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DOE OSTI2015

THE CONNECTIONS BETWEEN THE UV AND OPTICAL Fe ii EMISSION LINES IN TYPE 1 AGNs

We investigate the spectral properties of the UV (λλ2650–3050 Å) and optical (λλ4000–5500 Å) Fe ii emission features in a sample of 293 Type 1 active galactic nuclei (AGNs) from the Sloan Digital Sky Survey database. We explore different correlations between their emission line properties, as well as the correlations with other emission lines from the spectral range. We find several interesting correlations and outline the most interesting results as follows. (i) There is a kinematical connection between the UV and optical Fe ii lines, indicating that the UV and optical Fe ii lines originate from the outer part of the broad line region, the so-called intermediate line region. (ii) The unexplained anticorrelations of the optical Fe ii equivalent width (EW Fe ii{sub opt}) versus EW [O iii] 5007 Å and EW Fe ii{sub opt} versus FWHM Hβ have not been detected for the UV Fe ii lines. (iii) The significant averaged redshift in the UV Fe ii lines, which is not present in optical Fe ii, indicates an inflow in the UV Fe ii emitting clouds, and probably their asymmetric distribution. (iv) Also, we confirm the anticorrelation between the intensity ratio of the optical and UV Fe ii lines and the FWHM of Hβ, and we find the anticorrelations of this ratio with the widths of Mg ii 2800 Å, optical Fe ii, and UV Fe ii. This indicates a very important role for the column density and microturbulence in the emitting gas. We discuss the starburst activity in high-density regions of young AGNs as a possible explanation of the detected optical Fe ii correlations and intensity line ratios of the UV and optical Fe ii lines.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Pathosphere.org: pathogen detection and characterization through a web-based, open source informatics platform

The detection of pathogens in complex sample backgrounds has been revolutionized by wide access to next-generation sequencing (NGS) platforms. However, analytical methods to support NGS platforms are not as uniformly available. Pathosphere (found at Pathosphere.org) is a cloud - based open - sourced community tool that allows for communication, collaboration and sharing of NGS analytical tools and data amongst scientists working in academia, industry and government. The architecture allows for users to upload data and run available bioinformatics pipelines without the need for onsite processing hardware or technical support. The pathogen detection capabilities hosted on Pathosphere were tested by analyzing pathogen-containing samples sequenced by NGS with both spiked human samples as well as human and zoonotic host backgrounds. Pathosphere analytical pipelines developed by Edgewood Chemical Biological Center (ECBC) identified spiked pathogens within a common sample analyzed by 454, Ion Torrent, and Illumina sequencing platforms. ECBC pipelines also correctly identified pathogens in human samples containing arenavirus in addition to animal samples containing flavivirus and coronavirus. These analytical methods were limited in the detection of sequences with limited homology to previous annotations within NCBI databases, such as parvovirus. Utilizing the pipeline-hosting adaptability of Pathosphere, the analytical suite was supplemented by analytical pipelines designed by the United States Army Medical Research Institute of Infectious Diseases and Walter Reed Army Institute of Research (USAMRIID-WRAIR). These pipelines were implemented and detected parvovirus sequence in the sample that the ECBC iterative analysis previously failed to identify. By accurately detecting pathogens in a variety of samples, this work demonstrates the utility of Pathosphere and provides a platform for utilizing, modifying and creating pipelines for a variety of NGS technologies developed to detect pathogens in complex sample backgrounds. These results serve as an exhibition for the existing pipelines and web-based interface of Pathosphere as well as the plug-in adaptability that allows for integration of newer NGS analytical software as it becomes available.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Enabling a systems biology knowledgebase with gaggle and firegoose

The overall goal of this project was to extend the existing Gaggle and Firegoose systems to develop an open-source technology that runs over the web and links desktop applications with many databases and software applications. This technology would enable researchers to incorporate workflows for data analysis that can be executed from this interface to other online applications. The four specific aims were to (1) provide one-click mapping of genes, proteins, and complexes across databases and species; (2) enable multiple simultaneous workflows; (3) expand sophisticated data analysis for online resources; and enhance open-source development of the Gaggle-Firegoose infrastructure. Gaggle is an open-source Java software system that integrates existing bioinformatics programs and data sources into a user-friendly, extensible environment to allow interactive exploration, visualization, and analysis of systems biology data. Firegoose is an extension to the Mozilla Firefox web browser that enables data transfer between websites and desktop tools including Gaggle. In the last phase of this funding period, we have made substantial progress on development and application of the Gaggle integration framework. We implemented the workspace to the Network Portal. Users can capture data from Firegoose and save them to the workspace. Users can create workflows to start multiple software components programmatically and pass data between them. Results of analysis can be saved to the cloud so that they can be easily restored on any machine. We also developed the Gaggle Chrome Goose, a plugin for the Google Chrome browser in tandem with an opencpu server in the Amazon EC2 cloud. This allows users to interactively perform data analysis on a single web page using the R packages deployed on the opencpu server. The cloud-based framework facilitates collaboration between researchers from multiple organizations. We have made a number of enhancements to the cmonkey2 application to enable and improve the integration within different environments, and we have created a new tools pipeline for generating EGRIN2 models in a largely automated way.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

LARGE-SCALE FILAMENTARY STRUCTURES AROUND THE VIRGO CLUSTER REVISITED

We revisit the filamentary structures of galaxies around the Virgo cluster, exploiting a larger data set, based on the HyperLeda database, than previous studies. In particular, this includes a large number of low-luminosity galaxies, resulting in better sampled individual structures. We confirm seven known structures in the distance range 4 h {sup −1} Mpc < SGY < 16 h {sup −1} Mpc, now identified as filaments, where SGY is the axis of the supergalactic coordinate system roughly along the line of sight. The Hubble diagram of the filament galaxies suggests they are infalling toward the main body of the Virgo cluster. We propose that the collinear distribution of giant elliptical galaxies along the fundamental axis of the Virgo cluster is smoothly connected to two of these filaments (Leo II A and B). Behind the Virgo cluster (16 h {sup −1} Mpc < SGY < 27 h {sup −1} Mpc), we also identify a new filament elongated toward the NGC 5353/4 group (“NGC 5353/4 filament”) and confirm a sheet that includes galaxies from the W and M clouds of the Virgo cluster (“W–M sheet”). In the Hubble diagram, the NGC 5353/4 filament galaxies show infall toward the NGC 5353/4 group, whereas the W–M sheet galaxies do not show hints of gravitational influence from the Virgo cluster. The filamentary structures identified can now be used to better understand the generic role of filaments in the build-up of galaxy clusters at z ≈ 0.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Development of a MODIS-Derived Surface Albedo Data Set: An Improved Model Input for Processing the NSRDB

A significant source of bias in the transposition of global horizontal irradiance to plane-of-array (POA) irradiance arises from inaccurate estimations of surface albedo. The current physics-based model used to produce the National Solar Radiation Database (NSRDB) relies on model estimations of surface albedo from a reanalysis climatalogy produced at relatively coarse spatial resolution compared to that of the NSRDB. As an input to spectral decomposition and transposition models, more accurate surface albedo data from remotely sensed imagery at finer spatial resolutions would improve accuracy in the final product. The National Renewable Energy Laboratory (NREL) developed an improved white-sky (bi-hemispherical reflectance) broadband (0.3-5.0 ..mu..m) surface albedo data set for processing the NSRDB from two existing data sets: a gap-filled albedo product and a daily snow cover product. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Terra and Aqua satellites have provided high-quality measurements of surface albedo at 30 arc-second spatial resolution and 8-day temporal resolution since 2001. The high spatial and temporal resolutions and the temporal coverage of the MODIS sensor will allow for improved modeling of POA irradiance in the NSRDB. However, cloud and snow cover interfere with MODIS observations of ground surface albedo, and thus they require post-processing. The MODIS production team applied a gap-filling methodology to interpolate observations obscured by clouds or ephemeral snow. This approach filled pixels with ephemeral snow cover because the 8-day temporal resolution is too coarse to accurately capture the variability of snow cover and its impact on albedo estimates. However, for this project, accurate representation of daily snow cover change is important in producing the NSRDB. Therefore, NREL also used the Integrated Multisensor Snow and Ice Mapping System data set, which provides daily snow cover observations of the Northern Hemisphere for the temporal extent of the NSRDB (1998-2015). We provide a review of validation studies conducted on these two products and describe the methodology developed by NREL to remap the data products to the NSRDB grid and integrate them into a seamless daily data set.

14 SOLAR ENERGY↗

DOE OSTI2019

Method and System for Accessing PV Resource Data from the NSRDB: Preprint

This study developed a capability to provide spectrally resolved solar resource data from the National Solar Radiation Database (NSRDB), effectively providing 20 years of half-hourly data for all of the United States at a 4-km by 4-km spatial resolution. These data are now available freely to users directly through a geographic information system-based web interface (https://nsrdb.nrel.gov) as well as through an application programming interface. Users of these data can conduct more accurate prefeasibility studies and assess multiple photovoltaic (PV) technologies. It is expected that widely used models such as PVSyst, the National Renewable Energy Laboratory's System Advisor Model (SAM), and First Solar-designed PlantPredict will develop the capabilities to use these spectral data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

PAH EMISSION AT THE BRIGHT LOCATIONS OF PDRs: THE grandPAH HYPOTHESIS

The polycyclic aromatic hydrocarbon (PAH) emission observed in the Spitzer Infrared Spectrograph spectra of bright mid-IR locations of NGC 7023, NGC 2023, and NGC 1333 was analyzed. These objects show large variations in PAH band ratios when studied through spectral mapping. Nevertheless, the mid-IR spectra at these bright spots show a remarkably similar PAH emission. We used the NASA Ames PAH IR Spectroscopic Database to fit the observations and analyze the derived PAH populations. Our results show that PAH emission in the 5–15 μm range appears to be rather insensitive to variations of the radiation field. Similar PAH populations of neutral small to medium-sized PAHs (∼50%), with ionized species contributing in slightly less than 50%, provide very good fits. Analyzing the degeneracy of the results shows that subtle (but intrinsic) variations in the emission properties of individual PAHs lead to observable differences in the resulting spectra. On top of this, we found that variations of <30% in the PAH abundances would lead to noticeable spectral differences between the three photodissociation regions (PDRs). Therefore, PAH populations must be remarkably similar at these different lines of sight. To account for this, we suggest the concept of grandPAHs as a unique mixture of the most stable PAHs emitting at these spots. Using NGC 7023 as an example, the grandPAHs refer to the robust PAH population that results from the intense processing of PAHs at the border limit between the PDR and the molecular cloud, where, due to the UV radiation that destroys the PAH population, the abundance of PAHs starts decreasing as we move toward the star.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Bioblendstocks to Optimize Mixing Controlled Compression Ignition (MCCI) Engines

In this project, a team of researchers from the University of Massachusetts Lowell, the University of Maine, and Mainstream Engineering developed an integrated process for the product of bioblendstocks to optimize mixing controlled compression ignition (MCCI) engines. The objective was to improve the energy density, sooting propensity, and cetane number of base diesel fuel while maintaining cold weather behavior. The process converts woody biomass (e.g. sawmill residues) into bio-oil through selective fast pyrolysis; the bio-oil is then selectively upgraded to form selectively oxygenated, minimally-branched hydrocarbons using non-noble metal catalysts in combination with metal-catalyzed hydrogenation. Advanced predictive models, in conjunction with existing property databases, and experimental testing are used to evaluate overall bioblendstock properties and their impact on base diesel fuel. An iterative, targeted upgrading approach was implemented to optimize the proposed bioblendstock’s properties. Assessment methodologies included techno-economic analysis, life-cycle assessment, property testing, and engine testing. Ultimately, the project team successfully produced a viable bioblendstock while identifying critical process points related to scale-up efforts. It was found that producing pyrolysis oils at 500 degrees C and with pine particle sizes of 1-2 mm led to bio-oil with a higher yield (of approximately 45 wt%) and rich amounts of aromatic alcohols. The resultant pyrolysis oil was then upgraded using a sequence of mild hydrotreating, followed by catalytic etherification and esterification, followed by another final mild hydrotreating to produce a blendstock containing saturated species with a limited, but non-zero, amount of oxygen. The aromatic alcohols produced by pyrolysis were especially helpful in this regard, as the resulting bicycloethers and derivatives exhibited high cetane numbers. While most bulk properties of the bioblendstock met or exceeded targeted thresholds, viscosity and cloud point notably fell outside the expected range; this could be addressed by blending limits and/or through the use of additives that are commonplace in current refinding practices. Identification of a bioblendstock that can be produced economically at scale while improving the performance and emissions characteristics of internal combustion engines positively affects the economy by boosting domestic fuel production and the environment by decreasing harmful emissions and increasing efficiency.

09 BIOMASS FUELS↗

DOE OSTI2014

DARK MATTER HALOS IN GALAXIES AND GLOBULAR CLUSTER POPULATIONS

We combine a new, comprehensive database for globular cluster populations in all types of galaxies with a new calibration of galaxy halo masses based entirely on weak lensing. Correlating these two sets of data, we find that the mass ratio η ≡ M {sub GCS}/M {sub h} (total mass in globular clusters, divided by halo mass) is essentially constant at (η) ∼ 4 × 10{sup –5}, strongly confirming earlier suggestions in the literature. Globular clusters are the only known stellar population that formed in essentially direct proportion to host galaxy halo mass. The intrinsic scatter in η appears to be at most 0.2 dex; we argue that some of this scatter is due to differing degrees of tidal stripping of the globular cluster systems between central and satellite galaxies. We suggest that this correlation can be understood if most globular clusters form at very early stages in galaxy evolution, largely avoiding the feedback processes that inhibited the bulk of field-star formation in their host galaxies. The actual mean value of η also suggests that about one-fourth of the initial gas mass present in protogalaxies collected into giant molecular clouds large enough to form massive, dense star clusters. Finally, our calibration of (η) indicates that the halo masses of the Milky Way and M31 are (1.2 ± 0.5) × 10{sup 12} M {sub ☉} and (3.9 ± 1.8) × 10{sup 12} M {sub ☉}, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2018

Assessment of the National Solar Radiation Database (NSRDB 1998-2016)

Applying traceable and standardized uncertainty characterization for solar resource data provides confidence in the dataset for use by financiers, developers and site operators of solar energy conversion systems, and ultimately reduces deployment cost. Performance guarantees of solar energy conversion systems are based on the available solar resource from measurement stations or modeled dataset such as the National Solar Radiation Database (NSRDB). In this study we implemented a comprehensive uncertainty determination approach [1]. The study also analyzed how the NSRDB (19982016) - Version 3 compares with the previous NSRDB (19982015) - Version 2. The study also attempted to estimate the uncertainty differences derived by comparing theNSRDB data to the seven measurement stations from the National Oceanic and Atmospheric Administration's Surface Radiation Budget Network (SURFRAD) and University of Oregon Solar Radiation Monitoring Laboratory (SRML). The evaluation was conducted for hourly values, daily totals, monthly mean daily totals, and annual mean monthly mean daily totals and demonstrate the qualityof the new datasets currently available from the NSRDB.

14 SOLAR ENERGY↗

DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗

DOE OSTI2016

Particle Number Concentrations for HI-SCALE Field Campaign Report

In support of the Holistic Interactions of Shallow Clouds, Aerosols, and Ecosystems (HI-SCALE) project to study new particle formation in the atmosphere, a pair of custom water condensation particle counters were provided to the second intensive field campaign, from mid-August through mid-September 2017, at the U.S. Department of Energy Southern Great Plains Atmospheric Radiation Measurement (ARM) Climate Research Facility observatory. These custom instruments were developed by Aerosol Dynamics, Inc. (Hering et al. 2017) to detect particles into the nanometer size range. Referred to as “versatile water condensation particle counter (vWCPC)”, they are water-based, laminar-flow condensational growth instruments whose lower particle size threshold can be set based on user-selected operating temperatures. For HI-SCALE, the vWCPCs were configured to measure airborne particle number concentrations in the size range from approximately 2nm to 2μm. Both were installed in the particle sizing system operated by Chongai Kuang of Brookhaven National Laboratory (BNL). One of these was operated in parallel to a TSI Model 3776, upstream of the mobility particle sizing system, to measure total ambient particle concentrations. The airborne particle concentration data from this “total particle number vWCPC” (Ntot-vWCPC) system has been reported to the ARM database. The data are reported with one-second resolution. The second vWCPC was operated in parallel with the BNL diethylene glycol instrument to count particles downstream of a separate differential mobility size analyzer. Data from this “DMA-vWCPC” system was logged by BNL, and will eventually be provided by that laboratory.

HI-SCALE, aerosol particle number concentration, v↗

DOE OSTI2019

The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data

The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access to the three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), and PERSIANN-Climate Data Record (CDR). These datasets have the potential for widespread use by various researchers, professionals including engineers, city planners, and so forth, as well as the community at large. Researchers at CHRS created the CHRS Data Portal with an emphasis on simplicity and the intention of fostering synergistic relationships with scientists and experts from around the world. The following paper presents an outline of the hosted datasets and features available on the CHRS Data Portal, an examination of the necessity of easily accessible public data, a comprehensive overview of the PERSIANN algorithms and datasets, and a walk-through of the procedure to access and obtain the data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Integration of a physics-based direct normal irradiance ($\mathrm{DNI}$) model to enhance the National Solar Radiation Database ($\mathrm{NSRDB}$)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

14 SOLAR ENERGY↗

DOE OSTI2015

Analysis of Global Horizontal Irradiance in Version 3 of the National Solar Radiation Database

We report an analysis that compares global horizontal irradiance (GHI) estimates from version 3 of the National Solar Radiation Database (NSRDB v3) with surface measurements of GHI at a wide variety of locations over the period spanning from 2005 to 2012. The NSRDB v3 estimate of GHI are derived from the Physical Solar Model (PSM) which employs physics-based models to estimate GHI from measurements of reflected visible and infrared irradiance collected by Geostationary Operational Environment Satellites (GOES) and several other data sources. Because the ground measurements themselves are uncertain our analysis does not establish the absolute accuracy for PSM GHI. However by examining the comparison for trends and for consistency across a large number of sites, we may establish a level of confidence in PSM GHI and identify conditions which indicate opportunities to improve PSM. We focus our evaluation on annual and monthly insolation because these quantities directly relate to prediction of energy production from solar power systems. We find that generally, PSM GHI exhibits a bias towards overestimating insolation, on the order of 5% when all sky conditions are considered, and somewhat less (-3%) when only clear sky conditions are considered. The biases persist across multiple years and are evident at many locations. In our opinion the bias originates with PSM and we view as less credible that the bias stems from calibration drift or soiling of ground instruments. We observe that PSM GHI may significantly underestimate monthly insolation in locations subject to broad snow cover. We found examples of days where PSM GHI apparently misidentified snow cover as clouds, resulting in significant underestimates of GHI during these days and hence leading to substantial understatement of monthly insolation. Analysis of PSM GHI in adjacent pixels shows that the level of agreement between PSM GHI and ground data can vary substantially over distances on the order of 2 km. We conclude that the variance most likely originates from dramatic contrasts in the ground's appearance over these distances.

14 SOLAR ENERGY↗

DOE OSTI2021

Survival of Snow in the Melting Layer: Relative Humidity Influence

This study quantifies how far snow can fall into the melting layer (ML) before all snow has melted by examining a combination of in-situ observations from aircraft measurements in Lagrangian spiral descents from above through the ML and descents and ascents into the ML, as well as an extensive database of NOAA surface observer reports during the past 50 years. The airborne data contain information on the particle phase (solid, mixed, or liquid), population size distributions and shapes, along with temperature, relative humidity, and vertical velocity. A wide range of temperatures and ambient relative humidities are used for both the airborne and ground-based data. It is shown that an ice bulb temperature of 0°C, together with the air temperature and pressure (altitude), are good first order predictors of the highest temperature snowflakes can survive in the melting layer before completely melting. Particle size is also important, as is whether the particles are graupel or hail. If the relative humidity is too low, the particles will sublimate completely as they fall into the melting layer. Snow as warm as +7°C is observed from aircraft measurements and surface observations. Snow pellets survive to even warmer temperatures. Relationships are developed to represent the primary findings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Reaction studies of neutral atomic C with H{sub 3}{sup +} using a merged-beams apparatus

We have investigated the chemistry of C+H{sub 3}{sup +} forming CH{sup +}, CH{sub 2}{sup +}, and CH{sub 3}{sup +}. These reactions are believed to be some of the key gas-phase astrochemical processes initiating the formation of organic molecules in molecular clouds. For this work, we have constructed a novel merged fast-beams apparatus which overlaps a beam of molecular ions onto a beam of ground-term neutral atoms. Here, we describe the apparatus in detail and present cross section data for forming CH{sup +} and CH{sub 2}{sup +} at relative energies from ≈9 meV to ≈20 and 3 eV, respectively. Measurements were performed for statistically populated C ({sup 3}P{sub J}) in the ground term reacting with hot H{sub 3}{sup +} (at an internal temperature of ∼2550 K). Using these data, we have derived rate coefficients for translational temperatures from ≈72 K to ∼2.3×10{sup 5} and 3.4×10{sup 4} K, respectively. For the formation of CH{sub 3}{sup +}, we are only able to place an upper limit on the rate coefficient. Our results for CH{sup +} and CH{sub 2}{sup +} are in good agreement with the mass-scaled results from a previous ion trap study of C + D{sub 3}{sup +}, at a translational temperature of ∼1000 K. That work also used statistically populated C ({sup 3}P{sub J}) but internally cold D{sub 3}{sup +} (∼77 K). The good agreement between the two experiments implies that the internal excitation of the H{sub 3}{sup +} is not significant so long as the reaction proceeds adiabatically. At 300 K, the C fine-structure levels are predicted to be essentially statistically populated, enabling us to compare our translational temperature results to thermal equilibrium calculations. At this temperature, our rate coefficient for forming CH{sup +} lies a factor of ≈2.9 below the Langevin rate coefficient currently given in astrochemical databases, and a factor of ∼1.8−3.3 below the published classical trajectory studies using quantum mechanical potential energy surfaces. Our results for CH{sub 2}{sup +} formation at 300 K are a factor of ≈26.7 above these semi-classical results. Astrochemical databases do not currently include this channel. We also present a method for converting our translational temperature results to thermal rate coefficients for temperatures below ∼300 K. The results indicated that CH{sub 2}{sup +} formation dominates over that of CH{sup +} at temperatures ≲50 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Web-based visual data exploration for improved radiological source detection

Radiation detection can provide a reliable means of detecting radiological material. Such capabilities can help to prevent nuclear and/or radiological attacks, but reliable detection in uncontrolled surroundings requires algorithms that account for environmental background radiation. The Berkeley Data Cloud (BDC) facilitates the development of such methods by providing a framework to capture, store, analyze, and share data sets. In the era of big data, both the size and variety of data make it difficult to explore and find data sets of interest and manage the data. Thus, in the context of big data, visualization is critical for checking data consistency and validity, identifying gaps in data coverage, searching for data relevant to an analyst's use cases, and choosing input parameters for analysis. Downloading the data and exploring it on an analyst's desktop using traditional tools are no longer feasible due to the size of the data. This study describes the design and implementation of a visualization system that addresses the problems associated with data exploration within the context of the BDC. Finally, the visualization system is based on a JavaScript front end communicating via REST with a back end web server.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗