SEARCH · Engineering Papers

Results for “reprocessed data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

All sources NASA NTRS DOE OSTI

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 OSTI2014

HESS J1640–465 AND HESS J1641–463: TWO INTRIGUING TeV SOURCES IN LIGHT OF NEW FERMI-LAT OBSERVATIONS

We report on γ-ray analysis of the region containing the bright TeV source HESS J1640–465 and the close-by TeV source HESS J1641–463 using 64 months of observations with the Fermi Large Area Telescope (LAT). Previously only one GeV source was reported in this region and was associated with HESS J1640–465. With an increased data set and the improved sensitivity afforded by the reprocessed data (P7REP) of the LAT, we now report the detection, morphological study, and spectral analysis of two distinct sources above 100 MeV. The softest emission in this region comes from the TeV source HESS J1641–463 which is well fitted with a power law of index Γ = 2.47 ± 0.05 ± 0.06 and presents no significant γ-ray signal above 10 GeV, which contrasts with its hard spectrum at TeV energies. The Fermi-LAT spectrum of the second TeV source, HESS J1640–465 is well described by a power-law shape of index Γ = 1.99 ± 0.04 ± 0.07 that links up naturally with the spectral data points obtained by the High Energy Stereoscopic System (H.E.S.S.). These new results provide new constraints concerning the identification of these two puzzling γ-ray sources.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Data publication with the structural biology data grid supports live analysis

Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. SBDG has extended its services to the entire community and is used to develop support for other types of biomedical data sets. In conclusion, it is anticipated that access to the experimental data sets will enhance the paradigm shift in the community towards a much more dynamic body of continuously improving data analysis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Updated search for spectral lines from Galactic dark matter interactions with pass 8 data from the Fermi Large Area Telescope

Dark matter in the Milky Way may annihilate directly into γ rays, producing a monoenergetic spectral line. Therefore, detecting such a signature would be strong evidence for dark matter annihilation or decay. We search for spectral lines in the Fermi Large Area Telescope observations of the Milky Way halo in the energy range 200 MeV–500 GeV using analysis methods from our most recent line searches. The main improvements relative to previous works are our use of 5.8 years of data reprocessed with the Pass 8 event-level analysis and the additional data resulting from the modified observing strategy designed to increase exposure of the Galactic center region. Furthermore, we search in five sky regions selected to optimize sensitivity to different theoretically motivated dark matter scenarios and find no significant detections. In addition to presenting the results from our search for lines, we also investigate the previously reported tentative detection of a line at 133 GeV using the new Pass 8 data.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Search for Standard Model $ZH \to \ell^+\ell^-b\bar{b}$ at DØ

We present a search for the Standard Model Higgs boson in the ZH → ℓ + ℓ ₋ $b\bar{b}$ channel, using data collected with the DØ detector at the Fermilab Tevatron Collider. This analysis is based on a sample of reprocessed data incorporating several improve ments relative to a previous published result, and a modified multivariate analysis strategy. For a Standard Model Higgs boson of mass 125 GeV, the expected cross section limit over the Standard M odel prediction is improved by about 5% compared to the previous published results in this c hannel from the DØ Collaboration

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2014

A dark matter search using the final CDMS-II data and 100 mm SuperCDMS germanium detector ionization test

Astrophysical observations indicate that approximately 85% of the matter in the universe is nonluminous, nonbaryonic, and nonrelativistic (cold) dark matter. Weakly Interacting Massive Particles (WIMPs) are a particularly well motivated dark matter particle candidate. They would be thermally produced in the early universe and their relics account for the current dark matter abundance. WIMP candidate particles are naturally provided by extensions to the Standard Model of particle physics, such as supersymmetry. The Cryogenic Dark Matter Search (CDMS) experiment operates cryogenic germanium and silicon particle detectors in the low-background environment of the Soudan Underground Laboratory in northern Minnesota to search for WIMP-nucleus scatters while rejecting electron-recoil background. The detectors simultaneously measure the ionization and phonon energies of each scattering event. The difference in ionization yield (ratio of ionization energy to recoil energy) discriminates nuclear recoils from the electron-recoil background efficiently.More sensitive detectors are required to probe the WIMP parameter space with lower WIMP-nucleon scattering cross sections. To support the R&D effort especially the detector R&D and characterization of the SuperCDMS experiment, a new CDMS test facility has been developed on the University of Minnesota campus. This thesis documents the test facility and the work involved in its development. In the test facility, we performed the first ionization collection efficiency measurements of the ionization test devices. The test devices are fabricated with detector-grade germanium crystals that are 100 mm in diameter, which is the largest available, and 33 mm in thickness. The measured efficiencies are consistent with the earlier measurements conducted with smaller Ge crystals, demonstrating that these 100 mm crystals can be used for development of the next generation dark matter detectors.Improvements of data analysis methods can also potentially improve the sensitivity of an experiment. The data taken during the last four runs of CDMS II with total raw exposure 612 kg-day were reprocessed with improved ionization pulse reconstruction algorithm. We present the classic timing analysis with the reprocessed data in this thesis. For the four runs combined, this analysis resulted in a new WIMP-nucleon cross section 4.4×10 -44 cm 2 for a WIMP mass of 70 GeV, which is a factor of 1.6 improvement compared to the original c58 classic timing analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

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 OSTI2015

Study of TeV shell supernova remnants at gamma-ray energies

The breakthrough developments of Cherenkov telescopes in the past decade have led to angular resolution of 0.1° and an unprecedented sensitivity. This has allowed the current generation of Cherenkov telescopes (H.E.S.S., MAGIC, and VERITAS) to discover a population of supernova remnants (SNRs) radiating in very-high-energy (VHE; E > 100 GeV) γ-rays. A number of those VHE SNRs exhibit a shell-type morphology that is spatially coincident with the shock front of the SNR. The members of this VHE shell SNR club are RX J1713.7-3946, RX J0852.0-4622, RCW 86, SN 1006, and HESS J1731-347. The last two objects have been poorly studied in high-energy (HE; 0.1 < E < 100 GeV) γ-rays and need to be investigated in order to draw the overall picture of this class of SNRs and to constrain the characteristics of the underlying population of accelerated particles. Using 6 years of Fermi-LAT P7 reprocessed data, we studied the GeV counterpart of the SNRs HESS J1731-347 and SN 1006. The two SNRs are not detected in the data set, and given that there is no hint of detection, we do not expect any detection in coming years from the SNRs. However in both cases, we derived upper limits that significantly constrain the γ-ray emission mechanism and can rule out a standard hadronic scenario with a confidence level >5σ. In conclusion, with this Fermi analysis, we now have a complete view of the HE to VHE γ-ray emission of TeV shell SNRs. All five sources have a hard HE photon index (Γ < 1.8), which suggests a common scenario where the bulk of the emission is produced by accelerated electrons radiating from radio to VHE γ-rays through synchrotron and inverse Compton processes. In addition when correcting for the distance, all SNRs show a surprisingly similar γ-ray luminosity supporting the idea of a common emission mechanism. While the γ-ray emission is likely to be leptonic-dominated at the scale of the whole SNR, this does not rule out efficient hadron acceleration in those objects.

79 ASTRONOMY AND ASTROPHYSICS↗

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 OSTI2015

Extending the Fermi -LAT data processing pipeline to the grid

The Data Handling Pipeline ("Pipeline") has been developed for the Fermi Gamma-Ray Space Telescope (Fermi) Large Area Telescope (LAT) which launched in June 2008. Since then it has been in use to completely automate the production of data quality monitoring quantities, reconstruction and routine analysis of all data received from the satellite and to deliver science products to the collaboration and the Fermi Science Support Center. Aside from the reconstruction of raw data from the satellite (Level 1), data reprocessing and various event-level analyses are also reasonably heavy loads on the pipeline and computing resources. These other loads, unlike Level 1, can run continuously for weeks or months at a time. Additionally, it receives heavy use in performing production Monte Carlo tasks.

ASTRO↗

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 OSTI2016

Angular power spectrum of the diffuse gamma-ray emission as measured by the Fermi Large Area Telescope and constraints on its dark matter interpretation

The isotropic gamma-ray background arises from the contribution of unresolved sources, including members of confirmed source classes and proposed gamma-ray emitters such as the radiation induced by dark matter annihilation and decay. Clues about the properties of the contributing sources are imprinted in the anisotropy characteristics of the gamma-ray background. We use 81 months of Pass 7 Reprocessed data from the Fermi Large Area Telescope to perform a measurement of the anisotropy angular power spectrum of the gamma-ray background. Here, we analyze energies between 0.5 and 500 GeV, extending the range considered in the previous measurement based on 22 months of data. We also compute, for the first time, the cross-correlation angular power spectrum between different energy bins. The derived angular spectra are compatible with being Poissonian, i.e. constant in multipole. Furthermore, the energy dependence of the anisotropy suggests that the signal is due to two populations of sources, contributing, respectively, below and above ~ 2 GeV . Finally, using data from state-of-the-art numerical simulations to model the dark matter distribution, we constrain the contribution from dark matter annihilation and decay in Galactic and extra-Galactic structures to the measured anisotropy. These constraints are competitive with those that can be derived from the average intensity of the isotropic gamma-ray background.

79 ASTRONOMY AND ASTROPHYSICS↗

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 OSTI2014

Deep thermal infrared imaging of HR 8799 bcde: new atmospheric constraints and limits on a fifth planet

We present new L' (3.8 μm) and Brα (4.05 μm) data and reprocessed archival L' data for the young, planet-hosting star HR 8799 obtained with Keck/NIRC2, VLT/NaCo, and Subaru/IRCS. We detect all four HR 8799 planets in each data set at a moderate to high signal-to-noise ratio (S/N ≳ 6-15). We fail to identify a fifth planet, 'HR 8799 f', at r < 15 AU at a 5σ confidence level: one suggestive, marginally significant residual at 0.''2 is most likely a point-spread function artifact. Assuming companion ages of 30 Myr and the Baraffe planet cooling models, we rule out an HR 8799 f with a mass of 5 M{sub J} (7 M{sub J} ), 7 M{sub J} (10 M{sub J} ), or 12 M{sub J} (13 M{sub J} ) at r {sub proj} ∼ 12 AU, 9 AU, and 5 AU, respectively. All four HR 8799 planets have red early T dwarf-like L' – [4.05] colors, suggesting that their spectral energy distributions peak in between the L' and M' broadband filters. We find no statistically significant difference in HR 8799 cde's color. Atmosphere models assuming thick, patchy clouds appear to better match HR 8799 bcde's photometry than models assuming a uniform cloud layer. While non-equilibrium carbon chemistry is required to explain HR 8799 b and c's photometry/spectra, evidence for it from HR 8799 d and e's photometry is weaker. Future, deep-IR spectroscopy/spectrophotometry with the Gemini Planet Imager, SCExAO/CHARIS, and other facilities may clarify whether the planets are chemically similar or heterogeneous.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

AUTOMATED TRANSIENT IDENTIFICATION IN THE DARK ENERGY SURVEY

We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique known as Random Forest. We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline. After reprocessing the data collected during the first DES-SN observing season (2013 September through 2014 February) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1.0% of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithm’s performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at at http://portal.nersc.gov/project/dessn/autoscan.

79 ASTRONOMY AND ASTROPHYSICS↗

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↗