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The University of Alabama in Huntsville (UAH) has completed the research proposed. The major tasks under this contract were: (1) research into visualization of scientific data sets (browse); (2) studies of standard data formatting procedures; and (3) investigations of approaches for submission of scientific data sets for archival. Summaries of each activity are presented along with travel reports and conclusions and recommendations.
We made more tests of the version 2.0 daily Level 2 and Level 3 Land-Surface Temperature (LST) code (PGE 16) jointly with the MODIS Science Data Support Team (SDST). After making minor changes a few times, the PGE16 code has been successfully integrated and tested by MODIS SDST, and recently has passed the inspection at the Goddard Distributed Active Archive Center (DAAC). We conducted a field campaign in the area of Mono Lake, California on March 10, 1998, in order to validate the MODIS LST algorithm in cold and dry conditions. Two MODIS Airborne Simulator (MAS) flights were completed during the field campaign, one before noon, and another around 10 pm PST. The weather condition for the daytime flight was perfect: clear sky, the column water vapor measured by radiosonde around 0.3 cm, and wind speed less than a half meter per second. The quality of MAS data is good for both day and night flights. We analyzed the noise equivalent temperature difference (NE(delta)T) and the calibration accuracy of the seven MAS thermal infrared (TIR) bands, that are used in the MODIS day/night LST algorithm, with daytime MAS data over four flat homogeneous study areas: two on Grant Lake (covered with ice and snow, respectively), one on Mono Lake, and another on the snow field site where we made field measurements. NE(delta)T ranges from 0.2 to 0.6 k for bands 42, 45, 46, and 48. It ranges from 0.8 to 1.1 K for bands 30-32. The day and night MAS data have been used to retrieve surface temperature and emissivities in these bands. A simple method to correct the effect of night thin cirrus has been incorporated into the day/night LST algorithm in dry atmospheric conditions. We compared the retrieved surface temperatures with those measured with TIR spectrometer, radiometers and thermistors in the snow test site, and the retrieved emissivity images with topographic map. The daytime LST values match well within 1 K. The night LST retrieved from MAS data is 3.3 K colder than those from field measurements most likely because of the effect of haze at night. The good agreement among the regional averaged surface temperatures obtained from LST values retrieved at different resolutions increased our confidence in the MODIS day/night LST algorithm.
The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.
In support of the DOE Office of Science, Sandia National Labs is one of nine national laboratories which oversees the Atmospheric Radiation Measurement (ARM) Program. The Sandia ARM team has an obligation to fulfill its mission to provide the nation with data to improve the understanding of climate processes and the representation of those processes in climate models. Sandia’s ARM team’s ability to provide detailed and accurate descriptions of the Earth’s atmosphere in diverse climate regimes assists the DOE in development of sustainable solutions to the nation’s energy and environmental challenges. Sandia Labs manages ARM atmospheric facilities along the North Slope of Alaska (NSA) at Utqiagvik as well as the Tethered Balloon System (TBS). Activities conducted at NSA and with the TBS aide in data collection for the ARM data archive. An overview of these activities for the month of December follows.
Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current ‘state of the art’ from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of ‘soft recommendations’ about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage ‘open science’ practices.
Most meteorologists are really physicists in disguise. They use thermodynamics and hydrodynamics to understand snow squalls in Buffalo and typhoons in Japan.
In this field campaign, we used eddy covariance towers to quantify carbon, water, and energy fluxes from a pasture and a wheat field that were converted to switchgrass. The U.S. Department of Energy is investing in switchgrass as a cellulosic bioenergy crop, but there is little data available that could be used to develop or test land surface model representations of the crop. This campaign was a collaboration between Lawrence Berkeley National Laboratory and the U.S. Department of Agriculture Agricultural Research Service. Unfortunately, in 2011, Oklahoma had one of the most severe droughts on record, and the crop in one of the switchgrass fields experienced almost complete die-off. The crop was replanted, but subsequent drought conditions prevented its establishment. Then, in April 2012, a large tornado demolished the instruments at our site in Woodward, Oklahoma. These two events meant that we have some interesting data on land response to extreme weather; however, we were not able to collect continuous data for annual sums as originally intended. We did observe that, because of the drought, the net ecosystem exchange of CO 2 was much lower in 2011 than in 2010. Concomitantly, sensible heat fluxes increased and latent heat fluxes decreased. These conditions would have large consequences for land surface forcing of convection. Data from all years were submitted to the Atmospheric Radiation Measurement Climate Research Facility Data Archive, and the sites were registered in AmeriFlux.
The Advanced Microwave Scanning Radiometer (AMSR-E) is being built by NASDA to fly on NASA's PM Platform (now called "Aqua") in December 2000. This is in addition to a copy of AMSR that will be launched on Japan's ADEOS-11 satellite in 2001. The AMSRs improve upon the window frequency radiometer heritage of the SSM[l and SMMR instruments. Major improvements over those instruments include channels spanning the 6.9 GHz to 89 GHz frequency range, and higher spatial resolution from a 1.6 m reflector (AMSR-E) and 2.0 m reflector (ADEOS-11 AMSR). The ADEOS-11 AMSR also will have 50.3 and 52.8 GHz channels, providing sensitivity to lower tropospheric temperature. NASA funds an AMSR-E Science Team to provide algorithms for the routine production of a number of standard geophysical products. These products will be generated by the AMSR-E Science Investigator-led Processing System (SIPS) at the Global Hydrology Resource Center (GHRC) in Huntsville, Alabama. While there is a separate NASDA-sponsored activity to develop algorithms and produce products from AMSR, as well as a Joint (NASDA-NASA) AMSR Science Team activity, here I will review only the AMSR-E Team's algorithms and how they benefit from the new capabilities that AMSR-E will provide. The U.S. Team's products will be archived at the National Snow and Ice Data Center (NSIDC). Further information about AMSR-E can be obtained at http://www.jzhcc.msfc.nasa.Vov/AMSR.
Grid technology consists of middleware that permits distributed computations, data and sensors to be seamlessly integrated into a secure, single-sign-on processing environment. In &is environment, a user has to identify and authenticate himself once to the grid middleware, and then can utilize any of the distributed resources to which he has been,panted access. Grid technology allows resources that exist in enterprises that are under different administrative control to be securely integrated into a single processing environment The grid community has adopted commercial web services technology as a means for implementing persistent, re-usable grid services that sit on top of the basic distributed processing environment that grids provide. These grid services can then form building blocks for even more complex grid services. Each grid service is characterized using the Web Service Description Language, which provides a description of the interface and how other applications can access it. The emerging Semantic grid work seeks to associates sufficient semantic information with each grid service such that applications wii1 he able to automatically select, compose and if necessary substitute available equivalent services in order to assemble collections of services that are most appropriate for a particular application. Grid technology has been used to provide limited support to various Earth and space science applications. Looking to the future, this emerging grid service technology can provide a cyberinfrastructures for both the Earth and space science communities. Groups within these communities could transform those applications that have community-wide applicability into persistent grid services that are made widely available to their respective communities. In concert with grid-enabled data archives, users could easily create complex workflows that extract desired data from one or more archives and process it though an appropriate set of widely distributed grid services discovered using semantic grid technology. As required, high-end computational resources could be drawn from available grid resource pools. Using grid technology, this confluence of data, services and computational resources could easily be harnessed to transform data from many different sources into a desired product that is delivered to a user's workstation or to a web portal though which it could be accessed by its intended audience.
As part of an effort by the Lunar Data Node (LDN) we are restoring data returned by the Apollo Dust, Thermal, and Radiation Engineering Measurements (DTREM) packages emplaced on the lunar surface by the crews of Apollo 11, 12, 14, and 15. Also commonly known as the Dust Detector experiments, the DTREM packages measured the outputs of exposed solar cells and thermistors over time. They operated on the surface for up to nearly 8 years, returning data every 54 seconds. The Apollo 11 DTREM was part of the Early Apollo Surface Experiments Package (EASEP), and operated for a few months as planned following emplacement in July 1969. The Apollo 12, 14, and 15 DTREMs were mounted on the central station as part of the Apollo Lunar Surface Experiments Package (ALSEP) and operated from deployment until ALSEP shutdown in September 1977. The objective of the DTREM experiments was to determine the effects of lunar and meteoric dust, thermal stresses, and radiation exposure on solar cells. The LDN, part of the Geosciences Node of the Planetary Data System (PDS), operates out of the National Space Science Data Center (NSSDC) at Goddard Space Flight Center. The goal of the LDN is to extract lunar data stored on older media and/or in obsolete formats, restore the data into a usable digital format, and archive the data with PDS and NSSDC. For the DTREM data we plan to recover the raw telemetry, translate the raw counts into appropriate output units, and then apply calibrations. The final archived data will include the raw, translated, and calibrated data and the associated conversion tables produced from the microfilm, as well as ancillary supporting data (metadata) packaged in PDS format.
The FISO (Future In-Space Operations) Telecon Series was begun in 2006, by the then-active FISO Working Group, as an innovative outreach effort to bring space technology, engineering, and science to the widespread space community with presentations from leaders in their field. Although the telephone line telecon can host a limited number of participants, who are explicitly invited via our listserve, we are pleased to archive the slides and audio from those telecons for the general public. This presentation will describe spacecraft fire safety issues for future NASA exploration missions and highlight several experiments and research efforts being performed to address these issues.
The Apache Point Lunar Laser-ranging Operation (APOLLO) has been collecting lunar range measurements for 15 yr at millimeter accuracy. The median nightly range uncertainty since 2006 is 1.7 mm. A recently added Absolute Calibration System (ACS), providing an independent assessment of APOLLO system accuracy and the capability to correct lunar range data, revealed a ∼0.4% (10 ps) systematic error in the calibration of one piece of hardware that has been present for the entire history of APOLLO. The application of ACS-based timing corrections suggests systematic errors are reduced to <1 mm, such that overall data accuracy and precision are both ∼1 mm. This paper describes the processing of APOLLO/ACS data that converts photon-by-photon range measurements into the aggregated normal points that are used for science analyses. Additionally, we present methodologies to estimate timing corrections for range data lacking contemporaneous ACS photons, including range data collected prior to installation of the ACS. We also provide access to the full 15 yr archive of APOLLO normal points (2006 April 6–2020 December 27).
Individual datastreams from instrumentation at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility fixed and mobile research observatories (sites) are collected and routed to the ARM Data Center (ADC). The Data Management Facility (DMF), a component of the ADC, executes datastream processing in near-real time. Processed data are then delivered approximately daily to the ARM Data Archive, also a component of the ADC, where they are made freely available to the research community. For each instrument, ARM calculates the ratio of the actual number of processed data records received daily at the ARM Data Archive to the expected number of data records. DOE requires national user facilities to report time-based operating data.
We report the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.
The ESS-DIVE reporting format for Comma-separated Values (CSV) file structure is based on a combination of existing guidelines and recommendations including some found within the Earth Science Community with valuable input from the Environmental Systems Science (ESS) Community. The CSV reporting format is designed to promote interoperability and machine-readability of CSV data files while also facilitating the collection of some file-level metadata content. Tabular data in the form of rows and columns should be archived in its simplest form, and we recommend submitting these tabular data following the ESS-DIVE reporting format for generic comma-separated values (CSV) text format files. In general, the CSV file format is more likely accessible by future systems when compared to a proprietary format and CSV files are preferred because this format is easier to exchange between different programs increasing the interoperability of a data file. By defining the reporting format and providing guidelines for how to structure CSV files and some field content within, this can increase the machine-readability of the data file for extracting, compiling, and comparing the data across files and systems.Data package files are in .csv, .png, and .md. Open the .csv with e.g. Microsoft Excel, LibreOffice, or Google Sheets. Open the .md files by downloading and using a text editor (e.g., notepad or TextEdit). Open the .png in e.g. a web browser, photo viewer/editor, or Google Drive.
The waveform Light Detection and Ranging (LiDAR) data in this package were generated through a National Ecological Observatory Network Airborne Observation Platform (NEON AOP) acquisition over watersheds of interest surrounding Crested Butte, Colorado. The remote sensing imagery acquired by the NEON AOP supports an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in a snow-dominated headwater environment. These waveform LiDAR data enable spatially continuous estimation of vegetation structure parameters to facilitate analyses of the major environmental drivers of structural and compositional variability. The package contains 97 compressed file archives in 7-zip (.7z) format, each corresponding to one acquisition flightpath. Within each .7z archive is a set of constituent files describing properties of the LiDAR waveforms, such as return intensity, geolocation, outgoing pulse and other behavior of the sensor and signals. Once downloaded, the files must first be unzipped using the widely distributed command-line software utility 7z, using the command '7z x \[filename\].7z \[target_directory\]'. All files within the .7z archives can be opened in IDL, MatLab, or the open-source R statistical computing environment. Further details about the data package are in the attached user guide (neon_aop_crbu_waveformlidar_userguide.pdf). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.
The Advanced Microwave Scanning 2 Radiometer (AMSR-E) is being built by NASDA to fly on NASA's PM Platform (now called Aqua) in December 2000. This is in addition to a copy of AMSR that will be launched on Japan's ADEOS-II satellite in 2001. The AMSRs improve upon the window frequency radiometer heritage of the SSM/I and SMMR instruments. Major improvements over those instruments include channels spanning the 6.9 GHz to 89 GHz frequency range, and higher spatial resolution from a 1.6 m reflector (AMSR-E) and 2.0 m reflector (ADEOS-II AMSR). The ADEOS-II AMSR also will have 50.3 and 52.8 GHz channels, providing sensitivity to lower tropospheric temperature. NASA funds an AMSR-E Science Team to provide algorithms for the routine production of a number of standard geophysical products. These products will be generated by the AMSR-E Science Investigator-led Processing System (SIPS) at the Global Hydrology Resource Center (GHRC) in Huntsville, Alabama. While there is a separate NASDA-sponsored activity to develop algorithms and produce products from AMSR, as well as a Joint (NASDA-NASA) AMSR Science Team 3 activity, here I will review only the AMSR-E Team's algorithms and how they benefit from the new capabilities that AMSR-E will provide. The US Team's products will be archived at the National Snow and Ice Data Center (NSIDC).
The Sensor Web is a macroinstrument concept that allows for the spatio-temporal understanding of an environment through coordinated efforts between multiple numbers and types of sensing platforms, including, in its most general form, both orbital and terrestrial and both fixed and mobile. Each of these platforms, or pods, communicates within its local neighborhood and thus distributes information to the instrument as a whole. The result of sharing and continual processing of this information among all the Sensor Web elements will result in an information flow and a global perception of and reactive capability to the environment. As illustrated, the Sensor Web concept also allows for the recursive notion of a web of webs with individual distributed instruments possibly playing the role of a single node point on a larger Sensor Web instrument. In particular, the fusion of inexpensive, yet sophisticated, commercial technology from both the computation and telecommunication revolutions has enabled the development of practical, fielded, and embedded in situ systems that have been the focus of the NASA/JPL Sensor Webs Project (http://sensorwebs.jpl.nasa.gov/). These Sensor Webs are complete systems consisting of not only the pod elements that wirelessly communicate among themselves, but also interfacing and archiving software that allows for easy use by the end-user. Previous successful deployments have included environments as diverse as coastal regions, Antarctica, and desert areas. The Sensor Web has broad implications for Earth and planetary science and will revolutionize the way experiments and missions are conceived and performed. As part of our current efforts to develop a macrointelligence within the system, we have deployed a Sensor Web at the Central Avra Valley Storage and Recovery Project (CAVSARP) facility located west of Tucson, AZ. This particular site was selected because it is ideal for studying spatio-temporal phenomena and for providing a test site for more sophisticated hydrological studies in the future.