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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Process-control decisions made by applying rules to process data streams. Expert System Executive computer program demonstrates feasibility of automated real-time monitoring. Using Executives knowledge of system formally coded as set of rules specifying actions taken under various conditions. Program makes inferences about observed data based on rules. Written in C language.
A group of candidates for extreme carbon stars has been identified based on their IRAS low-resolution spectra being similar to those of AFGL 3068 and IRC +10 deg 216, which are prototypes of highly evolved carbon stars with very high mass-loss rates. Ground-based photometry has been obtained for five of these sources in order to determine the peaks of their energy distributions. All five sources have color temperatures between 300 and 500 K, and have energy distributions very similar to AFGL 3068. The observed data are fitted with radiative transfer models with optical depths at 11.3 microns ranging from 1.4 to 8. These results suggest that these stars have among the most optically thick circumstellar envelopes of all carbon stars observed to date.
In previously published work (Zhu and Kivelson, 1991) the spatial distribution of compressional magnetic pulsations of period 2 - 20 min in the outer magnetosphere was described. In this companion paper, we study some specific compressional events within our data set, seeking to determine the structure of the waves and identifying the wave generation mechanism. We use both the magnetic field and three-dimensional plasma data observed by the International Sun-Earth Explorer (ISEE) 1 and/or 2 spacecraft to characterize eight compressional ultra low frequency (ULF) wave events with frequencies below 8 mHz in the outer magnetosphere. High time resolution plasma data for the event of July 24, 1978, made possible a detailed analysis of the waves. Wave properties specific to the event of July 24, 1978, can be summarized as follows: (1) Partial plasma pressures in the different energy ranges responded to the magnetic field pressure differently. In the low-energy range they oscillated in phase with the magnetic pressure, while oscillations in higher-energy ranges were out-of-phase; (2) Perpendicular wavelengths for the event were determined to be 60,000 and 30,000 km in the radial and azimuthal directions, respectively. Wave properties common to all events can be summarized as follows: (1) Compressional Pc 5 wave activity is correlated with Beta, the ratio of the plasma pressure to the magnetic pressure; the absolute magnitude of the plasma pressure plays a minor role for the wave activity; (2) The magnetic equator is a node of the compressional perturbation of the magnetic field; (3) The criterion for the mirror mode instability is often satisfied near the equator in the outer magnetosphere when the compressional waves are present. We believe these waves are generated by internal magnetohydrodynamic (MHD) instabilities.
Assessing renewable energy resources under future climate scenarios has been highlighted in recent years to analyze and understand potential impacts of future change in renewable generation on the power sector. Solar energy is well-known as the most plentiful among various renewable resources and usually converted to electricity using photovoltaics (PV) technologies, and the global deployment of PV technology has increased rapidly in recent decades. In this study, we develop a statistical technique to downscale the future projection of solar irradiance for PV energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the proposed downscaling method and future extension of this research.
Solar flares and coronal mass ejections are the most powerful explosions in the Sun. They are major sources of potentially destructive space weather conditions. However, the possible causes of their initiation remain controversial. Using high-resolution data observed by the New Solar Telescope of Big Bear Solar Observatory, supplemented by Solar Dynamics Observatory observations, we present unusual observations of a small-scale emerging flux rope near a large sunspot, whose eruption produced an M-class flare and a coronal mass ejection. The presence of the small-scale flux rope was indicated by static nonlinear force-free field extrapolation as well as data-driven magnetohydrodynamics modeling of the dynamic evolution of the coronal three-dimensional magnetic field. During the emergence of the flux rope, rotation of satellite sunspots at the footpoints of the flux rope was observed. Meanwhile, the Lorentz force, magnetic energy, vertical current, and transverse fields were increasing during this phase. The free energy from the magnetic flux emergence and twisting magnetic fields is sufficient to power the M-class flare. These observations present, for the first time, the complete process, from the emergence of the small-scale flux rope, to the production of solar eruptions.
Although many long-term simulations of snow accumulation and oblation have been made using stand-alone land surface models and surface models coupled with GCMs, less research has focused on short-term event simulations. Actually, accurate event simulations of snow-related processes are the basis for successful long-term simulation. Three advantages of event simulations of snowfall and snow melting are availability of: (1) intensive observation data from field experiments for validation; (2) more physically-realistic precipitation schemes for use in atmospheric models to simulate snowfall; and (3) a more detailed analysis of the snow melting processes. In addition to the complexities of snow related processes themselves, terrain-induced effects on snowfall/snow melting make simulations of snow events more difficult. Climatological observations indicate that terrain features such as the Black Hills of South Dakota and Wyoming can exert important effects on snow accumulation and snow oblation processes. One of the primary effects is that the orography causes forced uplift of airflow and causes atmospheric waves to form both upwind and downwind of it. Airflow often splits around the obstacle, converging on the lee side. This convergence may lead to precipitation enhancement. It also provides an elevated heat and moisture source that enhances atmospheric instability. During the period of April 5-May 5, 1999, the Upper Missouri River Basin Pilot Project (UMRBPP) made intensive observations on precipitation events occurring in the Black Hills. Two moderate snowfall events were captured during the period. The resulting high temporal and spatial resolution data provides opportunities to investigate terrain effects on snowfall amount, distribution, and melting. Successful simulation of snowfall amount, distribution, and evolution using atmospheric models is important to subsequent modeling of snow melting using snow sub-models in land surface schemes. In this paper, a coupled model system, consisting of an atmosphere model (ARPS) and a land-surface model (revised NCAR LSM), is used to simulate one of these UMRBPP cases.
Earth observation data collected from orbital remote sensing systems are becoming increasingly critical to the short- and long-term operations of many commercial industries including agriculture, energy exploration, environmental management, transportation, and urban planning and operations. In this panel, I will present an overview of current and planned NASA remote sensing systems for Earth observation with relevance to commercial and industrial applications. Special emphasis will be given to the International Space Station (ISS) as a platform for both commercial technology demonstration/development and operational data collection through the ISS National Laboratory.
While climate models have evolved over time to produce high fidelity and high resolution climate forecasts, visualization and analysis of the output of the model simulations has been limited, typically constrained to single dimensional charts for visualization and basic aggregate statistics for analytics. Same is true for the large troves of observational data available from meteorological stations all over the world. For richer understanding of climate and the impact of climate change, one needs computational tools that allow researchers, policymakers, and general public, to interact with the climate data. In this paper, we describe, webGlobe, a browser based GIS framework for interacting with climate data, and other datasets available in similar format. webGlobe is a unique resource that allows unprecedented access to climate data through a browser. The framework also allows for deploying machine learning based analytical applications on the climate data without putting computational burden on the client. Instead, webGlobe uses a client-server framework, where the server, deployed on a cloud infrastructure, allows for dynamic allocation of resources for running compute-intensive applications. The capabilities of the framework will be discussed in context of a use case: identifying extreme events from real and simulated climate data using a Gaussian process based change detection algorithm.
Filtering is an approach for incorporating observed data into time-evolving systems. Instead of a family of Dirac delta masses that is widely used in Monte Carlo methods, we here use the Wiener chaos expansion for the parametrization of the conditioned probability distribution to solve the nonlinear filtering problem. The Wiener chaos expansion is not the best method for uncertainty propagation without observations. Nevertheless, the projection of the system variables in a fixed polynomial basis spanning the probability space might be a competitive representation in the presence of relatively frequent observations because the Wiener chaos approach not only leads to an accurate and efficient prediction for short time uncertainty quantification, but it also allows to apply several data assimilation methods that can be used to yield a better approximate filtering solution. The aim of the present paper is to investigate this hypothesis. We answer in the affirmative for the (stochastic) Lorenz-63 system based on numerical simulations in which the uncertainty quantification method and the data assimilation method are adaptively selected by whether the dynamics is driven by Brownian motion and the near-Gaussianity of the measure to be updated, respectively.
The NGEE-Arctic research team identified a common set of hierarchical plant functional types (PFTs) for pan-arctic vegetation that we will use across our research activities. Interdisciplinary work within a large team requires agreement regarding levels of functional organization so that knowledge, data, and technologies can be shared and combined effectively. The team has identified plant functional types as a crucial area where such interoperability is needed. PFTs are used to represent plant pools and fluxes within models, summarize observational data, and map vegetation across the landscape. Within each of these applications, varying levels of PFT specificity are needed according to the specific scientific research goal, computational limitations, and data availability. By agreeing on a specific hierarchical framework for grouping variables in our vegetation data, we ensure the resulting research products will be robust, flexible, and scalable. In this document, we lay out the agreed upon PFT framework with definitions and references to existing literature. Table 1 included in the "NGA700_Phase4PFTFramework_about*" file outlines the relationship between NGEE-Arctic Phase 4, Tier 1 PFTs and the PFTs used within prominent arctic literature as well as publications by the NGEE-Arctic team during phases 1-3.This dataset consists of a table detailing a hierarchical PFT framework that spans 4 tiers with the most granular PFTs listed in tier 1 and the most general PFTs in tier 4. The PFTs within each tier has a single column in the dataset where the PFTs are named and a separate column where the characteristics used to define that PFT are listed. Grey fill of the cells is used to indicate where a given PFT starts to “lose” tier 1 details as you look from left to right. Note the excel file has merged cells to indicate grouping of PFTs across the Tiers- it will not translate into a delimited filetype (.csv, .txt, etc) without modification thus the hierarchical PFT framework table is available in three different file formats: 1) NGA700_Phase4PTS.xlsx – maintains the merged cells and grey fill; 2) NGA700_Phase4PTS.csv – merged cells are split, and grey fill is removed; 3) NGA700_Phase4PTS.pdf – image of the table with merged cells and grey fill. Metadata document included as a *.pdf and file-level metadata and data dictionary as *.csv files.
We have constructed a series of nonrotating quasi-hydrostatic evolutionary models for the M2 Iab supergiant Betelgeuse (α Orionis). Our models are constrained by multiple observed values for the temperature, luminosity, surface composition, and mass loss for this star, along with the parallax distance and high-resolution imagery that determines its radius. We have then applied our best-fit models to analyze the observed variations in surface luminosity and the size of detected surface bright spots as the result of up-flowing convective material from regions of high temperature in the surface convective zone. We also attempt to explain the intermittently observed periodic variability in a simple radial linear adiabatic pulsation model. Based on the best fit to all observed data, we suggest a best progenitor mass estimate of 20{sub −3}{sup +5} M{sub ⊙} and a current age from the start of the zero-age main sequence of 8.0–8.5 Myr based on the observed ejected mass while on the giant branch.
The microwave emission from a half-space medium, characterized by coordinate dependent scattering and absorbing centers, was calculated by numerically solving the radiative transfer equation by the method of invariant imbedding. Rayleigh scattering phase functions and scattering induced polarization of the radiation were included in the calculation. Using the scattering and extinction data of polar firn the brightness temperature was calculated for the 1.55 cm wavelength. This study was the first quantitative comparison of the results of numerical calculation using the actual measured information of crystal size with the observed data.
Model calculations were performed to describe a mechanism that produces classical nova outbursts on white dwarfs of 1 solar mass or less and for accretion rates of 4 x 10 to the -10th solar mass/yr or greater, i.e., the parameters corresponding to observed data of nova systems. Calculations point to four factors that can induce nuclear runaways of sufficient strength to eject about 0.0001 solar mass at speeds of several hundred to a few thousand km per second, as is observed in classical novae. These are (1) the effects of storage of angular momentum in the star's envelope during the accretion phase; (2) the reduction of centrifugal forces in the star's outer layers during the early nuclear runaway phase, through the inward transport of angular momentum; (3) the inward movement of the zone of peak nuclear burning through the convectively induced shear instability during the runaway phase; and (4) the mixing of original CO stellar matter and H-rich matter, also through the convectively induced shear instability.
Both the observed and simulated time series of the Labrador Sea surface salinities show a major freshening event since the middles. It continues the series of decoder events of the 1970s and 1980s from which the freshening in the early 1970's was named as the Great Salinity Anomaly (GSA). These events are especially distinguishable in the late summer (August and September) time series. The observed data suggests that the 1990's freshening may equal the GSA in magnitude. This recent event is associated with a large reduction in the overturning rate between the early and latter part of the 1990s. Both the observations and model results indicate that the surface salinity conditions appear to be returning towards normal daring 1999 and 2000 in the coastal area, but offshore, the model predicts the freshening to linger on after peaking 1997.
In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).
Two methods for obtaining the required spectral signatures for a particular mixture model are considered. For the model considered, the spectral signatures become signature vectors. The first method is based upon determination of the signature vectors in such a way that a measure of the inconsistency between the mixture model and the observed data is minimized. The second method is based upon determination of the signature vectors in such a way that the estimated mean percentage coverage of individual species matches apriori or ground truth estimates. The two methods proposed are applied to actual multispectral data in order to verify the concepts presented.
The proposed research involves the application of multispectral satellite data in combination with ground truth measurements to monitor surface properties of the Greenland ice sheet which are essential for describing the energy and mass of the ice sheet. Several key components of the energy balance are parameterized using satellite data and in situ measurements. The analysis will be done for a ten year time period in order to get statistics on the seasonal and interannual variations of the surface processes and the climatology. Our goal is to investigate to what accuracy and over what geographic areas large scale snow properties and radiative fluxes can be derived based upon a combination of available remote sensing and meterological data sets. Data analysis showed the following results: (1)cloud classification based on longwave sky radiation revealed that overcast sky occurred for 25% of the time in winter, and for 15% in spring and summer respectively (winter and summer both show the same occurrence of clear sky of approximately 26%); (2) comparison of aerodynamic profile method with eddy correlation method to derive sensible and latent heat flux showed good agreement in the diurnal cycle and the turbulent fluxes were underestimated with the aerodynamic method by 10 - 30% as compared to the in situ eddy flux method; (3) the katabatic wind shows a distinct diurnal cycle with a maximum in the morning (7-9 h solar time) and a minimum in the later afternoon (18 h solar time); (4) snow grain size was modeled with a surface energy balance model (SNTHERM) and compared with in situ measurements. Sharp decreases in the modeled snow grain size, caused by accumulation events such as precipitation and deposition, could be verified with observational data; (4) radiative transfer modeling of firn supports our beliefs that the observed trends in 18 and 19 GHz passive microwave brightness temperatures are attributable to accumulation rate changes (modeling also indicates the above relationship is detectable because of the presence of depth hoar; (5) snow melt can be detected by a distinct signal in the passive microwave cross-polarized gradient ratio (19h-37v)/(19h+37v) and has been used for wet/dry snow classification; (6) top of the atmosphere (TOA) broadband albedos were derived from AVHRR visible and near infrared reflectances for the entire ice sheet from May 1990 - June 1991, and the highest albedo values are found along the southeast coast of the ice sheet which is consistent with the summer peak of precipitation due to onshore flow loaded with high water vapor content (TOA albedo values dropped to around 40% along the south-western coast during July and August due to bare ice surfaces); and (7) the net all-wave radiation balance at the top of the atmosphere is negative over the entire ice sheet except for the summer months - June-July-August (in June, the net radiation balance is slightly positive over the dry snow areas--15 W/m2).
Theoretical emission-line ratios involving multiplets near 1262, 1306, and 1530 A are derived on the basis of new calculations of electron-impact excitation rates for allowed transition in Si II. A comparison of these line ratios with observational data from a quiet solar region, a sunspot, and an active region, obtained with the HIRTS on board a sounding rocket flight reveals that the 1530-A multiplet is optically thick, which is consistent with a calculation of the optical depth of these lines through a model atmosphere. The 1262- and 1306-A multiplets appear to be effectively optically thin. The average discrepancy between the theoretical and observed ratios is about 40 percent, which may not be significant, since the estimated uncertainties in both the calculated and experimental data are approximately 30 percent.