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At least 955 records · Page 53

Uncertainties in Atomic Data and Their Propagation Through Spectral Models. I.

We present a method for computing uncertainties in spectral models, i.e., level populations, line emissivities, and emission line ratios, based upon the propagation of uncertainties originating from atomic data.We provide analytic expressions, in the form of linear sets of algebraic equations, for the coupled uncertainties among all levels. These equations can be solved efficiently for any set of physical conditions and uncertainties in the atomic data. We illustrate our method applied to spectral models of Oiii and Fe ii and discuss the impact of the uncertainties on atomic systems under different physical conditions. As to intrinsic uncertainties in theoretical atomic data, we propose that these uncertainties can be estimated from the dispersion in the results from various independent calculations. This technique provides excellent results for the uncertainties in A-values of forbidden transitions in [Fe ii]. Key words: atomic data - atomic processes - line: formation - methods: data analysis - molecular data - molecular processes - techniques: spectroscopic

linear sets↗

Land Boundary Conditions for the Goddard Earth Observing System Model Version 5 (GEOS-5) Climate Modeling System: Recent Updates and Data File Descriptions

The Earths land surface boundary conditions in the Goddard Earth Observing System version 5 (GEOS-5) modeling system were updated using recent high spatial and temporal resolution global data products. The updates include: (i) construction of a global 10-arcsec land-ocean lakes-ice mask; (ii) incorporation of a 10-arcsec Globcover 2009 land cover dataset; (iii) implementation of Level 12 Pfafstetter hydrologic catchments; (iv) use of hybridized SRTM global topography data; (v) construction of the HWSDv1.21-STATSGO2 merged global 30 arc second soil mineral and carbon data in conjunction with a highly-refined soil classification system; (vi) production of diffuse visible and near-infrared 8-day MODIS albedo climatologies at 30-arcsec from the period 2001-2011; and (vii) production of the GEOLAND2 and MODIS merged 8-day LAI climatology at 30-arcsec for GEOS-5. The global data sets were preprocessed and used to construct global raster data files for the software (mkCatchParam) that computes parameters on catchment-tiles for various atmospheric grids. The updates also include a few bug fixes in mkCatchParam, as well as changes (improvements in algorithms, etc.) to mkCatchParam that allow it to produce tile-space parameters efficiently for high resolution AGCM grids. The update process also includes the construction of data files describing the vegetation type fractions, soil background albedo, nitrogen deposition and mean annual 2m air temperature to be used with the future Catchment CN model and the global stream channel network to be used with the future global runoff routing model. This report provides detailed descriptions of the data production process and data file format of each updated data set.

GEOS-5↗

Methods and Apparatus for Aggregation of Multiple Pulse Code Modulation Channels into a Signal Time Division Multiplexing Stream

Methods and apparatus are provided for telemetry processing using a telemetry processor. The telemetry processor can include a plurality of communications interfaces, a computer processor, and data storage. The telemetry processor can buffer sensor data by: receiving a frame of sensor data using a first communications interface and clock data using a second communications interface, receiving an end of frame signal using a third communications interface, and storing the received frame of sensor data in the data storage. After buffering the sensor data, the telemetry processor can generate an encapsulated data packet including a single encapsulated data packet header, the buffered sensor data, and identifiers identifying telemetry devices that provided the sensor data. A format of the encapsulated data packet can comply with a Consultative Committee for Space Data Systems (CCSDS) standard. The telemetry processor can send the encapsulated data packet using a fourth and a fifth communications interfaces.

Chang, Chen J.↗

UH-60A Airloads Flight Test Program: Data Counter 9017

Aeromechanics Branch interns at Ames Research Center have been directly contributing to the data quality analysis and reporting of the UH-60A Airloads Flight Test Program for many years. In chronological order (together with the semester and year): Caroline Edwards (Summer 2011); Joni DeGuzman and Carson Turner (Fall 2011); Eric Fritz (Spring 2012); Connor Beierle (Fall 2012); Christopher Olinger (Spring and Summer 2013); Needa Lin, Anatole Levkoff, Maxwell Loebig, Jose Orejel, Megan Prout, and Albert Sue (Summer 2014); Jared Archey (Fall 2014); Alexander Crone (Summer 2015); Jeffrey Diament, Austin Djang, and Jessica Swan (Summer 2016); Makenzie Allen (Summer 2017); Colin Lauzon (Fall 2017); Eric Gilkey (Spring 2018); and Nicholas Masso (Summer 2019). These interns have spent their internships reviewing flight logs, extracting the data out of TRENDS, formatting the data into spreadsheets, writing code to automate the process, and plotting results. Without their efforts, much of the work would be unfinished. The authors appreciate the achievements of the UH-60A Airloads Working Group during its 20-year lifetime, as well the contributions of Randy Peterson, Tom Norman, and William Warmbrodt to the data processing and assistance with the report preparation. Lastly, this report is dedicated to William Bousman for his efforts preceding, during, and subsequent to the UH-60A Airloads Flight Test Program.

Kufeld, Robert M.↗

Sandia Data Archive (SDA) file specifications

The Sandia Data Archive (SDA) format is a specific implementation of the HDF5 (Hierarchal Data Format version 5) standard. The format was developed for storing data in a universally accessible manner. SDA files may contain one or more data records, each associated with a distinct text label. Primitive records provide basic data storage, while compound records support more elaborate grouping. External records allow text/binary files to be carried inside an archive and later recovered. This report documents version 1.0 of the SDA standard. The information provided here is sufficient for reading from and writing to an archive. Although the format was original designed for use in MATLAB, broader use is encouraged.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

BOREAS Soils Data over the SSA in Raster Format and AEAC Projection

This data set consists of GIS layers that describe the soils of the BOREAS SSA. The original data were submitted as vector layers that were gridded by BOREAS staff to a 30-meter pixel size in the AEAC projection. These data layers include the soil code (which relates to the soil name), modifier (which also relates to the soil name), and extent (indicating the extent that this soil exists within the polygon). There are three sets of these layers representing the primary, secondary, and tertiary soil characteristics. Thus, there is a total of nine layers in this data set along with supporting files. The data are stored in binary, image format files.

Knapp, David↗

Prediction of plant complex traits via integration of multi-omics data

The formation of complex traits is the consequence of genotype and activities at multiple molecular levels. However, connecting genotypes and these activities to complex traits remains challenging. Here, we investigate whether integrating genomic, transcriptomic, and methylomic data can improve prediction for six Arabidopsis traits. We find that transcriptome- and methylome-based models have performances comparable to those of genome-based models. However, models built for flowering time using different omics data identify different benchmark genes. Nine additional genes identified as important for flowering time from our models are experimentally validated as regulating flowering. Gene contributions to flowering time prediction are accession-dependent and distinct genes contribute to trait prediction in different genotypes. Models integrating multi-omics data perform best and reveal known and additional gene interactions, extending knowledge about existing regulatory networks underlying flowering time determination. These results demonstrate the feasibility of revealing molecular mechanisms underlying complex traits through multi-omics data integration.

59 BASIC BIOLOGICAL SCIENCES↗

Subsetting and Formatting Landsat-7 LOR ETM+ and Data Products

The Landsat-7 Processing System (LPS) processes Landsat-7 Enhanced Thematic Mapper (ETM+) instrument data into large, contiguous segments called "subintervals" and stores them in Level OR (LOR) data files. The LPS processed subinterval products must be subsetted and reformatted before the Level I processing systems can ingest them. The initial full subintervals produced by the LPS are stored mainly in HDF Earth Observing System (HDF-EOS) format which is an extension to the Hierarchical Data Format (HDF). The final LOR products are stored in native HDF format. Primarily the EOS Core System (ECS) and alternately the DAAC Emergency System (DES) subset the subinterval data for the operational Landsat-7 data processing systems. The HDF and HDF-EOS application programming interfaces (APIs) can be used for extensive data subsetting and data reorganization. A stand-alone subsetter tool has been developed which is based on some of the DES code. This tool makes use of the HDF and HDFEOS APIs to perform Landsat-7 LOR product subsetting and demonstrates how HDF and HDFEOS can be used for creating various configurations of full LOR products. How these APIs can be used to efficiently subset, format, and organize Landsat-7 LOR data as demonstrated by the subsetter tool and the DES is discussed.

Reid, Michael R.↗

mzapy : An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format

We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of a core library with standard mass spectrometry utilities greatly facilitates fast software development. Here, we present a Python library, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python with minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multi-omics domain. The free and open source mzapy is built with extensibility in mind, and future development will support cloud computing and artificial intelligence/machine learning applications. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Large Impact Basins on Mercury: Global Distribution, Characteristics, and Modification History from MESSENGER Orbital Data

The formation of large impact basins (diameter D greater than or equal to 300 km) was an important process in the early evolution of Mercury and influenced the planet's topography, stratigraphy, and crustal structure. We catalog and characterize this basin population on Mercury from global observations by the MESSENGER spacecraft, and we use the new data to evaluate basins suggested on the basis of the Mariner 10 flybys. Forty-two certain or probable impact basins are recognized a few additional basins that may have been degraded to the point of ambiguity are plausible on the basis of new data but are classified as uncertain. The spatial density of large basins (D greater than or equal to 500 km) on Mercury is lower than that on the Moon. Morphological characteristics of basins on Mercury suggest that on average they are more degraded than lunar basins. These observations are consistent with more efficient modification, degradation, and obliteration of the largest basins on Mercury than on the Moon. This distinction may be a result of differences in the basin formation process (producing fewer rings), greater relaxation of topography after basin formation (subduing relief), and/or higher rates of volcanism during the period of heavy bombardment on Mercury compared to the Moon (burying basin rings and interiors).

Fassett, Caleb I.↗

FIRST SIMULTANEOUS DETECTION OF MOVING MAGNETIC FEATURES IN PHOTOSPHERIC INTENSITY AND MAGNETIC FIELD DATA

The formation and the temporal evolution of a bipolar moving magnetic feature (MMF) was studied with high-spatial and temporal resolution. The photometric properties were observed with the New Solar Telescope at Big Bear Solar Observatory using a broadband TiO filter (705.7 nm), while the magnetic field was analyzed using the spectropolarimetric data obtained by Hinode. For the first time, we observed a bipolar MMF simultaneously in intensity images and magnetic field data, and studied the details of its structure. The vector magnetic field and the Doppler velocity of the MMF were also studied. A bipolar MMF with its positive polarity closer to the negative penumbra formed, accompanied by a bright, filamentary structure in the TiO data connecting the MMF and a dark penumbral filament. A fast downflow ({<=}2 km s{sup -1}) was detected at the positive polarity. The vector magnetic field obtained from the full Stokes inversion revealed that a bipolar MMF has a U-shaped magnetic field configuration. Our observations provide a clear intensity counterpart of the observed MMF in the photosphere, and strong evidence of the connection between the MMF and the penumbral filament as a serpentine field.

79 ASTRONOMY AND ASTROPHYSICS↗

An HDF5-Based Framework for the Distribution and Analysis of Ultrasonic Concrete Data

There are many commercial ultrasonic tomography devices (UTDs) available for use in nondestructive evaluation (NDE) of reinforced concrete structures. These devices emit, measure, and store ultrasonic signals typically in the 25 kHz to 5 MHz frequency range. UTDs are characterized by a composition of multiple transducers, also known as a transducer array or phased array. Often, UTDs data are in a proprietary format. Consequently, NDE research data is limited to those who have prior non-disclosure agreements or the appropriate licenses. Thus, there is a need for a proper universal data framework to exist such that proprietary file datasets for different concrete specimens can be converted, organized, and stored with relative metadata for individual or collaborative NDE research. Building upon the Hierarchical Data Format (HDF5) model, we have developed a UTD data management framework and Graphic User Interface (GUI) to promote the algorithmic reconstruction of ultrasonic data in a controlled environment for easily reproducible and publishable results.

concrete NDE↗

Restoration of the Potosi Dynamic Model 2010

In topical Report DOE/FE0002068-1 [2] technical performance evaluations on the Cambrian Potosi Formation were performed through reservoir modeling. The data included formation tops from mud logs, well logs from the VW1 and the CCS1 wells, structural and stratigraphic formation from three dimensional (3D) seismic data, and field data from several waste water injection wells for Potosi Formation. Intention was for two million tons per annum (MTPA) of CO2 to be injected for 20 years. In this Task the 2010 Potosi heterogeneous model (referred to as the "Potosi Dynamic Model 2010" in this report) was re-run using a new injection scenario; 3.2 MTPA for 30 years. The extent of the Potosi Dynamic Model 2010, however, appeared too small for the new injection target. It was not sufficiently large enough to accommodate the evolution of the plume. Also, it might have overestimated the injection capacity by enhancing too much the pressure relief due to the relatively close proximity between the injector and the infinite acting boundaries. The new model, Potosi Dynamic Model 2013a, was built by extending the Potosi Dynamic Model 2010 grid to 30 miles x 30 miles (48 km by 48 km), while preserving all property modeling workflows and layering. This model was retained as the base case. Potosi Dynamic Model 2013.a gives an average CO2 injection rate of 1.4 MTPA and cumulative injection of 43 Mt in 30 years, which corresponds to 45% of the injection target. This implies that according to this preliminary model, a minimum of three (3) wells could be required to achieve the injection target. The injectivity evaluation of the Potosi formation will be revisited in topical Report 15 during which more data will be integrated in the modeling exercise. A vertical flow performance evaluation could be considered for the succeeding task to determine the appropriate tubing size, the required injection tubing head pressure (THP) and to investigate whether the corresponding well injection rate falls within the tubing erosional velocity limit. After 30 years, the plume extends 15 miles (24 km) in E-W and 14 miles (22 km) in N-S directions. After injection is completed, the plume continues to migrate laterally, mainly driven by the remaining pressure gradient. After 100 years post-injection, the plume extends 17 miles (27 km) in E-W and 15 miles (24 km) in N-S directions. The increase of reservoir pressure at the end of injection is approximately 370 psia around the injector and gradually decreases away from the well. The reservoir pressure increase is less than 30 psia beyond 14 miles (22 km) away from injector. The initial reservoir pressure is restored after approximately 20 years post-injection. This result, however, is associated with uncertainties on the boundary conditions, and a sensitivity analysis could be considered for the succeeding tasks. It is important to remember that the respective plume extent and areal pressure increase corresponds to an injection of 43 Mt CO2. Should the targeted cumulative injection of 96 Mt be achieved; a much larger plume extent and areal pressure increase could be expected. Re-evaluating the permeability modeling, vugs and heterogeneity distributions, and relative permeability input could be considered for the succeeding Potosi formation evaluations. A simulation using several injectors could also be considered to determine the required number of wells to achieve the injection target while taking into account the pressure interference.

Adushita, Yasmin↗

ncompare: A Python Package for Comparing netCDF Structures

Earth science researchers and data engineers have a common problem: they often need to compare data files to see what is different between them. A lot of time is spent developing code to test differences. When it comes to comparing multidimensional data file formats like netCDFs (Network Common Data Form), this is particularly challenging and time-consuming, since there is frequently a need to evaluate the differences between dimension sizes, variable structures, and variable attributes, especially for regression testing. Since netCDFs are widely used in Earth science — with climate models, oceanographic or atmospheric reanalyses, and observational data — improved means of evaluating netCDF files can help enable a wide range of applications. We have developed a reusable open source approach through `ncompare`, which is a Python package for comparing netCDF structures [[https://github.com/nasa/ncompare]]. The `ncompare` tool compares the structure of two Network Common Data Form (NetCDF) files at the command line. It facilitates rapid comparisons by generating a formatted display of the matching and non-matching groups, variables, and associated metadata between two NetCDF datasets. The user has the option to colorize the terminal output for ease of viewing, and `ncompare` can optionally save comparison reports in text, comma-separated value (CSV), and/or Microsoft Excel formats. Despite the availability of tools (such as ncmpidiff or nccmp) that compare the values of variables, there was not previously a readily available, Python-based tool for rapid visual comparisons of group and variable structures, attributes, and chunking. `ncompare` was developed at NASA’s Atmospheric Science Data Center (ASDC) and is a collaboration with NASA Openscapes [[https://nasa-openscapes.github.io]] mentors across 11 of NASA’s data centers. Openscapes’ overarching vision is to support scientific researchers using NASA Earthdata as they migrate their workflows to the cloud. Relevant links: - https://github.com/nasa/ncompare - https://github.com/pyOpenSci/software-submission/issues/146 - https://nasa-openscapes.github.io

Daniel Kaufman↗

Tropical easterly jet located using TOMS data

The formative stages of the onset of the 1979 southwest monsoon was marked by a WNW-ESE oriented band of marine convection over the South Arabian Sea. This convection was first observed on June 10, 1979 using satellite cloud imagery. The marine convection appeared during a major acceleration of the upper troposphere easterly wind field. A composite vertical meridional cross-section of upper level winds for June 11, revealed the core of the Tropical Easterly Jet (TEJ) at 115 mb, 9.5 deg N. Time analysis of the upper level wind field over the Tropical Wind Observing Ship (TWOS) polygon show a lowering of both the pressure level of maximum wind and tropopause level with acceleration of the upper level easterlies. The tropopause was as much as 20 mb lower on the equatorial side of the TEJ. Streamline analysis of the maximum observed easterly winds over India did not reveal the horizontal position of the TEJ. Careful analysis of Total Ozone Mapping Spectrometer (TOMS) data for June 11, 1979 showed relatively high values of ozone south of India. It was observed that the latitudinal position of the TEJ on June 11, at approximately 70 deg E coincided with the northern edge of relatively high ozone values. Using this as a reference, the TEJ core was identified as far as NE Bay of Bengal (the limits of the available TOMS data).

Bolhofer, William C.↗