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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 NTRS2007

Solar Occultation Satellite Data and Derived Meteorological Products: Sampling Issues and Comparisons with Aura MLS

Derived Meteorological Products (DMPs, including potential temperature (theta), potential vorticity, equivalent latitude (EqL), horizontal winds and tropopause locations) have been produced for the locations and times of measurements by several solar occultation (SO) instruments and the Aura Microwave Limb Sounder (MLS). DMPs are calculated from several meteorological analyses for the Atmospheric Chemistry Experiment-Fourier Transform Spectrometer, Stratospheric Aerosol and Gas Experiment II and III, Halogen Occultation Experiment, and Polar Ozone and Aerosol Measurement II and III SO instruments and MLS. Time-series comparisons of MLS version 1.5 and SO data using DMPs show good qualitative agreement in time evolution of O3, N2O, H20, CO, HNO3, HCl and temperature; quantitative agreement is good in most cases. EqL-coordinate comparisons of MLS version 2.2 and SO data show good quantitative agreement throughout the stratosphere for most of these species, with significant biases for a few species in localized regions. Comparisons in EqL coordinates of MLS and SO data, and of SO data with geographically coincident MLS data provide insight into where and how sampling effects are important in interpretation of the sparse SO data, thus assisting in fully utilizing the SO data in scientific studies and comparisons with other sparse datasets. The DMPs are valuable for scientific studies and to facilitate validation of non-coincident measurements.

Manney, Gloria↗
NASA NTRS2009

Large Area Lunar Dust Flux Measurement Instrument

The instrument under development is designed to characterize the flux and size distribution of the lunar micrometeoroid and secondary ejecta environment. When deployed on the lunar surface, the data collected will benefit fundamental lunar science as well as enabling more reliable impact risk assessments for human lunar exploration activities. To perform this task, the instrument requirements are demanding. It must have as large a surface area as possible to sample the very sparse population of the larger potentially damage-inducing micrometeorites. It must also have very high sensitivity to enable it to measure the flux of small (<10 micron) micrometeorite and secondary ejecta dust particles. To be delivered to the lunar surface, it must also be very low mass, rugged and stow compactly. The instrument designed to meet these requirements is called FOMIS. It is a large-area thin film under tension (i.e. a drum) with multiple fiber optic displacement (FOD) sensors to monitor displacements of the film. This sensor was chosen since it can measure displacements over a wide dynamic range: 1 cm to sub-Angstrom. A prototype system was successfully demonstrated using the hypervelocity impact test facility at the University of Kent (Canterbury, UK). Based on these results, the prototype system can detect hypervelocity (approx.5 km/s) impacts by particles as small as 2 microns diameter. Additional tests using slow speeds find that it can detect secondary ejecta particles (which do not penetrate the film) with momentums as small as 15 pico-gram 100m/s, or nominally 5 microns diameter at 100 m/s.

Corsaro, R.↗
DOE OSTI2026

The two radiative states of the Arctic atmosphere and their impacts on the surface energy budget of sea ice

The surface energy budget (SEB) is a central regulator of Arctic climate and sea ice evolution, yet its processes remain poorly constrained due to sparse observations and complex, coupled surface-atmosphere interactions. This study leverages year-long measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) to provide the most comprehensive assessment to date of the central Arctic SEB and its modulation by atmospheric variability. Ship- and ice-based observations from October 2019 to September 2020 were used to directly measure or tightly constrain each term of the SEB, leading to exceptional energetic closure with the seasonal snow and ice mass balance. The analysis reveals strong seasonal transitions in atmosphere-surface energy transfer that are modulated by the atmospheric state and constrained by the ability of the surface temperature to respond. Classification of the atmosphere into its two dominant radiative states—the semi-transparent (ST) and opaque (OP)—highlights the central role of synoptic-scale variability in clouds. The ST atmospheric state dominated the long winter ice growth season, with limited cloudiness supporting persistent surface radiative cooling and ice growth. The OP state, associated with liquid-containing or thick ice clouds, became dominant in spring, with the combination of increased solar heating and cloud surface longwave warming driving ice and snow melt. Eddy covariance versus bulk approaches for deriving surface turbulent heat fluxes provide vastly different perspectives on the role of turbulence in modulating the SEB. These results establish a high-quality benchmark dataset for Arctic SEB studies and demonstrate how the balance of atmospheric radiative states exerts a first-order control on the annual evolution of the sea ice. The findings have broad implications for advancing observing technologies, understanding Arctic amplification, improving climate models, and predicting future sea ice change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

Thickness of western mare basalts

An isopach map of the basalt thickness in the western mare basins is constructed from measurements of the exposed external rim height of partially buried craters. The data, although numerically sparse, is sufficiently distributed to yield gross thickness variations. The average basalt thickness in Oceanus Procellarum and adjacent regions is 400 m with local lenses in excess of 1500 m in the circular maria. The total volume of basalt in the western maria is estimated to be in the range of 1.5 x 10 to the 6th power cu km. The chief distinction between the eastern and western maria appears to be one of basalt volumes erupted to the surface. Maximum volumes of basalt are deposited west of the central highlands and flood subjacent terrain to a greater extent than on the east. The surface structures of the western maria reflect the probability of a greater degree of isostatic response to a larger surface loading by the greater accumulation of mare basalt.

Dehon, R. A.↗
NASA NTRS2007

Improving an Atlantic Fisheries DSS using Sea Surface Salinity Data from NASA's Aquarius Mission

This report assesses the capacity of incorporating NASA#s Aquarius SSS (sea surface salinity) data into the SMAST (School of Marine Science and Technology) DSS for Fisheries Science. This data will enhance the SMAST DSS by providing SSS over a large area. Aquarius is a focused satellite mission designed to measure global SSS. SSS mapping is limited because conventional in situ SSS sampling is too sparse to give a large-scale view of the salinity variability. Aquarius will resolve missing physical processes that link the water cycle, the climate, and the ocean. The SMAST Fisheries program provides a DSS for fisheries science. It collects fisheries and environmental data, integrates them into a suite of data assimilation ocean models, and provides hindcasts, nowcasts, and forecasts for fisheries research, fisheries management, and the fishery industry. Currently, SMAST is using SSS data from the National Oceanic and Atmospheric Administration#s National Data Buoy Center. The SMAST DSS would be enhanced with SSS data from the Aquarius mission.

Guest, DeNeice↗
NASA NTRS2021

Chapter 10: Development of a Precipitation Retrieval Scheme for Cross-track Passive Microwave Sounding Instruments

The measurement of precipitation (snowfall and rainfall) across the globe is of great importance to both science and society. Despite the importance, measurements from surface-based instruments are limited in extent and are generally absent from sparsely populated or inhospitable regions, including over the world’s oceans. Satellite-based observations have the potential to provide the necessary information to generate estimates of precipitation across the whole of the Earth’s surface. However, adequate temporal and spatial sampling is required to capture vagaries of precipitation and it is necessary to exploit all possible satellite (and, where appropriate surface) information. While much research has been carried out into the retrieval of precipitation from observations gathered by conically-scanning passive microwave imagers in previous decades, relatively little has been done to fully utilise the observations from the cross-track scanning passive microwave sounding instruments. This paper will review a number of cross-track precipitation retrieval schemes, together an evaluation of their performance, and future directions.

Precipitation retrievals↗
NASA NTRS2010

An Investigation of Widespread Ozone Damage to the Soybean Crop in the Upper Midwest Determined From Ground-Based and Satellite Measurements

Elevated concentrations of ground-level ozone (O3) are frequently measured over farmland regions in many parts of the world. While numerous experimental studies show that O3 can significantly decrease crop productivity, independent verifications of yield losses at current ambient O3 concentrations in rural locations are sparse. In this study, soybean crop yield data during a 5-year period over the Midwest of the United States were combined with ground and satellite O3 measurements to provide evidence that yield losses on the order of 10% could be estimated through the use of a multiple linear regression model. Yield loss trends based on both conventional ground-based instrumentation and satellite-derived tropospheric O3 measurements were statistically significant and were consistent with results obtained from open-top chamber experiments and an open-air experimental facility (SoyFACE, Soybean Free Air Concentration Enrichment) in central Illinois. Our analysis suggests that such losses are a relatively new phenomenon due to the increase in background tropospheric O3 levels over recent decades. Extrapolation of these findings supports previous studies that estimate the global economic loss to the farming community of more than $10 billion annually.

Fishman, Jack↗
NASA NTRS1992

Requirements for facilities and measurement techniques to support CFD development for hypersonic aircraft

The design of a hypersonic aircraft poses unique challenges to the engineering community. Problems with duplicating flight conditions in ground based facilities have made performance predictions risky. Computational fluid dynamics (CFD) has been proposed as an additional means of providing design data. At the present time, CFD codes are being validated based on sparse experimental data and then used to predict performance at flight conditions with generally unknown levels of uncertainty. This paper will discuss the facility and measurement techniques that are required to support CFD development for the design of hypersonic aircraft. Illustrations are given of recent success in combining experimental and direct numerical simulation in CFD model development and validation for hypersonic perfect gas flows.

Sellers, William L., III↗
DOE OSTI2024

Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes

In recent years, there has been widespread adoption of machine learning-based approaches to automate the solving of partial differential equations (PDEs). Among these approaches, Gaussian processes (GPs) and kernel methods have garnered considerable interest due to their flexibility, robust theoretical guarantees, and close ties to traditional methods. They can transform the solving of general nonlinear PDEs into solving quadratic optimization problems with nonlinear, PDE-induced constraints. However, the complexity bottleneck lies in computing with dense kernel matrices obtained from pointwise evaluations of the covariance kernel, and its partial derivatives, a result of the PDE constraint and for which fast algorithms are scarce. The primary goal of this paper is to provide a near-linear complexity algorithm for working with such kernel matrices. We present a sparse Cholesky factorization algorithm for these matrices based on the near-sparsity of the Cholesky factor under a novel ordering of pointwise and derivative measurements. The near-sparsity is rigorously justified by directly connecting the factor to GP regression and exponential decay of basis functions in numerical homogenization. We then employ the Vecchia approximation of GPs, which is optimal in the Kullback-Leibler divergence, to compute the approximate factor. This enables us to compute ϵ-approximate inverse Cholesky factors of the kernel matrices with complexity O(N log d (N/ϵ)) in space and O(N log 2d (N/ϵ)) in time. We integrate sparse Cholesky factorizations into optimization algorithms to obtain fast solvers of the nonlinear PDE. We numerically illustrate our algorithm’s near-linear space/time complexity for a broad class of nonlinear PDEs such as the nonlinear elliptic, Burgers, and Monge-Ampère equations. In summary, we provide a fast, scalable, and accurate method for solving general PDEs with GPs and kernel methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2021

Improved Rainfall Data in the Philippines through Concurrent Use of GPM IMERG and Ground-Based Measurements

The availability of accurate and reliable rainfall data that are applicable to various phenomenological, climatological, and modeling studies is important, especially in the Philippines, which is considered to be highly vulnerable to natural hazards and a changing climate. The presented strategy involved constructing a dataset consisting of synoptic data, automatic rain gauge (ARG) measurements, and satellite data that are co-registered, consistent, and formatted in the same manner. Although sparse in number, the synoptic stations provide the most accurate rainfall information and were used as the baseline for creating the dataset. The ARGs that are within a distance of 1 km to the synoptic stations were used to determine the correction factors needed to make the synoptic and ARG data consistent. Subsequently, the corrected ARGs were used to make the satellite IMERG data consistent with both ARG and synoptic data. In case of the latter, only IMERG pixels with at least 10 ARGs within the relatively large footprint of the satellite sensor were used in estimating the required correction parameters derived from a combination of a power transform and linear regression correction techniques. The final results show good agreement of synoptic and corrected ARG data with correlation coefficients of 0.94 and 0.97 for the 10 day and monthly data, respectively, and improvement in the linear regression slope from 0.67 to 0.90 for 10 day data, and 0.70 to 0.94 for monthly data. In addition, the corrected ARG data agree well with the corrected IMERG data, with correlation coefficients of 0.88 and 0.93 for the 10 day and monthly data, respectively, and an improvement in slope from 0.66 to 0.87 for 10 day data, and 0.74 to 0.99 for monthly data. The merit of using a combined dataset is illustrated through comparative analyses of the IMERG data and spatially interpolated synoptic and ARG data. The results show general agreements in spatial patterns of rainfall across the datasets, especially in areas where in situ measurements are recorded. The observed discrepancy when ground data is limited emphasizes the need for satellite IMERG data to obtain the true spatial patterns of rainfall distribution.

rainfall↗
DOE OSTI2025

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records↗
DOE OSTI2024

Deep learning based event reconstruction for cyclotron radiation emission spectroscopy

The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β - -decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1993

System identification and model reduction using modulating function techniques

Weighted least squares (WLS) and adaptive weighted least squares (AWLS) algorithms are initiated for continuous-time system identification using Fourier type modulating function techniques. Two stochastic signal models are examined using the mean square properties of the stochastic calculus: an equation error signal model with white noise residuals, and a more realistic white measurement noise signal model. The covariance matrices in each model are shown to be banded and sparse, and a joint likelihood cost function is developed which links the real and imaginary parts of the modulated quantities. The superior performance of above algorithms is demonstrated by comparing them with the LS/MFT and popular predicting error method (PEM) through 200 Monte Carlo simulations. A model reduction problem is formulated with the AWLS/MFT algorithm, and comparisons are made via six examples with a variety of model reduction techniques, including the well-known balanced realization method. Here the AWLS/MFT algorithm manifests higher accuracy in almost all cases, and exhibits its unique flexibility and versatility. Armed with this model reduction, the AWLS/MFT algorithm is extended into MIMO transfer function system identification problems. The impact due to the discrepancy in bandwidths and gains among subsystem is explored through five examples. Finally, as a comprehensive application, the stability derivatives of the longitudinal and lateral dynamics of an F-18 aircraft are identified using physical flight data provided by NASA. A pole-constrained SIMO and MIMO AWLS/MFT algorithm is devised and analyzed. Monte Carlo simulations illustrate its high-noise rejecting properties. Utilizing the flight data, comparisons among different MFT algorithms are tabulated and the AWLS is found to be strongly favored in almost all facets.

Shen, Yan↗
NASA NTRS2011

NREPS Applications for Water Supply and Management in California and Tennessee

Management of water resources is a balancing act between temporally and spatially limited sources and competitive needs which can often exceed the supply. In order to manage water resources over a region such as the San Joaquin Valley or the Tennessee River Valley, it is pertinent to know the amount of water that has fallen in the watershed and where the water is going within it. Since rain gauge networks are typically sparsely spaced, it is typical that the majority of rainfall on the region may not be measured. To mitigate this under-sampling of rainfall, weather radar has long been employed to provide areal rainfall estimates. The Next-Generation Weather Radars (NEXRAD) make it possible to estimate rainfall over the majority of the conterminous United States. The NEXRAD Rainfall Estimation Processing System (NREPS) was developed specifically for the purpose of using weather radar to estimate rainfall for water resources management. The NREPS is tailored to meet customer needs on spatial and temporal scales relevant to the hydrologic or land-surface models of the end-user. It utilizes several techniques to mitigate artifacts in the NEXRAD data from contaminating the rainfall field. These techniques include clutter filtering, correction for occultation by topography as well as accounting for the vertical profile of reflectivity. This presentation will focus on improvements made to the NREPS system to map rainfall in the San Joaquin Valley for NASA s Water Supply and Management Project in California, but also ongoing rainfall mapping work in the Tennessee River watershed for the Tennessee Valley Authority and possible future applications in other areas of the continent.

Gatlin, P.↗
NASA NTRS2019

A Large Source of Cloud Condensation Nuclei from New Particle Formation in the Tropics

Cloud condensation nuclei (CCN) can affect cloud properties and therefore the Earth’s radiative balance. New particle formation (NPF) from condensable vapours in the free troposphere has been suggested to contribute to CCN, especially in remote, pristine atmospheric regions, but direct evidence is sparse, and the magnitude of this contribution is uncertain. Here we use in-situ aircraft measurements of vertical profiles of aerosol size distributions to present a global-scale survey of NPF occurrence. We observed intense NPF occurring at high altitude in tropical convective regions over both the Pacific and Atlantic Oceans. Together with the results of chemical-transport models, our findings indicate that NPF persists at all longitudes as a global-scale band in the tropical upper troposphere, covering about 40% of the Earth’s surface. Furthermore, we find that this NPF in the tropical upper troposphere is a globally important source of CCN in the lower troposphere, where they can affect cloud properties. Our findings suggest that the production of CCN, as these new particles descend towards the surface, is currently not adequately captured in global models, because they tend to underestimate both the magnitude of tropical upper tropospheric NPF and the subsequent growth to CCN sizes. This has potential implications for cloud albedo and the global radiative balance.

Atmospheric chemistry↗
NASA NTRS2012

Can Asian Dust Trigger Phytoplankton Blooms in the Oligotrophic Northern South China Sea?

Satellite data estimate a high dust deposition flux (approximately 18 g m(exp-2 a(exp-1) into the northern South China Sea (SCS). However, observational evidence concerning any biological response to dust fertilization is sparse. In this study, we combined long-term aerosol and chlorophyll-a (Chl-a) measurements from satellite sensors (MODIS and SeaWiFS) with a 16-year record of dust events from surface PM10 observations to investigate dust transport, flux, and the changes in Chl-a concentration over the northern SCS. Our result revealed that readily identifiable strong dust events over this region, although relatively rare (6 cases since 1994) and accounting for only a small proportion of the total dust deposition (approximately 0.28 g m(exp-2 a(exp-1), do occur and could significantly enhance phytoplankton blooms. Following such events, the Chl-a concentration increased up to 4-fold, and generally doubled the springtime background value (0.15 mg m(exp-3). We suggest these heavy dust events contain readily bioavailable iron and enhance the phytoplankton growth in the oligotrophic northern SCS.

northern South China sea↗
NASA NTRS2022

Drill-Site Selection for Cosmogenic-Nuclide Exposure Dating of the Bed of the Greenland Ice Sheet

Direct observations of the size of the Greenland Ice Sheet during Quaternary interglaciations are sparse yet valuable for testing numerical models of ice-sheet history and sea level contribution. Recent measurements of cosmogenic nuclides in bedrock from beneath the Greenland Ice Sheet collected during past deep-drilling campaigns reveal that the ice sheet was significantly smaller, and perhaps largely absent, sometime during the past 1.1 million years. These discoveries from decades-old basal samples motivate new, targeted sampling for cosmogenic-nuclide analysis beneath the ice sheet. Current drills available for retrieving bed material from the US Ice Drilling Program require < 700 m ice thickness and a frozen bed, while quartz-bearing bedrock lithologies are required for measuring a large suite of cosmogenic nuclides. We find that these and other requirements yield only ∼ 3.4 % of the Greenland Ice Sheet bed as a suitable drilling target using presently available technology. Additional factors related to scientific questions of interest are the following: which areas of the present ice sheet are the most sensitive to warming, where would a retreating ice sheet expose bare ground rather than leave a remnant ice cap, and which areas are most likely to remain frozen bedded throughout glacial cycles and thus best preserve cosmogenic nuclides? Here we identify locations beneath the Greenland Ice Sheet that are best suited for potential future drilling and analysis. These include sites bordering Inglefield Land in northwestern Greenland, near Victoria Fjord and Mylius-Erichsen Land in northern Greenland, and inland from the alpine topography along the ice margin in eastern and northeastern Greenland. Results from cosmogenic-nuclide analysis in new sub-ice bedrock cores from these areas would help to constrain dimensions of the Greenland Ice Sheet in the past.

Greenland↗
DOE OSTI2025

Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.

Bayesian inference↗