SEARCH · Engineering Papers

Results for “Data Variables”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 991 records · Page 55

All sources NASA NTRS DOE OSTI

NASA NTRS1991

Compositional variability of the Martian surface

Spectral reflectance data from Viking Landers and Orbiters and from telescopic observations were analyzed with the objective of isolating compositional information about the Martian surface and assessing compositional variability. Two approaches were used to calibrate the data to reflectance to permit direct comparisons with laboratory reference spectra of well characterized materials. In Viking Lander multispectral images (six spectral bands) most of the spectral variation is caused by changes in lighting geometry within individual scenes, from scene to scene, and over time. Lighting variations are both wavelength independent and wavelength dependent. By calibrating lander image radiance values to reflectance using spectral mixture analysis, the possible range of compositions was assessed with reference to a collection of laboratory samples, also resampled to the lander spectral bands. All spectra from the lander images studied plot (in six-space) within a planar triangle having at the apexes the respective spectra of tan basaltic palagonite, gray basalt, and shale. Within this plane all lander spectra fit as mixtures of these three endmembers. Reference spectra that plot outside of the triangle are unable to account for the spectral variation observed in the images.

Adams, John B.↗

DOE OSTI2025

The Observed and Projected Changes of Global Monsoons: Current Status and Future Perspectives

The global monsoon system, encompassing the Asian-Australian, African, and American monsoons, sustains two-thirds of the world’s population by regulating water resources and agriculture. Monsoon anomalies pose severe risks, including floods and droughts. Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms. Observational data reveal a 20th-century shift: increased rainfall pre-1950s, followed by aridification and partial recovery post-1980s, driven by both internal variability (e.g., Atlantic Multidecadal Oscillation) and external forcings (greenhouse gases, aerosols), while ENSO drives interannual variability through ocean-atmosphere interactions. Future projections under greenhouse forcing suggest long-term monsoon intensification, though regional disparities and model uncertainties persist. Models indicate robust trends but struggle to quantify extremes, where thermodynamic effects (warming-induced moisture rise) uniformly boost heavy rainfall, while dynamical shifts (circulation changes) create spatial heterogeneity. Volcanic eruptions and proposed solar radiation modification (SRM) further complicate predictions: tropical eruptions suppress monsoons, whereas high-latitude events alter cross-equatorial flows, highlighting unresolved feedbacks. The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons. Critical challenges remain. Model biases and sparse 20th-century observational data hinder accurate attribution. The interplay between natural variability and anthropogenic forcings, along with nonlinear extreme precipitation risks under warming, demands deeper mechanistic insights. Additionally, SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation. Addressing these gaps necessitates enhanced observational networks, refined climate models, and interdisciplinary efforts to disentangle multiscale drivers, ultimately improving resilience strategies for monsoon-dependent regions.

climate extreme events↗

NASA NTRS1989

Interannual variability in phytoplankton pigment distribution during the spring transition along the west coast of North America

A 5-year time series of coastal zone color scanner imagery (1980-1983, 1986) is used to examine changes in the large-scale pattern of chlorophyll pigment concentration coincident with the spring transition in winds and currents along the west coast of North America. The data show strong interannual variability in the timing and spatial patterns of pigment concentration at the time of the transition event. Interannual variability in the response of pigment concentration to the spring transition appears to be a function of spatial and temporal variability in vertical nutrient flux induced by wind mixing and/or the upwelling initiated at the time of the transition. Interannual differences in the mixing regime are illustrated with a one-dimensional mixing model.

Thomas, A. C.↗

NASA NTRS1999

Climatic Variability of Precipitation from the Seasonal Cycle to ENSO Using GPCP's Merged Data Product and SSM/I-Based Microwave Estimates

Satellite estimates and gauge observations of precipitation are useful in understanding the water cycle, analyzing climatic variability, and validating climate models. The Global Precipitation Climatology Project (GPCP) released a community merged precipitation data set for the period July 1987 through the present, and has recently extended that data set back to 1986. One objective of this study is to use GPCP estimates to describe and quantify the seasonal variation of precipitation, with emphasis on the Asian summer monsoon. Another focus is the 1997-98 El Nino Southern Oscillation (ENSO) and associated extreme precipitation events. The summer monsoon tends to be drier than normal in El Nino ears. This was not observed for 1997 or 1998, while for 1997 the NCEP model produced the largest summer rain rates over India in years. This inconsistency will be examined. The average annual global precipitation rate is 2.7 mm day as estimated by GPCP, which is similar to values computed from long-term climatologies. From 30 deg N to 30 deg S the average precipitation rate is 2.7 mm day over land with a maximum in the annual cycle occurring in February-March, when the Amazon basin receives abundant rainfall. The average precipitation rate is 3.1 mm day over the tropical oceans, with a peak earlier in the season (November-December), corresponding with the transition from a strong Pacific Intertropical Convergence Zone (ITCZ) from June to November to a strong South Pacific Convergence Zone (SPCZ) from December to March. The seasonal evolution of C, C, the Asian summer monsoon stands out with rains in excess of 15 mm day off the coast of Burma in June. The GPROF pentad data also captures the onset of the tropical Pacific rainfall patterns associated with the 1997-98 ENSO. From February to October 1997 at least four rain-producing systems traveled from West to East in the equatorial corridor. A rapid transition from El Nino to La Nina conditions occurred in May-June 1998. GPCP and GPROF were used to construct precipitation-based ENSO indices to monitor El Ninos (EL) and La Ninas and (LI).

Curtis, Scott↗

DOE OSTI2026

Characterization of Electronic Stress-Induced Changes in Multilayer MoS 2

Transition metal dichalcogenides like molybdenum disulfide (MoS 2 ) are compelling for next-generation electronic devices. In this work, we investigate the impact of electronic stress on MoS 2 to illustrate that observational and phenomenological information on multiple devices can be useful to describe changes in the device, and caution against the rationalization of paltry results as representative or correlative to device behavior. Here, we stress MoS 2 by applying a sustained 20 V DC bias to study the material’s response. Post-stress electronic characterization revealed nonuniform shifts in current–voltage (I–V) behavior alongside microscale changes. Complementary mechanical, spectroscopic, and scanning microwave impedance measurements showed that stress-induced features locally modulate stiffness, surface potential, Raman intensity, and charge carrier density. We correlated I–V behavior with morphological features (wrinkles, tears, folds, height) and device-level geometry (MoS 2 overlap with electrodes, channel area, contact length) on 50 test structures across five chips to move beyond anecdotal conclusions. We found no universal correlations before DC stress. However, device-level geometry was correlated with I–V behavior after DC stress, suggesting that electrode contacts play a more dominant role than morphology in determining performance. Delamination and thinning induced by DC stress led to localized reductions in charge carrier density within the affected regions. Further, delamination and thinning appear to map to I–V device performance in a few samples, but the correlation is lost when a larger sample size is considered. This suggests significant sample-to-sample variability in surface electronic states of the test structures. We also discuss how environmental factors introduced during fabrication may contribute to the observed heterogeneous device response. Progress will require high-resolution, multimodal analysis across many samples constructed under controlled, clean conditions. By building data sets that capture variability, we can better identify the true drivers of performance.

36 MATERIALS SCIENCE↗

NASA NTRS1992

Estimating net solar radiation using Landsat Thematic Mapper and digital elevation data

A radiative transfer algorithm is combined with digital elevation and satellite reflectance data to model spatial variability in net solar radiation at fine spatial resolution. The method is applied to the tall-grass prairie of the 16 x 16 sq km FIFE site (First ISLSCP Field Experiment) of the International Satellite Land Surface Climatology Project. Spectral reflectances as measured by the Landsat Thematic Mapper (TM) are corrected for atmospheric and topographic effects using field measurements and accurate 30-m digital elevation data in a detailed model of atmosphere-surface interaction. The spectral reflectances are then integrated to produce estimates of surface albedo in the range 0.3-3.0 microns. This map of albedo is used in an atmospheric and topographic radiative transfer model to produce a map of net solar radiation. A map of apparent net solar radiation is also derived using only the TM reflectance data, uncorrected for topography, and the average field-measured downwelling solar irradiance. Comparison with field measurements at 10 sites on the prairie shows that the topographically derived radiation map accurately captures the spatial variability in net solar radiation, but the apparent map does not.

Dubayah, R.↗

DOE OSTI2024

Comparison of Global Aboveground Biomass Estimates From Satellite Observations and Dynamic Global Vegetation Models

The global forest carbon stocks represent the amount of carbon stored in woody vegetation and are important for quantifying the ability of the global forests to sequester atmospheric CO 2 and to provide ecosystem services (e.g., timber) under climate change. The forest ecosystem carbon pool estimates are highly variable and poorly quantified in areas lacking forest inventory estimates. Here, we compare and analyze aboveground biomass (AGB) estimates from five satellite-based global data sets and nine dynamic global vegetation models (DVGMs). We find that across the data sets, mean AGB exhibits the largest variability around the tropical area. In addition, AGB shows a similar latitudinal trend but large variability among the data sets. Satellite-based AGB estimates are lower than those simulated by DVGMs. The divergence among the satellite-based AGB estimates can be driven by the methodology, input satellite products, and the forested areas used to estimate AGB. The modeled NPP, autotrophic respiration, and carbon allocation mostly drive the variability of AGB simulated by DGVMs. The future availability of a high-quality global forest area map is anticipated to improve AGB estimate accuracy and to reduce the discrepancies among different satellite- and model-based AGB estimates. Furthermore, we suggest the carbon-modeling community reexamine the methodology used to estimate AGB and forested areas for a more robust global forest carbon stock estimation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1994

Global QBO in circulation and ozone. Part 1: Reexamination of observational evidence

Observational evidence for a global quasi-biennial oscillation (QBO) pattern is reviewed. In particular, the presence of an extratropical, as well as an equatorial, component of the QBO signal in column ozone is established. It is found that the ozone interannual variability is such that as one moves away from the Tropics, the frequency spectrum of the anomaly changes from one that is dominated by the equatorial QBO frequency of 1/30 mo to a two-peak spectrum around the two frequencies: 1/30 mo and 1/20 mo. Instead of treating the 1/20 mo frequency as a separate phenomenon to be filtered away in extracting the QBO in the extratropics, as was previously done, the authors argue that both peaks are integral parts of the extratropical QBO phenomenon. The 1/20 mo frequency happens to be the difference combination of the QBO frequency 1/30 mo and the annual frequency 1/12 mo. Therefore, it can represent the result of the QBO modulating an annual cycle. The authors suggest that previous methods of extracting the extratropical QBO signal severely underestimated the contribution of the QBO to the interannual variability of ozone when data are filtered to pass only the component with the period of equatorial QBO. Further, it is argued that the transport of equatorial QBO ozone anomaly by a non-QBO circulation can at most account for 6-8 Dobson units (DU) of the observed interannual variability of column ozone in the extratropics. The remaining variability (up to 20 DU) probably cannot be produced without an anomaly in the transporting circulation in the extratropics.

Tung, K. K.↗

DOE OSTI2020

Science-Driven Data Management for Multi-Tiered Storage (Final Report)

Scientific discovery at the exascale will not be possible without significant new research in the management, storage and retrieval over the long lifespan of the extreme amounts of data that will be produced. Our thesis is that adding application level knowledge about data to guide the actions of the storage system provides substantial benefits to the organization, storage, and access to extreme scale data, resulting in improved productivity for computational science. In this project we will demonstrate novel techniques to facilitate efficient mapping of data objects, even partitioning individual variables, from the user space onto multiple storage tiers, and enable application-guided data reductions and transformations to address capacity and bandwidth bottlenecks. Our goal is to address the associated Input/ Output (I/O) and storage challenges in the context of current and emerging storage landscapes, and expedite insights into mission critical scientific processes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1983

Feature selection and information content of thematic mapper simulator data for a forested environment

Feature selection and the information content of Thematic Mapper Simulator (TMS) data are investigated for a forested region in northern Idaho. The optimal TMS channels for forest structural characteristics are determined, and the capability of TMS data to describe the structural variability within a forest stand is evaluated. The comparative performance of TMS and MSS data to discriminate forest structural factors using per-pixel maximum likelihood classification is examined, and four optimal TMS channels are classified in order to ascertain if the full complement of TM channels provide higher accuracies than the four optimal ones.

Spanner, M. A.↗

NASA NTRS2017

Interval Predictor Models for Data with Measurement Uncertainty

An interval predictor model (IPM) is a computational model that predicts the range of an output variable given input-output data. This paper proposes strategies for constructing IPMs based on semidefinite programming and sum of squares (SOS). The models are optimal in the sense that they yield an interval valued function of minimal spread containing all the observations. Two different scenarios are considered. The first one is applicable to situations where the data is measured precisely whereas the second one is applicable to data subject to known biases and measurement error. In the latter case, the IPMs are designed to fully contain regions in the input-output space where the data is expected to fall. Moreover, we propose a strategy for reducing the computational cost associated with generating IPMs as well as means to simulate them. Numerical examples illustrate the usage and performance of the proposed formulations.

Lacerda, Marcio J.↗

DOE OSTI2022

Long-term missing value imputation for time series data using deep neural networks

We present an approach that uses a deep learning model, in particular, a MultiLayer Perceptron, for estimating the missing values of a variable in multivariate time series data. We focus on filling a long continuous gap (e.g., multiple months of missing daily observations) rather than on individual randomly missing observations. Our proposed gap filling algorithm uses an automated method for determining the optimal MLP model architecture, thus allowing for optimal prediction performance for the given time series. We tested our approach by filling gaps of various lengths (three months to three years) in three environmental datasets with different time series characteristics, namely daily groundwater levels, daily soil moisture, and hourly Net Ecosystem Exchange. We compared the accuracy of the gap-filled values obtained with our approach to the widely used R-based time series gap filling methods ImputeTS and mtsdi. The results indicate that using an MLP for filling a large gap leads to better results, especially when the data behave nonlinearly. Thus, our approach enables the use of datasets that have a large gap in one variable, which is common in many long-term environmental monitoring observations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Development of a Machine-Learned Cruise Guide Indicator for Rotorcraft

This paper presents a machine-learned virtual cruise guide indicator (vCGI) for Chinook helicopters. Two temporal neural networks were trained and evaluated on measured data from 55 flight tests, one for the fore rotor and another for the aft rotor, to predict a vCGI value, which protects 23 components from fatigue damage during steady-state conditions. Three different classes of machine learning architectures were evaluated for prediction of the vCGI from time sequences: a temporal convolutional neural network with 1D dilated causal convolutions, a long short-term memory recurrent neural network, and an attention-based transformer architecture. The final average model accuracy on unseen flight data is currently greater than 93% for CGI values which could result in fatigue damage and 90% for normal operation CGI values. Model accuracy was improved through a series of advancements in:(1) selection of optimal training data using temporal collective variables and unsupervised learning, (2) dataset augmentation with maximum-entropy temporal collective variables, and (3) implementation of a mixture-of-experts classification- regression approach using an adversarial classification approach to assign maneuver labels. The results are presented for each advancement in model development along with lessons learned in training machine learning models on real- world, time-dependent rotorcraft data.

Boyer, Mathew↗

NASA NTRS1977

JPL field measurements at the Finney County, Kansas, test site, October 1976: Meteorological variables, surface reflectivity, surface and subsurface temperatures

Data collected at the Finney County, Kansas test site as part of the Joint Soil Moisture Experiment (JSME) are presented here, prior to analysis, to provide all JSME investigators with an immediate source of primary information. The ground-truth measurements were taken to verify and complement soil moisture data taken by microwave and infrared sensors during aircraft overflights. Measurements were made of meteorological variables (air speed, temperature, relative humidity, and rainfall), surface reflectivity, and temperatures at and below the surface.

Kahle, A. B.↗

NASA NTRS1977

Rocketsonde repeatability of temperature and wind measurements as obtained from rocketsonde network systems

Variability of temperature and wind data obtained close in time is of particular concern since questions exist on whether this variability is due to natural atmospheric variability or arises from instrumental causes. Previously conducted repeatability tests of the U.S. Loki Datasonde instrument indicated rms differences of 1C in temperature and 3 mps in wind. The newer Super Loki Datasonde instrument reaches apogee 20 km higher (about 82 km) than the older system and attains a higher initial fall velocity, thus the heat exchange effects on the measuring components are different. Data were obtained from routine rocketsonde launchings, available in pairs, with time differences of 12 to 120 minutes. Mean values calculated over the altitude range of 30 to 70 km indicate than an rms difference of 1-2 C in temperature and of 3-4 mps in the zonal and meridional components exist. Additional computations in 5-km altitude layers suggest that rms differences do not exceed 3 C and 7 mps at the highest altitudes.

Schmidlin, F. J.↗

NASA NTRS1998

CNO Processing in Massive Algol Binaries

This program, used ultraviolet observations by the IUE Observatory along with other tools to search for abundance anomalies indicative of CNO processing in the secondary (mass-donating) stars of six interacting binary systems. Related IUE-based activities were also undertaken under this grant. Two Supplements to the grant were awarded. Supplement No. 1 was in connection with the NASA Grant Supplements for Education program, for a workshop for elementary school science teachers. The two sessions of the workshop were held October 24 and November 14, 1992. Eighteen school teachers from central Pennsylvania, grades 1-7, participated in the workshop, for which they received one unit of in-service training credit from their Intermediate Unit. Supplement No. 2 was awarded for additional IUE observations of the Algol stars V342 Aql and TU Mon. Observations of all six candidate stars were made with IUE, and attention was narrowed to TU Mon in particular, for which further IUE observations were made using Director's discretionary time. Observations of TU Mon were also carried out with the Voyager UV spectrometers, and optical spectroscopy was obtained on several occasions at Penn State's Black Moshannon Observatory. Photometric data on TU Mon were acquired by Dr. Paul Etzel at Mt. Laguna Observatory (MLO). McGouldrick was employed part-time during the Fall academic semester to assist in accessing the IUE Archive, and to correlate data on some cataclysmic variables and related objects that were observed with both IUE and the Voyager Far Ultraviolet Spectrometers. Approximately 21 relevant binary systems were observed with the Voyager UVS over the past decade, and a paper is being prepared for eventual publication that will serve as an index to the UVS data archive on these stars, providing observation dates, mean count rates in far and extreme UV bands, and a discussion of the relevant literature concerning the far UV behavior (including correlative IUE information from the archive and the literature). Much of the activity under the grant was in connection with V342 Aquilae, a 3.39 day Algol system thought to be in a state of rapid mass transfer. The goal was to combine optical and ultraviolet data to arrive at a robust, informative interpretation of this unique binary system. Complete orbital phase coverage of V342 Aql was obtained spectroscopically and photometrically.

Wade, Richard A.↗

NASA NTRS1986

Variability of earth-emitted radiation from one year of Nimbus-6 ERB data

Outgoing longwave radiation (OLR) measurements from the Nimbus-6 ERB wide field-of-view instrument are used to study daytime and nighttime radiation variability on a 15 deg regional, zonal, and global scale. An analysis of components of variance is used to determine how much of the total variability is due to between-region and within-region variance. Most of the analysis is on July and January data from one year of Nimbus-6 ERB. Different geographical scales are considered: regions within latitude zones and latitude zones within hemispheres. Results show that much of the variability is spatial, peaks in the tropics and subtropics, and is concentrated in the Northern Hemisphere. Daytime variability is generally larger than nighttime variability for July but not for January. Variance in OLR in the tropics and subtropics is largely a function of cloud variability.

Bess, T. D.↗

DOE OSTI2020

Atmospheric condition identification in multivariate data through a metric for total variation

Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation.Atmospheric conditions relevant for wind energy research include stationary conditions, given the need for well-converged statistics for model validation, as well as conditions observed less frequently, such as extreme atmospheric events, which are used in wind turbine and wind plant design.Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of atmospheric conditions.Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation set the stage for more advanced model validation and the development of real-time control and operational strategies.The current work explores a single metric for variation in a multivariate data sample that quantifies variability within each channel as well as covariance between channels.The total variation is used to identify conditions of interest that conform to desired objective functions, such as stationary conditions, ramps or waves of wind speed, and changes in wind direction.Total variation is somewhat sensitive to the presence of outliers in the input data, and the method is best complemented by quality-control procedures to ensure reliable results.The direct detection and classification of events or conditions of interest within atmospheric data sets is vital to developing our understanding of wind plant response and to the formulation of forecasting and control models.

17 WIND ENERGY↗