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An end-to-end point design, including lidar, orbit, scanning, atmospheric, and data processing parameters, for space-based global profiling of atmospheric wind will be presented. The point design attempts to match the recent NASA/NOAA draft science requirements for wind measurement.
The Athena science payload on the Mars Exploration Rovers (MER) includes the Microscopic Imager (MI). The MI is a fixed-focus camera mounted on an extendable arm, the Instrument Deployment Device (IDD). The MI acquires images at a spatial resolution of 30 microns/pixel over a broad spectral range (400 - 700 nm). The MI uses the same electronics design as the other MER cameras but its optics yield a field of view of 31 x 31 mm across a 1024 x 1024 pixel CCD image. The MI acquires images using only solar or skylight illumination of the target surface. A contact sensor is used to place the MI slightly closer to the target surface than its best focus distance (about 69 mm), allowing concave surfaces to be imaged in good focus. Coarse focusing (approx. 2 mm precision) is achieved by moving the IDD away from a rock target after contact is sensed. The MI optics are protected from the Martian environment by a retractable dust cover. This cover includes a Kapton window that is tinted orange to restrict the spectral bandpass to 500 - 700 nm, allowing crude color information to be obtained by acquiring images with the cover open and closed. The MI science objectives, instrument design and calibration, operation, and data processing were described by Herkenhoff et al. Initial results of the MI experiment on both MER rovers ('Spirit' and 'Opportunity') are described below.
The Athena science payload on the Mars Exploration Rovers (MER) includes the Microscopic Imager (MI). The MI is a fixed-focus camera mounted on an extendable arm, the Instrument Deployment Device (IDD). The MI acquires images at a spatial resolution of 31 microns/pixel over a broad spectral range (400 - 700 nm). The MI uses the same electronics design as the other MER cameras but its optics yield a field of view of 32 32 mm across a 1024 1024 pixel CCD image. The MI acquires images using only solar or skylight illumination of the target surface. The MI science objectives, instrument design and calibration, operation, and data processing were described by Herkenhoff et al. Initial results of the MI experiment on both MER rovers (Spirit and Opportunity) have been published previously. Highlights of these and more recent results are described.
Rainfall-triggered landslides typically occur and are evaluated at local scales, using slope-stability models to calculate coincident changes in driving and resisting forces at the hillslope level in order to anticipate slope failures. Over larger areas, detailed high resolution landslide modeling is often infeasible due to difficulties in quantifying the complex interaction between rainfall infiltration and surface materials as well as the dearth of available in situ soil and rainfall estimates and accurate landslide validation data. This presentation will discuss how satellite precipitation and surface information can be applied within a landslide hazard assessment framework to improve landslide monitoring and early warning by considering two disparate approaches to landslide hazard assessment: an empirical landslide forecasting algorithm and a physical slope-stability model. The goal of this research is to advance near real-time landslide hazard assessment and early warning at larger spatial scales. This is done by employing high resolution surface and precipitation information within a probabilistic framework to provide more physically-based grounding to empirical landslide triggering thresholds. The empirical landslide forecasting tool, running in near real-time at http://trmm.nasa.gov, considers potential landslide activity at the global scale and relies on Tropical Rainfall Measuring Mission (TRMM) precipitation data and surface products to provide a near real-time picture of where landslides may be triggered. The physical approach considers how rainfall infiltration on a hillslope affects the in situ hydro-mechanical processes that may lead to slope failure. Evaluation of these empirical and physical approaches are performed within the Land Information System (LIS), a high performance land surface model processing and data assimilation system developed within the Hydrological Sciences Branch at NASA's Goddard Space Flight Center. LIS provides the capabilities to quantify uncertainty from model inputs and calculate probabilistic estimates for slope failures. Results indicate that remote sensing data can provide many of the spatiotemporal requirements for accurate landslide monitoring and early warning; however, higher resolution precipitation inputs will help to better identify small-scale precipitation forcings that contribute to significant landslide triggering. Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale, which will serve as key inputs to significantly advance the accuracy of landslide hazard assessment, particularly over larger spatial scales.
NASA's Earth Science Data and Information System (ESDIS) Project has been processing information for the registration of Digital Object Identifiers (DOI) for the last five years of which an automated system has been in operation for the last two years. The ESDIS DOI registration system has registered over 2000 DOIs with over 1000 DOIs held in reserve until all required information has been collected. By working towards the goal of assigning DOIs to the 8000+ data collections under its management, ESDIS has taken the first step towards facilitating the use of data citations with those products. Jeanne Behnke, ESDIS Deputy Project Manager has reviewed and approved the poster.
The Ocean Biology Processing Group has been working with the Korean Institute of Ocean Science and Technology (KIOST) to process geosynchronous ocean color data from the GOCI (Geostationary Ocean Color Instrument) aboard the COMS (Communications, Ocean and Meteorological Satellite). The level-2 processing program, l2gen has GOCI processing as an option. Improvements made to that processing are discussed here as well as a discussion about cloud motion effects.
The Ocean Biology Processing Group has been working with the Korean Institute of Ocean Science and Technology (KIOST) to process geosynchronous ocean color data from the GOCI (Geostationary Ocean Color Instrument) aboard the COMS (Communications, Ocean and Meteorological Satellite). The level-2 processing program, l2gen has GOCI processing as an option. Improvements made to that processing are discussed here as well as a discussion about cloud motion effects.
Fourteen types of geophysical instruments deployed at the Apollo 12, 14, 15, 16, and 17 sites by the astronauts for long-term observation were collectively called the Apollo Lunar Surface Experiments Package (ALSEP). These instruments were active from the times of their deployment (November 1969–December 1972) to September 1977. At the conclusion of the experiments, the raw instrument data received from the Moon prior to March 1976 were left unarchived. Portions of the data processed by the principal investigators (PIs) of these experiments had been archived at the NASA Space Science Data Coordinated Archive (NSSDCA) in various formats. The unarchived data, residing then on open-reel magnetic tapes, became lost in the decades since, along with much of the metadata (the supporting documents for these data). We have recently recovered 440 of the previously lost tapes, containing raw ALSEP instrument data from April through June of 1975. Here we describe the data extracted from these tapes and summarize the data products generated for archiving at the NASA Planetary Data System (PDS) and NSSDCA, along with their historical narrative. In addition, we have reformatted many of the datasets delivered to NSSDCA by the PIs in the 1970s for archiving at the PDS. Finally, we have compiled an online searchable repository of ALSEP-related documents by optically scanning tens of thousands of pages of them kept at the Lunar and Planetary Institute in Texas.
The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the NOAA-20 (N20) satellite was launched on November 18, 2017. The N20 VIIRS reflective solar bands (RSBs) are calibrated on-orbit using a solar diffuser. An accurate on-orbit calibration is crucial to the high-quality downstream products facilitating atmosphere, ocean and land applications. In this study, the stability of the Level 1B (L1B) reflectance product is investigated using measurements over deep convective clouds (DCCs) for M-bands M1-M5, M7-M11, and I-bands I1-I3. The methodologies developed previously for Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and Suomi National Polar-orbiting Partnership (SNPP) VIIRS are extended and applied to the N20 RSB to derive DCC-based trends. The Collection 2 L1B data produced by NASA Land Science Investigator-led Processing Systems (SIPS) is used to evaluate the performance of the N20 VIIRS RSB calibration. At nadir, the reflectance trends for M1, M5, M8-M11, and I3 are insignificant compared to their corresponding variations (STDs) except for bands M2-M4, M7, and I1-I2, whose trends are larger than or equivalent to their STDs. The reflectance is relatively stable compared to their STDs for all the study RSBs at six aggregation zones across the entire scan angle range. Also discussed in this paper are the detector-to-detector differences and half-angle mirror side differences using the DCCs. Future applications using DCCs, which include an intercomparison with SNPP VIIRS, are also discussed.
Why? - Interdisciplinary science depends on large amount of Earth science data and computational resources - Working with these datasets is non-trivial - Big data science requires advanced distributed computing knowledge What? VEDA is an open platform that brings key Earth science datasets next to open source tools for data processing, analysis, visualization, and exploration in a managed and more accessible computing environment.
NASA’s Goddard Earth Science (GES) Data and Information Services Center (DISC) is one of twelve data centers in NASA's Science Mission Directorate (SMD), providing vital earth science data to a diverse user base. To enhance the discoverability of this data, GES DISC employs a keyword search system, which leverages scientific keywords embedded in dataset metadata. However, the evolving nature of scientific applications of our data necessitates regular review and augmentation of these keywords. To address this, we developed a service to automatically predict missing science keywords in the metadata. This service constructs a knowledge graph from the latest GES DISC metadata within NASA’s Common Metadata Repository (CMR). Using an open-source library, we trained a machine learning model to predict absent science keywords in the metadata. Our preliminary results indicate that the model has high levels of accuracy at predicting science keywords in the dataset metadata when exposed to data not included in its training. These predicted keywords were then evaluated by GES DISC data curation scientists and compared against other AI tools for metadata augmentation. We aim to enhance the overall usability and accessibility of NASA’s earth science data by implementing this tool in our data curation processes.
The TERRA spacecraft was launched in December 1999 from Vandenberg Air Force Base, becoming the flagship of NASA's Earth Observing System program to gather data on how the planet's processes create climate. Originally planned as a 5 year mission, it still provides valuable science data after nearly 10 years on orbit. On October 13th, 2009 at 16:23z following a routine inclination maneuver, TERRA experienced a battery cell failure and a simultaneous failure of several battery heater control circuits used to maintain cell temperatures and gradients within the battery. With several cells nearing the minimum survival temperature, preventing the electrolyte from freezing was the first priority. After several reset attempts and power cycling of the control electronics failed to reestablish control authority on the primary side of the controller, it was switched to the redundant side, but anomalous performance again prevented full heater control of the battery cells. As the investigation into the cause of the anomaly and corrective action continued, a battery thermal model was developed to be used in determining the control ability remaining and to simulate and assess corrective actions. Although no thermal model or detailed reference data of the battery was available, sufficient information was found to allow a simplified model to be constructed, correlated against pre-anomaly telemetry, and used to simulate the thermal behavior at several points after the anomaly. It was then used to simulate subsequent corrective actions to assess their impact on cell temperatures. This paper describes the rapid development of this thermal model, including correlation to flight data before and after the anomaly., along with a comparative assessment of the analysis results used to interpret the telemetry to determine the extent of damage to the thermal control hardware, with near-term corrective actions and long-term operations plan to overcome the anomaly.
A challenging aspect of any project is finding all the relevant data and information needed to address the research objective. Searching for data and its contextual metadata can become overwhelming for both undergraduate and graduate students, potentially hindering their work and affecting the scientific discoveries that could be made in the long run. To ease this, the NASA Airborne Data Management Group (ADMG), part of the Interagency Implementation and Advanced Concepts Team (IMPACT), has developed the new Catalog of Archived Suborbital Earth science Investigations (CASEI). CASEI includes a web portal that users, be they professionals or students, can use to search, browse, discover, and locate relevant observations associated with NASA’s airborne and field campaigns. Users are able to query data in a variety of ways (via keywords, locations, timeframe, etc) from one online portal, minimizing the amount of time needed to search. CASEI also allows access to key contextual metadata and data from a wide array of Earth and Atmospheric Science topics such as aerosols and boundary layer processes, as well as ice and glacial properties or processes. Users are able to access the data via DOI links to data set landing pages. This presentation will demonstrate how CASEI can be used for classwork and student research. Teachers can provide CASEI to their students as a tool for their studies, or use it to find data themselves while constructing their curriculums. Additionally, users can leverage CASEI to learn about NASA’s Earth and Atmospheric Science research efforts and to find data relevant for assignments or other research projects. The metadata in CASEI has been carefully curated, and highlights important information about the campaigns and their data. Students can explore and learn about the scientific objectives of the campaigns, as well as descriptions of the campaign’s best research days. Having access to contextual metadata in an easy to understand way can help plant the seeds of new ideas in students at any point in their academic journey. From class projects to theses/dissertations and other research, CASEI is a valuable emerging tool for data discovery, giving access to all users and guiding researchers to NASA’s unique airborne data to answer the burning Earth Science questions of our time.
The Life Sciences Project Division (LSPD) at JSC, which manages human life sciences flight experiments for the NASA Life Sciences Division, augmented its Life Sciences Data System (LSDS) in support of the Spacelab Life Sciences-2 (SLS-2) mission, October 1993. The LSDS is a portable ground system supporting Shuttle, Spacelab, and Mir based life sciences experiments. The LSDS supports acquisition, processing, display, and storage of real-time experiment telemetry in a workstation environment. The system may acquire digital or analog data, storing the data in experiment packet format. Data packets from any acquisition source are archived and meta-parameters are derived through the application of mathematical and logical operators. Parameters may be displayed in text and/or graphical form, or output to analog devices. Experiment data packets may be retransmitted through the network interface and database applications may be developed to support virtually any data packet format. The user interface provides menu- and icon-driven program control and the LSDS system can be integrated with other workstations to perform a variety of functions. The generic capabilities, adaptability, and ease of use make the LSDS a cost-effective solution to many experiment data processing requirements. The same system is used for experiment systems functional and integration tests, flight crew training sessions and mission simulations. In addition, the system has provided the infrastructure for the development of the JSC Life Sciences Data Archive System scheduled for completion in December 1994.
The Mars 2020 spacecraft was spin-stabilized during cruise, just like its predecessor, the Mars Science Laboratory. This spinning motion imparts a signature in the radiometric tracking data that must be dealt with in order to properly model the motion of the spacecraft's center of mass. We discuss how the Orbit Determination team pre-processed the data for efficient computations while also providing other benefits such as high-fidelity attitude modeling and on-board clock verification. Finally, we discuss the analysis and results of the in-flight thruster calibration activity.
A method for ocean fish production estimation was proposed for development. The method was to use data acquired with the Coastal Zone Color Scanner, and processed into chlorophyll concentrations by the GSFC ocean Sciences Division, in combination with fish production and primary production data acquired from different ocean areas. A linear relation exits between annual fish production and annual phytoplankton carbon production for a wide range of coastal ocean environments. The uses of several existing algorithms which relate primary production to CZCS chlorophyll data as input to the fish production regression model is proposed. A question relating phytoplankton production to CZCS chlorophyll was obtained by Eppley (1984) using chlorophyll data obtained from field samples, equivalent to chlorophyll data obtained from CZCS imagery, and primary production data obtained from ship-board observations on a wide variety of coastal and open ocean environments. This equation was modified with additional data and was successfully tested using CZCS data and field chlorophyll and phytoplankton production data obtained from northeastern North American continental shelf waters and Atlantic open ocean waters. The modified Eppley (1984) relation also estimated phytoplankton annual carbon production in the Sargasso Sea within the confidence limits of a mean value obtained from the Eppley (1984) equation for oceanic waters that provide about 90 percent of total ocean primary production. The modified Eppley production formula applied to CZCS chlorophyll data obtained from several northeastern North American coastal environments gave phytoplankton annual carbon production values similar to the values used in the fish production regression equation.
The NASA Pilot Land Data System (PLDS) project is intended to enhance the effectiveness of data processing capabilities used by researchers applying remote sensing data in land science research. Two sites in the centerminous U.S. have been selected as study areas scanned by Landsat, Nimbus and GOES instruments. The data will be analyzed by teams of researchers representing different fields of expertise. The PLDS program will explore data management, networking and communications, system access capabilities, land analysis software, special processes and overall systems engineerng. The data will be processed by researchers working interactively through remote supermicrocomputer workstations using a variety of operating systems and on-site software capabilities.
On the Lunar Geoscience Observer project, a Visible and Infrared Mapping Spectrometer instrument has been proposed. This instrument will have science data input rates in the hundreds of kilobits per second (kbps) and an average telemetry output data rate of 4 kbps. Techniques that can be used to reduce the throughput of the instrument are editing, summing and averaging, data compression, data preprocessing, pattern recognition and snapshot data taking. Due to instrument limitations in the buffer memory size and processing speeds, a careful selection of the available techniques must be made.