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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Giovanni in the Cloud: Earth Science Data Exploration in Amazon Web Services

Giovanni is an exploration tool at the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), providing 22 analysis and visualization services for over 1600 Earth Science data variables. Owing to its popularity, Giovanni has experienced a consistent growth in overall demand, with periodic usage spikes attributed to trainings by education organizations, extensive data analysis in response to natural disasters, preparations for science meetings, etc. Furthermore, the new generation of spaceborne sensors and high resolution models have resulted in an exponential growth in data volume with data distributed across the traditional boundaries of data centers. Seamless exploration of data (without users having to worry about data center boundaries) has been a key recommendation of the GES DISC User Working Group. These factors have required new strategies for delivering acceptable performance. The cloud-based Giovanni, built on Amazon Web Services (AWS), evaluates (1) AWS native solutions to provide a scalable, serverless architecture; (2) open standards for data storage in the Cloud; (3) a cost model for operations; and (4) end-user performance. Our preliminary findings indicate that the use of serverless architecture has a potential to significantly reduce development and operational cost of Giovanni. The combination of using AWS managed services, storage of data in open standards, and schema-on-read data access strategy simplifies data access and analytics, in addition to making data more accessible to the end users of Giovanni through popular programming languages.

Giovanni↗
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 NTRS2017

Trade Study: Storing NASA HDF5/netCDF-4 Data in the Amazon Cloud and Retrieving Data Via Hyrax Server Data Server

This study explored three candidate architectures with different types of objects and access paths for serving NASA Earth Science HDF5 data via Hyrax running on Amazon Web Services (AWS). We studied the cost and performance for each architecture using several representative Use-Cases. The objectives of the study were: Conduct a trade study to identify one or more high performance integrated solutions for storing and retrieving NASA HDF5 and netCDF4 data in a cloud (web object store) environment. The target environment is Amazon Web Services (AWS) Simple Storage Service (S3). Conduct needed level of software development to properly evaluate solutions in the trade study and to obtain required benchmarking metrics for input into government decision of potential follow-on prototyping. Develop a cloud cost model for the preferred data storage solution (or solutions) that accounts for different granulation and aggregation schemes as well as cost and performance trades.We will describe the three architectures and the use cases along with performance results and recommendations for further work.

AWS cost↗
NASA NTRS2018

Data, Meet Compute: NASA's Cumulus Ingest Architecture

NASA's Earth Observing System Data and Information System (EOSDIS) houses nearly 30PBs of critical Earth Science data and with upcoming missions is expected to balloon to between 200PBs-300PBs over the next seven years. In addition to the massive increase in data collected, researchers and application developers want more and faster access - enabling complex visualizations, long time-series analysis, and cross dataset research without needing to copy and manage massive amounts of data locally. NASA has looked to the cloud to address these needs, building its Cumulus system to manage the ingest of diverse data in a wide variety of formats into the cloud. In this talk, we look at what Cumulus is from a high level and then take a deep dive into how it manages complexity and versioning associated with multiple AWS Lambda and ECS microservices communicating through AWS Step Functions across several disparate installations

EOSDIS↗
NASA NTRSDate not supplied

SatCORPS Global Cloud Composite (GCC): the Design and Delivery of A High Quality, High Resolution, Global Cloud Product Available in Near-Real Time

The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) supports the development of an analysis ready and cloud-optimized data transformation pipeline and geospatial service enablement of a global cloud composite (GCC) product derived from global geostationary satellite imagery. This geospatial service will be available at high temporal and spatial resolution via the SatCORPS web mapping application for visualization and analysis as well as direct ingestion to common geospatial software and custom programming. The resulting global cloud composite products from the processing pipeline can then be geospatially-service enabled as ArcGIS Image Services and Open Geospatial Consortium (OGC) Web Mapping/Coverage Services for visualization and analysis via a web mapping application and common geospatial software. Near real time global observations are created through the composition of five geostationary satellites that provides modelling and forecasting communities with the capability to provide high quality and timely information to start the projection process. The Global Cloud Composite product combines information from geostationary satellites, GOES-16, GOES-17, Himawari-8, Meteosat-11 and Meteosat-9 to create a single global composite netcdf file and images using the different products within the netcdf file. The SatCORPS team, though our Global Cloud Composite (GCC) product and web-based visualization tools including Geographical Information System (GIS) services provide near real time global cloud product information to both automated processes and traditional web users that is timely and high quality derived from geostationary satellites. The Global Cloud Composite product takes advantage of the scalable processing resources provided by the AWS batch service to provide new composites every thirty minutes. Because information from each of the low earth orbiting satellites is available on schedules tuned to the specific satellite, the processing algorithm temporally composites the final dataset as each satellite’s information becomes available. The SatCORPS team has leveraged our experience using Amazon Web Services (AWS) to build a low latency high availability tool that allows end users both human and automated to acquire high quality and high-resolution Geostationary Earth Orbiting (GEO) information at zero cost to the end user. This presentation will describe how we architected and implemented the service as well as lessons learned based on our experiences both developing and operating the system. The lessons learned include how we integrated multiple services including Amazon Batch, Amazon S3 and Amazon Lambda service to create a low cost but high-performance processing system that is capable of identifying and processing the most appropriate satellite overpass information into global cloud composites. We will also describe our web-based tools including our Geographic Information System that can be used for visualization and analysis. The products from the processing can be geospatially-service enabled as ArcGIS Image Services and Open Geospatial Consortium (OGC) Web Mapping/Coverage Services for visualization and analysis via a web mapping application and common geospatial software. The SatCORPS Global Composite Cloud product provides sophisticated global composited cloud research products with very low latency that we see that as filling a rapidly growing need in the research and modelling community with no up-front nor ongoing costs associated with downloading or using the information.

AWS AMCE SMCE GCC SATCORPS GLOBAL CLOUD COMPOSITE ↗
DOE OSTI2025

Search for an axion-like particle in B → K (*) a(→ γγ) decays at Belle

We report a search for an axion-like particle a in B → K (*) a decays using data collected with the Belle detector at the KEKB asymmetric-energy electron-positron collider. The search is based on a 711 fb −1 data sample collected at the Υ(4S) resonance energy, corresponding to a sample of 772 × 10 6 Υ(4S) events. In this study, we search for the decay of the axion-like particle into a pair of photons, a → γγ. We scan the two-photon invariant mass in the range 0.16 GeV–4.50 GeV for the K modes and 0.16 GeV–4.20 GeV for the K * modes. No significant signal is observed in any of the modes, and 90% confidence level upper limits are established on the coupling to the W boson, g aW , as a function of a mass. The limits range from 3 × 10 −6 GeV −1 to 3 × 10 −5 GeV −1 , improving the current constraints on g aW by a factor of two over the most stringent previous experimental results.

B physics↗
DOE OSTI2024

Influence of Antarctic and Greenland Continental Shelf Circulation on High‐Latitude Oceans in E3SM

The science objectives of this project are to simulate and understand the impacts of both deep-basin warm-water intrusions and land-ice melt on the continental shelf circulations and sea-ice distributions around the margins of Greenland and Antarctica. As well, the role of subsurface ocean heat from the Atlantic on declining sea-ice cover in the Arctic is explored. Mesoscale processes and fine bathymetry are implicated in cross-shelf property transports around both Greenland and Antarctica. Therefore, we configured and ran an atmospheric reanalysis-forced global ocean/sea-ice simulation on a grid that reduces from 8 km at the Equator to 2 km at the poles (UH8to2) with 60 vertical levels. It was produced using the Energy Exascale Earth System Model ‘‘HiLAT’’ code (E3SMv0-HiLAT) that uses the Parallel Ocean Program (POP) and CICE5 as its ocean and sea-ice components, respectively. Two main UH8to2 simulations were carried out: one for 1975-2009 and the other for July 2016-2020 after it was initialized from a 1/25° data-assimilative ocean/sea-ice prediction system ocean/sea-ice state. The UH8to2 is not coupled to an active land-ice model. Rather, land-ice melt is represented by observationally informed freshwater fluxes (FWFs). Short (multi-year) UH8to2 simulations were conducted to understand sensitivities when Greenland ice sheet (GrIS) melt is released only at the ocean surface or when it is distributed over the upper water column in accordance with fjord melt plume behavior; these cases were compared with a no GrIS melt case. West Greenland continental shelf currents were fastest in the vertical distribution case and an increase in baroclinic conversion at the shelf break associated with increased eddy kinetic energy was found relative to the surface release case. Further, salinity is lower and meltwater volume greater in the eastern Labrador Sea in the vertical distribution case. For the Arctic, the veracity of the UH8to2 was evaluated for 2017-2020 using available observations. Simulated seasonal sea-ice thickness and concentration are realistic, but the ice is unrealistically thin in the central and eastern Arctic in the fall. Comparisons of vertical sections of ocean temperature, salinity, and buoyancy collected from Ice-Tethered Profilers (ITPs) in the eastern Arctic in the fall and winter of 2019/2020 and co-located/concurrent UH8to2 fields show the stratification over the top 100 m of the water column is too low in the model, the simulated mixed layer too deep, and the simulated subsurface Atlantic Water (AW) too warm; these biases may contribute to the sea-ice biases. A model intercomparison study using the UH8to2 and a forced 1/25° regional Arctic ocean/sea-ice (uses the HYbrid Coordinate Ocean Model and CICE5) simulation further investigates the relationship between AW and sea-ice in the eastern Arctic. The models show a mesoscale-rich pulse of Atlantic Water extending into the eastern basin that reaches maximum intensity in late winter of 2018, after which it decreases in strength. Concurrent and co-located sea-ice melt or the inhibition of sea-ice growth is seen and is attributed to halocline mesoscale eddies doming into the mixed layer with convection bringing this heat into the vicinity of the sea-ice.

58 GEOSCIENCES↗
DOE OSTI2026

HydroGEN Consortium: Advancements in Hydrogen Production

HydroGEN Energy Materials Network (EMN) is an U.S. Department of Energy (DOE) EERE Hydrogen and Fuel Cell Technologies Office (HFTO)-funded consortium that aims to accelerate the discovery and development of advanced water splitting materials (AWSM) for clean, low-cost hydrogen production. Materials innovations are key to enhancing performance, durability, and cost of hydrogen generation technologies. HydroGEN is focused on low technology readiness level AWS technologies, including low- (alkaline exchanged membrane electrolysis) and high-temperature electrolysis (proton-conducting solid oxide electrolysis), photoelectrochecmical (PEC) and thermochemical (TCH) water splitting. The AWS technologies in this consortium study proton conduction in solid oxide electrolysis and hydroxide conduction in polymer electrolysis, and proton transport in photoelectrochemical water splitting. This presentation will provide an overview of the HydroGEN EMN and technical highlights of a few lab-led and DOE-awarded "seedling" R&D projects. HydroGEN continues to grow its community of industry, university, and national laboratories, forming a national innovation ecosystem focused on renewable hydrogen production.

08 HYDROGEN↗
DOE OSTI2026

HydroGEN Consortium

HydroGEN Energy Materials Network (EMN) is an U.S. Department of Energy (DOE) EERE Hydrogen and Fuel Cell Technologies Office (HFTO)-funded consortium that aims to accelerate the discovery and development of advanced water splitting materials (AWSM) for clean, low-cost hydrogen production. Materials innovations are key to enhancing performance, durability, and cost of hydrogen generation technologies. HydroGEN is focused on low technology readiness level AWS technologies, including low- (alkaline exchanged membrane electrolysis) and high-temperature electrolysis (proton-conducting solid oxide electrolysis), photoelectrochecmical (PEC) and thermochemical (TCH) water splitting. The AWS technologies in this consortium study proton conduction in solid oxide electrolysis and hydroxide conduction in polymer electrolysis, and proton transport in photoelectrochemical water splitting. This presentation will provide an overview of the HydroGEN EMN and technical highlights of a few lab-led and DOE-awarded "seedling" R&D projects. HydroGEN continues to grow its community of industry, university, and national laboratories, forming a national innovation ecosystem focused on renewable hydrogen production.

08 HYDROGEN↗
DOE OSTI2025

Methods for Measuring and Computing the Reference Temperature in Newton’s Law of Cooling for External Flows

Newton’s law of cooling requires a reference temperature (𝑇 𝑟𝑒𝑓 ) to define the heat-transfer coefficient (ℎ). For external flows with multiple temperatures in the freestream, obtaining 𝑇 𝑟𝑒𝑓 is a challenge. One widely used method, referred to as the adiabatic-wall (AW) method, obtains 𝑇 𝑟𝑒𝑓 by requiring the surface of the solid exposed to convective heat transfer to be adiabatic. Another widely used method, referred to as the linear-extrapolation (LE) method, obtains 𝑇 𝑟𝑒𝑓 by measuring/computing the heat flux ($𝑞^{′′}_𝑠$) on the solid surface at two different surface temperatures (𝑇 𝑠 ) and then linearly extrapolating to $𝑞^{′′}_𝑠$ = 0. A third recently developed method, referred to as the state-space (SS) method, obtains 𝑇 𝑟𝑒𝑓 by probing the temperature space between the highest and lowest in the flow to account for the effects of 𝑇 𝑠 or $𝑞^{′′}_𝑠$ on 𝑇 𝑟𝑒𝑓 . This study examines the foundation and accuracy of these methods via a test problem involving film cooling of a flat plate where $𝑞^{′′}_𝑠$ switches signs on the plate’s surface. Results obtained show that only the SS method could guarantee a unique and physically meaningful 𝑇 𝑟𝑒𝑓 where 𝑇 𝑠 =𝑇 𝑟𝑒𝑓 on a nonadiabatic surface $𝑞^{′′}_𝑠$ = 0. The AW and LE methods both assume 𝑇 𝑟𝑒𝑓 to be independent of 𝑇 𝑠 , which the SS method shows to be incorrect. Though this study also showed the adiabatic-wall temperature, 𝑇 𝐴𝑊 , to be a good approximation of 𝑇 𝑟𝑒𝑓 (<10% relative error), huge errors can occur in ℎ about the solid surface where |𝑇 𝑠 −𝑇 𝐴𝑊 | is near zero because where 𝑇 𝑠 =𝑇 𝐴𝑊 , $𝑞^{′′}_𝑠$ ≠ 0.

Newton's law of cooling↗
DOE OSTI2026

MSD CoP Webinar: "Advances in MSD-LIVE to Support the MSD Community of Practice"

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Advances in MSD-LIVE to Support the MSD Community of Practice Presenters: Casey Burleyson and Zoe Guillen (Pacific Northwest National Laboratory) Abstract: The MultiSector Dynamics Living, Intuitive, Value-adding, Environment (MSD-LIVE; msdlive.org) is a cloud-based data management system and advanced computing platform that enables MSD researchers to document and archive their data, run their models and analysis tools, and share their data, software, and workflows within the MSD Community of Practice. Recently, several high-profile datasets have attracted many new users to MSD-LIVE. This webinar has two goals: 1) To refamiliarize the MSD community and new users with the components of the platform (e.g., the data repository, model training notebooks, and data dashboards) and to highlight examples of how these components are advancing MSD science and 2) To demonstrate new features in v3 of the platform, released in late 2025. The main new feature in v3 is the ability to interactively explore data in MSD-LIVE without downloading it. MSD-LIVE users can now click a button in our data repository and launch a blank Jupyter notebook with access to the underlying data on AWS. Users can use the notebook to write analysis, visualization, or subsetting routines that process the data directly on the AWS cloud. We also added a GitHub integration feature that allows users to share analysis or visualization code they develop with the community of MSD-LIVE users. The webinar will wrap up with a look at what's coming next for MSD-LIVE in 2026. Moderator: Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: May 12th, 2026 from 1-2 PM EST.

Open Science↗
NASA NTRS1974

Comparison of the acoustic characteristics of large-scale models of several propulsive-lift concepts

Wind-tunnel acoustic investigations were performed to determine the acoustic characteristics and the effect of forward speed on the over-the-wing externally blown jet flap (OTW), the under-the-wing externally blown jet flap (UTW), the internally blown jet flap (IBF), and the augmentor wing (AW). The data presented represent the basic noise generated by the powered-lift system without acoustic treatment, assuming all other noise sources, such as the turbofan compressor noise, have been suppressed. Under these conditions, when scaled to a 100,000-lb aircraft, the OTW concept exhibited the lowest perceived noise levels, because of dominant low-frequency noise and wing shielding of the high-frequency noise. The AW was the loudest configuration, because of dominant high-frequency noise created by the high jet velocities and small nozzle dimensions. All four configurations emitted noise 10 to 15 PNdB higher than the noise goal of 95 PNdB at 500 ft.

Falarski, M. D.↗
NASA NTRS1995

The minimum mass ratio of W Ursae Majoris binaries

The minimum mass ratio for tidal stability of a contact binary containing two unevolved main-sequence stars is calculated to be q(sub min) approximately =0.09 in the case of a mostly radiative primary, and it is higher if an appreciable fraction of the mass lies in a convective envelope. At least one observed system, AW UMa, has a mass ratio just below this value (q = 0.075), implying that, if the system is stable, the primary must be slightly evolved and must have a very shallow convective envelope. Contact binaries with mass ratios significantly below that of AW UMa should not be observed, since they are tidally unstable and quickly merge into a single, rapidly rotating object, on a timescale approximately 10(exp 3)-10(exp 4) yr.

Rasio, Frederic A.↗
NASA NTRS2001

Intercomparison Between in situ and AVHRR Polar Pathfinder-Derived Surface Albedo over Greenland

The Advanced Very High Resolution (AVHRR) Polar Pathfinder Data (APP) provides the first long time series of consistent, calibrated surface albedo and surface temperature data for the polar regions. Validations of these products have consisted of individual studies that analyzed algorithm performance for limited regions and or time periods. This paper reports on comparisons made between the APP-derived surface albedo and that measured at fourteen automatic weather stations (AWS) around the Greenland ice sheet from January 1997 to August 1998. Results show that satellite-derived surface albedo values are on average 10% less than those measured by the AWS stations. However, the station measurements tend to be biased high by about 4% and thus the differences in absolute albedo may be less (e.g. 6%). In regions of the ice sheet where the albedo variability is small, such as the dry snow facies, the APP albedo uncertainty exceeds the natural variability. Further work is needed to improve the absolute accuracy of the APP-derived surface albedo. Even so, the data provide temporally and spatially consistent estimates of the Greenland ice sheet albedo.

Stroeve, Julienne C.↗
NASA NTRS2001

Seasonal and Interannual Variations of Ice Sheet Surface Elevation at the Summit of Greenland: Observed and Modeled

Observed seasonal and interannual variations in the surface elevation over the summit of the Greenland ice sheet are modeled using a new temperature-dependent formulation of firn-densification and observed accumulation variations. The observed elevation variations are derived from ERS (European Remote Sensing)-1 and ERS-2 radar altimeter data for the period between April 1992 and April 1999. A multivariate linear/sine function is fitted to an elevation time series constructed from elevation differences measured by radar altimetry at orbital crossovers. The amplitude of the seasonal elevation cycle is 0.25 m peak-to-peak, with a maximum in winter and a minimum in summer. Inter-annually, the elevation decreases to a minimum in 1995, followed by an increase to 1999, with an overall average increase of 4.2 cm a(exp -1) for 1992 to 1999. Our densification formulation uses an initial field-density profile, the AWS (automatic weather station) surface temperature record, and a temperature-dependent constitutive relation for the densification that is based on laboratory measurements of crystal growth rates. The rate constant and the activation energy commonly used in the Arrhenius-type constitutive relation for firn densification are also temperature dependent, giving a stronger temperature and seasonal amplitudes about 10 times greater than previous densification formulations. Summer temperatures are most important, because of the strong non-linear dependence on temperature. Much of firn densification and consequent surface lowering occurs within about three months of the summer season, followed by a surface build-up from snow accumulation until spring. Modeled interannual changes of the surface elevation, using the AWS measurements of surface temperature and accumulation and results of atmospheric modeling of precipitation variations, are in good agreement with the altimeter observations. In the model, the surface elevation decreases about 20 cm over the seven years due to more compaction driven by increasing summer temperatures. The minimum elevation in 1995 is driven mainly by a temporary accumulation decrease and secondarily by warmer temperatures. However, the overall elevation increase over the seven years is dominated by the accumulation increase in the later years.

Zwally, H. Jay↗
NASA NTRS2000

Wind Prediction Accuracy for Air Traffic Management Decision Support Tools

The performance of Air Traffic Management and flight deck decision support tools depends in large part on the accuracy of the supporting 4D trajectory predictions. This is particularly relevant to conflict prediction and active advisories for the resolution of conflicts and the conformance with of traffic-flow management flow-rate constraints (e.g., arrival metering / required time of arrival). Flight test results have indicated that wind prediction errors may represent the largest source of trajectory prediction error. The tests also discovered relatively large errors (e.g., greater than 20 knots), existing in pockets of space and time critical to ATM DST performance (one or more sectors, greater than 20 minutes), are inadequately represented by the classic RMS aggregate prediction-accuracy studies of the past. To facilitate the identification and reduction of DST-critical wind-prediction errors, NASA has lead a collaborative research and development activity with MIT Lincoln Laboratories and the Forecast Systems Lab of the National Oceanographic and Atmospheric Administration (NOAA). This activity, begun in 1996, has focussed on the development of key metrics for ATM DST performance, assessment of wind-prediction skill for state of the art systems, and development/validation of system enhancements to improve skill. A 13 month study was conducted for the Denver Center airspace in 1997. Two complementary wind-prediction systems were analyzed and compared to the forecast performance of the then standard 60 km Rapid Update Cycle - version 1 (RUC-1). One system, developed by NOAA, was the prototype 40-km RUC-2 that became operational at NCEP in 1999. RUC-2 introduced a faster cycle (1 hr vs. 3 hr) and improved mesoscale physics. The second system, Augmented Winds (AW), is a prototype en route wind application developed by MITLL based on the Integrated Terminal Wind System (ITWS). AW is run at a local facility (Center) level, and updates RUC predictions based on an optimal interpolation of the latest ACARS reports since the RUC run. This paper presents an overview of the study's results including the identification and use of new large mor wind-prediction accuracy metrics that are key to ATM DST performance.

Cole, Rod↗
NASA NTRS2015

Assessing the Amazon Cloud Suitability for CLARREO's Computational Needs

In this document we compare the performance of the Amazon Web Services (AWS), also known as Amazon Cloud, with the CLARREO (Climate Absolute Radiance and Refractivity Observatory) cluster and assess its suitability for computational needs of the CLARREO mission. A benchmark executable to process one month and one year of PARASOL (Polarization and Anistropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) data was used. With the optimal AWS configuration, adequate data-processing times, comparable to the CLARREO cluster, were found. The assessment of alternatives to the CLARREO cluster continues and several options, such as a NASA-based cluster, are being considered.

Goldin, Daniel↗