Chemically Controllable Magnetic Transition Temperature and Magneto-Elastic Coupling in MnZnSb Compo
Explore the source record for details and available documents.
Murgatroyd, Philip A.E.↗
SEARCH · Search NASA
Results for “proxy application”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
From NASA's EOS to ESO: Advancing Applications of the Future Atmosphere Observing (AOS) Mission
The NASA Earth System Observatory (ESO) Atmosphere Observing System (AOS) is being designed to explore the fundamental questions of how interconnections between aerosols, clouds and precipitation impact our weather and climate, addressing real-world challenges to benefit society. A fundamental component of the AOS mission is ensuring that applications for economic and societal benefit are considered to the greatest extent possible in mission design. As a result, the AOS Applications Impact Team (AIT) was formed to address this objective. The overarching goal of the AIT is to help improve capacity for transitioning science to applications to make it possible to more quickly and effectively inform decisions that will directly benefit society. We seek to maximize AOS benefit to impact decisions through early engagement in the mission development phase in order to prepare stakeholders to apply observations as soon as AOS mission data become available. A critical component of preparing future users of AOS mission data is building on the successes of applications of NASA’s existing Earth Observing System (EOS), A-Train, and sub-orbital campaigns with the goal of advancing current mission applications activities and preparing for innovative AOS mission observations. NASA’s GPM mission forms a framework to enhance AOS precipitation applications while AOS health and air quality applications benefit from the heritage of CALIPSO and MODIS. Additionally, AOS will likely benefit from current and future missions such as TROPICS, MAIA, TEMPO, and PACE which launch before AOS. Additionally, current sub-orbital field campaigns, such as NASA IMPACTS and ACTIVATE, provide rich data sources to highlight future AOS capabilities. Engaging with existing missions and sub-orbital field campaigns helps to identify and understand data needs, gaps, and opportunities for current and future stakeholders, determine what data products are of highest value and use, and connect stakeholders with current mission data that can serve as AOS proxy data, among others. In this presentation, we provide an overview of AOS AIT activities, initiatives, and the AOS Applications Seminar Series to highlight how existing EOS, A-Train, and sub-orbital missions can play a critical role in advancing AOS applications prior to launch.
Emily B. Berndt↗
Progress in Monitoring Strategies for Risk Reduction in Geologic CO2 Storage
Abstract: This geophysical monitoring review is intended to lay the ground work for an approach to integrating monitoring strategies with risk assessment and risk reduction and to summarize work conducted by U.S. Department of Energy's National Risk Assessment Partnership (NRAP) participants that addresses key questions related to monitoring strategies. Commercial, demonstration and pilot geologic CO2 storage (GCS) projects have provided a wealth of practical experience in monitoring technologies. In this manuscript we review a few key projects, specific geophysical methods, well-based methods, geophysical workflows and some suggested best practices. These practices are often driven and governed by regulatory requirements (e.g. U.S. Environmental Protection Agency [US-EPA] Class VI underground injection regulation) and the need for effective reservoir management of pressures and volumes as well as the site-specific risks to containment, plume migration, and underground sources of drinking water. Reducing uncertainty in monitoring is likely to lead to reducing uncertainty in risk. One of our conclusions is the need for field experiment testing that is risk-driven. It is notable that GCS pilot projects have been designed for safe storage, and to date they have been successful. Technical challenges at all geologic CO2 storage sites include assuring injectivity and capacity predictions are reliable, the fate and subsurface topology of the CO2 plume is acceptable, regulatory permitting and compliance are accomplished in a timely manner, needed business assurances are supported by quantitative technical data, and the storage operation is acceptable to stakeholders. There is risk associated with all of these challenges necessitating a monitoring program utilizing both direct and indirect monitoring methods. Because of the direct association of risk and monitoring in GCS, geophysical monitoring is viewed as a key component in approaching risk assessment. To address monitoring in the context of risk assessment the U.S. Department of Energy's National Risk Assessment Partnership (NRAP) has undertaken a review of work related to monitoring storage sites. In this review we provide a framework for monitoring and then present some specific monitoring tools that may be useful for regulatory compliance, as well as technologies that are applicable to leakage detection.
Harbert, William↗
High Impact Weather Forecasts and Warnings with the GOES-R Geostationary Lightning Mapper (GLM)
The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. A major advancement over the current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM). The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, a GOES-R Risk Reduction Science Team and Algorithm Working Group Lightning Applications Team have begun to develop cal/val performance monitoring tools and new applications using the GLM alone, in conjunction with other instruments, and merged or blended integrated observing system products combining satellite, radar, in-situ and numerical models. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms.
Goodman, Steven↗
High Impact Weather Forecasts and Warnings with the GOES-R Geostationary Lightning Mapper (GLM)
The Geostationary Operational Environmental Satellite (GOES-R) is the next series to follow the existing GOES system currently operating over the Western Hemisphere. A major advancement over the current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM). The GLM will operate continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions. This will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. In parallel with the instrument development, a GOES-R Risk Reduction Science Team and Algorithm Working Group Lightning Applications Team have begun to develop cal/val performance monitoring tools and new applications using the GLM alone, in conjunction with other instruments, and merged or blended integrated observing system products combining satellite, radar, in-situ and numerical models. Proxy total lightning data from the NASA Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional ground-based lightning networks are being used to develop the pre-launch algorithms, test data sets, and applications, as well as improve our knowledge of thunderstorm initiation and evolution. In this presentation we review the planned implementation of the instrument and suite of operational algorithms.
Goodman, Steven J.↗
Leveraging Existing Satellite Precipitation Missions for AOS Applications Development
The Atmosphere Observing System (AOS) seeks to explore fundamental questions of how interconnections between aerosols, clouds and precipitation impact our weather and climate, addressing real-world challenges to benefit society. AOS will provide key information to enhance the communities’ ability to improve weather and air quality forecasting today, seasonal to sub-seasonal changes in the near future, and societal challenges resulting from climate change in the decades to come. A fundamental component of the AOS mission is ensuring that applications are considered to the greatest extent possible in mission design.As a result, the Applications Impact Team (AIT) was implemented to address this objective.The overarching goal of the AIT is to help improve the capacity for transitioning science to applications to make it possible to more quickly and effectively inform decisions that will directly benefit society. We seek to maximize AOS benefit to impact decisions through early engagement in the mission development phase in order to prepare stakeholders to apply observations as soon as AOS mission data becomes available.To support these efforts, we leverage existing mission applications activities and initiatives, such as NASA’s GPM and TROPICS missions, to form a framework to enhance precipitation applications for AOS. Engaging with existing missions helps identify and understand data needs, gaps and opportunities for current and future stakeholders, determine what precipitation data products are of highest value and use, and helps connect stakeholders with current mission data that can serve as AOS proxy data, among others. In this presentation, we provide an overview of AOS AIT activities and initiatives and highlight how existing satellite precipitation missions and their applications activities can play a critical role in AOS applications development during mission design.
Andrea Portier↗
Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States
Abstract The U.S. Energy Information Administration (EIA) conducts a regular survey (form EIA-923) to collect annual and monthly net generation for more than ten thousand U.S. power plants. Approximately 90% of the ~1,500 hydroelectric plants included in this data release are surveyed at annual resolution only and thus lack actual observations of monthly generation. For each of these plants, EIA imputes monthly generation values using the combined monthly generating pattern of other hydropower plants within the corresponding census division. The imputation method neglects local hydrology and reservoir operations, rendering the monthly data unsuitable for various research applications. Here we present an alternative approach to disaggregate each unobserved plant’s reported annual generation using proxies of monthly generation—namely historical monthly reservoir releases and average river discharge rates recorded downstream of each dam. Evaluation of the new dataset demonstrates substantial and robust improvement over the current imputation method, particularly if reservoir release data are available. The new dataset—named RectifHyd—provides an alternative to EIA-923 for U.S. scale, plant-level, monthly hydropower net generation (2001–2020). RectifHyd may be used to support power system studies or analyze within-year hydropower generation behavior at various spatial scales.
13 HYDRO ENERGY↗
SPoRT's Participation in the GOES-R Proving Ground Activity
The next generation geostationary satellite, GOES-R, will carry two new instruments with unique atmospheric and surface observing capabilities, the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM), to study short-term weather processes. The ABI will bring enhanced multispectral observing capabilities with frequent refresh rates for regional and full disk coverage to geostationary orbit to address many existing and new forecast challenges. The GLM will, for the first time, provide the continuous monitoring of total lightning flashes over a hemispherical region from space. NOAA established the GOES-R Proving Ground activity several years ago to demonstrate the new capabilities of these instruments and to prepare forecasters for their day one use. Proving Ground partners work closely with algorithm developers and the end user community to develop and transition proxy data sets representing GOES-R observing capabilities. This close collaboration helps to maximize refine algorithms leading to the delivery of a product that effectively address a forecast challenge. The NASA Short-term Prediction Research and Transition (SPoRT) program has been a participant in the NOAA GOES-R Proving Ground activity by developing and disseminating selected GOES-R proxy products to collaborating WFOs and National Centers. Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the SPoRT program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral data from EOS satellites to improve short-term weather forecasts on a regional and local scale. Participation in the Proving Ground activities extends SPoRT s activities and taps its experience and expertise in diagnostic weather analysis, short-term weather forecasting, and the transition of research and experimental data to operational decision support systems like NAWIPS, AWIPS, AWIPS2, and Google Earth. Recent SPoRT Proving Ground activities supporting the development and use of a pseudo GLM total lightning product and the transition of the AWG s Convective Initiation (CI) product, both of which were available in AWIPS and AWIPS II environments, by forecasters during the Hazardous Weather Testbed (HWT) Spring Experiment. SPoRT is also providing a suite of SEVIRI and MODIS RGB image products, and a high resolution composite SST product to several National Centers for use in there ongoing demonstration activities. Additionally, SPoRT has involved numerous WFOs in the evaluation of a GOES-MODIS hybrid product which brings ABI-like data sets in front of the forecaster for everyday use. An overview of this activity will be presented at the conference.
Jedlovec, Gary↗
Development of a “fission-proxy” method for the measurement of 14-MeV neutron fission yields
Here, telative fission-yield measurements were made for 50 fission products from 25.6±0.5 MeV α -induced fission of Th-232. Quantitative comparison of these experimentally measured yields with the evaluated fission yields from 14-MeV neutrons on U-235 demonstrates the application of the Bohr-independence hypothesis for measuring fission yields. As optimum particle-target configurations may be impossible or compromised at a given facility, this new approach, fission-proxy, allows the measurement of fission yields for a given compound nucleus from an alternate reaction pathway since formation and subsequent decay are independent processes.
07 ISOTOPE AND RADIATION SOURCES↗
The Benefit of NASA's Atmosphere Observing System (AOS) Mission Lidar and Polarimeter Observations for Health and Air Quality Applications
The Atmosphere Observing System (AOS) seeks to explore fundamental questions of how interconnections between aerosols, clouds and precipitation impact our weather and climate, addressing real-world challenges to benefit society. AOS will provide key information to enhance the communities’ ability to improve weather and air quality forecasting today, seasonal to sub-seasonal changes in the near future, and societal challenges resulting from climate change in the decades to come. A fundamental component of the AOS mission is ensuring that health and air quality applications are considered to the greatest extent possible in mission design. As a result, the Applications Impact Team (AIT) was implemented to address this objective. The overarching goal of the AIT is to help improve the capacity for transitioning science to applications to make it possible to more quickly and effectively inform decisions that will directly benefit society. We seek to maximize AOS benefit to impact decisions through early engagement in the mission development phase in order to prepare stakeholders to apply observations as soon as AOS mission data becomes available. To support these efforts, we leverage existing and near future mission applications activities and initiatives, such as the NASA CALIPSO, MAIA, TEMPO, and PACE missions to form a framework to enhance health and air quality applications for AOS. The unique synergy between lidar and polarimeter instruments onboard the AOS constellation, as well as diurnally varying observations of aerosol profiles, will provide new opportunities to engage health and air quality stakeholders for forecasting, monitoring, and warning of hazardous events (e.g., wildfire smoke, volcanic ash) that impact human health. Engaging with existing missions helps identify and understand data needs, gaps and opportunities for current and future stakeholders, determine what aerosol data products are of highest value and use, and helps connect stakeholders with current mission data that can serve as AOS proxy data, among others. In this presentation, we provide an overview of AOS aerosol observations relevant for health and air quality applications, AIT activities and initiatives and how existing aerosol satellite missions and their applications activities can play a critical role in AOS applications development during mission design.
Melanie Follette-Cook↗
Joint Assimilation of Surface, GEO, and LEO CO, NO2, and O3 In Situ and Partial Column/Profile Retrievals with WRF-Chem/DART
Low earth orbiting (LEO) satellites like OMI or TROPOMI and geostationary satellites like TEMPO (to be launched January 2023) provide ozone (O3) retrieval profiles that extend into the stratosphere. Regional models like WRF-Chem generally do not include the stratosphere and have vertical grids that end near the tropopause. This causes difficulties when assimilating O3 retrievals (both column and profile retrievals) because the respective observation forward operators require model O3 profiles that extend into the stratosphere. In this presentation, we look at results from assimilating O3 observation from surface (in situ measurements), multiple LEO (profile retrievals), and GEO (proxy profile retrievals) platforms in as regional model where we use a global chemical transport model (GCTM) to provide O3 upper boundary conditions (BCs) to enable application of the O3 observation forward operators. We will also present results from assimilating O3 retrievals together with carbon monoxide (CO) observations from surface (in situ measurements) and multiple LEO platforms (column and profile retrievals), and nitrogen dioxide (NO2) observations from surface (in situ measurements), multiple LEO (column retrievals), and GEO (proxy column retrievals) platforms. Our joint assimilation experiments show that sometimes the observations complement each, e.g., when assimilating surface and satellite CO or NO2 observations, and sometimes the observations conflict with each other, e.g., when assimilating observations that are biased with respect to other observations (e.g., CO retrievals), or when the assimilation increments conflict due to their chemical interaction (e.g., surface O3 and NO2 retrievals). Results from assimilation of O3 retrieval profiles in regional model are preliminary and will also be discussed in our presentation. Our expectation is that using GCTM O3 as upper BCs in the regional model for evaluation of the O3 forward operator will improve our O3 forecasts. In summary, we will present results from jointly assimilating surface, LEO, and GEO CO, NO2, and O3 in situ and satellite observations in a regional model with dynamic emissions estimation. This study uses proxy GEO observations as a prelude to actual observations because the GEO platform will not be launched until after our presentation.
WRF-Chem/DART↗
PredCom: A Predictive Approach to Collecting Approximated Communication Traces
Communication traces collected from MPI applications are an important source of information for performance optimization as they can help analysts determine communication patterns and identify inefficiencies. However, their collection, especially at scale, is time consuming, since it usually requires running the complete target application on a large number of nodes. In this work, we present PredCom, a tool-chain to generate a predictive communication proxy based on information gathered from a few small scale runs, which allows us to extract approximate communication traces with an accuracy high enough for most analysis goals. For this, we combine LLVM passes on the original source code (to capture static program structure) with parameter prediction (to capture dynamic and scaling behavior). This approach drastically reduces the time needed for collecting the communication traces, even for traces on large numbers of MPI processes. Here, we demonstrate that PredCom generates communication traces of various applications up to 1612x faster with an accuracy loss of 0.11 on average compared to the original large-scale traces, and we show that the generated traces can be used to optimize process placement.
97 MATHEMATICS AND COMPUTING↗
An overview of uncertainty quantification techniques with application to oceanic and oil‐spill simulations
Abstract We give an overview of four different ensemble‐based techniques for uncertainty quantification and illustrate their application in the context of oil plume simulations. These techniques share the common paradigm of constructing a model proxy that efficiently captures the functional dependence of the model output on uncertain model inputs. This proxy is then used to explore the space of uncertain inputs using a large number of samples, so that reliable estimates of the model's output statistics can be calculated. Three of these techniques use polynomial chaos (PC) expansions to construct the model proxy, but they differ in their approach to determining the expansions' coefficients; the fourth technique uses Gaussian Process Regression (GPR). An integral plume model for simulating the Deepwater Horizon oil‐gas blowout provides examples for illustrating the different techniques. A Monte Carlo ensemble of 50,000 model simulations is used for gauging the performance of the different proxies. The examples illustrate how regression‐based techniques can outperform projection‐based techniques when the model output is noisy. They also demonstrate that robust uncertainty analysis can be performed at a fraction of the cost of the Monte Carlo calculation.
Iskandarani, Mohamed↗
The Use of the Deep Convective Cloud Technique (DCCT) to Monitor On-Orbit Performance of the Geostationary Lightning Mapper (GLM): Use of Lightning Imaging Sensor (LIS) Data as Proxy
The Geostationary Lightning Mapper (GLM) on the next generation Geostationary Operational Environmental Satellite-R (GOES-R) will not have onboard calibration capability to monitor its performance. The Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite has been providing observations of total lightning over the Earth's Tropics since 1997. The GLM design is based on LIS heritage, making it a good proxy dataset. This study examines the performance of LIS throughout its time in orbit. This was accomplished through application of the Deep Convective Cloud Technique (DCCT) (Doelling et al., 2004) to LIS background pixel radiance data. The DCCT identifies deep convective clouds by their cold Infrared (IR) brightness temperatures and using them as invariant targets in the solar reflective portion of the solar spectrum. The GLM and LIS operate in the near-IR at a wavelength of 777.4 nm. In the present study the IR data is obtained from the Visible Infrared Sensor (VIRS) which is collocated with LIS onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The DCCT is applied to LIS observations for July and August of each year from 1998-2010. The resulting distributions of LIS background DCC pixel radiance for each July August are very similar, indicating stable performance. The mean radiance of the DCCT analysis does not show a long term trend and the maximum deviation of the July August mean radiance for each year is within 0.7% of the overall mean. These results demonstrate that there has been no discernible change in LIS performance throughout its lifetime. A similar approach will used for monitoring the performance of GLM, with cold clouds identified using IR data from the Advanced Baseline Imager (ABI) which will also be located on GOES-R. Since GLM is based on LIS design heritage, the LIS results indicate that GLM should also experience stable performance over its lifetime.
Buechler, Dennis E.↗
Neural Networks as Surrogates of Nonlinear High-Dimensional Parameter-to-Prediction Maps.
We present a preliminary investigation of the use of Multi-Layer Perceptrons (MLP) and Recurrent Neural Networks (RNNs) as surrogates of parameter-to-prediction maps of com- putational expensive dynamical models. In particular, we target the approximation of Quan- tities of Interest (QoIs) derived from the solution of a Partial Differential Equations (PDEs) at different time instants. In order to limit the scope of our study while targeting a rele- vant application, we focus on the problem of computing variations in the ice sheets mass (our QoI), which is a proxy for global mean sea-level changes. We present a number of neural network formulations and compare their performance with that of Polynomial Chaos Expansions (PCE) constructed on the same data.
97 MATHEMATICS AND COMPUTING↗
Neural Networks as Surrogates of Nonlinear High-Dimensional Parameter-to-Prediction Maps
We present a preliminary investigation of the use of Multi-Layer Perceptrons (MLP) and Recurrent Neural Networks (RNNs) as surrogates of parameter-to-prediction maps of computational expensive dynamical models. In particular, we target the approximation of Quantities of Interest (QoIs) derived from the solution of a Partial Differential Equations (PDEs) at different time instants. In order to limit the scope of our study while targeting a relevant application, we focus on the problem of computing variations in the ice sheets mass (our QoI), which is a proxy for global mean sea-level changes. We present a number of neural network formulations and compare their performance with that of Polynomial Chaos Expansions (PCE) constructed on the same data.
97 MATHEMATICS AND COMPUTING↗
Operational Correction for the Temperature Dependence of A Class of Hyperspectral Radiometers
The correction for temperature response of optical radiometers implies access to their working temperature (i.e., either the internal or ambient one with the radiometers in thermal equilibrium). With specific reference to a class of widely used hyperspectral radiometers lacking a thermistor to measure the internal temperature, this work investigated the potential for operational corrections relying on the ambient temperature derived from the radiometer dark digital counts. With the objective to keep within approximately 1% the uncertainty for radiometric corrections in the 400–800-nm spectral range, findings showed the applicability of the method for ambient temperatures higher than approximately 30°C. The alternative use of an ambient temperature proxy, determined from the air temperature incremented by a few degrees Celsius, was shown a valid solution to constrain the uncertainty of radiometric corrections to within 1% over the entire 0°–40°C interval.
Giuseppe Zibordi↗
A Simple Computational Proxy for Screening Magnetocaloric Compounds
Alternating cycles of isothermal magnetization and adiabatic demagnetization applied to a magnetocaloric material can drive refrigeration in very much the same manner as cycles of gas compression and expansion. The material property of interest in finding candidate magnetocaloric materials is ΔS M , their gravimetric entropy change upon application of a magnetic field under isothermal conditions. There is, however, no general method for screening materials for such an entropy change without actually performing the relevant, time- and effort-intensive magnetic measurements. Here we propose a simple computational proxy based on performing nonmagnetic and magnetic density functional theory calculations on magnetic materials. This proxy, which we term the magnetic deformation Σ M , is a measure of how much the unit cell deforms comparing with the relaxed structures with and without the inclusion of spin polarization. Σ M appears to correlate very well with experimentally measured magnetic entropy change values. Here, the proxy has been tested against 33 ferromagnetic materials with known ΔS M , including nine materials newly measured for this study. It has then been used to screen 134 ferromagnetic materials for which the magnetic entropy has not yet been reported, identifying 30 compounds as being promising for further study. As a demonstration of the effectiveness of our approach, we have prepared one of these compounds and measured its isothermal entropy change. MnCoP, with a T C of 575 K, shows a maximal ΔS M of –6.0 J kg –1 K –1 for an H = 5 T applied field.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗