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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Magellan/M2FS and MMT/Hectochelle Spectroscopy of Dwarf Galaxies and Faint Star Clusters within the Galactic Halo*

We present spectroscopic data for 16,369 stellar targets within and/or toward 38 dwarf spheroidal galaxies and faint star clusters within the Milky Way halo environment. All spectra come from observations with the multiobject, fiber-fed echelle spectrographs M2FS at the Magellan/Clay telescope or Hectochelle at the MMT, reaching a typical limiting magnitude G ≲ 21. Data products include processed spectra from all observations and catalogs listing estimates—derived from template model fitting—of line-of-sight velocity (median uncertainty 1.4 km s –1 ) effective temperature (255 K), (base-10 logarithm of) surface gravity (0.59 dex in cgs units), [Fe/H] (0.4 dex) and [Mg/Fe] (0.27 dex) abundance ratios. The sample contains multiepoch measurements for 3720 sources, with up to 15 epochs per source, enabling studies of intrinsic spectroscopic variability. The sample contains 6087 likely red giant stars (based on surface gravity), and 4492 likely members (based on line-of-sight velocity and Gaia-measured proper motion) of the target systems. The number of member stars per individual target system ranges from a few, for the faintest systems, to ~850 for the most luminous. For most systems, our new samples extend over wider fields than have previously been observed; of the likely members in our samples, 820 lie beyond 2 times the projected half-light radius of their host system, and 42 lie beyond 5 R half .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Iterative and Self-Consistent Estimation of Degradation and Soiling Loss in PV Systems - A Case Study

In many parts of the world, soiling is a significant loss mechanism for PV energy production. Methods for accurately quantifying historical and current soiling losses are needed. Furthermore, state-of-the-art methods to estimate degradation rates based on production data do not handle soiling in a satisfactory manner. In order to accurately estimate either of these quantities, it is in many cases helpful to consider their combined effect on PV system performance. In this work we make use of the Combined Degradation and Soiling (CODS) algorithm, which iteratively decomposes time series of daily normalized energy yield into four components: a soiling ratio, a degradation trend, seasonal variations, and noise. We demonstrate the use of the model on field performance data from a utility-scale power plant in a high-soiling environment in the Middle East. We apply the algorithm to the temperature-corrected performance index of the DC power on inverter level, quantifying how soiling varies over time and through the plant. The irradiation-weighted average soiling loss in this plant is 2.3 %, with a median soiling rate of -0.086 %/day. Between the inverters in the site in study, estimated degradation rates range between -0.86 %/year and -0.15 %/year, with a mean rate of -0.48 %/year.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Differences in fine particle chemical composition on clear and cloudy days

Clouds are prevalent and alter fine particulate matter (PM 2.5 ) mass and chemical composition. Cloud-affected satellite retrievals are subject to higher uncertainty and are often removed from data products, hindering quantitative estimates of tropospheric chemical composition during cloudy times. We examine surface PM 2.5 chemical constituent concentrations in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network in the United States during cloudy and clear-sky times defined using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud flags from 2010 to 2014 with a focus on differences in particle species that affect hygroscopicity and aerosol liquid water (ALW). Cloudy and clear-sky periods exhibit significant differences in PM 2.5 mass and chemical composition that vary regionally and seasonally. In the eastern US, relative humidity alone cannot explain differences in ALW, suggesting that emissions and in situ chemistry related to anthropogenic sources exert determining impacts. An implicit clear-sky bias may hinder efforts to quantitatively understand and improve representation of aerosol–cloud interactions, which remain dominant uncertainties in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Propagating information content: an example with advection

The mathematical algorithm to derive geophysical information from remote sensing observations is called a retrieval. The mathematics of many retrieval problems are ill-posed, and thus a priori information is used to help constrain the derived geophysical variable to realistic values. One quantity of interest, therefore, is the information content of the observation. Perfect information content in the observation would be achieved if the retrieval were able to capture any perturbation in the desired geophysical variable with the proper magnitude. Many new data products can be derived by combining geophysical variables retrieved from multiple different remote sensors. This paper explores, for the first time, how to derive the information content of these derived products. The approach uses traditional error propagation techniques to derive the uncertainty of the derived field twice, both when the observations are used in the retrieval and also when only the a priori information from each remote sensor is propagated. These two uncertainties are then used to provide an estimate of the information content of the derived geophysical variable. This study demonstrates how to propagate the uncertainties from six different instruments to provide the information content for water vapor and temperature advection. A multi-month analysis demonstrates that, in a mean sense, the information content for temperature advection is nearly unity for all heights below 700 m while, the information content for water vapor advection is somewhat more variable but still larger than 0.6 in the convective boundary layer.

Turner, David D. [National Oceanic and Atmospheric↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin

Abstract. Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding predefined thresholds. When evaluated using historical data over the Susquehanna River basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gauge data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency, as well as choice of land surface model, play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Radar-derived precipitation climatology for wind turbine blade leading edge erosion

Wind turbine blade leading edge erosion (LEE) is a potentially significant source of revenue loss for wind farm operators. Thus, it is important to advance understanding of the underlying causes, to generate geospatial estimates of erosion potential to provide guidance in pre-deployment planning, and ultimately to advance methods to mitigate this effect and extend blade lifetimes. This study focuses on the second issue and presents a novel approach to characterizing the erosion potential across the contiguous USA based solely on publicly available data products from the National Weather Service dual-polarization radar. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments. Results from these locations demonstrate the high spatial variability in precipitation-induced erosion potential, illustrate the importance of low-probability high-impact events to cumulative annual total kinetic energy transfer and emphasize the importance of hail as a damage vector.

Letson, Frederick (ORCID:0000000192750359)↗
DOE OSTI2025

Dark Energy Survey Supernova 5YR Data Release

This data set was generated by the Dark Energy Survey (DES) team, and was used for the cosmological analysis in the DES key supernova cosmology paper (arXiv:2401.02929). When using this data please cite both the data release paper (Sanchez et al. 2024, ApJ, arXiv:2406.05046) and the DES key paper (Dark Energy Survey Collaboration 2024, ApJL, arXiv:2401.02929). At DES-SN5YR Github Repo (https://github.com/des-science/DES-SN5YR), we provide a comprehensive list of references for the different data products (SALT3 light-curve model, calibration, DCR corrections, SN classification, SN distances and uncertainties covariance matrix). If you have questions, post an issue on the DES-SN5YR Github repository and/or get in touch with the contact people indicated on the Github repository.

Contractor, Fermilab [Fermilab]↗
DOE OSTI2021

Multi-Angle Snowflake Camera b1

The b1 level data production for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and the IDs of the images taken for each snowflake.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

EPCAPE-PT-LANL Measurements: Proton Transfer Reaction Mass Spectrometer

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: IONICON High-Resolution PTR-TOF-MS - Trace VOC Analyzer. Concentrations were calculated based on theoretical reaction rates of each species with H3O+. Details of the calculation are found in the PTR-MS Ionicon Manual. Data Notes: It is a high-resolution PTR-TOF-MS we have sub-selected relevant species. Contact us if you want additional data products from this instrument. Header: - CalculatedAcetonitrile[ppbv]: The concentration of acetonitrile expressed in parts per billion by volume (ppbv). - CalculatedAcetaldehyde[ppbv]: The concentration of acetaldehyde expressed in parts per billion by volume (ppbv). - CalculatedAcetone[ppbv]: The concentration of acetone expressed in parts per billion by volume (ppbv). - CalculatedAceticAcid[ppbv]: The concentration of acetic acid expressed in parts per billion by volume (ppbv). - CalculatedIsoprene[ppbv]: The concentration of isoprene expressed in parts per billion by volume (ppbv). - CalculatedMVK[ppbv]: The concentration of methyl vinyl ketone (MVK) and methacrolein (MACR) expressed in parts per billion by volume (ppbv). - CalculatedMEK[ppbv]: The concentration of methyl ethyl ketone (MEK) expressed in parts per billion by volume (ppbv). - CalculatedBenzene[ppbv]: The concentration of benzene expressed in parts per billion by volume (ppbv). - CalculatedToluene[ppbv]: The concentration of toluene expressed in parts per billion by volume (ppbv).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Particle_hygroscopicity_growth_factor_HFIMS_TRACER_2022_HOUSTON_IOP

This dataset contains high-temporal-resolution measurements of hygroscopic growth factors (GF), and instrument diagnostic/environmental parameters collected during the TRACER campaign using a coupled Differential Mobility Analyzer (DMA) and Fast Integrated Mobility Spectrometer (FIMS) system (HFIMS). The dataset consists of 49,645 time steps and covers 20 discrete growth factor bins (FIMS_GFbinc). The primary data product is the Probability Density Function (FIMS_cPDF), which characterizes aerosol hygroscopic growth behavior under controlled relative humidity conditions. Instrument operational parameters, including flow temperatures, relative humidities, pressures, and counts for both DMA and FIMS, are provided for quality assurance.

DMA_Dp_mean↗
DOE OSTI2020

NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Council Road Mile Marker 71, Seward Peninsula, Alaska, 2017 - 2019

Measurements began in July 2017 and are made year-round (2017 seasonal only) on the tussock tundra with discontinuous permafrost at the Council Road Site on the Seward Peninsula. CO2, CH4 and energy fluxes using the Eddy Covariance (EC) technique (Baldocchi 2003) and meteorological measurements are reported as 30-minute averages. The tower site is registered with AmeriFlux as US-NGC. See http://ameriflux.lbl.gov/sites/siteinfo/US-NGC for more information. Reported data include: (1) additional/extra meteorological and eddy covariance CO2, CH4 and energy flux data, (2) binned spectra for the three wind components, the sonic temperature and gas densities together with the binned cospectra for covariances of w (vertical wind component) and gas densities, (3) binned ogives (cumulative (co)spectra) for the three wind components, the sonic temperature and gas densities, and (4) tower site footprint matrices. The majority of the files are *.csv with paired visualization footprint files as *.jpg. Meteorological and eddy covariance CO2, CH4 and energy flux data (AmeriFlux BASE data product) are available from AmeriFlux BASE US-NGC NGEE Arctic Council (Torn, 2020) https://doi.org/10.17190/AMF/1634883. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) near Utqiagvik (Barrow), Alaska and 2) multiple areas in the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Bayesian Inference of the Fluctuating Proton Shape in DIS and Hadronic Collisions

We determine the likelihood distribution for the model parameters describing the event-by-event fluctuating proton geometry at small $x$ by performing a Bayesian analysis within the Color Glass Condensate framework. The exclusive J/ψ production data from HERA is found to constrain the model parameters well, and we demonstrate that complementary constraints can be obtained from simulations of Pb+Pb collisions at the LHC.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Analysis of carbon capture at cellulosic biorefineries

The large-scale production of cellulosic biofuels would involve spatially distributed systems including biomass fields, logistics networks and biorefineries. Better understanding of the interactions between landscape-related decisions and the design of biorefineries with carbon capture and storage (CCS) in a supply chain context is needed to enable efficient systems. Here we analyse the cost and greenhouse gas mitigation potential for cellulosic biofuel supply chains in the US Midwest using realistic spatially explicit land availability and crop productivity data and consider fuel conversion technologies with detailed CCS design for their associated CO2 streams.

carbon capture and storage (CCS)↗