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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Evaluating A Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is primarily conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address these limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm uses a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB) O3 climatology). It has been shown that satellite O3 retrievals are sensitive to a priori O3 profiles and covariance matrices. During this work we investigate the climatological data to be used in TEMPO algorithms (TB O3) and simulated data from the NASA GMAO Goddard Earth Observing System (GEOS-5) Forward Processing (FP) near-real-time (NRT) model products. These two data products will be evaluated with ground-based lidar data from the Tropospheric Ozone Lidar Network (TOLNet) at various locations of the US. This study evaluates the TB climatology, GEOS-5 climatology, and 3-hourly GEOS-5 data compared to lower tropospheric observations to demonstrate the accuracy of a priori information to potentially be used in TEMPO O3 algorithms. Here we present our initial analysis and the theoretical impact on TEMPO retrievals in the lower troposphere.

ozone↗
NASA NTRS2016

Evaluating a Priori Ozone Profile Information Used in TEMPO Tropospheric Ozone Retrievals

Ozone (O3) is a greenhouse gas and toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is primarily conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address these limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product.TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME, GOME-2, and OMI. This algorithm uses a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB) O3 climatology). It has been shown that satellite O3 retrievals are sensitive to a priori O3 profiles and covariance matrices. During this work we investigate the climatological data to be used in TEMPO algorithms (TB O3) and simulated data from the NASA GMAO Goddard Earth Observing System (GEOS-5) Forward Processing (FP) near-real-time (NRT) model products. These two data products will be evaluated with ground-based lidar data from the Tropospheric Ozone Lidar Network (TOLNet) at various locations of the US. This study evaluates the TB climatology, GEOS-5 climatology, and 3-hourly GEOS-5 data compared to lower tropospheric observations to demonstrate the accuracy of a priori information to potentially be used in TEMPO O3 algorithms. Here we present our initial analysis and the theoretical impact on TEMPO retrievals in the lower troposphere.

TEMPO↗
NASA NTRS2000

BOREAS AFM-08 ECMWF Hourly Surface and Upper Air Data for the SSA and NSA

The Boreal Ecosystem-Atmosphere Study (BOREAS) Airborne Fluxes and Meteorology (AFM)-8 team focused on modeling efforts to improve the understanding of the diurnal evolution of the convective boundary layer over the boreal forest. This data set contains hourly data from the European Center for for Medium-Range Weather Forecasts (ECMWF) operational model from below the surface to the top of the atmosphere, including the model fluxes at the surface. Spatially, the data cover a pair of the points that enclose the rawinsonde sites at Candle Lake, Saskatchewan, in the Southern Study Area (SSA) and Thompson, Manitoba, in the Northern Study Area (NSA). Temporally, the data include the two time periods of 13 May 1994 to 30 Sept 1994 and 01 Mar 1996 to 31 Mar 1997. The data are stored in tabular ASCII files. The number of records in the upper air data files may exceed 20,000, causing a problem for some software packages. The ECMWF hourly surface and upper air data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Viterbo, Pedro↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2019

Method and System for Accessing PV Resource Data from the NSRDB

This study developed a capability to provide spectrally resolved solar resource data from the National Solar Radiation Data Base (NSRDB) effectively providing 20 years of half-hourly data for all of the US at a 4kmx4km spatial resolution. This data is now available freely to users directly through a GIS based web interface (https://nsrdb.nrel.gov) as well as through an Application Programming Interface. Users of this data can conduct more accurate pre-feasibility studies and assess multiple PV technologies. It is expected that widely used models such as PVSyst, National Renewable Energy Laboratory’s (NREL’s) System Advisor Model (SAM) and First Solar designed PlantPredict will develop the capabilities to use this spectral data.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2019

Reducing PV Performance Uncertainty by Accurately Quantifying the PV Resource

Reducing uncertainty in the prediction and/or verification of photovoltaic (PV) plant output can directly increase the expected return on investment for each party in a contract, likely leading to more favorable terms for the contract, including a possible reduction in interest rates. Note that a reduction of 1% in the interest rate is estimated to reduce the levelized cost of energy by approximately 0.5 cents/kWh. This project developed a capability to provide spectrally resolved solar information from the National Solar Radiation Data Base (NSRDB), effectively providing 20 years of half-hourly data for all of the United States at a 4-km by 4-km spatial resolution. These data are now available freely to users directly through a geographic information system-based Web interface (https://nsrdb.nrel.gov) as well as through an application programming interface. Users of these data can conduct more accurate prefeasibility studies and assess multiple PV technologies. It is expected that widely used models such as PVSyst, the National Renewable Energy Laboratory's System Advisor Model (SAM), and PlantPredict, designed by First Solar, will develop the capabilities to use these spectral data.

14 SOLAR ENERGY↗
NASA NTRS2013

File Specification for GEOS-5 FP (Forward Processing)

The GEOS-5 FP Atmospheric Data Assimilation System (GEOS-5 ADAS) uses an analysis developed jointly with NOAA's National Centers for Environmental Prediction (NCEP), which allows the Global Modeling and Assimilation Office (GMAO) to take advantage of the developments at NCEP and the Joint Center for Satellite Data Assimilation (JCSDA). The GEOS-5 AGCM uses the finite-volume dynamics (Lin, 2004) integrated with various physics packages (e.g, Bacmeister et al., 2006), under the Earth System Modeling Framework (ESMF) including the Catchment Land Surface Model (CLSM) (e.g., Koster et al., 2000). The GSI analysis is a three-dimensional variational (3DVar) analysis applied in grid-point space to facilitate the implementation of anisotropic, inhomogeneous covariances (e.g., Wu et al., 2002; Derber et al., 2003). The GSI implementation for GEOS-5 FP incorporates a set of recursive filters that produce approximately Gaussian smoothing kernels and isotropic correlation functions. The GEOS-5 ADAS is documented in Rienecker et al. (2008). More recent updates to the model are presented in Molod et al. (2011). The GEOS-5 system actively assimilates roughly 2 × 10(exp 6) observations for each analysis, including about 7.5 × 10(exp 5) AIRS radiance data. The input stream is roughly twice this volume, but because of the large volume, the data are thinned commensurate with the analysis grid to reduce the computational burden. Data are also rejected from the analysis through quality control procedures designed to detect, for example, the presence of cloud. To minimize the spurious periodic perturbations of the analysis, GEOS-5 FP uses the Incremental Analysis Update (IAU) technique developed by Bloom et al. (1996). More details of this procedure are given in Appendix A. The assimilation is performed at a horizontal resolution of 0.3125-degree longitude by 0.25- degree latitude and at 72 levels, extending to 0.01 hPa. All products are generated at the native resolution of the horizontal grid. The majority of data products are time-averaged, but four instantaneous products are also available. Hourly data intervals are used for two-dimensional products, while 3-hourly intervals are used for three-dimensional products. These may be on the model's native 72-layer vertical grid or at 42 pressure surfaces extending to 0.1 hPa. This document describes the gridded output files produced by the GMAO near real-time operational FP, using the most recent version of the GEOS-5 assimilation system. Additional details about variables listed in this file specification can be found in a separate document, the GEOS-5 File Specification Variable Definition Glossary. Documentation about the current access methods for products described in this document can be found on the GMAO products page: http://gmao.gsfc.nasa.gov/products/.

GSI↗
NASA NTRS2013

File Specification for GEOS-5 FP-IT (Forward Processing for Instrument Teams)

The GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS) uses an analysis developed jointly with NOAA's National Centers for Environmental Prediction (NCEP), which allows the Global Modeling and Assimilation Office (GMAO) to take advantage of the developments at NCEP and the Joint Center for Satellite Data Assimilation (JCSDA). The GEOS-5 AGCM uses the finite-volume dynamics (Lin, 2004) integrated with various physics packages (e.g, Bacmeister et al., 2006), under the Earth System Modeling Framework (ESMF) including the Catchment Land Surface Model (CLSM) (e.g., Koster et al., 2000). The GSI analysis is a three-dimensional variational (3DVar) analysis applied in grid-point space to facilitate the implementation of anisotropic, inhomogeneous covariances (e.g., Wu et al., 2002; Derber et al., 2003). The GSI implementation for GEOS-5 FP-IT incorporates a set of recursive filters that produce approximately Gaussian smoothing kernels and isotropic correlation functions. The GEOS-5 ADAS is documented in Rienecker et al. (2008). More recent updates to the model are presented in Molod et al. (2011). The GEOS-5 system actively assimilates roughly 2 × 10(exp 6) observations for each analysis, including about 7.5 × 10(exp 5) AIRS radiance data. The input stream is roughly twice this volume, but because of the large volume, the data are thinned commensurate with the analysis grid to reduce the computational burden. Data are also rejected from the analysis through quality control procedures designed to detect, for example, the presence of cloud. To minimize the spurious periodic perturbations of the analysis, GEOS-5 FP-IT uses the Incremental Analysis Update (IAU) technique developed by Bloom et al. (1996). More details of this procedure are given in Appendix A. The analysis is performed at a horizontal resolution of 0.625-degree longitude by 0.5-degree latitude and at 72 levels, extending to 0.01 hPa. All products are generated at the native resolution of the horizontal grid. The majority of data products are time-averaged, but four instantaneous products are also available. Hourly data intervals are used for two-dimensional products, while 3-hourly intervals are used for three-dimensional products. These may be on the model's native 72-layer vertical grid or at 42 pressure surfaces extending to 0.1 hPa. This document describes the gridded output files produced by the GMAO near real-time operational GEOS-5 FP-IT processing in support of the EOS instrument teams. Additional details about variables listed in this file specification can be found in a separate document, the GEOS-5 File Specification Variable Definition Glossary.

3D Var↗
NASA NTRS2012

Rapid and Reliable Damage Proxy Map from InSAR Coherence

Future radar satellites will visit SoCal within a day after a disaster event. Data acquisition latency in 2015-2020 is 8 to approx. 15 hours. Data transfer latency that often involves human/agency intervention far exceeds the data acquisition latency. Need interagency cooperation to establish automatic pipeline for data transfer. The algorithm is tested with ALOS PALSAR data of Pasadena, California. Quantitative quality assessment is being pursued: Meeting with Pasadena City Hall computer engineers for a complete list of demolition/construction project 1. Estimate the probability of detection and probability of false alarm 2. Estimate the optimal threshold value.

InSAR↗
NASA NTRS2012

Analysis of Water and Energy Budgets and Trends Using the NLDAS Monthly Data Sets

The North American Land Data Assimilation System (NLDAS) is a collaborative project between NASA GSFC, NOAA, Princeton University, and the University of Washington. NLDAS has created surface meteorological forcing data sets using the best-available observations and reanalyses. The forcing data sets are used to drive four separate land-surface models (LSMs), Mosaic, Noah, VIC, and SAC, to produce data sets of soil moisture, snow, runoff, and surface fluxes. NLDAS hourly data, accessible from the NASA GES DISC Hydrology Data Holdings Portal, http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings, are widely used by various user communities in modeling, research, and applications, such as drought and flood monitoring, watershed and water quality management, and case studies of extreme events. More information is available at http://ldas.gsfc.nasa.gov/. To further facilitate analysis of water and energy budgets and trends, NLDAS monthly data sets have been recently released by NASA GES DISC.

Vollmer, Bruce E.↗
NASA NTRS2015

Between a Map and a Data Rod

A Digital Divide has long stood between how NASA and other satellite-derived data are typically archived (time-step arrays or maps) and how hydrology and other point-time series oriented communities prefer to access those data. In essence, the desired method of data access is orthogonal to the way the data are archived. Our approach to bridging the Divide is part of a larger NASA-supported data rods project to enhance access to and use of NASA and other data by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS) and the larger hydrology community. Our main objective was to determine a way to reorganize data that is optimal for these communities. Two related objectives were to optimally reorganize data in a way that (1) is operational and fits in and leverages the existing Goddard Earth Sciences Data and Information Services Center (GES DISC) operational environment and (2) addresses the scaling up of data sets available as time series from those archived at the GES DISC to potentially include those from other Earth Observing System Data and Information System (EOSDIS) data archives. Through several prototype efforts and lessons learned, we arrived at a non-database solution that satisfied our objectivesconstraints. We describe, in this presentation, how we implemented the operational production of pre-generated data rods and, considering the tradeoffs between length of time series (or number of time steps), resources needed, and performance, how we implemented the operational production of on-the-fly (virtual) data rods. For the virtual data rods, we leveraged a number of existing resources, including the NASA Giovanni Cache and NetCDF Operators (NCO) and used data cubes processed in parallel. Our current benchmark performance for virtual generation of data rods is about a years worth of time series for hourly data (9,000 time steps) in 90 seconds. Our approach is a specific implementation of the general optimal strategy of reorganizing data to match the desired means of access. Results from our project have already significantly extended NASA data to the large and important hydrology user community that has been, heretofore, mostly unable to easily access and use NASA data.

machine learning↗
DOE OSTI2022

GE Renewable Energy: Perdigao wind dataset from Large Eddy Simulation and ERA5

Perdigao wind research dataset including ERA5 data, high resolution and low resolution Large Eddy Simulations for the year 2020 at 1 hour frequency. Generated using Copernicus Climate Change Service information 2022[1] [1] Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on 05-04-2022), 10.24381/cds.adbb2d47

17 WIND ENERGY↗
DOE OSTI2018

AmeriFlux US-SCg Southern California Climate Gradient - Grassland

This is the AmeriFlux version of the carbon flux data for the site US-SCg Southern California Climate Gradient - Grassland. Site Description - Half hourly data are available at https://www.ess.uci.edu/~california/. This site is one of six Southern California Climate Gradient flux towers operated along an elevation gradient (sites are US-SCg, US-SCs, US-SCf, US-SCw, US-SCc, US-SCd). This site is a grassland that was historically dominated by exotic annuals and that underwent restoration with a focus on native bunch grasses in the 2010s. The site has historically burned every 10-20 years, with a wildfire in October 2007. The restoration involved several years of mowing and herbicide application to suppress exotics followed by dense planting of Nasella bunch grasses.

Goulden, Mike↗
DOE OSTI2019

Method and System for Accessing PV Resource Data from the NSRDB: Preprint

This study developed a capability to provide spectrally resolved solar resource data from the National Solar Radiation Database (NSRDB), effectively providing 20 years of half-hourly data for all of the United States at a 4-km by 4-km spatial resolution. These data are now available freely to users directly through a geographic information system-based web interface (https://nsrdb.nrel.gov) as well as through an application programming interface. Users of these data can conduct more accurate prefeasibility studies and assess multiple photovoltaic (PV) technologies. It is expected that widely used models such as PVSyst, the National Renewable Energy Laboratory's System Advisor Model (SAM), and First Solar-designed PlantPredict will develop the capabilities to use these spectral data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

AmeriFlux US-SCs Southern California Climate Gradient - Coastal Sage

This is the AmeriFlux version of the carbon flux data for the site US-SCs Southern California Climate Gradient - Coastal Sage. Site Description - Half hourly data are available at https://www.ess.uci.edu/~california/. This site is one of six Southern California Climate Gradient flux towers operated along an elevation gradient (sites are US-SCg, US-SCs, US-SCf, US-SCw, US-SCc, US-SCd). This site is a coastal sage shrubland. Coastal sage is a small stature, closed canopy vegetation dominated by drought deciduous shrubs. The site has historically burned every 10-20 years, with the wild fire in October 2007. The tower data sets includes this recovery process.

Goulden, Mike↗
DOE OSTI2018

AmeriFlux US-SCf Southern California Climate Gradient - Oak/Pine Forest

This is the AmeriFlux version of the carbon flux data for the site US-SCf Southern California Climate Gradient - Oak/Pine Forest. Site Description - Half hourly data are available at https://www.ess.uci.edu/~california/. This site is one of six Southern California Climate Gradient flux towers operated along an elevation gradient (sites are US-SCg, US-SCs, US-SCf, US-SCw, US-SCc, US-SCd). This site is a mixed oak/pine forest. The site experiences episodic severe drought and mortality, and has also experienced occasional logging and wildfire. Drought and mortality was especially severe in the early 2000s.

Goulden, Mike↗
DOE OSTI2018

AmeriFlux US-SCw Southern California Climate Gradient - Pinyon/Juniper Woodland

This is the AmeriFlux version of the carbon flux data for the site US-SCw Southern California Climate Gradient - Pinyon/Juniper Woodland. Site Description - Half hourly data are available at https://www.ess.uci.edu/~california/. This site is one of six Southern California Climate Gradient flux towers operated along an elevation gradient (sites are US-SCg, US-SCs, US-SCf, US-SCw, US-SCc, US-SCd). This site is a Pinyon Juniper woodland with trees that are at least 150 years old, and ephemeral herbaceous cover following winter or spring rains. The site has experienced repeated drought during the record and roughly 50% Pinyon mortality over the last decade. A nearby tower site (US-SCc) burned in a 1994 wildfire; comparisons between US-SCw and US-SCc provide a measure of the effects of the 1994 on land-atmosphere exchange.

Goulden, Mike↗
DOE OSTI2018

AmeriFlux US-SCd Southern California Climate Gradient - Sonoran Desert

This is the AmeriFlux version of the carbon flux data for the site US-SCd Southern California Climate Gradient - Sonoran Desert. Site Description - Half hourly data are available at https://www.ess.uci.edu/~california/. This site is one of six Southern California Climate Gradient flux towers operated along an elevation gradient (sites are US-SCg, US-SCs, US-SCf, US-SCw, US-SCc, US-SCd). This site is a low desert site in Southern California's rain shadow; the climate is extremely dry and hot. The site has experience repeated droughts, with negligible rainfall during several years of the record.

Goulden, Mike↗