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NASA NTRS2019

Bringing Analysis Closer to Data: Developing a Visualization Tool for L2 Earth Science Satellite Data

Earth Science satellite missions provide a unique opportunity for scientists to visualize complex and multifaceted observations projected geospatially across maps of the Earth. While visualization tools can help scientists comprehend, analyze, and share data, visualizing Level-2 Earth Sciences data poses its own specific set of challenges. Since the geospatial information in Level-2 data files is stored as independent variables, the plotting process involves matching dimensional information from latitude and longitude with a desired variable. Variables are stored in different ways across various Earth Science data file formats, which complicates the process of extracting data and plotting variables from a given file without requiring extensive user input and prerequisite familiarity with the file type variable structure. In coordination with NASA’s Goddard Earth Sciences Data Information Services Center (GES DISC), the team developed a Level-2 Earth Science data visualization tool that aims to address some of the complexities associated with plotting Level-2 data. This tool offers command-line and user interface support for file and variable selection to accommodate varying use cases and degrees of user familiarity with the structure of a given file. The visualization tool is written in Python 3 and utilizes a modular approach to facilitate continued expansion and reuse. In addressing some common complications involved in plotting Level-2 Earth Sciences data, the tool aims to help to link the process of analysis more directly with data acquisition and visualization, bringing analysis closer to data across levels of processing.

Li, Angela W.↗
NASA NTRSDate not supplied

ICARTT File Format Enhancements: Supporting FAIRness and Data Discovery of Suborbital Campaign Data

Suborbital campaigns aim to accomplish a wide variety of goals and can include a variety of platforms, instruments, and parameters measured. In 2004, the ICARTT (International Consortium for Atmospheric Research on Transport and Transformation) standards were developed to fulfill data management needs for the ICARTT campaign. The ICARTT file format is text-based and composed of a header with important data description information and the data section. Built on the NASA Ames and GTE data formats, the ICARTT format was created to facilitate data exchange and promote collaborations among the science teams for achieving the ICARTT campaign goals. Due to its success and adaptation for use in many other field campaigns, the ICARTT file format became a NASA standard in 2010 and was amended in January 2017. These changes provided many enhancements, including the requirement for variable standard names. Primarily designed for airborne field studies, ICARTT has been further utilized for ground-based studies. NASA has made a commitment to build an inclusive open science community over the next decade. Open-source science strives to make publicly funded scientific research transparent, inclusive, accessible, and reproducible. The ICARTT format can host metadata that is critical for proper use of the data, particularly for in-situ measurements, and can enhance data discovery and accessibility. However, the required fields are often free text, meaning that the information is human readable, but not machine interpretable. Furthermore, the amount and type of information provided can vary significantly between principal investigators and campaigns. To support FAIR principles and interoperability, enhancements to the ICARTT standards are recommended. Possible recommendations include potential use of controlled and consistent vocabulary for variable standard name and certain common metadata elements; standardizing timestamps for easier data comparisons and analysis; and providing guidance on variable measurement units and how they are reported. Enhancing ICARTT metadata can further streamline the process to make suborbital data more readily available to the data user and improve variable-level metadata. Providing more variable-level metadata can enhance data searching and discovery, supporting NASA’s Open-Source Science Initiative (OSSI).

Megan Buzanowicz↗
NASA NTRS2016

National Climate Assessment - Land Data Assimilation System (NCA-LDAS) Data and Services at NASA GES DISC

The National Climate Assessment-Land Data Assimilation System (NCA-LDAS) is an Integrated Terrestrial Water Analysis, and is one of NASAs contributions to the NCA of the United States. The NCA-LDAS has undergone extensive development, including multi-variate assimilation of remotely-sensed water states and anomalies as well as evaluation and verification studies, led by the Goddard Space Flight Centers Hydrological Sciences Laboratory (HSL). The resulting NCA-LDAS data have recently been released to the general public and include those from the Noah land-surface model (LSM) version 3.3 (Noah-3.3) and the Catchment LSM version Fortuna-2.5 (CLSM-F2.5). Standard LSM output variables including soil moistures temperatures, surface fluxes, snow cover depth, groundwater, and runoff are provided, as well as streamflow using a river routing system. The NCA-LDAS data are archived at and distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The data can be accessed via HTTP, OPeNDAP, Mirador search and download, and NASA Earth data Search. To further facilitate access and use, the NCA-LDAS data are integrated into the NASA Giovanni, for quick visualization and analysis, and into the Data Rods system, for retrieval of time series of long time periods. The temporal and spatial resolutions of the NCA-LDAS data are, respectively, daily-averages and 0.125x0.125 degree, covering North America (25N 53N; 125W 67W) and the period January 1979 to December 2015. The data files are in self-describing, machine-independent, CF-compliant netCDF-4 format.

data assimiliation↗
NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) is a facility data management application developed for the NASA Ames arc jet facilities. The current decentralized data management practices limit statistical tracking, synchronization between video/time series, search capability, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRS1977

AOIPS data base management systems support for GARP data sets

A data base management system is identified, developed to provide flexible access to data sets produced by GARP during its data systems tests. The content and coverage of the data base are defined and a computer-aided, interactive information storage and retrieval system, implemented to facilitate access to user specified data subsets, is described. The computer programs developed to provide the capability were implemented on the highly interactive, minicomputer-based AOIPS and are referred to as the data retrieval system (DRS). Implemented as a user interactive but menu guided system, the DRS permits users to inventory the data tape library and create duplicate or subset data sets based on a user selected window defined by time and latitude/longitude boundaries. The DRS permits users to select, display, or produce formatted hard copy of individual data items contained within the data records.

Gary, J. P.↗
NASA NTRS1980

An improvement in land cover classification achieved by merging microwave data with Landsat multispectral scanner data

The improvement in land cover classification achieved by merging microwave data with Landsat MSS data is examined. To produce a merged data set for analysis and comparison, a registration procedure by which a set of Seasat SAR digital data was merged with the MSS data is described. The Landsat MSS data and the merged Landsat/Seasat data sets were processed using conventional multichannel spectral pattern recognition techniques. An analysis of the classified data sets indicates that while Landsat data delineate different forest types (i.e., deciduous/coniferous) and allow some species separation, SAR data provide additional information related to plant canopy configuration and vegetation density as associated with varying water regimes, and therefore allow for further subdivision in the classification of forested wetlands of the coastal region of the southern United States.

Wu, S. T.↗
NASA NTRS1986

Planning for the utilization of the PCDS in studying the interaction of clouds (ISCCP-C data) and the Earth radiation budget (ERBE data)

The Pilot Climate Data System (PCDS) affords an opportunity to analyze data from different but highly complementary data sets. Two of these highly complementary data sets supported by the PCDS are the International Satellite Cloud Climatology Project (ISCCP) and the Earth Radiation Budget Experiment (ERBE). Both data set sponsors are aware of the utility of one data set to the other, and both projects utilize gridded data on a 2.5 deg by 2.5 deg grid. The ISCCP data have been collected since July 1983, and the NOAA-9 data for ERBE have been collected for more than a year. Therefore, there is a good chance to use these temporally overlapping data sets to investigate hypothesized relationships. Changes in cloudiness affect both cloud albedo feedback (shortwave) and the greenhouse effect (longwave). The relative importance of the effects of clouds on albedo versus outgoing longwave radiation (OLR) in determining the radiation balance has long been a matter of controversy. Now, however, changes in cloud amount as observed by the ISCCPO can be correlated to corresponding changes in the albedo and changes in the OLR from ERBE. Monthly means can be utilized in all instances.

Jacobowitz, H.↗
NASA NTRS1991

Data space volumes and classification optimization of SPOT and Landsat TM data

In order to compare the data space volume of SPOT XS and Landsat TM images, three data sets, i.e., a wetlands/agricultural data set, an agricultural data set, and a forest data set, are examined. The comparisons are made for the same geographic area. The data space volumes for Landsat TM (2, 3, and 4) are found to be 70 to 100 percent larger than the volumes for the SPOT XS images. It is suggested that the additional midinfrared bands contribute to the difference in data space volumes between Landsat TM and SPOT XS. The data space volumes for Landsat TM bands 3, 4, and 5 are more than an order of magnitude greater than the volumes for the three band SPOT XS data sets. The volumes of the six-band Landsat TM images are four orders of magnitude greater than the SPOT XS. The analysis of the data space volumes is used to optimize the computation time and minimize the storage requirements of a maximum-likelihood classification based on a look-up table.

Ahearn, Sean C.↗
NASA NTRS1992

Earth observing system. Output data products and input requirements, version 2.0. Volume 3: Algorithm summary tables and non-EOS data products

Volume 3 assists Earth Observing System (EOS) investigators in locating required non-EOS data products by identifying their non-EOS input requirements and providing the information on data sets available at various Distributed Active Archive Centers (DAAC's), including those from Pathfinder Activities and Earth Probes. Volume 3 is intended to complement, not to duplicate, the the EOSDIS Science Data Plan (SDP) by providing detailed data set information which was not presented in the SDP. Section 9 of this volume discusses the algorithm summary tables containing information on retrieval algorithms, expected outputs and required input data. Section 10 describes the non-EOS input requirements of instrument teams and IDS investigators. Also described are the current and future data holdings of the original seven DAACS and data products planned from the future missions and projects including Earth Probes and Pathfinder Activities. Section 11 describes source of information used in compiling data set information presented in this volume. A list of data set attributes used to describe various data sets is presented in section 12 along with their descriptions. Finally, Section 13 presents the SPSO's future plan to improve this report .

Lu, Yun-Chi↗
NASA NTRS1995

Calibration of the NOAA 11 solar backscatter ultraviolet (SBUV/2) ozone data set from 1989 to 1993 using in-flight calibration data and SSBUV

Total ozone and ozone profiles are currently being measured by solar backscatter ultraviolet (SBUV/2) instruments onboard NOAA polar orbiting spacecraft using the backscattered ultraviolet technique. The NOAA 11 SBUV/2 operational data set, reprocessed from January 1989 to May 1993 (version 6), includes an updated algorithm and revised prelaunch and postlaunch calibrations of the geometrical albedo observations used to derive ozone values. The postlaunch calibration revisions remove time dependent errors in the ozone amounts due to instrument drift, while the revised prelaunch calibration corrects the absolute value of retrieved ozone. The prelaunch corrections are a result of calibration checks from in-orbit comparisons of ultraviolet geometric albedos measured by shuttle SBUV (SSBUV) and the NOAA 11 SBUV/2. Geometric albedo comparison data are further corrected using a radiative transfer code to account for the small difference in observing conditions between the two spacecraft. The postlaunch corrections rely on in-flight calibration and solar irradiance data to account for time dependent changes in instrument gain, thermal response, and instrument diffuser degradation over time. Comparison of data from three SSBUV flights, which occurred about one year apart, with concurrent SBUV/2 data provided an independent check of the time dependent change derived from the in-flight calibration data. Time independent corrections result in an increase of about 1% for total ozone, 5% for ozone at 1 mbar, and near 0% at 15 mbar. The time dependent corrections amount to an increase of 2% for total ozone, 10% for ozone near 1 mbar, and 3% at 15 mbar at the end of the current record in May 1993. Recent laboratory studies indicate that the absolute radiance calibrations may still be in error by a few percent which results in ozone profile values that are too low. The SBUV/2 total and ozone profile data are compared to the Nimbus SBUV data during the period when the data overlapped. Total ozone values agree to about 1%, while ozone profile differences range from -4% to +6%, depending on latitude and altitude, relative to SBUV. These differences are not statistically significant given the uncertainties of the two data sets.

Hilsenrath, E.↗
NASA NTRS1996

Application of Tracking and Data Relay Satellite (TDRS) Differenced One-Way Doppler (DOWD) Tracking Data for Orbit Determination and Station Acquisition Support of User Spacecraft Without TDRS Compatible Transponders

Many spacecraft are launched today with only an omni-directional (omni) antenna and do not have an onboard Tracking and Data Relay Satellite (TDRS) transponder that is capable of coherently returning a carrier signal through TDRS. Therefore, other means of tracking need to be explored and used to adequately acquire the spacecraft. Differenced One-Way Doppler (DOWD) tracking data are very useful in eliminating the problems associated with the instability of the onboard oscillators when using strictly one-way Doppler data. This paper investigates the TDRS DOWD tracking data received by the Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF) during the launch and early orbit phases for the the Interplanetary Physics Laboratory (WIND) and the National Oceanographic and Atmospheric Administration (NOAA)-J missions. In particular FDF personnel performed an investigation of the data residuals and made an assessment of the acquisition capabilities of DOWD-based solutions. Comparisons of DOWD solutions with existing data types were performed and analyzed in this study. The evaluation also includes atmospheric editing of the DOWD data and a study of the feasibility of solving for Doppler biases in an attempt to minimize error. Furthermore, by comparing the results from WIND and NOAA-J, an attempt is made to show the limitations involved in using DOWD data for the two different mission profiles. The techniques discussed in this paper benefit the launches of spacecraft that do not have TDRS transponders on board, particularly those launched into a low Earth orbit. The use of DOWD data is a valuable asset to missions which do not have a stable local oscillator to enable high-quality solutions from the one-way/return-link Doppler tracking data.

Olszewski, A. D., Jr.↗
NASA NTRS2000

Data Information for Global Change Studies: NASA's Distributed Active Archive Centers and Cooperating Data Centers

The Earth Observing System (EOS) is an integral part of the National Aeronautics and Space Administration's (NASA's) Earth Science Enterprise (ESE). ESE is a long-term global change research program designed to improve our understanding of the Earth's interrelated processes involving the atmosphere, oceans, land surfaces, and polar regions. Data from EOS instruments and other Earth science measurement systems are useful in understanding the causes and processes of global climate change and the consequences of human activities. The EOS Data and Information System (EOSDIS) provides a structure for data management and user services for products derived from EOS satellite instruments and other NASA Earth science data. Within the EOSDIS framework, the Distributed Active Archive Centers (DAACs) have been established to provide expertise in one or more Earth science disciplines. The DAACs and cooperating data centers provide data and information services to support the global change research community. Much of the development of the DAACs has been in anticipation of the enormous amount of data expected from EOS instruments to be launched within the next two decades. Terra, the EOS flagship launched in December 1999, is the first of a series of EOS satellites to carry several instruments with multispectral capabilities. Some data products from these instruments are now available from several of the DAACs. These and other data products can be ordered through the EOS Data Gateway (EDG) and DAAC-specific online ordering systems.

Source record↗
NASA NTRS2004

Enhancing the IGS Data and Products Infrastructure: A Data Center Perspective

The Data Transfer and Data Centers session will present papers on current data center activities as well as various topics geared towards enhancing the availability and acquisition of International Global Positions System Service (IGS) data and products. These topics include: strategies for reducing data file latency at IGS Data Centers, standardization of data file replacement notification methods, improving the efficiency of data file transfer among IGS Data Centers, and defining a network topology for GPS data files for use in applications.

Scharber, Michael↗
NASA NTRS2004

Circular Data Images for Directional Data

Directional data includes vectors, points on a unit sphere, axis orientation, angular direction, and circular or periodic data. The theoretical statistics for circular data (random points on a unit circle) or spherical data (random points on a unit sphere) are a recent development. An overview of existing graphical methods for the display of directional data is given. Cross-over occurs when periodic data are measured on a scale for the measurement of linear variables. For example, if angle is represented by a linear color gradient changing uniformly from dark blue at -180 degrees to bright red at +180 degrees, the color image will be discontinuous at +180 degrees and -180 degrees, which are the same location. The resultant color would depend on the direction of approach to the cross-over point. A new graphical method for imaging directional data is described, which affords high resolution without color discontinuity from "cross-over". It is called the circular data image. The circular data image uses a circular color scale in which colors repeat periodically. Some examples of the circular data image include direction of earth winds on a global scale, rocket motor internal flow, earth global magnetic field direction, and rocket motor nozzle vector direction vs. time.

Morpet, William J.↗
NASA NTRS2012

New and Improved GLDAS Data Sets and Data Services at NASA GES DISC

The goal of a Land Data Assimilation System (LDAS) is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes data and, thereby, facilitate hydrology and climate modeling, research, and forecast. With the motivation of creating more climatologically consistent data sets, NASA GSFC's Hydrological Sciences Laboratory has generated more than 60 years (Jan. 1948-- Dec. 2008) of Global LDAS Version 2 (GLDAS-2) data, by using the Princeton Forcing Data Set and upgraded versions of Land Surface Models (LSMs). GLDAS data and data services are provided at NASA GES DISC Hydrology Data and Information Services Center (HDISC), in collaboration with HSL and LDAS.

Rui, Hualan↗
NASA NTRS2014

Earth Science Data Analysis in the Era of Big Data

Anyone with even a cursory interest in information technology cannot help but recognize that "Big Data" is one of the most fashionable catchphrases of late. From accurate voice and facial recognition, language translation, and airfare prediction and comparison, to monitoring the real-time spread of flu, Big Data techniques have been applied to many seemingly intractable problems with spectacular successes. They appear to be a rewarding way to approach many currently unsolved problems. Few fields of research can claim a longer history with problems involving voluminous data than Earth science. The problems we are facing today with our Earth's future are more complex and carry potentially graver consequences than the examples given above. How has our climate changed? Beside natural variations, what is causing these changes? What are the processes involved and through what mechanisms are these connected? How will they impact life as we know it? In attempts to answer these questions, we have resorted to observations and numerical simulations with ever-finer resolutions, which continue to feed the "data deluge." Plausibly, many Earth scientists are wondering: How will Big Data technologies benefit Earth science research? As an example from the global water cycle, one subdomain among many in Earth science, how would these technologies accelerate the analysis of decades of global precipitation to ascertain the changes in its characteristics, to validate these changes in predictive climate models, and to infer the implications of these changes to ecosystems, economies, and public health? Earth science researchers need a viable way to harness the power of Big Data technologies to analyze large volumes and varieties of data with velocity and veracity. Beyond providing speedy data analysis capabilities, Big Data technologies can also play a crucial, albeit indirect, role in boosting scientific productivity by facilitating effective collaboration within an analysis environment. To illustrate the effects of combining a Big Data technology with an effective means of collaboration, we relate the (fictitious) experience of an early-career Earth science researcher a few years beyond the present, interlaced and contrasted with reminiscences of its recent past (i.e., the present).

Kuo, K.-S.↗
NASA NTRS2018

Digital Image Correlation Data Processing and Analysis Techniques to Enhance Test Data Assessment and Improve Structural Simulations

The NASA Shell Buckling Knockdown Factor Project (SBKF) was established in 2007 by the NASA Engineering and Safety Center (NESC) with the primary goal to develop new analysis-based buckling design factors (a.k.a. knockdown factors) and high-fidelity buckling simulations for selected launch-vehicle-like cylindrical shell structures. A series of tests are being conducted on large-scale metallic and composite cylindrical shells in order to provide validation data for these new factors and simulations. However, the validation of these new factors and simulations is quite demanding and requires test data that is commensurate with their fidelity. Traditional instrumentation, such as linear variable displacement transducers (LVDTs) and electrical-resistance strain gages serve a critical role in providing accurate displacement and strain measurements in these tests, but only allow for data to be recorded at a select number of point locations and are not sufficient to provide all the necessary validation data. Advanced measurement technologies can be used effectively to complement traditional instrumentation and gather additional data required to validate these structural simulations. In particular, three-dimensional digital image correlation (DIC) was implemented during SBKF cylinder testing to characterize the full-field displacement and strain behavior. Commercially available VIC-3DTM software and user-written data processing scripts were used to generate valuable data and insight into the complex buckling response of the cylinders that otherwise would be impossible to gather using traditional instrumentation. In addition, the measured data from DIC was used to verify measured test data obtained from other instrumentation, enhance test and analysis correlation, and help identify the root cause of anomalous test results that may have gone unexplained if only traditional instrumentation was used. Selected test results that demonstrate the use of DIC on the SBKF cylinders are presented and a portion of the data processing methods are described.

Gardner, Nathaniel W.↗
NASA NTRSDate not supplied

What is LAADS DAAC? How to Download Data from NASA's Source for Cloud and Aerosol Data

The Level-1 and Atmosphere Archive Distribution System (LAADS) Distributed Active Archive Center (DAAC) primarily archives and distributes data on clouds and aerosols in Earth’s atmosphere in addition to providing Level 0 and 1 Moderate Imaging Radiometer Suite (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS) and airborne data as well as data from the European Space Agency’s Sea and Land Surface Temperature Radiometer (SLSTR) and the Ocean and Land Colour Instrument (OLCI). Secondarily, LAADS DAAC serves as a backup source for MODIS and VIIRS land products. As one of twelve DAACS supported by NASA’s Earth Science Data and Information System (ESDIS), LAADS DAAC provides a gateway to data through multiple tools. The newest, View Data, allows users to preview data sets before downloading or applying any post-processing transformations. Learn about how to access LAADS DAAC data, get up-to-date information about LAADS DAAC's migration to hosting data in the cloud, and discover how to use the View Data tool.

Tassia Owen↗