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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.

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At least 145 records · Page 8

From particle attachment to space-filling coral skeletons

Reef-building corals and their aragonite (CaCO 3 ) skeletons support entire reef ecosystems, yet their formation mechanism is poorly understood. Here we used synchrotron spectromicroscopy to observe the nanoscale mineralogy of fresh, forming skeletons from six species spanning all reef-forming coral morphologies: Branching, encrusting, massive, and table. In all species, hydrated and anhydrous amorphous calcium carbonate nanoparticles were precursors for skeletal growth, as previously observed in a single species. The amorphous precursors here were observed in tissue, between tissue and skeleton, and at growth fronts of the skeleton, within a low-density nano- or microporous layer varying in thickness from 7 to 20 µm. Brunauer-Emmett-Teller measurements, however, indicated that the mature skeletons at the microscale were space-filling, comparable to single crystals of geologic aragonite. Nanoparticles alone can never fill space completely, thus ion-by-ion filling must be invoked to fill interstitial pores. Such ion-by-ion diffusion and attachment may occur from the supersaturated calcifying fluid known to exist in corals, or from a dense liquid precursor, observed in synthetic systems but never in biogenic ones. Concomitant particle attachment and ion-by-ion filling was previously observed in synthetic calcite rhombohedra, but never in aragonite pseudohexagonal prisms, synthetic or biogenic, as observed here. Models for biomineral growth, isotope incorporation, and coral skeletons’ resilience to ocean warming and acidification must take into account the dual formation mechanism, including particle attachment and ion-by-ion space filling.

59 BASIC BIOLOGICAL SCIENCES↗

Optical Measurement Center Status

Beginning in 2005, an optical measurement center (OMC) was created to measure the photometric signatures of debris pieces. Initially, the OMC was equipped with a 300 W xenon arc lamp, a SBIG 512 x 512 ST8X MEI CCD camera with standard Johnson filters, and a Lynx 6 robotic arm with five degrees of freedom. As research progressed, modifications were made to the equipment. A customized rotary table was built to overcome the robot s limitation of 180 degree wrist rotation and provide complete 360 degree rotation with little human interaction. This change allowed an initial phase angle (source-object-camera angle) of roughly 5 degrees to be adjusted to 7, 10, 15, 18, 20, 25, or 28 degrees. Additionally, the Johnson R and I CCD filters were replaced with the standard astronomical filters suite (Bessell R,I). In an effort to reduce object saturation, the two generic aperture stops were replaced with neutral density filters. Initially data were taken with aluminum debris pieces from the European Space Operations Centre ESOC2 ground test and more recently with samples from a thermal multi-layered insulation (MLI) commonly used on rocket bodies and satellites. The ESOC2 data provided light curve analysis for one type of material but many different shapes, including flat, bent, curled, folded, and torn. The MLI samples are roughly the same size and shape, but have different surfaces that give rise to interesting photometric light curves. In addition, filter photometry was conducted on the MLI pieces, a process that also will be used on the ESOC2 samples. While obtaining light curve data an anomalous drop in intensity was observed when the table revolved through the second 180 degree rotation. Investigation revealed that the robot s wrist rotation is not reliable past 80 degrees, thus the object may be at slightly different angles at the 180 degree transition. To limit this effect, the initial rotation position begins with the object s minimal surface area facing the camera.

Rodriguez, H.↗

Retrieval of the sea spray aerosol mode from submicron particle size distributions and supermicron scattering during LASIC

Abstract. Improved quantification of sea spray aerosol concentration and size is important for determining aerosol effects on clouds and the climate, though attempts to accurately capture the size distribution of the sea spray mode remain limited by the availability of supermicron size distributions. In this work, we introduce a new approach to retrieving lognormal mode fit parameters for a sea spray aerosol mode by combining submicron size distributions with supermicron scattering measurements using a Mie inversion. Submicron size distributions were measured by an ultra-high-sensitivity aerosol spectrometer (UHSAS), and supermicron scattering was taken as the difference between <10 µm and <1 µm three-wavelength integrating nephelometer measurements (NEPH). This UHSAS-NEPH method was applied during background marine periods of the Department of Energy Atmospheric Radiation Measurement Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign on Ascension Island (November 2016–May 2017), when the contribution of sea spray aerosol was expected to represent a large fraction of the aerosol mass and total scattering. Lognormal sea spray modal parameters were retrieved from comparisons between nephelometer measurements and a lookup table of Mie theory-simulated scattering coefficients for low-error solutions that minimized the 0.4–1 µm residual in the UHSAS size distribution. We evaluated the UHSAS-NEPH method with a set of clean marine measurements in the North Atlantic that included supermicron size and chemical measurements, showing that measured supermicron size distributions are needed to constrain the sea spray number concentration but that mass concentration was reasonably characterized using supermicron scattering. For LASIC, the UHSAS-NEPH method retrieved sea spray mode properties for approximately 88 % of the background marine times when the scattering variability and total particle concentration were low (<± 5 Mm−1 and <400 cm−3, respectively), with mass mean diameter ranging from 0.6 to 1.9 µm (1.47 ± 0.17 µm), modal width ranging from 1.1 to 3.97 (2.4±0.3), and mass concentration ranging from 0.18 to 23.0 µg m−3 (8.37. ± 4.1 µg m−3). The measured nephelometer scattering at three wavelengths was found to constrain the mode width marginally at the largest particle sizes in the absence of additional size and chemical measurements for defining parameters for the Mie solutions. Comparing UHSAS-NEPH retrievals to those of a fitting algorithm applied only to the submicron UHSAS number size distribution showed that correlations between retrieved mass concentration and the available mass-based sea spray tracers (coarse scattering, wind speed, and chloride) are low when supermicron measurements are not considered. This work demonstrates the added value of supermicron scattering measurements for retrieving reasonable sea spray mass concentrations, providing the best-available observationally constrained estimate of the sea spray mode properties when supermicron size distribution measurements are not available.

54 ENVIRONMENTAL SCIENCES↗

Meteoric 10Be Flux Calibration Data for the East River Watershed, Colorado, USA

This data package contains tabular and geospatial data used to quantify and model meteoric beryllium-10 fluxes in the East River watershed, Colorado, USA. The tabular component includes calibration-site data from five glacial moraine sites and includes environmental variables used to evaluate spatial controls on meteoric 10Be delivery, including elevation, mean annual precipitation (MAP), mean snow depth, and mean snow water equivalent (SWE). These site-level data were used to compare observed fluxes with environmental gradients across the watershed and to evaluate the effects of erosion correction on flux estimates. The package also includes supporting slope and curvature values used to assess topographic inputs to the erosion analysis. A second component of the data package contains updated manuscript tables and regression outputs used to summarize the relationships between meteoric 10Be flux and environmental predictors. These tables include meteoric 10Be sample information and AMS results, site-level environmental values, site-level meteoric 10Be inventory and flux values, watershed-averaged predicted fluxes, soil bulk density measurements, fine-fraction values, soil pH measurements, and regression statistics including slope, intercept, coefficient of determination, and p-value. The regression products include both standard linear regressions and regressions in which the intercept is constrained to pass through zero, and they support the analyses presented in the companion manuscript. Together, these tabular files provide the numerical basis for the manuscript tables and the regression-based interpretation of meteoric 10Be flux variability in a snow-dominated mountain watershed. The geospatial component of the package consists of GeoTIFF raster files used to generate the map products presented in Figures 2 and 6 of the companion manuscript. These rasters represent watershed-scale spatial layers for environmental variables and regression-based predictions of meteoric 10Be flux. This dataset contains comma-separated values files (.csv), Microsoft Excel files (.xlsx), GeoTIFF raster files (.tif), and upporting metadata files, including CSV data dictionaries and readme text files (.csv, .txt). The tabular files can be opened with standard spreadsheet software, and the raster files can be viewed and analyzed in GIS software such as ArcGIS Pro or QGIS. Together, these files document the numerical and spatial datasets used to calibrate and predict meteoric 10Be delivery in the East River watershed.

East River↗

Comets, volcanism, the salt rich regolith and cycling of volatiles on Mars

The composition of the Martian surface and its evolution are examined, reviewing the results of recent theoretical models and composition estimates based on Viking lander analyses. The data are compiled in tables and characterized in detail, and a high degree of variation among the predictions is noted. The discussion centers on the possible roles of comets (as sources of volatiles), the salt rich regolith (as an important water sink), and volcanic activity (interfering with volatile-recycling processes and eventually producing a volatile depleted surface layer).

Clark, Benton C.↗

A Deep Learning Approach to Fast Radiative Transfer

Due to the sheer volume of data, leveraging satellite instrument observations effectively in a data assimilation context for numerical weather prediction or for remote sensing requires a radiative transfer model as an observation operator that is both fast and accurate at the same time. Physics-based line-by-line radiative transfer (RT) models fulfil the requirement for accuracy, but are too slow and too costly in computational terms for operational applications. Therefore, fast methods were developed to be able to perform fast RT calculations using techniques such as spectral sampling or pre-computed look-up tables. The operational fast models currently calculate the absorption and scattering coefficients from the pre-computed regression coefficients and atmospheric state and cloud profiles. As a novel solution to this problem, this work investigates a deep learning approach to replace the regression coefficients in the fast RT models. A selection of hidden-layer neural network configurations is trained against atmospheric transmittance profile data computed by an accurate line-by-line model and their performance is evaluated and their advantages and disadvantages are discussed.

Machine learning↗

The Unified Phenotype Ontology : a framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

59 BASIC BIOLOGICAL SCIENCES↗

KBase Narrative - Porphyromonadaceae sp. W3.11 genome

Narratives for The phenotype and genotype of fermentative prokaryotes This is the Narrative for Porphyromonadaceae sp. W3.11. A complementary Narrative for Lachnospiraceae sp. C1.1 is available here. This is the Narrative for Lachnospiraceae sp. C1.1. A complementary Narrative for Porphyromonadaceae sp. W3.11 is available here. Background and Isolation This Narrative and its complementary Narrative contain assembly and annotation of two bacterial isolates that were isolated by our laboratory from the rumen of a Holstein heifer. All procedures with animals have been approved by University of California Davis’s Institutional Animal Care and Use Committee. Rumen contents were collected through a rumen fistula and strained through two layers of cheesecloth into a bottle. The bottle was sealed to exclude air and maintained at 39°C. Contents were brought to the laboratory and bubbled under O2-free CO2 within 15 min. At the laboratory, serial dilutions were made with anaerobic dilution solution for Lachnospiraceae sp. C1.1 and propionibacterium diluent for Porphyromonadaceae sp. W3.11 (table S2). Aliquots (0.1 ml) of each dilution were injected into anaerobic bottle plates (1) containing 9 ml of LH medium (table S2). After incubation at 37°C for 7 days, isolated colonies were picked. Lachnospiraceae sp. C1.1 was picked from a bottle inoculated with a 104 dilution of rumen contents, and Porphyromonadaceae sp. W3.11 was picked from a bottle inoculated with a 103 dilution. After initial isolation, these organisms were purified by growing on anaerobic roll tubes (2) and picking isolated colonies. We performed de novo sequencing of Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11. Aliquots of liquid culture (9 and 1.5 ml, respectively) were collected by syringe and centrifuged (21,000g for 10 min at 4°C). Cell pellets were submitted to Molecular Research LP for DNA extraction, library preparation, and sequencing. After resuspending pellets in 180 µl of ATL buffer (Qiagen), DNA was extracted using the MagAttract HMW DNA Kit (Qiagen). DNA was eluted in 100 µl of AE buffer (Qiagen) and then cleaned using the DNEasy PowerClean Pro Cleanup Kit (Qiagen). DNA was then sheared using the Covaris g-TUBE (Covaris). Sequencing libraries were prepared using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences) and 1500 ng of the sheared and purified DNA. The SMRTbell libraries were size-selected (>6 Kb) using a BluePippin instrument (Sage Science) and 0.75% agarose gel. Libraries were then sequenced using the PacBio Sequel II (Pacific Biosciences) platform and a 30-hour movie time. Narrative Summary In these Narratives, we filtered low-quality reads using Trimmomatic (v0.36), assembled filtered reads with SPAdes (v3.15.3), and then checked completeness and contamination of the assembled genomes with CheckM (v1.0.18). Statistics for sequencing and assembly are in table S3. Using the assembled contigs (genomes), we called genes and annotated them. Protein-coding genes were called using Prodigal (v2.6.3) (3) locally or using KBase via RASTtk (v1.073), with identical results. Genes were annotated with KO IDs using KAAS (4). They were further annotated with pfam and TIGRFAM IDs using KBase and the Annotate Domains in a Genome app. We classified putative genes for hydrogenases using HydDB. Genes for 16S ribosomal RNA (rRNA) were called using RASTtk (v1.073) in KBase. The contigs (genomes) were analyzed to determine whether they belonged to new species. Taxonomy was assigned using GTDB-Tk (v1.7.0) in KBase. The identity of 16S rRNA genes to other organisms was found using EzBioCloud (5). Values of digital DNA-DNA hybridization (dDDH) were found with Type (Strain) Genome Server (6). These analyses suggest that Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11 represent novel species or genera. GTDB-Tk assigned Lachnospiracae sp. C1.1 to family Lachnospiraceae and genus NK4A144, which contains no type strains. It assigned Porphyromonadaceae sp. W3.11 to Porphyromonadaceae and genus Porphyromonas_A. Values of 16S rRNA identity and dDDH with respect to type strains were low (table S4). Although more phenotypic data are needed, available evidence supports assignment of genomes to new species or genera. Related publication Hackmann TJ, Zhang B. The phenotype and genotype of fermentative prokaryotes. Sci Adv. 2023 Sep 29;9(39):eadg8687. doi: 10.1126/sciadv.adg8687. Epub 2023 Sep 27. PMID: 37756392; PMCID: PMC10530074.

Hackmann, Timothy↗

KBase Narrative - Lachnospiraceae sp. C1.1 genome

Narratives for The phenotype and genotype of fermentative prokaryotes This is the Narrative for Porphyromonadaceae sp. W3.11. A complementary Narrative for Lachnospiraceae sp. C1.1 is available here. This is the Narrative for Lachnospiraceae sp. C1.1. A complementary Narrative for Porphyromonadaceae sp. W3.11 is available here. Background and Isolation This Narrative and its complementary Narrative contain assembly and annotation of two bacterial isolates that were isolated by our laboratory from the rumen of a Holstein heifer. All procedures with animals have been approved by University of California Davis’s Institutional Animal Care and Use Committee. Rumen contents were collected through a rumen fistula and strained through two layers of cheesecloth into a bottle. The bottle was sealed to exclude air and maintained at 39°C. Contents were brought to the laboratory and bubbled under O2-free CO2 within 15 min. At the laboratory, serial dilutions were made with anaerobic dilution solution for Lachnospiraceae sp. C1.1 and propionibacterium diluent for Porphyromonadaceae sp. W3.11 (table S2). Aliquots (0.1 ml) of each dilution were injected into anaerobic bottle plates (1) containing 9 ml of LH medium (table S2). After incubation at 37°C for 7 days, isolated colonies were picked. Lachnospiraceae sp. C1.1 was picked from a bottle inoculated with a 104 dilution of rumen contents, and Porphyromonadaceae sp. W3.11 was picked from a bottle inoculated with a 103 dilution. After initial isolation, these organisms were purified by growing on anaerobic roll tubes (2) and picking isolated colonies. We performed de novo sequencing of Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11. Aliquots of liquid culture (9 and 1.5 ml, respectively) were collected by syringe and centrifuged (21,000g for 10 min at 4°C). Cell pellets were submitted to Molecular Research LP for DNA extraction, library preparation, and sequencing. After resuspending pellets in 180 µl of ATL buffer (Qiagen), DNA was extracted using the MagAttract HMW DNA Kit (Qiagen). DNA was eluted in 100 µl of AE buffer (Qiagen) and then cleaned using the DNEasy PowerClean Pro Cleanup Kit (Qiagen). DNA was then sheared using the Covaris g-TUBE (Covaris). Sequencing libraries were prepared using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences) and 1500 ng of the sheared and purified DNA. The SMRTbell libraries were size-selected (>6 Kb) using a BluePippin instrument (Sage Science) and 0.75% agarose gel. Libraries were then sequenced using the PacBio Sequel II (Pacific Biosciences) platform and a 30-hour movie time. Narrative Summary In these Narratives, we filtered low-quality reads using Trimmomatic (v0.36), assembled filtered reads with SPAdes (v3.15.3), and then checked completeness and contamination of the assembled genomes with CheckM (v1.0.18). Statistics for sequencing and assembly are in table S3. Using the assembled contigs (genomes), we called genes and annotated them. Protein-coding genes were called using Prodigal (v2.6.3) (3) locally or using KBase via RASTtk (v1.073), with identical results. Genes were annotated with KO IDs using KAAS (4). They were further annotated with pfam and TIGRFAM IDs using KBase and the Annotate Domains in a Genome app. We classified putative genes for hydrogenases using HydDB. Genes for 16S ribosomal RNA (rRNA) were called using RASTtk (v1.073) in KBase. The contigs (genomes) were analyzed to determine whether they belonged to new species. Taxonomy was assigned using GTDB-Tk (v1.7.0) in KBase. The identity of 16S rRNA genes to other organisms was found using EzBioCloud (5). Values of digital DNA-DNA hybridization (dDDH) were found with Type (Strain) Genome Server (6). These analyses suggest that Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11 represent novel species or genera. GTDB-Tk assigned Lachnospiracae sp. C1.1 to family Lachnospiraceae and genus NK4A144, which contains no type strains. It assigned Porphyromonadaceae sp. W3.11 to Porphyromonadaceae and genus Porphyromonas_A. Values of 16S rRNA identity and dDDH with respect to type strains were low (table S4). Although more phenotypic data are needed, available evidence supports assignment of genomes to new species or genera. Related publication Hackmann TJ, Zhang B. The phenotype and genotype of fermentative prokaryotes. Sci Adv. 2023 Sep 29;9(39):eadg8687. doi: 10.1126/sciadv.adg8687. Epub 2023 Sep 27. PMID: 37756392; PMCID: PMC10530074.

Hackmann, Timothy↗

A new template for developing C++ applications in NASA's Core Flight System

In this presentation, we will demonstrate an example Core Flight System (cFS) application written in C++, compatible with the Draco releases of the Core Flight Executive (cFE) and NASA Operating System Abstraction Layer (OSAL). The application boilerplate, supporting library, and associated generation script were recently developed and licensed under the permissive Apache License 2.0 with the goal of easing the cFS app development with C++. The design and features of this application will be presented, including a higher-level interface for interactions with the cFE software bus pipes, tables, and event services. Data structures are provided for centralized telecommand and telemetry parsing which isolates bookkeeping of message components from the calling code in an application's core logic. Specific advantages of writing a cFS application in C++ will be shown, including easier avoidance of symbol collisions via namespaces, expanded compile-time checks via constant expressions, default initialization for data structures, null safety via references, improved syntax for operating on multi-dimensional arrays, and reliable serialization of enumerations via enumeration classes. Special considerations needed for integrating a C++ application will be identified, including function linkage, exceptions, and stack unwinding. Evidence for the usefulness of this template will be discussed in the context of development of a flight software application used for interfacing with a solid-state data recorder.

Dominick Allen↗

Prediction of Thermal Conditions of DED With FEA Metal Additive Simulation

This paper presents the integration of wire-arc additive manufacturing (WAAM) using Gas Metal Arc Welding (GMAW) into a machine tool to create a retrofit hybrid computer numeric control (CNC) machine tool. GMAW, along with other direct energy deposition systems, has the capacity to deposit material faster than the excess thermal energy can dissipate. This results in the need to allow the part to cool between consecutive layers, which is the most time-consuming part of the additive process. Finite element analysis (FEA) was used in conjunction with monitored build plate surface temperatures during deposition samples to improve adequate dwell time prediction and to develop a cooling system. A deposition was completed where no dwell time was used and the build plate along with the machine table temperatures were monitored. A second deposition was completed where only one bead was deposited and the traverse speed was increased. The GMAW welder was mounted on a 3-axis CNC machine where two square deposition samples were completed. A FEA model was designed and verified using the monitored samples. The model will be used to determine improved depositions speeds and whether forced cooling would allow for an increased deposition rate without structural failure. It was determined the FEA software can be used to accurately model and predict the thermal response of WAAM AM components.

Heinrich, Lauren↗

Airborne lidar measurements of El Chichon stratospheric aerosols, October 1982 to November 1982

A coordinated flight mission to determine the spatial distribution and aerosol characteristics of the El Chichon produced stratospheric aerosol was flown in October to November 1982. The mission covered 46 deg N to 46 deg S and included rendezvous between balloon-, airplane-, and satellite-borne sensors. The lidar data from the flight mission are presented. Representative profiles of lidar backscatter ratio, plots of the integrated backscattering function versus latitude, and contours of backscatter mixing ratio versus altitude and latitude are given. In addition, tables containing numerical values of the backscatter ratio and backscattering functions versus altitude are supplied for each profile. The bulk of the material produced by the El Chichon eruptions of late March 10 to early April 1982 resided between latitudes from 5 to 7 deg S to 35 to 37 deg N and was concentrated above 21 km in a layer that peaked at 23 to 25 km. In this latitude region, peak scattering ratios at a wavelength of 0.6943 micron were approximately 24. The results of this mission are presented in a ready-to-use format for atmospheric and climatic studies.

Mccormick, M. P.↗

Aircraft Seat Cushion Fire-Blocking Layers

229-page report describes work done to determine burning characteristics of present and proposed seat-cushion materials and types of construction. Tested cushions classified in four groups: standard cushion construction, standard cushion construction with protective covering enveloping urethane-foam core, standard cushion construction with protective covering enveloping non-fire-retarded urethane-foam core, and standard cushion construction with urethane-foam core replaced by advanced fire-resistant foam. Report includes still photographs and presents quantitative data from each test in graphs and tables.

Schutter, Kenneth J.↗

Ocean observations with EOS/MODIS: Algorithm development and post launch studies

An investigation of the influence of stratospheric aerosol on the performance of the atmospheric correction algorithm was carried out. The results indicate how the performance of the algorithm is degraded if the stratospheric aerosol is ignored. Use of the MODIS 1380 nm band to effect a correction for stratospheric aerosols was also studied. The development of a multi-layer Monte Carlo radiative transfer code that includes polarization by molecular and aerosol scattering and wind-induced sea surface roughness has been completed. Comparison tests with an existing two-layer successive order of scattering code suggests that both codes are capable of producing top-of-atmosphere radiances with errors usually less than 0.1 percent. An initial set of simulations to study the effects of ignoring the polarization of the the ocean-atmosphere light field, in both the development of the atmospheric correction algorithm and the generation of the lookup tables used for operation of the algorithm, have been completed. An algorithm was developed that can be used to invert the radiance exiting the top and bottom of the atmosphere to yield the columnar optical properties of the atmospheric aerosol under clear sky conditions over the ocean, for aerosol optical thicknesses as large as 2. The algorithm is capable of retrievals with such large optical thicknesses because all significant orders of multiple scattering are included.

Gordon, Howard R.↗

Software for Processing Flight and Simulated Data of the ATIC Experiment

ATIC (Advanced Thin Ionization Calorimeter) is a balloon borne experiment designed to measure the cosmic ray composition for elements from hydrogen to iron and their energy spectra from approx.50 GeV to near 100 TeV. It consists of a Si-matrix detector to determine the charge of a CR particle, a scintillator hodoscope for tracking, carbon interaction targets and a fully active BGO calorimeter. ATIC had its first flight from McMurdo, Antarctica from 28/12/2000 to 13/01/2001. The ATIC flight collected approximately 25 million events. A C++-class library for building different programs for processing flight and simulated data of the ATIC balloon experiment is described. This library is compatible with the ROOT-system and includes classes and methods for solving a number of problems as the following: Reading data files in different formats (raw-data format, ROOT-format, ASCII-format, different formats for simulated data); Transferring all these formats to the only inner format of the library; Reconstruction of trajectories of primary particles with BGO calorimeter only. The Monte-Carlo simulations with GEANT code were used to obtain the basic tables for computing error corridors and chi(sup 2)-values for the trajectories. Obtaining error corridors for searching for signal of primary particle in the Si-matrix; Searching for hit of primary particle in the Si-matrix with using of error corridor and other criteria (chi(sup 2)-values, agreement between signals in Si-matrix and in the upper layer of scintillator and others); Determination of charge of primary particle; Determination of energy deposit in BGO calorimeter.

Panov, A. D.↗

AWAKEN Site A1 - NREL Ceilometer (Vaisala CL51) / Derived Data

Site A1 netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined

17 WIND ENERGY↗

WFIP3 - BARG site - NREL Ceilometer (Vaisala CL51) / Derived Data

Site A1 netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined

17 WIND ENERGY↗

WFIP3 - BARG site - NREL Ceilometer (Vaisala CL51) / Derived Data

BARG ceilometer: netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values, and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined

17 WIND ENERGY↗