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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
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

Inferring the photolysis rate of NO 2 in the stratosphere based on satellite observations

NO and NO 2 (NO x ) play major roles in both tropospheric and stratospheric chemistry. This paper provides a novel method to obtain a global and accurate photolysis rate for NO 2 based on satellite data. The photolysis rate J(NO 2 ) dominates the daytime diurnal variation of NO x photochemistry. Here the spatial variation of J(NO 2 ) at 50–90°S in December from 20–40km is obtained using data from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) experiment. Because NO and NO 2 rapidly exchange with one another in the daytime, J(NO 2 ) can be attained assuming steady state, and the results are shown to be consistent with model results. The J(NO 2 ) value decreases as the solar zenith angle increases and has a weak altitude dependence. A key finding is that satellite-derived J(NO 2 ) increases in the polar regions, in good agreement with model predictions, due to the effects of ice and snow on surface albedo. Thus, the method presented here provides an observation-based check on the role of albedo in driving polar photochemistry.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Interplanetary gas. XXVII - A catalog of disconnection events in cometary plasma tails

A catalog of 72 disconnection event (DES) in cometary plasma tails is presented as a basic data source for cometary and solar wind analysis. The events span the period 1892-1976, and are characterized on the basis of photographic observations. From the observed kinematics of rejected tails, methods are derived to calculate an estimated time of tail disconnection for each DE, either from the average law of motion for DEs lacking velocity measurements, or from the observed velocities. The geometric circumstances of each DE at the times of separation and observation are also tabulated, and a brief description of each event is presented.

Niedner, M. B., Jr.↗
NASA NTRS1993

An analysis of nearfield normal mode amplitude anomalies of the Landers earthquake

The 1992 Landers earthquake (M(sub w) = 7.3) occurred in the middle of the TERRAscope network. Long-period Rayleigh waves recorded at the TERRAscope stations (delta less than or = 3 deg) after traveling around the Earth show large amplitude anomalies, one order of magnitude larger than spherical Earth predictions up to a period of about 600 s. The ground motions over the epicentral region at and after the arrival of R4-5 are in phase at all stations. These observations are inconsistent with the nearly vertical strike slip mechanism of the Landers earthquake. Synthetic seismograms for a rotating, elliptic, and laterally heterogeneous Earth model calculated by the variational method agree well with the observed waveforms. Calculations for various 3D Earth models demonstrate that the amplitudes are very sensitive to the large scale aspherical structure in the crust and the mantle. The anomalies for modes shorter than 300 s period can be explained by lateral heterogeneity shallower than the upper mantle. Rotation of the Earth and lower mantle heterogeneity are required to explain mode amplitudes at longer periods. Current whole mantle seismic tomographic models can fully explain the observed amplitudes longer than 300 s. To assess the effect of the high order lateral heterogeneity in the mantle more precise estimate of the crustal correction is required.

Watada, Shingo↗
NASA NTRS1997

Infalling Envelopes and Pre-Main Sequence Disks

The goal of this project is to understand the observed infrared emission of young stellar objects, and explore the implications of this emission for the evolution of dusty envelopes and circumstellar disks. We are using sophisticated radiative transfer methods to compare models with observations, thereby making critical tests of the standard picture of low-mass star formation.

Hartmann, Lee W.↗
DOE OSTI2022

Characterizing the Reproducibility of Noisy Quantum Circuits

The ability of a quantum computer to reproduce or replicate the results of a quantum circuit is a key concern for verifying and validating applications of quantum computing. Statistical variations in circuit outcomes that arise from ill-characterized fluctuations in device noise may lead to computational errors and irreproducible results. While device characterization offers a direct assessment of noise, an outstanding concern is how such metrics bound the reproducibility of a given quantum circuit. Here, we first directly assess the reproducibility of a noisy quantum circuit, in terms of the Hellinger distance between the computational results, and then we show that device characterization offers an analytic bound on the observed variability. We validate the method using an ensemble of single qubit test circuits, executed on a superconducting transmon processor with well-characterized readout and gate error rates. The resulting description for circuit reproducibility, in terms of a composite device parameter, is confirmed to define an upper bound on the observed Hellinger distance, across the variable test circuits. This predictive correlation between circuit outcomes and device characterization offers an efficient method for assessing the reproducibility of noisy quantum circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

DC-Ripple-Energy Adaptive-Minimization (DREAM) Modulation Scheme for a High Power Density Inverter

The DC bus capacitor is one of the major power-density and reliability hurdles of electric drive systems. It is hard to shrink because it is constrained by the DC bus RMS ripple current, which is only load dependent. A dual-inverter based segmented drive can reduce the ripple current by ~50% compared to a non-segmented case. This paper analyzes the origin of this ripple current and points out the path for minimization. An optimal DC-ripple-energy adaptive-minimization (DREAM) modulation method is proposed to further reduce the ripple current. It is observed in experimental results that the proposed method can achieve additional 38% reduction over the traditional segmented drive system.

Xue, Lincoln↗
NASA NTRS1979

Utilization of nuclear methods for materials analysis and the determination of concentration gradients

The various types of nuclear chemical analysis methods are discussed. The possibilities of analysis through activation and direct observation of nuclear reactions are described. Such methods make it possible to analyze trace elements and impurities with selectivity, accuracy, and a high degree of sensitivity. Such methods are used in measuring major elements present in materials which are available for analysis only in small quantities. These methods are well suited to superficial analyses and to determination of concentration gradients; provided the nature and energy of the incident particles are chosen judiciously. Typical examples of steels, pure iron and refractory metals are illustrated.

Darras, R.↗
NASA NTRSDate not supplied

Separating Magnetospheric and Heliospheric SWCX in X-ray Spectra

Solar wind charge exchange (SWCX) emission contaminates all astrophysical observations in X-rays regardless of the direction. This contamination is particularly problematic when measuring astrophysical plasma temperatures due to the similar spectral distribution of the two phenomena. Since its discovery, literature has distinguished between SWCX emission resulting from solar wind-neutral interactions within the Earth’s magnetosphere, called magnetospheric SWCX, and similar interactions occurring more generally throughout the heliosphere, called heliospheric SWCX. Previous work demonstrated accurate modeling of the heliospheric SWCX contribution for astrophysical observations performed at low ecliptic latitude with HaloSat, a CubeSat X-ray mission of medium spectral resolution. Here, we apply this model to similarly orchestrated observations to measure the magnetospheric SWCX contribution separately from the heliospheric SWCX and the astrophysical background contributions. We describe our observational strategy, the heliospheric SWCX model used, and our spectral fitting methods. In particular, we report on four observations with lines of sight through the Earth’s magnetospheric flank with total O VII line fluxes at least 3σ above that predicted by the heliospheric SWCX model and the astrophysical background, possibly indicative of magnetospheric SWCX. We then discuss the excess emission in comparison with available magnetospheric SWCX simulations requested through the Community Coordinated Modeling Center.

X-ray↗
DOE OSTI2021

Secondary Crash Identification using Crowdsourced Waze User Reports

Secondary crashes are crashes that occur as a result of the nonrecurrent congestion originating from primary crashes, and always have a greater impact on safety and traffic than a single crash. A better understanding of secondary crashes would benefit traffic incident management, and this requires accurate identification of secondary crashes. This study explores using crowdsourced Waze user reports to identify secondary crashes. Here, a network-based clustering algorithm is proposed to extract the primary crash cluster, including all user reports originating from the primary crash, and any crash that occurred within the cluster would be a secondary crash. This method works as a filter to select accurate primary–secondary relationships, thus precisely identifying secondary crashes. A case study is performed with crashes occurring from June to December 2019 on a 30-mi stretch of I-40 in Knoxville, TN. A static threshold method (crash duration and 10 mi) was used to preselect the potential primary–secondary crash pairs, and 75 out of 708 crashes were identified as potential secondary crashes. Based on the preselected primary–secondary crash pairs, 17 secondary crashes were obtained with the proposed method and the results were compared with one of the commonly used methods, the speed contour plot method. Though the proposed method captured fewer secondary crashes, it did identify several secondary crashes that could not be observed with the speed contour plot method. The results showed the applicability of the method and the potential of crowdsourced Waze user reports in secondary crash identification.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2013

On the Reprocessing and Reanalysis of Observations for Climate

The long observational record is critical to our understanding of the Earths climate, but most observing systems were not developed with a climate objective in mind. As a result, tremendous efforts have gone into assessing and reprocessing the data records to improve their usefulness in climate studies. The purpose of this paper is to both review recent progress in reprocessing and reanalyzing observations, and to summarize the challenges that must be overcome in order to improve our understanding of climate and variability. Reprocessing improves data quality through more scrutiny and improved retrieval techniques for individual observing systems, while reanalysis merges many disparate observations with models through data assimilation, yet both aim to provide an climatology of Earth processes. Many challenges remain, such as tracking the improvement of processing algorithms and limited spatial coverage. Reanalyses have fostered significant research, yet reliable global trends in many physical fields are not yet attainable, despite significant advances in data assimilation and numerical modeling. Oceanic reanalyses have made significant advances in recent years, but will only be discussed here in terms of progress toward integrated Earth system analyses. Climate data sets are generally adequate for process studies and large-scale climate variability. Communication of the strengths, limitations and uncertainties of reprocessed observations and reanalysis data, not only among the community of developers, but also with the extended research community, including the new generations of researchers and the decision makers is crucial for further advancement of the observational data records. It must be emphasized that careful investigation of the data and processing methods are required to use the observations appropriately.

Uncertainty↗
DOE OSTI2020

Separating Cloud and Drizzle Signals in Radar Doppler Spectra Using a Parametric Time Domain Method

The ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s -1 , respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m -2 with the benchmark microwave method.

Engineering↗
DOE OSTI2021

Anthropogenic influence on extreme precipitation over global land areas seen in multiple observational datasets

The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Estimation of Seasonal Snow Water Equivalent Using Landsat Observations

This work presents a methodology for estimating seasonal snow water equivalent (SWE) from the use of remotely sensed Visible and Near Infrared observations from the Landsat mission. The method is comprised of two main components: (1) a coupled land surface model and snow depletion curve model, which is used to generate an ensemble of predictions of SWE and snow cover area for a given set of (uncertain) inputs, and (2) a reanalysis step, which updates estimation variables to be consistent with the satellite observed depletion of the fractional snow cover time series. This method was applied over the Sierra Nevada (USA) based on the assimilation of remotely sensed fractional snow covered area data over the Landsat 5-8 record (1985-2016). The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlations with in situ SWE observations of greater than 0.95. The method (fully Bayesian), resolution (daily, 90-meter), temporal extent (32 years), and accuracy provide a unique dataset for investigating snow processes. In particular, this presentation illustrates how the reanalysis dataset was used to provide climatology of the seasonal snowfall accumulation rates, distributions, and variability over the last three decades.

SWE↗
NASA NTRS2018

TPSAS-NF1676L-32014-DND

The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is a satellite-borne polarization sensitive lidar. It has been providing the vertical distributions of clouds and aerosols along with their microphysical and optical properties since 2006. One of its important Level 2 products, feature classification, has been determined using the lidar information from 532 nm parallel and perpendicular channels, and 1064 nm channel measurements of layer integrated backscatter. Deep machine learning methods which combine both the channel and texture information to recognize feature patterns is uniquely beneficial when applied to this data. In this study, we will use Convolutional Neural Network (CNN), a deep machine learning method, to classify lidar aerosol subtypes by using the lidar profile observations. This method uses additional information from the vertical texture of the feature instead of using only the layer information. Note that in the integrated layer properties, the texture information has been masked due to averaging. Our results will show how the texture information plays a role in the classification. This preliminary work explores the benefits and potential of deep machine learning methods for lidar retrievals and focuses on the aerosol subtype classification. The broader application extends to the classification of other feature types. Future applications include the developing deep machine learning methods with neural networks to retrieve properties of the features, and studies of indirect effect of cloud-aerosol interaction from lidar measurements.

Shan Zeng Kowalski↗
DOE OSTI2023

Correlated insulator of excitons in WSe 2 /WS 2 moiré superlattices

A panoply of unconventional electronic states has been observed in moiré superlattices. Engineering similar bosonic phases remains, however, largely unexplored. We report the observation of a bosonic correlated insulator in tungsten diselenide/tungsten disulfide (WSe 2 /WS 2 ) moiré superlattices composed of excitons, that is, tightly bound electron-hole pairs. We develop a pump probe spectroscopy method that we use to observe an exciton incompressible state at exciton filling ν ex = 1 and charge neutrality, indicating a correlated insulator of excitons. With varying charge density, the bosonic correlated insulator continuously transitions into an electron correlated insulator at charge filling ν e = 1, suggesting a mixed correlated insulating state between the two limits. Our studies establish semiconducting moiré superlattices as an intriguing platform for engineering bosonic phases.

Science & Technology - Other Topics↗
NASA NTRS1991

Spectroscopic investigations of microwave generated plasmas

The study deals with the plasma behavior as applied to spacecraft propulsion from the perspective of obtaining better design and modeling capabilities. The general theory of spectroscopy is reviewed, and existing methods for converting emission-line intensities into such quantities as temperatures and densities are outlined. Attention is focused on the single-atomic-line and two-line radiance ratio methods, atomic Boltzmann plot, and species concentration. Electronic temperatures for a helium plasma are determined as a function of pressure and a gas-flow rate using these methods, and the concentrations of ions and electrons are predicted from the Saha-Eggert equations using the sets of temperatures obtained as a function of the gas-flow rate. It is observed that the atomic Boltzmann method produces more reliable results for the electronic temperature, while the results obtained from the single-line method reflect the electron temperatures accurately.

Hawley, Martin C.↗
NASA NTRS1985

Limits on the Doppler factor in relativistic jets by means of gamma-ray observations

A new, simple and potentially useful method for constraining the kinematical parameters of relativistic jets based on gamma ray spectral measurements of Active Galaxies is presented. The application of this method to the Quasar 3C273 leads to a value of the Doppler factor of 3 to 4. This corresponds to jet parameters of mu 2 and theta 15 deg in good agreement with the values estimated independently from radio observations of superluminal motion. For the particular case of 3C273, the results are also compared to those given by a similar technique based on the comparison of the X-ray observational data with the synchrotron self Compton prediction from radio measurements. The application of the proposed technique to a significant sample of active galaxies as a result of future gamma ray surveys of the sky is briefly discussed, particularly with respect to possible ways to constrain the cosmological constants H sub o and q sub o.

Dean, A. J.↗