Distributed Active Archive Center Research Articles

Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977

Abstract. The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured the 19.35 GHz horizontally polarized brightness temperature (TB) from 11 December 1972 to 16 May 1977. The original tape archive data in swath projection have recently been made available online by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though the ESMR was a predecessor of modern multi-frequency radiometers, there are still parts of modern processing methodologies which can be applied to the data to derive the sea ice extent globally. Here, we have reprocessed the entire dataset using a modern processing methodology that includes the implementation of pre-processing filtering, dynamical tie points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one-channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include resampling to daily grids, land-spillover correction, the application of climatological masks, the setting of processing flags, and the estimation of sea ice extent, monthly means, and trends. This sea ice dataset derived from the NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid-1970s. To make it easier to perform a consistent analysis of sea ice development over time, the same grid and land mask as used for EUMETSAT's OSI-SAF SMMR-based sea-ice climate data record (CDR) were used for our ESMR dataset. SIC uncertainties were included to further ease comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which were derived from the same ESMR dataset, with mean differences of 240 000 and 590 000 km2, respectively. When comparing monthly sea ice extents, the largest differences reach up to 2 million km2. Such large differences cannot be explained by the different grids and land masks of the datasets alone and must therefore also result from the differences in data filtering and algorithms, such as the dynamical tie points and atmospheric correction. The new ESMR SIC dataset has been released as part of the ESA Climate Change Initiative (ESA CCI) program and is publicly available at https://doi.org/10.5285/34a15b96f1134d9e95b9e486d74e49cf (Tonboe et al., 2023).

Attenuated atmospheric backscatter profiles measured by the CO2 Sounder lidar in the 2017 ASCENDS/ABoVE airborne campaign

Abstract. A series of attenuated atmospheric backscatter profiles were measured at 1572 nm by the CO2 Sounder lidar during the eight flights of the 2017 ASCENDS/ABoVE (Active Sensing of CO2 Emission over Nights, Days, and Seasons, Arctic Boreal Vulnerability Experiment) airborne campaign. In addition to measuring the column average CO2 mixing ratio from the laser signals reflected by the ground, the CO2 Sounder lidar also recorded the height-resolved attenuated atmospheric backscatter profiles beneath the aircraft. We have recently processed these vertical profiles with a 15 m vertical spacing and 1 s integration time along the flight path (∼ 200 m) for all the 2017 flights and have posted the results at NASA Distributed Active Archive Center (DAAC) for Biogeochemical Dynamics https://doi.org/10.3334/ORNLDAAC/2051 (Sun et al., 2022). This paper describes the measurement principles, the data processing technique, and the signal to noise ratios.

Annual forest disturbance intensity mapped using Landsat time series and field inventory data for the conterminous United States (1986–2015)

Forest disturbances can have broad impact on the climate, local environment, and the regeneration of the forest ecosystem. The nature and magnitude of such impact is largely driven by disturbance intensity. In this study, by integrating field plot measurements collected by the Forest Inventory and Analysis program with time series Landsat observations, we produced the first set of annual forest disturbance intensity map products quantifying the percentage of basal area removal (PBAR) at the 30-m resolution for the conterminous United States (CONUS) from 1986 to 2015. The derived map products revealed that during the 30-year study period, the annual average PBAR values of all disturbed pixels across CONUS ranged from 66% to 70%, and the proportion of those pixels having stand-clearing disturbances ranged from 40% to 58%. High disturbance intensities were concentrated in the Southeastern states from Texas to Virginia and along the Pacific coast and the Cascades in the West. At the national scale, the annual mean PBAR and proportion of stand clearing area (PSCA) values both appeared to follow second order trajectories, with increasing trends at the beginning, decreasing trends towards the end, and turning points around 2003. Overall, there is a net increase of 2% in PBAR and 3% in PSCA from 1986 to 2015. The temporal trends of PBAR and PSCA were also investigated at state and ecoregion levels, with substantial differences found among many states and ecoregions. While states and ecoregions generally follow second order trajectories, the majority had increasing trends throughout much of the study period, reflecting higher disturbance intensities during the later years compared to earlier years. Large increase (>10%) in PBAR was seen in several states (e.g., Virginia, Arkansas, and Minnesota) and ecoregions (e.g., Northern Minnesota Wetlands); however, large decreases (>10%) in PBAR were not observed in any states, and were seen in only one ecoregion, the Blue Mountains in the southeast. The disturbance intensity maps are available from a web portal of the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) at https://doi.org/10.3334/ORNLDAAC/2059.

Developing Metrics for NASA Earth Science Interdisciplinary Data Products and Services

Metrics are measures that are able to produce quantifiable information. There are many applications of metrics in Earth science data and services; for example, metrics are frequently used to track service performance and progress. In short, developing, collecting and analyzing metrics are essential activities to better support Earth science research, applications, and education. As one of the largest repositories of Earth science data in the world, NASA’s Earth Science Data and Information System (ESDIS) Project supports twelve Distributed Active Archive Centers (DAACs). Standard metrics have been developed by the ESDIS Metrics System (EMS). These metrics are collected and analyzed routinely at each DAAC. As it is expected that the total data volume will continue to grow rapidly, and the timely developed technologies (e.g., cloud computing, AI/ML) will continue to improve data discovery and accessibility, opportunities for developing new data services for the Earth science community will also arise, especially in interdisciplinary research and applications. However, developing such metrics has become a challenge because multiple datasets are often needed. Current metrics are designed for a single predefined dataset or service, a disadvantage for collecting metrics for interdisciplinary data services. In this paper, we assess current metrics using one of the NASA DAACs, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), as an example, to discuss challenges and opportunities, along with recommendations for developing metrics addressing interdisciplinary satellite data products and services. <strong>Highlights</strong> <ul><li>Overview of NASA GES DISC Earth science datasets and services</li><li>Overview of existing metrics collection methods and analysis tools with examples</li><li>Discuss challenges and opportunities in collecting metrics for Earth science interdisciplinary data and services</li></ul>

Biomass partitioning of C3- and C4-dominated grasslands in response to climatic variability and climate extremes

The rising temperature, altering precipitation, and increasing extreme events under climate warming affect the stability and sustainability of grassland ecosystems. The dynamics of above-ground biomass (AGB), below-ground biomass (BGB), and biomass partitioning (BGB:AGB ratio) of grasslands are of fundamental importance to understand their feedback to climate change. In this study, we used grassland productivity data extracted from the Oak Ridge National Laboratory Distributed Active Archive Center, Tennessee, USA, in which the AGB was collected within a 1.0 m × 0.25 m quadrat and the BGB was sampled within the center of the quadrat. Using multiple pairwise tests and Pearson’s correlation analysis, we assessed the variations of grassland productivity and examined the response of single-harvest and annual biomass partitioning of C3- and C4-dominated grasslands to the growing-season and annual climatic variability and climate extremes in seven sites belonging to four ecoregions (i.e. cold steppe, humid temperate, humid savanna, and savanna). The results show that the annual and single-harvest BGB:AGB ratio varied significantly across the plant types and ecoregions. Overall, the C3-dominated grasslands exhibited a higher BGB:AGB ratio than that of C4-dominated grasslands. Growing-season temperatures (GSTs) were found to be the key determinants in explaining the single-harvest BGB:AGB ratio rather than growing-season precipitation. For instance, the single-harvest BGB:AGB ratio of C4-dominated grasslands increased, while that of C3-dominated grasslands decreased with elevated GSTs. The growing-season extreme dry climates significantly increased the single-harvest BGB:AGB ratio of C4 plants by a large reduction of AGB, potentially affecting the ecosystem functioning and stability. The C3-dominated grasslands in the cold steppe ecoregion are at great threat of drought-induced stress, as we observed that growing-season extreme dry climates reduced, albeit insignificantly, both the single-harvest AGB and BGB. This study provides key insights into factors influencing the biomass partitioning of C3- and C4-dominated grasslands and has important implications for assessing the grassland functioning and stability under increasing climate extremes.

Climatology of the Combined ASTER MODIS Emissivity over Land (CAMEL) Version 2

The Combined ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) MODIS (Moderate Resolution Imaging Spectroradiometer) Emissivity over Land (CAMEL) Version 2 (V002) has been available since March 2019 from the NASA LP DAAC (Land Processes Distributed Active Archive Center) and provides global, monthly infrared land surface emissivity and uncertainty at 0.05 degrees (~5 km) resolution. A climatology of the CAMEL V002 product is now available at the same spatial, temporal, and spectral resolution, covering the CAMEL record from 2000 to 2016. Characterization of the climatology over case sites and IGBP (International Geosphere-Biosphere Programme) land cover categories shows the climatology is a stable representation of the monthly CAMEL emissivity. Time series of the monthly CAMEL V002 product show realistic seasonal changes but also reveal subtle artifacts known to be from calibration and processing errors in the MODIS MxD11 emissivity. The use of the CAMEL V002 climatology mitigates many of these time dependent errors by providing an emissivity estimate which represents the complete 16-year record. The CAMEL V002 climatology’s integration into RTTOV (Radiative Transfer for TOVS) v12 is demonstrated through the simulation of IASI (Infrared Atmospheric Sounding Interferometer) radiances. Improved stability in CAMEL Version 3 is expected in the future with the incorporation of the new MxD21 and VIIRS VNP21 emissivity products in MODIS Collection 6.1.

The Vulcan Version 3.0 High-Resolution Fossil Fuel CO2 Emissions for the United States.

Estimates of high‐resolution greenhouse gas (GHG) emissions have become a critical component of climate change research and an aid to decision makers considering GHG mitigation opportunities. The “Vulcan Project” is an effort to estimate bottom‐up carbon dioxide emissions from fossil fuel combustion and cement production (FFCO2) for the U.S. landscape at space and time scales that satisfy both scientific and policy needs. Here, we report on the Vulcan version 3.0 which quantifies emissions at a resolution of 1 km2/hr for the 2010–2015 time period. We estimate 2011 FFCO2 emissions of 1,589.9 TgC with a 95% confidence interval of 1,367/1,853 TgC (−14.0%/+16.6%), implying a one‐sigma uncertainty of ~ ±8%. Per capita emissions are larger in states dominated by electricity production and industrial activity and smaller where onroad and building emissions dominate. The U.S. FFCO2 emissions center of mass (CoM) is located in the state of Missouri with mean seasonality that moves on a near‐elliptical NE/SW path. Comparison to ODIAC, a global gridded FFCO2 emissions estimate, shows large total emissions differences (100.4 TgC for year 2011), a spatial correlation of 0.68 (R2), and a mean absolute relative difference at the 1 km2 scale of 104.3%. The Vulcan data product offers a high‐resolution estimate of FFCO2 emissions in every U.S. city, obviating costly development of self‐reported urban inventories. The Vulcan v3.0 annual gridded emissions data product can be downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center (Gurney, Liang, et al., 2019, https://doi.org/10.3334/ORNLDAAC/1741).

OpenAltimetry - rapid analysis and visualization of Spaceborne altimeter data

NASA’s Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) carries a laser altimeter that fires 10,000 pulses per second towards Earth and records the travel time of individual photons to measure the elevation of the surface below. The volume of data produced by ICESat-2, nearly a TB per day, presents significant challenges for users wishing to efficiently explore the dataset. NASA’s National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which is responsible for archiving and distributing ICESat-2 data, provides search and subsetting services on mission data products, but providing interactive data discovery and visualization tools needed to assess data coverage and quality in a given area of interest is outside of NSIDC’s mandate. The OpenAltimetry project, a NASA-funded collaboration between NSIDC, UNAVCO and the University of California San Diego, has developed a web-based cyberinfrastructure platform that allows users to locate, visualize, and download ICESat-2 surface elevation data and photon clouds for any location on Earth, on demand. OpenAltimetry also provides access to elevations and waveforms for ICESat (the predecessor mission to ICESat-2). In addition, OpenAltimetry enables data access via APIs, opening opportunities for rapid access, experimentation, and computation via third party applications like Jupyter notebooks. OpenAltimetry emphasizes ease-of-use for new users and rapid access to entire altimetry datasets for experts and has been successful in meeting the needs of different user groups. In this paper we describe the principles that guided the design and development of the OpenAltimetry platform and provide a high-level overview of the cyberinfrastructure components of the system.

Oil palm dry season analysis based on moderate-resolution imaging spectroradiometer (MODIS) satellite indices

ABSTRACTOil palm is the most important crop in Malaysia. It plays a key role in maintaining carbon balance and it possesses high economic value. However, oil palm productivity is really sensitive to environmental changes especially during the long-term dry season. This has warranted studies on frequent observation of dry season for the plantation of oil palm. In this study, Moderate-Resolution Imaging Spectroradiometer (MODIS) surface reflectance data were utilized to generate an index for the crop assessment at Felcra Mendom Oil Plantation area, Negeri Sembilan. The indices of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and shortwave indices, namely Shortwave Infrared Water Stress Index (SWISI), at three different channels of 2, 5, and 6 were utilized. The main aim is to identify drought year based on MODIS-SPI-based index calculated and evaluated indices developed from MODIS images. The precipitation data retrieved from Malaysia Meteorology Department starting from year 1997 until 2017 and later are classified using Standardised Precipitation Index (SPI). Then by using the data from precipitation and SPI, the MODIS images were extracted from Land Process Distributed Active Archive Center (LP DAAC). Next, the images were processed and analysed by using ENVI Version 5.3 software to extract the indices used. The SPI-3 and SPI-1 were classified year 2015 as extreme drought with ‘extreme’ and ‘mild’, respectively. Meanwhile, the vegetation indices were calculated and average showed both NDVI and EVI approaching +1.00 value at the highest NDVI is 0.79 during 2017 and the highest EVI is 0.62 during years 2005 and 2013, respectively. Both SIWSI showed similar pattern, indicating high-moisture content as the index approaching −1.00. The result does not represent the overall condition of the specific study area, but the SPI is reliable to display the whole area and the utilization of MODIS and SPI can be used in further study.

The SMAP mission combined active-passive soil moisture product at 9 km and 3 km spatial resolutions

The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process ~2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP high-resolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m3/m3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of ~0.053 m3/m3. The SMAP L2SMAP product for ~2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined L-band radar-radiometer for proving high spatial resolution and accurate global soil moisture.

Vista-LA: Mapping methane-emitting infrastructure in the Los Angeles megacity

Abstract. Methane (CH4) is a potent greenhouse gas (GHG) and a critical target of climate mitigation efforts. However, actionable emission reduction efforts are complicated by large uncertainties in the methane budget on relevant scales. Here, we present Vista, a Geographic Information System (GIS)-based approach to map potential methane emissions sources in the South Coast Air Basin (SoCAB) that encompasses Los Angeles, an area with a dense, complex mixture of methane sources. The goal of this work is to provide a database that, together with atmospheric observations, improves methane emissions estimates in urban areas with complex infrastructure. We aggregated methane source location information into three sectors (energy, agriculture, and waste) following the frameworks used by the State of California GHG Inventory and the Intergovernmental Panel on Climate Change (IPCC) Guidelines for GHG Reporting. Geospatial modeling was applied to publicly available datasets to precisely geolocate facilities and infrastructure comprising major anthropogenic methane source sectors. The final database, Vista-Los Angeles (Vista-LA), is presented as maps of infrastructure known or expected to emit CH4. Vista-LA contains over 33 000 features concentrated on &lt; 1 % of land area in the region. Currently, Vista-LA is used as a planning and analysis tool for atmospheric measurement surveys of methane sources, particularly for airborne remote sensing, and methane hotspot detection using regional observations. This study represents a first step towards developing an accurate, spatially resolved methane flux estimate for point sources in SoCAB, with the potential to address discrepancies between bottom–up and top–down methane emissions accounting in this region. The Vista-LA datasets and associated metadata are available from the Oak Ridge National Laboratory Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC; https://doi.org/10.3334/ORNLDAAC/1525).

Development of Landsat-based annual US forest disturbance history maps (1986–2010) in support of the North American Carbon Program (NACP)

In Phase III of the North American Forest Dynamics (NAFD) study an automatic workflow has been developed for evaluating forest disturbance history using Landsat observations. It has four major components: an automated approach for image selection and preprocessing, the vegetation change tracker (VCT) forest disturbance analysis, postprocessing, and validation. This approach has been applied to the conterminous US (CONUS) to produce a comprehensive analysis of US forest disturbance history using the NASA Earth Exchange (NEX) cloud computing system. The resultant NAFD-NEX product includes 25 annual forest disturbance maps for 1986–2010 and two time-integrated maps to provide spatial-temporal synoptic view of disturbances over this time period. These maps were derived based on 24,000+ scenes selected from 350,000+ available Landsat images at 30-m resolution, and were validated using a visual assessment of Landsat time-series images in combination with high-resolution and other ancillary data sources over samples selected using a probability based sampling method. The validation revealed no major biases in the NAFD-NEX maps for disturbance events that resulted in at least 20% canopy cover loss. The average user's and producer's accuracies for the disturbance class were 53.6% and 53.3%, respectively, with the individual year's user's accuracy varying from 42.8% to 73.6% and producer's accuracy from 39.0% to 84.8% over the 25-year period. The NAFD-NEX disturbance maps are available from a web portal of the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) at https://doi.org/10.3334/ORNLDAAC/1290.

The art and science of data curation: Lessons learned from constructing a virtual collection

A digital, or virtual, collection is a value added service developed by libraries that curates information and resources around a topic, theme or organization. Adoption of the virtual collection concept as an Earth science data service improves the discoverability, accessibility and usability of data both within individual data centers but also across data centers and disciplines. In this paper, we introduce a methodology for systematically and rigorously curating Earth science data and information into a cohesive virtual collection. This methodology builds on the geocuration model of searching, selecting and synthesizing Earth science data, metadata and other information into a single and useful collection. We present our experiences curating a virtual collection for one of NASA's twelve Distributed Active Archive Centers (DAACs), the Global Hydrology Resource Center (GHRC), and describe lessons learned as a result of this curation effort. We also provide recommendations and best practices for data centers and data providers who wish to curate virtual collections for the Earth sciences.

NASA EOSDIS Data Identifiers: Approach and System

NASA’s Earth Science Data and Information System (ESDIS) Project began investigating the use of Digital Object Identifiers (DOIs) in 2010 with the goal of assigning DOIs to various data products. These Earth science research data products produced using Earth observations and models are archived and distributed by twelve Distributed Active Archive Centers (DAACs) located across the United States. Each data center serves a different Earth science discipline user community and, accordingly, has a unique approach and process for generating and archiving a variety of data products. These varied approaches present a challenge for developing a DOI solution. To address this challenge, the ESDIS Project has developed processes, guidelines, and several models for creating and assigning DOIs. Initially the DOI assignment and registration process was started as a prototype but now it is fully operational. In February 2012, the ESDIS Project started using the California Digital Library (CDL) EZID for registering DOIs. The DOI assignments were initially labor-intensive. The system is now automated, and the assignments are progressing rapidly. As of February 28, 2017, over 50% of the data products at the DAACs had been assigned DOIs. Citations using the DOIs increased from about 100 to over 370 between 2015 and 2016.

Spatial pattern of grassland aboveground biomass and its environmental controls in the Eurasian steppe

Vegetation biomass is an important component of terrestrial ecosystem carbon stocks. Grasslands are one of the most widespread biomes worldwide, playing an important role in global carbon cycling. Therefore, studying spatial patterns of biomass and their correlations to environment in grasslands is fundamental to quantifying terrestrial carbon budgets. The Eurasian steppe, an important part of global grasslands, is the largest and relatively well preserved grassland in the world. In this study, we analyzed the spatial pattern of aboveground biomass (AGB), and correlations of AGB to its environment in the Eurasian steppe by meta-analysis. AGB data used in this study were derived from the harvesting method and were obtained from three data sources (literature, global NPP database at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL), some data provided by other researchers). Our results demonstrated that: (1) as for the Eurasian steppe overall, the spatial variation in AGB exhibited significant horizontal and vertical zonality. In detail, AGB showed an inverted parabola curve with the latitude and with the elevation, while a parabola curve with the longitude. In addition, the spatial pattern of AGB had marked horizontal zonality in the Black Sea-Kazakhstan steppe subregion and the Mongolian Plateau steppe subregion, while horizontal and vertical zonality in the Tibetan Plateau alpine steppe subregion. (2) Of the examined environmental variables, the spatial variation of AGB was related to mean annual precipitation (MAP), mean annual temperature (MAT), mean annual solar radiation (MAR), soil Gravel content, soil pH and soil organic content (SOC) at the depth of 0–30 cm. Nevertheless, MAP dominated spatial patterns of AGB in the Eurasian steppe and its three subregions. (3) A Gaussian function was found between AGB and MAP in the Eurasian steppe overall, which was primarily determined by unique patterns of grasslands and environment in the Tibetan Plateau. AGB was significantly positively related to MAP in the Black Sea-Kazakhstan steppe subregion (elevation < 3000 m), the Mongolian Plateau steppe subregion (elevation < 3000 m) and the surface (elevation ≥ 4800 m) of the Tibetan Plateau. Nevertheless, the spatial variation in AGB exhibited a Gaussian function curve with the increasing MAP in the east and southeast margins (elevation < 4800 m) of the Tibetan Plateau. This study provided more knowledge of spatial patterns of AGB and their environmental controls in grasslands than previous studies only conducted in local regions like the Inner Mongolian temperate grassland, the Tibetan Plateau alpine grassland, etc.

In situ air temperature and humidity measurements over diverse land covers in Greenbelt, Maryland, November 2013–November 2015

Abstract. As our climate changes through time there is an ever-increasing need to quantify how and where it is changing so that mitigation strategies can be implemented. Urban areas have a disproportionate amount of warming due, in part, to the conductive properties of concrete and asphalt surfaces, surface albedo, heat capacity, lack of water, etc. that make up an urban environment. The NASA Climate Adaptation Science Investigation working group at Goddard Space Flight Center in Greenbelt, MD, conducted a study to collect temperature and humidity data at 15 min intervals from 12 sites at the center. These sites represent the major surface types at the center: asphalt, building roof, grass field, forest, and rain garden. The data show a strong distinction in the thermal properties of these surfaces at the center and the difference between the average values for the center compared to a local meteorological station. The data have been submitted to Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) for archival in comma separated value (csv) file format (Carroll et al., 2016) and can be found by following this link: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1319.

A Semi-Automated Workflow Solution for Data Set Publication

To address the need for published data, considerable effort has gone into formalizing the process of data publication. From funding agencies to publishers, data publication has rapidly become a requirement. Digital Object Identifiers (DOI) and data citations have enhanced the integration and availability of data. The challenge facing data publishers now is to deal with the increased number of publishable data products and most importantly the difficulties of publishing diverse data products into an online archive. The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), a NASA-funded data center, faces these challenges as it deals with data products created by individual investigators. This paper summarizes the challenges of curating data and provides a summary of a workflow solution that ORNL DAAC researcher and technical staffs have created to deal with publication of the diverse data products. The workflow solution presented here is generic and can be applied to data from any scientific domain and data located at any data center.

Implementation of data citations and persistent identifiers at the ORNL DAAC

A requirement of data archives is that data holdings can be easily discovered, accessed, and used. One approach to improving data discovery and access is through data citations coupled with Digital Object Identifiers (DOI). The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) since 1998 has issued data citations that have been accepted and used in peer-reviewed journals. Citation elements established by the ORNL DAAC are similar to those used for journal articles (authors, year, product title, and information to locate), and beginning in 2007 included a DOI that is persistent, actionable, specific, and complete. The approach used at the ORNL DAAC also allows for referring to subsets of the data, by including within the citation the temporal and spatial extent, and parameters used. Data citations allow readers to find data and reproduce the results of the research article, and also use those data to test new hypotheses, design new sample collections, or construct or evaluate models. The ORNL DAAC uses a manual method to compile data citations and has developed a database that links research articles and their use of specific ORNL DAAC data products. Automation of the data citation compilation process, as is the case for articles, will enable data citations to become a more common practice. In addition to enhancing discovery and access of the data used in a research article, the citation gives credit to data generators, data centers and their funders, and, through citation indices, determine the scientific impact of a data set.

Life Cycle Management Considerations of Remotely Sensed Geospatial Data and Documentation for Long Term Preservation

As geospatial missions age, one of the challenges for the usability of data is the availability of relevant and updated metadata with sufficient documentation that can be used by future generations of users to gain knowledge from the original data. Given that remote sensing data undergo many intermediate processing steps, for example, an understanding of the exact algorithms employed and the quality of that data produced could be key considerations for these users. As interest in global climate data is increasing, documentation about older data, their origins, and their provenance are valuable to first-time users attempting to perform historical climate research or comparative analysis of global change. Incomplete or missing documentation could be what stands in the way of a new researcher attempting to use the data. Therefore, preservation of documentation and related metadata is sometimes just as critical as the preservation of the original observational data. The Goddard Earth Sciences–Data and Information Service Center (GES DISC), a NASA Earth science Distributed Active Archive Center (DAAC) that falls under the management structure of the Earth Science Data and Information System (ESDIS), is actively pursuing the preservation of all necessary artifacts needed by future users.In this article, we will detail the data custodial planning and the data lifecycle process developed for content preservation, and our implementation of a Preservation System to safeguard documents and associated artifacts from legacy (older) missions, as well as detail lessons learned regarding access rights and confidentiality of information issues. We also elaborate on key points that made our preservation effort successful; the primary points being drafting of a governing baseline for historical data preservation from satellite missions and using the historical baseline as a guide to content filtering of what documents to preserve. The Preservation System currently archives documentation content for High Resolution Dynamics Limb Sounder (HIRDLS), Upper Atmosphere Research Satellite (UARS), Total Ozone Mapping Spectrometer (TOMS) mission data, and the 1960s era Nimbus mission. Documentation from other missions like the Tropical Rainfall Measuring Mission (TRMM), the Ozone Monitoring Instrument (OMI), and the Atmospheric Infra-Red Sounder (AIRS) are also slated to be added to this repository, as well as other mission datasets to be preserved at the GES DISC.

Data-Enabled Field Experiment Planning, Management, and Research Using Cyberinfrastructure

Abstract In the spring of 2013, NASA conducted a field campaign known as Iowa Flood Studies (IFloodS) as part of the Ground Validation (GV) program for the Global Precipitation Measurement (GPM) mission. The purpose of IFloodS was to enhance the understanding of flood-related, space-based observations of precipitation processes in events that transpire worldwide. NASA used a number of scientific instruments such as ground-based weather radars, rain and soil moisture gauges, stream gauges, and disdrometers to monitor rainfall events in Iowa. This article presents the cyberinfrastructure tools and systems that supported the planning, reporting, and management of the field campaign and that allow these data and models to be accessed, evaluated, and shared for research. The authors describe the collaborative informatics tools, which are suitable for the network design, that were used to select the locations in which to place the instruments. How the authors used information technology tools for instrument monitoring, data acquisition, and visualizations after deploying the instruments and how they used a different set of tools to support data analysis and modeling after the campaign are also explained. All data collected during the campaign are available through the Global Hydrology Resource Center (GHRC), a NASA Distributed Active Archive Center (DAAC).