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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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NASA NTRS1989

Ground data system architecture for precipitation determination from space-based radar

The Tropical Rain Mapping Radar (Tramar) is proposed as an attached payload as part of the Space Station Earth Observing System Program. Tramar would measure rainfall rates, rain velocity, and rain cell areal extent in the latitude band from 30 deg S to 30 deg N for use in studies of large-scale atmospheric circulation, variations of latent heating, tropical hydrologic processes, and mesoscale precipitation systems. The Tramar science requirements, radar design, and ground data system architecture are examined, including the three-dimensional scan geometry, the radar system performance parameters, the production of earth-gridded maps, and the telemetry, sensor, radiometric, and geophysical data that would be obtained by Tramar.

Hilland, Jeffrey E.↗
NASA NTRS2017

Optimal A-Train Data Utilization: A Use Case of Aura OMI L2G and MERRA-2 Aerosol Products

Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission measures ozone column and profile, aerosols, clouds, surface UV irradiance, and the trace gases including NO2, SO2, HCHO, BrO, and OClO using UltraViolet electromagnetic spectrum (280 - 400 nm) with a daily global coverage and a pixel spatial resolution of 13 km × 24 km at nadir, and it's been one of the key instruments to study the Earth's atmospheric composition and chemistry. The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is NASA's atmospheric reanalysis using an upgraded version of Goddard Earth Observing System Model, version 5 (GEOS-5) data assimilation system. Compared to its predecessor MERRA, MERRA-2 is enhanced with more aspects of the Earth system among which is aerosol assimilation. When comparing between satellite pixel measurements and modeled grid data, how to properly handle counterpart pairing is critical considering their spatial and temporal variations. The comparison between satellite and model data by simply using Level 3 (L3) products may result biases due to lack of detailed temporal information. It has been preferred to inter-compare or implement satellite derived physical quantity (i.e., Level 2 (L2) Swath type) directly with/to model measurements with higher temporal and spatial resolution as possible. However, this has posed a challenge in the community to handle. Rather than directly handling the L2 or L3 data, there is a Level 2G (L2G) product conserving L2 pixel scientific data quality but in Grid type with the global coverage. In this presentation, we would like to demonstrate the optimal utilization of OMI L2G daily aerosol products by comparing with MERRA-2 hourly aerosol simulations matched well in both space and time.

MERRA-2 reanalysis↗
NASA NTRS1997

An optical systems analysis approach to image resampling

All types of image registration require some type of resampling, either during the registration or as a final step in the registration process. Thus the image(s) must be regridded into a spatially uniform, or angularly uniform, coordinate system with some pre-defined resolution. Frequently the ending resolution is not the resolution at which the data was observed with. The registration algorithm designer and end product user are presented with a multitude of possible resampling methods each of which modify the spatial frequency content of the data in some way. The purpose of this paper is threefold: (1) to show how an imaging system modifies the scene from an end to end optical systems analysis approach, (2) to develop a generalized resampling model, and (3) empirically apply the model to simulated radiometric scene data and tabulate the results. A Hanning windowed sinc interpolator method will be developed based upon the optical characterization of the system. It will be discussed in terms of the effects and limitations of sampling, aliasing, spectral leakage, and computational complexity. Simulated radiometric scene data will be used to demonstrate each of the algorithms. A high resolution scene will be "grown" using a fractal growth algorithm based on mid-point recursion techniques. The result scene data will be convolved with a point spread function representing the optical response. The resultant scene will be convolved with the detection systems response and subsampled to the desired resolution. The resultant data product will be subsequently resampled to the correct grid using the Hanning windowed sinc interpolator and the results and errors tabulated and discussed.

Lyon, Richard G.↗
NASA NTRS2016

Addressing the Big-Earth-Data Variety Challenge with the Hierarchical Triangular Mesh

We have implemented an updated Hierarchical Triangular Mesh (HTM) as the basis for a unified data model and an indexing scheme for geoscience data to address the variety challenge of Big Earth Data. We observe that, in the absence of variety, the volume challenge of Big Data is relatively easily addressable with parallel processing. The more important challenge in achieving optimal value with a Big Data solution for Earth Science (ES) data analysis, however, is being able to achieve good scalability with variety. With HTM unifying at least the three popular data models, i.e. Grid, Swath, and Point, used by current ES data products, data preparation time for integrative analysis of diverse datasets can be drastically reduced and better variety scaling can be achieved. In addition, since HTM is also an indexing scheme, when it is used to index all ES datasets, data placement alignment (or co-location) on the shared nothing architecture, which most Big Data systems are based on, is guaranteed and better performance is ensured. Moreover, our updated HTM encoding turns most geospatial set operations into integer interval operations, gaining further performance advantages.

SciDB↗
NASA NTRS2002

MODIS Data from the GES DISC DAAC: Moderate-Resolution Imaging Spectroradiometer (MODIS)

The Goddard Earth Sciences (GES) Distributed Active Archive Center (DAAC) is responsible for the distribution of the Level 1 data, and the higher levels of all Ocean and Atmosphere products (Land products are distributed through the Land Processes (LP) DAAC DAAC, and the Snow and Ice products are distributed though the National Snow and Ice Data Center (NSIDC) DAAC). Ocean products include sea surface temperature (SST), concentrations of chlorophyll, pigment and coccolithophores, fluorescence, absorptions, and primary productivity. Atmosphere products include aerosols, atmospheric water vapor, clouds and cloud masks, and atmospheric profiles from 20 layers. While most MODIS data products are archived in the Hierarchical Data Format-Earth Observing System (HDF-EOS 2.7) format, the ocean binned products and primary productivity products (Level 4) are in the native HDF4 format. MODIS Level 1 and 2 data are of the Swath type and are packaged in files representing five minutes of Files for Level 3 and 4 are global products at daily, weekly, monthly or yearly resolutions. Apart from the ocean binned and Level 4 products, these are in Grid type, and the maps are in the Cylindrical Equidistant projection with rectangular grid. Terra viewing (scenes of approximately 2000 by 2330 km). MODIS data have several levels of maturity. Most products are released with a provisional level of maturity and only announced as validated after rigorous testing by the MODIS Science Teams. MODIS/Terra Level 1, and all MODIS/Terra 11 micron SST products are announced as validated. At the time of this publication, the MODIS Data Support Team (MDST) is working with the Ocean Science Team toward announcing the validated status of the remainder of MODIS/Terra Ocean products. MODIS/Aqua Level 1 and cloud mask products are released with provisional maturity.

Source record↗
NASA NTRS2004

MODIS Snow and Sea Ice Products

In this chapter, we describe the suite of Earth Observing System (EOS) Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow and sea ice products. Global, daily products, developed at Goddard Space Flight Center, are archived and distributed through the National Snow and Ice Data Center at various resolutions and on different grids useful for different communities Snow products include binary snow cover, snow albedo, and in the near future, fraction of snow in a 5OO-m pixel. Sea ice products include ice extent determined with two different algorithms, and sea ice surface temperature. The algorithms used to develop these products are described. Both the snow and sea ice products, available since February 24,2000, are useful for modelers. Validation of the products is also discussed.

Hall, Dorothy K.↗
NASA NTRS2010

Phenological Parameters Estimation Tool

The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGINE

McKellip, Rodney D.↗
NASA NTRS2019

The Value of Long-Term (40 years) Airborne Gamma Radiation SWE Record for Evaluating Three Observation-Based Gridded SWE Data Sets by Seasonal Snow and Land Cover Classifications

Observation‐based long‐term gridded snow water equivalent (SWE) products are important assets for hydrological and climate research. However, an evaluation of the currently available SWE products has been limited due to the lack of independent SWE data that extend over a large range of environmental conditions. In this study, three daily long‐term SWE products (Special Sensor Microwave Imager and Sounder [SSMI/S] SWE, GlobSnow‐2 SWE, and University of Arizona [UA] SWE) we reevaluated by seasonal snow cover and land cover classifications over the conterminous United States from1982 to 2017, using the historical airborne gamma radiation SWE observations (20,738 measurements).We found that there are similar patterns in SSMI/S and GlobSnow‐2 SWE when compared against the gamma SWE. However, GlobSnow‐2 SWE had better agreement with gamma SWE than SSMI/S SWE in some forested‐type classes and maritime and prairie snow classes. As compared to SSMI/S and GlobSnow‐2SWE, UA SWE has much better agreement with gamma SWE in all land cover types and snow classes. Tree cover and topographic heterogeneity affect the agreement between the gamma and gridded SWE and accuracy of gamma SWE itself with the largest differences typically occurring when the percent tree cover was 80% or higher, the terrain slope was steeper than 2.5°, and the elevation range exceeded 100 m. The results demonstrate the reliability of the UA SWE products and the benefits of the gamma radiation approach to measure SWE, especially in forested regions.

Eunsang Cho↗
DOE OSTI2024

Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

14 SOLAR ENERGY↗
NASA NTRS1998

Assessment of Anthropogenic and Climatic Impacts on the Global Carbon Cycle Using a 3-D Model Constrained by Isotopic Carbon Measurements and Remote Sensing of Vegetation

Our original proposal called for improved modeling of the terrestrial biospheric carbon cycle, specifically using biome-specific process models to account for both the energy and water budgets of plant growth, to facilitate investigations into recent changes in global atmospheric CO2 abundance and regional distribution. The carbon fluxes predicted by these models were to be incorporated into a global model of CO2 transport to establish large-scale regional fluxes of CO2 to and from the terrestrial biosphere subject to constraints imposed by direct measurements of atmospheric CO2 and its 13C/12C isotopic ratio. Our work was coordinated with a NASA project (NASA NAGW-3151) at the University of Montana under the direction of Steven Running, and was partially funded by the Electric Power Research Institute. The primary objective of this project was to develop and test the Biome-BGC model, a global biological process model with a daily time step which simulates the water, energy and carbon budgets of plant growth. The primary product, the unique global gridded daily land temperature, and the precipitation data set which was used to drive the process model is described. The Biome-BGC model was tested by comparison with a simpler biological model driven by satellite-derived (NDVI) Normalized Difference Vegetation Index and (PAR) Photosynthetically Active Radiation data and by comparison with atmospheric CO2 observations. The simple NDVI model is also described. To facilitate the comparison with atmospheric CO2 observations, a three-dimensional atmospheric transport model was used to produce predictions of atmospheric CO2 variations given CO2 fluxes owing to (NPP) Net Primary Productivity and heterotrophic respiration that were produced by the Biome-BGC model and by the NDVI model. The transport model that we used in this project, and errors associated with transport simulations, were characterized by a comparison of 12 transport models.

Keeling, Charles D.↗
DOE OSTI2026

Operational Simulation of Multi-Functional Charging Station for Sustainable Transportation

As transportation systems move toward electrification and decarbonization, multifunctional charging stations (MFCS) are emerging as key infrastructure for electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs). This paper presents a simulation model of an MFCS that integrates solar photovoltaic (PV), wind power, battery storage, hydrogen (\mathrm{H}_{2}) production, dual-pressure \mathrm{H}_{2} storage, fuel cells, and dynamic grid interactions. The model simulates daily operations using 5 -minute resolution data to capture realtime variability in renewable energy (RE), demand, and electricity prices. A flexible dispatch algorithm dynamically allocates energy for EV charging, \mathrm{H}_{2} production, storage, and grid transactions while respecting system constraints. Results show that the MFCS effectively prioritizes RE usage, minimizes waste, meets diverse energy demands, and achieves net operational profit. The model serves as a valuable decision-support tool for designing and optimizing integrated clean energy hubs for zero-emission transportation.

Gbadegoye, Jeremiah [University of Tennessee, Knox↗
NASA NTRS2010

Processing AIRS Scientific Data Through Level 3

The Atmospheric Infra-Red Sounder (AIRS) Science Processing System (SPS) is a collection of computer programs, known as product generation executives (PGEs). The AIRS SPS PGEs are used for processing measurements received from the AIRS suite of infrared and microwave instruments orbiting the Earth onboard NASA's Aqua spacecraft. Early stages of the AIRS SPS development were described in a prior NASA Tech Briefs article: Initial Processing of Infrared Spectral Data (NPO-35243), Vol. 28, No. 11 (November 2004), page 39. In summary: Starting from Level 0 (representing raw AIRS data), the AIRS SPS PGEs and the data products they produce are identified by alphanumeric labels (1A, 1B, 2, and 3) representing successive stages or levels of processing. The previous NASA Tech Briefs article described processing through Level 2, the output of which comprises geo-located atmospheric data products such as temperature and humidity profiles among others. The AIRS Level 3 PGE samples selected information from the Level 2 standard products to produce a single global gridded product. One Level 3 product is generated for each day s collection of Level 2 data. In addition, daily Level 3 products are aggregated into two multiday products: an eight-day (half the orbital repeat cycle) product and monthly (calendar month) product.

Granger, Stephanie↗
NASA NTRS1984

Postprocessing for the UCLA climate model

Postprocessing programs written to analyze the results of a general circulation model are described. The model history data is interpolated to pressure surfaces, converted from 64-bit Cyber words to 32 bit IBM words, and transferred to tape for further analysis. The time average program reads the interpolation output tape and produces 15-day time averages. The model outputs a very detailed record for selected grid points. This data is sent to a tape in Cyber full words. The conversion program changes the format of the tape from 64 bit Cyber words to 32 bit IBM words, processing a month of data at a time. The grid-point history sorting program inputs the converted grid point history tape and sorts it by time. The end product is a tape with a time history of each field for every point. After the PBP data has been stored, it can be plotted. The plotting package is flexible, and allows the choice of fields and grid points. The one-dimensional fields are plotted as functions of time, and a daily summary is also produced. For the two-dimensional fields, time-height cross sections and daily summaries are produced. The model plotting package produces plots for all fields on the time average and pressure tapes. These plots include world maps, polar projections, zonal means, vectors, streamlines, and latitude-height plots.

Abeles, J.↗
NASA NTRS2020

Evaluation and Validation of a High Spatial Resolution Satellite Soil Moisture Product over the Continental United States

The soil moisture (SM) data retrieved from the Soil Moisture Active and Passive (SMAP) satellite are available at a 9 km grid spacing since April 2015. This product can provide valuable information for research and applications in hydrology and other related fields. However, the resolution may be too coarse for applications at catchment or field scale. In this study, an established downscaling methodology, which had a major modification regarding its application on the SMAP 33 km domain, was implemented to develop a 1 km soil moisture product based on the SMAP 9 km data. The algorithm proposed here is based on the thermal inertia principle and developed by modeling the relationship between surface temperature difference and SM for different Normalized Difference Vegetation Index (NDVI) classes. The model functions were established and tuned using data from the NASA’s Land Information System (LIS) North America Land Data Assimilation System (NLDAS) and remotely sensed VISible/InfRared (VIS/IR) reflectance data from Long Term Data Record (LTDR) AVHRR (Advanced Very High Resolution Radiometer) for the growing season months of April-September 1981–2018. These were then implemented using the MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Continental United States (CONUS) domain. Validation activities were carried out using in situ measurements distributed through the International Soil Moisture Network (ISMN). The validation results computed using the 1 km SM data showed that the R2, unbiased RMSE (root mean square error) and bias were improved relative to the 9 km SMAP product by 0.045, 0.018m3/m3 and 0.001m3/m3, respectively. The 1 km SM also exhibited a strong time-series autocorrelation. Further accuracy assessment analyses indicated that precipitation might contribute to the uncertainties in both the 9 km SMAP and 1 km downscaled SMAP SM products.

SMAP↗
NASA NTRS2009

Global Land Data Assimilation System (GLDAS) Products, Services and Application from NASA Hydrology Data and Information Services Center (HDISC)

The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data holdings include a set of 1.0 degree resolution data products from the four models, covering 1979 to the present; and a 0.25 degree data product from the Noah model, covering 2000 to the present. The products are in Gridded Binary (GRIB) format and can be accessed through a number of interfaces. Users can search the products through keywords and perform on-the-fly spatial and parameter subsetting and format conversion of selected data. More advanced visualization, access and analysis capabilities will be available in the future. The long term GLDAS data are used to develop climatology of water cycle components and to explore the teleconnections of droughts and pluvial.

Fang, Hongliang↗
NASA NTRS2015

NASA'S Earth Science Data Stewardship Activities

NASA has been collecting Earth observation data for over 50 years using instruments on board satellites, aircraft and ground-based systems. With the inception of the Earth Observing System (EOS) Program in 1990, NASA established the Earth Science Data and Information System (ESDIS) Project and initiated development of the Earth Observing System Data and Information System (EOSDIS). A set of Distributed Active Archive Centers (DAACs) was established at locations based on science discipline expertise. Today, EOSDIS consists of 12 DAACs and 12 Science Investigator-led Processing Systems (SIPS), processing data from the EOS missions, as well as the Suomi National Polar Orbiting Partnership mission, and other satellite and airborne missions. The DAACs archive and distribute the vast majority of data from NASA’s Earth science missions, with data holdings exceeding 12 petabytes The data held by EOSDIS are available to all users consistent with NASA’s free and open data policy, which has been in effect since 1990. The EOSDIS archives consist of raw instrument data counts (level 0 data), as well as higher level standard products (e.g., geophysical parameters, products mapped to standard spatio-temporal grids, results of Earth system models using multi-instrument observations, and long time series of Earth System Data Records resulting from multiple satellite observations of a given type of phenomenon). EOSDIS data stewardship responsibilities include ensuring that the data and information content are reliable, of high quality, easily accessible, and usable for as long as they are considered to be of value.

metadata↗
NASA NTRS2002

A Variable-Resolution Stretched-Grid General Circulation Model and Data Assimilation System with Multiple Areas of Interest: Studying the Anomalous Regional Climate Events of 1998

The new stretched-grid design with multiple (four) areas of interest, one at each global quadrant, is implemented into both a stretched-grid GCM (general circulation model) and a stretched-grid data assimilation system (DAS). The four areas of interest include: the U.S./Northern Mexico, the El Nino area/Central South America, India/China, and the Eastern Indian Ocean/Australia. Both the stretched-grid GCM and DAS annual (November 1997 through December 1998) integrations are performed with 50 km regional resolution. The efficient regional down-scaling to mesoscales is obtained for each of the four areas of interest while the consistent interactions between regional and global scales and the high quality of global circulation, are preserved. This is the advantage of the stretched-grid approach. The global variable resolution DAS incorporating the stretched-grid GCM has been developed and tested as an efficient tool for producing regional analyses and diagnostics with enhanced mesoscale resolution. The anomalous regional climate events of 1998 that occurred over the U.S., Mexico, South America, China, India, African Sahel, and Australia are investigated in both simulation and data assimilation modes. Tree assimilated products are also used, along with gauge precipitation data, for validating the simulation results. The obtained results show that the stretched-grid GCM and DAS are capable of producing realistic high quality simulated and assimilated products at mesoscale resolution for regional climate studies and applications.

Fox-Rabinovitz, Michael S.↗