SEARCH · Search NASA
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.
Data from the Ulysses spacecraft was used for studying solar tangential discontinuities (TDs) at high latitudes. Significant numbers of TDs seem to exist at these latitudes.
Observing and better understanding clouds and aerosols are priorities in the NASA Earth Science Decadal Survey, US National Climate Assessment, and Intergovernmental Panel on Climate Change Report. The Global Learning & Observations to Benefit the Environment (GLOBE) Program is NASA's largest and longest running citizen science program in the Earth Sciences that collects cloud observations. Since January 2017, NASA GLOBE students and citizen scientists have submitted over 800,000 cloud observations worldwide using both paper-based and smartphone app platforms. In this presentation, we compare satellite, model, and volunteer-reported total cloud cover. The comparison reveals a systematic misreporting of obscured skies (sky not visible due to smoke, dust, haze, etc.) versus overcast skies (100% total cloud cover) by GLOBE participants. We discuss implications for improving procedures for volunteer reporting under overcast and obscured sky conditions, and relevance for reporting extreme air pollution events in areas with little or no formal institutional monitoring networks.
The first of a series of papers which explore the optical morphology, photometric properties, and colors of powerful radio galaxies is presented. This paper reports the observations and data reduction process. The surface photometry techniques used in the analysis are described and the calibration of the results is given. The resulting images reveal that a significant fraction of powerful radio galaxies are morphologically distorted and that many of these galaxies have unusual colors. Results for individual objects are discussed.
In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. Furthermore, one experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.
Since numerical weather prediction models are unable to accurately forecast the severity and the location of the storm cells several hours into the future when compared with observation data, there has been a growing interest in probabilistic description of convective weather. The classical approach for generating uncertainty bounds consists of integrating the state equations and covariance propagation equations forward in time. This step is readily recognized as the process update step of the Kalman Filter algorithm. The second well known method, known as the Monte Carlo method, consists of generating output samples by driving the forecast algorithm with input samples selected from distributions. The statistical properties of the distributions of the output samples are then used for defining the uncertainty bounds of the output variables. This method is computationally expensive for a complex model compared to the covariance propagation method. The main advantage of the Monte Carlo method is that a complex non-linear model can be easily handled. Recently, a few different methods for probabilistic forecasting have appeared in the literature. A method for computing probability of convection in a region using forecast data is described in Ref. 5. Probability at a grid location is computed as the fraction of grid points, within a box of specified dimensions around the grid location, with forecast convection precipitation exceeding a specified threshold. The main limitation of this method is that the results are dependent on the chosen dimensions of the box. The examples presented Ref. 5 show that this process is equivalent to low-pass filtering of the forecast data with a finite support spatial filter. References 6 and 7 describe the technique for computing percentage coverage within a 92 x 92 square-kilometer box and assigning the value to the center 4 x 4 square-kilometer box. This technique is same as that described in Ref. 5. Characterizing the forecast, following the process described in Refs. 5 through 7, in terms of percentage coverage or confidence level is notionally sound compared to characterizing in terms of probabilities because the probability of the forecast being correct can only be determined using actual observations. References 5 through 7 only use the forecast data and not the observations. The method for computing the probability of detection, false alarm ratio and several forecast quality metrics (Skill Scores) using both the forecast and observation data are given in Ref. 2. This paper extends the statistical verification method in Ref. 2 to determine co-occurrence probabilities. The method consists of computing the probability that a severe weather cell (grid location) is detected in the observation data in the neighborhood of the severe weather cell in the forecast data. Probabilities of occurrence at the grid location and in its neighborhood with higher severity, and with lower severity in the observation data compared to that in the forecast data are examined. The method proposed in Refs. 5 through 7 is used for computing the probability that a certain number of cells in the neighborhood of severe weather cells in the forecast data are seen as severe weather cells in the observation data. Finally, the probability of existence of gaps in the observation data in the neighborhood of severe weather cells in forecast data is computed. Gaps are defined as openings between severe weather cells through which an aircraft can safely fly to its intended destination. The rest of the paper is organized as follows. Section II summarizes the statistical verification method described in Ref. 2. The extension of this method for computing the co-occurrence probabilities in discussed in Section HI. Numerical examples using NCWF forecast data and NCWD observation data are presented in Section III to elucidate the characteristics of the co-occurrence probabilities. This section also discusses the procedure for computing throbabilities that the severity of convection in the observation data will be higher or lower in the neighborhood of grid locations compared to that indicated at the grid locations in the forecast data. The probability of coverage of neighborhood grid cells is also described via examples in this section. Section IV discusses the gap detection algorithm and presents a numerical example to illustrate the method. The locations of the detected gaps in the observation data are used along with the locations of convective weather cells in the forecast data to determine the probability of existence of gaps in the neighborhood of these cells. Finally, the paper is concluded in Section V.
Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.
Abstract Connecting observations of core-collapse supernova explosions to the properties of their massive star progenitors is a long-sought, and challenging, goal of supernova science. Recently, Barker et al. presented bolometric light curves for a landscape of progenitors from spherically symmetric neutrino-driven core-collapse supernova (CCSN) simulations using an effective model. They find a tight relationship between the plateau luminosity of the Type II-P CCSN light curve and the terminal iron-core mass of the progenitor. Remarkably, this allows us to constrain progenitor properties with photometry alone. We analyze a large observational sample of Type II-P CCSN light curves and estimate a distribution of iron-core masses using the relationship of Barker et al. The inferred distribution matches extremely well with the distribution of iron-core masses from stellar evolutionary models and namely, contains high-mass iron cores that suggest contributions from very massive progenitors in the observational data. We use this distribution of iron-core masses to infer minimum and maximum masses of progenitors in the observational data. Using Bayesian inference methods to locate optimal initial mass function parameters, we find M min = 9.8 − 0.27 + 0.37 and M max = 24.0 − 1.9 + 3.9 solar masses for the observational data.
Modeling strong gravitational lenses is prohibitively expensive for modern and next-generation cosmic survey data. Neural posterior estimation (NPE), a simulation-based inference (SBI) approach, has been studied as an avenue for efficient analysis of strong lensing data. However, NPE has not been demonstrated to perform well on out-of-domain target data -- e.g., when trained on simulated data and then applied to real, observational data. In this work, we perform the first study of the efficacy of NPE in combination with unsupervised domain adaptation (UDA). The source domain is noiseless, and the target domain has noise mimicking modern cosmology surveys. We find that combining UDA and NPE improves the accuracy of the inference by 1-2 orders of magnitude and significantly improves the posterior coverage over an NPE model without UDA. We anticipate that this combination of approaches will help enable future applications of NPE models to real observational data.
Efficient and accurate wake models are required for wind plant performance modeling and the suite of engineering processes that support wind plant layout, control, and monitoring. Although many analytical and engineering wake models with low computational costs have been proposed, their ability to represent the power production of large wind plants in a wide range of atmospheric conditions is not completely understood. The following validation study reviews the underlying theory for analytical wake models, outlines quality control procedures for observational data, and compares model results with observational data from the Lillgrund Wind Plant. Lillgrund makes a valuable case study for wake modeling because of its regular arrangement and the relatively close spacing of constituent wind turbines, which lead to regular and significant wake interactions within the wind plant and the development of deep array flow conditions. Formulations for the velocity deficit, wake-added turbulence, and wake superposition methods are considered in a modular sense, yielding many possible configurations to represent wind turbine wakes, of which seven are examined in detail. Velocity deficit models that account for flow conditions in the near wake are better able to reproduce power production for wind turbines in the transitional region of the wind plant, where wind turbines experience as many as five wakes from upstream turbines. Finally, in the deep array, where power production reaches asymptotic values and wake statistics become quasi-periodic, wake superposition schemes become the largest driver in error reduction; using the linear or maximum wake superposition methods can reduce the relative root mean square error by as much as 40% in the deep array.
The data listing process of the electronic data base of the Catalogs of Space Shuttle Earth Observations Photography is described. Similar data are recorded for each frame in each role from the mission. At the end of each roll, a computer printout is checked for mistakes, glitches, and typographical errors. After the roll and frames have been corrected, the data listings are ready for transfer to the data base and for development of the catalog.
The NEO (NASA Earth Observations) web space is currently under development with the goal of significantly increasing the demand for NASA remote sensing data while dramatically simplifying public access to georeferenced images. NEO will target the unsophisticated, nontraditional data users who are currently underserved by the existing data ordering systems. These users will include formal and informal educators, museum and science center personnel, professional communicators, and citizen scientists and amateur Earth observers. Users will be able to view and manipulate georeferenced browse imagery and, if they desire, download directly or order the source HDF data from the data provider (e.g., NASA DAAC or science team) via a single, integrated interface. NE0 will accomplish this goal by anticipating users expectations and knowledge level, thus providing an interface that presents material to users in a more simplified manner, without relying upon the jargon/technical terminology that make even the identification of the appropriate data set a significant hurdle. NEO will also act as a gateway that manages users expectations by providing specific details about images and data formats, developing tutorials regarding the manipulation of georeferenced imagery and raw data, links to software tools and ensuring that users are able to get the image they want in the format they want as easily as possible.
Currently, there is a common endeavor to detect magnetoacoustic waves in solar flares. This paper contributes to this topic using an approach of numerical simulations. We studied a spatial and temporal evolution of impulsively generated fast and slow magnetoacoustic waves propagating along the dense slab and Harris current sheet using two-dimensional magnetohydrodynamic numerical models. Wave signals computed in numerical models were used for computations of the temporal and spatial wavelet spectra for their possible comparison with those obtained from observations. It is shown that these wavelet spectra allow us to estimate basic parameters of waveguides and perturbations. It was found that the wavelet spectra of waves in the dense slab and current sheet differ in additional wavelet components that appear in association with the main tadpole structure. These additional components are new details in the wavelet spectrum of the signal. While in the dense slab this additional component is always delayed after the tadpole head, in the current sheet this component always precedes the tadpole head. It could help distinguish a type of the waveguide in observed data. We present a technique based on wavelets that separates wave structures according to their spatial scales. This technique shows not only how to separate the magnetoacoustic waves and waveguide structure in observed data, where the waveguide structure is not known, but also how propagating magnetoacoustic waves would appear in observations with limited spatial resolutions. The possibilities detecting these waves in observed data are mentioned.
Introduction The SpaceX Dragon 1 spacecraft performed 20 resupply missions to the International Space Station (ISS) between 2012 and 2020. After each mission, a team from the NASA John-son Space Center Hypervelocity Impact Technology Group inspected each Dragon 1 capsule for hypervelocity impact damage features. Data from these inspections are collected into a database that includes impact feature dimensions as well as the location on the vehicle. Additional details on the type and size of particle that produced the damage site are provided when sampling data and definitive spectroscopic analysis results are available. Observation data can be compared with impact estimates from risk assessment codes as a check on the micrometeoroid and orbital debris (MMOD) environment predictions. Scope A general description of the areas of the vehicle that were inspected are provided as well as mission details such as exposure duration and launch dates. The paper documents the general inspection procedure for collection of data and the post inspection data analysis process. It also provides details of the observation data collected as well as the results of analysis of intact samples collected for spectroscopic analysis to discern the source of the impacting particle. A comparison between observed impacts and the expected number of damage features calculated by Bumper 3 with the latest MMOD environments are also presented. Findings Statistics on the >300 impact features documented in the database will provide insight into the depth to diameter ratios and other relationships. The quality of the comparison between the observations and code predictions are dependent on several factors. The paper provides de-tails of each of these variables. (1) Damage equations (2) Impact condition assumptions a. Projectile density b. Impact speed c. Impact angle (3) Analysis results a. pre-flight vs. on-orbit damage b. MMOD vs. non-MMOD Conclusions and Recommendations The ISS visiting vehicle impact database is an ongoing project. SpaceX provides crew rotations as well as resupply and cargo return with the Dragon 2 spacecraft. Sierra Space is manifested for resupply and cargo return services starting in 2023. Boeing is expected to provide crew rotations as well. All of these spacecraft will provide additional opportunities for post flight MMOD inspections of space exposed hardware. Acknowledgements Over the course of the inspection campaign various personnel at the SpaceX Texas Test Site in McGregor and at the SpaceX headquarters in Hawthorne CA provided valuable support to this activity.
Using over five decades of field data on electric and non-electric parts, we revisit the environmental conversion methodology described in MIL-HDBKs -217 and -338. These Reliability Engineering Handbooks’ predicted values are compared to new tables, developed using observed data. This observed data approach is also used to develop environmental conversion factors for non-electric parts, which were out of scope of MIL-HDBK-217 and -338.
Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.
Here, we compare a large suite of theoretical cosmological models to observational data from the cosmic microwave background, baryon acoustic oscillation measurements of expansion, Type Ia supernova measurements of expansion, redshift space distortion measurements of the growth of structure, and the local Hubble constant. Our theoretical models include parametrizations of dark energy as well as physical models of dark energy and modified gravity. We determine the constraints on the model parameters, incorporating the redshift space distortion data directly in the analysis. To determine whether models can be ruled out, we evaluate the p-value (the probability under the model of obtaining data as bad or worse than the observed data). In our comparison, we find the well-known tension of H 0 with the other data; no model resolves this tension successfully. Among the models we consider, the large-scale growth of structure data does not affect the modified gravity models as a category particularly differently from dark energy models; it matters for some modified gravity models but not others, and the same is true for dark energy models. We compute predicted observables for each model under current observational constraints, and identify models for which future observational constraints will be particularly informative.
In order to understand the physical and chemical processes which produce the observed spatial morphology of the cometary coma, it is necessary to analyze observational data with physically meaningful models. Thus, a coupled program of theoretical modeling and complementary observational data analysis was undertaken regarding the spatial distributions of neutral gases in the coma. More, specifically, the particular topics of interest are: (1) the theoretical modeling of the nonequilibrium dynamics of the inner coma with emphasis on the region of the coma from the transition from collisional fluid flow out to the free-flow region and on observable conditions in the coma (i.e., density, outflow speed, and temperature); and (2) the model analysis of an important set of long-slit CCD spectra of comets. The side-by-side development of models along with the observation and analysis of data is an important and integral part of this project. The scientific community has in hand valuable observational and in situ data regarding one comet, Halley. It is important to use Halley as the benchmark by which other remotely observed comet data can be understood. Therefore, the self-consistant analysis of data with appropriate models is of the utmost importance. The data analysis work includes the analysis of the spatial profiles of (OI), NH2, CN, and C2.
The majority of research on efficient and scalable algorithms in computational science and engineering has focused on the forward problem : given parameter inputs, solve the governing equations to determine output quantities of interest. In contrast, in this paper, we consider the broader question: given a (large-scale) model containing uncertain parameters, (possibly) noisy observational data, and a prediction quantity of interest, how do we construct efficient and scalable algorithms to (1) infer the model parameters from the data (the deterministic inverse problem ), (2) quantify the uncertainty in the inferred parameters (the Bayesian inference problem), and (3) propagate the resulting uncertain parameters through the model to issue predictions with quantified uncertainties (the forward uncertainty propagation problem )? We present efficient and scalable algorithms for this end-to-end, data-to-prediction process under the Gaussian approximation and in the context of modeling the flow of the Antarctic ice sheet and its effect on loss of grounded ice to the ocean. The ice is modeled as a viscous, incompressible, creeping, shear-thinning fluid. The observational data come from satellite measurements of surface ice flow velocity, and the uncertain parameter field to be inferred is the basal sliding parameter, represented by a heterogeneous coefficient in a Robin boundary condition at the base of the ice sheet. The prediction quantity of interest is the present-day ice mass flux from the Antarctic continent to the ocean. We show that the work required for executing this data-to-prediction process—measured in number of forward (and adjoint) ice sheet model solves—is independent of the state dimension, parameter dimension, data dimension, and the number of processor cores. The key to achieving this dimension independence is to exploit the fact that, despite their large size, the observational data typically provide only sparse information on model parameters. This property can be exploited to construct a low rank approximation of the linearized parameter-to-observable map via randomized SVD methods and adjoint-based actions of Hessians of the data misfit functional.