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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Exploring and Analyzing Climate Variations Online by Using NASA MERRA-2 Data at GES DISC

NASA Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) (http:giovanni.sci.gsfc.nasa.govgiovanni) is a web-based data visualization and analysis system developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data analysis functions include Lat-Lon map, time series, scatter plot, correlation map, difference, cross-section, vertical profile, and animation etc. The system enables basic statistical analysis and comparisons of multiple variables. This web-based tool facilitates data discovery, exploration and analysis of large amount of global and regional remote sensing and model data sets from a number of NASA data centers. Long term global assimilated atmospheric, land, and ocean data have been integrated into the system that enables quick exploration and analysis of climate data without downloading, preprocessing, and learning data. Example data include climate reanalysis data from NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) which provides data beginning in 1980 to present; land data from NASA Global Land Data Assimilation System (GLDAS), which assimilates data from 1948 to 2012; as well as ocean biological data from NASA Ocean Biogeochemical Model (NOBM), which provides data from 1998 to 2012. This presentation, using surface air temperature, precipitation, ozone, and aerosol, etc. from MERRA-2, demonstrates climate variation analysis with Giovanni at selected regions.

knowledge base↗
NASA NTRS2019

Forecasting the Ambient Solar Wind with Numerical Models. I. On the Implementation of an Operational Framework

The ambient solar wind conditions in interplanetary space and in the near-Earth environment are determined by activity on the Sun. Steady solar wind streams modulate the propagation behavior of interplanetary coronal mass ejections and are themselves an important driver of recurrent geomagnetic storm activity. The knowledge of the ambient solar wind flows and fields is thus an essential component of successful space weather forecasting. Here, we present an implementation of an operational framework for operating, validating, and optimizing models of the ambient solar wind flow on the example of Carrington Rotation 2077. We reconstruct the global topology of the coronal magnetic field using the potential field source surface model (PFSS) and the Schatten current sheet model (SCS) and discuss three empirical relationships for specifying the solar wind conditions near the Sun, namely the Wang–Sheeley (WS) model, the distance from the coronal hole boundary model (DCHB), and the Wang-Sheeley–Arge (WSA) model. By adding uncertainty in the latitude about the sub-Earth point, we select an ensemble of initial conditions and map the solutions to Earth by the Heliospheric Upwind eXtrapolation (HUX) model. We assess the forecasting performance from a continuous variable validation and find that the WSA model most accurately predicts the solar wind speed time series (RMSE≈83 km/s). We note that the process of ensemble forecasting slightly improves the forecasting performance of all solar wind models investigated. We conclude that the implemented framework is well suited for studying the relationship between coronal magnetic fields and the properties of the ambient solar wind flow in the near-Earth environment.

Reiss, Martin A.↗
NASA NTRSDate not supplied

Regional variability of dust single scattering albedo due to mineral composition

Nearly all Earth System Models (ESMs) assume globally homogeneous dust aerosols, neglecting regional variations of the imaginary refractive index (IRI) due to varying mineral composition. This has led to a range of single scattering albedo (SSA) and direct radiative forcing (DRF) estimates, as models assign global properties using dust measurements from different regions. We use model and observational data to assess to what extent regionally varying mineral composition affects visible-band SSA and short-wave DRF of dust. We run global simulations with NASA GISS ModelE2.1, using optical properties for minerals based on laboratory-derived empirical relationships between dust IRI at visible wavelengths and the mineral content of iron oxides. When allowing mineral variations instead of homogeneous dust, we find regional differences in dust SSA up to ~0.06 and consequent variations in DRF up to ~6 and ~5 W/m2, at surface and top-of-atmosphere respectively. We compare model SSA with AERONET inversion data (Version 3.0, Level 2), filtered by aerosol size and optical properties to identify pure dust scenes. To investigate possible contamination by biomass burning aerosols, we use the dataset of Schuster et al. (2016, doi:10.5194/acp-16-1565-2016), who separately calculated the contribution of iron oxides and carbonaceous species to AERONET extinction and absorption optical depths. Our results show that: (1) the range of observed SSA (even without carbonaceous species) is larger than that resulting from homogeneous dust; (2) residual amounts of fine mode black and brown carbon may affect AERONET SSA even in seasons with limited biomass burning; (3) model SSA from our mineral scheme exceeds the AERONET range, possibly due also to an uncertain treatment of goethite. In summary, homogeneous dust cannot explain the variability of observed SSA, so regionally varying optical properties based on mineral content are necessary. Higher accuracy in soil mineralogy maps is needed to better reproduce SSA variations at regional scales. Also, distinct soil maps for hematite and goethite are required, given the high sensitivity of model SSA to their extreme optical properties.

V. Obiso↗
NASA NTRSDate not supplied

Future Lightning Instruments for Weather and Climate Monitoring From LEO

Lightning is not only a natural hazard, but it also holds information about physical processes at work in deep convection and influences atmospheric oxidants that modulate ozone and methane. Hence, spaceborne lightning observations are essential for monitoring extreme events and feedbacks in Earth’s climate system. Low-Earth orbiting (LEO) lightning mapping instruments established a 25-year record of global lightning activity and resulted in a wealth of studies that motivated its designation as an Essential Climate Variable by the World Meteorological Organization. Although geostationary observations of lightning have become increasingly available since 2017, LEO lightning mappers continue to serve critical needs given their unique vantage point and acceptance as a unifying reference for geostationary lightning mapper and ground-based global lightning datasets. At the close of 2023, the Lightning Imaging Sensor (LIS) will cease operations on the International Space Station, which introduces a critical gap in LEO-based monitoring of global lightning activity. To address this need, NASA is developing new lightning mapping technology that enables future LEO-based satellite missions. Existing LEO-based lightning mappers rely on narrowband optical emissions centered on 777.4-nm and detectors that capture 500 images per second to detect lightning both day and night. Lightning can also produce optical emissions in a narrowband centered on 337-nm without any corresponding emissions at the traditionally used near-infrared (NIR) wavelength. These near-ultraviolet (UV) emissions are associated with earlier stage electrical breakdown (e.g., streamers), may frequent intense regions of deep convective storms, and may be a significant source of nitrogen oxide production in the upper atmosphere. A new lightning mapping instrument called the CubeSat Lightning Imaging and Detection Experiment (CLIDE) is being designed at NASA Marshall Space Flight Center to use high-speed, scientific CMOS image sensors capable of detecting lightning’s transient optical emissions in the near-UV and NIR during both daylit and nighttime scenes. This will extend the record of global lightning activity and help pave the way for future small satellite missions that combine radio and optical lightning detectors to obtain novel three-dimensional maps of lightning on a global scale, which can revolutionize the way lightning is used to monitor weather and understand changes in climate.

lightning↗
DOE OSTI2021

Parallel projection—An improved return mapping algorithm for finite element modeling of shape memory alloys

Here, we present a novel finite element analysis of inelastic structures containing Shape Memory Alloys (SMAs). Phenomenological constitutive models for SMAs lead to material nonlinearities, that require substantial computational effort to resolve. Finite element analysis methods, which rely on Gauss quadrature integration schemes, must solve two sets of coupled differential equations: one at the global level and the other at the local, i.e. Gauss point level. In contrast to the conventional return mapping algorithm, which solves these two sets of coupled differential equations separately using a nested Newton procedure, we propose a scheme to solve the local and global differential equations simultaneously. In the process we also derive closed-form expressions used to update the internal/constitutive state variables, and unify the popular closest-point and cutting plane methods with our formulas. Numerical testing indicates that our method allows for larger thermomechanical loading steps and provides increased computational efficiency, over the standard return mapping algorithm.

42 ENGINEERING↗
NASA NTRS2008

Physical and Radiative Characteristic and Long-term Variability of the Okhotsk Sea Ice Cover

Much of what we know about the large scale characteristics of the Okhotsk Sea ice cover has been provided by ice concentration maps derived from passive microwave data. To understand what satellite data represent in a highly divergent and rapidly changing environment like the Okhotsk Sea, we take advantage of concurrent satellite, aircraft, and ship data acquired on 7 February and characterized the sea ice cover at different scales from meters to hundreds of kilometers. Through comparative analysis of surface features using co-registered data from visible, infrared and microwave channels we evaluated the general radiative and physical characteristics of the ice cover as well as quantify the distribution of different ice types in the region. Ice concentration maps from AMSR-E using the standard sets of channels, and also only the 89 GHz channel for optimal resolution, are compared with aircraft and high resolution visible data and while the standard set provides consistent results, the 89 GHz provides the means to observe mesoscale patterns and some unique features of the ice cover. Analysis of MODIS data reveals that thick ice types represents about 37% of the ice cover indicating that young and new ice types represent a large fraction of the ice cover that averages about 90% ice concentration according to passive microwave data. These results are used to interpret historical data that indicate that the Okhotsk Sea ice extent and area are declining at a rapid rate of about -9% and -12 % per decade, respectively.

Nishio, Fumihiko↗
DOE OSTI2021

An extended polynomial chaos expansion for PDF characterization and variation with aleatory and epistemic uncertainties

This paper presents an extended polynomial chaos formalism for epistemic uncertainties and a new framework for evaluating sensitivities and variations of output probability density functions (PDF) to uncertainty in probabilistic models of input variables. An ”extended” polynomial chaos expansion (PCE) approach is developed that accounts for both aleatory and epistemic uncertainties, modeled as random variables, thus allowing a unified treatment of both types of uncertainty. We explore in particular epistemic uncertainty associated with the choice of prior probabilistic models for input parameters. A PCE-based Kernel Density (KDE) construction provides a composite map from the PCE coefficients and germ to the PDF of quantities of interest (QoI). Here, the sensitivities of these PDF with respect to the input parameters are then evaluated. Input parameters of the probabilistic models are considered. By sampling over the epistemic random variable, a family of PDFs is generated and the failure probability is itself estimated as a random variable with its own PCE. Integrating epistemic uncertainties within the PCE framework results in a computationally efficient paradigm for propagation and sensitivity evaluation. Two typical illustrative examples are used to demonstrate the proposed approach.

Aleatory uncertainty↗
NASA NTRS2019

Snow Depth Variability in the Northern Hemisphere Mountains Observed from Space

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map the snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4,000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

Hans Lievens↗
NASA NTRS2016

Extreme Mapping: Looking for Water on the Moon

There are many challenges when exploring extreme environments. Gathering accurate data to build maps about places that you cannot go is incredibly complex. NASA supports scientists by remotely operating robotic rovers to explore uncharted territories. One potential upcoming mission is to look for water near a lunar pole (the Resource Prospector mission). Learn about the technical hurdles and research steps that NASA takes before the mission. NASA practices on Earth with Mission Analogs which simulate the proposed mission. This includes going to lunar-type landscapes, building field networks, testing out rovers, instruments and operational procedures. NASA sets up remote science back rooms just as there are for actual missions. NASA develops custom Ground Data Systems software to support scientific mission planning and monitoring over variable time delays, and separate commanding software and infrastructure to operate the rovers.

MOON↗
NASA NTRS2015

Modeling Landslide Susceptibility Over Large Regions with Fuzzy Overlay

Landslide susceptibility mapping is most effective if detailed surface and subsurface information can be combined with authoritative landslide catalogs or a deep understanding of local conditions. However, these types of homogeneous input data and catalogs are frequently not available over large areas. In this study, we model landslide susceptibility in Central America and the Caribbean islands by combining three globally available datasets and one regional dataset with fuzzy overlay. This primarily heuristic model provides the flexibility to test a range of different contributing variables and the capability to compare landslide inventories within the model framework that vary greatly in their size, spatiotemporal scope, and collection methods. We create a regional susceptibility map and evaluate its performance using receiver operating characteristics for both continuous and binned susceptibility values. This susceptibility map forms the basis for a near-real-time landslide hazard assessment system that couples susceptibility with rainfall and soil moisture triggers to estimate potential landslide activity at a regional scale. The application of this susceptibility model at the regional scale provides a foundation for transferring the methodology to other geographic areas.

Landslide↗
DOE OSTI2026

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also 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.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS1987

Ocean color spectral variability studies using solar-induced chlorophyll fluorescence

It is suggested that chlorophyll-induced ocean color spectral variability can be studied using only a passive airborne spectroradiometer instrument, with solar-induced chlorophyll fluorescence used as the standard against which all correlations are performed. The intraspectral correlation (ISC) method is demonstrated with results obtained during an airborne mapping mission in the New York Bight. The curvature algorithm is applied to the solar-induced chlorophyll fluorescence at about 690 nm, and good agreement is found with results obtained using active-passive correlation spectroscopy. The ISC method has application to spectral variability and resulting chlorophyll concentration measurement in different environmental conditions and in different water types.

Hoge, Frank E.↗
DOE OSTI2021

EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

In this work, we present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Spurious Correlations between Galaxies and Multiepoch Image Stacks in the DESI Legacy Surveys

A nonnegligible source of systematic bias in cosmological analyses of galaxy surveys is the on-sky modulation that is caused by foregrounds and variable image characteristics, such as observing conditions. Standard mitigation techniques perform a regression between the observed galaxy density field and sky maps of the potential contaminants. Such maps are ad hoc lossy summaries of the heterogeneous sets of coadded exposures that contribute to the survey. We present a methodology for addressing this limitation, and we extract spurious correlations between the observed distributions of galaxies and arbitrary stacks of single-epoch exposures. We study four types of galaxies (luminous red galaxies, emission-line galaxies, quasars, and Lyman-break galaxies) in the three regions of the DESI Legacy Surveys (North, South, and Dark Energy Survey), resulting in 12 samples with varying levels and types of contamination. We find that the new technique outperforms the traditional ones in all cases, and is able to remove higher levels of contamination. This paves the way for new methods that extract more information from multiepoch galaxy survey data and mitigate large-scale biases more effectively.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Climatological occurrences of hail and tornadoes associated with mesoscale convective systems in the United States

Hail and tornadoes are hazardous weather events responsible for significant property damage and economic loss worldwide. The most devastating occurrences of hail and tornadoes are commonly produced by supercells in the United States. However, these supercells may also grow upscale into mesoscale convective systems (MCSs) or be embedded within them. The relationship between hail and tornado occurrences with MCSs in the long-term climatology has not been thoroughly examined. In this study, radar features associated with MCSs are extracted from a 14-year MCS tracking database across the contiguous United States, and hazard reports are mapped to these MCS features. We investigate the characteristics of hail and tornadoes in relation to MCSs, considering seasonal and regional variabilities. On average, 8 %–17 % of hail events and 17 %–32 % of tornado events are associated with MCSs, depending on the criteria used to define MCSs. The highest total and MCS-associated hazard events occur from March to May, while the highest MCS-associated portion (23 % for hail and 45 % for tornadoes) is observed in winter (December–February) due to the dominance of MCSs caused by strong synoptic forcing. As hailstone size increases, the fraction associated with MCS decreases, but there is an increasing trend for tornado severity from EF0 to EF3 (Enhanced Fujita Scale). Violent tornadoes at EF4 and EF5 associated with MCSs were also observed, which are generated by supercells embedded within MCSs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Analysis of global oceanic rainfall from microwave data

A Global Rainfall Atlas was prepared from Nimbus 5 ESMR data. The Atlas includes global oceanic rainfall maps based on weekly, monthly and seasonal averages, complete through the end of 1975. Similar maps for 1973 and 1974 were studied. They reveal several previously unknown areas of enhanced rainfall and preliminary data on interannual variability of oceanic rainfall.

Rao, M.↗
NASA NTRS1979

The Surface Contour Radar, a unique remote sensing instrument

A 36 GHz computer controlled airborne Surface Contour Radar (SCR) is described, which was developed by the Naval Research Laboratory and NASA. The system uses pulse-compression techniques and dual frequency carriers spaced far enough apart to be decorrelated on the sea surface. The continuous wave transmitter is biphase modulated, the return signal is autocorrelated, and the code length and clock rate are variable, providing selectable range resolutions of 0.15, 0.30, 0.61 and 1.52 m. The SCR generates a false-color coded elevation map of the sea surface below the aircraft in real time, and can routinely produce ocean directional wave spectra with off-line data processing.

Kenney, J. E.↗
NASA NTRS1990

North Atlantic weather regimes: A synoptic study of phase space

In the phase space of weather, low frequency variability (LFV) of the atmosphere can be captured in a large scale subspace, where a trajectory connects consecutive large scale weather maps, thus revealing flow changes and recurrences. Using this approach, Vautard applied the trajectory speed minimization method (Vautard and Legras) to atmospheric data. From 37 winters of 700 mb geopotential height anomalies over the North Atlantic and the adjacent land masses, four persistent and recurrent weather patterns, interpreted as weather regimes, were discernable: a blocking regime, a zonal regime, a Greenland anticyclone regime, and an Atlantic regime. These regimes are studied further in terms of maintenance and transitions. A regime survey unveils preferences regarding event durations and precursors for the onset or break of an event. The transition frequencies between regimes vary, and together with the transition times, suggest the existence of easier transition routes. These matters are more systematically studied using complete synoptic map sequences from a number of events.

Orrhede, Anna Karin↗