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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Shifting institutional culture to develop climate solutions with Open Science

To address our climate emergency, “we must rapidly, radically reshape society”—Johnson & Wilkinson, All We Can Save. In science, reshaping requires formidable technical (cloud, coding, reproducibility) and cultural shifts (mindsets, hybrid collaboration, inclusion). We are a group of cross-government and academic scientists that are exploring better ways of working and not being too entrenched in our bureaucracies to do better science, support colleagues, and change the culture at our organizations. We share much-needed success stories and action for what we can all do to reshape science as part of the Open Science movement and 2023 Year of Open Science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Shedding light on U.S. small and midsize data centers: Exploring insights from the CBECS survey

As demand for digital services accelerates, the energy and environmental footprint of data centers faces increasing scrutiny. While hyperscale cloud facilities have driven efficiency gains, small and midsize U.S. data centers remain a critical yet underexamined segment with significant untapped potential for energy savings. This study leverages data from the Commercial Buildings Energy Consumption Survey (CBECS) to analyze trends in server stocks, computing customers, cooling system adoption and efficiency, and geospatial distribution from 2012 to 2018. Findings reveal a sharp decline in small and midsize data centers, from 1.764 million to 1.398 million, with server counts dropping from 5.177 million to 4.262 million—aligning with the broader shift toward cloud computing. More than 40 % of servers in small data centers and 55 % in midsize data centers are housed in office buildings, and over half of all servers are concentrated in climate zones 5A (cold), 3A (mixed-humid), and 4A (mixed-humid), with the highest densities in metropolitan hubs. While direct expansion units remain the dominant cooling system, a clear transition toward more energy-efficient solutions, particularly air economizers, is evident. By integrating server and cooling system distributions, we estimate Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) for U.S. data centers by size and year. Results show that midsize data centers are more energy-efficient but more water-intensive due to the widespread use of water-cooled chillers. These findings highlight the trade-offs in cooling system selection and provide a critical foundation for policies aimed at enhancing efficiency in an evolving data center landscape.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2015

A Fast Visible-Infrared Imaging Radiometer Suite Simulator for Cloudy Atmopheres

A fast instrument simulator is developed to simulate the observations made in cloudy atmospheres by the Visible Infrared Imaging Radiometer Suite (VIIRS). The correlated k-distribution (CKD) technique is used to compute the transmissivity of absorbing atmospheric gases. The bulk scattering properties of ice clouds used in this study are based on the ice model used for the MODIS Collection 6 ice cloud products. Two fast radiative transfer models based on pre-computed ice cloud look-up-tables are used for the VIIRS solar and infrared channels. The accuracy and efficiency of the fast simulator are quantify in comparison with a combination of the rigorous line-by-line (LBLRTM) and discrete ordinate radiative transfer (DISORT) models. Relative errors are less than 2 for simulated TOA reflectances for the solar channels and the brightness temperature differences for the infrared channels are less than 0.2 K. The simulator is over three orders of magnitude faster than the benchmark LBLRTM+DISORT model. Furthermore, the cloudy atmosphere reflectances and brightness temperatures from the fast VIIRS simulator compare favorably with those from VIIRS observations.

VIIRS Instrument Simulator↗
NASA NTRS2019

GeoNEX: A Cloud Gateway for Near Real-time Processing of Geostationary Satellite Products

The emergence of a new generation of geostationary satellite sensors provides land andatmosphere monitoring capabilities similar to MODIS and VIIRS with far greater temporal resolution (5-15 minutes). However, processing such large volume, highly dynamic datasets requires computing capabilities that (1) better support data access and knowledge discovery for scientists; (2) provide resources to enable real-time processing for emergency response (wildfire, smoke, dust, etc.); and (3) provide reliable and scalable services for the broader user community. This paper presents an implementation of GeoNEX (Geostationary NASA-NOAA Earth Exchange) services that integrate scientific algorithms with Amazon Web Services (AWS) to provide near realtime monitoring (~5 minute latency) capability in a hybrid cloud-computing environment. It offers a user-friendly, manageable and extendable interface and benefits from the scalability provided by Amazon Web Services. Four use cases are presented to illustrate how to (1) search and access geostationary data; (2) configure computing infrastructure to enable near real-time processing; (3) disseminate and utilize research results, visualizations, and animations to concurrent users; and (4) use a Jupyter Notebook-like interface for data exploration and rapid prototyping. As an example of (3), the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) was implemented on GOES-16 and -17 data to produce an active fire map every 5 minutes over the conterminous US. Details of the implementation strategies, architectures, and challenges of the use cases are discussed.

GeoNEX↗
DOE OSTI2024

Science Use Case Design Patterns for Autonomous Experiments

Connecting scientific instruments and robot-controlled laboratories with computing and data resources at the edge, the Cloud or the high-performance computing (HPC) center enables autonomous experiments, self-driving laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with a federated hardware/software architecture for the laboratory of the future. It relies on a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture. This paper introduces the science use case design patterns of the INTERSECT Architecture. It describes the overall background, the involved terminology and concepts, and the pattern format and classification. It further offers an overview of the 12 defined patterns and 4 examples of patterns of 2 different pattern classes. It also provides insight into building solutions from these patterns. The target audience are computer, computational, instrument and domain science experts working in the field of autonomous experiments.

Engelmann, Christian↗
DOE OSTI2023

INTERSECT Architecture Specification: Use Case Design Patterns (V.0.9)

Connecting scientific instruments and robot-controlled laboratories with computing and data resources at the edge, the Cloud or the high-performance computing (HPC) center enables autonomous experiments, self-driving laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) Open Architecture enables science breakthroughs using intelligent networked systems, instruments and facilities with a federated hardware/software architecture for the laboratory of the future. It relies on a novel approach, consisting of (1) science use case design patterns, (2) a system of systems architecture, and (3) a microservice architecture. This document introduces the science use case design patterns of the INTERSECT Architecture. It describes the overall background, the involved terminology and concepts, and the pattern format and classification. It further details the 12 defined patterns and provides insight into building solutions from these patterns. The document also describes the application of these patterns in the context of several INTERSECT autonomous laboratories. The target audience are computer, computational, instrument and domain science experts working in the field of autonomous experiments.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Surface Radiation Budget

The Surface Radiation Budget (SRB) data sets contain global 3-hourly, daily and monthly averages of surface longwave and shortwave radiative properties, cloud amount, and meteorological properties computed using models. The main input data for these models include cloud information, top-of-atmosphere radiances and profiles of atmospheric water vapor and temperature. Some of the input data include Earth Radiation Budget Energy (ERBE) top-of-atmosphere clear-sky albedo and International Satellite Cloud Climatology Project (ISCCP) radiances and cloud amount. SRB parameters derived for the renewable energy community are also available from the Surface meteorology and Solar Energy (SSE) data set. Other SRB data are available from Clouds and the Earth's Radiant Energy System (CERES) and Multi-angle Imaging SpectroRadiometer (MISR). [Mission Objectives] The objective of the SRB Project is to produce and archive a global data set of shortwave (SW) and longwave (LW) surface and top of the atmosphere parameters. The data generated in the SRB project may be used in conjunction with other data sets to facilitate the development of renewable energy resources and increase understanding of radiative properties within the meteorological community. [Temporal_Coverage: Start_Date=1983-07-01; Stop_Date=2005-06-30] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

Stackhouse, Paul W.↗
NASA NTRS1992

Cumulus cloud base height estimation from high spatial resolution Landsat data - A Hough transform approach

A semiautomated methodology is developed for estimating cumulus cloud base heights on the basis of high spatial resolution Landsat MSS data, using various image-processing techniques to match cloud edges with their corresponding shadow edges. The cloud base height is then estimated by computing the separation distance between the corresponding generalized Hough transform reference points. The differences between the cloud base heights computed by these means and a manual verification technique are of the order of 100 m or less; accuracies of 50-70 m may soon be possible via EOS instruments.

Berendes, Todd↗
NASA NTRS2018

High Performance Space Computing with System-on-Chip Instrument Avionics for Space-based Next Generation Imaging Spectrometers (NGIS).

The emergent technology of system-on-chip (SoC) devices promises lighter, smaller, cheaper, and more capable and reliable space electronic systems that could help to unveil some of the most treasured secrets in our universe. This technology is an improvement over the technology that is currently used in space applications, which lags behind stateof-the-art commercial-off-the-shelf (COTS) equipment by several generations. SoC technology integrates all computational power required by next-generation space exploration science instruments onto a single chip. This presentation will describe a Xilinx Zynq-based data acquisition, cloud-screening and compression computing system that has been developed at the Jet Propulsion Laboratory (JPL) for JPL’s Next Generation Imaging Spectrometers (NGIS). The Xilinx Zynq-based Alpha Data hardware assembly fits into a 120mm by 190m by 40mm assembly and uses 9 watts at peak performance. The computing element is a Xilinx Zynq Z7045Q which includes a Kintex-7 FPGA (equivalent to 3 RAD Virtex5 FPGAs in terms of logic cell resources) and dual-core ARM Cortex-A9 Processors (equivalent to 10 RAD750 Power PCs in term of processing capability).

Smith, Adam↗
NASA NTRS2018

High Performance Space Computing with System-on-Chip Instrument Avionics for Space-based Next Generation Imaging Spectrometers (NGIS).

The emergent technology of system-on-chip (SoC) devices promises lighter, smaller, cheaper, and more capable and reliable space electronic systems that could help to unveil some of the most treasured secrets in our universe. This technology is an improvement over the technology that is currently used in space applications, which lags behind state-of-the-art commercial-off-the-shelf (COTS) equipment by several generations. SoC technology integrates all computational power required by next-generation space exploration science instruments onto a single chip. This presentation will describe a Xilinx Zynq-based data acquisition, cloud-screening and compression computing system that has been developed at the Jet Propulsion Laboratory (JPL) for JPL’s Next Generation Imaging Spectrometers (NGIS). The Xilinx Zynq-based Alpha Data hardware assembly fits into a 120mm by 190m by 40mm assembly and uses 9 watts at peak performance. The computing element is a Xilinx Zynq Z7045Q which includes a Kintex-7 FPGA (equivalent to 3 RAD Virtex5 FPGAs in terms of logic cell resources) and dual-core ARM Cortex-A9 Processors (equivalent to 10 RAD750 Power PCs in term of processing capability).

Dolinar, Sam↗
NASA NTRS2019

High performance space computing with system-on-chip instrument avionics for space-based Next Generation Imaging Spectrometers (NGIS)

The emergent technology of system-on-chip (SoC) devices promises lighter, smaller, cheaper, and more capable and reliable space electronic systems that could help to unveil some of the most treasured secrets in our universe. This technology is an improvement over the technology that is currently used in space applications, which lags behind state-of-the-art commercial-off-the-shelf (COTS) equipment by several generations. SoC technology integrates all computational power required by next-generation space exploration science instruments onto a single chip. This presentation will describe a Xilinx Zynq-based data acquisition, cloud-screening and compression computing system that has been developed at the Jet Propulsion Laboratory (JPL) for JPL’s Next Generation Imaging Spectrometers (NGIS). The Xilinx Zynq-based Alpha Data hardware assembly fits into a 120mm by 190m by 40mm assembly and uses 9 watts at peak performance. The computing element is a Xilinx Zynq Z7045Q which includes a Kintex-7 FPGA (equivalent to 3 RAD Virtex5 FPGAs in terms of logic cell resources) and dual-core ARM Cortex-A9 Processors (equivalent to 10 RAD750 Power PCs in term of processing capability).

Dolinar, Sam↗
NASA NTRS1990

Cloud spatial structure during the FIRE MS IFO

The fractal properties of clouds observed during the FIRE Marine Stratocumulus Intensive Field Observations (MS IFO) and their effects on the large scale radiative properties of the atmosphere are examined. This involves three states: (1) analysis of LANDSAT Thematic Mapper (TM) cloud data to determine the scaling properties associated with various cloud types; (2) simulation of fractal clouds with realistic scaling properties; and (3) computation of mean radiative properties of fractal clouds as a function of their scaling properties. Thirty-three LANDSAT scenes were acquired as part of the FIRE Marine Stratocumulus IFO in July 1987. They exhibit a wide variety of stratocumulus structures. Analysis has so far focused upon the July 7 scene, in which the NASA ER-2, the BMO C130 and the NCAR Electra repeatedly gathered data across a stratocumulus-fair weather cumulus transition.

Cahalan, Robert F.↗
NASA NTRS2003

Precipitation Processes developed during ARM (1997), TOGA COARE(1992), GATE(1 974), SCSMEX(1998) and KWAJEX(1999): Consistent 2D and 3D Cloud Resolving Model Simulations

Real clouds and clouds systems are inherently three dimensional (3D). Because of the limitations in computer resources, however, most cloud-resolving models (CRMs) today are still two-dimensional (2D). A few 3D CRMs have been used to study the response of clouds to large-scale forcing. In these 3D simulations, the model domain was small, and the integration time was 6 hours. Only recently have 3D experiments been performed for multi-day periods for tropical cloud system with large horizontal domains at the National Center for Atmospheric Research. The results indicate that surface precipitation and latent heating profiles are very similar between the 2D and 3D simulations of these same cases. The reason for the strong similarity between the 2D and 3D CRM simulations is that the observed large-scale advective tendencies of potential temperature, water vapor mixing ratio, and horizontal momentum were used as the main forcing in both the 2D and 3D models. Interestingly, the 2D and 3D versions of the CRM used in CSU and U.K. Met Office showed significant differences in the rainfall and cloud statistics for three ARM cases. The major objectives of this project are to calculate and axamine: (1)the surface energy and water budgets, (2) the precipitation processes in the convective and stratiform regions, (3) the cloud upward and downward mass fluxes in the convective and stratiform regions; (4) cloud characteristics such as size, updraft intensity and lifetime, and (5) the entrainment and detrainment rates associated with clouds and cloud systems that developed in TOGA COARE, GATE, SCSMEX, ARM and KWAJEX. Of special note is that the analyzed (model generated) data sets are all produced by the same current version of the GCE model, i.e. consistent model physics and configurations. Trajectory analyse and inert tracer calculation will be conducted to identify the differences and similarities in the organization of convection between simulated 2D and 3D cloud systems.

Tao, W.-K.↗
NASA NTRS2006

Annual Cycle of Cloud Forcing of Surface Radiation Budget

The climate of the Earth is determined by its balance of radiation. The incoming and outgoing radiation fluxes are strongly modulated by clouds, which are not well understood. The Earth Radiation Budget Experiment (Barkstrom and Smith, 1986) provided data from which the effects of clouds on radiation at the top of the atmosphere (TOA) could be computed (Ramanathan, 1987). At TOA, clouds increase the reflected solar radiation, tending to cool the planet, and decrease the OLR, causing the planet to retain its heat (Ramanathan et al., 1989; Harrison et al., 1990). The effects of clouds on radiation fluxes are denoted cloud forcing. These shortwave and longwave forcings counter each other to various degrees, so that in the tropics the result is a near balance. Over mid and polar latitude oceans, cloud forcing at TOA results in large net loss of radiation. Here, there are large areas of stratus clouds and cloud systems associated with storms. These systems are sensitive to surface temperatures and vary strongly with the annual cycle. During winter, anticyclones form over the continents and move to the oceans during summer. This movement of major cloud systems causes large changes of surface radiation, which in turn drives the surface temperature and sensible and latent heat released to the atmosphere.

Wilber, Anne C.↗
DOE OSTI2021

Cloud forensics and incident response platform

A system, method, and device for cloud forensics and incident response is provided. In an embodiment, a computer-implemented method for performing cloud forensics and incident response includes intercepting, by a cloud incident response module (CIRM), communication between a virtual machine (VM) and a hypervisor. The method also includes extracting, by the CIRM, data from the communication between the VM and the hypervisor according to a forensic policy. Intercepting and extracting the data are transparent to the VM and to the hypervisor. Intercepting and extracting the data are independent of the VM and the hypervisor.

Urias, Vincent↗
DOE OSTI2023

US ATLAS and US CMS HPC and Cloud Blueprint

The Large Hadron Collider (LHC) at CERN houses two general purpose detectors - ATLAS and CMS - which conduct physics programs over multi-year runs to generate increasingly precise and extensive datasets. The efforts of the CMS and ATLAS collaborations lead to the discovery of the Higgs boson, a fundamental particle that gives mass to other particles, representing a monumental achievement in the field of particle physics that was recognized with the awarding of the Nobel Prize in Physics in 2013 to François Englert and Peter Higgs. These collaborations continue to analyze data from the LHC and are preparing for the high luminosity data taking phase at the end of the decade. The computing models of these detectors rely on a distributed processing grid hosted by more than 150 associated universities and laboratories worldwide. However, such new data will require a significant expansion of the existing computing infrastructure. To address this, both collaborations have been working for years on integrating High Performance Computers (HPC) and commercial cloud resources into their infrastructure and continue to assess the potential role of such resources in order to cope with the demands of the new high luminosity era. US ATLAS and US CMS computing management have charged the authors to provide a blueprint document looking at current and possibly future use of HPC and Cloud resources, outlining integration models, possibilities, challenges and costs. The document will address key questions such as the optimal use of resources for the experiments and funding agencies, the main obstacles that need to be overcome for resource adoption, and areas that require more attention.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

5G Energy FRAME: The Design and Implementation of Data, Model, and Use Case (Year 2 Report)

This report summarizes the Year 2 work of Pacific Northwest National Laboratory’s (PNNL’s) 5G Fabricated Resource and Asset Management Encompassment for energy infrastructure (Energy FRAME) project funded by the Department of Energy Office of Science’s Advanced Scientific Computing Research (ASCR) Program. In this report, newest 5G equipment testing results are presented, along with two 5G-enabled AI/ML examples for grid applications; in addition, the work flow of grid edge, cloud, and High Performance Computing (HPC) platform is introduced, to support and interface the cross-domain simulation for power system transmission, distribution, and communication networks. Last but not least, the outlook for Year 3 work and the overarching impact of 5G Energy FRAME work to a multitude of stakeholders are provided. Additional 5G performance data now is shared through the publicly available weblink, https://www.pnnl.gov/projects/5g-energy-frame/publications

24 POWER TRANSMISSION AND DISTRIBUTION↗