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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 469 records · Page 26

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NASA NTRS1981

Modeling the terrestrial hydrology for the global atmosphere - The future role of satellite data

A global terrestrial hydrology model has been developed for the transport and storage of moisture and heat in the ground surface layer where the hydrological parameters react to diurnal and seasonal changes in the atmosphere. The spatial and temporal variability of land surface features is considered in the model by means of large scale parameterizations. The model can be either forced by the atmosphere using conventional meteorological data or coupled to an atmospheric general circulation model (GCM) for interactive studies. The global surface is divided into 4 deg longitude by 5 deg latitude cells while the ground is represented by a thin surface layer, a bulk layer (the root zone), and a deep layer (the ground water zone). Results are presented from a seven-day global experiment which was conducted utilizing the GLAS GCM (NASA Goddard Laboratory for Atmospheric Sciences). The model has demonstrated its capability to predict, over a large region, the overall soil moisture storage and major flux exchanges with the atmosphere above and the ground water below.

Lin, J. D.↗

NASA NTRS1981

A study of model parameters associated with the urban climate using HCMM data

The use of infrared and visible data from the Heat Capacity Mapping Mission (HCMM) and in situ data to study the intensity of the urban heat island of Saint Louis is described. Analysis of HCMM data shows that an urban heat island exists day and night in all seasons when clear skies prevail. The lower albedo value of the urban region during the day suggests that the higher temperatures are due to more absorption of solar radiation. Preliminary analysis of in situ meteorological data was performed after merging with HCMM data, and surface roughness, the exchange coefficient, and the soil moisture were calculated.

Source record↗

NASA NTRS1999

The GEOS Ozone Data Assimilation System: Design and Validation

An ozone data assimilation system has been developed at the Data Assimilation Office of the NASA/Goddard Space Flight Center to provide global three-dimensional analyzed ozone mixing ratio and total column ozone. The Total Ozone Mapping Spectrometer (TOMS) total column ozone and the Solar Backscatter Ultraviolet (SBUV) or SBUV/2 partial ozone profile data are assimilated. The analyzed winds from the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) drive the ozone transport. Following every transport model timestep, the model prediction is combined with the observations using a global, physical-space based, statistical analysis scheme. Due to the smaller size of the ozone system than that of a global meteorological data assimilation system, new statistical analysis methodology, including anisotropic and flow-dependent forecast error correlation models, can be implemented and tested in the ozone system more easily. Sample results from the winter 1992 validation period are presented. There is a close agreement between the analyzed fields and the independent observations from ozone sondes and the Halogen Occultation Experiment (HALOE).

Stajner, I.↗

NASA NTRS2000

Estimating Long Term Surface Soil Moisture in the GCIP Area From Satellite Microwave Observations

Soil moisture is an important component of the water and energy balances of the Earth's surface. Furthermore, it has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, accurate estimates of surface soil moisture are often difficult to make, especially at large spatial scales. Soil moisture is a highly variable land surface parameter, and while point measurements are usually accurate, they are representative only of the immediate site which was sampled. Simple averaging of point values to obtain spatial means often leads to substantial errors. Since remotely sensed observations are already a spatially averaged or areally integrated value, they are ideally suited for measuring land surface parameters, and as such, are a logical input to regional or larger scale land process models. A nine-year database of surface soil moisture is being developed for the Central United States from satellite microwave observations. This region forms much of the GCIP study area, and contains most of the Mississippi, Rio Grande, and Red River drainages. Daytime and nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and an orbital frequency over any pixel-sized area of about 2 daytime and 2 nighttime passes per week. Ground measurements of surface soil moisture from various locations throughout the study area are used to calibrate the microwave observations. Because ground measurements are usually only single point values, and since the time of satellite coverage does not always coincide with the ground measurements, the soil moisture data were used to calibrate a regional water balance for the top 1, 5, and 10 cm surface layers in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area spatially averaged soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.

Owe, Manfred↗

NASA NTRS2010

The Wettzell System Monitoring Concept and First Realizations

Automated monitoring of operational system parameters for the geodetic space techniques is becoming more important in order to improve the geodetic data and to ensure the safety and stability of automatic and remote-controlled observations. Therefore, the Wettzell group has developed the system monitoring software, SysMon, which is based on a reliable, remotely-controllable hardware/software realization. A multi-layered data logging system based on a fanless, robust industrial PC with an internal database system is used to collect data from several external, serial, bus, or PCI-based sensors. The internal communication is realized with Remote Procedure Calls (RPC) and uses generative programming with the interface software generator idl2rpc.pl developed at Wettzell. Each data monitoring stream can be configured individually via configuration files to define the logging rates or analog-digital-conversion parameters. First realizations are currently installed at the new laser ranging system at Wettzell to address safety issues and at the VLBI station O Higgins as a meteorological data logger. The system monitoring concept should be realized for the Wettzell radio telescope in the near future.

Ettl, Martin↗

DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗

DOE OSTI2025

U.S. Agrivoltaics Irradiance Database

This is a foundational data set for research and deployment of agrivoltaics, which is the co-location of agriculture and solar power plants on the same land. This irradiance and shading dataset can be utilized to determine the suitability of agrivoltaics configurations for a given region and crop-type. The data is hourly, 4x4 km resolution across the contiguous United States and Hawaii. It is calculated from the National Solar Radiation Database sites, using the System Advisor Model (SAM) to simulate the shading patterns for 10 common agrivoltaics configurations. Sunlight availability data is reported for 10 locations on the ground between adjacent rows of solar panels, as well as averaged across areas of interest such as the average irradiance in the edge-to-edge open area or across 3-6 planting beds. Other available metrics include the input meteorological data from the NSRDB (e.g. global horizontal irradiance, wind speed, etc.) and estimates for comparing energy and agricultural characteristic across the 10 configurations, including power output per acre or per kW installed capacity and farmable land area per acre.

14 SOLAR ENERGY↗

NASA NTRS1978

Users' instructions for the NASA/MSFC cloud-rise preprocessor program, version 6, and the NASA/MSFC multilayer diffusion program, version 6: Research version for Univac 1108 system

The cloud-rise preprocessor and multilayer diffusion computer programs were used by NASA in predicting concentrations and dosages downwind from normal and abnormal launches of rocket vehicles. These programs incorporated: (1) the latest data for the heat content and chemistry of rocket exhaust clouds; (2) provision for the automated calculation of surface water pH due to deposition of HCl from precipitation scavenging; (3) provision for automated calculation of concentration and dosage parameters at any level within the vertical grounds for which meteorological inputs have been specified; and (4) provision for execution of multiple cases of meteorological data. Procedures used to automatically calculate wind direction shear in a layer were updated.

Bjorklund, J. R.↗

NASA NTRS1972

Application of ERTS-A data to the protection and management of New Jersey's coastal environment

The author has identified the following significant results. Apparent sewage sludge disposal by barge has been detected approximately 12 miles offshore in an area with an approximate radius of 2.5 nautical miles. Verification is underway to determine whether this dumping is within one of the approved dump sites in the Bight. Analysis of all available historical and routine meteorological data in correlation with the observed phenomenon is necessary before final conclusions can be reached with respect to the effects of currents on the disposal of dumped wastes. Four effluent plumes emanating from the shoreline just south of Sandy Hook were observed and are moving in a southerly direction. Another plume is evident north of Barnegat Inlet and is moving almost directly offshore. This suggests that the more northerly plumes are under the influence of the tidal regime around New York Harbor much more than are the plumes further south along the New Jersey coast. Of further interest are what appear to be an internal wave phenomena approximately 75 miles east of the New Jersey coast. This same sort of phenomena has been observed repetitively off the coast of Oregon.

Yunghans, R. S.↗

NASA NTRS2005

Improvements in the estimates of ice thickness and production in the Chukchi Sea polynyas derived from AMSR-E

For January-March 2003, we use 12.5-km resolution Advanced Microwave Scanning Radiometer (AMSR) data for the first time in a comparison with Synthetic Aperture Radar (SAR) and Special Sensor Microwave/Imager (SSM/I) data to study two Chukchi coast polynyas, one consisting of many, the other of only a few 25-km SSM/I pixels. Within these polynyas, the ice thicknesses are derived separately from the SMM/I 37-GHz and AMSR 36-GHz channels; the heat fluxes are derived by combining thicknesses with meteorological data. Comparison with ScanSAR data shows that for the large polynya, because AMSR provides better resolution of the surrounding coastline and first-year ice, the AMSR heat losses are greater than the SSM/I; for the small polynya, AMSR measures its variability even when its area is order of a single SSM/I pixel. This means that AMSR permits more accurate calculation of polynya heat losses, yielding the potential of improved estimates of Arctic polynya productivity.

ice thicknesses↗

NASA NTRS2005

Improvements in the estimates of ice thickness and production in the Chukchi Sea polynyas derived from AMSR-E

Chukchi coast polynyas↗

NASA NTRS2005

Improvements in the estimates of ice thickness and production in the Chukchi Sea polynyas derived from AMSR-E

Advanced Microwave Scanning Radiometer (AMSR)↗

NASA NTRS1999

Selecting Meteorological Input for the Global Modeling Initiative Assessments

The Global Modeling Initiative (GMI) science team has developed a three dimensional chemistry and transport model (CTM) to evaluate the impact of the exhaust of supersonic aircraft on the stratosphere. An important goal of the GMI is to test modules for numerical transport, photochemical integration, and model dynamics within a common framework. This work is focussed on the dependence of the overall assessment on the wind and temperature fields used by the CTM. Three meteorological data sets for the stratosphere were available to GMI: the National Center for Atmospheric Research Community Climate Model (CCM2), the Goddard Earth Observing System Data Assimilation System (GEOS-DAS), and the Goddard Institute for Space Studies general circulation model (GISS-2'). Objective criteria were established by the GMI team to evaluate which of these three data sets provided the best representation of trace gases in the stratosphere today. Tracer experiments were devised to test various aspects of model transport. Stratospheric measurements of long-lived trace gases were selected as a test of the CTM transport. This presentation describes the criteria used in grading the meteorological fields and the resulting choice of wind fields to be used in the GMI assessment. This type of objective model evaluation will lead to a higher level of confidence in these assessments. We suggest that the diagnostic tests shown here be used to augment traditional general circulation model evaluation methods.

Strahan, Susan↗

NASA NTRS1992

Atmosphere and climate studies of Mars using the Mars Observer pressure modulator infrared radiometer

Studies of the climate and atmosphere of Mars are limited at present by a lack of meteorological data having systematic global coverage with good horizontal and vertical resolution. The Mars Observer spacecraft in a low, nearly circular, polar orbit will provide an excellent platform for acquiring the data needed to advance significantly our understanding of the Martian atmosphere and its remarkable variability. The Mars Observer pressure modulator infrared radiometer (PMIRR) is a nine-channel limb and nadir scanning atmospheric sounder which will observe the atmosphere of Mars globally from 0 to 80 km for a full Martian year. PMIRR employs narrow-band radiometric channels and two pressure modulation cells to measure atmospheric and surface emission in the thermal infrared. PMIRR infrared and visible measurements will be combined to determine the radiative balance of the polar regions, where a sizeable fraction of the global atmospheric mass annually condenses onto and sublimes from the surface. Derived meteorological fields, including diabatic heating and cooling and the vertical variation of horizontal winds, are computed from the globally mapped fields retrieved from PMIRR data.

Mccleese, D. J.↗

NASA NTRS1980

Ground cloud hydrogen chloride measurements from three Titan launches at the Kennedy Space Center during 1978 and 1979

Total hydrogen chloride and gaseous hydrogen chloride concentrations were measured in the exhaust cloud produced at three Titan 3 launches at the Kennedy Space Center in March 1978, December 1978, and November 1979 to determine the degree of hydrogen chloride partitioning in a solid rocket exhaust cloud between gaseous hydrogen chloride and hydrochloric acid aerosol as a function of ambient relative humidity. Moderately low relative humidity conditions were present during a daytime launch on Mar 25, 1978, and high relative humidity conditions were present during a nighttime launch on December 13, 1978. Gaseous hydrogen chloride concentrations and meteorological data, as a function of time after launch, are presented. Hydrogen chloride is present in both the gaseous and aerosol phase in the exhaust cloud. Total HCl concentrations ranged from 18 parts per million by volume (ppm) several minutes after launch down to 1 ppm after the cloud stabilization period, depending on the meteorological conditions. Gaseous HCl concentrations ranged from 2 to 3 ppm several minutes after launch to less than 1 ppm after cloud stabilization. These measured concentrations indicated significant HCl aerosol formation.

Seabacher, D. I.↗

NASA NTRS2010

The Contribution of Soil Moisture Information to Forecast Skill: Two Studies

This talk briefly describes two recent studies on the impact of soil moisture information on hydrological and meteorological prediction. While the studies utilize soil moisture derived from the integration of large-scale land surface models with observations-based meteorological data, the results directly illustrate the potential usefulness of satellite-derived soil moisture information (e.g., from SMOS and SMAP) for applications in prediction. The first study, the GEWEX- and ClIVAR-sponsored GLACE-2 project, quantifies the contribution of realistic soil moisture initialization to skill in subseasonal forecasts of precipitation and air temperature (out to two months). The multi-model study shows that soil moisture information does indeed contribute skill to the forecasts, particularly for air temperature, and particularly when the initial local soil moisture anomaly is large. Furthermore, the skill contributions tend to be larger where the soil moisture initialization is more accurate, as measured by the density of the observational network contributing to the initialization. The second study focuses on streamflow prediction. The relative contributions of snow and soil moisture initialization to skill in streamflow prediction at seasonal lead, in the absence of knowledge of meteorological anomalies during the forecast period, were quantified with several land surface models using uniquely designed numerical experiments and naturalized streamflow data covering mUltiple decades over the western United States. In several basins, accurate soil moisture initialization is found to contribute significant levels of predictive skill. Depending on the date of forecast issue, the contributions can be significant out to leads of six months. Both studies suggest that improvements in soil moisture initialization would lead to increases in predictive skill. The relevance of SMOS and SMAP satellite-based soil moisture information to prediction are discussed in the context of these studies.

Koster, Randal↗

DOE OSTI2024

NDVI and Multispectral Images, UAS Flights, Council Road Site, Seward Peninsula, Alaska, June 2022

The dataset contains Normalized Difference Vegetation Index (NDVI) and multispectral images collected on June 18, 2022 using an UAS (Unmanned Aerial System) Yuneec H520, Mapir Survey 3, and Parrot Sequoia cameras. This data was created to accompany the following datasets:- NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Council Road Mile Marker 71, Seward Peninsula, Alaska (https://doi.org/10.5440/1526749)- Sun-induced chlorophyll fluorescence/eddy covariance measurements at NGEE Arctic Council site, Seward Peninsula, Alaska, 2022 (https://doi.org/10.15485/2466175)- NGEE Arctic Meteorological Data from Instrumented PCDC Stations, Council Site, Seward Peninsula, Alaska, 2018 - 2023 (https://doi.org/10.5440/1529604)Survey 3 is RGN (Red, Greeen, Near Infra Red) camera with 87° Horizontal Field of View (HFOV) (19mm) f/2.8 Aperture, 550nm/660nm/850nm (Red+Green+NIR) filter transmission and produced geo referenced jpg and raw format images. Parrot Sequoia is a camera with 16 megapixel RGB (Red, Green, Blue) sensor and 1.2 megapixel monochrome sensors in discrete spectral bands: Green (550nm - 40Nm Bandwidth), Red (660nm - 40nm Bandwidth), Red Edge (735nm - 10nm Bandwidth) and Near Infrared (790nm - 40nm Bandwidth).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗