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NASA NTRSDate not supplied

MISR Level 1A CCD Science data, all cameras (MIL1A_V2)

The Level 1A data are raw MISR data that are decommutated, reformatted 12-bit Level 0 data shifted to byte boundaries, i.e., reversal of square-root encoding applied and converted to 16 bit, and annotated (e.g., with time information). These data are used by the Level 1B1 processing algorithm to generate calibrated radiances. The science data output preserves the spatial sampling rate of the Level 0 raw MISR CCD science data. CCD data are collected during routine science observations of the sunlit portion of the Earth. Each product represents one 'granule' of data. A 'granule' is defined to be the smallest unit of data required for MISR processing. Also, included in the Level 1A product are pointers to calibration coefficient files provided for Level 1B processing. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

AM-1↗

NASA NTRS1993

The Payload Advisory Panel and the Data and Information System Advisory Panel of the Investigators Working Group of the Earth Observing System: A joint report

The Payload Advisory Panel of the Investigators Working Group (IWG) for the Earth Observing System (EOS) met 4 to 6 October 1993 in Herndon, Virginia. The Panel, originally composed of the Interdisciplinary Science Principal Investigators, was expanded to include all Principal Investigators and as such is now the IWG itself. The meeting also addressed directly a report from the EOS Data and Information System (EOSDIS) Advisory Panel. The meeting focused on payload issues in the years 2000 to 2005; however, some subjects in the nearer-term, most significantly EOSDIS, were considered. The overarching theme of convergence in Earth observations set a backdrop for the entire meeting. Other themes included: atmospheric chemistry; remote sensing of the global cycles of energy, water, and carbon in EOS; ocean and land-ice altimetry; and the EOSDIS. The Totol Solar Irradiance Monitoring Report and results from the Accelerated Canopy Chemistry Program are included as appendices.

Moore, Berrien, III↗

NASA NTRS2005

A Testbed Demonstration of an Intelligent Archive in a Knowledge Building System

The last decade's influx of raw data and derived geophysical parameters from several Earth observing satellites to NASA data centers has created a data-rich environment for Earth science research and applications. While advances in hardware and information management have made it possible to archive petabytes of data and distribute terabytes of data daily to a broad community of users, further progress is necessary in the transformation of data into information, and information into knowledge that can be used in particular applications in order to realize the full potential of these valuable datasets. In examining what is needed to enable this progress in the data provider environment that exists today and is expected to evolve in the next several years, we arrived at the concept of an Intelligent Archive in context of a Knowledge Building System (IA/KBS). Our prior work and associated papers investigated usage scenarios, required capabilities, system architecture, data volume issues, and supporting technologies. We identified six key capabilities of an IA/KBS: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting. Among these capabilities, large-scale data mining is perceived by many in the community to be an area of technical risk. One of the main reasons for this is that standard data mining research and algorithms operate on datasets that are several orders of magnitude smaller than the actual sizes of datasets maintained by realistic earth science data archives. Therefore, we defined a test-bed activity to implement a large-scale data mining algorithm in a pseudo-operational scale environment and to examine any issues involved. The application chosen for applying the data mining algorithm is wildfire prediction over the continental U.S. This paper reports a number of observations based on our experience with this test-bed. While proof-of-concept for data mining scalability and utility has been a major goal for the research reported here, it was not the only one. The other five capabilities of an WKBS named above have been considered as well, and an assessment of the implications of our experience for these other areas will also be presented. The lessons learned through the testbed effort and presented in this paper will benefit technologists, scientists, and system operators as they consider introducing IA/KBS capabilities into production systems.

Ramapriyan, Hampapuram↗

NASA NTRS1989

The NASA master directory: Quick reference guide

This is a quick reference guide to the NASA Master Directory (MD), which is a free, online, multidisciplinary directory of space and Earth science data sets (NASA and non-NASA data) that are of potential interest to the NASA-sponsored research community. The MD contains high-level descriptions of data sets, other data systems and archives, and campaigns and projects. It provides mechanisms for searching for data sets by important criteria such as geophysical parameters, time, and spatial coverage, and provides information on ordering the data. It also provides automatic connections to a number of data systems such as the NASA Climate Data System, the Planetary Data System, the NASA Ocean Data System, the Pilot Land Data System, and others. The MD includes general information about many data systems, data centers, and coordinated data analysis projects, It represents the first major step in the Catalog Interoperability project, whose objective is to enable researchers to quickly and efficiently identify, obtain information about, and get access to space and Earth science data. The guide describes how to access, use, and exit the MD and lists its features.

Satin, Karen↗

NASA NTRSDate not supplied

Integrating Multi-agency Data Products in a Cloud-based Platform for Streamlined Discovery, Visualization, and Use

Earth science data users almost always have an interest in utilizing geospatial data from multiple agencies. As computing capability and cloud-based infrastructures accelerate the pace at which scientific research can be done, there is a growing need to enable search, discovery, and use of multi-agency geospatial observations relevant for a common use case - without undergoing the search and discovery process in a less efficient, disparate path with each agency. NASA’s Earth Observing System Data and Information System (EOSDIS) and NOAA’s National Environmental Satellite, Data and Information Service (NESDIS) both support a wide range of Earth science disciplines’ research, operations, and applications activities. Presently, however, there are few examples of data discovery frameworks supporting an inquiry of both NASA’s and NOAA’s extensive archives of Earth observations that are equally suitable for a particular science scenario, regardless of the agency that “owns” the data. NASA and NOAA are collaborating on a data expedition platform for exploring fire weather using data products from both agencies. Users will be able to search, discover, and visualize NASA and NOAA products in one interface. Each agency will curate metadata for its respective datasets, providing for a rich search experience. The collaboration will pilot a shared search interface into these metadata datastores. Data products will be stored in the cloud in cloud-optimized format(s). These formats will allow for optimized data access and visualization to support the “data expedition”. Avenues for further development and application of this cloud-based, multi-agency data provisioning platform will also be discussed.

cloud-based technology↗

NASA NTRSDate not supplied

Application of ML/AI for Identifying Earth Science Datasets in Research Publications

NASA Data Active Archive Centers, or DAACs, ingest, store and distribute data acquired from satellites, ground systems as well as modelling data. These data are organized by the datasets, each presenting collection of files usually associated with the certain mission, instrument, processing level, parameter(s), algorithm and/or model. The number of datasets offered by a single DAAC to the public varies. GES DISC, for example, currently offers for public use approximately ~1,300 datasets. While each publicly offered dataset comes with supporting documentation, it is challenging for novice and even experienced scientists to navigate among the datasets that offer similar parameters to find the datasets for their particular research application. Supplying dataset documentation with the scientific paper citations that refer to that dataset provides means for the dataset users to educate themselves with the application research that dataset is being used in. Collecting citations of the papers that use the datasets for their research yield valuable insights into application areas of those datasets, information about usage of the dataset groups for specific applications and those application topics. It also gives insights into the “deep metrics” of the dataset usage, as opposed to the common metrics of the dataset usage such as number of users who downloaded the dataset files and volumes of downloaded data. Association of a certain scientific paper with the dataset(s) presents a challenge because most of the paper authors do not properly cite the datasets, datasets usually have cryptic names and Digital Object Identifiers (DOIs) that are used for dataset identification were assigned to the datasets only few years ago. Simple Google or online library search do not provide even meaningful fraction of the results when performed by the dataset name or DOI, however they provide too many results when the search is done by more broader terms such as mission and instrument names. Attempts to create an AI system capable to identify dataset in the scientific papers have already been made using neural networks classifiers on the basis of the dataset mission, instrument and variable name. This method was applied to NASA SEDAC, which has 41 datasets in total. In GES DISC there can be as many as ~100 datasets per mission/instrument with some of the datasets consisting of multiple variables so there is a need for more differentiating parameters for dataset identification in the paper. The approach we are currently investigating is creating AI classifiers that are based on multiple dataset features, or keywords, extracted from the NASA Earthdata Common Dataset Repository (CMR). The features are weighted based on how precisely they can identify a dataset. The classifier uses preprocessed paper text as input and searches for the CMR datasets whose feature sets are the closest to the feature sets contained in the paper. The challenges of dataset identification include variety of ways the paper authors describe the datasets in their papers and incomplete tagging of the CMR dataset description (DIFs).

Irina Gerasimov↗

NASA NTRS1999

Earth and Space Science

Workshop for middle and high school teachers to enhance their knowledge of the Earth as a system. NASA data and materials developed by teachers (all available via the Internet) will be used to engage participants in hands-on, investigative approaches to the Earth system. All materials are ready to be applied in pre-college classrooms. Remotely-sensed data will be used in combination with familiar resources, such as maps, to examine global climate change.

Meeson, Blanche W.↗

NASA NTRS2021

Next generation lunar laser retroreflectors for fundamental physics and lunar science

Lunar Laser Ranging (LLR) data represent a powerful tool to understand the dynamics of the Earth-Moon system and the deep lunar interior. Over the past five decades, the ground station technology has significantly improved, whereas the lunar laser retroreflector arrays (LRAs) on the lunar surface did not. Current instrumental LLR error budget is dominated by the spread of the returning laser pulse due to the large size of the arrays. Next-generation single solid lunar Cube Corner Retroreflectors (CCRs) of large optical diameter (whose LLR performance is unaffected by that time spread) aim to fully exploit the current laser ranging station capabilities to attain LLR accuracy below current centimeter value down to the desired millimeter level and much higher data collection rates. Such improvements will have a significant impact, enabling more refined ephemerides, improved tests of General Relativity (GR) and of other theories of relativistic gravity in the Sun-Earth-Moon system and improved knowledge of the properties of the lunar interior.

L. Porcelli↗

DOE OSTI2025

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗

NASA NTRS2019

Planetary Boundary Layer Height from AIRS and MERRA-2 Products at NASA GES DISC, and Insights from Data Intercomparison

The Atmospheric Infrared Sounder (AIRS) is the hyperspectral infrared sounder onboard NASA's Aqua satellite, launched in 2002. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), in collaboration with NASA Sounder Team at JPL, provides processing, archiving, and distribution services for NASA sounders: the Aqua AIRS mission and the subsequent Suomi-National Polar-orbiting Partnership Cross-track Infrared Sounder (CrIS) mission. The Planetary Boundary Layer (PBL) Height is a new variable added in the AIRS Version 6 support product. It is derived based on gradients of the retrieved atmospheric thermodynamic profile, and gives the pressure at the top of PBL over the ocean. The GES DISC also provides services for the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product generated by the Goddard Earth Observing System Model, Version 5 (GEOS-5) data assimilation system. The monthly PBL Height variable has been available in the Giovanni system, which is a Web-based application developed by the GES DISC providing a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. In this work, we will present the monthly PBL Height data from AIRS and MERRA-2 and the services to support data intercomparison, such as access, plotting, subsetting, re-gridding, and generation of a multi-year monthly mean. We will also show intercomparison results, and evaluate whether (over the ocean) AIRS can observe PBL features similar to the reanalysis product at monthly and longer-term scales.

AIRS↗

NASA NTRS2015

Total and Spectral Solar Irradiance Sensor (TSIS) Project Overview

The main objective of the Total and Spectral solar Irradiance Sensor (TSIS) is to acquire measurements to determine the direct and indirect effects of solar radiation on climate. TSIS total solar irradiance measurements will extend a 37-year long uninterrupted measurement record of incoming solar radiation, the dominant energy source driving the Earths climate and the most precise indicator of changes in the Suns energy output. TSIS solar spectral irradiance measurements will determine the regions of the Earths multi-layered atmosphere that are affected by solar variability, from which the solar forcing mechanisms causing changes in climate can be quantified. TSIS includes two instruments: the Total Irradiance Monitor (TIM) and the Spectral Irradiance Monitor (SIM), integrated into a single payload. The TSIS TIM and SIM instruments are upgraded versions of the two instruments that are flying on the Solar Radiation and Climate Experiment (SORCE) mission launched in January 2003. TSIS was originally planned for the nadir-pointing National Polar-orbiting Operational Environmental Satellite System (NPOESS) spacecraft. The TSIS instrument passed a Critical Design Review (CDR) for NPOESS in December 2009. In 2010, TSIS was re-planned for the Joint Polar Satellite System (JPSS) Polar Free Flyer (PFF). The TSIS TIM, SIM, and associated electronics were built, tested, and successfully completed pre-ship review as of December 2013.In early 2014, NOAA and NASA agreed to fly TSIS on the International Space Station (ISS). In the FY16 Presidents Budget, NASA assumes responsibility for the TSIS mission on ISS. The TSIS project includes requirements, interface, design, build and test of the TSIS payload, including an updated pointing system, for accommodation on the ISS. It takes advantage of the prior development of the TSIS sensors and electronics. The International Space Station (ISS) program contributions include launch services and robotic installation of the TSIS payload onto an ISS Express Logistics Carrier, mission operations, and communications. Total and Spectral solar irradiance data products will be produced, calibrated, and made publically available through the Goddard Earth Science Data and Information Services Center (GES DISC).The NASA GSFC TSIS project at GSFC is responsible for project management, system engineering, safety and mission assurance, and engineering oversight for the TSIS payload. The TSIS project has contracted with the University of Colorado Laboratory for Atmospheric and Space Physics (LASP) for the design, development and testing of TSIS, support for ISS integration, science operations of the TSIS instrument, data processing, data evaluation and delivery to the GES DISC. TSIS will be delivered to Kennedy Space Center for integration in 2017, with launch and installation onto ISS planned for late 2017-early 2018. After a 90-day check-out period, NASA plans five years of TSIS operations.

spectral emission↗

NASA NTRS1991

The NASA master directory quick reference guide

The NASA Master Directory (MD) is a free, online, multidisciplinary directory of space and Earth science data sets (NASA and non-NASA data) that are of potential interest to the NASA-sponsored research community. The MD contains high-level descriptions of data sets, other data systems and archives, and campaigns and projects. It provides mechanisms for searching for data sets by important criteria such as geophysical parameters, time, and spatial coverage. The MD also provides information on ordering the data. In order to simplify the process of finding more detailed information or accessing online data, the MD provides automatic connections to a number of data systems such as the NASA Climate Data System, the Planetary Data System, the NASA Ocean Data System, the Pilot Land Data System, and others. The MD also provides general information about many data systems, data centers, and coordinated data analysis projects. It represents the first major step in the Catalog Interoperability project, whose objective is to enable researchers to quickly and efficiently identity, obtain information about, and get access to space and Earth science data.

Source record↗

NASA NTRS2004

The Lifecycle of NASA's Earth Science Enterprise Data Resources

A major endeavor of NASA's Earth Science Enterprise (ESE) is to acquire, process, archive and distribute data from Earth observing satellites in support of a broad set of science research and applications in the U. S. and abroad. NASA policy directives specifically call for the agency to collect, announce, disseminate and archive all scientific and technical data resulting from NASA and NASA-funded research. During the active life of the satellite missions, while the data products are being created, validated and refined, a number of NASA organizations have the responsibility for data and information system functions. Following the completion of the missions, the responsibility for the long-term stewardship of the ocean and atmospheric, and land process data products transitions to the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS), respectively. Ensuring that long-term satellite data be preserved to support global climate change studies and other research topics and applications presents some major challenges to NASA and its partners. Over the last several years, with the launch and operation of the EOS satellites and the acquisition and production of an unprecedented volume of Earth science data, the importance of addressing these challenges has been elevated. The lifecycle of NASA's Earth science data has been the subject of several agency and interagency studies and reports and has implications and effects on agency charters, policies and budgets and on their data system's requirements, implementation plans and schedules. While much remains to be done, considerable progress has been made in understanding and addressing the data lifecycle issues.

McDonald, Kenneth R.↗

NASA NTRS1984

Earth Observing System. Volume 1, Part 2: Science and Mission Requirements. Working Group Report Appendix

The Earth Observing System (EOS) is a planned NASA program, which will carry out multidisciplinary Earth science studies employing a variety of remote sensing techniques in the 1990's, as a prime mission, using the Space Station polar platform. Its primary goal is the generation of long term Earth science data sets of measurements and processes in the areas of agriculture, forestry, geology, hydrology, oceanography, snow and ice, troposphere and upper atmospheric chemistry, radiation and dynamics pertaining to global studies of Earth as a system, emphasizing the interactions and couplings of the atmospbere-ocean-land-cryosphere system. These results will provide the information needed to qualify our understanding of the global hydrological cycle, global biogeochemical cycles and global climate processes. This report provides the science rationale, recommended observational needs, the broad system configuration and a recommended implementation strategy to achieve the stated mission goals. The report is one volume of a proposed five volume set constituting the Phase A report as the Earth Observing System (EOS).

Goddard Space Flight Center↗

NASA NTRS1992

Remote Earth Sciences data collection using ACTS

Given the focus on global change and the attendant scope of such research, we anticipate significant growth of requirements for investigator interaction, processing system capabilities, and availability of data sets. The increased complexity of global processes requires interdisciplinary teams to address them; the investigators will need to interact on a regular basis; however, it is unlikely that a single institution will house sufficient investigators with the required breadth of skills. The complexity of the computations may also require resources beyond those located within a single institution; this lack of sufficient computational resources leads to a distributed system located at geographically dispersed institutions. Finally the combination of long term data sets like the Pathfinder datasets and the data to be gathered by new generations of satellites such as SeaWiFS and MODIS-N yield extra-ordinarily large amounts of data. All of these factors combine to increase demands on the communications facilities available; the demands are generating requirements for highly flexible, high capacity networks. We have been examining the applicability of the Advanced Communications Technology Satellite (ACTS) to address the scientific, computational, and, primarily, communications questions resulting from global change research. As part of this effort three scenarios for oceanographic use of ACTS have been developed; a full discussion of this is contained in Appendix B.

Evans, Robert H.↗

NASA NTRS2004

Application of Bayesian Classification to Content-Based Data Management

The high volume of Earth Observing System data has proven to be challenging to manage for data centers and users alike. At the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC), about 1 TB of new data are archived each day. Distribution to users is also about 1 TB/day. A substantial portion of this distribution is MODIS calibrated radiance data, which has a wide variety of uses. However, much of the data is not useful for a particular user's needs: for example, ocean color users typically need oceanic pixels that are free of cloud and sun-glint. The GES DAAC is using a simple Bayesian classification scheme to rapidly classify each pixel in the scene in order to support several experimental content-based data services for near-real-time MODIS calibrated radiance products (from Direct Readout stations). Content-based subsetting would allow distribution of, say, only clear pixels to the user if desired. Content-based subscriptions would distribute data to users only when they fit the user's usability criteria in their area of interest within the scene. Content-based cache management would retain more useful data on disk for easy online access. The classification may even be exploited in an automated quality assessment of the geolocation product. Though initially to be demonstrated at the GES DAAC, these techniques have applicability in other resource-limited environments, such as spaceborne data systems.

Lynnes, Christopher↗

NASA NTRS2017

Exploiting Dark Information Resources to Create New Value Added Services to Study Earth Science Phenomena

This paper presents two research applications exploiting unused metadata resources in novel ways to aid data discovery and exploration capabilities. The results based on the experiments are encouraging and each application has the potential to serve as a useful standalone component or service in a data system. There were also some interesting lessons learned while designing the two applications and these are presented next.

Earth Science Informatics↗

NASA NTRS2012

An Update on the CDDIS

The Crustal Dynamics Data Inforn1ation System (CoorS) supports data archiving and distribution activities for the space geodesy and geodynamics community. The main objectives of the system are to store space geodesy and geodynamics related data products in a central data bank, to maintain infom1ation about the archival of these data, and to disseminate these data and information in a timely mam1er to a global scientific research community. The archive consists of GNSS, laser ranging, VLBI, and OORIS data sets and products derived from these data. The coors is one of NASA's Earth Observing System Oata and Infom1ation System (EOSorS) distributed data centers; EOSOIS data centers serve a diverse user community and are tasked to provide facilities to search and access science data and products. The coors data system and its archive have become increasingly important to many national and international science communities, in pal1icular several of the operational services within the International Association of Geodesy (lAG) and its project the Global Geodetic Observing System (GGOS), including the International OORIS Service (IDS), the International GNSS Service (IGS), the International Laser Ranging Service (ILRS), the International VLBI Service for Geodesy and Astrometry (IVS), and the International Earth Rotation Service (IERS). The coors has recently expanded its archive to supp011 the IGS Multi-GNSS Experiment (MGEX). The archive now contains daily and hourly 3D-second and subhourly I-second data from an additional 35+ stations in RINEX V3 fOm1at. The coors will soon install an Ntrip broadcast relay to support the activities of the IGS Real-Time Pilot Project (RTPP) and the future Real-Time IGS Service. The coors has also developed a new web-based application to aid users in data discovery, both within the current community and beyond. To enable this data discovery application, the CDDIS is currently implementing modifications to the metadata extracted from incoming data and product files pushed to its archive. This poster will include background information about the system and its user communities, archive contents and updates, enhancements for data discovery, new system architecture, and future plans.

Noll, Carey↗