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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Identifying Solar Wind and Other Volatiles in Space-Weathered Samples From Asteroid Bennu

On September 24, 2023, NASA’s Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission returned >70 g of material from asteroid Bennu to Earth. The Sample Return Capsule was opened at NASA Johnson Space Center (JSC), and a spillover sample was collected from the avionics deck to perform quick-look (QL) analyses. In addition to providing a reconnaissance investigation of its mineralogy, the sample was also examined for evidence of surface exposure on asteroid Bennu by identifying signatures of space weathering. Space weathering changes the morphology, microstructure, and chemistry of regolith on the surfaces of airless bodies. Driven by micrometeoroid bombardment and solar wind irradiation, our understanding of how space weathering modifies particles on the surfaces of carbonaceous asteroids is still developing. To bolster the interpretation of remote sensing data and to further address driving Hypothesis 10, we must investigate the nanoscale structural and chemical changes in returned samples from Bennu to understand how space weathering affects carbonaceous surface materials. Here we explore evidence for solar wind irradiation in QL samples from asteroid Bennu.

M S Thompson↗
NASA NTRS2024

A Review of Twenty-Six Years of U.S. Weather Radar Detection of Meteorite Falls

Weather radars have proven to be a valuable tool for detection, rapid recovery, and analysis of meteorite falls and fall statistics. The U.S. operates a nationwide weather radar network (NEXRAD) which will be discussed here, but it is important to acknowledge that weather radars operate worldwide and efforts are underway to utilize this rich resource. By the author’s count, since the NEXRAD system came online in the late 1990s it has detected 34 recovered meteorite falls and another 33 unrecovered probable falls. Weather radars are powerful tools because they directly detect meteorites falling after the end of their luminous flight, during the "dark flight" period. This allows rapid recovery through detection and modeling of fall locations down to as little as tens of meters. They are also the only tool that analyzes meteorites that survive their parent fireball, before many or all of them are lost to terrain. This allows direct measurement of meteorite mass, particle size distribution, and with additional work should allow calculation of pre-atmospheric mass and a measure of whole-body strength.

M D Fries↗
DOE OSTI2021

Weather and Random Forest-based Load Profiling Approximation Models and its Transferability across Climate Zones

This study is to provide predictive understanding of the associations of various weather attributes with residential and commercial load profiles, for a variety of climate zones and seasons. In this work, machine learning (ML) approaches were used to identify and quantify the impacts of various weather attributes on residential and commercial electricity demand and its components across the western United States. Performance and transferability of the developed ML models were then evaluated across different temperate zones (e.g., southern, middle, and northern US) and across coastal, mid-continent, and wet zones, with inputs of weather condition data from the National Oceanic and Atmospheric Administration (NOAA) at representative weather stations. The predictive models were developed based on the ranked/screened factors using the regression tree (RT) and random forest (RF) approaches, for five different scenarios (seasons).

load composite, Random Forest, regression tree, lo↗
DOE OSTI2021

Dataset for Evaluation of Extreme Weather Impacts on Utility-Scale Photovoltaic Plant Performance in the United States

This dataset is a fusion of three data types (operations and maintenance tickets, weather data, and production data) that was used to support machine learning analysis and evaluation of drivers for low performance at photovoltaic (PV) sites during compound, extreme weather events. After being processed with machine learning, the data was used in the "Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States" manuscript. Additional details are captured in the associated manuscript.

AI↗
NASA NTRS2014

Traffic Management Coordinator Evaluation of the Dynamic Weather Routes Concept and System

Dynamic Weather Routes (DWR) is a weather-avoidance system for airline dispatchers and FAA traffic managers that continually searches for and advises the user of more efficient routes around convective weather. NASA and American Airlines (AA) have been conducting an operational trial of DWR since July 17, 2012. The objective of this evaluation is to assess DWR from a traffic management coordinator (TMC) perspective, using recently retired TMCs and actual DWR reroutes advisories that were rated acceptable by AA during the operational trial. Results from the evaluation showed that the primary reasons for a TMC to modify or reject airline reroute requests were related to airspace configuration. Approximately 80 percent of the reroutes evaluated required some coordination before implementation. Analysis showed TMCs approved 62 percent of the requested DWR reroutes, resulting in 57 percent of the total requested DWR time savings.

weather avoidance↗
DOE OSTI2018

Corrosion Behavior of Weathering Steel Under Thin Electrolyte Layer at Different Relative Humidity

The corrosion behavior of weathering steel under thin electrolyte layer (TEL) at different relative humidity (RH) was investigated by cathodic polarization, electrochemical impedance spectroscopy, electrochemical noise, SEM/EDS, XRD and Raman spectroscopy. The results indicate that during the initial stage, the corrosion rate increases as the RH decreases when the initial thickness of TEL is above 100 μm. During the middle and final corrosion stages, the corrosion behavior of weathering steel is influenced by RH, the initial thickness of TEL and corrosion product. The TEL corrosion is divided into three types, and a weathering steel corrosion model under TEL and bulk solution is also proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Weather effects on the lifecycle of U.S. Department of Defense equipment replacement (WELDER)

Extreme weather has a direct and significant impact on buildings and infrastructure, resulting in billions of dollars of damage each year. This problem continues to grow as climate patterns change and buildings are exposed to new and different hazards than what they were designed to withstand. In order to better plan for the long-range sustainment, restoration, modernization, and eventual recapitalization of these buildings, organizations with large building portfolios, such as the U.S. Department of Defense (DoD), must have an awareness of the risks that these extreme weather events present. This research aimed to develop an approach to estimate condition loss and reduction in service life for the components of a building due to extreme weather hazards, to understand the risks that may be present in certain buildings and building systems. To achieve this objective, a damage association matrix was developed that categorizes climate hazards, the damage modes that they produce, and the individual component types impacted. This damage matrix formally links state-of-the-art climate model output, which provides projections of the probability of various climate hazards with a damage effects model that quantifies the consequence on component-level condition and service life. This method is applied to an actual portfolio of buildings in a particular geographic location and with a pre-defined component inventory that comprises the building. This approach can be aggregated to the system-, facility-, and site-level thus helping support billions of dollars in recapitalization decisions related to restoration/modernization of facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

FIND: A Synthetic weather generator to control drought Frequency, Intensity, and Duration

Water systems worldwide are experiencing climate change-induced shifts in drought properties like frequency, intensity, and duration, affecting water security and reliability. To develop and test effective drought preparedness plans, researchers often use synthetic weather generators to create hydrological scenarios that explore drought variability beyond historical records. Existing weather generators typically allow users to adjust streamflow statistics like percentiles or temporal correlation but do not directly control drought properties of frequency, intensity, and duration. To fill this gap, we propose FIND (Frequency, INtensity, and Duration) synthetic weather generator. FIND incorporates a standardized drought index to directly and in dependently control drought frequency, intensity, and duration in generated streamflow time series while preserving observed hydrological variability. Use cases for FIND include i) water systems analysis applications that seek to train and test drought strategies under historical and plausible future drought conditions, and ii) bottom-up vulnerability studies relating system vulnerability outcomes to specific changes in drought properties of frequency, intensity, and duration. Here, we demonstrate FIND’s versatility through three experiments: replicating historically observed drought properties, generating streamflow scenarios for multiple sites preserving correlation between their drought conditions, and generating a set of scenarios with direct and independent changes in drought properties. FIND source code is openly available for applications beyond the scope of this paper.

42 ENGINEERING↗
DOE OSTI2022

Weathering of chlorite from grain to watershed: The role and distribution of oxidation reactions in the subsurface

The reaction mechanism of weathering of chlorite, an important rock-forming phyllosilicate, is not well understood in natural settings. Here, in this work we investigated the weathering of Fe-rich chlorite from deep protolith to saprock to soil across a small shale-underlain watershed in the Appalachian Mountains, USA (Shale Hills). We found that oxidation of Fe(II) in chlorite always occurs prior to dissolution of the interlayers of the mineral. The oxidation of pyrite and chlorite commence near the water table across narrow depth intervals under the upper-catchment ridges, but well below the water table across wide depth intervals under the valley. We hypothesize that these patterns can be explained by hydrological and geochemical differences between the ridge and the valley: oxygenated water descends sub-vertically (1D flow) under the ridge, while under the valley, oxygen-depleted water moves upward to the stream and laterally out of the watershed in the subsurface (3D flow). Geochemical and mineralogical characterization indicates that the transformation of Fe-rich chlorite at Shale Hills is initiated by the oxidation of Fe(II). Next, the interlayer hydroxide sheet dissolves to form hydroxy-interlayered vermiculite and then vermiculite. During the transformation, Mg and Fe are released into solution and Fe is reprecipitated as goethite in pore space. Delivery of oxygen to the deep subsurface by infiltration of meteoric water is thought to control the initial transformation of chlorite at Shale Hills. It is possible that weathering of many Fe(II)-rich minerals is initiated by oxidation as mediated by rates of subsurface oxygen delivery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Machine learning based algorithms for uncertainty quantification in numerical weather prediction models

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works best for all times, at all locations, and under all conditions. It is therefore of considerable interest to understand the interplay between the choice of physics and the accuracy of the resulting forecasts under different conditions. This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. The first problem addressed herein is the estimation of systematic model errors in output quantities of interest at future times, and the use of this information to improve the model forecasts. The second problem considered is the identification of those specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. In order to address these questions we employ two machine learning approaches, random forests and artificial neural networks. The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors. Numerical experiments are carried out with the Weather Research and Forecasting (WRF) model. The output quantity of interest is the model precipitation, a variable that is both extremely important and very challenging to forecast. The physical processes under consideration include various micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. The experiments demonstrate the strong potential of machine learning approaches to aid the study of model errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Managing weather- and market price-related financial risks in algal biofuel production

Large-scale algae production has garnered interest due to its potential as a biofuel feedstock. Previous research assessing the profitability of algae products has been mostly based on values averaged over time, but algae production and resulting financial returns exhibit significant variability due to weather and fluctuations in selling prices for algae-based products. In other sectors, producers often reduce weather- and market price-related financial risk with financial instruments such as insurance, but little research has been performed on the design of insurance products to protect algae producers. Furthermore, this study develops a novel index-based insurance instrument that pays-out during unfavorable weather and market conditions, then explores the instrument's effectiveness, combined with a cash reserve, in reducing revenue stream variability for an algae producer. Results indicate that a biophysically based index-insurance product tailored to the specific financial risks in algae production can reduce variability in net revenues and can do so at a lower cost than relying solely on cash reserves, the most common financial risk management tool. Assessing the performance of index-insurance in algae production is particularly timely given the passage of the 2018 Farm Bill, which newly opens opportunities for the USDA to provide crop insurance to algae producers.

09 BIOMASS FUELS↗
DOE OSTI2021

Cohesive and adhesive degradation in PET-based photovoltaic backsheets subjected to ultraviolet accelerated weathering

Delamination between layers in photovoltaic (PV) backsheets is often reported in the literature, causing voids that can collect moisture, diminish module backside heat dissipation, and reduce the backsheet’s effectiveness as a physical barrier. While backsheets with weathering-resistant fluoropolymer outer layers have traditionally been used in modules, more recent backsheets using non-fluoropolymer outer layers, such as polyethylene terephthalate (PET), have been developed. These backsheets have shown signs of premature degradation, and their adhesion degradation, in particular, has not been widely studied. In the present work, the single cantilever beam (SCB) adhesion test was used to quantify the adhesion energy in two commercially available PET-based backsheets. To study the effect of minor changes in formulation, the backsheets were obtained from same manufacturer and product line but during different years. To study the effect of environmental variables on adhesion degradation, the backsheets were subjected to artificial weathering at controlled temperature, humidity, and ultraviolet (UV) radiation in an indoor weathering chamber, and the adhesion energy was quantified at several intervals of exposure time. Layering structure, composition, and adhesion failure mode were compared between the backsheets, using Raman and infrared spectroscopy and thermogravimetric analysis. The results show a large difference in initial (unexposed) adhesion energy between the backsheets, despite very similar structures and compositions. Following exposure, adhesion energy dropped significantly, primarily due to thermo-hydrolytic degradation of the polyurethane (PU)-based adhesive layers. Significant UV-induced adhesion degradation of the PET outer layer surface was also observed. The study represents an important step in understanding adhesion degradation in PET-based backsheets, suggesting ways in which adhesion integrity – and, correspondingly, module service life – can be improved.

14 SOLAR ENERGY↗
DOE OSTI2021

Concentration‐Discharge Relationships of Dissolved Rhenium in Alpine Catchments Reveal Its Use as a Tracer of Oxidative Weathering

Abstract Oxidative weathering of sedimentary rocks plays an important role in the global carbon cycle. Rhenium (Re) has been proposed as a tracer of rock organic carbon (OC petro ) oxidation. However, the sources of Re and its mobilization by hydrological processes remain poorly constrained. Here, we examine dissolved Re as a function of water discharge, using samples collected from three alpine catchments that drain sedimentary rocks in Switzerland (Erlenbach and Vogelbach) and Colorado, USA (East River). The Swiss catchments reveal a higher dissolved Re flux in the catchment with higher erosion rates, but have similar [Re]/[Na + ] and [Re]/[SO 4 2− ] ratios, which indicate a dominance of Re from OC petro . Despite differences in rock type and hydro‐climatic setting, the three catchments have a positive correlation between river water [Re]/[Na + ] and [Re]/[SO 4 2− ] and water discharge. We propose that this reflects preferential routing of Re from a near‐surface, oxidative weathering zone. The observations support the use of Re as a proxy to trace rock‐organic carbon oxidation, and suggest it may be a hydrological tracer of vadose zone processes. We apply the Re proxy and estimate CO 2 release by OC petro oxidation of 5.7 +6.6 / −2.0 tC km −2 yr −1 for the Erlenbach. The overall weathering intensity was ∼40%, meaning that the corresponding export of unweathered OC petro in river sediments is large, and the findings call for more measurements of OC petro oxidation in mountains and rivers as they cross floodplains.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantifying and simulating the weather forecast uncertainty for advanced building control

Weather forecast uncertainty is unavoidable despite technological advancements. Accurately quantifying and modelling this uncertainty is essential for developing and comparing advanced building controllers. In this study, we present a structured approach using a first-order autoregressive model (AR(1)) to model uncertainty in ambient temperature and global solar irradiation (GHI) forecasts. We analyzed weather data from four cities and employed Jensen–Shannon divergence (JSD) to evaluate the similarity between synthetic and actual forecast errors. The average JSD values for temperature are 0.027 (Berkeley), 0.021 (Leuven), 0.018 (Berlin), and 0.008 (Oslo), and for GHI, the average JSD values are 0.016 (Berkeley), 0.058 (Leuven), and 0.013 (Berlin). The low JSD values indicate a high similarity between the synthetic and real forecast error distributions. Further, our approach successfully generates synthetic weather forecasts that mirror the statistical properties of actual forecasts. The implementation of our method for uncertain forecast generation is being added to the BOPTEST framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Incorporation of the Rotor-Equivalent Wind Speed into the Weather Research and Forecasting Model’s Wind Farm Parameterization

Wind power installations have been increasing in recent years. Because wind turbines can influence local wind speeds, temperatures, and surface fluxes, weather forecasting models should consider their effects. Wind farm parameterizations do currently exist for numerical weather prediction models. They generally consider two turbine impacts: elevated drag in the region of the wind turbine rotor disk and increased turbulent kinetic energy production. The wind farm parameterization available in the Weather Research and Forecasting (WRF) Model calculates this drag and TKE as a function of hub-height wind speed. However, recent work has suggested that integrating momentum over the entire rotor disk [via a rotor-equivalent wind speed (REWS)] is more appropriate, especially for cases with high wind shear. In this study, we implement the REWS in the WRF wind farm parameterization and evaluate its impacts in an idealized environment, with varying amounts of wind speed shear and wind directional veer. Specifically, we evaluate three separate cases: neutral stability with low wind shear, high stability with high wind shear, and high stability with nonlinear wind shear. For most situations, use of the REWS with the wind farm parameterization has marginal impacts on model forecasts. However, for scenarios with highly nonlinear wind shear, the REWS can significantly affect results.

17 WIND ENERGY↗
DOE OSTI2021

Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction

Progress in numerical weather and climate prediction accuracy greatly depends on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

2D reactive transport model of shale chemical weathering and biogeochemical fluxes along a mountainous hillslope, East River Watershed, Colorado: Input files and simulation results

This data package contains input files and simulation results for a two-dimensional (2D) reactive transport model used to quantitatively analyze the coupled hydrological and biogeochemical processes governing shale weathering and associated biogeochemical fluxes under realistic environmental conditions in the high-elevation East River Watershed. These data support the conclusions presented in Stolze et al. (Water Resources Research, under review), "Model-based interpretation of solute exports and carbon partitioning during shale weathering in a mountainous hillslope". The model simulates atmospheric-subsurface gas exchange, subsurface water flow, and shale weathering processes under dynamic, year-scale conditions along a shale-underlain hillslope located in the East River watershed. The simulations were performed using the PFLOTRAN flow and reactive transport code and executed on the Perlmutter supercomputer to leverage its large-scale parallel computing capabilities. The data package contains two zipped folders, "model_input_files" and "simulation_results", and one readme.txt file. "model_input_files" contains the necessary input files to run the calibrated base-base model presented in Stolze et al. (Water Resources Research, under review). "simulation_results" contains a single hdf5 file ("Output_2D_hillslope_model.h5") which includes the results of simulation performed using the base-case model. This file can be opened with HDFView 3.1.4, Python, or MATLAB. "readme.txt" contains relevant information about the base-case model and provides guidelines on how to run the associated input files provided in the folder "model_input_files". Furthermore, readme.txt provides information regarding the model results provided in "Output_2D_hillslope_model.h5" such as matrix dimensionality and output units. Field datasets used to evaluate model performance were collected at three monitoring wells located along a hillslope transect (PLM1, PLM2, and PLM3). Dissolved ion concentration data were collected from November 2016 to October 2021 for Ca, Mg, DIC, Na, K, SO4 (Dong et al., 2025 - dic_npoc_data_2014_2024.zip - DOI:10.15485/1660459; Williams et al., 2025 - anion_data_2014_2024.zip - DOI:10.15485/1668054; Dong et al., 2025 - cation_data_2014_2024.zip - DOI:10.15485/1668055). Note that we used the files named er_PLM1_xx_yy, er_PLM2_xx_yy, and er_PLM3_xx_yy where xx stands for the name of the aqueous species and yy stands for the depth where the measurements were performed. Soil water content ([0 - 1] m) and water table depth were collected from November 2016 to October 2021 (Wan et al., 2024 - Dynamic_water_table__depthsFig2b.csv and Soil_water_content_Fig4e.csv - DOI:10.15485/2322567). Gaseous CO2 concentration were collected from October 2020 to December 2021(Wan et al., 2024 - Soil_CO2_concentrations_Fig4h.csv - DOI:10.15485/2322567) Gaseous CO2 flux from the subsurface to the atmosphere were collected in the vicinity of PLM2 from October 2019 to May 2022 (Wu et al., 2025). Soil microbial biomass concentration was measured from August 2016 to June 2017 (Sorensen et al., 2019 - 2017_East_River_Pumphouse_Microbial_Biomass__1_.csv - DOI:10.15485/1577267) All field data are published as CSV files compatible with Microsoft Excel, MATLAB, and Python, or as text files. The coordinates of the monitoring wells and the CO2(g) flux sensor in the coordinate system WGS84 are: -PLM1: [38.9197710 ; -106.9492750] -PLM2: [38.9201580 ; -106.9487170] -PLM3: [38.9207843 ; -106.9483668] -PLM4: 38.9210060 ; -106.9479528] -CO2(g) flux sensor: [38.9199180 ; -106.9489906] ------------------------------------------------------------------------------------------- This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility using NERSC award BER-ERCAP 23980, BER-ERCAP 28550, and BER-ERCAP 33789.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Weather Analysis for Xcel Energy's 2030 Colorado Preferred Plan

The Public Service Company of Colorado (PSCo), a subsidiary of Xcel Energy (Xcel), plans to meet its target of an 80% reduction in CO 2 emissions over 2005 levels by 2030 through its Preferred Plan1,2 that includes increasing the wind and solar energy on its system, reducing coal, and adjusting the operation and dispatch of new and existing thermal generation. The objective of our analysis reported here is to identify how the PSCo generation and transmission of its Preferred Plan might operate in the face of specific weather events. We investigate weather events that currently cause stress to PSCo’s system, such as winter storms and extreme heat. In addition, we explore how the future system responds to scenarios that may be considered normal weather conditions today, but that have a large impact on wind and solar generation potential.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗