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NASA NTRS2008

New Collections of Aura Atmospheric data Products at the GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is the primary archive of atmospheric composition data from the Aura Ozone Monitoring Instrument (OMI), Microwave Limb sounder (MLS), and High-Resolution Dynamics Limb Sounder (HIRDLS) instruments. The most recent versions of Aura OMI, MLS and HIRDLS data are available free to the public (http://disc.gsfc.nasa.gov/Aura). TES data are at ASDC (http://eosweb.larc.nasa.gov).

Johnson, James↗

NASA NTRS2009

Relating OGO-5 H(+) Plasmapause Transitions to Mid-Latitude Topside-Ionospheric Signatures

Plasmapause transitions, as seen in the H + and He+ density gradients measured by the Orbiting Geophysical Observatory 5 (OGO 5) ion spectrometer [Sharp, IEE Trans. in Geosci. Elect., 1969], have been investigated in an attempt to relate them to their topside ionospheric signatures as seen in the Alouette-1 & 2 and ISIS-1 data. The satellite data were obtained from the National Space Science Data Center (NSSDC). A search of the OGO-5 data revealed 54 sharp plasmapause crossings as evaluated from the H+ density. The ionospheric footprints (at 1400 km altitude) of the magnetic-field lines through the locations of these plasmapause crossings were then used to search for topside ionospheric electron-density profiles from the NSSDC. No profiles corresponding to these projections were identified. A similar search of the topside-sounder 35-mm ionogram-film database, however, identified 17 cases of candidate "conjunctions" involving Alouette l & 2 and ISIS 1. We will present samples of the plasmapause OGO-5 ion transitions and the related topside ionospheric signatures and discuss the observations in relation to the recent similar study based on Explorer-45 and ISIS-2 data [Grebowsky et al., JASTP, 2009].

Truhlik, Vladimir↗

NASA NTRS2010

Eight Year Climatologies from Observational (AIRS) and Model (MERRA) Data

We examine climatologies derived from eight years of temperature, water vapor, cloud, and trace gas observations made by the Atmospheric Infrared Sounder (AIRS) instrument flying on the Aqua satellite and compare them to similar climatologies constructed with data from a global assimilation model, the Modern Era Retrospective-Analysis for Research and Applications (MERRA). We use the AIRS climatologies to examine anomalies and trends in the AIRS data record. Since sampling can be an issue for infrared satellites in low earth orbit, we also use the MERRA data to examine the AIRS sampling biases. By sampling the MERRA data at the AIRS space-time locations both with and without the AIRS quality control we estimate the sampling bias of the AIRS climatology and the atmospheric conditions where AIRS has a lower sampling rate. While the AIRS temperature and water vapor sampling biases are small at low latitudes, they can be more than a few degrees in temperature or 10 percent in water vapor at higher latitudes. The largest sampling biases are over desert. The AIRS and MERRA data are available from the Goddard Earth Sciences Data and Information Services Center (GES DISC). The AIRS climatologies we used are available for analysis with the GIOVANNI data exploration tool. (see, http://disc.gsfc.nasa.gov).

Hearty, Thomas↗

NASA NTRS2012

Restoration of APOLLO Data by the NSSDC and PDS Lunar Data Node

The Apollo Lunar Surface Experiment Packages (ALSEPs), suites of instruments deployed by the Apollo 12. 14, 15, 16 and 17 astronauts on the lunar surface, still represent the only in-situ measurements of the Moon's environment taken over long time periods, Much of these data are housed at the National Space Science Data Center (NSSDC) at Goddard Space Flight Center but are in forms that are not readily usable, such as microfilm, hardcopy, and magnetic tapes with older, obsolete formats. The Lunar Data Node (LDN) has been formed under the auspices of the Planetary Data System (PDS) Geosciences Node to put relevant, scientifically important Apollo data into accessible digital form for use by researchers and mission planners. The LDN has prioritized the restoration of these data based on their scientific and engineering value and the level of effort required. We will report on progress made and plans for future data restorations.

Williams, David R.↗

NASA NTRS2015

Developing Data Citations from Digital Object Identifier Metadata

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.

digital object idenifiers↗

NASA NTRS2018

Studying Diurnal Variations of Aerosols with NASA MERRA-2 Reanalysis Data

Aerosols play an important role in atmospheric dynamics, climate variations, and Earth's energy cycle by altering the radiation balance in the atmosphere through interaction with clouds, providing fertilizer for forests and canopy, and as a supply of iron to the ocean over long time periods. Studies suggest that much of the feedback between dust aerosols and dynamics is associated with diurnal and synoptic scale variability. However, the lack of sub-daily resolution of aerosols from satellite observations makes it difficult to study the diurnal characteristics, especially over tropical and subtropical regions. Investigation of this topic utilizes over 37 years of simulated global aerosol products from NASA atmospheric reanalysis, in the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data set, available from NASA Goddard Earth Science Data and Information Services Center (GES DISC). MERRA-2 covers the period 1980-present, and is continuing as an ongoing climate analysis. Aerosol assimilation is included throughout the period, using data from MODIS, MISR, AERONET, and AVHRR (in the pre-EOS period). The aerosols are assimilated using the MERRA-2 aerosol model, which interacts directly with radiation parameterization, and is radiatively coupled with atmospheric model dynamics in the Goddard Earth Observing System Model, Version 5 (GEOS-5). Hourly, monthly, and monthly diurnal data are available at spatial resolution of 0.5o x 0.625o (latitude x longitude). By using MERRA-2 hourly and monthly diurnal products, different aerosol diurnal variabilities are observed over North America, Africa, Asia, and Australia, that may be due to different meteorological conditions and aerosol sources. The presentation will also provide an overview of MERRA-2 data services at GES DISC, such as how to find and download data, and how to quickly visualize and analyze data online with Giovanni.

aerosol↗

NASA NTRS2019

Fast Attitude Maneuvers for the Lunar Reconnaissance Orbiter

This paper describes a new operational capability for fast attitude maneuvering that is being developed for the Lunar Reconnaissance Orbiter (LRO). The LRO hosts seven scientific instruments. For some instruments, it is necessary to per-form large off-nadir slews to collect scientific data. The accessibility of off-nadir science targets has been limited by slew rates and/or occultation, thermal and power constraints along the standard slew path. The new fast maneuver (FastMan) algorithm employs a slew path that autonomously avoids constraint violations while simultaneously minimizing the slew time. The FastMan algo-rithm will open regions of observation that were not previously feasible and improve the overall science return for LRO's extended mission. The design of an example fast maneuver for LRO's Lunar Orbiter Laser Altimeter that reduc-es the slew time by nearly 40% is presented. Pre-flight, ground-test, end-to-end tests are also presented to demonstrate the readiness of FastMan. This pioneer-ing work is extensible and has potential to improve the science data collection return of other NASA spacecraft, especially those observatories in extended mission phases where new applications are proposed to expand their utility.

Karpenko, Mark↗

NASA NTRS2020

Recovery, Restoration and Archiving of Previously Lost Data and Metadata from the Apollo Lunar Surface Experiments Package (ALSEP)

The Apollo Lunar Surface Experiments Package (ALSEP) is the name used to collectively represent the geophysical instruments deployed on the lunar surface by the astronauts on Apollo 12, 14, 15, 16, and 17. These instruments were active from the times of their deployment (November 1969 – December 1972) to September 1977. During that time, fourteen types of experiments were conducted, and their data were transmitted to Earth. The experiment PIs processed them. At the conclusion of the experiments, some of these data were submitted to the NASA Space Science Data Coordinated Archive (NSSDCA) for archiving, while others were not. The raw instrument data received from the Moon prior to March 1976 were not archived, either. The unarchived data, resided on open-reel magnetic tapes, became lost in the decades since, along with much of the metadata (the information necessary/useful in properly processing/analyzing the data). This article retraces the history of the ALSEP data archiving efforts in the 1970s, the subsequent loss of the data tapes, and the search, recovery, and restoration of the lost data by contemporary researchers in the 21st century. In 2006, NSSDCA began reformatting some of the ALSEP data archived in the 1970s to conform with the current Planetary Data System (PDS). In 2010, 440 of the previously lost magnetic tapes containing the raw ALSEP data were recovered. From these tapes, the data were extracted, re-packaged for individual experiments, and, for those with sufficient metadata, processed into higher order data readily usable by researchers. All of these data products have been recently archived with either PDS or NSSDCA. These newly restored data fill a number of gaps in the previously existing archive of the ALSEP data. In addition, tens of thousands of pages of Apollo era documents have been optically scanned and compiled into an online searchable catalog. This article also describes the content, organization, and usage of the restored raw ALSEP data and metadata.

S Nagihara↗

NASA NTRS2019

EDOS Initiatives to Decrease Latency of NRT Data for LANCE

NASA's EOS Data and Operations System (EDOS) is the primary supplier of NRT (near real-time) data to the NASA NRT user community known as the Land, Atmosphere NRT Capability for EOS (LANCE). EDOS provides NRT data for various instruments on the EOS missions Terra, Aqua, Aura, as well as for the NOAA missions Suomi NPP and NOAA-20. This poster describes an overview of the EDOS multi-mission system with emphasis on the NRT products distributed for LANCE elements: AIRS, MISR, MLS, MODIS, MOPITT, OMPS, OMI and VIIRS. Remote EDOS high-rate data capture systems are deployed at NASA ground stations which provide data-driven capture of high-rate science for EOS missions. The remote EDOS components transfer the science data via high-rate WANs to the centralized EDOS Level-zero processing systems located at Goddard Space Flight Center. EDOS produces session-based data sets especially for LANCE NRT use from a single ground station contact; this data is sent to dual LANCE destinations as part of the standard redundancy requirement for LANCE elements. EDOS has implemented various latency improvements with the ultimate goal to have EDOS processing of NRT data keep up with the spacecraft data downlink. EDOS enhancements have included implementation of priority-based QoS, expanded network architecture to include open networks, and use of a delay-tolerant protocol. EDOS has streamlined its systems and infrastructure to minimize latency for NRT data delivery for LANCE. EDOS begins to transfer the NRT data to the LANCE elements within minutes of the end of the contact session with an average packet latency from instrument observation to Level 0 product delivery to each LANCE element of just over one hour.

Michael, Karen↗

NASA NTRSDate not supplied

Standard Measures During Spaceflight

The key goal of the Spaceflight Standard Measures project is to ensure that a set of measures, representing the Human Research Program’s key risks and acquired with minimal impact on time and resources, is consistently captured from crewmembers through the end of the International Space Station (ISS) Program. Data collected under the Spaceflight Standard Measures project include assessments of sleep/wake cycles, cognition, immune status and function, general blood and urine chemistry (urine is collected only before flight and after landing), microbiome composition (gastrointestinal tract, saliva, and body surface), cardiovascular structure and function (carotid intima-media thickness, orthostatic responses), sensorimotor function, and team processes. Data is collected once or twice before the flight (180 and 90 days before launch), twice during the 6-month missions (fight day 30 and 30 days before return to Earth) with the exception of actigraphy, which is recorded continuously during the mission, and during two-week periods before and after the mission. In this presentation, we will review the data collected to date on twelve ISS crew members. These data are placed in the NASA Life Sciences Data Archive and are available for occupational surveillance (using non-identifiable data) Institutional Review Board-approved data sharing requests, and retrospective data requests. This data repository enables high-level monitoring of the effectiveness of countermeasures and meaningful interpretation of health and performance outcomes for various mission durations. The knowledge gained from this project informs and supports future hypothesis-driven research that will enable the success of planetary missions.

G R Clement↗

NASA NTRSDate not supplied

Characterizing Wildfires in Western US.: A Cloud-based Case Study for Interdisciplinary Research using NASA Resources

This presentation will demonstrate a case study of interdisciplinary research done in the Amazon Web Services (AWS) cloud platform, in addition to in the local machine. We conduct data analysis next to data by leveraging various cloud-based data in NASA Earthdata Cloud, which are distributed by different missions/NASA Distributed Active Archive Centers (DAACs), and cloud computing resources at NASA. For instance, we directly access multiple datasets stored in the AWS Simple Storage Service (S3) buckets using a Python Jupyter notebook through a JupyterHub interface hosted in AWS (without having to download data), and conduct data analysis next to data in the cloud. We will also show how to share the research results following Open Source policy. This case study characterizes the change in wildfire events in the western United States during the past 20 years. In particular, we focus on the wildfires in California in 2021, one of the most severe wildfire years occurring in the most recent 20 years in California. We will analyze the possible causes of wildfires, such as drought conditions and climate variability, and examine the impacts of wildfires on air quality and atmospheric composition, and on land cover. We will examine the data distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), including aerosols and meteorological data from the NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), precipitation from the Global Precipitation Measurement (GPM) and Global Precipitation Climate Project (GPCP), and aerosol index from Ozone Monitoring Instrument (OMI). We also utilize the data distributed by the Physical Oceanography (PO) DAAC, such as Sea Surface Temperature (SST) data from the Group for High Resolution Sea Surface Temperature (GHRSST), and the data distributed by Land Processes (LP) DAAC, such as Normalized Difference Vegetation Index (NDVI).

Xiaohua Pan↗

NASA NTRS2023

Easy, Scalable Subsetting of GEDI Point Clouds

The GEDI Subsetter, a Python tool developed for NASA’s Multi-mission Algorithm and Analysis Platform (MAAP), optimizes the accessibility and visualization of GEDI point clouds by enabling users to efficiently subset data in a convenient, scalable manner. Complex science data often requires users to learn new software skills and handle many large files. Handling and cleaning large data sets is tedious and error-prone. These challenges significantly impede analysis. One of the goals of NASA's MAAP is to provide a platform that lowers the barrier to conducting research and analysis at scale. When a group of MAAP users wanted to conduct above-ground biomass estimation using GEDI data, we found that their existing workflow for leveraging GEDI data suffered from the barriers mentioned above. Furthermore, their workflow did not scale easily beyond a small number of granules. We found that existing tools related to GEDI data retrieval and subsetting were too limiting, so the GEDI Subsetter was written to support MAAP users’ needs. Being able to run many subsetting jobs simultaneously in the MAAP, and parallelizing the code itself, has led to significant speed improvements in obtaining relevant data, reducing subsetting time from hours to minutes. MAAP users can now more quickly and easily obtain only the data relevant to their research, by choosing which GEDI collection they want to work with (L1A, L2A, L2B, or L4A), and how they want to subset it, by specifying an area of interest, a temporal range, and relevant attributes. This has significantly reduced the feedback loop for users, allowing them to much more quickly subset GEDI data and begin their analysis. Although the GEDI Subsetter originally targeted users of the MAAP, it is generalized such that it can also be used outside of the MAAP and includes a command-line interface for convenience. Furthermore, with minor modifications, it should be possible to use it with non-GEDI data as the general pattern should be applicable to other sparse/track-based sensors.

Charles Daniels↗

NASA NTRS1995

Visualization of International Solar-Terrestrial Physics Program (ISTP) data

The International Solar-Terrestrial Physics Program (ISTP) is a multispacecraft, multinational program whose objective is to promote further understanding of the Earth's complex plasma environment. Extensive data sharing and data analysis will be needed to ensure the success of the overall ISTP program. For this reason, there has been a special emphasis on data standards throughout ISTP. One of the key tools will be the common data format (CDF), developed, maintained, and evolved at the National Space Science Data Center (NSSDC), with the set of ISTP implementation guidelines specially designed for space physics data sets by the Space Physics Data Facility (associated with the NSSDC). The ISTP guidelines were developed to facilitate searching, plotting, merging, and subsetting of data sets. We focus here on the plotting application. A prototype software package was developed to plot key parameter (KP) data from the ISTP program at the Science Planning and Operations Facility (SPOF). The ISTP Key Parameter Visualization Tool is based on the Interactive Data Language (IDL) and is keyed to the ISTP guidelines, reading data stored in CDF. With the combination of CDF, the ISTP guidelines, and the visualization software, we can look forward to easier and more effective data sharing and use among ISTP scientists.

Kessel, Ramona L.↗

NASA NTRS1999

TRMM Realtime Data Production, Transfer, and Future Plans

The Tropical Rainfall Measuring Mission, a joint US NASA and Japan NASDA mission, was launched on November 27, 1997 from Tanegashima Island, Japan on a NASDA provided H-II launch vehicle. The TRMM objectives are: to obtain and study multi-year science data sets of tropical and sub- tropical rainfall measurement; to understand how interactions between the sea, air, and land masses produce changes in global rainfall and climate; to help improve modeling of tropical rainfall processes; to test, evaluate, and improve the performance of satellite rainfall estimates measurements and techniques. The routine production and quick distribution of standard products provides an important underpinning to these objectives. The determination of standard TRMM algorithms and the requirements for routine production and distribution fall within the authority of the joint US-Japan Science Team. In April of 1998, the Joint TRMM Science team requested that TRMM data be made available in near real-time to approved international agencies and modelers. This request was an addition to the routine production of the standard TRMM data products. The science team requested that near-realtime products be as small as possible to facilitate their transfer via networks. Indeed, it was determined that only network transfer would be available. This presentation provides an overview of the type of products available from the realtime system and how the TRMM system was developed at the lowest cost in the shortest amount of time. It then describes the real-time data flow. The presentation also summarizes how the science quality of real-time data is maintained. It describes how interested groups can get access to this data and the importance of networks for the future of real-time data expansion. The presentation concludes real-time possibilities and improvements possible as part of the proposed Global Precipitation Mission. High speed network access availability is an important catalyst to real-time data flow during the proposed mission.

Stocker, Erich Franz↗

NASA NTRS2009

Constellations: A New Paradigm for Earth Observations

The last decade has seen a significant increase in the number and the capabilities of remote sensing satellites launched by the international community. A relatively new approach has been the launching of satellites into heterogeneous constellations. Constellations provide the scientists a capability to acquire science data, not only from specific instruments on a single satellite, but also from instruments on other satellites that fly in the same orbit. Initial results from the A-Train (especially following the CALIPSO/CloudSat launch) attest to the tremendous scientific value of constellation flying. This paper provides a history of the constellations (particularly the A-Train) and how the A-Train mission design was driven by science requirements. The A-Train has presented operational challenges which had not previously been encountered. Operations planning had to address not only how the satellites of each constellation operate safely together, but also how the two constellations fly in the same orbits without interfering with each other when commands are uplinked or data are downlinked to their respective ground stations. This paper discusses the benefits of joining an on-orbit constellation. When compared to a single, large satellite, a constellation infrastructure offers more than just the opportunities for coincidental science observations. For example, constellations reduce risks by distributing observing instruments among numerous satellites; in contrast, a failed launch or a system failure in a single satellite would lead to loss of all observations. Constellations allow for more focused, less complex satellites. Constellations distribute the development, testing, and operations costs among various agencies and organizations for example, the Morning and Afternoon Constellations involve several agencies within the U.S. and in other countries. Lastly, this paper addresses the need to plan for the long-term evolution of a constellation. Agencies need to have a replenishment strategy as some satellites age and eventually leave the constellation. This will ensure overlap of observations, thus providing continuous, calibrated science data over a much longer time period. Thoughts on the evolution of the A-Train will also be presented.

Kelly, Angelita C.↗

NASA NTRSDate not supplied

From the Knowledge-based Digital Platform (KbDP) Concept for Advanced Air Mobility Research to a Preliminary Prototype

Advanced Air Mobility (AAM) encompasses a range of innovative operational and technological changes to aviation (electric aircraft, increasingly automated aircraft, increasingly automated airspace operations, etc.) that are transforming aviation’s role in everyday movement of people and goods. There are multiple associated concepts and use cases for AAM, all interrelated, including small Unmanned Aircraft System (UAS) Traffic Management (UTM), Upper-Class E Traffic Management (ETM), Extensible Traffic Management (xTM), Regional Air Mobility (RAM), and Urban Air Mobility (UAM). These AAM operations must integrate with traditional Air Traffic Management (ATM) operations, as well as non-aviation modes of transportation and logistics. National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from the information database, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Expected benefits of this concept include improved technology transfers from research to production, improved research portfolio investments, and research outcomes that are more integrated with all aspects of the multi-modal transportation problem. The preliminary KbDP prototype has been realized using UAM as a pathfinder use case and developed by a team of system engineer, software developer, data scientist, and interns.

Systems Engineering↗

NASA NTRS2019

Assimilated Hydrological Data at NASA GES DISC with Examples of Extreme Events

Extreme weather and climate events, such as heavy rainfall, heatwave, floods and droughts, and strong wind, can have devastating impacts on society. NASA and NOAA, based on independent analyses, recently announced that global surface temperatures in 2018 are the fourth warmest since 1880, behind only those of 2016, 2017, and 2015 (nasa.gov). Also in 2018, the United States experienced 14 billion-dollar disasters, ranking as the fourth highest total number of such events, behind only the years 2017, 2011, and 2016 (climate.gov). Many research studies have focused on acquiring observational and modeling data, to reveal linkages between increasing extreme events, global water and energy cycle, and global climate change. However, draw conclusions is still a challenge. NASA Goddard Earth Sciences Data and Information Services Center is one of twelve NASA Earth Observing System (EOS) data centers that process, archive, document, and distribute data from Earth science missions and related projects. The GES DISC hosts a wide range of remotely-sensed and model data and provides reliable and robust data access and services to users worldwide. This presentation provides a few examples of extreme event study that use Land Surface Model (LSM) assimilated, quality-controlled, and spatially and temporally consistent, hydrological data from the GES DISC. Also provided is a summary table for the hydrological data holdings, along with discussions of recent updates to data and data services.

Rui, Hualan↗

NASA NTRS2016

A Natural Interaction Interface for UAVs Using Intuitive Gesture Recognition

The popularity of unmanned aerial vehicles (UAVs) is increasing as technological advancements boost their favorability for a broad range of applications. One application is science data collection. In fields like Earth and atmospheric science, researchers are seeking to use UAVs to augment their current portfolio of platforms and increase their accessibility to geographic areas of interest. By increasing the number of data collection platforms UAVs will significantly improve system robustness and allow for more sophisticated studies. Scientists would like be able to deploy an available fleet of UAVs to fly a desired flight path and collect sensor data without needing to understand the complex low-level controls required to describe and coordinate such a mission. A natural interaction interface for a Ground Control System (GCS) using gesture recognition is developed to allow non-expert users (e.g., scientists) to define a complex flight path for a UAV using intuitive hand gesture inputs from the constructed gesture library. The GCS calculates the combined trajectory on-line, verifies the trajectory with the user, and sends it to the UAV controller to be flown.

Chandarana, Meghan↗