by data users were addressed and case studies from two field experiments were examined. The most recent data from the ACSM installed in the newly commissioned SGP ARM Mobile Facility 7 (AMF7) were also evaluated. The participants recommended that the SGP ACSM data be reprocessed using calibration values averaged over the history of SGP ACSM calibrations. They also recommended that the data quality be evaluated by comparing (1) observed versus predicted particulate ammonium (NH4+) mass loadings, and (2) ACSM mass loadings versus mass loadings calculated from particle size and light scattering data. The contents of the datastreams from the ACSM were defined based on ARM requirements and the necessary tasks to implement these recommendations were assigned to the mentor and the instrument manufacturer. Some of the recommendations were implemented for the SGP data and are presented in this report. Data from two ARM sites located in marine environments were also analyzed. These analyses as well as instrument intercomparisons have led to further questions about data treatment. These will be addresses in the second users’ meeting scheduled for the summer of 2018.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

First ARM Aerosol Chemical Speciation Monitor Users’ Meeting Report

The first U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility aerosol chemical speciation monitor (ACSM) users’ meeting was held on April 11-13, 2017, at Aerodyne Research, Inc. in Billerica, Massachusetts, to discuss the Southern Great Plains (SGP) atmospheric observatory ACSM data quality and establish best practices for data collection and processing. The participants examined six years of calibration and processed data. Specific issues raised by data users were addressed and case studies from two field experiments were examined. The most recent data from the ACSM installed in the newly commissioned SGP ARM Mobile Facility 7 (AMF7) were also evaluated. The participants recommended that the SGP ACSM data be reprocessed using calibration values averaged over the history of SGP ACSM calibrations. They also recommended that the data quality be evaluated by comparing (1) observed versus predicted particulate ammonium (NH 4 + ) mass loadings, and (2) ACSM mass loadings versus mass loadings calculated from particle size and light scattering data. The contents of the datastreams from the ACSM were defined based on ARM requirements and the necessary tasks to implement these recommendations were assigned to the mentor and the instrument manufacturer. Some of the recommendations were implemented for the SGP data and are presented in this report. Data from two ARM sites located in marine environments were also analyzed. These analyses as well as instrument intercomparisons have led to further questions about data treatment. These will be addresses in the second users’ meeting scheduled for the summer of 2018.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

First ARM Aerosol Chemical Speciation Monitor Users’ Meeting Report

The first U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility aerosol chemical speciation monitor (ACSM) users’ meeting was held on April 11-13, 2017, at Aerodyne Research, Inc. in Billerica, Massachusetts, to discuss the Southern Great Plains (SGP) atmospheric observatory ACSM data quality and establish best practices for data collection and processing. The participants examined six years of calibration and processed data. Specific issues raised by data users were addressed and case studies from two field experiments were examined. The most recent data from the ACSM installed in the newly commissioned SGP ARM Mobile Facility 7 (AMF7) were also evaluated. The participants recommended that the SGP ACSM data be reprocessed using calibration values averaged over the history of SGP ACSM calibrations. They also recommended that the data quality be evaluated by comparing (1) observed versus predicted particulate ammonium (NH 4 + ) mass loadings, and (2) ACSM mass loadings versus mass loadings calculated from particle size and light scattering data. The contents of the datastreams from the ACSM were defined based on ARM requirements and the necessary tasks to implement these recommendations were assigned to the mentor and the instrument manufacturer. Some of the recommendations were implemented for the SGP data and are presented in this report. Data from two ARM sites located in marine environments were also analyzed. These analyses as well as instrument intercomparisons have led to further questions about data treatment. These will be addresses in the second users’ meeting scheduled for the summer of 2018.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

First ARM Aerosol Chemical Speciation Monitor Users’ Meeting Report

The first U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility aerosol chemical speciation monitor (ACSM) users’ meeting was held on April 11-13, 2017, at Aerodyne Research, Inc. in Billerica, Massachusetts, to discuss the Southern Great Plains (SGP) atmospheric observatory ACSM data quality and establish best practices for data collection and processing. The participants examined six years of calibration and processed data. Specific issues raised by data users were addressed and case studies from two field experiments were examined. The most recent data from the ACSM installed in the newly commissioned SGP ARM Mobile Facility 7 (AMF7) were also evaluated. The participants recommended that the SGP ACSM data be reprocessed using calibration values averaged over the history of SGP ACSM calibrations. They also recommended that the data quality be evaluated by comparing (1) observed versus predicted particulate ammonium (NH 4 + ) mass loadings, and (2) ACSM mass loadings versus mass loadings calculated from particle size and light scattering data. The contents of the datastreams from the ACSM were defined based on ARM requirements and the necessary tasks to implement these recommendations were assigned to the mentor and the instrument manufacturer. Some of the recommendations were implemented for the SGP data and are presented in this report. Data from two ARM sites located in marine environments were also analyzed. These analyses as well as instrument intercomparisons have led to further questions about data treatment. These will be addresses in the second users’ meeting scheduled for the summer of 2018.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Effects of Improved 17 O Correction on Interlaboratory Agreement in Clumped Isotope Calibrations, Estimates of Mineral-Specific Offsets, and Temperature Dependence of Acid Digestion Fractionation

The clumped isotopic composition of carbonate-CO 2 (denoted Δ 47 ) is a function of carbonate formation temperature and in natural samples can act as a recorder of paleoclimate, burial, or diagenetic conditions. The absolute abundance of heavy isotopes in the universal standards VPDB and VSMOW (defined by four parameters: R 13 VPDB , R 17 VSMOW , R 18 VSMOW , and λ ) impact calculated Δ 47 values. In this work, we investigate whether use of updated and more accurate values for these parameters can remove observed interlaboratory differences in the measured T-Δ 47 relationship. Using the updated parameters, we reprocess 14 published calibration data sets measured in 11 different laboratories, representing many mineralogies, bulk compositions, sample types, reaction temperatures, and sample preparation and analysis methods. Exploiting this large composite data set ( n = 1,253 sample replicates), we investigate the possibility for a “universal” clumped isotope calibration. We find that applying updated parameters improves the T-Δ 47 relationship (reduces residuals) within most labs and improves overall agreement but does not eliminate all interlaboratory differences. We reaffirm earlier findings that different mineralogies do not require different calibration equations and that cleaning procedures, method of pressure baseline correction, and mass spectrometer type do not affect interlaboratory agreement. We also present new estimates of the temperature dependence of the acid digestion fractionation for Δ 47 (Δ* 25-X ), based on combining reprocessed data from four studies, and new theoretical equilibrium values to be used in calculation of the empirical transfer function. Overall, we have ruled out a number of possible causes of interlaboratory disagreement in the T-Δ 47 relationship, but many more remain to be investigated.

58 GEOSCIENCES↗

DOE OSTI2019

Mid-Atlantic U.S. Offshore Carbon Storage Resource Assessment Project (Final Technical Report)

The greatest potential for carbon storage in the northeastern United States lies in the offshore geologic formations comprising the outer continental shelf (Monteverde et al., 2011). Offshore storage can be linked to large point-sources of carbon dioxide (CO 2 ) while avoiding many of the logistical difficulties and potential risks encountered when siting onshore projects, especially in densely populated areas of the East Coast. Offshore CO 2 storage assessment and research in the United States is underway to address uncertainty in potential storage resources, particularly in the mid- and north-Atlantic offshore area. Given the current knowledge base and access to publicly available data, the objectives of the Mid-Atlantic U.S. Offshore Carbon Storage Resource Assessment Project (MAOCSRAP) were fourfold: (1) complete a systematic carbon storage resource assessment of the mid-Atlantic offshore coastal region from the Georges Bank Basin (GBB) through the Long Island Platform (LIP) to the southern Baltimore Canyon Trough (BCT); (2) define key input parameters to reduce uncertainty for offshore storage resource and efficiency estimates; (3) perform a preliminary assessment of risk factors, uncertainties, and data gaps; and (4) engage industry and regulatory stakeholders through development of a Road Map to assist future project planning and implementation. The MAOCSRAP project has compiled, inventoried, and assimilated various publicly available data sets to provide a strong technical basis on which future carbon storage studies and applications can be built. The knowledge infrastructure necessary to support the development of full-scale offshore carbon storage include: Defining the geologic characteristics of candidate storage sites (Chapter 2). Using existing seismic data to better define the continuity of the storage zones and seals (Chapter 3). Cataloguing the hydrogeologic properties of mid-Atlantic offshore storage sites (Chapter 4). Calculating prospective CO 2 storage resources using net effective pore volumes and fluid displacement properties specific to offshore lithologies (Chapter 5). Examining risk factors related to offshore storage (Chapter 6). Communicating with industry and other stakeholders about the future prospects for offshore storage (Chapter 7). Ensuring technology transfer to industry and other stakeholders (Chapter 8). Led by Battelle, this project was conducted by public and private entities with expertise in offshore geology and resources for the study region, including state geological surveys of Delaware, Maryland, and Pennsylvania; United States Geological Survey-Woods Hole Coastal and Marine Science Center and Haifa University; Rutgers University; Harvard University; and Lamont-Doherty Earth Observatory (LDEO) at Columbia University. The storage resource assessment was completed for a broad region offshore of the U.S. East Coast, from Massachusetts to Virginia. The results include high-level storage resource estimates for areas not previously characterized and improved storage resource estimates for geographically expansive portions of offshore geologic units. Results of the project provide a foundation for developing CO 2 storage along offshore areas of the mid-Atlantic United States. A combination of information was used to provide confidence in project results. Key findings of the major project tasks are summarized as follows. Subsurface Data Analysis: Legacy seismic, well-log, core, and biostratigraphic data were digitized, reprocessed, and analyzed using modern techniques, augmenting previous characterization efforts. The sequence stratigraphic framework for the Cretaceous and Jurassic strata was developed by correlating well-log depositional sequences to seismic reflectors identified in the BCT, LIP, and GBB. The integration of well-log stratigraphy with seismic stratigraphy and petrophysical analysis helped to identify and map potential storage zones within sequences of the Logan Canyon (LC), Missisauga, and Mohawk formations, as well as the regionally extensive caprock comprised of the Dawson Canyon shale. Approximately 4,000 kilometers (km) of seismic data was reprocessed to create a structural framework that was used to tie together all available well data, constrain sequence stratigraphic interpretations, and make regional assessments of offshore carbon storage resources. Prospective storage resource estimates were 150 to 1136 megatons (Mt) for the combined storage zones. This suggests mid-Atlantic U.S. offshore formations can store decades of CO 2 from industrial sources in the region. Preliminary local reservoir simulations suggest that injection rates of 1 Mt CO 2 /year may be sustained for 30 years in single injection wells in the BCT. Advanced geologic modeling and new data acquisition are needed to address data gaps and advance carbon capture and storage (CCS) in key offshore areas selected for further investigation. Offshore Risk Factors: Offshore geologic risk factors include soft sediment deformation, unit continuity, sedimentological and structural features, seismicity and hydrates. CO 2 storage risks include inadequate seals, migration/leakage, and chemical interactions leading to decreased storage. Sensitive habitats, environmental impacts, disturbance to seafloor, and other risks need to be identified in advance of project activities and integrated into detailed mitigation plans for all project phases. Stakeholder Engagement: Input and participation from government, industry, and environmental groups provided input into the Road Map and address next steps needed for project deployment. Early engagement and ongoing communication, as well as policy framework development, is key to large-scale deployment of CCS. Next Steps: This project represents an important first step by completing a high-level CO 2 storage resource assessment and building the knowledge infrastructure necessary to improve quantitative storage resource estimates. The data sets that have been curated under this project provide an opportunity to conduct research and development (R&D) needed to address data gaps and reduce risk and uncertainty. Offshore characterization and validation strategies that are systematically designed to provide data and infrastructure that can be upscaled to meet commercial requirements could be developed off of this foundation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Utah FORGE: 2D and 3D Seismic Data

This set of data contains raw and Initially-processed 2D and 3D seismic data from the Utah FORGE study area near Roosevelt Hot Springs. Reprocessed versions of these data can be accessed at the linked submission in the Resources section titled "Reprocessed Seismic Reflection Data." The zipped archives numbered from 1-100 to 1001-1122 contain 3D seismic uncorrelated shot gatherers SEG-Y files. The zipped archives numbered from 1-100C to 1001-1122C contain 3D seismic correlated shot gatherers SEG-Y files. Other data have intuitive names.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Microwave Radiometer - UND Radiometrics MWR, Rufus - Reviewed Data

Reviewed dataset that also includes post-reprocessed level1 and level2 data files from November 2015 to May 2016 (refer to "Additional Information"). Monitor real-time profiles of temperature (K), water vapor (gm-3), relative humidity (%), and liquid water (gm-3) up to 10 km.

17 WIND ENERGY↗