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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 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Hydropower potential derived from streamflow extremes for Alaska, USA

Alaska is an expansive region known for its abundant natural resources, including thousands of miles of streams and rivers. These rivers represent potential opportunities for future hydropower development that could provide reliable energy supply for local communities. There is limited long-term high temporal resolution streamflow data available for the region, making data-driven estimates of potential hydropower and its variability across the state challenging. This study provides a novel data-driven approach for hydropower capacity estimation across Alaska. We use supervised machine learning to develop a relationship between the daily and peak flow duration curves in order to augment the size of our dataset from 44 sites to 67 sites. We perform a stochastic hydropower estimation across the 67 sites and identify approximately 1000 MW of total potential hydropower capacity distributed across these sites. Our study provides the first step towards more comprehensive hydropower estimation for this critical region, highlighting the need for future work integrating high-resolution spatial data, community needs, and economic constraints in estimates of potential hydropower development in Alaska.

Hydropower↗
NASA NTRS2002

Comparing Satellite Rainfall Estimates with Rain-Gauge Data: Optimal Strategies Suggested by a Spectral Model

Validation of satellite remote-sensing methods for estimating rainfall against rain-gauge data is attractive because of the direct nature of the rain-gauge measurements. Comparisons of satellite estimates to rain-gauge data are difficult, however, because of the extreme variability of rain and the fact that satellites view large areas over a short time while rain gauges monitor small areas continuously. In this paper, a statistical model of rainfall variability developed for studies of sampling error in averages of satellite data is used to examine the impact of spatial and temporal averaging of satellite and gauge data on intercomparison results. The model parameters were derived from radar observations of rain, but the model appears to capture many of the characteristics of rain-gauge data as well. The model predicts that many months of data from areas containing a few gauges are required to validate satellite estimates over the areas, and that the areas should be of the order of several hundred km in diameter. Over gauge arrays of sufficiently high density, the optimal areas and averaging times are reduced. The possibility of using time-weighted averages of gauge data is explored.

Bell, Thomas L.↗
DOE OSTI2020

India Solar Resource Data: National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) provides high-resolution solar radiation data for long periods of time, which allows user to have a clear understanding of the spatial and temporal correlation of the data to determine the true impact of solar variability on the grid. This fact sheet specifically outlines solar resource data for India included in NSRDB.

energy systems↗
NASA NTRS2006

Does The Nile Reflect Solar Variability?

Historical records of the Nile water level provide a unique opportunity to investigate the possibility that solar variability influences the Earth's climate. Particularly important are the annual records of the water level, which are uninterrupted for the years 622-1470 A.D. These records are non-stationary, so that standard spectral analyses cannot adequately characterize them. Here the Empirical Mode Decomposition technique, which is designed to deal with nonstationary, nonlinear time series, becomes useful. It allows the identification of two characteristic time scales in the water level data that can be linked to solar variability: the 88 year period and a time scale of about 200 years. These time scales are also present in the concurrent aurora data. Auroras are driven by coronal mass ejections and the rate of auroras is an excellent proxy for solar variability. Analysis of auroral data contemporaneous with the Nile data shows peaks at 88 years and about 200 years. This suggests a physical link between solar variability and the low frequency variations of the Nile water level. The link involves the influence of solar variability on the North Annual Mode of atmospheric variability and its North Atlantic and Indian Oceans patterns that affect rainfall over Eastern Equatorial Africa where the Nile originates.

statistical methods↗
NASA NTRS2012

Space Suit Joint Torque Measurement Method Validation

In 2009 and early 2010, a test method was developed and performed to quantify the torque required to manipulate joints in several existing operational and prototype space suits. This was done in an effort to develop joint torque requirements appropriate for a new Constellation Program space suit system. The same test method was levied on the Constellation space suit contractors to verify that their suit design met the requirements. However, because the original test was set up and conducted by a single test operator there was some question as to whether this method was repeatable enough to be considered a standard verification method for Constellation or other future development programs. In order to validate the method itself, a representative subset of the previous test was repeated, using the same information that would be available to space suit contractors, but set up and conducted by someone not familiar with the previous test. The resultant data was compared using graphical and statistical analysis; the results indicated a significant variance in values reported for a subset of the re-tested joints. Potential variables that could have affected the data were identified and a third round of testing was conducted in an attempt to eliminate and/or quantify the effects of these variables. The results of the third test effort will be used to determine whether or not the proposed joint torque methodology can be applied to future space suit development contracts.

Valish, Dana↗
NASA NTRSDate not supplied

Observation of Trace Gases Seasonal Variability in the Marine Boundary Layer over the Atlantic Ocean during the ACTIVATE Field Campaign

High resolution in-situ measurements of carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and water vapor (H2O) were made onboard the NASA HU-25 aircraft during the ACTIVATE (Aerosol Cloud meteorology Interactions oVer the western Atlantic Experiment) campaign during 2020 and 2021 in different seasons (winter through summer) over the mid-latitude western Atlantic Ocean. As most of the flights focused on the marine boundary layer (MBL) during the campaign, these trace gas observations are an excellent data set to examine seasonal variability of trace gas background values in the MBL without the influence of localized point sources. We will describe the variability of these trace gases in the MBL background by filtering out concentrated point sources using back trajectory analysis along with trace gas ratios. Additionally, the ocean is a significant sink of anthropogenic CO2 capturing about one quarter of total anthropogenic carbon. By looking at the MBL CO2 variation as a function of season, we discuss observed changes in CO2 uptake over the ocean. These high accuracy observations of trace gas backgrounds in the MBL along with characterizing seasonal effects on oceanic sequestering of anthropogenic CO2 will improve the understanding of seasonal variations and change in climate and inverse modelling over the ocean.

Yonghoon Choi↗
NASA NTRS1991

State estimation applications in aircraft flight-data analysis: A user's manual for SMACK

The evolution in the use of state estimation is traced for the analysis of aircraft flight data. A unifying mathematical framework for state estimation is reviewed, and several examples are presented that illustrate a general approach for checking instrument accuracy and data consistency, and for estimating variables that are difficult to measure. Recent applications associated with research aircraft flight tests and airline turbulence upsets are described. A computer program for aircraft state estimation is discussed in some detail. This document is intended to serve as a user's manual for the program called SMACK (SMoothing for AirCraft Kinematics). The diversity of the applications described emphasizes the potential advantages in using SMACK for flight-data analysis.

Bach, Ralph E., Jr.↗
NASA NTRS2023

Pypromice: A Python Package for Processing Automated Weather Station Data

The pypromice Python package is for processing and handling observation datasets from automated weather stations (AWS). It is primarily aimed at users of AWS data from the Geological Survey of Denmark and Greenland (GEUS), which collects and distributes in situ weather station observations to the cryospheric science research community. Functionality in pypromice is primarily handled using two key open-source Python packages, xarray (Hoyer & Hamman, 2017) and pandas (The pandas development team, 2020). A defined processing workflow is included in pypromice for transforming original AWS observations (Level 0, L0) to a usable, CF-convention-compliant dataset (Level 3, L3) (Figure 1). Intermediary processing levels (L1,L2) refer to key stages in the workflow, namely the conversion of variables to physical measurements and variable filtering (L1), cross-variable corrections and user-defined data flagging and fixing (L2), and derived variables (L3). Information regarding the station configuration is needed to perform the processing, such as instrument calibration coefficients and station type (one-boom tripod or two-boom mast station design, for example), which are held in a toml configuration file. Two example configuration files are provided with pypromice , which are also used in the package’s unit tests. More detailed documentation of the AWS design, instrumentation, and processing steps are described in Fausto et al. (2021).

pypromice↗
DOE OSTI2020

Developing a simulator-based satellite dataset for using machine learning techniques to derive aerosol-cloud-precipitation interactions in models and observations in a consistent framework

Aerosol-cloud-precipitation interactions (ACPI) remain a major uncertainty in understanding the Earth’s radiation budget and water cycle (including extremes). After decades of active research, various observationally based metrics have been developed to constrain ACPI in Earth System Models (ESMs), but direct comparison of model and data estimates can confound scientific understanding because limitations and uncertainties in sampling and retrieval procedures may combine with model deficiencies in process representations of ACPI to obstruct understanding. Furthermore, conventional ACPI metrics often vary from one regime to another, and the ACPI process representation in ESMs is also typically derived based on only a limited area/regime (even though the parameterization applies globally). To bridge the gap between models and data and to correctly describe ACPI in all regimes, we propose to construct a new CALIPSO-CloudSat merged dataset that is produced by the same algorithms used in satellite simulators in ESMs, and to use machine learning techniques to derive new ACPI metrics that can be accurately estimated by satellites and can provide meaningful constraints on cloud microphysical process representations in ESMs. The dataset will include measured and retrieved variables for aerosol, cloud, and precipitation from CALIPSO and CloudSat, and environmental variables from meteorological reanalysis. The data will be used to train a neural network to construct the ACPI metrics as a function of environmental conditions. The new ACPI formula will be used to constrain the ACPI in the Energy Exascale Earth System Model (E3SM), and to augment/reformulate the ACPI process representation in the E3SM to improve the simulation of the evolution of the atmosphere under different environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Visualizing large data sets in the earth sciences

The authors describe the capabilities of McIDAS, an interactive visualization system that is vastly increasing the ability of earth scientists to manage and analyze data from remote sensing instruments and numerical simulation models. McIDAS provides animated three-dimensionsal images and highly interactive displays. The software can manage, analyze, and visualize large data sets that span many physical variables (such as temperature, pressure, humidity, and wind speed), as well as time and three spatial dimensions. The McIDAS system manages data from at least 100 different sources. The data management tools consist of data structures for storing different data types in files, libraries of routines for accessing these data structures, system commands for performing housekeeping functions on the data files, and reformatting programs for converting external data to the system's data structures. The McIDAS tools for three-dimensional visualization of meteorological data run on an IBM mainframe and can load up to 128-frame animation sequences into the workstations. A highly interactive version of the system can provide an interactive window into data sets containing tens of millions of points produced by numerical models and remote sensing instruments. The visualizations are being used for teaching as well as by scientists.

Hibbard, William↗
DOE OSTI2022

Thermal Tolerance Metrics for Freshwater Fish, CONUS, Version 1

This dataset is a compilation of 13 thermal response metrics for 834 freshwater fish species across the conterminous United States (CONUS). The data were extracted from six published sources, many of which are compilations of data from other sources. The data were harmonized for comparison, and additional variables were added to summarize the metrics. The dataset is presented as a spreadsheet containing 17 sheets. The first sheet (datasets) describes the data sources. Other sheets describe the source and compilation variables in detail.

13 HYDRO ENERGY↗
NASA NTRS2001

Sources of Variability in the Stratospheric Column of Nitrogen Dioxide

Instruments such as the Global Ozone Monitoring Experiment (GOME, on the European Remote Sensing Satellite (ERS-2), launched 1995), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY, on ENVISAT, to be launched July 2001) and the Ozone Monitoring Instrument (OMI on EOS Aura, to be launched 2003) make measurements of the total column of NO2. There is interest in separating the stratospheric and tropospheric contributions to the column, as the tropospheric column provides a measure of pollution. We are using a 3D chemistry and transport model driven by winds from the Goddard Space Flight Center Data Assimilation System to examine variability in the stratospheric NO2 column. Model results for NOx = NO + NO2 + 2N2O5 will be shown to compare well with sunset observations from the Halogen Occultation Experiment on the Upper Atmosphere Research Satellite, and to exhibit similar temporal and spatial dependence. Partitioning between NO, NO2, and N2O5 is also shown to compare well with observations. This good agreement supports the use of simulated fields in the stratosphere to derive the tropospheric column from the total column. Preliminary comparisons of the tropospheric column with model simulations for the troposphere will also be shown.

Douglass, Anne↗
NASA NTRS1976

Correlation of turbulent trailing vortex decay data

A correlation function, derived on the basis of self-similar variable eddy-viscosity decay, is introduced and utilized to correlate aircraft trailing vortex velocity data from ground and flight experiments. The correlation function collapses maximum tangential velocity data from scale-model and flight tests to a single curve. The resulting curve clearly shows both the inviscid plateau and the downstream decay regions. A comparison between experimental data and numerical solution shows closer agreement with the variable eddy viscosity solution than the constant viscosity analytical solution.

Iversen, J. D.↗
NASA NTRS1977

A comparison between Nimbus 5 THIR and ITPR temperatures and derived winds with rawinsonde data obtained in the AVE II experiment

During the second Atmospheric Variability Experiment (AVE II), atmospheric temperature profiles were computed from Nimbus 5 data, which comprised ITPR, NEMS, and SCR measurements. Rawinsonde data were obtained from NWS stations in the AVE II network and processed for each pressure contact; the soundings closest in space and time were interpolated to the Nimbus 5 sounding points for comparison purposes. Cross sections of thermal and geostrophic winds were computed from satellite-derived cross sections of temperature along the Nimbus orbital track.

Arnold, J. E.↗
NASA NTRS1984

Calibration or inverse regression: Which is appropriate for crop surveys using LANDSAT data?

Calibration and inverse regression estimators of crop proportions are investigated where the auxiliary variable is obtained from binary classification of multivariate LANDSAT data. The appropriate model relating classifier proportions and ground observed proportions for a given crop type is the calibration model. Under this model the inverse regression estimator is superior to the calibration estimator in estimating the crop acreage or proportion for a region of interest.

Chhikara, R. S.↗
NASA NTRS1980

Einstein X-ray observations of Proxima Centauri and the surrounding region

The first detection of both quiescent and flaring soft X-ray emission from a dMe flare star, Proxima Centauri (dM5e) is reported. The data are analyzed for temporal variability and spectral characteristics. The quiescent state is characterized by a mean X-ray luminosity of 1.5 x 10 to the 27th erg s/s, corresponding to a mean surface flux of 700,000 erg s/sq cm-s, and an inferred temperature of 4-million K. The flare that is detected has a peak flux of 7.4 x 10 to the 27th erg s/s and a peak temperature of 17-million K. The implications of these data for models of the quiescent and flare coronae of dMe stars are discussed.

Haisch, B. M.↗
NASA NTRS2013

An Investigation of Flight Deck Data Link in the Terminal Area

The Next Generation Air Transportation System (NextGen) and Europe's Single European Sky ATM Research (SESAR) concepts require an increased use of trajectory-based operations, including extensive strategic air traffic control clearances. The clearances are lengthy and complex, which necessitate data link communications to allow for message permanence and integration into the autoflight systems (i.e., autoload capability). This paper examines the use of flight deck data link communications for strategic and tactical clearance usage in the terminal area. A human-in-the-loop simulation was conducted using a high-fidelity flight deck simulator, with ten commercial flight crews as participants. Data were collected from six flight scenarios in the San Francisco terminal airspace. The variables of interest were ATC message modality (voice v. data link), temporal quality of the message (tactical v. strategic) and message length. Dependent variables were message response times, communication clarifications, communication-related errors, and pilot workload. Response time results were longer in data link compared to voice, a finding that has been consistently revealed in a number of other simulations [1]. In addition, strategic clearances and longer messages resulted in a greater number of clarifications and errors, suggesting an increase in uncertainty of message interpretation for the flight crews when compared to tactical clearances. The implications for strategic and compound clearance usage in NextGen and SESAR are discussed

data link communication↗
DOE OSTI2026

Development of a 95-Year Solar Dataset for Resource Adequacy Studies

Long-term high-resolution solar data provides enhanced understanding of variability of solar generation and enhances our ability to develop strategies for a resilient and reliable electric grid under high deployment of solar energy. Therefore, it is important to develop long-term synthetic datasets that can provide multiple occurrences of various severe weather scenarios that are expected to test the limits of resource adequacy under scenarios contain various energy generation sources. Examples of such scenarios could be long periods of high temperatures when demand for electricity is high or periods where high winds could lead to a shut-down of transmission lines for long periods of time to ensure fire safety. NREL has developed the first version of such a dataset covering a 95-year period covering 2006-2100 at a 4km hourly resolution. This dataset contains all variables necessary to calculate solar generation. During development of this dataset, we focused on creating unbiased, high-resolution solar irradiance through statistical downscaling methods, using Regional Climate Model (RCM) simulations from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) as input. The National Solar Radiation Database (NSRDB) containing over 25 years of observations was used to calibrate the statistical downscaling models. This presentation will outline the primary steps in developing this dataset, including (1) regridding RCM data to a common grid at 20-km resolution, (2) correcting RCM biases with NSRDB, (3) applying temporal and spatial downscaling methods to generate high-resolution (4-km, hourly) solar and ancillary data. Additionally, we will present an evaluation of the downscaled data against the NSRDB across various zones in the CONUS. Lastly, we will present a user guide for accessing the datasets.

14 SOLAR ENERGY↗