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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
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NASA NTRS2021

NASA Leveraging Commercial Communication Ground Stations for Small Satellites

The Space Communications and Navigation (SCaN) program at NASA has reorganized its operations portfolio into two networks: the Deep Space Network and the new Near Space Network (NSN). With this reorganization, NASA can begin transforming to 100% direct-to-Earth commercial communications services for missions in the near-Earth region. NASA’s leveraging of commercial direct-to-Earth ground stations offers several benefits for the small satellite community, including lower cost, greater coverage, and increased technology infusion. In the fall of 2020, SCaN announced their intention to rely primarily on industry-provided communications services for missions close to Earth by 2030. Commercial services are one way to infuse new technology into the ground station network without requiring an investment from NASA. Digital Video Broadcast, Satellite Second Generation (DVB-S2) is one example of a current technology. When combined with variable coding and modulation (VCM), the system automatically optimizes the data rate based on signal performance, significantly increasing total downlink data volume without an increase in the spacecraft effective isotropic radiated power (EIRP). There are several commercial service providers, including Amazon Web Service (AWS) Ground Station (AGS) and the KSATLITE ground stations that support SmallSat missions using DVB-S2 waveforms for downlinks. This paper identifies some commercial off-the-shelf (COTS) CubeSat/SmallSat DVB-S2 X-band and Ka-band radios. Overall, NASA’s increased dependence on commercial direct-to-Earth ground stations is a significant benefit for the small satellite community.

Space Communications↗
NASA NTRSDate not supplied

Applying the Cognitive Space Gateway to Swarm Topologies

NASA’s future vision for interplanetary networking includes a lunar network, Cube Satellite (CubeSat) constellations, and deep space robotic missions, comprising what could be viewed as a network of networks. Delay-tolerant networking (DTN) architecture and protocols provide a standard network layer among these varying scenarios and mitigate many challenges of the space environment, such as long delays, unplanned service interruptions, and asymmetric links. The Cognitive Space Gateway (CSG) is a routing method in a DTN architecture that uses spiking neural networks as the learning element to optimize outing decisions in a complex environment. This work aims to further develop cognitive networking technologies in several critical areas, including DTN, the CSG algorithm, SmallSat swarm topologies, and cloud services. The CSG algorithm is tested in a realistic scenario in which the emulated network topology is based on a SmallSat swarm. The emulation environment will be built upon a commercial cloud service, such as Amazon Web Services (AWS) Elastic Compute Cloud. This work investigates the ability of such a platform to enable a flexible, lower maintenance approach to creating a multihop network outside of a physical laboratory. The cloud platform will provide a secure environment allowing for collaboration among government and academic entities.

Ricardo Lent↗
NASA NTRS2020

Connecting Users and Applications with Po.daac Hosted GHRSST Data

The 80+ GHRSST public datasets represent a rich resource for sea surface temperature research and applications given their time series length, resolution, spatial coverage, varying measurement types and processing levels, and availability in the full spectrum of PO.DAAC tools and services ecosystem. The PO.DAAC has created a publicly accessible recipe suite for the user community to perform straightforward yet powerful computations on GHRSST data using python recipes, Jupyter notebooks, R, Matlab, and the NCO programming language. These recipes include numerical computations for regional and global SST trends, anomaly derivations, EOF analysis, climate signal reproduction, and ocean phenology. For example, one recipe reproduces a famous SST based warming figure from the Fourth National Climate Assessment (USA) while another focuses on quantifying the regional changes in ocean SST phenology. Most are python-based while some contain hybrid calls and leverage the NCO programming interface too. All are available on the PO.DAAC user forum (https://podaac.jpl.nasa.gov/forum/) and/or via the open source NASA GitHub repository (https://github.com/nasa/podaac_tools_and_services). Several are available in the Jupyter notebook framework including podaacypy (https://github.com/nasa/podaacpy), a recipe for GHRSST granule metadata discovery and application, and more recently a Jupyter notebook developed to support data analysis and visualization of a cloud-based Zarr formatted Level 4 MUR dataset in the AWS Open Data Registry. Throughout the summer of 2020, the PO.DAAC intends to add and migrate more of its numerical recipes to the Jupyter notebook framework and publish them on its open source GitHub repository.

Gentemann, Chelle↗
NASA NTRSDate not supplied

AAM National Campaign Tech Talk: Data Pipeline Familiarization

NASA's AWS-based Data Pipeline allows real-time data submission and ingestion, with immediate monitoring of data rate, coverage and ingestion quality. Problems are immediately discovered and can be corrected with agility by both Partners and NASA during the a simulation or flight event​.

Data Pipeline↗
NASA NTRSDate not supplied

Cloud-Based Demodulation and Data Distribution of a Satellite Downlink

Ground station networks connected to the cloud allow space missions to have global communications coverage without operating their own infrastructure. In this work, we describe the communications architecture for the TechEdSat-13 mission, which performed the first in-space characterization of a neuromorphic processor. The mission utilizes a commercial provider for S-band downlinks. A suite of cloud services and open-source software such as GNU Radio are leveraged to demodulate signals received by an AWS ground station during passes with TechEdSat-13 and store recovered data. Once a pass is scheduled, the entire process takes place without human intervention. On-orbit results the past year of operations are presented, demonstrating the advantages of this approach over traditional operator-owned ground stations. Use of software-defined radio makes possible custom signal processing. The homogeneity of apertures and their interfaces to the cloud simplifies scaling across many sites. This abundance of candidate links lays the groundwork for intelligent scheduling agents to optimize pass selection across several factors, automatically recover from failed contacts, and gather metrics to learn from past performance.

cloud demodulation↗
NASA NTRSDate not supplied

The NASA Merra-2 Reanalysis Products: Data and Tools Used for Aerosol and Air Quality Studies

The NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is atmospheric reanalysis data spanning 1980 to present. It has been produced by the NASA Global Modeling and Assimilation Office (GMAO) and is distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). MERRA-2 data includes 100 collections of Earth system variables, mainly from the atmospheric model, such as aerosol fields and meteorological fields, radiation fields, and aerosol fields, guided by the assimilation of as many as six million observations every six hours. MERRA-2 has been one of the most popular datasets from NASA and is widely used in interdisciplinary research and applications, with increasing numbers of new users. For example, at least 7000 users accessed MERRA-2 data at GES DISC in the year 2021, ~1000 more users than in the year 2020. In this presentation, we will introduce the MERRA-2 datasets associated with aerosol and air quality studies and use a wildfire case study to demonstrate the data tools developed at GES DISC to analyze and visualize MERRA-2 data, such as Giovanni and the level 3 and level 4 subsetter, and Jupyter Python notebook. We will also update the status of cloud migration of the MERRA-2 data to Amazon Web Services (AWS).

Xiaohua Pan↗
NASA NTRSDate not supplied

Enhancing Autonomous Satellite Communication Systems with Weather-Aware Scheduling and Reconfiguration

NASA currently provides communication support to over 100 satellite missions, and the agency is driving developments in Ka-band communications and network management automation to support additional future missions. At Ka-band frequencies, rain can degrade a communication link by more than 10 dB, which may be mitigated by agile scheduling and data rate control. We present a weather forecasting module for Ka-band communications that is intended to be used in an autonomous network management service that employs machine-to-machine scheduling systems for dynamic user access opportunities. Our forecasting module (NIMBUS) runs on AWS Cloud, consumes the freely and publicly available NOAA MRMS precipitation rate dataset (1km x 1km x 2-min), produces 30-minute Nowcasts using the pySTEPS algorithm, and publishes high level ground station specific link quality predictions. We evaluate two potential NIMBUS outputs, a binary classifier that predicts rain attenuation greater than 3 dB and a rain attenuation estimator, and we backtest these outputs using one year of power measurement data collected from observations of the geostationary ANIK F2 satellite’s Ka-band beacon. We report, with a 30-minute lead time, a binary classifier accuracy of 84% and an estimator RMSE of 1.67 dB. Additionally, we discuss how the NIMBUS module could be incorporated into a user-initiated service framework to enable weather-aware scheduling and reconfiguration. Operating a static link budget with minimal link margin can increase network operation costs by creating additional scheduling tasks due to failed packets, which may require human intervention and/or lead to inefficient asset utilization. The proposed system aims to increase throughput by reducing link margin, while mitigating increases in network operations costs by leveraging autonomous machine-to-machine scheduling, shortened prediction lead times, and advances in precipitation Nowcasting.

satellite communications↗
NASA NTRSDate not supplied

Hydrological Data at the NASA GES DISC: Current Capabilities and New Opportunities

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelve NASA Earth science data centers that document, process, archive and distribute data from Earth observation missions and projects. GES DISC maintains an archive of several hydrology datasets, including the Land Data Assimilation Systems (LDAS) and the Gravity Recovery and Climate Experiment (GRACE) Data Assimilation for Drought Monitoring (GRACE-DA-DM) data products. These datasets include model output of heat fluxes, rain, snow, soil temperature, soil moisture, and runoff; and observational forcing data, including surface pressure, temperature, precipitation, downward shortwave and longwave radiation, humidity, and wind. The temporal resolution of the hydrology data at GES DISC ranges from hourly to monthly, and spatial resolutions range from 0.1° to 1.0°. The GES DISC provides services which enable users to aggregate, temporally and spatially subset, regrid, and visualize archived data including the GES DISC Subsetter, Hydrology Data Rods, and the Geospatial Interactive Online Visualization and Analysis Infrastructure (GIOVANNI). The Hydrology Data Rods service optimally reorganizes large hydrological data sets as extended time series, providing more efficient access for the hydrological community. The time series data (aka “data rods”) were integrated into hydrology community tools, such as the Data Rods Explorer on HydroShare. Furthermore, the GES DISC is in the process of migrating its data and services to the cloud. Hydrological data available at the GES DISC are now available in the Amazon Web Services (AWS) cloud (us-west-2 region) providing users Direct S3 data access and the capability for cloud computing operations. In this presentation, the hydrology data products and services currently available at the GES DISC will be summarized. Also discussed are the migration to the cloud, user support through this transition, and the status of migrating the data rods service to the cloud.

Ashley Heath↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO2. The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRSDate not supplied

Supporting Space Weather Modelling at the Community Coordinated Modeling Center (CCMC)

Space weather models are essential to our ability to understand and predict space weather events. Nonetheless, some of the most cutting-edge models may struggle to move past the initial research stage, remaining unknown and inaccessible to a wider research audience, thereby hindering validation, intercomparison and adoption of the models. The Community Coordinated Modeling Center (CCMC, https://ccmc.gsfc.nasa.gov) closes this gap by providing a convenient platform for hosting space weather models and associated services. Using these services, researchers and other end-users may exercise, evaluate, and intercompare contributed models, as well as collaborate on a growing archive of model run results. In this presentation, we will discuss current and planned capabilities in some of the model services at CCMC, including Runs-on-Request, Instant Runs, and Real-Time Continuous Runs. We will also review new models added to the extensive collection of space weather models hosted at CCMC. Finally, we will talk about our efforts at streamlining model delivery to CCMC, including support for containerized models and establishment of an open collaborative environment based on Amazon Web Services (AWS).

space weather↗
NASA NTRSDate not supplied

The Crustal Dynamics Data Information System (CDDIS) – Updates and Future Developments

As earth observing services and techniques have flourished, data and products have exponentially grown, like the proliferation of Global Navigation Satellite System (GNSS) stations capable of providing real-time data, Satellite Laser Ranging (SLR) stations shifting to kHz lasers, and Very-Long-Baseline Interferometry (VLBI)’s implementation of VLBI Global Observing System (VGOS) telescopes. The CDDIS is continually evolving to fulfill the new storage, quality check, and latency requirements that these changes bring, as well as meet new standards such as the shift toward FAIR and open science. These have shaped how the CDDIS develops new software and resources. Beginning next year, the CDDIS will begin to transition their data and products to the Amazon Web Services (AWS) cloud, beginning with DORIS data. This poster will highlight the CDDIS’s recently updated processing system, new data and products available, and future work.

Justine Woo↗
NASA NTRSDate not supplied

Open Science in Action: The Role of SWxSOC in Expedited Data Release and Cloud-based Data Processing for Heliophysics Missions

NASA's Space Weather Science Operations Center (SWxSOC) is an effort to develop a multi-mission Science Operations Center for the community and specifically Space Weather missions that require data products to be released consistently and quickly. The SWxSOC is committed to Open Science in its approach to software development and data product releases. We are currently supporting the Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) that will fly on the Lunar Gateway and the PADRE Small Sat mission. SWxSOC has established an open-source, reusable solution for transitioning data management from on-premises to the cloud, processing data files, and setting up a cloud-based analysis environment to monitor instrument anomalies. Leveraging Amazon Web Services (AWS) and making open-source tools available on GitHub, HERMES exemplifies NASA's commitment to open-source standardization, showcasing the real-world effectiveness of cloud technology in data processing, analysis, and observability. This enhances current operations and ensures faster, standardized deployments for future missions.

hermes↗
NASA NTRSDate not supplied

Improving Access to the GEOS Composition Forecast Model with API Development and Ingestion into Google Earth Engine

The GEOS Composition Forecast (GEOS-CF) model produces forecast and historical estimates of atmospheric composition and meteorology fields, which provide useful insight into air quality issues and events. In a year for which Canadian wildfires created adverse air quality conditions in the eastern United States, access to model fields such as PM2.5 are in high demand. The GEOS-CF team at the NASA Global Modeling and Assimilation Office (GMAO) first developed in-house solutions to improve data access via the CF API, and recently partnered with Google to ingest a collated set of model diagnostics into the Google Earth Engine (GEE) data repository. GEOS-CF model output is also being ingested into AWS storage. Creating these various open access points to GEOS-CF model diagnostics provides the public with an opportunity to easily interact with air quality information. Users are able to use a temporally consistent global grid of air quality fields in machine learning applications, mapping tools, and data informatics. Hosting GEOS-CF forecasts and the historical timeseries of these chemistry and meteorology fields in GEE allows users to create dynamic JavaScript-based air quality applications in the GEE code editor. GEOS-CF users can also access the model output via the GEE Python application programming interface (API), making it easy to perform various analyses with Python. This presentation will show two examples of accessing the GEOS-CF model through GEE. The first is an example application made in the GEE code editor which allows users to view time series plots and downscaled maps of surface level NO 2 . The second example exhibits using the GEE Python API to create a machine learning model to temporally gap-fill between air quality observations. These examples are an introduction to the many possible benefits of having open access to the GEOS-CF model through multiple platforms.

Callum Wayman↗
NASA NTRS2023

Using Dome Concordia to Characterize the Long-Term Stability of VIIRS Thermal Emissive Bands

The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments aboard the Suomi NPP and NOAA-20 spacecraft have successfully provided Earth image products since 2011 and 2017, respectively. Maintaining accurate radiometric calibration and calibration consistency between the two sensors is a necessity for the continued quality of long-term data records. In this work, the use of frequent VIIRS measurements of brightness temperature over the area surrounding Dome Concordia (Dome C), Antarctica (75.1 °S, 123.4°E) to track the long-term stability of its thermal emissive bands (TEB) is presented. The extremely dry, cold, and rarefied atmosphere of the site makes it ideal to track and detect long-term changes in the TEB responses via analysis of near-nadir and off-nadir VIIRS overpasses in reference to the surface temperature measurements provided by an automated weather station (AWS). Multi-year Dome C measurements have been used to assess the stability of the VIIRS response-versus-scan-angle (RVS) of the half-angle-mirror (HAM), derived from prelaunch characterization, and detector differences at multiple scan angles. Also, included in this work is the RVS stability assessments using the Dome C overpasses. The methodology developed via this work will also be applied to the recently launched VIIRS instrument onboard the NOAA-21 satellite (previously JPSS-2) in the future.

Amit Angal↗
NASA NTRSDate not supplied

The Crustal Dynamics Data Information System (CDDIS) – Updates and Future Developments

As earth observing services and techniques have flourished, data and products have exponentially grown, like the proliferation of GNSS stations capable of providing real-time data, SLR stations shifting to kHz lasers, and VLBI’s implementation of VGOS telescopes. The CDDIS is continually evolving to fulfill the new storage, quality check, and latency requirements that these changes bring, as well as meet new standards such as the shift toward FAIR and open science. These have shaped how the CDDIS develops new software and resources. Beginning next year, the CDDIS will begin to transition their data and products to the AWS cloud, beginning with DORIS data. This poster will highlight the CDDIS’s recently updated processing system, new data and products available, and future work.

Justine Woo↗
NASA NTRSDate not supplied

NASA Power: Global Solar Insolation, Meteorological Parameter Data, and Web Services to Support Sustainable Building Design and Operations

The buildings industry is currently striving to adopt green solutions to make infrastructure more energy-efficient in order to meet the 2050 net-zero climate goals. This planning requires reliable environmental datasets that are crucial in designing, building, and maintaining our world’s-built environment, as well as other energy-related processes and investments. This webinar for the National Institute of Building Sciences provides an overview of NASA’s Prediction Of Worldwide Energy Resources (POWER) Project that informs decision-making and development for sustainable building design and operations by enabling public open discovery, efficient access, and convenient distribution of NASA’s Earth Observations and global atmospheric model datasets. POWER’s datastore is comprised of solar radiation and surface meteorology parameters, spanning nearly 40 years of hourly data, that are easily accessible via several access methods and tools to support three focus areas: 1) renewable energy deployment and management, 2) sustainable infrastructure, and 3) agroclimatology applications. POWER and NASA Earth Science both plan future data parameters, updated tools, and improved observations that could directly support U.S. and international sustainable development goals, climate strategies, and building information modeling. To this end, solar data from several NASA projects and meteorological data from NASA assimilation models have already been reformatted and disseminated to the public via a user-friendly web GIS-enabled based data portal through the POWER Project. POWER data is analysis-ready and accessible through an Application Programming Interface (API), ArcGIS Image Services, and the project’s Data Access Viewer enhanced (DAVe), an interactive online tool. The POWER DAVe also features data consistent with ASHRAE Climate Design Conditions and has developed web image services showing Building Climate Zones and their variability. Through those tools, the data can be downloaded into multiple formats that support the infrastructure community, including CSV and Energy Plus Weather (EPW). POWER’s entire data product catalog is available through Amazon Web Services (AWS) Open Data Registry (ODR) via a free and publicly accessible Simple Storage Service (S3). This webinar provides a full overview of the NASA POWER Project's data and services developed in collaboration with the sustainable infrastructure community. Examples of how the renewable energy and building communities have utilized POWER data products to make decisions and a preview of future data product expansion, including climate projections, and web services will also be provided. Additionally, use case stories from our broad community of users will be presented.

Paul W. Stackhouse↗
NASA NTRSDate not supplied

Efficient Access to MERRA-2 Reanalysis Data from the Cloud Archives

The Modern-Era Retrospective analysis for Research and Applications, Version (MERRA-2) dataset is now available in the AWS West Region-2 cloud. The free data downloading policy remains unchanged. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) offers cloud services such as OPeNDP subsetting, and time series services. This presentation will showcase use cases that testing the procedures and find the most efficiency workflows to access and process the data for finding climate extremes. The use cases include a) accessing and processing data directly in the cloud S3 bucket without downloading it, and b) downloading subset data to process on the user’s local system.

data access, cloud computing, climate extremes↗
NASA NTRSDate not supplied

Open-Source Data Engineering at NASA: CCMC's Approach to Managing Petabyte-Scale Heliophysics Data

The Community Coordinated Modeling Center (CCMC) at NASA Goddard Space Flight Center (GSFC) leads heliophysics research by providing open access to numerous models and their outputs. Our resources are available on-demand and continuously updated with real-time data, covering sun-earth interactions across multiple domains. These domains include coronal, heliosphere, inner and global magnetosphere, ionosphere, thermosphere, and lower atmosphere interactions. Operating in a hybrid environment, CCMC utilizes both self-owned hardware and Amazon Web Services (AWS) cloud infrastructure. Managing petabytes of data across multiple locations necessitates robust data engineering solutions. To address this challenge, CCMC has adopted industry-standard and open-source tools. We use Apache Airflow as our primary data engineering platform, Python for scripting and data processing, and GitLab for version control and CI/CD. Additionally, we employ Kubernetes for containerized services, Grafana and Prometheus for metrics and monitoring, and Terraform and Puppet for reproducible infrastructure as code. This presentation will discuss lessons learned from our data engineering experiences, platforms evaluated but found unsuitable for our scientific data requirements, and specific techniques developed to enhance data transfer speed and reliability. By using these technologies effectively, CCMC continues to advance heliophysics research through efficient data management and open-access modeling.

space weather↗