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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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NASA NTRS1993

Hierarchical Poly Tree Configurations for the Solution of Dynamically Refined Finte Element Models

This paper demonstrates how a multilevel substructuring technique, called the Hierarchical Poly Tree (HPT), can be used to integrate a localized mesh refinement into the original finite element model more efficiently. The optimal HPT configurations for solving isoparametrically square h-, p-, and hp-extensions on single and multiprocessor computers is derived. In addition, the reduced number of stiffness matrix elements that must be stored when employing this type of solution strategy is quantified. Moreover, the HPT inherently provides localize 'error-trapping' and a logical, efficient means with which to isolate physically anomalous and analytically singular behavior.

Gute, G. D.↗
NASA NTRS1994

Development and initial test of the University of Wisconsin global isentropic-sigma model

The description of a global version of the University of Wisconsin (UW) hybrid isentropic-sigma (theta-sigma) model and the results from an initial numerical weather prediction experiment are presented in this paper. The main objectives of this initial test are to (1) discuss theta-sigma model development and computer requirements, (2) demonstrate the ability of the UW theta-sigma model for global numerical weather prediction using realistic orography and parameterized physical processes, and (3) compare the transport of an inert trace constituent against a nominally 'identical' sigma coordinate model. Initial and verifying data for the 5-day simulations presented in this work were supplied by the Goddard Earth Observing System (GEOS-1) data assimilation system. The time period studied is 1-6 February 1985. This validation experiment demonstrates that the global UW theta-sigma model produces a realistic 5-day simulation of the mass and momentum distributions when compared to both the identical sigma model and GEOS-1 verification. Root-mean-square errors demonstrate that the theta-sigma model is slightly more accurate than the nominally identical sigma model with respect to standard synoptic variables. Of particular importance, the UW theta-sigma model displays a distinct advantage over the conventional sigma model with respect to the prognostic simulation of inert trace constituent transport in amplifying baroclinic waves of the extratropics. This is especially true in the upper troposphere and stratosphere where the spatial integrity and conservation of an inert trace constituent is severely compromised in the sigma model compared to the theta-sigma model.

Zapotocny, Tom H.↗
NASA NTRS1996

Assessment of Rainfall Estimates Using a Standard Z-R Relationship and the Probability Matching Method Applied to Composite Radar Data in Central Florida

Precipitation estimates from radar systems are a crucial component of many hydrometeorological applications, from flash flood forecasting to regional water budget studies. For analyses on large spatial scales and long timescales, it is frequently necessary to use composite reflectivities from a network of radar systems. Such composite products are useful for regional or national studies, but introduce a set of difficulties not encountered when using single radars. For instance, each contributing radar has its own calibration and scanning characteristics, but radar identification may not be retained in the compositing procedure. As a result, range effects on signal return cannot be taken into account. This paper assesses the accuracy with which composite radar imagery can be used to estimate precipitation in the convective environment of Florida during the summer of 1991. Results using Z = 30OR(sup 1.4) (WSR-88D default Z-R relationship) are compared with those obtained using the probability matching method (PMM). Rainfall derived from the power law Z-R was found to he highly biased (+90%-l10%) compared to rain gauge measurements for various temporal and spatial integrations. Application of a 36.5-dBZ reflectivity threshold (determined via the PMM) was found to improve the performance of the power law Z-R, reducing the biases substantially to 20%-33%. Correlations between precipitation estimates obtained with either Z-R relationship and mean gauge values are much higher for areal averages than for point locations. Precipitation estimates from the PMM are an improvement over those obtained using the power law in that biases and root-mean-square errors are much lower. The minimum timescale for application of the PMM with the composite radar dataset was found to be several days for area-average precipitation. The minimum spatial scale is harder to quantify, although it is concluded that it is less than 350 sq km. Implications relevant to the WSR-88D system are discussed.

Crosson, William L.↗
NASA NTRS2022

Landslide Likelihood Prediction using Machine Learning Algorithms

The supply of electricity via power plants is criticalto the operation of many critical infrastructure systems in mod-ern society. Natural hazards can disrupt the power supply, causepower outages that can halt economic growth, and impede emer-gency response until power is restored. The proposed work aimsto predict the landslides likelihood in these critical infrastructurelocations in the Northeastern USA using integrated databases ofexplanatory variables and machine learning algorithms. First,data related to landslides are obtained and merged, includingtopographic, soil moisture, and precipitation-related data. Fiveregression algorithms, namely: Random Forest, Extreme Gradi-ent Boosting (XGBoost), K-Nearest Neighbor regression (KNN),Linear Support Vector Regressor (SVR), and Linear regression,are utilized to predict the landslide probability and evaluatedon the dataset. The accuracy of the models is assessed by usingstatistical metrics such as mean absolute error (MAE), meansquared error (MSE), and root mean squared error (RMSE).The study results show that Random Forest outperformed othermodels with the mutual information feature selection method.It achieved an MSE of 0.0011 with mutual information-basedfeature selection and an MSE of 0.00157 without feature selection.KNN regressor outperformed the other models with an MSEof 0.00139 with correlation-based information selection. Theproposed landslide identification model with Random Forestalgorithm shows outstanding robustness and great potential intackling the landslide likelihood prediction by employing MLalgorithms.

Vasundhara Acharya↗
NASA NTRS2016

Snow Process Estimation Over the Extratropical Andes Using a Data Assimilation Framework Integrating MERRA Data and Landsat Imagery

A data assimilation framework was implemented with the objective of obtaining high resolution retrospective snow water equivalent (SWE) estimates over several Andean study basins. The framework integrates Landsat fractional snow covered area (fSCA) images, a land surface and snow depletion model, and the Modern Era Retrospective Analysis for Research and Applications (MERRA) reanalysis as a forcing data set. The outputs are SWE and fSCA fields (1985-2015) at a resolution of 90 m that are consistent with the observed depletion record. Verification using in-situ snow surveys showed significant improvements in the accuracy of the SWE estimates relative to forward model estimates, with increases in correlation (0.49-0.87) and reductions in root mean square error (0.316 m to 0.129 m) and mean error (-0.221 m to 0.009 m). A sensitivity analysis showed that the framework is robust to variations in physiography, fSCA data availability and a priori precipitation biases. Results from the application to the headwater basin of the Aconcagua River showed how the forward model versus the fSCA-conditioned estimate resulted in different quantifications of the relationship between runoff and SWE, and different correlation patterns between pixel-wise SWE and ENSO. The illustrative results confirm the influence that ENSO has on snow accumulation for Andean basins draining into the Pacific, with ENSO explaining approximately 25% of the variability in near-peak (1 September) SWE values. Our results show how the assimilation of fSCA data results in a significant improvement upon MERRA-forced modeled SWE estimates, further increasing the utility of the MERRA data for high-resolution snow modeling applications.

SWE↗
NASA NTRS2013

Evaluation of MODIS Albedo Product (MCD43A) over Grassland, Agriculture and Forest Surface Types During Dormant and Snow-Covered Periods

This study assesses the Moderate-resolution Imaging Spectroradiometer (MODIS) BRDF/albedo 8 day standard product and products from the daily Direct Broadcast BRDF/albedo algorithm, and shows that these products agree well with ground-based albedo measurements during the more difficult periods of vegetation dormancy and snow cover. Cropland, grassland, deciduous and coniferous forests are considered. Using an integrated validation strategy, analyses of the representativeness of the surface heterogeneity under both dormant and snow-covered situations are performed to decide whether direct comparisons between ground measurements and 500-m satellite observations can be made or whether finer spatial resolution airborne or spaceborne data are required to scale the results at each location. Landsat Enhanced Thematic Mapper Plus (ETM +) data are used to generate finer scale representations of albedo at each location to fully link ground data with satellite data. In general, results indicate the root mean square errors (RMSEs) are less than 0.030 over spatially representative sites of agriculture/grassland during the dormant periods and less than 0.050 during the snow-covered periods for MCD43A albedo products. For forest, the RMSEs are less than 0.020 during the dormant period and 0.025 during the snow-covered periods. However, a daily retrieval strategy is necessary to capture ephemeral snow events or rapidly changing situations such as the spring snow melt.

MODIS↗
NASA NTRS2021

Characterizing Deformable Mirrors for the MagAO-X Instrument

The MagAO-X instrument is a new extreme adaptive optics system for high-contrast imaging at visibleand near-infrared wavelengths on the Magellan Clay Telescope. A central component of this system is a 2040-actuatormicroelectromechanical deformable mirror (DM) from Boston Micromachines Corp. that operates at 3.63 kHz forhigh-order wavefront control (the tweeter). Two additional DMs from ALPAO perform the low-order (the woofer)and non-common-path science-arm wavefront correction (the NCPC DM). Prior to integration with the instrument, wecharacterized these devices using a Zygo Verifire Interferometer to measure each DM surface. We present the resultsof the characterization effort here, demonstrating the ability to drive tweeter to a flat of 6.9 nm RMS surface (and 0.56nm RMS surface within its control bandwidth), the woofer to 2.2 nm root mean square (RMS) surface, and the NCPCDM to 2.1 nm RMS surface over the MagAO-X beam footprint on each device. Using focus-diversity phase retrievalon the MagAO-X science cameras to estimate the internal instrument wavefront error (WFE), we further show that theintegrated DMs correct the instrument WFE to 18.7 nm RMS, which, combined with a 11.7% pupil amplitude RMS,produces a Strehl ratio of 0.94 at H↵

Kyle Van Gorkom↗
NASA NTRS1994

Correlated environmental corrections in TOPEX/POSEIDON, with a note on ionospheric accuracy

Estimates of the effectiveness of an altimetric correction, and interpretation of sea level variability as a response to atmospheric forcing, both depend upon assuming that residual errors in altimetric corrections are uncorrelated among themselves and with residual sea level, or knowing the correlations. Not surprisingly, many corrections are highly correlated since they involve atmospheric properties and the ocean surface's response to them. The full corrections (including their geographically varying time mean values), show correlations between electromagnetic bias (mostly the height of wind waves) and either atmospheric pressure or water vapor of -40%, and between atmospheric pressure and water vapor of 28%. In the more commonly used collinear differences (after removal of the geographically varying time mean), atmospheric pressure and wave height show a -30% correlation, atmospheric pressure and water vapor a -10% correlation, both pressure and water vapor a 7% correlation with residual sea level, and a bit surprisingly, ionospheric electron content and wave height a 15% correlation. Only the ocean tide is totally uncorrelated with other corrections or residual sea level. The effectiveness of three ionospheric corrections (TOPEX dual-frequency, a smoothed version of the TOPEX dual-frequency, and Doppler orbitography and radiopositioning integrated by satellite (DORIS) is also evaluated in terms of their reduction in variance of residual sea level. Smooth (90-200 km along-track) versions of the dual-frequency altimeter ionosphere perform best both globally and within 20 deg in latitude from the equator. The noise variance in the 1/s TOPEX inospheric samples is approximately (11 mm) squared, about the same as noise in the DORIS-based correction; however, the latter has its error over scales of order 10(exp 3) km. Within 20 deg of the equator, the DORIS-based correction adds (14 mm) squared to the residual sea level variance.

Zlotnicki, V.↗
NASA NTRS1981

Model error estimation for large flexible spacecraft

The basic model obtained for spacecraft control system design applications is used to develop the control and estimation algorithms which constitute integral elements of the control system. The performance of this system is limited on account of modeling errors. The presence of such errors is inevitable in connection with truncated dynamics, parameter uncertainties, neglected nonlinearities, and external disturbances. In many cases, an approximate knowledge of the model errors can only be established by means of an estimation procedure. The present investigation has the objective to outline an approach to estimate errors inherent in a reduced-order model and to illustrate its application to modeling problems arising in spacecraft control system design. The considered procedure makes use of the principle of least-squares. In the discussed application this principle is employed to minimize a quadratic functional of the model errors.

Rodriguez, G.↗
NASA NTRS1992

Airborne gamma radiation measurements of soil moisture during FIFE: Activities and results

Soil moisture measurements were obtained during the summer of 1987 and 1989 near Manhattan, Kansas, using the National Weather Service (NWS) airborne gamma radiation system. A network of 24 flight lines were established over the research area. Airborne surveys were flown daily during two intensive field campaigns. The data collected was sufficient to modify the NWS standard operational method for estimating soil moisture for the Field Experiment (FIFE) flight lines. The average root mean square error of the soil moisture estimates for shorter FIFE flight lines was found to be 2.5 percent, compared with a reported value of 3.9 percent for NWS flight lines. Techniques were developed to compute soil moisture estimates for portions of the flight lines. Results of comparisons of the airborne gamma radiation soil moisture estimates with those obtained using the NASA Pushbroom Microwave Radiation (PBMR) system and hydrological model are presented. The airborne soil moisture measurements, and real averages computed using all remotely sensed and ground data, have been in support of the research of the many FIFE investigators whose overall goal was the upscale integration of models and the application of satellite remote sensing.

Peck, Eugene L.↗
NASA NTRS1986

Static shape control for flexible structures

An integrated methodology is described for defining static shape control laws for large flexible structures. The techniques include modeling, identifying and estimating the control laws of distributed systems characterized in terms of infinite dimensional state and parameter spaces. The models are expressed as interconnected elliptic partial differential equations governing a range of static loads, with the capability of analyzing electromagnetic fields around antenna systems. A second-order analysis is carried out for statistical errors, and model parameters are determined by maximizing an appropriate defined likelihood functional which adjusts the model to observational data. The parameter estimates are derived from the conditional mean of the observational data, resulting in a least squares superposition of shape functions obtained from the structural model.

Rodriguez, G.↗
NASA NTRS2009

Identifying Optimal Temporal Scale for the Correlation of AOD and Ground Measurements of PM2.5 to Improve the Model Performance in a Real-time Air Quality Estimation System

Aerosol optical depth (AOD), an indirect estimate of particle matter using satellite observations, has shown great promise in improving estimates of PM 2.5 air quality surface. Currently, few studies have been conducted to explore the optimal way to apply AOD data to improve the model accuracy of PM 2.5 surface estimation in a real-time air quality system. We believe that two major aspects may be worthy of consideration in that area: 1) the approach to integrate satellite measurements with ground measurements in the pollution estimation, and 2) identification of an optimal temporal scale to calculate the correlation of AOD and ground measurements. This paper is focused on the second aspect on the identifying the optimal temporal scale to correlate AOD with PM2.5. Five following different temporal scales were chosen to evaluate their impact on the model performance: 1) within the last 3 days, 2) within the last 10 days, 3) within the last 30 days, 4) within the last 90 days, and 5) the time period with the highest correlation in a year. The model performance is evaluated for its accuracy, bias, and errors based on the following selected statistics: the Mean Bias, the Normalized Mean Bias, the Root Mean Square Error, Normalized Mean Error, and the Index of Agreement. This research shows that the model with the temporal scale of within the last 30 days displays the best model performance in this study area using 2004 and 2005 data sets.

Li, Hui↗
NASA NTRS1995

Interaction of the terrestrial and atmospheric hydrological cycles in the context of the North American southwest summer monsoon

Work under this grant has used information on precipitation and water vapor fluxes in the area of the Mexican Monsoon to analyze the regional precipitation climatology, to understand the nature of water vapor transport during the monsoon using model and observational data, and to analyze the ability of the TRMM remote sensing algorithm to characterize precipitation. An algorithm for estimating daily surface rain volumes from hourly GOES infrared images was developed and compared to radar data. Estimates were usually within a factor of two, but different linear relations between satellite reflectances and rainfall rate were obtained for each day, storm type and storm development stage. This result suggests that using TRMM sensors to calibrate other satellite IR will need to be a complex process taking into account all three of the above factors. Another study, this one of the space-time variability of the Mexican Monsoon, indicate that TRMM will have a difficult time, over the course of its expected three year lifetime, identifying the diurnal cycle of precipitation over monsoon region. Even when considering monthly rainfalls, projected satellite estimates of August rainfall show a root mean square error of 38 percent. A related examination of spatial variability of mean monthly rainfall using a novel method for removing the effects of elevation from gridded gauge data, show wide variation from a satellite-based rainfall estimates for the same time and space resolution. One issue addressed by our research, relating to the basic character of the monsoon circulation, is the determination of the source region for moisture. The monthly maps produced from our study of monsoon variability show the presence of two rainfall maxima in the analysis normalized to sea level, one in south-central Arizona associated with the Mexican monsoon maximum and one in southeastern New Mexico associated with the Gulf of Mexico. From the point of view of vertically-integrated fluxes and flux divergence of water vapor from ECMWF data, most moisture at upper levels arrives from the Gulf of Mexico, while low level moisture comes from the northern Gulf of California. Composites of ECMWF analyses for wet and dry periods (classified by rain gauge data) show that both regimes show low level moisture arriving from northern and central Gulf of California. Above 700 MB, moisture comes from both source regions and the Sierra Madre Occidental. During wet periods a longer fetch through the moist air mass above western Mexico results in a greater moisture flux into the Sonoran Desert region, while there is less moisture from the Gulf of Mexico both above and below 700 mb. Work on the grant subcontract at the University of Colorado concentrated on the development of a technique useful to TRMM combining visible, infrared and passive microwave data for measuring precipitation. Two established techniques using either visible or infrared data applied over the US Southwest correlated with gauges at the 0.58 to 0.70 level. The application of some established passive microwave techniques were less successful for a variety of reason, including problems in both the gauge and satellite data quality, sampling problems and weaknesses inherent in the algorithms themselves. A more promising solution for accurate rainfall estimation was explored using visible and infrared data to perform a cloud classification, which when combined with information about the background (e.g. Iand/ocean), was used to select the most appropriate microwave algorithm from a suite of possibilities.

Dickinson, Robert E.↗
NASA NTRSDate not supplied

Expanding the Spatiotemporal Range of Soil Moisture Analysis Utilizing NASA Earth Observations and In Situ Measurements

Drought can cause immense agricultural and ecological damage resulting in high mitigation and compensation costs. Climate variability in future decades is expected to cause severe drought conditions and threaten necessary water resources. Stakeholders seek to implement effective drought assessments in preparation for potential economic and environmental damage invoked by drought. Although in-situ measurements are accurate, the current infrastructure is spatially limited and costly to maintain. A framework was created to compare modeled, satellite and in-situ data in drought monitoring. Here we show that the comparison of in-situ and remotely sensed soil moisture (SM) measurements can increase the spatiotemporal range of SM assessments. Data collected between 2003 and 2021 by NASA’s SPoRT Land Information System (SPoRT-LIS) and Soil Moisture Active Passive (SMAP) mission were standardized and compared with in-situ data provided through the Illinois Climate Network (WARM). Statistical analysis results including the Pearson correlation coefficient (r), root mean squared error, mean absolute error and others were calculated to compare the WARM measurements to the SMAP and SPoRT-LIS products. Results indicate that both satellite products demonstrate seasonally variable bias that is not present in the in-situ measurements. Bias was highest in the winter months and lowest in the late summer and early fall months in both satellite datasets. Overall, WARM-SPoRT comparisons resulted in lower seasonal variability. However, on average, the SMAP comparison demonstrated higher correlation values and lower error values. The WARM-SMAP average correlation (r) was 0.61 compared to the WARM-SPoRT average correlation (r) value of 0.54. Average mean absolute error values calculated for the SMAP and SPoRT comparisons were 0.07 and 0.08 percent soil moisture by volume, respectively. These analyses suggest integrating in-situ measurements and those provided by NASA Earth observations can be utilized in a multi-faceted SM evaluation, a valuable contribution to drought monitoring and water resource decision making.

Emma P Myrick↗
NASA NTRS2000

Postural control model interpretation of stabilogram diffusion analysis

Collins and De Luca [Collins JJ. De Luca CJ (1993) Exp Brain Res 95: 308-318] introduced a new method known as stabilogram diffusion analysis that provides a quantitative statistical measure of the apparently random variations of center-of-pressure (COP) trajectories recorded during quiet upright stance in humans. This analysis generates a stabilogram diffusion function (SDF) that summarizes the mean square COP displacement as a function of the time interval between COP comparisons. SDFs have a characteristic two-part form that suggests the presence of two different control regimes: a short-term open-loop control behavior and a longer-term closed-loop behavior. This paper demonstrates that a very simple closed-loop control model of upright stance can generate realistic SDFs. The model consists of an inverted pendulum body with torque applied at the ankle joint. This torque includes a random disturbance torque and a control torque. The control torque is a function of the deviation (error signal) between the desired upright body position and the actual body position, and is generated in proportion to the error signal, the derivative of the error signal, and the integral of the error signal [i.e. a proportional, integral and derivative (PID) neural controller]. The control torque is applied with a time delay representing conduction, processing, and muscle activation delays. Variations in the PID parameters and the time delay generate variations in SDFs that mimic real experimental SDFs. This model analysis allows one to interpret experimentally observed changes in SDFs in terms of variations in neural controller and time delay parameters rather than in terms of open-loop versus closed-loop behavior.

NASA Program Biomedical Research and Countermeasur↗
NASA NTRS2016

Validation and Expected Error Estimation of Suomi-NNP VIIRS Aerosol Optical Thickness and Angstrom Exponent with AERONET

The new-generation polar-orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET)measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of 0.0008 with root-mean-square error(RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06.The mean bias of VIIRS Ocean Angstrom Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S-NPP to future polar-orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations missions come to an end.

Huang, Jingfeng↗
NASA NTRS2003

Analytic Method for Computing Instrument Pointing Jitter

A new method of calculating the root-mean-square (rms) pointing jitter of a scientific instrument (e.g., a camera, radar antenna, or telescope) is introduced based on a state-space concept. In comparison with the prior method of calculating the rms pointing jitter, the present method involves significantly less computation. The rms pointing jitter of an instrument (the square root of the jitter variance shown in the figure) is an important physical quantity which impacts the design of the instrument, its actuators, controls, sensory components, and sensor- output-sampling circuitry. Using the Sirlin, San Martin, and Lucke definition of pointing jitter, the prior method of computing the rms pointing jitter involves a frequency-domain integral of a rational polynomial multiplied by a transcendental weighting function, necessitating the use of numerical-integration techniques. In practice, numerical integration complicates the problem of calculating the rms pointing error. In contrast, the state-space method provides exact analytic expressions that can be evaluated without numerical integration.

Bayard, David↗
NASA NTRS1994

Determination of ionospheric conductivities from FUV auroral emissions

The purpose of this paper is to examine the viability of using Lyman-Birge-Hopfeld (LBH) emission ratios to infer auroral conductances and to quantify the strengths and weaknesses of this technique. We show that column-integrated Hall and Pedersen conductances may be determined from a single remote measurement of a pair of auroral LBH emissions, one in the region of strong O2 absorption (1464 A) and one lying outside of this region (1838 A). The dependence of the determined conductivities on incident average energy, total energy flux, and changes in solar and magnetic activity levels is examined. We show that, for eneriges above 5 keV, auroral conductances may be scaled by the square root of the incident energy flux with errors less than about 20%. For lower energies, however, the scaling may deviate significantly from a power of 0.5. We provide appropriate scaling factors as a function of average energy. We also note that the choice of either a Gaussian or Maxwellian distribution can significantly affect low-energy (less than keV) conductance calculations. Finally, we quantify the conditions under which the mean energy of a Gaussian energy distribution may differ significantly from the characteristic energy.

Germany, G. A.↗