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At least 1,099 records · Page 61

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NASA NTRS2016

CME Flux Rope and Shock Identifications and Locations: Comparison of White Light Data, Graduated Cylindrical Shell Model, and MHD Simulations

Coronal mass ejections (CMEs) are major transient phenomena in the solar corona that are observed with ground-based and spacecraft-based coronagraphs in white light or with in situ measurements by spacecraft. CMEs transport mass and momentum and often drive shocks. In order to derive the CME and shock trajectories with high precision, we apply the graduated cylindrical shell (GCS) model to fit a flux rope to the CME directed toward STEREO A after about 19:00 UT on 29 November 2013 and check the quality of the heliocentric distance-time evaluations by carrying out a three-dimensional magnetohydrodynamic (MHD) simulation of the same CME with the Block Adaptive Tree Solar-Wind Roe Upwind Scheme (BATS-R-US) code. Heliocentric distances of the CME and shock leading edges are determined from the simulated white light images and magnetic field strength data. We find very good agreement between the predicted and observed heliocentric distances, showing that the GCS model and the BATS-R-US simulation approach work very well and are consistent. In order to assess the validity of CME and shock identification criteria in coronagraph images, we also compute synthetic white light images of the CME and shock. We find that the outer edge of a cloud-like illuminated area in the observed and predicted images in fact coincides with the leading edge of the CME flux rope and that the outer edge of a faint illuminated band in front of the CME leading edge coincides with the CME-driven shock front.

Schmidt, J. M.↗

NASA NTRSDate not supplied

A Principal-Component-Based Radiative Transfer Model (PCRTM) for Hyperspectral Shortwave and Longwave Satellite Sensors and Its Applications

The radiative transfer model (RTM) or forward model is an essential component in satellite remote sensing. For modern hyperspectral remote sensors, fast and accurate RTMs are needed due to a large number of spectral dimensions and high spatial resolutions. We will describe a Principal Component-based radiative transfer model (PCRTM) which can simulate the top-of-atmosphere (TOA) radiance or reflectance spectra 250 nm to 2000 micrometers quickly and accurately. The PCRTM has been demonstrated to be extremely accurate, compared to the line-by-line RTM benchmarks, and the former is several orders more computationally efficient than the latter. We will demonstrate how the PCRTM and the associated inversion algorithms are used to infer atmospheric temperature, moisture, and trace gas profiles, as well as cloud and surface properties from hyperspectral IR sounders such as Atomspheric Infrared Souder (AIRS) and Cross-track Infrared Sounder (CrIS). High-quality climate records for a 20-year duration have been derived from these IR hyperspectral data. Finally, we will show some examples of using PCRTM to retrieve cloud properties from Earth Surface Mineral Dust Source Investigation (EMIT) and its applicability of PCRTM to future missions such as the CLARREO (Climate Absolute Radiance and Refractivity Observatory) Pathfinder (CPF) CPF and the Surface Biology and Geology (SBG).

Xu Liu↗

DOE OSTI2021

Coding the Computing Continuum: Fluid Function Execution in Heterogeneous Computing Environments

Advances in network technologies have greatly decreased barriers to accessing physically distributed computers. This newfound accessibility coincides with increasing hardware specialization, creating exciting new opportunities to dispatch workloads to the best resource for a specific purpose, rather than those that are closest or most easily accessible. We present Delta, a service designed to intelligently schedule function-based workloads across a distributed set of heterogeneous computing resources. Delta implements an extensible architecture in which different predictors and scheduling algorithms can be integrated to provide dynamically evolving estimates of function execution times on different resources-estimates that can be used to determine the most appropriate location for execution. We describe predictors for function runtime, data transfer time, and cold-start resource provisioning and configuration delay; dynamic learning methods that update predictor models over time; and scheduling strategies that take into account both function and endpoint information. We show that these methods can halve workload makespan when compared with a strategy that selects the fastest resource, and decrease makespan by a factor of five when compared to a round robin strategy, when deployed on a heterogeneous testbed with resources ranging from a Raspberry Pi to a GPU node in an academic cloud.

Computing continuum↗

NASA NTRS1990

Estimated accuracy of ground-based liquid water measurements during FIRE

Since on goal of the First ISCCP Regional Experiment (FIRE) project is to improve our understanding of the relationships between cloud microphysics and cloud reflectivity, it is important that the accuracy of remote liquid measurements by microwave radiometry be thoroughly understood. The question is particularly relevant since the uncertainty in the absolute value of the radiometric liquid measurement is greatest at low liquid water contents (less than 0.1 mm). However it should be stressed that although uncertainty exists in the absolute value of liquid, it is well known that the observed radiometric signal is proportional to the amount of liquid in the antenna beam. As a result, changes in amounts of liquid are known to greater accuracy than the absolute value, which may contain a bias. Here, an assessment of the liquid measurement accuracy attained at San Nicolas Island (SNI) is presented. The vapor and liquid water data shown were computed from the radiometric brightness temperatures using statistical retrieval algorithms. The retrieval coefficients were derived from the 69 soundings made by Colorado State University during the SNI observations. Sources of error in the vapor and liquid measurements include cross-talk in the retrieval algorithms (not a factor at low liquid contents), uncertainties in the brightness temperature measurement, and uncertainties in the vapor and liquid attenuation coefficients. The relative importance of these errors is discussed. For the retrieval of path-integrated liquid water, the greatest uncertainty is caused by the temperature dependence of the absorption at microwave frequencies. As a result, the accuracy of statistical retrieval of liquid depends to large measure upon how representative the a priori radiosonde data are of the conditions prevailing during the measurements. The microwave radiometer measurements at SNI were supplemented by an infrared (IR) radiometer modified for measurement of cloud-base temperature. Thus, the IR system provides the means to incorporate continuous measurements of the liquid temperature into the retrieval process.

Snider, Jack B.↗

DOE OSTI2020

An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1990

The radiation budget of a Cirrus layer deduced from simultaneous aircraft observations and model calculations

Several aircraft were employed during the FIRE Cirrus IFO in order to make nearly simultaneous observations of cloud properties and fluxes. A segment of the flight data collected on 28 October 1988 during which the NASA Ames ER-2 overflew the NCAR King Air was analyzed. The ER-2 flew at high altitude making observations of visible and infrared radiances and infrared flux and cloud height and thickness. During this segment, the King Air flew just above the cloud base making observations of ice crystal size and shape, local meteorological variables, and infrared fluxes. While the two aircraft did not collect data exactly coincident in space and time, they did make observations within a few minutes of each other. For this case study, the infrared radiation balance of the cirrus layer is of primary concern. Observations of the upwelling 10 micron radiance, made from the ER-2, can be used to deduce the 10 micron optical depth of the layer. The upwelling broadband infrared flux is also measured from the ER-2. At the same time, the upwelling and downwelling infrared flux at the cloud base is obtained from the King Air measurements. Information on cloud microphysics is also available from the King Air. Using this data in conjunction with atmospheric temperature and humidity profiles from local radiosondes, the necessary inputs for an infrared radiative transfer model can be developed. Infrared radiative transfer calculations are performed with a multispectral two-stream model. The model fluxes at the cloud base and at 19 km are then compared with the aircraft observations to determine whether the model is performing well. Cloud layer heating rates can then be computed from the radiation exchange.

Ackerman, Thomas P.↗

NASA NTRS2001

Effects of Aircraft Wake Dynamics on Measured and Simulated NO(x) and HO(x) Wake Chemistry

High-resolution numerical large-eddy simulations of the near wake of a B757 including simplified NOx and HOx chemistry were performed to explore the effects of dynamics on chemistry in wakes of ages from a few seconds to several minutes. Dilution plays an important basic role in the NOx-O3 chemistry in the wake, while a more interesting interaction between the chemistry and dynamics occurs for the HOx species. These simulation results are compared with published measurements of OH and HO2 within a B757 wake under cruise conditions in the upper troposphere taken during the Subsonic Aircraft Contrail and Cloud Effects Special Study (SUCCESS) mission in May 1996. The simulation provides a much finer grained representation of the chemistry and dynamics of the early wake than is possible from the 1 s data samples taken in situ. The comparison suggests that the previously reported discrepancy of up to a factor of 20 - 50 between the SUCCESS measurements of the [HO2]/[OH] ratio and that predicted by simplified theoretical computations is due to the combined effects of large mixing rates around the wake plume edges and averaging over volumes containing large species fluctuations. The results demonstrate the feasibility of using three-dimensional unsteady large-eddy simulations with coupled chemistry to study such phenomena.

Lewellen, D. C.↗

NASA NTRS2019

Past and Future Operations Concepts of NASA's Earth Science Data and Information System

NASA committed to support the collection and distribution of Earth science data to study global change in the 1990's. A series of Earth science remote sensing satellites, the Earth Observing System (EOS), was to be the centerpiece. The concept for the science data system, the EOS Data and Information System (EOSDIS), created new challenges in the data processing of multiple satellite instrument observations for climate research and in the distribution of global-coverage remote sensor products to a large and growing science research community. EOSDIS was conceived to facilitate easy access to EOS science data for a wide heterogeneous national and international community of users. EOSDIS was to provide a spectrum of services designed for research scientists working on NASA focus areas but open to the general public and international science community. EOSDIS would give researchers tools and assistance in searching, selecting and acquiring data, allowing them to focus on Earth science climate research rather than complex product generation. Goals were to promote exchange of data and research results and expedite development of new geophysical algorithms. The system architecture had to accommodate a diversity of data types, data acquisition and product generation operations, data access requirements and different centers of science discipline expertise. Steps were taken early to make EOSDIS flexible by distributing responsibility for basic services. Many of the system operations concept decisions made in the 90s continued to this day. Once implemented, concepts such as the EOSDIS data model played a critical role developing effective data services, now a hallmark of EOSDIS. In other cases, EOSDIS architecture has evolved to enable more efficient operations, taking advantage of new technology and thereby shifting more resources on data services and less on operating and maintaining infrastructure. In looking to the future, EOSDIS may be able to take advantage of commercial compute environments for infrastructure and further enable large scale climate research. In this presentation, we will discuss key EOSDIS operations concepts from the 1990's, how they were implemented and evolved in the architecture, and look at concepts and architectural challenges for EOSDIS operations utilizing commercial cloud services.

Moses, John F.↗

DOE OSTI2023

Department of Energy’s Atmospheric System Research (ASR) Program’s Workshop on the Future of Atmospheric Large Eddy Simulation (LES): Workshop Report

Large-eddy simulation (LES) is used as a tool to understand physical processes such as turbulence, aerosols, clouds, precipitation, radiation, the interactions among all these, and their interactions with the underlying surface. Over the next 10 years, LES will drive fundamental progress in open scientific questions in these areas as LES is increasingly used to gain understanding of complex interacting physical processes involving atmospheric turbulence. This growth will be driven both by scientific demand and the expansion of computational resources needed to conduct LES, and the form that the growth takes will largely be determined by how computational resources are leveraged for scientific gain. In particular, we suggest that computational resources are likely to be leveraged in two separate but not necessarily distinct ways. On one hand, growth in computational resources will allow LES to be made more routine, that is, performed more frequently, while on the other hand, the computational expense (measured in total floating point operations) afforded to individual LES will expand dramatically, allowing simulations to increase in both domain size and resolution as well as physical detail. Current U.S. Department of Energy (DOE) projects such as LES ARM Symbiotic Simulation and Observation Activity (LASSO) are leading the way in conducting routine LES, building large, public databases that are accessible for data science, sensitivity studies, and training for machine learning. LES will also become more routine as it becomes more accessible for individual researchers to address their scientific questions of interest. Scientific questions addressed by LES over the next 10 years are likely to include cloud organization and aggregation; aerosol cloud interactions and atmospheric chemistry (including geo-engineering); urban-scale LES; atmospheric extreme events, ranging from small-scale severe weather to wildfires; and ocean-wave-atmosphere interactions. Further LES-related research will likely grow significantly in areas related to societal impact studies of air quality and extreme weather events, applications to renewable energy forecasting and resource assessment, and aid in decision-making processes. The growth in the use of LES in atmospheric science research will drive the need for better physical process representations (e.g., cloud aerosol microphysics, radiation, and atmospheric chemistry) at the scales resolved by LES. To date, many of the process representations used by LES have been taken directly from coarser-resolution models. Promising methods for LES process representations include superdroplet and quadrature methods for microphysics, 3D approaches for radiation, and better representation of chemistry and aerosol processes. At LES resolution, land-atmosphere interactions for complex terrains, land cover/types, biogeochemistry, and plant canopy models are needed as an improvement beyond traditional and widely used Monin-Obuhkov similarity theory.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Department of Energy’s Atmospheric System Research (ASR) Program’s Workshop on the Future of Atmospheric Large Eddy Simulation (LES) (Workshop Report)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Chemical Characterization of Individual Particles and Residuals of Cloud Droplets and Ice Crystals Collected On Board Research Aircraft in the ISDAC 2008 Study

Although it has been shown that size of atmospheric particles has a direct correlation with their ability to act as cloud droplet and ice nuclei, the influence of composition of freshly emitted and aged particles in nucleation processes is poorly understood. In this work we combine data from field measurements of ice nucleation with chemical imaging of the sampled particles to link aerosol composition with ice nucleation ability. Field measurements and sampling were conducted during the Indirect and Semidirect Aerosols Campaign (ISDAC) over Barrow, Alaska, in the springtime of 2008. In-situ ice nucleation measurements were conducted using a Continuous Flow Diffusion Chamber (CFDC). Measured number concentrations of ice nuclei (IN) varied from frequent values of 0.01 per liter to more than 10 per liter. Residuals of airborne droplets and ice crystals were collected through a counterflow virtual impactor (CVI). The compositions of individual atmospheric particles and the residuals were studied using Computer Controlled Scanning Electron Microscopy with Energy Dispersive X-ray analysis (CCSEM/EDX) and Scanning Transmission X-ray Microscopy coupled with Near Edge X-ray Absorption Fine Structure spectroscopy (STXM/NEXAFS). Chemical analysis of cloud particle residuals collected during an episode of high ice nucleation suggests that both size and composition may influence aerosol's ability to act as IN. The STXM/NEXAFS chemical composition maps of individual residuals have characteristic structures of either inorganic or black carbon cores coated by organic materials. In a separate flight, particle samples from a biomass burning plume were collected. Although it has previously been suggested that episodes of biomass burning contribute to increased numbers of highly effective ice nuclei, in this episode we observed that only a small fraction were effective ice nuclei. Most of the particles from the biomass plume episode were smaller in size and were composed of homogeneous organic material without identifiable cores.

Environmental Molecular Sciences Laboratory↗

NASA NTRS2011

Validation of Real-Time Data Processing for the Ground and Air-MSPI Systems

JPL is currently developing the multi-angle spectro-polarimetric imager (MSPI), targeted for the Aerosol-Cloud-Ecosystems (ACE) mission, as defined in the National Academies 2007 Decadal Survey. In preparation for the space instrument, the MSPI team has built two incremental camera systems (Ground- and Air-MSPI) to improve understanding of the proposed architecture. Ground-MSPI is a gimballed instrument used primarily for stationary observation and characterization of the imager and optics. The ER-2 based Air-MSPI operates in a step-and-stare mode, providing multi-angle imaging of a static target. This mode-of-operation simulates the observation scenario of the space instrument. Physically, MSPI is a pushbroom camera with a specialized frontend. Before imaging, light entering the camera passes through a pair of photoelastic modulators and a set of pattern polarizers. These optical elements act on the light to make polarimetric extraction computationally feasible. Calculating polarimetric parameters from the imager's data stream requires a real-time least-squares computation that produces coefficients of a truncated time-series expansion of the image. As reported in, the data processing algorithm can operate in real-time on a Xilinx Virtex-5 FPGA. Moving beyond verification with an onboard data source, the algorithm has been validated on a commercial development board interfaced with the ground camera. In addition, the algorithm has been instantiated within the Air-MSPI electronics board's FPGA, and in situ first-light has been achieved.

real-time data processing↗

DOE OSTI2019

Directed Gas‐Phase Synthesis of Triafulvene under Single‐Collision Conditions

Abstract The triafulvene molecule (c‐C 4 H 4 )—the simplest representative of the fulvene family—has been synthesized for the first time in the gas phase through the reaction of the methylidyne radical (CH) with methylacetylene (CH 3 CCH) and allene (H 2 CCCH 2 ) under single‐collision conditions. The experimental and computational data suggest triafulvene is formed by the barrierless cycloaddition of the methylidyne radical to the π‐electron density of either C 3 H 4 isomer followed by unimolecular decomposition through elimination of atomic hydrogen from the CH 3 or CH 2 groups of the reactants. The dipole moment of triafulvene of 1.90 D suggests that this molecule could represent a critical tracer of microwave‐inactive allene in cold molecular clouds, thus defining constraints on the largely elusive hydrocarbon chemistry in low‐temperature interstellar environments, such as that of the Taurus Molecular Cloud 1 (TMC‐1).

Thomas, Aaron M.↗

DOE OSTI2024

Towards Cross-Facility Workflows Orchestration through Distributed Automation

Modern science relies on end-to-end workflows that incorporate experimental instruments and utilize edge, cloud, or high-performance computing and storage resources. These components are geographically dispersed across various user facilities and interconnected through high-speed networks. In this paper, we present Zambeze, an automated distributed framework designed to facilitate this new class of cross-facility workflows. Utilizing swarm intelligence principles, Zambeze orchestrates science campaigns by managing distributed autonomous agents. These agents can offer a suite of services, including computing, storage, and data management. We demonstrate the feasibility of Zambeze through a real-world application involving electron microscopy, enhanced with Artificial Intelligence capabilities.

Skluzacek, Tyler↗

DOE OSTI2016

Sirepo for Synchrotron Radiation Workshop

Sirepo is an open source framework for cloud computing. The graphical user interface (GUI) for Sirepo, also known as the client, executes in any HTML5 compliant web browser on any computing platform, including tablets. The client is built in JavaScript, making use of the following open source libraries: Bootstrap, which is fundamental for cross-platform web applications; AngularJS, which provides a model–view–controller (MVC) architecture and GUI components; and D3.js, which provides interactive plots and data-driven transformations. The Sirepo server is built on the following Python technologies: Flask, which is a lightweight framework for web development; Jinja, which is a secure and widely used templating language; and Werkzeug, a utility library that is compliant with the WSGI standard. We use Nginx as the HTTP server and proxy, which provides a scalable event-driven architecture. The physics codes supported by Sirepo execute inside a Docker container. One of the codes supported by Sirepo is the Synchrotron Radiation Workshop (SRW). SRW computes synchrotron radiation from relativistic electrons in arbitrary magnetic fields and propagates the radiation wavefronts through optical beamlines. SRW is open source and is primarily supported by Dr. Oleg Chubar of NSLS-II at Brookhaven National Laboratory.

Nagler, Robert↗

DOE OSTI2016

Sirepo - Warp

Sirepo is an open source framework for cloud computing. The graphical user interface (GUI) for Sirepo, also known as the client, executes in any HTML5 compliant web browser on any computing platform, including tablets. The client is built in JavaScript, making use of the following open source libraries: Bootstrap, which is fundamental for cross-platform web applications; AngularJS, which provides a model–view–controller (MVC) architecture and GUI components; and D3.js, which provides interactive plots and data-driven transformations. The Sirepo server is built on the following Python technologies: Flask, which is a lightweight framework for web development; Jin-ja, which is a secure and widely used templating language; and Werkzeug, a utility library that is compliant with the WSGI standard. We use Nginx as the HTTP server and proxy, which provides a scalable event-driven architecture. The physics codes supported by Sirepo execute inside a Docker container. One of the codes supported by Sirepo is Warp. Warp is a particle-in-cell (PIC) code de-signed to simulate high-intensity charged particle beams and plasmas in both the electrostatic and electromagnetic regimes, with a wide variety of integrated physics models and diagnostics. At pre-sent, Sirepo supports a small subset of Warp’s capabilities. Warp is open source and is part of the Berkeley Lab Accelerator Simulation Toolkit.

Nagler, Robert↗

DOE OSTI2022

3DBFSVBF (3D BatFinder Smart Video BioFilter and Multi-class BatFinder Smart Video BioFilter) [SWR-22-88]

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.

Yarbrough, John↗

NASA NTRS2014

A Cloud-Based Global Flood Disaster Community Cyber-Infrastructure: Development and Demonstration

Flood disasters have significant impacts on the development of communities globally. This study describes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-of-the- art cloud computing and crowdsourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters.

CyberFlood↗