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Jet Propulsion Laboratory computer scientists developed a unique software framework to help NASA manage its massive amounts of science data. Through a partnership with the Apache Software Foundation of Forest Hill, Maryland, the technology is now available as an open-source solution and is in use by cancer researchers and pediatric hospitals.
This software was developed for the NPOESS (National Polar-orbiting Operational Environmental Satellite System) Preparatory Project (NPP) Science Data Segment. The purpose of this software is to compare HDF5 (Hierarchical Data Format) files specific to NPP and report whether the HDF5 files are identical. If the HDF5 files are different, users have the option of printing out the list of differences in the HDF5 data files. The user provides paths to two directories containing a list of HDF5 files to compare. The tool would select matching HDF5 file names from the two directories and run the comparison on each file. The user can also select from three levels of detail. Level 0 is the basic level, which simply states whether the files match or not. Level 1 is the intermediate level, which lists the differences between the files. Level 2 lists all the details regarding the comparison, such as which objects were compared, and how and where they are different. The HDF5 tool is written specifically for the NPP project. As such, it ignores certain attributes (such as creation_date, creation_ time, etc.) in the HDF5 files. This is because even though two HDF5 files could represent exactly the same granule, if they are created at different times, the creation date and time would be different. This tool is smart enough to ignore differences that are not relevant to NPP users.
Once a research or application problem has been identified, one logical next step is to search for available relevant data products. Thus, an early component of a potential GEOSS-AI system, in the continuum between observations and end point research, applications, and decision making, would be one that enables transparent data discovery and access by users. Such a component might be effected via the systems data agents. Presumably, some kind of data cataloging has already been implemented, e.g., in the GEOSS Common Infrastructure (GCI). Both the agents and cataloging could also leverage existing resources external to the system. The system would have some means to accept and integrate user-contributed agents. The need or desirability for some data format internal to the system should be evaluated. Another early component would be one that facilitates browsing visualization of the data, as well as some basic analyses.Three ongoing projects at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provide possible proto-examples of potential data access and visualization components of a cloud-based GEOSS-AI system. 1. Reorganizing data archived as time-step arrays to point-time series (data rods), as well as leveraging the NASA Simple Subset Wizard (SSW), to significantly increase the number of data products available, at multiple NASA data centers, for production as on-the-fly (virtual) data rods. SSWs data discovery is based on OpenSearch. Both pre-generated and virtual data rods are accessible via Web services. 2. Developing Web Feature Services to publish the metadata, and expose the locations, of pre-generated and virtual data rods in the GEOSS Portal and enable direct access of the data via Web services. SSW is also leveraged to increase the availability of both NASA and non-NASA data.3.Federating NASA Giovanni (Geospatial Interactive Online Visualization and Analysis Interface), for multi-sensor data exploration, that would allow each cooperating data center, currently the NASA Distributed Active Archive Centers (DAACs), to configure its own Giovanni deployment, while also allowing all the deployments to incorporate each others data. A federated Giovanni comprises Giovanni Virtual Machines, which can be run on local servers or in the cloud.
The Geospatial Interactive Online Visualization and Analysis Interface (Giovanni) is a popular tool for users of the Goddard Earth Sciences Data and Information Services Center (GES DISC) and has been in use for over a decade. It provides a wide variety of algorithms and visualizations to explore large remote sensing datasets without having to download the data and without having to write readers and visualizers for it. Giovanni is now being extended to enable its capabilities at other data centers within the Earth Observing System Data and Information System (EOSDIS). This Federated Giovanni will allow four other data centers to add and maintain their data within Giovanni on behalf of their user community. Those data centers are the Physical Oceanography Distributed Active Archive Center (PO.DAAC), MODIS Adaptive Processing System (MODAPS), Ocean Biology Processing Group (OBPG), and Land Processes Distributed Active Archive Center (LP DAAC). Three tiers are supported: Tier 1 (GES DISC-hosted) gives the remote data center a data management interface to add and maintain data, which are provided through the Giovanni instance at the GES DISC. Tier 2 packages Giovanni up as a virtual machine for distribution to and deployment by the other data centers. Data variables are shared among data centers by sharing documents from the Solr database that underpins Giovanni's data management capabilities. However, each data center maintains their own instance of Giovanni, exposing the variables of most interest to their user community. Tier 3 is a Shared Source model, in which the data centers cooperate to extend the infrastructure by contributing source code.
NASA has been monitoring ozone variations from space since 1970 with the launch of the Backscatter Ultraviolet instrument (BUV) on Nimbus- 4. Ozone in the Earth’s stratosphere and troposphere plays a crucial role in protecting life from harmful solar UV radiation, and it also influences Earth’s climate. The vertical distribution of ozone is used in modeling forecasts, verifying model analysis and simulations, and improving the measurement of tropospheric ozone that is a toxic constituent of air pollution. The Microwave Limb Sounder (MLS) on the EOS Aura spacecraft has been monitoring long-term continuous stratosphere ozone vertical behaviors since 2004. The Ozone Mapper Profile Suite (OMPS) on the Suomi National Polar-orbiting Partnership (S-NPP) was launched in 2011. Two of its three instruments, the Limb Profiler (LP) and the Nadir Profiler (NP), are designed to map ozone profiles with full global coverage (~6 days for NP) and a high level of vertical accuracy (1-km reporting record for LP) in the stratosphere and upper troposphere. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5 Precursor (S5P), launched in 2017, aims to retrieve ozone profiles in both the troposphere and up to top-of-atmosphere (TOA) by using UV radiation measurements in a spectral range of 270-320 nm. S5P/TROPOMI ozone profile products are planned to become available in April 2020. This presentation will concentrate on satellite ozone vertical profile data collections from OMPS and MLS which are archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). TROPOMI ozone vertical profiles for the troposphere and to top-of-atmosphere will become available at NASA GES DISC in 2020. The presentation briefly explores their potential complementary usability in determining three-dimensional ozone global distributions with high temporal and spatial resolutions.
Space missions are increasingly demanding higher data rates to support the growth in information-intensive mission operations. This growth is reflected in planned and operational missions from low Earth orbit, such as the upcoming NASA-Indian Space Research Organization (ISRO) Synthetic Aperture Radar (NISAR) and Plankton, Aerosol, Cloud and Ocean Ecosystem (PACE) missions, to the future Artemis lunar campaign, and the recently launched James Webb Space Telescope (JWST) orbiting at the Sun-Earth L2 Lagrange point. JWST was the first L2 mission to be defined as a high data rate mission transmitting at 28 Megabits per second (Mbps), or 270 Gigabits of science data per day. ISRO and PACE anticipate achieving data throughputs of 5-40 Terabits per day. These data rates exceed the capabilities of S-band and X-band frequency allocations and are a key driver for migrating to the 26 GHz Ka-band frequency allocation. The NASA Space Communications and Navigation (SCaN) program has been preparing the networks to support this demand by pursuing critical Ka-band infrastructure. The status of current and evolving network capability, including the Near Space Network’s Initiative for Ka-band Advancement (NIKA), and the Deep Space Network’s Lunar Exploration Upgrades (DLEU), as well as profiling mission usage of Ka-band services, are discussed in detail. The push toward Kaband, is not only an opportunity for increased performance, but alleviates current challenges with contentious and cluttered spectrum access in S- and X-band. The paper provides an overview of these advantages and advanced techniques that optimize its use before the transition to optical communications becomes an imperative. The challenges and potential mitigations for missions considering selection of Ka-band network services are also discussed.
Open science aims to foster transparent sharing of scientific processes including open access, incentivization, provenance, open source code and tools, metrics, and resource sharing. However, effective management of these processes remains a challenge. This paper explores the application of blockchain technology to address these key aspects of open science. Blockchain offers a decentralized and secureplatform for information exchange and verification. By leveraging blockchain, open science can enhance transparency and reproducibility. In this paper, we present an implementation of blockchain for Earth science data synchronization across organizations, enabling tracking of data copying, citation, anddownload. The findings highlight the potential of blockchain in supporting open science objectives.
RadLab, a new addition to the NASA Open Science Data Repository (OSDR), is a public platform for space radiation data relevant to human space exploration. RadLab consists of a database, a submission portal, and user-friendly visualization and data analysis tools, including a graphical user interface (GUI) and an application programming interface (API). Investigators from ISS partners including Germany, Italy, Canada, Hungary, the Czech Republic, Russia, Japan have committed to providing data from their instruments. RadLab will also include data from other spacecraft in LEO: the Space Shuttle, the Mir space station, biosatellites; and beyond LEO: the lunar and the Martian surface, the heliocentric orbit at 1 AU, Mars orbit, and Earth-Mars space. Once fully operational, RadLab will provide open, centralized access to space radiation physics data relevant to human space exploration; a platform for submission of data by agencies and research institutions responsible for radiation detectors deployed in space; analysis tools to facilitate detector and dataset intercomparison to better understand space habitat radiation environments; capabilities for space biology investigators to determine the radiation environment to which samples were exposed. A RadLab Working Group (RLWG) has been formed, modeled on the GeneLab Analysis Working Groups and comprised of data contributors and users. RLWG tasks include identifying data sources, normalizing data from diverse detectors, expanding the analysis toolkit and, perhaps most importantly, sharing ideas for research exploiting capabilities of RadLab. We will provide an overview of RadLab data and capabilities and discuss examples of its potential as a resource for open science.
The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission launched from Kennedy Space Center in the early morning of February 8, 2024. Just 63 days later, data from NASA’s newest Earth-observing satellite became available to the public. These data will extend and improve upon NASA’s 20+ years of global satellite observation of our living oceans, atmospheric aerosols, and cloud and initiate an advanced set of climate-relevant data records. Ultimately, PACE is the first mission to provide daily, global measurements that will enable prediction of the “boom-bust” cycle of fisheries, the appearance of harmful algae, and other factors that affect commercial and recreational industries. PACE also observes clouds and tiny airborne particles known as aerosols that influence air quality and absorb and reflect sunlight, thus warming and cooling the atmosphere. In the months since launch and initial data release, the PACE Project pursued instrument temporal and system vicarious calibrations, executed cross-instrument comparisons, conducted performance assessments, explored synergies with other missions, and released advanced science data products. In parallel, the PACE Validation Science Team left for the field and the Post-launch Airborne eXperiment (PACE-PAX) prepared for its mission. And, most importantly, preliminary science results were realized. Here, we present a snapshot of these activities and their impacts and outcomes, encompassing the first half year of the PACE mission.
Technical Interchange Meeting 2 of 2, with two linked events. Overall Purpose of the Technical Interchange Meeting was to facilitate collaboration on NAS data analytics between FAA and NASA teams, leveraging the great tool developed by NASA, Sherlock Data Warehouse. Conducted the two TIM events, next step is for the leadership to meet and discuss potential future collaborations. Purpose for Technical Interchange Meeting #2 (TIM 2o2); NASA presented data science related use-cases and applications where they have leveraged the NASA Sherlock Data Warehouse to address aviation transportation problems, with priority on aviation safety.
As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.
The NPP Instrument Calibration Support Element (NICSE) is one of the elements within the NASA NPP Science Data Segment (SDS). The primary responsibility of NICSE is to independently monitor and evaluate on-orbit radiometric and geometric performance of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument and to validate its Sensor Data Record (SDR) [1]. The NICSE interacts and works closely with other SDS Product Evaluation and Analysis Tools Elements (PEATE) and the NPP Science Team (ST) and supports their on-orbit data product calibration and validation efforts. The NICSE also works closely with the NPP Instrument Calibration Support Team (NICST) during sensor pre-launch testing in ambient and thermal vacuum environment [2]. This paper provides an overview of NICSE VIIRS sensor post-launch calibration support with a focus on the use of sensor on-board calibrators (OBC) for the radiometric calibration and characterization. It presents the current status of NICSE post-launch radiometric calibration tool development effort based on its design requirements
The NASA Dawn spacecraft was launched on September 27, 2007 on a mission to study the asteroid belt's two largest objects, Vesta and Ceres. It is the first deep space orbiting mission to demonstrate solar-electric ion propulsion, providing the necessary delta-V to enable capture and escape from two extraterrestrial bodies. At this time, Dawn has completed its science campaign at Vesta and is currently on its journey to Ceres, where it will arrive in mid-2015. The spacecraft spent over a year in orbit around Vesta from July 2011 through August 2012, capturing science data during four dedicated orbit phases. In order to maintain the reference orbits necessary for science and enable the transfers between those orbits, precise and timely orbit determination was required. The constraints associated with low-thrust ion propulsion coupled with the relatively unknown a priori gravity and rotation models for Vesta presented unique challenges for the Dawn orbit determination team. While [1] discusses the prediction performance of the orbit determination products, this paper discusses the dynamics models, filter configuration, and data processing implemented to deliver a rapid orbit determination capability to the Dawn project.
Short term Prediction Research and Transition (SPoRT) Center: Mission: Apply NASA and NOAA measurement systems and unique Earth science research to improve the accuracy of short term weather prediction at the regional/local scale. Goals: Evaluate and assess the utility of NASA and NOAA Earth science data and products and unique research capabilities to address operational weather forecast problems; Provide an environment which enables the development and testing of new capabilities to improve short term weather forecasts on a regional scale; Help ensure successful transition of new capabilities to operational weather entities for the benefit of society
Since the 2010 NASA directive to make the Life Sciences Data Archive (LSDA) and Lifetime Surveillance of Astronaut Health (LSAH) data archives more accessible by the research and operational communities, demand for astronaut medical data has increased greatly. LSAH and LSDA personnel are working with Human Research Program on many fronts to improve data access and decrease lead time for release of data. Some examples include the following: Feasibility reviews for NASA Research Announcement (NRA) data mining proposals; Improved communication, support for researchers, and process improvements for retrospective Institutional Review Board (IRB) protocols; Supplemental data sharing for flight investigators versus purely retrospective studies; Work with the Multilateral Human Research Panel for Exploration (MHRPE) to develop acceptable data sharing and crew consent processes and to organize inter-agency data coordinators to facilitate requests for international crewmember data. Current metrics on data requests crew consenting will be presented, along with limitations on contacting crew to obtain consent. Categories of medical monitoring data available for request will be presented as well as flow diagrams detailing data request processing and approval steps.
The 2019-2020 Australian fire season was particularly devastating, with millions of acres of land burned and impacts affecting Australian ecology, local populations and air quality, and the atmosphere. Australia saw unprecedented heat waves, with temperatures reaching 120 F (49.1 C) in January across central and eastern Australia. The fires gave rise to a host of atmospheric phenomenon, including smoke transport and lofting generated by storm-induced by fires, known as pyrocumulonimbus, reaching the stratosphere. NASA’s satellites not only tracked the event in real time, but also gathered data to further inform forecasting and response methods in the future. To better assist the public in understanding the lead up, impacts, and aftermath effects of these fires, the Science Outreach Team at NASA Langley Research Center’s Atmospheric Science Data Center (ASDC) Distributed Active Archive Center (DAAC) used Esri’s storymap tool to guide users through understanding relevant phenomenon, contributing factors, the effects this event has had on global atmospheric composition, and the science behind researching the tie between disasters and public health. The storymap uses data from the ASDC-supported NASA missions Measurements Of Pollution In The Troposphere (MOPITT), Cloud-Aerosol and Infrared Pathfinder Satellite Observation (CALIPSO), Clouds and the Earth’s Radiant Energy System (CERES), the Stratospheric Aerosol and Gas Experiment (SAGE III), and Multi-angle Imaging SpectroRadiometer (MISR). By using data-driven storytelling to communicate impacts of a large fire event, we hope to provide an accessible, engaging science outreach tool format.
Space weather refers to conditions around a star, like our Sun, and its interplanetary space that may affect space- and ground-based assets as well as human life. Space weather can manifest as many different phenomena, often simultaneously, and can create complex and sometimes dangerous conditions. The study of space weather is inherently trans-disciplinary, including subfields of solar, magnetospheric, ionospheric, and atmospheric research communities, but benefiting from collaborations with policymakers, industry, astrophysics, software engineering, and many more. Effective communication is required between scientists, the end-user community, and government organizations to ensure that we are prepared for any adverse space weather effects. With the rapid growth of the field in recent years, the upcoming Solar Cycle 25 maximum, and the evolution of research-ready technologies, we believe that space weather deserves a reexamination in terms of a “risk and resiliency” framework. By utilizing open data science, cross-disciplinary collaborations, information systems, and citizen science, we can forge stronger partnerships between science and industry and improve our readiness as a society to mitigate space weather impacts. The objective of this manuscript is to raise awareness of these concepts as we approach a solar maximum that coincides with an increasingly technology-dependent society, and introduce a unique way of approaching space weather through the lens of a risk and resiliency framework that can be used to further assess areas of improvement in the field.
Transient kinetic data contain a wealth of information about intrinsic features of a catalyst as well as the reaction mechanism. Currently, high volume transient data is underutilized, and data science methods could both increase the value of information that can be extracted from this data, integrate experimental with theoretical data sources, and accelerate the pace of catalyst technology advancement. Transient kinetic characterizations with simple probe molecules exhibiting reversible adsorption, irreversible adsorption and bulk-surface diffusion are presented as training components for similar experiments with more complex surface reactions. In conclusion, by increasing the availability and accessibility of transient kinetic data through details of its structure and acquisition, we aim to decrease the barrier for data scientists to apply machine learning methods to this valuable data source.