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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Aerosol EnKF at GMAO

In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASAs Global Modeling and Assimilation Office(GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. In line with the transition of the GEOS meteorological data assimilation system to a hybrid Ensemble-Variational formulation, we are updating the aerosol component of our assimilation system to an ensemble square root filter(EnSRF; Whitaker and Hamill (2002)) type of scheme.We present a summary of our preliminary results of the assimilation of column integrated aerosol observations (Aerosol Optical Depth; AOD) using an Ensemble Square Root Filters (EnSRF) scheme and the ensemble members produced routinely by the meteorological assimilation.

MERRA-2 EnSRF↗
NASA NTRS1994

A comparison between general circulation model simulations using two sea surface temperature datasets for January 1979

Simulations with the UCLA atmospheric general circulation model (AGCM) using two different global sea surface temperature (SST) datasets for January 1979 are compared. One of these datasets is based on Comprehensive Ocean-Atmosphere Data Set (COADS) (SSTs) at locations where there are ship reports, and climatology elsewhere; the other is derived from measurements by instruments onboard NOAA satellites. In the former dataset (COADS SST), data are concentrated along shipping routes in the Northern Hemisphere; in the latter dataset High Resolution Infrared Sounder (HIRS SST), data cover the global domain. Ensembles of five 30-day mean fields are obtained from integrations performed in the perpetual-January mode. The results are presented as anomalies, that is, departures of each ensemble mean from that produced in a control simulation with climatological SSTs. Large differences are found between the anomalies obtained using COADS and HIRS SSTs, even in the Northern Hemisphere where the datasets are most similar to each other. The internal variability of the circulation in the control simulation and the simulated atmospheric response to anomalous forcings appear to be linked in that the pattern of geopotential height anomalies obtained using COADS SSTs resembles the first empirical orthogonal function (EOF 1) in the control simulation. The corresponding pattern obtained using HIRS SSTs is substantially different and somewhat resembles EOF 2 in the sector from central North America to central Asia. To gain insight into the reasons for these results, three additional simulations are carried out with SST anomalies confined to regions where COADS SSTs are substantially warmer than HIRS SSTs. The regions correspond to warm pools in the northwest and northeast Pacific, and the northwest Atlantic. These warm pools tend to produce positive geopotential height anomalies in the northeastern part of the corresponding oceans. Both warm pools in the Pacific produce large-scale circulation anomalies with a pattern that resembles that obtained using COADS SSTs as well as EOF 1 of the control simulation; the warm pool in the Atlantic does not. These results suggest that the differences obtained with COADS SSTs and HIRS SSTs are mostly due to the differences in the datasets over the northern Pacific. There was a blocking episode near Greenland in late January 1979. Both simulations with warm SST anomalies over the northwest and northeast Pacific show a tendency toward increased incidence of North Atlantic blocking; the simulation with warm SST anomalies over the northwest Atlantic shows a tendency toward decreased incidence. These results suggest that features in both SST datasets that do not have a counterpart in the other dataset contribute signficantly to the differences between the simulated and observed fields. The results of this study imply that uncertainties in current SST distributions for the world oceans can be as important as the SST anomalies themselves in terms of their impact on the atmospheric circulation. Caution should be exercised, therefore, when linking anomalous circulation and SST patterns, especially in long-range prediction.

Ose, Tomoaki↗
DOE OSTI2026

Phosphoserine Charge State Drives Ion Condensation and Spatial Polyamine Presentation in Multirepeat Silaffin

Diatom silaffins direct silica biomineralization through heavily post-translationally modified repeat domains, yet how these modifications reshape the multirepeat conformational ensemble remains unknown. We report all-atom MD simulations of a 195-residue construct spanning repeats R1–R7 of Sil1p from Cylindrotheca fusiformis, carrying the full complement of native PTMs: phosphoserine (pSer), long-chain polyamines (LCPA), dimethyllysine (MLY), and trimethylhydroxylysine phosphate (TPL). We simulated three variants (Native, P1/singly deprotonated phosphate, and P2/doubly deprotonated phosphate) at two NaCl concentrations in triplicate for 500 ns each. All systems disorder from the AlphaFold 3 starting structure. Phosphate charge state, not ionic strength, is the dominant control of ensemble compaction and ion organization. Doubly deprotonated phosphate organizes an extensive Na + condensation shell (∼100 ions, 20% of box Na + ) and a heterogeneous bridging network that integrates both pSer and TPL phosphate groups. The resulting ensemble is compact with LCPA side chains exhibiting above-median solvent accessibility in 84% of simulation frames in P2 at 300 mM. This is higher than any other condition we simulated and indicates that polyamine groups are preferentially surface-presented in the most compact, ion-organized state. A charge-neutralization control confirms that this compact state is a structured intermediate maintained by the bridging network, not a simple collapsed globule. Here, this repeat-scale spatial organization is not captured by single-repeat peptide studies. Understanding the dynamics, mechanism, and spatial organization of PTM-rich silaffin at the repeat scale is a step closer to hierarchical biomimetic materials beyond simple silica morphologies.

Amines↗
DOE OSTI2022

Electric current paths in a Si:P delta-doped device imaged by nitrogen-vacancy diamond magnetic microscopy

The recently-developed ability to control phosphorous-doping of silicon at an atomic level using scanning tunneling microscopy, a technique known as atomic precision advanced manufacturing (APAM), has allowed us to tailor electronic devices with atomic precision, and thus has emerged as a way to explore new possibilities in Si electronics. In these applications, critical questions include where current flow is actually occurring in or near APAM structures as well as whether leakage currents are present. In general, detection and mapping of current flow in APAM structures are valuable diagnostic tools to obtain reliable devices in digital-enhanced applications. In this report, we used nitrogen-vacancy (NV) centers in diamond for wide-field magnetic imaging (with a few-mm field of view and micron-scale resolution) of magnetic fields from surface currents flowing in an APAM test device made of a P delta-doped layer on a Si substrate, a standard APAM witness material. We integrated a diamond having a surface NV ensemble with the device (patterned in two parallel mm-sized ribbons), then mapped the magnetic field from the DC current injected in the APAM device in a home-built NV wide-field microscope. The 2D magnetic field maps were used to reconstruct the surface current densities, allowing us to obtain information on current paths, device failures such as choke points where current flow is impeded, and current leakages outside the APAM-defined P-doped regions. Analysis on the current density reconstructed map showed a projected sensitivity of ~0.03 A m -1 , corresponding to a smallest-detectable current in the 200 μm wide APAM ribbon of ~6 μA. These results demonstrate the failure analysis capability of NV wide-field magnetometry for APAM materials, opening the possibility to investigate other cutting-edge microelectronic devices.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1979

Synthetic aperture radar imaging of moving ocean waves

A theory for the radar imaging of ocean waves is presented under the assumptions that a swell propagates through an ensemble of Bragg scatterers and that the integration time of the synthetic aperture radar (SAR) is small compared to the angular velocity of the swell. Results are presented which show image development and distortions caused by the radial velocities and accelerations of the swell. Neglecting small wave bunching and tilts due to the longer underlying waves, and considering only one-dimensional geometries, the mechanism of wave motions are considered and their efforts on the production of the usual intensity pattern representing the wave image are studied. The analysis shows that in certain situations a processed image can appear which has twice the spatial period of the actual long wave on the ocean, which can confuse the interpretation of ocean wave analysis.

Swift, C. T.↗
NASA NTRS2023

Observational Constraints Reduce Model Spread but Not Uncertainty in Global Wetland Methane Emission Estimates

The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.

Kuang-Yu Chang↗
DOE OSTI2022

Automated Stellar Spectra Classification with Ensemble Convolutional Neural Network

Large sky survey telescopes have produced a tremendous amount of astronomical data, including spectra. Machine learning methods must be employed to automatically process the spectral data obtained by these telescopes. Classification of stellar spectra by applying deep learning is an important research direction for the automatic classification of high-dimensional celestial spectra. In this paper, a robust ensemble convolutional neural network (ECNN) was designed and applied to improve the classification accuracy of massive stellar spectra from the Sloan digital sky survey. We designed six classifiers which consist six different convolutional neural networks (CNN), respectively, to recognize the spectra in DR16. Then, according the cross-entropy testing error of the spectra at different signal-to-noise ratios, we integrate the results of different classifiers in an ensemble learning way to improve the effect of classification. The experimental result proved that our one-dimensional ECNN strategy could achieve 95.0% accuracy in the classification task of the stellar spectra, a level of accuracy that exceeds that of the classical principal component analysis and support vector machine model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Chern-Simons invariants from ensemble averages

We discuss ensemble averages of two-dimensional conformal field theories associated with an arbitrary indefinite lattice with integral quadratic form Q. We provide evidence that the holographic dual after the ensemble average is the three-dimensional Abelian Chern-Simons theory with kinetic term determined by Q. The resulting partition function can be written as a modular form, expressed as a sum over the partition functions of Chern-Simons theories on lens spaces. For odd lattices, the dual bulk theory is a spin Chern-Simons theory, and we identify several novel phenomena in this case. We also discuss the holographic duality prior to averaging in terms of Maxwell-Chern-Simons theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Path integral factorization and the gravitational effective action

Abstract We discuss the factorization and continuity properties of fields in the Euclidean gravitational path integral with higher dimension operators constructed from powers of the Riemann tensor. We construct the boundary terms corresponding to the microcanonical ensemble and show that the saddle point approximation to the path integral with a quasilocal energy constraint generally yields a saddle point with discontinuous temperature. This extends a previous result for the Euclidean Schwarzschild–de Sitter geometry in Einstein gravity and shows that it is robust against at least some types of quantum corrections from heavy fields. As an application, we compute the entropy of SdS in D = 4 using the BTZ method. Our result matches the entropy calculated using Wald’s formula.

Astronomy & Astrophysics↗
NASA NTRS2014

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Error Covariance↗
NASA NTRS2014

Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

Data Assimilation↗
DOE OSTI2024

OpenFacadeControl: enabling integration of automated facades with other building systems

Automated facades are, for the most part, still considered as separate from other building systems throughout the design, installation, commissioning, operation, and maintenance cycle. This takes place despite the fact that their energy and comfort performance are deeply interlinked with the operation of lighting and HVAC systems. Over the last two decades, research has shown that there are significant advantages from operating facades as an integrated system with the rest of the building. Nevertheless, significant barriers prevent this type of integration becoming more common. One of them is the lack of a platform that is inexpensive to implement and that easily allows the practical implementation of integrated control algorithms across fenestration and other building systems, using a variety of communications protocols. This is particularly challenging when automated facades are installed in existing buildings, where interaction with legacy building systems that were installed over the past lifetime of the building can require a high degree of interoperability. OpenFacadeControl (OFC) is an open-source controls framework aimed at unified control of facades and other building systems, including the sharing of third-party sensor information. Through leveraging the Volttron controls platform, it allows the integration of systems and sensors that are manufactured by different companies and that use different communications protocols into an ensemble that functions as a single system. OFC is designed to enable integrated control algorithms of varying degrees of complexity, ranging from simple, heuristic controls to more sophisticated approaches like model-predictive control. Use of a research version to test advanced lighting and shading strategies in a full-scale experimental testbed has demonstrated the ease of deploying advanced control solutions using OpenFacadeControl. This paper presents the structure of OpenFacadeControl and a demonstration case showing the use of OFC in laboratory tests of advanced lighting and fenestration controls that coordinated motorized shades communicating via the BACnet building communications standard and lights communicating via internet-protocol-based application programming interface (API), based on the readings of a shared light level sensor communicating via a different API.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2022

Development and Evaluation of Ensemble Consensus Precipitation Estimates over High Mountain Asia

Precipitation estimates are highly uncertain in complex regions such as High-Mountain Asia (HMA), where ground measurements are very difficult to obtain, and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA’s strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets (the Integrated Multi-satellitE Retrieals for Global Precipitation Measurement IMERG, the Climate Hazards group Infrared Precipitation with Stations CHIRPS, and the ECMWF Reanalysis ERA5) with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatio-temporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends.

Fadji Z Maina↗
DOE OSTI2025

Integrated GW Farm ABM

This Data Repository includes data used for the integrated groundwater- farm ABM model, raw model output from scenario ensemble, and processed outputs that isolate the groundwater storage depletion outcomes for the 35,000 farm cells. Model Inputs: Farm ABM Inputs: This folder contains the input data used by the integrated groundwater - farm ABM modelling script (Python file) used for the high performance computing (HPC) experiments. The sub-folder "data inputs" contains all of the farm attribute data, while the three files in the folder have the hydrogeological data lookup table (NLDAS Cost Curve Attributes.csv), a lookup table (Theis well function table.csv) for the groundwater cost curve function, and the farm indexes and corresponding NLDAS ids for all of the cells run in this experiment (nldas farms subset final.csv). NLDAS Cost curve hydrogeological data: Hydrogeological data aggregated to 1/8 degree resolution and aligned with the NLDAS grid. Parameters include: water depth below ground surface [meters], subsurface porosity [unitless], aquifer depth from ground surface to aquifer bottom [meters], annual average recharge (USGS: mm, Doll: meters), and three different hydraulic conductivity (K) values (meters/day). The three K values represent the mean value from Gleeson et al. (2018), one standard deviation above the mean from Gleeson et al. (2018), and the de Graaf et al. 2020 modifications to certain lithologies. Additional information about these datasets and their processing are documented in the supplement to Yoon et al. 2025 (in review). Output: Raw outputs: This folder contains a .zip file that has model outputs for the entire scenario ensemble. There is one csv for each farm id, using the format "farm farmid cases.csv". The relationship between the farm id and NLDAS id is defined by the "nldas farms subset final.csv" located in the Farm ABM Inputs folder. Each csv has 625 rows, corresponding to 625 combinations of different scenario parameter values. Each row (scenario) represents the outcome of a 100 year simulation. Columns define scenario settings and summary statistics for each scenario. The first four columns define the scenario settings: "hydro ratio," "econ ratio," "K scenario," and "gamma scenario." The hydro and econ ratios are values passed to the modeling script that influence multipliers for other model parameters, as documented in the supplement to Yoon et al. 2025 (in review). The gamma multiplier is a coefficient multiplier applied to the baseline gamma values (values below 1 represent lower unobserved costs compared to baseline, values above 1 represent higher costs). The K scenario names represent K values of: "low": 0.5 m/d, "int 1": 2.5 m/d, "int 2": 10 m/d, "high": 50 m/d, and "gleeson": mean Gleeson K value. "Perc vol depleted" is the fraction of groundwater depleted at the end of the 100 simulation. Processed Output: Derived depletion outcomes from raw outputs: All of the individual csv files from the Raw outputs were aggregated into a single file that has the scenario settings and fraction depletion "Perc vol depleted" for every farm cell, for every scenario. The other two files define relationships between the farm id, NLDAS id, and local and major aquifer units, used for aquifer-level depletion analysis.

Agent based modeling↗
DOE OSTI2022

The WRF-Solar Ensemble Prediction System: Development, Test, and Validation

Providing reliable probabilistic solar radiation information is needed to improve management of the uncertainty and variability of solar generation. Thus, guidance on how to develop skillful and accurate ensemble forecasts is essential and it will ultimately contribute to integration of high amounts of solar energy on the grid. A team from the National Renewable Energy Laboratory and the National Center for Atmospheric Research had been collaborating to develop the WRF-Solar ensemble prediction system (WRF-Solar EPS) in the past three years to produce probabilistic solar irradiance forecasts and better predict solar energy by quantifying forecast uncertainty. The WRF-Solar EPS basically generates ensemble members for solar irradiance based on stochastic perturbations to provide intraday and day-ahead probabilistic forecasts. This study will present main research steps in developing the WRF-Solar EPS including: (a) tangent linear analysis for identifying key input variables of six WRF-Solar modules significantly related to predicting of cloud and solar irradiance, (b) combining stochastic perturbation technique with the WRF-Solar model, and (c) ensemble calibration method to decrease error and uncertainty of ensemble-based solar forecasts. The capability of WRF-Solar EPS is now updated to the most recent version of standard WRF model. This presentation will summarize comprehensive results from the evaluation of forecasts against the National Solar Radiation Data Base as well as ground-measured observations. Moreover, we will introduce the user's guide for WRF-Solar EPS (e.g., parameters to configure stochastic perturbations) and future extension of this research.

day-ahead forecast↗
DOE OSTI2022

Exploring cryo-electron microscopy with molecular dynamics

Single particle analysis cryo-electron microscopy (EM) and molecular dynamics (MD) have been complimentary methods since cryo-EM was first applied to the field of structural biology. The relationship started by biasing structural models to fit low-resolution cryo-EM maps of large macromolecular complexes not amenable to crystallization. The connection between cryo-EM and MD evolved as cryo-EM maps improved in resolution, allowing advanced sampling algorithms to simultaneously refine backbone and sidechains. Moving beyond a single static snapshot, modern inferencing approaches integrate cryo-EM and MD to generate structural ensembles from cryo-EM map data or directly from the particle images themselves. We summarize the recent history of MD innovations in the area of cryo-EM modeling. The merits for the myriad of MD based cryo-EM modeling methods are discussed, as well as, the discoveries that were made possible by the integration of molecular modeling with cryo-EM. Lastly, current challenges and potential opportunities are reviewed.

Biochemistry & Molecular Biology↗
DOE OSTI2021

Feasibility of DEIM for retrieving the initial field via dimensionality reduction

When parameter estimation is solved in a high-dimensional space, the dimensionality reduction strategy becomes the primary consideration for alleviating the tremendous computational cost. Here, the discrete empirical interpolation method (DEIM) is explored to retrieve the initial condition (IC) by combining the polynomial chaos (PC) based ensemble Kalman filter (i.e. PC-EnKF), where a non-intrusive PC expansion is considered as a surrogate model in place of the forward model in the prediction step of the ensemble Kalman filter, resulting in fewer forward model integrations but with a comparable accuracy as Monte Carlo-based approaches. The DEIM acts as a hyper-reduction tool to provide the low-dimensional input for the high-dimensional initial field, which can be reconstructed using the information on the sparse interpolation grid points that is adaptively obtained through PC-EnKF data assimilation method. Thus an innovative framework to reconstruct the IC is developed. The detailed procedure at each assimilation iteration includes: the determination of the spatial interpolation points, the estimation of the initial values on the interpolation locations using the optimal observations, and the reconstruction of IC in the full space. The current study uses the reconstruction field of initial conditions of the Navier-Stokes equations as an example to illustrate the efficacy of our method. The experimental results demonstrate the proposed algorithm achieves a satisfactory reconstruction for the initial field. The proposed method helps to extend the applicable area of DEIM in solving inverse problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗