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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Space-based Doppler lidar sampling strategies: Algorithm development and simulated observation experiments

Lidar Atmospheric Wind Sounder (LAWS) Simulation Models (LSM) were developed to evaluate the potential impact of global wind observations on the basic understanding of the Earth's atmosphere and on the predictive skills of current forecast models (GCM and regional scale). Fully integrated top to bottom LAWS Simulation Models for global and regional scale simulations were developed. The algorithm development incorporated the effects of aerosols, water vapor, clouds, terrain, and atmospheric turbulence into the models. Other additions include a new satellite orbiter, signal processor, line of sight uncertainty model, new Multi-Paired Algorithm and wind error analysis code. An atmospheric wind field library containing control fields, meteorological fields, phenomena fields, and new European Center for Medium Range Weather Forecasting (ECMWF) data was also added. The LSM was used to address some key LAWS issues and trades such as accuracy and interpretation of LAWS information, data density, signal strength, cloud obscuration, and temporal data resolution.

Emmitt, G. D.↗
NASA NTRS2003

Automated Monitoring with a BCP Fault-Decision Test

The Bayesian conditional probability (BCP) technique is a statistical fault-decision technique that is suitable as the mathematical basis of the fault-manager module in the automated-monitoring system and method described in the immediately preceding article. Within the automated-monitoring system, the fault-manager module operates in conjunction with the fault-detector module, which can be based on any one of several fault-detection techniques; examples include a threshold-limit-comparison technique or the BSP or SPRT technique mentioned in the preceding article. The present BCP technique is used to evaluate a series of one or more fault-detection events for the purpose of filtering out occasional false alarms produced by many types of statistical fault-detection procedures. The BCP technique increases the probability that an automated monitoring system produces a correct decision regarding the presence or absence of a fault. Because occasional false alarms are an inevitable consequence of the SPRT, BSP, or any other statistically based fault-detection test, there is a need for a logical procedure to distinguish between true and false alarms. Heretofore, it has been common practice to make a fault decision on an ad hoc basis for example by following a multiple-observation voting strategy in which a signal is declared to be indicative of a fault if m of the last n observations produced a fault-detection alarm. The BCP technique was developed to obtain results more reliable than those afforded by a voting strategy. The BCP technique involves a test in which one applies Bayesian inference techniques to a series of one or more single-observation alarms produced by a fault-detection test. One considers the last n decisions generated by a fault-detection test in order to evaluate the conditional probability that a failure is indicated (see figure). Each new decision reached by a fault-detection test is treated as a new piece of evidence about the state of the monitored asset, and the conditional probability of failure for the system is updated on the basis of this new evidence. The conditional probability of failure is compared with a predefined limit. For a probability below the limit, the asset is declared to be healthy. For a probability above the limit, the asset is declared to be faulty.

Bickford, Randall L.↗
NASA NTRSDate not supplied

Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separability of class distributions. Such challenges result in skewed or warped decision boundaries and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions within the feature space through partitioning. This enables the optimization of local models within the regions to overcome the impact of unobservability in different feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of unobservability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobservability in predictive models. Formal fusion methods, such as Dempster-Shafer theory, can better leverage the information from local regions to improve the performance of interpretable predictive models in the presence of unobservable variables.

Time Series Data↗
NASA NTRS1985

The Characterization of Surface Variegation Effects on Remote Sensing

Improvements in remote sensing capabilities hinge very directly upon attaining an understanding of the physical processes contributing to the measurements. In order to devise new measurement strategies and to learn better techniques for processing remotely gathered data, it is necessary to understand and to characterize the complex radiative interactions of the atmosphere-surface system. In particular, it is important to understand the role of atmospheric structure, ground reflectance inhomogeneity and ground bidirectional reflectance type. The goals, then, are to model, analyze, and parameterize the observable effects of three dimensional atmospheric structure and composition and two dimensional variations in ground albedo and bidirectional reflectance. To achieve these goals, a Monte Carlo radiative transfer code is employed to model and analyze the effects of many of the complications which are present in nature.

Pearce, W. A.↗
NASA NTRS1998

The Study of Clusters of Galaxies and Large Scale Structures

The work on the ROSAT Deep Survey has been successfully completed. A number of interesting results have been established within this joint MPE, Cal Tech, JHU, ST Scl, ESO collaboration. First, a very large fraction, 70-80 percent, of the X-ray background has been directly resolved into point sources. We have derived a new log N-log S for X- ray sources and have measured a source density of 970 sources per square degree at a limiting flux level of 10(exp -15)/erg s sq cm (0.5-2.0 keV). Care was taken in these studies to accurately model and measure the effects of sources confusion. This was possible because of our observing strategy which included both deep PSPC and HRI observations. In the last year we initiated work in the design and development of the Next Generation Space Telescope.

Source record↗
NASA NTRS2014

Preparing for Dawn's Mission at Ceres: Challenges and Opportunities in the Exploration of a Dwarf Planet

After escaping from Vesta in 2012, Dawn is continuing its 2.5-year flight to dwarf planet Ceres. Investigating this second destination promises to provide a view of an intriguing world of ice and rock, likely displaying fascinating geology entirely unlike any body yet orbited by a spacecraft. Dawn spends the significant majority of the time thrusting with its ion propulsion system to deliver the 3.6 km/s required to rendezvous with Ceres. Meanwhile, the operations team has developed the sequences that will be used there. Following orbit capture in March 2015, Dawn will fly to a series of four circular polar science orbits. The orbits, ranging from about 13,500 km to 375 km in altitude, are designed to optimize the scientific observations. The overall strategy for exploring Ceres is based strongly on the extremely successful 16 months of Vesta operations, during which Dawn met or exceeded all of its objectives. Nevertheless, the loss of two of the spacecraft's four reaction wheels has necessitated some important changes. Based on a very productive hydrazine conservation campaign in the interplanetary cruise and the development of new hydrazine-efficient methods of operating at Ceres, there is good reason to expect that Dawn will be able to accomplish all of its objectives regardless of the health of the reaction wheels. This paper describes the progress in traveling to Ceres as well as the plans for exploring this giant, icy world.

Vesta↗
NASA NTRS1994

Infrared algorithm development for ocean observations with EOS/MODIS

Efforts continue under this contract to develop algorithms for the computation of sea surface temperature (SST) from MODIS infrared retrievals. This effort includes radiative transfer modeling, comparison of in situ and satellite observations, development and evaluation of processing and networking methodologies for algorithm computation and data accession, evaluation of surface validation approaches for IR radiances, and participation in MODIS (project) related activities. Efforts in this contract period have focused on radiative transfer modeling and evaluation of atmospheric path radiance efforts on SST estimation, exploration of involvement in ongoing field studies, evaluation of new computer networking strategies, and objective analysis approaches.

Brown, Otis B.↗
NASA NTRS2015

Global Ocean Evaporation: How Well Can We Estimate Interannual to Decadal Variability?

Evaporation from the world's oceans constitutes the largest component of the global water balance. It is important not only as the ultimate source of moisture that is tied to the radiative processes determining Earth's energy balance but also to freshwater availability over land, governing habitability of the planet. Here we focus on variability of ocean evaporation on scales from interannual to decadal by appealing to three sources of data: the new MERRA-2 (Modern-Era Retrospective analysis for Research and Applications -2); climate models run with historical sea-surface temperatures, ice and atmospheric constituents (so-called AMIP experiments); and state-of-the-art satellite retrievals from the Seaflux and HOAPS (Hamburg Ocean-Atmosphere Parameters and Fluxes from Satellite) projects. Each of these sources has distinct advantages as well as drawbacks. MERRA-2, like other reanalyses, synthesizes evaporation estimates consistent with observationally constrained physical and dynamical models-but data stream discontinuities are a major problem for interpreting multi-decadal records. The climate models used in data assimilation can also be run with lesser constraints such as with SSTs and sea-ice (i.e. AMIPs) or with additional, minimal observations of surface pressure and marine observations that have longer and less fragmentary observational records. We use the new ERA-20C reanalysis produced by ECMWF embodying the latter methodology. Still, the model physics biases in climate models and the lack of a predicted surface energy balance are of concern. Satellite retrievals and comparisons to ship-based measurements offer the most observationally-based estimates, but sensor inter-calibration, algorithm retrieval assumptions, and short records are dominant issues. Our strategy depends on maximizing the advantages of these combined records. The primary diagnostic tool used here is an analysis of bulk aerodynamic computations produced by these sources and uses a first-order Taylor series analysis of wind speed, SST, near-surface stability and relative humidity variations around climatology to gauge the importance of these components. We find that the MERRA-2 evaporation record is strongly influenced by the availability of wind speed and humidity from passive microwave imagers beginning in the late 1980s as well as by the SST record. The trend over the period 1980 to present is nearly 10%. AMIP or the ERA-20C trends are much smaller. We find that ENSO-related signals involving both wind speed and thermodynamic variability remain the primary signal in the latter and are confirmed by satellite retrievals. We present uncertainty estimates based on the various data sources and discuss the implications for GEWEX water and energy budget science challenges.

Robertson, Franklin R.↗
NASA NTRSDate not supplied

Accelerating TEMPO Air Quality Science Through STAQS

Soon after the launch of Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission, NASA is supporting the Synergistic TEMPO Air Quality Science field study (STAQS) in summer 2023. This study’s main objective is to accelerate science with geostationary air quality observations from TEMPO to better understand its use in air pollution research and applications. Science objectives include, but are not limited to, TEMPO L2 product evaluation, the interpretation of the spatiotemporal evolution of TEMPO data during air quality events, assessment of anthropogenic emissions, contributions to chemical transport modeling, and environmental justice applications. These objectives can be accomplished through the integration of satellite data with systematically repeated high-resolution aircraft- and ground-based measurements in multiple urban environments. The primary urban cities considered include Los Angeles, New York City, and Chicago in June-August 2023. Measurements consist of airborne remote sensing observations of air quality constituents of nitrogen dioxide (NO2), formaldehyde (HCHO), ozone, and aerosols using the GeoCape Airborne Simulator and High-Spectral Resolution Lidar 2/Differential Absorption Lidar (HSRL2/DIAL) on the NASA JSC G-V aircraft, greenhouse gas observations from Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) and High Altitude Lidar Observatory (HALO) on the NASA LaRC G-III aircraft, and ground-based remote sensing and in situ observations of ozone, NO2, and HCHO from the Tropospheric Ozone Lidar Network (TOLNet), Pandora spectrometers, and ground-based monitoring networks. Key partnership studies providing in situ airborne observations include the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) field study on the NASA DC-8 in the same primary target areas as STAQS and Greater New York Oxidant, Tropospheric Halogens, and Aerosol Measurements and Modeling (GOTHAMM) field study on the NSF C-130 near New York City. This presentation will include a current status update of the STAQS mission and an overview of its measurement strategies and science objectives with the goal of promoting continued discussions for building and strengthening collaborations prior to the mission.

Laura Judd↗
NASA NTRS1978

Spatial land-use inventory, modeling, and projection/Denver metropolitan area, with inputs from existing maps, airphotos, and LANDSAT imagery

A landscape model was constructed with 34 land-use, physiographic, socioeconomic, and transportation maps. A simple Markov land-use trend model was constructed from observed rates of change and nonchange from photointerpreted 1963 and 1970 airphotos. Seven multivariate land-use projection models predicting 1970 spatial land-use changes achieved accuracies from 42 to 57 percent. A final modeling strategy was designed, which combines both Markov trend and multivariate spatial projection processes. Landsat-1 image preprocessing included geometric rectification/resampling, spectral-band, and band/insolation ratioing operations. A new, systematic grid-sampled point training-set approach proved to be useful when tested on the four orginal MSS bands, ten image bands and ratios, and all 48 image and map variables (less land use). Ten variable accuracy was raised over 15 percentage points from 38.4 to 53.9 percent, with the use of the 31 ancillary variables. A land-use classification map was produced with an optimal ten-channel subset of four image bands and six ancillary map variables. Point-by-point verification of 331,776 points against a 1972/1973 U.S. Geological Survey (UGSG) land-use map prepared with airphotos and the same classification scheme showed average first-, second-, and third-order accuracies of 76.3, 58.4, and 33.0 percent, respectively.

Tom, C.↗
NASA NTRS2010

Orbital Debris Detection and Tracking Strategies for the NASA/AFRL Meter Class Autonomous Telescope (MCAT)

MCAT (Meter-Class Autonomous Telescope) is a 1.3m f/4 Ritchey-Chr tien on a double horseshoe equatorial mount that will be deployed in early 2011 to the western pacific island of Legan in the Kwajalein Atoll to perform orbital debris observations. MCAT will be capable of tracking earth orbital objects at all inclinations and at altitudes from 200 km to geosynchronous. MCAT s primary objective is the detection of new orbital debris in both low-inclination low-earth orbits (LEO) and at geosynchronous earth orbit (GEO). MCAT was thus designed with a fast focal ratio and a large unvignetted image circle able to accommodate a detector sized to yield a large field of view. The selected primary detector is a close-cycle cooled 4Kx4K 15um pixel CCD camera that yields a 0.9 degree diagonal field. For orbital debris detection in widely spaced angular rate regimes, the camera must offer low read-noise performance over a wide range of framing rates. MCAT s 4-port camera operates from 100 kHz to 1.5 MHz per port at 2 e- and 10 e- read noise respectively. This enables low-noise multi-second exposures for GEO observations as well as rapid (several frames per second) exposures for LEO. GEO observations will be performed using a counter-sidereal time delay integration (TDI) technique which NASA has used successfully in the past. For MCAT the GEO survey, detection, and follow-up prediction algorithms will be automated. These algorithms will be detailed herein. For LEO observations two methods will be employed. The first, Orbit Survey Mode (OSM), will scan specific orbital inclination and altitude regimes, detect new orbital debris objects against trailed background stars, and adjust the telescope track to follow the detected object. The second, Stare and Chase Mode (SCM), will perform a stare, then detect and track objects that enter the field of view which satisfy specific rate and brightness criteria. As with GEO, the LEO operational modes will be fully automated and will be described herein. The automation of photometric and astrometric processing (thus streamlining data collection for environmental modeling) will also be discussed.

Mulrooney, M.↗
NASA NTRSDate not supplied

NASA’s Advance Information Systems Technology (AIST) Program

NASA’s Earth Science Technology Office (ESTO) develops early-stage technologies to enhance scientific understanding. ESTO’s technologies can be reliably and confidently applied to a broad range of science measurements and missions, as well as facilitate practical applications to benefit society at large. As the lead technology office within the Earth Science Division of the NASA Science Mission Directorate, ESTO is focused on the technological challenges inherent to space-based investigations of our planet's dynamic, interrelated systems. Through flexible, science-driven technology strategies and a competitive selection process, ESTO-funded technologies have supported numerous Earth science missions as well as commercial applications. From next generation sensors and instruments to communication and information systems, ESTO technologies enable many NASA missions and data services. The Advanced Information Systems Technology (AIST) Program is one focal area of ESTO. AIST innovates information system technologies that enable the development of new observing systems as well as agile science investigations through data analytics and artificial intelligence tools and algorithms. AIST utilizes an end-to-end development approach with the goal of infusing mature technologies into future missions, measurements, and analysis. One thrust of AIST is data exploitation and analysis using an analytic center framework to make observational data and model output more accessible and usable to scientists conducting specific investigations as well as to extract higher-level science content and information from the data. The framework accelerates scientific discovery by harmonizing the data, tools, and computational resources. NASA’s upcoming Surface, Biology and Geology (SBG) mission is one such mission whose data architecture will leverage many of the technologies developed through the AIST program. This talk will highlight current and future program development goals.

Laura Rogers↗
NASA NTRS1993

EOS SAR: A new approach

Viewgraphs on Earth Observing System (EOS) Synthetic Aperture Radar (SAR) are presented. The goal of the EOS is to develop the modeling and observational capabilities to predict and/or monitor atmospheric, terrestrial, and oceanic processes that are either causing global change or resulting from global change. The EOS SAR goal is to provide important geophysical products to the EOS data set to improve our understanding of the state and functioning of the Earth system. The EOS SAR strategy is to define the instrument requirements based on required input to geophysical algorithms, to provide the processing capability and algorithms to generate such products on the required spatial (global) and temporal (3-5 days) scales, and to provide the spaceborne instrumentation with international partnerships.

Way, Jobea↗
NASA NTRS1985

Nonlinear global stability analysis of compressor stall phenomena

Compressor stall phenomena are analyzed from the point of view of nonlinear control theory, based on bifurcation-catastrophe techniques. This new approach appears promising and offers insight into such well-known compressor instability problems as surge and rotating stall and suggests strategies for recovery. Three interlocking dynamic nonlinear state space models are developed. It is shown that the problem of rotating stall can be viewed as an induced bifurcation of solution of the unstalled model. Hysteresis effects are shown to exist in the stall/recovery process. Surge cycles are observed for some critical parameter values. The oscillatory behavior is seen to be due to development of limit cycles, generated by Hopf bifurcation of solutions. More specifically, it is observed that at certain critical values of parameters, a family of stable limit cycles with growning and then diminishing amplitudes is generated, then giving rise to an unstable family of limit cycles. This unstable family in turn bifurcates into other unstable families. To further illustrate the utility of the methodology, some partial computation of domains is carried out, and parameter sensitivity analysis is performed.

Razavi, H.↗
NASA NTRS2020

Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark Target Algorithm: 2. Nonspherical Dust Model

The Dark‐target (DT) aerosol algorithm retrieves spectral Aerosol Optical Depth (AOD) and other aerosol properties from Moderate‐resolution Imaging Spectrometer (MODIS) reflectance observations. Over the ocean, the DT algorithm is known to contain scattering‐angle‐dependent biases in its retrievals of AOD, Angstrom Exponent (AE), and Fine Mode Fraction (FMF) for dust aerosols. Following a two‐step strategy to improve the DT retrieval of dust over ocean, for which the first step is to identify dusty pixels (reported in “Part 1”), in this “Part 2,” we report on construction of a new dust model lookup table (LUT) and the strategy for applying it within the existing DT algorithm. In particular, we evaluate different characterizations of dust optical properties from a variety of frameworks and databases, and compare them with the current DT retrieval assumptions. Substituting the standard operational LUT with a spheroid dust model with identified dusty pixels shows significant improvement when compared with collocated AERONET‐identified dusty pixels. Specifically, the application of the new dust model to dusty pixels reduces their AOD bias from 0.06 to 0.02 while improving the fraction of retrievals within expected error from 64% to 82%. At the same time, the overall bias in AE is reduced from 0.13 to 0.06, and the scattering‐angle‐dependent AE bias is largely eliminated. In testing on two full months of data (April and July), the new retrieval will reduce the monthly mean AOD by up to 0.1 and 0.2 in the north Atlantic and Arabian seas, respectively. The average AE and FMF are also reduced in these dust heavy regions

dust aerosol retrieval↗
NASA NTRS2024

Intermountain West Wildland Fires: Mapping Tree Mortality and Burn Patches using NASA Earth Observations to Determine Fire Risk and Inform Fire Management Practices

Within the intermountain west, monitoring fuel loads is a major concern for wildland fire management efforts. To address this concern, we partnered with the U.S. Forest Service to inform the agency which forested areas should be prioritized for prescribed burning and fuel reduction near human communities in the Bridger-Teton National Forest, Wyoming. We created burn maps, fuel load maps, and a tutorial document to identify forest impact trends and provide the partner with the tools to replicate project methods for expansion to other wildfire crisis strategy sites. These end products were made using two NASA Earth observations: Landsat 8 Operational Land Imager and Shuttle Radar Topography Mission. Based on our random forest analysis, our maps identified 998 acres within the Wildland Urban Interface that are predicted to have high fuel loading and high burn severity within the Bridger-Teton National Forest. Forested areas closer to heavily populated areas such as Jackson, Kelly, Moran, New Forks Lake, and Star Valley Ranch should be prioritized for fuel reduction. However, our random forest model analysis was limited to using vegetation and topographical indices with no field data for model validation. Therefore, future studies should use field data for model validation to improve model accuracy and additionally incorporate Global Ecosystem Dynamics Investigation data into models to create better predictions of forested areas with high fuel load and high burn severity.

Remote Sensing↗
NASA NTRS1989

Report from upper atmospheric science

Most of the understanding of the thermosphere resulted from the analysis of data accrued through the Atmosphere Explorer satellites, the Dynamics Explorer 2 satellite, and observations from rockets, balloons, and ground based instruments. However, new questions were posed by the data that have not yet been answered. The mesosphere and lower thermosphere have been less thoroughly studied because of the difficulty of accessibility on a global scale, and many rather fundamental characteristics of these regions are not well understood. A wide variety of measurement platforms can be used to implement various parts of a measurement strategy, but the major thrusts of the International Solar Terrestrial Physics Program would require Explorer-class missions. A remote sensing mission to explore the mesosphere and lower thermosphere and one and two Explorer-type spacecraft to enable a mission into the thermosphere itself would provide the essential components of a productive program of exploration of this important region of the upper atomsphere. Theoretical mission options are explored.

Carignan, G. R.↗
NASA NTRS1985

Bifurcation techniques for nonlinear dynamic analysis of compressor stall phenomena

Compressor stall phenomena is analyzed from nonlinear control theory viewpoint, based on bifurcation-catastrophe techniques. This new approach appears promising and offers insight into such well known compressor instability problems as surge and rotating stall; furthermore it suggests strategies for recovery from stall. Three interlocking dynamic nonlinear state space models are developed. It is shown that the problem of rotating stall can be viewed as an (induced) bifurcation of solution of the unstalled model. Hysteresis effect is shown to exist in the stall/recovery process. Surge cycles are observed to develop for some critical parameter values. It is shown that the oscillatory behavior is due to development of limit cycles, generated by Hopf bifurcation of solutions. Both stable and unstable limit cycles are observed. To further illustrate the usefulness of the methodology some partial computation of domains of attraction of equilibria is carried out, and parameter sensitivity analysis is performed.

Razavi, H. C.↗