SEARCH · Search NASA

Results for “cloud computing, data cloud”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Entwine Point Tiles for 3D Visualization and Querying of ICESat-2

Point Cloud data from non-optical sensors present challenges in scientific computing in both volume of data and files, even for cloud services environments. As part of the Multi-Mission Algorithm and Analysis Platform (MAAP), a joint open science platform for global biomass modelling, we’ve developed a cloud optimized workflow for using ATL08 (ICESat-2) data as a point cloud. For MAAP, the ATL08 data product is published as Entwine Point Tiles (EPT), allowing users to visualize and query the full extent of this collection interactively without pre-downloading, or preprocessing. The EPT format is a cloud-optimized point cloud data format which re-organizes points into a cloud friendly spatially indexed data structure. MAAP uses AWS S3 to store these point clouds and serves them over OGC specified APIs, 3DTiles for visualization, and WFS for querying. This workflow allows for interactive 3D visualizations in a web browser, including notebook environments and facilitates on the fly subsetting for interactive data exploration, all of which can be applied to other similar sensors.

Alex Mandel↗

NASA NTRS1999

S'COOL Provides Research Opportunities and Current Data for Today's Technological Classroom

NASA's Students' Cloud Observations On-Line (S'COOL) project, a hands-on educational project, was an innovative idea conceived by the scientists in the Radiation Sciences Branch at NASA Langley Research Center, Hampton, Virginia, in 1996. It came about after a local teacher expressed the idea that she wanted her students to be involved in real-life science. S'COOL supports NASA's Clouds and the Earth's Radiant Energy System (CERES) instrument, which was launched on the Tropical Rainforest Measuring Mission (TRMM) in November, 1997, as part of NASA's Earth Science Enterprise. With the S'COOL project students observe clouds and related weather conditions, compute data and note vital information while obtaining ground truth observations for the CERES instrument. The observations can then be used to help validate the CERES measurements, particularly detection of clear sky from space. In addition to meeting math, science and geography standards, students are engaged in using the computer to obtain, report and analyze current data, thus bringing modern technology into the realm of classroom, a paradigm that demands our attention.

Green, Carolyn J.↗

NASA NTRS2018

Earthdata Cloud Analytics Project

This presentation describes a nascent project in NASA to develop a framework to support end-user analytics of NASA's Earth science data in the cloud. The chief benefit of migrating EOSDIS (Earth Observation System Data and Information Systems) data to the cloud is to position the data next to enormous computing capacity to allow end users to process data at scale. The Earthdata Cloud Analytics project will user a service-based approach to facilitate the infusion of evolving analytics technology and the integration with non-NASA analytics or other complementary functionality at other agencies and in other nations.

data services↗

NASA NTRSDate not supplied

Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)

The scientific community is faced with a need for greatly improved data sharing, analysis, visualization and advanced collaboration based firmly on open science principles. Recent and upcoming launches of new satellite missions with more complex and voluminous data, as well as the ever more urgent need to better understand the global carbon budget and related ecological processes, provided the immediate rational for the ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP). This highly collaborative joint project of ESA and NASA established a framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by ESA and NASA EO data users. Presented to the public in October 2021, the current version of MAAP provides a common cloud-based platform with computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of global above-ground biomass. Data from the Global Ecosystem Dynamics Investigation (GEDI) mission on the International Space Station and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) have been instrumental in the first products of MAAP including the first comprehensive map of Boreal above-ground Biomass and a current Global Biomass Harmonization Activity, but the platform is also being specifically designed to support the forthcoming ESA Biomass mission and incorporate data from the upcoming NASA-ISRO SAR (NISAR) mission. While these missions and the corresponding research which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of data and information relating to global biomass estimation, they also present data storing, processing and sharing challenges. The NISAR mission alone will produce about 80TB/day. These large data volumes present a challenge that would otherwise place accessibility limits on the scientific community and impact scientific progress. Other challenges being addressed by MAAP include: 1) Enabling researchers to easily discover, process, visualize and analyze large volumes of data from both agencies; 2) Providing a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation; 3) Providing a version-controlled science algorithm development environment that supports tools, co-located data and processing resources; and 4) Addressing intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms. MAAP products can be explored on the MAAP Dashboard at https://earthdata.nasa.gov/maap-biomass or the joint platform entrance at scimaap.net. MAAP also can be accessed through individual NASA (https://maap-project.org) and ESA (https://esa-maap.org/) landing pages.

cloud computing↗

NASA NTRS1998

S'COOL Takes Students to New Heights

Students Cloud Observations On-Line (S'COOL) is a hands-on educational project which supports NASA's Clouds and the Earth s Radiant Energy System (CERES) satellite instrument; part of the Earth Science Enterprise studying our planet. S'COOL meets science, math, technology and geography Standards of Learning (SOLs) as students observe clouds and related weather conditions, compute data and locate vital information while obtaining ground truth observations for the CERES instrument. These observations can then be used to help validate the CERES measurements; particularly detection of clear sky from space. Participants to date have been in 20 states and 5 countries and have reported great interest and learning among their students. Many have used this project as a stepping stone to further learning in other areas of Earth Science; and to do more with the Internet in the classroom. Satellite images and clues to their interpretation are used on the website ( http://asd-www.larc.nasa.gov/SCOOL/) . Background information is also given on Earth's Radiation Budget and it s importance in understanding our climate. Students can retrieve both their observations and the corresponding satellite data and participate in the validation efforts. A number of suggestions for studies to be done with the data, and related lesson plans, are available. Teachers can tailor this project to the appropriate level and subject matter needed for their students. The recommended grade level is 4th through 12th grade. The project is now open to new participants. We particularly seek schools in more remote areas, to obtain wider geographic coverage for ground truth data; so the project has been designed to use, but not require, computer technology. AGU participants attending the S'COOL presentation will be given a handout describing the project. Material for introducing the project in the classroom will be demonstrated in a participatory style.

Green, Carolyn J.↗

NASA NTRSDate not supplied

Data Quality Challenges for Analysis Ready Data (ARD)

Data quality plays a critical role in research and applications. The Earth Science Information Partners (ESIP) Information Quality Cluster (IQC) defines four aspects of information quality: Science, Product, Stewardship, and Services. The ESIP IQC has become internationally recognized as an authoritative and responsive resource of information and guidance to data producers and distributors on how to implement data quality standards and best practices for their science data systems, datasets, and data/metadata dissemination services. In recent years, cloud computing environments have provided scale-up capabilities such as data archives and services, enabling interdisciplinary science and applications. More value-added products are expected from data service providers, including Analysis Ready Data (ARD). ARD refers to data that has been preprocessed into a form that allows immediate analysis by the end user, processed to a minimum set of requirements and provides interoperability over time and across multiple datasets. Once a dataset has been developed from its original form to produce ARD, what quality characteristics should the derived dataset or ARD possess? Also, is it safe to assume that the quality of the ARD is consistent with the quality of the source data, or are there special attributes to an ARD that would warrant a secondary, independent quality assessment? What provenance (also called “data lineage”) information needs to be included in ARD? It is important to answer these questions, especially given the ease of use of ARD, and the consequent temptation by users to trust ARD without understanding the limitations or possible variations in quality compared to the source data. In this presentation, we will discuss data quality challenges for ARD products and services and introduce IQC for participation.

data quality↗

NASA NTRS2015

Climate Model Diagnostic Analyzer

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

cloud computing↗

NASA NTRSDate not supplied

Advancing Open Science through Public-Private Partnerships

Rapid technology developments are changing the way data-driven research is performed within the science community. With the emergence of cloud computing, this has quickly become a viable approach for enabling “science at scale”. Researchers are no longer hindered by obstacles of data management and data wrangling, allowing them to quickly discover, access and perform analysis on extremely large datasets. Infrastructures that move data out of institutional silos and into a computational platform, will ensure that data and tools are accessible to all users. NASA’s Interagency Implementation and Advanced Concepts Team (IMPACT) seeks to address these rapid technology developments by establishing Space Act Agreements with selected partners from the public-private sector working in the area of cloud computing. These agreements aim to explore new opportunities with commercial cloud providers to accelerate open science and enable discovery, access and use of data sets on the cloud. In addition, they will also help establish training workshops for the science community to help researchers utilize the cloud for science. In this talk, we will present an overview of current and new partnerships we are developing to support open science and open data initiatives.

Elizabeth Fancher↗

NASA NTRS1991

The derivation of cloud parameters from satellite-measured radiances for use in surface radiation calculations

A simple scheme is developed for calculating the cloud amount, optical thickness, and height from satellite-measured radiances and is applied to the International Satellite Cloud Climatology Project B3 radiance data to compute the surface radiative fluxes over the tropical and subtropical western Pacific regions for July 1983, using a radiative transfer model. Results are presented on the sensitivity of surface radiation to the cloud scheme. In addition, the validity of an empirical relationship between the solar fluxes at the surface and at the top of the atmosphere is examined.

Chou, Ming-Dah↗

NASA NTRSDate not supplied

TPSAS-NF1676L-33992-DND

The CERES Science Team integrates and fuses observations from 6 CERES instruments aboard the Terra, Aqua, S-NPP, and NOAA-20 missions with data from more than 20 other unique data sources. Following the November 2017 launch of CERES Flight Model 6 (FM6) onboard NOAA20, CERES has now amassed over 80 instrument-years of valuable Earth radiation budget data. The rapidly growing volume of CERES data coupled with the introduction of new data products alongside improvements to existing science algorithms fosters the requirement for faster, more flexible, and scalable data production and orchestration. New virtualized, cloud-centric compute hardware hosted by the NASA Langley Research Center’s (LaRC) Atmospheric Sciences Data Center (ASDC) provides an ideal environment for these ever-increasing data production demands for CERES. This poster discusses updates to the implementation of the CERES Data Management Team’s (DMT) CERES AuTomAted job Loading sYSTem (CATALYST), a custom data processing workflow engine for CERES, to use on-demand computing resources to perform automated CERES data production processing in a Linux-based container environment. Linux containers provide CERES the flexibility to build multiple production environments in containers tailored for specific workloads and allow effortless provisioning of resources based on the CERES Science Team’s data production requirements.

Thomas N. Hillyer↗

NASA NTRS2017

GES DISC Data Recipes in Jupyter Notebooks

The Earth Science Data and Information System (ESDIS) Project manages twelve Distributed Active Archive Centers (DAACs) which are geographically dispersed across the United States. The DAACs are responsible for ingesting, processing, archiving, and distributing Earth science data produced from various sources (satellites, aircraft, field measurements, etc.). In response to projections of an exponential increase in data production, there has been a recent effort to prototype various DAAC activities in the cloud computing environment. This, in turn, led to the creation of an initiative, called the Cloud Analysis Toolkit to Enable Earth Science (CATEES), to develop a Python software package in order to transition Earth science data processing to the cloud. This project, in particular, supports CATEES and has two primary goals. One, to transition data recipes created by the Goddard Earth Science Data and Information Service Center (GES DISC) into an interactive and educational environment using JupyterNotebooks. Two, to acclimate Earth scientists to cloud computing. To accomplish these goals, we create JupyterNotebooks to compartmentalize the different steps of data analysis and help users obtain and parse data from the command line. We also develop a Docker container, comprised of Jupyter Notebooks, Python dependencies, and command line tools, and configure it into an easy-to-deploy package. The end result is an end-to-end product that simulates the use case of end users working in the cloud computing environment.

discoverability↗

NASA NTRS2016

IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

visualization↗

NASA NTRS2013

An Offload NIC for NASA, NLR, and Grid Computing

This work addresses distributed data management and access dynamically configurable high-speed access to data distributed and shared over wide-area high-speed network environments. An offload engine NIC (network interface card) is proposed that scales at nX10-Gbps increments through 100-Gbps full duplex. The Globus de facto standard was used in projects requiring secure, robust, high-speed bulk data transport. Novel extension mechanisms were derived that will combine these technologies for use by GridFTP, bandwidth management resources, and host CPU (central processing unit) acceleration. The result will be wire-rate encrypted Globus grid data transactions through offload for splintering, encryption, and compression. As the need for greater network bandwidth increases, there is an inherent need for faster CPUs. The best way to accelerate CPUs is through a network acceleration engine. Grid computing data transfers for the Globus tool set did not have wire-rate encryption or compression. Existing technology cannot keep pace with the greater bandwidths of backplane and network connections. Present offload engines with ports to Ethernet are 32 to 40 Gbps f-d at best. The best of ultra-high-speed offload engines use expensive ASICs (application specific integrated circuits) or NPUs (network processing units). The present state of the art also includes bonding and the use of multiple NICs that are also in the planning stages for future portability to ASICs and software to accommodate data rates at 100 Gbps. The remaining industry solutions are for carrier-grade equipment manufacturers, with costly line cards having multiples of 10-Gbps ports, or 100-Gbps ports such as CFP modules that interface to costly ASICs and related circuitry. All of the existing solutions vary in configuration based on requirements of the host, motherboard, or carriergrade equipment. The purpose of the innovation is to eliminate data bottlenecks within cluster, grid, and cloud computing systems, and to add several more capabilities while reducing space consumption and cost. Provisions were designed for interoperability with systems used in the NASA HEC (High-End Computing) program. The new acceleration engine consists of state-ofthe- art FPGA (field-programmable gate array) core IP, C, and Verilog code; novel communication protocol; and extensions to the Globus structure. The engine provides the functions of network acceleration, encryption, compression, packet-ordering, and security added to Globus grid or for cloud data transfer. This system is scalable in nX10-Gbps increments through 100-Gbps f-d. It can be interfaced to industry-standard system-side or network-side devices or core IP in increments of 10 GigE, scaling to provide IEEE 40/100 GigE compliance.

Awrach, James↗

NASA NTRSDate not supplied

Integrating Multi-agency Data Products in a Cloud-based Platform for Streamlined Discovery, Visualization, and Use

Earth science data users almost always have an interest in utilizing geospatial data from multiple agencies. As computing capability and cloud-based infrastructures accelerate the pace at which scientific research can be done, there is a growing need to enable search, discovery, and use of multi-agency geospatial observations relevant for a common use case - without undergoing the search and discovery process in a less efficient, disparate path with each agency. NASA’s Earth Observing System Data and Information System (EOSDIS) and NOAA’s National Environmental Satellite, Data and Information Service (NESDIS) both support a wide range of Earth science disciplines’ research, operations, and applications activities. Presently, however, there are few examples of data discovery frameworks supporting an inquiry of both NASA’s and NOAA’s extensive archives of Earth observations that are equally suitable for a particular science scenario, regardless of the agency that “owns” the data. NASA and NOAA are collaborating on a data expedition platform for exploring fire weather using data products from both agencies. Users will be able to search, discover, and visualize NASA and NOAA products in one interface. Each agency will curate metadata for its respective datasets, providing for a rich search experience. The collaboration will pilot a shared search interface into these metadata datastores. Data products will be stored in the cloud in cloud-optimized format(s). These formats will allow for optimized data access and visualization to support the “data expedition”. Avenues for further development and application of this cloud-based, multi-agency data provisioning platform will also be discussed.

cloud-based technology↗

NASA NTRS2014

RECOVER: An Automated Cloud-Based Decision Support System for Post-fire Rehabilitation Planning

RECOVER is a site-specific decision support system that automatically brings together in a single analysis environment the information necessary for post-fire rehabilitation decision-making. After a major wildfire, law requires that the federal land management agencies certify a comprehensive plan for public safety, burned area stabilization, resource protection, and site recovery. These burned area emergency response (BAER) plans are a crucial part of our national response to wildfire disasters and depend heavily on data acquired from a variety of sources. Final plans are due within 21 days of control of a major wildfire and become the guiding document for managing the activities and budgets for all subsequent remediation efforts. There are few instances in the federal government where plans of such wide-ranging scope and importance are assembled on such short notice and translated into action more quickly. RECOVER has been designed in close collaboration with our agency partners and directly addresses their high-priority decision-making requirements. In response to a fire detection event, RECOVER uses the rapid resource allocation capabilities of cloud computing to automatically collect Earth observational data, derived decision products, and historic biophysical data so that when the fire is contained, BAER teams will have a complete and ready-to-use RECOVER dataset and GIS analysis environment customized for the target wildfire. Initial studies suggest that RECOVER can transform this information-intensive process by reducing from days to a matter of minutes the time required to assemble and deliver crucial wildfire-related data.

cloud computing↗

NASA NTRSDate not supplied

Breaking Barriers: Integrating Geo-Leo Aerosol Data with an Open-Source Approach

The scientific community is still examining the novel data from geostationary satellite observations and evaluating methods for effectively fusing the polar observations with various spatial and temporal resolutions. However, the merged data will present a significant ""Big Data"" challenge, including processing, storage, data discoverability, accessibility, and migration within cloud computing environments. We have developed an open-source package to fuse aerosol optical depths (AOD) products from six satellite sensors in the past four years (2019~2023), and this presentation will update our recent progress. Using this Python-based package, we produced a level 3 global (AOD) product in a quarter-degree spatial resolution every half-hour, fusing the Level 2 AOD data with the Dark Target aerosol retrieval algorithm from six satellites: three geostationary (GOES-16/17 and Himawari-8) with high temporal resolution, and three polar orbiting (TERRA/MODIS, AQUA/MODIS, and SNPP-VIIRS) with global coverage. By integrating these observations, the diurnal cycle of global AOD in this fused product can be characterized at local, regional, and global scales. Furthermore, we are committed to openness and transparency by providing our package and its associated functionalities as open-source. Our dedication to adhering to the FAIR, CARE, and TRUST principles ensures that our users can rely on the integrity and ethical standards of our work. For instance of Interoperability, this package fuses remote sensing products on demand into desired temporal and spatial domains. It can be run in a central processing unit (CPU) or a Graphics processing unit (GPU) mode. This package will empower researchers and practitioners to use satellite and sensor data efficiently in various applications and research.

Xiaohua Pan↗

NASA NTRS2014

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

Data Analytics↗

NASA NTRSDate not supplied

NASA Cloud Data Access

Explore the source record for details and available documents.

Cloud Computing↗