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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Semantics-enabled Spatio-Temporal Modeling of Earth Observation Data: An application to Flood Monitoring

Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatiotemporal analysis of such disastrous events is important for understanding the resiliency of an urban system during these events. Remote Sensing (RS) data is one of the crucial earth observation (EO) data sources that can facilitate such spatiotemporal analysis due to its wide spatial coverage and high temporal availability. In this paper, we propose a discrete mereotopology (DM) based approach to enable representation and querying of spatiotemporal information from a series of multitemporal RS images that are acquired during a flood disaster event. We represent this spatiotemporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatiotemporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t1 have been demonstrated with promising results.

Kurte, Kuldeep↗
DOE OSTI2026

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗
DOE OSTI2023

California - Quality-Controlled Reanalysis and Observational Data for Buoy (130), Morro Bay / Derived Data

This collection provides spatiotemporally paired reanalysis and satellite data to supplement the lidar buoy observations during the California deployments. Point time series of observed and reanalysis data are provided, using inverse distance weighting to geolocate the reanalysis data to the buoy location. Gridded time series of reanalysis data are provided, covering the California coast and extending into the Pacific Ocean. Satellite data in the coastal California region are also provided. The reanalysis collection includes the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP). The satellite data collection is that of Ribal and Young (2019). All datasets are publicly available with the sources provided in the References section.

17 WIND ENERGY↗
DOE OSTI2023

California - Quality-controlled Reanalysis and Observational Data for Buoy (120), Humboldt / Derived Data

This collection provides spatiotemporally paired reanalysis and satellite data to supplement the lidar buoy observations during the California deployments. Point time series of observed and reanalysis data are provided, using inverse distance weighting to geolocate the reanalysis data to the buoy location. Gridded time series of reanalysis data are provided, covering the California coast and extending into the Pacific Ocean. Satellite data in the coastal California region are also provided. The reanalysis collection includes the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP). The satellite data collection is that of Ribal and Young (2019). All datasets are publicly available with the sources provided in the References section.

17 WIND ENERGY↗
DOE OSTI2023

Reconstructing the neutron star equation of state from observational data via automatic differentiation

Neutron star observables like masses, radii, and tidal deformability are direct probes to the dense matter equation of state (EoS). Here, a novel deep learning method that optimizes an EoS in the automatic differentiation framework of solving inverse problems is presented. The trained neural network EoS yields narrow bands for the relationship between the pressure and speed of sound as a function of the mass density. The results are consistent with those obtained from conventional approaches and the observational bound on the tidal deformability inferred from the gravitational wave event, GW170817.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Observation and characterization of a cosmic muon neutrino flux from the northern hemisphere using six years of IceCube data

The IceCube Collaboration has previously discovered a high-energy astrophysical neutrino flux using neutrino events with interaction vertices contained within the instrumented volume of the IceCube detector. We present a complementary measurement using charged current muon neutrino events where the interaction vertex can be outside this volume. As a consequence of the large muon range the effective area is significantly larger but the field of view is restricted to the Northern Hemisphere. IceCube data from 2009 through 2015 have been analyzed using a likelihood approach based on the reconstructed muon energy and zenith angle. At the highest neutrino energies between 194 TeV and 7.8 PeV a significant astrophysical contribution is observed, excluding a purely atmospheric origin of these events at 5.6σ significance. The data are well described by an isotropic, unbroken power-law flux with a normalization at 100 TeV neutrino energy of (0.90{sub −0.27}{sup +0.30})×10{sup −18} GeV{sup −1} cm{sup −2} s{sup −1} sr{sup −1} and a hard spectral index of γ=2.13±0.13. The observed spectrum is harder in comparison to previous IceCube analyses with lower energy thresholds which may indicate a break in the astrophysical neutrino spectrum of unknown origin. The highest-energy event observed has a reconstructed muon energy of (4.5±1.2) PeV which implies a probability of less than 0.005% for this event to be of atmospheric origin. Analyzing the arrival directions of all events with reconstructed muon energies above 200 TeV no correlation with known γ-ray sources was found. Using the high statistics of atmospheric neutrinos we report the current best constraints on a prompt atmospheric muon neutrino flux originating from charmed meson decays which is below 1.06 in units of the flux normalization of the model in Enberg et al.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Observational data from uncrewed systems over Southern Great Plains

Uncrewed Systems (UxS), including uncrewed aerial systems (UAS) and tethered balloon/kite systems (TBS), are significantly expanding observational capabilities in atmospheric science. Rapid adaptation of these platforms and the advancement of miniaturized instruments have resulted in an expanding number of data sets captured under various environmental conditions by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. In 2021, observational data collected using ARM UxS platforms, including seven TigerShark UAS flights and 133 tethered balloon system (TBS) flights, were archived by the ARM Data Center and made publicly available to the user community https://adc.arm.gov/discovery/#/results/s::sgp*U3 and https://adc.arm.gov/discovery/#/results/s::sgp*tbs. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research. This manuscript describes the DOE UAS/TBS datasets, including information on the acquisition, collection, and quality control processes, and highlights the potential scientific contributions using UAS and TBS platforms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Empirical radius formulas for canonical neutron stars from bidirectionally selecting features of equations of state in extended Bayesian analyses of observational data

Significant advancement in Bayesian inference of nuclear equation of state (EOS) from gravitational wave and x-ray observations of neutron stars (NSs) has been made by the nuclear astrophysics community especially since GW170817. By extending the traditional Bayesian analysis which normally ends at presenting the marginalized posterior probability distribution functions (PDFs) of individual EOS parameters and their correlations (or sometimes only the Pearson correlation coefficients which are only reliably useful when the variables are linearly correlated while they are actually often not), we search for a data-driven and robust empirical formula for the radius 𝑅 1.4 of canonical NSs in terms of the characteristic EOS parameters (features). We also identify the single most important but currently poorly known EOS parameter for determining the 𝑅 1.4 . Using three regression-model-building methodologies: bidirectional stepwise feature selection, least absolute shrinkage selection operator (LASSO) regression, and neural network regression on a large set of posterior EOSs and the corresponding 𝑅 1.4 values inferred from earlier comprehensive Bayesian analyses of NS observational data, we systematically and rigorously develop the most probable 𝑅 1.4 formulas with varying statistical accuracy and technical complexity. Here, the most important EOS parameters for determining 𝑅 1.4 are found consistently in each of the feature selection processes to be (in order of decreasing importance): curvature 𝐾 sym , slope 𝐿, skewness 𝐽 sym of nuclear symmetry energy, skewness 𝐽 0 , incompressibility 𝐾 0 of symmetric nuclear matter, and the magnitude 𝐸 sym ⁡(𝜌 0 ) of symmetry energy at the saturation density 𝜌 0 of nuclear matter.

Bayesian methods↗
DOE OSTI2017

Management, Analysis, and Visualization of Experimental and Observational Data – The Convergence of Data and Computing

Scientific user facilities—particle accelerators, telescopes, colliders, supercomputers, light sources, sequencing facilities, and more—operated by the U.S. Department of Energy (DOE) Office of Science (SC) generate ever increasing volumes of data at unprecedented rates from experiments, observations, and simulations. At the same time there is a growing community of experimentalists that require real-time data analysis feedback, to enable them to steer their complex experimental instruments to optimized scientific outcomes and new discoveries. Recent efforts in DOE-SC have focused on articulating the data-centric challenges and opportunities facing these science communities. Key challenges include difficulties coping with data size, rate, and complexity in the context of both real-time and post-experiment data analysis and interpretation. Solutions will require algorithmic and mathematical advances, as well as hardware and software infrastructures that adequately support data-intensive scientific workloads. This paper presents the summary findings of a workshop held by DOE-SC in September 2015, convened to identify the major challenges and the research that is needed to meet those challenges.

Bethel, E. Wes↗
DOE OSTI2016

Management, Analysis, and Visualization of Experimental and Observational Data -- The Convergence of Data and Computing

Scientific user facilities---particle accelerators, telescopes, colliders, supercomputers, light sources, sequencing facilities, and more---operated by the U.S. Department of Energy (DOE) Office of Science (SC) generate ever increasing volumes of data at unprecedented rates from experiments, observations, and simulations. At the same time there is a growing community of experimentalists that require real-time data analysis feedback, to enable them to steer their complex experimental instruments to optimized scientific outcomes and new discoveries. Recent efforts in DOE-SC have focused on articulating the data-centric challenges and opportunities facing these science communities. Key challenges include difficulties coping with data size, rate, and complexity in the context of both real-time and post-experiment data analysis and interpretation. Solutions will require algorithmic and mathematical advances, as well as hardware and software infrastructures that adequately support data-intensive scientific workloads. This paper presents the summary findings of a workshop held by DOE-SC in September 2015, convened to identify the major challenges and the research that is needed to meet those challenges.

Bethel, E. Wes↗
DOE OSTI2018

An Observation Data Driven Simulation and Analysis Framework for Early Stage C. elegans Embryogenesis

Recent developments in cutting-edge live microscopy and image analysis provide a unique opportunity to systematically investigate individual cell’s dynamics as well as simulation-based hypothesis testing. After a summary of data generation and analysis in the observation and modeling efforts related to C. elegans embryogenesis, we develop a systematic approach to model the basic behaviors of individual cells. Next, we present our ideas to model cell fate, division, and movement using 3D time-lapse images within an agent-based modeling framework. Then, we summarize preliminary result and discuss efforts in cell fate, division, and movement modeling. Finally, we discuss the ongoing efforts and future directions for C. elegans embryo modeling, including an inferred developmental landscape for cell fate, a quasi-equilibrium model for cell division, and multi-agent, deep reinforcement learning for cell movement.

3D live images↗
DOE OSTI2015

Deformation analysis of Aceh April 11{sup th} 2012 earthquake using GPS observation data

This research tries to estimate the co-seismic deformation of intraplate earthquake occurred off northern Sumatra coast which is about 100-200 km southwest of Sumatrasubduction zone. The earthquake mechanism was strike-slip with magnitude 8.6 and triggering aftershock with magnitude 8.2 two hours later. We estimated the co-seismic deformation by using the GPS (Global Positioning System) continuous data along western Sumatra coast. The GPS observation derived from Sumatran GPS Array (SuGAr) and Geospatial Information Agency (BIG). For data processing we used GPS Analyze at Massachusetts Institute of Technology (GAMIT) software and Global Kalman Filter (GLOBK) to estimate the co-seismic deformation. From the GPS daily solution, the result shows that the earthquake caused displacement for the GPS stations in Sumatra. GPS stations in northern Sumatra showed the displacement to the northeast with the average displacement was 15 cm. The biggest displacement was found at station BSIM which is located at Simeuleu Island off north west Sumatra coast. GPS station in middle part of Sumatra, the displacement was northwest. The earthquake also caused subsidence for stations in northern Sumatra, but from the time series there was not sign of subsidence was found at middle part of Sumatra. In addition, the effect of the earthquake was worldwide and affected the other GPS Stations around Hindia oceanic.

58 GEOSCIENCES↗
DOE OSTI2021

AI-Based Upgrades to Observational Data Centers to Facilitate Data Interoperability

Focal Areas: (1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Focal areas 2 and 3 have critical dependencies to the modernization described. Key benefits to the focal areas: (1) Modernized observatory framework capable of agile adaptive observation, (2) Advanced instrument and data tagging supporting AI data acquisition for assimilation or validation, and (3) Widespread data interoperability bridging Earth system prediction scales

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Machine Learning for a-posteriori model-observed data fusion to enhance predictive value of ESM output

The proposed research is consistent with focal areas 2 and 3: when successful, this effort will enhance the predictive value of Earth System Model (ESM) output by identifying and quantifying its systematic deviations from available observations and by correcting and recalibrating to existing data. The product is a hybrid process & data-driven modeling tool whose estimation can also bring insights through pattern discovery recognition on model deficiencies on the one hand and data gaps on the other.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2022

First Sagittarius A* Event Horizon Telescope Results. II. EHT and Multiwavelength Observations, Data Processing, and Calibration

We present Event Horizon Telescope (EHT) 1.3 mm measurements of the radio source located at the position of the supermassive black hole Sagittarius A* (Sgr A*), collected during the 2017 April 5–11 campaign. The observations were carried out with eight facilities at six locations across the globe. Novel calibration methods are employed to account for Sgr A*'s flux variability. The majority of the 1.3 mm emission arises from horizon scales, where intrinsic structural source variability is detected on timescales of minutes to hours. The effects of interstellar scattering on the image and its variability are found to be subdominant to intrinsic source structure. The calibrated visibility amplitudes, particularly the locations of the visibility minima, are broadly consistent with a blurred ring with a diameter of ∼50 μas, as determined in later works in this series. Contemporaneous multiwavelength monitoring of Sgr A* was performed at 22, 43, and 86 GHz and at near-infrared and X-ray wavelengths. Several X-ray flares from Sgr A* are detected by Chandra, one at low significance jointly with Swift on 2017 April 7 and the other at higher significance jointly with NuSTAR on 2017 April 11. The brighter April 11 flare is not observed simultaneously by the EHT but is followed by a significant increase in millimeter flux variability immediately after the X-ray outburst, indicating a likely connection in the emission physics near the event horizon. We compare Sgr A*’s broadband flux during the EHT campaign to its historical spectral energy distribution and find that both the quiescent emission and flare emission are consistent with its long-term behavior.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The importance of Aitken mode aerosol particles for cloud sustenance in the summertime high Arctic – a simulation study supported by observational data

The potential importance of Aitken mode particles (diameters ~ 25–80 nm) for stratiform mixed-phase clouds in the summertime high Arctic (>80° N) has been investigated using two large-eddy simulation models. We find that, in both models, Aitken mode particles significantly affect the simulated microphysical and radiative properties of the cloud and can help sustain the cloud when accumulation mode concentrations are low (< 10–20 cm –3 ), even when the particles have low hygroscopicity (hygroscopicity parameter – κ=0.1). However, the influence of the Aitken mode decreases if the overall liquid water content of the cloud is low, either due to a higher ice fraction or due to low radiative cooling rates. An analysis of the simulated supersaturation (ss) statistics shows that the ss frequently reaches 0.5 % and sometimes even exceeds 1 %, which confirms that Aitken mode particles can be activated. The modelling results are in qualitative agreement with observations of the Hoppel minimum obtained from four different expeditions in the high Arctic. Our findings highlight the importance of better understanding Aitken mode particle formation, chemical properties and emissions, particularly in clean environments such as the high Arctic.

54 ENVIRONMENTAL SCIENCES↗